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	91.78 142	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	95.46 170	99.23 9	93.45 94	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	98.36 153

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	98.36 153

sc_t1		97.21 10	97.71 5	95.71 79	99.06 10	88.89 111	96.72 31	97.79 120	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	97.54 145	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	98.51 202	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	98.00 191

mvs_tets		96.83 16	96.71 26	97.17 31	98.83 29	92.51 52	96.58 37	97.61 136	87.57 241	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	92.91 115	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	95.95 141	96.41 195	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	97.86 107	96.10 37	98.14 31	99.28 8	97.94 3	98.21 236	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	88.07 223	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	97.90 271	89.32 217	99.23 94	98.19 173

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	97.90 271	89.32 217	99.23 94	98.19 173

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	90.11 199	99.44 52	98.31 160
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	97.61 136	87.68 239	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	97.94 100	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	96.73 174	95.09 53	99.43 37	92.99 109	98.71 173	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	97.62 134	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	97.37 158	88.98 202	98.26 27	98.86 15	93.35 99	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	96.84 164	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	93.15 106	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	91.76 145	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	97.42 155	86.96 255	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	98.00 264	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 97	94.58 56	94.38 223	96.49 188	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 94	92.35 100	95.57 163	96.61 182	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	97.74 124	92.59 94	95.47 168	96.68 178	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	95.08 199	96.39 200	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	95.34 177	96.36 203	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 94	92.26 103	95.28 182	96.57 184	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	96.57 184	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	95.63 160	96.47 189	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	95.76 151	96.87 160	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	96.85 161	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	95.74 154	96.28 209	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	97.53 147	97.60 11	98.34 23	97.52 98	91.98 138	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	97.78 122	92.73 90	93.48 253	96.72 175	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	94.75 323	94.20 23	97.34 13	97.98 91	97.31 15	95.32 178	96.77 167	93.08 109	99.20 90	91.79 143	98.16 239	97.44 257

MP-MVS-pluss		96.08 56	95.92 70	96.57 48	99.06 10	91.21 69	93.25 188	98.32 36	87.89 232	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	97.86 107	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	97.88 105	88.72 208	98.81 10	98.86 15	90.77 175	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	97.90 101	95.31 46	98.14 31	97.28 122	88.82 209	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	97.90 101	95.31 46	98.14 31	97.28 122	88.82 209	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	97.95 201

DVP-MVS++		95.93 62	96.34 44	94.70 123	96.54 206	86.66 165	98.45 4	98.22 50	93.26 85	97.54 49	97.36 112	93.12 107	99.38 61	93.88 68	98.68 177	98.04 186

APD_test1		95.91 63	95.42 95	97.36 27	98.82 30	96.62 7	95.64 88	97.64 132	93.38 83	95.89 146	97.23 128	93.35 99	97.66 300	88.20 252	98.66 181	97.79 227

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	86.25 267	97.56 48	98.66 24	95.73 23	98.44 214	97.35 4	98.99 123	98.27 164

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	96.68 178	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	97.82 115	96.13 36	94.74 214	97.23 128	91.33 157	99.16 93	93.25 99	98.30 224	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	93.41 255	99.29 7	90.25 188	97.27 326	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	97.00 191	91.85 119	97.40 61	97.35 115	95.58 28	99.34 68	93.44 89	99.31 78	98.13 179
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	92.48 298	94.79 288	93.56 90	99.49 30	93.47 86	99.05 115	97.89 213

SMA-MVS		95.77 70	95.54 89	96.47 53	98.27 75	91.19 70	95.09 115	97.79 120	86.48 262	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	97.24 175	96.88 21	97.69 42	97.77 78	94.12 83	99.13 99	91.54 155	99.29 83	97.88 214

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	96.47 189	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	96.76 169	92.91 115	98.72 168	91.19 162	99.42 54	98.32 158

DP-MVS		95.62 75	95.84 76	94.97 109	97.16 156	88.62 117	94.54 141	97.64 132	96.94 20	96.58 107	97.32 119	93.07 110	98.72 168	90.45 179	98.84 146	97.57 246

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	85.68 286	97.42 59	98.30 41	95.34 39	98.39 215	96.85 11	98.98 125	98.19 173

OPM-MVS		95.61 76	95.45 92	96.08 59	98.49 61	91.00 72	92.65 217	97.33 166	90.05 180	96.77 96	96.85 161	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	97.86 107	91.88 117	97.52 52	98.13 46	91.45 155	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	97.53 147	92.42 96	93.41 255	97.78 74	91.21 162	97.77 290	91.06 164	97.06 308	98.80 98

MIMVSNet1		95.52 80	95.45 92	95.72 78	99.14 5	89.02 108	96.23 63	96.87 207	93.73 74	97.87 36	98.49 34	90.73 179	99.05 113	86.43 289	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	96.77 215	96.64 24	97.61 47	98.05 51	93.23 103	98.79 155	88.60 244	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	94.11 229	98.07 50	92.02 136	99.44 34	93.38 94	97.67 279	97.85 219
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	92.77 289	94.52 300	93.95 86	99.49 30	93.62 77	99.22 97	97.51 251

test_fmvsmconf_n		95.43 84	95.50 90	95.22 101	96.48 214	89.19 105	93.23 190	98.36 33	85.61 289	96.92 87	98.02 55	95.23 45	98.38 218	96.69 14	98.95 134	98.09 181

pm-mvs1		95.43 84	95.94 67	93.93 163	98.38 66	85.08 206	95.46 98	97.12 184	91.84 122	97.28 67	98.46 36	95.30 42	97.71 297	90.17 197	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	87.35 244	96.22 128	97.99 59	94.48 77	99.05 113	92.73 115	99.68 20	97.93 204
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	94.29 312	96.48 27	95.38 173	93.63 333	94.89 63	97.94 270	95.38 41	96.92 316	95.17 361

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	93.75 243	96.25 213	98.03 2	97.02 342	92.08 132	95.55 353	98.45 144

UniMVSNet_NR-MVSNet		95.35 89	95.21 107	95.76 76	97.69 124	88.59 120	92.26 243	97.84 111	94.91 53	96.80 94	95.78 242	90.42 184	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	95.12 195	95.15 269	86.60 255	99.50 24	93.43 92	96.81 320	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	91.03 334	93.73 331	93.52 92	99.55 19	91.81 142	99.45 49	97.58 245

FC-MVSNet-test		95.32 91	95.88 73	93.62 178	98.49 61	81.77 264	95.90 77	98.32 36	93.93 70	97.53 51	97.56 93	88.48 214	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	95.97 231	90.14 192	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 97	96.69 22	91.78 321	98.85 17	91.77 143	95.49 390	91.72 147	99.08 111	95.02 370
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	96.42 218	83.08 241	97.35 12	99.28 3	96.44 29	96.20 130	99.65 2	84.10 281	98.01 262	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	97.81 116	93.99 67	96.80 94	95.90 232	90.10 195	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	97.37 158	93.92 72	97.65 43	95.90 232	90.10 195	99.33 73	90.11 199	99.66 24	99.26 35

TransMVSNet (Re)		95.27 98	96.04 62	92.97 207	98.37 68	81.92 263	95.07 117	96.76 216	93.97 69	97.77 39	98.57 29	95.72 24	97.90 271	88.89 234	99.23 94	99.08 56

fmvsm_s_conf0.5_n_3		95.20 99	95.95 66	92.94 211	96.60 201	82.18 260	93.13 193	98.39 30	91.44 144	97.16 72	97.68 82	93.03 112	97.82 282	97.54 3	98.63 182	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	87.21 248	96.59 106	97.76 79	94.20 81	98.11 248	95.90 26	98.40 206	98.42 147

SD-MVS		95.19 100	95.73 82	93.55 182	96.62 200	88.88 113	94.67 131	98.05 80	91.26 149	97.25 69	96.40 196	95.42 34	94.36 413	92.72 116	99.19 100	97.40 261
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	97.58 141	93.39 82	97.05 80	98.04 53	93.25 102	98.51 202	89.75 210	99.59 30	99.08 56

casdiffmvs_mvg		95.10 103	95.62 86	93.53 185	96.25 239	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	98.72 171	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	96.69 220	94.41 61	96.67 100	97.25 125	87.67 232	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	85.57 290	98.27 24	97.11 140	94.11 84	97.75 293	96.26 20	98.72 171	96.89 290

HPM-MVS++		95.02 106	94.39 145	96.91 41	97.88 107	93.58 41	94.09 158	96.99 193	91.05 154	92.40 303	95.22 268	91.03 171	99.25 84	92.11 130	98.69 176	97.90 211

APD-MVS		95.00 107	94.69 130	95.93 67	97.38 142	90.88 75	94.59 134	97.81 116	89.22 197	95.46 170	96.17 219	93.42 97	99.34 68	89.30 219	98.87 144	97.56 248
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	95.85 263	96.47 28	93.40 258	97.46 105	95.31 41	95.47 391	86.18 293	98.78 161	89.11 441
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	96.81 211	85.23 296	94.75 213	97.12 139	91.85 140	99.40 51	93.45 88	98.33 218	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	94.84 298	94.86 54	98.49 19	98.74 21	81.45 309	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	81.76 265	95.33 102	98.14 63	93.05 89	97.07 77	97.18 134	87.65 233	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	95.04 201	96.74 172	92.54 124	97.86 279	85.11 307	98.98 125	97.98 195

FMVSNet1		94.84 113	95.13 110	93.97 159	97.60 129	84.29 215	95.99 71	96.56 231	92.38 97	97.03 81	98.53 31	90.12 193	98.98 122	88.78 238	99.16 105	98.65 119

ANet_high		94.83 114	96.28 47	90.47 316	96.65 192	73.16 397	94.33 145	98.74 14	96.39 31	98.09 34	98.93 13	93.37 98	98.70 175	90.38 182	99.68 20	99.53 17

MVSMamba_PlusPlus		94.82 115	95.89 72	91.62 269	97.82 111	78.88 324	96.52 39	97.60 138	97.14 17	94.23 226	98.48 35	87.01 245	99.71 3	95.43 39	98.80 157	96.28 318

3Dnovator	92.54 3	94.80 116	94.90 118	94.47 141	95.47 297	87.06 150	96.63 35	97.28 172	91.82 125	94.34 225	97.41 106	90.60 182	98.65 184	92.47 124	98.11 244	97.70 236

CPTT-MVS		94.74 117	94.12 159	96.60 47	98.15 84	93.01 46	95.84 80	97.66 131	89.21 198	93.28 263	95.46 258	88.89 208	98.98 122	89.80 206	98.82 152	97.80 226

test_fmvsm_n_1920		94.72 118	94.74 128	94.67 126	96.30 233	88.62 117	93.19 191	98.07 76	85.63 288	97.08 76	97.35 115	90.86 172	97.66 300	95.70 30	98.48 200	97.74 234

XVG-OURS		94.72 118	94.12 159	96.50 51	98.00 99	94.23 22	91.48 275	98.17 59	90.72 163	95.30 179	96.47 189	87.94 228	96.98 343	91.41 158	97.61 283	98.30 162

fmvsm_s_conf0.5_n_8		94.70 120	95.34 100	92.78 220	96.77 184	81.50 272	92.64 218	98.50 19	91.51 141	97.22 70	97.93 62	88.07 223	98.45 212	96.62 16	98.80 157	98.39 151

