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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 2	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

pmmvs6		99.07 4	99.24 4	98.56 48	99.81 2	96.38 62	98.87 9	99.30 26	99.01 16	99.63 11	99.66 3	99.27 2	99.68 124	97.75 51	99.89 26	99.62 36

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 42	99.77 5	96.34 65	99.18 5	99.20 34	99.67 2	99.73 3	99.65 5	99.15 3	99.86 24	97.22 68	99.92 15	99.77 12

test_fmvsmconf0.01_n		98.57 17	98.74 16	98.06 88	99.39 47	94.63 136	96.70 145	99.82 1	95.44 166	99.64 10	99.52 7	98.96 4	99.74 77	99.38 3	99.86 31	99.81 8

XVG-OURS-SEG-HR		97.38 117	97.07 131	98.30 68	99.01 110	97.41 34	94.66 269	99.02 74	95.20 174	98.15 130	97.52 216	98.83 5	98.43 357	94.87 184	96.41 356	99.07 173

ACMH	93.61 9	98.44 25	98.76 13	97.51 128	99.43 40	93.54 179	98.23 46	99.05 65	97.40 79	99.37 23	99.08 51	98.79 6	99.47 197	97.74 52	99.71 75	99.50 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		98.90 5	98.94 6	98.75 31	99.69 10	96.48 60	98.54 23	99.22 31	96.23 121	99.71 4	99.48 10	98.77 7	99.93 3	98.89 17	99.95 5	99.84 5

test_fmvsmconf0.1_n		98.41 27	98.54 25	98.03 93	99.16 83	94.61 137	96.18 174	99.73 3	95.05 182	99.60 14	99.34 25	98.68 8	99.72 88	99.21 7	99.85 38	99.76 17

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 37	99.71 9	96.99 44	99.69 2	99.57 14	99.02 15	99.62 12	99.36 21	98.53 9	99.52 182	98.58 29	99.95 5	99.66 30
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
test_fmvsmconf_n		98.30 32	98.41 32	97.99 96	98.94 116	94.60 138	96.00 189	99.64 12	94.99 185	99.43 19	99.18 39	98.51 10	99.71 104	99.13 10	99.84 40	99.67 28

TransMVSNet (Re)		98.38 28	98.67 18	97.51 128	99.51 31	93.39 185	98.20 51	98.87 110	98.23 40	99.48 16	99.27 30	98.47 11	99.55 174	96.52 91	99.53 125	99.60 38

pm-mvs1		98.47 24	98.67 18	97.86 104	99.52 30	94.58 139	98.28 42	99.00 83	97.57 67	99.27 29	99.22 34	98.32 12	99.50 187	97.09 75	99.75 66	99.50 63

jajsoiax		98.77 9	98.79 12	98.74 34	99.66 13	96.48 60	98.45 31	99.12 48	95.83 147	99.67 7	99.37 19	98.25 13	99.92 5	98.77 20	99.94 8	99.82 6

sd_testset		97.97 52	98.12 41	97.51 128	99.41 43	93.44 182	97.96 63	98.25 213	98.58 28	98.78 65	99.39 16	98.21 14	99.56 170	92.65 252	99.86 31	99.52 59

ACMH+	93.58 10	98.23 36	98.31 35	97.98 97	99.39 47	95.22 118	97.55 92	99.20 34	98.21 41	99.25 31	98.51 105	98.21 14	99.40 222	94.79 188	99.72 72	99.32 116

HPM-MVS_fast		98.32 30	98.13 40	98.88 23	99.54 26	97.48 30	98.35 35	99.03 72	95.88 143	97.88 159	98.22 146	98.15 16	99.74 77	96.50 92	99.62 93	99.42 98

wuyk23d		93.25 297	95.20 214	87.40 384	96.07 347	95.38 105	97.04 122	94.97 333	95.33 169	99.70 6	98.11 158	98.14 17	91.94 402	77.76 393	99.68 83	74.89 402

ACMM	93.33 11	98.05 48	97.79 73	98.85 24	99.15 86	97.55 26	96.68 146	98.83 126	95.21 173	98.36 104	98.13 154	98.13 18	99.62 151	96.04 110	99.54 121	99.39 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 43	97.83 69	98.92 21	99.42 42	97.46 31	98.57 20	99.05 65	95.43 167	97.41 184	97.50 218	97.98 19	99.79 45	95.58 140	99.57 109	99.50 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testgi		96.07 185	96.50 169	94.80 288	99.26 60	87.69 306	95.96 194	98.58 178	95.08 180	98.02 146	96.25 298	97.92 20	97.60 382	88.68 328	98.74 262	99.11 166

LPG-MVS_test		97.94 62	97.67 86	98.74 34	99.15 86	97.02 42	97.09 119	99.02 74	95.15 177	98.34 107	98.23 143	97.91 21	99.70 112	94.41 203	99.73 68	99.50 63

LGP-MVS_train				98.74 34	99.15 86	97.02 42		99.02 74	95.15 177	98.34 107	98.23 143	97.91 21	99.70 112	94.41 203	99.73 68	99.50 63

SED-MVS		97.94 62	97.90 59	98.07 86	99.22 69	95.35 108	96.79 136	98.83 126	96.11 127	99.08 40	98.24 141	97.87 23	99.72 88	95.44 149	99.51 135	99.14 156

test_241102_ONE					99.22 69	95.35 108		98.83 126	96.04 132	99.08 40	98.13 154	97.87 23	99.33 245

SDMVSNet		97.97 52	98.26 39	97.11 163	99.41 43	92.21 214	96.92 127	98.60 173	98.58 28	98.78 65	99.39 16	97.80 25	99.62 151	94.98 182	99.86 31	99.52 59

testf1		98.57 17	98.45 29	98.93 18	99.79 3	98.78 2	97.69 81	99.42 21	97.69 63	98.92 51	98.77 79	97.80 25	99.25 264	96.27 100	99.69 79	98.76 221

APD_test2		98.57 17	98.45 29	98.93 18	99.79 3	98.78 2	97.69 81	99.42 21	97.69 63	98.92 51	98.77 79	97.80 25	99.25 264	96.27 100	99.69 79	98.76 221

SD-MVS		97.37 119	97.70 81	96.35 214	98.14 216	95.13 122	96.54 151	98.92 99	95.94 139	99.19 34	98.08 160	97.74 28	95.06 396	95.24 161	99.54 121	98.87 209
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
DeepC-MVS	95.41 4	97.82 81	97.70 81	98.16 79	98.78 135	95.72 86	96.23 172	99.02 74	93.92 220	98.62 76	98.99 57	97.69 29	99.62 151	96.18 105	99.87 29	99.15 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		98.54 21	98.62 22	98.32 65	99.22 69	95.66 91	97.90 68	99.08 57	98.31 36	99.02 43	98.74 82	97.68 30	99.61 158	97.77 50	99.85 38	99.70 26

ANet_high		98.31 31	98.94 6	96.41 213	99.33 54	89.64 263	97.92 67	99.56 16	99.27 6	99.66 9	99.50 9	97.67 31	99.83 33	97.55 59	99.98 2	99.77 12

test_fmvsmvis_n_1920		98.08 45	98.47 26	96.93 177	99.03 108	93.29 187	96.32 164	99.65 9	95.59 158	99.71 4	99.01 54	97.66 32	99.60 160	99.44 2	99.83 43	97.90 307

casdiffmvs_mvg		97.83 78	98.11 42	97.00 174	98.57 162	92.10 222	95.97 192	99.18 37	97.67 66	99.00 46	98.48 110	97.64 33	99.50 187	96.96 80	99.54 121	99.40 101
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
canonicalmvs		97.23 126	97.21 124	97.30 150	97.65 277	94.39 145	97.84 71	99.05 65	97.42 75	96.68 231	93.85 352	97.63 34	99.33 245	96.29 99	98.47 284	98.18 283

GeoE		97.75 87	97.70 81	97.89 102	98.88 123	94.53 140	97.10 118	98.98 89	95.75 151	97.62 170	97.59 211	97.61 35	99.77 57	96.34 98	99.44 155	99.36 113

TranMVSNet+NR-MVSNet		98.33 29	98.30 37	98.43 57	99.07 101	95.87 81	96.73 143	99.05 65	98.67 24	98.84 59	98.45 111	97.58 36	99.88 20	96.45 94	99.86 31	99.54 54

cdsmvs_eth3d_5k		24.22 373	32.30 376	0.00 391	0.00 414	0.00 416	0.00 402	98.10 237	0.00 409	0.00 410	95.06 333	97.54 37	0.00 410	0.00 409	0.00 408	0.00 406

ACMP	92.54 13	97.47 111	97.10 128	98.55 49	99.04 107	96.70 51	96.24 171	98.89 102	93.71 225	97.97 151	97.75 199	97.44 38	99.63 146	93.22 245	99.70 78	99.32 116
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		98.73 11	98.74 16	98.69 39	99.63 15	96.30 67	98.67 15	99.02 74	96.50 109	99.32 26	99.44 14	97.43 39	99.92 5	98.73 22	99.95 5	99.86 2

TDRefinement		98.90 5	98.86 8	99.02 6	99.54 26	98.06 8	99.34 4	99.44 19	98.85 21	99.00 46	99.20 35	97.42 40	99.59 161	97.21 69	99.76 59	99.40 101

anonymousdsp		98.72 14	98.63 20	98.99 10	99.62 16	97.29 37	98.65 19	99.19 36	95.62 156	99.35 25	99.37 19	97.38 41	99.90 14	98.59 28	99.91 18	99.77 12

PS-CasMVS		98.73 11	98.85 10	98.39 61	99.55 23	95.47 102	98.49 28	99.13 47	99.22 8	99.22 33	98.96 61	97.35 42	99.92 5	97.79 49	99.93 11	99.79 10

COLMAP_ROB	94.48 6	98.25 35	98.11 42	98.64 43	99.21 76	97.35 35	97.96 63	99.16 39	98.34 35	98.78 65	98.52 103	97.32 43	99.45 204	94.08 217	99.67 85	99.13 158
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		97.69 92	97.79 73	97.40 145	99.06 102	93.52 180	95.96 194	98.97 92	94.55 201	98.82 61	98.76 81	97.31 44	99.29 256	97.20 71	99.44 155	99.38 107

XXY-MVS		97.54 106	97.70 81	97.07 168	99.46 37	92.21 214	97.22 111	99.00 83	94.93 188	98.58 81	98.92 66	97.31 44	99.41 220	94.44 201	99.43 163	99.59 39

PEN-MVS		98.75 10	98.85 10	98.44 55	99.58 19	95.67 90	98.45 31	99.15 43	99.33 5	99.30 27	99.00 55	97.27 46	99.92 5	97.64 57	99.92 15	99.75 19

DTE-MVSNet		98.79 8	98.86 8	98.59 46	99.55 23	96.12 72	98.48 30	99.10 51	99.36 4	99.29 28	99.06 52	97.27 46	99.93 3	97.71 53	99.91 18	99.70 26

ZNCC-MVS		97.92 66	97.62 95	98.83 25	99.32 56	97.24 39	97.45 99	98.84 120	95.76 149	96.93 217	97.43 222	97.26 48	99.79 45	96.06 107	99.53 125	99.45 86

MP-MVS-pluss		97.69 92	97.36 115	98.70 38	99.50 34	96.84 47	95.38 231	98.99 86	92.45 270	98.11 133	98.31 125	97.25 49	99.77 57	96.60 88	99.62 93	99.48 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		97.89 72	97.63 93	98.67 40	99.35 52	96.84 47	96.36 161	98.79 136	95.07 181	97.88 159	98.35 121	97.24 50	99.72 88	96.05 109	99.58 106	99.45 86

Effi-MVS+		96.19 181	96.01 188	96.71 193	97.43 295	92.19 218	96.12 180	99.10 51	95.45 164	93.33 344	94.71 340	97.23 51	99.56 170	93.21 246	97.54 326	98.37 260

tt0805		97.44 113	97.56 102	97.11 163	99.55 23	96.36 63	98.66 18	95.66 319	98.31 36	97.09 205	95.45 327	97.17 52	98.50 352	98.67 25	97.45 332	96.48 365

