High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 28	98.81 28	93.86 34	99.07 2	98.98 6	97.01 13	98.92 4	98.78 14	95.22 39	98.61 183	96.85 2	99.77 10	99.31 29
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 93	99.51 2	87.38 140	97.70 8	97.54 113	98.16 2	98.94 2	99.33 2	97.84 4	99.08 102	90.73 133	99.73 14	99.59 13

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 7	98.61 3	98.81 8	97.41 10	97.28 53	98.46 27	94.62 60	98.84 140	94.64 20	99.53 37	98.99 56

PS-CasMVS		96.69 22	97.43 5	94.49 135	99.13 6	84.09 204	96.61 31	97.97 78	97.91 5	98.64 13	98.13 38	95.24 38	99.65 3	93.39 63	99.84 3	99.72 2

DTE-MVSNet		96.74 19	97.43 5	94.67 121	99.13 6	84.68 194	96.51 35	97.94 84	98.14 3	98.67 12	98.32 32	95.04 47	99.69 2	93.27 69	99.82 8	99.62 10

ACMH	88.36 12	96.59 29	97.43 5	94.07 148	98.56 41	85.33 188	96.33 47	98.30 26	94.66 42	98.72 8	98.30 33	97.51 5	98.00 239	94.87 17	99.59 29	98.86 76
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		96.69 22	97.39 8	94.61 124	99.16 4	84.50 195	96.54 34	98.05 63	98.06 4	98.64 13	98.25 34	95.01 50	99.65 3	92.95 82	99.83 6	99.68 4

pmmvs6		96.80 13	97.36 9	95.15 104	99.12 8	87.82 135	96.68 29	97.86 86	96.10 26	98.14 24	99.28 3	97.94 3	98.21 221	91.38 123	99.69 15	99.42 20

v7n		96.82 10	97.31 10	95.33 95	98.54 47	86.81 155	96.83 22	98.07 59	96.59 20	98.46 17	98.43 29	92.91 98	99.52 19	96.25 6	99.76 11	99.65 8

UA-Net		97.35 4	97.24 11	97.69 5	98.22 75	93.87 33	98.42 6	98.19 38	96.95 14	95.46 138	99.23 4	93.45 79	99.57 14	95.34 16	99.89 2	99.63 9

Anonymous20231211		96.60 27	97.13 12	95.00 108	97.46 129	86.35 170	97.11 18	98.24 33	97.58 8	98.72 8	98.97 7	93.15 90	99.15 90	93.18 72	99.74 13	99.50 17

abl_6		97.31 5	97.12 13	97.86 3	98.54 47	95.32 9	96.61 31	98.35 20	95.81 31	97.55 38	97.44 75	96.51 9	99.40 47	94.06 33	99.23 84	98.85 79

WR-MVS_H		96.60 27	97.05 14	95.24 100	99.02 12	86.44 166	96.78 26	98.08 56	97.42 9	98.48 16	97.86 56	91.76 124	99.63 6	94.23 29	99.84 3	99.66 6

HPM-MVS_fast		97.01 7	96.89 15	97.39 24	99.12 8	93.92 31	97.16 13	98.17 43	93.11 75	96.48 85	97.36 82	96.92 6	99.34 66	94.31 26	99.38 59	98.92 70

ACMH+	88.43 11	96.48 32	96.82 16	95.47 90	98.54 47	89.06 106	95.65 80	98.61 11	96.10 26	98.16 23	97.52 70	96.90 7	98.62 182	90.30 146	99.60 27	98.72 96

CP-MVSNet		96.19 48	96.80 17	94.38 141	98.99 16	83.82 207	96.31 50	97.53 115	97.60 7	98.34 19	97.52 70	91.98 120	99.63 6	93.08 78	99.81 9	99.70 3

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 38	99.03 11	91.85 61	97.98 7	98.01 72	94.15 53	98.93 3	99.07 5	88.07 188	99.57 14	95.86 9	99.69 15	99.46 19

mvs_tets		96.83 9	96.71 19	97.17 29	98.83 26	92.51 52	96.58 33	97.61 108	87.57 212	98.80 7	98.90 9	96.50 10	99.59 13	96.15 7	99.47 42	99.40 22

RE-MVS-def			96.66 20		98.07 84	95.27 10	96.37 44	98.12 49	95.66 33	97.00 64	97.03 105	95.40 29		93.49 51	98.84 132	98.00 160

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 27	97.95 97	93.82 36	96.31 50	98.25 30	95.51 35	96.99 66	97.05 104	95.63 23	99.39 52	93.31 66	98.88 127	98.75 90

APDe-MVS		96.46 34	96.64 22	95.93 67	97.68 114	89.38 103	96.90 21	98.41 17	92.52 82	97.43 46	97.92 52	95.11 44	99.50 21	94.45 22	99.30 69	98.92 70

HPM-MVS		96.81 12	96.62 23	97.36 26	98.89 21	93.53 41	97.51 9	98.44 13	92.35 87	95.95 115	96.41 147	96.71 8	99.42 33	93.99 36	99.36 60	99.13 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	91.06 5	96.75 18	96.62 23	97.13 30	98.38 64	94.31 19	96.79 25	98.32 23	96.69 17	96.86 71	97.56 67	95.48 27	98.77 159	90.11 155	99.44 49	98.31 132
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS-dyc-post		96.84 8	96.60 25	97.56 12	98.07 84	95.27 10	96.37 44	98.12 49	95.66 33	97.00 64	97.03 105	94.85 54	99.42 33	93.49 51	98.84 132	98.00 160

nrg030		96.32 43	96.55 26	95.62 84	97.83 101	88.55 120	95.77 75	98.29 29	92.68 78	98.03 26	97.91 53	95.13 42	98.95 124	93.85 39	99.49 41	99.36 25

test1172		96.79 15	96.52 27	97.60 9	98.03 90	94.87 12	96.07 62	98.06 62	95.76 32	96.89 69	96.85 117	94.85 54	99.42 33	93.35 65	98.81 140	98.53 117

FMVS1		96.77 16	96.49 28	97.60 9	99.01 14	96.70 3	96.31 50	98.33 21	94.96 38	97.30 51	97.93 49	96.05 17	97.90 245	89.32 171	99.23 84	98.19 143

