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

mamv4		98.21 2	97.86 3	99.26 1	98.24 79	99.36 1	96.10 67	99.32 2	98.75 2	99.58 2	98.70 23	91.78 144	99.88 1	98.60 1	99.67 23	98.54 135

LTVRE_ROB	93.87 1	97.93 3	98.16 2	97.26 30	98.81 32	93.86 35	99.07 2	98.98 9	97.01 18	98.92 6	98.78 19	95.22 46	98.61 187	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
TDRefinement		97.68 4	97.60 9	97.93 3	99.02 13	95.95 9	98.61 3	98.81 11	97.41 14	97.28 67	98.46 36	94.62 70	98.84 144	94.64 51	99.53 40	98.99 64

reproduce_model		97.35 5	97.24 16	97.70 5	98.44 63	95.08 12	95.88 78	98.50 19	96.62 25	98.27 24	97.93 62	94.57 72	99.50 24	95.57 34	99.35 67	98.52 138

UA-Net		97.35 5	97.24 16	97.69 6	98.22 80	93.87 34	98.42 6	98.19 53	96.95 19	95.46 171	99.23 9	93.45 95	99.57 15	95.34 43	99.89 2	99.63 12

lecture		97.32 7	97.64 7	96.33 55	99.01 15	90.77 80	96.90 21	98.60 16	96.30 34	97.74 41	98.00 56	96.87 8	99.39 54	95.95 24	99.42 54	98.84 93

reproduce-ours		97.28 8	97.19 18	97.57 12	98.37 68	94.84 13	95.57 93	98.40 28	96.36 32	98.18 28	97.78 74	95.47 32	99.50 24	95.26 44	99.33 73	98.36 154

our_new_method		97.28 8	97.19 18	97.57 12	98.37 68	94.84 13	95.57 93	98.40 28	96.36 32	98.18 28	97.78 74	95.47 32	99.50 24	95.26 44	99.33 73	98.36 154

sc_t1		97.21 10	97.71 5	95.71 79	99.06 10	88.89 111	96.72 31	97.79 121	98.34 3	98.97 3	99.40 5	96.81 9	98.79 155	92.58 121	99.72 15	99.45 23

UniMVSNet_ETH3D		97.13 11	97.72 4	95.35 90	99.51 2	87.38 142	97.70 8	97.54 146	98.16 6	98.94 4	99.33 6	97.84 4	99.08 106	90.73 172	99.73 14	99.59 15

HPM-MVS_fast		97.01 12	96.89 22	97.39 25	99.12 8	93.92 32	97.16 14	98.17 59	93.11 87	96.48 109	97.36 112	96.92 6	99.34 68	94.31 59	99.38 63	98.92 83

tt0320-xc		97.00 13	97.67 6	94.98 108	98.89 23	86.94 156	96.72 31	98.46 22	98.28 5	98.86 8	99.43 4	96.80 10	98.51 203	91.79 143	99.76 10	99.50 19

tt0320		96.97 14	97.64 7	94.96 110	98.89 23	86.86 158	96.85 23	98.45 23	98.29 4	98.88 7	99.45 3	96.48 13	98.54 197	91.73 146	99.72 15	99.47 21

SR-MVS-dyc-post		96.84 15	96.60 32	97.56 14	98.07 89	95.27 10	96.37 50	98.12 66	95.66 43	97.00 82	97.03 148	94.85 64	99.42 38	93.49 83	98.84 146	98.00 193

mvs_tets		96.83 16	96.71 26	97.17 31	98.83 29	92.51 52	96.58 37	97.61 137	87.57 243	98.80 11	98.90 14	96.50 12	99.59 14	96.15 22	99.47 45	99.40 27

v7n		96.82 17	97.31 15	95.33 92	98.54 51	86.81 159	96.83 24	98.07 76	96.59 26	98.46 21	98.43 38	92.91 116	99.52 20	96.25 21	99.76 10	99.65 11

APD-MVS_3200maxsize		96.82 17	96.65 28	97.32 29	97.95 103	93.82 37	96.31 56	98.25 43	95.51 45	96.99 84	97.05 147	95.63 27	99.39 54	93.31 95	98.88 141	98.75 104

HPM-MVS		96.81 19	96.62 30	97.36 27	98.89 23	93.53 42	97.51 10	98.44 24	92.35 100	95.95 142	96.41 196	96.71 11	99.42 38	93.99 67	99.36 66	99.13 48
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 20	97.36 14	95.15 104	99.12 8	87.82 136	96.68 33	97.86 108	96.10 37	98.14 31	99.28 8	97.94 3	98.21 238	91.38 159	99.69 17	99.42 24

OurMVSNet-221017-0		96.80 20	96.75 25	96.96 39	99.03 12	91.85 61	97.98 7	98.01 88	94.15 65	98.93 5	99.07 10	88.07 226	99.57 15	95.86 27	99.69 17	99.46 22

testf1		96.77 22	96.49 34	97.60 10	99.01 15	96.70 4	96.31 56	98.33 34	94.96 51	97.30 64	97.93 62	96.05 20	97.90 273	89.32 218	99.23 94	98.19 175

APD_test2		96.77 22	96.49 34	97.60 10	99.01 15	96.70 4	96.31 56	98.33 34	94.96 51	97.30 64	97.93 62	96.05 20	97.90 273	89.32 218	99.23 94	98.19 175

COLMAP_ROB	91.06 5	96.75 24	96.62 30	97.13 32	98.38 66	94.31 21	96.79 27	98.32 36	96.69 22	96.86 89	97.56 93	95.48 31	98.77 162	90.11 200	99.44 52	98.31 161
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 25	96.42 37	97.68 8	98.00 99	94.03 29	96.97 19	97.61 137	87.68 241	98.45 22	98.77 20	94.20 81	99.50 24	96.70 13	99.40 61	99.53 17

DTE-MVSNet		96.74 25	97.43 10	94.67 126	99.13 6	84.68 210	96.51 40	97.94 101	98.14 7	98.67 16	98.32 40	95.04 54	99.69 4	93.27 98	99.82 7	99.62 13

SR-MVS		96.70 27	96.42 37	97.54 15	98.05 91	94.69 15	96.13 66	98.07 76	95.17 49	96.82 93	96.73 175	95.09 53	99.43 37	92.99 109	98.71 174	98.50 139

PS-CasMVS		96.69 28	97.43 10	94.49 140	99.13 6	84.09 222	96.61 36	97.97 93	97.91 9	98.64 17	98.13 46	95.24 44	99.65 5	93.39 93	99.84 3	99.72 4

PEN-MVS		96.69 28	97.39 13	94.61 129	99.16 4	84.50 211	96.54 38	98.05 80	98.06 8	98.64 17	98.25 43	95.01 57	99.65 5	92.95 110	99.83 5	99.68 7

MTAPA		96.65 30	96.38 41	97.47 19	98.95 21	94.05 27	95.88 78	97.62 135	94.46 60	96.29 122	96.94 154	93.56 90	99.37 63	94.29 60	99.42 54	98.99 64

test_djsdf		96.62 31	96.49 34	97.01 36	98.55 49	91.77 63	97.15 15	97.37 160	88.98 202	98.26 27	98.86 15	93.35 100	99.60 10	96.41 18	99.45 49	99.66 9

ACMMP		96.61 32	96.34 44	97.43 22	98.61 42	93.88 33	96.95 20	98.18 55	92.26 103	96.33 117	96.84 165	95.10 52	99.40 51	93.47 86	99.33 73	99.02 61
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
Anonymous20231211		96.60 33	97.13 20	95.00 107	97.46 139	86.35 175	97.11 18	98.24 46	97.58 12	98.72 12	98.97 12	93.15 107	99.15 94	93.18 101	99.74 13	99.50 19

WR-MVS_H		96.60 33	97.05 21	95.24 98	99.02 13	86.44 171	96.78 28	98.08 73	97.42 13	98.48 20	97.86 72	91.76 147	99.63 8	94.23 61	99.84 3	99.66 9

jajsoiax		96.59 35	96.42 37	97.12 33	98.76 35	92.49 53	96.44 47	97.42 157	86.96 257	98.71 14	98.72 22	95.36 38	99.56 18	95.92 25	99.45 49	99.32 32

ACMH	88.36 12	96.59 35	97.43 10	94.07 156	98.56 46	85.33 202	96.33 53	98.30 39	94.66 55	98.72 12	98.30 41	97.51 5	98.00 266	94.87 48	99.59 30	98.86 89
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS		96.49 37	96.18 52	97.44 20	98.56 46	93.99 30	96.50 41	97.95 98	94.58 56	94.38 225	96.49 189	94.56 73	99.39 54	93.57 78	99.05 115	98.93 79

ACMH+	88.43 11	96.48 38	96.82 23	95.47 87	98.54 51	89.06 107	95.65 87	98.61 15	96.10 37	98.16 30	97.52 98	96.90 7	98.62 186	90.30 189	99.60 28	98.72 109

APDe-MVS		96.46 39	96.64 29	95.93 67	97.68 125	89.38 101	96.90 21	98.41 27	92.52 95	97.43 57	97.92 67	95.11 51	99.50 24	94.45 55	99.30 80	98.92 83
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		96.46 39	96.14 55	97.41 24	98.60 43	93.82 37	96.30 60	97.96 95	92.35 100	95.57 164	96.61 183	94.93 62	99.41 44	93.78 72	99.15 106	99.00 62

mPP-MVS		96.46 39	96.05 61	97.69 6	98.62 40	94.65 17	96.45 45	97.74 125	92.59 94	95.47 169	96.68 179	94.50 75	99.42 38	93.10 104	99.26 90	98.99 64

CP-MVS		96.44 42	96.08 59	97.54 15	98.29 73	94.62 18	96.80 26	98.08 73	92.67 93	95.08 201	96.39 201	94.77 66	99.42 38	93.17 102	99.44 52	98.58 132

ZNCC-MVS		96.42 43	96.20 51	97.07 34	98.80 34	92.79 50	96.08 69	98.16 62	91.74 130	95.34 178	96.36 204	95.68 25	99.44 34	94.41 57	99.28 88	98.97 71

region2R		96.41 44	96.09 57	97.38 26	98.62 40	93.81 39	96.32 55	97.96 95	92.26 103	95.28 183	96.57 185	95.02 56	99.41 44	93.63 76	99.11 109	98.94 77

SteuartSystems-ACMMP		96.40 45	96.30 46	96.71 44	98.63 39	91.96 59	95.70 84	98.01 88	93.34 84	96.64 103	96.57 185	94.99 58	99.36 64	93.48 85	99.34 71	98.82 94
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 46	96.17 54	97.04 35	98.51 54	93.37 43	96.30 60	97.98 91	92.35 100	95.63 161	96.47 190	95.37 36	99.27 83	93.78 72	99.14 107	98.48 142

LPG-MVS_test		96.38 47	96.23 49	96.84 42	98.36 71	92.13 56	95.33 102	98.25 43	91.78 126	97.07 77	97.22 130	96.38 16	99.28 81	92.07 133	99.59 30	99.11 52

nrg030		96.32 48	96.55 33	95.62 82	97.83 110	88.55 122	95.77 82	98.29 42	92.68 91	98.03 35	97.91 69	95.13 49	98.95 130	93.85 70	99.49 44	99.36 30

PGM-MVS		96.32 48	95.94 67	97.43 22	98.59 45	93.84 36	95.33 102	98.30 39	91.40 146	95.76 152	96.87 161	95.26 43	99.45 33	92.77 112	99.21 98	99.00 62

ACMM	88.83 9	96.30 50	96.07 60	96.97 38	98.39 65	92.95 48	94.74 127	98.03 85	90.82 161	97.15 73	96.85 162	96.25 18	99.00 120	93.10 104	99.33 73	98.95 76
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 51	95.99 65	97.00 37	98.65 38	92.71 51	95.69 86	98.01 88	92.08 110	95.74 155	96.28 210	95.22 46	99.42 38	93.17 102	99.06 112	98.88 88

ACMMP_NAP		96.21 52	96.12 56	96.49 52	98.90 22	91.42 67	94.57 137	98.03 85	90.42 175	96.37 115	97.35 115	95.68 25	99.25 84	94.44 56	99.34 71	98.80 98

CP-MVSNet		96.19 53	96.80 24	94.38 145	98.99 19	83.82 225	96.31 56	97.53 148	97.60 11	98.34 23	97.52 98	91.98 140	99.63 8	93.08 106	99.81 8	99.70 5

MP-MVS		96.14 54	95.68 84	97.51 17	98.81 32	94.06 25	96.10 67	97.78 123	92.73 90	93.48 256	96.72 176	94.23 80	99.42 38	91.99 136	99.29 83	99.05 59
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 55	95.83 77	96.95 40	94.75 327	94.20 23	97.34 13	97.98 91	97.31 15	95.32 179	96.77 168	93.08 110	99.20 90	91.79 143	98.16 240	97.44 261

MP-MVS-pluss		96.08 56	95.92 70	96.57 48	99.06 10	91.21 69	93.25 188	98.32 36	87.89 233	96.86 89	97.38 108	95.55 30	99.39 54	95.47 37	99.47 45	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 57	96.26 48	95.50 86	98.26 76	87.69 138	93.75 170	97.86 108	95.96 42	97.48 55	97.14 137	95.33 40	99.44 34	90.79 170	99.76 10	99.38 28

PS-MVSNAJss		96.01 58	96.04 62	95.89 72	98.82 30	88.51 123	95.57 93	97.88 106	88.72 208	98.81 10	98.86 15	90.77 178	99.60 10	95.43 39	99.53 40	99.57 16

Elysia		96.00 59	96.36 42	94.91 112	98.01 97	85.96 186	95.29 106	97.90 102	95.31 46	98.14 31	97.28 122	88.82 212	99.51 21	97.08 7	99.38 63	99.26 35

StellarMVS		96.00 59	96.36 42	94.91 112	98.01 97	85.96 186	95.29 106	97.90 102	95.31 46	98.14 31	97.28 122	88.82 212	99.51 21	97.08 7	99.38 63	99.26 35

SED-MVS		96.00 59	96.41 40	94.76 120	98.51 54	86.97 153	95.21 110	98.10 70	91.95 112	97.63 44	97.25 125	96.48 13	99.35 65	93.29 96	99.29 83	97.95 203

