High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		86.90 1	88.67 1	81.57 24	91.50 1	63.30 161	84.80 39	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 76	68.08 117	97.05 1	96.93 1

UA-Net		81.56 39	82.28 47	79.40 51	88.91 28	69.16 90	84.67 40	80.01 171	75.34 18	79.80 138	94.91 2	69.79 104	80.25 162	72.63 79	94.46 40	88.78 44

UniMVSNet_ETH3D		76.74 88	79.02 68	69.92 240	89.27 19	43.81 394	74.47 169	71.70 295	72.33 43	85.50 61	93.65 3	77.98 24	76.88 232	54.60 292	91.64 98	89.08 34

tt0320		71.34 191	73.47 144	64.97 332	79.92 154	40.81 426	65.22 357	69.07 336	66.72 78	76.15 223	93.36 4	70.35 94	66.90 387	49.31 342	91.09 119	87.21 63

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 63	80.48 146	64.16 150	80.24 85	82.06 121	61.89 132	88.77 15	93.32 5	57.15 271	82.60 112	70.08 100	92.80 80	89.25 30

tt0320-xc		71.50 186	73.63 140	65.08 330	79.77 156	40.46 435	64.80 365	68.86 342	67.08 73	76.84 199	93.24 6	70.33 95	66.77 394	49.76 334	92.02 94	88.02 53

K. test v3		73.67 126	73.61 141	73.87 135	79.78 155	55.62 248	74.69 165	62.04 404	66.16 84	84.76 73	93.23 7	49.47 332	80.97 148	65.66 146	86.67 241	85.02 126

LTVRE_ROB	75.46 1	84.22 9	84.98 11	81.94 23	84.82 80	75.40 36	91.60 3	87.80 8	73.52 28	88.90 14	93.06 8	71.39 85	81.53 134	81.53 4	92.15 93	88.91 40
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		80.35 55	82.89 40	72.74 173	89.84 7	37.34 469	77.16 123	81.81 126	80.45 3	90.92 3	92.95 9	74.57 55	86.12 32	63.65 172	94.68 36	94.76 6

Anonymous20231211		75.54 99	77.19 89	70.59 212	77.67 196	45.70 372	74.73 163	80.19 166	68.80 62	82.95 94	92.91 10	66.26 146	76.76 235	58.41 243	92.77 81	89.30 27

PEN-MVS		80.46 53	82.91 39	73.11 153	89.83 8	39.02 449	77.06 126	82.61 108	80.04 4	90.60 6	92.85 11	74.93 51	85.21 64	63.15 179	95.15 22	95.09 2

pmmvs6		71.82 180	73.66 138	66.31 317	75.94 235	42.01 412	66.99 326	72.53 287	63.45 118	76.43 217	92.78 12	72.95 68	69.69 353	51.41 320	90.46 138	87.22 62

PS-CasMVS		80.41 54	82.86 41	73.07 155	89.93 6	39.21 445	77.15 124	81.28 138	79.74 5	90.87 4	92.73 13	75.03 50	84.93 69	63.83 169	95.19 20	95.07 3

gg-mvs-nofinetune		55.75 430	56.75 418	52.72 466	62.87 471	28.04 524	68.92 279	41.36 534	71.09 50	50.80 523	92.63 14	20.74 532	66.86 391	29.97 514	72.41 473	63.25 498

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 5	88.84 4	90.72 1	78.27 11	87.95 18	92.53 15	79.37 15	84.79 73	74.51 59	96.15 2	92.88 7

v7n		79.37 63	80.41 59	76.28 100	78.67 181	55.81 244	79.22 98	82.51 112	70.72 53	87.54 26	92.44 16	68.00 124	81.34 136	72.84 77	91.72 96	91.69 10

PS-MVSNAJss		77.54 79	77.35 88	78.13 76	84.88 79	66.37 122	78.55 104	79.59 183	53.48 252	86.29 45	92.43 17	62.39 188	80.25 162	67.90 122	90.61 135	87.77 55

test_djsdf		78.88 66	78.27 77	80.70 38	81.42 135	71.24 65	83.98 45	75.72 248	52.27 267	87.37 31	92.25 18	68.04 123	80.56 155	72.28 84	91.15 114	90.32 20

SixPastTwentyTwo		75.77 94	76.34 95	74.06 131	81.69 132	54.84 255	76.47 131	75.49 250	64.10 109	87.73 22	92.24 19	50.45 325	81.30 138	67.41 127	91.46 104	86.04 94

reproduce_model		84.87 5	85.80 5	82.05 22	85.52 68	78.14 16	87.69 6	85.36 39	79.26 6	89.12 11	92.10 20	77.52 26	85.92 40	80.47 8	95.20 19	82.10 237

WR-MVS_H		80.22 57	82.17 48	74.39 125	89.46 14	42.69 408	78.24 109	82.24 118	78.21 12	89.57 9	92.10 20	68.05 122	85.59 53	66.04 142	95.62 9	94.88 5

PMVS	70.70 6	81.70 38	83.15 36	77.36 87	90.35 5	82.82 2	82.15 64	79.22 191	74.08 23	87.16 34	91.97 22	84.80 2	76.97 228	64.98 151	93.61 70	72.28 411
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lecture		83.41 20	85.02 10	78.58 65	83.87 98	67.26 108	84.47 41	88.27 6	73.64 27	87.35 32	91.96 23	78.55 21	82.92 105	81.59 3	95.50 10	85.56 108

MVSMamba_PlusPlus		76.88 86	78.21 78	72.88 167	80.83 142	48.71 311	83.28 57	82.79 102	72.78 31	79.17 147	91.94 24	56.47 281	83.95 82	70.51 98	86.15 245	85.99 96

ANet_high		67.08 282	69.94 220	58.51 429	57.55 515	27.09 528	58.43 451	76.80 234	63.56 115	82.40 102	91.93 25	59.82 230	64.98 409	50.10 332	88.86 185	83.46 186

reproduce-ours		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 219

our_new_method		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 219

mvs_tets		78.93 65	78.67 72	79.72 46	84.81 81	73.93 48	80.65 77	76.50 236	51.98 274	87.40 28	91.86 28	76.09 39	78.53 189	68.58 112	90.20 143	86.69 75

sc_t1		72.50 168	74.23 126	67.33 295	80.05 152	46.99 350	66.58 334	69.48 328	66.28 82	77.62 177	91.83 29	70.98 90	68.62 366	53.86 304	91.40 105	86.37 86

test_0402		78.17 75	79.48 66	74.24 127	83.50 101	59.15 209	72.52 197	74.60 260	75.34 18	88.69 17	91.81 30	75.06 49	82.37 118	65.10 149	88.68 186	81.20 257

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 82	66.72 117	86.54 23	85.11 43	72.00 45	86.65 39	91.75 31	78.20 23	87.04 10	77.93 30	94.32 52	83.47 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VDDNet		71.60 184	73.13 154	67.02 304	86.29 47	41.11 421	69.97 254	66.50 363	68.72 64	74.74 256	91.70 32	59.90 228	75.81 244	48.58 352	91.72 96	84.15 165

CP-MVSNet		79.48 61	81.65 52	72.98 159	89.66 12	39.06 448	76.76 127	80.46 161	78.91 8	90.32 7	91.70 32	68.49 115	84.89 70	63.40 176	95.12 23	95.01 4

HPM-MVS_fast		84.59 7	85.10 9	83.06 4	88.60 32	75.83 33	86.27 27	86.89 16	73.69 26	86.17 46	91.70 32	78.23 22	85.20 65	79.45 16	94.91 29	88.15 52

EGC-MVSNET		64.77 318	61.17 369	75.60 110	86.90 42	74.47 43	84.04 44	68.62 349	0.60 556	1.13 560	91.61 35	65.32 159	74.15 277	64.01 163	88.28 192	78.17 321

jajsoiax		78.51 70	78.16 79	79.59 48	84.65 84	73.83 50	80.42 80	76.12 243	51.33 285	87.19 33	91.51 36	73.79 62	78.44 194	68.27 115	90.13 147	86.49 83

SMA-MVS		82.12 33	82.68 44	80.43 39	88.90 29	69.52 82	85.12 36	84.76 54	63.53 116	84.23 80	91.47 37	72.02 74	87.16 7	79.74 13	94.36 49	84.61 145
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
COLMAP_ROB	72.78 3	83.75 14	84.11 19	82.68 12	82.97 113	74.39 45	87.18 11	88.18 7	78.98 7	86.11 49	91.47 37	79.70 14	85.76 47	66.91 137	95.46 13	87.89 54
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + MP.		79.05 64	78.81 69	79.74 45	88.94 27	67.52 105	86.61 22	81.38 136	51.71 276	77.15 189	91.42 39	65.49 156	87.20 6	79.44 17	87.17 231	84.51 154
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		82.54 30	83.46 30	79.76 44	88.88 30	68.44 96	81.57 69	86.33 19	63.17 122	85.38 64	91.26 40	76.33 36	84.67 75	83.30 1	94.96 27	86.17 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 19	84.33 16	80.90 35	87.00 39	70.41 75	82.04 66	86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

LGP-MVS_train				80.90 35	87.00 39	70.41 75		86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

ACMH+	66.64 10	81.20 43	82.48 45	77.35 88	81.16 140	62.39 167	80.51 78	87.80 8	73.02 30	87.57 25	91.08 43	80.28 9	82.44 115	64.82 153	96.10 4	87.21 63

ACMMP_NAP		82.33 32	83.28 33	79.46 50	89.28 18	69.09 93	83.62 51	84.98 48	64.77 104	83.97 83	91.02 44	75.53 45	85.93 39	82.00 2	94.36 49	83.35 193

MP-MVS		83.19 22	83.54 28	82.14 19	90.54 4	79.00 12	86.42 25	83.59 87	71.31 47	81.26 120	90.96 45	74.57 55	84.69 74	78.41 25	94.78 32	82.74 218
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testf1		75.66 97	76.57 92	72.95 160	67.07 421	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 324	60.46 209	91.13 116	79.56 294

APD_test2		75.66 97	76.57 92	72.95 160	67.07 421	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 324	60.46 209	91.13 116	79.56 294

anonymousdsp		78.60 68	77.80 81	81.00 34	78.01 190	74.34 46	80.09 87	76.12 243	50.51 301	89.19 10	90.88 48	71.45 83	77.78 212	73.38 71	90.60 136	90.90 16

PGM-MVS		83.07 25	83.25 35	82.54 15	89.57 13	77.21 28	82.04 66	85.40 37	67.96 68	84.91 71	90.88 48	75.59 42	86.57 15	78.16 27	94.71 35	83.82 172

mPP-MVS		84.01 13	84.39 15	82.88 6	90.65 3	81.38 4	87.08 13	82.79 102	72.41 41	85.11 67	90.85 50	76.65 33	84.89 70	79.30 20	94.63 37	82.35 230

MTAPA		83.19 22	83.87 23	81.13 33	91.16 2	78.16 15	84.87 37	80.63 157	72.08 44	84.93 68	90.79 51	74.65 54	84.42 79	80.98 5	94.75 33	80.82 269

MIMVSNet1		66.57 291	69.23 238	58.59 428	81.26 139	37.73 465	64.06 380	57.62 431	57.02 182	78.40 161	90.75 52	62.65 182	58.10 448	41.77 413	89.58 163	79.95 289

SR-MVS-dyc-post		84.75 6	85.26 8	83.21 3	86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	79.20 16	85.58 54	78.11 28	94.46 40	84.89 127

RE-MVS-def			85.50 6		86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	81.38 7		78.11 28	94.46 40	84.89 127

region2R		83.54 17	83.86 24	82.58 14	89.82 9	77.53 21	87.06 16	84.23 77	70.19 57	83.86 85	90.72 55	75.20 47	86.27 24	79.41 18	94.25 54	83.95 169

ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 25	87.01 17	84.19 78	70.23 55	84.49 76	90.67 56	75.15 48	86.37 19	79.58 14	94.26 53	84.18 163

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 31	87.65 7	85.89 27	71.03 51	85.85 51	90.58 57	78.77 18	85.78 46	79.37 19	95.17 21	84.62 144
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CP-MVS		84.12 11	84.55 14	82.80 10	89.42 17	79.74 9	88.19 5	84.43 68	71.96 46	84.70 74	90.56 58	77.12 29	86.18 29	79.24 21	95.36 14	82.49 227

Baseline_NR-MVSNet		70.62 206	73.19 152	62.92 367	76.97 210	34.44 490	68.84 282	70.88 316	60.25 146	79.50 143	90.53 59	61.82 198	69.11 360	54.67 291	95.27 15	85.22 115

DeepC-MVS	72.44 4	81.00 47	80.83 57	81.50 25	86.70 44	70.03 79	82.06 65	87.00 15	59.89 149	80.91 126	90.53 59	72.19 71	88.56 1	73.67 70	94.52 39	85.92 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS_3200maxsize		83.57 16	84.33 16	81.31 31	82.83 116	73.53 53	85.50 34	87.45 13	74.11 22	86.45 43	90.52 61	80.02 10	84.48 77	77.73 32	94.34 51	85.93 97

Anonymous20240529		72.56 164	73.79 136	68.86 267	76.89 218	45.21 376	68.80 288	77.25 227	67.16 72	76.89 195	90.44 62	65.95 150	74.19 276	50.75 325	90.00 150	87.18 66

HFP-MVS		83.39 21	84.03 20	81.48 26	89.25 20	75.69 35	87.01 17	84.27 74	70.23 55	84.47 77	90.43 63	76.79 30	85.94 37	79.58 14	94.23 55	82.82 215

HPM-MVS		84.12 11	84.63 13	82.60 13	88.21 35	74.40 44	85.24 35	87.21 14	70.69 54	85.14 66	90.42 64	78.99 17	86.62 14	80.83 6	94.93 28	86.79 72
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DVP-MVS		81.15 44	83.12 37	75.24 117	86.16 54	60.78 189	83.77 49	80.58 159	72.48 37	85.83 52	90.41 65	78.57 19	85.69 49	75.86 43	94.39 45	79.24 301
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									74.03 24	85.83 52	90.41 65	75.58 43	85.69 49	77.43 35	94.74 34	84.31 160

SteuartSystems-ACMMP		83.07 25	83.64 27	81.35 29	85.14 75	71.00 68	85.53 33	84.78 53	70.91 52	85.64 54	90.41 65	75.55 44	87.69 4	79.75 11	95.08 24	85.36 113
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		83.12 24	83.68 26	81.45 27	89.14 24	73.28 55	86.32 26	85.97 25	67.39 71	84.02 82	90.39 68	74.73 53	86.46 16	80.73 7	94.43 44	84.60 147

XVS		83.51 18	83.73 25	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	90.39 68	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

DVP-MVS++		81.24 42	82.74 43	76.76 92	83.14 106	60.90 187	91.64 1	85.49 33	74.03 24	84.93 68	90.38 70	66.82 137	85.90 41	77.43 35	90.78 131	83.49 182

test_one_0601					85.84 66	61.45 177		85.63 31	75.27 20	85.62 57	90.38 70	76.72 32

FC-MVSNet-test		73.32 139	74.78 112	68.93 265	79.21 166	36.57 472	71.82 222	79.54 185	57.63 176	82.57 101	90.38 70	59.38 238	78.99 181	57.91 248	94.56 38	91.23 12

GBi-Net		68.30 256	68.79 244	66.81 306	73.14 297	40.68 429	71.96 211	73.03 274	54.81 214	74.72 257	90.36 73	48.63 344	75.20 257	47.12 365	85.37 258	84.54 150

test1		68.30 256	68.79 244	66.81 306	73.14 297	40.68 429	71.96 211	73.03 274	54.81 214	74.72 257	90.36 73	48.63 344	75.20 257	47.12 365	85.37 258	84.54 150

FMVSNet1		71.06 195	72.48 172	66.81 306	77.65 197	40.68 429	71.96 211	73.03 274	61.14 137	79.45 144	90.36 73	60.44 220	75.20 257	50.20 331	88.05 198	84.54 150

SR-MVS		84.51 8	85.27 7	82.25 18	88.52 33	77.71 18	86.81 19	85.25 41	77.42 16	86.15 47	90.24 76	81.69 5	85.94 37	77.77 31	93.58 71	83.09 202

ACMH	63.62 14	77.50 82	80.11 61	69.68 244	79.61 158	56.28 238	78.81 101	83.62 86	63.41 120	87.14 35	90.23 77	76.11 38	73.32 288	67.58 124	94.44 43	79.44 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		82.79 28	83.27 34	81.34 30	88.99 26	73.29 54	85.94 32	85.13 42	68.58 66	84.14 81	90.21 78	73.37 64	86.41 17	79.09 22	93.98 63	84.30 162

