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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 46	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 73	99.27 1	99.54 1

FOURS1					96.08 11	87.41 18	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 50	91.91 18	84.21 192	96.51 7	57.84 433	88.93 96	92.84 115	91.92 3	96.16 3	96.23 23	86.95 56	95.99 11	79.05 155	98.57 14	98.80 6

PS-CasMVS		90.06 46	91.92 16	84.47 181	96.56 6	58.83 422	89.04 94	92.74 119	91.40 5	96.12 4	96.06 28	87.23 52	95.57 42	79.42 150	98.74 5	99.00 2

LCM-MVSNet-Re		83.48 207	85.06 155	78.75 351	85.94 328	55.75 453	80.05 330	94.27 25	76.47 149	96.09 5	94.54 73	83.31 104	89.75 280	59.95 409	94.89 195	90.75 295

PEN-MVS		90.03 48	91.88 19	84.48 180	96.57 5	58.88 419	88.95 95	93.19 94	91.62 4	96.01 6	96.16 26	87.02 55	95.60 41	78.69 159	98.72 8	98.97 3

CP-MVSNet		89.27 65	90.91 46	84.37 182	96.34 8	58.61 425	88.66 103	92.06 142	90.78 6	95.67 7	95.17 51	81.80 139	95.54 45	79.00 156	98.69 9	98.95 4

reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 12	92.87 13	94.32 22	90.25 10	95.65 8	95.74 32	87.75 45	95.72 37	89.60 4	98.27 27	92.08 252

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 42	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 87	93.16 153	91.10 1	97.53 81	96.58 33
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
WR-MVS_H		89.91 53	91.31 35	85.71 144	96.32 9	62.39 346	89.54 84	93.31 88	90.21 11	95.57 10	95.66 36	81.42 144	95.90 16	80.94 129	98.80 2	98.84 5

reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 27	89.13 6	98.26 29	91.76 263

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 27	89.13 6	98.26 29	91.76 263

OurMVSNet-221017-0		90.01 49	89.74 59	90.83 35	93.16 86	80.37 100	91.91 41	93.11 99	81.10 90	95.32 13	97.24 9	72.94 270	94.85 76	85.07 73	97.78 58	97.26 16

anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 190	86.67 24	90.51 59	90.20 214	69.87 276	95.06 14	96.14 27	84.28 92	93.07 157	87.68 23	96.34 121	97.09 20

wuyk23d		75.13 373	79.30 304	62.63 510	75.56 506	75.18 168	80.89 315	73.10 457	75.06 176	94.76 15	95.32 45	87.73 47	52.85 546	34.16 544	97.11 91	59.85 542

ACMH	76.49 14	89.34 62	91.14 37	83.96 200	92.50 103	70.36 235	89.55 82	93.84 55	81.89 82	94.70 16	95.44 44	90.69 9	88.31 320	83.33 96	98.30 26	93.20 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 96	87.45 96	86.45 121	92.52 102	69.19 253	87.84 117	88.05 267	81.66 84	94.64 17	96.53 19	65.94 329	94.75 80	83.02 102	96.83 101	95.41 59

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 53	89.89 73	90.63 193	70.00 275	94.55 18	96.67 16	87.94 42	93.59 135	84.27 88	95.97 140	95.52 57

lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 111	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 82	90.26 3	98.44 19	93.63 156

jajsoiax		89.41 60	88.81 80	91.19 31	93.38 78	84.72 54	89.70 76	90.29 211	69.27 284	94.39 20	96.38 20	86.02 72	93.52 140	83.96 90	95.92 146	95.34 61

test_0402		88.65 74	89.58 63	85.88 139	92.55 101	72.22 205	84.01 208	89.44 236	88.63 19	94.38 21	95.77 31	86.38 67	93.59 135	79.84 141	95.21 177	91.82 261

RoMa-HiRes		85.97 122	85.47 144	87.48 100	91.66 134	89.37 4	87.18 126	83.89 349	71.47 252	94.29 22	91.35 221	75.59 220	81.39 425	76.88 194	96.92 97	91.68 268

UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 189	97.00 2	64.33 314	89.67 79	88.38 258	88.84 16	94.29 22	97.57 7	90.48 14	91.26 212	72.57 277	97.65 69	97.34 15

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 225	89.99 69	93.05 103	86.53 34	94.29 22	96.27 22	82.69 112	94.08 110	86.25 52	97.63 70	97.82 8

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 107	82.98 72	91.98 39	90.08 217	71.54 249	94.28 25	96.54 18	81.57 142	94.27 97	86.26 50	96.49 114	97.09 20

PS-MVSNAJss		88.31 78	87.90 90	89.56 59	93.31 81	77.96 128	87.94 115	91.97 145	70.73 264	94.19 26	96.67 16	76.94 203	94.57 88	83.07 100	96.28 123	96.15 37

SED-MVS		90.46 39	91.64 22	86.93 111	94.18 54	72.65 191	90.47 60	93.69 64	83.77 60	94.11 27	94.27 85	90.28 15	95.84 25	86.03 56	97.92 51	92.29 241

test_241102_ONE					94.18 54	72.65 191		93.69 64	83.62 63	94.11 27	93.78 117	90.28 15	95.50 50

DPE-MVS		90.53 38	91.08 39	88.88 70	93.38 78	78.65 118	89.15 93	94.05 42	84.68 51	93.90 29	94.11 97	88.13 38	96.30 4	84.51 86	97.81 57	91.70 267
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		91.22 25	91.92 16	89.14 67	92.97 90	78.04 125	92.84 16	94.14 37	83.33 67	93.90 29	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 195
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMH+	77.89 11	90.73 33	91.50 26	88.44 82	93.00 89	76.26 152	89.65 80	95.55 8	87.72 26	93.89 31	94.94 56	91.62 3	93.44 144	78.35 163	98.76 3	95.61 56

TestfortrainingZip a		91.12 29	92.04 14	88.36 86	94.38 47	76.05 159	92.12 33	93.73 59	85.28 43	93.85 32	94.84 59	88.66 29	95.18 66	87.89 18	97.59 77	93.84 139

ACMM	79.39 9	90.65 34	90.99 43	89.63 57	95.03 33	83.53 65	89.62 81	93.35 84	79.20 116	93.83 33	93.60 126	90.81 8	92.96 160	85.02 76	98.45 18	92.41 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	88.83 27	95.51 48	87.16 37	97.60 74	92.73 204

RE-MVS-def			92.61 8		94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	90.64 11		87.16 37	97.60 74	92.73 204

DVP-MVS		90.06 46	91.32 34	86.29 124	94.16 57	72.56 197	90.54 57	91.01 181	83.61 64	93.75 36	94.65 67	89.76 19	95.78 33	86.42 46	97.97 48	90.55 306
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
test_0728_THIRD									85.33 41	93.75 36	94.65 67	87.44 50	95.78 33	87.41 30	98.21 33	92.98 195

test0726					94.16 57	72.56 197	90.63 54	93.90 49	83.61 64	93.75 36	94.49 75	89.76 19

LPG-MVS_test		91.47 21	91.68 21	90.82 36	94.75 40	81.69 83	90.00 67	94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

LGP-MVS_train				90.82 36	94.75 40	81.69 83		94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

DVP-MVS++		90.07 45	91.09 38	87.00 108	91.55 141	72.64 193	96.19 2	94.10 40	85.33 41	93.49 41	94.64 70	81.12 147	95.88 17	87.41 30	95.94 144	92.48 222

test_241102_TWO								93.71 60	83.77 60	93.49 41	94.27 85	89.27 24	95.84 25	86.03 56	97.82 56	92.04 254

test_one_0601					93.85 66	73.27 183		94.11 39	86.57 33	93.47 43	94.64 70	88.42 30

aaatest				88.50 80	94.38 47	76.12 156	92.12 33	93.85 53	77.53 142	93.24 44	93.18 141		95.85 23	84.99 77	97.69 66	93.54 166

MED-MVS		90.78 32	91.50 26	88.60 78	94.38 47	76.12 156	92.12 33	93.85 53	85.28 43	93.24 44	94.84 59	87.06 54	95.85 23	84.99 77	97.78 58	93.84 139

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 13	92.51 25	93.87 52	88.20 23	93.24 44	94.02 103	90.15 17	95.67 39	86.82 42	97.34 85	92.19 247

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 88	85.17 48	92.47 27	95.05 14	87.65 27	93.21 47	94.39 83	90.09 18	95.08 70	86.67 44	97.60 74	94.18 121

testf1		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 46	84.94 84	89.44 286	74.12 242	96.10 134	94.45 106

APD_test2		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 46	84.94 84	89.44 286	74.12 242	96.10 134	94.45 106

Anonymous20231211		88.40 76	89.62 62	84.73 171	90.46 174	65.27 301	88.86 97	93.02 107	87.15 29	93.05 50	97.10 10	82.28 125	92.02 186	76.70 195	97.99 45	96.88 26

RoMa-SfM		83.52 205	82.69 227	86.00 135	90.77 166	89.30 5	85.98 155	81.47 386	65.77 347	92.99 51	89.25 306	69.55 304	78.65 449	72.01 281	96.45 117	90.04 321

PMatch-Up-SfM		81.93 251	80.09 291	87.42 102	89.08 210	86.10 34	81.31 300	83.35 358	67.64 319	92.96 52	90.69 254	45.71 482	85.82 385	75.20 224	94.89 195	90.35 311

MP-MVS-pluss		90.81 31	91.08 39	89.99 49	95.97 13	79.88 103	88.13 110	94.51 19	75.79 163	92.94 53	94.96 55	88.36 32	95.01 72	90.70 2	98.40 21	95.09 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 70	89.88 56	86.22 128	91.63 135	77.07 142	89.82 74	93.77 57	78.90 120	92.88 54	92.29 184	86.11 70	90.22 257	86.24 53	97.24 88	91.36 278
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
ACMP	79.16 10	90.54 37	90.60 52	90.35 44	94.36 50	80.98 92	89.16 92	94.05 42	79.03 119	92.87 55	93.74 120	90.60 12	95.21 64	82.87 104	98.76 3	94.87 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 56	96.29 21	88.16 37	94.17 107	86.07 55	98.48 17	97.22 18

SMA-MVS		90.31 40	90.48 53	89.83 54	95.31 29	79.52 110	90.98 51	93.24 92	75.37 173	92.84 57	95.28 48	85.58 79	96.09 7	87.92 17	97.76 61	93.88 137
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
test_part2					93.86 65	77.77 130				92.84 57

v10		86.54 108	87.10 102	84.84 165	88.16 246	63.28 325	86.64 141	92.20 137	75.42 172	92.81 59	94.50 74	74.05 249	94.06 111	83.88 91	96.28 123	97.17 19

dcpmvs_2		84.23 176	85.14 153	81.50 284	88.61 229	61.98 354	82.90 256	93.11 99	68.66 298	92.77 60	92.39 176	78.50 174	87.63 336	76.99 192	92.30 309	94.90 78

v8		86.22 114	86.83 111	84.36 184	87.82 254	62.35 348	86.42 145	91.33 168	76.78 148	92.73 61	94.48 76	73.41 262	93.72 126	83.10 99	95.41 169	97.01 23

nrg030		87.85 88	88.49 82	85.91 137	90.07 185	69.73 243	87.86 116	94.20 31	74.04 192	92.70 62	94.66 66	85.88 73	91.50 200	79.72 143	97.32 86	96.50 34

fmvsm_l_mol_unc0.5_1		82.09 243	83.08 216	79.12 342	85.01 349	56.67 445	77.48 387	81.51 385	68.49 301	92.60 63	95.50 41	72.88 271	88.10 322	81.09 125	93.20 275	92.58 216

SteuartSystems-ACMMP		91.16 27	91.36 30	90.55 40	93.91 64	80.97 93	91.49 45	93.48 78	82.82 74	92.60 63	93.97 105	88.19 35	96.29 5	87.61 25	98.20 35	94.39 112
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 104	86.76 138	92.78 118	78.78 122	92.51 65	93.64 125	88.13 38	93.84 122	84.83 82	97.55 78	94.10 127
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 41	92.99 12	94.23 28	85.21 45	92.51 65	95.13 52	90.65 10	95.34 58	88.06 15	98.15 38	95.95 45

K. test v3		85.14 145	84.73 163	86.37 122	91.13 158	69.63 245	85.45 170	76.68 429	84.06 58	92.44 67	96.99 12	62.03 357	94.65 84	80.58 135	93.24 271	94.83 89

aaEdge-Enhanced		90.09 43	90.66 50	88.38 84	92.82 97	76.12 156	89.40 90	93.70 61	83.72 62	92.39 68	93.18 141	88.02 41	95.47 51	84.99 77	97.69 66	93.54 166

ACMMP		91.91 14	91.87 20	92.03 11	95.53 26	85.91 36	93.35 11	94.16 33	82.52 76	92.39 68	94.14 95	89.15 26	95.62 40	87.35 32	98.24 31	94.56 97
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
SF-MVS		90.27 41	90.80 48	88.68 77	92.86 94	77.09 141	91.19 49	95.74 5	81.38 87	92.28 70	93.80 115	86.89 57	94.64 85	85.52 67	97.51 82	94.30 117

PMatch-SfM		81.28 264	79.37 302	87.00 108	89.23 203	85.40 45	81.27 305	81.28 388	65.97 338	92.13 71	90.30 275	44.94 494	85.43 389	74.06 245	95.14 182	90.18 318

LoFTR		76.52 353	76.53 348	76.49 403	83.36 391	80.97 93	80.82 318	68.96 489	62.47 397	92.13 71	89.95 286	51.45 443	74.61 470	64.97 363	94.67 211	73.87 521

COLMAP_ROB	83.01 3	91.97 13	91.95 15	92.04 10	93.68 69	86.15 31	93.37 10	95.10 13	90.28 9	92.11 73	95.03 54	89.75 21	94.93 74	79.95 140	98.27 27	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 50	89.84 57	90.41 42	94.91 36	84.50 57	89.49 86	93.98 44	79.68 108	92.09 74	93.89 113	83.80 97	93.10 156	82.67 108	98.04 40	93.64 155

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 157	94.02 62	64.13 315	84.38 199	91.29 169	84.88 49	92.06 75	93.84 114	86.45 64	93.73 125	73.22 268	98.66 10	97.69 9

DKM		82.99 220	82.10 237	85.66 145	90.69 170	88.83 9	82.94 253	78.86 407	66.54 334	92.02 76	88.74 320	67.79 315	78.28 451	74.39 232	96.96 95	89.85 326

MTAPA		91.52 18	91.60 23	91.29 29	96.59 4	86.29 28	92.02 38	91.81 153	84.07 57	92.00 77	94.40 82	86.63 60	95.28 61	88.59 10	98.31 25	92.30 239

ACMMP_NAP		90.65 34	91.07 41	89.42 61	95.93 15	79.54 109	89.95 71	93.68 68	77.65 138	91.97 78	94.89 57	88.38 31	95.45 53	89.27 5	97.87 55	93.27 174

FC-MVSNet-test		85.93 124	87.05 104	82.58 251	92.25 111	56.44 446	85.75 162	93.09 101	77.33 143	91.94 79	94.65 67	74.78 234	93.41 146	75.11 226	98.58 13	97.88 7

test-260524					93.36 80	75.43 166		93.68 68		91.87 80		86.66 59	95.37 56	85.83 63	97.78 58

sc_t1		87.70 91	88.94 73	83.99 198	93.47 73	67.15 276	85.05 180	88.21 266	86.81 31	91.87 80	97.65 5	85.51 81	87.91 328	74.22 236	97.63 70	96.92 25

lessismore_v0				85.95 136	91.10 159	70.99 226		70.91 478		91.79 82	94.42 80	61.76 358	92.93 162	79.52 149	93.03 279	93.93 134

HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 55	93.51 8	94.85 15	82.88 73	91.77 83	93.94 111	90.55 13	95.73 36	88.50 11	98.23 32	95.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 26	90.95 45	91.93 14	95.67 22	85.85 39	90.00 67	93.90 49	80.32 99	91.74 84	94.41 81	88.17 36	95.98 12	86.37 48	97.99 45	93.96 133

mPP-MVS		91.69 15	91.47 28	92.37 5	96.04 12	88.48 11	92.72 18	92.60 126	83.09 70	91.54 85	94.25 89	87.67 48	95.51 48	87.21 36	98.11 39	93.12 185