PGM-MVS		97.88 73	97.52 106	98.96 13	99.20 78	97.62 21	97.09 119	99.06 61	95.45 164	97.55 172	97.94 180	97.11 53	99.78 48	94.77 191	99.46 151	99.48 77

test_0728_THIRD									96.62 99	98.40 98	98.28 134	97.10 54	99.71 104	95.70 128	99.62 93	99.58 40

APD-MVS_3200maxsize		98.13 42	97.90 59	98.79 29	98.79 132	97.31 36	97.55 92	98.92 99	97.72 59	98.25 118	98.13 154	97.10 54	99.75 68	95.44 149	99.24 206	99.32 116

OPM-MVS		97.54 106	97.25 121	98.41 59	99.11 95	96.61 56	95.24 242	98.46 187	94.58 200	98.10 135	98.07 162	97.09 56	99.39 226	95.16 167	99.44 155	99.21 141
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HFP-MVS		97.94 62	97.64 91	98.83 25	99.15 86	97.50 29	97.59 89	98.84 120	96.05 130	97.49 177	97.54 214	97.07 57	99.70 112	95.61 137	99.46 151	99.30 121

DVP-MVS		97.78 85	97.65 88	98.16 79	99.24 64	95.51 97	96.74 139	98.23 216	95.92 140	98.40 98	98.28 134	97.06 58	99.71 104	95.48 145	99.52 130	99.26 133
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					99.24 64	95.51 97	96.89 129	98.89 102	95.92 140	98.64 74	98.31 125	97.06 58

test_fmvsm_n_1920		98.08 45	98.29 38	97.43 141	98.88 123	93.95 164	96.17 178	99.57 14	95.66 153	99.52 15	98.71 85	97.04 60	99.64 142	99.21 7	99.87 29	98.69 230

casdiffmvs		97.50 108	97.81 71	96.56 203	98.51 171	91.04 242	95.83 203	99.09 56	97.23 85	98.33 110	98.30 129	97.03 61	99.37 234	96.58 90	99.38 173	99.28 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SteuartSystems-ACMMP		98.02 50	97.76 78	98.79 29	99.43 40	97.21 41	97.15 114	98.90 101	96.58 104	98.08 138	97.87 188	97.02 62	99.76 62	95.25 160	99.59 104	99.40 101
Skip Steuart: Steuart Systems R&D Blog.
PC_three_1452									87.24 339	98.37 101	97.44 221	97.00 63	96.78 392	92.01 260	99.25 203	99.21 141

EC-MVSNet		97.90 71	97.94 58	97.79 108	98.66 149	95.14 121	98.31 39	99.66 8	97.57 67	95.95 268	97.01 255	96.99 64	99.82 35	97.66 56	99.64 90	98.39 258

DVP-MVS++		97.96 54	97.90 59	98.12 84	97.75 264	95.40 103	99.03 7	98.89 102	96.62 99	98.62 76	98.30 129	96.97 65	99.75 68	95.70 128	99.25 203	99.21 141

OPU-MVS				97.64 119	98.01 225	95.27 113	96.79 136				97.35 233	96.97 65	98.51 351	91.21 278	99.25 203	99.14 156

RE-MVS-def			97.88 64		98.81 128	98.05 9	97.55 92	98.86 113	97.77 54	98.20 122	98.07 162	96.94 67		95.49 142	99.20 208	99.26 133

APDe-MVS		98.14 39	98.03 50	98.47 54	98.72 140	96.04 75	98.07 58	99.10 51	95.96 137	98.59 80	98.69 87	96.94 67	99.81 37	96.64 86	99.58 106	99.57 47
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_one_0601					99.05 106	95.50 100		98.87 110	97.21 86	98.03 145	98.30 129	96.93 69

GST-MVS		97.82 81	97.49 110	98.81 27	99.23 66	97.25 38	97.16 113	98.79 136	95.96 137	97.53 173	97.40 224	96.93 69	99.77 57	95.04 176	99.35 182	99.42 98

test_241102_TWO								98.83 126	96.11 127	98.62 76	98.24 141	96.92 71	99.72 88	95.44 149	99.49 142	99.49 71

LCM-MVSNet-Re		97.33 122	97.33 117	97.32 149	98.13 219	93.79 170	96.99 124	99.65 9	96.74 97	99.47 17	98.93 65	96.91 72	99.84 30	90.11 306	99.06 231	98.32 267

VPA-MVSNet		98.27 33	98.46 27	97.70 114	99.06 102	93.80 169	97.76 76	99.00 83	98.40 33	99.07 42	98.98 58	96.89 73	99.75 68	97.19 72	99.79 53	99.55 53

ACMMP		98.05 48	97.75 80	98.93 18	99.23 66	97.60 22	98.09 57	98.96 93	95.75 151	97.91 156	98.06 167	96.89 73	99.76 62	95.32 157	99.57 109	99.43 97
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CS-MVS		98.09 44	98.01 52	98.32 65	98.45 180	96.69 52	98.52 26	99.69 5	98.07 46	96.07 264	97.19 243	96.88 75	99.86 24	97.50 61	99.73 68	98.41 255

PMVS	89.60 17	96.71 158	96.97 137	95.95 233	99.51 31	97.81 16	97.42 103	97.49 275	97.93 50	95.95 268	98.58 97	96.88 75	96.91 389	89.59 314	99.36 177	93.12 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
region2R		97.92 66	97.59 99	98.92 21	99.22 69	97.55 26	97.60 87	98.84 120	96.00 135	97.22 189	97.62 209	96.87 77	99.76 62	95.48 145	99.43 163	99.46 82

CP-MVS		97.92 66	97.56 102	98.99 10	98.99 111	97.82 15	97.93 66	98.96 93	96.11 127	96.89 220	97.45 220	96.85 78	99.78 48	95.19 163	99.63 92	99.38 107

DPE-MVS		97.64 97	97.35 116	98.50 51	98.85 126	96.18 69	95.21 244	98.99 86	95.84 146	98.78 65	98.08 160	96.84 79	99.81 37	93.98 223	99.57 109	99.52 59
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_0402		97.84 77	97.97 55	97.47 137	99.19 80	94.07 159	96.71 144	98.73 148	98.66 25	98.56 82	98.41 115	96.84 79	99.69 119	94.82 186	99.81 48	98.64 234

CS-MVS-test		97.91 69	97.84 66	98.14 82	98.52 169	96.03 77	98.38 34	99.67 6	98.11 44	95.50 285	96.92 261	96.81 81	99.87 22	96.87 83	99.76 59	98.51 248

ACMMPR		97.95 58	97.62 95	98.94 15	99.20 78	97.56 25	97.59 89	98.83 126	96.05 130	97.46 182	97.63 208	96.77 82	99.76 62	95.61 137	99.46 151	99.49 71

Vis-MVSNet		98.27 33	98.34 34	98.07 86	99.33 54	95.21 120	98.04 60	99.46 17	97.32 82	97.82 166	99.11 47	96.75 83	99.86 24	97.84 46	99.36 177	99.15 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+		95.49 209	95.07 220	96.75 191	97.67 275	92.82 196	94.22 284	98.60 173	91.61 283	93.42 342	92.90 362	96.73 84	99.70 112	92.60 253	97.89 309	97.74 319

baseline		97.44 113	97.78 77	96.43 209	98.52 169	90.75 249	96.84 130	99.03 72	96.51 108	97.86 163	98.02 171	96.67 85	99.36 237	97.09 75	99.47 148	99.19 146

SR-MVS		98.00 51	97.66 87	99.01 8	98.77 136	97.93 11	97.38 104	98.83 126	97.32 82	98.06 141	97.85 189	96.65 86	99.77 57	95.00 179	99.11 222	99.32 116

tfpnnormal		97.72 90	97.97 55	96.94 176	99.26 60	92.23 213	97.83 72	98.45 188	98.25 39	99.13 38	98.66 89	96.65 86	99.69 119	93.92 225	99.62 93	98.91 199

DeepPCF-MVS	94.58 5	96.90 142	96.43 171	98.31 67	97.48 289	97.23 40	92.56 338	98.60 173	92.84 261	98.54 83	97.40 224	96.64 88	98.78 322	94.40 205	99.41 170	98.93 195

MVS_111021_LR		96.82 149	96.55 163	97.62 120	98.27 195	95.34 110	93.81 306	98.33 206	94.59 199	96.56 239	96.63 279	96.61 89	98.73 327	94.80 187	99.34 185	98.78 217

Gipuma		98.07 47	98.31 35	97.36 147	99.76 7	96.28 68	98.51 27	99.10 51	98.76 23	96.79 223	99.34 25	96.61 89	98.82 318	96.38 96	99.50 139	96.98 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SR-MVS-dyc-post		98.14 39	97.84 66	99.02 6	98.81 128	98.05 9	97.55 92	98.86 113	97.77 54	98.20 122	98.07 162	96.60 91	99.76 62	95.49 142	99.20 208	99.26 133

MVS_111021_HR		96.73 155	96.54 165	97.27 152	98.35 188	93.66 176	93.42 316	98.36 202	94.74 191	96.58 237	96.76 273	96.54 92	98.99 304	94.87 184	99.27 201	99.15 153

SMA-MVS		97.48 110	97.11 127	98.60 45	98.83 127	96.67 53	96.74 139	98.73 148	91.61 283	98.48 90	98.36 120	96.53 93	99.68 124	95.17 165	99.54 121	99.45 86
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
v7n		98.73 11	98.99 5	97.95 98	99.64 14	94.20 156	98.67 15	99.14 46	99.08 10	99.42 20	99.23 33	96.53 93	99.91 13	99.27 5	99.93 11	99.73 22

mPP-MVS		97.91 69	97.53 105	99.04 4	99.22 69	97.87 14	97.74 79	98.78 140	96.04 132	97.10 200	97.73 202	96.53 93	99.78 48	95.16 167	99.50 139	99.46 82

XVS		97.96 54	97.63 93	98.94 15	99.15 86	97.66 19	97.77 74	98.83 126	97.42 75	96.32 250	97.64 207	96.49 96	99.72 88	95.66 133	99.37 174	99.45 86

X-MVStestdata		92.86 302	90.83 329	98.94 15	99.15 86	97.66 19	97.77 74	98.83 126	97.42 75	96.32 250	36.50 404	96.49 96	99.72 88	95.66 133	99.37 174	99.45 86

9.14			96.69 153		98.53 168		96.02 187	98.98 89	93.23 241	97.18 194	97.46 219	96.47 98	99.62 151	92.99 249	99.32 192

UA-Net		98.88 7	98.76 13	99.22 2	99.11 95	97.89 13	99.47 3	99.32 24	99.08 10	97.87 162	99.67 2	96.47 98	99.92 5	97.88 43	99.98 2	99.85 3

fmvsm_l_conf0.5_n		97.68 95	97.81 71	97.27 152	98.92 119	92.71 202	95.89 200	99.41 23	93.36 236	99.00 46	98.44 113	96.46 100	99.65 138	99.09 11	99.76 59	99.45 86

SF-MVS		97.60 101	97.39 113	98.22 75	98.93 117	95.69 88	97.05 121	99.10 51	95.32 170	97.83 165	97.88 187	96.44 101	99.72 88	94.59 200	99.39 172	99.25 137

fmvsm_s_conf0.1_n_a		97.80 83	98.01 52	97.18 158	99.17 82	92.51 205	96.57 149	99.15 43	93.68 228	98.89 54	99.30 28	96.42 102	99.37 234	99.03 13	99.83 43	99.66 30

xiu_mvs_v1_base_debu		95.62 204	95.96 192	94.60 296	98.01 225	88.42 284	93.99 296	98.21 218	92.98 255	95.91 270	94.53 343	96.39 103	99.72 88	95.43 152	98.19 295	95.64 376

xiu_mvs_v1_base		95.62 204	95.96 192	94.60 296	98.01 225	88.42 284	93.99 296	98.21 218	92.98 255	95.91 270	94.53 343	96.39 103	99.72 88	95.43 152	98.19 295	95.64 376

xiu_mvs_v1_base_debi		95.62 204	95.96 192	94.60 296	98.01 225	88.42 284	93.99 296	98.21 218	92.98 255	95.91 270	94.53 343	96.39 103	99.72 88	95.43 152	98.19 295	95.64 376

