High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		86.90 1	88.67 1	81.57 24	91.50 1	63.30 162	84.80 40	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 77	68.08 118	97.05 1	96.93 1

FOURS1					89.19 23	77.84 18	91.64 1	89.11 2	84.05 2	91.57 2

DTE-MVSNet		80.35 55	82.89 40	72.74 173	89.84 7	37.34 470	77.16 124	81.81 127	80.45 3	90.92 3	92.95 9	74.57 56	86.12 33	63.65 173	94.68 36	94.76 6

PEN-MVS		80.46 53	82.91 39	73.11 153	89.83 8	39.02 450	77.06 127	82.61 108	80.04 4	90.60 6	92.85 11	74.93 52	85.21 65	63.15 180	95.15 22	95.09 2

PS-CasMVS		80.41 54	82.86 41	73.07 155	89.93 6	39.21 446	77.15 125	81.28 139	79.74 5	90.87 4	92.73 13	75.03 51	84.93 70	63.83 170	95.19 20	95.07 3

reproduce_model		84.87 5	85.80 5	82.05 22	85.52 69	78.14 16	87.69 6	85.36 39	79.26 6	89.12 11	92.10 20	77.52 26	85.92 41	80.47 8	95.20 19	82.10 237

COLMAP_ROB	72.78 3	83.75 14	84.11 19	82.68 12	82.97 114	74.39 46	87.18 11	88.18 7	78.98 7	86.11 49	91.47 37	79.70 14	85.76 48	66.91 138	95.46 13	87.89 54
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVSNet		79.48 61	81.65 52	72.98 159	89.66 12	39.06 449	76.76 128	80.46 162	78.91 8	90.32 7	91.70 32	68.49 116	84.89 71	63.40 177	95.12 23	95.01 4

reproduce-ours		84.97 3	85.93 3	82.10 20	86.11 60	77.53 22	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 34	79.90 9	95.21 17	82.72 219

our_new_method		84.97 3	85.93 3	82.10 20	86.11 60	77.53 22	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 34	79.90 9	95.21 17	82.72 219

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 5	88.84 4	90.72 1	78.27 11	87.95 18	92.53 15	79.37 15	84.79 74	74.51 59	96.15 2	92.88 7

WR-MVS_H		80.22 57	82.17 48	74.39 125	89.46 14	42.69 409	78.24 110	82.24 118	78.21 12	89.57 9	92.10 20	68.05 123	85.59 54	66.04 143	95.62 9	94.88 5

SR-MVS-dyc-post		84.75 6	85.26 8	83.21 3	86.19 53	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	79.20 16	85.58 55	78.11 28	94.46 40	84.89 127

RE-MVS-def			85.50 6		86.19 53	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	81.38 7		78.11 28	94.46 40	84.89 127

LS3D		80.99 48	80.85 57	81.41 28	78.37 183	71.37 64	87.45 8	85.87 28	77.48 15	81.98 107	89.95 85	69.14 108	85.26 62	66.15 140	91.24 111	87.61 58

SR-MVS		84.51 8	85.27 7	82.25 18	88.52 33	77.71 19	86.81 19	85.25 41	77.42 16	86.15 47	90.24 76	81.69 5	85.94 38	77.77 31	93.58 71	83.09 202

3Dnovator+	73.19 2	81.08 46	80.48 59	82.87 7	81.41 137	72.03 59	84.38 44	86.23 23	77.28 17	80.65 130	90.18 79	59.80 232	87.58 6	73.06 75	91.34 108	89.01 36

UA-Net		81.56 39	82.28 47	79.40 51	88.91 28	69.16 91	84.67 41	80.01 172	75.34 18	79.80 139	94.91 2	69.79 105	80.25 163	72.63 80	94.46 40	88.78 44

test_0402		78.17 75	79.48 67	74.24 127	83.50 102	59.15 210	72.52 198	74.60 261	75.34 18	88.69 17	91.81 30	75.06 50	82.37 119	65.10 150	88.68 187	81.20 257

test_one_0601					85.84 67	61.45 178		85.63 31	75.27 20	85.62 57	90.38 70	76.72 33

DP-MVS		78.44 73	79.29 68	75.90 105	81.86 131	65.33 135	79.05 100	84.63 62	74.83 21	80.41 134	86.27 185	71.68 77	83.45 97	62.45 186	92.40 88	78.92 308

APD-MVS_3200maxsize		83.57 16	84.33 16	81.31 31	82.83 117	73.53 54	85.50 35	87.45 13	74.11 22	86.45 43	90.52 61	80.02 10	84.48 78	77.73 32	94.34 51	85.93 97

PMVS	70.70 6	81.70 38	83.15 36	77.36 87	90.35 5	82.82 2	82.15 65	79.22 192	74.08 23	87.16 34	91.97 22	84.80 2	76.97 229	64.98 152	93.61 70	72.28 411
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		81.24 42	82.74 43	76.76 92	83.14 107	60.90 188	91.64 1	85.49 33	74.03 24	84.93 68	90.38 70	66.82 138	85.90 42	77.43 35	90.78 132	83.49 182

test_0728_THIRD									74.03 24	85.83 52	90.41 65	75.58 44	85.69 50	77.43 35	94.74 34	84.31 160

HPM-MVS_fast		84.59 7	85.10 9	83.06 4	88.60 32	75.83 34	86.27 28	86.89 16	73.69 26	86.17 46	91.70 32	78.23 22	85.20 66	79.45 16	94.91 29	88.15 52

lecture		83.41 20	85.02 10	78.58 65	83.87 99	67.26 109	84.47 42	88.27 6	73.64 27	87.35 32	91.96 23	78.55 21	82.92 106	81.59 3	95.50 10	85.56 108

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 72	67.25 110	82.91 60	84.98 48	73.52 28	85.43 62	90.03 80	76.37 36	86.97 13	74.56 57	94.02 62	82.62 223
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
LTVRE_ROB	75.46 1	84.22 9	84.98 11	81.94 23	84.82 81	75.40 37	91.60 3	87.80 8	73.52 28	88.90 14	93.06 8	71.39 86	81.53 135	81.53 4	92.15 94	88.91 40
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
ACMH+	66.64 10	81.20 43	82.48 45	77.35 88	81.16 141	62.39 168	80.51 79	87.80 8	73.02 30	87.57 25	91.08 43	80.28 9	82.44 116	64.82 154	96.10 4	87.21 63

MVSMamba_PlusPlus		76.88 86	78.21 79	72.88 167	80.83 143	48.71 312	83.28 58	82.79 102	72.78 31	79.17 148	91.94 24	56.47 282	83.95 83	70.51 99	86.15 246	85.99 96

XVS		83.51 18	83.73 25	82.85 8	89.43 15	77.61 20	86.80 20	84.66 60	72.71 32	82.87 96	90.39 68	73.86 61	86.31 23	78.84 23	94.03 60	84.64 142

X-MVStestdata		76.81 87	74.79 112	82.85 8	89.43 15	77.61 20	86.80 20	84.66 60	72.71 32	82.87 96	9.95 554	73.86 61	86.31 23	78.84 23	94.03 60	84.64 142

test_241102_ONE					86.12 57	61.06 184		84.72 56	72.64 34	87.38 29	89.47 92	77.48 27	85.74 49

SED-MVS		81.78 36	83.48 29	76.67 93	86.12 57	61.06 184	83.62 52	84.72 56	72.61 35	87.38 29	89.70 89	77.48 27	85.89 44	75.29 47	94.39 45	83.08 203

test_241102_TWO								84.80 51	72.61 35	84.93 68	89.70 89	77.73 25	85.89 44	75.29 47	94.22 56	83.25 195

DVP-MVS		81.15 44	83.12 37	75.24 117	86.16 55	60.78 190	83.77 50	80.58 160	72.48 37	85.83 52	90.41 65	78.57 19	85.69 50	75.86 43	94.39 45	79.24 301
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					86.16 55	60.78 190	83.81 49	85.10 44	72.48 37	85.27 65	89.96 84	78.57 19

MED-MVS		81.77 37	82.86 41	78.51 67	86.27 49	64.31 147	86.10 29	84.54 64	72.46 39	85.54 58	90.03 80	72.97 68	86.37 20	74.09 64	93.74 67	84.86 130

TestfortrainingZip a		82.48 31	83.93 21	78.11 77	86.27 49	64.11 153	86.10 29	85.02 46	72.46 39	86.32 44	90.03 80	76.75 32	85.37 57	78.23 26	94.22 56	84.86 130

mPP-MVS		84.01 13	84.39 15	82.88 6	90.65 3	81.38 4	87.08 13	82.79 102	72.41 41	85.11 67	90.85 50	76.65 34	84.89 71	79.30 20	94.63 37	82.35 230

TestfortrainingZip				73.58 141	79.21 167	57.65 231	86.10 29	81.22 142	72.34 42	72.08 324	83.19 266	58.95 245	83.71 89		84.76 279	79.38 300

UniMVSNet_ETH3D		76.74 88	79.02 69	69.92 240	89.27 19	43.81 395	74.47 170	71.70 296	72.33 43	85.50 61	93.65 3	77.98 24	76.88 233	54.60 293	91.64 99	89.08 34

MTAPA		83.19 22	83.87 23	81.13 33	91.16 2	78.16 15	84.87 38	80.63 158	72.08 44	84.93 68	90.79 51	74.65 55	84.42 80	80.98 5	94.75 33	80.82 269

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 83	66.72 118	86.54 24	85.11 43	72.00 45	86.65 39	91.75 31	78.20 23	87.04 11	77.93 30	94.32 52	83.47 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CP-MVS		84.12 11	84.55 14	82.80 10	89.42 17	79.74 9	88.19 5	84.43 68	71.96 46	84.70 75	90.56 58	77.12 30	86.18 30	79.24 21	95.36 14	82.49 227

MP-MVS		83.19 22	83.54 28	82.14 19	90.54 4	79.00 12	86.42 26	83.59 87	71.31 47	81.26 121	90.96 45	74.57 56	84.69 75	78.41 25	94.78 32	82.74 218
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
aaEdge-Enhanced		81.36 41	82.39 46	78.28 73	84.42 91	64.31 147	82.78 61	85.02 46	71.25 48	84.81 73	88.38 124	76.53 35	85.81 46	74.09 64	94.20 58	84.73 138

MM		78.15 76	77.68 83	79.55 49	80.10 152	65.47 133	80.94 75	78.74 202	71.22 49	72.40 316	88.70 114	60.51 219	87.70 3	77.40 37	89.13 178	85.48 110

gg-mvs-nofinetune		55.75 430	56.75 419	52.72 466	62.87 472	28.04 525	68.92 280	41.36 535	71.09 50	50.80 524	92.63 14	20.74 533	66.86 392	29.97 515	72.41 474	63.25 498

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 32	87.65 7	85.89 27	71.03 51	85.85 51	90.58 57	78.77 18	85.78 47	79.37 19	95.17 21	84.62 144
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SteuartSystems-ACMMP		83.07 25	83.64 27	81.35 29	85.14 76	71.00 69	85.53 34	84.78 53	70.91 52	85.64 54	90.41 65	75.55 45	87.69 4	79.75 11	95.08 24	85.36 113
Skip Steuart: Steuart Systems R&D Blog.
v7n		79.37 63	80.41 60	76.28 100	78.67 182	55.81 245	79.22 99	82.51 112	70.71 53	87.54 26	92.44 16	68.00 125	81.34 137	72.84 78	91.72 97	91.69 10

HPM-MVS		84.12 11	84.63 13	82.60 13	88.21 35	74.40 45	85.24 36	87.21 14	70.69 54	85.14 66	90.42 64	78.99 17	86.62 15	80.83 6	94.93 28	86.79 72
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HFP-MVS		83.39 21	84.03 20	81.48 26	89.25 20	75.69 36	87.01 17	84.27 74	70.23 55	84.47 78	90.43 63	76.79 31	85.94 38	79.58 14	94.23 55	82.82 215

ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 26	87.01 17	84.19 78	70.23 55	84.49 77	90.67 56	75.15 49	86.37 20	79.58 14	94.26 53	84.18 163

region2R		83.54 17	83.86 24	82.58 14	89.82 9	77.53 22	87.06 16	84.23 77	70.19 57	83.86 86	90.72 55	75.20 48	86.27 25	79.41 18	94.25 54	83.95 169

IS-MVSNet		75.10 106	75.42 107	74.15 130	79.23 166	48.05 326	79.43 95	78.04 216	70.09 58	79.17 148	88.02 135	53.04 306	83.60 91	58.05 248	93.76 66	90.79 17

LPG-MVS_test		83.47 19	84.33 16	80.90 35	87.00 40	70.41 76	82.04 67	86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 28	77.13 40	95.96 5	86.08 92

LGP-MVS_train				80.90 35	87.00 40	70.41 76		86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 28	77.13 40	95.96 5	86.08 92

APD-MVS		81.13 45	81.73 51	79.36 52	84.47 88	70.53 75	83.85 48	83.70 85	69.43 61	83.67 88	88.96 109	75.89 41	86.41 18	72.62 81	92.95 78	81.14 259
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		75.54 99	77.19 90	70.59 212	77.67 197	45.70 373	74.73 164	80.19 167	68.80 62	82.95 95	92.91 10	66.26 147	76.76 236	58.41 244	92.77 82	89.30 27

CPTT-MVS		81.51 40	81.76 50	80.76 37	89.20 22	78.75 13	86.48 25	82.03 122	68.80 62	80.92 126	88.52 120	72.00 76	82.39 118	74.80 50	93.04 77	81.14 259

VDDNet		71.60 184	73.13 155	67.02 304	86.29 48	41.11 422	69.97 255	66.50 364	68.72 64	74.74 257	91.70 32	59.90 229	75.81 245	48.58 353	91.72 97	84.15 165

TranMVSNet+NR-MVSNet		76.13 92	77.66 84	71.56 196	84.61 86	42.57 411	70.98 238	78.29 212	68.67 65	83.04 92	89.26 96	72.99 67	80.75 154	55.58 279	95.47 12	91.35 11

GST-MVS		82.79 28	83.27 34	81.34 30	88.99 26	73.29 55	85.94 33	85.13 42	68.58 66	84.14 82	90.21 78	73.37 65	86.41 18	79.09 22	93.98 63	84.30 162

SSC-MVS		61.79 365	66.08 298	48.89 492	76.91 216	10.00 559	53.56 486	47.37 500	68.20 67	76.56 211	89.21 98	54.13 299	57.59 451	54.75 290	74.07 462	79.08 304

PGM-MVS		83.07 25	83.25 35	82.54 15	89.57 13	77.21 29	82.04 67	85.40 37	67.96 68	84.91 72	90.88 48	75.59 43	86.57 16	78.16 27	94.71 35	83.82 172

Elysia		77.52 80	77.43 85	77.78 80	79.01 175	60.26 196	76.55 130	84.34 70	67.82 69	78.73 154	87.94 137	58.68 250	83.79 86	74.70 54	89.10 180	89.28 28

StellarMVS		77.52 80	77.43 85	77.78 80	79.01 175	60.26 196	76.55 130	84.34 70	67.82 69	78.73 154	87.94 137	58.68 250	83.79 86	74.70 54	89.10 180	89.28 28

ZNCC-MVS		83.12 24	83.68 26	81.45 27	89.14 24	73.28 56	86.32 27	85.97 25	67.39 71	84.02 83	90.39 68	74.73 54	86.46 17	80.73 7	94.43 44	84.60 147

Anonymous20240529		72.56 164	73.79 137	68.86 267	76.89 219	45.21 377	68.80 289	77.25 228	67.16 72	76.89 196	90.44 62	65.95 151	74.19 277	50.75 326	90.00 151	87.18 66

tt0320-xc		71.50 186	73.63 141	65.08 330	79.77 157	40.46 436	64.80 366	68.86 343	67.08 73	76.84 200	93.24 6	70.33 96	66.77 395	49.76 335	92.02 95	88.02 53

XVG-OURS		79.51 60	79.82 64	78.58 65	86.11 60	74.96 41	76.33 141	84.95 50	66.89 74	82.75 99	88.99 108	66.82 138	78.37 199	74.80 50	90.76 135	82.40 229

ITE_SJBPF				80.35 41	76.94 212	73.60 52		80.48 161	66.87 75	83.64 89	86.18 188	70.25 99	79.90 169	61.12 203	88.95 184	87.56 59

ACMP	69.50 8	82.64 29	83.38 31	80.40 40	86.50 46	69.44 85	82.30 64	86.08 24	66.80 76	86.70 38	89.99 83	81.64 6	85.95 37	74.35 61	96.11 3	85.81 99
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 59	79.99 63	78.49 68	86.46 47	74.79 42	77.15 125	85.39 38	66.73 77	80.39 135	88.85 112	74.43 59	78.33 201	74.73 52	85.79 253	82.35 230

tt0320		71.34 191	73.47 145	64.97 332	79.92 155	40.81 427	65.22 358	69.07 337	66.72 78	76.15 224	93.36 4	70.35 95	66.90 388	49.31 343	91.09 120	87.21 63

