High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 36	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 70	99.27 1	99.54 1

FOURS1					96.08 11	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 49	91.91 17	84.21 182	96.51 7	57.84 385	88.93 96	92.84 111	91.92 3	96.16 3	96.23 23	86.95 55	95.99 11	79.05 147	98.57 14	98.80 6

PEN-MVS		90.03 47	91.88 18	84.48 172	96.57 5	58.88 371	88.95 95	93.19 91	91.62 4	96.01 6	96.16 26	87.02 54	95.60 42	78.69 151	98.72 8	98.97 3

PS-CasMVS		90.06 45	91.92 15	84.47 173	96.56 6	58.83 374	89.04 94	92.74 115	91.40 5	96.12 4	96.06 28	87.23 50	95.57 43	79.42 143	98.74 5	99.00 2

CP-MVSNet		89.27 65	90.91 45	84.37 174	96.34 8	58.61 377	88.66 103	92.06 138	90.78 6	95.67 7	95.17 50	81.80 135	95.54 46	79.00 148	98.69 9	98.95 4

LS3D		90.60 34	90.34 54	91.38 27	89.03 204	84.23 48	93.58 6	94.68 17	90.65 7	90.33 103	93.95 107	84.50 86	95.37 57	80.87 123	95.50 158	94.53 98

TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 53	96.29 21	88.16 36	94.17 106	86.07 55	98.48 17	97.22 18

COLMAP_ROB	83.01 3	91.97 13	91.95 14	92.04 10	93.68 69	86.15 23	93.37 10	95.10 13	90.28 9	92.11 67	95.03 53	89.75 21	94.93 73	79.95 133	98.27 27	95.04 73
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 8	92.87 13	94.32 21	90.25 10	95.65 8	95.74 32	87.75 43	95.72 38	89.60 4	98.27 27	92.08 243

WR-MVS_H		89.91 53	91.31 33	85.71 137	96.32 9	62.39 302	89.54 84	93.31 85	90.21 11	95.57 10	95.66 36	81.42 140	95.90 16	80.94 122	98.80 2	98.84 5

reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 28	89.13 6	98.26 29	91.76 254

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 28	89.13 6	98.26 29	91.76 254

3Dnovator+	83.92 2	89.97 51	89.66 60	90.92 34	91.27 148	81.66 65	91.25 47	94.13 37	88.89 14	88.83 138	94.26 85	77.55 184	95.86 22	84.88 77	95.87 143	95.24 64

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 33	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 84	93.16 151	91.10 1	97.53 80	96.58 33
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 180	97.00 2	64.33 273	89.67 79	88.38 249	88.84 16	94.29 22	97.57 7	90.48 14	91.26 208	72.57 258	97.65 69	97.34 15

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 32	94.43 75	88.83 27	95.51 49	87.16 37	97.60 74	92.73 198

RE-MVS-def			92.61 8		94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 32	94.43 75	90.64 11		87.16 37	97.60 74	92.73 198

test_0402		88.65 74	89.58 63	85.88 133	92.55 100	72.22 170	84.01 201	89.44 231	88.63 19	94.38 21	95.77 31	86.38 65	93.59 133	79.84 134	95.21 167	91.82 252

PMVS	80.48 6	90.08 43	90.66 50	88.34 86	96.71 3	92.97 1	90.31 64	89.57 228	88.51 20	90.11 105	95.12 52	90.98 7	88.92 289	77.55 173	97.07 91	83.13 426
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 83	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 81	90.26 3	98.44 19	93.63 152

UA-Net		91.49 19	91.53 24	91.39 26	94.98 34	82.95 57	93.52 7	92.79 113	88.22 22	88.53 146	97.64 6	83.45 99	94.55 89	86.02 59	98.60 12	96.67 30

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 9	92.51 25	93.87 51	88.20 23	93.24 42	94.02 100	90.15 17	95.67 40	86.82 42	97.34 84	92.19 238

MVSMamba_PlusPlus		87.53 92	88.86 77	83.54 207	92.03 119	62.26 306	91.49 45	92.62 119	88.07 24	88.07 161	96.17 25	72.24 270	95.79 33	84.85 78	94.16 208	92.58 210

DP-MVS		88.60 75	89.01 70	87.36 101	91.30 146	77.50 103	87.55 119	92.97 107	87.95 25	89.62 121	92.87 154	84.56 85	93.89 118	77.65 171	96.62 104	90.70 287

ACMH+	77.89 11	90.73 31	91.50 25	88.44 82	93.00 88	76.26 121	89.65 80	95.55 8	87.72 26	93.89 30	94.94 55	91.62 3	93.44 142	78.35 155	98.76 3	95.61 54

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 87	85.17 38	92.47 27	95.05 14	87.65 27	93.21 46	94.39 80	90.09 18	95.08 69	86.67 44	97.60 74	94.18 118

tt0320-xc		86.67 104	88.41 83	81.44 272	93.45 74	60.44 342	83.96 203	88.50 245	87.26 28	90.90 93	97.90 3	85.61 75	86.40 355	70.14 284	98.01 44	97.47 14

Anonymous20231211		88.40 76	89.62 62	84.73 163	90.46 169	65.27 262	88.86 97	93.02 104	87.15 29	93.05 49	97.10 10	82.28 121	92.02 184	76.70 184	97.99 45	96.88 26

tt0320		86.63 106	88.36 84	81.41 273	93.57 71	60.73 339	84.37 194	88.61 244	87.00 30	90.75 96	97.98 2	85.54 77	86.45 352	69.75 289	97.70 63	97.06 22

sc_t1		87.70 90	88.94 73	83.99 188	93.47 73	67.15 239	85.05 175	88.21 257	86.81 31	91.87 73	97.65 5	85.51 78	87.91 319	74.22 221	97.63 70	96.92 25

gg-mvs-nofinetune		68.96 412	69.11 404	68.52 439	76.12 455	45.32 468	83.59 218	55.88 491	86.68 32	64.62 477	97.01 11	30.36 483	83.97 394	44.78 470	82.94 440	76.26 470

test_one_0601					93.85 66	73.27 147		94.11 38	86.57 33	93.47 41	94.64 67	88.42 29

v7n		90.13 41	90.96 43	87.65 98	91.95 121	71.06 190	89.99 69	93.05 100	86.53 34	94.29 22	96.27 22	82.69 108	94.08 109	86.25 52	97.63 70	97.82 8

VDDNet		84.35 166	85.39 144	81.25 275	95.13 31	59.32 360	85.42 166	81.11 369	86.41 35	87.41 187	96.21 24	73.61 248	90.61 242	66.33 322	96.85 95	93.81 141

IS-MVSNet		86.66 105	86.82 110	86.17 127	92.05 118	66.87 247	91.21 48	88.64 242	86.30 36	89.60 124	92.59 164	69.22 293	94.91 74	73.89 232	97.89 54	96.72 29

testf1		89.30 63	89.12 67	89.84 52	88.67 216	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 281	74.12 227	96.10 128	94.45 103

APD_test2		89.30 63	89.12 67	89.84 52	88.67 216	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 281	74.12 227	96.10 128	94.45 103

Anonymous20240529		86.20 114	87.13 100	83.42 209	90.19 174	64.55 270	84.55 188	90.71 186	85.85 39	89.94 112	95.24 49	82.13 124	90.40 248	69.19 296	96.40 114	95.31 61

SSC-MVS		77.55 310	81.64 240	65.29 455	90.46 169	20.33 502	73.56 412	68.28 456	85.44 40	88.18 159	94.64 67	70.93 283	81.33 409	71.25 268	92.03 290	94.20 115

DVP-MVS++		90.07 44	91.09 37	87.00 106	91.55 138	72.64 158	96.19 2	94.10 39	85.33 41	93.49 39	94.64 67	81.12 143	95.88 17	87.41 30	95.94 137	92.48 215

test_0728_THIRD									85.33 41	93.75 34	94.65 64	87.44 48	95.78 34	87.41 30	98.21 33	92.98 191

HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 32	92.99 12	94.23 27	85.21 43	92.51 61	95.13 51	90.65 10	95.34 58	88.06 15	98.15 38	95.95 45

tt0805		88.09 82	89.79 58	82.98 221	93.26 82	63.94 277	91.10 50	89.64 225	85.07 44	90.91 91	91.09 229	89.16 25	91.87 189	82.03 112	95.87 143	93.13 178

XVS		91.54 17	91.36 28	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 44	89.99 109	94.03 99	86.57 59	95.80 30	87.35 32	97.62 72	94.20 115

X-MVStestdata		85.04 145	82.70 221	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 44	89.99 109	16.05 498	86.57 59	95.80 30	87.35 32	97.62 72	94.20 115

TranMVSNet+NR-MVSNet		87.86 86	88.76 80	85.18 149	94.02 62	64.13 274	84.38 193	91.29 165	84.88 47	92.06 69	93.84 111	86.45 62	93.73 123	73.22 249	98.66 10	97.69 9

TestfortrainingZip				84.49 171	88.84 211	70.49 196	92.12 33	91.01 177	84.70 48	82.82 310	89.25 292	74.30 234	94.06 110		90.73 336	88.92 339

DPE-MVS		90.53 36	91.08 38	88.88 70	93.38 78	78.65 89	89.15 93	94.05 41	84.68 49	93.90 28	94.11 94	88.13 37	96.30 4	84.51 83	97.81 57	91.70 258
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		87.64 91	87.15 99	89.09 68	89.51 189	76.39 120	88.68 102	86.76 288	84.54 50	83.58 294	93.78 114	73.36 256	96.48 1	87.98 16	96.21 121	94.41 108

Elysia		88.71 72	88.89 74	88.19 89	91.26 149	72.96 152	88.10 111	93.59 71	84.31 51	90.42 99	94.10 95	74.07 238	94.82 76	88.19 13	95.92 139	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 89	91.26 149	72.96 152	88.10 111	93.59 71	84.31 51	90.42 99	94.10 95	74.07 238	94.82 76	88.19 13	95.92 139	96.80 27

APD_test1		88.40 76	87.91 88	89.88 51	89.50 190	86.65 19	89.98 70	91.91 144	84.26 53	90.87 95	93.92 109	82.18 123	89.29 285	73.75 235	94.81 186	93.70 145

Gipuma		84.44 163	86.33 119	78.78 325	84.20 351	73.57 142	89.55 82	90.44 196	84.24 54	84.38 272	94.89 56	76.35 211	80.40 418	76.14 197	96.80 100	82.36 436
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MTAPA		91.52 18	91.60 22	91.29 29	96.59 4	86.29 20	92.02 38	91.81 149	84.07 55	92.00 70	94.40 79	86.63 58	95.28 61	88.59 10	98.31 25	92.30 230

K. test v3		85.14 141	84.73 159	86.37 118	91.13 155	69.63 210	85.45 165	76.68 399	84.06 56	92.44 63	96.99 12	62.03 341	94.65 83	80.58 128	93.24 245	94.83 86

ANet_high		83.17 210	85.68 137	75.65 378	81.24 399	45.26 469	79.94 312	92.91 108	83.83 57	91.33 81	96.88 15	80.25 155	85.92 365	68.89 300	95.89 142	95.76 47

SED-MVS		90.46 38	91.64 21	86.93 108	94.18 54	72.65 156	90.47 60	93.69 63	83.77 58	94.11 26	94.27 82	90.28 15	95.84 26	86.03 56	97.92 51	92.29 232

test_241102_TWO								93.71 59	83.77 58	93.49 39	94.27 82	89.27 24	95.84 26	86.03 56	97.82 56	92.04 245

MED-MVS		90.48 37	91.14 35	88.50 79	94.38 47	76.12 125	92.12 33	93.85 52	83.72 60	93.24 42	93.18 135	87.06 52	95.85 23	84.99 74	97.69 64	93.54 162

ME-MVS		90.09 42	90.66 50	88.38 84	92.82 96	76.12 125	89.40 90	93.70 60	83.72 60	92.39 64	93.18 135	88.02 40	95.47 52	84.99 74	97.69 64	93.54 162

test_241102_ONE					94.18 54	72.65 156		93.69 63	83.62 62	94.11 26	93.78 114	90.28 15	95.50 51

DVP-MVS		90.06 45	91.32 32	86.29 120	94.16 57	72.56 162	90.54 57	91.01 177	83.61 63	93.75 34	94.65 64	89.76 19	95.78 34	86.42 46	97.97 48	90.55 295
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					94.16 57	72.56 162	90.63 54	93.90 48	83.61 63	93.75 34	94.49 72	89.76 19

pmmvs6		86.52 108	88.06 87	81.90 258	92.22 112	62.28 305	84.66 185	89.15 236	83.54 65	89.85 114	97.32 8	88.08 39	86.80 344	70.43 281	97.30 86	96.62 31

APDe-MVS		91.22 25	91.92 15	89.14 67	92.97 89	78.04 95	92.84 16	94.14 36	83.33 66	93.90 28	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 191
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
WB-MVS		76.06 333	80.01 281	64.19 458	89.96 183	20.58 501	72.18 425	68.19 457	83.21 67	86.46 217	93.49 123	70.19 288	78.97 426	65.96 324	90.46 344	93.02 185

CP-MVS		91.67 16	91.58 23	91.96 13	95.29 30	87.62 12	93.38 9	93.36 78	83.16 68	91.06 87	94.00 101	88.26 33	95.71 39	87.28 35	98.39 22	92.55 212

mPP-MVS		91.69 15	91.47 26	92.37 5	96.04 12	88.48 7	92.72 18	92.60 122	83.09 69	91.54 77	94.25 86	87.67 46	95.51 49	87.21 36	98.11 39	93.12 181

UniMVSNet_NR-MVSNet		86.84 100	87.06 102	86.17 127	92.86 93	67.02 243	82.55 256	91.56 155	83.08 70	90.92 89	91.82 198	78.25 173	93.99 112	74.16 225	98.35 23	97.49 13

LFMVS		80.15 281	80.56 267	78.89 320	89.19 198	55.93 398	85.22 171	73.78 419	82.96 71	84.28 279	92.72 162	57.38 376	90.07 265	63.80 349	95.75 150	90.68 288

HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 45	93.51 8	94.85 15	82.88 72	91.77 75	93.94 108	90.55 13	95.73 37	88.50 11	98.23 32	95.33 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 40	93.91 64	80.97 69	91.49 45	93.48 76	82.82 73	92.60 60	93.97 102	88.19 34	96.29 5	87.61 25	98.20 35	94.39 109
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 28	90.91 45	91.83 19	96.18 10	86.88 16	92.20 31	93.03 103	82.59 74	88.52 147	94.37 81	86.74 57	95.41 56	86.32 49	98.21 33	93.19 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		91.91 14	91.87 19	92.03 11	95.53 26	85.91 27	93.35 11	94.16 32	82.52 75	92.39 64	94.14 92	89.15 26	95.62 41	87.35 32	98.24 31	94.56 94
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
LPG-MVS_test		91.47 21	91.68 20	90.82 36	94.75 40	81.69 62	90.00 67	94.27 24	82.35 76	93.67 37	94.82 59	91.18 5	95.52 47	85.36 66	98.73 6	95.23 65