CSCG		94.69 121	94.75 126	94.52 137	97.55 133	87.87 134	95.01 120	97.57 142	92.68 91	96.20 130	93.44 339	91.92 139	98.78 159	89.11 228	99.24 93	96.92 288

v10		94.68 122	95.27 106	92.90 214	96.57 203	80.15 288	94.65 133	97.57 142	90.68 165	97.43 57	98.00 56	88.18 220	99.15 94	94.84 49	99.55 38	99.41 26

v8		94.65 123	95.29 104	92.74 221	96.65 192	79.77 303	94.59 134	97.17 179	91.86 118	97.47 56	97.93 62	88.16 221	99.08 106	94.32 58	99.47 45	99.38 28

sasdasda		94.59 124	94.69 130	94.30 146	95.60 290	87.03 151	95.59 89	98.24 46	91.56 136	95.21 188	92.04 373	94.95 59	98.66 181	91.45 156	97.57 285	97.20 272

canonicalmvs		94.59 124	94.69 130	94.30 146	95.60 290	87.03 151	95.59 89	98.24 46	91.56 136	95.21 188	92.04 373	94.95 59	98.66 181	91.45 156	97.57 285	97.20 272

CNVR-MVS		94.58 126	94.29 151	95.46 88	96.94 168	89.35 102	91.81 266	96.80 212	89.66 187	93.90 241	95.44 260	92.80 119	98.72 168	92.74 114	98.52 195	98.32 158

GeoE		94.55 127	94.68 134	94.15 151	97.23 151	85.11 205	94.14 155	97.34 165	88.71 209	95.26 183	95.50 256	94.65 69	99.12 100	90.94 168	98.40 206	98.23 167

EG-PatchMatch MVS		94.54 128	94.67 135	94.14 153	97.87 109	86.50 167	92.00 251	96.74 217	88.16 226	96.93 86	97.61 89	93.04 111	97.90 271	91.60 151	98.12 243	98.03 189

fmvsm_l_conf0.5_n_9		94.51 129	95.11 112	92.72 222	96.70 188	83.14 239	91.91 258	97.89 104	88.44 217	97.30 64	97.57 91	91.60 147	97.54 307	95.82 28	98.74 169	97.47 253

fmvsm_s_conf0.5_n_5		94.50 130	94.80 122	93.60 179	96.80 181	84.93 207	92.81 207	97.59 140	85.27 295	96.85 92	97.29 120	91.48 154	98.05 255	96.67 15	98.47 201	97.83 221

IS-MVSNet		94.49 131	94.35 150	94.92 111	98.25 78	86.46 170	97.13 17	94.31 311	96.24 35	96.28 124	96.36 203	82.88 291	99.35 65	88.19 253	99.52 42	98.96 75

Baseline_NR-MVSNet		94.47 132	95.09 114	92.60 233	98.50 60	80.82 284	92.08 247	96.68 222	93.82 73	96.29 122	98.56 30	90.10 195	97.75 293	90.10 201	99.66 24	99.24 39

MGCFI-Net		94.44 133	94.67 135	93.75 172	95.56 292	85.47 199	95.25 109	98.24 46	91.53 138	95.04 201	92.21 368	94.94 61	98.54 197	91.56 154	97.66 280	97.24 270

SDMVSNet		94.43 134	95.02 115	92.69 224	97.93 104	82.88 245	91.92 257	95.99 260	93.65 79	95.51 165	98.63 26	94.60 71	96.48 363	87.57 267	99.35 67	98.70 113

MM		94.41 135	94.14 158	95.22 101	95.84 271	87.21 146	94.31 147	90.92 375	94.48 59	92.80 287	97.52 98	85.27 271	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	97.84 111	91.46 142	95.70 158	96.56 186	92.50 128	99.08 106	88.83 235	98.23 231	97.98 195

fmvsm_s_conf0.1_n_2		94.38 136	94.78 125	93.19 201	97.07 162	81.72 267	91.97 252	97.51 150	87.05 254	97.31 63	97.92 67	88.29 218	98.15 244	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	93.28 333	94.49 58	96.24 126	97.78 74	87.99 227	98.79 155	88.92 232	99.14 107	98.34 157

EI-MVSNet-Vis-set		94.36 139	94.28 152	94.61 129	92.55 377	85.98 185	92.44 229	94.69 305	93.70 75	96.12 135	95.81 238	91.24 160	98.86 141	93.76 75	98.22 234	98.98 68

EI-MVSNet-UG-set		94.35 140	94.27 154	94.59 133	92.46 380	85.87 190	92.42 231	94.69 305	93.67 78	96.13 134	95.84 236	91.20 163	98.86 141	93.78 72	98.23 231	99.03 60

PHI-MVS		94.34 141	93.80 168	95.95 64	95.65 286	91.67 66	94.82 125	97.86 107	87.86 233	93.04 279	94.16 316	91.58 148	98.78 159	90.27 191	98.96 132	97.41 258

casdiffmvs		94.32 142	94.80 122	92.85 216	96.05 256	81.44 274	92.35 235	98.05 80	91.53 138	95.75 153	96.80 165	93.35 99	98.49 204	91.01 167	98.32 220	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	92.48 238	97.71 121	80.88 283	94.55 140	95.41 282	93.70 75	96.67 100	97.72 80	91.40 156	98.18 240	87.45 269	99.18 102	98.36 153

fmvsm_s_conf0.5_n_4		94.26 144	94.58 139	93.31 194	96.40 220	82.73 252	92.59 220	97.41 156	86.60 259	96.33 117	97.07 144	89.91 199	98.07 252	96.88 10	98.01 256	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	95.44 279	83.25 324	95.51 165	98.05 51	92.54 124	97.19 332	95.55 35	97.46 291	98.94 77

HQP_MVS		94.26 144	93.93 164	95.23 99	97.71 121	88.12 129	94.56 138	97.81 116	91.74 130	93.31 260	95.59 251	86.93 248	98.95 130	89.26 223	98.51 197	98.60 130

baseline		94.26 144	94.80 122	92.64 226	96.08 254	80.99 281	93.69 173	98.04 84	90.80 162	94.89 208	96.32 205	93.19 104	98.48 208	91.68 149	98.51 197	98.43 146

fmvsm_s_conf0.5_n_2		94.25 148	94.63 137	93.10 203	96.65 192	81.75 266	91.72 269	97.25 173	86.93 258	97.20 71	97.67 84	88.44 216	98.14 247	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	97.84 111	91.46 142	95.19 190	96.56 186	92.50 128	98.99 121	88.83 235	98.32 220	97.93 204

OMC-MVS		94.22 150	93.69 175	95.81 74	97.25 149	91.27 68	92.27 242	97.40 157	87.10 253	94.56 218	95.42 261	93.74 87	98.11 248	86.62 283	98.85 145	98.06 182

LCM-MVSNet-Re		94.20 151	94.58 139	93.04 204	95.91 266	83.13 240	93.79 169	99.19 6	92.00 111	98.84 9	98.04 53	93.64 89	99.02 118	81.28 349	98.54 192	96.96 287

DeepPCF-MVS	90.46 6	94.20 151	93.56 182	96.14 57	95.96 263	92.96 47	89.48 338	97.46 153	85.14 299	96.23 127	95.42 261	93.19 104	98.08 251	90.37 185	98.76 164	97.38 264

fmvsm_s_conf0.1_n		94.19 153	94.41 144	93.52 187	97.22 153	84.37 212	93.73 171	95.26 286	84.45 311	95.76 151	98.00 56	91.85 140	97.21 329	95.62 31	97.82 270	98.98 68

fmvsm_s_conf0.5_n_6		94.14 154	94.54 142	92.95 209	96.51 210	82.74 251	92.71 213	98.13 64	86.56 261	96.44 111	96.85 161	88.51 213	98.05 255	96.03 23	99.09 110	98.06 182

NormalMVS		94.10 155	93.36 188	96.31 56	99.01 15	90.84 77	94.70 129	97.90 101	90.98 155	93.22 269	95.73 245	78.94 329	99.12 100	90.38 182	99.42 54	98.97 71

KD-MVS_self_test		94.10 155	94.73 129	92.19 246	97.66 127	79.49 310	94.86 124	97.12 184	89.59 189	96.87 88	97.65 86	90.40 186	98.34 225	89.08 229	99.35 67	98.75 104

NCCC		94.08 157	93.54 183	95.70 81	96.49 212	89.90 90	92.39 233	96.91 200	90.64 166	92.33 310	94.60 296	90.58 183	98.96 128	90.21 195	97.70 277	98.23 167

VDDNet		94.03 158	94.27 154	93.31 194	98.87 26	82.36 257	95.51 97	91.78 366	97.19 16	96.32 119	98.60 28	84.24 279	98.75 163	87.09 276	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	95.23 288	83.30 322	95.13 194	97.56 93	92.22 132	97.17 333	95.51 36	97.41 293	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	95.13 290	84.50 310	95.40 172	97.55 97	91.77 143	97.20 330	95.59 32	97.79 271	98.69 116

dcpmvs_2		93.96 161	95.01 116	90.82 305	97.60 129	74.04 392	93.68 174	98.85 10	89.80 185	97.82 37	97.01 151	91.14 167	99.21 87	90.56 176	98.59 187	99.19 43

sd_testset		93.94 162	94.39 145	92.61 232	97.93 104	83.24 233	93.17 192	95.04 292	93.65 79	95.51 165	98.63 26	94.49 76	95.89 383	81.72 344	99.35 67	98.70 113

EPP-MVSNet		93.91 163	93.68 176	94.59 133	98.08 88	85.55 198	97.44 11	94.03 317	94.22 64	94.94 205	96.19 216	82.07 303	99.57 15	87.28 273	98.89 139	98.65 119

Effi-MVS+-dtu		93.90 164	92.60 215	97.77 4	94.74 324	96.67 6	94.00 160	95.41 282	89.94 181	91.93 320	92.13 371	90.12 193	98.97 127	87.68 266	97.48 289	97.67 239

viewmacassd2359aftdt		93.83 165	94.36 149	92.24 243	96.45 215	79.58 307	91.60 271	97.96 94	89.14 199	95.05 200	97.09 143	93.69 88	98.48 208	89.79 207	98.43 204	98.65 119

fmvsm_l_conf0.5_n		93.79 166	93.81 166	93.73 174	96.16 245	86.26 177	92.46 227	96.72 218	81.69 346	95.77 150	97.11 140	90.83 174	97.82 282	95.58 33	97.99 259	97.11 275

IterMVS-LS		93.78 167	94.28 152	92.27 242	96.27 236	79.21 318	91.87 262	96.78 213	91.77 128	96.57 108	97.07 144	87.15 242	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	93.44 185	94.60 132	96.14 248	87.90 133	93.36 186	97.14 181	85.53 291	93.90 241	95.45 259	91.30 159	98.59 191	89.51 213	98.62 183	97.31 267
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	93.28 191	94.80 118	96.25 239	90.95 73	90.21 315	95.43 281	87.91 230	93.74 245	94.40 306	92.88 117	96.38 369	90.39 181	98.28 225	97.07 280

MVS_111021_HR		93.63 170	93.42 187	94.26 148	96.65 192	86.96 155	89.30 345	96.23 248	88.36 221	93.57 249	94.60 296	93.45 94	97.77 290	90.23 194	98.38 211	98.03 189

fmvsm_s_conf0.5_n_7		93.61 171	93.94 163	92.63 229	96.11 251	82.76 250	90.81 293	97.55 144	86.57 260	93.14 275	97.69 81	90.17 191	96.83 352	94.46 54	98.93 135	98.31 160

mamba_0408		93.60 172	93.72 171	93.27 197	96.65 192	82.79 247	88.81 359	97.68 128	90.62 168	95.19 190	96.01 227	91.54 152	99.08 106	88.63 242	98.32 220	97.93 204

fmvsm_l_conf0.5_n_a		93.59 173	93.63 177	93.49 189	96.10 252	85.66 196	92.32 237	96.57 230	81.32 349	95.63 160	97.14 137	90.19 189	97.73 296	95.37 42	98.03 253	97.07 280

v1144		93.50 174	93.81 166	92.57 234	96.28 234	79.61 306	91.86 264	96.96 194	86.95 256	95.91 144	96.32 205	87.65 233	98.96 128	93.51 82	98.88 141	99.13 48

v1192		93.49 175	93.78 169	92.62 231	96.16 245	79.62 305	91.83 265	97.22 177	86.07 273	96.10 136	96.38 201	87.22 240	99.02 118	94.14 63	98.88 141	99.22 40