APD_test		96.77 16	96.49 28	97.60 9	99.01 14	96.70 3	96.31 50	98.33 21	94.96 38	97.30 51	97.93 49	96.05 17	97.90 245	89.32 171	99.23 84	98.19 143

test_djsdf		96.62 25	96.49 28	97.01 35	98.55 44	91.77 63	97.15 14	97.37 124	88.98 178	98.26 22	98.86 10	93.35 84	99.60 9	96.41 4	99.45 46	99.66 6

SR-MVS		96.70 21	96.42 31	97.54 13	98.05 86	94.69 13	96.13 59	98.07 59	95.17 37	96.82 73	96.73 129	95.09 46	99.43 32	92.99 81	98.71 149	98.50 119

anonymousdsp		96.74 19	96.42 31	97.68 7	98.00 93	94.03 28	96.97 19	97.61 108	87.68 209	98.45 18	98.77 15	94.20 69	99.50 21	96.70 3	99.40 57	99.53 15

jajsoiax		96.59 29	96.42 31	97.12 31	98.76 31	92.49 53	96.44 41	97.42 122	86.96 221	98.71 10	98.72 17	95.36 33	99.56 17	95.92 8	99.45 46	99.32 28

SED-MVS		96.00 54	96.41 34	94.76 117	98.51 51	86.97 151	95.21 95	98.10 52	91.95 97	97.63 34	97.25 91	96.48 11	99.35 63	93.29 67	99.29 72	97.95 168

MTAPA		96.65 24	96.38 35	97.47 17	98.95 18	94.05 25	95.88 71	97.62 105	94.46 47	96.29 96	96.94 110	93.56 75	99.37 60	94.29 27	99.42 51	98.99 56

DVP-MVS++		95.93 55	96.34 36	94.70 120	96.54 176	86.66 160	98.45 4	98.22 35	93.26 72	97.54 39	97.36 82	93.12 91	99.38 58	93.88 37	98.68 153	98.04 155

ACMMP		96.61 26	96.34 36	97.43 21	98.61 37	93.88 32	96.95 20	98.18 39	92.26 90	96.33 92	96.84 120	95.10 45	99.40 47	93.47 55	99.33 64	99.02 53
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SteuartSystems-ACMMP		96.40 40	96.30 38	96.71 44	98.63 34	91.96 59	95.70 77	98.01 72	93.34 71	96.64 80	96.57 139	94.99 51	99.36 62	93.48 54	99.34 62	98.82 81
Skip Steuart: Steuart Systems R&D Blog.
ANet_high		94.83 101	96.28 39	90.47 272	96.65 166	73.16 341	94.33 130	98.74 10	96.39 23	98.09 25	98.93 8	93.37 83	98.70 171	90.38 141	99.68 19	99.53 15

TranMVSNet+NR-MVSNet		96.07 52	96.26 40	95.50 89	98.26 72	87.69 136	93.75 150	97.86 86	95.96 30	97.48 44	97.14 99	95.33 34	99.44 28	90.79 132	99.76 11	99.38 23

LPG-MVS_test		96.38 42	96.23 41	96.84 42	98.36 67	92.13 56	95.33 91	98.25 30	91.78 110	97.07 59	97.22 94	96.38 13	99.28 76	92.07 101	99.59 29	99.11 45

test_0402		95.73 63	96.22 42	94.26 143	98.19 77	85.77 183	93.24 163	97.24 141	96.88 16	97.69 32	97.77 59	94.12 70	99.13 94	91.54 120	99.29 72	97.88 177

ZNCC-MVS		96.42 38	96.20 43	97.07 32	98.80 30	92.79 50	96.08 61	98.16 46	91.74 114	95.34 143	96.36 155	95.68 21	99.44 28	94.41 24	99.28 77	98.97 62

DVP-MVS		95.82 60	96.18 44	94.72 119	98.51 51	86.69 158	95.20 97	97.00 157	91.85 103	97.40 49	97.35 85	95.58 24	99.34 66	93.44 59	99.31 67	98.13 149
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
XVS		96.49 31	96.18 44	97.44 19	98.56 41	93.99 29	96.50 36	97.95 81	94.58 43	94.38 181	96.49 141	94.56 61	99.39 52	93.57 47	99.05 106	98.93 66

HFP-MVS		96.39 41	96.17 46	97.04 33	98.51 51	93.37 42	96.30 54	97.98 75	92.35 87	95.63 131	96.47 142	95.37 30	99.27 78	93.78 41	99.14 98	98.48 121

zzz-MVS		96.47 33	96.14 47	97.47 17	98.95 18	94.05 25	93.69 152	97.62 105	94.46 47	96.29 96	96.94 110	93.56 75	99.37 60	94.29 27	99.42 51	98.99 56

ACMMPR		96.46 34	96.14 47	97.41 23	98.60 38	93.82 36	96.30 54	97.96 79	92.35 87	95.57 134	96.61 137	94.93 53	99.41 40	93.78 41	99.15 97	99.00 54

ACMMP_NAP		96.21 47	96.12 49	96.49 52	98.90 20	91.42 67	94.57 122	98.03 68	90.42 148	96.37 89	97.35 85	95.68 21	99.25 80	94.44 23	99.34 62	98.80 85

region2R		96.41 39	96.09 50	97.38 25	98.62 35	93.81 38	96.32 49	97.96 79	92.26 90	95.28 147	96.57 139	95.02 49	99.41 40	93.63 45	99.11 101	98.94 65

CP-MVS		96.44 37	96.08 51	97.54 13	98.29 69	94.62 16	96.80 24	98.08 56	92.67 80	95.08 158	96.39 152	94.77 56	99.42 33	93.17 73	99.44 49	98.58 115

ACMM	88.83 9	96.30 45	96.07 52	96.97 37	98.39 63	92.95 48	94.74 114	98.03 68	90.82 137	97.15 56	96.85 117	96.25 15	99.00 116	93.10 76	99.33 64	98.95 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS		96.46 34	96.05 53	97.69 5	98.62 35	94.65 15	96.45 39	97.74 99	92.59 81	95.47 136	96.68 132	94.50 63	99.42 33	93.10 76	99.26 80	98.99 56

PS-MVSNAJss		96.01 53	96.04 54	95.89 72	98.82 27	88.51 122	95.57 84	97.88 85	88.72 184	98.81 6	98.86 10	90.77 149	99.60 9	95.43 15	99.53 37	99.57 14