LGP-MVS_train				90.82 36	94.75 40	81.69 62		94.27 24	82.35 76	93.67 37	94.82 59	91.18 5	95.52 47	85.36 66	98.73 6	95.23 65

HFP-MVS		91.30 23	91.39 27	91.02 32	95.43 28	84.66 46	92.58 23	93.29 87	81.99 78	91.47 78	93.96 105	88.35 32	95.56 44	87.74 21	97.74 61	92.85 195

ACMMPR		91.49 19	91.35 30	91.92 15	95.74 19	85.88 29	92.58 23	93.25 88	81.99 78	91.40 79	94.17 91	87.51 47	95.87 19	87.74 21	97.76 59	93.99 127

region2R		91.44 22	91.30 34	91.87 18	95.75 18	85.90 28	92.63 22	93.30 86	81.91 80	90.88 94	94.21 87	87.75 43	95.87 19	87.60 26	97.71 62	93.83 137

ACMH	76.49 14	89.34 62	91.14 35	83.96 190	92.50 102	70.36 200	89.55 82	93.84 55	81.89 81	94.70 16	95.44 43	90.69 9	88.31 313	83.33 93	98.30 26	93.20 175
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		86.80 101	86.99 105	86.21 125	93.24 83	67.02 243	83.16 239	92.21 132	81.73 82	90.92 89	91.97 189	77.20 192	93.99 112	74.16 225	98.35 23	97.61 10

SixPastTwentyTwo		87.20 95	87.45 95	86.45 117	92.52 101	69.19 218	87.84 117	88.05 258	81.66 83	94.64 17	96.53 19	65.94 314	94.75 79	83.02 99	96.83 97	95.41 57

ITE_SJBPF				90.11 48	90.72 164	84.97 40		90.30 205	81.56 84	90.02 108	91.20 225	82.40 114	90.81 232	73.58 242	94.66 192	94.56 94

EPP-MVSNet		85.47 128	85.04 152	86.77 112	91.52 141	69.37 213	91.63 44	87.98 261	81.51 85	87.05 197	91.83 197	66.18 313	95.29 59	70.75 275	96.89 94	95.64 52

SF-MVS		90.27 40	90.80 47	88.68 77	92.86 93	77.09 110	91.19 49	95.74 5	81.38 86	92.28 66	93.80 112	86.89 56	94.64 84	85.52 65	97.51 81	94.30 114

WR-MVS		83.56 198	84.40 177	81.06 281	93.43 77	54.88 413	78.67 338	85.02 320	81.24 87	90.74 97	91.56 208	72.85 262	91.08 219	68.00 310	98.04 40	97.23 17

Anonymous202405211		80.51 268	81.19 258	78.49 331	88.48 223	57.26 390	76.63 371	82.49 355	81.21 88	84.30 278	92.24 183	67.99 299	86.24 357	62.22 360	95.13 170	91.98 249

OurMVSNet-221017-0		90.01 48	89.74 59	90.83 35	93.16 85	80.37 73	91.91 41	93.11 96	81.10 89	95.32 13	97.24 9	72.94 261	94.85 75	85.07 70	97.78 58	97.26 16

NR-MVSNet		86.00 118	86.22 121	85.34 146	93.24 83	64.56 269	82.21 271	90.46 195	80.99 90	88.42 150	91.97 189	77.56 183	93.85 119	72.46 259	98.65 11	97.61 10

GST-MVS		90.96 29	91.01 41	90.82 36	95.45 27	82.73 58	91.75 43	93.74 58	80.98 91	91.38 80	93.80 112	87.20 51	95.80 30	87.10 39	97.69 64	93.93 131

EC-MVSNet		88.01 83	88.32 85	87.09 103	89.28 195	72.03 173	90.31 64	96.31 3	80.88 92	85.12 249	89.67 284	84.47 87	95.46 53	82.56 106	96.26 120	93.77 143

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 99	81.34 67	90.19 66	93.08 99	80.87 93	91.13 85	93.19 134	86.22 66	95.97 13	82.23 111	97.18 89	90.45 297
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-Vis-set		85.12 143	84.53 172	86.88 109	84.01 356	72.76 155	83.91 207	85.18 315	80.44 94	88.75 140	85.49 365	80.08 156	91.92 186	82.02 113	90.85 326	95.97 43

KinetiMVS		85.95 120	86.10 125	85.50 143	87.56 254	69.78 206	83.70 214	89.83 219	80.42 95	87.76 175	93.24 133	73.76 247	91.54 195	85.03 72	93.62 230	95.19 67

UniMVSNet (Re)		86.87 98	86.98 106	86.55 115	93.11 86	68.48 228	83.80 211	92.87 109	80.37 96	89.61 123	91.81 199	77.72 180	94.18 104	75.00 214	98.53 15	96.99 24

CSCG		86.26 111	86.47 113	85.60 139	90.87 161	74.26 138	87.98 114	91.85 145	80.35 97	89.54 127	88.01 313	79.09 164	92.13 180	75.51 206	95.06 174	90.41 298

PGM-MVS		91.20 26	90.95 44	91.93 14	95.67 22	85.85 30	90.00 67	93.90 48	80.32 98	91.74 76	94.41 78	88.17 35	95.98 12	86.37 48	97.99 45	93.96 130

EI-MVSNet-UG-set		85.04 145	84.44 175	86.85 110	83.87 360	72.52 164	83.82 209	85.15 316	80.27 99	88.75 140	85.45 367	79.95 158	91.90 187	81.92 116	90.80 330	96.13 38

XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 25	85.91 152	93.60 70	80.16 100	89.13 134	93.44 124	83.82 93	90.98 222	83.86 89	95.30 166	93.60 155

ZNCC-MVS		91.26 24	91.34 31	91.01 33	95.73 20	83.05 55	92.18 32	94.22 29	80.14 101	91.29 83	93.97 102	87.93 42	95.87 19	88.65 9	97.96 50	94.12 123

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 26	86.84 133	93.91 47	80.07 102	86.75 203	93.26 132	93.64 2	90.93 225	84.60 82	90.75 331	93.97 129

mvs5depth		83.82 189	84.54 171	81.68 266	82.23 385	68.65 226	86.89 131	89.90 217	80.02 103	87.74 176	97.86 4	64.19 326	82.02 405	76.37 191	95.63 156	94.35 110

VDD-MVS		84.23 172	84.58 169	83.20 215	91.17 154	65.16 265	83.25 233	84.97 323	79.79 104	87.18 190	94.27 82	74.77 227	90.89 228	69.24 293	96.54 107	93.55 161

CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 15	92.09 37	92.30 131	79.74 105	87.50 186	92.38 173	81.42 140	93.28 147	83.07 97	97.24 87	91.67 259

XVG-ACMP-BASELINE		89.98 49	89.84 57	90.41 42	94.91 36	84.50 47	89.49 86	93.98 43	79.68 106	92.09 68	93.89 110	83.80 94	93.10 154	82.67 105	98.04 40	93.64 151

TransMVSNet (Re)		84.02 182	85.74 136	78.85 323	91.00 158	55.20 411	82.29 267	87.26 273	79.65 107	88.38 152	95.52 40	83.00 103	86.88 341	67.97 311	96.60 105	94.45 103

AllTest		87.97 85	87.40 97	89.68 55	91.59 133	83.40 51	89.50 85	95.44 10	79.47 108	88.00 164	93.03 145	82.66 109	91.47 198	70.81 272	96.14 125	94.16 120

TestCases				89.68 55	91.59 133	83.40 51		95.44 10	79.47 108	88.00 164	93.03 145	82.66 109	91.47 198	70.81 272	96.14 125	94.16 120

HQP_MVS		87.75 89	87.43 96	88.70 76	93.45 74	76.42 118	89.45 87	93.61 68	79.44 110	86.55 209	92.95 151	74.84 224	95.22 62	80.78 125	95.83 145	94.46 101

plane_prior2							89.45 87		79.44 110

CS-MVS		88.14 80	87.67 92	89.54 60	89.56 188	79.18 84	90.47 60	94.77 16	79.37 112	84.32 275	89.33 291	83.87 92	94.53 91	82.45 107	94.89 182	94.90 75

RPSCF		88.00 84	86.93 107	91.22 30	90.08 177	89.30 4	89.68 78	91.11 173	79.26 113	89.68 118	94.81 62	82.44 112	87.74 324	76.54 189	88.74 369	96.61 32

ACMM	79.39 9	90.65 32	90.99 42	89.63 57	95.03 33	83.53 50	89.62 81	93.35 81	79.20 114	93.83 31	93.60 122	90.81 8	92.96 158	85.02 73	98.45 18	92.41 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		83.55 199	83.10 211	84.90 156	89.34 194	83.87 49	84.54 190	88.77 239	79.09 115	83.54 296	88.66 306	74.87 223	81.73 407	66.84 317	92.29 281	89.11 331

Baseline_NR-MVSNet		84.00 183	85.90 129	78.29 338	91.47 143	53.44 425	82.29 267	87.00 287	79.06 116	89.55 125	95.72 35	77.20 192	86.14 362	72.30 260	98.51 16	95.28 62

ACMP	79.16 10	90.54 35	90.60 52	90.35 44	94.36 50	80.98 68	89.16 92	94.05 41	79.03 117	92.87 52	93.74 117	90.60 12	95.21 64	82.87 101	98.76 3	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SD-MVS		88.96 70	89.88 56	86.22 124	91.63 132	77.07 111	89.82 74	93.77 57	78.90 118	92.88 51	92.29 180	86.11 67	90.22 253	86.24 53	97.24 87	91.36 267
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
Vis-MVSNet		86.86 99	86.58 111	87.72 96	92.09 116	77.43 106	87.35 123	92.09 137	78.87 119	84.27 280	94.05 98	78.35 172	93.65 126	80.54 129	91.58 305	92.08 243
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 77	86.76 137	92.78 114	78.78 120	92.51 61	93.64 121	88.13 37	93.84 121	84.83 79	97.55 77	94.10 124
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
NCCC		87.36 93	86.87 108	88.83 71	92.32 109	78.84 88	86.58 141	91.09 175	78.77 121	84.85 261	90.89 240	80.85 146	95.29 59	81.14 120	95.32 163	92.34 228

ETV-MVS		84.31 167	83.91 191	85.52 141	88.58 221	70.40 198	84.50 192	93.37 77	78.76 122	84.07 283	78.72 442	80.39 153	95.13 68	73.82 234	92.98 254	91.04 274

Effi-MVS+		83.90 188	84.01 186	83.57 205	87.22 266	65.61 261	86.55 142	92.40 125	78.64 123	81.34 342	84.18 388	83.65 97	92.93 160	74.22 221	87.87 383	92.17 240

FMVSNet1		84.55 161	85.45 142	81.85 260	90.27 173	61.05 329	86.83 134	88.27 254	78.57 124	89.66 120	95.64 37	75.43 216	90.68 237	69.09 297	95.33 162	93.82 138

MSP-MVS		89.08 69	88.16 86	91.83 19	95.76 17	86.14 24	92.75 17	93.90 48	78.43 125	89.16 132	92.25 182	72.03 275	96.36 3	88.21 12	90.93 321	92.98 191
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
testing3-2		70.72 393	70.97 385	69.95 423	88.93 208	34.80 493	69.85 444	66.59 467	78.42 126	77.58 395	85.55 362	31.83 479	82.08 404	46.28 464	93.73 225	92.98 191

API-MVS		82.28 228	82.61 224	81.30 274	86.29 302	69.79 205	88.71 101	87.67 267	78.42 126	82.15 322	84.15 389	77.98 175	91.59 194	65.39 333	92.75 261	82.51 435

HPM-MVS++		88.93 71	88.45 82	90.38 43	94.92 35	85.85 30	89.70 76	91.27 169	78.20 128	86.69 207	92.28 181	80.36 154	95.06 70	86.17 54	96.49 109	90.22 301

AdaColmap		83.66 193	83.69 193	83.57 205	90.05 180	72.26 169	86.29 146	90.00 215	78.19 129	81.65 336	87.16 339	83.40 100	94.24 99	61.69 369	94.76 190	84.21 408

PAPM_NR		83.23 208	83.19 207	83.33 211	90.90 160	65.98 257	88.19 109	90.78 185	78.13 130	80.87 347	87.92 318	73.49 252	92.42 171	70.07 285	88.40 372	91.60 261

mmtdpeth		85.13 142	85.78 134	83.17 217	84.65 341	74.71 134	85.87 154	90.35 201	77.94 131	83.82 287	96.96 14	77.75 178	80.03 421	78.44 152	96.21 121	94.79 89

casdiffmvs_mvg		86.72 102	87.51 94	84.36 176	87.09 273	65.22 263	84.16 197	94.23 27	77.89 132	91.28 84	93.66 120	84.35 88	92.71 164	80.07 130	94.87 185	95.16 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
RRT-MVS		82.97 214	83.44 198	81.57 268	85.06 334	58.04 383	87.20 124	90.37 199	77.88 133	88.59 144	93.70 119	63.17 335	93.05 156	76.49 190	88.47 371	93.62 153

SPE-MVS-test		87.00 97	86.43 114	88.71 75	89.46 191	77.46 104	89.42 89	95.73 6	77.87 134	81.64 337	87.25 337	82.43 113	94.53 91	77.65 171	96.46 111	94.14 122

plane_prior3						76.85 113			77.79 135	86.55 209

ACMMP_NAP		90.65 32	91.07 40	89.42 61	95.93 15	79.54 81	89.95 71	93.68 67	77.65 136	91.97 71	94.89 56	88.38 30	95.45 54	89.27 5	97.87 55	93.27 171

MSDG		80.06 283	79.99 282	80.25 300	83.91 359	68.04 234	77.51 356	89.19 234	77.65 136	81.94 327	83.45 395	76.37 210	86.31 356	63.31 354	86.59 402	86.41 379

MIMVSNet1		83.63 195	84.59 168	80.74 287	94.06 61	62.77 291	82.72 250	84.53 332	77.57 138	90.34 102	95.92 30	76.88 204	85.83 372	61.88 367	97.42 82	93.62 153

MGCNet		85.37 134	84.58 169	87.75 95	85.28 329	73.36 143	86.54 143	85.71 305	77.56 139	81.78 335	92.47 171	70.29 287	96.02 10	85.59 64	95.96 134	93.87 135

MED-MVS test				88.50 79	94.38 47	76.12 125	92.12 33	93.85 52	77.53 140	93.24 42	93.18 135		95.85 23	84.99 74	97.69 64	93.54 162