DVP-MVS++		95.93 62	96.34 44	94.70 123	96.54 207	86.66 165	98.45 4	98.22 50	93.26 85	97.54 49	97.36 112	93.12 108	99.38 61	93.88 68	98.68 178	98.04 188

APD_test1		95.91 63	95.42 95	97.36 27	98.82 30	96.62 7	95.64 88	97.64 133	93.38 83	95.89 147	97.23 128	93.35 100	97.66 302	88.20 254	98.66 182	97.79 230

test_fmvsmconf0.01_n		95.90 64	96.09 57	95.31 95	97.30 148	89.21 103	94.24 149	98.76 13	86.25 269	97.56 48	98.66 24	95.73 23	98.44 215	97.35 4	98.99 123	98.27 166

DPE-MVS		95.89 65	95.88 73	95.92 69	97.93 104	89.83 91	93.46 181	98.30 39	92.37 98	97.75 40	96.95 153	95.14 48	99.51 21	91.74 145	99.28 88	98.41 148
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		95.88 66	95.88 73	95.87 73	98.12 85	89.65 93	95.58 92	98.56 18	91.84 122	96.36 116	96.68 179	94.37 79	99.32 74	92.41 126	99.05 115	98.64 125

3Dnovator+	92.74 2	95.86 67	95.77 81	96.13 58	96.81 180	90.79 79	96.30 60	97.82 116	96.13 36	94.74 216	97.23 128	91.33 159	99.16 93	93.25 99	98.30 225	98.46 143

mmtdpeth		95.82 68	96.02 64	95.23 99	96.91 171	88.62 117	96.49 43	99.26 4	95.07 50	93.41 258	99.29 7	90.25 191	97.27 330	94.49 53	99.01 122	99.80 3

DVP-MVS		95.82 68	96.18 52	94.72 122	98.51 54	86.69 163	95.20 112	97.00 193	91.85 119	97.40 61	97.35 115	95.58 28	99.34 68	93.44 89	99.31 78	98.13 181
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
CS-MVS		95.77 70	95.58 88	96.37 54	96.84 177	91.72 65	96.73 30	99.06 8	94.23 63	92.48 302	94.79 292	93.56 90	99.49 30	93.47 86	99.05 115	97.89 215

SMA-MVS		95.77 70	95.54 89	96.47 53	98.27 75	91.19 70	95.09 115	97.79 121	86.48 264	97.42 59	97.51 102	94.47 78	99.29 77	93.55 80	99.29 83	98.93 79
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_0402		95.73 72	96.22 50	94.26 148	98.19 82	85.77 192	93.24 189	97.24 177	96.88 21	97.69 42	97.77 78	94.12 83	99.13 99	91.54 155	99.29 83	97.88 216

ACMP	88.15 13	95.71 73	95.43 94	96.54 49	98.17 83	91.73 64	94.24 149	98.08 73	89.46 190	96.61 105	96.47 190	95.85 22	99.12 100	90.45 179	99.56 37	98.77 103
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 74	95.34 100	96.69 45	98.40 64	93.04 45	94.54 141	98.05 80	90.45 174	96.31 120	96.76 170	92.91 116	98.72 168	91.19 162	99.42 54	98.32 159

DP-MVS		95.62 75	95.84 76	94.97 109	97.16 156	88.62 117	94.54 141	97.64 133	96.94 20	96.58 107	97.32 119	93.07 111	98.72 168	90.45 179	98.84 146	97.57 250

test_fmvsmconf0.1_n		95.61 76	95.72 83	95.26 96	96.85 176	89.20 104	93.51 179	98.60 16	85.68 288	97.42 59	98.30 41	95.34 39	98.39 216	96.85 11	98.98 125	98.19 175

OPM-MVS		95.61 76	95.45 92	96.08 59	98.49 61	91.00 72	92.65 217	97.33 168	90.05 180	96.77 96	96.85 162	95.04 54	98.56 194	92.77 112	99.06 112	98.70 113
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_9		95.58 78	95.91 71	94.59 133	97.25 149	86.26 177	92.96 200	97.86 108	91.88 117	97.52 52	98.13 46	91.45 157	98.54 197	97.17 5	98.99 123	98.98 68

RPSCF		95.58 78	94.89 119	97.62 9	97.58 131	96.30 8	95.97 74	97.53 148	92.42 96	93.41 258	97.78 74	91.21 164	97.77 292	91.06 164	97.06 311	98.80 98

MIMVSNet1		95.52 80	95.45 92	95.72 78	99.14 5	89.02 108	96.23 63	96.87 209	93.73 74	97.87 36	98.49 34	90.73 182	99.05 113	86.43 292	99.60 28	99.10 55

Anonymous20240529		95.50 81	95.83 77	94.50 138	97.33 146	85.93 188	95.19 114	96.77 218	96.64 24	97.61 47	98.05 51	93.23 104	98.79 155	88.60 245	99.04 120	98.78 100

Vis-MVSNet		95.50 81	95.48 91	95.56 85	98.11 86	89.40 100	95.35 100	98.22 50	92.36 99	94.11 232	98.07 50	92.02 138	99.44 34	93.38 94	97.67 281	97.85 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EC-MVSNet		95.44 83	95.62 86	94.89 114	96.93 170	87.69 138	96.48 44	99.14 7	93.93 70	92.77 293	94.52 304	93.95 86	99.49 30	93.62 77	99.22 97	97.51 255

test_fmvsmconf_n		95.43 84	95.50 90	95.22 101	96.48 215	89.19 105	93.23 190	98.36 33	85.61 291	96.92 87	98.02 55	95.23 45	98.38 219	96.69 14	98.95 134	98.09 183

pm-mvs1		95.43 84	95.94 67	93.93 163	98.38 66	85.08 206	95.46 98	97.12 186	91.84 122	97.28 67	98.46 36	95.30 42	97.71 299	90.17 198	99.42 54	98.99 64

DeepC-MVS	91.39 4	95.43 84	95.33 102	95.71 79	97.67 126	90.17 87	93.86 167	98.02 87	87.35 246	96.22 128	97.99 59	94.48 77	99.05 113	92.73 115	99.68 20	97.93 206
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0805		95.42 87	95.93 69	93.86 167	98.75 36	88.47 124	97.68 9	94.29 316	96.48 27	95.38 174	93.63 337	94.89 63	97.94 272	95.38 41	96.92 319	95.17 365

XVG-OURS-SEG-HR		95.38 88	95.00 117	96.51 50	98.10 87	94.07 24	92.46 227	98.13 64	90.69 164	93.75 246	96.25 214	98.03 2	97.02 346	92.08 132	95.55 357	98.45 144

UniMVSNet_NR-MVSNet		95.35 89	95.21 107	95.76 76	97.69 124	88.59 120	92.26 243	97.84 112	94.91 53	96.80 94	95.78 245	90.42 187	99.41 44	91.60 151	99.58 34	99.29 34

MSP-MVS		95.34 90	94.63 137	97.48 18	98.67 37	94.05 27	96.41 49	98.18 55	91.26 149	95.12 197	95.15 273	86.60 258	99.50 24	93.43 92	96.81 323	98.89 86
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
SPE-MVS-test		95.32 91	95.10 113	95.96 63	96.86 175	90.75 81	96.33 53	99.20 5	93.99 67	91.03 338	93.73 335	93.52 92	99.55 19	91.81 142	99.45 49	97.58 249

FC-MVSNet-test		95.32 91	95.88 73	93.62 178	98.49 61	81.77 265	95.90 77	98.32 36	93.93 70	97.53 51	97.56 93	88.48 217	99.40 51	92.91 111	99.83 5	99.68 7

UniMVSNet (Re)		95.32 91	95.15 109	95.80 75	97.79 114	88.91 110	92.91 203	98.07 76	93.46 81	96.31 120	95.97 234	90.14 195	99.34 68	92.11 130	99.64 26	99.16 45

Gipuma		95.31 94	95.80 80	93.81 170	97.99 102	90.91 74	96.42 48	97.95 98	96.69 22	91.78 325	98.85 17	91.77 145	95.49 394	91.72 147	99.08 111	95.02 374
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mvs5depth		95.28 95	95.82 79	93.66 176	96.42 219	83.08 241	97.35 12	99.28 3	96.44 29	96.20 130	99.65 2	84.10 284	98.01 264	94.06 64	98.93 135	99.87 1

DU-MVS		95.28 95	95.12 111	95.75 77	97.75 116	88.59 120	92.58 221	97.81 117	93.99 67	96.80 94	95.90 235	90.10 198	99.41 44	91.60 151	99.58 34	99.26 35

NR-MVSNet		95.28 95	95.28 105	95.26 96	97.75 116	87.21 146	95.08 116	97.37 160	93.92 72	97.65 43	95.90 235	90.10 198	99.33 73	90.11 200	99.66 24	99.26 35

TransMVSNet (Re)		95.27 98	96.04 62	92.97 207	98.37 68	81.92 264	95.07 117	96.76 219	93.97 69	97.77 39	98.57 29	95.72 24	97.90 273	88.89 235	99.23 94	99.08 56

fmvsm_s_conf0.5_n_3		95.20 99	95.95 66	92.94 211	96.60 202	82.18 261	93.13 193	98.39 30	91.44 144	97.16 72	97.68 82	93.03 113	97.82 284	97.54 3	98.63 183	98.81 96

fmvsm_l_conf0.5_n_3		95.19 100	95.36 98	94.68 125	96.79 183	87.49 140	93.05 196	98.38 31	87.21 250	96.59 106	97.76 79	94.20 81	98.11 250	95.90 26	98.40 207	98.42 147

SD-MVS		95.19 100	95.73 82	93.55 182	96.62 201	88.88 113	94.67 131	98.05 80	91.26 149	97.25 69	96.40 197	95.42 34	94.36 417	92.72 116	99.19 100	97.40 265
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
VPA-MVSNet		95.14 102	95.67 85	93.58 181	97.76 115	83.15 238	94.58 136	97.58 142	93.39 82	97.05 80	98.04 53	93.25 103	98.51 203	89.75 211	99.59 30	99.08 56

casdiffmvs_mvg		95.10 103	95.62 86	93.53 185	96.25 240	83.23 234	92.66 216	98.19 53	93.06 88	97.49 54	97.15 136	94.78 65	98.71 174	92.27 128	98.72 172	98.65 119
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		95.09 104	95.40 96	94.15 151	97.42 141	84.35 214	93.91 165	96.69 223	94.41 61	96.67 100	97.25 125	87.67 235	99.14 96	95.78 29	98.81 154	98.97 71

test_fmvsmvis_n_1920		95.08 105	95.40 96	94.13 154	96.66 191	87.75 137	93.44 183	98.49 21	85.57 292	98.27 24	97.11 140	94.11 84	97.75 295	96.26 20	98.72 172	96.89 294

HPM-MVS++		95.02 106	94.39 145	96.91 41	97.88 107	93.58 41	94.09 158	96.99 195	91.05 154	92.40 307	95.22 272	91.03 173	99.25 84	92.11 130	98.69 177	97.90 213

APD-MVS		95.00 107	94.69 130	95.93 67	97.38 142	90.88 75	94.59 134	97.81 117	89.22 197	95.46 171	96.17 221	93.42 98	99.34 68	89.30 220	98.87 144	97.56 252
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 108	95.33 102	93.91 164	98.97 20	97.16 3	95.54 96	95.85 267	96.47 28	93.40 261	97.46 105	95.31 41	95.47 395	86.18 296	98.78 161	89.11 445
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 109	94.75 126	95.57 84	98.86 27	88.69 114	96.37 50	96.81 214	85.23 299	94.75 215	97.12 139	91.85 142	99.40 51	93.45 88	98.33 219	98.62 129
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SixPastTwentyTwo		94.91 110	95.21 107	93.98 158	98.52 53	83.19 237	95.93 75	94.84 302	94.86 54	98.49 19	98.74 21	81.45 313	99.60 10	94.69 50	99.39 62	99.15 46

FIs		94.90 111	95.35 99	93.55 182	98.28 74	81.76 266	95.33 102	98.14 63	93.05 89	97.07 77	97.18 134	87.65 236	99.29 77	91.72 147	99.69 17	99.61 14

AllTest		94.88 112	94.51 143	96.00 60	98.02 95	92.17 54	95.26 108	98.43 25	90.48 172	95.04 203	96.74 173	92.54 125	97.86 281	85.11 311	98.98 125	97.98 197

FMVSNet1		94.84 113	95.13 110	93.97 159	97.60 129	84.29 215	95.99 71	96.56 235	92.38 97	97.03 81	98.53 31	90.12 196	98.98 122	88.78 239	99.16 105	98.65 119

ANet_high		94.83 114	96.28 47	90.47 320	96.65 192	73.16 401	94.33 145	98.74 14	96.39 31	98.09 34	98.93 13	93.37 99	98.70 175	90.38 182	99.68 20	99.53 17

MVSMamba_PlusPlus		94.82 115	95.89 72	91.62 271	97.82 111	78.88 327	96.52 39	97.60 139	97.14 17	94.23 228	98.48 35	87.01 248	99.71 3	95.43 39	98.80 157	96.28 322

3Dnovator	92.54 3	94.80 116	94.90 118	94.47 141	95.47 301	87.06 150	96.63 35	97.28 174	91.82 125	94.34 227	97.41 106	90.60 185	98.65 184	92.47 124	98.11 245	97.70 239

CPTT-MVS		94.74 117	94.12 159	96.60 47	98.15 84	93.01 46	95.84 80	97.66 132	89.21 198	93.28 266	95.46 261	88.89 211	98.98 122	89.80 207	98.82 152	97.80 229

test_fmvsm_n_1920		94.72 118	94.74 128	94.67 126	96.30 234	88.62 117	93.19 191	98.07 76	85.63 290	97.08 76	97.35 115	90.86 175	97.66 302	95.70 30	98.48 201	97.74 237

