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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 2

XVG-OURS-SEG-HR		95.38 91	95.00 128	96.51 49	98.10 90	94.07 24	92.46 249	98.13 73	90.69 172	93.75 280	96.25 234	98.03 2	97.02 389	92.08 142	95.55 425	98.45 158

pmmvs6		96.80 19	97.36 13	95.15 112	99.12 8	87.82 175	96.68 33	97.86 126	96.10 36	98.14 31	99.28 8	97.94 3	98.21 263	91.38 169	99.69 17	99.42 24

UniMVSNet_ETH3D		97.13 10	97.72 3	95.35 97	99.51 2	87.38 181	97.70 8	97.54 166	98.16 5	98.94 4	99.33 6	97.84 4	99.08 111	90.73 190	99.73 14	99.59 15

ACMH	88.36 12	96.59 34	97.43 9	94.07 169	98.56 49	85.33 243	96.33 54	98.30 41	94.66 54	98.72 12	98.30 41	97.51 5	98.00 298	94.87 50	99.59 29	98.86 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		97.01 11	96.89 21	97.39 24	99.12 8	93.92 36	97.16 14	98.17 67	93.11 89	96.48 119	97.36 121	96.92 6	99.34 70	94.31 62	99.38 63	98.92 87

ACMH+	88.43 11	96.48 38	96.82 22	95.47 92	98.54 55	89.06 138	95.65 91	98.61 15	96.10 36	98.16 30	97.52 101	96.90 7	98.62 195	90.30 210	99.60 27	98.72 121

lecture		97.32 6	97.64 6	96.33 54	99.01 15	90.77 107	96.90 21	98.60 16	96.30 33	97.74 42	98.00 56	96.87 8	99.39 54	95.95 24	99.42 54	98.84 98

sc_t1		97.21 9	97.71 4	95.71 78	99.06 10	88.89 142	96.72 31	97.79 139	98.34 2	98.97 2	99.40 5	96.81 9	98.79 160	92.58 130	99.72 15	99.45 23

tt0320-xc		97.00 12	97.67 5	94.98 117	98.89 23	86.94 195	96.72 31	98.46 24	98.28 4	98.86 8	99.43 4	96.80 10	98.51 222	91.79 153	99.76 10	99.50 19

HPM-MVS		96.81 18	96.62 31	97.36 26	98.89 23	93.53 51	97.51 10	98.44 26	92.35 104	95.95 158	96.41 215	96.71 11	99.42 37	93.99 71	99.36 66	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvs_tets		96.83 15	96.71 26	97.17 30	98.83 29	92.51 70	96.58 38	97.61 156	87.57 270	98.80 11	98.90 15	96.50 12	99.59 13	96.15 22	99.47 44	99.40 27

tt0320		96.97 13	97.64 6	94.96 120	98.89 23	86.86 197	96.85 23	98.45 25	98.29 3	98.88 7	99.45 3	96.48 13	98.54 214	91.73 156	99.72 15	99.47 21

SED-MVS		96.00 60	96.41 41	94.76 132	98.51 58	86.97 192	95.21 114	98.10 80	91.95 118	97.63 46	97.25 135	96.48 13	99.35 67	93.29 102	99.29 83	97.95 223

test_241102_ONE					98.51 58	86.97 192		98.10 80	91.85 125	97.63 46	97.03 161	96.48 13	98.95 135

LPG-MVS_test		96.38 47	96.23 50	96.84 41	98.36 75	92.13 77	95.33 106	98.25 46	91.78 132	97.07 84	97.22 140	96.38 16	99.28 85	92.07 143	99.59 29	99.11 54

LGP-MVS_train				96.84 41	98.36 75	92.13 77		98.25 46	91.78 132	97.07 84	97.22 140	96.38 16	99.28 85	92.07 143	99.59 29	99.11 54

ACMM	88.83 9	96.30 51	96.07 61	96.97 37	98.39 69	92.95 61	94.74 131	98.03 99	90.82 168	97.15 79	96.85 177	96.25 18	99.00 125	93.10 110	99.33 73	98.95 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		87.83 396	90.79 302	78.96 526	90.46 498	88.63 147	92.72 233	90.67 441	91.65 140	98.68 15	97.64 90	96.06 19	77.53 549	59.84 542	99.41 60	70.73 547

testf1		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 69	97.93 63	96.05 20	97.90 305	89.32 243	99.23 95	98.19 193

APD_test2		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 69	97.93 63	96.05 20	97.90 305	89.32 243	99.23 95	98.19 193

ACMP	88.15 13	95.71 74	95.43 99	96.54 48	98.17 86	91.73 86	94.24 155	98.08 83	89.46 207	96.61 115	96.47 209	95.85 22	99.12 105	90.45 199	99.56 36	98.77 114
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvsmconf0.01_n		95.90 65	96.09 58	95.31 102	97.30 155	89.21 133	94.24 155	98.76 12	86.25 306	97.56 50	98.66 24	95.73 23	98.44 236	97.35 3	98.99 134	98.27 183

TransMVSNet (Re)		95.27 101	96.04 63	92.97 227	98.37 72	81.92 311	95.07 121	96.76 246	93.97 70	97.77 39	98.57 29	95.72 24	97.90 305	88.89 264	99.23 95	99.08 58

ZNCC-MVS		96.42 43	96.20 52	97.07 33	98.80 34	92.79 64	96.08 73	98.16 70	91.74 136	95.34 202	96.36 224	95.68 25	99.44 33	94.41 60	99.28 88	98.97 73

ACMMP_NAP		96.21 53	96.12 57	96.49 51	98.90 22	91.42 92	94.57 142	98.03 99	90.42 184	96.37 128	97.35 124	95.68 25	99.25 89	94.44 59	99.34 71	98.80 104

APD-MVS_3200maxsize		96.82 16	96.65 28	97.32 28	97.95 106	93.82 42	96.31 61	98.25 46	95.51 44	96.99 91	97.05 160	95.63 27	99.39 54	93.31 100	98.88 160	98.75 115

DVP-MVS		95.82 69	96.18 53	94.72 134	98.51 58	86.69 202	95.20 116	97.00 216	91.85 125	97.40 64	97.35 124	95.58 28	99.34 70	93.44 94	99.31 78	98.13 201
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					98.51 58	86.69 202	95.34 105	98.18 63	91.85 125	97.63 46	97.37 116	95.58 28

MP-MVS-pluss		96.08 57	95.92 72	96.57 47	99.06 10	91.21 94	93.25 202	98.32 38	87.89 259	96.86 96	97.38 115	95.55 30	99.39 54	95.47 38	99.47 44	99.11 54
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 23	96.62 31	97.13 31	98.38 70	94.31 21	96.79 27	98.32 38	96.69 21	96.86 96	97.56 96	95.48 31	98.77 167	90.11 221	99.44 51	98.31 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
reproduce-ours		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 28	97.78 76	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

our_new_method		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 28	97.78 76	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

SD-MVS		95.19 103	95.73 84	93.55 197	96.62 218	88.88 144	94.67 136	98.05 92	91.26 156	97.25 75	96.40 216	95.42 34	94.36 469	92.72 125	99.19 102	97.40 296
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
RE-MVS-def			96.66 27		98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 89	97.03 161	95.40 35		93.49 88	98.84 165	98.00 213

test_241102_TWO								98.10 80	91.95 118	97.54 51	97.25 135	95.37 36	99.35 67	93.29 102	99.25 91	98.49 155

HFP-MVS		96.39 46	96.17 55	97.04 34	98.51 58	93.37 52	96.30 65	97.98 105	92.35 104	95.63 185	96.47 209	95.37 36	99.27 88	93.78 77	99.14 112	98.48 156

jajsoiax		96.59 34	96.42 38	97.12 32	98.76 35	92.49 71	96.44 48	97.42 178	86.96 290	98.71 14	98.72 23	95.36 38	99.56 17	95.92 25	99.45 48	99.32 32

test_fmvsmconf0.1_n		95.61 77	95.72 85	95.26 104	96.85 190	89.20 134	93.51 192	98.60 16	85.68 327	97.42 62	98.30 41	95.34 39	98.39 237	96.85 11	98.98 136	98.19 193

TranMVSNet+NR-MVSNet		96.07 58	96.26 49	95.50 90	98.26 80	87.69 177	93.75 180	97.86 126	95.96 41	97.48 57	97.14 149	95.33 40	99.44 33	90.79 188	99.76 10	99.38 28

PMVS	87.21 14	94.97 111	95.33 107	93.91 178	98.97 20	97.16 2	95.54 100	95.85 299	96.47 27	93.40 297	97.46 108	95.31 41	95.47 443	86.18 333	98.78 182	89.11 513
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pm-mvs1		95.43 87	95.94 69	93.93 177	98.38 70	85.08 247	95.46 102	97.12 209	91.84 128	97.28 72	98.46 36	95.30 42	97.71 333	90.17 219	99.42 54	98.99 66

Casviewmamba		95.48 85	95.97 67	94.04 170	96.94 181	84.57 252	93.96 171	98.29 44	93.94 71	96.76 105	97.14 149	95.27 43	98.72 174	92.37 137	99.02 130	98.82 99

PGM-MVS		96.32 49	95.94 69	97.43 21	98.59 48	93.84 41	95.33 106	98.30 41	91.40 153	95.76 171	96.87 176	95.26 44	99.45 32	92.77 121	99.21 99	99.00 64

PS-CasMVS		96.69 27	97.43 9	94.49 153	99.13 6	84.09 264	96.61 37	97.97 107	97.91 8	98.64 17	98.13 46	95.24 45	99.65 4	93.39 98	99.84 3	99.72 4

test_fmvsmconf_n		95.43 87	95.50 93	95.22 109	96.48 234	89.19 135	93.23 204	98.36 35	85.61 330	96.92 94	98.02 55	95.23 46	98.38 241	96.69 14	98.95 146	98.09 203

GST-MVS		96.24 52	95.99 66	97.00 36	98.65 41	92.71 66	95.69 90	98.01 102	92.08 116	95.74 176	96.28 230	95.22 47	99.42 37	93.17 108	99.06 119	98.88 93

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 29	98.81 32	93.86 40	99.07 2	98.98 8	97.01 17	98.92 6	98.78 20	95.22 47	98.61 196	96.85 11	99.77 9	99.31 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
DPE-MVS		95.89 66	95.88 75	95.92 68	97.93 108	89.83 121	93.46 194	98.30 41	92.37 102	97.75 40	96.95 167	95.14 49	99.51 20	91.74 155	99.28 88	98.41 164
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_one_0601					98.26 80	87.14 187		98.18 63	94.25 61	96.99 91	97.36 121	95.13 50

nrg030		96.32 49	96.55 34	95.62 84	97.83 114	88.55 153	95.77 86	98.29 44	92.68 94	98.03 35	97.91 71	95.13 50	98.95 135	93.85 75	99.49 43	99.36 30

MED-MVS		96.38 47	96.63 30	95.63 83	98.69 37	88.21 161	96.32 56	98.58 18	94.10 65	97.38 66	97.37 116	95.11 52	99.39 54	92.89 118	99.19 102	99.30 34

APDe-MVS		96.46 39	96.64 29	95.93 66	97.68 129	89.38 131	96.90 21	98.41 29	92.52 98	97.43 59	97.92 68	95.11 52	99.50 23	94.45 58	99.30 80	98.92 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMP		96.61 31	96.34 45	97.43 21	98.61 45	93.88 37	96.95 20	98.18 63	92.26 107	96.33 131	96.84 180	95.10 54	99.40 51	93.47 91	99.33 73	99.02 63
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
SR-MVS		96.70 26	96.42 38	97.54 14	98.05 94	94.69 15	96.13 71	98.07 86	95.17 48	96.82 100	96.73 190	95.09 55	99.43 36	92.99 115	98.71 199	98.50 153

OPM-MVS		95.61 77	95.45 95	96.08 58	98.49 65	91.00 98	92.65 239	97.33 189	90.05 194	96.77 104	96.85 177	95.04 56	98.56 211	92.77 121	99.06 119	98.70 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DTE-MVSNet		96.74 24	97.43 9	94.67 139	99.13 6	84.68 251	96.51 41	97.94 115	98.14 6	98.67 16	98.32 40	95.04 56	99.69 3	93.27 104	99.82 7	99.62 13

region2R		96.41 44	96.09 58	97.38 25	98.62 43	93.81 44	96.32 56	97.96 109	92.26 107	95.28 208	96.57 203	95.02 58	99.41 43	93.63 81	99.11 114	98.94 81

PEN-MVS		96.69 27	97.39 12	94.61 142	99.16 4	84.50 253	96.54 39	98.05 92	98.06 7	98.64 17	98.25 43	95.01 59	99.65 4	92.95 116	99.83 5	99.68 7

SteuartSystems-ACMMP		96.40 45	96.30 47	96.71 43	98.63 42	91.96 80	95.70 88	98.01 102	93.34 86	96.64 113	96.57 203	94.99 60	99.36 66	93.48 90	99.34 71	98.82 99
Skip Steuart: Steuart Systems R&D Blog.
sasdasda		94.59 131	94.69 142	94.30 159	95.60 323	87.03 190	95.59 93	98.24 54	91.56 143	95.21 217	92.04 440	94.95 61	98.66 188	91.45 166	97.57 331	97.20 307

canonicalmvs		94.59 131	94.69 142	94.30 159	95.60 323	87.03 190	95.59 93	98.24 54	91.56 143	95.21 217	92.04 440	94.95 61	98.66 188	91.45 166	97.57 331	97.20 307

MGCFI-Net		94.44 145	94.67 147	93.75 186	95.56 326	85.47 240	95.25 113	98.24 54	91.53 145	95.04 232	92.21 434	94.94 63	98.54 214	91.56 164	97.66 325	97.24 305

ACMMPR		96.46 39	96.14 56	97.41 23	98.60 46	93.82 42	96.30 65	97.96 109	92.35 104	95.57 188	96.61 200	94.93 64	99.41 43	93.78 77	99.15 111	99.00 64

hybridcas		94.81 120	95.45 95	92.88 236	96.74 201	81.36 323	93.32 201	98.13 73	92.16 113	96.79 103	96.98 166	94.91 65	98.53 218	91.16 174	98.90 155	98.75 115

tt0805		95.42 90	95.93 71	93.86 181	98.75 36	88.47 155	97.68 9	94.29 358	96.48 26	95.38 198	93.63 381	94.89 66	97.94 304	95.38 43	96.92 372	95.17 416

E6new		94.50 138	95.15 114	92.55 258	97.04 172	80.28 339	92.96 217	98.25 46	90.18 188	95.76 171	97.45 109	94.86 67	98.59 201	91.16 174	98.73 193	98.79 106

E6		94.50 138	95.15 114	92.55 258	97.04 172	80.28 339	92.96 217	98.25 46	90.18 188	95.76 171	97.45 109	94.86 67	98.59 201	91.16 174	98.73 193	98.79 106

E5new		94.50 138	95.15 114	92.55 258	97.04 172	80.27 341	92.96 217	98.25 46	90.18 188	95.77 168	97.45 109	94.85 69	98.59 201	91.16 174	98.73 193	98.79 106

E5		94.50 138	95.15 114	92.55 258	97.04 172	80.27 341	92.96 217	98.25 46	90.18 188	95.77 168	97.45 109	94.85 69	98.59 201	91.16 174	98.73 193	98.79 106

SR-MVS-dyc-post		96.84 14	96.60 33	97.56 13	98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 89	97.03 161	94.85 69	99.42 37	93.49 88	98.84 165	98.00 213

casdiffmvs_mvg		95.10 106	95.62 89	93.53 201	96.25 265	83.23 279	92.66 238	98.19 61	93.06 90	97.49 56	97.15 148	94.78 72	98.71 181	92.27 138	98.72 197	98.65 132
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVS		96.44 42	96.08 60	97.54 14	98.29 77	94.62 18	96.80 26	98.08 83	92.67 96	95.08 230	96.39 221	94.77 73	99.42 37	93.17 108	99.44 51	98.58 146

test_0728_THIRD									93.26 87	97.40 64	97.35 124	94.69 74	99.34 70	93.88 73	99.42 54	98.89 91

9.14			94.81 133		97.49 141		94.11 163	98.37 34	87.56 271	95.38 198	96.03 253	94.66 75	99.08 111	90.70 191	98.97 142

GeoE		94.55 135	94.68 146	94.15 164	97.23 158	85.11 246	94.14 162	97.34 188	88.71 229	95.26 211	95.50 287	94.65 76	99.12 105	90.94 184	98.40 239	98.23 186

TDRefinement		97.68 3	97.60 8	97.93 2	99.02 13	95.95 8	98.61 3	98.81 10	97.41 13	97.28 72	98.46 36	94.62 77	98.84 149	94.64 54	99.53 39	98.99 66