UniMVSNet_NR-MVSNet		74.90 112	75.65 103	72.64 176	83.04 112	45.79 369	69.26 271	78.81 198	66.66 79	81.74 114	86.88 156	63.26 176	81.07 145	56.21 269	94.98 25	91.05 13

XVG-ACMP-BASELINE		80.54 51	81.06 56	78.98 59	87.01 39	72.91 57	80.23 87	85.56 32	66.56 80	85.64 54	89.57 91	69.12 109	80.55 158	72.51 82	93.37 73	83.48 184

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 36	73.96 48	83.11 59	84.52 66	66.40 81	87.45 27	89.16 102	81.02 8	80.52 159	74.27 62	95.73 7	80.98 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
sc_t1		72.50 168	74.23 127	67.33 295	80.05 153	46.99 351	66.58 335	69.48 329	66.28 82	77.62 178	91.83 29	70.98 91	68.62 367	53.86 305	91.40 106	86.37 86

PAPM_NR		73.91 123	74.16 129	73.16 150	81.90 130	53.50 267	81.28 73	81.40 135	66.17 83	73.30 297	83.31 256	59.96 227	83.10 103	58.45 243	81.66 352	82.87 212

K. test v3		73.67 126	73.61 142	73.87 135	79.78 156	55.62 249	74.69 166	62.04 405	66.16 84	84.76 74	93.23 7	49.47 333	80.97 149	65.66 147	86.67 242	85.02 126

NCCC		78.25 74	78.04 81	78.89 61	85.61 68	69.45 84	79.80 94	80.99 150	65.77 85	75.55 233	86.25 187	67.42 130	85.42 56	70.10 100	90.88 130	81.81 248

OPM-MVS		80.99 48	81.63 54	79.07 56	86.86 44	69.39 86	79.41 97	84.00 83	65.64 86	85.54 58	89.28 95	76.32 38	83.47 96	74.03 68	93.57 72	84.35 159
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AdaColmap		74.22 118	74.56 116	73.20 149	81.95 129	60.97 186	79.43 95	80.90 151	65.57 87	72.54 314	81.76 297	70.98 91	85.26 62	47.88 362	90.00 151	73.37 393

APD_test1		75.04 108	75.38 108	74.02 132	69.89 369	70.15 78	76.46 133	79.71 178	65.50 88	82.99 94	88.60 119	66.94 135	72.35 306	59.77 224	88.54 189	79.56 294

HQP_MVS		78.77 67	78.78 72	78.72 62	85.18 73	65.18 137	82.74 62	85.49 33	65.45 89	78.23 164	89.11 103	60.83 215	86.15 31	71.09 91	90.94 124	84.82 134

plane_prior2							82.74 62		65.45 89

CNLPA		73.44 131	73.03 159	74.66 119	78.27 184	75.29 38	75.99 147	78.49 207	65.39 91	75.67 230	83.22 265	61.23 208	66.77 395	53.70 306	85.33 262	81.92 245

AllTest		77.66 78	77.43 85	78.35 71	79.19 169	70.81 71	78.60 104	88.64 3	65.37 92	80.09 137	88.17 130	70.33 96	78.43 196	55.60 276	90.90 128	85.81 99

TestCases				78.35 71	79.19 169	70.81 71		88.64 3	65.37 92	80.09 137	88.17 130	70.33 96	78.43 196	55.60 276	90.90 128	85.81 99

SF-MVS		80.72 50	81.80 49	77.48 84	82.03 128	64.40 145	83.41 56	88.46 5	65.28 94	84.29 80	89.18 100	73.73 64	83.22 100	76.01 42	93.77 65	84.81 136

DU-MVS		74.91 111	75.57 105	72.93 163	83.50 102	45.79 369	69.47 265	80.14 169	65.22 95	81.74 114	87.08 149	61.82 199	81.07 145	56.21 269	94.98 25	91.93 8

LFMVS		67.06 284	67.89 265	64.56 335	78.02 190	38.25 458	70.81 242	59.60 419	65.18 96	71.06 346	86.56 177	43.85 372	75.22 256	46.35 375	89.63 161	80.21 287

WB-MVS		60.04 386	64.19 330	47.59 495	76.09 232	10.22 558	52.44 494	46.74 502	65.17 97	74.07 278	87.48 144	53.48 303	55.28 459	49.36 341	72.84 471	77.28 334

EPP-MVSNet		73.86 125	73.38 148	75.31 114	78.19 186	53.35 269	80.45 80	77.32 226	65.11 98	76.47 217	86.80 161	49.47 333	83.77 88	53.89 303	92.72 84	88.81 43

WR-MVS		71.20 193	72.48 173	67.36 294	84.98 79	35.70 483	64.43 376	68.66 349	65.05 99	81.49 118	86.43 182	57.57 267	76.48 239	50.36 331	93.32 75	89.90 22

testf1		75.66 97	76.57 93	72.95 160	67.07 422	67.62 104	76.10 144	80.68 155	64.95 100	86.58 41	90.94 46	71.20 88	71.68 325	60.46 210	91.13 117	79.56 294

APD_test2		75.66 97	76.57 93	72.95 160	67.07 422	67.62 104	76.10 144	80.68 155	64.95 100	86.58 41	90.94 46	71.20 88	71.68 325	60.46 210	91.13 117	79.56 294

MSP-MVS		80.49 52	79.67 66	82.96 5	89.70 11	77.46 28	87.16 12	85.10 44	64.94 102	81.05 124	88.38 124	57.10 274	87.10 9	79.75 11	83.87 304	84.31 160
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
aaatest				78.47 70	86.27 49	64.31 147	86.10 29	84.54 64	64.93 103	85.54 58	88.38 124		86.37 20	74.09 64	94.20 58	84.73 138

ACMMP_NAP		82.33 32	83.28 33	79.46 50	89.28 18	69.09 94	83.62 52	84.98 48	64.77 104	83.97 84	91.02 44	75.53 46	85.93 40	82.00 2	94.36 49	83.35 193

HPM-MVS++		79.89 58	79.80 65	80.18 42	89.02 25	78.44 14	83.49 55	80.18 168	64.71 105	78.11 167	88.39 123	65.46 158	83.14 101	77.64 34	91.20 113	78.94 307

SD-MVS		80.28 56	81.55 55	76.47 98	83.57 101	67.83 103	83.39 57	85.35 40	64.42 106	86.14 48	87.07 151	74.02 60	80.97 149	77.70 33	92.32 91	80.62 277
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NR-MVSNet		73.62 127	74.05 132	72.33 184	83.50 102	43.71 396	65.65 349	77.32 226	64.32 107	75.59 232	87.08 149	62.45 188	81.34 137	54.90 288	95.63 8	91.93 8

Gipuma		69.55 229	72.83 164	59.70 413	63.63 469	53.97 263	80.08 89	75.93 247	64.24 108	73.49 293	88.93 110	57.89 265	62.46 419	59.75 226	91.55 103	62.67 501
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SixPastTwentyTwo		75.77 94	76.34 96	74.06 131	81.69 133	54.84 256	76.47 132	75.49 251	64.10 109	87.73 22	92.24 19	50.45 326	81.30 139	67.41 128	91.46 105	86.04 94

EI-MVSNet-Vis-set		72.78 158	71.87 186	75.54 111	74.77 254	59.02 214	72.24 203	71.56 300	63.92 110	78.59 157	71.59 450	66.22 148	78.60 189	67.58 125	80.32 383	89.00 37

CNVR-MVS		78.49 71	78.59 74	78.16 74	85.86 66	67.40 108	78.12 113	81.50 132	63.92 110	77.51 179	86.56 177	68.43 118	84.82 73	73.83 69	91.61 101	82.26 234

plane_prior3						65.67 131			63.82 112	78.23 164

tt0805		76.12 93	78.43 77	69.20 254	81.32 138	41.37 419	76.72 129	77.64 221	63.78 113	82.06 106	87.88 139	79.78 11	79.05 180	64.33 162	92.40 88	87.17 67

UniMVSNet (Re)		75.00 109	75.48 106	73.56 143	83.14 107	47.92 328	70.41 248	81.04 148	63.67 114	79.54 142	86.37 183	62.83 182	81.82 129	57.10 259	95.25 16	90.94 15

ANet_high		67.08 282	69.94 221	58.51 429	57.55 516	27.09 529	58.43 452	76.80 235	63.56 115	82.40 103	91.93 25	59.82 231	64.98 410	50.10 333	88.86 186	83.46 186

SMA-MVS		82.12 33	82.68 44	80.43 39	88.90 29	69.52 83	85.12 37	84.76 54	63.53 116	84.23 81	91.47 37	72.02 75	87.16 8	79.74 13	94.36 49	84.61 145
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
EI-MVSNet-UG-set		72.63 162	71.68 191	75.47 112	74.67 256	58.64 222	72.02 209	71.50 301	63.53 116	78.58 159	71.39 454	65.98 150	78.53 190	67.30 135	80.18 387	89.23 31

pmmvs6		71.82 180	73.66 139	66.31 317	75.94 236	42.01 413	66.99 327	72.53 288	63.45 118	76.43 218	92.78 12	72.95 69	69.69 354	51.41 321	90.46 139	87.22 62

EC-MVSNet		77.08 85	77.39 88	76.14 103	76.86 220	56.87 237	80.32 85	87.52 12	63.45 118	74.66 261	84.52 222	69.87 103	84.94 69	69.76 105	89.59 163	86.60 76

ACMH	63.62 14	77.50 82	80.11 62	69.68 244	79.61 159	56.28 239	78.81 102	83.62 86	63.41 120	87.14 35	90.23 77	76.11 39	73.32 289	67.58 125	94.44 43	79.44 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211		66.02 299	66.89 288	63.43 356	74.22 271	38.14 459	59.00 440	66.13 368	63.33 121	69.76 367	85.95 200	51.88 313	70.50 340	44.23 391	87.52 208	81.64 253

CANet		73.00 149	71.84 188	76.48 97	75.82 238	61.28 180	74.80 161	80.37 165	63.17 122	62.43 458	80.50 324	61.10 212	85.16 68	64.00 165	84.34 299	83.01 207

MP-MVS-pluss		82.54 30	83.46 30	79.76 44	88.88 30	68.44 97	81.57 70	86.33 19	63.17 122	85.38 64	91.26 40	76.33 37	84.67 76	83.30 1	94.96 27	86.17 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MGCNet		75.45 100	74.66 115	77.83 79	75.58 241	61.53 176	78.29 108	77.18 230	63.15 124	69.97 362	87.20 146	57.54 268	87.05 10	74.05 67	88.96 183	84.89 127

Casviewmamba		77.76 77	78.57 75	75.31 114	76.72 221	53.06 270	76.28 142	85.90 26	62.98 125	81.96 108	88.90 111	75.35 47	82.88 108	68.97 110	90.11 149	89.98 21

Vis-MVSNet		74.85 115	74.56 116	75.72 107	81.63 134	64.64 143	76.35 139	79.06 194	62.85 126	73.33 296	88.41 122	62.54 187	79.59 174	63.94 169	82.92 323	82.94 208
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		72.10 175	72.28 179	71.58 195	74.21 272	50.33 291	74.72 165	82.73 105	62.62 127	70.77 348	76.83 388	69.96 102	80.97 149	60.20 213	78.43 416	83.45 188

OMC-MVS		79.41 62	78.79 71	81.28 32	80.62 146	70.71 74	80.91 76	84.76 54	62.54 128	81.77 112	86.65 173	71.46 83	83.53 94	67.95 122	92.44 86	89.60 24

API-MVS		70.97 199	71.51 198	69.37 249	75.20 244	55.94 242	80.99 74	76.84 234	62.48 129	71.24 344	77.51 382	61.51 204	80.96 152	52.04 314	85.76 255	71.22 424

CSCG		74.12 120	74.39 122	73.33 146	79.35 163	61.66 175	77.45 120	81.98 123	62.47 130	79.06 150	80.19 331	61.83 198	78.79 186	59.83 223	87.35 215	79.54 297

ETV-MVS		72.72 160	72.16 182	74.38 126	76.90 218	55.95 241	73.34 187	84.67 59	62.04 131	72.19 320	70.81 458	65.90 152	85.24 64	58.64 239	84.96 270	81.95 244

OurMVSNet-221017-0		78.57 69	78.53 76	78.67 63	80.48 147	64.16 151	80.24 86	82.06 121	61.89 132	88.77 15	93.32 5	57.15 272	82.60 113	70.08 101	92.80 81	89.25 30

plane_prior						65.18 137	80.06 90		61.88 133						89.91 156

KinetiMVS		72.61 163	72.54 171	72.82 170	71.47 333	55.27 250	68.54 297	76.50 237	61.70 134	74.95 253	86.08 195	59.17 242	76.95 230	69.96 102	84.45 291	86.24 87

UGNet		70.20 215	69.05 241	73.65 137	76.24 229	63.64 156	75.87 149	72.53 288	61.48 135	60.93 470	86.14 191	52.37 311	77.12 228	50.67 327	85.21 264	80.17 288
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
VDD-MVS		70.81 203	71.44 199	68.91 266	79.07 174	46.51 361	67.82 308	70.83 318	61.23 136	74.07 278	88.69 115	59.86 230	75.62 250	51.11 323	90.28 143	84.61 145

FMVSNet1		71.06 195	72.48 173	66.81 306	77.65 198	40.68 430	71.96 212	73.03 275	61.14 137	79.45 145	90.36 73	60.44 221	75.20 258	50.20 332	88.05 199	84.54 150

TransMVSNet (Re)		69.62 227	71.63 193	63.57 350	76.51 225	35.93 481	65.75 348	71.29 308	61.05 138	75.02 251	89.90 86	65.88 153	70.41 343	49.79 334	89.48 166	84.38 158

testing3-2		56.85 420	57.62 409	54.53 457	75.84 237	22.23 547	51.26 501	49.10 490	61.04 139	63.74 446	79.73 341	22.29 530	59.44 435	31.16 510	84.43 294	81.92 245

EPNet		69.10 240	67.32 276	74.46 121	68.33 395	61.27 181	77.56 117	63.57 391	60.95 140	56.62 496	82.75 271	51.53 317	81.24 140	54.36 298	90.20 144	80.88 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		67.47 272	67.48 273	67.46 293	70.70 347	54.69 258	66.90 330	78.17 213	60.88 141	70.41 352	74.76 408	61.22 210	73.18 290	47.38 365	76.87 434	74.49 383

RRT-MVS		70.33 210	70.73 212	69.14 257	71.93 326	45.24 376	75.10 155	75.08 257	60.85 142	78.62 156	87.36 145	49.54 332	78.64 188	60.16 215	77.90 425	83.55 180

TSAR-MVS + GP.		73.08 144	71.60 196	77.54 83	78.99 178	70.73 73	74.96 157	69.38 330	60.73 143	74.39 270	78.44 370	57.72 266	82.78 110	60.16 215	89.60 162	79.11 303

MSLP-MVS++		74.48 117	75.78 102	70.59 212	84.66 84	62.40 167	78.65 103	84.24 76	60.55 144	77.71 175	81.98 290	63.12 177	77.64 215	62.95 182	88.14 196	71.73 417

CS-MVS		76.51 89	76.00 100	78.06 78	77.02 209	64.77 142	80.78 77	82.66 107	60.39 145	74.15 274	83.30 257	69.65 106	82.07 125	69.27 109	86.75 241	87.36 61

Baseline_NR-MVSNet		70.62 206	73.19 153	62.92 367	76.97 211	34.44 491	68.84 283	70.88 317	60.25 146	79.50 144	90.53 59	61.82 199	69.11 361	54.67 292	95.27 15	85.22 115

v8		75.07 107	75.64 104	73.35 145	73.42 291	47.46 340	75.20 154	81.45 134	60.05 147	85.64 54	89.26 96	58.08 261	81.80 132	69.71 107	87.97 202	90.79 17

9.14			80.22 61		80.68 145		80.35 84	87.69 11	59.90 148	83.00 93	88.20 129	74.57 56	81.75 133	73.75 70	93.78 64

DeepC-MVS	72.44 4	81.00 47	80.83 58	81.50 25	86.70 45	70.03 80	82.06 66	87.00 15	59.89 149	80.91 127	90.53 59	72.19 72	88.56 1	73.67 71	94.52 39	85.92 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
F-COLMAP		75.29 102	73.99 133	79.18 54	81.73 132	71.90 60	81.86 69	82.98 98	59.86 150	72.27 317	84.00 238	64.56 169	83.07 104	51.48 319	87.19 230	82.56 225

casdiffmvs_mvg		75.26 103	76.18 99	72.52 179	72.87 310	49.47 306	72.94 195	84.71 58	59.49 151	80.90 128	88.81 113	70.07 100	79.71 171	67.40 129	88.39 192	88.40 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
RPSCF		75.76 95	74.37 123	79.93 43	74.81 253	77.53 22	77.53 119	79.30 189	59.44 152	78.88 151	89.80 88	71.26 87	73.09 292	57.45 254	80.89 369	89.17 33