TestfortrainingZip a		89.97 51	90.77 48	87.58 99	94.38 47	73.21 149	92.12 33	93.85 52	77.53 140	93.24 42	93.18 135	87.06 52	95.85 23	87.89 18	97.69 64	93.68 146

FC-MVSNet-test		85.93 121	87.05 103	82.58 239	92.25 110	56.44 396	85.75 157	93.09 98	77.33 142	91.94 72	94.65 64	74.78 226	93.41 144	75.11 213	98.58 13	97.88 7

CNVR-MVS		87.81 88	87.68 91	88.21 88	92.87 91	77.30 109	85.25 170	91.23 170	77.31 143	87.07 196	91.47 213	82.94 104	94.71 80	84.67 81	96.27 119	92.62 206

CANet		83.79 191	82.85 219	86.63 113	86.17 306	72.21 171	83.76 212	91.43 159	77.24 144	74.39 424	87.45 333	75.36 217	95.42 55	77.03 181	92.83 259	92.25 236

UGNet		82.78 218	81.64 240	86.21 125	86.20 305	76.24 122	86.86 132	85.68 306	77.07 145	73.76 428	92.82 157	69.64 290	91.82 191	69.04 299	93.69 227	90.56 294
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
tfpnnormal		81.79 245	82.95 215	78.31 336	88.93 208	55.40 407	80.83 299	82.85 352	76.81 146	85.90 231	94.14 92	74.58 231	86.51 350	66.82 318	95.68 153	93.01 188

v8		86.22 113	86.83 109	84.36 176	87.82 244	62.35 304	86.42 144	91.33 164	76.78 147	92.73 58	94.48 73	73.41 253	93.72 124	83.10 96	95.41 159	97.01 23

LCM-MVSNet-Re		83.48 202	85.06 151	78.75 326	85.94 314	55.75 402	80.05 310	94.27 24	76.47 148	96.09 5	94.54 70	83.31 101	89.75 275	59.95 382	94.89 182	90.75 284

VPA-MVSNet		83.47 203	84.73 159	79.69 311	90.29 172	57.52 388	81.30 289	88.69 241	76.29 149	87.58 185	94.44 74	80.60 151	87.20 335	66.60 320	96.82 98	94.34 111

EPNet		80.37 273	78.41 302	86.23 122	76.75 447	73.28 146	87.18 126	77.45 390	76.24 150	68.14 458	88.93 300	65.41 318	93.85 119	69.47 291	96.12 127	91.55 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EI-MVSNet		82.61 220	82.42 228	83.20 215	83.25 376	63.66 278	83.50 225	85.07 317	76.06 151	86.55 209	85.10 373	73.41 253	90.25 250	78.15 162	90.67 338	95.68 51

IterMVS-LS		84.73 155	84.98 153	83.96 190	87.35 261	63.66 278	83.25 233	89.88 218	76.06 151	89.62 121	92.37 176	73.40 255	92.52 169	78.16 160	94.77 189	95.69 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OMC-MVS		88.19 79	87.52 93	90.19 47	91.94 123	81.68 64	87.49 122	93.17 92	76.02 153	88.64 143	91.22 223	84.24 90	93.37 145	77.97 169	97.03 92	95.52 55

test_yl		78.71 297	78.51 299	79.32 316	84.32 348	58.84 372	78.38 340	85.33 312	75.99 154	82.49 314	86.57 347	58.01 370	90.02 267	62.74 356	92.73 263	89.10 332

DCV-MVSNet		78.71 297	78.51 299	79.32 316	84.32 348	58.84 372	78.38 340	85.33 312	75.99 154	82.49 314	86.57 347	58.01 370	90.02 267	62.74 356	92.73 263	89.10 332

MSLP-MVS++		85.00 148	86.03 126	81.90 258	91.84 128	71.56 183	86.75 138	93.02 104	75.95 156	87.12 191	89.39 289	77.98 175	89.40 284	77.46 174	94.78 187	84.75 398

plane_prior						76.42 118	87.15 127		75.94 157						95.03 175

FIs		85.35 135	86.27 120	82.60 238	91.86 125	57.31 389	85.10 174	93.05 100	75.83 158	91.02 88	93.97 102	73.57 249	92.91 162	73.97 231	98.02 43	97.58 12

MP-MVS-pluss		90.81 30	91.08 38	89.99 49	95.97 13	79.88 76	88.13 110	94.51 18	75.79 159	92.94 50	94.96 54	88.36 31	95.01 71	90.70 2	98.40 21	95.09 72
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
thres100view900		75.45 342	75.05 342	76.66 366	87.27 262	51.88 437	81.07 292	73.26 424	75.68 160	83.25 301	86.37 350	45.54 437	88.80 292	51.98 437	90.99 317	89.31 322

BP-MVS1		82.81 216	81.67 239	86.23 122	87.88 243	68.53 227	86.06 151	84.36 333	75.65 161	85.14 248	90.19 271	45.84 435	94.42 93	85.18 68	94.72 191	95.75 48

3Dnovator	80.37 7	84.80 151	84.71 162	85.06 152	86.36 299	74.71 134	88.77 100	90.00 215	75.65 161	84.96 256	93.17 139	74.06 240	91.19 215	78.28 157	91.09 315	89.29 325

fmvsm_s_conf0.5_n_3		86.19 115	87.27 98	82.95 223	86.91 281	70.38 199	85.31 169	92.61 121	75.59 163	88.32 154	92.87 154	82.22 122	88.63 302	88.80 8	92.82 260	89.83 311

FA-MVS(test-final)		83.13 211	83.02 212	83.43 208	86.16 308	66.08 256	88.00 113	88.36 250	75.55 164	85.02 253	92.75 161	65.12 320	92.50 170	74.94 215	91.30 311	91.72 256

pm-mvs1		83.69 192	84.95 155	79.91 306	90.04 181	59.66 356	82.43 262	87.44 269	75.52 165	87.85 171	95.26 48	81.25 142	85.65 375	68.74 303	96.04 130	94.42 107

test_prior2							83.37 229		75.43 166	84.58 266	91.57 207	81.92 132		79.54 141	96.97 93

v10		86.54 107	87.10 101	84.84 157	88.16 236	63.28 284	86.64 140	92.20 133	75.42 167	92.81 56	94.50 71	74.05 241	94.06 110	83.88 88	96.28 117	97.17 19

SMA-MVS		90.31 39	90.48 53	89.83 54	95.31 29	79.52 82	90.98 51	93.24 89	75.37 168	92.84 54	95.28 47	85.58 76	96.09 7	87.92 17	97.76 59	93.88 134
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
thres600view7		75.97 336	75.35 336	77.85 348	87.01 277	51.84 438	80.45 306	73.26 424	75.20 169	83.10 304	86.31 353	45.54 437	89.05 286	55.03 417	92.24 283	92.66 204

9.14			89.29 65		91.84 128		88.80 99	95.32 12	75.14 170	91.07 86	92.89 153	87.27 49	93.78 122	83.69 92	97.55 77

wuyk23d		75.13 345	79.30 288	62.63 461	75.56 458	75.18 133	80.89 297	73.10 426	75.06 171	94.76 15	95.32 44	87.73 45	52.85 493	34.16 490	97.11 90	59.85 489

usedtu_blend_shiyan5		77.07 317	76.43 323	78.99 319	80.36 415	59.77 354	83.25 233	88.32 252	74.91 172	77.62 391	75.71 466	56.22 385	88.89 290	58.91 389	92.61 266	88.32 347

RPMNet		78.88 292	78.28 303	80.68 291	79.58 424	62.64 293	82.58 254	94.16 32	74.80 173	75.72 411	92.59 164	48.69 419	95.56 44	73.48 243	82.91 441	83.85 413

fmvsm_s_conf0.5_n_8		85.48 127	85.75 135	84.68 166	87.10 271	69.98 204	84.28 195	92.68 116	74.77 174	87.90 168	92.36 178	73.94 242	90.41 247	85.95 61	92.74 262	93.66 147

TSAR-MVS + GP.		83.95 185	82.69 222	87.72 96	89.27 196	81.45 66	83.72 213	81.58 367	74.73 175	85.66 236	86.06 356	72.56 267	92.69 166	75.44 208	95.21 167	89.01 338

casdiffmvs		85.21 137	85.85 131	83.31 212	86.17 306	62.77 291	83.03 241	93.93 46	74.69 176	88.21 157	92.68 163	82.29 120	91.89 188	77.87 170	93.75 224	95.27 63
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Effi-MVS+-dtu		85.82 123	83.38 202	93.14 3	87.13 268	91.15 2	87.70 118	88.42 248	74.57 177	83.56 295	85.65 361	78.49 171	94.21 100	72.04 261	92.88 256	94.05 126

NormalMVS		86.47 109	85.32 146	89.94 50	94.43 43	80.42 71	88.63 104	93.59 71	74.56 178	85.12 249	90.34 263	66.19 311	94.20 101	76.57 187	98.44 19	95.19 67

SymmetryMVS		84.79 153	83.54 194	88.55 78	92.44 104	80.42 71	88.63 104	82.37 358	74.56 178	85.12 249	90.34 263	66.19 311	94.20 101	76.57 187	95.68 153	91.03 275

baseline		85.20 138	85.93 128	83.02 219	86.30 301	62.37 303	84.55 188	93.96 44	74.48 180	87.12 191	92.03 188	82.30 118	91.94 185	78.39 153	94.21 205	94.74 90

SSM_0407		84.89 150	84.85 156	85.01 155	89.13 199	68.97 221	85.60 161	91.58 153	74.41 181	85.68 233	91.49 210	78.54 167	93.69 125	73.71 236	93.47 232	92.38 225

SSM_0404		85.16 140	85.09 150	85.36 145	90.14 176	69.52 211	86.17 149	91.58 153	74.41 181	86.55 209	91.49 210	78.54 167	93.97 114	73.71 236	93.21 248	92.59 209

fmvsm_s_conf0.5_n_9		87.04 96	87.02 104	87.08 104	89.67 186	75.87 128	84.60 186	89.74 220	74.40 183	89.92 113	93.41 125	80.45 152	90.63 240	86.66 45	94.37 201	94.73 91

VNet		79.31 287	80.27 272	76.44 368	87.92 241	53.95 421	75.58 389	84.35 334	74.39 184	82.23 320	90.72 247	72.84 263	84.39 388	60.38 380	93.98 214	90.97 277

BH-RMVSNet		80.53 267	80.22 275	81.49 271	87.19 267	66.21 254	77.79 351	86.23 293	74.21 185	83.69 291	88.50 307	73.25 258	90.75 234	63.18 355	87.90 382	87.52 366

usedtu_dtu_shiyan2		78.92 290	78.15 305	81.25 275	91.33 145	73.10 151	80.75 301	79.00 383	74.19 186	79.17 372	92.04 187	67.17 304	81.33 409	42.86 473	96.81 99	89.31 322

nrg030		87.85 87	88.49 81	85.91 131	90.07 179	69.73 208	87.86 116	94.20 30	74.04 187	92.70 59	94.66 63	85.88 70	91.50 196	79.72 136	97.32 85	96.50 34

Vis-MVSNet (Re-imp)		77.82 307	77.79 308	77.92 345	88.82 212	51.29 442	83.28 231	71.97 438	74.04 187	82.23 320	89.78 281	57.38 376	89.41 283	57.22 400	95.41 159	93.05 184

testdata1							79.62 316		73.95 189

Patchmtry		76.56 326	77.46 310	73.83 393	79.37 429	46.60 462	82.41 263	76.90 396	73.81 190	85.56 240	92.38 173	48.07 422	83.98 393	63.36 353	95.31 165	90.92 279

tttt0517		81.07 258	79.58 284	85.52 141	88.99 206	66.45 252	87.03 129	75.51 407	73.76 191	88.32 154	90.20 270	37.96 467	94.16 108	79.36 144	95.13 170	95.93 46

SDMVSNet		81.90 244	83.17 209	78.10 341	88.81 213	62.45 301	76.08 382	86.05 298	73.67 192	83.41 297	93.04 143	82.35 115	80.65 415	70.06 286	95.03 175	91.21 269

sd_testset		79.95 285	81.39 251	75.64 379	88.81 213	58.07 382	76.16 381	82.81 353	73.67 192	83.41 297	93.04 143	80.96 145	77.65 431	58.62 392	95.03 175	91.21 269

E5new		85.44 130	86.37 115	82.66 233	88.22 231	61.86 311	83.59 218	93.70 60	73.64 194	87.62 179	93.30 128	85.85 71	91.26 208	78.02 163	93.40 235	94.86 81

E6new		85.44 130	86.37 115	82.66 233	88.23 229	61.86 311	83.59 218	93.69 63	73.64 194	87.61 181	93.30 128	85.85 71	91.26 208	78.02 163	93.40 235	94.86 81

E6		85.44 130	86.37 115	82.66 233	88.23 229	61.86 311	83.59 218	93.69 63	73.64 194	87.61 181	93.30 128	85.85 71	91.26 208	78.02 163	93.40 235	94.86 81

E5		85.44 130	86.37 115	82.66 233	88.22 231	61.86 311	83.59 218	93.70 60	73.64 194	87.62 179	93.30 128	85.85 71	91.26 208	78.02 163	93.40 235	94.86 81

PatchT		70.52 394	72.76 369	63.79 460	79.38 428	33.53 494	77.63 353	65.37 471	73.61 198	71.77 438	92.79 160	44.38 450	75.65 439	64.53 345	85.37 414	82.18 438

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 62	93.64 70	79.07 85	88.54 106	94.20 30	73.53 199	89.71 117	94.82 59	85.09 80	95.77 36	84.17 86	98.03 42	93.26 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		85.04 145	85.95 127	82.31 249	87.52 255	63.59 280	86.23 148	93.96 44	73.46 200	88.07 161	87.83 324	86.46 61	90.87 230	76.17 196	93.89 217	92.47 217

VPNet		80.25 277	81.68 238	75.94 374	92.46 103	47.98 456	76.70 369	81.67 365	73.45 201	84.87 260	92.82 157	74.66 230	86.51 350	61.66 370	96.85 95	93.33 167

sasdasda		85.50 125	86.14 123	83.58 203	87.97 238	67.13 240	87.55 119	94.32 21	73.44 202	88.47 148	87.54 329	86.45 62	91.06 220	75.76 202	93.76 221	92.54 213

canonicalmvs		85.50 125	86.14 123	83.58 203	87.97 238	67.13 240	87.55 119	94.32 21	73.44 202	88.47 148	87.54 329	86.45 62	91.06 220	75.76 202	93.76 221	92.54 213