WR-MVS		93.49 175	93.72 171	92.80 218	97.57 132	80.03 294	90.14 318	95.68 267	93.70 75	96.62 104	95.39 265	87.21 241	99.04 116	87.50 268	99.64 26	99.33 31

balanced_conf03		93.45 177	94.17 157	91.28 284	95.81 275	78.40 332	96.20 64	97.48 152	88.56 215	95.29 181	97.20 133	85.56 270	99.21 87	92.52 123	98.91 138	96.24 321

LuminaMVS		93.43 178	93.18 194	94.16 150	97.32 147	85.29 203	93.36 186	93.94 322	88.09 227	97.12 75	96.43 192	80.11 320	98.98 122	93.53 81	98.76 164	98.21 169

V42		93.43 178	93.58 180	92.97 207	95.34 303	81.22 277	92.67 215	96.49 236	87.25 247	96.20 130	96.37 202	87.32 239	98.85 143	92.39 127	98.21 235	98.85 92

K. test v3		93.37 180	93.27 192	93.66 176	98.05 91	82.62 253	94.35 144	86.62 408	96.05 39	97.51 53	98.85 17	76.59 359	99.65 5	93.21 100	98.20 237	98.73 108

viewmsd2359difaftdt		93.36 181	93.99 162	91.48 275	95.50 296	78.39 334	90.47 305	96.69 220	88.59 213	96.03 139	96.88 159	93.48 93	97.63 303	90.20 196	98.07 249	98.41 148

PM-MVS		93.33 182	92.67 212	95.33 92	96.58 202	94.06 25	92.26 243	92.18 355	85.92 276	96.22 128	96.61 182	85.64 268	95.99 381	90.35 186	98.23 231	95.93 335

v1240		93.29 183	93.71 174	92.06 253	96.01 261	77.89 341	91.81 266	97.37 158	85.12 300	96.69 99	96.40 196	86.67 253	99.07 112	94.51 52	98.76 164	99.22 40

v2v482		93.29 183	93.63 177	92.29 241	96.35 226	78.82 326	91.77 268	96.28 244	88.45 216	95.70 158	96.26 212	86.02 262	98.90 134	93.02 107	98.81 154	99.14 47

SymmetryMVS		93.26 185	92.36 222	95.97 62	97.13 159	90.84 77	94.70 129	91.61 369	90.98 155	93.22 269	95.73 245	78.94 329	99.12 100	90.38 182	98.53 193	97.97 199

alignmvs		93.26 185	92.85 201	94.50 138	95.70 281	87.45 141	93.45 182	95.76 264	91.58 135	95.25 185	92.42 366	81.96 306	98.72 168	91.61 150	97.87 268	97.33 266

v1921920		93.26 185	93.61 179	92.19 246	96.04 260	78.31 335	91.88 261	97.24 175	85.17 298	96.19 133	96.19 216	86.76 252	99.05 113	94.18 62	98.84 146	99.22 40

SSM_04072		93.25 188	93.72 171	91.84 258	96.65 192	82.79 247	88.81 359	97.68 128	90.62 168	95.19 190	96.01 227	91.54 152	94.81 405	88.63 242	98.32 220	97.93 204

MSLP-MVS++		93.25 188	93.88 165	91.37 278	96.34 227	82.81 246	93.11 194	97.74 124	89.37 193	94.08 231	95.29 267	90.40 186	96.35 371	90.35 186	98.25 229	94.96 371

GBi-Net		93.21 190	92.96 197	93.97 159	95.40 299	84.29 215	95.99 71	96.56 231	88.63 210	95.10 196	98.53 31	81.31 311	98.98 122	86.74 279	98.38 211	98.65 119

test1		93.21 190	92.96 197	93.97 159	95.40 299	84.29 215	95.99 71	96.56 231	88.63 210	95.10 196	98.53 31	81.31 311	98.98 122	86.74 279	98.38 211	98.65 119

v144192		93.20 192	93.54 183	92.16 250	96.05 256	78.26 336	91.95 253	97.14 181	84.98 304	95.96 140	96.11 222	87.08 244	99.04 116	93.79 71	98.84 146	99.17 44

viewmanbaseed2359cas		93.08 193	93.43 186	92.01 255	95.69 282	79.29 314	91.15 283	97.70 127	87.45 243	94.18 228	96.12 221	92.31 130	98.37 222	88.58 245	97.73 273	98.38 152

VPNet		93.08 193	93.76 170	91.03 294	98.60 43	75.83 376	91.51 273	95.62 268	91.84 122	95.74 154	97.10 142	89.31 204	98.32 226	85.07 309	99.06 112	98.93 79

UGNet		93.08 193	92.50 218	94.79 119	93.87 350	87.99 132	95.07 117	94.26 314	90.64 166	87.33 402	97.67 84	86.89 250	98.49 204	88.10 256	98.71 173	97.91 210
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.		93.07 196	92.41 220	95.06 106	95.82 273	90.87 76	90.97 289	92.61 348	88.04 228	94.61 217	93.79 330	88.08 222	97.81 284	89.41 216	98.39 210	96.50 306

ETV-MVS		92.99 197	92.74 205	93.72 175	95.86 270	86.30 176	92.33 236	97.84 111	91.70 133	92.81 286	86.17 435	92.22 132	99.19 91	88.03 260	97.73 273	95.66 349

EI-MVSNet		92.99 197	93.26 193	92.19 246	92.12 390	79.21 318	92.32 237	94.67 307	91.77 128	95.24 186	95.85 234	87.14 243	98.49 204	91.99 136	98.26 227	98.86 89

MCST-MVS		92.91 199	92.51 217	94.10 155	97.52 134	85.72 194	91.36 279	97.13 183	80.33 358	92.91 285	94.24 312	91.23 161	98.72 168	89.99 203	97.93 264	97.86 217

h-mvs33		92.89 200	91.99 231	95.58 83	96.97 166	90.55 83	93.94 164	94.01 320	89.23 195	93.95 238	96.19 216	76.88 355	99.14 96	91.02 165	95.71 349	97.04 284

MVS_0304		92.88 201	92.27 223	94.69 124	92.35 381	86.03 184	92.88 205	89.68 383	90.53 171	91.52 324	96.43 192	82.52 299	99.32 74	95.01 46	99.54 39	98.71 112

QAPM		92.88 201	92.77 203	93.22 200	95.82 273	83.31 231	96.45 45	97.35 164	83.91 316	93.75 243	96.77 167	89.25 205	98.88 137	84.56 315	97.02 310	97.49 252

v148		92.87 203	93.29 189	91.62 269	96.25 239	77.72 344	91.28 280	95.05 291	89.69 186	95.93 143	96.04 225	87.34 238	98.38 218	90.05 202	97.99 259	98.78 100

Anonymous20240521		92.86 204	93.57 181	90.74 307	96.57 203	75.50 378	94.15 153	95.60 269	89.38 192	95.90 145	97.90 71	80.39 319	97.96 268	92.60 120	99.68 20	98.75 104

Effi-MVS+		92.79 205	92.74 205	92.94 211	95.10 311	83.30 232	94.00 160	97.53 147	91.36 147	89.35 366	90.65 397	94.01 85	98.66 181	87.40 271	95.30 362	96.88 292

FMVSNet2		92.78 206	92.73 207	92.95 209	95.40 299	81.98 262	94.18 152	95.53 277	88.63 210	96.05 137	97.37 109	81.31 311	98.81 151	87.38 272	98.67 179	98.06 182

Fast-Effi-MVS+-dtu		92.77 207	92.16 225	94.58 136	94.66 329	88.25 127	92.05 248	96.65 224	89.62 188	90.08 351	91.23 384	92.56 123	98.60 189	86.30 291	96.27 336	96.90 289

AstraMVS		92.75 208	92.73 207	92.79 219	97.02 163	81.48 273	92.88 205	90.62 379	87.99 229	96.48 109	96.71 176	82.02 304	98.48 208	92.44 125	98.46 202	98.40 150

LF4IMVS		92.72 209	92.02 230	94.84 117	95.65 286	91.99 58	92.92 202	96.60 227	85.08 302	92.44 301	93.62 334	86.80 251	96.35 371	86.81 278	98.25 229	96.18 324

train_agg		92.71 210	91.83 237	95.35 90	96.45 215	89.46 96	90.60 301	96.92 198	79.37 369	90.49 342	94.39 307	91.20 163	98.88 137	88.66 241	98.43 204	97.72 235

VNet		92.67 211	92.96 197	91.79 261	96.27 236	80.15 288	91.95 253	94.98 294	92.19 107	94.52 220	96.07 224	87.43 237	97.39 320	84.83 311	98.38 211	97.83 221

CDPH-MVS		92.67 211	91.83 237	95.18 103	96.94 168	88.46 125	90.70 298	97.07 187	77.38 385	92.34 309	95.08 275	92.67 122	98.88 137	85.74 296	98.57 189	98.20 171

guyue		92.60 213	92.62 213	92.52 237	96.73 185	81.00 280	93.00 198	91.83 365	88.28 222	96.38 114	96.23 214	80.71 317	98.37 222	92.06 135	98.37 216	98.20 171

Anonymous202405211		92.58 214	92.50 218	92.83 217	96.55 205	83.22 236	92.43 230	91.64 368	94.10 66	95.59 162	96.64 180	81.88 308	97.50 310	85.12 306	98.52 195	97.77 230

XXY-MVS		92.58 214	93.16 195	90.84 304	97.75 116	79.84 299	91.87 262	96.22 250	85.94 275	95.53 164	97.68 82	92.69 121	94.48 409	83.21 326	97.51 287	98.21 169

MVS_Test		92.57 216	93.29 189	90.40 319	93.53 356	75.85 374	92.52 223	96.96 194	88.73 207	92.35 307	96.70 177	90.77 175	98.37 222	92.53 122	95.49 355	96.99 286

TAPA-MVS	88.58 10	92.49 217	91.75 239	94.73 121	96.50 211	89.69 92	92.91 203	97.68 128	78.02 382	92.79 288	94.10 317	90.85 173	97.96 268	84.76 313	98.16 239	96.54 301
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 218	92.72 209	91.71 265	96.65 192	78.91 323	88.85 356	97.17 179	83.89 317	92.45 300	96.76 169	89.86 200	97.09 338	90.24 193	98.59 187	99.12 51

test_fmvs3		92.42 219	92.40 221	92.46 240	93.80 353	87.28 144	93.86 167	97.05 188	76.86 391	96.25 125	98.66 24	82.87 292	91.26 434	95.44 38	96.83 319	98.82 94

ab-mvs		92.40 220	92.62 213	91.74 263	97.02 163	81.65 268	95.84 80	95.50 278	86.95 256	92.95 284	97.56 93	90.70 180	97.50 310	79.63 368	97.43 292	96.06 329

CANet		92.38 221	91.99 231	93.52 187	93.82 352	83.46 229	91.14 284	97.00 191	89.81 184	86.47 406	94.04 319	87.90 229	99.21 87	89.50 214	98.27 226	97.90 211