SDMVSNet		94.43 146	95.02 126	92.69 247	97.93 108	82.88 291	91.92 281	95.99 296	93.65 81	95.51 190	98.63 26	94.60 78	96.48 414	87.57 305	99.35 67	98.70 125

reproduce_model		97.35 4	97.24 15	97.70 4	98.44 67	95.08 12	95.88 82	98.50 21	96.62 24	98.27 24	97.93 63	94.57 79	99.50 23	95.57 35	99.35 67	98.52 151

XVS		96.49 37	96.18 53	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 257	96.49 208	94.56 80	99.39 54	93.57 83	99.05 122	98.93 83

X-MVStestdata		90.70 301	88.45 362	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 257	26.89 555	94.56 80	99.39 54	93.57 83	99.05 122	98.93 83

TestfortrainingZip a		96.50 36	96.80 23	95.62 84	98.69 37	88.28 158	96.32 56	98.06 90	94.10 65	97.65 44	97.37 116	94.54 82	99.28 85	95.41 42	99.04 127	99.30 34

mPP-MVS		96.46 39	96.05 62	97.69 5	98.62 43	94.65 17	96.45 46	97.74 143	92.59 97	95.47 193	96.68 194	94.50 83	99.42 37	93.10 110	99.26 90	98.99 66

sd_testset		93.94 177	94.39 159	92.61 255	97.93 108	83.24 278	93.17 206	95.04 333	93.65 81	95.51 190	98.63 26	94.49 84	95.89 434	81.72 399	99.35 67	98.70 125

DeepC-MVS	91.39 4	95.43 87	95.33 107	95.71 78	97.67 130	90.17 117	93.86 176	98.02 101	87.35 274	96.22 143	97.99 59	94.48 85	99.05 118	92.73 124	99.68 20	97.93 228
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		95.77 71	95.54 92	96.47 52	98.27 79	91.19 95	95.09 119	97.79 139	86.48 298	97.42 62	97.51 105	94.47 86	99.29 81	93.55 85	99.29 83	98.93 83
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
viewdifsd2359ckpt07		93.63 185	94.33 165	91.55 310	96.19 271	77.86 415	90.11 363	97.74 143	90.76 170	96.11 151	96.61 200	94.37 87	98.27 255	88.82 267	98.23 265	98.51 152

SF-MVS		95.88 67	95.88 75	95.87 72	98.12 88	89.65 123	95.58 96	98.56 20	91.84 128	96.36 130	96.68 194	94.37 87	99.32 77	92.41 135	99.05 122	98.64 138

MP-MVS		96.14 55	95.68 86	97.51 16	98.81 32	94.06 25	96.10 72	97.78 141	92.73 93	93.48 292	96.72 191	94.23 89	99.42 37	91.99 146	99.29 83	99.05 61
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		95.19 103	95.36 103	94.68 137	96.79 197	87.49 179	93.05 210	98.38 33	87.21 280	96.59 116	97.76 81	94.20 90	98.11 277	95.90 26	98.40 239	98.42 161

anonymousdsp		96.74 24	96.42 38	97.68 7	98.00 102	94.03 29	96.97 19	97.61 156	87.68 267	98.45 22	98.77 21	94.20 90	99.50 23	96.70 13	99.40 61	99.53 17

test_0402		95.73 73	96.22 51	94.26 161	98.19 85	85.77 233	93.24 203	97.24 199	96.88 20	97.69 43	97.77 80	94.12 92	99.13 104	91.54 165	99.29 83	97.88 240

test_fmvsmvis_n_1920		95.08 108	95.40 101	94.13 167	96.66 208	87.75 176	93.44 196	98.49 23	85.57 331	98.27 24	97.11 153	94.11 93	97.75 329	96.26 20	98.72 197	96.89 330

casdiffseed414692147		94.56 134	94.90 129	93.54 199	96.60 219	83.33 275	93.57 189	98.06 90	91.57 142	95.26 211	97.31 129	94.06 94	98.39 237	88.67 272	98.95 146	98.91 89

aaEdge-Enhanced		95.61 77	95.65 88	95.49 91	97.62 133	88.21 161	94.21 158	97.87 125	92.48 99	96.38 126	96.22 236	94.06 94	99.32 77	92.89 118	99.10 115	98.96 77

Effi-MVS+		92.79 230	92.74 229	92.94 231	95.10 349	83.30 277	94.00 168	97.53 169	91.36 154	89.35 438	90.65 470	94.01 96	98.66 188	87.40 309	95.30 440	96.88 332

EC-MVSNet		95.44 86	95.62 89	94.89 124	96.93 184	87.69 177	96.48 45	99.14 6	93.93 72	92.77 333	94.52 342	93.95 97	99.49 29	93.62 82	99.22 98	97.51 283

E4		94.00 174	94.53 155	92.42 268	96.78 198	79.99 353	91.33 306	98.16 70	89.69 201	95.27 209	97.16 145	93.94 98	98.64 192	89.99 225	98.42 238	98.61 143

OMC-MVS		94.22 162	93.69 194	95.81 73	97.25 156	91.27 93	92.27 265	97.40 180	87.10 288	94.56 251	95.42 293	93.74 99	98.11 277	86.62 322	98.85 164	98.06 204

viewmacassd2359aftdt		93.83 180	94.36 163	92.24 273	96.45 236	79.58 371	91.60 296	97.96 109	89.14 216	95.05 231	97.09 156	93.69 100	98.48 229	89.79 230	98.43 236	98.65 132

LCM-MVSNet-Re		94.20 163	94.58 151	93.04 224	95.91 296	83.13 285	93.79 179	99.19 5	92.00 117	98.84 9	98.04 53	93.64 101	99.02 123	81.28 406	98.54 222	96.96 325

CS-MVS		95.77 71	95.58 91	96.37 53	96.84 191	91.72 87	96.73 30	99.06 7	94.23 62	92.48 343	94.79 329	93.56 102	99.49 29	93.47 91	99.05 122	97.89 239

MTAPA		96.65 29	96.38 42	97.47 18	98.95 21	94.05 27	95.88 82	97.62 154	94.46 59	96.29 137	96.94 168	93.56 102	99.37 65	94.29 63	99.42 54	98.99 66

SPE-MVS-test		95.32 94	95.10 123	95.96 62	96.86 189	90.75 108	96.33 54	99.20 4	93.99 68	91.03 394	93.73 378	93.52 104	99.55 18	91.81 152	99.45 48	97.58 277

viewdifsd2359ckpt11		93.36 200	93.99 178	91.48 315	95.50 330	78.39 403	90.47 341	96.69 251	88.59 233	96.03 155	96.88 174	93.48 105	97.63 340	90.20 217	98.07 286	98.41 164

viewmsd2359difaftdt		93.36 200	93.99 178	91.48 315	95.50 330	78.39 403	90.47 341	96.69 251	88.59 233	96.03 155	96.88 174	93.48 105	97.63 340	90.20 217	98.07 286	98.41 164

UA-Net		97.35 4	97.24 15	97.69 5	98.22 83	93.87 39	98.42 6	98.19 61	96.95 18	95.46 195	99.23 9	93.45 107	99.57 14	95.34 45	99.89 2	99.63 12

MVS_111021_HR		93.63 185	93.42 207	94.26 161	96.65 209	86.96 194	89.30 396	96.23 282	88.36 245	93.57 288	94.60 338	93.45 107	97.77 324	90.23 215	98.38 244	98.03 211

cdsmvs_eth3d_5k		23.35 521	31.13 523	0.00 543	0.00 567	0.00 570	0.00 555	95.58 311	0.00 562	0.00 563	91.15 457	93.43 109	0.00 563	0.00 562	0.00 562	0.00 559

APD-MVS		95.00 110	94.69 142	95.93 66	97.38 149	90.88 102	94.59 139	97.81 135	89.22 214	95.46 195	96.17 244	93.42 110	99.34 70	89.30 245	98.87 163	97.56 280
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ANet_high		94.83 118	96.28 48	90.47 374	96.65 209	73.16 484	94.33 150	98.74 13	96.39 30	98.09 34	98.93 14	93.37 111	98.70 182	90.38 202	99.68 20	99.53 17

APD_test1		95.91 64	95.42 100	97.36 26	98.82 30	96.62 6	95.64 92	97.64 152	93.38 85	95.89 163	97.23 138	93.35 112	97.66 336	88.20 287	98.66 208	97.79 254

casdiffmvs		94.32 154	94.80 134	92.85 238	96.05 285	81.44 322	92.35 257	98.05 92	91.53 145	95.75 175	96.80 181	93.35 112	98.49 224	91.01 183	98.32 253	98.64 138
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_djsdf		96.62 30	96.49 35	97.01 35	98.55 53	91.77 85	97.15 15	97.37 181	88.98 220	98.26 27	98.86 16	93.35 112	99.60 9	96.41 18	99.45 48	99.66 9

VPA-MVSNet		95.14 105	95.67 87	93.58 196	97.76 119	83.15 283	94.58 141	97.58 162	93.39 84	97.05 87	98.04 53	93.25 115	98.51 222	89.75 233	99.59 29	99.08 58

test-260524					97.94 107	87.97 171		97.94 115		96.37 128		93.24 116	99.34 70	94.10 67	99.19 102

Anonymous20240529		95.50 83	95.83 79	94.50 151	97.33 153	85.93 229	95.19 118	96.77 245	96.64 23	97.61 49	98.05 51	93.23 117	98.79 160	88.60 276	99.04 127	98.78 111

baseline		94.26 156	94.80 134	92.64 249	96.08 282	80.99 331	93.69 183	98.04 98	90.80 169	94.89 239	96.32 226	93.19 118	98.48 229	91.68 159	98.51 228	98.43 160

DeepPCF-MVS	90.46 6	94.20 163	93.56 201	96.14 56	95.96 292	92.96 60	89.48 388	97.46 176	85.14 345	96.23 142	95.42 293	93.19 118	98.08 282	90.37 205	98.76 185	97.38 299

Anonymous20231211		96.60 32	97.13 19	95.00 116	97.46 145	86.35 214	97.11 18	98.24 54	97.58 11	98.72 12	98.97 13	93.15 120	99.15 99	93.18 107	99.74 13	99.50 19

DVP-MVS++		95.93 63	96.34 45	94.70 135	96.54 225	86.66 204	98.45 4	98.22 58	93.26 87	97.54 51	97.36 121	93.12 121	99.38 63	93.88 73	98.68 204	98.04 208

OPU-MVS				95.15 112	96.84 191	89.43 128	95.21 114				95.66 279	93.12 121	98.06 288	86.28 332	98.61 212	97.95 223

E2		93.53 190	93.96 180	92.25 271	96.39 243	79.76 363	91.06 317	98.05 92	88.58 235	94.71 248	96.64 196	93.08 123	98.57 207	89.16 253	97.97 300	98.42 161

LS3D		96.11 56	95.83 79	96.95 39	94.75 361	94.20 23	97.34 13	97.98 105	97.31 14	95.32 203	96.77 183	93.08 123	99.20 95	91.79 153	98.16 274	97.44 290

E3		93.53 190	93.96 180	92.25 271	96.39 243	79.76 363	91.06 317	98.05 92	88.58 235	94.71 248	96.64 196	93.07 125	98.57 207	89.16 253	97.97 300	98.42 161

DP-MVS		95.62 76	95.84 78	94.97 118	97.16 163	88.62 148	94.54 146	97.64 152	96.94 19	96.58 117	97.32 128	93.07 125	98.72 174	90.45 199	98.84 165	97.57 278

EG-PatchMatch MVS		94.54 136	94.67 147	94.14 166	97.87 113	86.50 206	92.00 275	96.74 247	88.16 252	96.93 93	97.61 92	93.04 127	97.90 305	91.60 161	98.12 279	98.03 211

fmvsm_s_conf0.5_n_3		95.20 102	95.95 68	92.94 231	96.60 219	82.18 308	93.13 207	98.39 32	91.44 151	97.16 78	97.68 85	93.03 128	97.82 316	97.54 2	98.63 209	98.81 102

Fast-Effi-MVS+		91.28 288	90.86 297	92.53 263	95.45 333	82.53 301	89.25 399	96.52 267	85.00 351	89.91 425	88.55 492	92.94 129	98.84 149	84.72 360	95.44 429	96.22 372

PC_three_1452									75.31 480	95.87 164	95.75 273	92.93 130	96.34 424	87.18 312	98.68 204	98.04 208

v7n		96.82 16	97.31 14	95.33 99	98.54 55	86.81 198	96.83 24	98.07 86	96.59 25	98.46 21	98.43 38	92.91 131	99.52 19	96.25 21	99.76 10	99.65 11

XVG-ACMP-BASELINE		95.68 75	95.34 105	96.69 44	98.40 68	93.04 58	94.54 146	98.05 92	90.45 183	96.31 134	96.76 185	92.91 131	98.72 174	91.19 173	99.42 54	98.32 176

testgi		90.38 316	91.34 282	87.50 459	97.49 141	71.54 497	89.43 390	95.16 330	88.38 242	94.54 252	94.68 334	92.88 133	93.09 482	71.60 510	97.85 310	97.88 240

MVS_111021_LR		93.66 184	93.28 211	94.80 130	96.25 265	90.95 100	90.21 356	95.43 318	87.91 257	93.74 282	94.40 348	92.88 133	96.38 420	90.39 201	98.28 258	97.07 315

CNVR-MVS		94.58 133	94.29 166	95.46 93	96.94 181	89.35 132	91.81 290	96.80 241	89.66 203	93.90 276	95.44 291	92.80 135	98.72 174	92.74 123	98.52 226	98.32 176

ZD-MVS					97.23 158	90.32 113		97.54 166	84.40 364	94.78 243	95.79 267	92.76 136	99.39 54	88.72 271	98.40 239

XXY-MVS		92.58 242	93.16 216	90.84 357	97.75 120	79.84 357	91.87 286	96.22 285	85.94 316	95.53 189	97.68 85	92.69 137	94.48 465	83.21 378	97.51 334	98.21 189

CDPH-MVS		92.67 237	91.83 268	95.18 111	96.94 181	88.46 156	90.70 333	97.07 212	77.38 461	92.34 354	95.08 313	92.67 138	98.88 142	85.74 338	98.57 217	98.20 191

Fast-Effi-MVS+-dtu		92.77 232	92.16 254	94.58 149	94.66 369	88.25 159	92.05 272	96.65 256	89.62 204	90.08 420	91.23 456	92.56 139	98.60 199	86.30 331	96.27 400	96.90 328

fmvsm_s_conf0.1_n_a		94.26 156	94.37 161	93.95 176	97.36 151	85.72 235	94.15 160	95.44 316	83.25 383	95.51 190	98.05 51	92.54 140	97.19 378	95.55 36	97.46 339	98.94 81

AllTest		94.88 116	94.51 156	96.00 59	98.02 98	92.17 74	95.26 112	98.43 27	90.48 181	95.04 232	96.74 188	92.54 140	97.86 313	85.11 352	98.98 136	97.98 217

TestCases				96.00 59	98.02 98	92.17 74		98.43 27	90.48 181	95.04 232	96.74 188	92.54 140	97.86 313	85.11 352	98.98 136	97.98 217

TinyColmap		92.00 267	92.76 228	89.71 401	95.62 322	77.02 432	90.72 331	96.17 288	87.70 266	95.26 211	96.29 228	92.54 140	96.45 417	81.77 397	98.77 183	95.66 402

SSM_0407		94.23 161	94.56 153	93.24 218	96.65 209	82.79 294	93.66 185	97.84 130	91.46 149	95.19 219	96.56 205	92.50 144	98.99 126	88.83 265	98.32 253	97.93 228

SSM_0404		94.38 148	94.69 142	93.43 207	97.16 163	83.23 279	93.95 172	97.84 130	91.46 149	95.70 180	96.56 205	92.50 144	99.08 111	88.83 265	98.23 265	97.98 217

fmvsm_s_conf0.5_n_11		94.91 113	95.44 98	93.33 212	96.45 236	83.11 286	93.56 190	98.64 14	89.76 200	95.70 180	97.97 60	92.32 146	98.08 282	95.62 31	98.95 146	98.79 106

viewcassd2359sk11		93.16 213	93.51 204	92.13 283	96.07 283	79.59 368	90.88 323	97.97 107	87.82 261	94.23 260	96.19 240	92.31 147	98.53 218	88.58 277	97.51 334	98.28 181

viewmanbaseed2359cas		93.08 215	93.43 206	92.01 289	95.69 314	79.29 382	91.15 311	97.70 147	87.45 273	94.18 263	96.12 247	92.31 147	98.37 245	88.58 277	97.73 317	98.38 170

