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 1

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 28	93.86 31	99.07 2	98.98 6	97.01 13	98.92 4	98.78 14	95.22 40	98.61 170	96.85 3	99.77 9	99.31 28
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UniMVSNet_ETH3D		97.13 5	97.72 3	95.35 84	99.51 2	87.38 134	97.70 8	97.54 112	98.16 2	98.94 2	99.33 2	97.84 4	99.08 94	90.73 141	99.73 13	99.59 13

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 8	98.61 3	98.81 8	97.41 10	97.28 56	98.46 30	94.62 62	98.84 129	94.64 34	99.53 38	98.99 56

PS-CasMVS		96.69 20	97.43 5	94.49 128	99.13 6	84.09 205	96.61 32	97.97 77	97.91 5	98.64 13	98.13 41	95.24 38	99.65 3	93.39 72	99.84 3	99.72 2

DTE-MVSNet		96.74 17	97.43 5	94.67 113	99.13 6	84.68 194	96.51 35	97.94 83	98.14 3	98.67 12	98.32 34	95.04 48	99.69 2	93.27 77	99.82 7	99.62 10

ACMH	88.36 12	96.59 27	97.43 5	94.07 141	98.56 42	85.33 188	96.33 47	98.30 27	94.66 42	98.72 8	98.30 35	97.51 5	98.00 228	94.87 31	99.59 28	98.86 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		96.69 20	97.39 8	94.61 118	99.16 4	84.50 195	96.54 34	98.05 64	98.06 4	98.64 13	98.25 37	95.01 51	99.65 3	92.95 89	99.83 5	99.68 4

pmmvs6		96.80 12	97.36 9	95.15 97	99.12 8	87.82 129	96.68 30	97.86 85	96.10 27	98.14 24	99.28 3	97.94 3	98.21 209	91.38 129	99.69 14	99.42 19

v7n		96.82 9	97.31 10	95.33 86	98.54 48	86.81 148	96.83 23	98.07 60	96.59 20	98.46 17	98.43 32	92.91 102	99.52 19	96.25 12	99.76 10	99.65 8

UA-Net		97.35 4	97.24 11	97.69 4	98.22 74	93.87 30	98.42 6	98.19 39	96.95 14	95.46 144	99.23 4	93.45 82	99.57 14	95.34 30	99.89 2	99.63 9

Anonymous20231211		96.60 25	97.13 12	95.00 100	97.46 130	86.35 164	97.11 19	98.24 34	97.58 8	98.72 8	98.97 7	93.15 94	99.15 84	93.18 80	99.74 12	99.50 17

WR-MVS_H		96.60 25	97.05 13	95.24 92	99.02 12	86.44 160	96.78 27	98.08 57	97.42 9	98.48 16	97.86 62	91.76 128	99.63 6	94.23 42	99.84 3	99.66 6

HPM-MVS_fast		97.01 6	96.89 14	97.39 21	99.12 8	93.92 28	97.16 14	98.17 45	93.11 74	96.48 90	97.36 94	96.92 6	99.34 63	94.31 40	99.38 59	98.92 72

ACMH+	88.43 11	96.48 30	96.82 15	95.47 81	98.54 48	89.06 101	95.65 79	98.61 12	96.10 27	98.16 23	97.52 81	96.90 7	98.62 169	90.30 155	99.60 26	98.72 97

CP-MVSNet		96.19 45	96.80 16	94.38 133	98.99 16	83.82 208	96.31 50	97.53 114	97.60 7	98.34 19	97.52 81	91.98 122	99.63 6	93.08 85	99.81 8	99.70 3

OurMVSNet-221017-0		96.80 12	96.75 17	96.96 35	99.03 11	91.85 57	97.98 7	98.01 72	94.15 51	98.93 3	99.07 5	88.07 188	99.57 14	95.86 15	99.69 14	99.46 18

mvs_tets		96.83 8	96.71 18	97.17 27	98.83 25	92.51 48	96.58 33	97.61 107	87.57 207	98.80 7	98.90 9	96.50 9	99.59 13	96.15 13	99.47 43	99.40 21

RE-MVS-def			96.66 19		98.07 83	95.27 9	96.37 44	98.12 51	95.66 33	97.00 68	97.03 123	95.40 29		93.49 62	98.84 133	98.00 161

APD-MVS_3200maxsize		96.82 9	96.65 20	97.32 25	97.95 95	93.82 33	96.31 50	98.25 31	95.51 35	96.99 70	97.05 122	95.63 23	99.39 49	93.31 74	98.88 128	98.75 92

APDe-MVS		96.46 31	96.64 21	95.93 62	97.68 116	89.38 95	96.90 22	98.41 19	92.52 83	97.43 48	97.92 58	95.11 45	99.50 21	94.45 36	99.30 71	98.92 72
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
HPM-MVS		96.81 11	96.62 22	97.36 23	98.89 20	93.53 38	97.51 10	98.44 16	92.35 88	95.95 116	96.41 162	96.71 8	99.42 33	93.99 47	99.36 60	99.13 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	91.06 5	96.75 16	96.62 22	97.13 28	98.38 63	94.31 17	96.79 26	98.32 24	96.69 17	96.86 75	97.56 76	95.48 27	98.77 146	90.11 164	99.44 50	98.31 135
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS-dyc-post		96.84 7	96.60 24	97.56 10	98.07 83	95.27 9	96.37 44	98.12 51	95.66 33	97.00 68	97.03 123	94.85 56	99.42 33	93.49 62	98.84 133	98.00 161

nrg030		96.32 40	96.55 25	95.62 76	97.83 102	88.55 115	95.77 74	98.29 30	92.68 79	98.03 26	97.91 59	95.13 43	98.95 114	93.85 50	99.49 42	99.36 24

testf1		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 22	94.96 38	97.30 54	97.93 55	96.05 16	97.90 235	89.32 180	99.23 86	98.19 144

APD_test2		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 22	94.96 38	97.30 54	97.93 55	96.05 16	97.90 235	89.32 180	99.23 86	98.19 144

test_djsdf		96.62 23	96.49 26	97.01 32	98.55 45	91.77 59	97.15 15	97.37 123	88.98 174	98.26 22	98.86 10	93.35 87	99.60 9	96.41 9	99.45 47	99.66 6

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 85	94.69 11	96.13 59	98.07 60	95.17 37	96.82 77	96.73 146	95.09 47	99.43 32	92.99 88	98.71 151	98.50 122

anonymousdsp		96.74 17	96.42 29	97.68 6	98.00 91	94.03 25	96.97 20	97.61 107	87.68 205	98.45 18	98.77 15	94.20 72	99.50 21	96.70 5	99.40 57	99.53 15

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 31	92.49 49	96.44 41	97.42 121	86.96 216	98.71 10	98.72 17	95.36 32	99.56 17	95.92 14	99.45 47	99.32 27

SED-MVS		96.00 51	96.41 32	94.76 109	98.51 51	86.97 144	95.21 94	98.10 54	91.95 98	97.63 35	97.25 104	96.48 10	99.35 60	93.29 75	99.29 74	97.95 169

MTAPA		96.65 22	96.38 33	97.47 15	98.95 18	94.05 23	95.88 70	97.62 105	94.46 47	96.29 99	96.94 129	93.56 79	99.37 57	94.29 41	99.42 52	98.99 56

DVP-MVS++		95.93 52	96.34 34	94.70 112	96.54 179	86.66 154	98.45 4	98.22 36	93.26 71	97.54 40	97.36 94	93.12 95	99.38 55	93.88 48	98.68 155	98.04 156

ACMMP		96.61 24	96.34 34	97.43 18	98.61 38	93.88 29	96.95 21	98.18 41	92.26 91	96.33 95	96.84 137	95.10 46	99.40 46	93.47 65	99.33 66	99.02 53
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
SteuartSystems-ACMMP		96.40 37	96.30 36	96.71 40	98.63 35	91.96 55	95.70 76	98.01 72	93.34 70	96.64 85	96.57 155	94.99 52	99.36 58	93.48 64	99.34 64	98.82 82
Skip Steuart: Steuart Systems R&D Blog.
ANet_high		94.83 100	96.28 37	90.47 273	96.65 170	73.16 350	94.33 128	98.74 11	96.39 24	98.09 25	98.93 8	93.37 86	98.70 159	90.38 150	99.68 18	99.53 15

TranMVSNet+NR-MVSNet		96.07 49	96.26 38	95.50 80	98.26 71	87.69 131	93.75 150	97.86 85	95.96 32	97.48 46	97.14 114	95.33 34	99.44 29	90.79 139	99.76 10	99.38 22

LPG-MVS_test		96.38 39	96.23 39	96.84 38	98.36 66	92.13 52	95.33 90	98.25 31	91.78 111	97.07 63	97.22 108	96.38 12	99.28 72	92.07 108	99.59 28	99.11 44

test_0402		95.73 61	96.22 40	94.26 135	98.19 76	85.77 179	93.24 166	97.24 139	96.88 16	97.69 33	97.77 65	94.12 73	99.13 88	91.54 126	99.29 74	97.88 177

ZNCC-MVS		96.42 35	96.20 41	97.07 30	98.80 30	92.79 46	96.08 61	98.16 48	91.74 115	95.34 151	96.36 170	95.68 21	99.44 29	94.41 38	99.28 79	98.97 62

DVP-MVS		95.82 58	96.18 42	94.72 111	98.51 51	86.69 152	95.20 96	97.00 155	91.85 104	97.40 52	97.35 97	95.58 24	99.34 63	93.44 68	99.31 69	98.13 150
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
XVS		96.49 29	96.18 42	97.44 16	98.56 42	93.99 26	96.50 36	97.95 80	94.58 43	94.38 189	96.49 157	94.56 64	99.39 49	93.57 58	99.05 106	98.93 68

HFP-MVS		96.39 38	96.17 44	97.04 31	98.51 51	93.37 39	96.30 54	97.98 75	92.35 88	95.63 134	96.47 158	95.37 30	99.27 74	93.78 52	99.14 99	98.48 125

ACMMPR		96.46 31	96.14 45	97.41 20	98.60 39	93.82 33	96.30 54	97.96 78	92.35 88	95.57 137	96.61 153	94.93 54	99.41 39	93.78 52	99.15 98	99.00 54

ACMMP_NAP		96.21 44	96.12 46	96.49 48	98.90 19	91.42 63	94.57 119	98.03 69	90.42 148	96.37 93	97.35 97	95.68 21	99.25 75	94.44 37	99.34 64	98.80 86

test_fmvsmconf0.01_n		95.90 54	96.09 47	95.31 89	97.30 137	89.21 97	94.24 132	98.76 10	86.25 223	97.56 39	98.66 18	95.73 19	98.44 190	97.35 2	98.99 113	98.27 138

region2R		96.41 36	96.09 47	97.38 22	98.62 36	93.81 35	96.32 49	97.96 78	92.26 91	95.28 155	96.57 155	95.02 50	99.41 39	93.63 56	99.11 101	98.94 66

CP-MVS		96.44 34	96.08 49	97.54 11	98.29 68	94.62 14	96.80 25	98.08 57	92.67 81	95.08 167	96.39 167	94.77 58	99.42 33	93.17 81	99.44 50	98.58 119

ACMM	88.83 9	96.30 42	96.07 50	96.97 34	98.39 62	92.95 44	94.74 111	98.03 69	90.82 137	97.15 59	96.85 135	96.25 14	99.00 106	93.10 83	99.33 66	98.95 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS		96.46 31	96.05 51	97.69 4	98.62 36	94.65 13	96.45 39	97.74 98	92.59 82	95.47 142	96.68 149	94.50 66	99.42 33	93.10 83	99.26 82	98.99 56

PS-MVSNAJss		96.01 50	96.04 52	95.89 67	98.82 26	88.51 116	95.57 84	97.88 84	88.72 180	98.81 6	98.86 10	90.77 151	99.60 9	95.43 27	99.53 38	99.57 14

TransMVSNet (Re)		95.27 87	96.04 52	92.97 181	98.37 65	81.92 233	95.07 101	96.76 176	93.97 55	97.77 31	98.57 23	95.72 20	97.90 235	88.89 197	99.23 86	99.08 48