HQP-NCC					82.37 121		77.32 121		59.08 153	71.58 335

ACMP_Plane					82.37 121		77.32 121		59.08 153	71.58 335

HQP-MVS		75.24 104	75.01 111	75.94 104	82.37 121	58.80 218	77.32 121	84.12 79	59.08 153	71.58 335	85.96 199	58.09 259	85.30 60	67.38 132	89.16 174	83.73 177

FA-MVS(test-final)		71.27 192	71.06 205	71.92 193	73.96 280	52.32 276	76.45 134	76.12 244	59.07 156	74.04 280	86.18 188	52.18 312	79.43 176	59.75 226	81.76 343	84.03 167

v10		75.69 96	76.20 98	74.16 129	74.44 267	48.69 313	75.84 150	82.93 100	59.02 157	85.92 50	89.17 101	58.56 252	82.74 111	70.73 95	89.14 177	91.05 13

test_prior2							75.57 151		58.92 158	76.53 214	86.78 164	67.83 129		69.81 104	92.76 83

ZD-MVS					83.91 96	69.36 87		81.09 146	58.91 159	82.73 100	89.11 103	75.77 42	86.63 14	72.73 79	92.93 79

RA-MVS			81.65 52		88.08 37	78.10 17	86.76 22	81.91 125	58.85 160	84.92 71	89.88 87	77.32 29	87.68 5	74.24 63	92.84 80

SPE-MVS-test		74.89 113	74.23 127	76.86 91	77.01 210	62.94 165	78.98 101	84.61 63	58.62 161	70.17 358	80.79 317	66.74 142	81.96 127	61.74 193	89.40 170	85.69 106

NormalMVS		76.15 91	75.08 110	79.36 52	83.87 99	70.01 81	79.92 92	84.34 70	58.60 162	75.21 246	84.02 236	52.85 307	81.82 129	61.45 196	95.50 10	86.24 87

SymmetryMVS		74.00 121	72.85 162	77.43 86	85.17 75	70.01 81	79.92 92	68.48 351	58.60 162	75.21 246	84.02 236	52.85 307	81.82 129	61.45 196	89.99 153	80.47 280

casdiffseed414692147		74.13 119	74.76 114	72.25 188	73.89 283	49.89 303	75.54 152	82.35 115	58.57 164	77.77 172	87.76 141	69.09 110	78.46 193	59.77 224	88.10 198	88.41 48

MG-MVS		70.47 209	71.34 200	67.85 284	79.26 165	40.42 437	74.67 167	75.15 255	58.41 165	68.74 388	88.14 133	56.08 285	83.69 90	59.90 221	81.71 349	79.43 299

EI-MVSNet		69.61 228	69.01 243	71.41 200	73.94 281	49.90 299	71.31 233	71.32 306	58.22 166	75.40 239	70.44 462	58.16 256	75.85 243	62.51 184	79.81 394	88.48 46

IterMVS-LS		73.01 148	73.12 156	72.66 175	73.79 285	49.90 299	71.63 226	78.44 208	58.22 166	80.51 133	86.63 174	58.15 257	79.62 172	62.51 184	88.20 195	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SP-SuperGlue		66.58 290	67.36 274	64.24 337	68.59 390	66.47 120	68.14 303	61.29 408	58.07 168	71.67 330	75.95 394	46.37 356	50.95 475	74.72 53	81.46 360	75.29 372

BH-RMVSNet		68.69 250	68.20 261	70.14 231	76.40 227	53.90 265	64.62 371	73.48 270	58.01 169	73.91 285	81.78 295	59.09 243	78.22 203	48.59 352	77.96 424	78.31 317

test_yl		65.11 309	65.09 319	65.18 328	70.59 349	40.86 425	63.22 393	72.79 282	57.91 170	68.88 383	79.07 363	42.85 386	74.89 264	45.50 385	84.97 267	79.81 290

DCV-MVSNet		65.11 309	65.09 319	65.18 328	70.59 349	40.86 425	63.22 393	72.79 282	57.91 170	68.88 383	79.07 363	42.85 386	74.89 264	45.50 385	84.97 267	79.81 290

DP-MVS Recon		73.57 130	72.69 166	76.23 101	82.85 116	63.39 160	74.32 172	82.96 99	57.75 172	70.35 353	81.98 290	64.34 171	84.41 81	49.69 336	89.95 154	80.89 267

Effi-MVS+-dtu		75.43 101	72.28 179	84.91 2	77.05 207	83.58 1	78.47 106	77.70 220	57.68 173	74.89 255	78.13 376	64.80 166	84.26 82	56.46 267	85.32 263	86.88 71

MVS_111021_HR		72.98 151	72.97 161	72.99 158	80.82 144	65.47 133	68.81 287	72.77 284	57.67 174	75.76 227	82.38 281	71.01 90	77.17 223	61.38 198	86.15 246	76.32 356

3Dnovator	65.95 11	71.50 186	71.22 203	72.34 183	73.16 297	63.09 163	78.37 107	78.32 210	57.67 174	72.22 319	84.61 219	54.77 293	78.47 192	60.82 206	81.07 367	75.45 367

FE-MVS		68.29 258	66.96 285	72.26 186	74.16 273	54.24 261	77.55 118	73.42 273	57.65 176	72.66 311	84.91 213	32.02 467	81.49 136	48.43 355	81.85 341	81.04 261

FC-MVSNet-test		73.32 139	74.78 113	68.93 265	79.21 167	36.57 473	71.82 223	79.54 186	57.63 177	82.57 102	90.38 70	59.38 239	78.99 182	57.91 249	94.56 38	91.23 12

FPMVS		59.43 393	60.07 384	57.51 439	77.62 199	71.52 63	62.33 399	50.92 478	57.40 178	69.40 371	80.00 335	39.14 418	61.92 424	37.47 455	66.36 515	39.09 545

SP-LightGlue		66.16 298	66.97 284	63.75 346	68.62 388	66.76 117	68.82 286	62.15 399	57.30 179	70.52 351	75.63 399	43.02 382	48.82 488	75.09 49	81.55 355	75.66 363

BP-MVS1		71.60 184	70.06 219	76.20 102	74.07 277	55.22 251	74.29 174	73.44 272	57.29 180	73.87 287	84.65 217	32.57 457	83.49 95	72.43 84	87.94 203	89.89 23

testdata1							68.34 302		57.24 181

usedtu_blend_shiyan5		63.30 340	63.13 346	63.78 345	66.67 429	41.75 417	68.57 296	73.64 268	57.20 182	64.46 429	67.75 496	41.94 392	72.34 307	40.72 425	87.24 223	77.26 337

MIMVSNet1		66.57 291	69.23 239	58.59 428	81.26 140	37.73 466	64.06 381	57.62 432	57.02 183	78.40 162	90.75 52	62.65 183	58.10 449	41.77 414	89.58 164	79.95 289

hybridcas		73.97 122	75.17 109	70.38 216	73.56 286	47.22 345	72.99 194	82.30 116	56.94 184	79.54 142	88.05 134	72.64 70	76.88 233	63.11 181	87.43 212	87.04 69

MVS_111021_LR		72.10 175	71.82 189	72.95 160	79.53 161	73.90 50	70.45 247	66.64 363	56.87 185	76.81 201	81.76 297	68.78 111	71.76 323	61.81 191	83.74 309	73.18 395

fmvsm_s_conf0.5_n_3		72.97 152	74.13 130	69.47 248	71.40 335	58.36 224	73.07 190	80.64 157	56.86 186	75.49 236	84.67 216	67.86 128	72.33 309	75.68 45	81.54 357	77.73 331

LCM-MVSNet-Re		69.10 240	71.57 197	61.70 383	70.37 359	34.30 493	61.45 410	79.62 181	56.81 187	89.59 8	88.16 132	68.44 117	72.94 293	42.30 406	87.33 217	77.85 328

BH-untuned		69.39 232	69.46 230	69.18 255	77.96 192	56.88 236	68.47 300	77.53 222	56.77 188	77.79 171	79.63 344	60.30 224	80.20 166	46.04 379	80.65 377	70.47 431

mvs5depth		66.35 295	67.98 263	61.47 388	62.43 478	51.05 284	69.38 267	69.24 332	56.74 189	73.62 288	89.06 106	46.96 355	58.63 442	55.87 273	88.49 190	74.73 378

DeepC-MVS_fast	69.89 7	77.17 84	76.33 97	79.70 47	83.90 97	67.94 100	80.06 90	83.75 84	56.73 190	74.88 256	85.32 208	65.54 156	87.79 2	65.61 148	91.14 116	83.35 193
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	71.07 5	78.48 72	77.14 91	82.52 16	84.39 92	77.04 30	76.35 139	84.05 81	56.66 191	80.27 136	85.31 209	68.56 113	87.03 12	67.39 130	91.26 110	83.50 181

save fliter					87.00 40	67.23 112	79.24 98	77.94 218	56.65 192

VPA-MVSNet		68.71 248	70.37 217	63.72 348	76.13 231	38.06 461	64.10 380	71.48 302	56.60 193	74.10 276	88.31 127	64.78 167	69.72 353	47.69 364	90.15 146	83.37 192

fmvsm_s_conf0.5_n_8		72.87 156	72.85 162	72.93 163	72.25 320	59.01 215	72.35 201	80.13 170	56.32 194	75.74 228	84.12 231	60.14 225	75.05 262	71.71 88	82.90 324	84.75 137

GeoE		73.14 142	73.77 138	71.26 203	78.09 188	52.64 274	74.32 172	79.56 185	56.32 194	76.35 220	83.36 255	70.76 93	77.96 209	63.32 178	81.84 342	83.18 198

FIs		72.56 164	73.80 136	68.84 268	78.74 181	37.74 465	71.02 237	79.83 175	56.12 196	80.88 129	89.45 93	58.18 255	78.28 202	56.63 263	93.36 74	90.51 19

E5new		73.42 132	74.46 118	70.29 222	74.61 260	47.14 346	71.85 220	83.01 94	56.07 197	77.28 185	86.81 157	71.54 80	77.15 224	64.59 155	84.39 295	86.59 77

E6new		73.42 132	74.46 118	70.29 222	74.60 262	47.14 346	71.86 218	82.99 96	56.07 197	77.28 185	86.81 157	71.55 78	77.14 226	64.59 155	84.39 295	86.59 77

E6		73.42 132	74.46 118	70.29 222	74.60 262	47.14 346	71.86 218	82.99 96	56.07 197	77.28 185	86.81 157	71.55 78	77.14 226	64.59 155	84.39 295	86.59 77

E5		73.42 132	74.46 118	70.29 222	74.61 260	47.14 346	71.85 220	83.01 94	56.07 197	77.28 185	86.81 157	71.54 80	77.15 224	64.59 155	84.39 295	86.59 77

testing3		58.28 404	58.38 401	58.00 434	77.45 202	26.12 536	60.78 420	43.00 525	56.02 201	70.18 357	75.76 395	13.27 556	67.24 385	48.02 360	80.89 369	80.65 276

tfpnnormal		66.48 292	67.93 264	62.16 376	73.40 292	36.65 472	63.45 388	64.99 378	55.97 202	72.82 308	87.80 140	57.06 275	69.10 362	48.31 357	87.54 207	80.72 274

baseline		73.10 143	73.96 134	70.51 214	71.46 334	46.39 365	72.08 207	84.40 69	55.95 203	76.62 208	86.46 181	67.20 132	78.03 208	64.22 163	87.27 221	87.11 68

wuyk23d		61.97 361	66.25 295	49.12 490	58.19 513	60.77 192	66.32 339	52.97 468	55.93 204	90.62 5	86.91 155	73.07 66	35.98 544	20.63 548	91.63 100	50.62 529

Fast-Effi-MVS+-dtu		70.00 219	68.74 248	73.77 136	73.47 290	64.53 144	71.36 231	78.14 215	55.81 205	68.84 385	74.71 410	65.36 159	75.75 247	52.00 315	79.00 406	81.03 262

casdiffmvs		73.06 146	73.84 135	70.72 210	71.32 337	46.71 356	70.93 239	84.26 75	55.62 206	77.46 182	87.10 148	67.09 134	77.81 211	63.95 167	86.83 238	87.64 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E4		72.74 159	73.54 143	70.35 219	74.85 251	46.82 353	69.53 262	82.80 101	55.60 207	76.23 221	86.50 179	69.87 103	77.45 217	63.72 171	82.77 327	86.76 74

pm-mvs1		68.40 254	69.85 224	64.04 342	73.10 301	39.94 440	64.61 372	70.50 320	55.52 208	73.97 282	89.33 94	63.91 174	68.38 369	49.68 337	88.02 200	83.81 173

mmtdpeth		68.76 246	70.55 215	63.40 357	67.06 425	56.26 240	68.73 293	71.22 312	55.47 209	70.09 359	88.64 118	65.29 161	56.89 454	58.94 235	89.50 165	77.04 347

v2v482		72.55 166	72.58 170	72.43 181	72.92 309	46.72 355	71.41 230	79.13 193	55.27 210	81.17 123	85.25 210	55.41 290	81.13 142	67.25 136	85.46 258	89.43 26

thres100view900		61.17 373	61.09 371	61.39 389	72.14 323	35.01 487	65.42 354	56.99 441	55.23 211	70.71 349	79.90 338	32.07 465	72.09 312	35.61 476	81.73 346	77.08 344

TAPA-MVS	65.27 12	75.16 105	74.29 126	77.77 82	74.86 250	68.08 98	77.89 114	84.04 82	55.15 212	76.19 223	83.39 251	66.91 136	80.11 167	60.04 219	90.14 147	85.13 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EG-PatchMatch MVS		70.70 205	70.88 208	70.16 230	82.64 120	58.80 218	71.48 228	73.64 268	54.98 213	76.55 212	81.77 296	61.10 212	78.94 183	54.87 289	80.84 372	72.74 403

LuminaMVS		71.15 194	70.79 211	72.24 189	77.20 203	58.34 225	72.18 205	76.20 242	54.91 214	77.74 173	81.93 293	49.17 338	76.31 241	62.12 190	85.66 256	82.07 238

GBi-Net		68.30 256	68.79 245	66.81 306	73.14 298	40.68 430	71.96 212	73.03 275	54.81 215	74.72 258	90.36 73	48.63 345	75.20 258	47.12 366	85.37 259	84.54 150

test1		68.30 256	68.79 245	66.81 306	73.14 298	40.68 430	71.96 212	73.03 275	54.81 215	74.72 258	90.36 73	48.63 345	75.20 258	47.12 366	85.37 259	84.54 150

FMVSNet2		67.48 270	68.21 259	65.29 326	73.14 298	38.94 451	68.81 287	71.21 313	54.81 215	76.73 205	86.48 180	48.63 345	74.60 268	47.98 361	86.11 249	82.35 230

v148		69.38 233	69.39 231	69.36 250	69.14 382	44.56 385	68.83 285	72.70 286	54.79 218	78.59 157	84.12 231	54.69 294	76.74 237	59.40 229	82.20 334	86.79 72

thres600view7		61.82 364	61.38 368	63.12 360	71.81 328	34.93 488	64.64 370	56.99 441	54.78 219	70.33 354	79.74 340	32.07 465	72.42 304	38.61 440	83.46 317	82.02 239

tttt0517		69.46 230	67.79 268	74.46 121	75.34 242	52.72 273	75.05 156	63.27 395	54.69 220	78.87 152	84.37 225	26.63 504	81.15 141	63.95 167	87.93 204	89.51 25

RPMNet		65.77 302	65.08 321	67.84 285	66.37 432	48.24 322	70.93 239	86.27 20	54.66 221	61.35 463	86.77 165	33.29 449	85.67 52	55.93 271	70.17 495	69.62 441

VNet		64.01 331	65.15 316	60.57 401	73.28 294	35.61 484	57.60 457	67.08 360	54.61 222	66.76 408	83.37 253	56.28 283	66.87 391	42.19 408	85.20 265	79.23 302

MGCFI-Net		71.70 182	73.10 157	67.49 292	73.23 295	43.08 405	72.06 208	82.43 113	54.58 223	75.97 225	82.00 288	72.42 71	75.22 256	57.84 250	87.34 216	84.18 163

PLC	62.01 16	71.79 181	70.28 218	76.33 99	80.31 150	68.63 96	78.18 112	81.24 140	54.57 224	67.09 406	80.63 321	59.44 237	81.74 134	46.91 369	84.17 301	78.63 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
nrg030		74.87 114	75.99 101	71.52 197	74.90 249	49.88 304	74.10 177	82.58 109	54.55 225	83.50 90	89.21 98	71.51 82	75.74 248	61.24 200	92.34 90	88.94 39

fmvsm_s_conf0.5_n_9		74.56 116	74.30 125	75.34 113	77.17 204	64.87 141	72.62 197	76.17 243	54.54 226	78.32 163	86.14 191	65.14 164	75.72 249	73.10 74	85.55 257	85.42 111