EGC-MVSNET		80.97 489	75.73 509	96.67 45	98.85 28	94.55 19	96.83 24	96.60 259	2.44 557	5.32 560	98.25 43	92.24 149	98.02 295	91.85 151	99.21 99	97.45 288

fmvsm_s_conf0.5_n_a		94.02 172	94.08 177	93.84 182	96.72 204	85.73 234	93.65 187	95.23 327	83.30 381	95.13 224	97.56 96	92.22 150	97.17 379	95.51 37	97.41 342	98.64 138

ETV-MVS		92.99 219	92.74 229	93.72 189	95.86 300	86.30 215	92.33 259	97.84 130	91.70 139	92.81 330	86.17 511	92.22 150	99.19 96	88.03 298	97.73 317	95.66 402

CLD-MVS		91.82 269	91.41 279	93.04 224	96.37 245	83.65 269	86.82 455	97.29 194	84.65 358	92.27 356	89.67 480	92.20 152	97.85 315	83.95 372	99.47 44	97.62 273
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
segment_acmp												92.14 153

Vis-MVSNet		95.50 83	95.48 94	95.56 88	98.11 89	89.40 130	95.35 104	98.22 58	92.36 103	94.11 264	98.07 50	92.02 154	99.44 33	93.38 99	97.67 324	97.85 246
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ITE_SJBPF				95.95 63	97.34 152	93.36 54		96.55 266	91.93 120	94.82 241	95.39 298	91.99 155	97.08 385	85.53 341	97.96 303	97.41 292

CP-MVSNet		96.19 54	96.80 23	94.38 158	98.99 19	83.82 267	96.31 61	97.53 169	97.60 10	98.34 23	97.52 101	91.98 156	99.63 7	93.08 112	99.81 8	99.70 5

CSCG		94.69 126	94.75 138	94.52 150	97.55 138	87.87 173	95.01 124	97.57 163	92.68 94	96.20 145	93.44 387	91.92 157	98.78 164	89.11 257	99.24 93	96.92 327

fmvsm_s_conf0.1_n		94.19 165	94.41 158	93.52 203	97.22 160	84.37 254	93.73 181	95.26 325	84.45 362	95.76 171	98.00 56	91.85 158	97.21 375	95.62 31	97.82 311	98.98 70

TSAR-MVS + MP.		94.96 112	94.75 138	95.57 87	98.86 27	88.69 145	96.37 51	96.81 240	85.23 340	94.75 244	97.12 152	91.85 158	99.40 51	93.45 93	98.33 251	98.62 142
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n		94.00 174	94.20 172	93.42 208	96.69 206	84.37 254	93.38 198	95.13 331	84.50 361	95.40 197	97.55 100	91.77 160	97.20 376	95.59 33	97.79 312	98.69 128

Gipuma		95.31 97	95.80 82	93.81 184	97.99 105	90.91 101	96.42 49	97.95 112	96.69 21	91.78 372	98.85 18	91.77 160	95.49 442	91.72 157	99.08 118	95.02 425
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		96.60 32	97.05 20	95.24 106	99.02 13	86.44 210	96.78 28	98.08 83	97.42 12	98.48 20	97.86 74	91.76 162	99.63 7	94.23 64	99.84 3	99.66 9

AdaColmap		91.63 276	91.36 280	92.47 266	95.56 326	86.36 213	92.24 268	96.27 279	88.88 224	89.90 426	92.69 414	91.65 163	98.32 249	77.38 450	97.64 326	92.72 486

fmvsm_l_mol_unc0.5_1		94.01 173	95.09 124	90.74 363	96.48 234	76.52 448	89.38 393	97.59 160	89.00 219	98.96 3	98.98 12	91.62 164	97.76 327	94.82 52	99.01 131	97.93 228

fmvsm_l_conf0.5_n_9		94.51 137	95.11 121	92.72 245	96.70 205	83.14 284	91.91 282	97.89 122	88.44 240	97.30 69	97.57 94	91.60 165	97.54 345	95.82 28	98.74 191	97.47 286

PHI-MVS		94.34 153	93.80 187	95.95 63	95.65 318	91.67 88	94.82 129	97.86 126	87.86 260	93.04 322	94.16 360	91.58 166	98.78 164	90.27 212	98.96 144	97.41 292

E3new		92.83 229	93.10 217	92.04 286	95.78 307	79.45 376	90.76 328	97.90 118	87.23 279	93.79 279	95.70 277	91.55 167	98.49 224	88.17 290	96.99 370	98.16 196

xiu_mvs_v1_base_debu		91.47 282	91.52 274	91.33 326	95.69 314	81.56 317	89.92 368	96.05 293	83.22 384	91.26 384	90.74 465	91.55 167	98.82 151	89.29 246	95.91 413	93.62 470

xiu_mvs_v1_base		91.47 282	91.52 274	91.33 326	95.69 314	81.56 317	89.92 368	96.05 293	83.22 384	91.26 384	90.74 465	91.55 167	98.82 151	89.29 246	95.91 413	93.62 470

xiu_mvs_v1_base_debi		91.47 282	91.52 274	91.33 326	95.69 314	81.56 317	89.92 368	96.05 293	83.22 384	91.26 384	90.74 465	91.55 167	98.82 151	89.29 246	95.91 413	93.62 470

mamba_0408		93.60 188	93.72 190	93.27 216	96.65 209	82.79 294	88.81 412	97.68 148	90.62 177	95.19 219	96.01 255	91.54 171	99.08 111	88.63 274	98.32 253	97.93 228

SSM_04072		93.25 208	93.72 190	91.84 294	96.65 209	82.79 294	88.81 412	97.68 148	90.62 177	95.19 219	96.01 255	91.54 171	94.81 461	88.63 274	98.32 253	97.93 228

fmvsm_s_conf0.5_n_5		94.50 138	94.80 134	93.60 194	96.80 195	84.93 248	92.81 229	97.59 160	85.27 339	96.85 99	97.29 130	91.48 173	98.05 289	96.67 15	98.47 232	97.83 248

fmvsm_s_conf0.5_n_9		95.58 80	95.91 73	94.59 146	97.25 156	86.26 216	92.96 217	97.86 126	91.88 123	97.52 54	98.13 46	91.45 174	98.54 214	97.17 4	98.99 134	98.98 70

FE-MVSNET2		94.07 170	94.47 157	92.90 234	97.45 147	81.26 325	93.58 188	97.54 166	88.28 246	96.46 121	97.92 68	91.41 175	98.74 171	88.12 292	99.44 51	98.69 128

tfpnnormal		94.27 155	94.87 132	92.48 265	97.71 125	80.88 334	94.55 145	95.41 319	93.70 77	96.67 110	97.72 82	91.40 176	98.18 267	87.45 307	99.18 106	98.36 171

3Dnovator+	92.74 2	95.86 68	95.77 83	96.13 57	96.81 194	90.79 106	96.30 65	97.82 134	96.13 35	94.74 245	97.23 138	91.33 177	99.16 98	93.25 105	98.30 257	98.46 157

TEST9					96.45 236	89.46 126	90.60 337	96.92 224	79.09 449	90.49 405	94.39 349	91.31 178	98.88 142

DeepC-MVS_fast	89.96 7	93.73 183	93.44 205	94.60 145	96.14 276	87.90 172	93.36 199	97.14 205	85.53 332	93.90 276	95.45 290	91.30 179	98.59 201	89.51 239	98.62 211	97.31 302
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		94.36 151	94.28 168	94.61 142	92.55 429	85.98 226	92.44 251	94.69 348	93.70 77	96.12 150	95.81 266	91.24 180	98.86 146	93.76 80	98.22 269	98.98 70

MCST-MVS		92.91 222	92.51 241	94.10 168	97.52 139	85.72 235	91.36 305	97.13 207	80.33 431	92.91 329	94.24 355	91.23 181	98.72 174	89.99 225	97.93 305	97.86 244

RPSCF		95.58 80	94.89 131	97.62 8	97.58 136	96.30 7	95.97 78	97.53 169	92.42 100	93.41 294	97.78 76	91.21 182	97.77 324	91.06 180	97.06 362	98.80 104

train_agg		92.71 235	91.83 268	95.35 97	96.45 236	89.46 126	90.60 337	96.92 224	79.37 443	90.49 405	94.39 349	91.20 183	98.88 142	88.66 273	98.43 236	97.72 265

test_8					96.37 245	89.14 136	90.51 340	96.89 228	79.37 443	90.42 407	94.36 353	91.20 183	98.82 151

EI-MVSNet-UG-set		94.35 152	94.27 170	94.59 146	92.46 432	85.87 231	92.42 253	94.69 348	93.67 80	96.13 149	95.84 264	91.20 183	98.86 146	93.78 77	98.23 265	99.03 62

EIA-MVS		92.35 252	92.03 259	93.30 215	95.81 305	83.97 265	92.80 231	98.17 67	87.71 265	89.79 429	87.56 499	91.17 186	99.18 97	87.97 299	97.27 348	96.77 338

dcpmvs_2		93.96 176	95.01 127	90.82 359	97.60 134	74.04 478	93.68 184	98.85 9	89.80 199	97.82 37	97.01 164	91.14 187	99.21 92	90.56 194	98.59 215	99.19 45

icg_test_0407_2		91.18 290	91.92 265	88.94 424	95.19 344	76.72 441	84.66 501	96.89 228	85.92 317	93.55 289	94.50 343	91.06 188	92.99 484	88.49 281	97.07 358	97.10 311

IMVS_0407		92.28 255	92.83 226	90.63 370	95.19 344	76.72 441	92.79 232	96.89 228	85.92 317	93.55 289	94.50 343	91.06 188	98.07 286	88.49 281	97.07 358	97.10 311

xiu_mvs_v2_base		89.00 363	89.19 342	88.46 441	94.86 355	74.63 467	86.97 449	95.60 305	80.88 426	87.83 471	88.62 491	91.04 190	98.81 156	82.51 389	94.38 468	91.93 493

HPM-MVS++		95.02 109	94.39 159	96.91 40	97.88 111	93.58 50	94.09 165	96.99 218	91.05 161	92.40 348	95.22 305	91.03 191	99.25 89	92.11 140	98.69 203	97.90 237

viewmamba		92.69 236	93.03 218	91.69 304	93.92 392	79.50 374	89.92 368	97.33 189	88.86 225	93.13 318	95.79 267	90.97 192	97.65 338	90.86 186	96.45 394	97.94 225

viewdifsd2359ckpt13		92.57 244	92.48 244	92.83 239	95.60 323	82.35 306	91.80 292	97.49 174	85.04 350	93.14 316	95.41 296	90.94 193	98.25 257	86.68 320	96.24 403	97.87 243

test_fmvsm_n_1920		94.72 123	94.74 140	94.67 139	96.30 258	88.62 148	93.19 205	98.07 86	85.63 329	97.08 83	97.35 124	90.86 194	97.66 336	95.70 30	98.48 231	97.74 264

TAPA-MVS	88.58 10	92.49 246	91.75 270	94.73 133	96.50 231	89.69 122	92.91 224	97.68 148	78.02 458	92.79 332	94.10 361	90.85 195	97.96 302	84.76 359	98.16 274	96.54 344
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n		93.79 181	93.81 185	93.73 188	96.16 273	86.26 216	92.46 249	96.72 248	81.69 413	95.77 168	97.11 153	90.83 196	97.82 316	95.58 34	97.99 298	97.11 310

pcd_1.5k_mvsjas		7.56 526	10.09 528	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	90.77 197	0.00 563	0.00 562	0.00 562	0.00 559

PS-MVSNAJss		96.01 59	96.04 63	95.89 71	98.82 30	88.51 154	95.57 97	97.88 123	88.72 228	98.81 10	98.86 16	90.77 197	99.60 9	95.43 40	99.53 39	99.57 16

PS-MVSNAJ		88.86 367	88.99 348	88.48 440	94.88 353	74.71 465	86.69 459	95.60 305	80.88 426	87.83 471	87.37 503	90.77 197	98.82 151	82.52 388	94.37 469	91.93 493

MVS_Test		92.57 244	93.29 209	90.40 377	93.53 403	75.85 456	92.52 245	96.96 220	88.73 227	92.35 352	96.70 193	90.77 197	98.37 245	92.53 131	95.49 427	96.99 321

MIMVSNet1		95.52 82	95.45 95	95.72 77	99.14 5	89.02 139	96.23 68	96.87 234	93.73 76	97.87 36	98.49 34	90.73 201	99.05 118	86.43 329	99.60 27	99.10 57

ab-mvs		92.40 249	92.62 237	91.74 300	97.02 176	81.65 316	95.84 84	95.50 315	86.95 291	92.95 327	97.56 96	90.70 202	97.50 348	79.63 426	97.43 341	96.06 380

Test By Simon												90.61 203

3Dnovator	92.54 3	94.80 121	94.90 129	94.47 154	95.47 332	87.06 189	96.63 36	97.28 196	91.82 131	94.34 259	97.41 113	90.60 204	98.65 191	92.47 133	98.11 280	97.70 266

NCCC		94.08 169	93.54 202	95.70 80	96.49 232	89.90 120	92.39 255	96.91 227	90.64 174	92.33 355	94.60 338	90.58 205	98.96 133	90.21 216	97.70 322	98.23 186

dtuonlycased		90.11 328	90.39 317	89.28 414	97.09 170	72.61 491	85.75 480	95.27 324	81.57 416	94.42 254	94.89 320	90.47 206	96.81 402	78.74 436	95.27 442	98.41 164

UniMVSNet_NR-MVSNet		95.35 92	95.21 112	95.76 75	97.69 128	88.59 151	92.26 266	97.84 130	94.91 52	96.80 101	95.78 271	90.42 207	99.41 43	91.60 161	99.58 33	99.29 36

test_prior2							90.21 356		89.33 211	90.77 400	94.81 326	90.41 208		88.21 286	98.55 219

KD-MVS_self_test		94.10 167	94.73 141	92.19 277	97.66 131	79.49 375	94.86 128	97.12 209	89.59 205	96.87 95	97.65 89	90.40 209	98.34 248	89.08 258	99.35 67	98.75 115

MSLP-MVS++		93.25 208	93.88 184	91.37 323	96.34 252	82.81 293	93.11 208	97.74 143	89.37 210	94.08 266	95.29 303	90.40 209	96.35 422	90.35 206	98.25 262	94.96 427

mmtdpeth		95.82 69	96.02 65	95.23 107	96.91 185	88.62 148	96.49 44	99.26 3	95.07 49	93.41 294	99.29 7	90.25 211	97.27 368	94.49 56	99.01 131	99.80 3

fmvsm_l_conf0.5_n_a		93.59 189	93.63 196	93.49 205	96.10 280	85.66 237	92.32 260	96.57 262	81.32 421	95.63 185	97.14 149	90.19 212	97.73 332	95.37 44	98.03 291	97.07 315

IMVS_0403		92.20 260	92.70 234	90.69 366	95.19 344	76.72 441	92.39 255	96.89 228	85.92 317	93.66 286	94.50 343	90.18 213	98.24 259	88.49 281	97.07 358	97.10 311

fmvsm_s_conf0.5_n_7		93.61 187	93.94 182	92.63 252	96.11 279	82.76 297	90.81 326	97.55 165	86.57 295	93.14 316	97.69 84	90.17 214	96.83 400	94.46 57	98.93 149	98.31 178

UniMVSNet (Re)		95.32 94	95.15 114	95.80 74	97.79 118	88.91 141	92.91 224	98.07 86	93.46 83	96.31 134	95.97 259	90.14 215	99.34 70	92.11 140	99.64 25	99.16 47

Effi-MVS+-dtu		93.90 179	92.60 239	97.77 3	94.74 364	96.67 5	94.00 168	95.41 319	89.94 195	91.93 369	92.13 437	90.12 216	98.97 132	87.68 304	97.48 337	97.67 269

FMVSNet1		94.84 117	95.13 119	93.97 173	97.60 134	84.29 257	95.99 75	96.56 263	92.38 101	97.03 88	98.53 31	90.12 216	98.98 127	88.78 269	99.16 110	98.65 132

DU-MVS		95.28 98	95.12 120	95.75 76	97.75 120	88.59 151	92.58 243	97.81 135	93.99 68	96.80 101	95.90 260	90.10 218	99.41 43	91.60 161	99.58 33	99.26 37

NR-MVSNet		95.28 98	95.28 110	95.26 104	97.75 120	87.21 185	95.08 120	97.37 181	93.92 74	97.65 44	95.90 260	90.10 218	99.33 76	90.11 221	99.66 23	99.26 37

Baseline_NR-MVSNet		94.47 144	95.09 124	92.60 256	98.50 64	80.82 335	92.08 271	96.68 254	93.82 75	96.29 137	98.56 30	90.10 218	97.75 329	90.10 223	99.66 23	99.24 41