HFP-MVS		91.30 23	91.39 29	91.02 32	95.43 28	84.66 56	92.58 23	93.29 90	81.99 79	91.47 86	93.96 108	88.35 33	95.56 43	87.74 21	97.74 63	92.85 201

ACMMPR		91.49 19	91.35 32	91.92 15	95.74 19	85.88 38	92.58 23	93.25 91	81.99 79	91.40 87	94.17 94	87.51 49	95.87 19	87.74 21	97.76 61	93.99 130

GST-MVS		90.96 30	91.01 42	90.82 36	95.45 27	82.73 74	91.75 43	93.74 58	80.98 92	91.38 88	93.80 115	87.20 53	95.80 29	87.10 39	97.69 66	93.93 134

test_fmvsmvis_n_1920		85.22 140	85.36 149	84.81 167	85.80 331	76.13 155	85.15 178	92.32 134	61.40 414	91.33 89	90.85 248	83.76 99	86.16 373	84.31 87	93.28 269	92.15 250

ANet_high		83.17 216	85.68 140	75.65 418	81.24 424	45.26 521	79.94 332	92.91 112	83.83 59	91.33 89	96.88 15	80.25 159	85.92 377	68.89 320	95.89 149	95.76 48

ZNCC-MVS		91.26 24	91.34 33	91.01 33	95.73 20	83.05 71	92.18 32	94.22 30	80.14 102	91.29 91	93.97 105	87.93 43	95.87 19	88.65 9	97.96 50	94.12 126

casdiffmvs_mvg		86.72 103	87.51 95	84.36 184	87.09 287	65.22 302	84.16 204	94.23 28	77.89 134	91.28 92	93.66 124	84.35 91	92.71 166	80.07 137	94.87 200	95.16 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DKM-HiRes		83.22 214	82.10 237	86.59 117	91.79 132	88.73 10	82.92 254	77.76 415	69.00 292	91.15 93	89.69 294	63.65 349	81.20 429	76.19 206	96.70 107	89.86 325

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 100	81.34 90	90.19 66	93.08 102	80.87 94	91.13 94	93.19 140	86.22 68	95.97 13	82.23 114	97.18 90	90.45 308
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 65		91.84 129		88.80 99	95.32 12	75.14 175	91.07 95	92.89 157	87.27 51	93.78 124	83.69 95	97.55 78

CP-MVS		91.67 16	91.58 24	91.96 13	95.29 30	87.62 16	93.38 9	93.36 81	83.16 69	91.06 96	94.00 104	88.26 34	95.71 38	87.28 35	98.39 22	92.55 219

FIs		85.35 139	86.27 122	82.60 250	91.86 126	57.31 438	85.10 179	93.05 103	75.83 162	91.02 97	93.97 105	73.57 257	92.91 164	73.97 247	98.02 43	97.58 12

Casviewmamba		88.12 82	88.82 79	86.03 134	89.14 206	68.35 264	86.40 146	94.70 17	79.80 105	90.92 98	93.72 122	87.83 44	93.81 123	81.09 125	95.75 157	95.92 47

UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 131	92.86 94	67.02 281	82.55 265	91.56 159	83.08 71	90.92 98	91.82 202	78.25 177	93.99 113	74.16 240	98.35 23	97.49 13

DU-MVS		86.80 102	86.99 107	86.21 129	93.24 84	67.02 281	83.16 246	92.21 136	81.73 83	90.92 98	91.97 193	77.20 197	93.99 113	74.16 240	98.35 23	97.61 10

tt0805		88.09 83	89.79 58	82.98 233	93.26 83	63.94 318	91.10 50	89.64 230	85.07 46	90.91 101	91.09 234	89.16 25	91.87 191	82.03 116	95.87 150	93.13 182

V42		83.47 208	83.37 207	83.75 207	83.16 399	63.33 324	81.31 300	90.23 213	69.51 281	90.91 101	90.81 250	74.16 245	92.29 180	80.06 138	90.22 383	95.62 55

tt0320-xc		86.67 105	88.41 84	81.44 287	93.45 74	60.44 387	83.96 210	88.50 253	87.26 28	90.90 103	97.90 3	85.61 78	86.40 367	70.14 304	98.01 44	97.47 14

region2R		91.44 22	91.30 36	91.87 18	95.75 18	85.90 37	92.63 22	93.30 89	81.91 81	90.88 104	94.21 90	87.75 45	95.87 19	87.60 26	97.71 64	93.83 142

APD_test1		88.40 76	87.91 89	89.88 51	89.50 196	86.65 26	89.98 70	91.91 148	84.26 55	90.87 105	93.92 112	82.18 127	89.29 290	73.75 251	94.81 205	93.70 150

tt0320		86.63 107	88.36 85	81.41 288	93.57 71	60.73 384	84.37 200	88.61 252	87.00 30	90.75 106	97.98 2	85.54 80	86.45 364	69.75 309	97.70 65	97.06 22

WR-MVS		83.56 202	84.40 181	81.06 296	93.43 77	54.88 464	78.67 363	85.02 330	81.24 88	90.74 107	91.56 212	72.85 272	91.08 223	68.00 331	98.04 40	97.23 17

v1240		84.30 172	84.51 177	83.65 211	87.65 263	61.26 370	82.85 257	91.54 160	67.94 312	90.68 108	90.65 260	71.71 290	93.64 129	82.84 105	94.78 206	96.07 40

Elysia		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 109	94.10 98	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 109	94.10 98	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

ZD-MVS					92.22 113	80.48 97		91.85 149	71.22 257	90.38 111	92.98 151	86.06 71	96.11 6	81.99 118	96.75 105

MIMVSNet1		83.63 199	84.59 172	80.74 302	94.06 61	62.77 333	82.72 259	84.53 342	77.57 140	90.34 112	95.92 30	76.88 209	85.83 384	61.88 394	97.42 83	93.62 157

LS3D		90.60 36	90.34 54	91.38 27	89.03 213	84.23 58	93.58 6	94.68 18	90.65 7	90.33 113	93.95 110	84.50 89	95.37 56	80.87 130	95.50 168	94.53 101

KD-MVS_self_test		81.93 251	83.14 214	78.30 362	84.75 357	52.75 480	80.37 327	89.42 237	70.24 273	90.26 114	93.39 131	74.55 241	86.77 357	68.61 326	96.64 108	95.38 60

PMVS	80.48 6	90.08 44	90.66 50	88.34 87	96.71 3	92.97 1	90.31 64	89.57 233	88.51 20	90.11 115	95.12 53	90.98 7	88.92 295	77.55 182	97.07 92	83.13 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PC_three_1452									58.96 442	90.06 116	91.33 222	80.66 154	93.03 159	75.78 213	95.94 144	92.48 222

v1921920		84.23 176	84.37 182	83.79 205	87.64 264	61.71 361	82.91 255	91.20 175	67.94 312	90.06 116	90.34 269	72.04 285	93.59 135	82.32 112	94.91 193	96.07 40

ITE_SJBPF				90.11 48	90.72 168	84.97 50		90.30 209	81.56 85	90.02 118	91.20 230	82.40 118	90.81 236	73.58 260	94.66 212	94.56 97

XVS		91.54 17	91.36 30	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 119	94.03 102	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

X-MVStestdata		85.04 149	82.70 226	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 119	16.05 553	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

v1192		84.57 162	84.69 168	84.21 192	87.75 258	62.88 329	83.02 249	91.43 163	69.08 289	89.98 121	90.89 245	72.70 275	93.62 133	82.41 111	94.97 192	96.13 38

Anonymous20240529		86.20 115	87.13 101	83.42 220	90.19 180	64.55 309	84.55 193	90.71 190	85.85 39	89.94 122	95.24 50	82.13 128	90.40 252	69.19 316	96.40 120	95.31 63

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 106	89.67 192	75.87 161	84.60 191	89.74 225	74.40 188	89.92 123	93.41 129	80.45 156	90.63 244	86.66 45	94.37 224	94.73 94

hybridcas		86.07 119	87.02 105	83.19 228	87.76 257	62.85 331	84.53 197	93.42 79	75.52 169	89.88 124	93.31 133	86.15 69	91.68 196	77.76 179	94.89 195	95.05 75

pmmvs6		86.52 109	88.06 88	81.90 271	92.22 113	62.28 349	84.66 190	89.15 242	83.54 66	89.85 125	97.32 8	88.08 40	86.80 356	70.43 301	97.30 87	96.62 31

v144192		84.24 175	84.41 180	83.71 209	87.59 266	61.57 362	82.95 252	91.03 180	67.82 316	89.80 126	90.49 266	73.28 266	93.51 141	81.88 121	94.89 195	96.04 42

v1144		84.54 166	84.72 165	84.00 197	87.67 262	62.55 338	82.97 251	90.93 185	70.32 270	89.80 126	90.99 238	73.50 258	93.48 142	81.69 122	94.65 213	95.97 43

MatchFormer		68.98 457	69.54 447	67.33 490	76.37 500	74.77 169	79.54 337	57.73 543	46.87 517	89.77 128	86.43 371	41.98 506	65.54 525	52.83 479	94.31 227	61.67 540

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 62	93.64 70	79.07 114	88.54 106	94.20 31	73.53 204	89.71 129	94.82 62	85.09 83	95.77 35	84.17 89	98.03 42	93.26 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 85	86.93 109	91.22 30	90.08 183	89.30 5	89.68 78	91.11 177	79.26 115	89.68 130	94.81 65	82.44 116	87.74 333	76.54 200	88.74 412	96.61 32

IU-MVS					94.18 54	72.64 193		90.82 188	56.98 460	89.67 131				85.78 64	97.92 51	93.28 173

FMVSNet1		84.55 165	85.45 146	81.85 273	90.27 178	61.05 374	86.83 135	88.27 263	78.57 126	89.66 132	95.64 37	75.43 222	90.68 241	69.09 317	95.33 172	93.82 143

DenseAffine		81.00 272	79.38 301	85.84 140	90.25 179	87.48 17	81.47 295	78.40 411	65.68 350	89.63 133	86.45 370	58.79 380	82.05 420	67.78 334	95.99 139	87.99 377

IterMVS-LS		84.73 159	84.98 157	83.96 200	87.35 275	63.66 319	83.25 240	89.88 223	76.06 153	89.62 134	92.37 180	73.40 264	92.52 171	78.16 168	94.77 208	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 75	89.01 70	87.36 103	91.30 149	77.50 134	87.55 119	92.97 111	87.95 25	89.62 134	92.87 158	84.56 88	93.89 119	77.65 180	96.62 109	90.70 298

UniMVSNet (Re)		86.87 99	86.98 108	86.55 119	93.11 87	68.48 263	83.80 218	92.87 113	80.37 97	89.61 136	91.81 203	77.72 185	94.18 105	75.00 227	98.53 15	96.99 24

IS-MVSNet		86.66 106	86.82 112	86.17 131	92.05 119	66.87 285	91.21 48	88.64 250	86.30 36	89.60 137	92.59 168	69.22 307	94.91 75	73.89 248	97.89 54	96.72 29

v2v482		84.09 179	84.24 186	83.62 212	87.13 282	61.40 365	82.71 260	89.71 228	72.19 239	89.55 138	91.41 218	70.70 297	93.20 151	81.02 128	93.76 247	96.25 36

Baseline_NR-MVSNet		84.00 187	85.90 132	78.29 363	91.47 146	53.44 476	82.29 276	87.00 296	79.06 118	89.55 138	95.72 35	77.20 197	86.14 374	72.30 279	98.51 16	95.28 64

ArgMatch-SfM		79.08 304	77.37 334	84.22 191	87.80 255	86.73 23	79.32 347	78.45 409	56.81 462	89.54 140	84.95 402	55.35 418	79.21 443	68.89 320	95.21 177	86.73 402

CSCG		86.26 112	86.47 115	85.60 147	90.87 164	74.26 174	87.98 114	91.85 149	80.35 98	89.54 140	88.01 334	79.09 168	92.13 182	75.51 218	95.06 187	90.41 309

ambc				82.98 233	90.55 173	64.86 305	88.20 108	89.15 242		89.40 142	93.96 108	71.67 291	91.38 208	78.83 157	96.55 111	92.71 207

DeepPCF-MVS	81.24 5	87.28 95	86.21 124	90.49 41	91.48 145	84.90 51	83.41 235	92.38 131	70.25 272	89.35 143	90.68 256	82.85 111	94.57 88	79.55 147	95.95 143	92.00 256

LuminaMVS		83.94 190	83.51 199	85.23 155	89.78 191	71.74 211	84.76 186	87.27 281	72.60 230	89.31 144	90.60 264	64.04 342	90.95 227	79.08 154	94.11 234	92.99 193

MVStest1		70.05 445	69.26 448	72.41 454	58.62 553	55.59 455	76.61 404	65.58 507	53.44 484	89.28 145	93.32 132	22.91 552	71.44 482	74.08 244	89.52 394	90.21 317

ELoFTR		73.12 405	73.47 388	72.08 456	81.84 413	77.60 133	80.51 325	66.79 503	49.99 510	89.23 146	88.83 316	47.19 465	65.24 529	61.99 391	94.85 203	73.39 522

test_fmvsmconf0.01_n		86.68 104	86.52 114	87.18 104	85.94 328	78.30 121	86.93 131	92.20 137	65.94 340	89.16 147	93.16 144	83.10 105	89.89 274	87.81 20	94.43 220	93.35 169

MSP-MVS		89.08 69	88.16 87	91.83 19	95.76 17	86.14 32	92.75 17	93.90 49	78.43 127	89.16 147	92.25 186	72.03 286	96.36 3	88.21 12	90.93 355	92.98 195
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
XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 33	85.91 156	93.60 72	80.16 101	89.13 149	93.44 128	83.82 96	90.98 226	83.86 92	95.30 176	93.60 159

fmvsm_s_conf0.5_n_10		85.20 142	85.25 152	85.02 162	86.01 326	71.31 220	84.96 181	91.76 155	69.10 287	88.90 150	92.56 171	73.84 253	90.63 244	86.88 40	93.26 270	93.13 182

MDA-MVSNet-bldmvs		77.47 335	76.90 342	79.16 341	79.03 468	64.59 306	66.58 509	75.67 436	73.15 217	88.86 151	88.99 314	66.94 321	81.23 428	64.71 365	88.22 424	91.64 270

EG-PatchMatch MVS		84.08 180	84.11 188	83.98 199	92.22 113	72.61 196	82.20 282	87.02 293	72.63 229	88.86 151	91.02 237	78.52 173	91.11 222	73.41 262	91.09 349	88.21 370

3Dnovator+	83.92 2	89.97 52	89.66 60	90.92 34	91.27 151	81.66 87	91.25 47	94.13 38	88.89 14	88.83 153	94.26 88	77.55 189	95.86 22	84.88 80	95.87 150	95.24 66

ArgMatch-Sym		78.58 319	76.86 343	83.71 209	87.61 265	86.40 27	78.19 369	77.45 418	55.72 467	88.82 154	82.01 454	59.68 373	78.75 448	67.43 337	94.86 201	85.98 408

fmvsm_s_conf0.5_n_5		84.56 163	84.71 166	84.11 196	87.92 251	72.09 207	84.80 182	88.64 250	64.43 371	88.77 155	91.78 205	78.07 179	87.95 327	85.85 62	92.18 317	92.30 239

EI-MVSNet-UG-set		85.04 149	84.44 179	86.85 113	83.87 378	72.52 199	83.82 216	85.15 326	80.27 100	88.75 156	85.45 391	79.95 162	91.90 189	81.92 120	90.80 364	96.13 38

EI-MVSNet-Vis-set		85.12 147	84.53 176	86.88 112	84.01 374	72.76 190	83.91 214	85.18 325	80.44 95	88.75 156	85.49 389	80.08 160	91.92 188	82.02 117	90.85 360	95.97 43

BridgeMVS		84.80 155	85.40 147	83.00 232	88.95 216	61.44 364	90.42 63	92.37 133	71.48 251	88.72 158	93.13 145	70.16 302	95.15 67	79.26 153	94.11 234	92.41 228