ETV-MVS		96.13 184	95.90 196	96.82 186	97.76 262	93.89 165	95.40 229	98.95 95	95.87 144	95.58 284	91.00 386	96.36 106	99.72 88	93.36 239	98.83 254	96.85 352

fmvsm_l_conf0.5_n_a		97.60 101	97.76 78	97.11 163	98.92 119	92.28 211	95.83 203	99.32 24	93.22 242	98.91 53	98.49 106	96.31 107	99.64 142	99.07 12	99.76 59	99.40 101

fmvsm_s_conf0.1_n		97.73 88	98.02 51	96.85 183	99.09 98	91.43 237	96.37 160	99.11 49	94.19 210	99.01 44	99.25 31	96.30 108	99.38 229	99.00 14	99.88 27	99.73 22

MP-MVS		97.64 97	97.18 125	99.00 9	99.32 56	97.77 17	97.49 98	98.73 148	96.27 118	95.59 283	97.75 199	96.30 108	99.78 48	93.70 233	99.48 146	99.45 86
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TinyColmap		96.00 190	96.34 176	94.96 279	97.90 237	87.91 299	94.13 291	98.49 185	94.41 203	98.16 128	97.76 196	96.29 110	98.68 336	90.52 299	99.42 166	98.30 271

Fast-Effi-MVS+-dtu		96.44 172	96.12 183	97.39 146	97.18 311	94.39 145	95.46 223	98.73 148	96.03 134	94.72 303	94.92 337	96.28 111	99.69 119	93.81 228	97.98 303	98.09 286

fmvsm_s_conf0.5_n_a		97.65 96	97.83 69	97.13 162	98.80 130	92.51 205	96.25 170	99.06 61	93.67 229	98.64 74	99.00 55	96.23 112	99.36 237	98.99 15	99.80 51	99.53 57

fmvsm_s_conf0.5_n		97.62 99	97.89 62	96.80 187	98.79 132	91.44 236	96.14 179	99.06 61	94.19 210	98.82 61	98.98 58	96.22 113	99.38 229	98.98 16	99.86 31	99.58 40

APD_test1		97.95 58	97.68 85	98.75 31	99.60 17	98.60 5	97.21 112	99.08 57	96.57 107	98.07 140	98.38 119	96.22 113	99.14 282	94.71 195	99.31 195	98.52 247

OMC-MVS		96.48 170	96.00 189	97.91 100	98.30 190	96.01 78	94.86 261	98.60 173	91.88 279	97.18 194	97.21 242	96.11 115	99.04 298	90.49 302	99.34 185	98.69 230

xiu_mvs_v2_base		94.22 266	94.63 244	92.99 341	97.32 305	84.84 349	92.12 350	97.84 255	91.96 277	94.17 315	93.43 353	96.07 116	99.71 104	91.27 275	97.48 329	94.42 386

CSCG		97.40 116	97.30 118	97.69 116	98.95 113	94.83 128	97.28 107	98.99 86	96.35 117	98.13 132	95.95 313	95.99 117	99.66 136	94.36 208	99.73 68	98.59 240

PHI-MVS		96.96 138	96.53 166	98.25 73	97.48 289	96.50 59	96.76 138	98.85 117	93.52 231	96.19 260	96.85 264	95.94 118	99.42 211	93.79 229	99.43 163	98.83 212

TSAR-MVS + MP.		97.42 115	97.23 123	98.00 95	99.38 49	95.00 125	97.63 86	98.20 221	93.00 254	98.16 128	98.06 167	95.89 119	99.72 88	95.67 132	99.10 224	99.28 128
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVG-ACMP-BASELINE		97.58 104	97.28 120	98.49 52	99.16 83	96.90 46	96.39 156	98.98 89	95.05 182	98.06 141	98.02 171	95.86 120	99.56 170	94.37 206	99.64 90	99.00 182

AllTest		97.20 127	96.92 142	98.06 88	99.08 99	96.16 70	97.14 116	99.16 39	94.35 205	97.78 167	98.07 162	95.84 121	99.12 286	91.41 272	99.42 166	98.91 199

TestCases				98.06 88	99.08 99	96.16 70		99.16 39	94.35 205	97.78 167	98.07 162	95.84 121	99.12 286	91.41 272	99.42 166	98.91 199

APD-MVS		97.00 133	96.53 166	98.41 59	98.55 165	96.31 66	96.32 164	98.77 141	92.96 259	97.44 183	97.58 213	95.84 121	99.74 77	91.96 261	99.35 182	99.19 146
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pcd_1.5k_mvsjas		7.98 376	10.65 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	95.82 124	0.00 410	0.00 409	0.00 408	0.00 406

PS-MVSNAJss		98.53 22	98.63 20	98.21 78	99.68 11	94.82 129	98.10 56	99.21 32	96.91 92	99.75 2	99.45 13	95.82 124	99.92 5	98.80 19	99.96 4	99.89 1

PS-MVSNAJ		94.10 272	94.47 254	93.00 340	97.35 300	84.88 347	91.86 355	97.84 255	91.96 277	94.17 315	92.50 371	95.82 124	99.71 104	91.27 275	97.48 329	94.40 387

3Dnovator	96.53 2	97.61 100	97.64 91	97.50 132	97.74 267	93.65 177	98.49 28	98.88 108	96.86 94	97.11 199	98.55 101	95.82 124	99.73 83	95.94 118	99.42 166	99.13 158

MTAPA		98.14 39	97.84 66	99.06 3	99.44 39	97.90 12	97.25 108	98.73 148	97.69 63	97.90 157	97.96 177	95.81 128	99.82 35	96.13 106	99.61 99	99.45 86

DP-MVS		97.87 74	97.89 62	97.81 107	98.62 156	94.82 129	97.13 117	98.79 136	98.98 17	98.74 71	98.49 106	95.80 129	99.49 192	95.04 176	99.44 155	99.11 166

Anonymous20240529		97.96 54	98.04 49	97.71 113	98.69 147	94.28 153	97.86 70	98.31 210	98.79 22	99.23 32	98.86 74	95.76 130	99.61 158	95.49 142	99.36 177	99.23 139

LS3D		97.77 86	97.50 109	98.57 47	96.24 335	97.58 24	98.45 31	98.85 117	98.58 28	97.51 175	97.94 180	95.74 131	99.63 146	95.19 163	98.97 236	98.51 248

EIA-MVS		96.04 187	95.77 202	96.85 183	97.80 252	92.98 194	96.12 180	99.16 39	94.65 195	93.77 328	91.69 380	95.68 132	99.67 130	94.18 213	98.85 251	97.91 306

CNVR-MVS		96.92 140	96.55 163	98.03 93	98.00 229	95.54 95	94.87 260	98.17 227	94.60 197	96.38 247	97.05 251	95.67 133	99.36 237	95.12 173	99.08 226	99.19 146

CLD-MVS		95.47 212	95.07 220	96.69 195	98.27 195	92.53 204	91.36 361	98.67 163	91.22 291	95.78 277	94.12 350	95.65 134	98.98 306	90.81 287	99.72 72	98.57 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		98.55 20	98.76 13	97.94 99	98.79 132	94.37 147	98.84 11	99.15 43	99.37 3	99.67 7	99.43 15	95.61 135	99.72 88	98.12 35	99.86 31	99.73 22

EGC-MVSNET		83.08 370	77.93 373	98.53 50	99.57 20	97.55 26	98.33 38	98.57 179	4.71 406	10.38 407	98.90 70	95.60 136	99.50 187	95.69 130	99.61 99	98.55 244

ITE_SJBPF				97.85 105	98.64 150	96.66 54		98.51 184	95.63 155	97.22 189	97.30 237	95.52 137	98.55 348	90.97 282	98.90 244	98.34 266

DeepC-MVS_fast	94.34 7	96.74 153	96.51 168	97.44 140	97.69 271	94.15 157	96.02 187	98.43 191	93.17 249	97.30 186	97.38 230	95.48 138	99.28 258	93.74 230	99.34 185	98.88 207
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		98.65 15	98.62 22	98.75 31	99.51 31	96.61 56	98.55 22	99.17 38	99.05 13	99.17 35	98.79 76	95.47 139	99.89 18	97.95 42	99.91 18	99.75 19

FMVSNet1		97.95 58	98.08 44	97.56 123	99.14 93	93.67 173	98.23 46	98.66 165	97.41 78	99.00 46	99.19 36	95.47 139	99.73 83	95.83 125	99.76 59	99.30 121

MIMVSNet1		98.51 23	98.45 29	98.67 40	99.72 8	96.71 50	98.76 12	98.89 102	98.49 31	99.38 22	99.14 46	95.44 141	99.84 30	96.47 93	99.80 51	99.47 80

CP-MVSNet		98.42 26	98.46 27	98.30 68	99.46 37	95.22 118	98.27 44	98.84 120	99.05 13	99.01 44	98.65 92	95.37 142	99.90 14	97.57 58	99.91 18	99.77 12

segment_acmp												95.34 143

CDPH-MVS		95.45 214	94.65 241	97.84 106	98.28 193	94.96 126	93.73 308	98.33 206	85.03 364	95.44 286	96.60 280	95.31 144	99.44 207	90.01 308	99.13 218	99.11 166

3Dnovator+	96.13 3	97.73 88	97.59 99	98.15 81	98.11 220	95.60 92	98.04 60	98.70 157	98.13 43	96.93 217	98.45 111	95.30 145	99.62 151	95.64 135	98.96 237	99.24 138

MVS_Test		96.27 178	96.79 150	94.73 292	96.94 321	86.63 325	96.18 174	98.33 206	94.94 186	96.07 264	98.28 134	95.25 146	99.26 262	97.21 69	97.90 308	98.30 271

XVG-OURS		97.12 128	96.74 151	98.26 70	98.99 111	97.45 32	93.82 304	99.05 65	95.19 175	98.32 111	97.70 204	95.22 147	98.41 358	94.27 210	98.13 298	98.93 195

dcpmvs_2		97.12 128	97.99 54	94.51 302	99.11 95	84.00 358	97.75 77	99.65 9	97.38 80	99.14 37	98.42 114	95.16 148	99.96 2	95.52 141	99.78 56	99.58 40

MCST-MVS		96.24 179	95.80 200	97.56 123	98.75 137	94.13 158	94.66 269	98.17 227	90.17 306	96.21 258	96.10 307	95.14 149	99.43 209	94.13 216	98.85 251	99.13 158

EI-MVSNet-Vis-set		97.32 123	97.39 113	97.11 163	97.36 299	92.08 223	95.34 235	97.65 268	97.74 57	98.29 116	98.11 158	95.05 150	99.68 124	97.50 61	99.50 139	99.56 51

EI-MVSNet-UG-set		97.32 123	97.40 112	97.09 167	97.34 302	92.01 225	95.33 236	97.65 268	97.74 57	98.30 115	98.14 152	95.04 151	99.69 119	97.55 59	99.52 130	99.58 40

KD-MVS_self_test		97.86 76	98.07 45	97.25 155	99.22 69	92.81 197	97.55 92	98.94 96	97.10 88	98.85 57	98.88 72	95.03 152	99.67 130	97.39 65	99.65 88	99.26 133

ZD-MVS					98.43 182	95.94 79		98.56 180	90.72 296	96.66 233	97.07 249	95.02 153	99.74 77	91.08 279	98.93 242

DELS-MVS		96.17 182	96.23 179	95.99 229	97.55 285	90.04 257	92.38 347	98.52 182	94.13 212	96.55 241	97.06 250	94.99 154	99.58 163	95.62 136	99.28 199	98.37 260
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
patch_mono-2		96.59 164	96.93 140	95.55 252	98.88 123	87.12 317	94.47 274	99.30 26	94.12 213	96.65 235	98.41 115	94.98 155	99.87 22	95.81 127	99.78 56	99.66 30

ab-mvs		96.59 164	96.59 159	96.60 198	98.64 150	92.21 214	98.35 35	97.67 264	94.45 202	96.99 212	98.79 76	94.96 156	99.49 192	90.39 303	99.07 228	98.08 287

MSLP-MVS++		96.42 174	96.71 152	95.57 249	97.82 247	90.56 253	95.71 207	98.84 120	94.72 192	96.71 230	97.39 228	94.91 157	98.10 374	95.28 158	99.02 233	98.05 296

QAPM		95.88 194	95.57 209	96.80 187	97.90 237	91.84 229	98.18 53	98.73 148	88.41 327	96.42 245	98.13 154	94.73 158	99.75 68	88.72 326	98.94 240	98.81 214