GST-MVS		96.24 43	95.99 54	97.00 33	98.65 34	92.71 47	95.69 78	98.01 72	92.08 96	95.74 129	96.28 176	95.22 40	99.42 33	93.17 81	99.06 103	98.88 77

pm-mvs1		95.43 73	95.94 55	93.93 148	98.38 63	85.08 191	95.46 87	97.12 148	91.84 107	97.28 56	98.46 30	95.30 36	97.71 260	90.17 162	99.42 52	98.99 56

PGM-MVS		96.32 40	95.94 55	97.43 18	98.59 41	93.84 32	95.33 90	98.30 27	91.40 124	95.76 126	96.87 134	95.26 37	99.45 27	92.77 91	99.21 90	99.00 54

tt0805		95.42 76	95.93 57	93.86 152	98.75 32	88.47 117	97.68 9	94.29 270	96.48 21	95.38 147	93.63 283	94.89 55	97.94 234	95.38 28	96.92 269	95.17 307

MP-MVS-pluss		96.08 48	95.92 58	96.57 44	99.06 10	91.21 65	93.25 165	98.32 24	87.89 198	96.86 75	97.38 90	95.55 26	99.39 49	95.47 25	99.47 43	99.11 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		95.88 56	95.88 59	95.87 68	98.12 79	89.65 87	95.58 83	98.56 14	91.84 107	96.36 94	96.68 149	94.37 70	99.32 69	92.41 101	99.05 106	98.64 112

DPE-MVS		95.89 55	95.88 59	95.92 64	97.93 96	89.83 85	93.46 159	98.30 27	92.37 86	97.75 32	96.95 128	95.14 42	99.51 20	91.74 118	99.28 79	98.41 129
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
FC-MVSNet-test		95.32 81	95.88 59	93.62 159	98.49 58	81.77 234	95.90 69	98.32 24	93.93 56	97.53 42	97.56 76	88.48 181	99.40 46	92.91 90	99.83 5	99.68 4

DP-MVS		95.62 64	95.84 62	94.97 101	97.16 144	88.62 111	94.54 123	97.64 103	96.94 15	96.58 88	97.32 101	93.07 98	98.72 152	90.45 147	98.84 133	97.57 204

Anonymous20240529		95.50 70	95.83 63	94.50 126	97.33 136	85.93 174	95.19 98	96.77 175	96.64 19	97.61 38	98.05 46	93.23 91	98.79 140	88.60 203	99.04 111	98.78 88

LS3D		96.11 47	95.83 63	96.95 36	94.75 277	94.20 19	97.34 13	97.98 75	97.31 11	95.32 152	96.77 139	93.08 97	99.20 80	91.79 117	98.16 206	97.44 214

Gipuma		95.31 84	95.80 65	93.81 155	97.99 94	90.91 70	96.42 42	97.95 80	96.69 17	91.78 272	98.85 12	91.77 126	95.49 341	91.72 119	99.08 102	95.02 315
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
3Dnovator+	92.74 2	95.86 57	95.77 66	96.13 53	96.81 163	90.79 73	96.30 54	97.82 90	96.13 26	94.74 180	97.23 106	91.33 135	99.16 83	93.25 78	98.30 192	98.46 126

SD-MVS		95.19 88	95.73 67	93.55 162	96.62 174	88.88 107	94.67 113	98.05 64	91.26 126	97.25 58	96.40 163	95.42 28	94.36 361	92.72 95	99.19 92	97.40 218
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
test_fmvsmconf0.1_n		95.61 65	95.72 68	95.26 90	96.85 159	89.20 98	93.51 157	98.60 13	85.68 236	97.42 50	98.30 35	95.34 33	98.39 191	96.85 3	98.98 114	98.19 144

MP-MVS		96.14 46	95.68 69	97.51 13	98.81 28	94.06 21	96.10 60	97.78 96	92.73 78	93.48 214	96.72 147	94.23 71	99.42 33	91.99 110	99.29 74	99.05 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
VPA-MVSNet		95.14 89	95.67 70	93.58 161	97.76 106	83.15 218	94.58 118	97.58 109	93.39 68	97.05 66	98.04 48	93.25 90	98.51 182	89.75 174	99.59 28	99.08 48

casdiffmvs_mvg		95.10 90	95.62 71	93.53 165	96.25 205	83.23 215	92.66 185	98.19 39	93.06 75	97.49 44	97.15 113	94.78 57	98.71 158	92.27 103	98.72 149	98.65 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		95.44 72	95.62 71	94.89 103	96.93 154	87.69 131	96.48 38	99.14 4	93.93 56	92.77 243	94.52 255	93.95 76	99.49 24	93.62 57	99.22 89	97.51 209

CS-MVS		95.77 59	95.58 73	96.37 50	96.84 160	91.72 61	96.73 29	99.06 5	94.23 49	92.48 252	94.79 245	93.56 79	99.49 24	93.47 65	99.05 106	97.89 176

SMA-MVS		95.77 59	95.54 74	96.47 49	98.27 70	91.19 66	95.09 99	97.79 95	86.48 219	97.42 50	97.51 84	94.47 69	99.29 70	93.55 60	99.29 74	98.93 68
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_fmvsmconf_n		95.43 73	95.50 75	95.22 94	96.48 186	89.19 99	93.23 167	98.36 21	85.61 239	96.92 73	98.02 50	95.23 39	98.38 194	96.69 6	98.95 123	98.09 152

Vis-MVSNet		95.50 70	95.48 76	95.56 79	98.11 80	89.40 94	95.35 88	98.22 36	92.36 87	94.11 192	98.07 45	92.02 120	99.44 29	93.38 73	97.67 239	97.85 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		95.61 65	95.45 77	96.08 54	98.49 58	91.00 68	92.65 186	97.33 131	90.05 153	96.77 80	96.85 135	95.04 48	98.56 177	92.77 91	99.06 103	98.70 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MIMVSNet1		95.52 69	95.45 77	95.72 73	99.14 5	89.02 102	96.23 57	96.87 167	93.73 60	97.87 28	98.49 29	90.73 155	99.05 99	86.43 243	99.60 26	99.10 47

ACMP	88.15 13	95.71 62	95.43 79	96.54 45	98.17 77	91.73 60	94.24 132	98.08 57	89.46 163	96.61 87	96.47 158	95.85 18	99.12 91	90.45 147	99.56 36	98.77 91
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
APD_test1		95.91 53	95.42 80	97.36 23	98.82 26	96.62 6	95.64 80	97.64 103	93.38 69	95.89 121	97.23 106	93.35 87	97.66 263	88.20 206	98.66 159	97.79 188

test_fmvsmvis_n_1920		95.08 91	95.40 81	94.13 139	96.66 169	87.75 130	93.44 161	98.49 15	85.57 241	98.27 20	97.11 117	94.11 74	97.75 256	96.26 11	98.72 149	96.89 241

mvsmamba		95.61 65	95.40 81	96.22 51	98.44 60	89.86 84	97.14 17	97.45 120	91.25 128	97.49 44	98.14 39	83.49 242	99.45 27	95.52 22	99.66 21	99.36 24

FIs		94.90 97	95.35 83	93.55 162	98.28 69	81.76 235	95.33 90	98.14 49	93.05 76	97.07 63	97.18 111	87.65 195	99.29 70	91.72 119	99.69 14	99.61 11

XVG-ACMP-BASELINE		95.68 63	95.34 84	96.69 41	98.40 61	93.04 41	94.54 123	98.05 64	90.45 147	96.31 97	96.76 141	92.91 102	98.72 152	91.19 130	99.42 52	98.32 133

DeepC-MVS	91.39 4	95.43 73	95.33 85	95.71 74	97.67 117	90.17 80	93.86 147	98.02 71	87.35 209	96.22 105	97.99 53	94.48 68	99.05 99	92.73 94	99.68 18	97.93 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PMVS	87.21 14	94.97 94	95.33 85	93.91 149	98.97 17	97.16 2	95.54 85	95.85 220	96.47 22	93.40 217	97.46 87	95.31 35	95.47 342	86.18 247	98.78 144	89.11 384
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v8		94.65 107	95.29 87	92.74 192	96.65 170	79.77 269	94.59 116	97.17 143	91.86 103	97.47 47	97.93 55	88.16 186	99.08 94	94.32 39	99.47 43	99.38 22

NR-MVSNet		95.28 85	95.28 88	95.26 90	97.75 107	87.21 138	95.08 100	97.37 123	93.92 58	97.65 34	95.90 193	90.10 168	99.33 68	90.11 164	99.66 21	99.26 30

v10		94.68 106	95.27 89	92.90 187	96.57 176	80.15 254	94.65 115	97.57 110	90.68 141	97.43 48	98.00 51	88.18 185	99.15 84	94.84 32	99.55 37	99.41 20

UniMVSNet_NR-MVSNet		95.35 79	95.21 90	95.76 71	97.69 115	88.59 113	92.26 208	97.84 88	94.91 40	96.80 78	95.78 202	90.42 160	99.41 39	91.60 123	99.58 33	99.29 29

SixPastTwentyTwo		94.91 96	95.21 90	93.98 143	98.52 50	83.19 217	95.93 67	94.84 256	94.86 41	98.49 15	98.74 16	81.45 266	99.60 9	94.69 33	99.39 58	99.15 39

RRT_MVS		95.41 77	95.20 92	96.05 55	98.86 22	88.92 104	97.49 11	94.48 266	93.12 73	97.94 27	98.54 25	81.19 272	99.63 6	95.48 24	99.69 14	99.60 12

UniMVSNet (Re)		95.32 81	95.15 93	95.80 70	97.79 105	88.91 105	92.91 176	98.07 60	93.46 67	96.31 97	95.97 192	90.14 165	99.34 63	92.11 105	99.64 24	99.16 38

FMVSNet1		94.84 99	95.13 94	93.97 144	97.60 120	84.29 198	95.99 63	96.56 188	92.38 85	97.03 67	98.53 26	90.12 166	98.98 107	88.78 199	99.16 97	98.65 107

DU-MVS		95.28 85	95.12 95	95.75 72	97.75 107	88.59 113	92.58 188	97.81 91	93.99 53	96.80 78	95.90 193	90.10 168	99.41 39	91.60 123	99.58 33	99.26 30

CS-MVS-test		95.32 81	95.10 96	95.96 58	96.86 158	90.75 74	96.33 47	99.20 2	93.99 53	91.03 285	93.73 281	93.52 81	99.55 18	91.81 116	99.45 47	97.58 203

Baseline_NR-MVSNet		94.47 113	95.09 97	92.60 201	98.50 57	80.82 250	92.08 212	96.68 180	93.82 59	96.29 99	98.56 24	90.10 168	97.75 256	90.10 166	99.66 21	99.24 32

SDMVSNet		94.43 114	95.02 98	92.69 194	97.93 96	82.88 223	91.92 221	95.99 216	93.65 65	95.51 139	98.63 20	94.60 63	96.48 316	87.57 221	99.35 61	98.70 101

dcpmvs_2		93.96 135	95.01 99	90.82 265	97.60 120	74.04 345	93.68 154	98.85 7	89.80 158	97.82 29	97.01 126	91.14 145	99.21 78	90.56 145	98.59 164	99.19 36

XVG-OURS-SEG-HR		95.38 78	95.00 100	96.51 46	98.10 81	94.07 20	92.46 194	98.13 50	90.69 140	93.75 206	96.25 179	98.03 2	97.02 297	92.08 107	95.55 301	98.45 127

3Dnovator	92.54 3	94.80 101	94.90 101	94.47 129	95.47 255	87.06 142	96.63 31	97.28 137	91.82 110	94.34 191	97.41 88	90.60 158	98.65 167	92.47 100	98.11 210	97.70 196

RPSCF		95.58 68	94.89 102	97.62 7	97.58 122	96.30 7	95.97 66	97.53 114	92.42 84	93.41 215	97.78 63	91.21 140	97.77 253	91.06 132	97.06 261	98.80 86

tfpnnormal		94.27 121	94.87 103	92.48 205	97.71 112	80.88 249	94.55 122	95.41 240	93.70 61	96.67 84	97.72 66	91.40 134	98.18 213	87.45 223	99.18 94	98.36 131