MVS_111021_HR		84.63 156	84.34 180	85.49 144	90.18 175	75.86 129	79.23 329	87.13 278	73.35 204	85.56 240	89.34 290	83.60 98	90.50 244	76.64 186	94.05 213	90.09 307

tfpn200view9		74.86 352	74.23 350	76.74 365	86.24 303	52.12 434	79.24 327	73.87 417	73.34 205	81.82 331	84.60 382	46.02 430	88.80 292	51.98 437	90.99 317	89.31 322

thres400		75.14 344	74.23 350	77.86 347	86.24 303	52.12 434	79.24 327	73.87 417	73.34 205	81.82 331	84.60 382	46.02 430	88.80 292	51.98 437	90.99 317	92.66 204

HQP-NCC					91.19 151		84.77 178		73.30 207	80.55 351

ACMP_Plane					91.19 151		84.77 178		73.30 207	80.55 351

HQP-MVS		84.61 157	84.06 185	86.27 121	91.19 151	70.66 193	84.77 178	92.68 116	73.30 207	80.55 351	90.17 274	72.10 271	94.61 85	77.30 178	94.47 197	93.56 159

alignmvs		83.94 186	83.98 187	83.80 194	87.80 245	67.88 235	84.54 190	91.42 161	73.27 210	88.41 151	87.96 314	72.33 268	90.83 231	76.02 199	94.11 209	92.69 202

F-COLMAP		84.97 149	83.42 200	89.63 57	92.39 105	83.40 51	88.83 98	91.92 143	73.19 211	80.18 359	89.15 296	77.04 196	93.28 147	65.82 330	92.28 282	92.21 237

MDA-MVSNet-bldmvs		77.47 311	76.90 318	79.16 318	79.03 432	64.59 267	66.58 463	75.67 405	73.15 212	88.86 136	88.99 299	66.94 306	81.23 411	64.71 340	88.22 379	91.64 260

PHI-MVS		86.38 110	85.81 132	88.08 91	88.44 225	77.34 107	89.35 91	93.05 100	73.15 212	84.76 264	87.70 326	78.87 166	94.18 104	80.67 127	96.29 116	92.73 198

E4		84.75 154	85.46 141	82.61 237	88.17 234	61.55 318	81.39 285	93.55 74	73.13 214	86.83 200	92.83 156	84.17 91	91.48 197	76.92 183	92.19 286	94.80 88

Fast-Effi-MVS+-dtu		82.54 223	81.41 249	85.90 132	85.60 322	76.53 117	83.07 240	89.62 227	73.02 215	79.11 373	83.51 393	80.74 149	90.24 252	68.76 302	89.29 359	90.94 278

viewdifsd2359ckpt07		83.41 207	84.35 179	80.56 294	85.84 316	58.93 370	79.47 321	91.28 166	73.01 216	87.59 183	92.07 185	85.24 79	88.68 299	73.59 241	91.11 313	94.09 125

mamba_0408		83.44 206	82.88 217	85.11 150	89.13 199	68.97 221	72.73 421	91.28 166	72.90 217	85.68 233	90.61 256	76.78 205	93.97 114	73.37 246	93.47 232	92.38 225

SSM_04072		81.44 251	82.88 217	77.10 358	89.13 199	68.97 221	72.73 421	91.28 166	72.90 217	85.68 233	90.61 256	76.78 205	69.94 458	73.37 246	93.47 232	92.38 225

v148		82.31 227	82.48 227	81.81 263	85.59 323	59.66 356	81.47 283	86.02 299	72.85 219	88.05 163	90.65 254	70.73 284	90.91 227	75.15 212	91.79 297	94.87 77

testing3		71.53 384	70.79 386	73.77 395	88.89 210	41.86 479	76.60 374	59.12 486	72.83 220	80.97 343	82.08 411	19.80 502	87.33 334	65.12 336	91.68 302	92.13 242

FE-MVS		79.98 284	78.86 292	83.36 210	86.47 290	66.45 252	89.73 75	84.74 330	72.80 221	84.22 282	91.38 215	44.95 447	93.60 132	63.93 347	91.50 306	90.04 308

BH-untuned		80.96 260	80.99 260	80.84 286	88.55 222	68.23 229	80.33 308	88.46 246	72.79 222	86.55 209	86.76 345	74.72 228	91.77 192	61.79 368	88.99 364	82.52 434

MVS_111021_LR		84.28 169	83.76 192	85.83 135	89.23 197	83.07 54	80.99 295	83.56 343	72.71 223	86.07 223	89.07 298	81.75 137	86.19 360	77.11 180	93.36 239	88.24 350

EG-PatchMatch MVS		84.08 176	84.11 184	83.98 189	92.22 112	72.61 161	82.20 273	87.02 284	72.63 224	88.86 136	91.02 232	78.52 169	91.11 218	73.41 244	91.09 315	88.21 351

LuminaMVS		83.94 186	83.51 195	85.23 147	89.78 185	71.74 176	84.76 181	87.27 272	72.60 225	89.31 130	90.60 258	64.04 327	90.95 223	79.08 146	94.11 209	92.99 189

test1111		78.53 299	78.85 293	77.56 350	92.22 112	47.49 458	82.61 252	69.24 454	72.43 226	85.28 246	94.20 88	51.91 406	90.07 265	65.36 334	96.45 112	95.11 71

IterMVS-SCA-FT		80.64 266	79.41 285	84.34 178	83.93 358	69.66 209	76.28 378	81.09 370	72.43 226	86.47 216	90.19 271	60.46 348	93.15 152	77.45 175	86.39 405	90.22 301

GBi-Net		82.02 239	82.07 230	81.85 260	86.38 296	61.05 329	86.83 134	88.27 254	72.43 226	86.00 227	95.64 37	63.78 331	90.68 237	65.95 325	93.34 240	93.82 138

test1		82.02 239	82.07 230	81.85 260	86.38 296	61.05 329	86.83 134	88.27 254	72.43 226	86.00 227	95.64 37	63.78 331	90.68 237	65.95 325	93.34 240	93.82 138

FMVSNet2		81.31 253	81.61 242	80.41 297	86.38 296	58.75 375	83.93 206	86.58 290	72.43 226	87.65 178	92.98 147	63.78 331	90.22 253	66.86 315	93.92 216	92.27 234

GeoE		85.45 129	85.81 132	84.37 174	90.08 177	67.07 242	85.86 155	91.39 162	72.33 231	87.59 183	90.25 269	84.85 83	92.37 174	78.00 167	91.94 294	93.66 147

test2506		74.12 359	73.39 360	76.28 371	91.85 126	44.20 472	84.06 200	48.20 497	72.30 232	81.90 328	94.20 88	27.22 495	89.77 273	64.81 339	96.02 131	94.87 77

ECVR-MVS		78.44 302	78.63 297	77.88 346	91.85 126	48.95 452	83.68 215	69.91 450	72.30 232	84.26 281	94.20 88	51.89 407	89.82 270	63.58 350	96.02 131	94.87 77

v2v482		84.09 175	84.24 182	83.62 201	87.13 268	61.40 320	82.71 251	89.71 223	72.19 234	89.55 125	91.41 214	70.70 285	93.20 149	81.02 121	93.76 221	96.25 36

SSC-MVS3.2		73.90 362	75.67 332	68.61 438	84.11 353	41.28 480	64.17 471	72.83 429	72.09 235	79.08 374	87.94 315	70.31 286	73.89 446	55.99 407	94.49 196	90.67 290

DP-MVS Recon		84.05 179	83.22 205	86.52 116	91.73 131	75.27 132	83.23 236	92.40 125	72.04 236	82.04 326	88.33 309	77.91 177	93.95 116	66.17 323	95.12 172	90.34 300

MG-MVS		80.32 275	80.94 261	78.47 332	88.18 233	52.62 432	82.29 267	85.01 321	72.01 237	79.24 370	92.54 169	69.36 292	93.36 146	70.65 277	89.19 362	89.45 318

FPMVS		72.29 377	72.00 376	73.14 400	88.63 219	85.00 39	74.65 400	67.39 460	71.94 238	77.80 388	87.66 327	50.48 414	75.83 438	49.95 446	79.51 458	58.58 491

E2		84.06 177	84.61 166	82.40 247	87.49 257	61.31 322	81.03 293	93.36 78	71.83 239	86.02 225	91.87 191	82.91 105	91.37 205	75.66 204	91.33 309	94.53 98

E3		84.06 177	84.61 166	82.40 247	87.49 257	61.30 323	81.03 293	93.36 78	71.83 239	86.01 226	91.87 191	82.91 105	91.36 206	75.66 204	91.33 309	94.53 98

balanced_ft_v1		83.49 201	83.93 189	82.19 251	86.46 291	59.61 358	90.81 52	90.92 182	71.78 241	88.08 160	92.56 167	66.97 305	94.54 90	75.34 210	92.42 275	92.42 218

viewmacassd2359aftdt		84.04 181	84.78 158	81.81 263	86.43 293	60.32 344	81.95 275	92.82 112	71.56 242	86.06 224	92.98 147	81.79 136	90.28 249	76.18 195	93.24 245	94.82 87

MVSFormer		82.23 229	81.57 245	84.19 184	85.54 324	69.26 215	91.98 39	90.08 213	71.54 243	76.23 404	85.07 376	58.69 363	94.27 96	86.26 50	88.77 367	89.03 336

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 106	82.98 56	91.98 39	90.08 213	71.54 243	94.28 24	96.54 18	81.57 138	94.27 96	86.26 50	96.49 109	97.09 20

balanced_conf03		84.80 151	85.40 143	83.00 220	88.95 207	61.44 319	90.42 63	92.37 129	71.48 245	88.72 142	93.13 141	70.16 289	95.15 66	79.26 145	94.11 209	92.41 220

h-mvs33		84.25 170	82.76 220	88.72 74	91.82 130	82.60 59	84.00 202	84.98 322	71.27 246	86.70 205	90.55 259	63.04 338	93.92 117	78.26 158	94.20 206	89.63 315

hse-mvs2		83.47 203	81.81 237	88.47 81	91.03 157	82.27 60	82.61 252	83.69 341	71.27 246	86.70 205	86.05 357	63.04 338	92.41 172	78.26 158	93.62 230	90.71 286

TinyColmap		81.25 254	82.34 229	77.99 344	85.33 328	60.68 340	82.32 266	88.33 251	71.26 248	86.97 198	92.22 184	77.10 195	86.98 339	62.37 359	95.17 169	86.31 381

FE-MVSNET2		82.80 217	83.51 195	80.67 292	89.08 202	58.46 378	82.40 264	89.26 233	71.25 249	88.24 156	94.07 97	75.75 213	89.56 276	65.91 328	95.67 155	93.98 128

ZD-MVS					92.22 112	80.48 70		91.85 145	71.22 250	90.38 101	92.98 147	86.06 68	96.11 6	81.99 114	96.75 101

MVS_Test		82.47 224	83.22 205	80.22 301	82.62 384	57.75 387	82.54 257	91.96 142	71.16 251	82.89 307	92.52 170	77.41 185	90.50 244	80.04 132	87.84 385	92.40 222

MonoMVSNet		76.66 323	77.26 314	74.86 384	79.86 422	54.34 417	86.26 147	86.08 296	71.08 252	85.59 238	88.68 303	53.95 398	85.93 364	63.86 348	80.02 457	84.32 404

viewcassd2359sk11		83.53 200	83.96 188	82.25 250	86.97 280	61.13 327	80.80 300	93.22 90	70.97 253	85.36 244	91.08 230	81.84 134	91.29 207	74.79 216	90.58 343	94.33 112

DELS-MVS		81.44 251	81.25 254	82.03 255	84.27 350	62.87 289	76.47 376	92.49 124	70.97 253	81.64 337	83.83 390	75.03 220	92.70 165	74.29 218	92.22 285	90.51 296
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
save fliter					93.75 67	77.44 105	86.31 145	89.72 222	70.80 255

PS-MVSNAJss		88.31 78	87.90 89	89.56 59	93.31 80	77.96 98	87.94 115	91.97 141	70.73 256	94.19 25	96.67 16	76.94 198	94.57 87	83.07 97	96.28 117	96.15 37

DeepC-MVS_fast	80.27 8	86.23 112	85.65 138	87.96 94	91.30 146	76.92 112	87.19 125	91.99 140	70.56 257	84.96 256	90.69 249	80.01 157	95.14 67	78.37 154	95.78 149	91.82 252
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_11		84.56 159	84.69 164	84.15 185	86.53 287	71.29 186	85.53 162	92.62 119	70.54 258	82.75 312	91.20 225	77.33 187	88.55 307	83.80 91	91.93 295	92.61 208

EIA-MVS		82.19 232	81.23 256	85.10 151	87.95 240	69.17 219	83.22 237	93.33 82	70.42 259	78.58 378	79.77 433	77.29 189	94.20 101	71.51 267	88.96 365	91.93 250

test20.03		73.75 364	74.59 347	71.22 416	81.11 401	51.12 444	70.15 442	72.10 437	70.42 259	80.28 357	91.50 209	64.21 325	74.72 444	46.96 463	94.58 194	87.82 364

JIA-IIPM		69.41 407	66.64 425	77.70 349	73.19 474	71.24 187	75.67 386	65.56 470	70.42 259	65.18 472	92.97 150	33.64 475	83.06 397	53.52 426	69.61 487	78.79 466

v1144		84.54 162	84.72 161	84.00 187	87.67 250	62.55 295	82.97 244	90.93 181	70.32 262	89.80 115	90.99 233	73.50 250	93.48 140	81.69 118	94.65 193	95.97 43

E3new		83.08 213	83.39 201	82.14 253	86.49 289	61.00 332	80.64 302	93.12 95	70.30 263	84.78 263	90.34 263	80.85 146	91.24 213	74.20 224	89.83 352	94.17 119

DeepPCF-MVS	81.24 5	87.28 94	86.21 122	90.49 41	91.48 142	84.90 41	83.41 228	92.38 127	70.25 264	89.35 129	90.68 250	82.85 107	94.57 87	79.55 140	95.95 136	92.00 247

KD-MVS_self_test		81.93 242	83.14 210	78.30 337	84.75 340	52.75 429	80.37 307	89.42 232	70.24 265	90.26 104	93.39 126	74.55 233	86.77 345	68.61 305	96.64 103	95.38 58

thres200		72.34 376	71.55 382	74.70 388	83.48 366	51.60 439	75.02 396	73.71 420	70.14 266	78.56 379	80.57 424	46.20 428	88.20 314	46.99 462	89.29 359	84.32 404

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 43	89.89 73	90.63 189	70.00 267	94.55 18	96.67 16	87.94 41	93.59 133	84.27 85	95.97 133	95.52 55

anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 184	86.67 17	90.51 59	90.20 210	69.87 268	95.06 14	96.14 27	84.28 89	93.07 155	87.68 23	96.34 115	97.09 20

guyue		81.57 248	81.37 252	82.15 252	86.39 294	66.13 255	81.54 282	83.21 347	69.79 269	87.77 174	89.95 277	65.36 319	87.64 326	75.88 200	92.49 273	92.67 203