3Dnovator+	73.19 2	81.08 46	80.48 58	82.87 7	81.41 136	72.03 58	84.38 43	86.23 23	77.28 17	80.65 129	90.18 79	59.80 231	87.58 5	73.06 74	91.34 107	89.01 36

MED-MVS		81.77 37	82.86 41	78.51 67	86.27 48	64.31 146	86.10 28	84.54 64	72.46 39	85.54 58	90.03 80	72.97 67	86.37 19	74.09 63	93.74 67	84.86 130

TestfortrainingZip a		82.48 31	83.93 21	78.11 77	86.27 48	64.11 152	86.10 28	85.02 46	72.46 39	86.32 44	90.03 80	76.75 31	85.37 56	78.23 26	94.22 56	84.86 130

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 71	67.25 109	82.91 59	84.98 48	73.52 28	85.43 62	90.03 80	76.37 35	86.97 12	74.56 57	94.02 62	82.62 223
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMP	69.50 8	82.64 29	83.38 31	80.40 40	86.50 45	69.44 84	82.30 63	86.08 24	66.80 76	86.70 38	89.99 83	81.64 6	85.95 36	74.35 61	96.11 3	85.81 99
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0726					86.16 54	60.78 189	83.81 48	85.10 44	72.48 37	85.27 65	89.96 84	78.57 19

LS3D		80.99 48	80.85 56	81.41 28	78.37 182	71.37 63	87.45 8	85.87 28	77.48 15	81.98 106	89.95 85	69.14 107	85.26 61	66.15 139	91.24 110	87.61 58

TransMVSNet (Re)		69.62 227	71.63 192	63.57 350	76.51 224	35.93 480	65.75 347	71.29 307	61.05 138	75.02 250	89.90 86	65.88 152	70.41 342	49.79 333	89.48 165	84.38 158

RPSCF		75.76 95	74.37 122	79.93 43	74.81 252	77.53 21	77.53 118	79.30 188	59.44 152	78.88 150	89.80 87	71.26 86	73.09 291	57.45 253	80.89 368	89.17 33

SED-MVS		81.78 36	83.48 29	76.67 93	86.12 56	61.06 183	83.62 51	84.72 56	72.61 35	87.38 29	89.70 88	77.48 27	85.89 43	75.29 47	94.39 45	83.08 203

test_241102_TWO								84.80 51	72.61 35	84.93 68	89.70 88	77.73 25	85.89 43	75.29 47	94.22 56	83.25 195

XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 59	87.01 38	72.91 56	80.23 86	85.56 32	66.56 80	85.64 54	89.57 90	69.12 108	80.55 157	72.51 81	93.37 73	83.48 184

test_241102_ONE					86.12 56	61.06 183		84.72 56	72.64 34	87.38 29	89.47 91	77.48 27	85.74 48

FIs		72.56 164	73.80 135	68.84 268	78.74 180	37.74 464	71.02 236	79.83 174	56.12 195	80.88 128	89.45 92	58.18 254	78.28 201	56.63 262	93.36 74	90.51 19

pm-mvs1		68.40 254	69.85 223	64.04 342	73.10 300	39.94 439	64.61 371	70.50 319	55.52 207	73.97 281	89.33 93	63.91 173	68.38 368	49.68 336	88.02 199	83.81 173

OPM-MVS		80.99 48	81.63 53	79.07 56	86.86 43	69.39 85	79.41 96	84.00 83	65.64 86	85.54 58	89.28 94	76.32 37	83.47 95	74.03 67	93.57 72	84.35 159
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v8		75.07 107	75.64 103	73.35 145	73.42 290	47.46 339	75.20 153	81.45 133	60.05 147	85.64 54	89.26 95	58.08 260	81.80 131	69.71 106	87.97 201	90.79 17

TranMVSNet+NR-MVSNet		76.13 92	77.66 83	71.56 196	84.61 85	42.57 410	70.98 237	78.29 211	68.67 65	83.04 91	89.26 95	72.99 66	80.75 153	55.58 278	95.47 12	91.35 11

SSC-MVS		61.79 365	66.08 297	48.89 492	76.91 215	10.00 558	53.56 485	47.37 499	68.20 67	76.56 210	89.21 97	54.13 298	57.59 450	54.75 289	74.07 461	79.08 304

nrg030		74.87 114	75.99 100	71.52 197	74.90 248	49.88 303	74.10 176	82.58 109	54.55 224	83.50 89	89.21 97	71.51 81	75.74 247	61.24 199	92.34 89	88.94 39

SF-MVS		80.72 50	81.80 49	77.48 84	82.03 127	64.40 144	83.41 55	88.46 5	65.28 94	84.29 79	89.18 99	73.73 63	83.22 99	76.01 42	93.77 65	84.81 136

v10		75.69 96	76.20 97	74.16 129	74.44 266	48.69 312	75.84 149	82.93 100	59.02 157	85.92 50	89.17 100	58.56 251	82.74 110	70.73 94	89.14 176	91.05 13

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 36	73.96 47	83.11 58	84.52 66	66.40 81	87.45 27	89.16 101	81.02 8	80.52 158	74.27 62	95.73 7	80.98 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZD-MVS					83.91 95	69.36 86		81.09 145	58.91 159	82.73 99	89.11 102	75.77 41	86.63 13	72.73 78	92.93 79

HQP_MVS		78.77 67	78.78 71	78.72 62	85.18 72	65.18 136	82.74 61	85.49 33	65.45 89	78.23 163	89.11 102	60.83 214	86.15 30	71.09 90	90.94 123	84.82 134

plane_prior4											89.11 102

mvs5depth		66.35 295	67.98 262	61.47 388	62.43 477	51.05 283	69.38 266	69.24 331	56.74 188	73.62 287	89.06 105	46.96 354	58.63 441	55.87 272	88.49 189	74.73 378

lessismore_v0				72.75 172	79.60 159	56.83 237		57.37 435		83.80 86	89.01 106	47.45 352	78.74 186	64.39 160	86.49 244	82.69 221

XVG-OURS		79.51 60	79.82 63	78.58 65	86.11 59	74.96 40	76.33 140	84.95 50	66.89 74	82.75 98	88.99 107	66.82 137	78.37 198	74.80 50	90.76 134	82.40 229

APD-MVS		81.13 45	81.73 51	79.36 52	84.47 87	70.53 74	83.85 47	83.70 85	69.43 61	83.67 87	88.96 108	75.89 40	86.41 17	72.62 80	92.95 78	81.14 259
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Gipuma		69.55 229	72.83 163	59.70 413	63.63 468	53.97 262	80.08 88	75.93 246	64.24 108	73.49 292	88.93 109	57.89 264	62.46 418	59.75 225	91.55 102	62.67 501
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Casviewmamba		77.76 77	78.57 74	75.31 114	76.72 220	53.06 269	76.28 141	85.90 26	62.98 125	81.96 107	88.90 110	75.35 46	82.88 107	68.97 109	90.11 148	89.98 21

XVG-OURS-SEG-HR		79.62 59	79.99 62	78.49 68	86.46 46	74.79 41	77.15 124	85.39 38	66.73 77	80.39 134	88.85 111	74.43 58	78.33 200	74.73 52	85.79 252	82.35 230

casdiffmvs_mvg		75.26 103	76.18 98	72.52 179	72.87 309	49.47 305	72.94 194	84.71 58	59.49 151	80.90 127	88.81 112	70.07 99	79.71 170	67.40 128	88.39 191	88.40 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MM		78.15 76	77.68 82	79.55 49	80.10 151	65.47 132	80.94 74	78.74 201	71.22 49	72.40 315	88.70 113	60.51 218	87.70 3	77.40 37	89.13 177	85.48 110

VDD-MVS		70.81 203	71.44 198	68.91 266	79.07 173	46.51 360	67.82 307	70.83 317	61.23 136	74.07 277	88.69 114	59.86 229	75.62 249	51.11 322	90.28 142	84.61 145

test2506		61.23 372	60.85 375	62.38 373	78.80 178	27.88 525	67.33 317	37.42 544	54.23 233	67.55 400	88.68 115	17.87 547	74.39 272	46.33 375	89.41 167	84.86 130

ECVR-MVS		64.82 316	65.22 311	63.60 349	78.80 178	31.14 509	66.97 327	56.47 446	54.23 233	69.94 362	88.68 115	37.23 429	74.81 265	45.28 387	89.41 167	84.86 130

mmtdpeth		68.76 246	70.55 214	63.40 357	67.06 424	56.26 239	68.73 292	71.22 311	55.47 208	70.09 358	88.64 117	65.29 160	56.89 453	58.94 234	89.50 164	77.04 347

APD_test1		75.04 108	75.38 107	74.02 132	69.89 368	70.15 77	76.46 132	79.71 177	65.50 88	82.99 93	88.60 118	66.94 134	72.35 305	59.77 223	88.54 188	79.56 294

CPTT-MVS		81.51 40	81.76 50	80.76 37	89.20 22	78.75 13	86.48 24	82.03 122	68.80 62	80.92 125	88.52 119	72.00 75	82.39 117	74.80 50	93.04 77	81.14 259

test1111		64.62 320	65.19 312	62.93 366	79.01 174	29.91 517	65.45 352	54.41 457	54.09 238	71.47 341	88.48 120	37.02 430	74.29 275	46.83 370	89.94 154	84.58 148

Vis-MVSNet		74.85 115	74.56 115	75.72 107	81.63 133	64.64 142	76.35 138	79.06 193	62.85 126	73.33 295	88.41 121	62.54 186	79.59 173	63.94 168	82.92 322	82.94 208
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HPM-MVS++		79.89 58	79.80 64	80.18 42	89.02 25	78.44 14	83.49 54	80.18 167	64.71 105	78.11 166	88.39 122	65.46 157	83.14 100	77.64 34	91.20 112	78.94 307

aaatest				78.47 70	86.27 48	64.31 146	86.10 28	84.54 64	64.93 103	85.54 58	88.38 123		86.37 19	74.09 63	94.20 58	84.73 138

aaEdge-Enhanced		81.36 41	82.39 46	78.28 73	84.42 90	64.31 146	82.78 60	85.02 46	71.25 48	84.81 72	88.38 123	76.53 34	85.81 45	74.09 63	94.20 58	84.73 138

MSP-MVS		80.49 52	79.67 65	82.96 5	89.70 11	77.46 27	87.16 12	85.10 44	64.94 102	81.05 123	88.38 123	57.10 273	87.10 8	79.75 11	83.87 303	84.31 160
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
VPA-MVSNet		68.71 248	70.37 216	63.72 348	76.13 230	38.06 460	64.10 379	71.48 301	56.60 192	74.10 275	88.31 126	64.78 166	69.72 352	47.69 363	90.15 145	83.37 192

ambc				70.10 234	77.74 194	50.21 293	74.28 174	77.93 218		79.26 145	88.29 127	54.11 299	79.77 169	64.43 159	91.10 118	80.30 284

9.14			80.22 60		80.68 144		80.35 83	87.69 11	59.90 148	83.00 92	88.20 128	74.57 55	81.75 132	73.75 69	93.78 64

AllTest		77.66 78	77.43 84	78.35 71	79.19 168	70.81 70	78.60 103	88.64 3	65.37 92	80.09 136	88.17 129	70.33 95	78.43 195	55.60 275	90.90 127	85.81 99

TestCases				78.35 71	79.19 168	70.81 70		88.64 3	65.37 92	80.09 136	88.17 129	70.33 95	78.43 195	55.60 275	90.90 127	85.81 99

LCM-MVSNet-Re		69.10 240	71.57 196	61.70 383	70.37 358	34.30 492	61.45 409	79.62 180	56.81 186	89.59 8	88.16 131	68.44 116	72.94 292	42.30 405	87.33 216	77.85 328

MG-MVS		70.47 209	71.34 199	67.85 284	79.26 164	40.42 436	74.67 166	75.15 254	58.41 164	68.74 387	88.14 132	56.08 284	83.69 89	59.90 220	81.71 348	79.43 299

hybridcas		73.97 122	75.17 108	70.38 216	73.56 285	47.22 344	72.99 193	82.30 116	56.94 183	79.54 141	88.05 133	72.64 69	76.88 232	63.11 180	87.43 211	87.04 69

IS-MVSNet		75.10 106	75.42 106	74.15 130	79.23 165	48.05 325	79.43 94	78.04 215	70.09 58	79.17 147	88.02 134	53.04 305	83.60 90	58.05 247	93.76 66	90.79 17

fmvsm_l_mol_unc0.5_1		67.37 274	67.71 268	66.34 316	68.12 401	43.59 398	61.82 402	58.96 424	48.28 338	80.57 131	88.00 135	54.81 291	72.39 304	65.22 148	83.61 314	83.05 205

Elysia		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 153	87.94 136	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

StellarMVS		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 153	87.94 136	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

tt0805		76.12 93	78.43 76	69.20 254	81.32 137	41.37 418	76.72 128	77.64 220	63.78 113	82.06 105	87.88 138	79.78 11	79.05 179	64.33 161	92.40 87	87.17 67

tfpnnormal		66.48 292	67.93 263	62.16 376	73.40 291	36.65 471	63.45 387	64.99 377	55.97 201	72.82 307	87.80 139	57.06 274	69.10 361	48.31 356	87.54 206	80.72 274

casdiffseed414692147		74.13 119	74.76 113	72.25 188	73.89 282	49.89 302	75.54 151	82.35 115	58.57 163	77.77 171	87.76 140	69.09 109	78.46 192	59.77 223	88.10 197	88.41 48

FE-MVSNET2		68.70 249	69.85 223	65.22 327	74.82 251	37.95 462	67.28 321	73.47 270	53.40 253	77.65 176	87.72 141	59.72 232	73.17 290	46.39 373	88.23 193	84.56 149

BridgeMVS		73.59 129	74.06 130	72.17 190	77.48 200	47.72 333	81.43 71	82.20 119	54.38 228	79.19 146	87.68 142	54.41 296	83.57 91	63.98 165	85.78 253	85.22 115

WB-MVS		60.04 386	64.19 329	47.59 495	76.09 231	10.22 557	52.44 493	46.74 501	65.17 97	74.07 277	87.48 143	53.48 302	55.28 458	49.36 340	72.84 470	77.28 334

RRT-MVS		70.33 210	70.73 211	69.14 257	71.93 325	45.24 375	75.10 154	75.08 256	60.85 142	78.62 155	87.36 144	49.54 331	78.64 187	60.16 214	77.90 424	83.55 180

MGCNet		75.45 100	74.66 114	77.83 79	75.58 240	61.53 175	78.29 107	77.18 229	63.15 124	69.97 361	87.20 145	57.54 267	87.05 9	74.05 66	88.96 182	84.89 127

CDPH-MVS		77.33 83	77.06 91	78.14 75	84.21 92	63.98 154	76.07 145	83.45 88	54.20 235	77.68 175	87.18 146	69.98 100	85.37 56	68.01 119	92.72 83	85.08 123

casdiffmvs		73.06 146	73.84 134	70.72 210	71.32 336	46.71 355	70.93 238	84.26 75	55.62 205	77.46 181	87.10 147	67.09 133	77.81 210	63.95 166	86.83 237	87.64 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DU-MVS		74.91 111	75.57 104	72.93 163	83.50 101	45.79 368	69.47 264	80.14 168	65.22 95	81.74 113	87.08 148	61.82 198	81.07 144	56.21 268	94.98 25	91.93 8

NR-MVSNet		73.62 127	74.05 131	72.33 184	83.50 101	43.71 395	65.65 348	77.32 225	64.32 107	75.59 231	87.08 148	62.45 187	81.34 136	54.90 287	95.63 8	91.93 8

SD-MVS		80.28 56	81.55 54	76.47 98	83.57 100	67.83 102	83.39 56	85.35 40	64.42 106	86.14 48	87.07 150	74.02 59	80.97 148	77.70 33	92.32 90	80.62 277
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
旧先验1					84.55 86	60.36 194		63.69 389			87.05 151	54.65 294			83.34 318	69.66 440

ttmdpeth		56.40 425	55.45 436	59.25 418	55.63 525	40.69 428	58.94 441	49.72 483	36.22 484	65.39 418	86.97 152	23.16 524	56.69 454	42.30 405	80.74 374	80.36 283