9.14			94.81 104		97.49 127		94.11 139	98.37 20	87.56 208	95.38 147	96.03 189	94.66 60	99.08 94	90.70 142	98.97 119

casdiffmvs		94.32 120	94.80 105	92.85 189	96.05 221	81.44 241	92.35 201	98.05 64	91.53 122	95.75 128	96.80 138	93.35 87	98.49 183	91.01 135	98.32 191	98.64 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		94.26 123	94.80 105	92.64 196	96.08 219	80.99 247	93.69 153	98.04 68	90.80 138	94.89 174	96.32 172	93.19 92	98.48 187	91.68 121	98.51 173	98.43 128

TSAR-MVS + MP.		94.96 95	94.75 107	95.57 78	98.86 22	88.69 108	96.37 44	96.81 171	85.23 246	94.75 179	97.12 116	91.85 124	99.40 46	93.45 67	98.33 189	98.62 116
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		94.69 105	94.75 107	94.52 125	97.55 124	87.87 127	95.01 104	97.57 110	92.68 79	96.20 107	93.44 289	91.92 123	98.78 143	89.11 191	99.24 85	96.92 239

test_fmvsm_n_1920		94.72 103	94.74 109	94.67 113	96.30 200	88.62 111	93.19 168	98.07 60	85.63 238	97.08 62	97.35 97	90.86 148	97.66 263	95.70 16	98.48 176	97.74 194

KD-MVS_self_test		94.10 130	94.73 110	92.19 212	97.66 118	79.49 275	94.86 108	97.12 148	89.59 162	96.87 74	97.65 70	90.40 162	98.34 199	89.08 192	99.35 61	98.75 92

canonicalmvs		94.59 108	94.69 111	94.30 134	95.60 252	87.03 143	95.59 81	98.24 34	91.56 121	95.21 161	92.04 321	94.95 53	98.66 165	91.45 127	97.57 243	97.20 228

APD-MVS		95.00 93	94.69 111	95.93 62	97.38 132	90.88 71	94.59 116	97.81 91	89.22 170	95.46 144	96.17 184	93.42 85	99.34 63	89.30 182	98.87 131	97.56 206
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GeoE		94.55 110	94.68 113	94.15 137	97.23 139	85.11 190	94.14 138	97.34 130	88.71 181	95.26 156	95.50 214	94.65 61	99.12 91	90.94 136	98.40 179	98.23 140

EG-PatchMatch MVS		94.54 111	94.67 114	94.14 138	97.87 101	86.50 156	92.00 216	96.74 177	88.16 194	96.93 72	97.61 73	93.04 99	97.90 235	91.60 123	98.12 209	98.03 159

MSP-MVS		95.34 80	94.63 115	97.48 14	98.67 33	94.05 23	96.41 43	98.18 41	91.26 126	95.12 163	95.15 228	86.60 217	99.50 21	93.43 71	96.81 273	98.89 75
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
LCM-MVSNet-Re		94.20 127	94.58 116	93.04 178	95.91 232	83.13 219	93.79 149	99.19 3	92.00 97	98.84 5	98.04 48	93.64 78	99.02 104	81.28 298	98.54 169	96.96 238

AllTest		94.88 98	94.51 117	96.00 56	98.02 89	92.17 50	95.26 93	98.43 17	90.48 145	95.04 168	96.74 144	92.54 111	97.86 243	85.11 260	98.98 114	97.98 165

fmvsm_s_conf0.1_n		94.19 129	94.41 118	93.52 167	97.22 141	84.37 196	93.73 151	95.26 245	84.45 261	95.76 126	98.00 51	91.85 124	97.21 285	95.62 18	97.82 231	98.98 60

sd_testset		93.94 136	94.39 119	92.61 200	97.93 96	83.24 214	93.17 169	95.04 250	93.65 65	95.51 139	98.63 20	94.49 67	95.89 334	81.72 294	99.35 61	98.70 101

HPM-MVS++		95.02 92	94.39 119	96.91 37	97.88 99	93.58 37	94.09 140	96.99 157	91.05 132	92.40 257	95.22 227	91.03 147	99.25 75	92.11 105	98.69 154	97.90 174

fmvsm_s_conf0.1_n_a		94.26 123	94.37 121	93.95 147	97.36 134	85.72 181	94.15 136	95.44 237	83.25 273	95.51 139	98.05 46	92.54 111	97.19 288	95.55 21	97.46 248	98.94 66

VDD-MVS		94.37 116	94.37 121	94.40 132	97.49 127	86.07 172	93.97 144	93.28 289	94.49 45	96.24 103	97.78 63	87.99 191	98.79 140	88.92 195	99.14 99	98.34 132

IS-MVSNet		94.49 112	94.35 123	94.92 102	98.25 73	86.46 159	97.13 18	94.31 269	96.24 25	96.28 101	96.36 170	82.88 250	99.35 60	88.19 207	99.52 41	98.96 64

CNVR-MVS		94.58 109	94.29 124	95.46 82	96.94 152	89.35 96	91.81 229	96.80 172	89.66 160	93.90 204	95.44 217	92.80 106	98.72 152	92.74 93	98.52 171	98.32 133

EI-MVSNet-Vis-set		94.36 117	94.28 125	94.61 118	92.55 326	85.98 173	92.44 196	94.69 262	93.70 61	96.12 111	95.81 198	91.24 138	98.86 126	93.76 55	98.22 201	98.98 60

IterMVS-LS		93.78 141	94.28 125	92.27 209	96.27 202	79.21 282	91.87 225	96.78 173	91.77 113	96.57 89	97.07 120	87.15 204	98.74 150	91.99 110	99.03 112	98.86 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet-UG-set		94.35 118	94.27 127	94.59 122	92.46 329	85.87 176	92.42 198	94.69 262	93.67 64	96.13 110	95.84 197	91.20 141	98.86 126	93.78 52	98.23 199	99.03 52

VDDNet		94.03 132	94.27 127	93.31 173	98.87 21	82.36 229	95.51 86	91.78 318	97.19 12	96.32 96	98.60 22	84.24 238	98.75 147	87.09 230	98.83 138	98.81 84

fmvsm_s_conf0.5_n		94.00 134	94.20 129	93.42 171	96.69 167	84.37 196	93.38 163	95.13 248	84.50 260	95.40 146	97.55 80	91.77 126	97.20 286	95.59 19	97.79 232	98.69 104

bld_raw_dy_0_64		94.27 121	94.15 130	94.65 116	98.55 45	86.28 166	95.80 73	95.55 233	88.41 188	97.09 61	98.08 44	78.69 286	98.87 125	95.63 17	99.53 38	98.81 84

MM		94.41 115	94.14 131	95.22 94	95.84 235	87.21 138	94.31 131	90.92 326	94.48 46	92.80 241	97.52 81	85.27 230	99.49 24	96.58 8	99.57 35	98.97 62

XVG-OURS		94.72 103	94.12 132	96.50 47	98.00 91	94.23 18	91.48 235	98.17 45	90.72 139	95.30 153	96.47 158	87.94 192	96.98 298	91.41 128	97.61 242	98.30 136

CPTT-MVS		94.74 102	94.12 132	96.60 43	98.15 78	93.01 42	95.84 71	97.66 102	89.21 171	93.28 221	95.46 215	88.89 179	98.98 107	89.80 171	98.82 139	97.80 187

fmvsm_s_conf0.5_n_a		94.02 133	94.08 134	93.84 153	96.72 166	85.73 180	93.65 155	95.23 246	83.30 271	95.13 162	97.56 76	92.22 116	97.17 289	95.51 23	97.41 250	98.64 112

HQP_MVS		94.26 123	93.93 135	95.23 93	97.71 112	88.12 122	94.56 120	97.81 91	91.74 115	93.31 218	95.59 209	86.93 209	98.95 114	89.26 186	98.51 173	98.60 117

MSLP-MVS++		93.25 156	93.88 136	91.37 241	96.34 195	82.81 224	93.11 170	97.74 98	89.37 166	94.08 194	95.29 226	90.40 162	96.35 323	90.35 152	98.25 197	94.96 316

fmvsm_l_conf0.5_n		93.79 140	93.81 137	93.73 156	96.16 211	86.26 167	92.46 194	96.72 178	81.69 295	95.77 125	97.11 117	90.83 150	97.82 246	95.58 20	97.99 221	97.11 230

v1144		93.50 146	93.81 137	92.57 202	96.28 201	79.61 272	91.86 227	96.96 158	86.95 217	95.91 119	96.32 172	87.65 195	98.96 112	93.51 61	98.88 128	99.13 41

PHI-MVS		94.34 119	93.80 139	95.95 59	95.65 248	91.67 62	94.82 109	97.86 85	87.86 199	93.04 233	94.16 266	91.58 130	98.78 143	90.27 157	98.96 121	97.41 215

v1192		93.49 147	93.78 140	92.62 199	96.16 211	79.62 271	91.83 228	97.22 141	86.07 228	96.10 112	96.38 168	87.22 202	99.02 104	94.14 44	98.88 128	99.22 33

VPNet		93.08 160	93.76 141	91.03 255	98.60 39	75.83 331	91.51 234	95.62 225	91.84 107	95.74 129	97.10 119	89.31 176	98.32 200	85.07 262	99.06 103	98.93 68

WR-MVS		93.49 147	93.72 142	92.80 191	97.57 123	80.03 260	90.14 273	95.68 224	93.70 61	96.62 86	95.39 222	87.21 203	99.04 102	87.50 222	99.64 24	99.33 26

v1240		93.29 152	93.71 143	92.06 219	96.01 226	77.89 301	91.81 229	97.37 123	85.12 250	96.69 83	96.40 163	86.67 215	99.07 98	94.51 35	98.76 146	99.22 33

OMC-MVS		94.22 126	93.69 144	95.81 69	97.25 138	91.27 64	92.27 207	97.40 122	87.10 215	94.56 184	95.42 218	93.74 77	98.11 218	86.62 237	98.85 132	98.06 153

MVS_0304		93.92 137	93.68 145	94.64 117	95.94 231	85.83 178	94.34 127	88.14 343	92.98 77	91.09 284	97.68 67	86.73 214	99.36 58	96.64 7	99.59 28	98.72 97

EPP-MVSNet		93.91 138	93.68 145	94.59 122	98.08 82	85.55 185	97.44 12	94.03 275	94.22 50	94.94 171	96.19 181	82.07 261	99.57 14	87.28 227	98.89 126	98.65 107

fmvsm_l_conf0.5_n_a		93.59 145	93.63 147	93.49 169	96.10 217	85.66 183	92.32 203	96.57 187	81.32 298	95.63 134	97.14 114	90.19 164	97.73 259	95.37 29	98.03 217	97.07 231

v2v482		93.29 152	93.63 147	92.29 208	96.35 194	78.82 289	91.77 231	96.28 200	88.45 186	95.70 133	96.26 178	86.02 223	98.90 118	93.02 86	98.81 141	99.14 40

v1921920		93.26 154	93.61 149	92.19 212	96.04 225	78.31 295	91.88 224	97.24 139	85.17 248	96.19 109	96.19 181	86.76 213	99.05 99	94.18 43	98.84 133	99.22 33

V42		93.43 149	93.58 150	92.97 181	95.34 261	81.22 244	92.67 184	96.49 193	87.25 211	96.20 107	96.37 169	87.32 201	98.85 128	92.39 102	98.21 202	98.85 81

Anonymous20240521		92.86 169	93.57 151	90.74 267	96.57 176	75.50 333	94.15 136	95.60 226	89.38 165	95.90 120	97.90 61	80.39 276	97.96 232	92.60 98	99.68 18	98.75 92

DeepPCF-MVS	90.46 6	94.20 127	93.56 152	96.14 52	95.96 228	92.96 43	89.48 293	97.46 118	85.14 249	96.23 104	95.42 218	93.19 92	98.08 220	90.37 151	98.76 146	97.38 221

v144192		93.20 159	93.54 153	92.16 216	96.05 221	78.26 296	91.95 217	97.14 145	84.98 254	95.96 115	96.11 185	87.08 206	99.04 102	93.79 51	98.84 133	99.17 37

NCCC		94.08 131	93.54 153	95.70 75	96.49 184	89.90 83	92.39 200	96.91 164	90.64 142	92.33 263	94.60 252	90.58 159	98.96 112	90.21 161	97.70 237	98.23 140