XVG-OURS		94.72 118	94.12 159	96.50 51	98.00 99	94.23 22	91.48 277	98.17 59	90.72 163	95.30 180	96.47 190	87.94 231	96.98 347	91.41 158	97.61 285	98.30 163

fmvsm_s_conf0.5_n_8		94.70 120	95.34 100	92.78 221	96.77 184	81.50 273	92.64 218	98.50 19	91.51 141	97.22 70	97.93 62	88.07 226	98.45 213	96.62 16	98.80 157	98.39 152

CSCG		94.69 121	94.75 126	94.52 137	97.55 133	87.87 134	95.01 120	97.57 143	92.68 91	96.20 130	93.44 343	91.92 141	98.78 159	89.11 229	99.24 93	96.92 292

v10		94.68 122	95.27 106	92.90 214	96.57 204	80.15 289	94.65 133	97.57 143	90.68 165	97.43 57	98.00 56	88.18 223	99.15 94	94.84 49	99.55 38	99.41 26

v8		94.65 123	95.29 104	92.74 222	96.65 192	79.77 304	94.59 134	97.17 181	91.86 118	97.47 56	97.93 62	88.16 224	99.08 106	94.32 58	99.47 45	99.38 28

sasdasda		94.59 124	94.69 130	94.30 146	95.60 292	87.03 151	95.59 89	98.24 46	91.56 136	95.21 189	92.04 377	94.95 59	98.66 181	91.45 156	97.57 287	97.20 276

canonicalmvs		94.59 124	94.69 130	94.30 146	95.60 292	87.03 151	95.59 89	98.24 46	91.56 136	95.21 189	92.04 377	94.95 59	98.66 181	91.45 156	97.57 287	97.20 276

CNVR-MVS		94.58 126	94.29 151	95.46 88	96.94 168	89.35 102	91.81 266	96.80 215	89.66 187	93.90 244	95.44 263	92.80 120	98.72 168	92.74 114	98.52 196	98.32 159

GeoE		94.55 127	94.68 134	94.15 151	97.23 151	85.11 205	94.14 155	97.34 167	88.71 209	95.26 184	95.50 259	94.65 69	99.12 100	90.94 168	98.40 207	98.23 169

EG-PatchMatch MVS		94.54 128	94.67 135	94.14 153	97.87 109	86.50 167	92.00 251	96.74 220	88.16 227	96.93 86	97.61 89	93.04 112	97.90 273	91.60 151	98.12 244	98.03 191

fmvsm_l_conf0.5_n_9		94.51 129	95.11 112	92.72 223	96.70 188	83.14 239	91.91 258	97.89 105	88.44 218	97.30 64	97.57 91	91.60 149	97.54 310	95.82 28	98.74 170	97.47 257

fmvsm_s_conf0.5_n_5		94.50 130	94.80 122	93.60 179	96.80 181	84.93 207	92.81 207	97.59 141	85.27 298	96.85 92	97.29 120	91.48 156	98.05 257	96.67 15	98.47 202	97.83 224

IS-MVSNet		94.49 131	94.35 150	94.92 111	98.25 78	86.46 170	97.13 17	94.31 315	96.24 35	96.28 124	96.36 204	82.88 295	99.35 65	88.19 255	99.52 42	98.96 75

Baseline_NR-MVSNet		94.47 132	95.09 114	92.60 234	98.50 60	80.82 285	92.08 247	96.68 226	93.82 73	96.29 122	98.56 30	90.10 198	97.75 295	90.10 202	99.66 24	99.24 39

MGCFI-Net		94.44 133	94.67 135	93.75 172	95.56 295	85.47 199	95.25 109	98.24 46	91.53 138	95.04 203	92.21 372	94.94 61	98.54 197	91.56 154	97.66 282	97.24 274

SDMVSNet		94.43 134	95.02 115	92.69 225	97.93 104	82.88 245	91.92 257	95.99 264	93.65 79	95.51 166	98.63 26	94.60 71	96.48 367	87.57 269	99.35 67	98.70 113

MM		94.41 135	94.14 158	95.22 101	95.84 273	87.21 146	94.31 147	90.92 379	94.48 59	92.80 291	97.52 98	85.27 274	99.49 30	96.58 17	99.57 36	98.97 71

SSM_0404		94.38 136	94.69 130	93.43 191	97.16 156	83.23 234	93.95 163	97.84 112	91.46 142	95.70 159	96.56 187	92.50 129	99.08 106	88.83 236	98.23 232	97.98 197

fmvsm_s_conf0.1_n_2		94.38 136	94.78 125	93.19 201	97.07 162	81.72 268	91.97 252	97.51 151	87.05 256	97.31 63	97.92 67	88.29 221	98.15 246	97.10 6	98.81 154	99.70 5

VDD-MVS		94.37 138	94.37 147	94.40 144	97.49 136	86.07 183	93.97 162	93.28 337	94.49 58	96.24 126	97.78 74	87.99 230	98.79 155	88.92 233	99.14 107	98.34 158

EI-MVSNet-Vis-set		94.36 139	94.28 152	94.61 129	92.55 381	85.98 185	92.44 229	94.69 309	93.70 75	96.12 135	95.81 241	91.24 162	98.86 141	93.76 75	98.22 235	98.98 68

EI-MVSNet-UG-set		94.35 140	94.27 154	94.59 133	92.46 384	85.87 190	92.42 231	94.69 309	93.67 78	96.13 134	95.84 239	91.20 165	98.86 141	93.78 72	98.23 232	99.03 60

PHI-MVS		94.34 141	93.80 169	95.95 64	95.65 288	91.67 66	94.82 125	97.86 108	87.86 234	93.04 283	94.16 320	91.58 150	98.78 159	90.27 191	98.96 132	97.41 262

casdiffmvs		94.32 142	94.80 122	92.85 216	96.05 258	81.44 275	92.35 235	98.05 80	91.53 138	95.75 154	96.80 166	93.35 100	98.49 205	91.01 167	98.32 221	98.64 125
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpnnormal		94.27 143	94.87 120	92.48 239	97.71 121	80.88 284	94.55 140	95.41 286	93.70 75	96.67 100	97.72 80	91.40 158	98.18 242	87.45 271	99.18 102	98.36 154

fmvsm_s_conf0.5_n_4		94.26 144	94.58 139	93.31 194	96.40 221	82.73 252	92.59 220	97.41 158	86.60 261	96.33 117	97.07 144	89.91 202	98.07 254	96.88 10	98.01 258	99.13 48

fmvsm_s_conf0.1_n_a		94.26 144	94.37 147	93.95 162	97.36 144	85.72 194	94.15 153	95.44 283	83.25 328	95.51 166	98.05 51	92.54 125	97.19 336	95.55 35	97.46 294	98.94 77

HQP_MVS		94.26 144	93.93 165	95.23 99	97.71 121	88.12 129	94.56 138	97.81 117	91.74 130	93.31 263	95.59 254	86.93 251	98.95 130	89.26 224	98.51 198	98.60 130

baseline		94.26 144	94.80 122	92.64 227	96.08 255	80.99 282	93.69 173	98.04 84	90.80 162	94.89 210	96.32 206	93.19 105	98.48 209	91.68 149	98.51 198	98.43 146

fmvsm_s_conf0.5_n_2		94.25 148	94.63 137	93.10 203	96.65 192	81.75 267	91.72 270	97.25 175	86.93 260	97.20 71	97.67 84	88.44 219	98.14 249	97.06 9	98.77 162	99.42 24

SSM_0407		94.23 149	94.56 141	93.24 199	96.65 192	82.79 247	93.66 175	97.84 112	91.46 142	95.19 191	96.56 187	92.50 129	98.99 121	88.83 236	98.32 221	97.93 206

OMC-MVS		94.22 150	93.69 176	95.81 74	97.25 149	91.27 68	92.27 242	97.40 159	87.10 255	94.56 220	95.42 264	93.74 87	98.11 250	86.62 286	98.85 145	98.06 184

LCM-MVSNet-Re		94.20 151	94.58 139	93.04 204	95.91 268	83.13 240	93.79 169	99.19 6	92.00 111	98.84 9	98.04 53	93.64 89	99.02 118	81.28 353	98.54 193	96.96 291

DeepPCF-MVS	90.46 6	94.20 151	93.56 183	96.14 57	95.96 265	92.96 47	89.48 342	97.46 155	85.14 302	96.23 127	95.42 264	93.19 105	98.08 253	90.37 185	98.76 164	97.38 268

fmvsm_s_conf0.1_n		94.19 153	94.41 144	93.52 187	97.22 153	84.37 212	93.73 171	95.26 290	84.45 315	95.76 152	98.00 56	91.85 142	97.21 333	95.62 31	97.82 272	98.98 68

fmvsm_s_conf0.5_n_6		94.14 154	94.54 142	92.95 209	96.51 211	82.74 251	92.71 213	98.13 64	86.56 263	96.44 111	96.85 162	88.51 216	98.05 257	96.03 23	99.09 110	98.06 184

NormalMVS		94.10 155	93.36 190	96.31 56	99.01 15	90.84 77	94.70 129	97.90 102	90.98 155	93.22 272	95.73 248	78.94 333	99.12 100	90.38 182	99.42 54	98.97 71

KD-MVS_self_test		94.10 155	94.73 129	92.19 247	97.66 127	79.49 312	94.86 124	97.12 186	89.59 189	96.87 88	97.65 86	90.40 189	98.34 226	89.08 230	99.35 67	98.75 104

NCCC		94.08 157	93.54 184	95.70 81	96.49 213	89.90 90	92.39 233	96.91 202	90.64 166	92.33 314	94.60 300	90.58 186	98.96 128	90.21 195	97.70 279	98.23 169

VDDNet		94.03 158	94.27 154	93.31 194	98.87 26	82.36 257	95.51 97	91.78 370	97.19 16	96.32 119	98.60 28	84.24 282	98.75 163	87.09 278	98.83 151	98.81 96

fmvsm_s_conf0.5_n_a		94.02 159	94.08 161	93.84 168	96.72 187	85.73 193	93.65 177	95.23 292	83.30 326	95.13 196	97.56 93	92.22 134	97.17 337	95.51 36	97.41 296	98.64 125

fmvsm_s_conf0.5_n		94.00 160	94.20 156	93.42 192	96.69 189	84.37 212	93.38 185	95.13 294	84.50 314	95.40 173	97.55 97	91.77 145	97.20 334	95.59 32	97.79 273	98.69 116

dcpmvs_2		93.96 161	95.01 116	90.82 309	97.60 129	74.04 396	93.68 174	98.85 10	89.80 185	97.82 37	97.01 151	91.14 169	99.21 87	90.56 176	98.59 188	99.19 43

sd_testset		93.94 162	94.39 145	92.61 233	97.93 104	83.24 233	93.17 192	95.04 296	93.65 79	95.51 166	98.63 26	94.49 76	95.89 387	81.72 348	99.35 67	98.70 113

EPP-MVSNet		93.91 163	93.68 177	94.59 133	98.08 88	85.55 198	97.44 11	94.03 321	94.22 64	94.94 207	96.19 217	82.07 307	99.57 15	87.28 275	98.89 139	98.65 119

Effi-MVS+-dtu		93.90 164	92.60 217	97.77 4	94.74 328	96.67 6	94.00 160	95.41 286	89.94 181	91.93 324	92.13 375	90.12 196	98.97 127	87.68 268	97.48 292	97.67 242

viewmacassd2359aftdt		93.83 165	94.36 149	92.24 244	96.45 216	79.58 309	91.60 272	97.96 95	89.14 199	95.05 202	97.09 143	93.69 88	98.48 209	89.79 208	98.43 205	98.65 119

fmvsm_l_conf0.5_n		93.79 166	93.81 167	93.73 174	96.16 246	86.26 177	92.46 227	96.72 221	81.69 350	95.77 151	97.11 140	90.83 177	97.82 284	95.58 33	97.99 261	97.11 279

IterMVS-LS		93.78 167	94.28 152	92.27 243	96.27 237	79.21 320	91.87 262	96.78 216	91.77 128	96.57 108	97.07 144	87.15 245	98.74 166	91.99 136	99.03 121	98.86 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 168	93.44 187	94.60 132	96.14 249	87.90 133	93.36 186	97.14 183	85.53 293	93.90 244	95.45 262	91.30 161	98.59 191	89.51 214	98.62 184	97.31 271
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_LR		93.66 169	93.28 193	94.80 118	96.25 240	90.95 73	90.21 319	95.43 285	87.91 231	93.74 248	94.40 310	92.88 118	96.38 373	90.39 181	98.28 226	97.07 284

MVS_111021_HR		93.63 170	93.42 189	94.26 148	96.65 192	86.96 155	89.30 349	96.23 252	88.36 222	93.57 252	94.60 300	93.45 95	97.77 292	90.23 194	98.38 212	98.03 191

fmvsm_s_conf0.5_n_7		93.61 171	93.94 164	92.63 230	96.11 252	82.76 250	90.81 296	97.55 145	86.57 262	93.14 278	97.69 81	90.17 194	96.83 356	94.46 54	98.93 135	98.31 161

mamba_0408		93.60 172	93.72 172	93.27 197	96.65 192	82.79 247	88.81 363	97.68 129	90.62 168	95.19 191	96.01 230	91.54 154	99.08 106	88.63 243	98.32 221	97.93 206

fmvsm_l_conf0.5_n_a		93.59 173	93.63 178	93.49 189	96.10 253	85.66 196	92.32 237	96.57 234	81.32 353	95.63 161	97.14 137	90.19 192	97.73 298	95.37 42	98.03 255	97.07 284

v1144		93.50 174	93.81 167	92.57 235	96.28 235	79.61 307	91.86 264	96.96 196	86.95 258	95.91 145	96.32 206	87.65 236	98.96 128	93.51 82	98.88 141	99.13 48

v1192		93.49 175	93.78 170	92.62 232	96.16 246	79.62 306	91.83 265	97.22 179	86.07 275	96.10 136	96.38 202	87.22 243	99.02 118	94.14 63	98.88 141	99.22 40

WR-MVS		93.49 175	93.72 172	92.80 219	97.57 132	80.03 295	90.14 322	95.68 271	93.70 75	96.62 104	95.39 269	87.21 244	99.04 116	87.50 270	99.64 26	99.33 31

balanced_conf03		93.45 177	94.17 157	91.28 288	95.81 277	78.40 335	96.20 64	97.48 154	88.56 216	95.29 182	97.20 133	85.56 273	99.21 87	92.52 123	98.91 138	96.24 325