E2		71.98 177	72.60 168	70.13 232	74.09 275	46.61 357	69.15 275	82.56 110	54.40 227	75.32 244	85.35 205	68.51 114	77.34 219	62.30 188	81.74 345	86.44 84

E3		71.98 177	72.60 168	70.13 232	74.09 275	46.61 357	69.15 275	82.56 110	54.40 227	75.31 245	85.35 205	68.51 114	77.34 219	62.30 188	81.75 344	86.44 84

BridgeMVS		73.59 129	74.06 131	72.17 190	77.48 201	47.72 334	81.43 72	82.20 119	54.38 229	79.19 147	87.68 143	54.41 297	83.57 92	63.98 166	85.78 254	85.22 115

sasdasda		72.29 172	73.38 148	69.04 258	74.23 269	47.37 341	73.93 179	83.18 90	54.36 230	76.61 209	81.64 301	72.03 73	75.34 253	57.12 257	87.28 219	84.40 156

canonicalmvs		72.29 172	73.38 148	69.04 258	74.23 269	47.37 341	73.93 179	83.18 90	54.36 230	76.61 209	81.64 301	72.03 73	75.34 253	57.12 257	87.28 219	84.40 156

h-mvs33		73.08 144	71.61 195	77.48 84	83.89 98	72.89 58	70.47 246	71.12 314	54.28 232	77.89 168	83.41 250	49.04 339	80.98 148	63.62 174	90.77 134	78.58 312

hse-mvs2		72.32 170	70.66 214	77.31 89	83.10 111	71.77 61	69.19 274	71.45 303	54.28 232	77.89 168	78.26 372	49.04 339	79.23 177	63.62 174	89.13 178	80.92 266

test2506		61.23 372	60.85 376	62.38 373	78.80 179	27.88 526	67.33 318	37.42 545	54.23 234	67.55 401	88.68 116	17.87 548	74.39 273	46.33 376	89.41 168	84.86 130

ECVR-MVS		64.82 316	65.22 312	63.60 349	78.80 179	31.14 510	66.97 328	56.47 447	54.23 234	69.94 363	88.68 116	37.23 430	74.81 266	45.28 388	89.41 168	84.86 130

CDPH-MVS		77.33 83	77.06 92	78.14 75	84.21 93	63.98 155	76.07 146	83.45 88	54.20 236	77.68 176	87.18 147	69.98 101	85.37 57	68.01 120	92.72 84	85.08 123

VPNet		65.58 305	67.56 270	59.65 415	79.72 158	30.17 516	60.27 427	62.14 400	54.19 237	71.24 344	86.63 174	58.80 248	67.62 378	44.17 392	90.87 131	81.18 258

PHI-MVS		74.92 110	74.36 124	76.61 94	76.40 227	62.32 169	80.38 82	83.15 92	54.16 238	73.23 298	80.75 318	62.19 194	83.86 85	68.02 119	90.92 127	83.65 178

test1111		64.62 320	65.19 313	62.93 366	79.01 175	29.91 518	65.45 353	54.41 458	54.09 239	71.47 342	88.48 121	37.02 431	74.29 276	46.83 371	89.94 155	84.58 148

Patchmtry		60.91 378	63.01 350	54.62 456	66.10 439	26.27 535	67.47 313	56.40 448	54.05 240	72.04 325	86.66 171	33.19 450	60.17 432	43.69 393	87.45 211	77.42 332

train_agg		76.38 90	76.55 95	75.86 106	85.47 70	69.32 88	76.42 136	78.69 203	54.00 241	76.97 192	86.74 166	66.60 143	81.10 143	72.50 83	91.56 102	77.15 341

test_8					85.09 77	67.89 101	76.26 143	78.66 205	54.00 241	76.89 196	86.72 169	66.60 143	80.89 153

DELS-MVS		68.83 244	68.31 254	70.38 216	70.55 353	48.31 320	63.78 385	82.13 120	54.00 241	68.96 376	75.17 406	58.95 245	80.06 168	58.55 240	82.74 328	82.76 216
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
alignmvs		70.54 207	71.00 206	69.15 256	73.50 288	48.04 327	69.85 258	79.62 181	53.94 244	76.54 213	82.00 288	59.00 244	74.68 267	57.32 255	87.21 228	84.72 140

v1144		73.29 140	73.39 147	73.01 157	74.12 274	48.11 324	72.01 210	81.08 147	53.83 245	81.77 112	84.68 215	58.07 262	81.91 128	68.10 117	86.86 236	88.99 38

TEST9					85.47 70	69.32 88	76.42 136	78.69 203	53.73 246	76.97 192	86.74 166	66.84 137	81.10 143

SSM_0407		72.15 174	71.85 187	73.06 156	76.92 213	55.22 251	73.59 181	79.83 175	53.69 247	73.08 302	84.18 228	62.26 192	81.98 126	58.21 245	84.91 274	81.99 241

SSM_0404		72.51 167	72.15 183	73.60 140	78.20 185	55.86 244	74.41 171	79.83 175	53.69 247	73.98 281	84.18 228	62.26 192	82.50 114	58.21 245	84.60 285	82.43 228

balanced_ft_v1		71.65 183	72.22 181	69.92 240	74.26 268	45.74 371	81.54 71	79.66 179	53.65 249	79.77 140	86.74 166	51.20 321	80.64 155	58.70 238	84.47 290	83.40 189

viewcassd2359sk11		71.41 189	71.89 185	69.98 238	73.50 288	46.46 362	68.91 282	82.39 114	53.62 250	74.57 265	84.41 224	67.40 131	77.27 221	61.35 199	80.89 369	86.21 90

viewdifsd2359ckpt07		70.24 212	71.30 201	67.05 302	70.55 353	43.90 394	67.15 324	77.48 224	53.60 251	75.49 236	85.35 205	71.42 85	72.13 311	59.03 233	81.60 354	85.12 120

Vis-MVSNet (Re-imp)		62.74 350	63.21 345	61.34 391	72.19 322	31.56 507	67.31 319	53.87 460	53.60 251	69.88 364	83.37 253	40.52 407	70.98 335	41.40 416	86.78 240	81.48 255

PS-MVSNAJss		77.54 79	77.35 89	78.13 76	84.88 80	66.37 123	78.55 105	79.59 184	53.48 253	86.29 45	92.43 17	62.39 189	80.25 163	67.90 123	90.61 136	87.77 55

FE-MVSNET2		68.70 249	69.85 224	65.22 327	74.82 252	37.95 463	67.28 322	73.47 271	53.40 254	77.65 177	87.72 142	59.72 233	73.17 291	46.39 374	88.23 194	84.56 149

MDA-MVSNet-bldmvs		62.34 356	61.73 362	64.16 338	61.64 483	49.90 299	48.11 512	57.24 439	53.31 255	80.95 125	79.39 351	49.00 341	61.55 426	45.92 381	80.05 389	81.03 262

TinyColmap		67.98 262	69.28 236	64.08 340	67.98 405	46.82 353	70.04 253	75.26 253	53.05 256	77.36 183	86.79 162	59.39 238	72.59 301	45.64 383	88.01 201	72.83 401

E3new		70.94 200	71.30 201	69.86 242	72.98 308	46.34 366	68.74 292	82.28 117	53.01 257	73.95 283	83.57 248	66.41 146	77.21 222	60.68 208	80.06 388	86.03 95

tfpn200view9		60.35 384	59.97 385	61.51 386	70.78 343	35.35 485	63.27 391	57.47 434	53.00 258	68.31 393	77.09 386	32.45 460	72.09 312	35.61 476	81.73 346	77.08 344

thres400		60.77 381	59.97 385	63.15 359	70.78 343	35.35 485	63.27 391	57.47 434	53.00 258	68.31 393	77.09 386	32.45 460	72.09 312	35.61 476	81.73 346	82.02 239

viewmamba		69.26 234	69.34 234	69.03 260	64.17 463	47.67 336	67.23 323	76.95 233	52.82 260	73.15 301	83.23 261	62.99 180	74.06 279	63.71 172	79.80 396	85.36 113

v1192		73.40 137	73.42 146	73.32 147	74.65 259	48.67 314	72.21 204	81.73 128	52.76 261	81.85 110	84.56 220	57.12 273	82.24 123	68.58 113	87.33 217	89.06 35

MVS_Test		69.84 223	70.71 213	67.24 297	67.49 415	43.25 404	69.87 257	81.22 142	52.69 262	71.57 338	86.68 170	62.09 195	74.51 269	66.05 142	78.74 410	83.96 168

viewmacassd2359aftdt		71.41 189	72.29 178	68.78 269	71.32 337	44.81 381	70.11 252	81.51 131	52.64 263	74.95 253	86.79 162	66.02 149	74.50 270	62.43 187	84.86 278	87.03 70

SP-DiffGlue		64.90 314	65.69 305	62.51 371	69.18 379	64.39 146	69.79 259	60.46 413	52.50 264	75.70 229	72.08 442	44.17 370	48.59 493	67.84 124	79.52 401	74.54 381

MonoMVSNet		62.75 349	63.42 340	60.73 400	65.60 444	40.77 428	72.49 199	70.56 319	52.49 265	75.07 250	79.42 349	39.52 416	69.97 351	46.59 373	69.06 501	71.44 420

EIA-MVS		68.59 252	67.16 279	72.90 165	75.18 245	55.64 248	69.39 266	81.29 138	52.44 266	64.53 427	70.69 459	60.33 223	82.30 121	54.27 299	76.31 439	80.75 272

usedtu_dtu_shiyan2		62.25 357	62.27 357	62.18 375	77.08 206	52.84 272	62.56 397	56.33 450	52.43 267	64.22 436	83.26 259	48.47 348	58.06 450	25.75 535	90.34 142	75.64 364

MVSFormer		69.93 221	69.03 242	72.63 177	74.93 247	59.19 207	83.98 46	75.72 249	52.27 268	63.53 451	76.74 389	43.19 379	80.56 156	72.28 85	78.67 412	78.14 322

test_djsdf		78.88 66	78.27 78	80.70 38	81.42 136	71.24 66	83.98 46	75.72 249	52.27 268	87.37 31	92.25 18	68.04 124	80.56 156	72.28 85	91.15 115	90.32 20

mamba_0408		70.32 211	69.35 232	73.24 148	76.92 213	55.22 251	56.61 463	79.27 190	52.14 270	73.08 302	83.14 267	60.53 217	82.50 114	57.51 252	84.91 274	81.99 241

SSM_04072		67.23 279	69.35 232	60.89 398	76.92 213	55.22 251	56.61 463	79.27 190	52.14 270	73.08 302	83.14 267	60.53 217	45.46 513	57.51 252	84.91 274	81.99 241

CLD-MVS		72.88 155	72.36 177	74.43 124	77.03 208	54.30 260	68.77 290	83.43 89	52.12 272	76.79 203	74.44 414	69.54 107	83.91 84	55.88 272	93.25 76	85.09 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		53.35 450	56.47 422	43.99 514	64.19 462	17.46 551	59.15 437	43.10 523	52.11 273	54.74 509	86.95 154	29.97 490	49.98 482	43.62 394	74.40 458	64.53 494

CANet_DTU		64.04 330	63.83 333	64.66 334	68.39 391	42.97 407	73.45 185	74.50 263	52.05 274	54.78 508	75.44 404	43.99 371	70.42 342	53.49 308	78.41 418	80.59 278

mvs_tets		78.93 65	78.67 73	79.72 46	84.81 82	73.93 49	80.65 78	76.50 237	51.98 275	87.40 28	91.86 28	76.09 40	78.53 190	68.58 113	90.20 144	86.69 75

v1240		73.06 146	73.14 154	72.84 169	74.74 255	47.27 344	71.88 217	81.11 144	51.80 276	82.28 104	84.21 227	56.22 284	82.34 120	68.82 112	87.17 232	88.91 40

TSAR-MVS + MP.		79.05 64	78.81 70	79.74 45	88.94 27	67.52 106	86.61 23	81.38 137	51.71 277	77.15 190	91.42 39	65.49 157	87.20 7	79.44 17	87.17 232	84.51 154
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1921920		72.96 153	72.98 160	72.89 166	74.67 256	47.58 337	71.92 215	80.69 154	51.70 278	81.69 116	83.89 243	56.58 280	82.25 122	68.34 115	87.36 214	88.82 42

v144192		72.99 150	73.06 158	72.77 171	74.58 264	47.48 339	71.90 216	80.44 163	51.57 279	81.46 119	84.11 233	58.04 263	82.12 124	67.98 121	87.47 210	88.70 45

FMVSNet3		65.00 313	65.16 314	64.52 336	69.47 377	37.56 468	66.63 333	70.38 321	51.55 280	74.72 258	83.27 258	37.89 427	74.44 272	47.12 366	85.37 259	81.57 254

c3_l		69.82 224	69.89 222	69.61 246	66.24 435	43.48 400	68.12 305	79.61 183	51.43 281	77.72 174	80.18 332	54.61 296	78.15 207	63.62 174	87.50 209	87.20 65

SDMVSNet		66.36 294	67.85 267	61.88 380	73.04 304	46.14 368	58.54 450	71.36 305	51.42 282	68.93 379	82.72 273	65.62 155	62.22 423	54.41 296	84.67 281	77.28 334

sd_testset		63.55 334	65.38 310	58.07 432	73.04 304	38.83 453	57.41 458	65.44 375	51.42 282	68.93 379	82.72 273	63.76 175	58.11 448	41.05 419	84.67 281	77.28 334

SSC-MVS3.2		57.01 419	59.50 390	49.57 486	67.73 410	25.95 537	46.68 518	51.75 475	51.41 284	63.84 443	79.66 343	53.28 305	50.34 479	37.85 450	83.28 320	72.41 407

V42		71.06 195	70.83 209	71.72 194	67.25 417	47.14 346	65.94 343	80.35 166	51.35 285	83.40 91	83.23 261	59.25 240	78.80 185	65.91 144	80.81 373	89.23 31

jajsoiax		78.51 70	78.16 80	79.59 48	84.65 85	73.83 51	80.42 81	76.12 244	51.33 286	87.19 33	91.51 36	73.79 63	78.44 195	68.27 116	90.13 148	86.49 83

GA-MVS		62.91 345	61.66 363	66.66 311	67.09 420	44.49 389	61.18 415	69.36 331	51.33 286	69.33 372	74.47 413	36.83 432	74.94 263	50.60 329	74.72 453	80.57 279

RoMa-HiRes		73.61 128	73.51 144	73.92 133	82.27 125	81.71 3	77.59 115	64.83 381	51.32 288	88.72 16	83.92 241	60.47 220	61.70 425	60.01 220	92.44 86	78.34 315

CL-MVSNet_self_test		62.44 355	63.40 342	59.55 417	72.34 319	32.38 502	56.39 465	64.84 380	51.21 289	67.46 402	81.01 313	50.75 324	63.51 417	38.47 442	88.12 197	82.75 217

PM-MVS		64.49 323	63.61 337	67.14 300	76.68 222	75.15 40	68.49 299	42.85 526	51.17 290	77.85 170	80.51 323	45.76 358	66.31 401	52.83 313	76.35 438	59.96 514

onestephybrid01		68.67 251	68.21 259	70.07 235	64.40 461	49.83 305	67.51 311	76.41 239	51.08 291	71.78 327	81.97 292	59.69 234	75.32 255	59.85 222	81.20 362	85.06 125

fmvsm_s_conf0.5_n_11		71.06 195	70.91 207	71.51 198	72.09 324	59.40 204	73.49 183	79.97 173	50.98 292	68.33 392	81.50 304	61.82 199	72.64 297	69.54 108	80.43 381	82.51 226

原ACMM1				73.90 134	85.90 63	65.15 139		81.67 129	50.97 293	74.25 273	86.16 190	61.60 202	83.54 93	56.75 262	91.08 121	73.00 397

viewmanbaseed2359cas		70.24 212	70.83 209	68.48 274	69.99 368	44.55 387	69.48 264	81.01 149	50.87 294	73.61 289	84.84 214	64.00 172	74.31 275	60.24 212	83.43 318	86.56 81

JIA-IIPM		54.03 444	51.62 466	61.25 393	59.14 506	55.21 255	59.10 439	47.72 497	50.85 295	50.31 528	85.81 202	20.10 539	63.97 413	36.16 471	55.41 543	64.55 493