PM-MVS		80.20 279	79.00 290	83.78 196	88.17 234	86.66 18	81.31 287	66.81 466	69.64 270	88.33 153	90.19 271	64.58 321	83.63 396	71.99 262	90.03 348	81.06 454

viewmanbaseed2359cas		82.95 215	83.43 199	81.52 269	85.18 332	60.03 349	81.36 286	92.38 127	69.55 271	84.84 262	91.38 215	79.85 160	90.09 263	74.22 221	92.09 289	94.43 106

V42		83.47 203	83.37 203	83.75 197	83.16 379	63.33 283	81.31 287	90.23 209	69.51 272	90.91 91	90.81 245	74.16 237	92.29 178	80.06 131	90.22 345	95.62 53

viewdifsd2359ckpt11		82.46 225	82.98 214	80.88 284	83.53 363	61.00 332	79.46 322	85.97 301	69.48 273	87.89 169	91.31 219	82.10 125	88.61 303	74.28 219	92.86 257	93.02 185

viewmsd2359difaftdt		82.46 225	82.99 213	80.88 284	83.52 364	61.00 332	79.46 322	85.97 301	69.48 273	87.89 169	91.31 219	82.10 125	88.61 303	74.28 219	92.86 257	93.02 185

jajsoiax		89.41 60	88.81 79	91.19 31	93.38 78	84.72 44	89.70 76	90.29 207	69.27 275	94.39 20	96.38 20	86.02 69	93.52 138	83.96 87	95.92 139	95.34 59

TAPA-MVS	77.73 12	85.71 124	84.83 157	88.37 85	88.78 215	79.72 78	87.15 127	93.50 75	69.17 276	85.80 232	89.56 285	80.76 148	92.13 180	73.21 254	95.51 157	93.25 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet_DTU		77.81 308	77.05 315	80.09 304	81.37 398	59.90 352	83.26 232	88.29 253	69.16 277	67.83 461	83.72 391	60.93 345	89.47 278	69.22 295	89.70 354	90.88 281

fmvsm_s_conf0.5_n_10		85.20 138	85.25 148	85.02 154	86.01 312	71.31 185	84.96 176	91.76 151	69.10 278	88.90 135	92.56 167	73.84 245	90.63 240	86.88 40	93.26 244	93.13 178

v1192		84.57 158	84.69 164	84.21 182	87.75 246	62.88 288	83.02 242	91.43 159	69.08 279	89.98 111	90.89 240	72.70 265	93.62 131	82.41 108	94.97 179	96.13 38

FMVSNet3		78.80 294	78.55 298	79.57 313	82.89 383	56.89 394	81.76 277	85.77 304	69.04 280	86.00 227	90.44 261	51.75 408	90.09 263	65.95 325	93.34 240	91.72 256

FE-MVSNET		78.46 300	79.36 287	75.75 376	86.53 287	54.53 415	78.03 344	85.35 311	69.01 281	85.41 243	90.68 250	64.27 323	85.73 373	62.59 358	92.35 278	87.00 374

ab-mvs		79.67 286	80.56 267	76.99 359	88.48 223	56.93 392	84.70 184	86.06 297	68.95 282	80.78 348	93.08 142	75.30 218	84.62 383	56.78 401	90.90 322	89.43 320

thisisatest0530		79.07 288	77.33 313	84.26 181	87.13 268	64.58 268	83.66 216	75.95 402	68.86 283	85.22 247	87.36 335	38.10 464	93.57 136	75.47 207	94.28 204	94.62 92

AstraMVS		81.67 246	81.40 250	82.48 244	87.06 276	66.47 251	81.41 284	81.68 364	68.78 284	88.00 164	90.95 238	65.70 316	87.86 323	76.66 185	92.38 276	93.12 181

Anonymous20240521		80.18 280	81.25 254	76.95 360	83.15 380	60.84 337	82.46 259	85.99 300	68.76 285	86.78 201	93.73 118	59.13 360	77.44 432	73.71 236	97.55 77	92.56 211

GA-MVS		75.83 337	74.61 345	79.48 315	81.87 388	59.25 362	73.42 414	82.88 351	68.68 286	79.75 360	81.80 414	50.62 413	89.46 279	66.85 316	85.64 412	89.72 312

dcpmvs_2		84.23 172	85.14 149	81.50 270	88.61 220	61.98 310	82.90 247	93.11 96	68.66 287	92.77 57	92.39 172	78.50 170	87.63 327	76.99 182	92.30 279	94.90 75

fmvsm_s_conf0.5_n_4		84.38 164	84.27 181	84.74 162	87.25 264	70.84 192	83.55 223	88.45 247	68.64 288	86.29 219	91.31 219	74.97 222	88.42 309	87.87 19	90.07 347	94.95 74

GDP-MVS		82.17 233	80.85 264	86.15 129	88.65 218	68.95 224	85.65 160	93.02 104	68.42 289	83.73 289	89.54 286	45.07 446	94.31 95	79.66 138	93.87 218	95.19 67

c3_l		81.64 247	81.59 243	81.79 265	80.86 406	59.15 366	78.61 339	90.18 211	68.36 290	87.20 189	87.11 341	69.39 291	91.62 193	78.16 160	94.43 199	94.60 93

CLD-MVS		83.18 209	82.64 223	84.79 160	89.05 203	67.82 236	77.93 348	92.52 123	68.33 291	85.07 252	81.54 417	82.06 127	92.96 158	69.35 292	97.91 53	93.57 158
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CL-MVSNet_self_test		76.81 321	77.38 312	75.12 382	86.90 282	51.34 440	73.20 416	80.63 374	68.30 292	81.80 333	88.40 308	66.92 307	80.90 412	55.35 414	94.90 181	93.12 181

testing91		69.94 403	68.99 407	72.80 403	83.81 361	45.89 465	71.57 430	73.64 422	68.24 293	70.77 446	77.82 448	34.37 472	84.44 387	53.64 424	87.00 398	88.07 353

PLC	73.85 16	82.09 236	80.31 271	87.45 100	90.86 162	80.29 74	85.88 153	90.65 188	68.17 294	76.32 403	86.33 351	73.12 259	92.61 168	61.40 374	90.02 349	89.44 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_s_conf0.5_n_7		82.04 238	82.05 232	82.01 256	86.98 279	71.07 189	78.70 336	89.45 230	68.07 295	78.14 382	91.61 206	74.19 236	85.92 365	79.61 139	91.73 300	89.05 335

Fast-Effi-MVS+		81.04 259	80.57 266	82.46 245	87.50 256	63.22 285	78.37 342	89.63 226	68.01 296	81.87 329	82.08 411	82.31 117	92.65 167	67.10 314	88.30 378	91.51 265

LF4IMVS		82.75 219	81.93 235	85.19 148	82.08 386	80.15 75	85.53 162	88.76 240	68.01 296	85.58 239	87.75 325	71.80 277	86.85 343	74.02 230	93.87 218	88.58 344

QAPM		82.59 221	82.59 225	82.58 239	86.44 292	66.69 248	89.94 72	90.36 200	67.97 298	84.94 258	92.58 166	72.71 264	92.18 179	70.63 278	87.73 386	88.85 340

v1921920		84.23 172	84.37 178	83.79 195	87.64 252	61.71 316	82.91 246	91.20 171	67.94 299	90.06 106	90.34 263	72.04 274	93.59 133	82.32 109	94.91 180	96.07 40

v1240		84.30 168	84.51 173	83.65 200	87.65 251	61.26 325	82.85 248	91.54 156	67.94 299	90.68 98	90.65 254	71.71 279	93.64 127	82.84 102	94.78 187	96.07 40

fmvsm_s_conf0.5_n_6		84.05 179	84.14 183	83.81 193	87.75 246	71.17 188	83.42 227	91.10 174	67.90 301	84.53 267	90.70 248	73.01 260	88.73 297	85.09 69	93.72 226	91.53 264

TSAR-MVS + MP.		88.14 80	87.82 90	89.09 68	95.72 21	76.74 114	92.49 26	91.19 172	67.85 302	86.63 208	94.84 58	79.58 161	95.96 14	87.62 24	94.50 195	94.56 94
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v144192		84.24 171	84.41 176	83.71 199	87.59 253	61.57 317	82.95 245	91.03 176	67.82 303	89.80 115	90.49 260	73.28 257	93.51 139	81.88 117	94.89 182	96.04 42

diffmvs_AUTHOR		81.24 255	81.55 246	80.30 299	80.61 411	60.22 345	77.98 347	90.48 193	67.77 304	83.34 299	89.50 287	74.69 229	87.42 331	78.78 150	90.81 329	93.27 171

fmvsm_l_conf0.5_n_9		83.98 184	84.46 174	82.53 242	86.11 309	70.65 195	82.45 261	89.17 235	67.72 305	86.74 204	91.49 210	79.20 162	85.86 371	84.71 80	92.60 270	91.07 273

fmvsm_l_conf0.5_n_3		85.11 144	84.96 154	85.56 140	87.49 257	75.69 130	84.71 183	90.61 191	67.64 306	84.88 259	92.05 186	82.30 118	88.36 311	83.84 90	91.10 314	92.62 206

myMVS_eth3d28		65.83 431	65.85 427	65.78 451	83.42 369	35.71 491	67.29 459	68.01 458	67.58 307	69.80 451	77.72 451	32.29 477	74.30 445	37.49 486	89.06 363	87.32 369

DIV-MVS_self_test		80.43 270	80.23 273	81.02 282	79.99 420	59.25 362	77.07 364	87.02 284	67.38 308	86.19 220	89.22 293	63.09 336	90.16 257	76.32 192	95.80 147	93.66 147

cl____		80.42 271	80.23 273	81.02 282	79.99 420	59.25 362	77.07 364	87.02 284	67.37 309	86.18 222	89.21 294	63.08 337	90.16 257	76.31 193	95.80 147	93.65 150

testing99		69.27 409	68.15 415	72.63 405	83.29 374	45.45 467	71.15 432	71.08 444	67.34 310	70.43 447	77.77 450	32.24 478	84.35 389	53.72 423	86.33 406	88.10 352

eth_miper_zixun_eth		80.84 262	80.22 275	82.71 231	81.41 397	60.98 335	77.81 350	90.14 212	67.31 311	86.95 199	87.24 338	64.26 324	92.31 176	75.23 211	91.61 303	94.85 85

fmvsm_s_conf0.1_n_2		83.82 189	83.49 197	84.84 157	85.99 313	70.19 202	80.93 296	87.58 268	67.26 312	87.94 167	92.37 176	71.40 281	88.01 315	86.03 56	91.87 296	96.31 35

fmvsm_s_conf0.5_n_2		83.62 196	83.29 204	84.62 167	85.43 327	70.18 203	80.61 304	87.24 274	67.14 313	87.79 173	91.87 191	71.79 278	87.98 317	86.00 60	91.77 299	95.71 49

VortexMVS		80.51 268	80.63 265	80.15 303	83.36 372	61.82 315	80.63 303	88.00 260	67.11 314	87.23 188	89.10 297	63.98 328	88.00 316	73.63 240	92.63 265	90.64 292

EMVS		61.10 450	60.81 449	61.99 463	65.96 496	55.86 400	53.10 490	58.97 488	67.06 315	56.89 494	63.33 487	40.98 459	67.03 474	54.79 418	86.18 408	63.08 486

OpenMVS	76.72 13	81.98 241	82.00 233	81.93 257	84.42 346	68.22 230	88.50 107	89.48 229	66.92 316	81.80 333	91.86 194	72.59 266	90.16 257	71.19 270	91.25 312	87.40 368

testgi		72.36 375	74.61 345	65.59 452	80.56 412	42.82 477	68.29 451	73.35 423	66.87 317	81.84 330	89.93 278	72.08 273	66.92 475	46.05 467	92.54 272	87.01 373

E-PMN		61.59 447	61.62 447	61.49 465	66.81 493	55.40 407	53.77 489	60.34 485	66.80 318	58.90 488	65.50 486	40.48 461	66.12 478	55.72 409	86.25 407	62.95 487

diffmvs		80.40 272	80.48 270	80.17 302	79.02 433	60.04 347	77.54 355	90.28 208	66.65 319	82.40 316	87.33 336	73.50 250	87.35 333	77.98 168	89.62 355	93.13 178
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet_dtu		72.87 372	71.33 384	77.49 354	77.72 438	60.55 341	82.35 265	75.79 403	66.49 320	58.39 490	81.06 420	53.68 399	85.98 363	53.55 425	92.97 255	85.95 384
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gbinet_0.2-2-1-0.02		76.14 331	74.88 343	79.92 305	80.33 418	60.02 350	75.80 385	82.44 356	66.36 321	79.24 370	75.07 472	56.11 388	90.17 256	64.60 344	93.95 215	89.58 316

viewdifsd2359ckpt13		82.22 230	81.98 234	82.95 223	85.48 326	64.44 271	83.17 238	92.11 136	65.97 322	83.72 290	89.73 283	77.60 182	90.80 233	70.61 279	89.42 357	93.59 156

test_fmvsmconf0.01_n		86.68 103	86.52 112	87.18 102	85.94 314	78.30 91	86.93 130	92.20 133	65.94 323	89.16 132	93.16 140	83.10 102	89.89 269	87.81 20	94.43 199	93.35 166

baseline1		73.26 367	73.54 358	72.43 409	84.92 336	47.79 457	79.89 313	74.00 415	65.93 324	78.81 376	86.28 354	56.36 382	81.63 408	56.63 402	79.04 464	87.87 362

CDPH-MVS		86.17 117	85.54 139	88.05 93	92.25 110	75.45 131	83.85 208	92.01 139	65.91 325	86.19 220	91.75 203	83.77 95	94.98 72	77.43 176	96.71 102	93.73 144

reproduce_monomvs		74.09 360	73.23 362	76.65 367	76.52 449	54.54 414	77.50 357	81.40 368	65.85 326	82.86 309	86.67 346	27.38 493	84.53 385	70.24 283	90.66 340	90.89 280

cl22		78.97 289	78.21 304	81.24 278	77.74 437	59.01 368	77.46 359	87.13 278	65.79 327	84.32 275	85.10 373	58.96 362	90.88 229	75.36 209	92.03 290	93.84 136

train_agg		85.98 119	85.28 147	88.07 92	92.34 107	79.70 79	83.94 204	90.32 202	65.79 327	84.49 269	90.97 234	81.93 130	93.63 128	81.21 119	96.54 107	90.88 281

test_8					92.09 116	78.87 87	83.82 209	90.31 204	65.79 327	84.36 273	90.96 236	81.93 130	93.44 142

miper_ehance_all_eth		80.34 274	80.04 280	81.24 278	79.82 423	58.95 369	77.66 352	89.66 224	65.75 330	85.99 230	85.11 372	68.29 298	91.42 202	76.03 198	92.03 290	93.33 167