TransMVSNet (Re)		95.27 86	96.04 54	92.97 184	98.37 66	81.92 227	95.07 103	96.76 179	93.97 58	97.77 30	98.57 20	95.72 20	97.90 245	88.89 187	99.23 84	99.08 49

GST-MVS		96.24 46	95.99 56	97.00 36	98.65 33	92.71 51	95.69 79	98.01 72	92.08 95	95.74 126	96.28 161	95.22 39	99.42 33	93.17 73	99.06 103	98.88 75

pm-mvs1		95.43 74	95.94 57	93.93 154	98.38 64	85.08 191	95.46 88	97.12 150	91.84 106	97.28 53	98.46 27	95.30 36	97.71 267	90.17 153	99.42 51	98.99 56

PGM-MVS		96.32 43	95.94 57	97.43 21	98.59 40	93.84 35	95.33 91	98.30 26	91.40 123	95.76 124	96.87 116	95.26 37	99.45 26	92.77 84	99.21 89	99.00 54

MP-MVS-pluss		96.08 51	95.92 59	96.57 48	99.06 10	91.21 69	93.25 162	98.32 23	87.89 202	96.86 71	97.38 78	95.55 26	99.39 52	95.47 13	99.47 42	99.11 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		95.88 58	95.88 60	95.87 73	98.12 80	89.65 95	95.58 83	98.56 12	91.84 106	96.36 90	96.68 132	94.37 66	99.32 72	92.41 94	99.05 106	98.64 106

DPE-MVS		95.89 56	95.88 60	95.92 69	97.93 98	89.83 92	93.46 158	98.30 26	92.37 85	97.75 31	96.95 109	95.14 41	99.51 20	91.74 112	99.28 77	98.41 126
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
FC-MVSNet-test		95.32 80	95.88 60	93.62 164	98.49 59	81.77 228	95.90 70	98.32 23	93.93 59	97.53 41	97.56 67	88.48 181	99.40 47	92.91 83	99.83 6	99.68 4

DP-MVS		95.62 66	95.84 63	94.97 109	97.16 142	88.62 117	94.54 126	97.64 104	96.94 15	96.58 83	97.32 88	93.07 94	98.72 165	90.45 138	98.84 132	97.57 202

Anonymous20240529		95.50 71	95.83 64	94.50 133	97.33 135	85.93 180	95.19 99	96.77 178	96.64 19	97.61 37	98.05 43	93.23 87	98.79 151	88.60 194	99.04 112	98.78 87

LS3D		96.11 50	95.83 64	96.95 39	94.75 269	94.20 21	97.34 12	97.98 75	97.31 11	95.32 144	96.77 122	93.08 93	99.20 86	91.79 111	98.16 208	97.44 212

Gipuma		95.31 83	95.80 66	93.81 161	97.99 96	90.91 74	96.42 42	97.95 81	96.69 17	91.78 263	98.85 12	91.77 123	95.49 334	91.72 113	99.08 102	95.02 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
3Dnovator+	92.74 2	95.86 59	95.77 67	96.13 57	96.81 162	90.79 77	96.30 54	97.82 92	96.13 25	94.74 172	97.23 93	91.33 134	99.16 89	93.25 70	98.30 193	98.46 123

SD-MVS		95.19 87	95.73 68	93.55 167	96.62 170	88.88 113	94.67 116	98.05 63	91.26 126	97.25 55	96.40 148	95.42 28	94.36 351	92.72 88	99.19 91	97.40 216
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
MP-MVS		96.14 49	95.68 69	97.51 15	98.81 28	94.06 23	96.10 60	97.78 98	92.73 77	93.48 207	96.72 130	94.23 68	99.42 33	91.99 103	99.29 72	99.05 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
VPA-MVSNet		95.14 88	95.67 70	93.58 166	97.76 105	83.15 215	94.58 121	97.58 110	93.39 70	97.05 62	98.04 44	93.25 86	98.51 196	89.75 165	99.59 29	99.08 49

DROMVSNet		95.44 73	95.62 71	94.89 111	96.93 153	87.69 136	96.48 38	99.14 4	93.93 59	92.77 234	94.52 244	93.95 72	99.49 24	93.62 46	99.22 88	97.51 207

CS-MVS		95.77 61	95.58 72	96.37 54	96.84 158	91.72 65	96.73 28	99.06 5	94.23 51	92.48 242	94.79 236	93.56 75	99.49 24	93.47 55	99.05 106	97.89 176

SMA-MVS		95.77 61	95.54 73	96.47 53	98.27 71	91.19 70	95.09 101	97.79 97	86.48 224	97.42 48	97.51 72	94.47 65	99.29 74	93.55 49	99.29 72	98.93 66
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#		95.89 56	95.51 74	97.04 33	98.51 51	93.37 42	95.14 100	97.98 75	89.34 169	95.63 131	96.47 142	95.37 30	99.27 78	91.99 103	99.14 98	98.48 121

Vis-MVSNet		95.50 71	95.48 75	95.56 88	98.11 81	89.40 102	95.35 89	98.22 35	92.36 86	94.11 184	98.07 42	92.02 117	99.44 28	93.38 64	97.67 240	97.85 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		95.61 67	95.45 76	96.08 58	98.49 59	91.00 72	92.65 179	97.33 133	90.05 153	96.77 76	96.85 117	95.04 47	98.56 191	92.77 84	99.06 103	98.70 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MIMVSNet1		95.52 70	95.45 76	95.72 81	99.14 5	89.02 107	96.23 57	96.87 170	93.73 63	97.87 28	98.49 26	90.73 153	99.05 107	86.43 235	99.60 27	99.10 48

ACMP	88.15 13	95.71 64	95.43 78	96.54 49	98.17 78	91.73 64	94.24 133	98.08 56	89.46 165	96.61 82	96.47 142	95.85 19	99.12 98	90.45 138	99.56 35	98.77 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
mvsmamba		95.61 67	95.40 79	96.22 55	98.44 61	89.86 91	97.14 16	97.45 121	91.25 128	97.49 43	98.14 36	83.49 238	99.45 26	95.52 11	99.66 22	99.36 25

FIs		94.90 95	95.35 80	93.55 167	98.28 70	81.76 229	95.33 91	98.14 47	93.05 76	97.07 59	97.18 97	87.65 195	99.29 74	91.72 113	99.69 15	99.61 11