KD-MVS_self_test		66.38 293	67.51 271	62.97 365	61.76 482	34.39 492	58.11 455	75.30 252	50.84 296	77.12 191	85.42 204	56.84 277	69.44 358	51.07 324	91.16 114	85.08 123

eth_miper_zixun_eth		69.42 231	68.73 249	71.50 199	67.99 404	46.42 363	67.58 310	78.81 198	50.72 297	78.13 166	80.34 327	50.15 328	80.34 161	60.18 214	84.65 283	87.74 56

FE-MVSNET		62.77 348	64.36 326	57.97 435	70.52 355	33.96 494	61.66 407	67.88 356	50.67 298	73.18 299	82.58 277	48.03 350	68.22 371	43.21 397	81.55 355	71.74 416

Fast-Effi-MVS+		68.81 245	68.30 255	70.35 219	74.66 258	48.61 319	66.06 341	78.32 210	50.62 299	71.48 341	75.54 401	68.75 112	79.59 174	50.55 330	78.73 411	82.86 213

viewdifsd2359ckpt11		69.22 235	69.68 228	67.83 286	68.17 399	46.57 359	66.42 337	68.93 339	50.60 300	77.47 181	83.95 239	68.16 120	73.84 285	58.49 241	84.92 272	83.10 200

viewmsd2359difaftdt		69.22 235	69.68 228	67.83 286	68.17 399	46.57 359	66.42 337	68.93 339	50.60 300	77.48 180	83.94 240	68.16 120	73.84 285	58.49 241	84.92 272	83.10 200

anonymousdsp		78.60 68	77.80 82	81.00 34	78.01 191	74.34 47	80.09 88	76.12 244	50.51 302	89.19 10	90.88 48	71.45 84	77.78 213	73.38 72	90.60 137	90.90 16

SP-NN		62.65 352	63.58 338	59.87 412	64.90 457	59.38 205	64.50 375	60.00 417	50.42 303	66.09 414	73.43 428	43.16 381	46.39 506	71.17 90	78.53 414	73.85 390

testing91		55.74 431	55.29 441	57.08 441	70.63 348	30.85 512	54.94 479	56.31 451	50.34 304	57.08 490	70.10 470	24.50 518	65.86 402	36.98 460	76.75 435	74.53 382

dcpmvs_2		71.02 198	72.65 167	66.16 318	76.06 235	50.49 289	71.97 211	79.36 187	50.34 304	82.81 98	83.63 247	64.38 170	67.27 384	61.54 195	83.71 312	80.71 275

thres200		57.55 412	57.02 415	59.17 419	67.89 408	34.93 488	58.91 443	57.25 438	50.24 306	64.01 440	71.46 452	32.49 458	71.39 329	31.31 508	79.57 400	71.19 426

thisisatest0530		67.05 285	65.16 314	72.73 174	73.10 301	50.55 288	71.26 235	63.91 389	50.22 307	74.46 268	80.75 318	26.81 503	80.25 163	59.43 228	86.50 244	87.37 60

test20.03		55.74 431	57.51 412	50.42 479	59.89 500	32.09 504	50.63 502	49.01 491	50.11 308	65.07 423	83.23 261	45.61 360	48.11 497	30.22 513	83.82 306	71.07 428

BH-w/o		64.81 317	64.29 329	66.36 315	76.08 234	54.71 257	65.61 350	75.23 254	50.10 309	71.05 347	71.86 449	54.33 298	79.02 181	38.20 445	76.14 440	65.36 484

cl____		68.26 261	68.26 256	68.29 278	64.98 454	43.67 397	65.89 344	74.67 259	50.04 310	76.86 198	82.42 279	48.74 343	75.38 251	60.92 205	89.81 158	85.80 103

DIV-MVS_self_test		68.27 259	68.26 256	68.29 278	64.98 454	43.67 397	65.89 344	74.67 259	50.04 310	76.86 198	82.43 278	48.74 343	75.38 251	60.94 204	89.81 158	85.81 99

guyue		66.95 287	66.74 291	67.56 291	70.12 367	51.14 283	65.05 362	68.68 348	49.98 312	74.64 262	80.83 316	50.77 323	70.34 344	57.72 251	82.89 325	81.21 256

EPNet_dtu		58.93 398	58.52 398	60.16 411	67.91 407	47.70 335	69.97 255	58.02 431	49.73 313	47.28 535	73.02 434	38.14 423	62.34 421	36.57 466	85.99 251	70.43 432
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GDP-MVS		70.84 201	69.24 238	75.62 109	76.44 226	55.65 247	74.62 169	82.78 104	49.63 314	72.10 322	83.79 245	31.86 468	82.84 109	64.93 153	87.01 235	88.39 50

QAPM		69.18 238	69.26 237	68.94 264	71.61 331	52.58 275	80.37 83	78.79 201	49.63 314	73.51 291	85.14 211	53.66 302	79.12 179	55.11 282	75.54 446	75.11 374

fmvsm_s_conf0.5_n_7		67.30 276	66.92 286	68.43 275	72.78 312	58.22 227	60.90 418	72.51 290	49.62 316	63.66 448	80.65 320	58.56 252	68.63 366	62.83 183	80.76 374	78.45 314

PAPR		69.20 237	68.66 250	70.82 208	75.15 246	47.77 332	75.31 153	81.11 144	49.62 316	66.33 413	79.27 357	61.53 203	82.96 105	48.12 359	81.50 359	81.74 252

testing99		55.16 437	54.56 448	56.98 443	70.13 366	30.58 514	54.55 482	54.11 459	49.53 318	56.76 494	70.14 469	22.76 527	65.79 404	36.99 459	76.04 442	74.57 380

viewdifsd2359ckpt09		72.87 156	72.43 175	74.17 128	74.45 265	51.70 277	76.39 138	84.50 67	49.48 319	75.34 243	83.23 261	63.12 177	82.43 117	56.99 261	88.41 191	88.37 51

SP-MNN		63.33 338	64.30 327	60.41 407	66.01 440	60.04 199	65.58 352	60.61 410	49.33 320	69.45 369	73.75 424	41.65 396	48.61 492	69.96 102	82.36 332	72.57 404

TR-MVS		64.59 321	63.54 339	67.73 290	75.75 240	50.83 287	63.39 389	70.29 322	49.33 320	71.55 339	74.55 412	50.94 322	78.46 193	40.43 427	75.69 444	73.89 389

gbinet_0.2-2-1-0.02		62.58 353	61.83 359	64.86 333	67.07 422	41.37 419	61.56 408	67.91 355	49.27 322	66.62 410	67.23 504	41.53 398	74.46 271	45.94 380	89.31 173	78.74 309

diffmvs_AUTHOR		68.27 259	68.59 251	67.32 296	63.76 466	45.37 374	65.31 356	77.19 229	49.25 323	72.68 310	82.19 284	59.62 235	71.17 331	65.75 146	81.53 358	85.42 111

DKM-HiRes		70.49 208	69.89 222	72.31 185	81.51 135	80.92 7	73.23 189	58.80 427	49.23 324	84.44 79	81.39 305	49.91 329	61.22 428	59.28 231	91.22 112	74.79 376

cl22		67.14 280	66.51 292	69.03 260	63.20 470	43.46 401	66.88 331	76.25 241	49.22 325	74.48 267	77.88 378	45.49 361	77.40 218	60.64 209	84.59 286	86.24 87

AUN-MVS		70.22 214	67.88 266	77.22 90	82.96 115	71.61 62	69.08 277	71.39 304	49.17 326	71.70 329	78.07 377	37.62 429	79.21 178	61.81 191	89.15 176	80.82 269

miper_ehance_all_eth		68.36 255	68.16 262	68.98 262	65.14 453	43.34 402	67.07 326	78.92 197	49.11 327	76.21 222	77.72 379	53.48 303	77.92 210	61.16 202	84.59 286	85.68 107

fmvsm_s_conf0.5_n_6		70.08 217	69.97 220	70.39 215	72.99 307	58.93 216	68.84 283	76.40 240	49.08 328	68.75 387	81.65 300	57.34 270	71.97 316	70.91 93	83.81 307	80.26 285

ab-mvs		64.11 329	65.13 317	61.05 395	71.99 325	38.03 462	67.59 309	68.79 347	49.08 328	65.32 421	86.26 186	58.02 264	66.85 393	39.33 432	79.79 397	78.27 318

AstraMVS		67.11 281	66.84 290	67.92 282	70.75 346	51.36 281	64.77 367	67.06 361	49.03 330	75.40 239	82.05 286	51.26 320	70.65 337	58.89 236	82.32 333	81.77 250

myMVS_eth3d28		51.35 467	51.99 465	49.44 487	69.21 378	22.51 545	49.82 507	49.11 489	49.00 331	55.03 506	70.31 465	22.73 528	52.88 468	24.33 541	78.39 419	72.92 398

fmvsm_l_conf0.5_n_3		71.98 177	71.68 191	72.88 167	72.84 311	64.15 152	73.48 184	77.11 231	48.97 332	71.31 343	84.18 228	67.98 126	71.60 327	68.86 111	80.43 381	82.89 210

testing915		59.64 390	60.70 378	56.44 446	71.85 327	30.24 515	65.32 355	58.86 426	48.85 333	60.89 471	78.77 367	42.12 390	67.38 382	35.37 479	84.31 300	71.72 418

OpenMVS	62.51 15	68.76 246	68.75 247	68.78 269	70.56 351	53.91 264	78.29 108	77.35 225	48.85 333	70.22 355	83.52 249	52.65 310	76.93 231	55.31 280	81.99 337	75.49 366

fmvsm_s_conf0.5_n_10		72.30 171	72.02 184	73.15 152	70.76 345	59.05 213	73.40 186	79.63 180	48.80 335	75.39 242	84.03 235	59.60 236	75.18 261	72.85 77	83.68 314	85.21 118

fmvsm_s_conf0.5_n_4		70.18 216	69.83 226	71.24 204	71.65 330	58.59 223	69.29 270	71.66 297	48.69 336	71.62 332	82.11 285	59.94 228	70.03 349	74.52 58	78.96 407	85.10 121

MAR-MVS		67.72 267	66.16 297	72.40 182	74.45 265	64.99 140	74.87 158	77.50 223	48.67 337	65.78 418	68.58 492	57.01 276	77.79 212	46.68 372	81.92 338	74.42 385
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PVSNet_Blended_VisFu		70.04 218	68.88 244	73.53 144	82.71 118	63.62 157	74.81 160	81.95 124	48.53 338	67.16 405	79.18 360	51.42 318	78.38 198	54.39 297	79.72 399	78.60 311

fmvsm_l_mol_unc0.5_1		67.37 274	67.71 269	66.34 316	68.12 402	43.59 399	61.82 403	58.96 425	48.28 339	80.57 132	88.00 136	54.81 292	72.39 305	65.22 149	83.61 315	83.05 205

hybridnocas07		66.30 297	66.22 296	66.51 313	60.68 490	44.53 388	64.01 382	74.60 261	48.26 340	70.21 356	81.74 299	56.61 278	71.06 333	60.70 207	79.20 404	83.94 170

fmvsm_s_conf0.1_n_2		69.14 239	68.42 253	71.28 202	68.30 396	57.60 232	65.06 361	69.91 324	48.24 341	74.56 266	82.84 270	55.55 289	69.73 352	70.66 97	80.69 376	86.52 82

diffmvs		67.42 273	67.50 272	67.20 298	62.26 480	45.21 377	64.87 364	77.04 232	48.21 342	71.74 328	79.70 342	58.40 254	71.17 331	64.99 151	80.27 384	85.22 115
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt13		69.89 222	69.74 227	70.32 221	70.82 342	48.73 311	72.39 200	81.39 136	48.20 343	72.73 309	82.73 272	62.61 184	76.50 238	55.87 273	80.93 368	85.73 105

fmvsm_s_conf0.5_n_2		68.93 242	68.23 258	71.02 206	67.78 409	57.58 233	64.74 368	69.56 328	48.16 344	74.38 271	82.32 282	56.00 286	69.68 355	70.65 98	80.52 380	85.80 103

fmvsm_l_conf0.5_n_9		70.73 204	71.08 204	69.67 245	70.44 357	58.80 218	70.21 251	75.11 256	48.15 345	73.50 292	82.69 275	65.69 154	68.05 375	70.87 94	83.02 322	82.16 235

IterMVS-SCA-FT		67.68 268	66.07 300	72.49 180	73.34 293	58.20 228	63.80 384	65.55 374	48.10 346	76.91 195	82.64 276	45.20 362	78.84 184	61.20 201	77.89 426	80.44 282

xiu_mvs_v1_base_debu		67.87 264	67.07 281	70.26 226	79.13 171	61.90 172	67.34 315	71.25 309	47.98 347	67.70 398	74.19 420	61.31 205	72.62 298	56.51 264	78.26 420	76.27 357

xiu_mvs_v1_base		67.87 264	67.07 281	70.26 226	79.13 171	61.90 172	67.34 315	71.25 309	47.98 347	67.70 398	74.19 420	61.31 205	72.62 298	56.51 264	78.26 420	76.27 357

xiu_mvs_v1_base_debi		67.87 264	67.07 281	70.26 226	79.13 171	61.90 172	67.34 315	71.25 309	47.98 347	67.70 398	74.19 420	61.31 205	72.62 298	56.51 264	78.26 420	76.27 357

testdata				64.13 339	85.87 65	63.34 161		61.80 406	47.83 350	76.42 219	86.60 176	48.83 342	62.31 422	54.46 295	81.26 361	66.74 470

DPM-MVS		69.98 220	69.22 240	72.26 186	82.69 119	58.82 217	70.53 245	81.23 141	47.79 351	64.16 438	80.21 329	51.32 319	83.12 102	60.14 217	84.95 271	74.83 375

无先验							74.82 159	70.94 316	47.75 352				76.85 235	54.47 294		72.09 413

blend_shiyan4		57.39 415	55.27 442	63.73 347	67.25 417	41.75 417	60.08 429	69.15 333	47.57 353	64.19 437	67.14 506	20.46 536	72.34 307	40.73 424	60.88 530	77.11 342

IB-MVS	49.67 18	59.69 389	56.96 417	67.90 283	68.19 398	50.30 292	61.42 411	65.18 377	47.57 353	55.83 501	67.15 505	23.77 522	79.60 173	43.56 395	79.97 390	73.79 391
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmvs		55.84 429	55.45 437	57.01 442	60.33 493	33.20 499	65.89 344	59.29 421	47.52 355	56.04 499	73.60 425	31.05 478	68.06 374	40.64 426	64.64 519	69.77 439

VortexMVS		65.93 300	66.04 302	65.58 325	67.63 413	47.55 338	64.81 365	72.75 285	47.37 356	75.17 249	79.62 345	49.28 336	71.00 334	55.20 281	82.51 330	78.21 320

PMatch-Up-SfM		68.45 253	66.90 287	73.11 153	77.17 204	76.10 33	71.60 227	62.67 397	47.32 357	87.78 19	82.41 280	24.19 521	66.58 398	58.86 237	90.11 149	76.66 348

PatchMatch-RL		58.68 400	57.72 407	61.57 385	76.21 230	73.59 53	61.83 402	49.00 492	47.30 358	61.08 466	68.97 485	50.16 327	59.01 438	36.06 474	68.84 503	52.10 526

blended_shiyan8		62.19 359	61.77 360	63.46 354	68.01 403	40.65 433	60.47 424	69.13 336	47.24 359	66.44 411	70.55 461	43.75 374	71.91 319	43.18 398	87.19 230	77.81 330

blended_shiyan6		62.20 358	61.77 360	63.47 353	67.98 405	40.64 434	60.46 425	69.15 333	47.24 359	66.43 412	70.57 460	43.73 375	71.93 318	43.16 399	87.24 223	77.85 328

DKM		69.82 224	69.29 235	71.40 201	80.33 149	80.76 8	73.05 191	60.16 416	47.00 361	85.42 63	79.91 337	48.29 349	58.24 446	57.18 256	92.25 92	75.19 373

Anonymous20240521		63.55 334	66.07 300	55.99 449	66.18 437	44.04 393	68.77 290	68.80 346	46.99 362	72.57 312	85.84 201	39.87 411	50.22 481	53.40 311	92.23 93	73.71 392

PC_three_1452									46.98 363	81.83 111	86.28 184	66.55 145	84.47 79	63.31 179	90.78 132	83.49 182

EMVS		44.61 500	44.45 507	45.10 510	48.91 544	43.00 406	37.92 539	41.10 539	46.75 364	38.00 550	48.43 544	26.42 505	46.27 507	37.11 458	75.38 449	46.03 538

wanda-best-256-512		61.16 374	60.55 380	62.98 362	66.67 429	39.85 442	58.66 445	68.87 341	46.67 365	64.46 429	67.75 496	41.94 392	71.84 320	42.67 402	87.24 223	77.26 337

FE-blended-shiyan7		61.16 374	60.55 380	62.98 362	66.67 429	39.85 442	58.66 445	68.87 341	46.67 365	64.46 429	67.75 496	41.94 392	71.84 320	42.67 402	87.24 223	77.26 337