PatchT		53.35 450	56.47 421	43.99 514	64.19 461	17.46 550	59.15 436	43.10 522	52.11 272	54.74 508	86.95 153	29.97 489	49.98 481	43.62 393	74.40 457	64.53 494

wuyk23d		61.97 361	66.25 294	49.12 490	58.19 512	60.77 191	66.32 338	52.97 467	55.93 203	90.62 5	86.91 154	73.07 65	35.98 543	20.63 547	91.63 99	50.62 529

UniMVSNet_NR-MVSNet		74.90 112	75.65 102	72.64 176	83.04 111	45.79 368	69.26 270	78.81 197	66.66 79	81.74 113	86.88 155	63.26 175	81.07 144	56.21 268	94.98 25	91.05 13

E5new		73.42 132	74.46 117	70.29 222	74.61 259	47.14 345	71.85 219	83.01 94	56.07 196	77.28 184	86.81 156	71.54 79	77.15 223	64.59 154	84.39 294	86.59 77

E6new		73.42 132	74.46 117	70.29 222	74.60 261	47.14 345	71.86 217	82.99 96	56.07 196	77.28 184	86.81 156	71.55 77	77.14 225	64.59 154	84.39 294	86.59 77

E6		73.42 132	74.46 117	70.29 222	74.60 261	47.14 345	71.86 217	82.99 96	56.07 196	77.28 184	86.81 156	71.55 77	77.14 225	64.59 154	84.39 294	86.59 77

E5		73.42 132	74.46 117	70.29 222	74.61 259	47.14 345	71.85 219	83.01 94	56.07 196	77.28 184	86.81 156	71.54 79	77.15 223	64.59 154	84.39 294	86.59 77

EPP-MVSNet		73.86 125	73.38 147	75.31 114	78.19 185	53.35 268	80.45 79	77.32 225	65.11 98	76.47 216	86.80 160	49.47 332	83.77 87	53.89 302	92.72 83	88.81 43

viewmacassd2359aftdt		71.41 189	72.29 177	68.78 269	71.32 336	44.81 380	70.11 251	81.51 130	52.64 262	74.95 252	86.79 161	66.02 148	74.50 269	62.43 186	84.86 277	87.03 70

TinyColmap		67.98 262	69.28 235	64.08 340	67.98 404	46.82 352	70.04 252	75.26 252	53.05 255	77.36 182	86.79 161	59.39 237	72.59 300	45.64 382	88.01 200	72.83 401

test_prior2							75.57 150		58.92 158	76.53 213	86.78 163	67.83 128		69.81 103	92.76 82

RPMNet		65.77 302	65.08 320	67.84 285	66.37 431	48.24 321	70.93 238	86.27 20	54.66 220	61.35 462	86.77 164	33.29 448	85.67 51	55.93 270	70.17 494	69.62 441

TEST9					85.47 69	69.32 87	76.42 135	78.69 202	53.73 245	76.97 191	86.74 165	66.84 136	81.10 142

train_agg		76.38 90	76.55 94	75.86 106	85.47 69	69.32 87	76.42 135	78.69 202	54.00 240	76.97 191	86.74 165	66.60 142	81.10 142	72.50 82	91.56 101	77.15 341

balanced_ft_v1		71.65 183	72.22 180	69.92 240	74.26 267	45.74 370	81.54 70	79.66 178	53.65 248	79.77 139	86.74 165	51.20 320	80.64 154	58.70 237	84.47 289	83.40 189

test_8					85.09 76	67.89 100	76.26 142	78.66 204	54.00 240	76.89 195	86.72 168	66.60 142	80.89 152

MVS_Test		69.84 223	70.71 212	67.24 297	67.49 414	43.25 403	69.87 256	81.22 141	52.69 261	71.57 337	86.68 169	62.09 194	74.51 268	66.05 141	78.74 409	83.96 168

CR-MVSNet		58.96 396	58.49 398	60.36 408	66.37 431	48.24 321	70.93 238	56.40 447	32.87 506	61.35 462	86.66 170	33.19 449	63.22 417	48.50 353	70.17 494	69.62 441

Patchmtry		60.91 378	63.01 349	54.62 456	66.10 438	26.27 534	67.47 312	56.40 447	54.05 239	72.04 324	86.66 170	33.19 449	60.17 431	43.69 392	87.45 210	77.42 332

OMC-MVS		79.41 62	78.79 70	81.28 32	80.62 145	70.71 73	80.91 75	84.76 54	62.54 128	81.77 111	86.65 172	71.46 82	83.53 93	67.95 121	92.44 85	89.60 24

VPNet		65.58 305	67.56 269	59.65 415	79.72 157	30.17 515	60.27 426	62.14 399	54.19 236	71.24 343	86.63 173	58.80 247	67.62 377	44.17 391	90.87 130	81.18 258

IterMVS-LS		73.01 148	73.12 155	72.66 175	73.79 284	49.90 298	71.63 225	78.44 207	58.22 165	80.51 132	86.63 173	58.15 256	79.62 171	62.51 183	88.20 194	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testdata				64.13 339	85.87 64	63.34 160		61.80 405	47.83 349	76.42 218	86.60 175	48.83 341	62.31 421	54.46 294	81.26 360	66.74 470

LFMVS		67.06 284	67.89 264	64.56 335	78.02 189	38.25 457	70.81 241	59.60 418	65.18 96	71.06 345	86.56 176	43.85 371	75.22 255	46.35 374	89.63 160	80.21 287

CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 107	78.12 112	81.50 131	63.92 110	77.51 178	86.56 176	68.43 117	84.82 72	73.83 68	91.61 100	82.26 234

E4		72.74 159	73.54 142	70.35 219	74.85 250	46.82 352	69.53 261	82.80 101	55.60 206	76.23 220	86.50 178	69.87 102	77.45 216	63.72 170	82.77 326	86.76 74

FMVSNet2		67.48 270	68.21 258	65.29 326	73.14 297	38.94 450	68.81 286	71.21 312	54.81 214	76.73 204	86.48 179	48.63 344	74.60 267	47.98 360	86.11 248	82.35 230

baseline		73.10 143	73.96 133	70.51 214	71.46 333	46.39 364	72.08 206	84.40 69	55.95 202	76.62 207	86.46 180	67.20 131	78.03 207	64.22 162	87.27 220	87.11 68

WR-MVS		71.20 193	72.48 172	67.36 294	84.98 78	35.70 482	64.43 375	68.66 348	65.05 99	81.49 117	86.43 181	57.57 266	76.48 238	50.36 330	93.32 75	89.90 22

UniMVSNet (Re)		75.00 109	75.48 105	73.56 143	83.14 106	47.92 327	70.41 247	81.04 147	63.67 114	79.54 141	86.37 182	62.83 181	81.82 128	57.10 258	95.25 16	90.94 15

PC_three_1452									46.98 362	81.83 110	86.28 183	66.55 144	84.47 78	63.31 178	90.78 131	83.49 182

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 130	65.33 134	79.05 99	84.63 62	74.83 21	80.41 133	86.27 184	71.68 76	83.45 96	62.45 185	92.40 87	78.92 308

ab-mvs		64.11 329	65.13 316	61.05 395	71.99 324	38.03 461	67.59 308	68.79 346	49.08 327	65.32 420	86.26 185	58.02 263	66.85 392	39.33 431	79.79 396	78.27 318

NCCC		78.25 74	78.04 80	78.89 61	85.61 67	69.45 83	79.80 93	80.99 149	65.77 85	75.55 232	86.25 186	67.42 129	85.42 55	70.10 99	90.88 129	81.81 248

FA-MVS(test-final)		71.27 192	71.06 204	71.92 193	73.96 279	52.32 275	76.45 133	76.12 243	59.07 156	74.04 279	86.18 187	52.18 311	79.43 175	59.75 225	81.76 342	84.03 167

ITE_SJBPF				80.35 41	76.94 211	73.60 51		80.48 160	66.87 75	83.64 88	86.18 187	70.25 98	79.90 168	61.12 202	88.95 183	87.56 59

原ACMM1				73.90 134	85.90 62	65.15 138		81.67 128	50.97 292	74.25 272	86.16 189	61.60 201	83.54 92	56.75 261	91.08 120	73.00 397

fmvsm_s_conf0.5_n_9		74.56 116	74.30 124	75.34 113	77.17 203	64.87 140	72.62 196	76.17 242	54.54 225	78.32 162	86.14 190	65.14 163	75.72 248	73.10 73	85.55 256	85.42 111

UGNet		70.20 215	69.05 240	73.65 137	76.24 228	63.64 155	75.87 148	72.53 287	61.48 135	60.93 469	86.14 190	52.37 310	77.12 227	50.67 326	85.21 263	80.17 288
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
OPU-MVS				78.65 64	83.44 104	66.85 115	83.62 51				86.12 192	66.82 137	86.01 35	61.72 193	89.79 159	83.08 203

新几何1				69.99 237	88.37 34	71.34 64		62.08 401	43.85 408	74.99 251	86.11 193	52.85 306	70.57 338	50.99 324	83.23 320	68.05 459

KinetiMVS		72.61 163	72.54 170	72.82 170	71.47 332	55.27 249	68.54 296	76.50 236	61.70 134	74.95 252	86.08 194	59.17 241	76.95 229	69.96 101	84.45 290	86.24 87

mvs_anonymous		65.08 311	65.49 307	63.83 344	63.79 464	37.60 466	66.52 335	69.82 325	43.44 417	73.46 293	86.08 194	58.79 248	71.75 323	51.90 315	75.63 444	82.15 236

114514_t		73.40 137	73.33 151	73.64 138	84.15 94	57.11 234	78.20 110	80.02 170	43.76 411	72.55 312	86.07 196	64.00 171	83.35 98	60.14 216	91.03 121	80.45 281

NP-MVS					83.34 105	63.07 163					85.97 197

HQP-MVS		75.24 104	75.01 110	75.94 104	82.37 120	58.80 217	77.32 120	84.12 79	59.08 153	71.58 334	85.96 198	58.09 258	85.30 59	67.38 131	89.16 173	83.73 177

Anonymous202405211		66.02 299	66.89 287	63.43 356	74.22 270	38.14 458	59.00 439	66.13 367	63.33 121	69.76 366	85.95 199	51.88 312	70.50 339	44.23 390	87.52 207	81.64 253

Anonymous20240521		63.55 334	66.07 299	55.99 449	66.18 436	44.04 392	68.77 289	68.80 345	46.99 361	72.57 311	85.84 200	39.87 410	50.22 480	53.40 310	92.23 92	73.71 392

JIA-IIPM		54.03 444	51.62 465	61.25 393	59.14 505	55.21 254	59.10 438	47.72 496	50.85 294	50.31 527	85.81 201	20.10 538	63.97 412	36.16 470	55.41 542	64.55 493

test222					87.30 37	69.15 92	67.85 306	59.59 419	41.06 443	73.05 305	85.72 202	48.03 349			80.65 376	66.92 466

KD-MVS_self_test		66.38 293	67.51 270	62.97 365	61.76 481	34.39 491	58.11 454	75.30 251	50.84 295	77.12 190	85.42 203	56.84 276	69.44 357	51.07 323	91.16 113	85.08 123

E2		71.98 177	72.60 167	70.13 232	74.09 274	46.61 356	69.15 274	82.56 110	54.40 226	75.32 243	85.35 204	68.51 113	77.34 218	62.30 187	81.74 344	86.44 84

E3		71.98 177	72.60 167	70.13 232	74.09 274	46.61 356	69.15 274	82.56 110	54.40 226	75.31 244	85.35 204	68.51 113	77.34 218	62.30 187	81.75 343	86.44 84

viewdifsd2359ckpt07		70.24 212	71.30 200	67.05 302	70.55 352	43.90 393	67.15 323	77.48 223	53.60 250	75.49 235	85.35 204	71.42 84	72.13 310	59.03 232	81.60 353	85.12 120

DeepC-MVS_fast	69.89 7	77.17 84	76.33 96	79.70 47	83.90 96	67.94 99	80.06 89	83.75 84	56.73 189	74.88 255	85.32 207	65.54 155	87.79 2	65.61 147	91.14 115	83.35 193
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	71.07 5	78.48 72	77.14 90	82.52 16	84.39 91	77.04 29	76.35 138	84.05 81	56.66 190	80.27 135	85.31 208	68.56 112	87.03 11	67.39 129	91.26 109	83.50 181

v2v482		72.55 166	72.58 169	72.43 181	72.92 308	46.72 354	71.41 229	79.13 192	55.27 209	81.17 122	85.25 209	55.41 289	81.13 141	67.25 135	85.46 257	89.43 26

QAPM		69.18 238	69.26 236	68.94 264	71.61 330	52.58 274	80.37 82	78.79 200	49.63 313	73.51 290	85.14 210	53.66 301	79.12 178	55.11 281	75.54 445	75.11 374

test_fmvsmconf0.01_n		73.91 123	73.64 139	74.71 118	69.79 372	66.25 123	75.90 147	79.90 173	46.03 374	76.48 215	85.02 211	67.96 126	73.97 279	74.47 60	87.22 226	83.90 171

FE-MVS		68.29 258	66.96 284	72.26 186	74.16 272	54.24 260	77.55 117	73.42 272	57.65 175	72.66 310	84.91 212	32.02 466	81.49 135	48.43 354	81.85 340	81.04 261

viewmanbaseed2359cas		70.24 212	70.83 208	68.48 274	69.99 367	44.55 386	69.48 263	81.01 148	50.87 293	73.61 288	84.84 213	64.00 171	74.31 274	60.24 211	83.43 317	86.56 81

v1144		73.29 140	73.39 146	73.01 157	74.12 273	48.11 323	72.01 209	81.08 146	53.83 244	81.77 111	84.68 214	58.07 261	81.91 127	68.10 116	86.86 235	88.99 38

fmvsm_s_conf0.5_n_3		72.97 152	74.13 129	69.47 248	71.40 334	58.36 223	73.07 189	80.64 156	56.86 185	75.49 235	84.67 215	67.86 127	72.33 308	75.68 45	81.54 356	77.73 331

BP-MVS1		71.60 184	70.06 218	76.20 102	74.07 276	55.22 250	74.29 173	73.44 271	57.29 179	73.87 286	84.65 216	32.57 456	83.49 94	72.43 83	87.94 202	89.89 23

MVStest1		55.38 435	54.97 443	56.58 445	43.72 551	40.07 438	59.13 437	47.09 500	34.83 493	76.53 213	84.65 216	13.55 554	53.30 465	55.04 286	80.23 384	76.38 355

3Dnovator	65.95 11	71.50 186	71.22 202	72.34 183	73.16 296	63.09 162	78.37 106	78.32 209	57.67 173	72.22 318	84.61 218	54.77 292	78.47 191	60.82 205	81.07 366	75.45 367

v1192		73.40 137	73.42 145	73.32 147	74.65 258	48.67 313	72.21 203	81.73 127	52.76 260	81.85 109	84.56 219	57.12 272	82.24 122	68.58 112	87.33 216	89.06 35

mvsmamba		68.87 243	67.30 277	73.57 142	76.58 223	53.70 265	84.43 42	74.25 263	45.38 384	76.63 206	84.55 220	35.85 436	85.27 60	49.54 338	78.49 414	81.75 251

EC-MVSNet		77.08 85	77.39 87	76.14 103	76.86 219	56.87 236	80.32 84	87.52 12	63.45 118	74.66 260	84.52 221	69.87 102	84.94 68	69.76 104	89.59 162	86.60 76

USDC		62.80 347	63.10 346	61.89 379	65.19 449	43.30 402	67.42 313	74.20 265	35.80 489	72.25 317	84.48 222	45.67 358	71.95 316	37.95 448	84.97 266	70.42 433

viewcassd2359sk11		71.41 189	71.89 184	69.98 238	73.50 287	46.46 361	68.91 281	82.39 114	53.62 249	74.57 264	84.41 223	67.40 130	77.27 220	61.35 198	80.89 368	86.21 90

tttt0517		69.46 230	67.79 267	74.46 121	75.34 241	52.72 272	75.05 155	63.27 394	54.69 219	78.87 151	84.37 224	26.63 503	81.15 140	63.95 166	87.93 203	89.51 25

PCF-MVS	63.80 13	72.70 161	71.69 189	75.72 107	78.10 186	60.01 199	73.04 191	81.50 131	45.34 385	79.66 140	84.35 225	65.15 161	82.65 111	48.70 350	89.38 170	84.50 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1240		73.06 146	73.14 153	72.84 169	74.74 254	47.27 343	71.88 216	81.11 143	51.80 275	82.28 103	84.21 226	56.22 283	82.34 119	68.82 111	87.17 231	88.91 40

SSM_0407		72.15 174	71.85 186	73.06 156	76.92 212	55.22 250	73.59 180	79.83 174	53.69 246	73.08 301	84.18 227	62.26 191	81.98 125	58.21 244	84.91 273	81.99 241