EIA-MVS		92.35 222	92.03 229	93.30 196	95.81 275	83.97 223	92.80 209	98.17 59	87.71 237	89.79 359	87.56 425	91.17 166	99.18 92	87.97 261	97.27 297	96.77 296

diffmvs_AUTHOR		92.34 223	92.70 210	91.26 285	94.20 339	78.42 331	89.12 350	97.60 138	87.16 249	93.17 274	95.50 256	88.66 211	97.57 306	91.30 160	97.61 283	97.79 227

DP-MVS Recon		92.31 224	91.88 235	93.60 179	97.18 155	86.87 157	91.10 286	97.37 158	84.92 305	92.08 317	94.08 318	88.59 212	98.20 237	83.50 323	98.14 241	95.73 344

IMVS_0407		92.28 225	92.83 202	90.63 312	95.19 307	76.72 360	92.79 210	96.89 201	85.92 276	93.55 250	94.50 301	91.06 168	98.07 252	88.49 247	97.07 304	97.10 276

RRT-MVS		92.28 225	93.01 196	90.07 328	94.06 345	73.01 399	95.36 99	97.88 105	92.24 105	95.16 193	97.52 98	78.51 337	99.29 77	90.55 177	95.83 347	97.92 209

F-COLMAP		92.28 225	91.06 256	95.95 64	97.52 134	91.90 60	93.53 178	97.18 178	83.98 315	88.70 380	94.04 319	88.41 217	98.55 196	80.17 361	95.99 342	97.39 262

OpenMVS	89.45 8	92.27 228	92.13 228	92.68 225	94.53 333	84.10 221	95.70 84	97.03 189	82.44 338	91.14 333	96.42 194	88.47 215	98.38 218	85.95 294	97.47 290	95.55 354

hse-mvs2		92.24 229	91.20 251	95.38 89	96.16 245	90.65 82	92.52 223	92.01 362	89.23 195	93.95 238	92.99 350	76.88 355	98.69 177	91.02 165	96.03 340	96.81 294

IMVS_0403		92.20 230	92.70 210	90.69 308	95.19 307	76.72 360	92.39 233	96.89 201	85.92 276	93.66 247	94.50 301	90.18 190	98.24 234	88.49 247	97.07 304	97.10 276

MVSFormer		92.18 231	92.23 224	92.04 254	94.74 324	80.06 292	97.15 15	97.37 158	88.98 202	88.83 372	92.79 355	77.02 352	99.60 10	96.41 18	96.75 323	96.46 310

VortexMVS		92.13 232	92.56 216	90.85 303	94.54 332	76.17 370	92.30 240	96.63 226	86.20 269	96.66 102	96.79 166	79.87 322	98.16 242	91.27 161	98.76 164	98.24 166

HQP-MVS		92.09 233	91.49 245	93.88 165	96.36 223	84.89 208	91.37 276	97.31 167	87.16 249	88.81 374	93.40 340	84.76 276	98.60 189	86.55 286	97.73 273	98.14 178

DELS-MVS		92.05 234	92.16 225	91.72 264	94.44 334	80.13 290	87.62 376	97.25 173	87.34 245	92.22 312	93.18 347	89.54 203	98.73 167	89.67 211	98.20 237	96.30 316
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
TinyColmap		92.00 235	92.76 204	89.71 337	95.62 289	77.02 353	90.72 297	96.17 253	87.70 238	95.26 183	96.29 207	92.54 124	96.45 366	81.77 342	98.77 162	95.66 349

CLD-MVS		91.82 236	91.41 247	93.04 204	96.37 221	83.65 227	86.82 395	97.29 170	84.65 309	92.27 311	89.67 406	92.20 134	97.85 281	83.95 321	99.47 45	97.62 241
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)		91.81 237	91.85 236	91.68 267	94.95 314	79.99 296	96.00 70	93.44 331	87.80 234	94.02 236	97.29 120	77.60 343	98.45 212	88.04 259	97.49 288	96.61 300

BP-MVS1		91.77 238	91.10 255	93.75 172	96.42 218	83.40 230	94.10 157	91.89 363	91.27 148	93.36 259	94.85 283	64.43 413	99.29 77	94.88 47	98.74 169	98.56 134

diffmvs		91.74 239	91.93 233	91.15 292	93.06 365	78.17 337	88.77 362	97.51 150	86.28 266	92.42 302	93.96 324	88.04 225	97.46 313	90.69 174	96.67 326	97.82 224
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		91.72 240	91.20 251	93.26 198	96.17 244	91.02 71	91.14 284	95.55 276	90.16 179	90.87 335	93.56 337	86.31 258	94.40 412	79.92 367	97.12 302	94.37 389

IterMVS-SCA-FT		91.65 241	91.55 241	91.94 256	93.89 349	79.22 317	87.56 379	93.51 329	91.53 138	95.37 175	96.62 181	78.65 333	98.90 134	91.89 140	94.95 371	97.70 236

PVSNet_Blended_VisFu		91.63 242	91.20 251	92.94 211	97.73 119	83.95 224	92.14 246	97.46 153	78.85 378	92.35 307	94.98 278	84.16 280	99.08 106	86.36 290	96.77 322	95.79 342

AdaColmap		91.63 242	91.36 248	92.47 239	95.56 292	86.36 174	92.24 245	96.27 245	88.88 206	89.90 356	92.69 358	91.65 146	98.32 226	77.38 387	97.64 281	92.72 423

GDP-MVS		91.56 244	90.83 263	93.77 171	96.34 227	83.65 227	93.66 175	98.12 66	87.32 246	92.98 282	94.71 291	63.58 419	99.30 76	92.61 119	98.14 241	98.35 156

pmmvs-eth3d		91.54 245	90.73 268	93.99 157	95.76 279	87.86 135	90.83 292	93.98 321	78.23 381	94.02 236	96.22 215	82.62 298	96.83 352	86.57 284	98.33 218	97.29 268

API-MVS		91.52 246	91.61 240	91.26 285	94.16 340	86.26 177	94.66 132	94.82 299	91.17 152	92.13 316	91.08 387	90.03 198	97.06 341	79.09 375	97.35 296	90.45 439

xiu_mvs_v1_base_debu		91.47 247	91.52 242	91.33 280	95.69 282	81.56 269	89.92 325	96.05 257	83.22 325	91.26 329	90.74 392	91.55 149	98.82 146	89.29 220	95.91 343	93.62 408

xiu_mvs_v1_base		91.47 247	91.52 242	91.33 280	95.69 282	81.56 269	89.92 325	96.05 257	83.22 325	91.26 329	90.74 392	91.55 149	98.82 146	89.29 220	95.91 343	93.62 408

xiu_mvs_v1_base_debi		91.47 247	91.52 242	91.33 280	95.69 282	81.56 269	89.92 325	96.05 257	83.22 325	91.26 329	90.74 392	91.55 149	98.82 146	89.29 220	95.91 343	93.62 408

LFMVS		91.33 250	91.16 254	91.82 260	96.27 236	79.36 312	95.01 120	85.61 421	96.04 40	94.82 210	97.06 146	72.03 378	98.46 211	84.96 310	98.70 175	97.65 240

c3_l		91.32 251	91.42 246	91.00 297	92.29 383	76.79 359	87.52 382	96.42 239	85.76 284	94.72 216	93.89 327	82.73 295	98.16 242	90.93 169	98.55 190	98.04 186

Fast-Effi-MVS+		91.28 252	90.86 261	92.53 236	95.45 298	82.53 254	89.25 348	96.52 235	85.00 303	89.91 355	88.55 418	92.94 113	98.84 144	84.72 314	95.44 357	96.22 322

icg_test_0407_2		91.18 253	91.92 234	88.94 351	95.19 307	76.72 360	84.66 427	96.89 201	85.92 276	93.55 250	94.50 301	91.06 168	92.99 426	88.49 247	97.07 304	97.10 276

MDA-MVSNet-bldmvs		91.04 254	90.88 260	91.55 272	94.68 328	80.16 287	85.49 417	92.14 358	90.41 176	94.93 206	95.79 239	85.10 273	96.93 347	85.15 304	94.19 393	97.57 246

PAPM_NR		91.03 255	90.81 264	91.68 267	96.73 185	81.10 279	93.72 172	96.35 242	88.19 224	88.77 378	92.12 372	85.09 274	97.25 327	82.40 337	93.90 398	96.68 299

MSDG		90.82 256	90.67 269	91.26 285	94.16 340	83.08 241	86.63 400	96.19 251	90.60 170	91.94 319	91.89 375	89.16 206	95.75 385	80.96 354	94.51 382	94.95 372

test20.03		90.80 257	90.85 262	90.63 312	95.63 288	79.24 316	89.81 329	92.87 339	89.90 182	94.39 222	96.40 196	85.77 263	95.27 398	73.86 412	99.05 115	97.39 262

FMVSNet3		90.78 258	90.32 278	92.16 250	93.03 367	79.92 298	92.54 222	94.95 295	86.17 272	95.10 196	96.01 227	69.97 386	98.75 163	86.74 279	98.38 211	97.82 224

viewmambaseed2359dif		90.77 259	90.81 264	90.64 311	93.46 357	77.04 352	88.83 357	96.29 243	80.79 356	92.21 313	95.11 272	88.99 207	97.28 324	85.39 301	96.20 338	97.59 244

eth_miper_zixun_eth		90.72 260	90.61 270	91.05 293	92.04 393	76.84 358	86.91 391	96.67 223	85.21 297	94.41 221	93.92 325	79.53 325	98.26 232	89.76 209	97.02 310	98.06 182

X-MVStestdata		90.70 261	88.45 312	97.44 20	98.56 46	93.99 30	96.50 41	97.95 97	94.58 56	94.38 223	26.89 463	94.56 73	99.39 54	93.57 78	99.05 115	98.93 79

BH-untuned		90.68 262	90.90 259	90.05 331	95.98 262	79.57 308	90.04 321	94.94 296	87.91 230	94.07 232	93.00 349	87.76 230	97.78 289	79.19 374	95.17 366	92.80 422

IMVS_0404		90.67 263	91.06 256	89.50 339	95.19 307	76.72 360	86.58 403	96.89 201	85.92 276	89.17 367	94.50 301	85.77 263	94.67 406	88.49 247	97.07 304	97.10 276

cl____		90.65 264	90.56 272	90.91 301	91.85 398	76.98 356	86.75 396	95.36 284	85.53 291	94.06 233	94.89 281	77.36 349	97.98 267	90.27 191	98.98 125	97.76 231

DIV-MVS_self_test		90.65 264	90.56 272	90.91 301	91.85 398	76.99 355	86.75 396	95.36 284	85.52 293	94.06 233	94.89 281	77.37 348	97.99 266	90.28 190	98.97 130	97.76 231

test_fmvs2		90.62 266	90.40 276	91.29 283	91.93 397	85.46 200	92.70 214	96.48 237	74.44 406	94.91 207	97.59 90	75.52 363	90.57 437	93.44 89	96.56 328	97.84 220

114514_t		90.51 267	89.80 288	92.63 229	98.00 99	82.24 259	93.40 184	97.29 170	65.84 449	89.40 365	94.80 287	86.99 246	98.75 163	83.88 322	98.61 184	96.89 290

miper_ehance_all_eth		90.48 268	90.42 275	90.69 308	91.62 405	76.57 366	86.83 394	96.18 252	83.38 321	94.06 233	92.66 360	82.20 301	98.04 257	89.79 207	97.02 310	97.45 255