XVG-ACMP-BASELINE		95.68 65	95.34 81	96.69 45	98.40 62	93.04 45	94.54 126	98.05 63	90.45 147	96.31 94	96.76 124	92.91 98	98.72 165	91.19 124	99.42 51	98.32 130

DeepC-MVS	91.39 4	95.43 74	95.33 82	95.71 82	97.67 115	90.17 85	93.86 147	98.02 70	87.35 214	96.22 103	97.99 47	94.48 64	99.05 107	92.73 87	99.68 19	97.93 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PMVS	87.21 14	94.97 92	95.33 82	93.91 156	98.97 17	97.16 2	95.54 85	95.85 219	96.47 21	93.40 210	97.46 74	95.31 35	95.47 335	86.18 239	98.78 144	89.11 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v8		94.65 108	95.29 84	92.74 195	96.65 166	79.77 262	94.59 119	97.17 145	91.86 102	97.47 45	97.93 49	88.16 186	99.08 102	94.32 25	99.47 42	99.38 23

NR-MVSNet		95.28 84	95.28 85	95.26 99	97.75 106	87.21 145	95.08 102	97.37 124	93.92 61	97.65 33	95.90 177	90.10 167	99.33 71	90.11 155	99.66 22	99.26 31

v10		94.68 107	95.27 86	92.90 190	96.57 173	80.15 247	94.65 118	97.57 111	90.68 141	97.43 46	98.00 46	88.18 185	99.15 90	94.84 18	99.55 36	99.41 21

UniMVSNet_NR-MVSNet		95.35 78	95.21 87	95.76 79	97.69 113	88.59 118	92.26 199	97.84 90	94.91 40	96.80 74	95.78 187	90.42 158	99.41 40	91.60 117	99.58 33	99.29 30

SixPastTwentyTwo		94.91 94	95.21 87	93.98 150	98.52 50	83.19 214	95.93 68	94.84 250	94.86 41	98.49 15	98.74 16	81.45 262	99.60 9	94.69 19	99.39 58	99.15 40

RRT_MVS		95.41 76	95.20 89	96.05 59	98.86 23	88.92 109	97.49 10	94.48 261	93.12 74	97.94 27	98.54 22	81.19 268	99.63 6	95.48 12	99.69 15	99.60 12

UniMVSNet (Re)		95.32 80	95.15 90	95.80 76	97.79 104	88.91 110	92.91 170	98.07 59	93.46 69	96.31 94	95.97 176	90.14 163	99.34 66	92.11 98	99.64 25	99.16 39

FMVSNet1		94.84 100	95.13 91	93.97 151	97.60 119	84.29 197	95.99 64	96.56 188	92.38 84	97.03 63	98.53 23	90.12 164	98.98 117	88.78 189	99.16 96	98.65 102

DU-MVS		95.28 84	95.12 92	95.75 80	97.75 106	88.59 118	92.58 180	97.81 93	93.99 55	96.80 74	95.90 177	90.10 167	99.41 40	91.60 117	99.58 33	99.26 31

CS-MVS-test		95.32 80	95.10 93	95.96 63	96.86 157	90.75 78	96.33 47	99.20 2	93.99 55	91.03 274	93.73 272	93.52 78	99.55 18	91.81 110	99.45 46	97.58 201

Baseline_NR-MVSNet		94.47 115	95.09 94	92.60 202	98.50 58	80.82 243	92.08 205	96.68 182	93.82 62	96.29 96	98.56 21	90.10 167	97.75 265	90.10 157	99.66 22	99.24 33

dcpmvs_2		93.96 134	95.01 95	90.82 264	97.60 119	74.04 336	93.68 154	98.85 7	89.80 159	97.82 29	97.01 108	91.14 145	99.21 84	90.56 136	98.59 160	99.19 37

XVG-OURS-SEG-HR		95.38 77	95.00 96	96.51 50	98.10 82	94.07 22	92.46 186	98.13 48	90.69 140	93.75 198	96.25 164	98.03 2	97.02 296	92.08 100	95.55 296	98.45 124

xxxxxxxxxxxxxcwj		95.03 89	94.93 97	95.33 95	97.46 129	88.05 129	92.04 207	98.42 16	87.63 210	96.36 90	96.68 132	94.37 66	99.32 72	92.41 94	99.05 106	98.64 106

3Dnovator	92.54 3	94.80 103	94.90 98	94.47 136	95.47 247	87.06 148	96.63 30	97.28 139	91.82 109	94.34 183	97.41 76	90.60 156	98.65 180	92.47 93	98.11 214	97.70 193

RPSCF		95.58 69	94.89 99	97.62 8	97.58 121	96.30 6	95.97 67	97.53 115	92.42 83	93.41 208	97.78 57	91.21 140	97.77 262	91.06 125	97.06 259	98.80 85

tfpnnormal		94.27 123	94.87 100	92.48 206	97.71 110	80.88 242	94.55 125	95.41 237	93.70 64	96.67 79	97.72 60	91.40 132	98.18 225	87.45 215	99.18 93	98.36 128

9.14			94.81 101		97.49 126		94.11 138	98.37 18	87.56 213	95.38 140	96.03 173	94.66 58	99.08 102	90.70 134	98.97 119

casdiffmvs		94.32 121	94.80 102	92.85 192	96.05 215	81.44 235	92.35 194	98.05 63	91.53 121	95.75 125	96.80 121	93.35 84	98.49 197	91.01 128	98.32 190	98.64 106

baseline		94.26 125	94.80 102	92.64 198	96.08 213	80.99 240	93.69 152	98.04 67	90.80 138	94.89 166	96.32 157	93.19 88	98.48 201	91.68 115	98.51 170	98.43 125

TSAR-MVS + MP.		94.96 93	94.75 104	95.57 87	98.86 23	88.69 114	96.37 44	96.81 174	85.23 244	94.75 171	97.12 100	91.85 122	99.40 47	93.45 57	98.33 188	98.62 110
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		94.69 106	94.75 104	94.52 132	97.55 123	87.87 133	95.01 106	97.57 111	92.68 78	96.20 105	93.44 279	91.92 121	98.78 155	89.11 182	99.24 83	96.92 233