OMC-MVS		88.19 79	87.52 94	90.19 47	91.94 124	81.68 85	87.49 122	93.17 95	76.02 155	88.64 159	91.22 228	84.24 93	93.37 147	77.97 177	97.03 93	95.52 57

RRT-MVS		82.97 221	83.44 202	81.57 281	85.06 348	58.04 431	87.20 124	90.37 203	77.88 135	88.59 160	93.70 123	63.17 351	93.05 158	76.49 201	88.47 416	93.62 157

test_fmvsmconf0.1_n		86.18 117	85.88 133	87.08 106	85.26 344	78.25 122	85.82 160	91.82 151	65.33 357	88.55 161	92.35 183	82.62 115	89.80 276	86.87 41	94.32 226	93.18 181

UA-Net		91.49 19	91.53 25	91.39 26	94.98 34	82.95 73	93.52 7	92.79 117	88.22 22	88.53 162	97.64 6	83.45 102	94.55 90	86.02 59	98.60 12	96.67 30

MP-MVS		91.14 28	90.91 46	91.83 19	96.18 10	86.88 22	92.20 31	93.03 106	82.59 75	88.52 163	94.37 84	86.74 58	95.41 55	86.32 49	98.21 33	93.19 180
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
sasdasda		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 164	87.54 350	86.45 64	91.06 224	75.76 214	93.76 247	92.54 220

canonicalmvs		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 164	87.54 350	86.45 64	91.06 224	75.76 214	93.76 247	92.54 220

NR-MVSNet		86.00 120	86.22 123	85.34 154	93.24 84	64.56 308	82.21 280	90.46 199	80.99 91	88.42 166	91.97 193	77.56 188	93.85 120	72.46 278	98.65 11	97.61 10

alignmvs		83.94 190	83.98 191	83.80 204	87.80 255	67.88 271	84.54 195	91.42 165	73.27 215	88.41 167	87.96 335	72.33 279	90.83 235	76.02 211	94.11 234	92.69 208

TransMVSNet (Re)		84.02 186	85.74 139	78.85 348	91.00 161	55.20 462	82.29 276	87.26 282	79.65 109	88.38 168	95.52 40	83.00 107	86.88 352	67.97 332	96.60 110	94.45 106

PM-MVS		80.20 292	79.00 306	83.78 206	88.17 244	86.66 25	81.31 300	66.81 502	69.64 278	88.33 169	90.19 279	64.58 336	83.63 410	71.99 282	90.03 386	81.06 482

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 235	86.91 295	70.38 234	85.31 174	92.61 125	75.59 167	88.32 170	92.87 158	82.22 126	88.63 309	88.80 8	92.82 288	89.83 327

tttt0517		81.07 270	79.58 298	85.52 149	88.99 215	66.45 290	87.03 130	75.51 438	73.76 196	88.32 170	90.20 278	37.96 515	94.16 109	79.36 152	95.13 183	95.93 46

FE-MVSNET2		82.80 224	83.51 199	80.67 308	89.08 210	58.46 426	82.40 273	89.26 238	71.25 256	88.24 172	94.07 100	75.75 218	89.56 281	65.91 352	95.67 163	93.98 131

casdiffmvs		85.21 141	85.85 134	83.31 223	86.17 320	62.77 333	83.03 248	93.93 47	74.69 181	88.21 173	92.68 167	82.29 124	91.89 190	77.87 178	93.75 250	95.27 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_vis3_rt		71.42 429	70.67 430	73.64 440	69.66 537	70.46 232	66.97 508	89.73 226	42.68 536	88.20 174	83.04 437	43.77 498	60.07 537	65.35 359	86.66 451	90.39 310

SSC-MVS		77.55 334	81.64 249	65.29 503	90.46 174	20.33 557	73.56 452	68.28 491	85.44 40	88.18 175	94.64 70	70.93 295	81.33 426	71.25 288	92.03 321	94.20 118

balanced_ft_v1		83.49 206	83.93 193	82.19 263	86.46 305	59.61 403	90.81 52	90.92 186	71.78 246	88.08 176	92.56 171	66.97 320	94.54 91	75.34 222	92.42 305	92.42 226

MVSMamba_PlusPlus		87.53 93	88.86 77	83.54 218	92.03 120	62.26 350	91.49 45	92.62 123	88.07 24	88.07 177	96.17 25	72.24 281	95.79 32	84.85 81	94.16 233	92.58 216

MGCFI-Net		85.04 149	85.95 130	82.31 261	87.52 268	63.59 321	86.23 150	93.96 45	73.46 205	88.07 177	87.83 345	86.46 63	90.87 234	76.17 208	93.89 242	92.47 224

v148		82.31 234	82.48 233	81.81 276	85.59 337	59.66 401	81.47 295	86.02 309	72.85 224	88.05 179	90.65 260	70.73 296	90.91 231	75.15 225	91.79 329	94.87 80

AstraMVS		81.67 256	81.40 259	82.48 256	87.06 290	66.47 289	81.41 297	81.68 381	68.78 295	88.00 180	90.95 243	65.70 331	87.86 332	76.66 196	92.38 306	93.12 185

AllTest		87.97 86	87.40 98	89.68 55	91.59 136	83.40 66	89.50 85	95.44 10	79.47 110	88.00 180	93.03 149	82.66 113	91.47 202	70.81 292	96.14 131	94.16 123

TestCases				89.68 55	91.59 136	83.40 66		95.44 10	79.47 110	88.00 180	93.03 149	82.66 113	91.47 202	70.81 292	96.14 131	94.16 123

fmvsm_s_conf0.1_n_2		83.82 193	83.49 201	84.84 165	85.99 327	70.19 237	80.93 314	87.58 277	67.26 326	87.94 183	92.37 180	71.40 293	88.01 324	86.03 56	91.87 328	96.31 35

fmvsm_s_conf0.5_n_8		85.48 131	85.75 138	84.68 174	87.10 285	69.98 239	84.28 202	92.68 120	74.77 179	87.90 184	92.36 182	73.94 250	90.41 251	85.95 61	92.74 290	93.66 151

viewdifsd2359ckpt11		82.46 232	82.98 219	80.88 299	83.53 382	61.00 377	79.46 344	85.97 311	69.48 282	87.89 185	91.31 224	82.10 129	88.61 310	74.28 234	92.86 285	93.02 189

viewmsd2359difaftdt		82.46 232	82.99 218	80.88 299	83.52 383	61.00 377	79.46 344	85.97 311	69.48 282	87.89 185	91.31 224	82.10 129	88.61 310	74.28 234	92.86 285	93.02 189

pm-mvs1		83.69 196	84.95 159	79.91 324	90.04 187	59.66 401	82.43 271	87.44 278	75.52 169	87.85 187	95.26 49	81.25 146	85.65 388	68.74 324	96.04 136	94.42 110

PCF-MVS	74.62 15	82.15 242	80.92 271	85.84 140	89.43 198	72.30 203	80.53 324	91.82 151	57.36 456	87.81 188	89.92 289	77.67 186	93.63 130	58.69 418	95.08 186	91.58 272
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_2		83.62 200	83.29 208	84.62 175	85.43 341	70.18 238	80.61 323	87.24 283	67.14 327	87.79 189	91.87 195	71.79 289	87.98 326	86.00 60	91.77 331	95.71 50

guyue		81.57 258	81.37 261	82.15 265	86.39 308	66.13 293	81.54 294	83.21 360	69.79 277	87.77 190	89.95 286	65.36 334	87.64 335	75.88 212	92.49 303	92.67 209

KinetiMVS		85.95 123	86.10 127	85.50 151	87.56 267	69.78 241	83.70 221	89.83 224	80.42 96	87.76 191	93.24 139	73.76 255	91.54 199	85.03 75	93.62 256	95.19 69

mvs5depth		83.82 193	84.54 175	81.68 279	82.23 407	68.65 261	86.89 132	89.90 222	80.02 104	87.74 192	97.86 4	64.19 341	82.02 421	76.37 202	95.63 165	94.35 113

test_fmvsmconf_n		85.88 125	85.51 143	86.99 110	84.77 356	78.21 123	85.40 172	91.39 166	65.32 358	87.72 193	91.81 203	82.33 120	89.78 277	86.68 43	94.20 231	92.99 193

FMVSNet2		81.31 263	81.61 251	80.41 314	86.38 310	58.75 423	83.93 213	86.58 300	72.43 231	87.65 194	92.98 151	63.78 346	90.22 257	66.86 339	93.92 241	92.27 243

E5new		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 195	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E5		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 195	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E6new		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 197	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E6		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 197	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

viewdifsd2359ckpt07		83.41 212	84.35 183	80.56 310	85.84 330	58.93 418	79.47 342	91.28 170	73.01 221	87.59 199	92.07 189	85.24 82	88.68 306	73.59 259	91.11 347	94.09 128

GeoE		85.45 133	85.81 135	84.37 182	90.08 183	67.07 280	85.86 159	91.39 166	72.33 236	87.59 199	90.25 276	84.85 86	92.37 176	78.00 175	91.94 325	93.66 151

VPA-MVSNet		83.47 208	84.73 163	79.69 329	90.29 177	57.52 436	81.30 303	88.69 249	76.29 151	87.58 201	94.44 77	80.60 155	87.20 345	66.60 344	96.82 102	94.34 114

casdiffseed414692147		85.64 128	86.08 128	84.32 187	87.49 270	65.55 300	85.81 161	93.00 110	75.85 161	87.50 202	93.40 130	83.10 105	91.71 195	73.70 256	94.84 204	95.69 51

CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 20	92.09 37	92.30 135	79.74 107	87.50 202	92.38 177	81.42 144	93.28 149	83.07 100	97.24 88	91.67 269

VDDNet		84.35 170	85.39 148	81.25 290	95.13 31	59.32 407	85.42 171	81.11 389	86.41 35	87.41 204	96.21 24	73.61 256	90.61 246	66.33 346	96.85 99	93.81 146

VortexMVS		80.51 281	80.63 275	80.15 320	83.36 391	61.82 360	80.63 322	88.00 269	67.11 328	87.23 205	89.10 312	63.98 343	88.00 325	73.63 258	92.63 293	90.64 303

ALIKED-LG		78.19 326	77.07 337	81.54 282	84.95 350	86.95 20	86.16 152	83.96 348	56.64 464	87.21 206	90.05 285	51.36 444	78.05 453	57.73 428	95.60 166	79.63 494

c3_l		81.64 257	81.59 252	81.79 278	80.86 432	59.15 413	78.61 364	90.18 215	68.36 303	87.20 207	87.11 362	69.39 305	91.62 197	78.16 168	94.43 220	94.60 96

VDD-MVS		84.23 176	84.58 173	83.20 226	91.17 157	65.16 304	83.25 240	84.97 333	79.79 106	87.18 208	94.27 85	74.77 235	90.89 232	69.24 313	96.54 112	93.55 165

SIFT-NCMNet		71.70 424	70.97 427	73.90 434	77.55 484	81.03 91	71.58 475	63.31 520	63.91 380	87.12 209	81.00 465	50.00 456	64.64 532	49.37 500	94.86 201	76.04 516

MSLP-MVS++		85.00 152	86.03 129	81.90 271	91.84 129	71.56 218	86.75 139	93.02 107	75.95 158	87.12 209	89.39 300	77.98 180	89.40 289	77.46 184	94.78 206	84.75 424

baseline		85.20 142	85.93 131	83.02 231	86.30 315	62.37 347	84.55 193	93.96 45	74.48 185	87.12 209	92.03 192	82.30 122	91.94 187	78.39 161	94.21 229	94.74 93

YYNet1		70.06 444	70.44 434	68.90 478	73.76 518	53.42 477	58.99 533	67.20 498	58.42 445	87.10 212	85.39 393	59.82 371	67.32 514	59.79 410	83.50 491	85.96 409

MDA-MVSNet_test_wron		70.05 445	70.44 434	68.88 479	73.84 517	53.47 475	58.93 534	67.28 497	58.43 444	87.09 213	85.40 392	59.80 372	67.25 515	59.66 411	83.54 490	85.92 411

test_fmvs3		75.72 367	75.20 365	77.27 386	75.01 513	69.47 247	78.93 356	84.88 336	46.67 519	87.08 214	87.84 344	50.44 454	71.62 480	77.42 187	88.53 414	90.72 296

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 140	85.25 175	91.23 174	77.31 144	87.07 215	91.47 217	82.94 108	94.71 81	84.67 84	96.27 125	92.62 212

EPP-MVSNet		85.47 132	85.04 156	86.77 115	91.52 144	69.37 248	91.63 44	87.98 270	81.51 86	87.05 216	91.83 201	66.18 328	95.29 59	70.75 295	96.89 98	95.64 54

TinyColmap		81.25 265	82.34 235	77.99 369	85.33 342	60.68 385	82.32 275	88.33 260	71.26 255	86.97 217	92.22 188	77.10 200	86.98 350	62.37 385	95.17 180	86.31 406

eth_miper_zixun_eth		80.84 275	80.22 285	82.71 243	81.41 422	60.98 380	77.81 377	90.14 216	67.31 325	86.95 218	87.24 359	64.26 339	92.31 178	75.23 223	91.61 336	94.85 88

E4		84.75 158	85.46 145	82.61 249	88.17 244	61.55 363	81.39 298	93.55 76	73.13 219	86.83 219	92.83 160	84.17 94	91.48 201	76.92 193	92.19 316	94.80 91

Anonymous20240521		80.18 293	81.25 263	76.95 393	83.15 400	60.84 382	82.46 268	85.99 310	68.76 296	86.78 220	93.73 121	59.13 377	77.44 455	73.71 252	97.55 78	92.56 218

Patchmatch-RL test		74.48 385	73.68 384	76.89 396	84.83 354	66.54 287	72.29 467	69.16 488	57.70 451	86.76 221	86.33 374	45.79 481	82.59 414	69.63 310	90.65 375	81.54 473

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 35	86.84 134	93.91 48	80.07 103	86.75 222	93.26 138	93.64 2	90.93 229	84.60 85	90.75 365	93.97 132

fmvsm_l_conf0.5_n_9		83.98 188	84.46 178	82.53 254	86.11 323	70.65 230	82.45 270	89.17 241	67.72 318	86.74 223	91.49 214	79.20 166	85.86 383	84.71 83	92.60 299	91.07 284

h-mvs33		84.25 174	82.76 225	88.72 74	91.82 131	82.60 75	84.00 209	84.98 332	71.27 253	86.70 224	90.55 265	63.04 354	93.92 118	78.26 166	94.20 231	89.63 331

hse-mvs2		83.47 208	81.81 246	88.47 81	91.03 160	82.27 79	82.61 261	83.69 353	71.27 253	86.70 224	86.05 380	63.04 354	92.41 174	78.26 166	93.62 256	90.71 297

HPM-MVS++		88.93 71	88.45 83	90.38 43	94.92 35	85.85 39	89.70 76	91.27 173	78.20 130	86.69 226	92.28 185	80.36 158	95.06 71	86.17 54	96.49 114	90.22 313

TSAR-MVS + MP.		88.14 80	87.82 91	89.09 68	95.72 21	76.74 145	92.49 26	91.19 176	67.85 315	86.63 227	94.84 59	79.58 165	95.96 14	87.62 24	94.50 216	94.56 97
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SSM_0404		85.16 144	85.09 154	85.36 153	90.14 182	69.52 246	86.17 151	91.58 157	74.41 186	86.55 228	91.49 214	78.54 171	93.97 115	73.71 252	93.21 274	92.59 215

EI-MVSNet		82.61 227	82.42 234	83.20 226	83.25 396	63.66 319	83.50 232	85.07 327	76.06 153	86.55 228	85.10 397	73.41 262	90.25 254	78.15 170	90.67 372	95.68 53

HQP_MVS		87.75 90	87.43 97	88.70 76	93.45 74	76.42 149	89.45 87	93.61 70	79.44 112	86.55 228	92.95 155	74.84 232	95.22 62	80.78 132	95.83 152	94.46 104

plane_prior3						76.85 144			77.79 137	86.55 228

BH-untuned		80.96 273	80.99 269	80.84 301	88.55 232	68.23 265	80.33 328	88.46 255	72.79 227	86.55 228	86.76 366	74.72 236	91.77 194	61.79 395	88.99 407	82.52 462