API-MVS		91.52 280	91.61 272	91.26 331	94.16 382	86.26 216	94.66 137	94.82 341	91.17 159	92.13 364	91.08 460	90.03 221	97.06 388	79.09 435	97.35 345	90.45 508

fmvsm_s_conf0.5_n_4		94.26 156	94.58 151	93.31 213	96.40 242	82.73 299	92.59 242	97.41 179	86.60 294	96.33 131	97.07 157	89.91 222	98.07 286	96.88 10	98.01 294	99.13 50

viewdifsd2359ckpt09		92.60 240	92.34 250	93.36 210	95.94 295	83.36 274	92.35 257	97.93 117	83.17 387	92.92 328	94.66 335	89.87 223	98.57 207	86.51 327	97.71 321	98.15 198

patch_mono-2		92.46 247	92.72 233	91.71 302	96.65 209	78.91 392	88.85 409	97.17 203	83.89 374	92.45 345	96.76 185	89.86 224	97.09 384	90.24 214	98.59 215	99.12 53

test12				94.43 156	95.95 293	86.75 200		96.24 281		89.76 430		89.79 225	98.79 160		97.95 304	97.75 263

usedtu_dtu_shiyan2		93.15 214	92.40 246	95.41 95	98.56 49	90.53 111	94.71 133	94.14 364	92.10 115	93.73 283	96.94 168	89.66 226	97.77 324	72.97 501	98.81 173	97.92 234

旧先验1					96.20 269	84.17 262		94.82 341			95.57 285	89.57 227			97.89 307	96.32 364

DELS-MVS		92.05 265	92.16 254	91.72 301	94.44 375	80.13 347	87.62 432	97.25 197	87.34 275	92.22 358	93.18 396	89.54 228	98.73 173	89.67 236	98.20 272	96.30 365
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
VPNet		93.08 215	93.76 189	91.03 343	98.60 46	75.83 459	91.51 299	95.62 304	91.84 128	95.74 176	97.10 155	89.31 229	98.32 249	85.07 354	99.06 119	98.93 83

QAPM		92.88 224	92.77 227	93.22 219	95.82 303	83.31 276	96.45 46	97.35 187	83.91 373	93.75 280	96.77 183	89.25 230	98.88 142	84.56 361	97.02 365	97.49 285

MSDG		90.82 296	90.67 305	91.26 331	94.16 382	83.08 287	86.63 461	96.19 286	90.60 179	91.94 368	91.89 444	89.16 231	95.75 436	80.96 411	94.51 464	94.95 428

viewmambaseed2359dif		90.77 299	90.81 300	90.64 369	93.46 406	77.04 431	88.83 410	96.29 277	80.79 429	92.21 360	95.11 310	88.99 232	97.28 365	85.39 345	96.20 406	97.59 276

fmvsm_s_conf0.5_n_10		94.63 130	95.11 121	93.18 221	96.28 259	83.51 271	93.00 214	98.25 46	88.37 244	97.43 59	97.70 83	88.90 233	98.63 194	97.15 5	98.90 155	97.41 292

CPTT-MVS		94.74 122	94.12 175	96.60 46	98.15 87	93.01 59	95.84 84	97.66 151	89.21 215	93.28 303	95.46 289	88.89 234	98.98 127	89.80 229	98.82 171	97.80 253

Elysia		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 31	97.28 132	88.82 235	99.51 20	97.08 7	99.38 63	99.26 37

StellarMVS		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 31	97.28 132	88.82 235	99.51 20	97.08 7	99.38 63	99.26 37

onestephybrid01		92.06 264	92.07 258	92.04 286	93.45 407	80.93 333	89.82 374	96.78 242	87.60 269	91.68 374	95.43 292	88.73 237	97.43 355	88.32 285	96.85 375	97.76 259

diffmvs_AUTHOR		92.34 253	92.70 234	91.26 331	94.20 381	78.42 400	89.12 401	97.60 158	87.16 283	93.17 315	95.50 287	88.66 238	97.57 344	91.30 171	97.61 328	97.79 254

dtuplus		90.63 307	90.59 310	90.74 363	93.85 396	77.43 425	89.01 404	96.16 289	81.42 418	92.77 333	95.54 286	88.59 239	97.28 365	81.99 395	96.00 409	97.50 284

DP-MVS Recon		92.31 254	91.88 266	93.60 194	97.18 162	86.87 196	91.10 314	97.37 181	84.92 353	92.08 366	94.08 362	88.59 239	98.20 264	83.50 375	98.14 277	95.73 397

RoMa-HiRes		94.64 129	94.29 166	95.68 81	97.47 144	93.88 37	93.83 178	96.23 282	88.05 254	97.75 40	96.20 239	88.58 241	94.93 460	91.33 170	99.17 109	98.22 188

fmvsm_s_conf0.5_n_6		94.14 166	94.54 154	92.95 229	96.51 230	82.74 298	92.71 235	98.13 73	86.56 296	96.44 122	96.85 177	88.51 242	98.05 289	96.03 23	99.09 117	98.06 204

FC-MVSNet-test		95.32 94	95.88 75	93.62 193	98.49 65	81.77 312	95.90 81	98.32 38	93.93 72	97.53 53	97.56 96	88.48 243	99.40 51	92.91 117	99.83 5	99.68 7

OpenMVS	89.45 8	92.27 258	92.13 257	92.68 248	94.53 373	84.10 263	95.70 88	97.03 214	82.44 402	91.14 392	96.42 214	88.47 244	98.38 241	85.95 336	97.47 338	95.55 407

fmvsm_s_conf0.5_n_2		94.25 160	94.63 149	93.10 223	96.65 209	81.75 314	91.72 294	97.25 197	86.93 293	97.20 77	97.67 87	88.44 245	98.14 276	97.06 9	98.77 183	99.42 24

F-COLMAP		92.28 255	91.06 291	95.95 63	97.52 139	91.90 81	93.53 191	97.18 202	83.98 372	88.70 455	94.04 363	88.41 246	98.55 213	80.17 418	95.99 411	97.39 297

fmvsm_s_conf0.1_n_2		94.38 148	94.78 137	93.19 220	97.07 171	81.72 315	91.97 276	97.51 172	87.05 289	97.31 68	97.92 68	88.29 247	98.15 273	97.10 6	98.81 173	99.70 5

ambc				92.98 226	96.88 187	83.01 289	95.92 80	96.38 274		96.41 125	97.48 107	88.26 248	97.80 319	89.96 227	98.93 149	98.12 202

v10		94.68 127	95.27 111	92.90 234	96.57 222	80.15 345	94.65 138	97.57 163	90.68 173	97.43 59	98.00 56	88.18 249	99.15 99	94.84 51	99.55 37	99.41 26

v8		94.65 128	95.29 109	92.74 244	96.65 209	79.77 362	94.59 139	97.17 203	91.86 124	97.47 58	97.93 63	88.16 250	99.08 111	94.32 61	99.47 44	99.38 28

TSAR-MVS + GP.		93.07 218	92.41 245	95.06 114	95.82 303	90.87 103	90.97 319	92.61 409	88.04 255	94.61 250	93.79 376	88.08 251	97.81 318	89.41 242	98.39 243	96.50 351

fmvsm_s_conf0.5_n_8		94.70 125	95.34 105	92.78 243	96.77 199	81.50 320	92.64 240	98.50 21	91.51 148	97.22 76	97.93 63	88.07 252	98.45 234	96.62 16	98.80 177	98.39 169

OurMVSNet-221017-0		96.80 19	96.75 25	96.96 38	99.03 12	91.85 82	97.98 7	98.01 102	94.15 64	98.93 5	99.07 10	88.07 252	99.57 14	95.86 27	99.69 17	99.46 22

diffmvs		91.74 273	91.93 264	91.15 339	93.06 417	78.17 409	88.77 415	97.51 172	86.28 305	92.42 347	93.96 368	88.04 254	97.46 352	90.69 192	96.67 384	97.82 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
原ACMM1				92.87 237	96.91 185	84.22 260		97.01 215	76.84 468	89.64 432	94.46 347	88.00 255	98.70 182	81.53 402	98.01 294	95.70 400

VDD-MVS		94.37 150	94.37 161	94.40 157	97.49 141	86.07 223	93.97 170	93.28 392	94.49 57	96.24 141	97.78 76	87.99 256	98.79 160	88.92 262	99.14 112	98.34 175

XVG-OURS		94.72 123	94.12 175	96.50 50	98.00 102	94.23 22	91.48 301	98.17 67	90.72 171	95.30 204	96.47 209	87.94 257	96.98 390	91.41 168	97.61 328	98.30 180

CANet		92.38 250	91.99 261	93.52 203	93.82 397	83.46 272	91.14 312	97.00 216	89.81 198	86.47 484	94.04 363	87.90 258	99.21 92	89.50 240	98.27 259	97.90 237

BH-untuned		90.68 302	90.90 294	90.05 391	95.98 291	79.57 372	90.04 364	94.94 337	87.91 257	94.07 267	93.00 398	87.76 259	97.78 323	79.19 433	95.17 446	92.80 485

SD_0403		88.79 369	88.88 352	88.51 438	95.89 299	72.58 492	94.27 154	95.24 326	83.77 377	87.92 470	94.38 352	87.70 260	96.47 416	66.36 531	94.40 466	96.49 352

hybridnocas07		91.51 281	91.66 271	91.04 342	93.14 415	78.03 410	88.75 417	96.92 224	85.97 315	91.63 377	95.31 302	87.67 261	97.31 363	88.97 260	96.61 388	97.79 254

KinetiMVS		95.09 107	95.40 101	94.15 164	97.42 148	84.35 256	93.91 174	96.69 251	94.41 60	96.67 110	97.25 135	87.67 261	99.14 101	95.78 29	98.81 173	98.97 73

FIs		94.90 115	95.35 104	93.55 197	98.28 78	81.76 313	95.33 106	98.14 72	93.05 91	97.07 84	97.18 144	87.65 263	99.29 81	91.72 157	99.69 17	99.61 14

v1144		93.50 192	93.81 185	92.57 257	96.28 259	79.61 367	91.86 288	96.96 220	86.95 291	95.91 161	96.32 226	87.65 263	98.96 133	93.51 87	98.88 160	99.13 50

mvs_anonymous		90.37 317	91.30 283	87.58 458	92.17 444	68.00 514	89.84 373	94.73 347	83.82 375	93.22 310	97.40 114	87.54 265	97.40 359	87.94 300	95.05 450	97.34 300

PCF-MVS	84.52 17	89.12 355	87.71 384	93.34 211	96.06 284	85.84 232	86.58 464	97.31 191	68.46 527	93.61 287	93.89 371	87.51 266	98.52 221	67.85 526	98.11 280	95.66 402
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		92.67 237	92.96 220	91.79 297	96.27 262	80.15 345	91.95 277	94.98 335	92.19 111	94.52 253	96.07 251	87.43 267	97.39 360	84.83 357	98.38 244	97.83 248

v148		92.87 226	93.29 209	91.62 307	96.25 265	77.72 420	91.28 307	95.05 332	89.69 201	95.93 160	96.04 252	87.34 268	98.38 241	90.05 224	97.99 298	98.78 111

V42		93.43 197	93.58 199	92.97 227	95.34 338	81.22 327	92.67 237	96.49 268	87.25 278	96.20 145	96.37 223	87.32 269	98.85 148	92.39 136	98.21 270	98.85 97

TestfortrainingZip				93.68 190	95.25 340	86.20 219	96.32 56	96.38 274	92.81 92	92.13 364	93.87 374	87.28 270	98.61 196		95.07 449	96.23 371

v1192		93.49 193	93.78 188	92.62 254	96.16 273	79.62 366	91.83 289	97.22 201	86.07 312	96.10 152	96.38 222	87.22 271	99.02 123	94.14 66	98.88 160	99.22 42

WR-MVS		93.49 193	93.72 190	92.80 241	97.57 137	80.03 351	90.14 360	95.68 303	93.70 77	96.62 114	95.39 298	87.21 272	99.04 121	87.50 306	99.64 25	99.33 31

RoMa-SfM		93.45 195	92.92 224	95.03 115	96.77 199	94.01 31	93.01 212	95.19 329	83.99 371	97.28 72	95.33 301	87.17 273	93.66 476	88.55 279	99.00 133	97.42 291

IterMVS-LS		93.78 182	94.28 168	92.27 270	96.27 262	79.21 386	91.87 286	96.78 242	91.77 134	96.57 118	97.07 157	87.15 274	98.74 171	91.99 146	99.03 129	98.86 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hybrid		91.14 291	91.24 284	90.83 358	93.15 413	77.49 423	88.76 416	96.87 234	84.51 360	91.25 387	95.23 304	87.14 275	97.25 371	88.05 295	96.24 403	97.76 259

EI-MVSNet		92.99 219	93.26 213	92.19 277	92.12 446	79.21 386	92.32 260	94.67 350	91.77 134	95.24 215	95.85 262	87.14 275	98.49 224	91.99 146	98.26 260	98.86 94

v144192		93.20 212	93.54 202	92.16 281	96.05 285	78.26 408	91.95 277	97.14 205	84.98 352	95.96 157	96.11 249	87.08 277	99.04 121	93.79 76	98.84 165	99.17 46

MVSMamba_PlusPlus		94.82 119	95.89 74	91.62 307	97.82 115	78.88 393	96.52 40	97.60 158	97.14 16	94.23 260	98.48 35	87.01 278	99.71 2	95.43 40	98.80 177	96.28 367

114514_t		90.51 309	89.80 331	92.63 252	98.00 102	82.24 307	93.40 197	97.29 194	65.84 536	89.40 437	94.80 328	86.99 279	98.75 168	83.88 373	98.61 212	96.89 330

新几何1				93.17 222	97.16 163	87.29 182		94.43 355	67.95 528	91.29 383	94.94 318	86.97 280	98.23 261	81.06 410	97.75 315	93.98 460

HQP_MVS		94.26 156	93.93 183	95.23 107	97.71 125	88.12 164	94.56 143	97.81 135	91.74 136	93.31 300	95.59 281	86.93 281	98.95 135	89.26 249	98.51 228	98.60 144

plane_prior6					97.21 161	88.23 160						86.93 281

UGNet		93.08 215	92.50 242	94.79 131	93.87 394	87.99 168	95.07 121	94.26 361	90.64 174	87.33 480	97.67 87	86.89 283	98.49 224	88.10 293	98.71 199	97.91 236
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
LF4IMVS		92.72 234	92.02 260	94.84 128	95.65 318	91.99 79	92.92 223	96.60 259	85.08 348	92.44 346	93.62 382	86.80 284	96.35 422	86.81 316	98.25 262	96.18 374

v1921920		93.26 205	93.61 198	92.19 277	96.04 289	78.31 407	91.88 285	97.24 199	85.17 343	96.19 148	96.19 240	86.76 285	99.05 118	94.18 65	98.84 165	99.22 42

PRO-TEST		90.68 302	90.65 307	90.79 361	93.47 404	76.93 437	92.17 269	96.97 219	84.00 370	89.28 439	92.10 439	86.75 286	98.48 229	85.17 346	95.93 412	96.95 326

v1240		93.29 203	93.71 193	92.06 285	96.01 290	77.89 414	91.81 290	97.37 181	85.12 346	96.69 109	96.40 216	86.67 287	99.07 117	94.51 55	98.76 185	99.22 42

MAR-MVS		90.32 321	88.87 353	94.66 141	94.82 356	91.85 82	94.22 157	94.75 346	80.91 425	87.52 478	88.07 497	86.63 288	97.87 312	76.67 458	96.21 405	94.25 452
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
MSP-MVS		95.34 93	94.63 149	97.48 17	98.67 40	94.05 27	96.41 50	98.18 63	91.26 156	95.12 225	95.15 307	86.60 289	99.50 23	93.43 97	96.81 377	98.89 91
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
BH-RMVSNet		90.47 311	90.44 314	90.56 373	95.21 343	78.65 399	89.15 400	93.94 372	88.21 249	92.74 335	94.22 356	86.38 290	97.88 309	78.67 438	95.39 432	95.14 419

SSC-MVS3.2		89.88 338	91.06 291	86.31 482	95.90 297	63.76 535	82.68 520	92.43 413	91.42 152	92.37 351	94.58 340	86.34 291	96.60 410	84.35 366	99.50 42	98.57 147

CNLPA		91.72 274	91.20 285	93.26 217	96.17 272	91.02 96	91.14 312	95.55 313	90.16 192	90.87 398	93.56 385	86.31 292	94.40 468	79.92 424	97.12 356	94.37 449

PVSNet_BlendedMVS		90.35 318	89.96 327	91.54 312	94.81 357	78.80 397	90.14 360	96.93 222	79.43 442	88.68 457	95.06 314	86.27 293	98.15 273	80.27 414	98.04 290	97.68 268