PRO-TEST		65.07 312	64.53 325	66.68 310	71.39 336	50.28 293	70.38 249	74.81 258	46.63 367	61.27 465	74.26 417	54.06 301	73.83 287	51.83 317	76.14 440	75.93 362

IterMVS		63.12 343	62.48 356	65.02 331	66.34 434	52.86 271	63.81 383	62.25 398	46.57 368	71.51 340	80.40 325	44.60 367	66.82 394	51.38 322	75.47 447	75.38 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hybrid		65.62 304	65.49 308	66.01 320	60.48 492	44.28 391	64.13 378	74.21 265	46.41 369	69.84 365	80.86 315	55.77 287	70.28 345	59.30 230	78.42 417	83.46 186

E-PMN		45.17 496	45.36 500	44.60 511	50.07 541	42.75 408	38.66 538	42.29 531	46.39 370	39.55 548	51.15 539	26.00 509	45.37 515	37.68 451	76.41 437	45.69 539

RoMa-SfM		70.84 201	70.47 216	71.95 192	80.95 142	81.09 6	76.44 135	62.08 402	46.25 371	87.14 35	80.63 321	55.60 288	58.69 441	54.19 300	90.98 123	76.07 361

testing222		53.37 449	52.50 461	55.98 450	70.51 356	29.68 519	56.20 468	51.85 473	46.19 372	56.76 494	68.94 486	19.18 543	65.39 406	25.87 534	76.98 433	72.87 400

PMatch-SfM		67.96 263	66.40 293	72.63 177	78.06 189	75.26 39	71.85 220	59.63 418	46.07 373	86.78 37	82.02 287	26.32 506	66.37 400	57.00 260	89.87 157	76.27 357

baseline1		57.82 409	58.36 402	56.19 448	69.17 381	30.76 513	62.94 395	55.21 453	46.04 374	63.83 444	78.47 369	41.20 401	63.68 415	39.44 431	68.99 502	74.13 386

test_fmvsmconf0.01_n		73.91 123	73.64 140	74.71 118	69.79 373	66.25 124	75.90 148	79.90 174	46.03 375	76.48 216	85.02 212	67.96 127	73.97 280	74.47 60	87.22 227	83.90 171

DenseAffine		67.25 278	66.08 298	70.76 209	80.22 151	77.51 26	70.65 244	58.59 429	45.98 376	81.51 117	76.48 391	41.58 397	62.36 420	49.23 344	90.48 138	72.40 408

FBQ-MVS		59.22 394	57.87 405	63.30 358	73.18 296	39.68 444	68.92 280	63.38 393	45.87 377	60.72 472	69.03 483	27.40 501	73.66 288	33.33 498	78.95 408	76.57 349

reproduce_monomvs		58.94 397	58.14 403	61.35 390	59.70 502	40.98 424	60.24 428	63.51 392	45.85 378	68.95 377	75.31 405	18.27 546	65.82 403	51.47 320	79.97 390	77.26 337

test_fmvsmconf_n		72.91 154	72.40 176	74.46 121	68.62 388	66.12 127	74.21 176	78.80 200	45.64 379	74.62 263	83.25 260	66.80 141	73.86 284	72.97 76	86.66 243	83.39 190

test_fmvsmconf0.1_n		73.26 141	72.82 165	74.56 120	69.10 383	66.18 126	74.65 168	79.34 188	45.58 380	75.54 234	83.91 242	67.19 133	73.88 283	73.26 73	86.86 236	83.63 179

MCST-MVS		73.42 132	73.34 151	73.63 139	81.28 139	59.17 209	74.80 161	83.13 93	45.50 381	72.84 307	83.78 246	65.15 162	80.99 147	64.54 159	89.09 182	80.73 273

PVSNet_BlendedMVS		65.38 306	64.30 327	68.61 272	69.81 370	49.36 307	65.60 351	78.96 195	45.50 381	59.98 475	78.61 368	51.82 314	78.20 204	44.30 389	84.11 302	78.27 318

IU-MVS					86.12 57	60.90 188		80.38 164	45.49 383	81.31 120				75.64 46	94.39 45	84.65 141

testgi		54.00 446	56.86 418	45.45 507	58.20 512	25.81 538	49.05 508	49.50 487	45.43 384	67.84 396	81.17 308	51.81 316	43.20 527	29.30 518	79.41 402	67.34 463

mvsmamba		68.87 243	67.30 278	73.57 142	76.58 224	53.70 266	84.43 43	74.25 264	45.38 385	76.63 207	84.55 221	35.85 437	85.27 61	49.54 339	78.49 415	81.75 251

PCF-MVS	63.80 13	72.70 161	71.69 190	75.72 107	78.10 187	60.01 200	73.04 192	81.50 132	45.34 386	79.66 141	84.35 226	65.15 162	82.65 112	48.70 351	89.38 171	84.50 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
icg_test_0407_2		63.88 333	65.59 306	58.75 424	72.47 314	48.64 315	53.19 487	72.98 278	45.33 387	68.91 381	79.37 352	61.91 196	51.11 472	55.06 283	81.11 363	76.49 350

IMVS_0407		67.26 277	67.35 275	66.97 305	72.47 314	48.64 315	69.03 278	72.98 278	45.33 387	68.91 381	79.37 352	61.91 196	75.77 246	55.06 283	81.11 363	76.49 350

IMVS_0404		62.18 360	63.05 348	59.58 416	72.47 314	48.64 315	55.47 473	72.98 278	45.33 387	55.80 503	79.37 352	49.84 330	53.60 465	55.06 283	81.11 363	76.49 350

IMVS_0403		67.07 283	67.08 280	67.03 303	72.47 314	48.64 315	68.44 301	72.98 278	45.33 387	68.63 389	79.37 352	60.38 222	75.97 242	55.06 283	81.11 363	76.49 350

SIFT-MNN		59.60 391	58.57 397	62.71 369	68.39 391	69.16 91	63.67 386	48.13 496	45.22 391	73.92 284	73.85 423	30.71 482	50.57 476	39.45 430	83.78 308	68.40 451

TAMVS		65.31 307	63.75 334	69.97 239	82.23 126	59.76 203	66.78 332	63.37 394	45.20 392	69.79 366	79.37 352	47.42 354	72.17 310	34.48 488	85.15 266	77.99 326

fmvsm_s_conf0.5_n_5		71.46 188	71.62 194	70.99 207	73.89 283	59.95 201	73.02 193	73.08 274	45.15 393	77.30 184	84.06 234	64.73 168	70.08 348	71.20 89	82.10 336	82.92 209

旧先验2							71.17 236		45.11 394	78.54 160			61.28 427	59.19 232

PS-MVSNAJ		64.27 328	63.73 335	65.90 322	77.82 194	51.42 280	63.33 390	72.33 292	45.09 395	61.60 461	68.04 494	62.39 189	73.95 281	49.07 346	73.87 464	72.34 409

xiu_mvs_v2_base		64.43 325	63.96 332	65.85 323	77.72 196	51.32 282	63.63 387	72.31 293	45.06 396	61.70 460	69.66 474	62.56 185	73.93 282	49.06 347	73.91 463	72.31 410

usedtu_dtu_shiyan1		61.16 374	60.92 373	61.90 377	69.70 375	36.41 476	58.57 448	68.86 343	44.94 397	65.02 424	75.67 397	43.00 383	70.28 345	40.83 422	81.68 350	78.99 305

FE-MVSNET3		61.16 374	60.92 373	61.90 377	69.70 375	36.41 476	58.57 448	68.86 343	44.94 397	65.02 424	75.67 397	43.00 383	70.28 345	40.82 423	81.68 350	78.99 305

testing11		53.13 451	52.26 463	55.75 451	70.44 357	31.73 506	54.75 480	52.40 471	44.81 399	52.36 519	68.40 493	21.83 531	65.74 405	32.64 504	72.73 472	69.78 438

LF4IMVS		67.50 269	67.31 277	68.08 281	58.86 508	61.93 171	71.43 229	75.90 248	44.67 400	72.42 315	80.20 330	57.16 271	70.44 341	58.99 234	86.12 248	71.88 414

SIFT-NCM-Cal		58.68 400	57.65 408	61.77 382	67.58 414	68.99 95	62.62 396	43.04 524	44.65 401	75.91 226	72.23 440	33.66 447	49.28 487	34.36 489	84.76 279	67.03 465

SD_0403		61.63 368	62.83 352	58.03 433	72.21 321	32.43 501	69.33 268	69.00 338	44.54 402	62.01 459	79.42 349	55.27 291	66.88 390	36.07 473	77.63 428	74.78 377

SIFT-ConvMatch		58.61 402	57.61 410	61.63 384	65.55 445	67.97 99	62.24 400	42.52 527	44.40 403	77.28 185	73.28 432	30.00 489	50.42 477	36.36 467	86.82 239	66.50 472

CDS-MVSNet		64.33 327	62.66 354	69.35 251	80.44 148	58.28 226	65.26 357	65.66 371	44.36 404	67.30 404	75.54 401	43.27 378	71.77 322	37.68 451	84.44 293	78.01 325
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SIFT-UMatch		58.13 405	57.37 414	60.42 406	65.49 447	67.10 113	61.52 409	43.57 519	44.20 405	76.80 202	72.60 436	29.70 492	47.95 499	36.61 464	85.82 252	66.20 476

SIFT-UM-Cal		57.67 410	56.99 416	59.70 413	64.92 456	66.46 121	59.84 433	46.03 505	44.18 406	76.77 204	71.89 448	29.03 497	48.71 490	33.08 501	87.13 234	63.93 496

SIFT-CM-Cal		57.90 408	56.75 419	61.34 391	65.62 443	67.48 107	60.91 417	44.69 511	44.05 407	73.16 300	71.09 457	30.69 483	50.23 480	33.27 499	87.25 222	66.31 474

miper_lstm_enhance		61.97 361	61.63 365	62.98 362	60.04 495	45.74 371	47.53 514	70.95 315	44.04 408	73.06 305	78.84 366	39.72 413	60.33 431	55.82 275	84.64 284	82.88 211

新几何1				69.99 237	88.37 34	71.34 65		62.08 402	43.85 409	74.99 252	86.11 194	52.85 307	70.57 339	50.99 325	83.23 321	68.05 459

Syy-MVS		54.13 442	55.45 437	50.18 480	68.77 386	23.59 541	55.02 476	44.55 512	43.80 410	58.05 487	64.07 513	46.22 357	58.83 439	46.16 378	72.36 475	68.12 457

myMVS_eth3d		50.36 473	50.52 479	49.88 481	68.77 386	22.69 543	55.02 476	44.55 512	43.80 410	58.05 487	64.07 513	14.16 554	58.83 439	33.90 494	72.36 475	68.12 457

dtuplus		65.20 308	64.80 323	66.40 314	65.25 449	44.86 380	64.55 373	72.19 295	43.76 412	72.09 323	81.87 294	57.49 269	71.49 328	48.79 349	77.23 432	82.85 214

114514_t		73.40 137	73.33 152	73.64 138	84.15 95	57.11 235	78.20 111	80.02 171	43.76 412	72.55 313	86.07 197	64.00 172	83.35 99	60.14 217	91.03 122	80.45 281

OpenMVS_ROB	54.93 17	63.23 342	63.28 343	63.07 361	69.81 370	45.34 375	68.52 298	67.14 359	43.74 414	70.61 350	79.22 358	47.90 352	72.66 296	48.75 350	73.84 465	71.21 425

FMVSNet5		55.08 438	55.54 435	53.71 459	65.80 441	33.50 498	56.22 467	52.50 470	43.72 415	61.06 467	83.38 252	25.46 512	54.87 460	30.11 514	81.64 353	72.75 402

MVP-Stereo		61.56 369	59.22 391	68.58 273	79.28 164	60.44 194	69.20 273	71.57 299	43.58 416	56.42 497	78.37 371	39.57 415	76.46 240	34.86 484	60.16 532	68.86 450
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ETVMVS		50.32 474	49.87 483	51.68 472	70.30 362	26.66 531	52.33 496	43.93 517	43.54 417	54.91 507	67.95 495	20.01 540	60.17 432	22.47 544	73.40 467	68.22 455

mvs_anonymous		65.08 311	65.49 308	63.83 344	63.79 465	37.60 467	66.52 336	69.82 326	43.44 418	73.46 294	86.08 195	58.79 249	71.75 324	51.90 316	75.63 445	82.15 236

SIFT-NCMNet		56.27 426	55.94 431	57.26 440	62.54 475	64.28 150	59.61 435	41.26 537	43.43 419	78.50 161	69.35 481	32.26 462	45.98 508	27.16 528	89.34 172	61.53 510

test-LLR		50.43 472	50.69 478	49.64 484	60.76 488	41.87 414	53.18 488	45.48 508	43.41 420	49.41 529	60.47 527	29.22 494	44.73 519	42.09 410	72.14 478	62.33 507

test0.0.03 1		47.72 488	48.31 488	45.93 505	55.53 527	29.39 520	46.40 520	41.21 538	43.41 420	55.81 502	67.65 499	29.22 494	43.77 526	25.73 536	69.87 497	64.62 492

SCA		58.57 403	58.04 404	60.17 410	70.17 363	41.07 423	65.19 359	53.37 466	43.34 422	61.00 469	73.48 426	45.20 362	69.38 359	40.34 428	70.31 494	70.05 434

ET-MVSNet_ETH3D		63.32 339	60.69 379	71.20 205	70.15 365	55.66 246	65.02 363	64.32 386	43.28 423	68.99 375	72.05 445	25.46 512	78.19 206	54.16 302	82.80 326	79.74 293

LoFTR		61.29 371	62.50 355	57.67 438	69.07 384	65.66 132	68.96 279	48.59 493	43.15 424	86.65 39	79.95 336	32.68 456	53.14 467	46.21 377	87.20 229	54.22 525

viewmambaseed2359dif		65.63 303	65.13 317	67.11 301	64.57 459	44.73 384	64.12 379	72.48 291	43.08 425	71.59 333	81.17 308	58.90 247	72.46 302	52.94 312	77.33 430	84.13 166

SIFT-NN-UMatch		57.27 417	56.18 425	60.54 403	62.85 473	66.67 119	61.19 414	41.27 536	43.01 426	70.01 361	72.44 439	32.76 454	49.32 486	38.19 446	83.87 304	65.63 480

SIFT-PointCN		56.55 423	55.82 432	58.75 424	62.59 474	63.48 159	59.22 436	45.58 507	42.97 427	74.44 269	69.65 475	25.00 516	47.28 503	35.25 480	87.73 205	65.49 481

SIFT-NN-PointCN		57.17 418	56.12 427	60.35 409	62.47 477	65.79 130	59.98 430	44.36 515	42.73 428	72.13 321	71.16 456	30.84 480	48.08 498	36.92 461	84.45 291	67.17 464

SIFT-NN		56.62 422	55.34 440	60.47 404	67.01 426	67.25 110	61.73 406	45.38 510	42.69 429	64.49 428	71.36 455	28.48 498	47.55 500	36.68 463	80.23 385	66.63 471

miper_enhance_ethall		65.86 301	65.05 322	68.28 280	61.62 484	42.62 410	64.74 368	77.96 217	42.52 430	73.42 295	72.79 435	49.66 331	77.68 214	58.12 247	84.59 286	84.54 150

cascas		64.59 321	62.77 353	70.05 236	75.27 243	50.02 296	61.79 404	71.61 298	42.46 431	63.68 447	68.89 488	49.33 335	80.35 160	47.82 363	84.05 303	79.78 292

SIFT-PCN-Cal		56.03 428	55.47 436	57.69 436	63.19 471	62.93 166	58.63 447	43.46 521	42.37 432	75.62 231	69.51 479	25.32 514	44.67 521	33.77 495	87.41 213	65.45 483

SIFT-NN-NCMNet		57.48 413	56.02 429	61.86 381	66.93 427	69.26 90	62.14 401	44.46 514	42.32 433	67.01 407	71.93 447	32.46 459	50.96 474	35.06 483	81.87 340	65.36 484

PVSNet_Blended		62.90 346	61.64 364	66.69 309	69.81 370	49.36 307	61.23 413	78.96 195	42.04 434	59.98 475	68.86 489	51.82 314	78.20 204	44.30 389	77.77 427	72.52 405

SIFT-NN-CMatch		57.48 413	56.23 424	61.21 394	63.66 468	67.89 101	60.78 420	40.90 540	41.97 435	71.65 331	71.96 446	32.11 463	49.35 485	38.19 446	84.88 277	66.37 473

dongtai		31.66 514	32.98 518	27.71 531	58.58 510	12.61 556	45.02 525	14.24 561	41.90 436	47.93 532	43.91 547	10.65 557	41.81 535	14.06 550	20.53 554	28.72 548