LuminaMVS		93.43 178	93.18 196	94.16 150	97.32 147	85.29 203	93.36 186	93.94 326	88.09 228	97.12 75	96.43 193	80.11 324	98.98 122	93.53 81	98.76 164	98.21 171

V42		93.43 178	93.58 181	92.97 207	95.34 307	81.22 278	92.67 215	96.49 240	87.25 249	96.20 130	96.37 203	87.32 242	98.85 143	92.39 127	98.21 236	98.85 92

K. test v3		93.37 180	93.27 194	93.66 176	98.05 91	82.62 253	94.35 144	86.62 412	96.05 39	97.51 53	98.85 17	76.59 363	99.65 5	93.21 100	98.20 238	98.73 108

viewdifsd2359ckpt11		93.36 181	93.99 162	91.48 277	95.50 299	78.39 337	90.47 308	96.69 223	88.59 213	96.03 139	96.88 159	93.48 93	97.63 305	90.20 196	98.07 250	98.41 148

viewmsd2359difaftdt		93.36 181	93.99 162	91.48 277	95.50 299	78.39 337	90.47 308	96.69 223	88.59 213	96.03 139	96.88 159	93.48 93	97.63 305	90.20 196	98.07 250	98.41 148

PM-MVS		93.33 183	92.67 214	95.33 92	96.58 203	94.06 25	92.26 243	92.18 359	85.92 278	96.22 128	96.61 183	85.64 271	95.99 385	90.35 186	98.23 232	95.93 339

v1240		93.29 184	93.71 175	92.06 255	96.01 263	77.89 345	91.81 266	97.37 160	85.12 303	96.69 99	96.40 197	86.67 256	99.07 112	94.51 52	98.76 164	99.22 40

v2v482		93.29 184	93.63 178	92.29 242	96.35 227	78.82 329	91.77 269	96.28 248	88.45 217	95.70 159	96.26 213	86.02 265	98.90 134	93.02 107	98.81 154	99.14 47

SymmetryMVS		93.26 186	92.36 225	95.97 62	97.13 159	90.84 77	94.70 129	91.61 373	90.98 155	93.22 272	95.73 248	78.94 333	99.12 100	90.38 182	98.53 194	97.97 201

alignmvs		93.26 186	92.85 203	94.50 138	95.70 283	87.45 141	93.45 182	95.76 268	91.58 135	95.25 186	92.42 370	81.96 310	98.72 168	91.61 150	97.87 270	97.33 270

v1921920		93.26 186	93.61 180	92.19 247	96.04 262	78.31 339	91.88 261	97.24 177	85.17 301	96.19 133	96.19 217	86.76 255	99.05 113	94.18 62	98.84 146	99.22 40

SSM_04072		93.25 189	93.72 172	91.84 260	96.65 192	82.79 247	88.81 363	97.68 129	90.62 168	95.19 191	96.01 230	91.54 154	94.81 409	88.63 243	98.32 221	97.93 206

MSLP-MVS++		93.25 189	93.88 166	91.37 282	96.34 228	82.81 246	93.11 194	97.74 125	89.37 193	94.08 234	95.29 271	90.40 189	96.35 375	90.35 186	98.25 230	94.96 375

GBi-Net		93.21 191	92.96 199	93.97 159	95.40 303	84.29 215	95.99 71	96.56 235	88.63 210	95.10 198	98.53 31	81.31 315	98.98 122	86.74 281	98.38 212	98.65 119

test1		93.21 191	92.96 199	93.97 159	95.40 303	84.29 215	95.99 71	96.56 235	88.63 210	95.10 198	98.53 31	81.31 315	98.98 122	86.74 281	98.38 212	98.65 119

v144192		93.20 193	93.54 184	92.16 251	96.05 258	78.26 340	91.95 253	97.14 183	84.98 308	95.96 141	96.11 225	87.08 247	99.04 116	93.79 71	98.84 146	99.17 44

viewcassd2359sk11		93.16 194	93.51 186	92.13 253	96.07 256	79.59 308	90.88 293	97.97 93	87.82 235	94.23 228	96.19 217	92.31 131	98.53 200	88.58 246	97.51 289	98.28 164

viewmanbaseed2359cas		93.08 195	93.43 188	92.01 257	95.69 284	79.29 316	91.15 285	97.70 128	87.45 245	94.18 231	96.12 224	92.31 131	98.37 223	88.58 246	97.73 275	98.38 153

VPNet		93.08 195	93.76 171	91.03 298	98.60 43	75.83 380	91.51 275	95.62 272	91.84 122	95.74 155	97.10 142	89.31 207	98.32 227	85.07 313	99.06 112	98.93 79

UGNet		93.08 195	92.50 220	94.79 119	93.87 354	87.99 132	95.07 117	94.26 318	90.64 166	87.33 406	97.67 84	86.89 253	98.49 205	88.10 258	98.71 174	97.91 212
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.		93.07 198	92.41 223	95.06 106	95.82 275	90.87 76	90.97 291	92.61 352	88.04 229	94.61 219	93.79 334	88.08 225	97.81 286	89.41 217	98.39 211	96.50 310

ETV-MVS		92.99 199	92.74 207	93.72 175	95.86 272	86.30 176	92.33 236	97.84 112	91.70 133	92.81 290	86.17 439	92.22 134	99.19 91	88.03 262	97.73 275	95.66 353

EI-MVSNet		92.99 199	93.26 195	92.19 247	92.12 394	79.21 320	92.32 237	94.67 311	91.77 128	95.24 187	95.85 237	87.14 246	98.49 205	91.99 136	98.26 228	98.86 89

MCST-MVS		92.91 201	92.51 219	94.10 155	97.52 134	85.72 194	91.36 281	97.13 185	80.33 362	92.91 289	94.24 316	91.23 163	98.72 168	89.99 204	97.93 266	97.86 220

h-mvs33		92.89 202	91.99 235	95.58 83	96.97 166	90.55 83	93.94 164	94.01 324	89.23 195	93.95 241	96.19 217	76.88 359	99.14 96	91.02 165	95.71 353	97.04 288

MVS_0304		92.88 203	92.27 226	94.69 124	92.35 385	86.03 184	92.88 205	89.68 387	90.53 171	91.52 328	96.43 193	82.52 303	99.32 74	95.01 46	99.54 39	98.71 112

QAPM		92.88 203	92.77 205	93.22 200	95.82 275	83.31 231	96.45 45	97.35 166	83.91 320	93.75 246	96.77 168	89.25 208	98.88 137	84.56 319	97.02 313	97.49 256

v148		92.87 205	93.29 191	91.62 271	96.25 240	77.72 348	91.28 282	95.05 295	89.69 186	95.93 144	96.04 228	87.34 241	98.38 219	90.05 203	97.99 261	98.78 100

Anonymous20240521		92.86 206	93.57 182	90.74 311	96.57 204	75.50 382	94.15 153	95.60 273	89.38 192	95.90 146	97.90 71	80.39 323	97.96 270	92.60 120	99.68 20	98.75 104

Effi-MVS+		92.79 207	92.74 207	92.94 211	95.10 315	83.30 232	94.00 160	97.53 148	91.36 147	89.35 370	90.65 401	94.01 85	98.66 181	87.40 273	95.30 366	96.88 296

FMVSNet2		92.78 208	92.73 209	92.95 209	95.40 303	81.98 263	94.18 152	95.53 281	88.63 210	96.05 137	97.37 109	81.31 315	98.81 151	87.38 274	98.67 180	98.06 184

Fast-Effi-MVS+-dtu		92.77 209	92.16 229	94.58 136	94.66 333	88.25 127	92.05 248	96.65 228	89.62 188	90.08 355	91.23 388	92.56 124	98.60 189	86.30 294	96.27 339	96.90 293

AstraMVS		92.75 210	92.73 209	92.79 220	97.02 163	81.48 274	92.88 205	90.62 383	87.99 230	96.48 109	96.71 177	82.02 308	98.48 209	92.44 125	98.46 203	98.40 151

LF4IMVS		92.72 211	92.02 234	94.84 117	95.65 288	91.99 58	92.92 202	96.60 231	85.08 305	92.44 305	93.62 338	86.80 254	96.35 375	86.81 280	98.25 230	96.18 328

train_agg		92.71 212	91.83 241	95.35 90	96.45 216	89.46 96	90.60 304	96.92 200	79.37 373	90.49 346	94.39 311	91.20 165	98.88 137	88.66 242	98.43 205	97.72 238

VNet		92.67 213	92.96 199	91.79 263	96.27 237	80.15 289	91.95 253	94.98 298	92.19 107	94.52 222	96.07 227	87.43 240	97.39 324	84.83 315	98.38 212	97.83 224

CDPH-MVS		92.67 213	91.83 241	95.18 103	96.94 168	88.46 125	90.70 301	97.07 189	77.38 389	92.34 313	95.08 279	92.67 123	98.88 137	85.74 299	98.57 190	98.20 173

guyue		92.60 215	92.62 215	92.52 238	96.73 185	81.00 281	93.00 198	91.83 369	88.28 223	96.38 114	96.23 215	80.71 321	98.37 223	92.06 135	98.37 217	98.20 173

Anonymous202405211		92.58 216	92.50 220	92.83 217	96.55 206	83.22 236	92.43 230	91.64 372	94.10 66	95.59 163	96.64 181	81.88 312	97.50 313	85.12 310	98.52 196	97.77 233

XXY-MVS		92.58 216	93.16 197	90.84 308	97.75 116	79.84 300	91.87 262	96.22 254	85.94 277	95.53 165	97.68 82	92.69 122	94.48 413	83.21 330	97.51 289	98.21 171

viewdifsd2359ckpt13		92.57 218	92.48 222	92.83 217	95.60 292	82.35 259	91.80 268	97.49 153	85.04 306	93.14 278	95.41 267	90.94 174	98.25 234	86.68 284	96.24 340	97.87 219

MVS_Test		92.57 218	93.29 191	90.40 323	93.53 360	75.85 378	92.52 223	96.96 196	88.73 207	92.35 311	96.70 178	90.77 178	98.37 223	92.53 122	95.49 359	96.99 290

TAPA-MVS	88.58 10	92.49 220	91.75 243	94.73 121	96.50 212	89.69 92	92.91 203	97.68 129	78.02 386	92.79 292	94.10 321	90.85 176	97.96 270	84.76 317	98.16 240	96.54 305
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 221	92.72 211	91.71 267	96.65 192	78.91 326	88.85 360	97.17 181	83.89 321	92.45 304	96.76 170	89.86 203	97.09 342	90.24 193	98.59 188	99.12 51

test_fmvs3		92.42 222	92.40 224	92.46 241	93.80 357	87.28 144	93.86 167	97.05 190	76.86 395	96.25 125	98.66 24	82.87 296	91.26 438	95.44 38	96.83 322	98.82 94

ab-mvs		92.40 223	92.62 215	91.74 265	97.02 163	81.65 269	95.84 80	95.50 282	86.95 258	92.95 288	97.56 93	90.70 183	97.50 313	79.63 372	97.43 295	96.06 333

CANet		92.38 224	91.99 235	93.52 187	93.82 356	83.46 229	91.14 286	97.00 193	89.81 184	86.47 410	94.04 323	87.90 232	99.21 87	89.50 215	98.27 227	97.90 213

EIA-MVS		92.35 225	92.03 233	93.30 196	95.81 277	83.97 223	92.80 209	98.17 59	87.71 239	89.79 363	87.56 429	91.17 168	99.18 92	87.97 263	97.27 300	96.77 300

diffmvs_AUTHOR		92.34 226	92.70 212	91.26 289	94.20 343	78.42 334	89.12 354	97.60 139	87.16 251	93.17 277	95.50 259	88.66 214	97.57 309	91.30 160	97.61 285	97.79 230

DP-MVS Recon		92.31 227	91.88 239	93.60 179	97.18 155	86.87 157	91.10 288	97.37 160	84.92 309	92.08 321	94.08 322	88.59 215	98.20 239	83.50 327	98.14 242	95.73 348

IMVS_0407		92.28 228	92.83 204	90.63 316	95.19 311	76.72 364	92.79 210	96.89 203	85.92 278	93.55 253	94.50 305	91.06 170	98.07 254	88.49 249	97.07 307	97.10 280

RRT-MVS		92.28 228	93.01 198	90.07 332	94.06 349	73.01 403	95.36 99	97.88 106	92.24 105	95.16 194	97.52 98	78.51 341	99.29 77	90.55 177	95.83 351	97.92 211

F-COLMAP		92.28 228	91.06 260	95.95 64	97.52 134	91.90 60	93.53 178	97.18 180	83.98 319	88.70 384	94.04 323	88.41 220	98.55 196	80.17 365	95.99 346	97.39 266

OpenMVS	89.45 8	92.27 231	92.13 232	92.68 226	94.53 337	84.10 221	95.70 84	97.03 191	82.44 342	91.14 337	96.42 195	88.47 218	98.38 219	85.95 297	97.47 293	95.55 358

hse-mvs2		92.24 232	91.20 255	95.38 89	96.16 246	90.65 82	92.52 223	92.01 366	89.23 195	93.95 241	92.99 354	76.88 359	98.69 177	91.02 165	96.03 344	96.81 298

IMVS_0403		92.20 233	92.70 212	90.69 312	95.19 311	76.72 364	92.39 233	96.89 203	85.92 278	93.66 250	94.50 305	90.18 193	98.24 236	88.49 249	97.07 307	97.10 280

MVSFormer		92.18 234	92.23 227	92.04 256	94.74 328	80.06 293	97.15 15	97.37 160	88.98 202	88.83 376	92.79 359	77.02 356	99.60 10	96.41 18	96.75 326	96.46 314

VortexMVS		92.13 235	92.56 218	90.85 307	94.54 336	76.17 374	92.30 240	96.63 230	86.20 271	96.66 102	96.79 167	79.87 326	98.16 244	91.27 161	98.76 164	98.24 168