SSM_0404		72.51 167	72.15 182	73.60 140	78.20 184	55.86 243	74.41 170	79.83 174	53.69 246	73.98 280	84.18 227	62.26 191	82.50 113	58.21 244	84.60 284	82.43 228

fmvsm_l_conf0.5_n_3		71.98 177	71.68 190	72.88 167	72.84 310	64.15 151	73.48 183	77.11 230	48.97 331	71.31 342	84.18 227	67.98 125	71.60 326	68.86 110	80.43 380	82.89 210

fmvsm_s_conf0.5_n_8		72.87 156	72.85 161	72.93 163	72.25 319	59.01 214	72.35 200	80.13 169	56.32 193	75.74 227	84.12 230	60.14 224	75.05 261	71.71 87	82.90 323	84.75 137

v148		69.38 233	69.39 230	69.36 250	69.14 381	44.56 384	68.83 284	72.70 285	54.79 217	78.59 156	84.12 230	54.69 293	76.74 236	59.40 228	82.20 333	86.79 72

v144192		72.99 150	73.06 157	72.77 171	74.58 263	47.48 338	71.90 215	80.44 162	51.57 278	81.46 118	84.11 232	58.04 262	82.12 123	67.98 120	87.47 209	88.70 45

fmvsm_s_conf0.5_n_5		71.46 188	71.62 193	70.99 207	73.89 282	59.95 200	73.02 192	73.08 273	45.15 392	77.30 183	84.06 233	64.73 167	70.08 347	71.20 88	82.10 335	82.92 209

fmvsm_s_conf0.5_n_10		72.30 171	72.02 183	73.15 152	70.76 344	59.05 212	73.40 185	79.63 179	48.80 334	75.39 241	84.03 234	59.60 235	75.18 260	72.85 76	83.68 313	85.21 118

NormalMVS		76.15 91	75.08 109	79.36 52	83.87 98	70.01 80	79.92 91	84.34 70	58.60 161	75.21 245	84.02 235	52.85 306	81.82 128	61.45 195	95.50 10	86.24 87

SymmetryMVS		74.00 121	72.85 161	77.43 86	85.17 74	70.01 80	79.92 91	68.48 350	58.60 161	75.21 245	84.02 235	52.85 306	81.82 128	61.45 195	89.99 152	80.47 280

F-COLMAP		75.29 102	73.99 132	79.18 54	81.73 131	71.90 59	81.86 68	82.98 98	59.86 150	72.27 316	84.00 237	64.56 168	83.07 103	51.48 318	87.19 229	82.56 225

viewdifsd2359ckpt11		69.22 235	69.68 227	67.83 286	68.17 398	46.57 358	66.42 336	68.93 338	50.60 299	77.47 180	83.95 238	68.16 119	73.84 284	58.49 240	84.92 271	83.10 200

viewmsd2359difaftdt		69.22 235	69.68 227	67.83 286	68.17 398	46.57 358	66.42 336	68.93 338	50.60 299	77.48 179	83.94 239	68.16 119	73.84 284	58.49 240	84.92 271	83.10 200

RoMa-HiRes		73.61 128	73.51 143	73.92 133	82.27 124	81.71 3	77.59 114	64.83 380	51.32 287	88.72 16	83.92 240	60.47 219	61.70 424	60.01 219	92.44 85	78.34 315

test_fmvsmconf0.1_n		73.26 141	72.82 164	74.56 120	69.10 382	66.18 125	74.65 167	79.34 187	45.58 379	75.54 233	83.91 241	67.19 132	73.88 282	73.26 72	86.86 235	83.63 179

v1921920		72.96 153	72.98 159	72.89 166	74.67 255	47.58 336	71.92 214	80.69 153	51.70 277	81.69 115	83.89 242	56.58 279	82.25 121	68.34 114	87.36 213	88.82 42

MIMVSNet		54.39 441	56.12 426	49.20 488	72.57 312	30.91 510	59.98 429	48.43 494	41.66 437	55.94 499	83.86 243	41.19 401	50.42 476	26.05 530	75.38 448	66.27 475

GDP-MVS		70.84 201	69.24 237	75.62 109	76.44 225	55.65 246	74.62 168	82.78 104	49.63 313	72.10 321	83.79 244	31.86 467	82.84 108	64.93 152	87.01 234	88.39 50

MCST-MVS		73.42 132	73.34 150	73.63 139	81.28 138	59.17 208	74.80 161	83.13 93	45.50 380	72.84 306	83.78 245	65.15 161	80.99 146	64.54 158	89.09 181	80.73 273

dcpmvs_2		71.02 198	72.65 166	66.16 318	76.06 234	50.49 288	71.97 210	79.36 186	50.34 303	82.81 97	83.63 246	64.38 169	67.27 383	61.54 194	83.71 311	80.71 275

E3new		70.94 200	71.30 200	69.86 242	72.98 307	46.34 365	68.74 291	82.28 117	53.01 256	73.95 282	83.57 247	66.41 145	77.21 221	60.68 207	80.06 387	86.03 95

OpenMVS	62.51 15	68.76 246	68.75 246	68.78 269	70.56 350	53.91 263	78.29 107	77.35 224	48.85 332	70.22 354	83.52 248	52.65 309	76.93 230	55.31 279	81.99 336	75.49 366

h-mvs33		73.08 144	71.61 194	77.48 84	83.89 97	72.89 57	70.47 245	71.12 313	54.28 231	77.89 167	83.41 249	49.04 338	80.98 147	63.62 173	90.77 133	78.58 312

TAPA-MVS	65.27 12	75.16 105	74.29 125	77.77 82	74.86 249	68.08 97	77.89 113	84.04 82	55.15 211	76.19 222	83.39 250	66.91 135	80.11 166	60.04 218	90.14 146	85.13 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		55.08 438	55.54 434	53.71 459	65.80 440	33.50 497	56.22 466	52.50 469	43.72 414	61.06 466	83.38 251	25.46 511	54.87 459	30.11 513	81.64 352	72.75 402

VNet		64.01 331	65.15 315	60.57 401	73.28 293	35.61 483	57.60 456	67.08 359	54.61 221	66.76 407	83.37 252	56.28 282	66.87 390	42.19 407	85.20 264	79.23 302

Vis-MVSNet (Re-imp)		62.74 350	63.21 344	61.34 391	72.19 321	31.56 506	67.31 318	53.87 459	53.60 250	69.88 363	83.37 252	40.52 406	70.98 334	41.40 415	86.78 239	81.48 255

GeoE		73.14 142	73.77 137	71.26 203	78.09 187	52.64 273	74.32 171	79.56 184	56.32 193	76.35 219	83.36 254	70.76 92	77.96 208	63.32 177	81.84 341	83.18 198

PAPM_NR		73.91 123	74.16 128	73.16 150	81.90 129	53.50 266	81.28 72	81.40 134	66.17 83	73.30 296	83.31 255	59.96 226	83.10 102	58.45 242	81.66 351	82.87 212

CS-MVS		76.51 89	76.00 99	78.06 78	77.02 208	64.77 141	80.78 76	82.66 107	60.39 145	74.15 273	83.30 256	69.65 105	82.07 124	69.27 108	86.75 240	87.36 61

FMVSNet3		65.00 313	65.16 313	64.52 336	69.47 376	37.56 467	66.63 332	70.38 320	51.55 279	74.72 257	83.27 257	37.89 426	74.44 271	47.12 365	85.37 258	81.57 254

usedtu_dtu_shiyan2		62.25 357	62.27 356	62.18 375	77.08 205	52.84 271	62.56 396	56.33 449	52.43 266	64.22 435	83.26 258	48.47 347	58.06 449	25.75 534	90.34 141	75.64 364

test_fmvsmconf_n		72.91 154	72.40 175	74.46 121	68.62 387	66.12 126	74.21 175	78.80 199	45.64 378	74.62 262	83.25 259	66.80 140	73.86 283	72.97 75	86.66 242	83.39 190

viewmamba		69.26 234	69.34 233	69.03 260	64.17 462	47.67 335	67.23 322	76.95 232	52.82 259	73.15 300	83.23 260	62.99 179	74.06 278	63.71 171	79.80 395	85.36 113

viewdifsd2359ckpt09		72.87 156	72.43 174	74.17 128	74.45 264	51.70 276	76.39 137	84.50 67	49.48 318	75.34 242	83.23 260	63.12 176	82.43 116	56.99 260	88.41 190	88.37 51

V42		71.06 195	70.83 208	71.72 194	67.25 416	47.14 345	65.94 342	80.35 165	51.35 284	83.40 90	83.23 260	59.25 239	78.80 184	65.91 143	80.81 372	89.23 31

test20.03		55.74 431	57.51 411	50.42 479	59.89 499	32.09 503	50.63 501	49.01 490	50.11 307	65.07 422	83.23 260	45.61 359	48.11 496	30.22 512	83.82 305	71.07 428

CNLPA		73.44 131	73.03 158	74.66 119	78.27 183	75.29 37	75.99 146	78.49 206	65.39 91	75.67 229	83.22 264	61.23 207	66.77 394	53.70 305	85.33 261	81.92 245

TestfortrainingZip				73.58 141	79.21 166	57.65 230	86.10 28	81.22 141	72.34 42	72.08 323	83.19 265	58.95 244	83.71 88		84.76 278	79.38 300

mamba_0408		70.32 211	69.35 231	73.24 148	76.92 212	55.22 250	56.61 462	79.27 189	52.14 269	73.08 301	83.14 266	60.53 216	82.50 113	57.51 251	84.91 273	81.99 241

SSM_04072		67.23 279	69.35 231	60.89 398	76.92 212	55.22 250	56.61 462	79.27 189	52.14 269	73.08 301	83.14 266	60.53 216	45.46 512	57.51 251	84.91 273	81.99 241

ALIKED-LG		64.85 315	64.54 323	65.79 324	74.03 277	74.67 42	73.55 181	67.52 357	36.17 485	78.83 152	83.08 268	34.08 442	59.10 436	42.05 411	91.51 103	63.61 497

fmvsm_s_conf0.1_n_2		69.14 239	68.42 252	71.28 202	68.30 395	57.60 231	65.06 360	69.91 323	48.24 340	74.56 265	82.84 269	55.55 288	69.73 351	70.66 96	80.69 375	86.52 82

EPNet		69.10 240	67.32 275	74.46 121	68.33 394	61.27 180	77.56 116	63.57 390	60.95 140	56.62 495	82.75 270	51.53 316	81.24 139	54.36 297	90.20 143	80.88 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		69.89 222	69.74 226	70.32 221	70.82 341	48.73 310	72.39 199	81.39 135	48.20 342	72.73 308	82.73 271	62.61 183	76.50 237	55.87 272	80.93 367	85.73 105

SDMVSNet		66.36 294	67.85 266	61.88 380	73.04 303	46.14 367	58.54 449	71.36 304	51.42 281	68.93 378	82.72 272	65.62 154	62.22 422	54.41 295	84.67 280	77.28 334

sd_testset		63.55 334	65.38 309	58.07 432	73.04 303	38.83 452	57.41 457	65.44 374	51.42 281	68.93 378	82.72 272	63.76 174	58.11 447	41.05 418	84.67 280	77.28 334

fmvsm_l_conf0.5_n_9		70.73 204	71.08 203	69.67 245	70.44 356	58.80 217	70.21 250	75.11 255	48.15 344	73.50 291	82.69 274	65.69 153	68.05 374	70.87 93	83.02 321	82.16 235

IterMVS-SCA-FT		67.68 268	66.07 299	72.49 180	73.34 292	58.20 227	63.80 383	65.55 373	48.10 345	76.91 194	82.64 275	45.20 361	78.84 183	61.20 200	77.89 425	80.44 282

FE-MVSNET		62.77 348	64.36 325	57.97 435	70.52 354	33.96 493	61.66 406	67.88 355	50.67 297	73.18 298	82.58 276	48.03 349	68.22 370	43.21 396	81.55 354	71.74 416

DIV-MVS_self_test		68.27 259	68.26 255	68.29 278	64.98 453	43.67 396	65.89 343	74.67 258	50.04 309	76.86 197	82.43 277	48.74 342	75.38 250	60.94 203	89.81 157	85.81 99

cl____		68.26 261	68.26 255	68.29 278	64.98 453	43.67 396	65.89 343	74.67 258	50.04 309	76.86 197	82.42 278	48.74 342	75.38 250	60.92 204	89.81 157	85.80 103

PMatch-Up-SfM		68.45 253	66.90 286	73.11 153	77.17 203	76.10 32	71.60 226	62.67 396	47.32 356	87.78 19	82.41 279	24.19 520	66.58 397	58.86 236	90.11 148	76.66 348

MVS_111021_HR		72.98 151	72.97 160	72.99 158	80.82 143	65.47 132	68.81 286	72.77 283	57.67 173	75.76 226	82.38 280	71.01 89	77.17 222	61.38 197	86.15 245	76.32 356

fmvsm_s_conf0.5_n_2		68.93 242	68.23 257	71.02 206	67.78 408	57.58 232	64.74 367	69.56 327	48.16 343	74.38 270	82.32 281	56.00 285	69.68 354	70.65 97	80.52 379	85.80 103

pmmvs-eth3d		64.41 326	63.27 343	67.82 289	75.81 238	60.18 197	69.49 262	62.05 403	38.81 464	74.13 274	82.23 282	43.76 372	68.65 364	42.53 403	80.63 378	74.63 379

diffmvs_AUTHOR		68.27 259	68.59 250	67.32 296	63.76 465	45.37 373	65.31 355	77.19 228	49.25 322	72.68 309	82.19 283	59.62 234	71.17 330	65.75 145	81.53 357	85.42 111

fmvsm_s_conf0.5_n_4		70.18 216	69.83 225	71.24 204	71.65 329	58.59 222	69.29 269	71.66 296	48.69 335	71.62 331	82.11 284	59.94 227	70.03 348	74.52 58	78.96 406	85.10 121

AstraMVS		67.11 281	66.84 289	67.92 282	70.75 345	51.36 280	64.77 366	67.06 360	49.03 329	75.40 238	82.05 285	51.26 319	70.65 336	58.89 235	82.32 332	81.77 250

PMatch-SfM		67.96 263	66.40 292	72.63 177	78.06 188	75.26 38	71.85 219	59.63 417	46.07 372	86.78 37	82.02 286	26.32 505	66.37 399	57.00 259	89.87 156	76.27 357

MGCFI-Net		71.70 182	73.10 156	67.49 292	73.23 294	43.08 404	72.06 207	82.43 113	54.58 222	75.97 224	82.00 287	72.42 70	75.22 255	57.84 249	87.34 215	84.18 163

alignmvs		70.54 207	71.00 205	69.15 256	73.50 287	48.04 326	69.85 257	79.62 180	53.94 243	76.54 212	82.00 287	59.00 243	74.68 266	57.32 254	87.21 227	84.72 140

MSLP-MVS++		74.48 117	75.78 101	70.59 212	84.66 83	62.40 166	78.65 102	84.24 76	60.55 144	77.71 174	81.98 289	63.12 176	77.64 214	62.95 181	88.14 195	71.73 417

DP-MVS Recon		73.57 130	72.69 165	76.23 101	82.85 115	63.39 159	74.32 171	82.96 99	57.75 171	70.35 352	81.98 289	64.34 170	84.41 80	49.69 335	89.95 153	80.89 267

onestephybrid01		68.67 251	68.21 258	70.07 235	64.40 460	49.83 304	67.51 310	76.41 238	51.08 290	71.78 326	81.97 291	59.69 233	75.32 254	59.85 221	81.20 361	85.06 125

LuminaMVS		71.15 194	70.79 210	72.24 189	77.20 202	58.34 224	72.18 204	76.20 241	54.91 213	77.74 172	81.93 292	49.17 337	76.31 240	62.12 189	85.66 255	82.07 238

dtuplus		65.20 308	64.80 322	66.40 314	65.25 448	44.86 379	64.55 372	72.19 294	43.76 411	72.09 322	81.87 293	57.49 268	71.49 327	48.79 348	77.23 431	82.85 214

BH-RMVSNet		68.69 250	68.20 260	70.14 231	76.40 226	53.90 264	64.62 370	73.48 269	58.01 168	73.91 284	81.78 294	59.09 242	78.22 202	48.59 351	77.96 423	78.31 317

EG-PatchMatch MVS		70.70 205	70.88 207	70.16 230	82.64 119	58.80 217	71.48 227	73.64 267	54.98 212	76.55 211	81.77 295	61.10 211	78.94 182	54.87 288	80.84 371	72.74 403