MVSTER		77.09 341	75.70 358	81.25 290	75.27 510	61.08 373	77.49 386	85.07 327	60.78 428	86.55 228	88.68 321	43.14 503	90.25 254	73.69 257	90.67 372	92.42 226

旧先验2							81.73 290		56.88 461	86.54 234			84.90 395	72.81 275

IterMVS-SCA-FT		80.64 279	79.41 299	84.34 186	83.93 376	69.66 244	76.28 410	81.09 390	72.43 231	86.47 235	90.19 279	60.46 364	93.15 154	77.45 185	86.39 455	90.22 313

WB-MVS		76.06 361	80.01 293	64.19 507	89.96 189	20.58 556	72.18 469	68.19 492	83.21 68	86.46 236	93.49 127	70.19 301	78.97 445	65.96 348	90.46 381	93.02 189

test_fmvsm_n_1920		83.60 201	82.89 221	85.74 143	85.22 345	77.74 131	84.12 206	90.48 197	59.87 439	86.45 237	91.12 233	75.65 219	85.89 381	82.28 113	90.87 358	93.58 161

fmvsm_s_conf0.5_n_4		84.38 168	84.27 185	84.74 170	87.25 278	70.84 227	83.55 230	88.45 256	68.64 299	86.29 238	91.31 224	74.97 229	88.42 316	87.87 19	90.07 385	94.95 77

DIV-MVS_self_test		80.43 283	80.23 283	81.02 297	79.99 453	59.25 409	77.07 393	87.02 293	67.38 322	86.19 239	89.22 308	63.09 352	90.16 261	76.32 203	95.80 154	93.66 151

CDPH-MVS		86.17 118	85.54 142	88.05 94	92.25 111	75.45 165	83.85 215	92.01 143	65.91 342	86.19 239	91.75 207	83.77 98	94.98 73	77.43 186	96.71 106	93.73 149

cl____		80.42 284	80.23 283	81.02 297	79.99 453	59.25 409	77.07 393	87.02 293	67.37 323	86.18 241	89.21 309	63.08 353	90.16 261	76.31 204	95.80 154	93.65 154

MVS_111021_LR		84.28 173	83.76 196	85.83 142	89.23 203	83.07 70	80.99 312	83.56 355	72.71 228	86.07 242	89.07 313	81.75 141	86.19 372	77.11 190	93.36 265	88.24 369

viewmacassd2359aftdt		84.04 185	84.78 162	81.81 276	86.43 307	60.32 389	81.95 285	92.82 116	71.56 248	86.06 243	92.98 151	81.79 140	90.28 253	76.18 207	93.24 271	94.82 90

SP-DiffGlue		78.90 309	78.86 309	79.02 343	80.36 443	79.68 108	81.86 286	80.17 397	71.69 247	86.02 244	83.77 422	57.33 397	69.38 490	79.38 151	89.12 404	88.02 376

E2		84.06 181	84.61 170	82.40 259	87.49 270	61.31 367	81.03 310	93.36 81	71.83 244	86.02 244	91.87 195	82.91 109	91.37 209	75.66 216	91.33 342	94.53 101

E3		84.06 181	84.61 170	82.40 259	87.49 270	61.30 368	81.03 310	93.36 81	71.83 244	86.01 246	91.87 195	82.91 109	91.36 210	75.66 216	91.33 342	94.53 101

GBi-Net		82.02 247	82.07 239	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 247	95.64 37	63.78 346	90.68 241	65.95 349	93.34 266	93.82 143

test1		82.02 247	82.07 239	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 247	95.64 37	63.78 346	90.68 241	65.95 349	93.34 266	93.82 143

FMVSNet3		78.80 313	78.55 316	79.57 332	82.89 404	56.89 443	81.76 289	85.77 314	69.04 290	86.00 247	90.44 267	51.75 442	90.09 267	65.95 349	93.34 266	91.72 265

miper_ehance_all_eth		80.34 287	80.04 292	81.24 293	79.82 457	58.95 417	77.66 379	89.66 229	65.75 348	85.99 250	85.11 396	68.29 312	91.42 206	76.03 210	92.03 321	93.33 170

tfpnnormal		81.79 255	82.95 220	78.31 361	88.93 217	55.40 458	80.83 317	82.85 365	76.81 147	85.90 251	94.14 95	74.58 239	86.51 362	66.82 342	95.68 161	93.01 192

TAPA-MVS	77.73 12	85.71 127	84.83 161	88.37 85	88.78 224	79.72 105	87.15 128	93.50 77	69.17 285	85.80 252	89.56 296	80.76 152	92.13 182	73.21 273	95.51 167	93.25 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mamba_0408		83.44 211	82.88 222	85.11 158	89.13 207	68.97 256	72.73 464	91.28 170	72.90 222	85.68 253	90.61 262	76.78 210	93.97 115	73.37 264	93.47 258	92.38 233

SSM_04072		81.44 261	82.88 222	77.10 389	89.13 207	68.97 256	72.73 464	91.28 170	72.90 222	85.68 253	90.61 262	76.78 210	69.94 488	73.37 264	93.47 258	92.38 233

SSM_0407		84.89 154	84.85 160	85.01 163	89.13 207	68.97 256	85.60 166	91.58 157	74.41 186	85.68 253	91.49 214	78.54 171	93.69 127	73.71 252	93.47 258	92.38 233

TSAR-MVS + GP.		83.95 189	82.69 227	87.72 97	89.27 202	81.45 89	83.72 220	81.58 384	74.73 180	85.66 256	86.06 379	72.56 277	92.69 168	75.44 220	95.21 177	89.01 355

EU-MVSNet		75.12 374	74.43 377	77.18 388	83.11 401	59.48 405	85.71 164	82.43 372	39.76 541	85.64 257	88.76 318	44.71 496	87.88 330	73.86 249	85.88 463	84.16 435

SIFT-ConvMatch		74.17 389	72.94 398	77.87 372	80.47 440	83.15 69	74.56 436	63.87 517	63.44 382	85.61 258	83.95 419	53.15 430	69.97 487	57.21 432	94.21 229	80.48 487

MonoMVSNet		76.66 348	77.26 336	74.86 426	79.86 456	54.34 468	86.26 149	86.08 306	71.08 259	85.59 259	88.68 321	53.95 425	85.93 376	63.86 373	80.02 511	84.32 430

LF4IMVS		82.75 226	81.93 244	85.19 156	82.08 408	80.15 102	85.53 167	88.76 247	68.01 309	85.58 260	87.75 346	71.80 288	86.85 354	74.02 246	93.87 243	88.58 362

Patchmtry		76.56 352	77.46 331	73.83 436	79.37 464	46.60 514	82.41 272	76.90 426	73.81 195	85.56 261	92.38 177	48.07 463	83.98 407	63.36 379	95.31 175	90.92 290

MVS_111021_HR		84.63 160	84.34 184	85.49 152	90.18 181	75.86 162	79.23 353	87.13 287	73.35 209	85.56 261	89.34 302	83.60 101	90.50 248	76.64 197	94.05 238	90.09 320

testdata				79.54 334	92.87 92	72.34 202		80.14 398	59.91 438	85.47 263	91.75 207	67.96 314	85.24 391	68.57 328	92.18 317	81.06 482

FE-MVSNET		78.46 321	79.36 303	75.75 415	86.53 301	54.53 466	78.03 370	85.35 321	69.01 291	85.41 264	90.68 256	64.27 338	85.73 386	62.59 384	92.35 308	87.00 397

viewcassd2359sk11		83.53 204	83.96 192	82.25 262	86.97 294	61.13 372	80.80 319	93.22 93	70.97 261	85.36 265	91.08 235	81.84 138	91.29 211	74.79 229	90.58 377	94.33 115

mvsmamba		80.30 289	78.87 308	84.58 177	88.12 247	67.55 273	92.35 30	84.88 336	63.15 386	85.33 266	90.91 244	50.71 450	95.20 65	66.36 345	87.98 426	90.99 287

SIFT-UM-Cal		73.50 399	72.76 401	75.71 417	79.21 466	81.68 85	72.85 463	68.91 490	62.93 387	85.31 267	83.39 433	52.88 432	67.56 513	54.97 457	94.42 223	77.89 510

test1111		78.53 320	78.85 311	77.56 377	92.22 113	47.49 509	82.61 261	69.24 487	72.43 231	85.28 268	94.20 91	51.91 439	90.07 269	65.36 358	96.45 117	95.11 73

thisisatest0530		79.07 305	77.33 335	84.26 190	87.13 282	64.58 307	83.66 223	75.95 433	68.86 294	85.22 269	87.36 356	38.10 512	93.57 138	75.47 219	94.28 228	94.62 95

XFeat-MNN		64.44 486	63.82 486	66.28 496	61.83 552	67.23 274	61.52 526	63.95 516	44.72 527	85.19 270	74.40 524	36.05 520	66.04 523	55.58 447	91.14 346	65.57 535

BP-MVS1		82.81 223	81.67 248	86.23 126	87.88 253	68.53 262	86.06 154	84.36 343	75.65 165	85.14 271	90.19 279	45.84 480	94.42 94	85.18 71	94.72 210	95.75 49

NormalMVS		86.47 110	85.32 150	89.94 50	94.43 43	80.42 98	88.63 104	93.59 73	74.56 183	85.12 272	90.34 269	66.19 326	94.20 102	76.57 198	98.44 19	95.19 69

SymmetryMVS		84.79 157	83.54 198	88.55 79	92.44 105	80.42 98	88.63 104	82.37 373	74.56 183	85.12 272	90.34 269	66.19 326	94.20 102	76.57 198	95.68 161	91.03 286

EC-MVSNet		88.01 84	88.32 86	87.09 105	89.28 201	72.03 208	90.31 64	96.31 3	80.88 93	85.12 272	89.67 295	84.47 90	95.46 52	82.56 109	96.26 126	93.77 148

dtuonlycased		77.13 340	76.99 340	77.55 380	88.60 230	57.48 437	74.18 441	81.70 380	55.62 469	85.10 275	88.40 326	74.87 230	82.26 418	56.73 436	87.66 434	92.90 200

CLD-MVS		83.18 215	82.64 229	84.79 168	89.05 212	67.82 272	77.93 375	92.52 127	68.33 304	85.07 276	81.54 461	82.06 131	92.96 160	69.35 312	97.91 53	93.57 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
fmvsm_s_conf0.1_n_a		82.58 229	81.93 244	84.50 178	87.68 261	73.35 180	86.14 153	77.70 416	61.64 412	85.02 277	91.62 209	77.75 183	86.24 369	82.79 106	87.07 443	93.91 136

FA-MVS(test-final)		83.13 217	83.02 217	83.43 219	86.16 322	66.08 294	88.00 113	88.36 259	75.55 168	85.02 277	92.75 165	65.12 335	92.50 172	74.94 228	91.30 344	91.72 265

viewdifsd2359ckpt09		83.64 198	83.18 212	85.03 161	87.26 277	66.99 283	85.32 173	93.83 56	65.57 352	84.99 279	89.40 299	77.30 193	93.57 138	71.16 291	93.80 245	94.54 100

DeepC-MVS_fast	80.27 8	86.23 113	85.65 141	87.96 95	91.30 149	76.92 143	87.19 125	91.99 144	70.56 265	84.96 280	90.69 254	80.01 161	95.14 68	78.37 162	95.78 156	91.82 261
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 155	84.71 166	85.06 160	86.36 313	74.71 170	88.77 100	90.00 219	75.65 165	84.96 280	93.17 143	74.06 248	91.19 219	78.28 165	91.09 349	89.29 342

QAPM		82.59 228	82.59 231	82.58 251	86.44 306	66.69 286	89.94 72	90.36 204	67.97 311	84.94 282	92.58 170	72.71 274	92.18 181	70.63 298	87.73 431	88.85 357

fmvsm_l_conf0.5_n_3		85.11 148	84.96 158	85.56 148	87.49 270	75.69 163	84.71 188	90.61 195	67.64 319	84.88 283	92.05 190	82.30 122	88.36 318	83.84 93	91.10 348	92.62 212

VPNet		80.25 290	81.68 247	75.94 412	92.46 104	47.98 507	76.70 400	81.67 382	73.45 206	84.87 284	92.82 161	74.66 238	86.51 362	61.66 397	96.85 99	93.33 170

NCCC		87.36 94	86.87 110	88.83 71	92.32 110	78.84 117	86.58 142	91.09 179	78.77 123	84.85 285	90.89 245	80.85 150	95.29 59	81.14 124	95.32 173	92.34 236

viewmanbaseed2359cas		82.95 222	83.43 203	81.52 283	85.18 346	60.03 394	81.36 299	92.38 131	69.55 280	84.84 286	91.38 219	79.85 164	90.09 267	74.22 236	92.09 319	94.43 109

SIFT-NCM-Cal		73.77 395	72.70 403	76.99 391	82.03 409	83.73 63	75.59 421	63.01 523	63.50 381	84.80 287	83.94 420	55.86 411	67.80 509	52.94 476	92.62 294	79.44 496

E3new		83.08 219	83.39 205	82.14 266	86.49 303	61.00 377	80.64 321	93.12 98	70.30 271	84.78 288	90.34 269	80.85 150	91.24 217	74.20 239	89.83 390	94.17 122

PHI-MVS		86.38 111	85.81 135	88.08 92	88.44 235	77.34 138	89.35 91	93.05 103	73.15 217	84.76 289	87.70 347	78.87 170	94.18 105	80.67 134	96.29 122	92.73 204

SIFT-UMatch		73.61 397	72.65 405	76.46 404	80.19 451	82.31 78	74.23 440	64.86 511	64.03 377	84.69 290	84.19 416	50.89 448	67.79 510	57.03 433	93.79 246	79.28 498

pmmvs-eth3d		78.42 325	77.04 339	82.57 253	87.44 274	74.41 173	80.86 316	79.67 400	55.68 468	84.69 290	90.31 274	60.91 362	85.42 390	62.20 387	91.59 337	87.88 382

test_prior2							83.37 236		75.43 171	84.58 292	91.57 211	81.92 136		79.54 148	96.97 94

fmvsm_s_conf0.5_n_6		84.05 183	84.14 187	83.81 203	87.75 258	71.17 223	83.42 234	91.10 178	67.90 314	84.53 293	90.70 253	73.01 269	88.73 303	85.09 72	93.72 252	91.53 275

fmvsm_s_conf0.5_n_a		82.21 238	81.51 257	84.32 187	86.56 300	73.35 180	85.46 169	77.30 422	61.81 408	84.51 294	90.88 247	77.36 191	86.21 371	82.72 107	86.97 448	93.38 168

TEST9					92.34 108	79.70 106	83.94 211	90.32 206	65.41 356	84.49 295	90.97 239	82.03 132	93.63 130

train_agg		85.98 121	85.28 151	88.07 93	92.34 108	79.70 106	83.94 211	90.32 206	65.79 344	84.49 295	90.97 239	81.93 134	93.63 130	81.21 123	96.54 112	90.88 292

fmvsm_s_conf0.1_n		82.17 240	81.59 252	83.94 202	86.87 298	71.57 217	85.19 177	77.42 420	62.27 403	84.47 297	91.33 222	76.43 213	85.91 379	83.14 97	87.14 441	94.33 115

Gipuma		84.44 167	86.33 121	78.78 350	84.20 369	73.57 178	89.55 82	90.44 200	84.24 56	84.38 298	94.89 57	76.35 216	80.40 436	76.14 209	96.80 104	82.36 464
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		64.31 488	65.85 474	59.67 521	66.54 543	62.24 352	57.76 536	70.96 477	40.13 539	84.36 299	82.09 451	46.93 466	51.67 547	61.99 391	81.89 501	65.12 536

test_8					92.09 117	78.87 116	83.82 216	90.31 208	65.79 344	84.36 299	90.96 241	81.93 134	93.44 144