MVSTER		63.29 341	61.60 366	68.36 276	59.77 501	46.21 367	60.62 422	71.32 306	41.83 437	75.40 239	79.12 361	30.25 486	75.85 243	56.30 268	79.81 394	83.03 206

MIMVSNet		54.39 441	56.12 427	49.20 488	72.57 313	30.91 511	59.98 430	48.43 495	41.66 438	55.94 500	83.86 244	41.19 402	50.42 477	26.05 531	75.38 449	66.27 475

KD-MVS_2432*1600		52.05 462	51.58 467	53.44 462	52.11 538	31.20 508	44.88 526	64.83 381	41.53 439	64.37 432	70.03 471	15.61 552	64.20 411	36.25 468	74.61 455	64.93 490

miper_refine_blended		52.05 462	51.58 467	53.44 462	52.11 538	31.20 508	44.88 526	64.83 381	41.53 439	64.37 432	70.03 471	15.61 552	64.20 411	36.25 468	74.61 455	64.93 490

dmvs_testset		45.26 495	47.51 491	38.49 526	59.96 498	14.71 554	58.50 451	43.39 522	41.30 441	51.79 521	56.48 532	39.44 417	49.91 484	21.42 546	55.35 544	50.85 528

patch_mono-2		62.73 351	64.08 331	58.68 427	70.36 360	55.87 243	60.84 419	64.11 388	41.23 442	64.04 439	78.22 373	60.00 226	48.80 489	54.17 301	83.71 312	71.37 421

new-patchmatchnet		52.89 455	55.76 434	44.26 513	59.94 499	6.31 562	37.36 541	50.76 480	41.10 443	64.28 434	79.82 339	44.77 365	48.43 496	36.24 470	87.61 206	78.03 324

test222					87.30 38	69.15 93	67.85 307	59.59 420	41.06 444	73.05 306	85.72 203	48.03 350			80.65 377	66.92 466

Patchmatch-RL test		59.95 387	59.12 392	62.44 372	72.46 318	54.61 259	59.63 434	47.51 499	41.05 445	74.58 264	74.30 416	31.06 477	65.31 407	51.61 318	79.85 393	67.39 461

fmvsm_s_conf0.5_n_a		67.00 286	65.95 304	70.17 229	69.72 374	61.16 183	73.34 187	56.83 443	40.96 446	68.36 391	80.08 334	62.84 181	67.57 380	66.90 139	74.50 457	81.78 249

fmvsm_s_conf0.5_n		66.34 296	65.27 311	69.57 247	68.20 397	59.14 212	71.66 225	56.48 446	40.92 447	67.78 397	79.46 347	61.23 208	66.90 388	67.39 130	74.32 461	82.66 222

thisisatest0515		60.48 383	57.86 406	68.34 277	67.25 417	46.42 363	60.58 423	62.14 400	40.82 448	63.58 450	69.12 482	26.28 507	78.34 200	48.83 348	82.13 335	80.26 285

fmvsm_s_conf0.1_n_a		67.37 274	66.36 294	70.37 218	70.86 341	61.17 182	74.00 178	57.18 440	40.77 449	68.83 386	80.88 314	63.11 179	67.61 379	66.94 137	74.72 453	82.33 233

ppachtmachnet_test		60.26 385	59.61 388	62.20 374	67.70 411	44.33 390	58.18 454	60.96 409	40.75 450	65.80 417	72.57 438	41.23 400	63.92 414	46.87 370	82.42 331	78.33 316

fmvsm_s_conf0.1_n		66.60 289	65.54 307	69.77 243	68.99 385	59.15 210	72.12 206	56.74 445	40.72 451	68.25 395	80.14 333	61.18 211	66.92 387	67.34 134	74.40 458	83.23 197

PAPM		61.79 365	60.37 383	66.05 319	76.09 232	41.87 414	69.30 269	76.79 236	40.64 452	53.80 513	79.62 345	44.38 368	82.92 106	29.64 517	73.11 470	73.36 394

our_test_3		56.46 424	56.51 421	56.30 447	67.70 411	39.66 445	55.36 475	52.34 472	40.57 453	63.85 442	69.91 473	40.04 410	58.22 447	43.49 396	75.29 451	71.03 429

test_fmvsmvis_n_1920		72.36 169	72.49 172	71.96 191	71.29 339	64.06 154	72.79 196	81.82 126	40.23 454	81.25 122	81.04 312	70.62 94	68.69 364	69.74 106	83.60 316	83.14 199

PatchmatchNet		54.60 440	54.27 449	55.59 452	65.17 452	39.08 447	66.92 329	51.80 474	39.89 455	58.39 484	73.12 433	31.69 471	58.33 445	43.01 401	58.38 538	69.38 445
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dmvs_re		49.91 479	50.77 477	47.34 496	59.98 496	38.86 452	53.18 488	53.58 463	39.75 456	55.06 505	61.58 523	36.42 435	44.40 522	29.15 522	68.23 505	58.75 518

0.4-1-1-0.1		51.02 469	48.31 488	59.15 420	60.95 487	37.94 464	53.17 492	59.12 424	39.52 457	47.88 533	50.31 542	20.36 538	69.99 350	35.79 475	67.66 511	69.51 443

fmvsm_l_conf0.5_n		67.48 270	66.88 289	69.28 253	67.41 416	62.04 170	70.69 243	69.85 325	39.46 458	69.59 368	81.09 311	58.15 257	68.73 363	67.51 127	78.16 423	77.07 346

WBMVS		53.38 448	54.14 450	51.11 476	70.16 364	26.66 531	50.52 504	51.64 476	39.32 459	63.08 454	77.16 385	23.53 523	55.56 457	31.99 505	79.88 392	71.11 427

D2MVS		62.58 353	61.05 372	67.20 298	63.85 464	47.92 328	56.29 466	69.58 327	39.32 459	70.07 360	78.19 374	34.93 441	72.68 295	53.44 309	83.74 309	81.00 264

Patchmatch-test		47.93 487	49.96 482	41.84 519	57.42 517	24.26 540	48.75 509	41.49 534	39.30 461	56.79 493	73.48 426	30.48 485	33.87 545	29.29 519	72.61 473	67.39 461

HY-MVS	49.31 19	57.96 407	57.59 411	59.10 422	66.85 428	36.17 478	65.13 360	65.39 376	39.24 462	54.69 510	78.14 375	44.28 369	67.18 386	33.75 496	70.79 490	73.95 388

baseline2		55.57 434	52.74 457	64.05 341	65.26 448	44.11 392	62.38 398	54.43 457	39.03 463	51.21 522	67.35 502	33.66 447	72.45 303	37.14 457	64.22 521	75.60 365

XXY-MVS		55.19 436	57.40 413	48.56 494	64.45 460	34.84 490	51.54 498	53.59 462	38.99 464	63.79 445	79.43 348	56.59 279	45.57 511	36.92 461	71.29 486	65.25 486

pmmvs-eth3d		64.41 326	63.27 344	67.82 289	75.81 239	60.18 198	69.49 263	62.05 404	38.81 465	74.13 275	82.23 283	43.76 373	68.65 365	42.53 404	80.63 379	74.63 379

fmvsm_l_conf0.5_n_a		66.66 288	65.97 303	68.72 271	67.09 420	61.38 179	70.03 254	69.15 333	38.59 466	68.41 390	80.36 326	56.56 281	68.32 370	66.10 141	77.45 429	76.46 354

UWE-MVS		52.94 454	52.70 458	53.65 460	73.56 286	27.49 528	57.30 459	49.57 485	38.56 467	62.79 456	71.42 453	19.49 542	60.41 429	24.33 541	77.33 430	73.06 396

0.3-1-1-0.015		49.68 480	46.67 495	58.69 426	58.94 507	37.51 469	51.35 500	59.18 422	38.35 468	44.62 544	47.14 545	18.49 544	69.68 355	35.13 482	66.84 514	68.87 449

MDA-MVSNet_test_wron		52.57 458	53.49 455	49.81 483	54.24 531	36.47 474	40.48 535	46.58 503	38.13 469	75.47 238	73.32 430	41.05 405	43.85 525	40.98 420	71.20 487	69.10 448

YYNet1		52.58 457	53.50 453	49.85 482	54.15 532	36.45 475	40.53 534	46.55 504	38.09 470	75.52 235	73.30 431	41.08 404	43.88 524	41.10 418	71.14 488	69.21 446

0.4-1-1-0.2		49.48 481	46.57 496	58.21 430	58.02 514	36.93 471	50.24 505	59.18 422	37.97 471	44.94 540	46.16 546	20.52 535	69.54 357	34.84 485	67.28 513	68.17 456

1112_ss		59.48 392	58.99 394	60.96 397	77.84 193	42.39 412	61.42 411	68.45 352	37.96 472	59.93 478	67.46 500	45.11 364	65.07 409	40.89 421	71.81 480	75.41 368

WB-MVSnew		53.94 447	54.76 446	51.49 474	71.53 332	28.05 524	58.22 453	50.36 481	37.94 473	59.16 482	70.17 468	49.21 337	51.94 470	24.49 539	71.80 481	74.47 384

test_fmvsm_n_1920		69.63 226	68.45 252	73.16 150	70.56 351	65.86 129	70.26 250	78.35 209	37.69 474	74.29 272	78.89 365	61.10 212	68.10 373	65.87 145	79.07 405	85.53 109

UnsupCasMVSNet_eth		52.26 460	53.29 456	49.16 489	55.08 528	33.67 497	50.03 506	58.79 428	37.67 475	63.43 453	74.75 409	41.82 395	45.83 509	38.59 441	59.42 534	67.98 460

nomal-1		49.95 478	49.18 486	52.26 467	57.73 515	44.81 381	46.14 522	49.57 485	37.60 476	56.41 498	65.96 508	24.21 520	52.60 469	33.97 492	71.04 489	59.37 516

UBG		49.18 483	49.35 484	48.66 493	70.36 360	26.56 533	50.53 503	45.61 506	37.43 477	53.37 515	65.97 507	23.03 526	54.20 463	26.29 529	71.54 482	65.20 487

tpm		50.60 471	52.42 462	45.14 509	65.18 451	26.29 534	60.30 426	43.50 520	37.41 478	57.01 491	79.09 362	30.20 488	42.32 529	32.77 503	66.36 515	66.81 469

gm-plane-assit					62.51 476	33.91 496			37.25 479		62.71 519		72.74 294	38.70 438

CostFormer		57.35 416	56.14 426	60.97 396	63.76 466	38.43 455	67.50 312	60.22 414	37.14 480	59.12 483	76.34 392	32.78 453	71.99 315	39.12 436	69.27 500	72.47 406

pmmvs4		60.78 380	59.04 393	66.00 321	73.06 303	57.67 230	64.53 374	60.22 414	36.91 481	65.96 415	77.27 384	39.66 414	68.54 368	38.87 437	74.89 452	71.80 415

UWE-MVS-28		44.18 502	44.37 508	43.61 516	60.10 494	16.96 552	52.62 493	33.27 551	36.79 482	48.86 531	69.47 480	19.96 541	45.65 510	13.40 551	64.83 518	68.23 454

ArgMatch-SfM		64.74 319	63.70 336	67.83 286	77.62 199	76.78 31	67.30 320	58.21 430	36.64 483	81.94 109	73.41 429	38.67 421	56.92 453	50.66 328	88.89 185	69.81 437

PVSNet	43.83 21	51.56 465	51.17 471	52.73 465	68.34 394	38.27 457	48.22 511	53.56 464	36.41 484	54.29 511	64.94 512	34.60 442	54.20 463	30.34 512	69.87 497	65.71 479

ttmdpeth		56.40 425	55.45 437	59.25 418	55.63 526	40.69 429	58.94 442	49.72 484	36.22 485	65.39 419	86.97 153	23.16 525	56.69 455	42.30 406	80.74 375	80.36 283

ALIKED-LG		64.85 315	64.54 324	65.79 324	74.03 278	74.67 43	73.55 182	67.52 358	36.17 486	78.83 153	83.08 269	34.08 443	59.10 437	42.05 412	91.51 104	63.61 497

tpmrst		50.15 475	51.38 469	46.45 504	56.05 522	24.77 539	64.40 377	49.98 482	36.14 487	53.32 516	69.59 477	35.16 440	48.69 491	39.24 434	58.51 537	65.89 477

MS-PatchMatch		55.59 433	54.89 445	57.68 437	69.18 379	49.05 310	61.00 416	62.93 396	35.98 488	58.36 485	68.93 487	36.71 433	66.59 397	37.62 453	63.30 523	57.39 521

MDTV_nov1_ep13			54.05 452		65.54 446	29.30 521	59.00 440	55.22 452	35.96 489	52.44 517	75.98 393	30.77 481	59.62 434	38.21 444	73.33 469

USDC		62.80 347	63.10 347	61.89 379	65.19 450	43.30 403	67.42 314	74.20 266	35.80 490	72.25 318	84.48 223	45.67 359	71.95 317	37.95 449	84.97 267	70.42 433

ArgMatch-Sym		63.94 332	63.05 348	66.61 312	76.68 222	75.81 35	65.98 342	57.57 433	35.60 491	80.60 131	69.62 476	43.62 376	55.74 456	49.14 345	88.61 188	68.29 453

jason		64.47 324	62.84 351	69.34 252	76.91 216	59.20 206	67.15 324	65.66 371	35.29 492	65.16 422	76.74 389	44.67 366	70.68 336	54.74 291	79.28 403	78.14 322
jason: jason.
Anonymous20231206		54.13 442	55.82 432	49.04 491	70.89 340	35.96 480	51.73 497	50.87 479	34.86 493	62.49 457	79.22 358	42.52 389	44.29 523	27.95 526	81.88 339	66.88 467

MVStest1		55.38 435	54.97 444	56.58 445	43.72 552	40.07 439	59.13 438	47.09 501	34.83 494	76.53 214	84.65 217	13.55 555	53.30 466	55.04 287	80.23 385	76.38 355

dp		44.09 503	44.88 505	41.72 521	58.53 511	23.18 542	54.70 481	42.38 530	34.80 495	44.25 545	65.61 510	24.48 519	44.80 518	29.77 516	49.42 546	57.18 522

Test_1112_low_res		58.78 399	58.69 396	59.04 423	79.41 162	38.13 460	57.62 456	66.98 362	34.74 496	59.62 481	77.56 381	42.92 385	63.65 416	38.66 439	70.73 491	75.35 370

EPMVS		45.74 493	46.53 497	43.39 517	54.14 533	22.33 546	55.02 476	35.00 550	34.69 497	51.09 523	70.20 467	25.92 510	42.04 532	37.19 456	55.50 542	65.78 478

lupinMVS		63.36 337	61.49 367	68.97 263	74.93 247	59.19 207	65.80 347	64.52 385	34.68 498	63.53 451	74.25 418	43.19 379	70.62 338	53.88 304	78.67 412	77.10 343

UnsupCasMVSNet_bld		50.01 477	51.03 474	46.95 498	58.61 509	32.64 500	48.31 510	53.27 467	34.27 499	60.47 473	71.53 451	41.40 399	47.07 504	30.68 511	60.78 531	61.13 511

CMPMVS	48.73 20	61.54 370	60.89 375	63.52 351	61.08 486	51.55 279	68.07 306	68.00 354	33.88 500	65.87 416	81.25 307	37.91 426	67.71 376	49.32 342	82.60 329	71.31 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WTY-MVS		49.39 482	50.31 481	46.62 503	61.22 485	32.00 505	46.61 519	49.77 483	33.87 501	54.12 512	69.55 478	41.96 391	45.40 514	31.28 509	64.42 520	62.47 504

N_pmnet		52.06 461	51.11 472	54.92 453	59.64 503	71.03 68	37.42 540	61.62 407	33.68 502	57.12 489	72.10 441	37.94 425	31.03 547	29.13 523	71.35 485	62.70 500

HyFIR lowres test		63.01 344	60.47 382	70.61 211	83.04 112	54.10 262	59.93 432	72.24 294	33.67 503	69.00 374	75.63 399	38.69 420	76.93 231	36.60 465	75.45 448	80.81 271

tpm2		56.12 427	54.64 447	60.55 402	66.24 435	36.01 479	68.14 303	56.77 444	33.60 504	58.25 486	75.52 403	30.25 486	74.33 274	33.27 499	69.76 499	71.32 422

1314		59.83 388	58.86 395	62.74 368	65.71 442	44.78 383	68.59 294	72.63 287	33.54 505	61.05 468	67.29 503	43.62 376	71.26 330	49.49 340	67.84 509	72.19 412

dtuonlycased		61.79 365	62.24 358	60.43 405	73.00 306	39.07 448	61.74 405	60.61 410	33.09 506	74.10 276	80.34 327	59.20 241	60.39 430	38.34 443	79.76 398	81.83 247

CR-MVSNet		58.96 396	58.49 399	60.36 408	66.37 432	48.24 322	70.93 239	56.40 448	32.87 507	61.35 463	86.66 171	33.19 450	63.22 418	48.50 354	70.17 495	69.62 441