BH-RMVSNet		90.47 269	90.44 274	90.56 315	95.21 306	78.65 330	89.15 349	93.94 322	88.21 223	92.74 290	94.22 313	86.38 256	97.88 275	78.67 377	95.39 359	95.14 364

Vis-MVSNet (Re-imp)		90.42 270	90.16 279	91.20 290	97.66 127	77.32 349	94.33 145	87.66 400	91.20 151	92.99 280	95.13 271	75.40 364	98.28 228	77.86 380	99.19 100	97.99 194

test_vis3_rt		90.40 271	90.03 283	91.52 274	92.58 375	88.95 109	90.38 310	97.72 126	73.30 414	97.79 38	97.51 102	77.05 351	87.10 452	89.03 230	94.89 372	98.50 139

PLC	85.34 15	90.40 271	88.92 303	94.85 116	96.53 209	90.02 88	91.58 272	96.48 237	80.16 359	86.14 408	92.18 369	85.73 265	98.25 233	76.87 390	94.61 381	96.30 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 273	90.61 270	89.74 336	98.04 94	71.50 410	95.59 89	79.72 452	89.41 191	95.94 142	98.14 45	70.79 382	98.81 151	88.52 246	99.32 77	98.90 85

testgi		90.38 274	91.34 249	87.50 380	97.49 136	71.54 409	89.43 340	95.16 289	88.38 219	94.54 219	94.68 293	92.88 117	93.09 425	71.60 425	97.85 269	97.88 214

mvs_anonymous		90.37 275	91.30 250	87.58 379	92.17 389	68.00 426	89.84 328	94.73 304	83.82 318	93.22 269	97.40 107	87.54 235	97.40 319	87.94 262	95.05 369	97.34 265

PVSNet_BlendedMVS		90.35 276	89.96 284	91.54 273	94.81 319	78.80 328	90.14 318	96.93 196	79.43 368	88.68 381	95.06 276	86.27 259	98.15 244	80.27 357	98.04 252	97.68 238

UnsupCasMVSNet_eth		90.33 277	90.34 277	90.28 321	94.64 330	80.24 286	89.69 333	95.88 261	85.77 283	93.94 240	95.69 248	81.99 305	92.98 427	84.21 319	91.30 431	97.62 241

MAR-MVS		90.32 278	88.87 307	94.66 128	94.82 318	91.85 61	94.22 151	94.75 303	80.91 352	87.52 400	88.07 423	86.63 254	97.87 278	76.67 391	96.21 337	94.25 392
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		90.31 279	90.14 282	90.81 306	91.01 413	78.93 320	92.52 223	98.12 66	91.91 115	89.10 368	96.89 158	68.84 388	99.41 44	90.17 197	92.70 420	94.08 393

mvsmamba		90.24 280	89.43 294	92.64 226	95.52 294	82.36 257	96.64 34	92.29 353	81.77 344	92.14 315	96.28 209	70.59 383	99.10 105	84.44 317	95.22 365	96.47 309

IterMVS		90.18 281	90.16 279	90.21 325	93.15 363	75.98 373	87.56 379	92.97 338	86.43 264	94.09 230	96.40 196	78.32 338	97.43 316	87.87 263	94.69 379	97.23 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS		90.16 282	92.96 197	81.78 431	97.88 107	48.48 464	90.75 295	87.69 399	96.02 41	96.70 98	97.63 88	85.60 269	97.80 285	85.73 297	98.60 186	99.06 58

TAMVS		90.16 282	89.05 299	93.49 189	96.49 212	86.37 173	90.34 312	92.55 349	80.84 355	92.99 280	94.57 299	81.94 307	98.20 237	73.51 413	98.21 235	95.90 338

ECVR-MVS		90.12 284	90.16 279	90.00 332	97.81 112	72.68 403	95.76 83	78.54 455	89.04 200	95.36 176	98.10 48	70.51 384	98.64 185	87.10 275	99.18 102	98.67 117

test_yl		90.11 285	89.73 291	91.26 285	94.09 343	79.82 300	90.44 306	92.65 345	90.90 157	93.19 272	93.30 342	73.90 368	98.03 258	82.23 338	96.87 317	95.93 335

DCV-MVSNet		90.11 285	89.73 291	91.26 285	94.09 343	79.82 300	90.44 306	92.65 345	90.90 157	93.19 272	93.30 342	73.90 368	98.03 258	82.23 338	96.87 317	95.93 335

Patchmtry		90.11 285	89.92 285	90.66 310	90.35 424	77.00 354	92.96 200	92.81 340	90.25 178	94.74 214	96.93 155	67.11 395	97.52 309	85.17 302	98.98 125	97.46 254

MVP-Stereo		90.07 288	88.92 303	93.54 184	96.31 231	86.49 168	90.93 290	95.59 273	79.80 361	91.48 325	95.59 251	80.79 315	97.39 320	78.57 378	91.19 432	96.76 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		90.05 289	88.30 316	95.32 94	96.09 253	90.52 84	92.42 231	92.05 361	82.08 342	88.45 384	92.86 352	65.76 405	98.69 177	88.91 233	96.07 339	96.75 298

CL-MVSNet_self_test		90.04 290	89.90 286	90.47 316	95.24 305	77.81 342	86.60 402	92.62 347	85.64 287	93.25 267	93.92 325	83.84 282	96.06 378	79.93 365	98.03 253	97.53 250

D2MVS		89.93 291	89.60 293	90.92 299	94.03 346	78.40 332	88.69 364	94.85 297	78.96 376	93.08 276	95.09 274	74.57 366	96.94 345	88.19 253	98.96 132	97.41 258

miper_lstm_enhance		89.90 292	89.80 288	90.19 327	91.37 409	77.50 346	83.82 436	95.00 293	84.84 307	93.05 278	94.96 279	76.53 360	95.20 399	89.96 204	98.67 179	97.86 217

SSC-MVS3.2		89.88 293	91.06 256	86.31 399	95.90 267	63.76 447	82.68 441	92.43 352	91.42 145	92.37 306	94.58 298	86.34 257	96.60 359	84.35 318	99.50 43	98.57 133

CANet_DTU		89.85 294	89.17 297	91.87 257	92.20 387	80.02 295	90.79 294	95.87 262	86.02 274	82.53 439	91.77 377	80.01 321	98.57 193	85.66 298	97.70 277	97.01 285

tttt0517		89.81 295	88.90 305	92.55 235	97.00 165	79.73 304	95.03 119	83.65 434	89.88 183	95.30 179	94.79 288	53.64 442	99.39 54	91.99 136	98.79 160	98.54 135

EPNet		89.80 296	88.25 320	94.45 142	83.91 461	86.18 180	93.87 166	87.07 406	91.16 153	80.64 449	94.72 290	78.83 331	98.89 136	85.17 302	98.89 139	98.28 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 297	88.22 323	93.53 185	95.37 302	86.49 168	89.26 346	93.59 326	79.76 363	91.15 332	92.31 367	77.12 350	98.38 218	77.51 385	97.92 265	95.71 345
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 298	89.80 288	88.76 355	94.88 315	72.47 406	89.60 334	92.44 351	85.82 282	89.48 363	95.98 230	82.85 293	97.74 295	81.87 341	95.27 363	96.08 328

OpenMVS_ROB	85.12 16	89.52 299	89.05 299	90.92 299	94.58 331	81.21 278	91.10 286	93.41 332	77.03 390	93.41 255	93.99 323	83.23 287	97.80 285	79.93 365	94.80 376	93.74 404

test_vis1_n_1920		89.45 300	89.85 287	88.28 367	93.59 355	76.71 364	90.67 299	97.78 122	79.67 365	90.30 348	96.11 222	76.62 358	92.17 430	90.31 188	93.57 403	95.96 333

WB-MVS		89.44 301	92.15 227	81.32 432	97.73 119	48.22 465	89.73 331	87.98 397	95.24 48	96.05 137	96.99 152	85.18 272	96.95 344	82.45 336	97.97 261	98.78 100

DPM-MVS		89.35 302	88.40 313	92.18 249	96.13 250	84.20 219	86.96 390	96.15 254	75.40 400	87.36 401	91.55 382	83.30 286	98.01 262	82.17 340	96.62 327	94.32 391

MVSTER		89.32 303	88.75 308	91.03 294	90.10 427	76.62 365	90.85 291	94.67 307	82.27 339	95.24 186	95.79 239	61.09 429	98.49 204	90.49 178	98.26 227	97.97 199

PatchMatch-RL		89.18 304	88.02 328	92.64 226	95.90 267	92.87 49	88.67 366	91.06 372	80.34 357	90.03 353	91.67 379	83.34 285	94.42 411	76.35 395	94.84 375	90.64 438

jason		89.17 305	88.32 315	91.70 266	95.73 280	80.07 291	88.10 371	93.22 334	71.98 422	90.09 350	92.79 355	78.53 336	98.56 194	87.43 270	97.06 308	96.46 310
jason: jason.
PCF-MVS	84.52 17	89.12 306	87.71 331	93.34 193	96.06 255	85.84 191	86.58 403	97.31 167	68.46 442	93.61 248	93.89 327	87.51 236	98.52 201	67.85 438	98.11 244	95.66 349
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		89.11 307	88.21 324	91.83 259	91.30 410	90.25 86	88.09 372	78.76 453	76.37 394	96.43 112	98.39 39	83.79 283	90.43 440	86.57 284	94.20 391	94.80 378

FE-MVS		89.06 308	88.29 317	91.36 279	94.78 321	79.57 308	96.77 29	90.99 373	84.87 306	92.96 283	96.29 207	60.69 431	98.80 154	80.18 360	97.11 303	95.71 345

cl22		89.02 309	88.50 311	90.59 314	89.76 429	76.45 367	86.62 401	94.03 317	82.98 331	92.65 292	92.49 361	72.05 377	97.53 308	88.93 231	97.02 310	97.78 229

USDC		89.02 309	89.08 298	88.84 354	95.07 312	74.50 386	88.97 352	96.39 240	73.21 415	93.27 264	96.28 209	82.16 302	96.39 368	77.55 384	98.80 157	95.62 352

test_vis1_n		89.01 311	89.01 301	89.03 349	92.57 376	82.46 256	92.62 219	96.06 255	73.02 417	90.40 345	95.77 243	74.86 365	89.68 443	90.78 171	94.98 370	94.95 372

xiu_mvs_v2_base		89.00 312	89.19 296	88.46 365	94.86 317	74.63 383	86.97 389	95.60 269	80.88 353	87.83 394	88.62 417	91.04 170	98.81 151	82.51 335	94.38 385	91.93 429

new-patchmatchnet		88.97 313	90.79 266	83.50 424	94.28 338	55.83 460	85.34 419	93.56 328	86.18 271	95.47 168	95.73 245	83.10 288	96.51 362	85.40 300	98.06 250	98.16 176

pmmvs4		88.95 314	87.70 332	92.70 223	94.30 337	85.60 197	87.22 385	92.16 357	74.62 405	89.75 361	94.19 314	77.97 341	96.41 367	82.71 330	96.36 333	96.09 327

N_pmnet		88.90 315	87.25 339	93.83 169	94.40 336	93.81 39	84.73 423	87.09 404	79.36 371	93.26 265	92.43 365	79.29 327	91.68 432	77.50 386	97.22 299	96.00 331

PS-MVSNAJ		88.86 316	88.99 302	88.48 364	94.88 315	74.71 381	86.69 398	95.60 269	80.88 353	87.83 394	87.37 428	90.77 175	98.82 146	82.52 334	94.37 386	91.93 429

Patchmatch-RL test		88.81 317	88.52 310	89.69 338	95.33 304	79.94 297	86.22 409	92.71 344	78.46 379	95.80 149	94.18 315	66.25 403	95.33 396	89.22 225	98.53 193	93.78 402