SIFT-MNN		74.38 388	73.27 391	77.72 375	82.37 406	83.68 64	76.29 409	67.76 494	64.16 374	84.33 301	84.30 411	50.36 455	68.84 499	57.79 427	92.07 320	80.66 486

cl22		78.97 307	78.21 322	81.24 293	77.74 479	59.01 416	77.46 388	87.13 287	65.79 344	84.32 302	85.10 397	58.96 379	90.88 233	75.36 221	92.03 321	93.84 139

CS-MVS		88.14 80	87.67 93	89.54 60	89.56 194	79.18 113	90.47 60	94.77 16	79.37 114	84.32 302	89.33 303	83.87 95	94.53 92	82.45 110	94.89 195	94.90 78

agg_prior					91.58 139	77.69 132		90.30 209		84.32 302			93.18 152

Anonymous202405211		80.51 281	81.19 267	78.49 356	88.48 233	57.26 439	76.63 402	82.49 369	81.21 89	84.30 305	92.24 187	67.99 313	86.24 369	62.22 386	95.13 183	91.98 258

LFMVS		80.15 294	80.56 277	78.89 345	89.19 205	55.93 449	85.22 176	73.78 450	82.96 72	84.28 306	92.72 166	57.38 395	90.07 269	63.80 374	95.75 157	90.68 299

Vis-MVSNet		86.86 100	86.58 113	87.72 97	92.09 117	77.43 137	87.35 123	92.09 141	78.87 121	84.27 307	94.05 101	78.35 176	93.65 128	80.54 136	91.58 338	92.08 252
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ECVR-MVS		78.44 324	78.63 315	77.88 371	91.85 127	48.95 503	83.68 222	69.91 482	72.30 237	84.26 308	94.20 91	51.89 440	89.82 275	63.58 375	96.02 137	94.87 80

FE-MVS		79.98 298	78.86 309	83.36 221	86.47 304	66.45 290	89.73 75	84.74 340	72.80 226	84.22 309	91.38 219	44.95 493	93.60 134	63.93 372	91.50 339	90.04 321

SIFT-PointCN		72.17 418	71.14 426	75.23 422	77.93 478	79.30 112	72.22 468	64.71 513	62.60 391	84.13 310	81.00 465	46.91 467	67.69 512	55.17 454	95.64 164	78.70 504

ETV-MVS		84.31 171	83.91 195	85.52 149	88.58 231	70.40 233	84.50 198	93.37 80	78.76 124	84.07 311	78.72 489	80.39 157	95.13 69	73.82 250	92.98 281	91.04 285

SIFT-PCN-Cal		71.86 420	71.21 424	73.82 437	77.43 485	78.37 120	71.75 472	65.73 506	62.15 405	84.04 312	81.59 460	50.59 452	64.96 530	52.46 481	95.15 181	78.14 509

fmvsm_s_conf0.5_n		81.91 253	81.30 262	83.75 207	86.02 325	71.56 218	84.73 187	77.11 425	62.44 400	84.00 313	90.68 256	76.42 214	85.89 381	83.14 97	87.11 442	93.81 146

MCST-MVS		84.36 169	83.93 193	85.63 146	91.59 136	71.58 216	83.52 231	92.13 139	61.82 407	83.96 314	89.75 292	79.93 163	93.46 143	78.33 164	94.34 225	91.87 260

新几何1				82.95 235	93.96 63	78.56 119		80.24 396	55.45 471	83.93 315	91.08 235	71.19 294	88.33 319	65.84 353	93.07 278	81.95 469

mmtdpeth		85.13 146	85.78 137	83.17 229	84.65 358	74.71 170	85.87 158	90.35 205	77.94 133	83.82 316	96.96 14	77.75 183	80.03 439	78.44 160	96.21 127	94.79 92

fmvsm_l_conf0.5_n		82.06 245	81.54 256	83.60 213	83.94 375	73.90 176	83.35 237	86.10 305	58.97 441	83.80 317	90.36 268	74.23 243	86.94 351	82.90 103	90.22 383	89.94 323

GDP-MVS		82.17 240	80.85 274	86.15 133	88.65 227	68.95 259	85.65 165	93.02 107	68.42 302	83.73 318	89.54 297	45.07 492	94.31 96	79.66 145	93.87 243	95.19 69

viewdifsd2359ckpt13		82.22 237	81.98 243	82.95 235	85.48 340	64.44 311	83.17 245	92.11 140	65.97 338	83.72 319	89.73 293	77.60 187	90.80 237	70.61 299	89.42 396	93.59 160

BH-RMVSNet		80.53 280	80.22 285	81.49 285	87.19 281	66.21 292	77.79 378	86.23 303	74.21 190	83.69 320	88.50 325	73.25 267	90.75 238	63.18 381	87.90 427	87.52 389

USDC		76.63 349	76.73 346	76.34 407	83.46 386	57.20 440	80.02 331	88.04 268	52.14 496	83.65 321	91.25 227	63.24 350	86.65 359	54.66 460	94.11 234	85.17 419

miper_enhance_ethall		77.83 329	76.93 341	80.51 311	76.15 501	58.01 432	75.47 424	88.82 244	58.05 449	83.59 322	80.69 468	64.41 337	91.20 218	73.16 274	92.03 321	92.33 238

MM		87.64 92	87.15 100	89.09 68	89.51 195	76.39 151	88.68 102	86.76 297	84.54 52	83.58 323	93.78 117	73.36 265	96.48 1	87.98 16	96.21 127	94.41 111

Effi-MVS+-dtu		85.82 126	83.38 206	93.14 3	87.13 282	91.15 2	87.70 118	88.42 257	74.57 182	83.56 324	85.65 385	78.49 175	94.21 101	72.04 280	92.88 284	94.05 129

CNLPA		83.55 203	83.10 215	84.90 164	89.34 200	83.87 61	84.54 195	88.77 245	79.09 117	83.54 325	88.66 324	74.87 230	81.73 423	66.84 341	92.29 311	89.11 348

SIFT-CM-Cal		73.20 404	71.85 413	77.25 387	79.80 458	82.49 77	73.51 453	64.83 512	62.27 403	83.49 326	82.81 445	51.79 441	69.71 489	53.70 467	94.43 220	79.53 495

SDMVSNet		81.90 254	83.17 213	78.10 366	88.81 222	62.45 345	76.08 414	86.05 308	73.67 197	83.41 327	93.04 147	82.35 119	80.65 433	70.06 306	95.03 188	91.21 280

sd_testset		79.95 299	81.39 260	75.64 419	88.81 222	58.07 430	76.16 413	82.81 366	73.67 197	83.41 327	93.04 147	80.96 149	77.65 454	58.62 419	95.03 188	91.21 280

viewmamba		81.97 250	82.13 236	81.47 286	80.43 441	62.46 340	79.31 348	89.99 221	71.08 259	83.39 329	90.21 277	78.08 178	88.73 303	77.55 182	89.16 403	93.23 178

diffmvs_AUTHOR		81.24 266	81.55 255	80.30 316	80.61 437	60.22 390	77.98 374	90.48 197	67.77 317	83.34 330	89.50 298	74.69 237	87.42 340	78.78 158	90.81 363	93.27 174

OpenMVS_ROB	70.19 17	77.77 332	77.46 331	78.71 352	84.39 365	61.15 371	81.18 308	82.52 368	62.45 399	83.34 330	87.37 355	66.20 325	88.66 308	64.69 366	85.02 473	86.32 405

thres100view900		75.45 370	75.05 370	76.66 400	87.27 276	51.88 488	81.07 309	73.26 455	75.68 164	83.25 332	86.37 373	45.54 483	88.80 298	51.98 485	90.99 351	89.31 339

miper_lstm_enhance		76.45 355	76.10 354	77.51 381	76.72 494	60.97 381	64.69 516	85.04 329	63.98 379	83.20 333	88.22 330	56.67 400	78.79 447	73.22 268	93.12 277	92.78 203

IterMVS		76.91 344	76.34 352	78.64 353	80.91 429	64.03 316	76.30 408	79.03 404	64.88 366	83.11 334	89.16 310	59.90 370	84.46 400	68.61 326	85.15 471	87.42 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 364	75.35 364	77.85 374	87.01 291	51.84 489	80.45 326	73.26 455	75.20 174	83.10 335	86.31 376	45.54 483	89.05 292	55.03 456	92.24 313	92.66 210

mvs_anonymous		78.13 327	78.76 313	76.23 410	79.24 465	50.31 499	78.69 362	84.82 338	61.60 413	83.09 336	92.82 161	73.89 252	87.01 347	68.33 330	86.41 454	91.37 277

MASt3R-SfM		63.18 490	63.70 488	61.64 515	63.57 549	67.13 277	64.25 519	57.31 544	37.50 547	82.96 337	80.95 467	45.96 477	49.82 548	54.93 458	85.89 462	67.95 532

fmvsm_l_conf0.5_n_a		81.46 260	80.87 273	83.25 224	83.73 381	73.21 185	83.00 250	85.59 318	58.22 447	82.96 337	90.09 284	72.30 280	86.65 359	81.97 119	89.95 388	89.88 324

SP-SuperGlue		80.13 295	80.14 287	80.11 321	79.95 455	80.97 93	80.94 313	80.77 393	76.46 150	82.92 339	85.73 384	58.75 381	70.83 484	85.20 70	90.50 378	88.53 363

dtuplus		78.46 321	78.13 324	79.45 337	80.90 431	59.52 404	77.65 380	86.72 298	61.21 421	82.91 340	89.26 305	73.46 261	87.27 344	63.53 377	87.49 436	91.55 273

test_fmvs2		73.57 398	72.80 399	75.90 413	72.74 528	68.84 260	77.07 393	84.32 345	45.14 525	82.89 341	84.22 415	48.37 461	70.36 486	73.40 263	87.03 445	88.52 364

MVS_Test		82.47 231	83.22 209	80.22 318	82.62 405	57.75 435	82.54 266	91.96 146	71.16 258	82.89 341	92.52 174	77.41 190	90.50 248	80.04 139	87.84 430	92.40 230

reproduce_monomvs		74.09 391	73.23 392	76.65 402	76.52 495	54.54 465	77.50 385	81.40 387	65.85 343	82.86 343	86.67 367	27.38 545	84.53 399	70.24 303	90.66 374	90.89 291

TestfortrainingZip				84.49 179	88.84 220	70.49 231	92.12 33	91.01 181	84.70 50	82.82 344	89.25 306	74.30 242	94.06 111		90.73 370	88.92 356

test12				86.57 118	90.74 167	72.63 195		90.69 191		82.76 345		79.20 166	94.80 79		95.32 173	92.27 243

fmvsm_s_conf0.5_n_11		84.56 163	84.69 168	84.15 195	86.53 301	71.29 221	85.53 167	92.62 123	70.54 266	82.75 346	91.20 230	77.33 192	88.55 314	83.80 94	91.93 326	92.61 214

原ACMM1				84.60 176	92.81 98	74.01 175		91.50 161	62.59 392	82.73 347	90.67 259	76.53 212	94.25 99	69.24 313	95.69 160	85.55 415

test_yl		78.71 317	78.51 317	79.32 339	84.32 366	58.84 420	78.38 365	85.33 322	75.99 156	82.49 348	86.57 368	58.01 389	90.02 271	62.74 382	92.73 291	89.10 349

DCV-MVSNet		78.71 317	78.51 317	79.32 339	84.32 366	58.84 420	78.38 365	85.33 322	75.99 156	82.49 348	86.57 368	58.01 389	90.02 271	62.74 382	92.73 291	89.10 349

diffmvs		80.40 285	80.48 280	80.17 319	79.02 469	60.04 392	77.54 383	90.28 212	66.65 333	82.40 350	87.33 357	73.50 258	87.35 342	77.98 176	89.62 393	93.13 182
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmambaseed2359dif		78.80 313	78.47 319	79.78 325	80.26 449	59.28 408	77.31 390	87.13 287	60.42 432	82.37 351	88.67 323	74.58 239	87.87 331	67.78 334	87.73 431	92.19 247

test222					93.31 81	76.54 146	79.38 346	77.79 414	52.59 490	82.36 352	90.84 249	66.83 323			91.69 334	81.25 477

D2MVS		76.84 345	75.67 359	80.34 315	80.48 439	62.16 353	73.50 454	84.80 339	57.61 453	82.24 353	87.54 350	51.31 445	87.65 334	70.40 302	93.19 276	91.23 279

VNet		79.31 303	80.27 282	76.44 405	87.92 251	53.95 472	75.58 422	84.35 344	74.39 189	82.23 354	90.72 252	72.84 273	84.39 402	60.38 407	93.98 239	90.97 288

Vis-MVSNet (Re-imp)		77.82 330	77.79 329	77.92 370	88.82 221	51.29 493	83.28 238	71.97 470	74.04 192	82.23 354	89.78 291	57.38 395	89.41 288	57.22 431	95.41 169	93.05 188

API-MVS		82.28 235	82.61 230	81.30 289	86.29 316	69.79 240	88.71 101	87.67 276	78.42 128	82.15 356	84.15 418	77.98 180	91.59 198	65.39 357	92.75 289	82.51 463

icg_test_0407_2		78.46 321	79.68 296	74.78 428	85.76 332	62.46 340	68.51 496	87.91 271	65.23 359	82.12 357	87.92 339	77.27 195	72.67 475	71.67 283	90.74 366	89.20 343

IMVS_0407		81.08 269	81.23 265	80.62 309	85.76 332	62.46 340	82.46 268	87.91 271	65.23 359	82.12 357	87.92 339	77.27 195	90.18 259	71.67 283	90.74 366	89.20 343

IMVS_0403		80.93 274	81.00 268	80.72 304	85.76 332	62.46 340	81.82 288	87.91 271	65.23 359	82.07 359	87.92 339	75.91 217	90.50 248	71.67 283	90.74 366	89.20 343

DP-MVS Recon		84.05 183	83.22 209	86.52 120	91.73 133	75.27 167	83.23 243	92.40 129	72.04 241	82.04 360	88.33 329	77.91 182	93.95 117	66.17 347	95.12 185	90.34 312

onestephybrid01		81.22 267	80.90 272	82.18 264	80.05 452	64.49 310	79.47 342	89.23 239	69.10 287	81.96 361	89.27 304	75.02 227	89.12 291	73.71 252	90.24 382	92.92 199

MSDG		80.06 297	79.99 294	80.25 317	83.91 377	68.04 270	77.51 384	89.19 240	77.65 138	81.94 362	83.45 430	76.37 215	86.31 368	63.31 380	86.59 452	86.41 404

test2506		74.12 390	73.39 389	76.28 408	91.85 127	44.20 524	84.06 207	48.20 551	72.30 237	81.90 363	94.20 91	27.22 547	89.77 278	64.81 364	96.02 137	94.87 80

Fast-Effi-MVS+		81.04 271	80.57 276	82.46 257	87.50 269	63.22 326	78.37 367	89.63 231	68.01 309	81.87 364	82.08 452	82.31 121	92.65 169	67.10 338	88.30 423	91.51 276

testgi		72.36 414	74.61 373	65.59 500	80.56 438	42.82 530	68.29 497	73.35 454	66.87 331	81.84 365	89.93 288	72.08 284	66.92 517	46.05 521	92.54 301	87.01 396

tfpn200view9		74.86 380	74.23 378	76.74 399	86.24 317	52.12 485	79.24 351	73.87 448	73.34 210	81.82 366	84.60 408	46.02 474	88.80 298	51.98 485	90.99 351	89.31 339

thres400		75.14 372	74.23 378	77.86 373	86.24 317	52.12 485	79.24 351	73.87 448	73.34 210	81.82 366	84.60 408	46.02 474	88.80 298	51.98 485	90.99 351	92.66 210

CL-MVSNet_self_test		76.81 346	77.38 333	75.12 424	86.90 296	51.34 491	73.20 458	80.63 395	68.30 305	81.80 368	88.40 326	66.92 322	80.90 430	55.35 452	94.90 194	93.12 185

OpenMVS	76.72 13	81.98 249	82.00 242	81.93 270	84.42 364	68.22 266	88.50 107	89.48 234	66.92 330	81.80 368	91.86 198	72.59 276	90.16 261	71.19 290	91.25 345	87.40 391