ALIKED-MNN		63.44 336	63.42 340	63.48 352	73.99 279	70.97 70	71.80 224	66.48 365	32.46 508	71.87 326	81.60 303	36.54 434	58.50 443	42.45 405	93.63 69	60.97 512

MVS		60.62 382	59.97 385	62.58 370	68.13 401	47.28 343	68.59 294	73.96 267	32.19 509	59.94 477	68.86 489	50.48 325	77.64 215	41.85 413	75.74 443	62.83 499

tpm cat1		54.02 445	52.63 459	58.19 431	64.85 458	39.86 441	66.26 340	57.28 437	32.16 510	56.90 492	70.39 464	32.75 455	65.30 408	34.29 490	58.79 535	69.41 444

pmmvs5		52.49 459	52.58 460	52.21 469	54.99 529	32.38 502	55.45 474	53.84 461	32.15 511	55.49 504	74.81 407	38.08 424	57.37 452	34.02 491	74.40 458	66.88 467

PMMVS2		37.74 511	40.87 511	28.36 530	42.41 554	5.35 564	24.61 548	27.75 553	32.15 511	47.85 534	70.27 466	35.85 437	29.51 551	19.08 549	67.85 508	50.22 530

PatchmatchNet2					0.00 568	8.37 560	35.35 544	35.51 549	32.14 513
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
sss		47.59 489	48.32 487	45.40 508	56.73 521	33.96 494	45.17 524	48.51 494	32.11 514	52.37 518	65.79 509	40.39 408	41.91 533	31.85 506	61.97 527	60.35 513

test-mter		48.56 486	48.20 490	49.64 484	60.76 488	41.87 414	53.18 488	45.48 508	31.91 515	49.41 529	60.47 527	18.34 545	44.73 519	42.09 410	72.14 478	62.33 507

MDTV_nov1_ep13_2view						18.41 549	53.74 485		31.57 516	44.89 541		29.90 491		32.93 502		71.48 419

ADS-MVSNet2		48.76 484	47.25 493	53.29 464	55.90 524	40.54 435	47.34 515	54.99 455	31.41 517	50.48 525	72.06 443	31.23 474	54.26 462	25.93 532	55.93 540	65.07 488

ADS-MVSNet		44.62 499	45.58 499	41.73 520	55.90 524	20.83 548	47.34 515	39.94 542	31.41 517	50.48 525	72.06 443	31.23 474	39.31 540	25.93 532	55.93 540	65.07 488

PVSNet_0	36.71 22	41.12 508	40.78 512	42.14 518	59.97 497	40.13 438	40.97 533	42.24 532	30.81 519	44.86 542	49.41 543	40.70 406	45.12 516	23.15 543	34.96 551	41.16 544

test_vis1_n_1920		52.96 453	53.50 453	51.32 475	59.15 505	44.90 379	56.13 469	64.29 387	30.56 520	59.87 479	60.68 525	40.16 409	47.47 501	48.25 358	62.46 525	61.58 509

ELoFTR		57.63 411	59.55 389	51.85 471	66.16 438	61.46 177	69.66 261	43.94 516	30.20 521	82.28 104	77.47 383	33.76 446	42.30 530	42.10 409	90.40 141	51.81 527

ALIKED-NN		61.86 363	61.18 369	63.92 343	71.72 329	71.04 67	69.24 272	66.41 366	29.80 522	64.25 435	81.10 310	35.56 439	58.35 444	41.25 417	91.30 109	62.35 506

MVS-HIRNet		45.53 494	47.29 492	40.24 523	62.29 479	26.82 530	56.02 470	37.41 546	29.74 523	43.69 547	81.27 306	33.96 444	55.48 458	24.46 540	56.79 539	38.43 546

dtuonly		50.13 476	51.25 470	46.77 501	53.07 537	30.10 517	52.41 495	49.25 488	28.98 524	53.76 514	72.59 437	39.83 412	41.82 534	37.58 454	73.80 466	68.37 452

CHOSEN 1792x2688		58.09 406	56.30 423	63.45 355	79.95 154	50.93 286	54.07 484	65.59 373	28.56 525	61.53 462	74.33 415	41.09 403	66.52 399	33.91 493	67.69 510	72.92 398

TESTMET0.1,1		45.17 496	44.93 503	45.89 506	56.02 523	38.31 456	53.18 488	41.94 533	27.85 526	44.86 542	56.47 533	17.93 547	41.50 536	38.08 448	68.06 506	57.85 519

test_fmvs3		56.78 421	55.99 430	59.12 421	53.96 535	48.09 325	58.76 444	66.22 367	27.54 527	76.66 206	68.69 491	25.32 514	51.31 471	53.42 310	73.38 468	77.97 327

CHOSEN 280x420		41.62 507	39.89 513	46.80 500	61.81 481	51.59 278	33.56 546	35.74 548	27.48 528	37.64 552	53.53 535	23.24 524	42.09 531	27.39 527	58.64 536	46.72 534

MatchFormer		53.09 452	55.03 443	47.30 497	59.31 504	57.25 234	67.30 320	37.25 547	27.23 529	82.61 101	74.56 411	26.23 508	42.89 528	34.73 486	86.00 250	41.75 543

XFeat-MNN		48.68 485	49.35 484	46.65 502	44.49 551	46.89 352	46.91 517	43.80 518	27.16 530	75.21 246	60.05 529	22.65 529	46.52 505	39.33 432	84.57 289	46.53 536

EU-MVSNet		60.82 379	60.80 377	60.86 399	68.37 393	41.16 421	72.27 202	68.27 353	26.96 531	69.08 373	75.71 396	32.09 464	67.44 381	55.59 278	78.90 409	73.97 387

test_cas_vis1_n_1920		50.90 470	50.92 475	50.83 478	54.12 534	47.80 331	51.44 499	54.61 456	26.95 532	63.95 441	60.85 524	37.86 428	44.97 517	45.53 384	62.97 524	59.72 515

CVMVSNet		59.21 395	58.44 400	61.51 386	73.94 281	47.76 333	71.31 233	64.56 384	26.91 533	60.34 474	70.44 462	36.24 436	67.65 377	53.57 307	68.66 504	69.12 447

test_fmvs2		54.80 439	54.11 451	56.88 444	51.76 540	49.95 298	56.70 462	65.80 370	26.22 534	69.42 370	65.25 511	31.82 469	49.98 482	49.63 338	70.36 493	70.71 430

kuosan		22.02 516	23.52 521	17.54 534	41.56 556	11.24 557	41.99 531	13.39 562	26.13 535	28.87 553	30.75 550	9.72 559	21.94 556	4.77 557	14.49 555	19.43 550

test_vis1_n		51.27 468	50.41 480	53.83 458	56.99 518	50.01 297	56.75 461	60.53 412	25.68 536	59.74 480	57.86 531	29.40 493	47.41 502	43.10 400	63.66 522	64.08 495

new_pmnet		37.55 512	39.80 514	30.79 529	56.83 519	16.46 553	39.35 537	30.65 552	25.59 537	45.26 539	61.60 522	24.54 517	28.02 552	21.60 545	52.80 545	47.90 532

test_fmvs1_n		52.70 456	52.01 464	54.76 454	53.83 536	50.36 290	55.80 471	65.90 369	24.96 538	65.39 419	60.64 526	27.69 500	48.46 494	45.88 382	67.99 507	65.46 482

MVE	27.91 23	36.69 513	35.64 517	39.84 524	43.37 553	35.85 482	19.49 549	24.61 555	24.68 539	39.05 549	62.63 520	38.67 421	27.10 553	21.04 547	47.25 548	56.56 523
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_fmvs1		51.51 466	50.86 476	53.48 461	49.72 543	49.35 309	54.11 483	64.96 379	24.64 540	63.66 448	59.61 530	28.33 499	48.45 495	45.38 387	67.30 512	62.66 502

pmmvs3		46.71 490	45.09 502	51.55 473	56.76 520	48.25 321	55.78 472	39.53 543	24.13 541	50.35 527	63.40 515	15.90 551	51.08 473	29.29 519	70.69 492	55.33 524

test_vis3_rt		51.94 464	51.04 473	54.65 455	46.32 549	50.13 295	44.34 528	78.17 213	23.62 542	68.95 377	62.81 518	21.41 532	38.52 542	41.49 415	72.22 477	75.30 371

XFeat-NN		44.60 501	44.89 504	43.74 515	46.61 548	44.56 385	41.07 532	40.59 541	23.40 543	66.73 409	54.97 534	20.65 534	40.41 538	33.52 497	76.49 436	46.25 537

mvsany_test3		43.76 505	41.01 510	52.01 470	48.09 545	57.74 229	42.47 530	23.85 557	23.30 544	64.80 426	62.17 521	27.12 502	40.59 537	29.17 521	48.11 547	57.69 520

PDCNetPlus		38.77 509	39.67 515	36.07 528	38.82 557	27.82 527	36.52 543	51.55 477	22.53 545	37.81 551	50.69 541	7.16 560	32.98 546	28.21 525	83.73 311	47.40 533

PMMVS		44.69 498	43.95 509	46.92 499	50.05 542	53.47 268	48.08 513	42.40 529	22.36 546	44.01 546	53.05 537	42.60 388	45.49 512	31.69 507	61.36 529	41.79 542

test_f		43.79 504	45.63 498	38.24 527	42.29 555	38.58 454	34.76 545	47.68 498	22.22 547	67.34 403	63.15 516	31.82 469	30.60 549	39.19 435	62.28 526	45.53 540

test_vis1_rt		46.70 491	45.24 501	51.06 477	44.58 550	51.04 285	39.91 536	67.56 357	21.84 548	51.94 520	50.79 540	33.83 445	39.77 539	35.25 480	61.50 528	62.38 505

MASt3R-SfM		45.75 492	47.16 494	41.50 522	47.00 547	47.91 330	45.50 523	38.10 544	21.81 549	73.91 285	62.86 517	29.14 496	29.95 550	34.59 487	71.54 482	46.65 535

mvsany_test1		37.88 510	35.74 516	44.28 512	47.28 546	49.90 299	36.54 542	24.37 556	19.56 550	45.76 537	53.46 536	32.99 452	37.97 543	26.17 530	35.52 550	44.99 541

DSMNet-mixed		43.18 506	44.66 506	38.75 525	54.75 530	28.88 523	57.06 460	27.42 554	13.47 551	47.27 536	77.67 380	38.83 419	39.29 541	25.32 538	60.12 533	48.08 531

GLUNet-SfM		24.03 515	24.76 519	21.84 532	12.84 559	18.20 550	27.35 547	15.92 559	9.48 552	63.07 455	34.11 549	10.20 558	23.13 555	9.60 555	40.26 549	24.18 549

DeepMVS_CX				11.83 535	15.51 558	13.86 555		11.25 563	5.76 553	20.85 555	26.46 551	17.06 550	9.22 557	9.69 554	13.82 557	12.42 551

VLMVS_CLIP		7.76 521	8.41 525	5.81 536	6.67 562	5.99 563	6.46 553	9.96 564	2.09 554	12.33 557	14.87 553	5.07 561	8.68 558	4.33 558	13.87 556	2.74 553

MVS_clip		7.93 520	9.12 524	4.36 537	9.81 560	6.92 561	6.89 552	1.72 565	1.89 555	16.36 556	21.19 552	4.56 562	2.56 560	6.56 556	13.13 558	3.60 552

test_method		19.26 517	19.12 522	19.71 533	9.09 561	1.91 566	7.79 551	53.44 465	1.42 556	10.27 558	35.80 548	17.42 549	25.11 554	12.44 552	24.38 553	32.10 547

EGC-MVSNET		64.77 318	61.17 370	75.60 110	86.90 43	74.47 44	84.04 45	68.62 350	0.60 557	1.13 561	91.61 35	65.32 160	74.15 278	64.01 164	88.28 193	78.17 321

tmp_tt		11.98 519	14.73 523	3.72 538	2.28 563	4.62 565	19.44 550	14.50 560	0.47 558	21.55 554	9.58 555	25.78 511	4.57 559	11.61 553	27.37 552	1.96 554

test123		4.43 524	5.78 528	0.39 542	0.97 566	0.28 568	46.33 521	0.45 567	0.31 559	0.62 562	1.50 560	0.61 566	0.11 563	0.56 561	0.63 561	0.77 558

testmvs		4.06 525	5.28 529	0.41 541	0.64 567	0.16 570	42.54 529	0.31 569	0.26 560	0.50 563	1.40 561	0.77 565	0.17 562	0.56 561	0.55 562	0.90 557

VLMVS		1.59 527	1.75 531	1.12 539	1.56 565	1.00 567	0.99 555	0.58 566	0.08 561	2.81 560	3.50 557	2.79 563	0.76 561	0.70 560	2.74 560	1.60 555

MVS_baseline		2.33 526	2.94 530	0.51 540	2.02 564	0.19 569	1.06 554	0.36 568	0.07 562	6.71 559	7.92 556	1.17 564	0.00 564	0.96 559	6.20 559	1.34 556

mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

cdsmvs_eth3d_5k		17.71 518	23.62 520	0.00 543	0.00 568	0.00 571	0.00 556	70.17 323	0.00 563	0.00 564	74.25 418	68.16 120	0.00 564	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		5.20 523	6.93 527	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	62.39 189	0.00 564	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

ab-mvs-re		5.62 522	7.50 526	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	67.46 500	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet1														28.98 524	71.38 484	62.61 503
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													30.98 548
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					85.04 78	63.52 158		84.79 52		83.97 84		74.92 53	85.60 53	74.59 56	93.74 67

WAC-MVS						22.69 543								36.10 472

MSC_two_6792asdad				79.02 57	83.14 107	67.03 114		80.75 152					86.24 26	77.27 38	94.85 30	83.78 174

No_MVS				79.02 57	83.14 107	67.03 114		80.75 152					86.24 26	77.27 38	94.85 30	83.78 174

eth-test2					0.00 568

eth-test					0.00 568

OPU-MVS				78.65 64	83.44 105	66.85 116	83.62 52				86.12 193	66.82 138	86.01 36	61.72 194	89.79 160	83.08 203

test_0728_SECOND				76.57 95	86.20 52	60.57 193	83.77 50	85.49 33					85.90 42	75.86 43	94.39 45	83.25 195

GSMVS																70.05 434

test_part2					85.90 63	66.44 122				84.61 76

sam_mvs1												31.41 472				70.05 434

sam_mvs												31.21 476

ambc				70.10 234	77.74 195	50.21 294	74.28 175	77.93 219		79.26 146	88.29 128	54.11 300	79.77 170	64.43 160	91.10 119	80.30 284

MTGPA								80.63 158

test_post1							66.63 333				2.08 558	30.66 484	59.33 436	40.34 428

test_post											1.99 559	30.91 479	54.76 461

patchmatchnet-post											68.99 484	31.32 473	69.38 359

GG-mvs-BLEND				52.24 468	60.64 491	29.21 522	69.73 260	42.41 528		45.47 538	52.33 538	20.43 537	68.16 372	25.52 537	65.42 517	59.36 517

MTMP							84.83 39	19.26 558

test9_res														72.12 87	91.37 107	77.40 333

agg_prior2														70.70 96	90.93 126	78.55 313

agg_prior					84.44 90	66.02 128		78.62 206		76.95 194			80.34 161

test_prior4						70.14 79	77.57 116

test_prior				75.27 116	82.15 127	59.85 202		84.33 73					83.39 98			82.58 224

新几何2							71.33 232

旧先验1					84.55 87	60.36 195		63.69 390			87.05 152	54.65 295			83.34 319	69.66 440

原ACMM2							74.78 163

testdata2													67.30 383	48.34 356

segment_acmp												68.30 119

test12				76.51 96	82.28 124	60.94 187		81.64 130		73.60 290		64.88 165	85.19 67		90.42 140	83.38 191

plane_prior7					85.18 73	66.21 125

plane_prior6					84.18 94	65.31 136						60.83 215

plane_prior5								85.49 33					86.15 31	71.09 91	90.94 124	84.82 134

plane_prior4											89.11 103

plane_prior1					84.46 89

n2								0.00 570

nn								0.00 570

door-mid								55.02 454

lessismore_v0				72.75 172	79.60 160	56.83 238		57.37 436		83.80 87	89.01 107	47.45 353	78.74 187	64.39 161	86.49 245	82.69 221

test11								82.71 106

door								52.91 469

HQP5-MVS						58.80 218

BP-MVS														67.38 132

HQP4-MVS										71.59 333			85.31 59			83.74 176

HQP3-MVS								84.12 79							89.16 174

HQP2-MVS												58.09 259

NP-MVS					83.34 106	63.07 164					85.97 198

ACMMP++_ref															89.47 167

ACMMP++															91.96 96

Test By Simon												62.56 185

High-resolution multi-view benchmark