DeepC-MVS_fast	89.96 7	93.73 142	93.44 155	94.60 121	96.14 214	87.90 126	93.36 164	97.14 145	85.53 242	93.90 204	95.45 216	91.30 137	98.59 174	89.51 177	98.62 160	97.31 224
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		93.63 144	93.42 156	94.26 135	96.65 170	86.96 146	89.30 300	96.23 204	88.36 190	93.57 212	94.60 252	93.45 82	97.77 253	90.23 160	98.38 183	98.03 159

v148		92.87 168	93.29 157	91.62 233	96.25 205	77.72 304	91.28 240	95.05 249	89.69 159	95.93 118	96.04 188	87.34 200	98.38 194	90.05 167	97.99 221	98.78 88

MVS_Test		92.57 179	93.29 157	90.40 276	93.53 308	75.85 329	92.52 190	96.96 158	88.73 179	92.35 260	96.70 148	90.77 151	98.37 198	92.53 99	95.49 303	96.99 237

MVS_111021_LR		93.66 143	93.28 159	94.80 107	96.25 205	90.95 69	90.21 270	95.43 239	87.91 196	93.74 208	94.40 257	92.88 104	96.38 321	90.39 149	98.28 193	97.07 231

K. test v3		93.37 150	93.27 160	93.66 158	98.05 85	82.62 225	94.35 126	86.62 356	96.05 29	97.51 43	98.85 12	76.59 312	99.65 3	93.21 79	98.20 204	98.73 96

EI-MVSNet		92.99 163	93.26 161	92.19 212	92.12 338	79.21 282	92.32 203	94.67 264	91.77 113	95.24 159	95.85 195	87.14 205	98.49 183	91.99 110	98.26 195	98.86 78

XXY-MVS		92.58 177	93.16 162	90.84 264	97.75 107	79.84 265	91.87 225	96.22 206	85.94 230	95.53 138	97.68 67	92.69 108	94.48 357	83.21 277	97.51 244	98.21 142

SSC-MVS		90.16 235	92.96 163	81.78 375	97.88 99	48.48 407	90.75 251	87.69 348	96.02 31	96.70 82	97.63 72	85.60 229	97.80 248	85.73 251	98.60 163	99.06 50

VNet		92.67 175	92.96 163	91.79 225	96.27 202	80.15 254	91.95 217	94.98 252	92.19 94	94.52 186	96.07 187	87.43 199	97.39 279	84.83 264	98.38 183	97.83 183

GBi-Net		93.21 157	92.96 163	93.97 144	95.40 257	84.29 198	95.99 63	96.56 188	88.63 182	95.10 164	98.53 26	81.31 268	98.98 107	86.74 233	98.38 183	98.65 107

test1		93.21 157	92.96 163	93.97 144	95.40 257	84.29 198	95.99 63	96.56 188	88.63 182	95.10 164	98.53 26	81.31 268	98.98 107	86.74 233	98.38 183	98.65 107

alignmvs		93.26 154	92.85 167	94.50 126	95.70 244	87.45 133	93.45 160	95.76 221	91.58 120	95.25 158	92.42 315	81.96 263	98.72 152	91.61 122	97.87 229	97.33 223

QAPM		92.88 167	92.77 168	93.22 176	95.82 237	83.31 212	96.45 39	97.35 129	83.91 266	93.75 206	96.77 139	89.25 177	98.88 121	84.56 268	97.02 263	97.49 210

TinyColmap		92.00 193	92.76 169	89.71 293	95.62 251	77.02 312	90.72 253	96.17 209	87.70 204	95.26 156	96.29 174	92.54 111	96.45 318	81.77 292	98.77 145	95.66 296

ETV-MVS		92.99 163	92.74 170	93.72 157	95.86 234	86.30 165	92.33 202	97.84 88	91.70 118	92.81 240	86.17 380	92.22 116	99.19 81	88.03 214	97.73 234	95.66 296

Effi-MVS+		92.79 170	92.74 170	92.94 185	95.10 265	83.30 213	94.00 142	97.53 114	91.36 125	89.35 315	90.65 343	94.01 75	98.66 165	87.40 225	95.30 310	96.88 243

FMVSNet2		92.78 171	92.73 172	92.95 183	95.40 257	81.98 232	94.18 135	95.53 235	88.63 182	96.05 113	97.37 91	81.31 268	98.81 136	87.38 226	98.67 157	98.06 153

patch_mono-2		92.46 181	92.72 173	91.71 229	96.65 170	78.91 287	88.85 310	97.17 143	83.89 267	92.45 254	96.76 141	89.86 172	97.09 293	90.24 159	98.59 164	99.12 43

PM-MVS		93.33 151	92.67 174	95.33 86	96.58 175	94.06 21	92.26 208	92.18 309	85.92 231	96.22 105	96.61 153	85.64 228	95.99 332	90.35 152	98.23 199	95.93 282

ab-mvs		92.40 183	92.62 175	91.74 227	97.02 148	81.65 236	95.84 71	95.50 236	86.95 217	92.95 237	97.56 76	90.70 156	97.50 270	79.63 317	97.43 249	96.06 276

Effi-MVS+-dtu		93.90 139	92.60 176	97.77 3	94.74 278	96.67 5	94.00 142	95.41 240	89.94 154	91.93 271	92.13 319	90.12 166	98.97 111	87.68 220	97.48 246	97.67 199

MCST-MVS		92.91 165	92.51 177	94.10 140	97.52 125	85.72 181	91.36 239	97.13 147	80.33 306	92.91 238	94.24 262	91.23 139	98.72 152	89.99 168	97.93 226	97.86 179

Anonymous202405211		92.58 177	92.50 178	92.83 190	96.55 178	83.22 216	92.43 197	91.64 320	94.10 52	95.59 136	96.64 151	81.88 265	97.50 270	85.12 259	98.52 171	97.77 190

UGNet		93.08 160	92.50 178	94.79 108	93.87 302	87.99 125	95.07 101	94.26 272	90.64 142	87.33 348	97.67 69	86.89 211	98.49 183	88.10 210	98.71 151	97.91 173
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
TSAR-MVS + GP.		93.07 162	92.41 180	95.06 99	95.82 237	90.87 72	90.97 246	92.61 304	88.04 195	94.61 183	93.79 280	88.08 187	97.81 247	89.41 179	98.39 182	96.50 257

test_fmvs3		92.42 182	92.40 181	92.46 207	93.80 305	87.28 136	93.86 147	97.05 152	76.86 335	96.25 102	98.66 18	82.87 251	91.26 380	95.44 26	96.83 272	98.82 82

MVSFormer		92.18 190	92.23 182	92.04 220	94.74 278	80.06 258	97.15 15	97.37 123	88.98 174	88.83 319	92.79 304	77.02 305	99.60 9	96.41 9	96.75 276	96.46 259

Fast-Effi-MVS+-dtu		92.77 172	92.16 183	94.58 124	94.66 283	88.25 120	92.05 213	96.65 182	89.62 161	90.08 301	91.23 331	92.56 110	98.60 172	86.30 245	96.27 287	96.90 240

DELS-MVS		92.05 192	92.16 183	91.72 228	94.44 288	80.13 256	87.62 325	97.25 138	87.34 210	92.22 265	93.18 296	89.54 175	98.73 151	89.67 175	98.20 204	96.30 265
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
WB-MVS		89.44 253	92.15 185	81.32 376	97.73 110	48.22 408	89.73 286	87.98 346	95.24 36	96.05 113	96.99 127	85.18 231	96.95 299	82.45 286	97.97 223	98.78 88

OpenMVS	89.45 8	92.27 188	92.13 186	92.68 195	94.53 287	84.10 204	95.70 76	97.03 153	82.44 288	91.14 283	96.42 161	88.47 182	98.38 194	85.95 248	97.47 247	95.55 301

EIA-MVS		92.35 185	92.03 187	93.30 174	95.81 239	83.97 206	92.80 179	98.17 45	87.71 203	89.79 309	87.56 370	91.17 144	99.18 82	87.97 215	97.27 254	96.77 247

LF4IMVS		92.72 173	92.02 188	94.84 106	95.65 248	91.99 54	92.92 175	96.60 184	85.08 252	92.44 255	93.62 284	86.80 212	96.35 323	86.81 232	98.25 197	96.18 271

h-mvs33		92.89 166	91.99 189	95.58 77	96.97 150	90.55 76	93.94 145	94.01 278	89.23 168	93.95 201	96.19 181	76.88 308	99.14 86	91.02 133	95.71 298	97.04 235

CANet		92.38 184	91.99 189	93.52 167	93.82 304	83.46 211	91.14 242	97.00 155	89.81 157	86.47 352	94.04 269	87.90 193	99.21 78	89.50 178	98.27 194	97.90 174

diffmvs		91.74 196	91.93 191	91.15 253	93.06 315	78.17 297	88.77 313	97.51 117	86.28 222	92.42 256	93.96 274	88.04 189	97.46 273	90.69 143	96.67 278	97.82 185
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DP-MVS Recon		92.31 186	91.88 192	93.60 160	97.18 143	86.87 147	91.10 244	97.37 123	84.92 255	92.08 268	94.08 268	88.59 180	98.20 210	83.50 274	98.14 208	95.73 291

FA-MVS(test-final)		91.81 195	91.85 193	91.68 231	94.95 268	79.99 262	96.00 62	93.44 287	87.80 200	94.02 199	97.29 102	77.60 296	98.45 189	88.04 213	97.49 245	96.61 251

train_agg		92.71 174	91.83 194	95.35 84	96.45 187	89.46 90	90.60 257	96.92 162	79.37 315	90.49 292	94.39 258	91.20 141	98.88 121	88.66 202	98.43 178	97.72 195

CDPH-MVS		92.67 175	91.83 194	95.18 96	96.94 152	88.46 118	90.70 254	97.07 151	77.38 330	92.34 262	95.08 233	92.67 109	98.88 121	85.74 250	98.57 166	98.20 143

TAPA-MVS	88.58 10	92.49 180	91.75 196	94.73 110	96.50 183	89.69 86	92.91 176	97.68 101	78.02 328	92.79 242	94.10 267	90.85 149	97.96 232	84.76 266	98.16 206	96.54 252
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
API-MVS		91.52 202	91.61 197	91.26 247	94.16 293	86.26 167	94.66 114	94.82 257	91.17 130	92.13 267	91.08 334	90.03 171	97.06 296	79.09 324	97.35 253	90.45 382

IterMVS-SCA-FT		91.65 198	91.55 198	91.94 221	93.89 301	79.22 281	87.56 328	93.51 285	91.53 122	95.37 149	96.62 152	78.65 287	98.90 118	91.89 114	94.95 318	97.70 196

xiu_mvs_v1_base_debu		91.47 203	91.52 199	91.33 243	95.69 245	81.56 237	89.92 280	96.05 213	83.22 274	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 293	93.62 353

xiu_mvs_v1_base		91.47 203	91.52 199	91.33 243	95.69 245	81.56 237	89.92 280	96.05 213	83.22 274	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 293	93.62 353

xiu_mvs_v1_base_debi		91.47 203	91.52 199	91.33 243	95.69 245	81.56 237	89.92 280	96.05 213	83.22 274	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 293	93.62 353

HQP-MVS		92.09 191	91.49 202	93.88 150	96.36 191	84.89 192	91.37 236	97.31 132	87.16 212	88.81 321	93.40 290	84.76 235	98.60 172	86.55 240	97.73 234	98.14 149

c3_l		91.32 207	91.42 203	91.00 258	92.29 331	76.79 319	87.52 331	96.42 196	85.76 234	94.72 182	93.89 277	82.73 254	98.16 215	90.93 137	98.55 167	98.04 156

CLD-MVS		91.82 194	91.41 204	93.04 178	96.37 189	83.65 210	86.82 344	97.29 135	84.65 259	92.27 264	89.67 352	92.20 118	97.85 245	83.95 272	99.47 43	97.62 201
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AdaColmap		91.63 199	91.36 205	92.47 206	95.56 253	86.36 163	92.24 210	96.27 201	88.88 178	89.90 306	92.69 307	91.65 129	98.32 200	77.38 336	97.64 240	92.72 366

testgi		90.38 227	91.34 206	87.50 331	97.49 127	71.54 360	89.43 295	95.16 247	88.38 189	94.54 185	94.68 249	92.88 104	93.09 372	71.60 372	97.85 230	97.88 177

mvs_anonymous		90.37 228	91.30 207	87.58 330	92.17 337	68.00 375	89.84 283	94.73 261	83.82 268	93.22 227	97.40 89	87.54 197	97.40 278	87.94 216	95.05 316	97.34 222

hse-mvs2		92.24 189	91.20 208	95.38 83	96.16 211	90.65 75	92.52 190	92.01 316	89.23 168	93.95 201	92.99 299	76.88 308	98.69 161	91.02 133	96.03 290	96.81 245