KD-MVS_self_test		94.10 130	94.73 106	92.19 213	97.66 116	79.49 268	94.86 110	97.12 150	89.59 164	96.87 70	97.65 63	90.40 161	98.34 211	89.08 183	99.35 61	98.75 90

canonicalmvs		94.59 109	94.69 107	94.30 142	95.60 244	87.03 150	95.59 81	98.24 33	91.56 120	95.21 153	92.04 311	94.95 52	98.66 178	91.45 121	97.57 244	97.20 225

APD-MVS		95.00 91	94.69 107	95.93 67	97.38 132	90.88 75	94.59 119	97.81 93	89.22 174	95.46 138	96.17 169	93.42 82	99.34 66	89.30 173	98.87 130	97.56 204
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GeoE		94.55 111	94.68 109	94.15 145	97.23 137	85.11 190	94.14 137	97.34 132	88.71 185	95.26 148	95.50 201	94.65 59	99.12 98	90.94 129	98.40 176	98.23 138

Regformer-4		94.90 95	94.67 110	95.59 85	92.78 315	89.02 107	92.39 191	95.91 216	94.50 45	96.41 87	95.56 198	92.10 116	99.01 115	94.23 29	98.14 210	98.74 93

EG-PatchMatch MVS		94.54 113	94.67 110	94.14 146	97.87 100	86.50 162	92.00 210	96.74 180	88.16 197	96.93 68	97.61 65	93.04 95	97.90 245	91.60 117	98.12 213	98.03 158

MSP-MVS		95.34 79	94.63 112	97.48 16	98.67 32	94.05 25	96.41 43	98.18 39	91.26 126	95.12 154	95.15 215	86.60 217	99.50 21	93.43 61	96.81 270	98.89 73
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
LCM-MVSNet-Re		94.20 128	94.58 113	93.04 181	95.91 226	83.13 216	93.79 149	99.19 3	92.00 96	98.84 5	98.04 44	93.64 74	99.02 113	81.28 286	98.54 166	96.96 232

ETH3D-3000-0.1		94.86 98	94.55 114	95.81 74	97.61 118	89.72 93	94.05 140	98.37 18	88.09 198	95.06 159	95.85 179	92.58 106	99.10 101	90.33 145	98.99 114	98.62 110

test_part1		94.39 116	94.55 114	93.92 155	96.14 208	82.86 219	95.54 85	98.09 55	95.36 36	98.27 20	98.36 31	75.91 306	99.44 28	93.41 62	99.84 3	99.47 18

Regformer-2		94.86 98	94.55 114	95.77 78	92.83 313	89.98 87	91.87 219	96.40 196	94.38 49	96.19 107	95.04 222	92.47 111	99.04 110	93.49 51	98.31 191	98.28 134

AllTest		94.88 97	94.51 117	96.00 61	98.02 91	92.17 54	95.26 94	98.43 14	90.48 145	95.04 160	96.74 127	92.54 108	97.86 253	85.11 250	98.98 115	97.98 164

testtj		94.81 102	94.42 118	96.01 60	97.23 137	90.51 82	94.77 113	97.85 89	91.29 125	94.92 165	95.66 191	91.71 125	99.40 47	88.07 204	98.25 198	98.11 151

HPM-MVS++		95.02 90	94.39 119	96.91 40	97.88 99	93.58 40	94.09 139	96.99 159	91.05 132	92.40 247	95.22 214	91.03 147	99.25 80	92.11 98	98.69 152	97.90 174

VDD-MVS		94.37 117	94.37 120	94.40 140	97.49 126	86.07 178	93.97 144	93.28 283	94.49 46	96.24 101	97.78 57	87.99 191	98.79 151	88.92 185	99.14 98	98.34 129

IS-MVSNet		94.49 114	94.35 121	94.92 110	98.25 74	86.46 165	97.13 17	94.31 265	96.24 24	96.28 99	96.36 155	82.88 245	99.35 63	88.19 199	99.52 40	98.96 63

Regformer-1		94.55 111	94.33 122	95.19 102	92.83 313	88.54 121	91.87 219	95.84 220	93.99 55	95.95 115	95.04 222	92.00 118	98.79 151	93.14 75	98.31 191	98.23 138

CNVR-MVS		94.58 110	94.29 123	95.46 91	96.94 151	89.35 104	91.81 225	96.80 175	89.66 161	93.90 196	95.44 205	92.80 102	98.72 165	92.74 86	98.52 168	98.32 130

EI-MVSNet-Vis-set		94.36 118	94.28 124	94.61 124	92.55 318	85.98 179	92.44 187	94.69 257	93.70 64	96.12 110	95.81 183	91.24 138	98.86 137	93.76 44	98.22 203	98.98 61

IterMVS-LS		93.78 137	94.28 124	92.27 210	96.27 197	79.21 275	91.87 219	96.78 176	91.77 112	96.57 84	97.07 102	87.15 204	98.74 163	91.99 103	99.03 113	98.86 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet-UG-set		94.35 119	94.27 126	94.59 129	92.46 319	85.87 181	92.42 189	94.69 257	93.67 68	96.13 109	95.84 182	91.20 141	98.86 137	93.78 41	98.23 201	99.03 52

VDDNet		94.03 132	94.27 126	93.31 176	98.87 22	82.36 223	95.51 87	91.78 313	97.19 12	96.32 93	98.60 19	84.24 234	98.75 160	87.09 222	98.83 137	98.81 83

Regformer-3		94.28 122	94.23 128	94.46 137	92.78 315	86.28 172	92.39 191	94.70 256	93.69 67	95.97 113	95.56 198	91.34 133	98.48 201	93.45 57	98.14 210	98.62 110

bld_raw_dy_0_64		94.27 123	94.15 129	94.65 123	98.55 44	86.28 172	95.80 74	95.55 232	88.41 192	97.09 58	98.08 41	78.69 281	98.87 136	95.63 10	99.53 37	98.81 83

XVG-OURS		94.72 105	94.12 130	96.50 51	98.00 93	94.23 20	91.48 231	98.17 43	90.72 139	95.30 145	96.47 142	87.94 192	96.98 297	91.41 122	97.61 243	98.30 133

CPTT-MVS		94.74 104	94.12 130	96.60 47	98.15 79	93.01 46	95.84 72	97.66 103	89.21 175	93.28 214	95.46 203	88.89 178	98.98 117	89.80 162	98.82 138	97.80 186