RPSCF		97.87 74	97.51 107	98.95 14	99.15 86	98.43 6	97.56 91	99.06 61	96.19 124	98.48 90	98.70 86	94.72 159	99.24 268	94.37 206	99.33 190	99.17 150

DU-MVS		97.79 84	97.60 98	98.36 63	98.73 138	95.78 84	95.65 214	98.87 110	97.57 67	98.31 113	97.83 190	94.69 160	99.85 27	97.02 78	99.71 75	99.46 82

Baseline_NR-MVSNet		97.72 90	97.79 73	97.50 132	99.56 21	93.29 187	95.44 224	98.86 113	98.20 42	98.37 101	99.24 32	94.69 160	99.55 174	95.98 116	99.79 53	99.65 33

TEST9					97.84 244	95.23 115	93.62 310	98.39 198	86.81 345	93.78 326	95.99 309	94.68 162	99.52 182

UniMVSNet (Re)		97.83 78	97.65 88	98.35 64	98.80 130	95.86 83	95.92 198	99.04 71	97.51 72	98.22 121	97.81 194	94.68 162	99.78 48	97.14 73	99.75 66	99.41 100

UniMVSNet_NR-MVSNet		97.83 78	97.65 88	98.37 62	98.72 140	95.78 84	95.66 212	99.02 74	98.11 44	98.31 113	97.69 205	94.65 164	99.85 27	97.02 78	99.71 75	99.48 77

VPNet		97.26 125	97.49 110	96.59 199	99.47 36	90.58 251	96.27 166	98.53 181	97.77 54	98.46 93	98.41 115	94.59 165	99.68 124	94.61 196	99.29 198	99.52 59

train_agg		95.46 213	94.66 240	97.88 103	97.84 244	95.23 115	93.62 310	98.39 198	87.04 341	93.78 326	95.99 309	94.58 166	99.52 182	91.76 269	98.90 244	98.89 203

test_8					97.81 248	95.07 124	93.54 313	98.38 200	87.04 341	93.71 330	95.96 312	94.58 166	99.52 182

API-MVS		95.09 230	95.01 223	95.31 262	96.61 327	94.02 161	96.83 131	97.18 284	95.60 157	95.79 275	94.33 348	94.54 168	98.37 363	85.70 357	98.52 281	93.52 391

Test By Simon												94.51 169

MSDG		95.33 218	95.13 217	95.94 235	97.40 297	91.85 228	91.02 372	98.37 201	95.30 171	96.31 252	95.99 309	94.51 169	98.38 361	89.59 314	97.65 323	97.60 327

TSAR-MVS + GP.		96.47 171	96.12 183	97.49 135	97.74 267	95.23 115	94.15 288	96.90 295	93.26 240	98.04 144	96.70 275	94.41 171	98.89 313	94.77 191	99.14 216	98.37 260

NR-MVSNet		97.96 54	97.86 65	98.26 70	98.73 138	95.54 95	98.14 54	98.73 148	97.79 53	99.42 20	97.83 190	94.40 172	99.78 48	95.91 120	99.76 59	99.46 82

AdaColmap		95.11 228	94.62 245	96.58 200	97.33 304	94.45 144	94.92 258	98.08 240	93.15 250	93.98 324	95.53 325	94.34 173	99.10 292	85.69 358	98.61 276	96.20 370

FC-MVSNet-test		98.16 37	98.37 33	97.56 123	99.49 35	93.10 192	98.35 35	99.21 32	98.43 32	98.89 54	98.83 75	94.30 174	99.81 37	97.87 44	99.91 18	99.77 12

Effi-MVS+-dtu		96.81 150	96.09 185	98.99 10	96.90 323	98.69 4	96.42 155	98.09 239	95.86 145	95.15 293	95.54 324	94.26 175	99.81 37	94.06 218	98.51 283	98.47 252

ambc				96.56 203	98.23 200	91.68 232	97.88 69	98.13 235		98.42 96	98.56 100	94.22 176	99.04 298	94.05 220	99.35 182	98.95 189

test20.03		96.58 166	96.61 158	96.48 207	98.49 175	91.72 231	95.68 211	97.69 263	96.81 95	98.27 117	97.92 183	94.18 177	98.71 330	90.78 289	99.66 87	99.00 182

HPM-MVS++		96.99 134	96.38 174	98.81 27	98.64 150	97.59 23	95.97 192	98.20 221	95.51 162	95.06 295	96.53 284	94.10 178	99.70 112	94.29 209	99.15 215	99.13 158

test_vis3_rt		97.04 131	96.98 136	97.23 157	98.44 181	95.88 80	96.82 132	99.67 6	90.30 303	99.27 29	99.33 27	94.04 179	96.03 395	97.14 73	97.83 310	99.78 11

test_fmvs3		97.38 117	97.56 102	96.84 185	98.63 154	92.81 197	97.60 87	99.61 13	90.87 294	98.76 70	99.66 3	94.03 180	97.90 376	99.24 6	99.68 83	99.81 8

PM-MVS		97.36 121	97.10 128	98.14 82	98.91 121	96.77 49	96.20 173	98.63 171	93.82 222	98.54 83	98.33 123	93.98 181	99.05 297	95.99 115	99.45 154	98.61 239

mvsany_test3		96.21 180	95.93 195	97.05 169	97.40 297	94.33 149	95.76 206	94.20 341	89.10 316	99.36 24	99.60 6	93.97 182	97.85 377	95.40 156	98.63 274	98.99 185

OpenMVS	94.22 8	95.48 211	95.20 214	96.32 216	97.16 312	91.96 226	97.74 79	98.84 120	87.26 338	94.36 312	98.01 173	93.95 183	99.67 130	90.70 295	98.75 261	97.35 338

v8		97.60 101	98.06 47	96.23 219	98.71 143	89.44 267	97.43 102	98.82 134	97.29 84	98.74 71	99.10 48	93.86 184	99.68 124	98.61 27	99.94 8	99.56 51

diffmvs		96.04 187	96.23 179	95.46 257	97.35 300	88.03 297	93.42 316	99.08 57	94.09 216	96.66 233	96.93 259	93.85 185	99.29 256	96.01 114	98.67 269	99.06 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NCCC		96.52 168	95.99 190	98.10 85	97.81 248	95.68 89	95.00 256	98.20 221	95.39 168	95.40 288	96.36 294	93.81 186	99.45 204	93.55 236	98.42 287	99.17 150

TAPA-MVS	93.32 12	94.93 235	94.23 261	97.04 171	98.18 207	94.51 141	95.22 243	98.73 148	81.22 383	96.25 256	95.95 313	93.80 187	98.98 306	89.89 310	98.87 248	97.62 325
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FIs		97.93 65	98.07 45	97.48 136	99.38 49	92.95 195	98.03 62	99.11 49	98.04 48	98.62 76	98.66 89	93.75 188	99.78 48	97.23 67	99.84 40	99.73 22

OurMVSNet-221017-0		98.61 16	98.61 24	98.63 44	99.77 5	96.35 64	99.17 6	99.05 65	98.05 47	99.61 13	99.52 7	93.72 189	99.88 20	98.72 24	99.88 27	99.65 33

test_prior2							93.33 320		94.21 208	94.02 322	96.25 298	93.64 190		91.90 263	98.96 237

mvsany_test1		93.47 291	93.03 287	94.79 289	94.05 390	92.12 219	90.82 374	90.01 387	85.02 365	97.26 188	98.28 134	93.57 191	97.03 386	92.51 256	95.75 371	95.23 382

旧先验1					97.80 252	93.87 166		97.75 260			97.04 252	93.57 191			98.68 268	98.72 226

v10		97.55 105	97.97 55	96.31 217	98.60 158	89.64 263	97.44 100	99.02 74	96.60 101	98.72 73	99.16 43	93.48 193	99.72 88	98.76 21	99.92 15	99.58 40

v148		96.58 166	96.97 137	95.42 259	98.63 154	87.57 307	95.09 248	97.90 250	95.91 142	98.24 119	97.96 177	93.42 194	99.39 226	96.04 110	99.52 130	99.29 127

V42		97.04 131	97.16 126	96.68 196	98.59 160	91.05 241	96.33 163	98.36 202	94.60 197	97.99 147	98.30 129	93.32 195	99.62 151	97.40 64	99.53 125	99.38 107

new-patchmatchnet		95.67 202	96.58 160	92.94 343	97.48 289	80.21 383	92.96 326	98.19 226	94.83 189	98.82 61	98.79 76	93.31 196	99.51 186	95.83 125	99.04 232	99.12 163

test12				97.46 138	97.61 280	94.07 159		97.78 259		93.57 336		93.31 196	99.42 211		98.78 258	98.89 203

UGNet		96.81 150	96.56 162	97.58 122	96.64 326	93.84 168	97.75 77	97.12 287	96.47 112	93.62 333	98.88 72	93.22 198	99.53 179	95.61 137	99.69 79	99.36 113
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
pmmvs-eth3d		96.49 169	96.18 182	97.42 143	98.25 197	94.29 150	94.77 265	98.07 244	89.81 310	97.97 151	98.33 123	93.11 199	99.08 294	95.46 148	99.84 40	98.89 203

v1144		96.84 145	97.08 130	96.13 226	98.42 183	89.28 270	95.41 228	98.67 163	94.21 208	97.97 151	98.31 125	93.06 200	99.65 138	98.06 39	99.62 93	99.45 86

PVSNet_BlendedMVS		95.02 234	94.93 226	95.27 263	97.79 257	87.40 312	94.14 290	98.68 160	88.94 320	94.51 308	98.01 173	93.04 201	99.30 252	89.77 312	99.49 142	99.11 166

PVSNet_Blended		93.96 277	93.65 276	94.91 280	97.79 257	87.40 312	91.43 360	98.68 160	84.50 371	94.51 308	94.48 346	93.04 201	99.30 252	89.77 312	98.61 276	98.02 299

mvs_anonymous		95.36 216	96.07 187	93.21 334	96.29 334	81.56 375	94.60 271	97.66 266	93.30 239	96.95 216	98.91 69	93.03 203	99.38 229	96.60 88	97.30 337	98.69 230

v1192		96.83 148	97.06 132	96.15 225	98.28 193	89.29 269	95.36 232	98.77 141	93.73 224	98.11 133	98.34 122	93.02 204	99.67 130	98.35 33	99.58 106	99.50 63

F-COLMAP		95.30 220	94.38 258	98.05 92	98.64 150	96.04 75	95.61 218	98.66 165	89.00 319	93.22 345	96.40 292	92.90 205	99.35 241	87.45 346	97.53 327	98.77 220

WR-MVS		96.90 142	96.81 147	97.16 159	98.56 164	92.20 217	94.33 277	98.12 236	97.34 81	98.20 122	97.33 235	92.81 206	99.75 68	94.79 188	99.81 48	99.54 54

v1240		96.74 153	97.02 135	95.91 236	98.18 207	88.52 283	95.39 230	98.88 108	93.15 250	98.46 93	98.40 118	92.80 207	99.71 104	98.45 31	99.49 142	99.49 71

MVS_0304		96.62 163	96.40 173	97.28 151	97.91 235	92.30 210	96.47 154	89.74 388	97.52 71	95.38 289	98.63 94	92.76 208	99.81 37	99.28 4	99.93 11	99.75 19

MVE	73.61 22	86.48 368	85.92 367	88.18 382	96.23 337	85.28 341	81.78 400	75.79 406	86.01 351	82.53 402	91.88 377	92.74 209	87.47 405	71.42 402	94.86 379	91.78 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DP-MVS Recon		95.55 207	95.13 217	96.80 187	98.51 171	93.99 163	94.60 271	98.69 158	90.20 305	95.78 277	96.21 300	92.73 210	98.98 306	90.58 298	98.86 250	97.42 335

CANet		95.86 195	95.65 206	96.49 206	96.41 332	90.82 246	94.36 276	98.41 195	94.94 186	92.62 361	96.73 274	92.68 211	99.71 104	95.12 173	99.60 102	98.94 191

v1921920		96.72 156	96.96 139	95.99 229	98.21 201	88.79 280	95.42 226	98.79 136	93.22 242	98.19 126	98.26 139	92.68 211	99.70 112	98.34 34	99.55 118	99.49 71

BH-untuned		94.69 247	94.75 238	94.52 301	97.95 234	87.53 308	94.07 293	97.01 291	93.99 218	97.10 200	95.65 320	92.65 213	98.95 311	87.60 341	96.74 349	97.09 342