PVSNet_Blended		88.74 371	88.16 377	90.46 376	94.81 357	78.80 397	86.64 460	96.93 222	74.67 482	88.68 457	89.18 487	86.27 293	98.15 273	80.27 414	96.00 409	94.44 448

PAPR		87.65 402	86.77 414	90.27 380	92.85 424	77.38 426	88.56 422	96.23 282	76.82 469	84.98 498	89.75 478	86.08 295	97.16 381	72.33 504	93.35 493	96.26 369

v2v482		93.29 203	93.63 196	92.29 269	96.35 251	78.82 395	91.77 293	96.28 278	88.45 239	95.70 180	96.26 233	86.02 296	98.90 139	93.02 113	98.81 173	99.14 49

IMVS_0404		90.67 304	91.06 291	89.50 404	95.19 344	76.72 441	86.58 464	96.89 228	85.92 317	89.17 441	94.50 343	85.77 297	94.67 462	88.49 281	97.07 358	97.10 311

test20.03		90.80 297	90.85 298	90.63 370	95.63 321	79.24 384	89.81 375	92.87 399	89.90 196	94.39 256	96.40 216	85.77 297	95.27 451	73.86 495	99.05 122	97.39 297

PLC	85.34 15	90.40 313	88.92 349	94.85 127	96.53 228	90.02 118	91.58 297	96.48 269	80.16 432	86.14 487	92.18 435	85.73 299	98.25 257	76.87 456	94.61 463	96.30 365
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		84.98 447	84.30 448	87.01 466	91.03 481	77.69 421	91.94 279	94.16 363	59.36 546	84.23 507	87.50 502	85.66 300	96.80 403	71.79 507	93.05 502	86.54 534

testdata				91.03 343	96.87 188	82.01 309		94.28 359	71.55 507	92.46 344	95.42 293	85.65 301	97.38 362	82.64 383	97.27 348	93.70 467

PM-MVS		93.33 202	92.67 236	95.33 99	96.58 221	94.06 25	92.26 266	92.18 416	85.92 317	96.22 143	96.61 200	85.64 302	95.99 432	90.35 206	98.23 265	95.93 387

SSC-MVS		90.16 325	92.96 220	81.78 518	97.88 111	48.48 555	90.75 329	87.69 468	96.02 40	96.70 108	97.63 91	85.60 303	97.80 319	85.73 339	98.60 214	99.06 60

BridgeMVS		93.45 195	94.17 173	91.28 330	95.81 305	78.40 401	96.20 69	97.48 175	88.56 238	95.29 206	97.20 143	85.56 304	99.21 92	92.52 132	98.91 154	96.24 370

MM		94.41 147	94.14 174	95.22 109	95.84 301	87.21 185	94.31 152	90.92 438	94.48 58	92.80 331	97.52 101	85.27 305	99.49 29	96.58 17	99.57 35	98.97 73

WB-MVS		89.44 348	92.15 256	81.32 519	97.73 123	48.22 556	89.73 378	87.98 464	95.24 47	96.05 153	96.99 165	85.18 306	96.95 392	82.45 390	97.97 300	98.78 111

MDA-MVSNet-bldmvs		91.04 292	90.88 296	91.55 310	94.68 368	80.16 344	85.49 487	92.14 419	90.41 185	94.93 237	95.79 267	85.10 307	96.93 395	85.15 349	94.19 476	97.57 278

PAPM_NR		91.03 293	90.81 300	91.68 305	96.73 202	81.10 329	93.72 182	96.35 276	88.19 250	88.77 453	92.12 438	85.09 308	97.25 371	82.40 391	93.90 482	96.68 341

WB-MVSnew		84.20 456	83.89 456	85.16 493	91.62 465	66.15 525	88.44 425	81.00 533	76.23 472	87.98 468	87.77 498	84.98 309	93.35 480	62.85 540	94.10 479	95.98 384

HQP2-MVS												84.76 310

HQP-MVS		92.09 263	91.49 277	93.88 179	96.36 248	84.89 249	91.37 302	97.31 191	87.16 283	88.81 449	93.40 388	84.76 310	98.60 199	86.55 325	97.73 317	98.14 200

test222					96.95 180	85.27 245	88.83 410	93.61 382	65.09 538	90.74 401	94.85 324	84.62 312			97.36 344	93.91 461

VDDNet		94.03 171	94.27 170	93.31 213	98.87 26	82.36 304	95.51 101	91.78 428	97.19 15	96.32 133	98.60 28	84.24 313	98.75 168	87.09 314	98.83 170	98.81 102

PVSNet_Blended_VisFu		91.63 276	91.20 285	92.94 231	97.73 123	83.95 266	92.14 270	97.46 176	78.85 453	92.35 352	94.98 316	84.16 314	99.08 111	86.36 330	96.77 379	95.79 395

mvs5depth		95.28 98	95.82 81	93.66 191	96.42 240	83.08 287	97.35 12	99.28 2	96.44 28	96.20 145	99.65 2	84.10 315	98.01 296	94.06 68	98.93 149	99.87 1

FE-MVSNET		92.02 266	92.22 253	91.41 320	96.63 217	79.08 388	91.53 298	96.84 238	85.52 335	95.16 222	96.14 245	83.97 316	97.50 348	85.48 342	98.75 189	97.64 271

CL-MVSNet_self_test		90.04 335	89.90 329	90.47 374	95.24 341	77.81 416	86.60 463	92.62 408	85.64 328	93.25 308	93.92 369	83.84 317	96.06 429	79.93 422	98.03 291	97.53 282

mvsany_test3		89.11 356	88.21 375	91.83 295	91.30 472	90.25 115	88.09 427	78.76 543	76.37 471	96.43 123	98.39 39	83.79 318	90.43 504	86.57 323	94.20 474	94.80 435

BH-w/o		87.21 417	87.02 408	87.79 457	94.77 360	77.27 429	87.90 429	93.21 395	81.74 411	89.99 424	88.39 494	83.47 319	96.93 395	71.29 511	92.43 509	89.15 512

PatchMatch-RL		89.18 351	88.02 379	92.64 249	95.90 297	92.87 62	88.67 421	91.06 434	80.34 430	90.03 423	91.67 450	83.34 320	94.42 467	76.35 463	94.84 457	90.64 506

balanced_ft_v1		92.65 239	93.17 215	91.10 340	94.47 374	77.32 427	96.67 34	96.70 250	88.23 248	93.70 284	97.16 145	83.33 321	99.41 43	90.51 197	97.76 314	96.57 343

DPM-MVS		89.35 349	88.40 363	92.18 280	96.13 278	84.20 261	86.96 450	96.15 290	75.40 478	87.36 479	91.55 454	83.30 322	98.01 296	82.17 394	96.62 387	94.32 451

OpenMVS_ROB	85.12 16	89.52 346	89.05 345	90.92 352	94.58 371	81.21 328	91.10 314	93.41 391	77.03 466	93.41 294	93.99 367	83.23 323	97.80 319	79.93 422	94.80 458	93.74 466

DKM		92.97 221	92.35 249	94.81 129	96.53 228	93.72 46	90.94 320	94.88 338	85.21 341	96.42 124	95.18 306	83.11 324	93.06 483	89.66 237	99.24 93	97.64 271

new-patchmatchnet		88.97 364	90.79 302	83.50 510	94.28 380	55.83 550	85.34 490	93.56 385	86.18 310	95.47 193	95.73 274	83.10 325	96.51 413	85.40 343	98.06 288	98.16 196

mvsany_test1		83.91 461	82.93 465	86.84 472	86.18 536	85.93 229	81.11 528	75.03 550	70.80 516	88.57 459	94.63 336	83.08 326	87.38 526	80.39 412	86.57 534	87.21 528

1314		86.46 434	86.33 428	86.87 471	91.65 464	74.54 469	91.94 279	94.10 365	74.28 487	84.78 501	87.33 504	83.03 327	95.00 455	78.72 437	91.16 519	91.06 502

IS-MVSNet		94.49 143	94.35 164	94.92 121	98.25 82	86.46 209	97.13 17	94.31 357	96.24 34	96.28 139	96.36 224	82.88 328	99.35 67	88.19 288	99.52 41	98.96 77

test_fmvs3		92.42 248	92.40 246	92.46 267	93.80 398	87.28 183	93.86 176	97.05 213	76.86 467	96.25 140	98.66 24	82.87 329	91.26 498	95.44 39	96.83 376	98.82 99

MG-MVS		89.54 345	89.80 331	88.76 428	94.88 353	72.47 494	89.60 382	92.44 412	85.82 323	89.48 435	95.98 258	82.85 330	97.74 331	81.87 396	95.27 442	96.08 379

TR-MVS		87.70 399	87.17 400	89.27 415	94.11 384	79.26 383	88.69 419	91.86 426	81.94 407	90.69 403	89.79 476	82.82 331	97.42 357	72.65 503	91.98 513	91.14 501

c3_l		91.32 286	91.42 278	91.00 346	92.29 437	76.79 439	87.52 438	96.42 272	85.76 325	94.72 247	93.89 371	82.73 332	98.16 271	90.93 185	98.55 219	98.04 208

YYNet1		88.17 385	88.24 372	87.93 452	92.21 440	73.62 481	80.75 529	88.77 453	82.51 399	94.99 235	95.11 310	82.70 333	93.70 475	83.33 376	93.83 483	96.48 353

MDA-MVSNet_test_wron		88.16 387	88.23 373	87.93 452	92.22 439	73.71 480	80.71 530	88.84 452	82.52 398	94.88 240	95.14 308	82.70 333	93.61 477	83.28 377	93.80 484	96.46 355

pmmvs-eth3d		91.54 279	90.73 304	93.99 171	95.76 310	87.86 174	90.83 325	93.98 371	78.23 457	94.02 271	96.22 236	82.62 335	96.83 400	86.57 323	98.33 251	97.29 303

MGCNet		92.88 224	92.27 251	94.69 136	92.35 435	86.03 224	92.88 226	89.68 446	90.53 180	91.52 378	96.43 212	82.52 336	99.32 77	95.01 48	99.54 38	98.71 124

Anonymous20231206		88.77 370	88.29 368	90.20 384	96.31 256	78.81 396	89.56 385	93.49 388	74.26 488	92.38 349	95.58 284	82.21 337	95.43 445	72.07 505	98.75 189	96.34 360

miper_ehance_all_eth		90.48 310	90.42 315	90.69 366	91.62 465	76.57 447	86.83 454	96.18 287	83.38 380	94.06 268	92.66 416	82.20 338	98.04 291	89.79 230	97.02 365	97.45 288

USDC		89.02 360	89.08 344	88.84 427	95.07 350	74.50 471	88.97 405	96.39 273	73.21 495	93.27 304	96.28 230	82.16 339	96.39 419	77.55 446	98.80 177	95.62 405

EPP-MVSNet		93.91 178	93.68 195	94.59 146	98.08 91	85.55 239	97.44 11	94.03 366	94.22 63	94.94 236	96.19 240	82.07 340	99.57 14	87.28 311	98.89 158	98.65 132

AstraMVS		92.75 233	92.73 231	92.79 242	97.02 176	81.48 321	92.88 226	90.62 442	87.99 256	96.48 119	96.71 192	82.02 341	98.48 229	92.44 134	98.46 233	98.40 168

UnsupCasMVSNet_eth		90.33 320	90.34 318	90.28 379	94.64 370	80.24 343	89.69 380	95.88 297	85.77 324	93.94 275	95.69 278	81.99 342	92.98 485	84.21 367	91.30 517	97.62 273

alignmvs		93.26 205	92.85 225	94.50 151	95.70 313	87.45 180	93.45 195	95.76 300	91.58 141	95.25 214	92.42 427	81.96 343	98.72 174	91.61 160	97.87 309	97.33 301

TAMVS		90.16 325	89.05 345	93.49 205	96.49 232	86.37 212	90.34 351	92.55 410	80.84 428	92.99 323	94.57 341	81.94 344	98.20 264	73.51 496	98.21 270	95.90 390

Anonymous202405211		92.58 242	92.50 242	92.83 239	96.55 224	83.22 281	92.43 252	91.64 430	94.10 65	95.59 187	96.64 196	81.88 345	97.50 348	85.12 351	98.52 226	97.77 258

SixPastTwentyTwo		94.91 113	95.21 112	93.98 172	98.52 57	83.19 282	95.93 79	94.84 340	94.86 53	98.49 19	98.74 22	81.45 346	99.60 9	94.69 53	99.39 62	99.15 48

cascas		87.02 425	86.28 429	89.25 416	91.56 467	76.45 449	84.33 507	96.78 242	71.01 513	86.89 483	85.91 512	81.35 347	96.94 393	83.09 379	95.60 424	94.35 450

GBi-Net		93.21 210	92.96 220	93.97 173	95.40 334	84.29 257	95.99 75	96.56 263	88.63 230	95.10 227	98.53 31	81.31 348	98.98 127	86.74 317	98.38 244	98.65 132

test1		93.21 210	92.96 220	93.97 173	95.40 334	84.29 257	95.99 75	96.56 263	88.63 230	95.10 227	98.53 31	81.31 348	98.98 127	86.74 317	98.38 244	98.65 132

FMVSNet2		92.78 231	92.73 231	92.95 229	95.40 334	81.98 310	94.18 159	95.53 314	88.63 230	96.05 153	97.37 116	81.31 348	98.81 156	87.38 310	98.67 206	98.06 204

MVE	59.87 23	73.86 513	72.65 514	77.47 527	87.00 534	74.35 472	61.37 548	60.93 555	67.27 530	69.69 550	86.49 509	81.24 351	72.33 552	56.45 546	83.45 539	85.74 536
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MVP-Stereo		90.07 332	88.92 349	93.54 199	96.31 256	86.49 207	90.93 321	95.59 309	79.80 435	91.48 379	95.59 281	80.79 352	97.39 360	78.57 440	91.19 518	96.76 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		88.50 375	88.03 378	89.90 394	95.52 328	78.88 393	87.39 441	94.02 368	79.32 446	93.06 320	94.02 365	80.72 353	94.27 470	75.16 476	93.08 501	96.54 344

guyue		92.60 240	92.62 237	92.52 264	96.73 202	81.00 330	93.00 214	91.83 427	88.28 246	96.38 126	96.23 235	80.71 354	98.37 245	92.06 145	98.37 249	98.20 191

ArgMatch-Sym		90.98 294	89.75 334	94.68 137	95.17 348	92.64 69	89.09 402	93.46 389	78.60 454	95.11 226	92.37 428	80.44 355	95.24 452	85.04 355	98.44 235	96.18 374

MS-PatchMatch		88.05 389	87.75 383	88.95 423	93.28 410	77.93 412	87.88 430	92.49 411	75.42 477	92.57 341	93.59 384	80.44 355	94.24 472	81.28 406	92.75 504	94.69 442

Anonymous20240521		92.86 228	93.57 200	90.74 363	96.57 222	75.50 461	94.15 160	95.60 305	89.38 209	95.90 162	97.90 73	80.39 357	97.96 302	92.60 129	99.68 20	98.75 115

LuminaMVS		93.43 197	93.18 214	94.16 163	97.32 154	85.29 244	93.36 199	93.94 372	88.09 253	97.12 82	96.43 212	80.11 358	98.98 127	93.53 86	98.76 185	98.21 189

CANet_DTU		89.85 339	89.17 343	91.87 293	92.20 441	80.02 352	90.79 327	95.87 298	86.02 313	82.53 524	91.77 447	80.01 359	98.57 207	85.66 340	97.70 322	97.01 320

SIFT-NCM-Cal		87.99 390	87.39 393	89.77 397	92.16 445	93.98 34	86.51 467	82.96 520	85.99 314	91.10 393	92.99 399	80.00 360	87.11 530	77.21 452	97.60 330	88.22 518

VortexMVS		92.13 262	92.56 240	90.85 356	94.54 372	76.17 452	92.30 263	96.63 258	86.20 308	96.66 112	96.79 182	79.87 361	98.16 271	91.27 172	98.76 185	98.24 185

SP-SuperGlue		91.30 287	91.15 289	91.75 299	91.06 479	90.99 99	90.32 352	93.55 386	90.63 176	91.17 389	93.82 375	79.84 362	88.92 517	93.30 101	96.63 386	95.34 414

PMMVS		83.00 471	81.11 480	88.66 433	83.81 545	86.44 210	82.24 523	85.65 488	61.75 545	82.07 527	85.64 515	79.75 363	91.59 496	75.99 467	93.09 500	87.94 521

ppachtmachnet_test		88.61 374	88.64 356	88.50 439	91.76 458	70.99 501	84.59 503	92.98 397	79.30 447	92.38 349	93.53 386	79.57 364	97.45 353	86.50 328	97.17 355	97.07 315

eth_miper_zixun_eth		90.72 300	90.61 308	91.05 341	92.04 449	76.84 438	86.91 451	96.67 255	85.21 341	94.41 255	93.92 369	79.53 365	98.26 256	89.76 232	97.02 365	98.06 204

test_vis1_rt		85.58 442	84.58 445	88.60 435	87.97 525	86.76 199	85.45 489	93.59 383	66.43 533	87.64 474	89.20 486	79.33 366	85.38 540	81.59 400	89.98 525	93.66 468