SD_0403		88.79 318	88.88 306	88.51 362	95.89 269	72.58 404	94.27 148	95.24 287	83.77 320	87.92 393	94.38 309	87.70 231	96.47 365	66.36 442	94.40 383	96.49 307

Anonymous20231206		88.77 319	88.29 317	90.20 326	96.31 231	78.81 327	89.56 336	93.49 330	74.26 409	92.38 304	95.58 254	82.21 300	95.43 393	72.07 421	98.75 168	96.34 314

PVSNet_Blended		88.74 320	88.16 326	90.46 318	94.81 319	78.80 328	86.64 399	96.93 196	74.67 404	88.68 381	89.18 413	86.27 259	98.15 244	80.27 357	96.00 341	94.44 388

test_fmvs1_n		88.73 321	88.38 314	89.76 335	92.06 392	82.53 254	92.30 240	96.59 229	71.14 427	92.58 295	95.41 264	68.55 389	89.57 445	91.12 163	95.66 350	97.18 274

thisisatest0530		88.69 322	87.52 334	92.20 245	96.33 229	79.36 312	92.81 207	84.01 433	86.44 263	93.67 246	92.68 359	53.62 443	99.25 84	89.65 212	98.45 203	98.00 191

ppachtmachnet_test		88.61 323	88.64 309	88.50 363	91.76 400	70.99 413	84.59 428	92.98 337	79.30 373	92.38 304	93.53 338	79.57 324	97.45 314	86.50 288	97.17 301	97.07 280

UnsupCasMVSNet_bld		88.50 324	88.03 327	89.90 333	95.52 294	78.88 324	87.39 383	94.02 319	79.32 372	93.06 277	94.02 321	80.72 316	94.27 414	75.16 403	93.08 416	96.54 301

MonoMVSNet		88.46 325	89.28 295	85.98 401	90.52 420	70.07 419	95.31 105	94.81 301	88.38 219	93.47 254	96.13 220	73.21 371	95.07 400	82.61 332	89.12 440	92.81 421

miper_enhance_ethall		88.42 326	87.87 329	90.07 328	88.67 442	75.52 377	85.10 420	95.59 273	75.68 396	92.49 297	89.45 409	78.96 328	97.88 275	87.86 264	97.02 310	96.81 294

1112_ss		88.42 326	87.41 335	91.45 276	96.69 189	80.99 281	89.72 332	96.72 218	73.37 413	87.00 404	90.69 395	77.38 347	98.20 237	81.38 348	93.72 401	95.15 363

lupinMVS		88.34 328	87.31 336	91.45 276	94.74 324	80.06 292	87.23 384	92.27 354	71.10 428	88.83 372	91.15 385	77.02 352	98.53 200	86.67 282	96.75 323	95.76 343

test_cas_vis1_n_1920		88.25 329	88.27 319	88.20 369	92.19 388	78.92 322	89.45 339	95.44 279	75.29 403	93.23 268	95.65 250	71.58 379	90.23 441	88.05 258	93.55 405	95.44 357

YYNet1		88.17 330	88.24 321	87.93 373	92.21 386	73.62 394	80.75 447	88.77 387	82.51 337	94.99 204	95.11 272	82.70 296	93.70 419	83.33 324	93.83 399	96.48 308

MDA-MVSNet_test_wron		88.16 331	88.23 322	87.93 373	92.22 385	73.71 393	80.71 448	88.84 386	82.52 336	94.88 209	95.14 270	82.70 296	93.61 420	83.28 325	93.80 400	96.46 310

MS-PatchMatch		88.05 332	87.75 330	88.95 350	93.28 360	77.93 339	87.88 374	92.49 350	75.42 399	92.57 296	93.59 336	80.44 318	94.24 416	81.28 349	92.75 419	94.69 384

CR-MVSNet		87.89 333	87.12 344	90.22 324	91.01 413	78.93 320	92.52 223	92.81 340	73.08 416	89.10 368	96.93 155	67.11 395	97.64 302	88.80 237	92.70 420	94.08 393

pmmvs5		87.87 334	87.14 342	90.07 328	93.26 362	76.97 357	88.89 354	92.18 355	73.71 412	88.36 385	93.89 327	76.86 357	96.73 356	80.32 356	96.81 320	96.51 303

wuyk23d		87.83 335	90.79 266	78.96 438	90.46 423	88.63 116	92.72 211	90.67 378	91.65 134	98.68 15	97.64 87	96.06 19	77.53 460	59.84 453	99.41 60	70.73 458

FMVSNet5		87.82 336	86.56 355	91.62 269	92.31 382	79.81 302	93.49 180	94.81 301	83.26 323	91.36 327	96.93 155	52.77 444	97.49 312	76.07 397	98.03 253	97.55 249

GA-MVS		87.70 337	86.82 349	90.31 320	93.27 361	77.22 351	84.72 425	92.79 342	85.11 301	89.82 357	90.07 398	66.80 398	97.76 292	84.56 315	94.27 389	95.96 333

TR-MVS		87.70 337	87.17 341	89.27 346	94.11 342	79.26 315	88.69 364	91.86 364	81.94 343	90.69 340	89.79 403	82.82 294	97.42 317	72.65 419	91.98 428	91.14 435

thres600view7		87.66 339	87.10 345	89.36 344	96.05 256	73.17 396	92.72 211	85.31 424	91.89 116	93.29 262	90.97 389	63.42 420	98.39 215	73.23 415	96.99 315	96.51 303

PAPR		87.65 340	86.77 351	90.27 322	92.85 372	77.38 348	88.56 367	96.23 248	76.82 393	84.98 417	89.75 405	86.08 261	97.16 335	72.33 420	93.35 408	96.26 320

baseline1		87.62 341	87.31 336	88.54 360	94.71 327	74.27 389	93.10 195	88.20 393	86.20 269	92.18 314	93.04 348	73.21 371	95.52 388	79.32 372	85.82 448	95.83 340

test_fmvs1		87.59 342	87.27 338	88.54 360	88.32 443	81.26 276	90.43 309	95.72 266	70.55 433	91.70 322	94.63 294	68.13 390	89.42 447	90.59 175	95.34 361	94.94 374

our_test_3		87.55 343	87.59 333	87.44 381	91.76 400	70.48 414	83.83 435	90.55 380	79.79 362	92.06 318	92.17 370	78.63 335	95.63 386	84.77 312	94.73 377	96.22 322

PatchT		87.51 344	88.17 325	85.55 405	90.64 417	66.91 430	92.02 250	86.09 412	92.20 106	89.05 371	97.16 135	64.15 415	96.37 370	89.21 226	92.98 418	93.37 412

Test_1112_low_res		87.50 345	86.58 353	90.25 323	96.80 181	77.75 343	87.53 381	96.25 246	69.73 438	86.47 406	93.61 335	75.67 362	97.88 275	79.95 363	93.20 411	95.11 367

SCA		87.43 346	87.21 340	88.10 371	92.01 394	71.98 408	89.43 340	88.11 395	82.26 340	88.71 379	92.83 353	78.65 333	97.59 304	79.61 369	93.30 409	94.75 381

EU-MVSNet		87.39 347	86.71 352	89.44 341	93.40 358	76.11 371	94.93 123	90.00 382	57.17 458	95.71 157	97.37 109	64.77 412	97.68 299	92.67 117	94.37 386	94.52 386

thres100view900		87.35 348	86.89 348	88.72 356	96.14 248	73.09 398	93.00 198	85.31 424	92.13 109	93.26 265	90.96 390	63.42 420	98.28 228	71.27 427	96.54 329	94.79 379

CMPMVS	68.83 22	87.28 349	85.67 365	92.09 252	88.77 441	85.42 201	90.31 313	94.38 310	70.02 436	88.00 390	93.30 342	73.78 370	94.03 418	75.96 399	96.54 329	96.83 293
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 350	86.82 349	88.46 365	93.96 347	77.94 338	86.84 393	92.78 343	77.59 384	87.61 399	91.83 376	78.75 332	91.92 431	77.84 381	94.20 391	95.52 356

BH-w/o		87.21 351	87.02 346	87.79 378	94.77 322	77.27 350	87.90 373	93.21 336	81.74 345	89.99 354	88.39 420	83.47 284	96.93 347	71.29 426	92.43 424	89.15 440

thres400		87.20 352	86.52 357	89.24 348	95.77 277	72.94 400	91.89 259	86.00 413	90.84 159	92.61 293	89.80 401	63.93 416	98.28 228	71.27 427	96.54 329	96.51 303

CHOSEN 1792x2688		87.19 353	85.92 364	91.00 297	97.13 159	79.41 311	84.51 429	95.60 269	64.14 452	90.07 352	94.81 285	78.26 339	97.14 336	73.34 414	95.38 360	96.46 310

HyFIR lowres test		87.19 353	85.51 366	92.24 243	97.12 161	80.51 285	85.03 421	96.06 255	66.11 448	91.66 323	92.98 351	70.12 385	99.14 96	75.29 402	95.23 364	97.07 280

reproduce_monomvs		87.13 355	86.90 347	87.84 377	90.92 415	68.15 425	91.19 282	93.75 324	85.84 281	94.21 227	95.83 237	42.99 460	97.10 337	89.46 215	97.88 267	98.26 165

MIMVSNet		87.13 355	86.54 356	88.89 353	96.05 256	76.11 371	94.39 143	88.51 389	81.37 348	88.27 387	96.75 171	72.38 375	95.52 388	65.71 444	95.47 356	95.03 369

tfpn200view9		87.05 357	86.52 357	88.67 357	95.77 277	72.94 400	91.89 259	86.00 413	90.84 159	92.61 293	89.80 401	63.93 416	98.28 228	71.27 427	96.54 329	94.79 379

cascas		87.02 358	86.28 361	89.25 347	91.56 407	76.45 367	84.33 431	96.78 213	71.01 429	86.89 405	85.91 436	81.35 310	96.94 345	83.09 327	95.60 352	94.35 390

WTY-MVS		86.93 359	86.50 359	88.24 368	94.96 313	74.64 382	87.19 386	92.07 360	78.29 380	88.32 386	91.59 381	78.06 340	94.27 414	74.88 404	93.15 413	95.80 341

ttmdpeth		86.91 360	86.57 354	87.91 375	89.68 431	74.24 390	91.49 274	87.09 404	79.84 360	89.46 364	97.86 72	65.42 407	91.04 435	81.57 346	96.74 325	98.44 145

HY-MVS	82.50 18	86.81 361	85.93 363	89.47 340	93.63 354	77.93 339	94.02 159	91.58 370	75.68 396	83.64 429	93.64 332	77.40 346	97.42 317	71.70 424	92.07 427	93.05 417

test_f		86.65 362	87.13 343	85.19 409	90.28 425	86.11 182	86.52 405	91.66 367	69.76 437	95.73 156	97.21 132	69.51 387	81.28 459	89.15 227	94.40 383	88.17 445

1314		86.46 363	86.33 360	86.87 389	91.65 404	74.54 384	91.94 255	94.10 316	74.28 408	84.78 419	87.33 429	83.03 290	95.00 401	78.72 376	91.16 433	91.06 436

ET-MVSNet_ETH3D		86.15 364	84.27 375	91.79 261	93.04 366	81.28 275	87.17 387	86.14 411	79.57 366	83.65 428	88.66 415	57.10 435	98.18 240	87.74 265	95.40 358	95.90 338