LF4IMVS		96.07 185	95.63 207	97.36 147	98.19 204	95.55 94	95.44 224	98.82 134	92.29 273	95.70 281	96.55 282	92.63 214	98.69 333	91.75 270	99.33 190	97.85 311

v2v482		96.78 152	97.06 132	95.95 233	98.57 162	88.77 281	95.36 232	98.26 212	95.18 176	97.85 164	98.23 143	92.58 215	99.63 146	97.80 48	99.69 79	99.45 86

WB-MVSnew		91.50 325	91.29 318	92.14 360	94.85 377	80.32 382	93.29 321	88.77 391	88.57 326	94.03 321	92.21 373	92.56 216	98.28 368	80.21 386	97.08 338	97.81 315

EI-MVSNet		96.63 162	96.93 140	95.74 242	97.26 307	88.13 294	95.29 240	97.65 268	96.99 89	97.94 154	98.19 148	92.55 217	99.58 163	96.91 81	99.56 112	99.50 63

IterMVS-LS		96.92 140	97.29 119	95.79 240	98.51 171	88.13 294	95.10 247	98.66 165	96.99 89	98.46 93	98.68 88	92.55 217	99.74 77	96.91 81	99.79 53	99.50 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		97.37 119	97.25 121	97.74 111	98.69 147	94.50 143	97.04 122	95.61 323	98.59 27	98.51 85	98.72 83	92.54 219	99.58 163	96.02 112	99.49 142	99.12 163

MVS		90.02 337	89.20 344	92.47 355	94.71 379	86.90 321	95.86 201	96.74 302	64.72 402	90.62 374	92.77 365	92.54 219	98.39 360	79.30 388	95.56 373	92.12 395

test_vis1_rt		94.03 276	93.65 276	95.17 268	95.76 360	93.42 183	93.97 299	98.33 206	84.68 368	93.17 346	95.89 315	92.53 221	94.79 397	93.50 237	94.97 377	97.31 339

v144192		96.69 159	96.90 144	96.03 228	98.25 197	88.92 275	95.49 222	98.77 141	93.05 252	98.09 136	98.29 133	92.51 222	99.70 112	98.11 36	99.56 112	99.47 80

原ACMM1				96.58 200	98.16 212	92.12 219		98.15 233	85.90 354	93.49 338	96.43 289	92.47 223	99.38 229	87.66 340	98.62 275	98.23 278

VNet		96.84 145	96.83 146	96.88 181	98.06 221	92.02 224	96.35 162	97.57 274	97.70 62	97.88 159	97.80 195	92.40 224	99.54 177	94.73 193	98.96 237	99.08 171

114514_t		93.96 277	93.22 284	96.19 222	99.06 102	90.97 244	95.99 190	98.94 96	73.88 400	93.43 341	96.93 259	92.38 225	99.37 234	89.09 321	99.28 199	98.25 277

CPTT-MVS		96.69 159	96.08 186	98.49 52	98.89 122	96.64 55	97.25 108	98.77 141	92.89 260	96.01 267	97.13 245	92.23 226	99.67 130	92.24 258	99.34 185	99.17 150

MSP-MVS		97.45 112	96.92 142	99.03 5	99.26 60	97.70 18	97.66 83	98.89 102	95.65 154	98.51 85	96.46 288	92.15 227	99.81 37	95.14 170	98.58 279	99.58 40
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
MAR-MVS		94.21 268	93.03 287	97.76 110	96.94 321	97.44 33	96.97 125	97.15 285	87.89 336	92.00 366	92.73 367	92.14 228	99.12 286	83.92 372	97.51 328	96.73 359
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PVSNet_Blended_VisFu		95.95 191	95.80 200	96.42 211	99.28 58	90.62 250	95.31 238	99.08 57	88.40 328	96.97 215	98.17 151	92.11 229	99.78 48	93.64 234	99.21 207	98.86 210

BH-RMVSNet		94.56 255	94.44 257	94.91 280	97.57 282	87.44 311	93.78 307	96.26 308	93.69 227	96.41 246	96.50 287	92.10 230	99.00 302	85.96 355	97.71 317	98.31 269

新几何1				97.25 155	98.29 191	94.70 133		97.73 261	77.98 394	94.83 302	96.67 277	92.08 231	99.45 204	88.17 335	98.65 273	97.61 326

testdata				95.70 245	98.16 212	90.58 251		97.72 262	80.38 386	95.62 282	97.02 253	92.06 232	98.98 306	89.06 323	98.52 281	97.54 330

YYNet1		94.73 242	94.84 232	94.41 306	97.47 293	85.09 345	90.29 379	95.85 317	92.52 267	97.53 173	97.76 196	91.97 233	99.18 275	93.31 242	96.86 343	98.95 189

Anonymous20231206		95.27 221	95.06 222	95.88 237	98.72 140	89.37 268	95.70 208	97.85 253	88.00 334	96.98 214	97.62 209	91.95 234	99.34 243	89.21 319	99.53 125	98.94 191

MS-PatchMatch		94.83 239	94.91 228	94.57 299	96.81 324	87.10 318	94.23 283	97.34 279	88.74 323	97.14 196	97.11 247	91.94 235	98.23 370	92.99 249	97.92 306	98.37 260

MDA-MVSNet_test_wron		94.73 242	94.83 234	94.42 305	97.48 289	85.15 343	90.28 380	95.87 316	92.52 267	97.48 179	97.76 196	91.92 236	99.17 279	93.32 241	96.80 348	98.94 191

HQP_MVS		96.66 161	96.33 177	97.68 117	98.70 145	94.29 150	96.50 152	98.75 145	96.36 115	96.16 261	96.77 271	91.91 237	99.46 200	92.59 254	99.20 208	99.28 128

plane_prior6					98.38 185	94.37 147						91.91 237

MVP-Stereo		95.69 200	95.28 212	96.92 178	98.15 214	93.03 193	95.64 217	98.20 221	90.39 302	96.63 236	97.73 202	91.63 239	99.10 292	91.84 266	97.31 336	98.63 236
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchMatch-RL		94.61 253	93.81 274	97.02 173	98.19 204	95.72 86	93.66 309	97.23 281	88.17 332	94.94 300	95.62 322	91.43 240	98.57 345	87.36 347	97.68 320	96.76 358

MDA-MVSNet-bldmvs		95.69 200	95.67 204	95.74 242	98.48 177	88.76 282	92.84 328	97.25 280	96.00 135	97.59 171	97.95 179	91.38 241	99.46 200	93.16 247	96.35 358	98.99 185

SSC-MVS		95.92 192	97.03 134	92.58 352	99.28 58	78.39 388	96.68 146	95.12 332	98.90 19	99.11 39	98.66 89	91.36 242	99.68 124	95.00 179	99.16 214	99.67 28

mvsmamba		98.16 37	98.06 47	98.44 55	99.53 29	95.87 81	98.70 13	98.94 96	97.71 61	98.85 57	99.10 48	91.35 243	99.83 33	98.47 30	99.90 24	99.64 35

PAPR		92.22 312	91.27 320	95.07 272	95.73 362	88.81 279	91.97 353	97.87 252	85.80 355	90.91 373	92.73 367	91.16 244	98.33 365	79.48 387	95.76 370	98.08 287

1314		92.38 309	92.30 304	92.64 351	95.42 369	85.15 343	95.86 201	96.97 293	85.40 360	90.62 374	93.06 360	91.12 245	97.80 379	86.74 352	95.49 374	94.97 384

WB-MVS		95.50 208	96.62 156	92.11 361	99.21 76	77.26 396	96.12 180	95.40 329	98.62 26	98.84 59	98.26 139	91.08 246	99.50 187	93.37 238	98.70 267	99.58 40

ppachtmachnet_test		94.49 260	94.84 232	93.46 327	96.16 341	82.10 370	90.59 376	97.48 276	90.53 300	97.01 211	97.59 211	91.01 247	99.36 237	93.97 224	99.18 212	98.94 191

PLC	91.02 16	94.05 275	92.90 290	97.51 128	98.00 229	95.12 123	94.25 281	98.25 213	86.17 350	91.48 371	95.25 329	91.01 247	99.19 274	85.02 367	96.69 351	98.22 279
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test222					98.17 210	93.24 190	92.74 333	97.61 273	75.17 398	94.65 305	96.69 276	90.96 249			98.66 271	97.66 322

CL-MVSNet_self_test		95.04 231	94.79 237	95.82 239	97.51 287	89.79 261	91.14 369	96.82 298	93.05 252	96.72 229	96.40 292	90.82 250	99.16 280	91.95 262	98.66 271	98.50 250

USDC		94.56 255	94.57 251	94.55 300	97.78 260	86.43 328	92.75 331	98.65 170	85.96 352	96.91 219	97.93 182	90.82 250	98.74 326	90.71 294	99.59 104	98.47 252

PCF-MVS	89.43 18	92.12 315	90.64 332	96.57 202	97.80 252	93.48 181	89.88 386	98.45 188	74.46 399	96.04 266	95.68 319	90.71 252	99.31 249	73.73 398	99.01 235	96.91 349
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PAPM_NR		94.61 253	94.17 265	95.96 231	98.36 187	91.23 239	95.93 197	97.95 247	92.98 255	93.42 342	94.43 347	90.53 253	98.38 361	87.60 341	96.29 360	98.27 275

our_test_3		94.20 270	94.58 249	93.07 336	96.16 341	81.20 378	90.42 378	96.84 296	90.72 296	97.14 196	97.13 245	90.47 254	99.11 289	94.04 221	98.25 293	98.91 199

MM		96.87 144	96.62 156	97.62 120	97.72 269	93.30 186	96.39 156	92.61 361	97.90 52	96.76 228	98.64 93	90.46 255	99.81 37	99.16 9	99.94 8	99.76 17

test_f		95.82 197	95.88 198	95.66 246	97.61 280	93.21 191	95.61 218	98.17 227	86.98 343	98.42 96	99.47 11	90.46 255	94.74 398	97.71 53	98.45 285	99.03 178

OpenMVS_ROB	91.80 14	93.64 287	93.05 285	95.42 259	97.31 306	91.21 240	95.08 250	96.68 305	81.56 380	96.88 221	96.41 290	90.44 257	99.25 264	85.39 363	97.67 321	95.80 374

HQP2-MVS												90.33 258

N_pmnet		95.18 225	94.23 261	98.06 88	97.85 239	96.55 58	92.49 339	91.63 369	89.34 313	98.09 136	97.41 223	90.33 258	99.06 296	91.58 271	99.31 195	98.56 242

HQP-MVS		95.17 227	94.58 249	96.92 178	97.85 239	92.47 207	94.26 278	98.43 191	93.18 246	92.86 352	95.08 331	90.33 258	99.23 270	90.51 300	98.74 262	99.05 177

CNLPA		95.04 231	94.47 254	96.75 191	97.81 248	95.25 114	94.12 292	97.89 251	94.41 203	94.57 306	95.69 318	90.30 261	98.35 364	86.72 353	98.76 260	96.64 360

PMMVS		92.39 308	91.08 323	96.30 218	93.12 397	92.81 197	90.58 377	95.96 314	79.17 391	91.85 368	92.27 372	90.29 262	98.66 338	89.85 311	96.68 352	97.43 334

TR-MVS		92.54 307	92.20 306	93.57 325	96.49 330	86.66 324	93.51 314	94.73 335	89.96 308	94.95 299	93.87 351	90.24 263	98.61 342	81.18 383	94.88 378	95.45 380

TAMVS		95.49 209	94.94 224	97.16 159	98.31 189	93.41 184	95.07 251	96.82 298	91.09 292	97.51 175	97.82 193	89.96 264	99.42 211	88.42 331	99.44 155	98.64 234

DPM-MVS		93.68 284	92.77 297	96.42 211	97.91 235	92.54 203	91.17 368	97.47 277	84.99 366	93.08 348	94.74 339	89.90 265	99.00 302	87.54 343	98.09 300	97.72 320

PMMVS2		93.66 285	94.07 267	92.45 356	97.57 282	80.67 381	86.46 394	96.00 312	93.99 218	97.10 200	97.38 230	89.90 265	97.82 378	88.76 325	99.47 148	98.86 210