SIFT-NN-CMatch		72.68 410	71.28 421	76.88 397	78.79 471	82.59 76	73.68 449	61.02 533	60.35 433	81.79 370	83.09 436	52.94 431	68.88 498	57.28 430	92.53 302	79.16 500

MGCNet		85.37 138	84.58 173	87.75 96	85.28 343	73.36 179	86.54 144	85.71 315	77.56 141	81.78 371	92.47 175	70.29 300	96.02 10	85.59 66	95.96 141	93.87 138

AdaColmap		83.66 197	83.69 197	83.57 216	90.05 186	72.26 204	86.29 148	90.00 219	78.19 131	81.65 372	87.16 360	83.40 103	94.24 100	61.69 396	94.76 209	84.21 434

SPE-MVS-test		87.00 98	86.43 116	88.71 75	89.46 197	77.46 135	89.42 89	95.73 6	77.87 136	81.64 373	87.25 358	82.43 117	94.53 92	77.65 180	96.46 116	94.14 125

DELS-MVS		81.44 261	81.25 263	82.03 268	84.27 368	62.87 330	76.47 407	92.49 128	70.97 261	81.64 373	83.83 421	75.03 226	92.70 167	74.29 233	92.22 315	90.51 307
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
SP-LightGlue		79.92 300	79.74 295	80.46 312	80.22 450	81.52 88	81.28 304	81.81 378	75.89 160	81.60 375	84.90 403	55.82 412	71.10 483	85.62 65	90.47 379	88.76 359

SP-MNN		77.71 333	77.85 327	77.29 385	78.48 474	75.90 160	79.14 354	79.46 401	69.61 279	81.56 376	84.60 408	54.98 422	69.02 497	81.08 127	91.72 333	86.95 398

114514_t		83.10 218	82.54 232	84.77 169	92.90 91	69.10 255	86.65 140	90.62 194	54.66 477	81.46 377	90.81 250	76.98 202	94.38 95	72.62 276	96.18 129	90.82 294

TR-MVS		76.77 347	75.79 356	79.72 328	86.10 324	65.79 297	77.14 391	83.02 363	65.20 363	81.40 378	82.10 450	66.30 324	90.73 240	55.57 448	85.27 467	82.65 457

TAMVS		78.08 328	76.36 351	83.23 225	90.62 171	72.87 189	79.08 355	80.01 399	61.72 410	81.35 379	86.92 365	63.96 345	88.78 301	50.61 491	93.01 280	88.04 375

Effi-MVS+		83.90 192	84.01 190	83.57 216	87.22 280	65.61 299	86.55 143	92.40 129	78.64 125	81.34 380	84.18 417	83.65 100	92.93 162	74.22 236	87.87 428	92.17 249

hybridnocas07		79.65 302	79.65 297	79.63 331	78.06 475	59.34 406	77.00 397	88.72 248	66.51 335	81.08 381	89.36 301	72.35 278	87.12 346	74.56 230	89.20 401	92.44 225

testing3		71.53 428	70.79 429	73.77 439	88.89 219	41.86 532	76.60 405	59.12 538	72.83 225	80.97 382	82.08 452	19.80 554	87.33 343	65.12 360	91.68 335	92.13 251

new-patchmatchnet		70.10 443	73.37 390	60.29 520	81.23 425	16.95 559	59.54 530	74.62 441	62.93 387	80.97 382	87.93 338	62.83 356	71.90 478	55.24 453	95.01 191	92.00 256

SIFT-NN-PointCN		72.35 415	71.17 425	75.90 413	77.68 481	80.93 96	73.48 455	63.14 522	60.88 426	80.94 384	82.91 442	52.54 436	67.74 511	55.98 443	92.95 283	79.05 502

PVSNet_Blended_VisFu		81.55 259	80.49 279	84.70 173	91.58 139	73.24 184	84.21 203	91.67 156	62.86 389	80.94 384	87.16 360	67.27 318	92.87 165	69.82 308	88.94 409	87.99 377

BH-w/o		76.57 350	76.07 355	78.10 366	86.88 297	65.92 296	77.63 381	86.33 301	65.69 349	80.89 386	79.95 477	68.97 310	90.74 239	53.01 475	85.25 468	77.62 512

PAPM_NR		83.23 213	83.19 211	83.33 222	90.90 163	65.98 295	88.19 109	90.78 189	78.13 132	80.87 387	87.92 339	73.49 260	92.42 173	70.07 305	88.40 417	91.60 271

ab-mvs		79.67 301	80.56 277	76.99 391	88.48 233	56.93 441	84.70 189	86.06 307	68.95 293	80.78 388	93.08 146	75.30 224	84.62 397	56.78 435	90.90 356	89.43 337

XXY-MVS		74.44 387	76.19 353	69.21 476	84.61 359	52.43 484	71.70 473	77.18 424	60.73 429	80.60 389	90.96 241	75.44 221	69.35 493	56.13 441	88.33 419	85.86 412

HQP4-MVS										80.56 390			94.61 86			93.56 163

HQP-NCC					91.19 154		84.77 183		73.30 212	80.55 391

ACMP_Plane					91.19 154		84.77 183		73.30 212	80.55 391

HQP-MVS		84.61 161	84.06 189	86.27 125	91.19 154	70.66 228	84.77 183	92.68 120	73.30 212	80.55 391	90.17 282	72.10 282	94.61 86	77.30 188	94.47 218	93.56 163

ALIKED-MNN		76.42 356	75.39 363	79.52 335	84.57 360	84.06 60	84.33 201	82.48 370	49.85 511	80.53 394	88.35 328	54.52 423	77.10 458	56.89 434	96.96 95	77.39 513

hybrid		79.06 306	78.94 307	79.40 338	77.99 477	59.05 415	77.07 393	88.49 254	64.42 372	80.52 395	88.78 317	71.45 292	86.82 355	73.23 267	88.52 415	92.34 236

test_cas_vis1_n_1920		69.20 456	69.12 449	69.43 475	73.68 519	62.82 332	70.38 487	77.21 423	46.18 522	80.46 396	78.95 486	52.03 438	65.53 526	65.77 355	77.45 525	79.95 491

AUN-MVS		81.18 268	78.78 312	88.39 83	90.93 162	82.14 80	82.51 267	83.67 354	64.69 368	80.29 397	85.91 383	51.07 447	92.38 175	76.29 205	93.63 255	90.65 302

HyFIR lowres test		75.12 374	72.66 404	82.50 255	91.44 147	65.19 303	72.47 466	87.31 280	46.79 518	80.29 397	84.30 411	52.70 435	92.10 185	51.88 489	86.73 450	90.22 313

test20.03		73.75 396	74.59 375	71.22 461	81.11 426	51.12 495	70.15 488	72.10 469	70.42 267	80.28 399	91.50 213	64.21 340	74.72 469	46.96 516	94.58 214	87.82 385

mvsany_test3		65.48 481	62.97 492	73.03 446	69.99 536	76.17 154	64.83 514	43.71 553	43.68 531	80.25 400	87.05 364	52.83 434	63.09 536	51.92 488	72.44 534	79.84 493

SIFT-NN-UMatch		72.46 412	71.25 422	76.08 411	78.57 473	81.88 82	74.36 437	61.59 531	61.99 406	80.24 401	83.46 429	51.20 446	68.08 508	57.95 426	91.91 327	78.28 507

F-COLMAP		84.97 153	83.42 204	89.63 57	92.39 106	83.40 66	88.83 98	91.92 147	73.19 216	80.18 402	89.15 311	77.04 201	93.28 149	65.82 354	92.28 312	92.21 246

GA-MVS		75.83 365	74.61 373	79.48 336	81.87 411	59.25 409	73.42 456	82.88 364	68.68 297	79.75 403	81.80 456	50.62 451	89.46 284	66.85 340	85.64 464	89.72 328

xiu_mvs_v1_base_debu		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 353	79.59 404	82.73 446	76.94 203	90.14 264	73.22 268	88.33 419	86.90 399

xiu_mvs_v1_base		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 353	79.59 404	82.73 446	76.94 203	90.14 264	73.22 268	88.33 419	86.90 399

xiu_mvs_v1_base_debi		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 353	79.59 404	82.73 446	76.94 203	90.14 264	73.22 268	88.33 419	86.90 399

test_fmvs1_n		70.94 434	70.41 436	72.53 452	73.92 516	66.93 284	75.99 415	84.21 347	43.31 533	79.40 407	79.39 482	43.47 499	68.55 502	69.05 318	84.91 476	82.10 467

blended_shiyan8		76.05 362	75.11 366	78.86 347	81.76 414	59.18 412	75.09 428	83.81 350	64.70 367	79.37 408	78.35 492	58.30 385	88.68 306	62.03 390	92.56 300	88.73 360

blended_shiyan6		76.05 362	75.11 366	78.87 346	81.74 415	59.15 413	75.08 429	83.79 351	64.69 368	79.37 408	78.37 491	58.30 385	88.69 305	61.99 391	92.61 295	88.77 358

patch_mono-2		78.89 310	79.39 300	77.41 383	84.78 355	68.11 268	75.60 419	83.11 362	60.96 425	79.36 410	89.89 290	75.18 225	72.97 474	73.32 266	92.30 309	91.15 282

UnsupCasMVSNet_eth		71.63 426	72.30 410	69.62 473	76.47 497	52.70 482	70.03 489	80.97 391	59.18 440	79.36 410	88.21 331	60.50 363	69.12 495	58.33 422	77.62 523	87.04 395

ppachtmachnet_test		74.73 384	74.00 380	76.90 395	80.71 435	56.89 443	71.53 477	78.42 410	58.24 446	79.32 412	82.92 441	57.91 392	84.26 404	65.60 356	91.36 341	89.56 333

gbinet_0.2-2-1-0.02		76.14 359	74.88 371	79.92 323	80.33 448	60.02 395	75.80 417	82.44 371	66.36 337	79.24 413	75.07 521	56.11 408	90.17 260	64.60 369	93.95 240	89.58 332

MG-MVS		80.32 288	80.94 270	78.47 357	88.18 243	52.62 483	82.29 276	85.01 331	72.01 242	79.24 413	92.54 173	69.36 306	93.36 148	70.65 297	89.19 402	89.45 334

usedtu_dtu_shiyan2		78.92 308	78.15 323	81.25 290	91.33 148	73.10 186	80.75 320	79.00 406	74.19 191	79.17 415	92.04 191	67.17 319	81.33 426	42.86 527	96.81 103	89.31 339

Fast-Effi-MVS+-dtu		82.54 230	81.41 258	85.90 138	85.60 336	76.53 148	83.07 247	89.62 232	73.02 220	79.11 416	83.51 427	80.74 153	90.24 256	68.76 323	89.29 398	90.94 289

SSC-MVS3.2		73.90 393	75.67 359	68.61 484	84.11 371	41.28 533	64.17 520	72.83 460	72.09 240	79.08 417	87.94 336	70.31 299	73.89 472	55.99 442	94.49 217	90.67 301

CDS-MVSNet		77.32 337	75.40 361	83.06 230	89.00 214	72.48 200	77.90 376	82.17 375	60.81 427	78.94 418	83.49 428	59.30 375	88.76 302	54.64 461	92.37 307	87.93 381
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 401	73.54 386	72.43 453	84.92 352	47.79 508	79.89 333	74.00 446	65.93 341	78.81 419	86.28 377	56.36 402	81.63 424	56.63 437	79.04 518	87.87 383

SIFT-NN-NCMNet		72.70 409	71.25 422	77.06 390	81.65 418	84.07 59	75.19 426	63.15 521	61.29 418	78.74 420	83.21 434	53.60 427	69.25 494	53.99 464	90.47 379	77.86 511

ttmdpeth		71.72 423	70.67 430	74.86 426	73.08 525	55.88 450	77.41 389	69.27 486	55.86 466	78.66 421	93.77 119	38.01 514	75.39 466	60.12 408	89.87 389	93.31 172

EIA-MVS		82.19 239	81.23 265	85.10 159	87.95 250	69.17 254	83.22 244	93.33 85	70.42 267	78.58 422	79.77 480	77.29 194	94.20 102	71.51 287	88.96 408	91.93 259

thres200		72.34 416	71.55 418	74.70 430	83.48 385	51.60 490	75.02 430	73.71 451	70.14 274	78.56 423	80.57 471	46.20 472	88.20 321	46.99 515	89.29 398	84.32 430

usedtu_dtu_shiyan1		75.70 368	75.08 368	77.56 377	84.10 372	55.50 456	73.58 450	84.89 334	62.48 394	78.16 424	84.24 413	58.14 387	87.47 338	59.35 413	90.82 361	89.72 328

FE-MVSNET3		75.70 368	75.08 368	77.56 377	84.10 372	55.50 456	73.58 450	84.89 334	62.48 394	78.16 424	84.24 413	58.14 387	87.47 338	59.34 414	90.82 361	89.72 328

fmvsm_s_conf0.5_n_7		82.04 246	82.05 241	82.01 269	86.98 293	71.07 224	78.70 361	89.45 235	68.07 308	78.14 426	91.61 210	74.19 244	85.92 377	79.61 146	91.73 332	89.05 352

our_test_3		71.85 421	71.59 415	72.62 450	80.71 435	53.78 473	69.72 491	71.71 474	58.80 443	78.03 427	80.51 473	56.61 401	78.84 446	62.20 387	86.04 461	85.23 418

KD-MVS_2432*1600		66.87 468	65.81 476	70.04 467	67.50 540	47.49 509	62.56 523	79.16 402	61.21 421	77.98 428	80.61 469	25.29 550	82.48 415	53.02 473	84.92 474	80.16 489

miper_refine_blended		66.87 468	65.81 476	70.04 467	67.50 540	47.49 509	62.56 523	79.16 402	61.21 421	77.98 428	80.61 469	25.29 550	82.48 415	53.02 473	84.92 474	80.16 489

jason		77.42 336	75.75 357	82.43 258	87.10 285	69.27 249	77.99 373	81.94 377	51.47 500	77.84 430	85.07 400	60.32 366	89.00 293	70.74 296	89.27 400	89.03 353
jason: jason.
MAR-MVS		80.24 291	78.74 314	84.73 171	86.87 298	78.18 124	85.75 162	87.81 275	65.67 351	77.84 430	78.50 490	73.79 254	90.53 247	61.59 398	90.87 358	85.49 417
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
FPMVS		72.29 417	72.00 411	73.14 444	88.63 228	85.00 49	74.65 434	67.39 496	71.94 243	77.80 432	87.66 348	50.48 453	75.83 463	49.95 495	79.51 512	58.58 544

test_fmvs1		69.57 451	69.05 451	71.14 463	69.15 539	65.77 298	73.98 445	83.32 359	42.83 535	77.77 433	78.27 494	43.39 502	68.50 503	68.39 329	84.38 483	79.15 501

pmmvs4		74.92 379	72.98 397	80.73 303	84.95 350	71.71 215	76.23 411	77.59 417	52.83 489	77.73 434	86.38 372	56.35 403	84.97 394	57.72 429	87.05 444	85.51 416

wanda-best-256-512		74.97 377	73.85 381	78.35 359	80.36 443	58.13 427	73.10 460	83.53 356	64.04 376	77.62 435	75.71 515	56.22 405	88.60 312	61.42 399	92.61 295	88.32 366

FE-blended-shiyan7		74.97 377	73.85 381	78.35 359	80.36 443	58.13 427	73.10 460	83.53 356	64.03 377	77.62 435	75.71 515	56.22 405	88.60 312	61.42 399	92.61 295	88.32 366

usedtu_blend_shiyan5		77.07 342	76.43 350	78.99 344	80.36 443	59.77 399	83.25 240	88.32 261	74.91 177	77.62 435	75.71 515	56.22 405	88.89 296	58.91 416	92.61 295	88.32 366

ET-MVSNet_ETH3D		75.28 371	72.77 400	82.81 242	83.03 402	68.11 268	77.09 392	76.51 430	60.67 430	77.60 438	80.52 472	38.04 513	91.15 221	70.78 294	90.68 371	89.17 347

testing3-2		70.72 438	70.97 427	69.95 469	88.93 217	34.80 546	69.85 490	66.59 504	78.42 128	77.58 439	85.55 386	31.83 531	82.08 419	46.28 518	93.73 251	92.98 195