Patchmatch-test		86.10 365	86.01 362	86.38 397	90.63 418	74.22 391	89.57 335	86.69 407	85.73 285	89.81 358	92.83 353	65.24 410	91.04 435	77.82 383	95.78 348	93.88 401

thres200		85.85 366	85.18 367	87.88 376	94.44 334	72.52 405	89.08 351	86.21 410	88.57 214	91.44 326	88.40 419	64.22 414	98.00 264	68.35 436	95.88 346	93.12 414

EPNet_dtu		85.63 367	84.37 373	89.40 343	86.30 453	74.33 388	91.64 270	88.26 391	84.84 307	72.96 459	89.85 399	71.27 381	97.69 298	76.60 392	97.62 282	96.18 324
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_vis1_rt		85.58 368	84.58 371	88.60 359	87.97 444	86.76 160	85.45 418	93.59 326	66.43 446	87.64 397	89.20 412	79.33 326	85.38 456	81.59 345	89.98 439	93.66 406

test2506		85.42 369	84.57 372	87.96 372	97.81 112	66.53 433	96.14 65	56.35 466	89.04 200	93.55 250	98.10 48	42.88 463	98.68 179	88.09 257	99.18 102	98.67 117

PatchmatchNet		85.22 370	84.64 370	86.98 385	89.51 435	69.83 421	90.52 303	87.34 403	78.87 377	87.22 403	92.74 357	66.91 397	96.53 360	81.77 342	86.88 446	94.58 385
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 371	84.72 369	86.48 393	92.12 390	70.19 415	92.32 237	88.17 394	56.15 459	90.64 341	95.85 234	67.97 393	96.69 357	88.78 238	90.52 436	92.56 424

JIA-IIPM		85.08 372	83.04 387	91.19 291	87.56 446	86.14 181	89.40 342	84.44 432	88.98 202	82.20 440	97.95 61	56.82 437	96.15 374	76.55 394	83.45 452	91.30 434

MVS		84.98 373	84.30 374	87.01 384	91.03 412	77.69 345	91.94 255	94.16 315	59.36 457	84.23 424	87.50 427	85.66 266	96.80 354	71.79 422	93.05 417	86.54 449

Syy-MVS		84.81 374	84.93 368	84.42 416	91.71 402	63.36 449	85.89 412	81.49 443	81.03 350	85.13 414	81.64 453	77.44 345	95.00 401	85.94 295	94.12 394	94.91 375

MVStest1		84.79 375	84.06 378	86.98 385	77.73 466	74.76 380	91.08 288	85.63 418	77.70 383	96.86 89	97.97 60	41.05 465	88.24 450	92.22 129	96.28 335	97.94 203

thisisatest0515		84.72 376	82.99 388	89.90 333	92.96 369	75.33 379	84.36 430	83.42 435	77.37 386	88.27 387	86.65 430	53.94 441	98.72 168	82.56 333	97.40 294	95.67 348

dmvs_re		84.69 377	83.94 380	86.95 387	92.24 384	82.93 244	89.51 337	87.37 402	84.38 313	85.37 411	85.08 443	72.44 374	86.59 453	68.05 437	91.03 435	91.33 433

FPMVS		84.50 378	83.28 385	88.16 370	96.32 230	94.49 20	85.76 415	85.47 422	83.09 328	85.20 413	94.26 311	63.79 418	86.58 454	63.72 448	91.88 430	83.40 452

tpm		84.38 379	84.08 377	85.30 408	90.47 422	63.43 448	89.34 343	85.63 418	77.24 389	87.62 398	95.03 277	61.00 430	97.30 323	79.26 373	91.09 434	95.16 362

tpmvs		84.22 380	83.97 379	84.94 411	87.09 450	65.18 440	91.21 281	88.35 390	82.87 332	85.21 412	90.96 390	65.24 410	96.75 355	79.60 371	85.25 449	92.90 420

WB-MVSnew		84.20 381	83.89 381	85.16 410	91.62 405	66.15 437	88.44 370	81.00 446	76.23 395	87.98 391	87.77 424	84.98 275	93.35 423	62.85 451	94.10 396	95.98 332

ADS-MVSNet2		84.01 382	82.20 395	89.41 342	89.04 438	76.37 369	87.57 377	90.98 374	72.71 420	84.46 420	92.45 362	68.08 391	96.48 363	70.58 432	83.97 450	95.38 358

WBMVS		84.00 383	83.48 383	85.56 404	92.71 373	61.52 451	83.82 436	89.38 385	79.56 367	90.74 338	93.20 346	48.21 447	97.28 324	75.63 401	98.10 246	97.88 214

testing3-2		83.95 384	84.22 376	83.13 426	96.28 234	54.34 463	88.51 368	83.01 438	92.19 107	89.09 370	90.98 388	45.51 453	97.44 315	74.38 408	98.01 256	97.60 243

mvsany_test1		83.91 385	82.93 389	86.84 390	86.18 454	85.93 188	81.11 446	75.03 460	70.80 432	88.57 383	94.63 294	83.08 289	87.38 451	80.39 355	86.57 447	87.21 447

testing3		83.66 386	82.52 391	87.08 383	95.84 271	65.84 438	89.80 330	77.17 459	88.17 225	90.84 336	88.63 416	30.95 468	98.11 248	84.05 320	97.19 300	97.28 269

test-LLR		83.58 387	83.17 386	84.79 413	89.68 431	66.86 431	83.08 438	84.52 430	83.07 329	82.85 435	84.78 444	62.86 423	93.49 421	82.85 328	94.86 373	94.03 396

testing91		83.56 388	82.45 392	86.91 388	92.92 370	67.29 427	86.33 407	88.07 396	86.22 268	84.26 423	85.76 437	48.15 448	97.17 333	76.27 396	94.08 397	96.27 319

baseline2		83.38 389	81.54 399	88.90 352	91.38 408	72.84 402	88.78 361	81.22 445	78.97 375	79.82 451	87.56 425	61.73 427	97.80 285	74.30 409	90.05 438	96.05 330

IB-MVS	77.21 19	83.11 390	81.05 402	89.29 345	91.15 411	75.85 374	85.66 416	86.00 413	79.70 364	82.02 443	86.61 431	48.26 446	98.39 215	77.84 381	92.22 425	93.63 407
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		83.09 391	82.21 394	85.73 402	89.27 437	67.01 429	90.35 311	86.47 409	70.42 434	83.52 431	93.23 345	61.18 428	96.85 351	77.21 388	88.26 444	93.34 413

PMMVS		83.00 392	81.11 401	88.66 358	83.81 462	86.44 171	82.24 443	85.65 417	61.75 456	82.07 441	85.64 439	79.75 323	91.59 433	75.99 398	93.09 415	87.94 446

testing99		82.94 393	81.72 396	86.59 391	92.55 377	66.53 433	86.08 411	85.70 416	85.47 294	83.95 426	85.70 438	45.87 452	97.07 340	76.58 393	93.56 404	96.17 326

PVSNet	76.22 20	82.89 394	82.37 393	84.48 415	93.96 347	64.38 445	78.60 450	88.61 388	71.50 425	84.43 422	86.36 434	74.27 367	94.60 408	69.87 434	93.69 402	94.46 387

tpmrst		82.85 395	82.93 389	82.64 427	87.65 445	58.99 457	90.14 318	87.90 398	75.54 398	83.93 427	91.63 380	66.79 400	95.36 394	81.21 351	81.54 456	93.57 411

test0.0.03 1		82.48 396	81.47 400	85.48 406	89.70 430	73.57 395	84.73 423	81.64 442	83.07 329	88.13 389	86.61 431	62.86 423	89.10 449	66.24 443	90.29 437	93.77 403

ADS-MVSNet		82.25 397	81.55 398	84.34 417	89.04 438	65.30 439	87.57 377	85.13 428	72.71 420	84.46 420	92.45 362	68.08 391	92.33 429	70.58 432	83.97 450	95.38 358

DSMNet-mixed		82.21 398	81.56 397	84.16 419	89.57 434	70.00 420	90.65 300	77.66 457	54.99 460	83.30 433	97.57 91	77.89 342	90.50 439	66.86 441	95.54 354	91.97 428

KD-MVS_2432*1600		82.17 399	80.75 406	86.42 395	82.04 463	70.09 417	81.75 444	90.80 376	82.56 334	90.37 346	89.30 410	42.90 461	96.11 376	74.47 406	92.55 422	93.06 415

miper_refine_blended		82.17 399	80.75 406	86.42 395	82.04 463	70.09 417	81.75 444	90.80 376	82.56 334	90.37 346	89.30 410	42.90 461	96.11 376	74.47 406	92.55 422	93.06 415

gg-mvs-nofinetune		82.10 401	81.02 403	85.34 407	87.46 448	71.04 411	94.74 127	67.56 462	96.44 29	79.43 452	98.99 11	45.24 454	96.15 374	67.18 440	92.17 426	88.85 442

testing11		81.98 402	80.52 409	86.38 397	92.69 374	67.13 428	85.79 414	84.80 429	82.16 341	81.19 448	85.41 440	45.24 454	96.88 350	74.14 410	93.24 410	95.14 364

PAPM		81.91 403	80.11 414	87.31 382	93.87 350	72.32 407	84.02 433	93.22 334	69.47 439	76.13 457	89.84 400	72.15 376	97.23 328	53.27 458	89.02 441	92.37 426

tpm2		81.46 404	80.35 412	84.80 412	89.90 428	65.14 441	90.44 306	85.36 423	65.82 450	82.05 442	92.44 364	57.94 434	96.69 357	70.71 431	88.49 443	92.56 424

PMMVS2		81.31 405	83.44 384	74.92 441	90.52 420	46.49 467	69.19 457	85.23 427	84.30 314	87.95 392	94.71 291	76.95 354	84.36 458	64.07 447	98.09 247	93.89 400

new_pmnet		81.22 406	81.01 404	81.86 430	90.92 415	70.15 416	84.03 432	80.25 451	70.83 430	85.97 409	89.78 404	67.93 394	84.65 457	67.44 439	91.90 429	90.78 437

test-mter		81.21 407	80.01 415	84.79 413	89.68 431	66.86 431	83.08 438	84.52 430	73.85 411	82.85 435	84.78 444	43.66 459	93.49 421	82.85 328	94.86 373	94.03 396

EPMVS		81.17 408	80.37 411	83.58 423	85.58 456	65.08 442	90.31 313	71.34 461	77.31 388	85.80 410	91.30 383	59.38 432	92.70 428	79.99 362	82.34 455	92.96 419

myMVS_eth3d28		80.97 409	80.42 410	82.62 428	93.35 359	58.25 458	84.70 426	85.62 420	86.31 265	84.04 425	85.20 442	46.00 451	94.07 417	62.93 450	95.65 351	95.53 355

EGC-MVSNET		80.97 409	75.73 427	96.67 46	98.85 28	94.55 19	96.83 24	96.60 227	2.44 465	5.32 466	98.25 43	92.24 131	98.02 261	91.85 141	99.21 98	97.45 255

pmmvs3		80.83 411	78.96 419	86.45 394	87.23 449	77.48 347	84.87 422	82.31 440	63.83 453	85.03 416	89.50 408	49.66 445	93.10 424	73.12 417	95.10 367	88.78 444

E-PMN		80.72 412	80.86 405	80.29 435	85.11 458	68.77 423	72.96 454	81.97 441	87.76 236	83.25 434	83.01 451	62.22 426	89.17 448	77.15 389	94.31 388	82.93 453

tpm cat1		80.61 413	79.46 416	84.07 420	88.78 440	65.06 443	89.26 346	88.23 392	62.27 455	81.90 444	89.66 407	62.70 425	95.29 397	71.72 423	80.60 457	91.86 431

testing222		80.54 414	78.53 422	86.58 392	92.54 379	68.60 424	86.24 408	82.72 439	83.78 319	82.68 438	84.24 446	39.25 466	95.94 382	60.25 452	95.09 368	95.20 360