HQP-MVS		92.09 236	91.49 249	93.88 165	96.36 224	84.89 208	91.37 278	97.31 169	87.16 251	88.81 378	93.40 344	84.76 279	98.60 189	86.55 289	97.73 275	98.14 180

DELS-MVS		92.05 237	92.16 229	91.72 266	94.44 338	80.13 291	87.62 380	97.25 175	87.34 247	92.22 316	93.18 351	89.54 206	98.73 167	89.67 212	98.20 238	96.30 320
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
FE-MVSNET		92.02 238	92.22 228	91.41 281	96.63 200	79.08 322	91.53 274	96.84 212	85.52 295	95.16 194	96.14 222	83.97 285	97.50 313	85.48 303	98.75 168	97.64 244

TinyColmap		92.00 239	92.76 206	89.71 341	95.62 291	77.02 357	90.72 300	96.17 257	87.70 240	95.26 184	96.29 208	92.54 125	96.45 370	81.77 346	98.77 162	95.66 353

CLD-MVS		91.82 240	91.41 251	93.04 204	96.37 222	83.65 227	86.82 399	97.29 172	84.65 313	92.27 315	89.67 410	92.20 136	97.85 283	83.95 325	99.47 45	97.62 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FA-MVS(test-final)		91.81 241	91.85 240	91.68 269	94.95 318	79.99 297	96.00 70	93.44 335	87.80 236	94.02 239	97.29 120	77.60 347	98.45 213	88.04 261	97.49 291	96.61 304

BP-MVS1		91.77 242	91.10 259	93.75 172	96.42 219	83.40 230	94.10 157	91.89 367	91.27 148	93.36 262	94.85 287	64.43 417	99.29 77	94.88 47	98.74 170	98.56 134

diffmvs		91.74 243	91.93 237	91.15 296	93.06 369	78.17 341	88.77 366	97.51 151	86.28 268	92.42 306	93.96 328	88.04 228	97.46 317	90.69 174	96.67 329	97.82 227
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		91.72 244	91.20 255	93.26 198	96.17 245	91.02 71	91.14 286	95.55 280	90.16 179	90.87 339	93.56 341	86.31 261	94.40 416	79.92 371	97.12 305	94.37 393

IterMVS-SCA-FT		91.65 245	91.55 245	91.94 258	93.89 353	79.22 319	87.56 383	93.51 333	91.53 138	95.37 176	96.62 182	78.65 337	98.90 134	91.89 140	94.95 375	97.70 239

PVSNet_Blended_VisFu		91.63 246	91.20 255	92.94 211	97.73 119	83.95 224	92.14 246	97.46 155	78.85 382	92.35 311	94.98 282	84.16 283	99.08 106	86.36 293	96.77 325	95.79 346

AdaColmap		91.63 246	91.36 252	92.47 240	95.56 295	86.36 174	92.24 245	96.27 249	88.88 206	89.90 360	92.69 362	91.65 148	98.32 227	77.38 391	97.64 283	92.72 427

GDP-MVS		91.56 248	90.83 267	93.77 171	96.34 228	83.65 227	93.66 175	98.12 66	87.32 248	92.98 286	94.71 295	63.58 423	99.30 76	92.61 119	98.14 242	98.35 157

pmmvs-eth3d		91.54 249	90.73 272	93.99 157	95.76 281	87.86 135	90.83 295	93.98 325	78.23 385	94.02 239	96.22 216	82.62 302	96.83 356	86.57 287	98.33 219	97.29 272

API-MVS		91.52 250	91.61 244	91.26 289	94.16 344	86.26 177	94.66 132	94.82 303	91.17 152	92.13 320	91.08 391	90.03 201	97.06 345	79.09 379	97.35 299	90.45 443

xiu_mvs_v1_base_debu		91.47 251	91.52 246	91.33 284	95.69 284	81.56 270	89.92 329	96.05 261	83.22 329	91.26 333	90.74 396	91.55 151	98.82 146	89.29 221	95.91 347	93.62 412

xiu_mvs_v1_base		91.47 251	91.52 246	91.33 284	95.69 284	81.56 270	89.92 329	96.05 261	83.22 329	91.26 333	90.74 396	91.55 151	98.82 146	89.29 221	95.91 347	93.62 412

xiu_mvs_v1_base_debi		91.47 251	91.52 246	91.33 284	95.69 284	81.56 270	89.92 329	96.05 261	83.22 329	91.26 333	90.74 396	91.55 151	98.82 146	89.29 221	95.91 347	93.62 412

LFMVS		91.33 254	91.16 258	91.82 262	96.27 237	79.36 314	95.01 120	85.61 425	96.04 40	94.82 212	97.06 146	72.03 382	98.46 212	84.96 314	98.70 176	97.65 243

c3_l		91.32 255	91.42 250	91.00 301	92.29 387	76.79 363	87.52 386	96.42 243	85.76 286	94.72 218	93.89 331	82.73 299	98.16 244	90.93 169	98.55 191	98.04 188

Fast-Effi-MVS+		91.28 256	90.86 265	92.53 237	95.45 302	82.53 254	89.25 352	96.52 239	85.00 307	89.91 359	88.55 422	92.94 114	98.84 144	84.72 318	95.44 361	96.22 326

icg_test_0407_2		91.18 257	91.92 238	88.94 355	95.19 311	76.72 364	84.66 431	96.89 203	85.92 278	93.55 253	94.50 305	91.06 170	92.99 430	88.49 249	97.07 307	97.10 280

MDA-MVSNet-bldmvs		91.04 258	90.88 264	91.55 274	94.68 332	80.16 288	85.49 421	92.14 362	90.41 176	94.93 208	95.79 242	85.10 276	96.93 351	85.15 308	94.19 397	97.57 250

PAPM_NR		91.03 259	90.81 268	91.68 269	96.73 185	81.10 280	93.72 172	96.35 246	88.19 225	88.77 382	92.12 376	85.09 277	97.25 331	82.40 341	93.90 402	96.68 303

MSDG		90.82 260	90.67 273	91.26 289	94.16 344	83.08 241	86.63 404	96.19 255	90.60 170	91.94 323	91.89 379	89.16 209	95.75 389	80.96 358	94.51 386	94.95 376

test20.03		90.80 261	90.85 266	90.63 316	95.63 290	79.24 318	89.81 333	92.87 343	89.90 182	94.39 224	96.40 197	85.77 266	95.27 402	73.86 416	99.05 115	97.39 266

FMVSNet3		90.78 262	90.32 282	92.16 251	93.03 371	79.92 299	92.54 222	94.95 299	86.17 274	95.10 198	96.01 230	69.97 390	98.75 163	86.74 281	98.38 212	97.82 227

viewmambaseed2359dif		90.77 263	90.81 268	90.64 315	93.46 361	77.04 356	88.83 361	96.29 247	80.79 360	92.21 317	95.11 276	88.99 210	97.28 328	85.39 305	96.20 342	97.59 248

eth_miper_zixun_eth		90.72 264	90.61 274	91.05 297	92.04 397	76.84 362	86.91 395	96.67 227	85.21 300	94.41 223	93.92 329	79.53 329	98.26 233	89.76 210	97.02 313	98.06 184

X-MVStestdata		90.70 265	88.45 316	97.44 20	98.56 46	93.99 30	96.50 41	97.95 98	94.58 56	94.38 225	26.89 467	94.56 73	99.39 54	93.57 78	99.05 115	98.93 79

BH-untuned		90.68 266	90.90 263	90.05 335	95.98 264	79.57 310	90.04 325	94.94 300	87.91 231	94.07 235	93.00 353	87.76 233	97.78 291	79.19 378	95.17 370	92.80 426

IMVS_0404		90.67 267	91.06 260	89.50 343	95.19 311	76.72 364	86.58 407	96.89 203	85.92 278	89.17 371	94.50 305	85.77 266	94.67 410	88.49 249	97.07 307	97.10 280

cl____		90.65 268	90.56 276	90.91 305	91.85 402	76.98 360	86.75 400	95.36 288	85.53 293	94.06 236	94.89 285	77.36 353	97.98 269	90.27 191	98.98 125	97.76 234

DIV-MVS_self_test		90.65 268	90.56 276	90.91 305	91.85 402	76.99 359	86.75 400	95.36 288	85.52 295	94.06 236	94.89 285	77.37 352	97.99 268	90.28 190	98.97 130	97.76 234

test_fmvs2		90.62 270	90.40 280	91.29 287	91.93 401	85.46 200	92.70 214	96.48 241	74.44 410	94.91 209	97.59 90	75.52 367	90.57 441	93.44 89	96.56 331	97.84 223

114514_t		90.51 271	89.80 292	92.63 230	98.00 99	82.24 260	93.40 184	97.29 172	65.84 453	89.40 369	94.80 291	86.99 249	98.75 163	83.88 326	98.61 185	96.89 294

miper_ehance_all_eth		90.48 272	90.42 279	90.69 312	91.62 409	76.57 370	86.83 398	96.18 256	83.38 325	94.06 236	92.66 364	82.20 305	98.04 259	89.79 208	97.02 313	97.45 259

BH-RMVSNet		90.47 273	90.44 278	90.56 319	95.21 310	78.65 333	89.15 353	93.94 326	88.21 224	92.74 294	94.22 317	86.38 259	97.88 277	78.67 381	95.39 363	95.14 368

Vis-MVSNet (Re-imp)		90.42 274	90.16 283	91.20 294	97.66 127	77.32 353	94.33 145	87.66 404	91.20 151	92.99 284	95.13 275	75.40 368	98.28 229	77.86 384	99.19 100	97.99 196

test_vis3_rt		90.40 275	90.03 287	91.52 276	92.58 379	88.95 109	90.38 314	97.72 127	73.30 418	97.79 38	97.51 102	77.05 355	87.10 456	89.03 231	94.89 376	98.50 139

PLC	85.34 15	90.40 275	88.92 307	94.85 116	96.53 210	90.02 88	91.58 273	96.48 241	80.16 363	86.14 412	92.18 373	85.73 268	98.25 234	76.87 394	94.61 385	96.30 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 277	90.61 274	89.74 340	98.04 94	71.50 414	95.59 89	79.72 456	89.41 191	95.94 143	98.14 45	70.79 386	98.81 151	88.52 248	99.32 77	98.90 85

testgi		90.38 278	91.34 253	87.50 384	97.49 136	71.54 413	89.43 344	95.16 293	88.38 220	94.54 221	94.68 297	92.88 118	93.09 429	71.60 429	97.85 271	97.88 216

mvs_anonymous		90.37 279	91.30 254	87.58 383	92.17 393	68.00 430	89.84 332	94.73 308	83.82 322	93.22 272	97.40 107	87.54 238	97.40 323	87.94 264	95.05 373	97.34 269

PVSNet_BlendedMVS		90.35 280	89.96 288	91.54 275	94.81 323	78.80 331	90.14 322	96.93 198	79.43 372	88.68 385	95.06 280	86.27 262	98.15 246	80.27 361	98.04 254	97.68 241

UnsupCasMVSNet_eth		90.33 281	90.34 281	90.28 325	94.64 334	80.24 287	89.69 337	95.88 265	85.77 285	93.94 243	95.69 251	81.99 309	92.98 431	84.21 323	91.30 435	97.62 245

MAR-MVS		90.32 282	88.87 311	94.66 128	94.82 322	91.85 61	94.22 151	94.75 307	80.91 356	87.52 404	88.07 427	86.63 257	97.87 280	76.67 395	96.21 341	94.25 396
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
RPMNet		90.31 283	90.14 286	90.81 310	91.01 417	78.93 323	92.52 223	98.12 66	91.91 115	89.10 372	96.89 158	68.84 392	99.41 44	90.17 198	92.70 424	94.08 397

mvsmamba		90.24 284	89.43 298	92.64 227	95.52 297	82.36 257	96.64 34	92.29 357	81.77 348	92.14 319	96.28 210	70.59 387	99.10 105	84.44 321	95.22 369	96.47 313

IterMVS		90.18 285	90.16 283	90.21 329	93.15 367	75.98 377	87.56 383	92.97 342	86.43 266	94.09 233	96.40 197	78.32 342	97.43 320	87.87 265	94.69 383	97.23 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS		90.16 286	92.96 199	81.78 435	97.88 107	48.48 468	90.75 298	87.69 403	96.02 41	96.70 98	97.63 88	85.60 272	97.80 287	85.73 300	98.60 187	99.06 58

TAMVS		90.16 286	89.05 303	93.49 189	96.49 213	86.37 173	90.34 316	92.55 353	80.84 359	92.99 284	94.57 303	81.94 311	98.20 239	73.51 417	98.21 236	95.90 342

ECVR-MVS		90.12 288	90.16 283	90.00 336	97.81 112	72.68 407	95.76 83	78.54 459	89.04 200	95.36 177	98.10 48	70.51 388	98.64 185	87.10 277	99.18 102	98.67 117

test_yl		90.11 289	89.73 295	91.26 289	94.09 347	79.82 301	90.44 310	92.65 349	90.90 157	93.19 275	93.30 346	73.90 372	98.03 260	82.23 342	96.87 320	95.93 339

DCV-MVSNet		90.11 289	89.73 295	91.26 289	94.09 347	79.82 301	90.44 310	92.65 349	90.90 157	93.19 275	93.30 346	73.90 372	98.03 260	82.23 342	96.87 320	95.93 339

Patchmtry		90.11 289	89.92 289	90.66 314	90.35 428	77.00 358	92.96 200	92.81 344	90.25 178	94.74 216	96.93 155	67.11 399	97.52 312	85.17 306	98.98 125	97.46 258

MVP-Stereo		90.07 292	88.92 307	93.54 184	96.31 232	86.49 168	90.93 292	95.59 277	79.80 365	91.48 329	95.59 254	80.79 319	97.39 324	78.57 382	91.19 436	96.76 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		90.05 293	88.30 320	95.32 94	96.09 254	90.52 84	92.42 231	92.05 365	82.08 346	88.45 388	92.86 356	65.76 409	98.69 177	88.91 234	96.07 343	96.75 302

CL-MVSNet_self_test		90.04 294	89.90 290	90.47 320	95.24 309	77.81 346	86.60 406	92.62 351	85.64 289	93.25 270	93.92 329	83.84 286	96.06 382	79.93 369	98.03 255	97.53 254