PVSNet_Blended_VisFu		91.63 199	91.20 208	92.94 185	97.73 110	83.95 207	92.14 211	97.46 118	78.85 324	92.35 260	94.98 236	84.16 239	99.08 94	86.36 244	96.77 275	95.79 289

CNLPA		91.72 197	91.20 208	93.26 175	96.17 210	91.02 67	91.14 242	95.55 233	90.16 152	90.87 286	93.56 287	86.31 219	94.40 360	79.92 316	97.12 259	94.37 334

LFMVS		91.33 206	91.16 211	91.82 224	96.27 202	79.36 277	95.01 104	85.61 367	96.04 30	94.82 176	97.06 121	72.03 330	98.46 188	84.96 263	98.70 153	97.65 200

F-COLMAP		92.28 187	91.06 212	95.95 59	97.52 125	91.90 56	93.53 156	97.18 142	83.98 265	88.70 327	94.04 269	88.41 183	98.55 179	80.17 310	95.99 292	97.39 219

BH-untuned		90.68 216	90.90 213	90.05 287	95.98 227	79.57 273	90.04 276	94.94 254	87.91 196	94.07 195	93.00 298	87.76 194	97.78 252	79.19 323	95.17 313	92.80 365

MDA-MVSNet-bldmvs		91.04 209	90.88 214	91.55 235	94.68 282	80.16 253	85.49 365	92.14 312	90.41 149	94.93 172	95.79 199	85.10 232	96.93 302	85.15 257	94.19 339	97.57 204

Fast-Effi-MVS+		91.28 208	90.86 215	92.53 204	95.45 256	82.53 226	89.25 303	96.52 192	85.00 253	89.91 305	88.55 364	92.94 100	98.84 129	84.72 267	95.44 305	96.22 269

test20.03		90.80 212	90.85 216	90.63 270	95.63 250	79.24 280	89.81 284	92.87 295	89.90 155	94.39 188	96.40 163	85.77 224	95.27 349	73.86 359	99.05 106	97.39 219

PAPM_NR		91.03 210	90.81 217	91.68 231	96.73 165	81.10 246	93.72 152	96.35 199	88.19 192	88.77 325	92.12 320	85.09 233	97.25 283	82.40 287	93.90 344	96.68 250

new-patchmatchnet		88.97 265	90.79 218	83.50 370	94.28 292	55.83 405	85.34 367	93.56 284	86.18 226	95.47 142	95.73 205	83.10 247	96.51 315	85.40 254	98.06 214	98.16 147

wuyk23d		87.83 286	90.79 218	78.96 381	90.46 369	88.63 110	92.72 181	90.67 329	91.65 119	98.68 11	97.64 71	96.06 15	77.53 403	59.84 397	99.41 56	70.73 401

pmmvs-eth3d		91.54 201	90.73 220	93.99 142	95.76 242	87.86 128	90.83 249	93.98 279	78.23 327	94.02 199	96.22 180	82.62 257	96.83 307	86.57 238	98.33 189	97.29 225

MSDG		90.82 211	90.67 221	91.26 247	94.16 293	83.08 220	86.63 349	96.19 207	90.60 144	91.94 270	91.89 322	89.16 178	95.75 336	80.96 303	94.51 329	94.95 317

test1111		90.39 226	90.61 222	89.74 292	98.04 88	71.50 361	95.59 81	79.72 396	89.41 164	95.94 117	98.14 39	70.79 334	98.81 136	88.52 204	99.32 68	98.90 74

eth_miper_zixun_eth		90.72 214	90.61 222	91.05 254	92.04 341	76.84 318	86.91 340	96.67 181	85.21 247	94.41 187	93.92 275	79.53 280	98.26 206	89.76 173	97.02 263	98.06 153

cl____		90.65 217	90.56 224	90.91 262	91.85 346	76.98 315	86.75 345	95.36 243	85.53 242	94.06 196	94.89 239	77.36 302	97.98 231	90.27 157	98.98 114	97.76 191

DIV-MVS_self_test		90.65 217	90.56 224	90.91 262	91.85 346	76.99 314	86.75 345	95.36 243	85.52 244	94.06 196	94.89 239	77.37 301	97.99 230	90.28 156	98.97 119	97.76 191

BH-RMVSNet		90.47 222	90.44 226	90.56 272	95.21 264	78.65 293	89.15 304	93.94 280	88.21 191	92.74 244	94.22 263	86.38 218	97.88 239	78.67 326	95.39 307	95.14 310

miper_ehance_all_eth		90.48 221	90.42 227	90.69 268	91.62 353	76.57 322	86.83 343	96.18 208	83.38 270	94.06 196	92.66 309	82.20 259	98.04 222	89.79 172	97.02 263	97.45 212

test_fmvs2		90.62 219	90.40 228	91.29 246	91.93 345	85.46 186	92.70 183	96.48 194	74.44 350	94.91 173	97.59 74	75.52 316	90.57 382	93.44 68	96.56 280	97.84 182

UnsupCasMVSNet_eth		90.33 230	90.34 229	90.28 278	94.64 285	80.24 252	89.69 288	95.88 218	85.77 233	93.94 203	95.69 206	81.99 262	92.98 373	84.21 270	91.30 375	97.62 201

FMVSNet3		90.78 213	90.32 230	92.16 216	93.03 317	79.92 264	92.54 189	94.95 253	86.17 227	95.10 164	96.01 190	69.97 337	98.75 147	86.74 233	98.38 183	97.82 185

ECVR-MVS		90.12 237	90.16 231	90.00 288	97.81 103	72.68 355	95.76 75	78.54 399	89.04 172	95.36 150	98.10 42	70.51 335	98.64 168	87.10 229	99.18 94	98.67 105

IterMVS		90.18 234	90.16 231	90.21 282	93.15 313	75.98 328	87.56 328	92.97 294	86.43 221	94.09 193	96.40 163	78.32 291	97.43 275	87.87 217	94.69 326	97.23 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		90.42 223	90.16 231	91.20 251	97.66 118	77.32 309	94.33 128	87.66 349	91.20 129	92.99 234	95.13 230	75.40 317	98.28 202	77.86 329	99.19 92	97.99 164

RPMNet		90.31 232	90.14 234	90.81 266	91.01 361	78.93 284	92.52 190	98.12 51	91.91 101	89.10 316	96.89 133	68.84 339	99.41 39	90.17 162	92.70 364	94.08 338

test_vis3_rt		90.40 224	90.03 235	91.52 237	92.58 324	88.95 103	90.38 265	97.72 100	73.30 357	97.79 30	97.51 84	77.05 304	87.10 395	89.03 193	94.89 319	98.50 122

PVSNet_BlendedMVS		90.35 229	89.96 236	91.54 236	94.81 273	78.80 291	90.14 273	96.93 160	79.43 314	88.68 328	95.06 234	86.27 220	98.15 216	80.27 306	98.04 216	97.68 198

Patchmtry		90.11 238	89.92 237	90.66 269	90.35 370	77.00 313	92.96 174	92.81 296	90.25 151	94.74 180	96.93 130	67.11 346	97.52 269	85.17 255	98.98 114	97.46 211

CL-MVSNet_self_test		90.04 243	89.90 238	90.47 273	95.24 263	77.81 302	86.60 351	92.62 303	85.64 237	93.25 225	93.92 275	83.84 240	96.06 330	79.93 314	98.03 217	97.53 208

test_vis1_n_1920		89.45 252	89.85 239	88.28 320	93.59 307	76.71 320	90.67 255	97.78 96	79.67 312	90.30 298	96.11 185	76.62 311	92.17 376	90.31 154	93.57 349	95.96 280

miper_lstm_enhance		89.90 245	89.80 240	90.19 284	91.37 357	77.50 306	83.82 381	95.00 251	84.84 257	93.05 232	94.96 237	76.53 313	95.20 350	89.96 169	98.67 157	97.86 179

114514_t		90.51 220	89.80 240	92.63 198	98.00 91	82.24 230	93.40 162	97.29 135	65.84 391	89.40 314	94.80 244	86.99 207	98.75 147	83.88 273	98.61 161	96.89 241

MG-MVS		89.54 250	89.80 240	88.76 309	94.88 269	72.47 357	89.60 289	92.44 307	85.82 232	89.48 313	95.98 191	82.85 252	97.74 258	81.87 291	95.27 311	96.08 275

test_yl		90.11 238	89.73 243	91.26 247	94.09 296	79.82 266	90.44 261	92.65 301	90.90 133	93.19 228	93.30 292	73.90 321	98.03 223	82.23 288	96.87 270	95.93 282

DCV-MVSNet		90.11 238	89.73 243	91.26 247	94.09 296	79.82 266	90.44 261	92.65 301	90.90 133	93.19 228	93.30 292	73.90 321	98.03 223	82.23 288	96.87 270	95.93 282

D2MVS		89.93 244	89.60 245	90.92 260	94.03 298	78.40 294	88.69 315	94.85 255	78.96 322	93.08 230	95.09 232	74.57 319	96.94 300	88.19 207	98.96 121	97.41 215

iter_conf_final		90.23 233	89.32 246	92.95 183	94.65 284	81.46 240	94.32 130	95.40 242	85.61 239	92.84 239	95.37 224	54.58 388	99.13 88	92.16 104	98.94 124	98.25 139

xiu_mvs_v2_base		89.00 264	89.19 247	88.46 318	94.86 271	74.63 337	86.97 338	95.60 226	80.88 302	87.83 340	88.62 363	91.04 146	98.81 136	82.51 285	94.38 331	91.93 372

CANet_DTU		89.85 246	89.17 248	91.87 222	92.20 335	80.02 261	90.79 250	95.87 219	86.02 229	82.53 383	91.77 324	80.01 277	98.57 176	85.66 252	97.70 237	97.01 236

USDC		89.02 261	89.08 249	88.84 308	95.07 266	74.50 340	88.97 306	96.39 197	73.21 358	93.27 222	96.28 176	82.16 260	96.39 320	77.55 333	98.80 142	95.62 299

TAMVS		90.16 235	89.05 250	93.49 169	96.49 184	86.37 162	90.34 267	92.55 305	80.84 304	92.99 234	94.57 254	81.94 264	98.20 210	73.51 360	98.21 202	95.90 285

OpenMVS_ROB	85.12 16	89.52 251	89.05 250	90.92 260	94.58 286	81.21 245	91.10 244	93.41 288	77.03 334	93.41 215	93.99 273	83.23 246	97.80 248	79.93 314	94.80 323	93.74 349

test_vis1_n		89.01 263	89.01 252	89.03 304	92.57 325	82.46 228	92.62 187	96.06 211	73.02 360	90.40 295	95.77 203	74.86 318	89.68 388	90.78 140	94.98 317	94.95 317

PS-MVSNAJ		88.86 269	88.99 253	88.48 317	94.88 269	74.71 335	86.69 347	95.60 226	80.88 302	87.83 340	87.37 373	90.77 151	98.82 131	82.52 284	94.37 332	91.93 372

MVP-Stereo		90.07 241	88.92 254	93.54 164	96.31 198	86.49 157	90.93 247	95.59 230	79.80 308	91.48 275	95.59 209	80.79 273	97.39 279	78.57 327	91.19 376	96.76 248
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PLC	85.34 15	90.40 224	88.92 254	94.85 105	96.53 182	90.02 81	91.58 233	96.48 194	80.16 307	86.14 354	92.18 317	85.73 225	98.25 207	76.87 339	94.61 328	96.30 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		89.81 247	88.90 256	92.55 203	97.00 149	79.73 270	95.03 103	83.65 380	89.88 156	95.30 153	94.79 245	53.64 391	99.39 49	91.99 110	98.79 143	98.54 120