HQP_MVS		94.26 125	93.93 132	95.23 101	97.71 110	88.12 127	94.56 123	97.81 93	91.74 114	93.31 211	95.59 193	86.93 209	98.95 124	89.26 177	98.51 170	98.60 113

MSLP-MVS++		93.25 152	93.88 133	91.37 241	96.34 191	82.81 220	93.11 164	97.74 99	89.37 168	94.08 186	95.29 213	90.40 161	96.35 318	90.35 143	98.25 198	94.96 301

v1144		93.50 141	93.81 134	92.57 203	96.28 196	79.61 265	91.86 223	96.96 160	86.95 222	95.91 119	96.32 157	87.65 195	98.96 122	93.51 50	98.88 127	99.13 42

PHI-MVS		94.34 120	93.80 135	95.95 64	95.65 240	91.67 66	94.82 111	97.86 86	87.86 203	93.04 225	94.16 256	91.58 128	98.78 155	90.27 148	98.96 121	97.41 213

v1192		93.49 142	93.78 136	92.62 201	96.16 206	79.62 264	91.83 224	97.22 143	86.07 231	96.10 111	96.38 153	87.22 202	99.02 113	94.14 32	98.88 127	99.22 34

VPNet		93.08 156	93.76 137	91.03 254	98.60 38	75.83 322	91.51 230	95.62 224	91.84 106	95.74 126	97.10 101	89.31 175	98.32 212	85.07 252	99.06 103	98.93 66

WR-MVS		93.49 142	93.72 138	92.80 194	97.57 122	80.03 253	90.14 267	95.68 223	93.70 64	96.62 81	95.39 209	87.21 203	99.04 110	87.50 214	99.64 25	99.33 27

v1240		93.29 147	93.71 139	92.06 220	96.01 220	77.89 293	91.81 225	97.37 124	85.12 248	96.69 78	96.40 148	86.67 215	99.07 106	94.51 21	98.76 146	99.22 34

OMC-MVS		94.22 127	93.69 140	95.81 74	97.25 136	91.27 68	92.27 198	97.40 123	87.10 220	94.56 176	95.42 206	93.74 73	98.11 230	86.62 229	98.85 131	98.06 152

EPP-MVSNet		93.91 135	93.68 141	94.59 129	98.08 83	85.55 186	97.44 11	94.03 270	94.22 52	94.94 163	96.19 166	82.07 257	99.57 14	87.28 219	98.89 125	98.65 102

v2v482		93.29 147	93.63 142	92.29 209	96.35 190	78.82 281	91.77 227	96.28 200	88.45 190	95.70 130	96.26 163	86.02 223	98.90 128	93.02 79	98.81 140	99.14 41

v1921920		93.26 150	93.61 143	92.19 213	96.04 219	78.31 287	91.88 218	97.24 141	85.17 246	96.19 107	96.19 166	86.76 214	99.05 107	94.18 31	98.84 132	99.22 34

V42		93.43 144	93.58 144	92.97 184	95.34 253	81.22 237	92.67 178	96.49 193	87.25 216	96.20 105	96.37 154	87.32 201	98.85 139	92.39 96	98.21 204	98.85 79

Anonymous20240521		92.86 166	93.57 145	90.74 266	96.57 173	75.50 324	94.15 136	95.60 225	89.38 167	95.90 120	97.90 55	80.39 272	97.96 243	92.60 91	99.68 19	98.75 90

DeepPCF-MVS	90.46 6	94.20 128	93.56 146	96.14 56	95.96 222	92.96 47	89.48 284	97.46 119	85.14 247	96.23 102	95.42 206	93.19 88	98.08 231	90.37 142	98.76 146	97.38 219

v144192		93.20 155	93.54 147	92.16 217	96.05 215	78.26 288	91.95 211	97.14 147	84.98 252	95.96 114	96.11 170	87.08 206	99.04 110	93.79 40	98.84 132	99.17 38

NCCC		94.08 131	93.54 147	95.70 83	96.49 181	89.90 90	92.39 191	96.91 166	90.64 142	92.33 253	94.60 241	90.58 157	98.96 122	90.21 152	97.70 238	98.23 138

DeepC-MVS_fast	89.96 7	93.73 138	93.44 149	94.60 128	96.14 208	87.90 132	93.36 161	97.14 147	85.53 241	93.90 196	95.45 204	91.30 136	98.59 187	89.51 168	98.62 157	97.31 222
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		93.63 140	93.42 150	94.26 143	96.65 166	86.96 153	89.30 290	96.23 204	88.36 194	93.57 205	94.60 241	93.45 79	97.77 262	90.23 151	98.38 181	98.03 158

ETH3D cwj APD-0.16		93.99 133	93.38 151	95.80 76	96.82 160	89.92 88	92.72 175	98.02 70	84.73 257	93.65 202	95.54 200	91.68 126	99.22 83	88.78 189	98.49 173	98.26 136

v148		92.87 165	93.29 152	91.62 234	96.25 200	77.72 296	91.28 236	95.05 244	89.69 160	95.93 118	96.04 172	87.34 200	98.38 207	90.05 158	97.99 224	98.78 87

MVS_Test		92.57 177	93.29 152	90.40 275	93.53 300	75.85 320	92.52 182	96.96 160	88.73 183	92.35 250	96.70 131	90.77 149	98.37 210	92.53 92	95.49 298	96.99 231

MVS_111021_LR		93.66 139	93.28 154	94.80 115	96.25 200	90.95 73	90.21 263	95.43 236	87.91 200	93.74 200	94.40 247	92.88 100	96.38 316	90.39 140	98.28 194	97.07 226

K. test v3		93.37 145	93.27 155	93.66 163	98.05 86	82.62 221	94.35 129	86.62 345	96.05 28	97.51 42	98.85 12	76.59 304	99.65 3	93.21 71	98.20 206	98.73 95

EI-MVSNet		92.99 160	93.26 156	92.19 213	92.12 326	79.21 275	92.32 196	94.67 259	91.77 112	95.24 151	95.85 179	87.14 205	98.49 197	91.99 103	98.26 196	98.86 76

XXY-MVS		92.58 175	93.16 157	90.84 263	97.75 106	79.84 258	91.87 219	96.22 206	85.94 233	95.53 135	97.68 61	92.69 104	94.48 347	83.21 268	97.51 245	98.21 141

VNet		92.67 172	92.96 158	91.79 226	96.27 197	80.15 247	91.95 211	94.98 246	92.19 93	94.52 178	96.07 171	87.43 199	97.39 284	84.83 254	98.38 181	97.83 182