D2MVS		89.93 295	89.60 297	90.92 303	94.03 350	78.40 335	88.69 368	94.85 301	78.96 380	93.08 280	95.09 278	74.57 370	96.94 349	88.19 255	98.96 132	97.41 262

miper_lstm_enhance		89.90 296	89.80 292	90.19 331	91.37 413	77.50 350	83.82 440	95.00 297	84.84 311	93.05 282	94.96 283	76.53 364	95.20 403	89.96 205	98.67 180	97.86 220

SSC-MVS3.2		89.88 297	91.06 260	86.31 403	95.90 269	63.76 451	82.68 445	92.43 356	91.42 145	92.37 310	94.58 302	86.34 260	96.60 363	84.35 322	99.50 43	98.57 133

CANet_DTU		89.85 298	89.17 301	91.87 259	92.20 391	80.02 296	90.79 297	95.87 266	86.02 276	82.53 443	91.77 381	80.01 325	98.57 193	85.66 301	97.70 279	97.01 289

tttt0517		89.81 299	88.90 309	92.55 236	97.00 165	79.73 305	95.03 119	83.65 438	89.88 183	95.30 180	94.79 292	53.64 446	99.39 54	91.99 136	98.79 160	98.54 135

EPNet		89.80 300	88.25 324	94.45 142	83.91 465	86.18 180	93.87 166	87.07 410	91.16 153	80.64 453	94.72 294	78.83 335	98.89 136	85.17 306	98.89 139	98.28 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 301	88.22 327	93.53 185	95.37 306	86.49 168	89.26 350	93.59 330	79.76 367	91.15 336	92.31 371	77.12 354	98.38 219	77.51 389	97.92 267	95.71 349
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 302	89.80 292	88.76 359	94.88 319	72.47 410	89.60 338	92.44 355	85.82 284	89.48 367	95.98 233	82.85 297	97.74 297	81.87 345	95.27 367	96.08 332

OpenMVS_ROB	85.12 16	89.52 303	89.05 303	90.92 303	94.58 335	81.21 279	91.10 288	93.41 336	77.03 394	93.41 258	93.99 327	83.23 291	97.80 287	79.93 369	94.80 380	93.74 408

test_vis1_n_1920		89.45 304	89.85 291	88.28 371	93.59 359	76.71 368	90.67 302	97.78 123	79.67 369	90.30 352	96.11 225	76.62 362	92.17 434	90.31 188	93.57 407	95.96 337

WB-MVS		89.44 305	92.15 231	81.32 436	97.73 119	48.22 469	89.73 335	87.98 401	95.24 48	96.05 137	96.99 152	85.18 275	96.95 348	82.45 340	97.97 263	98.78 100

DPM-MVS		89.35 306	88.40 317	92.18 250	96.13 251	84.20 219	86.96 394	96.15 258	75.40 404	87.36 405	91.55 386	83.30 290	98.01 264	82.17 344	96.62 330	94.32 395

MVSTER		89.32 307	88.75 312	91.03 298	90.10 431	76.62 369	90.85 294	94.67 311	82.27 343	95.24 187	95.79 242	61.09 433	98.49 205	90.49 178	98.26 228	97.97 201

PatchMatch-RL		89.18 308	88.02 332	92.64 227	95.90 269	92.87 49	88.67 370	91.06 376	80.34 361	90.03 357	91.67 383	83.34 289	94.42 415	76.35 399	94.84 379	90.64 442

jason		89.17 309	88.32 319	91.70 268	95.73 282	80.07 292	88.10 375	93.22 338	71.98 426	90.09 354	92.79 359	78.53 340	98.56 194	87.43 272	97.06 311	96.46 314
jason: jason.
PCF-MVS	84.52 17	89.12 310	87.71 335	93.34 193	96.06 257	85.84 191	86.58 407	97.31 169	68.46 446	93.61 251	93.89 331	87.51 239	98.52 202	67.85 442	98.11 245	95.66 353
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		89.11 311	88.21 328	91.83 261	91.30 414	90.25 86	88.09 376	78.76 457	76.37 398	96.43 112	98.39 39	83.79 287	90.43 444	86.57 287	94.20 395	94.80 382

FE-MVS		89.06 312	88.29 321	91.36 283	94.78 325	79.57 310	96.77 29	90.99 377	84.87 310	92.96 287	96.29 208	60.69 435	98.80 154	80.18 364	97.11 306	95.71 349

cl22		89.02 313	88.50 315	90.59 318	89.76 433	76.45 371	86.62 405	94.03 321	82.98 335	92.65 296	92.49 365	72.05 381	97.53 311	88.93 232	97.02 313	97.78 232

USDC		89.02 313	89.08 302	88.84 358	95.07 316	74.50 390	88.97 356	96.39 244	73.21 419	93.27 267	96.28 210	82.16 306	96.39 372	77.55 388	98.80 157	95.62 356

test_vis1_n		89.01 315	89.01 305	89.03 353	92.57 380	82.46 256	92.62 219	96.06 259	73.02 421	90.40 349	95.77 246	74.86 369	89.68 447	90.78 171	94.98 374	94.95 376

xiu_mvs_v2_base		89.00 316	89.19 300	88.46 369	94.86 321	74.63 387	86.97 393	95.60 273	80.88 357	87.83 398	88.62 421	91.04 172	98.81 151	82.51 339	94.38 389	91.93 433

new-patchmatchnet		88.97 317	90.79 270	83.50 428	94.28 342	55.83 464	85.34 423	93.56 332	86.18 273	95.47 169	95.73 248	83.10 292	96.51 366	85.40 304	98.06 252	98.16 178

pmmvs4		88.95 318	87.70 336	92.70 224	94.30 341	85.60 197	87.22 389	92.16 361	74.62 409	89.75 365	94.19 318	77.97 345	96.41 371	82.71 334	96.36 336	96.09 331

N_pmnet		88.90 319	87.25 343	93.83 169	94.40 340	93.81 39	84.73 427	87.09 408	79.36 375	93.26 268	92.43 369	79.29 331	91.68 436	77.50 390	97.22 302	96.00 335

PS-MVSNAJ		88.86 320	88.99 306	88.48 368	94.88 319	74.71 385	86.69 402	95.60 273	80.88 357	87.83 398	87.37 432	90.77 178	98.82 146	82.52 338	94.37 390	91.93 433

Patchmatch-RL test		88.81 321	88.52 314	89.69 342	95.33 308	79.94 298	86.22 413	92.71 348	78.46 383	95.80 150	94.18 319	66.25 407	95.33 400	89.22 226	98.53 194	93.78 406

SD_0403		88.79 322	88.88 310	88.51 366	95.89 271	72.58 408	94.27 148	95.24 291	83.77 324	87.92 397	94.38 313	87.70 234	96.47 369	66.36 446	94.40 387	96.49 311

Anonymous20231206		88.77 323	88.29 321	90.20 330	96.31 232	78.81 330	89.56 340	93.49 334	74.26 413	92.38 308	95.58 257	82.21 304	95.43 397	72.07 425	98.75 168	96.34 318

PVSNet_Blended		88.74 324	88.16 330	90.46 322	94.81 323	78.80 331	86.64 403	96.93 198	74.67 408	88.68 385	89.18 417	86.27 262	98.15 246	80.27 361	96.00 345	94.44 392

test_fmvs1_n		88.73 325	88.38 318	89.76 339	92.06 396	82.53 254	92.30 240	96.59 233	71.14 431	92.58 299	95.41 267	68.55 393	89.57 449	91.12 163	95.66 354	97.18 278

thisisatest0530		88.69 326	87.52 338	92.20 246	96.33 230	79.36 314	92.81 207	84.01 437	86.44 265	93.67 249	92.68 363	53.62 447	99.25 84	89.65 213	98.45 204	98.00 193

ppachtmachnet_test		88.61 327	88.64 313	88.50 367	91.76 404	70.99 417	84.59 432	92.98 341	79.30 377	92.38 308	93.53 342	79.57 328	97.45 318	86.50 291	97.17 304	97.07 284

UnsupCasMVSNet_bld		88.50 328	88.03 331	89.90 337	95.52 297	78.88 327	87.39 387	94.02 323	79.32 376	93.06 281	94.02 325	80.72 320	94.27 418	75.16 407	93.08 420	96.54 305

MonoMVSNet		88.46 329	89.28 299	85.98 405	90.52 424	70.07 423	95.31 105	94.81 305	88.38 220	93.47 257	96.13 223	73.21 375	95.07 404	82.61 336	89.12 444	92.81 425

miper_enhance_ethall		88.42 330	87.87 333	90.07 332	88.67 446	75.52 381	85.10 424	95.59 277	75.68 400	92.49 301	89.45 413	78.96 332	97.88 277	87.86 266	97.02 313	96.81 298

1112_ss		88.42 330	87.41 339	91.45 279	96.69 189	80.99 282	89.72 336	96.72 221	73.37 417	87.00 408	90.69 399	77.38 351	98.20 239	81.38 352	93.72 405	95.15 367

lupinMVS		88.34 332	87.31 340	91.45 279	94.74 328	80.06 293	87.23 388	92.27 358	71.10 432	88.83 376	91.15 389	77.02 356	98.53 200	86.67 285	96.75 326	95.76 347

test_cas_vis1_n_1920		88.25 333	88.27 323	88.20 373	92.19 392	78.92 325	89.45 343	95.44 283	75.29 407	93.23 271	95.65 253	71.58 383	90.23 445	88.05 260	93.55 409	95.44 361

YYNet1		88.17 334	88.24 325	87.93 377	92.21 390	73.62 398	80.75 451	88.77 391	82.51 341	94.99 206	95.11 276	82.70 300	93.70 423	83.33 328	93.83 403	96.48 312

MDA-MVSNet_test_wron		88.16 335	88.23 326	87.93 377	92.22 389	73.71 397	80.71 452	88.84 390	82.52 340	94.88 211	95.14 274	82.70 300	93.61 424	83.28 329	93.80 404	96.46 314

MS-PatchMatch		88.05 336	87.75 334	88.95 354	93.28 364	77.93 343	87.88 378	92.49 354	75.42 403	92.57 300	93.59 340	80.44 322	94.24 420	81.28 353	92.75 423	94.69 388

CR-MVSNet		87.89 337	87.12 348	90.22 328	91.01 417	78.93 323	92.52 223	92.81 344	73.08 420	89.10 372	96.93 155	67.11 399	97.64 304	88.80 238	92.70 424	94.08 397

pmmvs5		87.87 338	87.14 346	90.07 332	93.26 366	76.97 361	88.89 358	92.18 359	73.71 416	88.36 389	93.89 331	76.86 361	96.73 360	80.32 360	96.81 323	96.51 307

wuyk23d		87.83 339	90.79 270	78.96 442	90.46 427	88.63 116	92.72 211	90.67 382	91.65 134	98.68 15	97.64 87	96.06 19	77.53 464	59.84 457	99.41 60	70.73 462

FMVSNet5		87.82 340	86.56 359	91.62 271	92.31 386	79.81 303	93.49 180	94.81 305	83.26 327	91.36 331	96.93 155	52.77 448	97.49 316	76.07 401	98.03 255	97.55 253

GA-MVS		87.70 341	86.82 353	90.31 324	93.27 365	77.22 355	84.72 429	92.79 346	85.11 304	89.82 361	90.07 402	66.80 402	97.76 294	84.56 319	94.27 393	95.96 337

TR-MVS		87.70 341	87.17 345	89.27 350	94.11 346	79.26 317	88.69 368	91.86 368	81.94 347	90.69 344	89.79 407	82.82 298	97.42 321	72.65 423	91.98 432	91.14 439

thres600view7		87.66 343	87.10 349	89.36 348	96.05 258	73.17 400	92.72 211	85.31 428	91.89 116	93.29 265	90.97 393	63.42 424	98.39 216	73.23 419	96.99 318	96.51 307

PAPR		87.65 344	86.77 355	90.27 326	92.85 376	77.38 352	88.56 371	96.23 252	76.82 397	84.98 421	89.75 409	86.08 264	97.16 339	72.33 424	93.35 412	96.26 324

baseline1		87.62 345	87.31 340	88.54 364	94.71 331	74.27 393	93.10 195	88.20 397	86.20 271	92.18 318	93.04 352	73.21 375	95.52 392	79.32 376	85.82 452	95.83 344

test_fmvs1		87.59 346	87.27 342	88.54 364	88.32 447	81.26 277	90.43 313	95.72 270	70.55 437	91.70 326	94.63 298	68.13 394	89.42 451	90.59 175	95.34 365	94.94 378

our_test_3		87.55 347	87.59 337	87.44 385	91.76 404	70.48 418	83.83 439	90.55 384	79.79 366	92.06 322	92.17 374	78.63 339	95.63 390	84.77 316	94.73 381	96.22 326

PatchT		87.51 348	88.17 329	85.55 409	90.64 421	66.91 434	92.02 250	86.09 416	92.20 106	89.05 375	97.16 135	64.15 419	96.37 374	89.21 227	92.98 422	93.37 416

Test_1112_low_res		87.50 349	86.58 357	90.25 327	96.80 181	77.75 347	87.53 385	96.25 250	69.73 442	86.47 410	93.61 339	75.67 366	97.88 277	79.95 367	93.20 415	95.11 371

SCA		87.43 350	87.21 344	88.10 375	92.01 398	71.98 412	89.43 344	88.11 399	82.26 344	88.71 383	92.83 357	78.65 337	97.59 307	79.61 373	93.30 413	94.75 385

EU-MVSNet		87.39 351	86.71 356	89.44 345	93.40 362	76.11 375	94.93 123	90.00 386	57.17 462	95.71 158	97.37 109	64.77 416	97.68 301	92.67 117	94.37 390	94.52 390

thres100view900		87.35 352	86.89 352	88.72 360	96.14 249	73.09 402	93.00 198	85.31 428	92.13 109	93.26 268	90.96 394	63.42 424	98.28 229	71.27 431	96.54 332	94.79 383