MAR-MVS		90.32 231	88.87 257	94.66 115	94.82 272	91.85 57	94.22 134	94.75 260	80.91 301	87.52 346	88.07 368	86.63 216	97.87 242	76.67 340	96.21 288	94.25 337
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
MVSTER		89.32 255	88.75 258	91.03 255	90.10 373	76.62 321	90.85 248	94.67 264	82.27 289	95.24 159	95.79 199	61.09 377	98.49 183	90.49 146	98.26 195	97.97 168

ppachtmachnet_test		88.61 275	88.64 259	88.50 316	91.76 348	70.99 364	84.59 374	92.98 293	79.30 319	92.38 258	93.53 288	79.57 279	97.45 274	86.50 242	97.17 258	97.07 231

Patchmatch-RL test		88.81 270	88.52 260	89.69 294	95.33 262	79.94 263	86.22 357	92.71 300	78.46 325	95.80 124	94.18 265	66.25 354	95.33 347	89.22 188	98.53 170	93.78 347

cl22		89.02 261	88.50 261	90.59 271	89.76 375	76.45 323	86.62 350	94.03 275	82.98 280	92.65 246	92.49 310	72.05 329	97.53 268	88.93 194	97.02 263	97.78 189

X-MVStestdata		90.70 215	88.45 262	97.44 16	98.56 42	93.99 26	96.50 36	97.95 80	94.58 43	94.38 189	26.89 404	94.56 64	99.39 49	93.57 58	99.05 106	98.93 68

DPM-MVS		89.35 254	88.40 263	92.18 215	96.13 216	84.20 202	86.96 339	96.15 210	75.40 344	87.36 347	91.55 329	83.30 245	98.01 227	82.17 290	96.62 279	94.32 336

test_fmvs1_n		88.73 273	88.38 264	89.76 291	92.06 340	82.53 226	92.30 206	96.59 186	71.14 369	92.58 249	95.41 221	68.55 340	89.57 390	91.12 131	95.66 299	97.18 229

jason		89.17 257	88.32 265	91.70 230	95.73 243	80.07 257	88.10 321	93.22 290	71.98 365	90.09 300	92.79 304	78.53 290	98.56 177	87.43 224	97.06 261	96.46 259
jason: jason.
AUN-MVS		90.05 242	88.30 266	95.32 88	96.09 218	90.52 77	92.42 198	92.05 315	82.08 292	88.45 331	92.86 301	65.76 356	98.69 161	88.91 196	96.07 289	96.75 249

FE-MVS		89.06 260	88.29 267	91.36 242	94.78 275	79.57 273	96.77 28	90.99 324	84.87 256	92.96 236	96.29 174	60.69 379	98.80 139	80.18 309	97.11 260	95.71 292

Anonymous20231206		88.77 271	88.29 267	90.20 283	96.31 198	78.81 290	89.56 291	93.49 286	74.26 352	92.38 258	95.58 212	82.21 258	95.43 344	72.07 368	98.75 148	96.34 263

test_cas_vis1_n_1920		88.25 280	88.27 269	88.20 322	92.19 336	78.92 286	89.45 294	95.44 237	75.29 347	93.23 226	95.65 208	71.58 331	90.23 386	88.05 212	93.55 351	95.44 303

EPNet		89.80 248	88.25 270	94.45 130	83.91 405	86.18 169	93.87 146	87.07 354	91.16 131	80.64 393	94.72 247	78.83 284	98.89 120	85.17 255	98.89 126	98.28 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
YYNet1		88.17 281	88.24 271	87.93 326	92.21 334	73.62 347	80.75 390	88.77 335	82.51 287	94.99 170	95.11 231	82.70 255	93.70 366	83.33 275	93.83 345	96.48 258

MDA-MVSNet_test_wron		88.16 282	88.23 272	87.93 326	92.22 333	73.71 346	80.71 391	88.84 334	82.52 286	94.88 175	95.14 229	82.70 255	93.61 367	83.28 276	93.80 346	96.46 259

CDS-MVSNet		89.55 249	88.22 273	93.53 165	95.37 260	86.49 157	89.26 301	93.59 282	79.76 310	91.15 282	92.31 316	77.12 303	98.38 194	77.51 334	97.92 227	95.71 292
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsany_test3		89.11 259	88.21 274	91.83 223	91.30 358	90.25 79	88.09 322	78.76 397	76.37 338	96.43 91	98.39 33	83.79 241	90.43 385	86.57 238	94.20 337	94.80 323

PatchT		87.51 295	88.17 275	85.55 352	90.64 364	66.91 379	92.02 215	86.09 360	92.20 93	89.05 318	97.16 112	64.15 364	96.37 322	89.21 189	92.98 362	93.37 357

PVSNet_Blended		88.74 272	88.16 276	90.46 275	94.81 273	78.80 291	86.64 348	96.93 160	74.67 348	88.68 328	89.18 359	86.27 220	98.15 216	80.27 306	96.00 291	94.44 333

iter_conf05		88.94 267	88.09 277	91.50 238	92.74 322	76.97 316	92.80 179	95.92 217	82.82 282	93.65 210	95.37 224	49.41 395	99.13 88	90.82 138	99.28 79	98.40 130

UnsupCasMVSNet_bld		88.50 276	88.03 278	89.90 289	95.52 254	78.88 288	87.39 332	94.02 277	79.32 318	93.06 231	94.02 271	80.72 274	94.27 362	75.16 351	93.08 360	96.54 252

PatchMatch-RL		89.18 256	88.02 279	92.64 196	95.90 233	92.87 45	88.67 317	91.06 323	80.34 305	90.03 303	91.67 326	83.34 244	94.42 359	76.35 344	94.84 322	90.64 381

miper_enhance_ethall		88.42 277	87.87 280	90.07 285	88.67 387	75.52 332	85.10 368	95.59 230	75.68 340	92.49 251	89.45 355	78.96 283	97.88 239	87.86 218	97.02 263	96.81 245

MS-PatchMatch		88.05 283	87.75 281	88.95 305	93.28 310	77.93 299	87.88 324	92.49 306	75.42 343	92.57 250	93.59 286	80.44 275	94.24 364	81.28 298	92.75 363	94.69 329

PCF-MVS	84.52 17	89.12 258	87.71 282	93.34 172	96.06 220	85.84 177	86.58 352	97.31 132	68.46 384	93.61 211	93.89 277	87.51 198	98.52 181	67.85 385	98.11 210	95.66 296
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs4		88.95 266	87.70 283	92.70 193	94.30 291	85.60 184	87.22 334	92.16 311	74.62 349	89.75 311	94.19 264	77.97 294	96.41 319	82.71 281	96.36 285	96.09 274

our_test_3		87.55 294	87.59 284	87.44 332	91.76 348	70.48 365	83.83 380	90.55 330	79.79 309	92.06 269	92.17 318	78.63 289	95.63 337	84.77 265	94.73 324	96.22 269

thisisatest0530		88.69 274	87.52 285	92.20 211	96.33 196	79.36 277	92.81 178	84.01 379	86.44 220	93.67 209	92.68 308	53.62 392	99.25 75	89.65 176	98.45 177	98.00 161

1112_ss		88.42 277	87.41 286	91.45 239	96.69 167	80.99 247	89.72 287	96.72 178	73.37 356	87.00 350	90.69 341	77.38 300	98.20 210	81.38 297	93.72 347	95.15 309

baseline1		87.62 292	87.31 287	88.54 314	94.71 281	74.27 343	93.10 171	88.20 341	86.20 225	92.18 266	93.04 297	73.21 324	95.52 339	79.32 321	85.82 391	95.83 287

lupinMVS		88.34 279	87.31 287	91.45 239	94.74 278	80.06 258	87.23 333	92.27 308	71.10 370	88.83 319	91.15 332	77.02 305	98.53 180	86.67 236	96.75 276	95.76 290

test_fmvs1		87.59 293	87.27 289	88.54 314	88.32 388	81.26 243	90.43 264	95.72 223	70.55 375	91.70 273	94.63 250	68.13 341	89.42 391	90.59 144	95.34 309	94.94 319

N_pmnet		88.90 268	87.25 290	93.83 154	94.40 290	93.81 35	84.73 371	87.09 353	79.36 317	93.26 223	92.43 314	79.29 282	91.68 378	77.50 335	97.22 256	96.00 278

SCA		87.43 297	87.21 291	88.10 324	92.01 342	71.98 359	89.43 295	88.11 344	82.26 290	88.71 326	92.83 302	78.65 287	97.59 266	79.61 318	93.30 354	94.75 326

TR-MVS		87.70 288	87.17 292	89.27 301	94.11 295	79.26 279	88.69 315	91.86 317	81.94 293	90.69 290	89.79 349	82.82 253	97.42 276	72.65 366	91.98 372	91.14 378

pmmvs5		87.87 285	87.14 293	90.07 285	93.26 312	76.97 316	88.89 308	92.18 309	73.71 355	88.36 332	93.89 277	76.86 310	96.73 310	80.32 305	96.81 273	96.51 254

test_f		86.65 311	87.13 294	85.19 356	90.28 371	86.11 171	86.52 353	91.66 319	69.76 379	95.73 131	97.21 110	69.51 338	81.28 402	89.15 190	94.40 330	88.17 388

CR-MVSNet		87.89 284	87.12 295	90.22 281	91.01 361	78.93 284	92.52 190	92.81 296	73.08 359	89.10 316	96.93 130	67.11 346	97.64 265	88.80 198	92.70 364	94.08 338

thres600view7		87.66 290	87.10 296	89.36 299	96.05 221	73.17 349	92.72 181	85.31 370	91.89 102	93.29 220	90.97 335	63.42 368	98.39 191	73.23 362	96.99 268	96.51 254

BH-w/o		87.21 302	87.02 297	87.79 329	94.77 276	77.27 310	87.90 323	93.21 292	81.74 294	89.99 304	88.39 366	83.47 243	96.93 302	71.29 373	92.43 368	89.15 383

thres100view900		87.35 299	86.89 298	88.72 310	96.14 214	73.09 351	93.00 173	85.31 370	92.13 95	93.26 223	90.96 336	63.42 368	98.28 202	71.27 374	96.54 281	94.79 324

GA-MVS		87.70 288	86.82 299	90.31 277	93.27 311	77.22 311	84.72 373	92.79 298	85.11 251	89.82 307	90.07 344	66.80 349	97.76 255	84.56 268	94.27 335	95.96 280

sss		87.23 301	86.82 299	88.46 318	93.96 299	77.94 298	86.84 342	92.78 299	77.59 329	87.61 345	91.83 323	78.75 285	91.92 377	77.84 330	94.20 337	95.52 302

PAPR		87.65 291	86.77 301	90.27 279	92.85 321	77.38 308	88.56 318	96.23 204	76.82 337	84.98 363	89.75 351	86.08 222	97.16 291	72.33 367	93.35 353	96.26 268

EU-MVSNet		87.39 298	86.71 302	89.44 296	93.40 309	76.11 326	94.93 107	90.00 332	57.17 400	95.71 132	97.37 91	64.77 362	97.68 262	92.67 96	94.37 332	94.52 331

Test_1112_low_res		87.50 296	86.58 303	90.25 280	96.80 164	77.75 303	87.53 330	96.25 202	69.73 380	86.47 352	93.61 285	75.67 315	97.88 239	79.95 312	93.20 356	95.11 313

FMVSNet5		87.82 287	86.56 304	91.62 233	92.31 330	79.81 268	93.49 158	94.81 259	83.26 272	91.36 277	96.93 130	52.77 393	97.49 272	76.07 346	98.03 217	97.55 207

MIMVSNet		87.13 306	86.54 305	88.89 307	96.05 221	76.11 326	94.39 125	88.51 337	81.37 297	88.27 334	96.75 143	72.38 327	95.52 339	65.71 390	95.47 304	95.03 314

tfpn200view9		87.05 307	86.52 306	88.67 311	95.77 240	72.94 352	91.89 222	86.00 361	90.84 135	92.61 247	89.80 347	63.93 365	98.28 202	71.27 374	96.54 281	94.79 324

thres400		87.20 303	86.52 306	89.24 303	95.77 240	72.94 352	91.89 222	86.00 361	90.84 135	92.61 247	89.80 347	63.93 365	98.28 202	71.27 374	96.54 281	96.51 254