N_pmnet		88.90 366	87.25 397	93.83 183	94.40 377	93.81 44	84.73 496	87.09 473	79.36 445	93.26 306	92.43 426	79.29 367	91.68 494	77.50 448	97.22 353	96.00 382

SIFT-NN-NCMNet		86.55 433	85.56 438	89.51 403	91.84 457	94.02 30	85.72 481	81.31 530	84.33 366	86.13 488	91.77 447	79.22 368	87.46 525	74.06 493	95.70 421	87.07 531

miper_enhance_ethall		88.42 378	87.87 382	90.07 387	88.67 523	75.52 460	85.10 491	95.59 309	75.68 473	92.49 342	89.45 483	78.96 369	97.88 309	87.86 302	97.02 365	96.81 335

NormalMVS		94.10 167	93.36 208	96.31 55	99.01 15	90.84 104	94.70 134	97.90 118	90.98 162	93.22 310	95.73 274	78.94 370	99.12 105	90.38 202	99.42 54	98.97 73

SymmetryMVS		93.26 205	92.36 248	95.97 61	97.13 167	90.84 104	94.70 134	91.61 431	90.98 162	93.22 310	95.73 274	78.94 370	99.12 105	90.38 202	98.53 223	97.97 221

EPNet		89.80 341	88.25 371	94.45 155	83.91 544	86.18 220	93.87 175	87.07 475	91.16 160	80.64 536	94.72 331	78.83 372	98.89 141	85.17 346	98.89 158	98.28 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-UM-Cal		87.93 393	87.42 391	89.44 407	90.95 484	92.71 66	84.33 507	88.32 457	86.32 303	90.41 408	92.73 413	78.78 373	88.31 520	76.83 457	98.16 274	87.31 526

sss		87.23 416	86.82 412	88.46 441	93.96 390	77.94 411	86.84 453	92.78 403	77.59 460	87.61 477	91.83 446	78.75 374	91.92 492	77.84 443	94.20 474	95.52 409

SIFT-NN-CMatch		86.64 431	85.79 433	89.18 418	91.21 476	93.07 56	84.60 502	80.33 538	84.07 369	89.10 442	91.58 453	78.69 375	87.33 528	75.28 474	97.28 347	87.13 530

IterMVS-SCA-FT		91.65 275	91.55 273	91.94 291	93.89 393	79.22 385	87.56 435	93.51 387	91.53 145	95.37 200	96.62 199	78.65 376	98.90 139	91.89 150	94.95 453	97.70 266

SCA		87.43 411	87.21 398	88.10 448	92.01 450	71.98 496	89.43 390	88.11 462	82.26 404	88.71 454	92.83 407	78.65 376	97.59 342	79.61 428	93.30 494	94.75 438

our_test_3		87.55 405	87.59 386	87.44 460	91.76 458	70.48 502	83.83 514	90.55 443	79.79 436	92.06 367	92.17 436	78.63 378	95.63 437	84.77 358	94.73 459	96.22 372

jason		89.17 354	88.32 366	91.70 303	95.73 311	80.07 348	88.10 426	93.22 393	71.98 504	90.09 416	92.79 410	78.53 379	98.56 211	87.43 308	97.06 362	96.46 355
jason: jason.
RRT-MVS		92.28 255	93.01 219	90.07 387	94.06 387	73.01 486	95.36 103	97.88 123	92.24 109	95.16 222	97.52 101	78.51 380	99.29 81	90.55 195	95.83 417	97.92 234

ArgMatch-SfM		91.28 288	90.08 325	94.88 125	95.22 342	92.66 68	89.81 375	94.51 354	79.15 448	95.27 209	93.71 379	78.33 381	95.52 439	86.11 334	98.63 209	96.46 355

IterMVS		90.18 324	90.16 321	90.21 383	93.15 413	75.98 455	87.56 435	92.97 398	86.43 301	94.09 265	96.40 216	78.32 382	97.43 355	87.87 301	94.69 461	97.23 306
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		87.19 419	85.92 432	91.00 346	97.13 167	79.41 379	84.51 504	95.60 305	64.14 540	90.07 422	94.81 326	78.26 383	97.14 382	73.34 497	95.38 433	96.46 355

SP-DiffGlue		90.34 319	90.20 320	90.76 362	90.52 494	90.29 114	90.37 348	94.02 368	87.19 281	93.85 278	92.55 419	78.24 384	87.50 524	89.68 235	95.41 430	94.49 445

SIFT-PCN-Cal		87.04 424	86.65 417	88.22 445	90.09 506	90.20 116	83.84 513	85.36 494	85.16 344	91.83 371	91.84 445	78.22 385	87.02 534	74.79 480	98.71 199	87.44 524

DenseAffine		91.92 268	90.90 294	94.97 118	96.37 245	93.07 56	90.35 349	93.65 380	84.62 359	95.66 184	94.39 349	78.19 386	94.97 459	86.02 335	98.90 155	96.87 333

dtuonly		84.38 453	85.24 440	81.80 517	87.13 531	58.46 547	81.58 527	92.71 404	74.41 485	85.68 491	92.62 417	78.17 387	92.13 491	79.15 434	95.73 419	94.82 433

SP-MNN		89.68 343	89.55 339	90.06 390	90.43 499	88.06 166	89.60 382	92.13 420	86.42 302	89.57 434	92.55 419	78.14 388	87.91 523	90.35 206	96.74 382	94.22 453

SIFT-ConvMatch		87.94 392	87.21 398	90.11 386	91.67 463	93.60 49	85.55 486	83.12 518	86.48 298	92.15 362	92.98 401	78.11 389	88.58 519	76.60 459	98.25 262	88.14 520

WTY-MVS		86.93 427	86.50 424	88.24 444	94.96 351	74.64 466	87.19 445	92.07 422	78.29 456	88.32 462	91.59 452	78.06 390	94.27 470	74.88 479	93.15 498	95.80 394

SIFT-NCMNet		87.31 414	87.07 407	88.02 449	90.01 507	91.85 82	82.65 521	89.57 448	86.52 297	93.34 299	92.51 421	78.05 391	86.22 538	71.95 506	98.98 136	86.01 535

pmmvs4		88.95 365	87.70 385	92.70 246	94.30 379	85.60 238	87.22 444	92.16 418	74.62 483	89.75 431	94.19 358	77.97 392	96.41 418	82.71 382	96.36 396	96.09 378

DSMNet-mixed		82.21 478	81.56 476	84.16 503	89.57 514	70.00 508	90.65 336	77.66 547	54.99 549	83.30 518	97.57 94	77.89 393	90.50 503	66.86 530	95.54 426	91.97 492

SIFT-NN-PointCN		86.59 432	85.79 433	88.99 420	90.15 503	92.46 72	84.96 494	82.76 522	83.11 388	88.70 455	92.34 429	77.62 394	87.10 531	75.03 478	97.44 340	87.42 525

FA-MVS(test-final)		91.81 270	91.85 267	91.68 305	94.95 352	79.99 353	96.00 74	93.44 390	87.80 262	94.02 271	97.29 130	77.60 395	98.45 234	88.04 297	97.49 336	96.61 342

DKM-HiRes		92.87 226	91.94 263	95.65 82	97.16 163	93.66 47	90.90 322	94.27 360	87.11 287	95.29 206	95.39 298	77.59 396	95.36 446	90.86 186	98.92 153	97.94 225

lessismore_v0				93.87 180	98.05 94	83.77 268		80.32 539		97.13 80	97.91 71	77.49 397	99.11 109	92.62 127	98.08 284	98.74 119

Syy-MVS		84.81 448	84.93 442	84.42 500	91.71 461	63.36 537	85.89 476	81.49 527	81.03 422	85.13 495	81.64 538	77.44 398	95.00 455	85.94 337	94.12 477	94.91 431

ALIKED-MNN		88.42 378	87.16 401	92.21 275	93.47 404	93.93 35	92.87 228	95.20 328	71.10 511	87.62 475	93.76 377	77.41 399	91.34 497	74.50 485	98.53 223	91.36 498

HY-MVS	82.50 18	86.81 429	85.93 431	89.47 405	93.63 400	77.93 412	94.02 166	91.58 432	75.68 473	83.64 514	93.64 380	77.40 400	97.42 357	71.70 509	92.07 512	93.05 480

1112_ss		88.42 378	87.41 392	91.45 317	96.69 206	80.99 331	89.72 379	96.72 248	73.37 493	87.00 482	90.69 468	77.38 401	98.20 264	81.38 405	93.72 485	95.15 418

DIV-MVS_self_test		90.65 305	90.56 312	90.91 354	91.85 455	76.99 434	86.75 456	95.36 321	85.52 335	94.06 268	94.89 320	77.37 402	97.99 300	90.28 211	98.97 142	97.76 259

cl____		90.65 305	90.56 312	90.91 354	91.85 455	76.98 435	86.75 456	95.36 321	85.53 332	94.06 268	94.89 320	77.36 403	97.98 301	90.27 212	98.98 136	97.76 259

SP-NN		88.21 384	87.96 381	88.97 422	89.33 517	87.99 168	88.06 428	90.93 437	85.48 337	84.50 502	91.11 459	77.25 404	84.79 541	90.55 195	94.42 465	94.14 454

CDS-MVSNet		89.55 344	88.22 374	93.53 201	95.37 337	86.49 207	89.26 397	93.59 383	79.76 437	91.15 391	92.31 430	77.12 405	98.38 241	77.51 447	97.92 306	95.71 398
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SP-LightGlue		90.98 294	90.67 305	91.92 292	91.04 480	91.02 96	90.68 334	94.22 362	89.56 206	90.35 413	92.90 405	77.08 406	89.38 513	93.92 72	96.27 400	95.35 413

test_vis3_rt		90.40 313	90.03 326	91.52 313	92.58 427	88.95 140	90.38 347	97.72 146	73.30 494	97.79 38	97.51 105	77.05 407	87.10 531	89.03 259	94.89 454	98.50 153

usedtu_dtu_shiyan1		89.18 351	88.59 357	90.95 350	94.75 361	77.79 417	86.25 470	94.63 352	81.61 414	90.88 396	92.24 432	77.03 408	98.08 282	82.62 384	97.27 348	96.97 323

FE-MVSNET3		89.18 351	88.59 357	90.95 350	94.75 361	77.79 417	86.25 470	94.63 352	81.61 414	90.88 396	92.25 431	77.03 408	98.08 282	82.62 384	97.27 348	96.97 323

MVSFormer		92.18 261	92.23 252	92.04 286	94.74 364	80.06 349	97.15 15	97.37 181	88.98 220	88.83 447	92.79 410	77.02 410	99.60 9	96.41 18	96.75 380	96.46 355

lupinMVS		88.34 382	87.31 394	91.45 317	94.74 364	80.06 349	87.23 443	92.27 415	71.10 511	88.83 447	91.15 457	77.02 410	98.53 218	86.67 321	96.75 380	95.76 396

SIFT-CM-Cal		87.51 408	86.76 415	89.76 398	91.48 468	93.30 55	84.73 496	84.04 507	85.53 332	91.66 375	92.58 418	77.01 412	88.75 518	75.29 472	98.56 218	87.24 527

PMMVS2		81.31 485	83.44 459	74.92 529	90.52 494	46.49 558	69.19 546	85.23 499	84.30 367	87.95 469	94.71 332	76.95 413	84.36 545	64.07 536	98.09 283	93.89 462

SIFT-NN-UMatch		86.43 435	85.66 436	88.76 428	90.73 488	92.76 65	84.99 493	81.25 531	84.13 368	88.17 465	92.04 440	76.90 414	86.62 535	76.34 464	96.36 396	86.91 532

h-mvs33		92.89 223	91.99 261	95.58 86	96.97 179	90.55 110	93.94 173	94.01 370	89.23 212	93.95 273	96.19 240	76.88 415	99.14 101	91.02 181	95.71 420	97.04 319

hse-mvs2		92.24 259	91.20 285	95.38 96	96.16 273	90.65 109	92.52 245	92.01 424	89.23 212	93.95 273	92.99 399	76.88 415	98.69 184	91.02 181	96.03 408	96.81 335

pmmvs5		87.87 395	87.14 402	90.07 387	93.26 412	76.97 436	88.89 407	92.18 416	73.71 491	88.36 461	93.89 371	76.86 417	96.73 405	80.32 413	96.81 377	96.51 348

SIFT-UMatch		87.96 391	87.52 387	89.29 412	91.48 468	92.84 63	85.46 488	83.94 509	87.47 272	91.86 370	92.92 403	76.78 418	87.35 527	79.73 425	98.00 297	87.69 522

test_vis1_n_1920		89.45 347	89.85 330	88.28 443	93.59 402	76.71 445	90.67 335	97.78 141	79.67 439	90.30 414	96.11 249	76.62 419	92.17 490	90.31 209	93.57 488	95.96 385

K. test v3		93.37 199	93.27 212	93.66 191	98.05 94	82.62 300	94.35 149	86.62 477	96.05 38	97.51 55	98.85 18	76.59 420	99.65 4	93.21 106	98.20 272	98.73 120

miper_lstm_enhance		89.90 337	89.80 331	90.19 385	91.37 471	77.50 422	83.82 515	95.00 334	84.84 355	93.05 321	94.96 317	76.53 421	95.20 453	89.96 227	98.67 206	97.86 244

dmvs_testset		78.23 506	78.99 499	75.94 528	91.99 451	55.34 552	88.86 408	78.70 544	82.69 394	81.64 532	79.46 540	75.93 422	85.74 539	48.78 549	82.85 541	86.76 533

PMatch-Up-SfM		92.38 250	91.36 280	95.46 93	96.22 268	92.32 73	89.61 381	95.31 323	85.08 348	96.71 107	96.12 247	75.90 423	97.27 368	89.73 234	97.54 333	96.78 337

Test_1112_low_res		87.50 410	86.58 418	90.25 381	96.80 195	77.75 419	87.53 437	96.25 280	69.73 523	86.47 484	93.61 383	75.67 424	97.88 309	79.95 420	93.20 496	95.11 422

test_fmvs2		90.62 308	90.40 316	91.29 329	91.93 453	85.46 241	92.70 236	96.48 269	74.44 484	94.91 238	97.59 93	75.52 425	90.57 501	93.44 94	96.56 389	97.84 247

Vis-MVSNet (Re-imp)		90.42 312	90.16 321	91.20 336	97.66 131	77.32 427	94.33 150	87.66 469	91.20 158	92.99 323	95.13 309	75.40 426	98.28 251	77.86 442	99.19 102	97.99 216

SIFT-PointCN		87.02 425	86.47 425	88.65 434	90.27 502	91.47 90	83.91 511	84.08 506	84.84 355	91.35 382	92.24 432	75.25 427	87.29 529	77.11 455	99.20 101	87.20 529

ELoFTR		89.04 359	88.72 355	89.99 393	94.38 378	89.08 137	90.15 359	89.10 451	75.60 475	95.85 165	96.52 207	75.00 428	89.26 514	83.82 374	98.08 284	91.61 497

test_vis1_n		89.01 362	89.01 347	89.03 419	92.57 428	82.46 303	92.62 241	96.06 291	73.02 497	90.40 409	95.77 272	74.86 429	89.68 508	90.78 189	94.98 451	94.95 428

D2MVS		89.93 336	89.60 337	90.92 352	94.03 388	78.40 401	88.69 419	94.85 339	78.96 451	93.08 319	95.09 312	74.57 430	96.94 393	88.19 288	98.96 144	97.41 292

blended_shiyan8		88.43 377	87.44 389	91.40 321	92.37 433	79.45 376	87.43 439	93.92 374	82.51 399	91.24 388	85.42 517	74.35 431	98.23 261	84.43 364	95.28 441	96.52 347

blended_shiyan6		88.42 378	87.43 390	91.40 321	92.37 433	79.43 378	87.41 440	93.91 375	82.51 399	91.17 389	85.44 516	74.34 432	98.24 259	84.38 365	95.32 436	96.53 346

PVSNet	76.22 20	82.89 473	82.37 469	84.48 499	93.96 390	64.38 533	78.60 535	88.61 454	71.50 508	84.43 505	86.36 510	74.27 433	94.60 464	69.87 520	93.69 487	94.46 447

LoFTR		90.05 333	89.57 338	91.50 314	93.73 399	91.47 90	90.72 331	89.37 450	81.71 412	97.13 80	96.40 216	74.09 434	92.38 488	84.18 368	98.79 180	90.63 507

test_yl		90.11 328	89.73 335	91.26 331	94.09 385	79.82 358	90.44 343	92.65 406	90.90 164	93.19 313	93.30 390	73.90 435	98.03 292	82.23 392	96.87 373	95.93 387