CMPMVS	68.83 22	87.28 353	85.67 369	92.09 254	88.77 445	85.42 201	90.31 317	94.38 314	70.02 440	88.00 394	93.30 346	73.78 374	94.03 422	75.96 403	96.54 332	96.83 297
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 354	86.82 353	88.46 369	93.96 351	77.94 342	86.84 397	92.78 347	77.59 388	87.61 403	91.83 380	78.75 336	91.92 435	77.84 385	94.20 395	95.52 360

BH-w/o		87.21 355	87.02 350	87.79 382	94.77 326	77.27 354	87.90 377	93.21 340	81.74 349	89.99 358	88.39 424	83.47 288	96.93 351	71.29 430	92.43 428	89.15 444

thres400		87.20 356	86.52 361	89.24 352	95.77 279	72.94 404	91.89 259	86.00 417	90.84 159	92.61 297	89.80 405	63.93 420	98.28 229	71.27 431	96.54 332	96.51 307

CHOSEN 1792x2688		87.19 357	85.92 368	91.00 301	97.13 159	79.41 313	84.51 433	95.60 273	64.14 456	90.07 356	94.81 289	78.26 343	97.14 340	73.34 418	95.38 364	96.46 314

HyFIR lowres test		87.19 357	85.51 370	92.24 244	97.12 161	80.51 286	85.03 425	96.06 259	66.11 452	91.66 327	92.98 355	70.12 389	99.14 96	75.29 406	95.23 368	97.07 284

reproduce_monomvs		87.13 359	86.90 351	87.84 381	90.92 419	68.15 429	91.19 284	93.75 328	85.84 283	94.21 230	95.83 240	42.99 464	97.10 341	89.46 216	97.88 269	98.26 167

MIMVSNet		87.13 359	86.54 360	88.89 357	96.05 258	76.11 375	94.39 143	88.51 393	81.37 352	88.27 391	96.75 172	72.38 379	95.52 392	65.71 448	95.47 360	95.03 373

tfpn200view9		87.05 361	86.52 361	88.67 361	95.77 279	72.94 404	91.89 259	86.00 417	90.84 159	92.61 297	89.80 405	63.93 420	98.28 229	71.27 431	96.54 332	94.79 383

cascas		87.02 362	86.28 365	89.25 351	91.56 411	76.45 371	84.33 435	96.78 216	71.01 433	86.89 409	85.91 440	81.35 314	96.94 349	83.09 331	95.60 356	94.35 394

WTY-MVS		86.93 363	86.50 363	88.24 372	94.96 317	74.64 386	87.19 390	92.07 364	78.29 384	88.32 390	91.59 385	78.06 344	94.27 418	74.88 408	93.15 417	95.80 345

ttmdpeth		86.91 364	86.57 358	87.91 379	89.68 435	74.24 394	91.49 276	87.09 408	79.84 364	89.46 368	97.86 72	65.42 411	91.04 439	81.57 350	96.74 328	98.44 145

HY-MVS	82.50 18	86.81 365	85.93 367	89.47 344	93.63 358	77.93 343	94.02 159	91.58 374	75.68 400	83.64 433	93.64 336	77.40 350	97.42 321	71.70 428	92.07 431	93.05 421

test_f		86.65 366	87.13 347	85.19 413	90.28 429	86.11 182	86.52 409	91.66 371	69.76 441	95.73 157	97.21 132	69.51 391	81.28 463	89.15 228	94.40 387	88.17 449

1314		86.46 367	86.33 364	86.87 393	91.65 408	74.54 388	91.94 255	94.10 320	74.28 412	84.78 423	87.33 433	83.03 294	95.00 405	78.72 380	91.16 437	91.06 440

ET-MVSNet_ETH3D		86.15 368	84.27 379	91.79 263	93.04 370	81.28 276	87.17 391	86.14 415	79.57 370	83.65 432	88.66 419	57.10 439	98.18 242	87.74 267	95.40 362	95.90 342

Patchmatch-test		86.10 369	86.01 366	86.38 401	90.63 422	74.22 395	89.57 339	86.69 411	85.73 287	89.81 362	92.83 357	65.24 414	91.04 439	77.82 387	95.78 352	93.88 405

thres200		85.85 370	85.18 371	87.88 380	94.44 338	72.52 409	89.08 355	86.21 414	88.57 215	91.44 330	88.40 423	64.22 418	98.00 266	68.35 440	95.88 350	93.12 418

EPNet_dtu		85.63 371	84.37 377	89.40 347	86.30 457	74.33 392	91.64 271	88.26 395	84.84 311	72.96 463	89.85 403	71.27 385	97.69 300	76.60 396	97.62 284	96.18 328
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_vis1_rt		85.58 372	84.58 375	88.60 363	87.97 448	86.76 160	85.45 422	93.59 330	66.43 450	87.64 401	89.20 416	79.33 330	85.38 460	81.59 349	89.98 443	93.66 410

test2506		85.42 373	84.57 376	87.96 376	97.81 112	66.53 437	96.14 65	56.35 470	89.04 200	93.55 253	98.10 48	42.88 467	98.68 179	88.09 259	99.18 102	98.67 117

PatchmatchNet		85.22 374	84.64 374	86.98 389	89.51 439	69.83 425	90.52 306	87.34 407	78.87 381	87.22 407	92.74 361	66.91 401	96.53 364	81.77 346	86.88 450	94.58 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 375	84.72 373	86.48 397	92.12 394	70.19 419	92.32 237	88.17 398	56.15 463	90.64 345	95.85 237	67.97 397	96.69 361	88.78 239	90.52 440	92.56 428

JIA-IIPM		85.08 376	83.04 391	91.19 295	87.56 450	86.14 181	89.40 346	84.44 436	88.98 202	82.20 444	97.95 61	56.82 441	96.15 378	76.55 398	83.45 456	91.30 438

MVS		84.98 377	84.30 378	87.01 388	91.03 416	77.69 349	91.94 255	94.16 319	59.36 461	84.23 428	87.50 431	85.66 269	96.80 358	71.79 426	93.05 421	86.54 453

Syy-MVS		84.81 378	84.93 372	84.42 420	91.71 406	63.36 453	85.89 416	81.49 447	81.03 354	85.13 418	81.64 457	77.44 349	95.00 405	85.94 298	94.12 398	94.91 379

MVStest1		84.79 379	84.06 382	86.98 389	77.73 470	74.76 384	91.08 290	85.63 422	77.70 387	96.86 89	97.97 60	41.05 469	88.24 454	92.22 129	96.28 338	97.94 205

thisisatest0515		84.72 380	82.99 392	89.90 337	92.96 373	75.33 383	84.36 434	83.42 439	77.37 390	88.27 391	86.65 434	53.94 445	98.72 168	82.56 337	97.40 297	95.67 352

dmvs_re		84.69 381	83.94 384	86.95 391	92.24 388	82.93 244	89.51 341	87.37 406	84.38 317	85.37 415	85.08 447	72.44 378	86.59 457	68.05 441	91.03 439	91.33 437

FPMVS		84.50 382	83.28 389	88.16 374	96.32 231	94.49 20	85.76 419	85.47 426	83.09 332	85.20 417	94.26 315	63.79 422	86.58 458	63.72 452	91.88 434	83.40 456

tpm		84.38 383	84.08 381	85.30 412	90.47 426	63.43 452	89.34 347	85.63 422	77.24 393	87.62 402	95.03 281	61.00 434	97.30 327	79.26 377	91.09 438	95.16 366

tpmvs		84.22 384	83.97 383	84.94 415	87.09 454	65.18 444	91.21 283	88.35 394	82.87 336	85.21 416	90.96 394	65.24 414	96.75 359	79.60 375	85.25 453	92.90 424

WB-MVSnew		84.20 385	83.89 385	85.16 414	91.62 409	66.15 441	88.44 374	81.00 450	76.23 399	87.98 395	87.77 428	84.98 278	93.35 427	62.85 455	94.10 400	95.98 336

ADS-MVSNet2		84.01 386	82.20 399	89.41 346	89.04 442	76.37 373	87.57 381	90.98 378	72.71 424	84.46 424	92.45 366	68.08 395	96.48 367	70.58 436	83.97 454	95.38 362

WBMVS		84.00 387	83.48 387	85.56 408	92.71 377	61.52 455	83.82 440	89.38 389	79.56 371	90.74 342	93.20 350	48.21 451	97.28 328	75.63 405	98.10 247	97.88 216

testing3-2		83.95 388	84.22 380	83.13 430	96.28 235	54.34 467	88.51 372	83.01 442	92.19 107	89.09 374	90.98 392	45.51 457	97.44 319	74.38 412	98.01 258	97.60 247

mvsany_test1		83.91 389	82.93 393	86.84 394	86.18 458	85.93 188	81.11 450	75.03 464	70.80 436	88.57 387	94.63 298	83.08 293	87.38 455	80.39 359	86.57 451	87.21 451

testing3		83.66 390	82.52 395	87.08 387	95.84 273	65.84 442	89.80 334	77.17 463	88.17 226	90.84 340	88.63 420	30.95 472	98.11 250	84.05 324	97.19 303	97.28 273

test-LLR		83.58 391	83.17 390	84.79 417	89.68 435	66.86 435	83.08 442	84.52 434	83.07 333	82.85 439	84.78 448	62.86 427	93.49 425	82.85 332	94.86 377	94.03 400

testing91		83.56 392	82.45 396	86.91 392	92.92 374	67.29 431	86.33 411	88.07 400	86.22 270	84.26 427	85.76 441	48.15 452	97.17 337	76.27 400	94.08 401	96.27 323

baseline2		83.38 393	81.54 403	88.90 356	91.38 412	72.84 406	88.78 365	81.22 449	78.97 379	79.82 455	87.56 429	61.73 431	97.80 287	74.30 413	90.05 442	96.05 334

IB-MVS	77.21 19	83.11 394	81.05 406	89.29 349	91.15 415	75.85 378	85.66 420	86.00 417	79.70 368	82.02 447	86.61 435	48.26 450	98.39 216	77.84 385	92.22 429	93.63 411
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
CostFormer		83.09 395	82.21 398	85.73 406	89.27 441	67.01 433	90.35 315	86.47 413	70.42 438	83.52 435	93.23 349	61.18 432	96.85 355	77.21 392	88.26 448	93.34 417

PMMVS		83.00 396	81.11 405	88.66 362	83.81 466	86.44 171	82.24 447	85.65 421	61.75 460	82.07 445	85.64 443	79.75 327	91.59 437	75.99 402	93.09 419	87.94 450

testing99		82.94 397	81.72 400	86.59 395	92.55 381	66.53 437	86.08 415	85.70 420	85.47 297	83.95 430	85.70 442	45.87 456	97.07 344	76.58 397	93.56 408	96.17 330

PVSNet	76.22 20	82.89 398	82.37 397	84.48 419	93.96 351	64.38 449	78.60 454	88.61 392	71.50 429	84.43 426	86.36 438	74.27 371	94.60 412	69.87 438	93.69 406	94.46 391

tpmrst		82.85 399	82.93 393	82.64 431	87.65 449	58.99 461	90.14 322	87.90 402	75.54 402	83.93 431	91.63 384	66.79 404	95.36 398	81.21 355	81.54 460	93.57 415

test0.0.03 1		82.48 400	81.47 404	85.48 410	89.70 434	73.57 399	84.73 427	81.64 446	83.07 333	88.13 393	86.61 435	62.86 427	89.10 453	66.24 447	90.29 441	93.77 407

ADS-MVSNet		82.25 401	81.55 402	84.34 421	89.04 442	65.30 443	87.57 381	85.13 432	72.71 424	84.46 424	92.45 366	68.08 395	92.33 433	70.58 436	83.97 454	95.38 362

DSMNet-mixed		82.21 402	81.56 401	84.16 423	89.57 438	70.00 424	90.65 303	77.66 461	54.99 464	83.30 437	97.57 91	77.89 346	90.50 443	66.86 445	95.54 358	91.97 432

KD-MVS_2432*1600		82.17 403	80.75 410	86.42 399	82.04 467	70.09 421	81.75 448	90.80 380	82.56 338	90.37 350	89.30 414	42.90 465	96.11 380	74.47 410	92.55 426	93.06 419

miper_refine_blended		82.17 403	80.75 410	86.42 399	82.04 467	70.09 421	81.75 448	90.80 380	82.56 338	90.37 350	89.30 414	42.90 465	96.11 380	74.47 410	92.55 426	93.06 419

gg-mvs-nofinetune		82.10 405	81.02 407	85.34 411	87.46 452	71.04 415	94.74 127	67.56 466	96.44 29	79.43 456	98.99 11	45.24 458	96.15 378	67.18 444	92.17 430	88.85 446

testing11		81.98 406	80.52 413	86.38 401	92.69 378	67.13 432	85.79 418	84.80 433	82.16 345	81.19 452	85.41 444	45.24 458	96.88 354	74.14 414	93.24 414	95.14 368

PAPM		81.91 407	80.11 418	87.31 386	93.87 354	72.32 411	84.02 437	93.22 338	69.47 443	76.13 461	89.84 404	72.15 380	97.23 332	53.27 462	89.02 445	92.37 430

tpm2		81.46 408	80.35 416	84.80 416	89.90 432	65.14 445	90.44 310	85.36 427	65.82 454	82.05 446	92.44 368	57.94 438	96.69 361	70.71 435	88.49 447	92.56 428

PMMVS2		81.31 409	83.44 388	74.92 445	90.52 424	46.49 471	69.19 461	85.23 431	84.30 318	87.95 396	94.71 295	76.95 358	84.36 462	64.07 451	98.09 248	93.89 404

new_pmnet		81.22 410	81.01 408	81.86 434	90.92 419	70.15 420	84.03 436	80.25 455	70.83 434	85.97 413	89.78 408	67.93 398	84.65 461	67.44 443	91.90 433	90.78 441

test-mter		81.21 411	80.01 419	84.79 417	89.68 435	66.86 435	83.08 442	84.52 434	73.85 415	82.85 439	84.78 448	43.66 463	93.49 425	82.85 332	94.86 377	94.03 400

EPMVS		81.17 412	80.37 415	83.58 427	85.58 460	65.08 446	90.31 317	71.34 465	77.31 392	85.80 414	91.30 387	59.38 436	92.70 432	79.99 366	82.34 459	92.96 423

myMVS_eth3d28		80.97 413	80.42 414	82.62 432	93.35 363	58.25 462	84.70 430	85.62 424	86.31 267	84.04 429	85.20 446	46.00 455	94.07 421	62.93 454	95.65 355	95.53 359