RRT_MVS		97.95 58	97.79 73	98.43 57	99.67 12	95.56 93	98.86 10	96.73 304	97.99 49	99.15 36	99.35 23	89.84 267	99.90 14	98.64 26	99.90 24	99.82 6

bld_raw_dy_0_64		97.69 92	97.61 97	97.91 100	99.54 26	94.27 154	98.06 59	98.60 173	96.60 101	98.79 64	98.95 63	89.62 268	99.84 30	98.43 32	99.91 18	99.62 36

BH-w/o		92.14 314	91.94 308	92.73 349	97.13 314	85.30 339	92.46 341	95.64 320	89.33 314	94.21 314	92.74 366	89.60 269	98.24 369	81.68 381	94.66 380	94.66 385

Anonymous20240521		97.07 130	97.51 107	95.76 241	99.35 52	88.18 291	97.78 73	98.40 197	97.11 87	98.34 107	99.04 53	89.58 270	99.79 45	98.09 37	99.93 11	99.30 121

UnsupCasMVSNet_bld		94.72 246	94.26 260	96.08 227	98.62 156	90.54 254	93.38 318	98.05 246	90.30 303	97.02 210	96.80 270	89.54 271	99.16 280	88.44 330	96.18 362	98.56 242

MG-MVS		94.08 274	94.00 269	94.32 309	97.09 315	85.89 333	93.19 324	95.96 314	92.52 267	94.93 301	97.51 217	89.54 271	98.77 323	87.52 345	97.71 317	98.31 269

UnsupCasMVSNet_eth		95.91 193	95.73 203	96.44 208	98.48 177	91.52 234	95.31 238	98.45 188	95.76 149	97.48 179	97.54 214	89.53 273	98.69 333	94.43 202	94.61 381	99.13 158

GBi-Net		96.99 134	96.80 148	97.56 123	97.96 231	93.67 173	98.23 46	98.66 165	95.59 158	97.99 147	99.19 36	89.51 274	99.73 83	94.60 197	99.44 155	99.30 121

test1		96.99 134	96.80 148	97.56 123	97.96 231	93.67 173	98.23 46	98.66 165	95.59 158	97.99 147	99.19 36	89.51 274	99.73 83	94.60 197	99.44 155	99.30 121

FMVSNet2		96.72 156	96.67 155	96.87 182	97.96 231	91.88 227	97.15 114	98.06 245	95.59 158	98.50 87	98.62 95	89.51 274	99.65 138	94.99 181	99.60 102	99.07 173

pmmvs4		94.82 240	94.19 264	96.70 194	97.42 296	92.75 201	92.09 352	96.76 300	86.80 346	95.73 280	97.22 241	89.28 277	98.89 313	93.28 243	99.14 216	98.46 254

cascas		91.89 320	91.35 317	93.51 326	94.27 385	85.60 335	88.86 391	98.61 172	79.32 390	92.16 365	91.44 382	89.22 278	98.12 373	90.80 288	97.47 331	96.82 355

DSMNet-mixed		92.19 313	91.83 310	93.25 331	96.18 340	83.68 361	96.27 166	93.68 346	76.97 397	92.54 362	99.18 39	89.20 279	98.55 348	83.88 373	98.60 278	97.51 331

c3_l		95.20 224	95.32 211	94.83 287	96.19 339	86.43 328	91.83 356	98.35 205	93.47 233	97.36 185	97.26 239	88.69 280	99.28 258	95.41 155	99.36 177	98.78 217

test_fmvs2		96.38 175	96.45 170	96.16 224	97.85 239	91.30 238	96.81 133	99.45 18	89.24 315	98.49 88	99.38 18	88.68 281	97.62 381	98.83 18	99.32 192	99.57 47

CANet_DTU		94.65 251	94.21 263	95.96 231	95.90 350	89.68 262	93.92 301	97.83 257	93.19 245	90.12 382	95.64 321	88.52 282	99.57 169	93.27 244	99.47 148	98.62 237

EPP-MVSNet		96.84 145	96.58 160	97.65 118	99.18 81	93.78 171	98.68 14	96.34 307	97.91 51	97.30 186	98.06 167	88.46 283	99.85 27	93.85 227	99.40 171	99.32 116

SixPastTwentyTwo		97.49 109	97.57 101	97.26 154	99.56 21	92.33 209	98.28 42	96.97 293	98.30 38	99.45 18	99.35 23	88.43 284	99.89 18	98.01 40	99.76 59	99.54 54

miper_ehance_all_eth		94.69 247	94.70 239	94.64 293	95.77 359	86.22 330	91.32 365	98.24 215	91.67 281	97.05 207	96.65 278	88.39 285	99.22 272	94.88 183	98.34 289	98.49 251

IS-MVSNet		96.93 139	96.68 154	97.70 114	99.25 63	94.00 162	98.57 20	96.74 302	98.36 34	98.14 131	97.98 176	88.23 286	99.71 104	93.10 248	99.72 72	99.38 107

jason		94.39 263	94.04 268	95.41 261	98.29 191	87.85 302	92.74 333	96.75 301	85.38 361	95.29 290	96.15 302	88.21 287	99.65 138	94.24 211	99.34 185	98.74 223
jason: jason.
IterMVS-SCA-FT		95.86 195	96.19 181	94.85 285	97.68 272	85.53 336	92.42 344	97.63 272	96.99 89	98.36 104	98.54 102	87.94 288	99.75 68	97.07 77	99.08 226	99.27 132

SCA		93.38 294	93.52 279	92.96 342	96.24 335	81.40 377	93.24 322	94.00 342	91.58 285	94.57 306	96.97 256	87.94 288	99.42 211	89.47 316	97.66 322	98.06 293

sss		94.22 266	93.72 275	95.74 242	97.71 270	89.95 259	93.84 303	96.98 292	88.38 329	93.75 329	95.74 317	87.94 288	98.89 313	91.02 281	98.10 299	98.37 260

IterMVS		95.42 215	95.83 199	94.20 313	97.52 286	83.78 360	92.41 345	97.47 277	95.49 163	98.06 141	98.49 106	87.94 288	99.58 163	96.02 112	99.02 233	99.23 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		94.10 272	93.41 281	96.18 223	99.16 83	90.04 257	92.15 349	98.68 160	79.90 388	96.22 257	97.83 190	87.92 292	99.42 211	89.18 320	99.65 88	99.08 171

VDDNet		96.98 137	96.84 145	97.41 144	99.40 46	93.26 189	97.94 65	95.31 330	99.26 7	98.39 100	99.18 39	87.85 293	99.62 151	95.13 172	99.09 225	99.35 115

pmmvs5		94.63 252	94.34 259	95.50 254	97.63 279	88.34 287	94.02 294	97.13 286	87.15 340	95.22 292	97.15 244	87.50 294	99.27 261	93.99 222	99.26 202	98.88 207

D2MVS		95.18 225	95.17 216	95.21 265	97.76 262	87.76 305	94.15 288	97.94 248	89.77 311	96.99 212	97.68 206	87.45 295	99.14 282	95.03 178	99.81 48	98.74 223

test_vis1_n_1920		95.77 198	96.41 172	93.85 318	98.55 165	84.86 348	95.91 199	99.71 4	92.72 264	97.67 169	98.90 70	87.44 296	98.73 327	97.96 41	98.85 251	97.96 303

PVSNet	86.72 19	91.10 329	90.97 326	91.49 365	97.56 284	78.04 390	87.17 393	94.60 337	84.65 369	92.34 363	92.20 374	87.37 297	98.47 355	85.17 366	97.69 319	97.96 303

Anonymous202405211		96.34 176	95.98 191	97.43 141	98.25 197	93.85 167	96.74 139	94.41 339	97.72 59	98.37 101	98.03 170	87.15 298	99.53 179	94.06 218	99.07 228	98.92 198

MVSFormer		96.14 183	96.36 175	95.49 255	97.68 272	87.81 303	98.67 15	99.02 74	96.50 109	94.48 310	96.15 302	86.90 299	99.92 5	98.73 22	99.13 218	98.74 223

lupinMVS		93.77 280	93.28 282	95.24 264	97.68 272	87.81 303	92.12 350	96.05 310	84.52 370	94.48 310	95.06 333	86.90 299	99.63 146	93.62 235	99.13 218	98.27 275

eth_miper_zixun_eth		94.89 237	94.93 226	94.75 291	95.99 348	86.12 331	91.35 362	98.49 185	93.40 234	97.12 198	97.25 240	86.87 301	99.35 241	95.08 175	98.82 255	98.78 217

test_vis1_n		95.67 202	95.89 197	95.03 274	98.18 207	89.89 260	96.94 126	99.28 28	88.25 331	98.20 122	98.92 66	86.69 302	97.19 384	97.70 55	98.82 255	98.00 301

WTY-MVS		93.55 289	93.00 289	95.19 266	97.81 248	87.86 300	93.89 302	96.00 312	89.02 318	94.07 319	95.44 328	86.27 303	99.33 245	87.69 339	96.82 346	98.39 258

CDS-MVSNet		94.88 238	94.12 266	97.14 161	97.64 278	93.57 178	93.96 300	97.06 290	90.05 307	96.30 253	96.55 282	86.10 304	99.47 197	90.10 307	99.31 195	98.40 256
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1112_ss		94.12 271	93.42 280	96.23 219	98.59 160	90.85 245	94.24 282	98.85 117	85.49 357	92.97 350	94.94 335	86.01 305	99.64 142	91.78 268	97.92 306	98.20 281

dmvs_testset		87.30 365	86.99 362	88.24 381	96.71 325	77.48 393	94.68 268	86.81 398	92.64 266	89.61 386	87.01 400	85.91 306	93.12 401	61.04 405	88.49 397	94.13 388

miper_enhance_ethall		93.14 299	92.78 296	94.20 313	93.65 393	85.29 340	89.97 382	97.85 253	85.05 363	96.15 263	94.56 342	85.74 307	99.14 282	93.74 230	98.34 289	98.17 284

new_pmnet		92.34 310	91.69 314	94.32 309	96.23 337	89.16 272	92.27 348	92.88 355	84.39 373	95.29 290	96.35 295	85.66 308	96.74 393	84.53 370	97.56 325	97.05 343

Syy-MVS		92.09 316	91.80 312	92.93 344	95.19 372	82.65 366	92.46 341	91.35 371	90.67 298	91.76 369	87.61 398	85.64 309	98.50 352	94.73 193	96.84 344	97.65 323

alignmvs		96.01 189	95.52 210	97.50 132	97.77 261	94.71 131	96.07 183	96.84 296	97.48 73	96.78 227	94.28 349	85.50 310	99.40 222	96.22 102	98.73 265	98.40 256

lessismore_v0				97.05 169	99.36 51	92.12 219		84.07 401		98.77 69	98.98 58	85.36 311	99.74 77	97.34 66	99.37 174	99.30 121

HY-MVS	91.43 15	92.58 306	91.81 311	94.90 282	96.49 330	88.87 277	97.31 105	94.62 336	85.92 353	90.50 377	96.84 265	85.05 312	99.40 222	83.77 375	95.78 369	96.43 366

EPNet		93.72 282	92.62 301	97.03 172	87.61 409	92.25 212	96.27 166	91.28 373	96.74 97	87.65 395	97.39 228	85.00 313	99.64 142	92.14 259	99.48 146	99.20 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		94.81 241	94.80 236	94.85 285	96.16 341	86.45 327	91.14 369	98.20 221	93.49 232	97.03 209	97.37 232	84.97 314	99.26 262	95.28 158	99.56 112	98.83 212

Test_1112_low_res		93.53 290	92.86 291	95.54 253	98.60 158	88.86 278	92.75 331	98.69 158	82.66 377	92.65 358	96.92 261	84.75 315	99.56 170	90.94 283	97.76 313	98.19 282

MVS-HIRNet		88.40 356	90.20 337	82.99 385	97.01 317	60.04 410	93.11 325	85.61 400	84.45 372	88.72 391	99.09 50	84.72 316	98.23 370	82.52 379	96.59 354	90.69 400