MVS_111021_LR		72.10 175	71.82 188	72.95 160	79.53 160	73.90 49	70.45 246	66.64 362	56.87 184	76.81 200	81.76 296	68.78 110	71.76 322	61.81 190	83.74 308	73.18 395

AdaColmap		74.22 118	74.56 115	73.20 149	81.95 128	60.97 185	79.43 94	80.90 150	65.57 87	72.54 313	81.76 296	70.98 90	85.26 61	47.88 361	90.00 150	73.37 393

hybridnocas07		66.30 297	66.22 295	66.51 313	60.68 489	44.53 387	64.01 381	74.60 260	48.26 339	70.21 355	81.74 298	56.61 277	71.06 332	60.70 206	79.20 403	83.94 170

fmvsm_s_conf0.5_n_6		70.08 217	69.97 219	70.39 215	72.99 306	58.93 215	68.84 282	76.40 239	49.08 327	68.75 386	81.65 299	57.34 269	71.97 315	70.91 92	83.81 306	80.26 285

sasdasda		72.29 172	73.38 147	69.04 258	74.23 268	47.37 340	73.93 178	83.18 90	54.36 229	76.61 208	81.64 300	72.03 72	75.34 252	57.12 256	87.28 218	84.40 156

canonicalmvs		72.29 172	73.38 147	69.04 258	74.23 268	47.37 340	73.93 178	83.18 90	54.36 229	76.61 208	81.64 300	72.03 72	75.34 252	57.12 256	87.28 218	84.40 156

ALIKED-MNN		63.44 336	63.42 339	63.48 352	73.99 278	70.97 69	71.80 223	66.48 364	32.46 507	71.87 325	81.60 302	36.54 433	58.50 442	42.45 404	93.63 69	60.97 512

fmvsm_s_conf0.5_n_11		71.06 195	70.91 206	71.51 198	72.09 323	59.40 203	73.49 182	79.97 172	50.98 291	68.33 391	81.50 303	61.82 198	72.64 296	69.54 107	80.43 380	82.51 226

DKM-HiRes		70.49 208	69.89 221	72.31 185	81.51 134	80.92 7	73.23 188	58.80 426	49.23 323	84.44 78	81.39 304	49.91 328	61.22 427	59.28 230	91.22 111	74.79 376

MVS-HIRNet		45.53 494	47.29 491	40.24 523	62.29 478	26.82 529	56.02 469	37.41 545	29.74 522	43.69 546	81.27 305	33.96 443	55.48 457	24.46 539	56.79 538	38.43 546

CMPMVS	48.73 20	61.54 370	60.89 374	63.52 351	61.08 485	51.55 278	68.07 305	68.00 353	33.88 499	65.87 415	81.25 306	37.91 425	67.71 375	49.32 341	82.60 328	71.31 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
viewmambaseed2359dif		65.63 303	65.13 316	67.11 301	64.57 458	44.73 383	64.12 378	72.48 290	43.08 424	71.59 332	81.17 307	58.90 246	72.46 301	52.94 311	77.33 429	84.13 166

testgi		54.00 446	56.86 417	45.45 507	58.20 511	25.81 537	49.05 507	49.50 486	45.43 383	67.84 395	81.17 307	51.81 315	43.20 526	29.30 517	79.41 401	67.34 463

ALIKED-NN		61.86 363	61.18 368	63.92 343	71.72 328	71.04 66	69.24 271	66.41 365	29.80 521	64.25 434	81.10 309	35.56 438	58.35 443	41.25 416	91.30 108	62.35 506

fmvsm_l_conf0.5_n		67.48 270	66.88 288	69.28 253	67.41 415	62.04 169	70.69 242	69.85 324	39.46 457	69.59 367	81.09 310	58.15 256	68.73 362	67.51 126	78.16 422	77.07 346

test_fmvsmvis_n_1920		72.36 169	72.49 171	71.96 191	71.29 338	64.06 153	72.79 195	81.82 125	40.23 453	81.25 121	81.04 311	70.62 93	68.69 363	69.74 105	83.60 315	83.14 199

CL-MVSNet_self_test		62.44 355	63.40 341	59.55 417	72.34 318	32.38 501	56.39 464	64.84 379	51.21 288	67.46 401	81.01 312	50.75 323	63.51 416	38.47 441	88.12 196	82.75 217

fmvsm_s_conf0.1_n_a		67.37 274	66.36 293	70.37 218	70.86 340	61.17 181	74.00 177	57.18 439	40.77 448	68.83 385	80.88 313	63.11 178	67.61 378	66.94 136	74.72 452	82.33 233

hybrid		65.62 304	65.49 307	66.01 320	60.48 491	44.28 390	64.13 377	74.21 264	46.41 368	69.84 364	80.86 314	55.77 286	70.28 344	59.30 229	78.42 416	83.46 186

guyue		66.95 287	66.74 290	67.56 291	70.12 366	51.14 282	65.05 361	68.68 347	49.98 311	74.64 261	80.83 315	50.77 322	70.34 343	57.72 250	82.89 324	81.21 256

SPE-MVS-test		74.89 113	74.23 126	76.86 91	77.01 209	62.94 164	78.98 100	84.61 63	58.62 160	70.17 357	80.80 316	66.74 141	81.96 126	61.74 192	89.40 169	85.69 106

thisisatest0530		67.05 285	65.16 313	72.73 174	73.10 300	50.55 287	71.26 234	63.91 388	50.22 306	74.46 267	80.75 317	26.81 502	80.25 162	59.43 227	86.50 243	87.37 60

PHI-MVS		74.92 110	74.36 123	76.61 94	76.40 226	62.32 168	80.38 81	83.15 92	54.16 237	73.23 297	80.75 317	62.19 193	83.86 84	68.02 118	90.92 126	83.65 178

fmvsm_s_conf0.5_n_7		67.30 276	66.92 285	68.43 275	72.78 311	58.22 226	60.90 417	72.51 289	49.62 315	63.66 447	80.65 319	58.56 251	68.63 365	62.83 182	80.76 373	78.45 314

RoMa-SfM		70.84 201	70.47 215	71.95 192	80.95 141	81.09 6	76.44 134	62.08 401	46.25 370	87.14 35	80.63 320	55.60 287	58.69 440	54.19 299	90.98 122	76.07 361

PLC	62.01 16	71.79 181	70.28 217	76.33 99	80.31 149	68.63 95	78.18 111	81.24 139	54.57 223	67.09 405	80.63 320	59.44 236	81.74 133	46.91 368	84.17 300	78.63 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PM-MVS		64.49 323	63.61 336	67.14 300	76.68 221	75.15 39	68.49 298	42.85 525	51.17 289	77.85 169	80.51 322	45.76 357	66.31 400	52.83 312	76.35 437	59.96 514

CANet		73.00 149	71.84 187	76.48 97	75.82 237	61.28 179	74.81 159	80.37 164	63.17 122	62.43 457	80.50 323	61.10 211	85.16 67	64.00 164	84.34 298	83.01 207

IterMVS		63.12 343	62.48 355	65.02 331	66.34 433	52.86 270	63.81 382	62.25 397	46.57 367	71.51 339	80.40 324	44.60 366	66.82 393	51.38 321	75.47 446	75.38 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_a		66.66 288	65.97 302	68.72 271	67.09 419	61.38 178	70.03 253	69.15 332	38.59 465	68.41 389	80.36 325	56.56 280	68.32 369	66.10 140	77.45 428	76.46 354

dtuonlycased		61.79 365	62.24 357	60.43 405	73.00 305	39.07 447	61.74 404	60.61 409	33.09 505	74.10 275	80.34 326	59.20 240	60.39 429	38.34 442	79.76 397	81.83 247

eth_miper_zixun_eth		69.42 231	68.73 248	71.50 199	67.99 403	46.42 362	67.58 309	78.81 197	50.72 296	78.13 165	80.34 326	50.15 327	80.34 160	60.18 213	84.65 282	87.74 56

DPM-MVS		69.98 220	69.22 239	72.26 186	82.69 118	58.82 216	70.53 244	81.23 140	47.79 350	64.16 437	80.21 328	51.32 318	83.12 101	60.14 216	84.95 270	74.83 375

LF4IMVS		67.50 269	67.31 276	68.08 281	58.86 507	61.93 170	71.43 228	75.90 247	44.67 399	72.42 314	80.20 329	57.16 270	70.44 340	58.99 233	86.12 247	71.88 414

CSCG		74.12 120	74.39 121	73.33 146	79.35 162	61.66 174	77.45 119	81.98 123	62.47 130	79.06 149	80.19 330	61.83 197	78.79 185	59.83 222	87.35 214	79.54 297

c3_l		69.82 224	69.89 221	69.61 246	66.24 434	43.48 399	68.12 304	79.61 182	51.43 280	77.72 173	80.18 331	54.61 295	78.15 206	63.62 173	87.50 208	87.20 65

fmvsm_s_conf0.1_n		66.60 289	65.54 306	69.77 243	68.99 384	59.15 209	72.12 205	56.74 444	40.72 450	68.25 394	80.14 332	61.18 210	66.92 386	67.34 133	74.40 457	83.23 197

fmvsm_s_conf0.5_n_a		67.00 286	65.95 303	70.17 229	69.72 373	61.16 182	73.34 186	56.83 442	40.96 445	68.36 390	80.08 333	62.84 180	67.57 379	66.90 138	74.50 456	81.78 249

FPMVS		59.43 393	60.07 383	57.51 439	77.62 198	71.52 62	62.33 398	50.92 477	57.40 177	69.40 370	80.00 334	39.14 417	61.92 423	37.47 454	66.36 514	39.09 545

LoFTR		61.29 371	62.50 354	57.67 438	69.07 383	65.66 131	68.96 278	48.59 492	43.15 423	86.65 39	79.95 335	32.68 455	53.14 466	46.21 376	87.20 228	54.22 525

DKM		69.82 224	69.29 234	71.40 201	80.33 148	80.76 8	73.05 190	60.16 415	47.00 360	85.42 63	79.91 336	48.29 348	58.24 445	57.18 255	92.25 91	75.19 373

thres100view900		61.17 373	61.09 370	61.39 389	72.14 322	35.01 486	65.42 353	56.99 440	55.23 210	70.71 348	79.90 337	32.07 464	72.09 311	35.61 475	81.73 345	77.08 344

new-patchmatchnet		52.89 455	55.76 433	44.26 513	59.94 498	6.31 561	37.36 540	50.76 479	41.10 442	64.28 433	79.82 338	44.77 364	48.43 495	36.24 469	87.61 205	78.03 324

thres600view7		61.82 364	61.38 367	63.12 360	71.81 327	34.93 487	64.64 369	56.99 440	54.78 218	70.33 353	79.74 339	32.07 464	72.42 303	38.61 439	83.46 316	82.02 239

testing3-2		56.85 420	57.62 408	54.53 457	75.84 236	22.23 546	51.26 500	49.10 489	61.04 139	63.74 445	79.73 340	22.29 529	59.44 434	31.16 509	84.43 293	81.92 245

diffmvs		67.42 273	67.50 271	67.20 298	62.26 479	45.21 376	64.87 363	77.04 231	48.21 341	71.74 327	79.70 341	58.40 253	71.17 330	64.99 150	80.27 383	85.22 115
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSC-MVS3.2		57.01 419	59.50 389	49.57 486	67.73 409	25.95 536	46.68 517	51.75 474	51.41 283	63.84 442	79.66 342	53.28 304	50.34 478	37.85 449	83.28 319	72.41 407

BH-untuned		69.39 232	69.46 229	69.18 255	77.96 191	56.88 235	68.47 299	77.53 221	56.77 187	77.79 170	79.63 343	60.30 223	80.20 165	46.04 378	80.65 376	70.47 431

VortexMVS		65.93 300	66.04 301	65.58 325	67.63 412	47.55 337	64.81 364	72.75 284	47.37 355	75.17 248	79.62 344	49.28 335	71.00 333	55.20 280	82.51 329	78.21 320

PAPM		61.79 365	60.37 382	66.05 319	76.09 231	41.87 413	69.30 268	76.79 235	40.64 451	53.80 512	79.62 344	44.38 367	82.92 105	29.64 516	73.11 469	73.36 394

fmvsm_s_conf0.5_n		66.34 296	65.27 310	69.57 247	68.20 396	59.14 211	71.66 224	56.48 445	40.92 446	67.78 396	79.46 346	61.23 207	66.90 387	67.39 129	74.32 460	82.66 222

XXY-MVS		55.19 436	57.40 412	48.56 494	64.45 459	34.84 489	51.54 497	53.59 461	38.99 463	63.79 444	79.43 347	56.59 278	45.57 510	36.92 460	71.29 485	65.25 486

SD_0403		61.63 368	62.83 351	58.03 433	72.21 320	32.43 500	69.33 267	69.00 337	44.54 401	62.01 458	79.42 348	55.27 290	66.88 389	36.07 472	77.63 427	74.78 377

MonoMVSNet		62.75 349	63.42 339	60.73 400	65.60 443	40.77 427	72.49 198	70.56 318	52.49 264	75.07 249	79.42 348	39.52 415	69.97 350	46.59 372	69.06 500	71.44 420

MDA-MVSNet-bldmvs		62.34 356	61.73 361	64.16 338	61.64 482	49.90 298	48.11 511	57.24 438	53.31 254	80.95 124	79.39 350	49.00 340	61.55 425	45.92 380	80.05 388	81.03 262

icg_test_0407_2		63.88 333	65.59 305	58.75 424	72.47 313	48.64 314	53.19 486	72.98 277	45.33 386	68.91 380	79.37 351	61.91 195	51.11 471	55.06 282	81.11 362	76.49 350

IMVS_0407		67.26 277	67.35 274	66.97 305	72.47 313	48.64 314	69.03 277	72.98 277	45.33 386	68.91 380	79.37 351	61.91 195	75.77 245	55.06 282	81.11 362	76.49 350

IMVS_0404		62.18 360	63.05 347	59.58 416	72.47 313	48.64 314	55.47 472	72.98 277	45.33 386	55.80 502	79.37 351	49.84 329	53.60 464	55.06 282	81.11 362	76.49 350

IMVS_0403		67.07 283	67.08 279	67.03 303	72.47 313	48.64 314	68.44 300	72.98 277	45.33 386	68.63 388	79.37 351	60.38 221	75.97 241	55.06 282	81.11 362	76.49 350

TAMVS		65.31 307	63.75 333	69.97 239	82.23 125	59.76 202	66.78 331	63.37 393	45.20 391	69.79 365	79.37 351	47.42 353	72.17 309	34.48 487	85.15 265	77.99 326

PAPR		69.20 237	68.66 249	70.82 208	75.15 245	47.77 331	75.31 152	81.11 143	49.62 315	66.33 412	79.27 356	61.53 202	82.96 104	48.12 358	81.50 358	81.74 252

Anonymous20231206		54.13 442	55.82 431	49.04 491	70.89 339	35.96 479	51.73 496	50.87 478	34.86 492	62.49 456	79.22 357	42.52 388	44.29 522	27.95 525	81.88 338	66.88 467

OpenMVS_ROB	54.93 17	63.23 342	63.28 342	63.07 361	69.81 369	45.34 374	68.52 297	67.14 358	43.74 413	70.61 349	79.22 357	47.90 351	72.66 295	48.75 349	73.84 464	71.21 425

PVSNet_Blended_VisFu		70.04 218	68.88 243	73.53 144	82.71 117	63.62 156	74.81 159	81.95 124	48.53 337	67.16 404	79.18 359	51.42 317	78.38 197	54.39 296	79.72 398	78.60 311

MVSTER		63.29 341	61.60 365	68.36 276	59.77 500	46.21 366	60.62 421	71.32 305	41.83 436	75.40 238	79.12 360	30.25 485	75.85 242	56.30 267	79.81 393	83.03 206

tpm		50.60 471	52.42 461	45.14 509	65.18 450	26.29 533	60.30 425	43.50 519	37.41 477	57.01 490	79.09 361	30.20 487	42.32 528	32.77 502	66.36 514	66.81 469

test_yl		65.11 309	65.09 318	65.18 328	70.59 348	40.86 424	63.22 392	72.79 281	57.91 169	68.88 382	79.07 362	42.85 385	74.89 263	45.50 384	84.97 266	79.81 290