EGC-MVSNET		80.97 413	75.73 431	96.67 46	98.85 28	94.55 19	96.83 24	96.60 231	2.44 469	5.32 470	98.25 43	92.24 133	98.02 263	91.85 141	99.21 98	97.45 259

pmmvs3		80.83 415	78.96 423	86.45 398	87.23 453	77.48 351	84.87 426	82.31 444	63.83 457	85.03 420	89.50 412	49.66 449	93.10 428	73.12 421	95.10 371	88.78 448

E-PMN		80.72 416	80.86 409	80.29 439	85.11 462	68.77 427	72.96 458	81.97 445	87.76 238	83.25 438	83.01 455	62.22 430	89.17 452	77.15 393	94.31 392	82.93 457

tpm cat1		80.61 417	79.46 420	84.07 424	88.78 444	65.06 447	89.26 350	88.23 396	62.27 459	81.90 448	89.66 411	62.70 429	95.29 401	71.72 427	80.60 461	91.86 435

testing222		80.54 418	78.53 426	86.58 396	92.54 383	68.60 428	86.24 412	82.72 443	83.78 323	82.68 442	84.24 450	39.25 470	95.94 386	60.25 456	95.09 372	95.20 364

EMVS		80.35 419	80.28 417	80.54 438	84.73 464	69.07 426	72.54 460	80.73 452	87.80 236	81.66 449	81.73 456	62.89 426	89.84 446	75.79 404	94.65 384	82.71 458

UWE-MVS		80.29 420	79.10 421	83.87 425	91.97 400	59.56 459	86.50 410	77.43 462	75.40 404	87.79 400	88.10 426	44.08 462	96.90 353	64.23 450	96.36 336	95.14 368

UBG		80.28 421	78.94 424	84.31 422	92.86 375	61.77 454	83.87 438	83.31 441	77.33 391	82.78 441	83.72 452	47.60 454	96.06 382	65.47 449	93.48 410	95.11 371

CHOSEN 280x420		80.04 422	77.97 429	86.23 404	90.13 430	74.53 389	72.87 459	89.59 388	66.38 451	76.29 460	85.32 445	56.96 440	95.36 398	69.49 439	94.72 382	88.79 447

ETVMVS		79.85 423	77.94 430	85.59 407	92.97 372	66.20 440	86.13 414	80.99 451	81.41 351	83.52 435	83.89 451	41.81 468	94.98 408	56.47 460	94.25 394	95.61 357

myMVS_eth3d		79.62 424	78.26 427	83.72 426	91.71 406	61.25 457	85.89 416	81.49 447	81.03 354	85.13 418	81.64 457	32.12 471	95.00 405	71.17 434	94.12 398	94.91 379

dp		79.28 425	78.62 425	81.24 437	85.97 459	56.45 463	86.91 395	85.26 430	72.97 422	81.45 451	89.17 418	56.01 443	95.45 396	73.19 420	76.68 462	91.82 436

TESTMET0.1,1		79.09 426	78.04 428	82.25 433	87.52 451	64.03 450	83.08 442	80.62 453	70.28 439	80.16 454	83.22 454	44.13 461	90.56 442	79.95 367	93.36 411	92.15 431

MVS-HIRNet		78.83 427	80.60 412	73.51 446	93.07 368	47.37 470	87.10 392	78.00 460	68.94 444	77.53 458	97.26 124	71.45 384	94.62 411	63.28 453	88.74 446	78.55 461

dmvs_testset		78.23 428	78.99 422	75.94 444	91.99 399	55.34 466	88.86 359	78.70 458	82.69 337	81.64 450	79.46 459	75.93 365	85.74 459	48.78 464	82.85 458	86.76 452

UWE-MVS-28		74.73 429	73.18 432	79.35 441	85.42 461	55.55 465	87.63 379	65.92 467	74.39 411	77.33 459	88.19 425	47.63 453	89.48 450	39.01 466	93.14 418	93.03 422

PVSNet_0	70.34 21	74.58 430	72.96 433	79.47 440	90.63 422	66.24 439	73.26 457	83.40 440	63.67 458	78.02 457	78.35 461	72.53 377	89.59 448	56.68 459	60.05 465	82.57 459

MVE	59.87 23	73.86 431	72.65 434	77.47 443	87.00 456	74.35 391	61.37 463	60.93 469	67.27 448	69.69 464	86.49 437	81.24 318	72.33 466	56.45 461	83.45 456	85.74 454
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		53.72 432	53.79 435	53.51 449	79.69 469	36.70 473	77.18 455	32.53 475	71.69 427	68.63 465	60.79 464	26.65 473	73.11 465	30.67 468	36.29 467	50.73 463

test_method		50.44 433	48.94 436	54.93 447	39.68 473	12.38 476	28.59 464	90.09 385	6.82 467	41.10 469	78.41 460	54.41 444	70.69 467	50.12 463	51.26 466	81.72 460

kuosan		43.63 434	44.25 438	41.78 450	66.04 472	34.37 474	75.56 456	32.62 474	53.25 465	50.46 468	51.18 465	25.28 474	49.13 468	13.44 469	30.41 468	41.84 465

tmp_tt		37.97 435	44.33 437	18.88 451	11.80 474	21.54 475	63.51 462	45.66 473	4.23 468	51.34 467	50.48 466	59.08 437	22.11 470	44.50 465	68.35 464	13.00 466

cdsmvs_eth3d_5k		23.35 436	31.13 439	0.00 454	0.00 477	0.00 479	0.00 465	95.58 279	0.00 472	0.00 473	91.15 389	93.43 97	0.00 473	0.00 472	0.00 471	0.00 469

test123		9.49 437	12.01 440	1.91 452	2.87 475	1.30 477	82.38 446	1.34 477	1.36 470	2.84 471	6.56 469	2.45 475	0.97 471	2.73 470	5.56 469	3.47 467

testmvs		9.02 438	11.42 441	1.81 453	2.77 476	1.13 478	79.44 453	1.90 476	1.18 471	2.65 472	6.80 468	1.95 476	0.87 472	2.62 471	3.45 470	3.44 468

pcd_1.5k_mvsjas		7.56 439	10.09 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	90.77 178	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		7.56 439	10.08 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	90.69 399	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						61.25 457								74.55 409

FOURS1					99.21 3	94.68 16	98.45 4	98.81 11	97.73 10	98.27 24

MSC_two_6792asdad				95.90 70	96.54 207	89.57 94		96.87 209					99.41 44	94.06 64	99.30 80	98.72 109

PC_three_1452									75.31 406	95.87 148	95.75 247	92.93 115	96.34 377	87.18 276	98.68 178	98.04 188

No_MVS				95.90 70	96.54 207	89.57 94		96.87 209					99.41 44	94.06 64	99.30 80	98.72 109

test_one_0601					98.26 76	87.14 148		98.18 55	94.25 62	96.99 84	97.36 112	95.13 49

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					97.23 151	90.32 85		97.54 146	84.40 316	94.78 214	95.79 242	92.76 121	99.39 54	88.72 241	98.40 207

RE-MVS-def			96.66 27		98.07 89	95.27 10	96.37 50	98.12 66	95.66 43	97.00 82	97.03 148	95.40 35		93.49 83	98.84 146	98.00 193

IU-MVS					98.51 54	86.66 165		96.83 213	72.74 423	95.83 149				93.00 108	99.29 83	98.64 125

OPU-MVS				95.15 104	96.84 177	89.43 98	95.21 110				95.66 252	93.12 108	98.06 256	86.28 295	98.61 185	97.95 203

test_241102_TWO								98.10 70	91.95 112	97.54 49	97.25 125	95.37 36	99.35 65	93.29 96	99.25 91	98.49 141

test_241102_ONE					98.51 54	86.97 153		98.10 70	91.85 119	97.63 44	97.03 148	96.48 13	98.95 130

9.14			94.81 121		97.49 136		94.11 156	98.37 32	87.56 244	95.38 174	96.03 229	94.66 68	99.08 106	90.70 173	98.97 130

save fliter					97.46 139	88.05 131	92.04 249	97.08 188	87.63 242

test_0728_THIRD									93.26 85	97.40 61	97.35 115	94.69 67	99.34 68	93.88 68	99.42 54	98.89 86

test_0728_SECOND				94.88 115	98.55 49	86.72 162	95.20 112	98.22 50					99.38 61	93.44 89	99.31 78	98.53 137

test0726					98.51 54	86.69 163	95.34 101	98.18 55	91.85 119	97.63 44	97.37 109	95.58 28

GSMVS																94.75 385

test_part2					98.21 81	89.41 99				96.72 97

sam_mvs1												66.64 405				94.75 385

sam_mvs												66.41 406

ambc				92.98 206	96.88 173	83.01 243	95.92 76	96.38 245		96.41 113	97.48 104	88.26 222	97.80 287	89.96 205	98.93 135	98.12 182

MTGPA								97.62 135

test_post1							90.21 319				5.85 471	65.36 412	96.00 384	79.61 373

test_post											6.07 470	65.74 410	95.84 388

patchmatchnet-post											91.71 382	66.22 408	97.59 307

GG-mvs-BLEND				83.24 429	85.06 463	71.03 416	94.99 122	65.55 468		74.09 462	75.51 462	44.57 460	94.46 414	59.57 458	87.54 449	84.24 455

MTMP							94.82 125	54.62 471

gm-plane-assit					87.08 455	59.33 460			71.22 430		83.58 453		97.20 334	73.95 415

test9_res														88.16 257	98.40 207	97.83 224

TEST9					96.45 216	89.46 96	90.60 304	96.92 200	79.09 378	90.49 346	94.39 311	91.31 160	98.88 137

test_8					96.37 222	89.14 106	90.51 307	96.89 203	79.37 373	90.42 348	94.36 314	91.20 165	98.82 146

agg_prior2														87.06 279	98.36 218	97.98 197

agg_prior					96.20 243	88.89 111		96.88 208		90.21 353			98.78 159

TestCases				96.00 60	98.02 95	92.17 54		98.43 25	90.48 172	95.04 203	96.74 173	92.54 125	97.86 281	85.11 311	98.98 125	97.98 197

test_prior4						89.91 89	90.74 299

test_prior2							90.21 319		89.33 194	90.77 341	94.81 289	90.41 188		88.21 253	98.55 191

test_prior				94.61 129	95.95 266	87.23 145		97.36 165					98.68 179			97.93 206

旧先验2							90.00 327		68.65 445	92.71 295			96.52 365	85.15 308

新几何2							90.02 326

新几何1				93.17 202	97.16 156	87.29 143		94.43 313	67.95 447	91.29 332	94.94 284	86.97 250	98.23 237	81.06 357	97.75 274	93.98 402

旧先验1					96.20 243	84.17 220		94.82 303			95.57 258	89.57 205			97.89 268	96.32 319

无先验							89.94 328	95.75 269	70.81 435				98.59 191	81.17 356		94.81 381

原ACMM2							89.34 347

原ACMM1				92.87 215	96.91 171	84.22 218		97.01 192	76.84 396	89.64 366	94.46 309	88.00 229	98.70 175	81.53 351	98.01 258	95.70 351

test222					96.95 167	85.27 204	88.83 361	93.61 329	65.09 455	90.74 342	94.85 287	84.62 281			97.36 298	93.91 403

testdata2													98.03 260	80.24 363

segment_acmp												92.14 137

testdata				91.03 298	96.87 174	82.01 262		94.28 317	71.55 428	92.46 303	95.42 264	85.65 270	97.38 326	82.64 335	97.27 300	93.70 409

testdata1							88.96 357		88.44 218

test12				94.43 143	95.95 266	86.75 161		96.24 251		89.76 364		89.79 204	98.79 155		97.95 265	97.75 236

plane_prior7					97.71 121	88.68 115

plane_prior6					97.21 154	88.23 128						86.93 251

plane_prior5								97.81 117					98.95 130	89.26 224	98.51 198	98.60 130

plane_prior4											95.59 254

plane_prior3						88.43 126			90.35 177	93.31 263

plane_prior2							94.56 138		91.74 130

plane_prior1					97.38 142

plane_prior						88.12 129	93.01 197		88.98 202						98.06 252

n2								0.00 478

nn								0.00 478

door-mid								92.13 363

lessismore_v0				93.87 166	98.05 91	83.77 226		80.32 454		97.13 74	97.91 69	77.49 348	99.11 104	92.62 118	98.08 249	98.74 107

LGP-MVS_train				96.84 42	98.36 71	92.13 56		98.25 43	91.78 126	97.07 77	97.22 130	96.38 16	99.28 81	92.07 133	99.59 30	99.11 52

test11								96.65 228

door								91.26 375

HQP5-MVS						84.89 208

HQP-NCC					96.36 224		91.37 278		87.16 251	88.81 378

ACMP_Plane					96.36 224		91.37 278		87.16 251	88.81 378

BP-MVS														86.55 289

HQP4-MVS										88.81 378			98.61 187			98.15 179

HQP3-MVS								97.31 169							97.73 275

HQP2-MVS												84.76 279

NP-MVS					96.82 179	87.10 149					93.40 344

MDTV_nov1_ep13_2view						42.48 472	88.45 373		67.22 449	83.56 434		66.80 402		72.86 422		94.06 399

MDTV_nov1_ep13			83.88 386		89.42 440	61.52 455	88.74 367	87.41 405	73.99 414	84.96 422	94.01 326	65.25 413	95.53 391	78.02 383	93.16 416

ACMMP++_ref															98.82 152

ACMMP++															99.25 91

Test By Simon												90.61 184

ITE_SJBPF				95.95 64	97.34 145	93.36 44		96.55 238	91.93 114	94.82 212	95.39 269	91.99 139	97.08 343	85.53 302	97.96 264	97.41 262

DeepMVS_CX				53.83 448	70.38 471	64.56 448		48.52 472	33.01 466	65.50 466	74.21 463	56.19 442	46.64 469	38.45 467	70.07 463	50.30 464

High-resolution multi-view benchmark