DCV-MVSNet		90.11 328	89.73 335	91.26 331	94.09 385	79.82 358	90.44 343	92.65 406	90.90 164	93.19 313	93.30 390	73.90 435	98.03 292	82.23 392	96.87 373	95.93 387

CMPMVS	68.83 22	87.28 415	85.67 435	92.09 284	88.77 522	85.42 242	90.31 354	94.38 356	70.02 520	88.00 467	93.30 390	73.78 437	94.03 474	75.96 468	96.54 390	96.83 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SIFT-MNN		87.81 398	87.11 405	89.90 394	92.19 442	93.62 48	86.73 458	84.68 502	87.19 281	90.95 395	92.80 409	73.54 438	87.09 533	78.62 439	97.32 346	88.98 514

MonoMVSNet		88.46 376	89.28 341	85.98 484	90.52 494	70.07 507	95.31 109	94.81 343	88.38 242	93.47 293	96.13 246	73.21 439	95.07 454	82.61 386	89.12 526	92.81 484

baseline1		87.62 403	87.31 394	88.54 436	94.71 367	74.27 474	93.10 209	88.20 460	86.20 308	92.18 361	93.04 397	73.21 439	95.52 439	79.32 431	85.82 535	95.83 393

ALIKED-NN		85.96 438	84.14 451	91.44 319	91.73 460	93.37 52	90.32 352	93.65 380	67.84 529	82.08 526	92.92 403	72.88 441	90.01 506	69.17 522	96.64 385	90.93 503

gbinet_0.2-2-1-0.02		88.14 388	86.86 411	91.99 290	90.70 489	80.51 337	87.36 442	93.01 396	83.45 379	90.38 410	82.42 536	72.73 442	98.54 214	85.40 343	96.27 400	96.90 328

PVSNet_0	70.34 21	74.58 512	72.96 513	79.47 523	90.63 491	66.24 523	73.26 541	83.40 514	63.67 542	78.02 540	78.35 542	72.53 443	89.59 509	56.68 544	60.05 552	82.57 543

ALIKED-LG		89.78 342	88.57 359	93.39 209	93.97 389	95.11 11	94.30 153	95.57 312	79.81 434	93.27 304	94.93 319	72.44 444	92.52 487	75.11 477	97.77 313	92.53 489

dmvs_re		84.69 451	83.94 455	86.95 469	92.24 438	82.93 290	89.51 386	87.37 471	84.38 365	85.37 492	85.08 521	72.44 444	86.59 536	68.05 525	91.03 521	91.33 499

MIMVSNet		87.13 421	86.54 421	88.89 426	96.05 285	76.11 453	94.39 148	88.51 455	81.37 420	88.27 463	96.75 187	72.38 446	95.52 439	65.71 533	95.47 428	95.03 424

wanda-best-256-512		87.53 406	86.39 426	90.97 348	91.29 473	78.39 403	85.63 484	93.75 377	81.91 408	90.09 416	83.30 530	72.25 447	98.18 267	83.96 370	95.32 436	96.33 361

FE-blended-shiyan7		87.53 406	86.39 426	90.97 348	91.29 473	78.39 403	85.63 484	93.75 377	81.91 408	90.09 416	83.30 530	72.25 447	98.18 267	83.96 370	95.32 436	96.33 361

usedtu_blend_shiyan5		89.08 357	88.33 365	91.34 325	91.29 473	79.59 368	94.02 166	97.13 207	90.07 193	90.09 416	83.30 530	72.25 447	98.10 280	81.45 403	95.32 436	96.33 361

PAPM		81.91 483	80.11 494	87.31 462	93.87 394	72.32 495	84.02 510	93.22 393	69.47 524	76.13 544	89.84 473	72.15 450	97.23 373	53.27 547	89.02 527	92.37 490

cl22		89.02 360	88.50 361	90.59 372	89.76 509	76.45 449	86.62 462	94.03 366	82.98 392	92.65 337	92.49 422	72.05 451	97.53 346	88.93 261	97.02 365	97.78 257

LFMVS		91.33 285	91.16 288	91.82 296	96.27 262	79.36 380	95.01 124	85.61 492	96.04 39	94.82 241	97.06 159	72.03 452	98.46 233	84.96 356	98.70 202	97.65 270

PMatch-SfM		91.76 272	90.58 311	95.30 103	95.64 320	91.67 88	89.49 387	94.79 345	84.45 362	96.31 134	96.02 254	71.68 453	97.26 370	89.13 256	97.75 315	96.98 322

test_cas_vis1_n_1920		88.25 383	88.27 370	88.20 446	92.19 442	78.92 391	89.45 389	95.44 316	75.29 481	93.23 309	95.65 280	71.58 454	90.23 505	88.05 295	93.55 490	95.44 410

MVS-HIRNet		78.83 505	80.60 488	73.51 530	93.07 416	47.37 557	87.10 447	78.00 546	68.94 525	77.53 541	97.26 134	71.45 455	94.62 463	63.28 538	88.74 528	78.55 546

EPNet_dtu		85.63 441	84.37 447	89.40 410	86.30 535	74.33 473	91.64 295	88.26 458	84.84 355	72.96 547	89.85 472	71.27 456	97.69 334	76.60 459	97.62 327	96.18 374
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1111		90.39 315	90.61 308	89.74 400	98.04 97	71.50 498	95.59 93	79.72 541	89.41 208	95.94 159	98.14 45	70.79 457	98.81 156	88.52 280	99.32 77	98.90 90

mvsmamba		90.24 323	89.43 340	92.64 249	95.52 328	82.36 304	96.64 35	92.29 414	81.77 410	92.14 363	96.28 230	70.59 458	99.10 110	84.44 363	95.22 445	96.47 354

ECVR-MVS		90.12 327	90.16 321	90.00 392	97.81 116	72.68 490	95.76 87	78.54 545	89.04 217	95.36 201	98.10 48	70.51 459	98.64 192	87.10 313	99.18 106	98.67 130

testing915		88.17 385	88.00 380	88.67 431	95.72 312	74.55 468	93.05 210	87.71 467	87.29 277	90.08 420	93.23 393	70.40 460	96.73 405	77.45 449	97.05 364	94.74 441

HyFIR lowres test		87.19 419	85.51 439	92.24 273	97.12 169	80.51 337	85.03 492	96.06 291	66.11 535	91.66 375	92.98 401	70.12 461	99.14 101	75.29 472	95.23 444	97.07 315

SIFT-NN		84.10 457	83.04 462	87.28 463	90.76 487	92.16 76	84.45 505	81.34 529	83.54 378	83.80 512	89.75 478	70.08 462	82.09 546	68.68 523	94.96 452	87.60 523

FMVSNet3		90.78 298	90.32 319	92.16 281	93.03 419	79.92 356	92.54 244	94.95 336	86.17 311	95.10 227	96.01 255	69.97 463	98.75 168	86.74 317	98.38 244	97.82 251

test_f		86.65 430	87.13 403	85.19 492	90.28 501	86.11 222	86.52 466	91.66 429	69.76 522	95.73 178	97.21 142	69.51 464	81.28 547	89.15 255	94.40 466	88.17 519

MatchFormer		85.84 440	85.60 437	86.56 475	90.63 491	87.98 170	89.85 372	83.79 510	72.98 498	95.69 183	94.88 323	69.40 465	87.92 522	74.60 481	98.55 219	83.77 539

MASt3R-SfM		82.76 475	82.17 472	84.53 498	83.29 547	86.01 225	82.08 524	80.49 537	63.10 543	92.22 358	94.20 357	69.18 466	77.62 548	79.63 426	95.37 434	89.94 511

RPMNet		90.31 322	90.14 324	90.81 360	91.01 482	78.93 389	92.52 245	98.12 76	91.91 121	89.10 442	96.89 173	68.84 467	99.41 43	90.17 219	92.70 505	94.08 455

test_fmvs1_n		88.73 372	88.38 364	89.76 398	92.06 448	82.53 301	92.30 263	96.59 261	71.14 510	92.58 340	95.41 296	68.55 468	89.57 510	91.12 179	95.66 422	97.18 309

test_fmvs1		87.59 404	87.27 396	88.54 436	88.32 524	81.26 325	90.43 346	95.72 302	70.55 517	91.70 373	94.63 336	68.13 469	89.42 512	90.59 193	95.34 435	94.94 430

ADS-MVSNet2		84.01 458	82.20 471	89.41 409	89.04 519	76.37 451	87.57 433	90.98 436	72.71 501	84.46 503	92.45 423	68.08 470	96.48 414	70.58 517	83.97 537	95.38 411

ADS-MVSNet		82.25 477	81.55 477	84.34 501	89.04 519	65.30 527	87.57 433	85.13 500	72.71 501	84.46 503	92.45 423	68.08 470	92.33 489	70.58 517	83.97 537	95.38 411

CVMVSNet		85.16 445	84.72 443	86.48 476	92.12 446	70.19 503	92.32 260	88.17 461	56.15 548	90.64 404	95.85 262	67.97 472	96.69 407	88.78 269	90.52 522	92.56 487

new_pmnet		81.22 486	81.01 483	81.86 516	90.92 485	70.15 504	84.03 509	80.25 540	70.83 514	85.97 489	89.78 477	67.93 473	84.65 542	67.44 527	91.90 514	90.78 505

CR-MVSNet		87.89 394	87.12 404	90.22 382	91.01 482	78.93 389	92.52 245	92.81 400	73.08 496	89.10 442	96.93 170	67.11 474	97.64 339	88.80 268	92.70 505	94.08 455

Patchmtry		90.11 328	89.92 328	90.66 368	90.35 500	77.00 433	92.96 217	92.81 400	90.25 187	94.74 245	96.93 170	67.11 474	97.52 347	85.17 346	98.98 136	97.46 287

PatchmatchNet		85.22 444	84.64 444	86.98 467	89.51 515	69.83 509	90.52 339	87.34 472	78.87 452	87.22 481	92.74 412	66.91 476	96.53 411	81.77 397	86.88 533	94.58 443
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GA-MVS		87.70 399	86.82 412	90.31 378	93.27 411	77.22 430	84.72 499	92.79 402	85.11 347	89.82 427	90.07 471	66.80 477	97.76 327	84.56 361	94.27 472	95.96 385

MDTV_nov1_ep13_2view						42.48 559	88.45 424		67.22 531	83.56 515		66.80 477		72.86 502		94.06 457

tpmrst		82.85 474	82.93 465	82.64 513	87.65 526	58.99 546	90.14 360	87.90 465	75.54 476	83.93 510	91.63 451	66.79 479	95.36 446	81.21 408	81.54 543	93.57 474

sam_mvs1												66.64 480				94.75 438

sam_mvs												66.41 481

Patchmatch-RL test		88.81 368	88.52 360	89.69 402	95.33 339	79.94 355	86.22 473	92.71 404	78.46 455	95.80 167	94.18 359	66.25 482	95.33 449	89.22 251	98.53 223	93.78 464

patchmatchnet-post											91.71 449	66.22 483	97.59 342

AUN-MVS		90.05 333	88.30 367	95.32 101	96.09 281	90.52 112	92.42 253	92.05 423	82.08 406	88.45 460	92.86 406	65.76 484	98.69 184	88.91 263	96.07 407	96.75 340

test_post											6.07 559	65.74 485	95.84 435

ttmdpeth		86.91 428	86.57 419	87.91 454	89.68 511	74.24 475	91.49 300	87.09 473	79.84 433	89.46 436	97.86 74	65.42 486	91.04 499	81.57 401	96.74 382	98.44 159

test_post1							90.21 356				5.85 560	65.36 487	96.00 431	79.61 428

MDTV_nov1_ep13			83.88 457		89.42 516	61.52 539	88.74 418	87.41 470	73.99 489	84.96 499	94.01 366	65.25 488	95.53 438	78.02 441	93.16 497

Patchmatch-test		86.10 437	86.01 430	86.38 480	90.63 491	74.22 476	89.57 384	86.69 476	85.73 326	89.81 428	92.83 407	65.24 489	91.04 499	77.82 445	95.78 418	93.88 463

tpmvs		84.22 455	83.97 454	84.94 494	87.09 532	65.18 528	91.21 308	88.35 456	82.87 393	85.21 493	90.96 463	65.24 489	96.75 404	79.60 430	85.25 536	92.90 483

EU-MVSNet		87.39 412	86.71 416	89.44 407	93.40 408	76.11 453	94.93 127	90.00 445	57.17 547	95.71 179	97.37 116	64.77 491	97.68 335	92.67 126	94.37 469	94.52 444

BP-MVS1		91.77 271	91.10 290	93.75 186	96.42 240	83.40 273	94.10 164	91.89 425	91.27 155	93.36 298	94.85 324	64.43 492	99.29 81	94.88 49	98.74 191	98.56 148

thres200		85.85 439	85.18 441	87.88 455	94.44 375	72.52 493	89.08 403	86.21 480	88.57 237	91.44 380	88.40 493	64.22 493	98.00 298	68.35 524	95.88 416	93.12 477

PatchT		87.51 408	88.17 376	85.55 488	90.64 490	66.91 518	92.02 274	86.09 483	92.20 110	89.05 446	97.16 145	64.15 494	96.37 421	89.21 252	92.98 503	93.37 475

tfpn200view9		87.05 423	86.52 422	88.67 431	95.77 308	72.94 487	91.89 283	86.00 484	90.84 166	92.61 338	89.80 474	63.93 495	98.28 251	71.27 512	96.54 390	94.79 436

thres400		87.20 418	86.52 422	89.24 417	95.77 308	72.94 487	91.89 283	86.00 484	90.84 166	92.61 338	89.80 474	63.93 495	98.28 251	71.27 512	96.54 390	96.51 348

FPMVS		84.50 452	83.28 460	88.16 447	96.32 255	94.49 20	85.76 479	85.47 493	83.09 389	85.20 494	94.26 354	63.79 497	86.58 537	63.72 537	91.88 515	83.40 540

GDP-MVS		91.56 278	90.83 299	93.77 185	96.34 252	83.65 269	93.66 185	98.12 76	87.32 276	92.98 325	94.71 332	63.58 498	99.30 80	92.61 128	98.14 277	98.35 174

thres100view900		87.35 413	86.89 410	88.72 430	96.14 276	73.09 485	93.00 214	85.31 496	92.13 114	93.26 306	90.96 463	63.42 499	98.28 251	71.27 512	96.54 390	94.79 436

thres600view7		87.66 401	87.10 406	89.36 411	96.05 285	73.17 483	92.72 233	85.31 496	91.89 122	93.29 302	90.97 462	63.42 499	98.39 237	73.23 498	96.99 370	96.51 348

EMVS		80.35 496	80.28 493	80.54 521	84.73 543	69.07 510	72.54 544	80.73 535	87.80 262	81.66 531	81.73 537	62.89 501	89.84 507	75.79 469	94.65 462	82.71 542

test-LLR		83.58 464	83.17 461	84.79 496	89.68 511	66.86 519	83.08 517	84.52 503	83.07 390	82.85 520	84.78 522	62.86 502	93.49 478	82.85 380	94.86 455	94.03 458

test0.0.03 1		82.48 476	81.47 479	85.48 489	89.70 510	73.57 482	84.73 496	81.64 526	83.07 390	88.13 466	86.61 507	62.86 502	89.10 516	66.24 532	90.29 523	93.77 465

tpm cat1		80.61 494	79.46 497	84.07 504	88.78 521	65.06 531	89.26 397	88.23 459	62.27 544	81.90 530	89.66 481	62.70 504	95.29 450	71.72 508	80.60 544	91.86 495

E-PMN		80.72 493	80.86 484	80.29 522	85.11 541	68.77 511	72.96 542	81.97 525	87.76 264	83.25 519	83.01 534	62.22 505	89.17 515	77.15 454	94.31 471	82.93 541

baseline2		83.38 467	81.54 478	88.90 425	91.38 470	72.84 489	88.78 414	81.22 532	78.97 450	79.82 538	87.56 499	61.73 506	97.80 319	74.30 490	90.05 524	96.05 381

CostFormer		83.09 470	82.21 470	85.73 485	89.27 518	67.01 517	90.35 349	86.47 478	70.42 518	83.52 516	93.23 393	61.18 507	96.85 399	77.21 452	88.26 530	93.34 476

MVSTER		89.32 350	88.75 354	91.03 343	90.10 505	76.62 446	90.85 324	94.67 350	82.27 403	95.24 215	95.79 267	61.09 508	98.49 224	90.49 198	98.26 260	97.97 221

tpm		84.38 453	84.08 452	85.30 491	90.47 497	63.43 536	89.34 394	85.63 489	77.24 465	87.62 475	95.03 315	61.00 509	97.30 364	79.26 432	91.09 520	95.16 417