GBi-Net		93.21 153	92.96 158	93.97 151	95.40 249	84.29 197	95.99 64	96.56 188	88.63 186	95.10 155	98.53 23	81.31 264	98.98 117	86.74 225	98.38 181	98.65 102

test1		93.21 153	92.96 158	93.97 151	95.40 249	84.29 197	95.99 64	96.56 188	88.63 186	95.10 155	98.53 23	81.31 264	98.98 117	86.74 225	98.38 181	98.65 102

alignmvs		93.26 150	92.85 161	94.50 133	95.70 236	87.45 138	93.45 159	95.76 221	91.58 119	95.25 150	92.42 305	81.96 259	98.72 165	91.61 116	97.87 230	97.33 221

test_prior3		93.29 147	92.85 161	94.61 124	95.95 223	87.23 143	90.21 263	97.36 129	89.33 170	90.77 277	94.81 232	90.41 159	98.68 175	88.21 197	98.55 163	97.93 170

QAPM		92.88 164	92.77 163	93.22 179	95.82 229	83.31 211	96.45 39	97.35 131	83.91 262	93.75 198	96.77 122	89.25 176	98.88 131	84.56 258	97.02 261	97.49 208

TinyColmap		92.00 191	92.76 164	89.71 291	95.62 243	77.02 304	90.72 248	96.17 209	87.70 208	95.26 148	96.29 159	92.54 108	96.45 313	81.77 282	98.77 145	95.66 286

ETV-MVS		92.99 160	92.74 165	93.72 162	95.86 228	86.30 171	92.33 195	97.84 90	91.70 117	92.81 232	86.17 367	92.22 113	99.19 87	88.03 206	97.73 234	95.66 286

Effi-MVS+		92.79 167	92.74 165	92.94 188	95.10 257	83.30 212	94.00 142	97.53 115	91.36 124	89.35 305	90.65 333	94.01 71	98.66 178	87.40 217	95.30 304	96.88 236

FMVSNet2		92.78 168	92.73 167	92.95 186	95.40 249	81.98 226	94.18 135	95.53 234	88.63 186	96.05 112	97.37 79	81.31 264	98.81 147	87.38 218	98.67 155	98.06 152

patch_mono-2		92.46 179	92.72 168	91.71 230	96.65 166	78.91 279	88.85 299	97.17 145	83.89 263	92.45 244	96.76 124	89.86 171	97.09 293	90.24 150	98.59 160	99.12 44

PM-MVS		93.33 146	92.67 169	95.33 95	96.58 172	94.06 23	92.26 199	92.18 304	85.92 234	96.22 103	96.61 137	85.64 228	95.99 327	90.35 143	98.23 201	95.93 272

ab-mvs		92.40 181	92.62 170	91.74 228	97.02 147	81.65 230	95.84 72	95.50 235	86.95 222	92.95 229	97.56 67	90.70 154	97.50 275	79.63 305	97.43 249	96.06 267

Effi-MVS+-dtu		93.90 136	92.60 171	97.77 4	94.74 270	96.67 5	94.00 142	95.41 237	89.94 154	91.93 261	92.13 309	90.12 164	98.97 121	87.68 212	97.48 247	97.67 196

MCST-MVS		92.91 162	92.51 172	94.10 147	97.52 124	85.72 184	91.36 235	97.13 149	80.33 292	92.91 230	94.24 252	91.23 139	98.72 165	89.99 159	97.93 227	97.86 179

Anonymous202405211		92.58 175	92.50 173	92.83 193	96.55 175	83.22 213	92.43 188	91.64 315	94.10 54	95.59 133	96.64 135	81.88 261	97.50 275	85.12 249	98.52 168	97.77 188

UGNet		93.08 156	92.50 173	94.79 116	93.87 295	87.99 131	95.07 103	94.26 267	90.64 142	87.33 334	97.67 62	86.89 212	98.49 197	88.10 202	98.71 149	97.91 173
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
TSAR-MVS + GP.		93.07 158	92.41 175	95.06 107	95.82 229	90.87 76	90.97 242	92.61 298	88.04 199	94.61 175	93.79 270	88.08 187	97.81 257	89.41 170	98.39 179	96.50 250

FMVS2		92.42 180	92.40 176	92.46 208	93.80 298	87.28 142	93.86 147	97.05 154	76.86 322	96.25 100	98.66 18	82.87 246	91.26 368	95.44 14	96.83 269	98.82 81

MVSFormer		92.18 188	92.23 177	92.04 221	94.74 270	80.06 251	97.15 14	97.37 124	88.98 178	88.83 309	92.79 294	77.02 298	99.60 9	96.41 4	96.75 273	96.46 252

Fast-Effi-MVS+-dtu		92.77 169	92.16 178	94.58 131	94.66 276	88.25 125	92.05 206	96.65 184	89.62 162	90.08 290	91.23 321	92.56 107	98.60 185	86.30 237	96.27 282	96.90 234

DELS-MVS		92.05 190	92.16 178	91.72 229	94.44 281	80.13 249	87.62 313	97.25 140	87.34 215	92.22 255	93.18 286	89.54 174	98.73 164	89.67 166	98.20 206	96.30 258
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
OpenMVS	89.45 8	92.27 186	92.13 180	92.68 197	94.53 280	84.10 203	95.70 77	97.03 155	82.44 280	91.14 273	96.42 146	88.47 182	98.38 207	85.95 240	97.47 248	95.55 290

EIA-MVS		92.35 183	92.03 181	93.30 177	95.81 231	83.97 205	92.80 173	98.17 43	87.71 207	89.79 299	87.56 357	91.17 144	99.18 88	87.97 207	97.27 253	96.77 240

LF4IMVS		92.72 170	92.02 182	94.84 114	95.65 240	91.99 58	92.92 169	96.60 186	85.08 250	92.44 245	93.62 274	86.80 213	96.35 318	86.81 224	98.25 198	96.18 263

h-mvs33		92.89 163	91.99 183	95.58 86	96.97 149	90.55 80	93.94 145	94.01 273	89.23 172	93.95 193	96.19 166	76.88 301	99.14 92	91.02 126	95.71 293	97.04 229