BH-w/o		76.57 325	76.07 328	78.10 341	86.88 283	65.92 258	77.63 353	86.33 291	65.69 331	80.89 346	79.95 430	68.97 296	90.74 235	53.01 430	85.25 416	77.62 468

MAR-MVS		80.24 278	78.74 296	84.73 163	86.87 284	78.18 94	85.75 157	87.81 266	65.67 332	77.84 386	78.50 443	73.79 246	90.53 243	61.59 371	90.87 324	85.49 391
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
viewdifsd2359ckpt09		83.64 194	83.18 208	85.03 153	87.26 263	66.99 245	85.32 168	93.83 56	65.57 333	84.99 255	89.40 288	77.30 188	93.57 136	71.16 271	93.80 220	94.54 97

xiu_mvs_v1_base_debu		80.84 262	80.14 277	82.93 226	88.31 226	71.73 177	79.53 317	87.17 275	65.43 334	79.59 361	82.73 405	76.94 198	90.14 260	73.22 249	88.33 374	86.90 375

xiu_mvs_v1_base		80.84 262	80.14 277	82.93 226	88.31 226	71.73 177	79.53 317	87.17 275	65.43 334	79.59 361	82.73 405	76.94 198	90.14 260	73.22 249	88.33 374	86.90 375

xiu_mvs_v1_base_debi		80.84 262	80.14 277	82.93 226	88.31 226	71.73 177	79.53 317	87.17 275	65.43 334	79.59 361	82.73 405	76.94 198	90.14 260	73.22 249	88.33 374	86.90 375

TEST9					92.34 107	79.70 79	83.94 204	90.32 202	65.41 337	84.49 269	90.97 234	82.03 128	93.63 128

test_fmvsmconf0.1_n		86.18 116	85.88 130	87.08 104	85.26 330	78.25 92	85.82 156	91.82 147	65.33 338	88.55 145	92.35 179	82.62 111	89.80 271	86.87 41	94.32 203	93.18 177

test_fmvsmconf_n		85.88 122	85.51 140	86.99 107	84.77 339	78.21 93	85.40 167	91.39 162	65.32 339	87.72 177	91.81 199	82.33 116	89.78 272	86.68 43	94.20 206	92.99 189

icg_test_0407_2		78.46 300	79.68 283	74.78 386	85.76 318	62.46 297	68.51 450	87.91 262	65.23 340	82.12 323	87.92 318	77.27 190	72.67 448	71.67 263	90.74 332	89.20 326

IMVS_0407		81.08 257	81.23 256	80.62 293	85.76 318	62.46 297	82.46 259	87.91 262	65.23 340	82.12 323	87.92 318	77.27 190	90.18 255	71.67 263	90.74 332	89.20 326

IMVS_0404		77.24 314	77.75 309	75.73 377	85.76 318	62.46 297	70.84 436	87.91 262	65.23 340	72.21 436	87.92 318	67.48 301	75.53 440	71.67 263	90.74 332	89.20 326

IMVS_0403		80.93 261	81.00 259	80.72 289	85.76 318	62.46 297	81.82 276	87.91 262	65.23 340	82.07 325	87.92 318	75.91 212	90.50 244	71.67 263	90.74 332	89.20 326

TR-MVS		76.77 322	75.79 329	79.72 310	86.10 310	65.79 259	77.14 362	83.02 350	65.20 344	81.40 340	82.10 409	66.30 309	90.73 236	55.57 411	85.27 415	82.65 429

tpmvs		70.16 397	69.56 401	71.96 412	74.71 466	48.13 454	79.63 315	75.45 408	65.02 345	70.26 448	81.88 413	45.34 442	85.68 374	58.34 394	75.39 474	82.08 440

blend_shiyan4		70.82 391	68.15 415	78.83 324	81.06 402	59.77 354	74.58 401	83.79 339	64.94 346	77.34 397	75.47 470	29.39 486	88.89 290	58.91 389	67.86 490	87.84 363

IterMVS		76.91 319	76.34 325	78.64 328	80.91 404	64.03 275	76.30 377	79.03 381	64.88 347	83.11 303	89.16 295	59.90 354	84.46 386	68.61 305	85.15 419	87.42 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		76.05 334	75.11 338	78.86 322	81.76 390	59.18 365	75.09 394	83.81 338	64.70 348	79.37 365	78.35 445	58.30 366	88.68 299	62.03 364	92.56 271	88.73 342

blended_shiyan6		76.05 334	75.11 338	78.87 321	81.74 391	59.15 366	75.08 395	83.79 339	64.69 349	79.37 365	78.37 444	58.30 366	88.69 298	61.99 365	92.61 266	88.77 341

AUN-MVS		81.18 256	78.78 294	88.39 83	90.93 159	82.14 61	82.51 258	83.67 342	64.69 349	80.29 355	85.91 360	51.07 410	92.38 173	76.29 194	93.63 229	90.65 291

fmvsm_s_conf0.5_n_5		84.56 159	84.71 162	84.11 186	87.92 241	72.09 172	84.80 177	88.64 242	64.43 351	88.77 139	91.78 201	78.07 174	87.95 318	85.85 62	92.18 287	92.30 230

PatchMatch-RL		74.48 356	73.22 363	78.27 339	87.70 248	85.26 37	75.92 384	70.09 448	64.34 352	76.09 407	81.25 419	65.87 315	78.07 430	53.86 422	83.82 434	71.48 477

testing222		66.93 420	65.30 433	71.81 413	83.38 370	45.83 466	72.06 426	67.50 459	64.12 353	69.68 452	76.37 463	27.34 494	83.00 398	38.88 481	88.38 373	86.62 378

wanda-best-256-512		74.97 349	73.85 353	78.35 334	80.36 415	58.13 379	73.10 418	83.53 344	64.04 354	77.62 391	75.71 466	56.22 385	88.60 305	61.42 372	92.61 266	88.32 347

FE-blended-shiyan7		74.97 349	73.85 353	78.35 334	80.36 415	58.13 379	73.10 418	83.53 344	64.03 355	77.62 391	75.71 466	56.22 385	88.60 305	61.42 372	92.61 266	88.32 347

miper_lstm_enhance		76.45 328	76.10 327	77.51 353	76.72 448	60.97 336	64.69 468	85.04 319	63.98 356	83.20 302	88.22 310	56.67 380	78.79 428	73.22 249	93.12 250	92.78 197

SD_0403		76.08 332	76.77 319	73.98 391	87.08 275	49.45 451	83.62 217	84.68 331	63.31 357	75.13 420	87.47 332	71.85 276	84.56 384	49.97 445	87.86 384	87.94 359

FMVSNet5		72.10 378	71.69 378	73.32 397	81.57 395	53.02 428	76.77 368	78.37 385	63.31 357	76.37 401	91.85 195	36.68 469	78.98 425	47.87 459	92.45 274	87.95 358

IB-MVS	62.13 19	71.64 382	68.97 408	79.66 312	80.80 408	62.26 306	73.94 407	76.90 396	63.27 359	68.63 457	76.79 459	33.83 473	91.84 190	59.28 388	87.26 390	84.88 396
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
mvsmamba		80.30 276	78.87 291	84.58 169	88.12 237	67.55 237	92.35 30	84.88 326	63.15 360	85.33 245	90.91 239	50.71 412	95.20 65	66.36 321	87.98 381	90.99 276

new-patchmatchnet		70.10 398	73.37 361	60.29 469	81.23 400	16.95 504	59.54 480	74.62 410	62.93 361	80.97 343	87.93 317	62.83 340	71.90 451	55.24 415	95.01 178	92.00 247

PVSNet_Blended_VisFu		81.55 249	80.49 269	84.70 165	91.58 136	73.24 148	84.21 196	91.67 152	62.86 362	80.94 345	87.16 339	67.27 303	92.87 163	69.82 288	88.94 366	87.99 357

原ACMM1				84.60 168	92.81 97	74.01 139		91.50 157	62.59 363	82.73 313	90.67 253	76.53 207	94.25 98	69.24 293	95.69 152	85.55 389

PAPR		78.84 293	78.10 306	81.07 280	85.17 333	60.22 345	82.21 271	90.57 192	62.51 364	75.32 417	84.61 381	74.99 221	92.30 177	59.48 385	88.04 380	90.68 288

usedtu_dtu_shiyan1		75.70 340	75.08 340	77.56 350	84.10 354	55.50 405	73.58 410	84.89 324	62.48 365	78.16 380	84.24 385	58.14 368	87.47 329	59.35 386	90.82 327	89.72 312

FE-MVSNET3		75.70 340	75.08 340	77.56 350	84.10 354	55.50 405	73.58 410	84.89 324	62.48 365	78.16 380	84.24 385	58.14 368	87.47 329	59.34 387	90.82 327	89.72 312

Patchmatch-test		65.91 429	67.38 418	61.48 466	75.51 459	43.21 476	68.84 448	63.79 475	62.48 365	72.80 433	83.42 396	44.89 448	59.52 489	48.27 458	86.45 403	81.70 442

testing11		67.38 418	65.93 426	71.73 414	83.37 371	46.60 462	70.95 435	69.40 452	62.47 368	66.14 465	76.66 460	31.22 480	84.10 391	49.10 452	84.10 433	84.49 400

OpenMVS_ROB	70.19 17	77.77 309	77.46 310	78.71 327	84.39 347	61.15 326	81.18 291	82.52 354	62.45 369	83.34 299	87.37 334	66.20 310	88.66 301	64.69 341	85.02 421	86.32 380

fmvsm_s_conf0.5_n		81.91 243	81.30 253	83.75 197	86.02 311	71.56 183	84.73 182	77.11 395	62.44 370	84.00 284	90.68 250	76.42 209	85.89 369	83.14 94	87.11 393	93.81 141

test-LLR		67.21 419	66.74 423	68.63 436	76.45 452	55.21 409	67.89 452	67.14 463	62.43 371	65.08 473	72.39 476	43.41 453	69.37 459	61.00 375	84.89 425	81.31 447

test0.0.03 1		64.66 436	64.36 435	65.57 453	75.03 464	46.89 461	64.69 468	61.58 483	62.43 371	71.18 442	77.54 452	43.41 453	68.47 468	40.75 479	82.65 444	81.35 446

fmvsm_s_conf0.1_n		82.17 233	81.59 243	83.94 192	86.87 284	71.57 182	85.19 172	77.42 391	62.27 373	84.47 271	91.33 217	76.43 208	85.91 367	83.14 94	87.14 392	94.33 112

MCST-MVS		84.36 165	83.93 189	85.63 138	91.59 133	71.58 181	83.52 224	92.13 135	61.82 374	83.96 285	89.75 282	79.93 159	93.46 141	78.33 156	94.34 202	91.87 251

fmvsm_s_conf0.5_n_a		82.21 231	81.51 248	84.32 179	86.56 286	73.35 144	85.46 164	77.30 392	61.81 375	84.51 268	90.88 242	77.36 186	86.21 359	82.72 104	86.97 399	93.38 165

SCA		73.32 366	72.57 372	75.58 380	81.62 394	55.86 400	78.89 333	71.37 443	61.73 376	74.93 421	83.42 396	60.46 348	87.01 336	58.11 397	82.63 446	83.88 410

TAMVS		78.08 305	76.36 324	83.23 214	90.62 166	72.87 154	79.08 330	80.01 377	61.72 377	81.35 341	86.92 344	63.96 330	88.78 295	50.61 443	93.01 253	88.04 356

PVSNet_BlendedMVS		78.80 294	77.84 307	81.65 267	84.43 344	63.41 281	79.49 320	90.44 196	61.70 378	75.43 414	87.07 342	69.11 294	91.44 200	60.68 378	92.24 283	90.11 306

fmvsm_s_conf0.1_n_a		82.58 222	81.93 235	84.50 170	87.68 249	73.35 144	86.14 150	77.70 388	61.64 379	85.02 253	91.62 205	77.75 178	86.24 357	82.79 103	87.07 394	93.91 133

mvs_anonymous		78.13 304	78.76 295	76.23 373	79.24 430	50.31 448	78.69 337	84.82 328	61.60 380	83.09 305	92.82 157	73.89 244	87.01 336	68.33 309	86.41 404	91.37 266

test_fmvsmvis_n_1920		85.22 136	85.36 145	84.81 159	85.80 317	76.13 124	85.15 173	92.32 130	61.40 381	91.33 81	90.85 243	83.76 96	86.16 361	84.31 84	93.28 243	92.15 241

Syy-MVS		69.40 408	70.03 397	67.49 443	81.72 392	38.94 485	71.00 433	61.99 477	61.38 382	70.81 444	72.36 478	61.37 344	79.30 423	64.50 346	85.18 417	84.22 406

myMVS_eth3d		64.66 436	63.89 437	66.97 446	81.72 392	37.39 488	71.00 433	61.99 477	61.38 382	70.81 444	72.36 478	20.96 501	79.30 423	49.59 449	85.18 417	84.22 406

ETVMVS		64.67 435	63.34 441	68.64 435	83.44 368	41.89 478	69.56 447	61.70 482	61.33 384	68.74 455	75.76 465	28.76 489	79.35 422	34.65 489	86.16 409	84.67 399

PS-MVSNAJ		77.04 318	76.53 322	78.56 329	87.09 273	61.40 320	75.26 392	87.13 278	61.25 385	74.38 425	77.22 457	76.94 198	90.94 224	64.63 342	84.83 427	83.35 421

xiu_mvs_v2_base		77.19 315	76.75 320	78.52 330	87.01 277	61.30 323	75.55 390	87.12 282	61.24 386	74.45 423	78.79 441	77.20 192	90.93 225	64.62 343	84.80 428	83.32 422

KD-MVS_2432*1600		66.87 422	65.81 429	70.04 421	67.50 491	47.49 458	62.56 474	79.16 379	61.21 387	77.98 384	80.61 422	25.29 498	82.48 401	53.02 428	84.92 422	80.16 459

miper_refine_blended		66.87 422	65.81 429	70.04 421	67.50 491	47.49 458	62.56 474	79.16 379	61.21 387	77.98 384	80.61 422	25.29 498	82.48 401	53.02 428	84.92 422	80.16 459

patch_mono-2		78.89 291	79.39 286	77.41 355	84.78 338	68.11 232	75.60 387	83.11 349	60.96 389	79.36 367	89.89 280	75.18 219	72.97 447	73.32 248	92.30 279	91.15 271

CDS-MVSNet		77.32 313	75.40 334	83.06 218	89.00 205	72.48 165	77.90 349	82.17 360	60.81 390	78.94 375	83.49 394	59.30 358	88.76 296	54.64 420	92.37 277	87.93 360
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		77.09 316	75.70 331	81.25 275	75.27 462	61.08 328	77.49 358	85.07 317	60.78 391	86.55 209	88.68 303	43.14 456	90.25 250	73.69 239	90.67 338	92.42 218