DCV-MVSNet		65.11 309	65.09 318	65.18 328	70.59 348	40.86 424	63.22 392	72.79 281	57.91 169	68.88 382	79.07 362	42.85 385	74.89 263	45.50 384	84.97 266	79.81 290

test_fmvsm_n_1920		69.63 226	68.45 251	73.16 150	70.56 350	65.86 128	70.26 249	78.35 208	37.69 473	74.29 271	78.89 364	61.10 211	68.10 372	65.87 144	79.07 404	85.53 109

miper_lstm_enhance		61.97 361	61.63 364	62.98 362	60.04 494	45.74 370	47.53 513	70.95 314	44.04 407	73.06 304	78.84 365	39.72 412	60.33 430	55.82 274	84.64 283	82.88 211

testing915		59.64 390	60.70 377	56.45 446	71.85 326	30.24 514	65.32 354	58.86 425	48.85 332	60.89 470	78.77 366	42.12 389	67.38 381	35.37 478	84.31 299	71.72 418

PVSNet_BlendedMVS		65.38 306	64.30 326	68.61 272	69.81 369	49.36 306	65.60 350	78.96 194	45.50 380	59.98 474	78.61 367	51.82 313	78.20 203	44.30 388	84.11 301	78.27 318

baseline1		57.82 409	58.36 401	56.19 448	69.17 380	30.76 512	62.94 394	55.21 452	46.04 373	63.83 443	78.47 368	41.20 400	63.68 414	39.44 430	68.99 501	74.13 386

TSAR-MVS + GP.		73.08 144	71.60 195	77.54 83	78.99 177	70.73 72	74.96 156	69.38 329	60.73 143	74.39 269	78.44 369	57.72 265	82.78 109	60.16 214	89.60 161	79.11 303

MVP-Stereo		61.56 369	59.22 390	68.58 273	79.28 163	60.44 193	69.20 272	71.57 298	43.58 415	56.42 496	78.37 370	39.57 414	76.46 239	34.86 483	60.16 531	68.86 450
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
hse-mvs2		72.32 170	70.66 213	77.31 89	83.10 110	71.77 60	69.19 273	71.45 302	54.28 231	77.89 167	78.26 371	49.04 338	79.23 176	63.62 173	89.13 177	80.92 266

patch_mono-2		62.73 351	64.08 330	58.68 427	70.36 359	55.87 242	60.84 418	64.11 387	41.23 441	64.04 438	78.22 372	60.00 225	48.80 488	54.17 300	83.71 311	71.37 421

D2MVS		62.58 353	61.05 371	67.20 298	63.85 463	47.92 327	56.29 465	69.58 326	39.32 458	70.07 359	78.19 373	34.93 440	72.68 294	53.44 308	83.74 308	81.00 264

HY-MVS	49.31 19	57.96 407	57.59 410	59.10 422	66.85 427	36.17 477	65.13 359	65.39 375	39.24 461	54.69 509	78.14 374	44.28 368	67.18 385	33.75 495	70.79 489	73.95 388

Effi-MVS+-dtu		75.43 101	72.28 178	84.91 2	77.05 206	83.58 1	78.47 105	77.70 219	57.68 172	74.89 254	78.13 375	64.80 165	84.26 81	56.46 266	85.32 262	86.88 71

AUN-MVS		70.22 214	67.88 265	77.22 90	82.96 114	71.61 61	69.08 276	71.39 303	49.17 325	71.70 328	78.07 376	37.62 428	79.21 177	61.81 190	89.15 175	80.82 269

cl22		67.14 280	66.51 291	69.03 260	63.20 469	43.46 400	66.88 330	76.25 240	49.22 324	74.48 266	77.88 377	45.49 360	77.40 217	60.64 208	84.59 285	86.24 87

miper_ehance_all_eth		68.36 255	68.16 261	68.98 262	65.14 452	43.34 401	67.07 325	78.92 196	49.11 326	76.21 221	77.72 378	53.48 302	77.92 209	61.16 201	84.59 285	85.68 107

DSMNet-mixed		43.18 506	44.66 505	38.75 525	54.75 529	28.88 522	57.06 459	27.42 553	13.47 550	47.27 535	77.67 379	38.83 418	39.29 540	25.32 537	60.12 532	48.08 531

Test_1112_low_res		58.78 399	58.69 395	59.04 423	79.41 161	38.13 459	57.62 455	66.98 361	34.74 495	59.62 480	77.56 380	42.92 384	63.65 415	38.66 438	70.73 490	75.35 370

API-MVS		70.97 199	71.51 197	69.37 249	75.20 243	55.94 241	80.99 73	76.84 233	62.48 129	71.24 343	77.51 381	61.51 203	80.96 151	52.04 313	85.76 254	71.22 424

ELoFTR		57.63 411	59.55 388	51.85 471	66.16 437	61.46 176	69.66 260	43.94 515	30.20 520	82.28 103	77.47 382	33.76 445	42.30 529	42.10 408	90.40 140	51.81 527

pmmvs4		60.78 380	59.04 392	66.00 321	73.06 302	57.67 229	64.53 373	60.22 413	36.91 480	65.96 414	77.27 383	39.66 413	68.54 367	38.87 436	74.89 451	71.80 415

WBMVS		53.38 448	54.14 449	51.11 476	70.16 363	26.66 530	50.52 503	51.64 475	39.32 458	63.08 453	77.16 384	23.53 522	55.56 456	31.99 504	79.88 391	71.11 427

tfpn200view9		60.35 384	59.97 384	61.51 386	70.78 342	35.35 484	63.27 390	57.47 433	53.00 257	68.31 392	77.09 385	32.45 459	72.09 311	35.61 475	81.73 345	77.08 344

thres400		60.77 381	59.97 384	63.15 359	70.78 342	35.35 484	63.27 390	57.47 433	53.00 257	68.31 392	77.09 385	32.45 459	72.09 311	35.61 475	81.73 345	82.02 239

Effi-MVS+		72.10 175	72.28 178	71.58 195	74.21 271	50.33 290	74.72 164	82.73 105	62.62 127	70.77 347	76.83 387	69.96 101	80.97 148	60.20 212	78.43 415	83.45 188

MVSFormer		69.93 221	69.03 241	72.63 177	74.93 246	59.19 206	83.98 45	75.72 248	52.27 267	63.53 450	76.74 388	43.19 378	80.56 155	72.28 84	78.67 411	78.14 322

jason		64.47 324	62.84 350	69.34 252	76.91 215	59.20 205	67.15 323	65.67 370	35.29 491	65.16 421	76.74 388	44.67 365	70.68 335	54.74 290	79.28 402	78.14 322
jason: jason.
DenseAffine		67.25 278	66.08 297	70.76 209	80.22 150	77.51 25	70.65 243	58.59 428	45.98 375	81.51 116	76.48 390	41.58 396	62.36 419	49.23 343	90.48 137	72.40 408

CostFormer		57.35 416	56.14 425	60.97 396	63.76 465	38.43 454	67.50 311	60.22 413	37.14 479	59.12 482	76.34 391	32.78 452	71.99 314	39.12 435	69.27 499	72.47 406

MDTV_nov1_ep13			54.05 451		65.54 445	29.30 520	59.00 439	55.22 451	35.96 488	52.44 516	75.98 392	30.77 480	59.62 433	38.21 443	73.33 468

SP-SuperGlue		66.58 290	67.36 273	64.24 337	68.59 389	66.47 119	68.14 302	61.29 407	58.07 167	71.67 329	75.95 393	46.37 355	50.95 474	74.72 53	81.46 359	75.29 372

testing3		58.28 404	58.38 400	58.00 434	77.45 201	26.12 535	60.78 419	43.00 524	56.02 200	70.18 356	75.76 394	13.27 555	67.24 384	48.02 359	80.89 368	80.65 276

EU-MVSNet		60.82 379	60.80 376	60.86 399	68.37 392	41.16 420	72.27 201	68.27 352	26.96 530	69.08 372	75.71 395	32.09 463	67.44 380	55.59 277	78.90 408	73.97 387

usedtu_dtu_shiyan1		61.16 374	60.92 372	61.90 377	69.70 374	36.41 475	58.57 447	68.86 342	44.94 396	65.02 423	75.67 396	43.00 382	70.28 344	40.83 421	81.68 349	78.99 305

FE-MVSNET3		61.16 374	60.92 372	61.90 377	69.70 374	36.41 475	58.57 447	68.86 342	44.94 396	65.02 423	75.67 396	43.00 382	70.28 344	40.82 422	81.68 349	78.99 305

SP-LightGlue		66.16 298	66.97 283	63.75 346	68.62 387	66.76 116	68.82 285	62.15 398	57.30 178	70.52 350	75.63 398	43.02 381	48.82 487	75.09 49	81.55 354	75.66 363

HyFIR lowres test		63.01 344	60.47 381	70.61 211	83.04 111	54.10 261	59.93 431	72.24 293	33.67 502	69.00 373	75.63 398	38.69 419	76.93 230	36.60 464	75.45 447	80.81 271

Fast-Effi-MVS+		68.81 245	68.30 254	70.35 219	74.66 257	48.61 318	66.06 340	78.32 209	50.62 298	71.48 340	75.54 400	68.75 111	79.59 173	50.55 329	78.73 410	82.86 213

CDS-MVSNet		64.33 327	62.66 353	69.35 251	80.44 147	58.28 225	65.26 356	65.66 371	44.36 403	67.30 403	75.54 400	43.27 377	71.77 321	37.68 450	84.44 292	78.01 325
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		56.12 427	54.64 446	60.55 402	66.24 434	36.01 478	68.14 302	56.77 443	33.60 503	58.25 485	75.52 402	30.25 485	74.33 273	33.27 498	69.76 498	71.32 422

CANet_DTU		64.04 330	63.83 332	64.66 334	68.39 390	42.97 406	73.45 184	74.50 262	52.05 273	54.78 507	75.44 403	43.99 370	70.42 341	53.49 307	78.41 417	80.59 278

reproduce_monomvs		58.94 397	58.14 402	61.35 390	59.70 501	40.98 423	60.24 427	63.51 391	45.85 377	68.95 376	75.31 404	18.27 545	65.82 402	51.47 319	79.97 389	77.26 337

DELS-MVS		68.83 244	68.31 253	70.38 216	70.55 352	48.31 319	63.78 384	82.13 120	54.00 240	68.96 375	75.17 405	58.95 244	80.06 167	58.55 239	82.74 327	82.76 216
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
pmmvs5		52.49 459	52.58 459	52.21 469	54.99 528	32.38 501	55.45 473	53.84 460	32.15 510	55.49 503	74.81 406	38.08 423	57.37 451	34.02 490	74.40 457	66.88 467

MSDG		67.47 272	67.48 272	67.46 293	70.70 346	54.69 257	66.90 329	78.17 212	60.88 141	70.41 351	74.76 407	61.22 209	73.18 289	47.38 364	76.87 433	74.49 383

UnsupCasMVSNet_eth		52.26 460	53.29 455	49.16 489	55.08 527	33.67 496	50.03 505	58.79 427	37.67 474	63.43 452	74.75 408	41.82 394	45.83 508	38.59 440	59.42 533	67.98 460

Fast-Effi-MVS+-dtu		70.00 219	68.74 247	73.77 136	73.47 289	64.53 143	71.36 230	78.14 214	55.81 204	68.84 384	74.71 409	65.36 158	75.75 246	52.00 314	79.00 405	81.03 262

MatchFormer		53.09 452	55.03 442	47.30 497	59.31 503	57.25 233	67.30 319	37.25 546	27.23 528	82.61 100	74.56 410	26.23 507	42.89 527	34.73 485	86.00 249	41.75 543

TR-MVS		64.59 321	63.54 338	67.73 290	75.75 239	50.83 286	63.39 388	70.29 321	49.33 319	71.55 338	74.55 411	50.94 321	78.46 192	40.43 426	75.69 443	73.89 389

GA-MVS		62.91 345	61.66 362	66.66 311	67.09 419	44.49 388	61.18 414	69.36 330	51.33 285	69.33 371	74.47 412	36.83 431	74.94 262	50.60 328	74.72 452	80.57 279

CLD-MVS		72.88 155	72.36 176	74.43 124	77.03 207	54.30 259	68.77 289	83.43 89	52.12 271	76.79 202	74.44 413	69.54 106	83.91 83	55.88 271	93.25 76	85.09 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 1792x2688		58.09 406	56.30 422	63.45 355	79.95 153	50.93 285	54.07 483	65.59 372	28.56 524	61.53 461	74.33 414	41.09 402	66.52 398	33.91 492	67.69 509	72.92 398

Patchmatch-RL test		59.95 387	59.12 391	62.44 372	72.46 317	54.61 258	59.63 433	47.51 498	41.05 444	74.58 263	74.30 415	31.06 476	65.31 406	51.61 317	79.85 392	67.39 461

PRO-TEST		65.07 312	64.53 324	66.68 310	71.39 335	50.28 292	70.38 248	74.81 257	46.63 366	61.27 464	74.26 416	54.06 300	73.83 286	51.83 316	76.14 439	75.93 362

cdsmvs_eth3d_5k		17.71 518	23.62 519	0.00 543	0.00 567	0.00 570	0.00 555	70.17 322	0.00 562	0.00 563	74.25 417	68.16 119	0.00 563	0.00 562	0.00 562	0.00 559

lupinMVS		63.36 337	61.49 366	68.97 263	74.93 246	59.19 206	65.80 346	64.52 384	34.68 497	63.53 450	74.25 417	43.19 378	70.62 337	53.88 303	78.67 411	77.10 343

xiu_mvs_v1_base_debu		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 346	67.70 397	74.19 419	61.31 204	72.62 297	56.51 263	78.26 419	76.27 357

xiu_mvs_v1_base		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 346	67.70 397	74.19 419	61.31 204	72.62 297	56.51 263	78.26 419	76.27 357

xiu_mvs_v1_base_debi		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 346	67.70 397	74.19 419	61.31 204	72.62 297	56.51 263	78.26 419	76.27 357

SIFT-MNN		59.60 391	58.57 396	62.71 369	68.39 390	69.16 90	63.67 385	48.13 495	45.22 390	73.92 283	73.85 422	30.71 481	50.57 475	39.45 429	83.78 307	68.40 451

SP-MNN		63.33 338	64.30 326	60.41 407	66.01 439	60.04 198	65.58 351	60.61 409	49.33 319	69.45 368	73.75 423	41.65 395	48.61 491	69.96 101	82.36 331	72.57 404

tpmvs		55.84 429	55.45 436	57.01 442	60.33 492	33.20 498	65.89 343	59.29 420	47.52 354	56.04 498	73.60 424	31.05 477	68.06 373	40.64 425	64.64 518	69.77 439

SCA		58.57 403	58.04 403	60.17 410	70.17 362	41.07 422	65.19 358	53.38 465	43.34 421	61.00 468	73.48 425	45.20 361	69.38 358	40.34 427	70.31 493	70.05 434

Patchmatch-test		47.93 487	49.96 481	41.84 519	57.42 516	24.26 539	48.75 508	41.49 533	39.30 460	56.79 492	73.48 425	30.48 484	33.87 544	29.29 518	72.61 472	67.39 461

SP-NN		62.65 352	63.58 337	59.87 412	64.90 456	59.38 204	64.50 374	60.00 416	50.42 302	66.09 413	73.43 427	43.16 380	46.39 505	71.17 89	78.53 413	73.85 390

ArgMatch-SfM		64.74 319	63.70 335	67.83 286	77.62 198	76.78 30	67.30 319	58.21 429	36.64 482	81.94 108	73.41 428	38.67 420	56.92 452	50.66 327	88.89 184	69.81 437

MDA-MVSNet_test_wron		52.57 458	53.49 454	49.81 483	54.24 530	36.47 473	40.48 534	46.58 502	38.13 468	75.47 237	73.32 429	41.05 404	43.85 524	40.98 419	71.20 486	69.10 448

YYNet1		52.58 457	53.50 452	49.85 482	54.15 531	36.45 474	40.53 533	46.55 503	38.09 469	75.52 234	73.31 430	41.08 403	43.88 523	41.10 417	71.14 487	69.21 446

SIFT-ConvMatch		58.61 402	57.61 409	61.63 384	65.55 444	67.97 98	62.24 399	42.52 526	44.40 402	77.28 184	73.28 431	30.00 488	50.42 476	36.36 466	86.82 238	66.50 472

PatchmatchNet		54.60 440	54.27 448	55.59 452	65.17 451	39.08 446	66.92 328	51.80 473	39.89 454	58.39 483	73.12 432	31.69 470	58.33 444	43.01 400	58.38 537	69.38 445
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		58.93 398	58.52 397	60.16 411	67.91 406	47.70 334	69.97 254	58.02 430	49.73 312	47.28 534	73.02 433	38.14 422	62.34 420	36.57 465	85.99 250	70.43 432
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		65.86 301	65.05 321	68.28 280	61.62 483	42.62 409	64.74 367	77.97 216	42.52 429	73.42 294	72.79 434	49.66 330	77.68 213	58.12 246	84.59 285	84.54 150