K. test v3		96.44 172	96.28 178	96.95 175	99.41 43	91.53 233	97.65 84	90.31 383	98.89 20	98.93 50	99.36 21	84.57 317	99.92 5	97.81 47	99.56 112	99.39 105

test_cas_vis1_n_1920		95.34 217	95.67 204	94.35 308	98.21 201	86.83 323	95.61 218	99.26 29	90.45 301	98.17 127	98.96 61	84.43 318	98.31 366	96.74 84	99.17 213	97.90 307

h-mvs33		96.29 177	95.63 207	98.26 70	98.50 174	96.11 73	96.90 128	97.09 288	96.58 104	97.21 191	98.19 148	84.14 319	99.78 48	95.89 121	96.17 363	98.89 203

hse-mvs2		95.77 198	95.09 219	97.79 108	97.84 244	95.51 97	95.66 212	95.43 328	96.58 104	97.21 191	96.16 301	84.14 319	99.54 177	95.89 121	96.92 340	98.32 267

DIV-MVS_self_test		94.73 242	94.64 242	95.01 275	95.86 353	87.00 319	91.33 363	98.08 240	93.34 237	97.10 200	97.34 234	84.02 321	99.31 249	95.15 169	99.55 118	98.72 226

cl____		94.73 242	94.64 242	95.01 275	95.85 354	87.00 319	91.33 363	98.08 240	93.34 237	97.10 200	97.33 235	84.01 322	99.30 252	95.14 170	99.56 112	98.71 229

Vis-MVSNet (Re-imp)		95.11 228	94.85 231	95.87 238	99.12 94	89.17 271	97.54 97	94.92 334	96.50 109	96.58 237	97.27 238	83.64 323	99.48 195	88.42 331	99.67 85	98.97 187

FA-MVS(test-final)		94.91 236	94.89 229	94.99 277	97.51 287	88.11 296	98.27 44	95.20 331	92.40 272	96.68 231	98.60 96	83.44 324	99.28 258	93.34 240	98.53 280	97.59 328

dmvs_re		92.08 317	91.27 320	94.51 302	97.16 312	92.79 200	95.65 214	92.64 360	94.11 214	92.74 355	90.98 387	83.41 325	94.44 400	80.72 384	94.07 384	96.29 368

PVSNet_0	81.89 21	84.49 369	83.21 372	88.34 380	95.76 360	74.97 403	83.49 397	92.70 359	78.47 393	87.94 394	86.90 401	83.38 326	96.63 394	73.44 399	66.86 405	93.40 392

test_fmvs1_n		95.21 223	95.28 212	94.99 277	98.15 214	89.13 274	96.81 133	99.43 20	86.97 344	97.21 191	98.92 66	83.00 327	97.13 385	98.09 37	98.94 240	98.72 226

CMPMVS	73.10 23	92.74 304	91.39 316	96.77 190	93.57 395	94.67 134	94.21 285	97.67 264	80.36 387	93.61 334	96.60 280	82.85 328	97.35 383	84.86 368	98.78 258	98.29 274
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_fmvs1		94.51 259	94.60 246	94.26 312	95.91 349	87.92 298	95.35 234	99.02 74	86.56 348	96.79 223	98.52 103	82.64 329	97.00 388	97.87 44	98.71 266	97.88 309

EU-MVSNet		94.25 265	94.47 254	93.60 324	98.14 216	82.60 368	97.24 110	92.72 358	85.08 362	98.48 90	98.94 64	82.59 330	98.76 325	97.47 63	99.53 125	99.44 96

baseline1		93.14 299	92.64 300	94.62 295	97.34 302	87.20 316	96.67 148	93.02 353	94.71 193	96.51 242	95.83 316	81.64 331	98.60 344	90.00 309	88.06 398	98.07 289

test1111		94.53 258	94.81 235	93.72 321	99.06 102	81.94 373	98.31 39	83.87 402	96.37 114	98.49 88	99.17 42	81.49 332	99.73 83	96.64 86	99.86 31	99.49 71

CVMVSNet		92.33 311	92.79 294	90.95 368	97.26 307	75.84 400	95.29 240	92.33 363	81.86 378	96.27 254	98.19 148	81.44 333	98.46 356	94.23 212	98.29 292	98.55 244

EPNet_dtu		91.39 327	90.75 330	93.31 329	90.48 406	82.61 367	94.80 262	92.88 355	93.39 235	81.74 403	94.90 338	81.36 334	99.11 289	88.28 333	98.87 248	98.21 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ECVR-MVS		94.37 264	94.48 253	94.05 317	98.95 113	83.10 363	98.31 39	82.48 404	96.20 122	98.23 120	99.16 43	81.18 335	99.66 136	95.95 117	99.83 43	99.38 107

test_yl		94.40 261	94.00 269	95.59 247	96.95 319	89.52 265	94.75 266	95.55 325	96.18 125	96.79 223	96.14 304	81.09 336	99.18 275	90.75 290	97.77 311	98.07 289

DCV-MVSNet		94.40 261	94.00 269	95.59 247	96.95 319	89.52 265	94.75 266	95.55 325	96.18 125	96.79 223	96.14 304	81.09 336	99.18 275	90.75 290	97.77 311	98.07 289

MIMVSNet		93.42 292	92.86 291	95.10 271	98.17 210	88.19 290	98.13 55	93.69 344	92.07 274	95.04 298	98.21 147	80.95 338	99.03 301	81.42 382	98.06 301	98.07 289

PAPM		87.64 361	85.84 368	93.04 337	96.54 328	84.99 346	88.42 392	95.57 324	79.52 389	83.82 400	93.05 361	80.57 339	98.41 358	62.29 404	92.79 388	95.71 375

HyFIR lowres test		93.72 282	92.65 299	96.91 180	98.93 117	91.81 230	91.23 367	98.52 182	82.69 376	96.46 244	96.52 286	80.38 340	99.90 14	90.36 304	98.79 257	99.03 178

FMVSNet3		95.26 222	94.94 224	96.22 221	96.53 329	90.06 256	95.99 190	97.66 266	94.11 214	97.99 147	97.91 184	80.22 341	99.63 146	94.60 197	99.44 155	98.96 188

RPMNet		94.68 249	94.60 246	94.90 282	95.44 367	88.15 292	96.18 174	98.86 113	97.43 74	94.10 317	98.49 106	79.40 342	99.76 62	95.69 130	95.81 366	96.81 356

LFMVS		95.32 219	94.88 230	96.62 197	98.03 222	91.47 235	97.65 84	90.72 379	99.11 9	97.89 158	98.31 125	79.20 343	99.48 195	93.91 226	99.12 221	98.93 195

ADS-MVSNet2		91.47 326	90.51 334	94.36 307	95.51 365	85.63 334	95.05 253	95.70 318	83.46 374	92.69 356	96.84 265	79.15 344	99.41 220	85.66 359	90.52 392	98.04 297

ADS-MVSNet		90.95 332	90.26 336	93.04 337	95.51 365	82.37 369	95.05 253	93.41 350	83.46 374	92.69 356	96.84 265	79.15 344	98.70 331	85.66 359	90.52 392	98.04 297

MDTV_nov1_ep13_2view						57.28 411	94.89 259		80.59 385	94.02 322		78.66 346		85.50 361		97.82 313

cl22		93.25 297	92.84 293	94.46 304	94.30 384	86.00 332	91.09 371	96.64 306	90.74 295	95.79 275	96.31 296	78.24 347	98.77 323	94.15 215	98.34 289	98.62 237

PatchmatchNet		91.98 319	91.87 309	92.30 358	94.60 381	79.71 384	95.12 246	93.59 349	89.52 312	93.61 334	97.02 253	77.94 348	99.18 275	90.84 286	94.57 383	98.01 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												77.80 349				98.06 293

CR-MVSNet		93.29 296	92.79 294	94.78 290	95.44 367	88.15 292	96.18 174	97.20 282	84.94 367	94.10 317	98.57 98	77.67 350	99.39 226	95.17 165	95.81 366	96.81 356

Patchmtry		95.03 233	94.59 248	96.33 215	94.83 378	90.82 246	96.38 159	97.20 282	96.59 103	97.49 177	98.57 98	77.67 350	99.38 229	92.95 251	99.62 93	98.80 215

tpmrst		90.31 335	90.61 333	89.41 376	94.06 389	72.37 407	95.06 252	93.69 344	88.01 333	92.32 364	96.86 263	77.45 352	98.82 318	91.04 280	87.01 399	97.04 344

sam_mvs												77.38 353

patchmatchnet-post											96.84 265	77.36 354	99.42 211

Patchmatch-RL test		94.66 250	94.49 252	95.19 266	98.54 167	88.91 276	92.57 337	98.74 147	91.46 286	98.32 111	97.75 199	77.31 355	98.81 320	96.06 107	99.61 99	97.85 311

tpmvs		90.79 333	90.87 327	90.57 371	92.75 401	76.30 398	95.79 205	93.64 348	91.04 293	91.91 367	96.26 297	77.19 356	98.86 317	89.38 318	89.85 395	96.56 363

test_post											10.87 407	76.83 357	99.07 295

Patchmatch-test		93.60 288	93.25 283	94.63 294	96.14 345	87.47 309	96.04 185	94.50 338	93.57 230	96.47 243	96.97 256	76.50 358	98.61 342	90.67 296	98.41 288	97.81 315

MDTV_nov1_ep13			91.28 319		94.31 383	73.51 405	94.80 262	93.16 352	86.75 347	93.45 340	97.40 224	76.37 359	98.55 348	88.85 324	96.43 355

EMVS		89.06 350	89.22 342	88.61 379	93.00 398	77.34 394	82.91 399	90.92 376	94.64 196	92.63 360	91.81 378	76.30 360	97.02 387	83.83 374	96.90 342	91.48 398

test_post1							94.98 257				10.37 408	76.21 361	99.04 298	89.47 316

GA-MVS		92.83 303	92.15 307	94.87 284	96.97 318	87.27 315	90.03 381	96.12 309	91.83 280	94.05 320	94.57 341	76.01 362	98.97 310	92.46 257	97.34 335	98.36 265

PatchT		93.75 281	93.57 278	94.29 311	95.05 375	87.32 314	96.05 184	92.98 354	97.54 70	94.25 313	98.72 83	75.79 363	99.24 268	95.92 119	95.81 366	96.32 367

E-PMN		89.52 347	89.78 339	88.73 378	93.14 396	77.61 392	83.26 398	92.02 365	94.82 190	93.71 330	93.11 355	75.31 364	96.81 390	85.81 356	96.81 347	91.77 397

DeepMVS_CX				77.17 386	90.94 405	85.28 341		74.08 409	52.51 403	80.87 404	88.03 397	75.25 365	70.63 406	59.23 406	84.94 401	75.62 401

AUN-MVS		93.95 279	92.69 298	97.74 111	97.80 252	95.38 105	95.57 221	95.46 327	91.26 290	92.64 359	96.10 307	74.67 366	99.55 174	93.72 232	96.97 339	98.30 271

CHOSEN 280x420		89.98 339	89.19 345	92.37 357	95.60 364	81.13 379	86.22 395	97.09 288	81.44 382	87.44 396	93.15 354	73.99 367	99.47 197	88.69 327	99.07 228	96.52 364

thres200		91.00 331	90.42 335	92.77 348	97.47 293	83.98 359	94.01 295	91.18 375	95.12 179	95.44 286	91.21 384	73.93 368	99.31 249	77.76 393	97.63 324	95.01 383

test-LLR		89.97 340	89.90 338	90.16 372	94.24 386	74.98 401	89.89 383	89.06 389	92.02 275	89.97 383	90.77 388	73.92 369	98.57 345	91.88 264	97.36 333	96.92 347

test0.0.03 1		90.11 336	89.21 343	92.83 346	93.89 391	86.87 322	91.74 357	88.74 392	92.02 275	94.71 304	91.14 385	73.92 369	94.48 399	83.75 376	92.94 387	97.16 341

tpm cat1		88.01 359	87.33 360	90.05 375	94.48 382	76.28 399	94.47 274	94.35 340	73.84 401	89.26 388	95.61 323	73.64 371	98.30 367	84.13 371	86.20 400	95.57 379

tfpn200view9		91.55 324	91.00 324	93.21 334	98.02 223	84.35 354	95.70 208	90.79 377	96.26 119	95.90 273	92.13 375	73.62 372	99.42 211	78.85 390	97.74 314	95.85 372

thres400		91.68 323	91.00 324	93.71 322	98.02 223	84.35 354	95.70 208	90.79 377	96.26 119	95.90 273	92.13 375	73.62 372	99.42 211	78.85 390	97.74 314	97.36 336

test_method		66.88 371	66.13 374	69.11 387	62.68 410	25.73 413	49.76 401	96.04 311	14.32 405	64.27 406	91.69 380	73.45 374	88.05 404	76.06 395	66.94 404	93.54 390

thres100view900		91.76 322	91.26 322	93.26 330	98.21 201	84.50 352	96.39 156	90.39 380	96.87 93	96.33 249	93.08 359	73.44 375	99.42 211	78.85 390	97.74 314	95.85 372

thres600view7		92.03 318	91.43 315	93.82 319	98.19 204	84.61 351	96.27 166	90.39 380	96.81 95	96.37 248	93.11 355	73.44 375	99.49 192	80.32 385	97.95 305	97.36 336