XXY-MVS		74.44 358	76.19 326	69.21 430	84.61 342	52.43 433	71.70 428	77.18 394	60.73 392	80.60 349	90.96 236	75.44 215	69.35 461	56.13 406	88.33 374	85.86 386

ET-MVSNet_ETH3D		75.28 343	72.77 368	82.81 230	83.03 382	68.11 232	77.09 363	76.51 400	60.67 393	77.60 394	80.52 425	38.04 465	91.15 217	70.78 274	90.68 337	89.17 330

dmvs_testset		60.59 453	62.54 445	54.72 475	77.26 441	27.74 498	74.05 405	61.00 484	60.48 394	65.62 470	67.03 485	55.93 389	68.23 470	32.07 493	69.46 488	68.17 482

viewmambaseed2359dif		78.80 294	78.47 301	79.78 307	80.26 419	59.28 361	77.31 361	87.13 278	60.42 395	82.37 317	88.67 305	74.58 231	87.87 322	67.78 313	87.73 386	92.19 238

MVP-Stereo		75.81 338	73.51 359	82.71 231	89.35 193	73.62 141	80.06 309	85.20 314	60.30 396	73.96 426	87.94 315	57.89 374	89.45 280	52.02 436	74.87 475	85.06 395
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
dmvs_re		66.81 424	66.98 420	66.28 449	76.87 446	58.68 376	71.66 429	72.24 434	60.29 397	69.52 454	73.53 475	52.38 404	64.40 483	44.90 469	81.44 451	75.76 471

DPM-MVS		80.10 282	79.18 289	82.88 229	90.71 165	69.74 207	78.87 334	90.84 183	60.29 397	75.64 413	85.92 359	67.28 302	93.11 153	71.24 269	91.79 297	85.77 387

MIMVSNet		71.09 388	71.59 379	69.57 428	87.23 265	50.07 449	78.91 332	71.83 439	60.20 399	71.26 440	91.76 202	55.08 396	76.09 436	41.06 477	87.02 397	82.54 433

testdata				79.54 314	92.87 91	72.34 167		80.14 376	59.91 400	85.47 242	91.75 203	67.96 300	85.24 377	68.57 307	92.18 287	81.06 454

test_fmvsm_n_1920		83.60 197	82.89 216	85.74 136	85.22 331	77.74 101	84.12 199	90.48 193	59.87 401	86.45 218	91.12 228	75.65 214	85.89 369	82.28 110	90.87 324	93.58 157

UnsupCasMVSNet_eth		71.63 383	72.30 375	69.62 427	76.47 451	52.70 431	70.03 443	80.97 371	59.18 402	79.36 367	88.21 311	60.50 347	69.12 462	58.33 395	77.62 469	87.04 372

fmvsm_l_conf0.5_n		82.06 237	81.54 247	83.60 202	83.94 357	73.90 140	83.35 230	86.10 295	58.97 403	83.80 288	90.36 262	74.23 235	86.94 340	82.90 100	90.22 345	89.94 309

PC_three_1452									58.96 404	90.06 106	91.33 217	80.66 150	93.03 157	75.78 201	95.94 137	92.48 215

our_test_3		71.85 379	71.59 379	72.62 406	80.71 409	53.78 422	69.72 445	71.71 442	58.80 405	78.03 383	80.51 426	56.61 381	78.84 427	62.20 361	86.04 410	85.23 392

MDA-MVSNet_test_wron		70.05 400	70.44 391	68.88 433	73.84 469	53.47 424	58.93 484	67.28 461	58.43 406	87.09 194	85.40 368	59.80 356	67.25 473	59.66 384	83.54 436	85.92 385

YYNet1		70.06 399	70.44 391	68.90 432	73.76 470	53.42 426	58.99 483	67.20 462	58.42 407	87.10 193	85.39 369	59.82 355	67.32 472	59.79 383	83.50 437	85.96 383

ppachtmachnet_test		74.73 355	74.00 352	76.90 362	80.71 409	56.89 394	71.53 431	78.42 384	58.24 408	79.32 369	82.92 402	57.91 373	84.26 390	65.60 332	91.36 308	89.56 317

fmvsm_l_conf0.5_n_a		81.46 250	80.87 263	83.25 213	83.73 362	73.21 149	83.00 243	85.59 308	58.22 409	82.96 306	90.09 276	72.30 269	86.65 347	81.97 115	89.95 350	89.88 310

无先验							82.81 249	85.62 307	58.09 410				91.41 203	67.95 312		84.48 401

miper_enhance_ethall		77.83 306	76.93 317	80.51 295	76.15 454	58.01 384	75.47 391	88.82 238	58.05 411	83.59 293	80.69 421	64.41 322	91.20 214	73.16 255	92.03 290	92.33 229

thisisatest0515		73.00 371	70.52 390	80.46 296	81.45 396	59.90 352	73.16 417	74.31 414	57.86 412	76.08 408	77.78 449	37.60 468	92.12 182	65.00 337	91.45 307	89.35 321

Patchmatch-RL test		74.48 356	73.68 356	76.89 363	84.83 337	66.54 249	72.29 424	69.16 455	57.70 413	86.76 202	86.33 351	45.79 436	82.59 400	69.63 290	90.65 341	81.54 445

PatchmatchNet		69.71 405	68.83 409	72.33 411	77.66 439	53.60 423	79.29 325	69.99 449	57.66 414	72.53 434	82.93 401	46.45 427	80.08 420	60.91 377	72.09 481	83.31 423
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
D2MVS		76.84 320	75.67 332	80.34 298	80.48 413	62.16 309	73.50 413	84.80 329	57.61 415	82.24 319	87.54 329	51.31 409	87.65 325	70.40 282	93.19 249	91.23 268

baseline2		69.77 404	66.89 421	78.41 333	79.51 426	58.09 381	76.23 379	69.57 451	57.50 416	64.82 476	77.45 454	46.02 430	88.44 308	53.08 427	77.83 466	88.70 343

dongtai		41.90 461	42.65 464	39.67 478	70.86 485	21.11 500	61.01 478	21.42 505	57.36 417	57.97 491	50.06 494	16.40 503	58.73 491	21.03 497	27.69 498	39.17 494

PVSNet_Blended		76.49 327	75.40 334	79.76 309	84.43 344	63.41 281	75.14 393	90.44 196	57.36 417	75.43 414	78.30 446	69.11 294	91.44 200	60.68 378	87.70 388	84.42 403

PCF-MVS	74.62 15	82.15 235	80.92 262	85.84 134	89.43 192	72.30 168	80.53 305	91.82 147	57.36 417	87.81 172	89.92 279	77.67 181	93.63 128	58.69 391	95.08 173	91.58 262
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WBMVS		68.76 413	68.43 412	69.75 426	83.29 374	40.30 483	67.36 458	72.21 436	57.09 420	77.05 398	85.53 364	33.68 474	80.51 416	48.79 454	90.90 322	88.45 346

IU-MVS					94.18 54	72.64 158		90.82 184	56.98 421	89.67 119				85.78 63	97.92 51	93.28 170

旧先验2							81.73 278		56.88 422	86.54 215			84.90 381	72.81 256

HY-MVS	64.64 18	73.03 370	72.47 374	74.71 387	83.36 372	54.19 419	82.14 274	81.96 361	56.76 423	69.57 453	86.21 355	60.03 352	84.83 382	49.58 450	82.65 444	85.11 394

cascas		76.29 330	74.81 344	80.72 289	84.47 343	62.94 287	73.89 408	87.34 270	55.94 424	75.16 419	76.53 462	63.97 329	91.16 216	65.00 337	90.97 320	88.06 355

ttmdpeth		71.72 381	70.67 387	74.86 384	73.08 477	55.88 399	77.41 360	69.27 453	55.86 425	78.66 377	93.77 116	38.01 466	75.39 441	60.12 381	89.87 351	93.31 169

pmmvs-eth3d		78.42 303	77.04 316	82.57 241	87.44 260	74.41 137	80.86 298	79.67 378	55.68 426	84.69 265	90.31 268	60.91 346	85.42 376	62.20 361	91.59 304	87.88 361

0.4-1-1-0.1		64.02 440	60.59 450	74.31 390	73.99 467	55.62 403	67.66 456	72.78 430	55.53 427	60.35 484	58.45 490	29.26 487	86.88 341	52.84 432	74.42 476	80.42 458

新几何1				82.95 223	93.96 63	78.56 90		80.24 375	55.45 428	83.93 286	91.08 230	71.19 282	88.33 312	65.84 329	93.07 251	81.95 441

WB-MVSnew		68.72 414	69.01 406	67.85 440	83.22 378	43.98 473	74.93 397	65.98 468	55.09 429	73.83 427	79.11 436	65.63 317	71.89 452	38.21 485	85.04 420	87.69 365

N_pmnet		70.20 396	68.80 410	74.38 389	80.91 404	84.81 42	59.12 482	76.45 401	55.06 430	75.31 418	82.36 408	55.74 390	54.82 492	47.02 461	87.24 391	83.52 417

tpm		67.95 416	68.08 417	67.55 442	78.74 435	43.53 475	75.60 387	67.10 465	54.92 431	72.23 435	88.10 312	42.87 457	75.97 437	52.21 434	80.95 456	83.15 425

UWE-MVS		66.43 426	65.56 432	69.05 431	84.15 352	40.98 481	73.06 420	64.71 473	54.84 432	76.18 406	79.62 434	29.21 488	80.50 417	38.54 484	89.75 353	85.66 388

UBG		64.34 438	63.35 440	67.30 444	83.50 365	40.53 482	67.46 457	65.02 472	54.77 433	67.54 463	74.47 474	32.99 476	78.50 429	40.82 478	83.58 435	82.88 428

114514_t		83.10 212	82.54 226	84.77 161	92.90 90	69.10 220	86.65 139	90.62 190	54.66 434	81.46 339	90.81 245	76.98 197	94.38 94	72.62 257	96.18 123	90.82 283

1112_ss		74.82 353	73.74 355	78.04 343	89.57 187	60.04 347	76.49 375	87.09 283	54.31 435	73.66 429	79.80 431	60.25 351	86.76 346	58.37 393	84.15 432	87.32 369

0.3-1-1-0.015		62.57 441	58.82 456	73.82 394	71.85 483	54.96 412	65.63 465	72.97 428	54.16 436	56.95 493	55.43 491	26.76 497	86.59 349	52.05 435	73.55 478	79.92 462

UnsupCasMVSNet_bld		69.21 410	69.68 400	67.82 441	79.42 427	51.15 443	67.82 455	75.79 403	54.15 437	77.47 396	85.36 371	59.26 359	70.64 456	48.46 456	79.35 460	81.66 443

EPMVS		62.47 442	62.63 444	62.01 462	70.63 487	38.74 486	74.76 398	52.86 493	53.91 438	67.71 462	80.01 429	39.40 462	66.60 476	55.54 412	68.81 489	80.68 456

0.4-1-1-0.2		62.43 444	58.81 457	73.31 398	70.85 486	54.20 418	64.36 470	72.99 427	53.70 439	57.51 492	54.59 492	29.52 485	86.44 353	51.70 442	74.02 477	79.30 464

WTY-MVS		67.91 417	68.35 413	66.58 448	80.82 407	48.12 455	65.96 464	72.60 431	53.67 440	71.20 441	81.68 416	58.97 361	69.06 463	48.57 455	81.67 448	82.55 432

MVStest1		70.05 400	69.26 402	72.41 410	58.62 501	55.59 404	76.61 373	65.58 469	53.44 441	89.28 131	93.32 127	22.91 500	71.44 455	74.08 229	89.52 356	90.21 305

PAPM		71.77 380	70.06 396	76.92 361	86.39 294	53.97 420	76.62 372	86.62 289	53.44 441	63.97 478	84.73 380	57.79 375	92.34 175	39.65 480	81.33 452	84.45 402

PMMVS2		55.64 459	59.27 455	44.74 477	64.30 499	12.32 505	40.60 492	49.79 495	53.19 443	65.06 475	84.81 378	53.60 400	49.76 495	32.68 492	89.41 358	72.15 476

tpmrst		66.28 428	66.69 424	65.05 456	72.82 479	39.33 484	78.20 343	70.69 447	53.16 444	67.88 460	80.36 427	48.18 421	74.75 443	58.13 396	70.79 483	81.08 452

UWE-MVS-28		58.44 456	57.71 458	60.65 468	73.58 472	31.23 495	69.68 446	48.80 496	53.12 445	61.79 480	78.83 440	30.98 481	68.40 469	21.58 496	80.99 455	82.33 437

pmmvs4		74.92 351	72.98 366	80.73 288	84.95 335	71.71 180	76.23 379	77.59 389	52.83 446	77.73 390	86.38 349	56.35 383	84.97 380	57.72 399	87.05 395	85.51 390

test222					93.31 80	76.54 115	79.38 324	77.79 387	52.59 447	82.36 318	90.84 244	66.83 308			91.69 301	81.25 449

Anonymous20231206		71.38 386	71.88 377	69.88 424	86.31 300	54.37 416	70.39 440	74.62 410	52.57 448	76.73 399	88.76 301	59.94 353	72.06 450	44.35 471	93.23 247	83.23 424

MS-PatchMatch		70.93 390	70.22 394	73.06 401	81.85 389	62.50 296	73.82 409	77.90 386	52.44 449	75.92 409	81.27 418	55.67 391	81.75 406	55.37 413	77.70 468	74.94 473

gm-plane-assit					75.42 461	44.97 471			52.17 450		72.36 478		87.90 320	54.10 421

MDTV_nov1_ep13			68.29 414		78.03 436	43.87 474	74.12 404	72.22 435	52.17 450	67.02 464	85.54 363	45.36 441	80.85 413	55.73 408	84.42 430

USDC		76.63 324	76.73 321	76.34 370	83.46 367	57.20 391	80.02 311	88.04 259	52.14 452	83.65 292	91.25 222	63.24 334	86.65 347	54.66 419	94.11 209	85.17 393

sss		66.92 421	67.26 419	65.90 450	77.23 442	51.10 445	64.79 467	71.72 441	52.12 453	70.13 449	80.18 428	57.96 372	65.36 481	50.21 444	81.01 454	81.25 449

CostFormer		69.98 402	68.68 411	73.87 392	77.14 443	50.72 446	79.26 326	74.51 412	51.94 454	70.97 443	84.75 379	45.16 445	87.49 328	55.16 416	79.23 461	83.40 420