FE-MVS		89.06 358	88.29 368	91.36 324	94.78 359	79.57 372	96.77 29	90.99 435	84.87 354	92.96 326	96.29 228	60.69 510	98.80 159	80.18 417	97.11 357	95.71 398

EPMVS		81.17 488	80.37 491	83.58 509	85.58 538	65.08 530	90.31 354	71.34 551	77.31 464	85.80 490	91.30 455	59.38 511	92.70 486	79.99 419	82.34 542	92.96 482

tmp_tt		37.97 518	44.33 520	18.88 537	11.80 562	21.54 563	63.51 547	45.66 560	4.23 556	51.34 554	50.48 552	59.08 512	22.11 558	44.50 550	68.35 551	13.00 553

tpm2		81.46 484	80.35 492	84.80 495	89.90 508	65.14 529	90.44 343	85.36 494	65.82 537	82.05 528	92.44 425	57.94 513	96.69 407	70.71 516	88.49 529	92.56 487

XFeat-MNN		80.76 492	79.73 496	83.85 507	79.29 553	82.86 292	76.90 538	83.32 516	69.86 521	92.27 356	87.53 501	57.82 514	84.65 542	74.17 491	96.44 395	84.03 538

ET-MVSNet_ETH3D		86.15 436	84.27 449	91.79 297	93.04 418	81.28 324	87.17 446	86.14 481	79.57 440	83.65 513	88.66 489	57.10 515	98.18 267	87.74 303	95.40 431	95.90 390

CHOSEN 280x420		80.04 499	77.97 507	86.23 483	90.13 504	74.53 470	72.87 543	89.59 447	66.38 534	76.29 543	85.32 519	56.96 516	95.36 446	69.49 521	94.72 460	88.79 516

JIA-IIPM		85.08 446	83.04 462	91.19 337	87.56 527	86.14 221	89.40 392	84.44 505	88.98 220	82.20 525	97.95 62	56.82 517	96.15 425	76.55 462	83.45 539	91.30 500

DeepMVS_CX				53.83 533	70.38 556	64.56 532		48.52 559	33.01 552	65.50 552	74.21 544	56.19 518	46.64 556	38.45 552	70.07 550	50.30 550

dp		79.28 503	78.62 502	81.24 520	85.97 537	56.45 549	86.91 451	85.26 498	72.97 499	81.45 533	89.17 488	56.01 519	95.45 444	73.19 499	76.68 548	91.82 496

test_method		50.44 516	48.94 519	54.93 532	39.68 559	12.38 565	28.59 550	90.09 444	6.82 555	41.10 556	78.41 541	54.41 520	70.69 553	50.12 548	51.26 553	81.72 544

thisisatest0515		84.72 450	82.99 464	89.90 394	92.96 421	75.33 462	84.36 506	83.42 513	77.37 462	88.27 463	86.65 506	53.94 521	98.72 174	82.56 387	97.40 343	95.67 401

tttt0517		89.81 340	88.90 351	92.55 258	97.00 178	79.73 365	95.03 123	83.65 511	89.88 197	95.30 204	94.79 329	53.64 522	99.39 54	91.99 146	98.79 180	98.54 149

thisisatest0530		88.69 373	87.52 387	92.20 276	96.33 254	79.36 380	92.81 229	84.01 508	86.44 300	93.67 285	92.68 415	53.62 523	99.25 89	89.65 238	98.45 234	98.00 213

XFeat-NN		75.97 508	74.88 510	79.25 525	77.98 554	79.81 360	70.81 545	79.50 542	64.75 539	86.32 486	82.83 535	53.44 524	76.70 550	66.89 529	91.40 516	81.23 545

FMVSNet5		87.82 397	86.56 420	91.62 307	92.31 436	79.81 360	93.49 193	94.81 343	83.26 382	91.36 381	96.93 170	52.77 525	97.49 351	76.07 466	98.03 291	97.55 281

nomal-1		83.48 466	81.65 475	88.98 421	91.07 478	80.73 336	85.66 482	86.34 479	80.98 424	83.93 510	86.95 505	51.44 526	91.71 493	74.53 484	93.93 481	94.49 445

pmmvs3		80.83 491	78.96 500	86.45 477	87.23 530	77.48 424	84.87 495	82.31 524	63.83 541	85.03 497	89.50 482	49.66 527	93.10 481	73.12 500	95.10 447	88.78 517

FBQ-MVS		83.72 462	81.80 473	89.47 405	93.62 401	76.73 440	91.20 309	87.89 466	81.52 417	84.88 500	83.74 526	49.19 528	96.66 409	70.51 519	93.70 486	95.00 426

IB-MVS	77.21 19	83.11 469	81.05 481	89.29 412	91.15 477	75.85 456	85.66 482	86.00 484	79.70 438	82.02 529	86.61 507	48.26 529	98.39 237	77.84 443	92.22 510	93.63 469
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
WBMVS		84.00 459	83.48 458	85.56 487	92.71 425	61.52 539	83.82 515	89.38 449	79.56 441	90.74 401	93.20 395	48.21 530	97.28 365	75.63 470	98.10 282	97.88 240

testing91		83.56 465	82.45 468	86.91 470	92.92 422	67.29 515	86.33 469	88.07 463	86.22 307	84.26 506	85.76 513	48.15 531	97.17 379	76.27 465	94.08 480	96.27 368

UWE-MVS-28		74.73 511	73.18 512	79.35 524	85.42 540	55.55 551	87.63 431	65.92 553	74.39 486	77.33 542	88.19 495	47.63 532	89.48 511	39.01 551	93.14 499	93.03 481

UBG		80.28 498	78.94 501	84.31 502	92.86 423	61.77 538	83.87 512	83.31 517	77.33 463	82.78 522	83.72 527	47.60 533	96.06 429	65.47 534	93.48 491	95.11 422

myMVS_eth3d28		80.97 489	80.42 490	82.62 514	93.35 409	58.25 548	84.70 500	85.62 491	86.31 304	84.04 508	85.20 520	46.00 534	94.07 473	62.93 539	95.65 423	95.53 408

testing99		82.94 472	81.72 474	86.59 473	92.55 429	66.53 521	86.08 475	85.70 487	85.47 338	83.95 509	85.70 514	45.87 535	97.07 387	76.58 461	93.56 489	96.17 377

testing3-2		83.95 460	84.22 450	83.13 512	96.28 259	54.34 554	88.51 423	83.01 519	92.19 111	89.09 445	90.98 461	45.51 536	97.44 354	74.38 488	98.01 294	97.60 275

testing11		81.98 482	80.52 489	86.38 480	92.69 426	67.13 516	85.79 478	84.80 501	82.16 405	81.19 535	85.41 518	45.24 537	96.88 398	74.14 492	93.24 495	95.14 419

gg-mvs-nofinetune		82.10 481	81.02 482	85.34 490	87.46 529	71.04 499	94.74 131	67.56 552	96.44 28	79.43 539	98.99 11	45.24 537	96.15 425	67.18 528	92.17 511	88.85 515

GG-mvs-BLEND				83.24 511	85.06 542	71.03 500	94.99 126	65.55 554		74.09 545	75.51 543	44.57 539	94.46 466	59.57 543	87.54 531	84.24 537

TESTMET0.1,1		79.09 504	78.04 506	82.25 515	87.52 528	64.03 534	83.08 517	80.62 536	70.28 519	80.16 537	83.22 533	44.13 540	90.56 502	79.95 420	93.36 492	92.15 491

UWE-MVS		80.29 497	79.10 498	83.87 506	91.97 452	59.56 544	86.50 468	77.43 548	75.40 478	87.79 473	88.10 496	44.08 541	96.90 397	64.23 535	96.36 396	95.14 419

test-mter		81.21 487	80.01 495	84.79 496	89.68 511	66.86 519	83.08 517	84.52 503	73.85 490	82.85 520	84.78 522	43.66 542	93.49 478	82.85 380	94.86 455	94.03 458

0.4-1-1-0.2		75.80 509	72.05 515	87.04 465	82.70 548	74.17 477	77.51 536	83.48 512	71.80 505	71.57 548	65.16 548	43.07 543	96.96 391	74.34 489	78.78 546	90.00 510

reproduce_monomvs		87.13 421	86.90 409	87.84 456	90.92 485	68.15 513	91.19 310	93.75 377	85.84 322	94.21 262	95.83 265	42.99 544	97.10 383	89.46 241	97.88 308	98.26 184

KD-MVS_2432*1600		82.17 479	80.75 485	86.42 478	82.04 549	70.09 505	81.75 525	90.80 439	82.56 396	90.37 411	89.30 484	42.90 545	96.11 427	74.47 486	92.55 507	93.06 478

miper_refine_blended		82.17 479	80.75 485	86.42 478	82.04 549	70.09 505	81.75 525	90.80 439	82.56 396	90.37 411	89.30 484	42.90 545	96.11 427	74.47 486	92.55 507	93.06 478

test2506		85.42 443	84.57 446	87.96 450	97.81 116	66.53 521	96.14 70	56.35 556	89.04 217	93.55 289	98.10 48	42.88 547	98.68 186	88.09 294	99.18 106	98.67 130

0.4-1-1-0.1		77.15 507	73.55 511	87.95 451	85.49 539	75.84 458	80.59 532	82.87 521	73.51 492	73.61 546	68.65 546	42.84 548	97.22 374	75.20 475	79.18 545	90.80 504

blend_shiyan4		83.29 468	80.66 487	91.19 337	91.86 454	79.59 368	87.05 448	93.91 375	82.66 395	89.60 433	83.36 529	42.82 549	98.10 280	81.45 403	73.26 549	95.87 392

ETVMVS		79.85 500	77.94 508	85.59 486	92.97 420	66.20 524	86.13 474	80.99 534	81.41 419	83.52 516	83.89 525	41.81 550	94.98 458	56.47 545	94.25 473	95.61 406

MVStest1		84.79 449	84.06 453	86.98 467	77.73 555	74.76 464	91.08 316	85.63 489	77.70 459	96.86 96	97.97 60	41.05 551	88.24 521	92.22 139	96.28 399	97.94 225

0.3-1-1-0.015		75.73 510	71.83 516	87.44 460	83.47 546	74.98 463	78.69 534	83.38 515	72.24 503	70.43 549	65.81 547	39.55 552	97.08 385	74.57 482	78.30 547	90.28 509

testing222		80.54 495	78.53 503	86.58 474	92.54 431	68.60 512	86.24 472	82.72 523	83.78 376	82.68 523	84.24 524	39.25 553	95.94 433	60.25 541	95.09 448	95.20 415

GLUNet-SfM		58.71 514	56.43 517	65.55 531	45.28 558	59.80 543	54.31 549	55.90 557	37.80 551	81.24 534	73.75 545	38.27 554	70.23 554	34.22 553	87.09 532	66.64 548

myMVS_eth3d		79.62 502	78.26 504	83.72 508	91.71 461	61.25 541	85.89 476	81.49 527	81.03 422	85.13 495	81.64 538	32.12 555	95.00 455	71.17 515	94.12 477	94.91 431

testing3		83.66 463	82.52 467	87.08 464	95.84 301	65.84 526	89.80 377	77.17 549	88.17 251	90.84 399	88.63 490	30.95 556	98.11 277	84.05 369	97.19 354	97.28 304

PDCNetPlus		79.66 501	78.21 505	84.01 505	79.49 552	73.91 479	75.29 540	96.44 271	66.51 532	89.20 440	91.98 443	30.56 557	84.51 544	75.48 471	98.93 149	93.62 470

dongtai		53.72 515	53.79 518	53.51 534	79.69 551	36.70 560	77.18 537	32.53 563	71.69 506	68.63 551	60.79 550	26.65 558	73.11 551	30.67 554	36.29 556	50.73 549

kuosan		43.63 517	44.25 521	41.78 535	66.04 557	34.37 561	75.56 539	32.62 562	53.25 550	50.46 555	51.18 551	25.28 559	49.13 555	13.44 557	30.41 557	41.84 551

MVS_clip		28.84 519	32.57 522	17.67 538	37.77 560	25.94 562	27.92 551	7.17 564	9.16 554	54.91 553	62.94 549	20.70 560	10.56 559	26.96 555	45.58 554	16.52 552

VLMVS_CLIP		26.72 520	28.23 524	22.16 536	23.46 561	19.29 564	25.04 552	38.45 561	10.30 553	37.65 557	43.37 553	16.55 561	34.48 557	19.59 556	39.68 555	12.71 554

VLMVS		7.75 525	8.50 530	5.52 539	7.85 564	5.47 566	5.34 553	3.06 565	0.41 560	11.88 559	15.91 556	11.95 562	3.89 560	3.42 559	16.65 559	7.20 555

MVS_baseline		9.63 522	12.05 525	2.37 540	9.15 563	0.73 569	5.23 554	1.75 567	0.31 561	26.23 558	30.60 554	5.95 563	0.00 563	4.43 558	24.78 558	6.38 556

test123		9.49 523	12.01 526	1.91 541	2.87 565	1.30 567	82.38 522	1.34 568	1.36 558	2.84 561	6.56 558	2.45 564	0.97 561	2.73 560	5.56 560	3.47 557

testmvs		9.02 524	11.42 527	1.81 542	2.77 566	1.13 568	79.44 533	1.90 566	1.18 559	2.65 562	6.80 557	1.95 565	0.87 562	2.62 561	3.45 561	3.44 558

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

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

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

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

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

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

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

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

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

ab-mvs-re		7.56 526	10.08 529	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	90.69 468	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

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

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
PatchmatchNet2					0.00 567	54.43 553	80.66 531	86.13 482	76.71 470
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														77.38 450	97.25 352	96.00 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.63 495
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
aaatest				95.52 89	98.69 37	88.21 161	96.32 56	98.58 18	88.79 226	97.38 66	96.22 236		99.39 54	92.89 118	99.10 115	98.96 77

WAC-MVS						61.25 541								74.55 483

FOURS1					99.21 3	94.68 16	98.45 4	98.81 10	97.73 9	98.27 24

MSC_two_6792asdad				95.90 69	96.54 225	89.57 124		96.87 234					99.41 43	94.06 68	99.30 80	98.72 121

No_MVS				95.90 69	96.54 225	89.57 124		96.87 234					99.41 43	94.06 68	99.30 80	98.72 121

eth-test2					0.00 567

eth-test					0.00 567

IU-MVS					98.51 58	86.66 204		96.83 239	72.74 500	95.83 166				93.00 114	99.29 83	98.64 138

save fliter					97.46 145	88.05 167	92.04 273	97.08 211	87.63 268

test_0728_SECOND				94.88 125	98.55 53	86.72 201	95.20 116	98.22 58					99.38 63	93.44 94	99.31 78	98.53 150

GSMVS																94.75 438

test_part2					98.21 84	89.41 129				96.72 106

MTGPA								97.62 154

MTMP							94.82 129	54.62 558

gm-plane-assit					87.08 533	59.33 545			71.22 509		83.58 528		97.20 376	73.95 494

test9_res														88.16 291	98.40 239	97.83 248

agg_prior2														87.06 315	98.36 250	97.98 217

agg_prior					96.20 269	88.89 142		96.88 233		90.21 415			98.78 164

test_prior4						89.91 119	90.74 330

test_prior				94.61 142	95.95 293	87.23 184		97.36 186					98.68 186			97.93 228

旧先验2							90.00 366		68.65 526	92.71 336			96.52 412	85.15 349

新几何2							90.02 365

无先验							89.94 367	95.75 301	70.81 515				98.59 201	81.17 409		94.81 434

原ACMM2							89.34 394

testdata2													98.03 292	80.24 416

testdata1							88.96 406		88.44 240

plane_prior7					97.71 125	88.68 146

plane_prior5								97.81 135					98.95 135	89.26 249	98.51 228	98.60 144

plane_prior4											95.59 281

plane_prior3						88.43 157			90.35 186	93.31 300

plane_prior2							94.56 143		91.74 136

plane_prior1					97.38 149

plane_prior						88.12 164	93.01 212		88.98 220						98.06 288

n2								0.00 569

nn								0.00 569

door-mid								92.13 420

test11								96.65 256

door								91.26 433

HQP5-MVS						84.89 249

HQP-NCC					96.36 248		91.37 302		87.16 283	88.81 449

ACMP_Plane					96.36 248		91.37 302		87.16 283	88.81 449

BP-MVS														86.55 325

HQP4-MVS										88.81 449			98.61 196			98.15 198

HQP3-MVS								97.31 191							97.73 317

NP-MVS					96.82 193	87.10 188					93.40 388

ACMMP++_ref															98.82 171

ACMMP++															99.25 91

High-resolution multi-view benchmark