WTY-MVS		86.93 309	86.50 308	88.24 321	94.96 267	74.64 336	87.19 335	92.07 314	78.29 326	88.32 333	91.59 328	78.06 293	94.27 362	74.88 352	93.15 358	95.80 288

1314		86.46 312	86.33 309	86.87 339	91.65 352	74.54 338	91.94 219	94.10 274	74.28 351	84.78 365	87.33 374	83.03 249	95.00 351	78.72 325	91.16 377	91.06 379

cascas		87.02 308	86.28 310	89.25 302	91.56 355	76.45 323	84.33 377	96.78 173	71.01 371	86.89 351	85.91 381	81.35 267	96.94 300	83.09 278	95.60 300	94.35 335

Patchmatch-test		86.10 314	86.01 311	86.38 347	90.63 365	74.22 344	89.57 290	86.69 355	85.73 235	89.81 308	92.83 302	65.24 360	91.04 381	77.82 332	95.78 297	93.88 346

HY-MVS	82.50 18	86.81 310	85.93 312	89.47 295	93.63 306	77.93 299	94.02 141	91.58 321	75.68 340	83.64 374	93.64 282	77.40 299	97.42 276	71.70 371	92.07 371	93.05 362

CHOSEN 1792x2688		87.19 304	85.92 313	91.00 258	97.13 146	79.41 276	84.51 375	95.60 226	64.14 394	90.07 302	94.81 242	78.26 292	97.14 292	73.34 361	95.38 308	96.46 259

CMPMVS	68.83 22	87.28 300	85.67 314	92.09 218	88.77 386	85.42 187	90.31 268	94.38 268	70.02 378	88.00 337	93.30 292	73.78 323	94.03 365	75.96 348	96.54 281	96.83 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HyFIR lowres test		87.19 304	85.51 315	92.24 210	97.12 147	80.51 251	85.03 369	96.06 211	66.11 390	91.66 274	92.98 300	70.12 336	99.14 86	75.29 350	95.23 312	97.07 231

thres200		85.85 315	85.18 316	87.88 328	94.44 288	72.52 356	89.08 305	86.21 358	88.57 185	91.44 276	88.40 365	64.22 363	98.00 228	68.35 383	95.88 296	93.12 359

Syy-MVS		84.81 323	84.93 317	84.42 363	91.71 350	63.36 397	85.89 360	81.49 387	81.03 299	85.13 360	81.64 396	77.44 298	95.00 351	85.94 249	94.12 340	94.91 320

CVMVSNet		85.16 320	84.72 318	86.48 343	92.12 338	70.19 366	92.32 203	88.17 342	56.15 401	90.64 291	95.85 195	67.97 344	96.69 311	88.78 199	90.52 380	92.56 367

PatchmatchNet		85.22 319	84.64 319	86.98 336	89.51 380	69.83 371	90.52 259	87.34 352	78.87 323	87.22 349	92.74 306	66.91 348	96.53 313	81.77 292	86.88 389	94.58 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_rt		85.58 317	84.58 320	88.60 313	87.97 389	86.76 149	85.45 366	93.59 282	66.43 388	87.64 343	89.20 358	79.33 281	85.38 399	81.59 295	89.98 383	93.66 351

test2506		85.42 318	84.57 321	87.96 325	97.81 103	66.53 382	96.14 58	56.35 409	89.04 172	93.55 213	98.10 42	42.88 407	98.68 163	88.09 211	99.18 94	98.67 105

EPNet_dtu		85.63 316	84.37 322	89.40 298	86.30 398	74.33 342	91.64 232	88.26 339	84.84 257	72.96 402	89.85 345	71.27 333	97.69 261	76.60 341	97.62 241	96.18 271
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS		84.98 322	84.30 323	87.01 335	91.03 360	77.69 305	91.94 219	94.16 273	59.36 399	84.23 370	87.50 372	85.66 226	96.80 308	71.79 369	93.05 361	86.54 392

ET-MVSNet_ETH3D		86.15 313	84.27 324	91.79 225	93.04 316	81.28 242	87.17 336	86.14 359	79.57 313	83.65 373	88.66 361	57.10 383	98.18 213	87.74 219	95.40 306	95.90 285

tpm		84.38 327	84.08 325	85.30 355	90.47 368	63.43 396	89.34 298	85.63 366	77.24 333	87.62 344	95.03 235	61.00 378	97.30 282	79.26 322	91.09 378	95.16 308

tpmvs		84.22 328	83.97 326	84.94 358	87.09 395	65.18 389	91.21 241	88.35 338	82.87 281	85.21 358	90.96 336	65.24 360	96.75 309	79.60 320	85.25 392	92.90 364

dmvs_re		84.69 325	83.94 327	86.95 337	92.24 332	82.93 222	89.51 292	87.37 351	84.38 263	85.37 357	85.08 387	72.44 326	86.59 396	68.05 384	91.03 379	91.33 376

WB-MVSnew		84.20 329	83.89 328	85.16 357	91.62 353	66.15 386	88.44 320	81.00 390	76.23 339	87.98 338	87.77 369	84.98 234	93.35 370	62.85 395	94.10 342	95.98 279

MDTV_nov1_ep13			83.88 329		89.42 381	61.52 398	88.74 314	87.41 350	73.99 353	84.96 364	94.01 272	65.25 359	95.53 338	78.02 328	93.16 357

PMMVS2		81.31 351	83.44 330	74.92 384	90.52 367	46.49 410	69.19 398	85.23 373	84.30 264	87.95 339	94.71 248	76.95 307	84.36 401	64.07 392	98.09 212	93.89 345

FPMVS		84.50 326	83.28 331	88.16 323	96.32 197	94.49 16	85.76 363	85.47 368	83.09 277	85.20 359	94.26 261	63.79 367	86.58 397	63.72 393	91.88 374	83.40 395

test-LLR		83.58 333	83.17 332	84.79 360	89.68 377	66.86 380	83.08 382	84.52 376	83.07 278	82.85 380	84.78 388	62.86 371	93.49 368	82.85 279	94.86 320	94.03 341

JIA-IIPM		85.08 321	83.04 333	91.19 252	87.56 391	86.14 170	89.40 297	84.44 378	88.98 174	82.20 384	97.95 54	56.82 385	96.15 326	76.55 343	83.45 395	91.30 377

thisisatest0515		84.72 324	82.99 334	89.90 289	92.96 319	75.33 334	84.36 376	83.42 381	77.37 331	88.27 334	86.65 375	53.94 390	98.72 152	82.56 283	97.40 251	95.67 295

mvsany_test1		83.91 331	82.93 335	86.84 340	86.18 399	85.93 174	81.11 389	75.03 404	70.80 374	88.57 330	94.63 250	83.08 248	87.38 394	80.39 304	86.57 390	87.21 390

tpmrst		82.85 341	82.93 335	82.64 372	87.65 390	58.99 403	90.14 273	87.90 347	75.54 342	83.93 372	91.63 327	66.79 351	95.36 345	81.21 300	81.54 399	93.57 356

testing3		83.66 332	82.52 337	87.08 334	95.84 235	65.84 387	89.80 285	77.17 403	88.17 193	90.84 287	88.63 362	30.95 411	98.11 218	84.05 271	97.19 257	97.28 226

testing91		83.56 334	82.45 338	86.91 338	92.92 320	67.29 376	86.33 355	88.07 345	86.22 224	84.26 369	85.76 382	48.15 397	97.17 289	76.27 345	94.08 343	96.27 267

PVSNet	76.22 20	82.89 340	82.37 339	84.48 362	93.96 299	64.38 394	78.60 393	88.61 336	71.50 367	84.43 368	86.36 379	74.27 320	94.60 356	69.87 381	93.69 348	94.46 332

CostFormer		83.09 337	82.21 340	85.73 350	89.27 382	67.01 378	90.35 266	86.47 357	70.42 376	83.52 376	93.23 295	61.18 376	96.85 306	77.21 337	88.26 387	93.34 358

ADS-MVSNet2		84.01 330	82.20 341	89.41 297	89.04 383	76.37 325	87.57 326	90.98 325	72.71 363	84.46 366	92.45 311	68.08 342	96.48 316	70.58 379	83.97 393	95.38 304

testing99		82.94 339	81.72 342	86.59 341	92.55 326	66.53 382	86.08 359	85.70 364	85.47 245	83.95 371	85.70 383	45.87 398	97.07 295	76.58 342	93.56 350	96.17 273

DSMNet-mixed		82.21 344	81.56 343	84.16 365	89.57 379	70.00 370	90.65 256	77.66 401	54.99 402	83.30 378	97.57 75	77.89 295	90.50 384	66.86 388	95.54 302	91.97 371

ADS-MVSNet		82.25 343	81.55 344	84.34 364	89.04 383	65.30 388	87.57 326	85.13 374	72.71 363	84.46 366	92.45 311	68.08 342	92.33 375	70.58 379	83.97 393	95.38 304

baseline2		83.38 335	81.54 345	88.90 306	91.38 356	72.84 354	88.78 312	81.22 389	78.97 321	79.82 395	87.56 370	61.73 375	97.80 248	74.30 356	90.05 382	96.05 277

test0.0.03 1		82.48 342	81.47 346	85.48 353	89.70 376	73.57 348	84.73 371	81.64 386	83.07 278	88.13 336	86.61 376	62.86 371	89.10 393	66.24 389	90.29 381	93.77 348

PMMVS		83.00 338	81.11 347	88.66 312	83.81 406	86.44 160	82.24 386	85.65 365	61.75 398	82.07 385	85.64 384	79.75 278	91.59 379	75.99 347	93.09 359	87.94 389

IB-MVS	77.21 19	83.11 336	81.05 348	89.29 300	91.15 359	75.85 329	85.66 364	86.00 361	79.70 311	82.02 387	86.61 376	48.26 396	98.39 191	77.84 330	92.22 369	93.63 352
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
gg-mvs-nofinetune		82.10 347	81.02 349	85.34 354	87.46 393	71.04 362	94.74 111	67.56 406	96.44 23	79.43 396	98.99 6	45.24 399	96.15 326	67.18 387	92.17 370	88.85 385

new_pmnet		81.22 352	81.01 350	81.86 374	90.92 363	70.15 367	84.03 378	80.25 395	70.83 372	85.97 355	89.78 350	67.93 345	84.65 400	67.44 386	91.90 373	90.78 380

E-PMN		80.72 357	80.86 351	80.29 379	85.11 402	68.77 373	72.96 395	81.97 385	87.76 202	83.25 379	83.01 394	62.22 374	89.17 392	77.15 338	94.31 334	82.93 396

KD-MVS_2432*1600		82.17 345	80.75 352	86.42 345	82.04 407	70.09 368	81.75 387	90.80 327	82.56 284	90.37 296	89.30 356	42.90 405	96.11 328	74.47 354	92.55 366	93.06 360

miper_refine_blended		82.17 345	80.75 352	86.42 345	82.04 407	70.09 368	81.75 387	90.80 327	82.56 284	90.37 296	89.30 356	42.90 405	96.11 328	74.47 354	92.55 366	93.06 360

MVS-HIRNet		78.83 367	80.60 354	73.51 385	93.07 314	47.37 409	87.10 337	78.00 400	68.94 382	77.53 398	97.26 103	71.45 332	94.62 355	63.28 394	88.74 385	78.55 400

testing11		81.98 348	80.52 355	86.38 347	92.69 323	67.13 377	85.79 362	84.80 375	82.16 291	81.19 392	85.41 385	45.24 399	96.88 305	74.14 357	93.24 355	95.14 310

EPMVS		81.17 354	80.37 356	83.58 369	85.58 401	65.08 391	90.31 268	71.34 405	77.31 332	85.80 356	91.30 330	59.38 380	92.70 374	79.99 311	82.34 398	92.96 363

tpm2		81.46 350	80.35 357	84.80 359	89.90 374	65.14 390	90.44 261	85.36 369	65.82 392	82.05 386	92.44 313	57.94 382	96.69 311	70.71 378	88.49 386	92.56 367

EMVS		80.35 360	80.28 358	80.54 378	84.73 404	69.07 372	72.54 397	80.73 392	87.80 200	81.66 389	81.73 395	62.89 370	89.84 387	75.79 349	94.65 327	82.71 397