SIFT-UMatch		58.13 405	57.37 413	60.42 406	65.49 446	67.10 112	61.52 408	43.57 518	44.20 404	76.80 201	72.60 435	29.70 491	47.95 498	36.61 463	85.82 251	66.20 476

dtuonly		50.13 476	51.25 469	46.77 501	53.07 536	30.10 516	52.41 494	49.25 487	28.98 523	53.76 513	72.59 436	39.83 411	41.82 533	37.58 453	73.80 465	68.37 452

ppachtmachnet_test		60.26 385	59.61 387	62.20 374	67.70 410	44.33 389	58.18 453	60.96 408	40.75 449	65.80 416	72.57 437	41.23 399	63.92 413	46.87 369	82.42 330	78.33 316

SIFT-NN-UMatch		57.27 417	56.18 424	60.54 403	62.85 472	66.67 118	61.19 413	41.27 535	43.01 425	70.01 360	72.44 438	32.76 453	49.32 485	38.19 445	83.87 303	65.63 480

SIFT-NCM-Cal		58.68 400	57.65 407	61.77 382	67.58 413	68.99 94	62.62 395	43.04 523	44.65 400	75.91 225	72.23 439	33.66 446	49.28 486	34.36 488	84.76 278	67.03 465

N_pmnet		52.06 461	51.11 471	54.92 453	59.64 502	71.03 67	37.42 539	61.62 406	33.68 501	57.12 488	72.10 440	37.94 424	31.03 546	29.13 522	71.35 484	62.70 500

SP-DiffGlue		64.90 314	65.69 304	62.51 371	69.18 378	64.39 145	69.79 258	60.46 412	52.50 263	75.70 228	72.08 441	44.17 369	48.59 492	67.84 123	79.52 400	74.54 381

ADS-MVSNet2		48.76 484	47.25 492	53.29 464	55.90 523	40.54 434	47.34 514	54.99 454	31.41 516	50.48 524	72.06 442	31.23 473	54.26 461	25.93 531	55.93 539	65.07 488

ADS-MVSNet		44.62 499	45.58 498	41.73 520	55.90 523	20.83 547	47.34 514	39.94 541	31.41 516	50.48 524	72.06 442	31.23 473	39.31 539	25.93 531	55.93 539	65.07 488

ET-MVSNet_ETH3D		63.32 339	60.69 378	71.20 205	70.15 364	55.66 245	65.02 362	64.32 385	43.28 422	68.99 374	72.05 444	25.46 511	78.19 205	54.16 301	82.80 325	79.74 293

SIFT-NN-CMatch		57.48 413	56.23 423	61.21 394	63.66 467	67.89 100	60.78 419	40.90 539	41.97 434	71.65 330	71.96 445	32.11 462	49.35 484	38.19 445	84.88 276	66.37 473

SIFT-NN-NCMNet		57.48 413	56.02 428	61.86 381	66.93 426	69.26 89	62.14 400	44.46 513	42.32 432	67.01 406	71.93 446	32.46 458	50.96 473	35.06 482	81.87 339	65.36 484

SIFT-UM-Cal		57.67 410	56.99 415	59.70 413	64.92 455	66.46 120	59.84 432	46.03 504	44.18 405	76.77 203	71.89 447	29.03 496	48.71 489	33.08 500	87.13 233	63.93 496

BH-w/o		64.81 317	64.29 328	66.36 315	76.08 233	54.71 256	65.61 349	75.23 253	50.10 308	71.05 346	71.86 448	54.33 297	79.02 180	38.20 444	76.14 439	65.36 484

EI-MVSNet-Vis-set		72.78 158	71.87 185	75.54 111	74.77 253	59.02 213	72.24 202	71.56 299	63.92 110	78.59 156	71.59 449	66.22 147	78.60 188	67.58 124	80.32 382	89.00 37

UnsupCasMVSNet_bld		50.01 477	51.03 473	46.95 498	58.61 508	32.64 499	48.31 509	53.27 466	34.27 498	60.47 472	71.53 450	41.40 398	47.07 503	30.68 510	60.78 530	61.13 511

thres200		57.55 412	57.02 414	59.17 419	67.89 407	34.93 487	58.91 442	57.25 437	50.24 305	64.01 439	71.46 451	32.49 457	71.39 328	31.31 507	79.57 399	71.19 426

UWE-MVS		52.94 454	52.70 457	53.65 460	73.56 285	27.49 527	57.30 458	49.57 484	38.56 466	62.79 455	71.42 452	19.49 541	60.41 428	24.33 540	77.33 429	73.06 396

EI-MVSNet-UG-set		72.63 162	71.68 190	75.47 112	74.67 255	58.64 221	72.02 208	71.50 300	63.53 116	78.58 158	71.39 453	65.98 149	78.53 189	67.30 134	80.18 386	89.23 31

SIFT-NN		56.62 422	55.34 439	60.47 404	67.01 425	67.25 109	61.74 404	45.38 509	42.69 428	64.49 427	71.36 454	28.48 497	47.55 499	36.68 462	80.23 384	66.63 471

SIFT-NN-PointCN		57.17 418	56.12 426	60.35 409	62.47 476	65.79 129	59.98 429	44.36 514	42.73 427	72.13 320	71.16 455	30.84 479	48.08 497	36.92 460	84.45 290	67.17 464

SIFT-CM-Cal		57.90 408	56.75 418	61.34 391	65.62 442	67.48 106	60.91 416	44.69 510	44.05 406	73.16 299	71.09 456	30.69 482	50.23 479	33.27 498	87.25 221	66.31 474

ETV-MVS		72.72 160	72.16 181	74.38 126	76.90 217	55.95 240	73.34 186	84.67 59	62.04 131	72.19 319	70.81 457	65.90 151	85.24 63	58.64 238	84.96 269	81.95 244

EIA-MVS		68.59 252	67.16 278	72.90 165	75.18 244	55.64 247	69.39 265	81.29 137	52.44 265	64.53 426	70.69 458	60.33 222	82.30 120	54.27 298	76.31 438	80.75 272

blended_shiyan6		62.20 358	61.77 359	63.47 353	67.98 404	40.64 433	60.46 424	69.15 332	47.24 358	66.43 411	70.57 459	43.73 374	71.93 317	43.16 398	87.24 222	77.85 328

blended_shiyan8		62.19 359	61.77 359	63.46 354	68.01 402	40.65 432	60.47 423	69.13 335	47.24 358	66.44 410	70.55 460	43.75 373	71.91 318	43.18 397	87.19 229	77.81 330

EI-MVSNet		69.61 228	69.01 242	71.41 200	73.94 280	49.90 298	71.31 232	71.32 305	58.22 165	75.40 238	70.44 461	58.16 255	75.85 242	62.51 183	79.81 393	88.48 46

CVMVSNet		59.21 395	58.44 399	61.51 386	73.94 280	47.76 332	71.31 232	64.56 383	26.91 532	60.34 473	70.44 461	36.24 435	67.65 376	53.57 306	68.66 503	69.12 447

tpm cat1		54.02 445	52.63 458	58.19 431	64.85 457	39.86 440	66.26 339	57.28 436	32.16 509	56.90 491	70.39 463	32.75 454	65.30 407	34.29 489	58.79 534	69.41 444

myMVS_eth3d28		51.35 467	51.99 464	49.44 487	69.21 377	22.51 544	49.82 506	49.11 488	49.00 330	55.03 505	70.31 464	22.73 527	52.88 467	24.33 540	78.39 418	72.92 398

PMMVS2		37.74 511	40.87 510	28.36 530	42.41 553	5.35 563	24.61 547	27.75 552	32.15 510	47.85 533	70.27 465	35.85 436	29.51 550	19.08 548	67.85 507	50.22 530

EPMVS		45.74 493	46.53 496	43.39 517	54.14 532	22.33 545	55.02 475	35.00 549	34.69 496	51.09 522	70.20 466	25.92 509	42.04 531	37.19 455	55.50 541	65.78 478

WB-MVSnew		53.94 447	54.76 445	51.49 474	71.53 331	28.05 523	58.22 452	50.36 480	37.94 472	59.16 481	70.17 467	49.21 336	51.94 469	24.49 538	71.80 480	74.47 384

testing99		55.16 437	54.56 447	56.98 443	70.13 365	30.58 513	54.55 481	54.11 458	49.53 317	56.76 493	70.14 468	22.76 526	65.79 403	36.99 458	76.04 441	74.57 380

testing91		55.74 431	55.29 440	57.08 441	70.63 347	30.85 511	54.94 478	56.31 450	50.34 303	57.08 489	70.10 469	24.50 517	65.86 401	36.98 459	76.75 434	74.53 382

KD-MVS_2432*1600		52.05 462	51.58 466	53.44 462	52.11 537	31.20 507	44.88 525	64.83 380	41.53 438	64.37 431	70.03 470	15.61 551	64.20 410	36.25 467	74.61 454	64.93 490

miper_refine_blended		52.05 462	51.58 466	53.44 462	52.11 537	31.20 507	44.88 525	64.83 380	41.53 438	64.37 431	70.03 470	15.61 551	64.20 410	36.25 467	74.61 454	64.93 490

our_test_3		56.46 424	56.51 420	56.30 447	67.70 410	39.66 444	55.36 474	52.34 471	40.57 452	63.85 441	69.91 472	40.04 409	58.22 446	43.49 395	75.29 450	71.03 429

xiu_mvs_v2_base		64.43 325	63.96 331	65.85 323	77.72 195	51.32 281	63.63 386	72.31 292	45.06 395	61.70 459	69.66 473	62.56 184	73.93 281	49.06 346	73.91 462	72.31 410

SIFT-PointCN		56.55 423	55.82 431	58.75 424	62.59 473	63.48 158	59.22 435	45.58 506	42.97 426	74.44 268	69.65 474	25.00 515	47.28 502	35.25 479	87.73 204	65.49 481

ArgMatch-Sym		63.94 332	63.05 347	66.61 312	76.68 221	75.81 34	65.98 341	57.57 432	35.60 490	80.60 130	69.62 475	43.62 375	55.74 455	49.14 344	88.61 187	68.29 453

tpmrst		50.15 475	51.38 468	46.45 504	56.05 521	24.77 538	64.40 376	49.98 481	36.14 486	53.32 515	69.59 476	35.16 439	48.69 490	39.24 433	58.51 536	65.89 477

WTY-MVS		49.39 482	50.31 480	46.62 503	61.22 484	32.00 504	46.61 518	49.77 482	33.87 500	54.12 511	69.55 477	41.96 390	45.40 513	31.28 508	64.42 519	62.47 504

SIFT-PCN-Cal		56.03 428	55.47 435	57.69 436	63.19 470	62.93 165	58.63 446	43.46 520	42.37 431	75.62 230	69.51 478	25.32 513	44.67 520	33.77 494	87.41 212	65.45 483

UWE-MVS-28		44.18 502	44.37 507	43.61 516	60.10 493	16.96 551	52.62 492	33.27 550	36.79 481	48.86 530	69.47 479	19.96 540	45.65 509	13.40 550	64.83 517	68.23 454

SIFT-NCMNet		56.27 426	55.94 430	57.26 440	62.54 474	64.28 149	59.61 434	41.26 536	43.43 418	78.50 160	69.35 480	32.26 461	45.98 507	27.16 527	89.34 171	61.53 510

thisisatest0515		60.48 383	57.86 405	68.34 277	67.25 416	46.42 362	60.58 422	62.14 399	40.82 447	63.58 449	69.12 481	26.28 506	78.34 199	48.83 347	82.13 334	80.26 285

FBQ-MVS		59.22 394	57.87 404	63.30 358	73.18 295	39.68 443	68.92 279	63.38 392	45.87 376	60.72 471	69.03 482	27.40 500	73.66 287	33.33 497	78.95 407	76.57 349

patchmatchnet-post											68.99 483	31.32 472	69.38 358

PatchMatch-RL		58.68 400	57.72 406	61.57 385	76.21 229	73.59 52	61.83 401	49.00 491	47.30 357	61.08 465	68.97 484	50.16 326	59.01 437	36.06 473	68.84 502	52.10 526

testing222		53.37 449	52.50 460	55.98 450	70.51 355	29.68 518	56.20 467	51.85 472	46.19 371	56.76 493	68.94 485	19.18 542	65.39 405	25.87 533	76.98 432	72.87 400

MS-PatchMatch		55.59 433	54.89 444	57.68 437	69.18 378	49.05 309	61.00 415	62.93 395	35.98 487	58.36 484	68.93 486	36.71 432	66.59 396	37.62 452	63.30 522	57.39 521

cascas		64.59 321	62.77 352	70.05 236	75.27 242	50.02 295	61.79 403	71.61 297	42.46 430	63.68 446	68.89 487	49.33 334	80.35 159	47.82 362	84.05 302	79.78 292

MVS		60.62 382	59.97 384	62.58 370	68.13 400	47.28 342	68.59 293	73.96 266	32.19 508	59.94 476	68.86 488	50.48 324	77.64 214	41.85 412	75.74 442	62.83 499

PVSNet_Blended		62.90 346	61.64 363	66.69 309	69.81 369	49.36 306	61.23 412	78.96 194	42.04 433	59.98 474	68.86 488	51.82 313	78.20 203	44.30 388	77.77 426	72.52 405

test_fmvs3		56.78 421	55.99 429	59.12 421	53.96 534	48.09 324	58.76 443	66.22 366	27.54 526	76.66 205	68.69 490	25.32 513	51.31 470	53.42 309	73.38 467	77.97 327

MAR-MVS		67.72 267	66.16 296	72.40 182	74.45 264	64.99 139	74.87 157	77.50 222	48.67 336	65.78 417	68.58 491	57.01 275	77.79 211	46.68 371	81.92 337	74.42 385
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
testing11		53.13 451	52.26 462	55.75 451	70.44 356	31.73 505	54.75 479	52.40 470	44.81 398	52.36 518	68.40 492	21.83 530	65.74 404	32.64 503	72.73 471	69.78 438

PS-MVSNAJ		64.27 328	63.73 334	65.90 322	77.82 193	51.42 279	63.33 389	72.33 291	45.09 394	61.60 460	68.04 493	62.39 188	73.95 280	49.07 345	73.87 463	72.34 409

ETVMVS		50.32 474	49.87 482	51.68 472	70.30 361	26.66 530	52.33 495	43.93 516	43.54 416	54.91 506	67.95 494	20.01 539	60.17 431	22.47 543	73.40 466	68.22 455

wanda-best-256-512		61.16 374	60.55 379	62.98 362	66.67 428	39.85 441	58.66 444	68.87 340	46.67 364	64.46 428	67.75 495	41.94 391	71.84 319	42.67 401	87.24 222	77.26 337

FE-blended-shiyan7		61.16 374	60.55 379	62.98 362	66.67 428	39.85 441	58.66 444	68.87 340	46.67 364	64.46 428	67.75 495	41.94 391	71.84 319	42.67 401	87.24 222	77.26 337

usedtu_blend_shiyan5		63.30 340	63.13 345	63.78 345	66.67 428	41.75 416	68.57 295	73.64 267	57.20 181	64.46 428	67.75 495	41.94 391	72.34 306	40.72 424	87.24 222	77.26 337

test0.0.03 1		47.72 488	48.31 487	45.93 505	55.53 526	29.39 519	46.40 519	41.21 537	43.41 419	55.81 501	67.65 498	29.22 493	43.77 525	25.73 535	69.87 496	64.62 492

1112_ss		59.48 392	58.99 393	60.96 397	77.84 192	42.39 411	61.42 410	68.45 351	37.96 471	59.93 477	67.46 499	45.11 363	65.07 408	40.89 420	71.81 479	75.41 368

ab-mvs-re		5.62 522	7.50 525	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	67.46 499	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

baseline2		55.57 434	52.74 456	64.05 341	65.26 447	44.11 391	62.38 397	54.43 456	39.03 462	51.21 521	67.35 501	33.66 446	72.45 302	37.14 456	64.22 520	75.60 365

1314		59.83 388	58.86 394	62.74 368	65.71 441	44.78 382	68.59 293	72.63 286	33.54 504	61.05 467	67.29 502	43.62 375	71.26 329	49.49 339	67.84 508	72.19 412

gbinet_0.2-2-1-0.02		62.58 353	61.83 358	64.86 333	67.07 421	41.37 418	61.56 407	67.91 354	49.27 321	66.62 409	67.23 503	41.53 397	74.46 270	45.94 379	89.31 172	78.74 309