MVSTER		94.21 268	93.93 272	95.05 273	95.83 355	86.46 326	95.18 245	97.65 268	92.41 271	97.94 154	98.00 175	72.39 377	99.58 163	96.36 97	99.56 112	99.12 163

JIA-IIPM		91.79 321	90.69 331	95.11 269	93.80 392	90.98 243	94.16 287	91.78 368	96.38 113	90.30 380	99.30 28	72.02 378	98.90 312	88.28 333	90.17 394	95.45 380

tpm		91.08 330	90.85 328	91.75 364	95.33 370	78.09 389	95.03 255	91.27 374	88.75 322	93.53 337	97.40 224	71.24 379	99.30 252	91.25 277	93.87 385	97.87 310

baseline2		89.65 346	88.44 352	93.25 331	95.62 363	82.71 365	93.82 304	85.94 399	88.89 321	87.35 397	92.54 369	71.23 380	99.33 245	86.01 354	94.60 382	97.72 320

CostFormer		89.75 343	89.25 341	91.26 367	94.69 380	78.00 391	95.32 237	91.98 366	81.50 381	90.55 376	96.96 258	71.06 381	98.89 313	88.59 329	92.63 389	96.87 350

FPMVS		89.92 341	88.63 349	93.82 319	98.37 186	96.94 45	91.58 358	93.34 351	88.00 334	90.32 379	97.10 248	70.87 382	91.13 403	71.91 401	96.16 364	93.39 393

EPMVS		89.26 348	88.55 350	91.39 366	92.36 402	79.11 387	95.65 214	79.86 405	88.60 325	93.12 347	96.53 284	70.73 383	98.10 374	90.75 290	89.32 396	96.98 345

FE-MVS		92.95 301	92.22 305	95.11 269	97.21 310	88.33 288	98.54 23	93.66 347	89.91 309	96.21 258	98.14 152	70.33 384	99.50 187	87.79 337	98.24 294	97.51 331

tmp_tt		57.23 372	62.50 375	41.44 388	34.77 411	49.21 412	83.93 396	60.22 412	15.31 404	71.11 405	79.37 403	70.09 385	44.86 407	64.76 403	82.93 403	30.25 403

ET-MVSNet_ETH3D		91.12 328	89.67 340	95.47 256	96.41 332	89.15 273	91.54 359	90.23 384	89.07 317	86.78 399	92.84 364	69.39 386	99.44 207	94.16 214	96.61 353	97.82 313

dp		88.08 358	88.05 354	88.16 383	92.85 399	68.81 409	94.17 286	92.88 355	85.47 358	91.38 372	96.14 304	68.87 387	98.81 320	86.88 351	83.80 402	96.87 350

iter_conf_final		94.54 257	93.91 273	96.43 209	97.23 309	90.41 255	96.81 133	98.10 237	93.87 221	96.80 222	97.89 185	68.02 388	99.72 88	96.73 85	99.77 58	99.18 149

tpm2		88.47 355	87.69 358	90.79 369	94.98 376	77.34 394	95.09 248	91.83 367	77.51 396	89.40 387	96.41 290	67.83 389	98.73 327	83.58 377	92.60 390	96.29 368

pmmvs3		90.00 338	88.90 348	93.32 328	94.20 388	85.34 338	91.25 366	92.56 362	78.59 392	93.82 325	95.17 330	67.36 390	98.69 333	89.08 322	98.03 302	95.92 371

thisisatest0515		90.43 334	89.18 346	94.17 315	97.07 316	85.44 337	89.75 387	87.58 394	88.28 330	93.69 332	91.72 379	65.27 391	99.58 163	90.59 297	98.67 269	97.50 333

tttt0517		93.31 295	92.56 302	95.57 249	98.71 143	87.86 300	97.44 100	87.17 396	95.79 148	97.47 181	96.84 265	64.12 392	99.81 37	96.20 103	99.32 192	99.02 181

thisisatest0530		92.71 305	91.76 313	95.56 251	98.42 183	88.23 289	96.03 186	87.35 395	94.04 217	96.56 239	95.47 326	64.03 393	99.77 57	94.78 190	99.11 222	98.68 233

iter_conf05		93.65 286	93.05 285	95.46 257	96.13 346	87.45 310	95.95 196	98.22 217	92.66 265	97.04 208	97.89 185	63.52 394	99.72 88	96.19 104	99.82 47	99.21 141

FMVSNet5		93.39 293	92.35 303	96.50 205	95.83 355	90.81 248	97.31 105	98.27 211	92.74 263	96.27 254	98.28 134	62.23 395	99.67 130	90.86 285	99.36 177	99.03 178

IB-MVS	85.98 20	88.63 354	86.95 364	93.68 323	95.12 374	84.82 350	90.85 373	90.17 385	87.55 337	88.48 392	91.34 383	58.01 396	99.59 161	87.24 349	93.80 386	96.63 362
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
testing91		89.67 345	88.55 350	93.04 337	95.90 350	81.80 374	92.71 335	93.71 343	93.71 225	90.18 381	90.15 392	57.11 397	99.22 272	87.17 350	96.32 359	98.12 285

gg-mvs-nofinetune		88.28 357	86.96 363	92.23 359	92.84 400	84.44 353	98.19 52	74.60 407	99.08 10	87.01 398	99.47 11	56.93 398	98.23 370	78.91 389	95.61 372	94.01 389

KD-MVS_2432*1600		88.93 351	87.74 356	92.49 353	88.04 407	81.99 371	89.63 388	95.62 321	91.35 288	95.06 295	93.11 355	56.58 399	98.63 340	85.19 364	95.07 375	96.85 352

miper_refine_blended		88.93 351	87.74 356	92.49 353	88.04 407	81.99 371	89.63 388	95.62 321	91.35 288	95.06 295	93.11 355	56.58 399	98.63 340	85.19 364	95.07 375	96.85 352

GG-mvs-BLEND				90.60 370	91.00 404	84.21 357	98.23 46	72.63 410		82.76 401	84.11 402	56.14 401	96.79 391	72.20 400	92.09 391	90.78 399

TESTMET0.1,1		87.20 366	86.57 366	89.07 377	93.62 394	72.84 406	89.89 383	87.01 397	85.46 359	89.12 389	90.20 391	56.00 402	97.72 380	90.91 284	96.92 340	96.64 360

testing99		89.21 349	88.04 355	92.70 350	95.78 358	81.00 380	92.65 336	92.03 364	93.20 244	89.90 385	90.08 394	55.25 403	99.14 282	87.54 343	95.95 365	97.97 302

UWE-MVS		87.57 363	86.72 365	90.13 374	95.21 371	73.56 404	91.94 354	83.78 403	88.73 324	93.00 349	92.87 363	55.22 404	99.25 264	81.74 380	97.96 304	97.59 328

test2506		89.86 342	89.16 347	91.97 362	98.95 113	76.83 397	98.54 23	61.07 411	96.20 122	97.07 206	99.16 43	55.19 405	99.69 119	96.43 95	99.83 43	99.38 107

testing11		88.93 351	87.63 359	92.80 347	95.87 352	81.49 376	92.48 340	91.54 370	91.62 282	88.27 393	90.24 390	55.12 406	99.11 289	87.30 348	96.28 361	97.81 315

test-mter		87.92 360	87.17 361	90.16 372	94.24 386	74.98 401	89.89 383	89.06 389	86.44 349	89.97 383	90.77 388	54.96 407	98.57 345	91.88 264	97.36 333	96.92 347

ETVMVS		87.62 362	85.75 369	93.22 333	96.15 344	83.26 362	92.94 327	90.37 382	91.39 287	90.37 378	88.45 396	51.93 408	98.64 339	73.76 397	96.38 357	97.75 318

testing222		87.35 364	85.50 371	92.93 344	95.79 357	82.83 364	92.40 346	90.10 386	92.80 262	88.87 390	89.02 395	48.34 409	98.70 331	75.40 396	96.74 349	97.27 340

myMVS_eth3d		87.16 367	85.61 370	91.82 363	95.19 372	79.32 385	92.46 341	91.35 371	90.67 298	91.76 369	87.61 398	41.96 410	98.50 352	82.66 378	96.84 344	97.65 323

testing3		89.72 344	88.26 353	94.10 316	97.66 276	84.30 356	94.80 262	88.25 393	94.66 194	95.07 294	92.51 370	41.15 411	99.43 209	91.81 267	98.44 286	98.55 244

test123		12.59 374	15.49 377	3.87 389	6.07 412	2.55 414	90.75 375	2.59 414	2.52 407	5.20 409	13.02 406	4.96 412	1.85 409	5.20 407	9.09 406	7.23 404

testmvs		12.33 375	15.23 378	3.64 390	5.77 413	2.23 415	88.99 390	3.62 413	2.30 408	5.29 408	13.09 405	4.52 413	1.95 408	5.16 408	8.32 407	6.75 405

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		7.91 377	10.55 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	94.94 335	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						79.32 385								85.41 362

FOURS1					99.59 18	98.20 7	99.03 7	99.25 30	98.96 18	98.87 56

MSC_two_6792asdad				98.22 75	97.75 264	95.34 110		98.16 231					99.75 68	95.87 123	99.51 135	99.57 47

No_MVS				98.22 75	97.75 264	95.34 110		98.16 231					99.75 68	95.87 123	99.51 135	99.57 47

eth-test2					0.00 414

eth-test					0.00 414

IU-MVS					99.22 69	95.40 103		98.14 234	85.77 356	98.36 104				95.23 162	99.51 135	99.49 71

save fliter					98.48 177	94.71 131	94.53 273	98.41 195	95.02 184

test_0728_SECOND				98.25 73	99.23 66	95.49 101	96.74 139	98.89 102					99.75 68	95.48 145	99.52 130	99.53 57

GSMVS																98.06 293

test_part2					99.03 108	96.07 74				98.08 138

MTGPA								98.73 148

MTMP							96.55 150	74.60 407

gm-plane-assit					91.79 403	71.40 408			81.67 379		90.11 393		98.99 304	84.86 368

test9_res														91.29 274	98.89 247	99.00 182

agg_prior2														90.34 305	98.90 244	99.10 170

agg_prior					97.80 252	94.96 126		98.36 202		93.49 338			99.53 179

test_prior4						95.38 105	93.61 312

test_prior				97.46 138	97.79 257	94.26 155		98.42 194					99.34 243			98.79 216

旧先验2							93.35 319		77.95 395	95.77 279			98.67 337	90.74 293

新几何2							93.43 315

无先验							93.20 323	97.91 249	80.78 384				99.40 222	87.71 338		97.94 305

原ACMM2							92.82 329

testdata2													99.46 200	87.84 336

testdata1							92.77 330		93.78 223

plane_prior7					98.70 145	94.67 134

plane_prior5								98.75 145					99.46 200	92.59 254	99.20 208	99.28 128

plane_prior4											96.77 271

plane_prior3						94.51 141			95.29 172	96.16 261

plane_prior2							96.50 152		96.36 115

plane_prior1					98.49 175

plane_prior						94.29 150	95.42 226		94.31 207						98.93 242

n2								0.00 415

nn								0.00 415

door-mid								98.17 227

test11								98.08 240

door								97.81 258

HQP5-MVS						92.47 207

HQP-NCC					97.85 239		94.26 278		93.18 246	92.86 352

ACMP_Plane					97.85 239		94.26 278		93.18 246	92.86 352

BP-MVS														90.51 300

HQP4-MVS										92.87 351			99.23 270			99.06 175

HQP3-MVS								98.43 191							98.74 262

NP-MVS					98.14 216	93.72 172					95.08 331

ACMMP++_ref															99.52 130

ACMMP++															99.55 118

High-resolution multi-view benchmark