PAPM		81.91 349	80.11 359	87.31 333	93.87 302	72.32 358	84.02 379	93.22 290	69.47 381	76.13 400	89.84 346	72.15 328	97.23 284	53.27 402	89.02 384	92.37 369

test-mter		81.21 353	80.01 360	84.79 360	89.68 377	66.86 380	83.08 382	84.52 376	73.85 354	82.85 380	84.78 388	43.66 404	93.49 368	82.85 279	94.86 320	94.03 341

tpm cat1		80.61 358	79.46 361	84.07 366	88.78 385	65.06 392	89.26 301	88.23 340	62.27 397	81.90 388	89.66 353	62.70 373	95.29 348	71.72 370	80.60 400	91.86 374

UWE-MVS		80.29 361	79.10 362	83.87 367	91.97 344	59.56 401	86.50 354	77.43 402	75.40 344	87.79 342	88.10 367	44.08 403	96.90 304	64.23 391	96.36 285	95.14 310

dmvs_testset		78.23 368	78.99 363	75.94 383	91.99 343	55.34 406	88.86 309	78.70 398	82.69 283	81.64 390	79.46 398	75.93 314	85.74 398	48.78 404	82.85 397	86.76 391

pmmvs3		80.83 356	78.96 364	86.45 344	87.23 394	77.48 307	84.87 370	82.31 384	63.83 395	85.03 362	89.50 354	49.66 394	93.10 371	73.12 364	95.10 314	88.78 387

dp		79.28 365	78.62 365	81.24 377	85.97 400	56.45 404	86.91 340	85.26 372	72.97 361	81.45 391	89.17 360	56.01 387	95.45 343	73.19 363	76.68 401	91.82 375

testing222		80.54 359	78.53 366	86.58 342	92.54 328	68.60 374	86.24 356	82.72 383	83.78 269	82.68 382	84.24 390	39.25 409	95.94 333	60.25 396	95.09 315	95.20 306

myMVS_eth3d		79.62 364	78.26 367	83.72 368	91.71 350	61.25 399	85.89 360	81.49 387	81.03 299	85.13 360	81.64 396	32.12 410	95.00 351	71.17 377	94.12 340	94.91 320

TESTMET0.1,1		79.09 366	78.04 368	82.25 373	87.52 392	64.03 395	83.08 382	80.62 393	70.28 377	80.16 394	83.22 393	44.13 402	90.56 383	79.95 312	93.36 352	92.15 370

CHOSEN 280x420		80.04 362	77.97 369	86.23 349	90.13 372	74.53 339	72.87 396	89.59 333	66.38 389	76.29 399	85.32 386	56.96 384	95.36 345	69.49 382	94.72 325	88.79 386

ETVMVS		79.85 363	77.94 370	85.59 351	92.97 318	66.20 385	86.13 358	80.99 391	81.41 296	83.52 376	83.89 391	41.81 408	94.98 354	56.47 400	94.25 336	95.61 300

EGC-MVSNET		80.97 355	75.73 371	96.67 42	98.85 24	94.55 15	96.83 23	96.60 184	2.44 406	5.32 407	98.25 37	92.24 115	98.02 226	91.85 115	99.21 90	97.45 212

PVSNet_0	70.34 21	74.58 369	72.96 372	79.47 380	90.63 365	66.24 384	73.26 394	83.40 382	63.67 396	78.02 397	78.35 400	72.53 325	89.59 389	56.68 399	60.05 404	82.57 398

MVE	59.87 23	73.86 370	72.65 373	77.47 382	87.00 397	74.35 341	61.37 400	60.93 408	67.27 386	69.69 403	86.49 378	81.24 271	72.33 404	56.45 401	83.45 395	85.74 393
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 371	48.94 374	54.93 386	39.68 410	12.38 413	28.59 401	90.09 331	6.82 404	41.10 406	78.41 399	54.41 389	70.69 405	50.12 403	51.26 405	81.72 399

tmp_tt		37.97 372	44.33 375	18.88 388	11.80 411	21.54 412	63.51 399	45.66 412	4.23 405	51.34 405	50.48 403	59.08 381	22.11 407	44.50 405	68.35 403	13.00 403

cdsmvs_eth3d_5k		23.35 373	31.13 376	0.00 391	0.00 414	0.00 416	0.00 402	95.58 232	0.00 409	0.00 410	91.15 332	93.43 84	0.00 410	0.00 409	0.00 408	0.00 406

test123		9.49 374	12.01 377	1.91 389	2.87 412	1.30 414	82.38 385	1.34 414	1.36 407	2.84 408	6.56 406	2.45 412	0.97 408	2.73 407	5.56 406	3.47 404

testmvs		9.02 375	11.42 378	1.81 390	2.77 413	1.13 415	79.44 392	1.90 413	1.18 408	2.65 409	6.80 405	1.95 413	0.87 409	2.62 408	3.45 407	3.44 405

pcd_1.5k_mvsjas		7.56 376	10.09 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	90.77 151	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		7.56 376	10.08 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	90.69 341	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

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

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

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

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

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

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

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

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

WAC-MVS						61.25 399								74.55 353

FOURS1					99.21 3	94.68 12	98.45 4	98.81 8	97.73 6	98.27 20

MSC_two_6792asdad				95.90 65	96.54 179	89.57 88		96.87 167					99.41 39	94.06 45	99.30 71	98.72 97

PC_three_1452									75.31 346	95.87 122	95.75 204	92.93 101	96.34 325	87.18 228	98.68 155	98.04 156

No_MVS				95.90 65	96.54 179	89.57 88		96.87 167					99.41 39	94.06 45	99.30 71	98.72 97

test_one_0601					98.26 71	87.14 140		98.18 41	94.25 48	96.99 70	97.36 94	95.13 43

eth-test2					0.00 414

eth-test					0.00 414

ZD-MVS					97.23 139	90.32 78		97.54 112	84.40 262	94.78 178	95.79 199	92.76 107	99.39 49	88.72 201	98.40 179

IU-MVS					98.51 51	86.66 154		96.83 170	72.74 362	95.83 123				93.00 87	99.29 74	98.64 112

OPU-MVS				95.15 97	96.84 160	89.43 92	95.21 94				95.66 207	93.12 95	98.06 221	86.28 246	98.61 161	97.95 169

test_241102_TWO								98.10 54	91.95 98	97.54 40	97.25 104	95.37 30	99.35 60	93.29 75	99.25 83	98.49 124

test_241102_ONE					98.51 51	86.97 144		98.10 54	91.85 104	97.63 35	97.03 123	96.48 10	98.95 114

save fliter					97.46 130	88.05 124	92.04 214	97.08 150	87.63 206

test_0728_THIRD									93.26 71	97.40 52	97.35 97	94.69 59	99.34 63	93.88 48	99.42 52	98.89 75

test_0728_SECOND				94.88 104	98.55 45	86.72 151	95.20 96	98.22 36					99.38 55	93.44 68	99.31 69	98.53 121

test0726					98.51 51	86.69 152	95.34 89	98.18 41	91.85 104	97.63 35	97.37 91	95.58 24

GSMVS																94.75 326

test_part2					98.21 75	89.41 93				96.72 81

sam_mvs1												66.64 352				94.75 326

sam_mvs												66.41 353

ambc				92.98 180	96.88 156	83.01 221	95.92 68	96.38 198		96.41 92	97.48 86	88.26 184	97.80 248	89.96 169	98.93 125	98.12 151

MTGPA								97.62 105

test_post1							90.21 270				5.85 408	65.36 358	96.00 331	79.61 318

test_post											6.07 407	65.74 357	95.84 335

patchmatchnet-post											91.71 325	66.22 355	97.59 266

GG-mvs-BLEND				83.24 371	85.06 403	71.03 363	94.99 106	65.55 407		74.09 401	75.51 401	44.57 401	94.46 358	59.57 398	87.54 388	84.24 394

MTMP							94.82 109	54.62 410

gm-plane-assit					87.08 396	59.33 402			71.22 368		83.58 392		97.20 286	73.95 358

test9_res														88.16 209	98.40 179	97.83 183

TEST9					96.45 187	89.46 90	90.60 257	96.92 162	79.09 320	90.49 292	94.39 258	91.31 136	98.88 121

test_8					96.37 189	89.14 100	90.51 260	96.89 165	79.37 315	90.42 294	94.36 260	91.20 141	98.82 131

agg_prior2														87.06 231	98.36 188	97.98 165

agg_prior					96.20 208	88.89 106		96.88 166		90.21 299			98.78 143

TestCases				96.00 56	98.02 89	92.17 50		98.43 17	90.48 145	95.04 168	96.74 144	92.54 111	97.86 243	85.11 260	98.98 114	97.98 165

test_prior4						89.91 82	90.74 252

test_prior2							90.21 270		89.33 167	90.77 288	94.81 242	90.41 161		88.21 205	98.55 167

test_prior				94.61 118	95.95 229	87.23 137		97.36 128					98.68 163			97.93 171

旧先验2							90.00 278		68.65 383	92.71 245			96.52 314	85.15 257

新几何2							90.02 277

新几何1				93.17 177	97.16 144	87.29 135		94.43 267	67.95 385	91.29 278	94.94 238	86.97 208	98.23 208	81.06 302	97.75 233	93.98 343

旧先验1					96.20 208	84.17 203		94.82 257			95.57 213	89.57 174			97.89 228	96.32 264

无先验							89.94 279	95.75 222	70.81 373				98.59 174	81.17 301		94.81 322

原ACMM2							89.34 298

原ACMM1				92.87 188	96.91 155	84.22 201		97.01 154	76.84 336	89.64 312	94.46 256	88.00 190	98.70 159	81.53 296	98.01 220	95.70 294

test222					96.95 151	85.27 189	88.83 311	93.61 281	65.09 393	90.74 289	94.85 241	84.62 237			97.36 252	93.91 344

testdata2													98.03 223	80.24 308

segment_acmp												92.14 119

testdata				91.03 255	96.87 157	82.01 231		94.28 271	71.55 366	92.46 253	95.42 218	85.65 227	97.38 281	82.64 282	97.27 254	93.70 350

testdata1							88.96 307		88.44 187

test12				94.43 131	95.95 229	86.75 150		96.24 203		89.76 310		89.79 173	98.79 140		97.95 225	97.75 193

plane_prior7					97.71 112	88.68 109

plane_prior6					97.21 142	88.23 121						86.93 209

plane_prior5								97.81 91					98.95 114	89.26 186	98.51 173	98.60 117

plane_prior4											95.59 209

plane_prior3						88.43 119			90.35 150	93.31 218

plane_prior2							94.56 120		91.74 115

plane_prior1					97.38 132

plane_prior						88.12 122	93.01 172		88.98 174						98.06 214

n2								0.00 415

nn								0.00 415

door-mid								92.13 313

lessismore_v0				93.87 151	98.05 85	83.77 209		80.32 394		97.13 60	97.91 59	77.49 297	99.11 93	92.62 97	98.08 213	98.74 95

LGP-MVS_train				96.84 38	98.36 66	92.13 52		98.25 31	91.78 111	97.07 63	97.22 108	96.38 12	99.28 72	92.07 108	99.59 28	99.11 44

test11								96.65 182

door								91.26 322

HQP5-MVS						84.89 192

HQP-NCC					96.36 191		91.37 236		87.16 212	88.81 321

ACMP_Plane					96.36 191		91.37 236		87.16 212	88.81 321

BP-MVS														86.55 240

HQP4-MVS										88.81 321			98.61 170			98.15 148

HQP3-MVS								97.31 132							97.73 234

HQP2-MVS												84.76 235

NP-MVS					96.82 162	87.10 141					93.40 290

MDTV_nov1_ep13_2view						42.48 411	88.45 319		67.22 387	83.56 375		66.80 349		72.86 365		94.06 340

ACMMP++_ref															98.82 139

ACMMP++															99.25 83

Test By Simon												90.61 157

ITE_SJBPF				95.95 59	97.34 135	93.36 40		96.55 191	91.93 100	94.82 176	95.39 222	91.99 121	97.08 294	85.53 253	97.96 224	97.41 215

DeepMVS_CX				53.83 387	70.38 409	64.56 393		48.52 411	33.01 403	65.50 404	74.21 402	56.19 386	46.64 406	38.45 406	70.07 402	50.30 402

High-resolution multi-view benchmark