IB-MVS	49.67 18	59.69 389	56.96 416	67.90 283	68.19 397	50.30 291	61.42 410	65.18 376	47.57 352	55.83 500	67.15 504	23.77 521	79.60 172	43.56 394	79.97 389	73.79 391
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
blend_shiyan4		57.39 415	55.27 441	63.73 347	67.25 416	41.75 416	60.08 428	69.15 332	47.57 352	64.19 436	67.14 505	20.46 535	72.34 306	40.73 423	60.88 529	77.11 342

UBG		49.18 483	49.35 483	48.66 493	70.36 359	26.56 532	50.53 502	45.61 505	37.43 476	53.37 514	65.97 506	23.03 525	54.20 462	26.29 528	71.54 481	65.20 487

nomal-1		49.95 478	49.18 485	52.26 467	57.73 514	44.81 380	46.14 521	49.57 484	37.60 475	56.41 497	65.96 507	24.21 519	52.60 468	33.97 491	71.04 488	59.37 516

sss		47.59 489	48.32 486	45.40 508	56.73 520	33.96 493	45.17 523	48.51 493	32.11 513	52.37 517	65.79 508	40.39 407	41.91 532	31.85 505	61.97 526	60.35 513

dp		44.09 503	44.88 504	41.72 521	58.53 510	23.18 541	54.70 480	42.38 529	34.80 494	44.25 544	65.61 509	24.48 518	44.80 517	29.77 515	49.42 545	57.18 522

test_fmvs2		54.80 439	54.11 450	56.88 444	51.76 539	49.95 297	56.70 461	65.80 369	26.22 533	69.42 369	65.25 510	31.82 468	49.98 481	49.63 337	70.36 492	70.71 430

PVSNet	43.83 21	51.56 465	51.17 470	52.73 465	68.34 393	38.27 456	48.22 510	53.56 463	36.41 483	54.29 510	64.94 511	34.60 441	54.20 462	30.34 511	69.87 496	65.71 479

Syy-MVS		54.13 442	55.45 436	50.18 480	68.77 385	23.59 540	55.02 475	44.55 511	43.80 409	58.05 486	64.07 512	46.22 356	58.83 438	46.16 377	72.36 474	68.12 457

myMVS_eth3d		50.36 473	50.52 478	49.88 481	68.77 385	22.69 542	55.02 475	44.55 511	43.80 409	58.05 486	64.07 512	14.16 553	58.83 438	33.90 493	72.36 474	68.12 457

pmmvs3		46.71 490	45.09 501	51.55 473	56.76 519	48.25 320	55.78 471	39.53 542	24.13 540	50.35 526	63.40 514	15.90 550	51.08 472	29.29 518	70.69 491	55.33 524

test_f		43.79 504	45.63 497	38.24 527	42.29 554	38.58 453	34.76 544	47.68 497	22.22 546	67.34 402	63.15 515	31.82 468	30.60 548	39.19 434	62.28 525	45.53 540

MASt3R-SfM		45.75 492	47.16 493	41.50 522	47.00 546	47.91 329	45.50 522	38.10 543	21.81 548	73.91 284	62.86 516	29.14 495	29.95 549	34.59 486	71.54 481	46.65 535

test_vis3_rt		51.94 464	51.04 472	54.65 455	46.32 548	50.13 294	44.34 527	78.17 212	23.62 541	68.95 376	62.81 517	21.41 531	38.52 541	41.49 414	72.22 476	75.30 371

gm-plane-assit					62.51 475	33.91 495			37.25 478		62.71 518		72.74 293	38.70 437

MVE	27.91 23	36.69 513	35.64 516	39.84 524	43.37 552	35.85 481	19.49 548	24.61 554	24.68 538	39.05 548	62.63 519	38.67 420	27.10 552	21.04 546	47.25 547	56.56 523
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test3		43.76 505	41.01 509	52.01 470	48.09 544	57.74 228	42.47 529	23.85 556	23.30 543	64.80 425	62.17 520	27.12 501	40.59 536	29.17 520	48.11 546	57.69 520

new_pmnet		37.55 512	39.80 513	30.79 529	56.83 518	16.46 552	39.35 536	30.65 551	25.59 536	45.26 538	61.60 521	24.54 516	28.02 551	21.60 544	52.80 544	47.90 532

dmvs_re		49.91 479	50.77 476	47.34 496	59.98 495	38.86 451	53.18 487	53.58 462	39.75 455	55.06 504	61.58 522	36.42 434	44.40 521	29.15 521	68.23 504	58.75 518

test_cas_vis1_n_1920		50.90 470	50.92 474	50.83 478	54.12 533	47.80 330	51.44 498	54.61 455	26.95 531	63.95 440	60.85 523	37.86 427	44.97 516	45.53 383	62.97 523	59.72 515

test_vis1_n_1920		52.96 453	53.50 452	51.32 475	59.15 504	44.90 378	56.13 468	64.29 386	30.56 519	59.87 478	60.68 524	40.16 408	47.47 500	48.25 357	62.46 524	61.58 509

test_fmvs1_n		52.70 456	52.01 463	54.76 454	53.83 535	50.36 289	55.80 470	65.90 368	24.96 537	65.39 418	60.64 525	27.69 499	48.46 493	45.88 381	67.99 506	65.46 482

test-LLR		50.43 472	50.69 477	49.64 484	60.76 487	41.87 413	53.18 487	45.48 507	43.41 419	49.41 528	60.47 526	29.22 493	44.73 518	42.09 409	72.14 477	62.33 507

test-mter		48.56 486	48.20 489	49.64 484	60.76 487	41.87 413	53.18 487	45.48 507	31.91 514	49.41 528	60.47 526	18.34 544	44.73 518	42.09 409	72.14 477	62.33 507

XFeat-MNN		48.68 485	49.35 483	46.65 502	44.49 550	46.89 351	46.91 516	43.80 517	27.16 529	75.21 245	60.05 528	22.65 528	46.52 504	39.33 431	84.57 288	46.53 536

test_fmvs1		51.51 466	50.86 475	53.48 461	49.72 542	49.35 308	54.11 482	64.96 378	24.64 539	63.66 447	59.61 529	28.33 498	48.45 494	45.38 386	67.30 511	62.66 502

test_vis1_n		51.27 468	50.41 479	53.83 458	56.99 517	50.01 296	56.75 460	60.53 411	25.68 535	59.74 479	57.86 530	29.40 492	47.41 501	43.10 399	63.66 521	64.08 495

dmvs_testset		45.26 495	47.51 490	38.49 526	59.96 497	14.71 553	58.50 450	43.39 521	41.30 440	51.79 520	56.48 531	39.44 416	49.91 483	21.42 545	55.35 543	50.85 528

TESTMET0.1,1		45.17 496	44.93 502	45.89 506	56.02 522	38.31 455	53.18 487	41.94 532	27.85 525	44.86 541	56.47 532	17.93 546	41.50 535	38.08 447	68.06 505	57.85 519

XFeat-NN		44.60 501	44.89 503	43.74 515	46.61 547	44.56 384	41.07 531	40.59 540	23.40 542	66.73 408	54.97 533	20.65 533	40.41 537	33.52 496	76.49 435	46.25 537

CHOSEN 280x420		41.62 507	39.89 512	46.80 500	61.81 480	51.59 277	33.56 545	35.74 547	27.48 527	37.64 551	53.53 534	23.24 523	42.09 530	27.39 526	58.64 535	46.72 534

mvsany_test1		37.88 510	35.74 515	44.28 512	47.28 545	49.90 298	36.54 541	24.37 555	19.56 549	45.76 536	53.46 535	32.99 451	37.97 542	26.17 529	35.52 549	44.99 541

PMMVS		44.69 498	43.95 508	46.92 499	50.05 541	53.47 267	48.08 512	42.40 528	22.36 545	44.01 545	53.05 536	42.60 387	45.49 511	31.69 506	61.36 528	41.79 542

GG-mvs-BLEND				52.24 468	60.64 490	29.21 521	69.73 259	42.41 527		45.47 537	52.33 537	20.43 536	68.16 371	25.52 536	65.42 516	59.36 517

E-PMN		45.17 496	45.36 499	44.60 511	50.07 540	42.75 407	38.66 537	42.29 530	46.39 369	39.55 547	51.15 538	26.00 508	45.37 514	37.68 450	76.41 436	45.69 539

test_vis1_rt		46.70 491	45.24 500	51.06 477	44.58 549	51.04 284	39.91 535	67.56 356	21.84 547	51.94 519	50.79 539	33.83 444	39.77 538	35.25 479	61.50 527	62.38 505

PDCNetPlus		38.77 509	39.67 514	36.07 528	38.82 556	27.82 526	36.52 542	51.55 476	22.53 544	37.81 550	50.69 540	7.16 559	32.98 545	28.21 524	83.73 310	47.40 533

0.4-1-1-0.1		51.02 469	48.31 487	59.15 420	60.95 486	37.94 463	53.17 491	59.12 423	39.52 456	47.88 532	50.31 541	20.36 537	69.99 349	35.79 474	67.66 510	69.51 443

PVSNet_0	36.71 22	41.12 508	40.78 511	42.14 518	59.97 496	40.13 437	40.97 532	42.24 531	30.81 518	44.86 541	49.41 542	40.70 405	45.12 515	23.15 542	34.96 550	41.16 544

EMVS		44.61 500	44.45 506	45.10 510	48.91 543	43.00 405	37.92 538	41.10 538	46.75 363	38.00 549	48.43 543	26.42 504	46.27 506	37.11 457	75.38 448	46.03 538

0.3-1-1-0.015		49.68 480	46.67 494	58.69 426	58.94 506	37.51 468	51.35 499	59.18 421	38.35 467	44.62 543	47.14 544	18.49 543	69.68 354	35.13 481	66.84 513	68.87 449

0.4-1-1-0.2		49.48 481	46.57 495	58.21 430	58.02 513	36.93 470	50.24 504	59.18 421	37.97 470	44.94 539	46.16 545	20.52 534	69.54 356	34.84 484	67.28 512	68.17 456

dongtai		31.66 514	32.98 517	27.71 531	58.58 509	12.61 555	45.02 524	14.24 560	41.90 435	47.93 531	43.91 546	10.65 556	41.81 534	14.06 549	20.53 553	28.72 548

test_method		19.26 517	19.12 521	19.71 533	9.09 560	1.91 565	7.79 550	53.44 464	1.42 555	10.27 557	35.80 547	17.42 548	25.11 553	12.44 551	24.38 552	32.10 547

GLUNet-SfM		24.03 515	24.76 518	21.84 532	12.84 558	18.20 549	27.35 546	15.92 558	9.48 551	63.07 454	34.11 548	10.20 557	23.13 554	9.60 554	40.26 548	24.18 549

kuosan		22.02 516	23.52 520	17.54 534	41.56 555	11.24 556	41.99 530	13.39 561	26.13 534	28.87 552	30.75 549	9.72 558	21.94 555	4.77 556	14.49 554	19.43 550

DeepMVS_CX				11.83 535	15.51 557	13.86 554		11.25 562	5.76 552	20.85 554	26.46 550	17.06 549	9.22 556	9.69 553	13.82 556	12.42 551

MVS_clip		7.93 520	9.12 523	4.36 537	9.81 559	6.92 560	6.89 551	1.72 564	1.89 554	16.36 555	21.19 551	4.56 561	2.56 559	6.56 555	13.13 557	3.60 552

VLMVS_CLIP		7.76 521	8.41 524	5.81 536	6.67 561	5.99 562	6.46 552	9.96 563	2.09 553	12.33 556	14.87 552	5.07 560	8.68 557	4.33 557	13.87 555	2.74 553

X-MVStestdata		76.81 87	74.79 111	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	9.95 553	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

tmp_tt		11.98 519	14.73 522	3.72 538	2.28 562	4.62 564	19.44 549	14.50 559	0.47 557	21.55 553	9.58 554	25.78 510	4.57 558	11.61 552	27.37 551	1.96 554

MVS_baseline		2.33 526	2.94 529	0.51 540	2.02 563	0.19 568	1.06 553	0.36 567	0.07 561	6.71 558	7.92 555	1.17 563	0.00 563	0.96 558	6.20 558	1.34 556

VLMVS		1.59 527	1.75 530	1.12 539	1.56 564	1.00 566	0.99 554	0.58 565	0.08 560	2.81 559	3.50 556	2.79 562	0.76 560	0.70 559	2.74 559	1.60 555

test_post1							66.63 332				2.08 557	30.66 483	59.33 435	40.34 427

test_post											1.99 558	30.91 478	54.76 460

test123		4.43 524	5.78 527	0.39 542	0.97 565	0.28 567	46.33 520	0.45 566	0.31 558	0.62 561	1.50 559	0.61 565	0.11 562	0.56 560	0.63 560	0.77 558

testmvs		4.06 525	5.28 528	0.41 541	0.64 566	0.16 569	42.54 528	0.31 568	0.26 559	0.50 562	1.40 560	0.77 564	0.17 561	0.56 560	0.55 561	0.90 557

mmdepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

monomultidepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

test_blank		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet_test		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

DCPMVS		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

pcd_1.5k_mvsjas		5.20 523	6.93 526	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	62.39 188	0.00 563	0.00 562	0.00 562	0.00 559

sosnet-low-res		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

sosnet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uncertanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Regformer		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
PatchmatchNet2					0.00 567	8.37 559	35.35 543	35.51 548	32.14 512
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														28.98 523	71.38 483	62.61 503
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													30.98 547
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					85.04 77	63.52 157		84.79 52		83.97 83		74.92 52	85.60 52	74.59 56	93.74 67

WAC-MVS						22.69 542								36.10 471

FOURS1					89.19 23	77.84 17	91.64 1	89.11 2	84.05 2	91.57 2

MSC_two_6792asdad				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

No_MVS				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

eth-test2					0.00 567

eth-test					0.00 567

IU-MVS					86.12 56	60.90 187		80.38 163	45.49 382	81.31 119				75.64 46	94.39 45	84.65 141

save fliter					87.00 39	67.23 111	79.24 97	77.94 217	56.65 191

test_0728_SECOND				76.57 95	86.20 51	60.57 192	83.77 49	85.49 33					85.90 41	75.86 43	94.39 45	83.25 195

GSMVS																70.05 434

test_part2					85.90 62	66.44 121				84.61 75

sam_mvs1												31.41 471				70.05 434

sam_mvs												31.21 475

MTGPA								80.63 157

MTMP							84.83 38	19.26 557

test9_res														72.12 86	91.37 106	77.40 333

agg_prior2														70.70 95	90.93 125	78.55 313

agg_prior					84.44 89	66.02 127		78.62 205		76.95 193			80.34 160

test_prior4						70.14 78	77.57 115

test_prior				75.27 116	82.15 126	59.85 201		84.33 73					83.39 97			82.58 224

旧先验2							71.17 235		45.11 393	78.54 159			61.28 426	59.19 231

新几何2							71.33 231

无先验							74.82 158	70.94 315	47.75 351				76.85 234	54.47 293		72.09 413

原ACMM2							74.78 162

testdata2													67.30 382	48.34 355

segment_acmp												68.30 118

testdata1							68.34 301		57.24 180

test12				76.51 96	82.28 123	60.94 186		81.64 129		73.60 289		64.88 164	85.19 66		90.42 139	83.38 191

plane_prior7					85.18 72	66.21 124

plane_prior6					84.18 93	65.31 135						60.83 214

plane_prior5								85.49 33					86.15 30	71.09 90	90.94 123	84.82 134

plane_prior3						65.67 130			63.82 112	78.23 163

plane_prior2							82.74 61		65.45 89

plane_prior1					84.46 88

plane_prior						65.18 136	80.06 89		61.88 133						89.91 155

n2								0.00 569

nn								0.00 569

door-mid								55.02 453

test11								82.71 106

door								52.91 468

HQP5-MVS						58.80 217

HQP-NCC					82.37 120		77.32 120		59.08 153	71.58 334

ACMP_Plane					82.37 120		77.32 120		59.08 153	71.58 334

BP-MVS														67.38 131

HQP4-MVS										71.59 332			85.31 58			83.74 176

HQP3-MVS								84.12 79							89.16 173

HQP2-MVS												58.09 258

MDTV_nov1_ep13_2view						18.41 548	53.74 484		31.57 515	44.89 540		29.90 490		32.93 501		71.48 419

ACMMP++_ref															89.47 166

ACMMP++															91.96 95

Test By Simon												62.56 184

High-resolution multi-view benchmark