EMVS		80.35 415	80.28 413	80.54 434	84.73 460	69.07 422	72.54 456	80.73 448	87.80 234	81.66 445	81.73 452	62.89 422	89.84 442	75.79 400	94.65 380	82.71 454

UWE-MVS		80.29 416	79.10 417	83.87 421	91.97 396	59.56 455	86.50 406	77.43 458	75.40 400	87.79 396	88.10 422	44.08 458	96.90 349	64.23 446	96.36 333	95.14 364

UBG		80.28 417	78.94 420	84.31 418	92.86 371	61.77 450	83.87 434	83.31 437	77.33 387	82.78 437	83.72 448	47.60 450	96.06 378	65.47 445	93.48 406	95.11 367

CHOSEN 280x420		80.04 418	77.97 425	86.23 400	90.13 426	74.53 385	72.87 455	89.59 384	66.38 447	76.29 456	85.32 441	56.96 436	95.36 394	69.49 435	94.72 378	88.79 443

ETVMVS		79.85 419	77.94 426	85.59 403	92.97 368	66.20 436	86.13 410	80.99 447	81.41 347	83.52 431	83.89 447	41.81 464	94.98 404	56.47 456	94.25 390	95.61 353

myMVS_eth3d		79.62 420	78.26 423	83.72 422	91.71 402	61.25 453	85.89 412	81.49 443	81.03 350	85.13 414	81.64 453	32.12 467	95.00 401	71.17 430	94.12 394	94.91 375

dp		79.28 421	78.62 421	81.24 433	85.97 455	56.45 459	86.91 391	85.26 426	72.97 418	81.45 447	89.17 414	56.01 439	95.45 392	73.19 416	76.68 458	91.82 432

TESTMET0.1,1		79.09 422	78.04 424	82.25 429	87.52 447	64.03 446	83.08 438	80.62 449	70.28 435	80.16 450	83.22 450	44.13 457	90.56 438	79.95 363	93.36 407	92.15 427

MVS-HIRNet		78.83 423	80.60 408	73.51 442	93.07 364	47.37 466	87.10 388	78.00 456	68.94 440	77.53 454	97.26 124	71.45 380	94.62 407	63.28 449	88.74 442	78.55 457

dmvs_testset		78.23 424	78.99 418	75.94 440	91.99 395	55.34 462	88.86 355	78.70 454	82.69 333	81.64 446	79.46 455	75.93 361	85.74 455	48.78 460	82.85 454	86.76 448

UWE-MVS-28		74.73 425	73.18 428	79.35 437	85.42 457	55.55 461	87.63 375	65.92 463	74.39 407	77.33 455	88.19 421	47.63 449	89.48 446	39.01 462	93.14 414	93.03 418

PVSNet_0	70.34 21	74.58 426	72.96 429	79.47 436	90.63 418	66.24 435	73.26 453	83.40 436	63.67 454	78.02 453	78.35 457	72.53 373	89.59 444	56.68 455	60.05 461	82.57 455

MVE	59.87 23	73.86 427	72.65 430	77.47 439	87.00 452	74.35 387	61.37 459	60.93 465	67.27 444	69.69 460	86.49 433	81.24 314	72.33 462	56.45 457	83.45 452	85.74 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		53.72 428	53.79 431	53.51 445	79.69 465	36.70 469	77.18 451	32.53 471	71.69 423	68.63 461	60.79 460	26.65 469	73.11 461	30.67 464	36.29 463	50.73 459

test_method		50.44 429	48.94 432	54.93 443	39.68 469	12.38 472	28.59 460	90.09 381	6.82 463	41.10 465	78.41 456	54.41 440	70.69 463	50.12 459	51.26 462	81.72 456

kuosan		43.63 430	44.25 434	41.78 446	66.04 468	34.37 470	75.56 452	32.62 470	53.25 461	50.46 464	51.18 461	25.28 470	49.13 464	13.44 465	30.41 464	41.84 461

tmp_tt		37.97 431	44.33 433	18.88 447	11.80 470	21.54 471	63.51 458	45.66 469	4.23 464	51.34 463	50.48 462	59.08 433	22.11 466	44.50 461	68.35 460	13.00 462

cdsmvs_eth3d_5k		23.35 432	31.13 435	0.00 450	0.00 473	0.00 475	0.00 461	95.58 275	0.00 468	0.00 469	91.15 385	93.43 96	0.00 469	0.00 468	0.00 467	0.00 465

test123		9.49 433	12.01 436	1.91 448	2.87 471	1.30 473	82.38 442	1.34 473	1.36 466	2.84 467	6.56 465	2.45 471	0.97 467	2.73 466	5.56 465	3.47 463

testmvs		9.02 434	11.42 437	1.81 449	2.77 472	1.13 474	79.44 449	1.90 472	1.18 467	2.65 468	6.80 464	1.95 472	0.87 468	2.62 467	3.45 466	3.44 464

pcd_1.5k_mvsjas		7.56 435	10.09 438	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	90.77 175	0.00 469	0.00 468	0.00 467	0.00 465

ab-mvs-re		7.56 435	10.08 439	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	90.69 395	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

WAC-MVS						61.25 453								74.55 405

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	96.54 206	89.57 94		96.87 207					99.41 44	94.06 64	99.30 80	98.72 109

PC_three_1452									75.31 402	95.87 147	95.75 244	92.93 114	96.34 373	87.18 274	98.68 177	98.04 186

No_MVS				95.90 70	96.54 206	89.57 94		96.87 207					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					0.00 473

eth-test					0.00 473

ZD-MVS					97.23 151	90.32 85		97.54 145	84.40 312	94.78 212	95.79 239	92.76 120	99.39 54	88.72 240	98.40 206

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	98.00 191

IU-MVS					98.51 54	86.66 165		96.83 210	72.74 419	95.83 148				93.00 108	99.29 83	98.64 125

OPU-MVS				95.15 104	96.84 177	89.43 98	95.21 110				95.66 249	93.12 107	98.06 254	86.28 292	98.61 184	97.95 201

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	87.56 242	95.38 173	96.03 226	94.66 68	99.08 106	90.70 173	98.97 130

save fliter					97.46 139	88.05 131	92.04 249	97.08 186	87.63 240

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																94.75 381

test_part2					98.21 81	89.41 99				96.72 97

sam_mvs1												66.64 401				94.75 381

sam_mvs												66.41 402

ambc				92.98 206	96.88 173	83.01 243	95.92 76	96.38 241		96.41 113	97.48 104	88.26 219	97.80 285	89.96 204	98.93 135	98.12 180

MTGPA								97.62 134

test_post1							90.21 315				5.85 467	65.36 408	96.00 380	79.61 369

test_post											6.07 466	65.74 406	95.84 384

patchmatchnet-post											91.71 378	66.22 404	97.59 304

GG-mvs-BLEND				83.24 425	85.06 459	71.03 412	94.99 122	65.55 464		74.09 458	75.51 458	44.57 456	94.46 410	59.57 454	87.54 445	84.24 451

MTMP							94.82 125	54.62 467

gm-plane-assit					87.08 451	59.33 456			71.22 426		83.58 449		97.20 330	73.95 411

test9_res														88.16 255	98.40 206	97.83 221

TEST9					96.45 215	89.46 96	90.60 301	96.92 198	79.09 374	90.49 342	94.39 307	91.31 158	98.88 137

test_8					96.37 221	89.14 106	90.51 304	96.89 201	79.37 369	90.42 344	94.36 310	91.20 163	98.82 146

agg_prior2														87.06 277	98.36 217	97.98 195

agg_prior					96.20 242	88.89 111		96.88 206		90.21 349			98.78 159

TestCases				96.00 60	98.02 95	92.17 54		98.43 25	90.48 172	95.04 201	96.74 172	92.54 124	97.86 279	85.11 307	98.98 125	97.98 195

test_prior4						89.91 89	90.74 296

test_prior2							90.21 315		89.33 194	90.77 337	94.81 285	90.41 185		88.21 251	98.55 190

test_prior				94.61 129	95.95 264	87.23 145		97.36 163					98.68 179			97.93 204

旧先验2							90.00 323		68.65 441	92.71 291			96.52 361	85.15 304

新几何2							90.02 322

新几何1				93.17 202	97.16 156	87.29 143		94.43 309	67.95 443	91.29 328	94.94 280	86.97 247	98.23 235	81.06 353	97.75 272	93.98 398

旧先验1					96.20 242	84.17 220		94.82 299			95.57 255	89.57 202			97.89 266	96.32 315

无先验							89.94 324	95.75 265	70.81 431				98.59 191	81.17 352		94.81 377

原ACMM2							89.34 343

原ACMM1				92.87 215	96.91 171	84.22 218		97.01 190	76.84 392	89.64 362	94.46 305	88.00 226	98.70 175	81.53 347	98.01 256	95.70 347

test222					96.95 167	85.27 204	88.83 357	93.61 325	65.09 451	90.74 338	94.85 283	84.62 278			97.36 295	93.91 399

testdata2													98.03 258	80.24 359

segment_acmp												92.14 135

testdata				91.03 294	96.87 174	82.01 261		94.28 313	71.55 424	92.46 299	95.42 261	85.65 267	97.38 322	82.64 331	97.27 297	93.70 405

testdata1							88.96 353		88.44 217

test12				94.43 143	95.95 264	86.75 161		96.24 247		89.76 360		89.79 201	98.79 155		97.95 263	97.75 233

plane_prior7					97.71 121	88.68 115

plane_prior6					97.21 154	88.23 128						86.93 248

plane_prior5								97.81 116					98.95 130	89.26 223	98.51 197	98.60 130

plane_prior4											95.59 251

plane_prior3						88.43 126			90.35 177	93.31 260

plane_prior2							94.56 138		91.74 130

plane_prior1					97.38 142

plane_prior						88.12 129	93.01 197		88.98 202						98.06 250

n2								0.00 474

nn								0.00 474

door-mid								92.13 359

lessismore_v0				93.87 166	98.05 91	83.77 226		80.32 450		97.13 74	97.91 69	77.49 344	99.11 104	92.62 118	98.08 248	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								96.65 224

door								91.26 371

HQP5-MVS						84.89 208

HQP-NCC					96.36 223		91.37 276		87.16 249	88.81 374

ACMP_Plane					96.36 223		91.37 276		87.16 249	88.81 374

BP-MVS														86.55 286

HQP4-MVS										88.81 374			98.61 187			98.15 177

HQP3-MVS								97.31 167							97.73 273

HQP2-MVS												84.76 276

NP-MVS					96.82 179	87.10 149					93.40 340

MDTV_nov1_ep13_2view						42.48 468	88.45 369		67.22 445	83.56 430		66.80 398		72.86 418		94.06 395

MDTV_nov1_ep13			83.88 382		89.42 436	61.52 451	88.74 363	87.41 401	73.99 410	84.96 418	94.01 322	65.25 409	95.53 387	78.02 379	93.16 412

ACMMP++_ref															98.82 152

ACMMP++															99.25 91

Test By Simon												90.61 181

ITE_SJBPF				95.95 64	97.34 145	93.36 44		96.55 234	91.93 114	94.82 210	95.39 265	91.99 137	97.08 339	85.53 299	97.96 262	97.41 258

DeepMVS_CX				53.83 444	70.38 467	64.56 444		48.52 468	33.01 462	65.50 462	74.21 459	56.19 438	46.64 465	38.45 463	70.07 459	50.30 460

High-resolution multi-view benchmark