UnsupCasMVSNet_bld		69.21 455	69.68 443	67.82 487	79.42 462	51.15 494	67.82 501	75.79 434	54.15 480	77.47 440	85.36 395	59.26 376	70.64 485	48.46 506	79.35 514	81.66 471

blend_shiyan4		70.82 436	68.15 461	78.83 349	81.06 427	59.77 399	74.58 435	83.79 351	64.94 365	77.34 441	75.47 519	29.39 538	88.89 296	58.91 416	67.86 544	87.84 384

XFeat-NN		59.92 504	59.04 506	62.58 511	63.37 550	64.42 312	55.18 539	60.26 536	41.73 537	77.26 442	69.20 533	31.98 530	58.40 542	48.23 510	84.12 485	64.93 537

WBMVS		68.76 459	68.43 458	69.75 472	83.29 394	40.30 536	67.36 504	72.21 467	57.09 459	77.05 443	85.53 388	33.68 525	80.51 434	48.79 504	90.90 356	88.45 365

Anonymous20231206		71.38 430	71.88 412	69.88 470	86.31 314	54.37 467	70.39 486	74.62 441	52.57 491	76.73 444	88.76 318	59.94 369	72.06 477	44.35 525	93.23 273	83.23 452

CMPMVS	59.41 20	75.12 374	73.57 385	79.77 326	75.84 504	67.22 275	81.21 307	82.18 374	50.78 505	76.50 445	87.66 348	55.20 419	82.99 413	62.17 389	90.64 376	89.09 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 419	71.69 414	73.32 441	81.57 420	53.02 479	76.77 399	78.37 412	63.31 383	76.37 446	91.85 199	36.68 518	78.98 444	47.87 511	92.45 304	87.95 379

CVMVSNet		72.62 411	71.41 419	76.28 408	83.25 396	60.34 388	83.50 232	79.02 405	37.77 546	76.33 447	85.10 397	49.60 459	87.41 341	70.54 300	77.54 524	81.08 480

PLC	73.85 16	82.09 243	80.31 281	87.45 101	90.86 165	80.29 101	85.88 157	90.65 192	68.17 307	76.32 448	86.33 374	73.12 268	92.61 170	61.40 401	90.02 387	89.44 335
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 236	81.57 254	84.19 194	85.54 338	69.26 250	91.98 39	90.08 217	71.54 249	76.23 449	85.07 400	58.69 382	94.27 97	86.26 50	88.77 410	89.03 353

lupinMVS		76.37 357	74.46 376	82.09 267	85.54 338	69.26 250	76.79 398	80.77 393	50.68 507	76.23 449	82.82 443	58.69 382	88.94 294	69.85 307	88.77 410	88.07 372

UWE-MVS		66.43 474	65.56 479	69.05 477	84.15 370	40.98 534	73.06 462	64.71 513	54.84 475	76.18 451	79.62 481	29.21 540	80.50 435	38.54 538	89.75 391	85.66 414

PatchMatch-RL		74.48 385	73.22 393	78.27 364	87.70 260	85.26 47	75.92 416	70.09 480	64.34 373	76.09 452	81.25 463	65.87 330	78.07 452	53.86 465	83.82 488	71.48 526

thisisatest0515		73.00 407	70.52 433	80.46 312	81.45 421	59.90 397	73.16 459	74.31 445	57.86 450	76.08 453	77.78 496	37.60 516	92.12 184	65.00 361	91.45 340	89.35 338

SP-NN		76.57 350	76.54 347	76.66 400	77.40 486	75.50 164	78.02 371	78.77 408	68.60 300	75.98 454	83.71 424	55.56 415	66.71 518	82.06 115	88.74 412	87.76 386

SIFT-NN		71.05 433	69.58 445	75.45 421	80.35 447	81.93 81	74.31 438	63.57 519	61.17 424	75.98 454	81.67 459	46.63 470	65.25 528	53.44 471	89.09 405	79.18 499

MS-PatchMatch		70.93 435	70.22 437	73.06 445	81.85 412	62.50 339	73.82 448	77.90 413	52.44 492	75.92 456	81.27 462	55.67 414	81.75 422	55.37 451	77.70 522	74.94 519

CHOSEN 1792x2688		72.45 413	70.56 432	78.13 365	90.02 188	63.08 327	68.72 495	83.16 361	42.99 534	75.92 456	85.46 390	57.22 398	85.18 393	49.87 497	81.67 502	86.14 407

CR-MVSNet		74.00 392	73.04 396	76.85 398	79.58 459	62.64 336	82.58 263	76.90 426	50.50 508	75.72 458	92.38 177	48.07 463	84.07 406	68.72 325	82.91 495	83.85 439

RPMNet		78.88 311	78.28 321	80.68 307	79.58 459	62.64 336	82.58 263	94.16 33	74.80 178	75.72 458	92.59 168	48.69 460	95.56 43	73.48 261	82.91 495	83.85 439

DPM-MVS		80.10 296	79.18 305	82.88 241	90.71 169	69.74 242	78.87 359	90.84 187	60.29 435	75.64 460	85.92 382	67.28 317	93.11 155	71.24 289	91.79 329	85.77 413

test_vis1_n		70.29 440	69.99 441	71.20 462	75.97 503	66.50 288	76.69 401	80.81 392	44.22 529	75.43 461	77.23 504	50.00 456	68.59 501	66.71 343	82.85 497	78.52 506

PVSNet_BlendedMVS		78.80 313	77.84 328	81.65 280	84.43 362	63.41 322	79.49 341	90.44 200	61.70 411	75.43 461	87.07 363	69.11 308	91.44 204	60.68 405	92.24 313	90.11 319

PVSNet_Blended		76.49 354	75.40 361	79.76 327	84.43 362	63.41 322	75.14 427	90.44 200	57.36 456	75.43 461	78.30 493	69.11 308	91.44 204	60.68 405	87.70 433	84.42 429

PAPR		78.84 312	78.10 326	81.07 295	85.17 347	60.22 390	82.21 280	90.57 196	62.51 393	75.32 464	84.61 407	74.99 228	92.30 179	59.48 412	88.04 425	90.68 299

N_pmnet		70.20 441	68.80 456	74.38 431	80.91 429	84.81 52	59.12 532	76.45 432	55.06 473	75.31 465	82.36 449	55.74 413	54.82 544	47.02 514	87.24 440	83.52 444

cascas		76.29 358	74.81 372	80.72 304	84.47 361	62.94 328	73.89 447	87.34 279	55.94 465	75.16 466	76.53 510	63.97 344	91.16 220	65.00 361	90.97 354	88.06 374

SD_0403		76.08 360	76.77 344	73.98 433	87.08 289	49.45 502	83.62 224	84.68 341	63.31 383	75.13 467	87.47 353	71.85 287	84.56 398	49.97 494	87.86 429	87.94 380

PRO-TEST		78.77 316	78.12 325	80.70 306	83.83 379	62.76 335	82.20 282	88.77 245	64.67 370	75.01 468	83.52 426	70.67 298	89.92 273	67.67 336	86.75 449	89.44 335

SCA		73.32 400	72.57 407	75.58 420	81.62 419	55.86 451	78.89 358	71.37 475	61.73 409	74.93 469	83.42 431	60.46 364	87.01 347	58.11 424	82.63 500	83.88 436

test_vis1_n_1920		71.30 431	71.58 417	70.47 465	77.58 483	59.99 396	74.25 439	84.22 346	51.06 502	74.85 470	79.10 484	55.10 420	68.83 500	68.86 322	79.20 517	82.58 459

xiu_mvs_v2_base		77.19 339	76.75 345	78.52 355	87.01 291	61.30 368	75.55 423	87.12 291	61.24 420	74.45 471	78.79 488	77.20 197	90.93 229	64.62 368	84.80 480	83.32 450

CANet		83.79 195	82.85 224	86.63 116	86.17 320	72.21 206	83.76 219	91.43 163	77.24 145	74.39 472	87.45 354	75.36 223	95.42 54	77.03 191	92.83 287	92.25 245

PS-MVSNAJ		77.04 343	76.53 348	78.56 354	87.09 287	61.40 365	75.26 425	87.13 287	61.25 419	74.38 473	77.22 505	76.94 203	90.94 228	64.63 367	84.83 479	83.35 449

ALIKED-NN		74.80 382	73.22 393	79.55 333	82.93 403	83.79 62	81.84 287	82.56 367	47.43 516	74.33 474	88.03 333	53.21 429	76.31 460	54.08 463	94.57 215	78.54 505

MVP-Stereo		75.81 366	73.51 387	82.71 243	89.35 199	73.62 177	80.06 329	85.20 324	60.30 434	73.96 475	87.94 336	57.89 393	89.45 285	52.02 484	74.87 529	85.06 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WB-MVSnew		68.72 460	69.01 452	67.85 486	83.22 398	43.98 525	74.93 431	65.98 505	55.09 472	73.83 476	79.11 483	65.63 332	71.89 479	38.21 539	85.04 472	87.69 387

UGNet		82.78 225	81.64 249	86.21 129	86.20 319	76.24 153	86.86 133	85.68 316	77.07 146	73.76 477	92.82 161	69.64 303	91.82 193	69.04 319	93.69 253	90.56 305
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
1112_ss		74.82 381	73.74 383	78.04 368	89.57 193	60.04 392	76.49 406	87.09 292	54.31 478	73.66 478	79.80 478	60.25 367	86.76 358	58.37 420	84.15 484	87.32 392

FBQ-MVS		71.59 427	69.67 444	77.34 384	84.84 353	56.41 447	81.26 306	76.51 430	62.70 390	73.28 479	75.95 512	36.93 517	88.04 323	48.28 508	87.27 438	87.56 388

Test_1112_low_res		73.90 393	73.08 395	76.35 406	90.35 176	55.95 448	73.40 457	86.17 304	50.70 506	73.14 480	85.94 381	58.31 384	85.90 380	56.51 438	83.22 492	87.20 394

1314		73.22 402	72.56 408	75.20 423	80.41 442	57.84 433	81.64 292	85.36 320	51.68 499	73.10 481	76.65 509	61.45 359	85.19 392	63.54 376	79.21 516	82.59 458

test_vis1_rt		65.64 480	64.09 484	70.31 466	66.09 544	70.20 236	61.16 527	81.60 383	38.65 543	72.87 482	69.66 532	52.84 433	60.04 538	56.16 440	77.77 521	80.68 484

Patchmatch-test		65.91 477	67.38 464	61.48 517	75.51 507	43.21 529	68.84 494	63.79 518	62.48 394	72.80 483	83.42 431	44.89 495	59.52 539	48.27 509	86.45 453	81.70 470

PatchmatchNet		69.71 450	68.83 455	72.33 455	77.66 482	53.60 474	79.29 349	69.99 481	57.66 452	72.53 484	82.93 440	46.45 471	80.08 438	60.91 404	72.09 535	83.31 451
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 462	68.08 463	67.55 488	78.74 472	43.53 527	75.60 419	67.10 501	54.92 474	72.23 485	88.10 332	42.87 504	75.97 462	52.21 482	80.95 510	83.15 453

IMVS_0404		77.24 338	77.75 330	75.73 416	85.76 332	62.46 340	70.84 482	87.91 271	65.23 359	72.21 486	87.92 339	67.48 316	75.53 465	71.67 283	90.74 366	89.20 343

pmmvs5		70.73 437	70.07 438	72.72 448	77.03 491	52.73 481	74.14 442	75.65 437	50.36 509	72.17 487	85.37 394	55.42 417	80.67 432	52.86 477	87.59 435	84.77 423

PatchT		70.52 439	72.76 401	63.79 509	79.38 463	33.53 547	77.63 381	65.37 509	73.61 203	71.77 488	92.79 164	44.38 497	75.65 464	64.53 370	85.37 466	82.18 466

MVS		73.21 403	72.59 406	75.06 425	80.97 428	60.81 383	81.64 292	85.92 313	46.03 523	71.68 489	77.54 499	68.47 311	89.77 278	55.70 446	85.39 465	74.60 520

MIMVSNet		71.09 432	71.59 415	69.57 474	87.23 279	50.07 500	78.91 357	71.83 471	60.20 437	71.26 490	91.76 206	55.08 421	76.09 461	41.06 531	87.02 446	82.54 461

WTY-MVS		67.91 463	68.35 459	66.58 495	80.82 433	48.12 506	65.96 511	72.60 462	53.67 483	71.20 491	81.68 458	58.97 378	69.06 496	48.57 505	81.67 502	82.55 460

test0.0.03 1		64.66 484	64.36 483	65.57 501	75.03 512	46.89 513	64.69 516	61.58 532	62.43 401	71.18 492	77.54 499	43.41 500	68.47 504	40.75 533	82.65 498	81.35 474

CostFormer		69.98 447	68.68 457	73.87 435	77.14 489	50.72 497	79.26 350	74.51 443	51.94 498	70.97 493	84.75 405	45.16 491	87.49 337	55.16 455	79.23 515	83.40 448

Syy-MVS		69.40 453	70.03 440	67.49 489	81.72 416	38.94 538	71.00 479	61.99 525	61.38 415	70.81 494	72.36 529	61.37 360	79.30 441	64.50 371	85.18 469	84.22 432

myMVS_eth3d		64.66 484	63.89 485	66.97 493	81.72 416	37.39 541	71.00 479	61.99 525	61.38 415	70.81 494	72.36 529	20.96 553	79.30 441	49.59 498	85.18 469	84.22 432

nomal-1		66.61 472	65.11 482	71.13 464	75.60 505	61.96 355	65.47 513	69.28 485	57.45 455	70.78 496	77.26 503	35.65 521	73.16 473	50.42 492	84.07 487	78.25 508

testing91		69.94 448	68.99 453	72.80 447	83.81 380	45.89 517	71.57 476	73.64 453	68.24 306	70.77 497	77.82 495	34.37 523	84.44 401	53.64 468	87.00 447	88.07 372

GLUNet-SfM		36.71 514	36.32 517	37.87 531	23.81 557	32.04 548	38.61 546	29.05 557	18.10 550	70.60 498	50.66 546	18.79 555	40.81 553	17.68 553	59.57 547	40.74 547

testing99		69.27 454	68.15 461	72.63 449	83.29 394	45.45 519	71.15 478	71.08 476	67.34 324	70.43 499	77.77 497	32.24 529	84.35 403	53.72 466	86.33 456	88.10 371

tpmvs		70.16 442	69.56 446	71.96 457	74.71 514	48.13 505	79.63 335	75.45 439	65.02 364	70.26 500	81.88 455	45.34 488	85.68 387	58.34 421	75.39 528	82.08 468

sss		66.92 467	67.26 465	65.90 498	77.23 488	51.10 496	64.79 515	71.72 473	52.12 497	70.13 501	80.18 475	57.96 391	65.36 527	50.21 493	81.01 508	81.25 477

tpm2		68.45 461	66.83 469	73.30 443	78.93 470	48.50 504	79.76 334	71.76 472	47.50 515	69.92 502	83.60 425	42.07 505	88.40 317	48.44 507	79.51 512	83.01 455

myMVS_eth3d28		65.83 479	65.85 474	65.78 499	83.42 388	35.71 544	67.29 505	68.01 493	67.58 321	69.80 503	77.72 498	32.29 528	74.30 471	37.49 540	89.06 406	87.32 392

testing222		66.93 466	65.30 480	71.81 458	83.38 389	45.83 518	72.06 470	67.50 495	64.12 375	69.68 504	76.37 511	27.34 546	83.00 412	38.88 535	88.38 418	86.62 403

HY-MVS	64.64 18	73.03 406	72.47 409	74.71 429	83.36 391	54.19 470	82.14 284	81.96 376	56.76 463	69.57 505	86.21 378	60.03 368	84.83 396	49.58 499	82.65 498	85.11 420

dmvs_re		66.81 470	66.98 467	66.28 496	76.87 492	58.68 424	71.66 474	72.24 465	60.29 435	69.52 506	73.53 525	52.38 437	64.40 533	44.90 523	81.44 505	75.76 517