1314		73.22 368	72.56 373	75.20 381	80.41 414	57.84 385	81.64 280	85.36 310	51.68 455	73.10 431	76.65 461	61.45 343	85.19 378	63.54 351	79.21 462	82.59 430

jason		77.42 312	75.75 330	82.43 246	87.10 271	69.27 214	77.99 346	81.94 362	51.47 456	77.84 386	85.07 376	60.32 350	89.00 287	70.74 276	89.27 361	89.03 336
jason: jason.
dp		60.70 452	60.29 453	61.92 464	72.04 482	38.67 487	70.83 437	64.08 474	51.28 457	60.75 482	77.28 455	36.59 470	71.58 454	47.41 460	62.34 492	75.52 472

test_vis1_n_1920		71.30 387	71.58 381	70.47 419	77.58 440	59.99 351	74.25 402	84.22 336	51.06 458	74.85 422	79.10 437	55.10 395	68.83 464	68.86 301	79.20 463	82.58 431

PVSNet	58.17 21	66.41 427	65.63 431	68.75 434	81.96 387	49.88 450	62.19 476	72.51 433	51.03 459	68.04 459	75.34 471	50.84 411	74.77 442	45.82 468	82.96 439	81.60 444

test-mter		65.00 434	63.79 438	68.63 436	76.45 452	55.21 409	67.89 452	67.14 463	50.98 460	65.08 473	72.39 476	28.27 491	69.37 459	61.00 375	84.89 425	81.31 447

CMPMVS	59.41 20	75.12 346	73.57 357	79.77 308	75.84 457	67.22 238	81.21 290	82.18 359	50.78 461	76.50 400	87.66 327	55.20 394	82.99 399	62.17 363	90.64 342	89.09 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Test_1112_low_res		73.90 362	73.08 364	76.35 369	90.35 171	55.95 397	73.40 415	86.17 294	50.70 462	73.14 430	85.94 358	58.31 365	85.90 368	56.51 403	83.22 438	87.20 371

lupinMVS		76.37 329	74.46 348	82.09 254	85.54 324	69.26 215	76.79 367	80.77 373	50.68 463	76.23 404	82.82 403	58.69 363	88.94 288	69.85 287	88.77 367	88.07 353

CR-MVSNet		74.00 361	73.04 365	76.85 364	79.58 424	62.64 293	82.58 254	76.90 396	50.50 464	75.72 411	92.38 173	48.07 422	84.07 392	68.72 304	82.91 441	83.85 413

pmmvs5		70.73 392	70.07 395	72.72 404	77.03 445	52.73 430	74.14 403	75.65 406	50.36 465	72.17 437	85.37 370	55.42 393	80.67 414	52.86 431	87.59 389	84.77 397

ADS-MVSNet2		65.87 430	63.64 439	72.55 407	73.16 475	56.92 393	67.10 460	74.81 409	49.74 466	66.04 467	82.97 399	46.71 425	77.26 433	42.29 474	69.96 485	83.46 418

ADS-MVSNet		61.90 445	62.19 446	61.03 467	73.16 475	36.42 490	67.10 460	61.75 480	49.74 466	66.04 467	82.97 399	46.71 425	63.21 484	42.29 474	69.96 485	83.46 418

tpm2		68.45 415	66.83 422	73.30 399	78.93 434	48.50 453	79.76 314	71.76 440	47.50 468	69.92 450	83.60 392	42.07 458	88.40 310	48.44 457	79.51 458	83.01 427

HyFIR lowres test		75.12 346	72.66 370	82.50 243	91.44 144	65.19 264	72.47 423	87.31 271	46.79 469	80.29 355	84.30 384	52.70 403	92.10 183	51.88 441	86.73 400	90.22 301

test_fmvs3		75.72 339	75.20 337	77.27 356	75.01 465	69.47 212	78.93 331	84.88 326	46.67 470	87.08 195	87.84 323	50.44 415	71.62 453	77.42 177	88.53 370	90.72 285

MVS-HIRNet		61.16 449	62.92 443	55.87 473	79.09 431	35.34 492	71.83 427	57.98 490	46.56 471	59.05 487	91.14 227	49.95 417	76.43 435	38.74 482	71.92 482	55.84 492

MDTV_nov1_ep13_2view						27.60 499	70.76 438		46.47 472	61.27 481		45.20 443		49.18 451		83.75 415

test_cas_vis1_n_1920		69.20 411	69.12 403	69.43 429	73.68 471	62.82 290	70.38 441	77.21 393	46.18 473	80.46 354	78.95 439	52.03 405	65.53 480	65.77 331	77.45 471	79.95 461

MVS		73.21 369	72.59 371	75.06 383	80.97 403	60.81 338	81.64 280	85.92 303	46.03 474	71.68 439	77.54 452	68.47 297	89.77 273	55.70 410	85.39 413	74.60 474

TESTMET0.1,1		61.29 448	60.32 452	64.19 458	72.06 481	51.30 441	67.89 452	62.09 476	45.27 475	60.65 483	69.01 482	27.93 492	64.74 482	56.31 404	81.65 450	76.53 469

test_fmvs2		73.57 365	72.80 367	75.90 375	72.74 480	68.84 225	77.07 364	84.32 335	45.14 476	82.89 307	84.22 387	48.37 420	70.36 457	73.40 245	87.03 396	88.52 345

tpm cat1		66.76 425	65.21 434	71.42 415	77.09 444	50.62 447	78.01 345	73.68 421	44.89 477	68.64 456	79.00 438	45.51 439	82.42 403	49.91 447	70.15 484	81.23 451

PVSNet_0	51.08 22	56.10 457	54.97 462	59.48 471	75.12 463	53.28 427	55.16 488	61.89 479	44.30 478	59.16 486	62.48 488	54.22 397	65.91 479	35.40 488	47.01 494	59.25 490

test_vis1_n		70.29 395	69.99 398	71.20 417	75.97 456	66.50 250	76.69 370	80.81 372	44.22 479	75.43 414	77.23 456	50.00 416	68.59 465	66.71 319	82.85 443	78.52 467

CHOSEN 280x420		59.08 454	56.52 460	66.76 447	76.51 450	64.39 272	49.62 491	59.00 487	43.86 480	55.66 495	68.41 484	35.55 471	68.21 471	43.25 472	76.78 473	67.69 483

mvsany_test3		65.48 433	62.97 442	73.03 402	69.99 488	76.17 123	64.83 466	43.71 499	43.68 481	80.25 358	87.05 343	52.83 402	63.09 486	51.92 440	72.44 480	79.84 463

new_pmnet		55.69 458	57.66 459	49.76 476	75.47 460	30.59 496	59.56 479	51.45 494	43.62 482	62.49 479	75.48 469	40.96 460	49.15 496	37.39 487	72.52 479	69.55 480

test_fmvs1_n		70.94 389	70.41 393	72.53 408	73.92 468	66.93 246	75.99 383	84.21 337	43.31 483	79.40 364	79.39 435	43.47 452	68.55 466	69.05 298	84.91 424	82.10 439

CHOSEN 1792x2688		72.45 374	70.56 389	78.13 340	90.02 182	63.08 286	68.72 449	83.16 348	42.99 484	75.92 409	85.46 366	57.22 378	85.18 379	49.87 448	81.67 448	86.14 382

test_fmvs1		69.57 406	69.05 405	71.14 418	69.15 490	65.77 260	73.98 406	83.32 346	42.83 485	77.77 389	78.27 447	43.39 455	68.50 467	68.39 308	84.38 431	79.15 465

test_vis3_rt		71.42 385	70.67 387	73.64 396	69.66 489	70.46 197	66.97 462	89.73 221	42.68 486	88.20 158	83.04 398	43.77 451	60.07 487	65.35 335	86.66 401	90.39 299

MVE	40.22 23	51.82 460	50.47 463	55.87 473	62.66 500	51.91 436	31.61 494	39.28 501	40.65 487	50.76 496	74.98 473	56.24 384	44.67 497	33.94 491	64.11 491	71.04 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		64.31 439	65.85 427	59.67 470	66.54 494	62.24 308	57.76 486	70.96 445	40.13 488	84.36 273	82.09 410	46.93 424	51.67 494	61.99 365	81.89 447	65.12 485

pmmvs3		62.47 442	60.02 454	69.80 425	71.58 484	64.00 276	70.52 439	58.44 489	39.77 489	66.05 466	75.84 464	27.10 496	72.28 449	46.15 466	84.77 429	73.11 475

EU-MVSNet		75.12 346	74.43 349	77.18 357	83.11 381	59.48 359	85.71 159	82.43 357	39.76 490	85.64 237	88.76 301	44.71 449	87.88 321	73.86 233	85.88 411	84.16 409

test_vis1_rt		65.64 432	64.09 436	70.31 420	66.09 495	70.20 201	61.16 477	81.60 366	38.65 491	72.87 432	69.66 481	52.84 401	60.04 488	56.16 405	77.77 467	80.68 456

mvsany_test1		58.48 455	56.47 461	64.50 457	65.90 497	68.21 231	56.95 487	42.11 500	38.30 492	65.69 469	77.19 458	56.96 379	59.35 490	46.16 465	58.96 493	65.93 484

kuosan		30.83 462	32.17 465	26.83 480	53.36 502	19.02 503	57.90 485	20.44 506	38.29 493	38.01 497	37.82 496	15.18 504	33.45 499	7.74 499	20.76 499	28.03 495

CVMVSNet		72.62 373	71.41 383	76.28 371	83.25 376	60.34 343	83.50 225	79.02 382	37.77 494	76.33 402	85.10 373	49.60 418	87.41 332	70.54 280	77.54 470	81.08 452

PMMVS		61.65 446	60.38 451	65.47 454	65.40 498	69.26 215	63.97 472	61.73 481	36.80 495	60.11 485	68.43 483	59.42 357	66.35 477	48.97 453	78.57 465	60.81 488

DSMNet-mixed		60.98 451	61.61 448	59.09 472	72.88 478	45.05 470	74.70 399	46.61 498	26.20 496	65.34 471	90.32 267	55.46 392	63.12 485	41.72 476	81.30 453	69.09 481

DeepMVS_CX				24.13 481	32.95 503	29.49 497		21.63 504	12.07 497	37.95 498	45.07 495	30.84 482	19.21 500	17.94 498	33.06 497	23.69 496

test_method		30.46 463	29.60 466	33.06 479	17.99 504	3.84 507	13.62 495	73.92 416	2.79 498	18.29 500	53.41 493	28.53 490	43.25 498	22.56 494	35.27 496	52.11 493

EGC-MVSNET		74.79 354	69.99 398	89.19 66	94.89 37	87.00 14	91.89 42	86.28 292	1.09 499	2.23 501	95.98 29	81.87 133	89.48 277	79.76 135	95.96 134	91.10 272

tmp_tt		20.25 465	24.50 468	7.49 482	4.47 505	8.70 506	34.17 493	25.16 503	1.00 500	32.43 499	18.49 497	39.37 463	9.21 501	21.64 495	43.75 495	4.57 497

test123		6.27 468	8.08 471	0.84 483	1.11 507	0.57 508	62.90 473	0.82 507	0.54 501	1.07 503	2.75 502	1.26 505	0.30 502	1.04 500	1.26 501	1.66 498

testmvs		5.91 469	7.65 472	0.72 484	1.20 506	0.37 509	59.14 481	0.67 508	0.49 502	1.11 502	2.76 501	0.94 506	0.24 503	1.02 501	1.47 500	1.55 499

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

cdsmvs_eth3d_5k		20.81 464	27.75 467	0.00 485	0.00 508	0.00 510	0.00 496	85.44 309	0.00 503	0.00 504	82.82 403	81.46 139	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		6.41 467	8.55 470	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	76.94 198	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		6.65 466	8.87 469	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	79.80 431	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

WAC-MVS						37.39 488								52.61 433

MSC_two_6792asdad				88.81 72	91.55 138	77.99 96		91.01 177					96.05 8	87.45 28	98.17 36	92.40 222

No_MVS				88.81 72	91.55 138	77.99 96		91.01 177					96.05 8	87.45 28	98.17 36	92.40 222

eth-test2					0.00 508

eth-test					0.00 508

OPU-MVS				88.27 87	91.89 124	77.83 99	90.47 60				91.22 223	81.12 143	94.68 81	74.48 217	95.35 161	92.29 232

test_0728_SECOND				86.79 111	94.25 52	72.45 166	90.54 57	94.10 39					95.88 17	86.42 46	97.97 48	92.02 246

GSMVS																83.88 410

test_part2					93.86 65	77.77 100				92.84 54

sam_mvs1												46.11 429				83.88 410

sam_mvs												45.92 434

ambc				82.98 221	90.55 168	64.86 266	88.20 108	89.15 236		89.40 128	93.96 105	71.67 280	91.38 204	78.83 149	96.55 106	92.71 201

MTGPA								91.81 149

test_post1							78.85 335				3.13 499	45.19 444	80.13 419	58.11 397

test_post											3.10 500	45.43 440	77.22 434

patchmatchnet-post											81.71 415	45.93 433	87.01 336

GG-mvs-BLEND				67.16 445	73.36 473	46.54 464	84.15 198	55.04 492		58.64 489	61.95 489	29.93 484	83.87 395	38.71 483	76.92 472	71.07 478

MTMP							90.66 53	33.14 502

test9_res														80.83 124	96.45 112	90.57 293

agg_prior2														79.68 137	96.16 124	90.22 301

agg_prior					91.58 136	77.69 102		90.30 205		84.32 275			93.18 150

test_prior4						78.97 86	84.59 187

test_prior				86.32 119	90.59 167	71.99 174		92.85 110					94.17 106			92.80 196

新几何2							81.72 279

旧先验1					91.97 120	71.77 175		81.78 363			91.84 196	73.92 243			93.65 228	83.61 416

原ACMM2							82.26 270

testdata2													86.43 354	63.52 352

segment_acmp												81.94 129

test12				86.57 114	90.74 163	72.63 160		90.69 187		82.76 311		79.20 162	94.80 78		95.32 163	92.27 234

plane_prior7					93.45 74	77.31 108

plane_prior6					92.61 98	76.54 115						74.84 224

plane_prior5								93.61 68					95.22 62	80.78 125	95.83 145	94.46 101

plane_prior4											92.95 151

plane_prior1					92.83 95

n2								0.00 509

nn								0.00 509

door-mid								74.45 413

lessismore_v0				85.95 130	91.10 156	70.99 191		70.91 446		91.79 74	94.42 77	61.76 342	92.93 160	79.52 142	93.03 252	93.93 131

test11								91.46 158

door								72.57 432

HQP5-MVS						70.66 193

BP-MVS														77.30 178

HQP4-MVS										80.56 350			94.61 85			93.56 159

HQP3-MVS								92.68 116							94.47 197

HQP2-MVS												72.10 271

NP-MVS					91.95 121	74.55 136					90.17 274

ACMMP++_ref															95.74 151

ACMMP++															97.35 83

Test By Simon												79.09 164

High-resolution multi-view benchmark