ETVMVS		64.67 483	63.34 491	68.64 481	83.44 387	41.89 531	69.56 493	61.70 530	61.33 417	68.74 507	75.76 514	28.76 541	79.35 440	34.65 543	86.16 460	84.67 425

tpm cat1		66.76 471	65.21 481	71.42 460	77.09 490	50.62 498	78.01 372	73.68 452	44.89 526	68.64 508	79.00 485	45.51 485	82.42 417	49.91 496	70.15 538	81.23 479

IB-MVS	62.13 19	71.64 425	68.97 454	79.66 330	80.80 434	62.26 350	73.94 446	76.90 426	63.27 385	68.63 509	76.79 507	33.83 524	91.84 192	59.28 415	87.26 439	84.88 422
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
EPNet		80.37 286	78.41 320	86.23 126	76.75 493	73.28 182	87.18 126	77.45 418	76.24 152	68.14 510	88.93 315	65.41 333	93.85 120	69.47 311	96.12 133	91.55 273
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dtuonly		66.56 473	67.23 466	64.55 505	69.44 538	43.53 527	66.34 510	72.11 468	48.23 514	68.04 511	83.21 434	55.95 409	66.59 520	55.55 449	86.17 459	83.53 443

PVSNet	58.17 21	66.41 475	65.63 478	68.75 480	81.96 410	49.88 501	62.19 525	72.51 464	51.03 503	68.04 511	75.34 520	50.84 449	74.77 467	45.82 522	82.96 493	81.60 472

tpmrst		66.28 476	66.69 471	65.05 504	72.82 527	39.33 537	78.20 368	70.69 479	53.16 487	67.88 513	80.36 474	48.18 462	74.75 468	58.13 423	70.79 537	81.08 480

CANet_DTU		77.81 331	77.05 338	80.09 322	81.37 423	59.90 397	83.26 239	88.29 262	69.16 286	67.83 514	83.72 423	60.93 361	89.47 283	69.22 315	89.70 392	90.88 292

EPMVS		62.47 492	62.63 494	62.01 512	70.63 535	38.74 539	74.76 432	52.86 547	53.91 481	67.71 515	80.01 476	39.40 510	66.60 519	55.54 450	68.81 543	80.68 484

UBG		64.34 487	63.35 490	67.30 491	83.50 384	40.53 535	67.46 503	65.02 510	54.77 476	67.54 516	74.47 523	32.99 527	78.50 450	40.82 532	83.58 489	82.88 456

MDTV_nov1_ep13			68.29 460		78.03 476	43.87 526	74.12 443	72.22 466	52.17 494	67.02 517	85.54 387	45.36 487	80.85 431	55.73 444	84.42 482

testing11		67.38 464	65.93 473	71.73 459	83.37 390	46.60 514	70.95 481	69.40 484	62.47 397	66.14 518	76.66 508	31.22 532	84.10 405	49.10 502	84.10 486	84.49 426

pmmvs3		62.47 492	60.02 504	69.80 471	71.58 532	64.00 317	70.52 485	58.44 541	39.77 540	66.05 519	75.84 513	27.10 548	72.28 476	46.15 520	84.77 481	73.11 524

ADS-MVSNet2		65.87 478	63.64 489	72.55 451	73.16 523	56.92 442	67.10 506	74.81 440	49.74 512	66.04 520	82.97 438	46.71 468	77.26 456	42.29 528	69.96 539	83.46 446

ADS-MVSNet		61.90 495	62.19 496	61.03 518	73.16 523	36.42 543	67.10 506	61.75 528	49.74 512	66.04 520	82.97 438	46.71 468	63.21 534	42.29 528	69.96 539	83.46 446

mvsany_test1		58.48 506	56.47 513	64.50 506	65.90 546	68.21 267	56.95 537	42.11 554	38.30 544	65.69 522	77.19 506	56.96 399	59.35 540	46.16 519	58.96 548	65.93 534

dmvs_testset		60.59 503	62.54 495	54.72 527	77.26 487	27.74 552	74.05 444	61.00 534	60.48 431	65.62 523	67.03 537	55.93 410	68.23 506	32.07 547	69.46 542	68.17 531

DSMNet-mixed		60.98 501	61.61 498	59.09 524	72.88 526	45.05 522	74.70 433	46.61 552	26.20 549	65.34 524	90.32 273	55.46 416	63.12 535	41.72 530	81.30 507	69.09 530

JIA-IIPM		69.41 452	66.64 472	77.70 376	73.19 522	71.24 222	75.67 418	65.56 508	70.42 267	65.18 525	92.97 154	33.64 526	83.06 411	53.52 470	69.61 541	78.79 503

test-LLR		67.21 465	66.74 470	68.63 482	76.45 498	55.21 460	67.89 498	67.14 499	62.43 401	65.08 526	72.39 527	43.41 500	69.37 491	61.00 402	84.89 477	81.31 475

test-mter		65.00 482	63.79 487	68.63 482	76.45 498	55.21 460	67.89 498	67.14 499	50.98 504	65.08 526	72.39 527	28.27 543	69.37 491	61.00 402	84.89 477	81.31 475

PMMVS2		55.64 511	59.27 505	44.74 529	64.30 548	12.32 561	40.60 545	49.79 549	53.19 486	65.06 528	84.81 404	53.60 427	49.76 549	32.68 546	89.41 397	72.15 525

baseline2		69.77 449	66.89 468	78.41 358	79.51 461	58.09 429	76.23 411	69.57 483	57.50 454	64.82 529	77.45 501	46.02 474	88.44 315	53.08 472	77.83 520	88.70 361

gg-mvs-nofinetune		68.96 458	69.11 450	68.52 485	76.12 502	45.32 520	83.59 225	55.88 545	86.68 32	64.62 530	97.01 11	30.36 535	83.97 408	44.78 524	82.94 494	76.26 515

PAPM		71.77 422	70.06 439	76.92 394	86.39 308	53.97 471	76.62 403	86.62 299	53.44 484	63.97 531	84.73 406	57.79 394	92.34 177	39.65 534	81.33 506	84.45 428

PDCNetPlus		57.49 508	56.93 511	59.15 523	56.36 554	47.35 512	52.32 543	77.34 421	39.50 542	63.50 532	73.19 526	13.19 558	56.86 543	47.51 512	89.48 395	73.22 523

new_pmnet		55.69 510	57.66 510	49.76 528	75.47 508	30.59 550	59.56 529	51.45 548	43.62 532	62.49 533	75.48 518	40.96 508	49.15 550	37.39 541	72.52 533	69.55 529

UWE-MVS-28		58.44 507	57.71 509	60.65 519	73.58 520	31.23 549	69.68 492	48.80 550	53.12 488	61.79 534	78.83 487	30.98 533	68.40 505	21.58 550	80.99 509	82.33 465

MDTV_nov1_ep13_2view						27.60 553	70.76 484		46.47 521	61.27 535		45.20 489		49.18 501		83.75 441

dp		60.70 502	60.29 503	61.92 514	72.04 530	38.67 540	70.83 483	64.08 515	51.28 501	60.75 536	77.28 502	36.59 519	71.58 481	47.41 513	62.34 546	75.52 518

TESTMET0.1,1		61.29 498	60.32 502	64.19 507	72.06 529	51.30 492	67.89 498	62.09 524	45.27 524	60.65 537	69.01 534	27.93 544	64.74 531	56.31 439	81.65 504	76.53 514

0.4-1-1-0.1		64.02 489	60.59 500	74.31 432	73.99 515	55.62 454	67.66 502	72.78 461	55.53 470	60.35 538	58.45 542	29.26 539	86.88 352	52.84 478	74.42 530	80.42 488

PMMVS		61.65 496	60.38 501	65.47 502	65.40 547	69.26 250	63.97 521	61.73 529	36.80 548	60.11 539	68.43 535	59.42 374	66.35 521	48.97 503	78.57 519	60.81 541

PVSNet_0	51.08 22	56.10 509	54.97 514	59.48 522	75.12 511	53.28 478	55.16 540	61.89 527	44.30 528	59.16 540	62.48 540	54.22 424	65.91 524	35.40 542	47.01 549	59.25 543

MVS-HIRNet		61.16 499	62.92 493	55.87 525	79.09 467	35.34 545	71.83 471	57.98 542	46.56 520	59.05 541	91.14 232	49.95 458	76.43 459	38.74 536	71.92 536	55.84 545

E-PMN		61.59 497	61.62 497	61.49 516	66.81 542	55.40 458	53.77 541	60.34 535	66.80 332	58.90 542	65.50 538	40.48 509	66.12 522	55.72 445	86.25 457	62.95 539

GG-mvs-BLEND				67.16 492	73.36 521	46.54 516	84.15 205	55.04 546		58.64 543	61.95 541	29.93 536	83.87 409	38.71 537	76.92 526	71.07 527

EPNet_dtu		72.87 408	71.33 420	77.49 382	77.72 480	60.55 386	82.35 274	75.79 434	66.49 336	58.39 544	81.06 464	53.68 426	85.98 375	53.55 469	92.97 282	85.95 410
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai		41.90 513	42.65 516	39.67 530	70.86 533	21.11 554	61.01 528	21.42 560	57.36 456	57.97 545	50.06 547	16.40 556	58.73 541	21.03 551	27.69 553	39.17 548

0.4-1-1-0.2		62.43 494	58.81 508	73.31 442	70.85 534	54.20 469	64.36 518	72.99 458	53.70 482	57.51 546	54.59 544	29.52 537	86.44 365	51.70 490	74.02 531	79.30 497

0.3-1-1-0.015		62.57 491	58.82 507	73.82 437	71.85 531	54.96 463	65.63 512	72.97 459	54.16 479	56.95 547	55.43 543	26.76 549	86.59 361	52.05 483	73.55 532	79.92 492

EMVS		61.10 500	60.81 499	61.99 513	65.96 545	55.86 451	53.10 542	58.97 540	67.06 329	56.89 548	63.33 539	40.98 507	67.03 516	54.79 459	86.18 458	63.08 538

CHOSEN 280x420		59.08 505	56.52 512	66.76 494	76.51 496	64.39 313	49.62 544	59.00 539	43.86 530	55.66 549	68.41 536	35.55 522	68.21 507	43.25 526	76.78 527	67.69 533

MVE	40.22 23	51.82 512	50.47 515	55.87 525	62.66 551	51.91 487	31.61 548	39.28 555	40.65 538	50.76 550	74.98 522	56.24 404	44.67 551	33.94 545	64.11 545	71.04 528
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
kuosan		30.83 515	32.17 518	26.83 533	53.36 555	19.02 558	57.90 535	20.44 561	38.29 545	38.01 551	37.82 549	15.18 557	33.45 554	7.74 556	20.76 556	28.03 549

DeepMVS_CX				24.13 534	32.95 556	29.49 551		21.63 559	12.07 551	37.95 552	45.07 548	30.84 534	19.21 555	17.94 552	33.06 552	23.69 550

tmp_tt		20.25 518	24.50 521	7.49 537	4.47 561	8.70 563	34.17 547	25.16 558	1.00 556	32.43 553	18.49 552	39.37 511	9.21 557	21.64 549	43.75 550	4.57 553

MVS_clip		14.31 519	16.37 522	8.11 536	18.08 558	12.42 560	12.95 550	3.12 563	3.73 553	28.79 554	35.98 550	8.84 559	4.85 558	12.31 554	23.54 554	7.07 551

VLMVS_CLIP		13.55 520	14.55 523	10.53 535	11.59 560	10.03 562	11.68 551	18.47 562	4.20 552	20.50 555	24.42 551	8.69 560	16.48 556	8.18 555	23.25 555	5.10 552

test_method		30.46 516	29.60 519	33.06 532	17.99 559	3.84 564	13.62 549	73.92 447	2.79 554	18.29 556	53.41 545	28.53 542	43.25 552	22.56 548	35.27 551	52.11 546

MVS_baseline		4.35 525	5.47 528	0.99 539	3.75 562	0.34 568	2.10 552	0.79 566	0.13 560	12.26 557	14.40 554	2.36 562	0.00 562	1.87 557	11.56 557	2.62 555

VLMVS		3.03 526	3.34 529	2.13 538	3.00 563	1.87 565	1.95 553	1.16 564	0.16 559	5.10 558	6.49 555	5.23 561	1.51 559	1.34 558	5.59 558	3.02 554

EGC-MVSNET		74.79 383	69.99 441	89.19 66	94.89 37	87.00 19	91.89 42	86.28 302	1.09 555	2.23 559	95.98 29	81.87 137	89.48 282	79.76 142	95.96 141	91.10 283

testmvs		5.91 524	7.65 527	0.72 541	1.20 564	0.37 567	59.14 531	0.67 567	0.49 558	1.11 560	2.76 558	0.94 564	0.24 561	1.02 560	1.47 559	1.55 557

test123		6.27 523	8.08 526	0.84 540	1.11 565	0.57 566	62.90 522	0.82 565	0.54 557	1.07 561	2.75 559	1.26 563	0.30 560	1.04 559	1.26 560	1.66 556

mmdepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

monomultidepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

test_blank		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet_test		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

DCPMVS		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

cdsmvs_eth3d_5k		20.81 517	27.75 520	0.00 542	0.00 566	0.00 569	0.00 554	85.44 319	0.00 561	0.00 562	82.82 443	81.46 143	0.00 562	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		6.41 522	8.55 525	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	76.94 203	0.00 562	0.00 561	0.00 561	0.00 558

sosnet-low-res		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

sosnet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uncertanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Regformer		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

ab-mvs-re		6.65 521	8.87 524	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	79.80 478	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

PatchmatchNet2					0.00 566	20.88 555	55.62 538	59.13 537	52.38 493
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														46.85 517	87.28 437	83.48 445
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													54.72 545
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						37.39 541								52.61 480

MSC_two_6792asdad				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 230

No_MVS				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 230

eth-test2					0.00 566

eth-test					0.00 566

OPU-MVS				88.27 88	91.89 125	77.83 129	90.47 60				91.22 228	81.12 147	94.68 82	74.48 231	95.35 171	92.29 241

save fliter					93.75 67	77.44 136	86.31 147	89.72 227	70.80 263

test_0728_SECOND				86.79 114	94.25 52	72.45 201	90.54 57	94.10 40					95.88 17	86.42 46	97.97 48	92.02 255

GSMVS																83.88 436

sam_mvs1												46.11 473				83.88 436

sam_mvs												45.92 479

MTGPA								91.81 153

test_post1							78.85 360				3.13 556	45.19 490	80.13 437	58.11 424

test_post											3.10 557	45.43 486	77.22 457

patchmatchnet-post											81.71 457	45.93 478	87.01 347

MTMP							90.66 53	33.14 556

gm-plane-assit					75.42 509	44.97 523			52.17 494		72.36 529		87.90 329	54.10 462

test9_res														80.83 131	96.45 117	90.57 304

agg_prior2														79.68 144	96.16 130	90.22 313

test_prior4						78.97 115	84.59 192

test_prior				86.32 123	90.59 172	71.99 209		92.85 114					94.17 107			92.80 202

新几何2							81.72 291

旧先验1					91.97 121	71.77 210		81.78 379			91.84 200	73.92 251			93.65 254	83.61 442

无先验							82.81 258	85.62 317	58.09 448				91.41 207	67.95 333		84.48 427

原ACMM2							82.26 279

testdata2													86.43 366	63.52 378

segment_acmp												81.94 133

testdata1							79.62 336		73.95 194

plane_prior7					93.45 74	77.31 139

plane_prior6					92.61 99	76.54 146						74.84 232

plane_prior5								93.61 70					95.22 62	80.78 132	95.83 152	94.46 104

plane_prior4											92.95 155

plane_prior2							89.45 87		79.44 112

plane_prior1					92.83 96

plane_prior						76.42 149	87.15 128		75.94 159						95.03 188

n2								0.00 568

nn								0.00 568

door-mid								74.45 444

test11								91.46 162

door								72.57 463

HQP5-MVS						70.66 228

BP-MVS														77.30 188

HQP3-MVS								92.68 120							94.47 218

HQP2-MVS												72.10 282

NP-MVS					91.95 122	74.55 172					90.17 282

ACMMP++_ref															95.74 159

ACMMP++															97.35 84

Test By Simon												79.09 168

High-resolution multi-view benchmark