CANet		92.38 182	91.99 183	93.52 171	93.82 297	83.46 210	91.14 238	97.00 157	89.81 158	86.47 338	94.04 259	87.90 193	99.21 84	89.50 169	98.27 195	97.90 174

diffmvs		91.74 195	91.93 185	91.15 252	93.06 308	78.17 289	88.77 302	97.51 118	86.28 227	92.42 246	93.96 264	88.04 189	97.46 278	90.69 135	96.67 275	97.82 184

DP-MVS Recon		92.31 184	91.88 186	93.60 165	97.18 141	86.87 154	91.10 240	97.37 124	84.92 253	92.08 258	94.08 258	88.59 180	98.20 222	83.50 265	98.14 210	95.73 281

FA-MVS(test-final)		91.81 194	91.85 187	91.68 232	94.95 260	79.99 255	96.00 63	93.44 281	87.80 204	94.02 191	97.29 89	77.60 291	98.45 204	88.04 205	97.49 246	96.61 244

train_agg		92.71 171	91.83 188	95.35 93	96.45 183	89.46 98	90.60 251	96.92 164	79.37 301	90.49 282	94.39 248	91.20 141	98.88 131	88.66 193	98.43 175	97.72 192

CDPH-MVS		92.67 172	91.83 188	95.18 103	96.94 151	88.46 123	90.70 249	97.07 153	77.38 317	92.34 252	95.08 220	92.67 105	98.88 131	85.74 241	98.57 162	98.20 142

mvs-test1		93.07 158	91.80 190	96.89 41	94.74 270	95.83 8	92.17 202	95.41 237	89.94 154	89.85 296	90.59 334	90.12 164	98.88 131	87.68 212	95.66 294	95.97 270

agg_prior1		92.60 174	91.76 191	95.10 106	96.20 202	88.89 111	90.37 258	96.88 168	79.67 298	90.21 287	94.41 246	91.30 136	98.78 155	88.46 196	98.37 186	97.64 198

TAPA-MVS	88.58 10	92.49 178	91.75 192	94.73 118	96.50 180	89.69 94	92.91 170	97.68 102	78.02 315	92.79 233	94.10 257	90.85 148	97.96 243	84.76 256	98.16 208	96.54 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
API-MVS		91.52 201	91.61 193	91.26 246	94.16 286	86.26 174	94.66 117	94.82 251	91.17 130	92.13 257	91.08 324	90.03 170	97.06 295	79.09 312	97.35 252	90.45 362

IterMVS-SCA-FT		91.65 197	91.55 194	91.94 222	93.89 294	79.22 274	87.56 316	93.51 279	91.53 121	95.37 141	96.62 136	78.65 282	98.90 128	91.89 108	94.95 310	97.70 193

xiu_mvs_v1_base_debu		91.47 202	91.52 195	91.33 243	95.69 237	81.56 231	89.92 274	96.05 212	83.22 267	91.26 269	90.74 328	91.55 129	98.82 142	89.29 174	95.91 288	93.62 334

xiu_mvs_v1_base		91.47 202	91.52 195	91.33 243	95.69 237	81.56 231	89.92 274	96.05 212	83.22 267	91.26 269	90.74 328	91.55 129	98.82 142	89.29 174	95.91 288	93.62 334

xiu_mvs_v1_base_debi		91.47 202	91.52 195	91.33 243	95.69 237	81.56 231	89.92 274	96.05 212	83.22 267	91.26 269	90.74 328	91.55 129	98.82 142	89.29 174	95.91 288	93.62 334

HQP-MVS		92.09 189	91.49 198	93.88 158	96.36 187	84.89 192	91.37 232	97.31 134	87.16 217	88.81 311	93.40 280	84.76 231	98.60 185	86.55 232	97.73 234	98.14 147

c3_l		91.32 206	91.42 199	91.00 257	92.29 321	76.79 311	87.52 319	96.42 195	85.76 237	94.72 174	93.89 267	82.73 249	98.16 227	90.93 130	98.55 163	98.04 155

CLD-MVS		91.82 193	91.41 200	93.04 181	96.37 185	83.65 209	86.82 332	97.29 137	84.65 258	92.27 254	89.67 343	92.20 114	97.85 255	83.95 262	99.47 42	97.62 199
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AdaColmap		91.63 198	91.36 201	92.47 207	95.56 245	86.36 169	92.24 201	96.27 201	88.88 182	89.90 295	92.69 297	91.65 127	98.32 212	77.38 324	97.64 241	92.72 347

testgi		90.38 225	91.34 202	87.50 324	97.49 126	71.54 351	89.43 285	95.16 243	88.38 193	94.54 177	94.68 240	92.88 100	93.09 361	71.60 356	97.85 231	97.88 177

mvs_anonymous		90.37 226	91.30 203	87.58 323	92.17 325	68.00 365	89.84 277	94.73 255	83.82 264	93.22 219	97.40 77	87.54 197	97.40 283	87.94 208	95.05 309	97.34 220

ETH3 D test6400		91.91 192	91.25 204	93.89 157	96.59 171	84.41 196	92.10 204	97.72 101	78.52 311	91.82 262	93.78 271	88.70 179	99.13 94	83.61 264	98.39 179	98.14 147

hse-mvs2		92.24 187	91.20 205	95.38 92	96.16 206	90.65 79	92.52 182	92.01 311	89.23 172	93.95 193	92.99 289	76.88 301	98.69 173	91.02 126	96.03 285	96.81 238

PVSNet_Blended_VisFu		91.63 198	91.20 205	92.94 188	97.73 109	83.95 206	92.14 203	97.46 119	78.85 310	92.35 250	94.98 225	84.16 235	99.08 102	86.36 236	96.77 272	95.79 279

CNLPA		91.72 196	91.20 205	93.26 178	96.17 205	91.02 71	91.14 238	95.55 232	90.16 152	90.87 276	93.56 277	86.31 219	94.40 350	79.92 304	97.12 257	94.37 315

LFMVS		91.33 205	91.16 208	91.82 225	96.27 197	79.36 270	95.01 106	85.61 355	96.04 29	94.82 168	97.06 103	72.03 319	98.46 203	84.96 253	98.70 151	97.65 197

F-COLMAP		92.28 185	91.06 209	95.95 64	97.52 124	91.90 60	93.53 156	97.18 144	83.98 261	88.70 317	94.04 259	88.41 183	98.55 193	80.17 298	95.99 287	97.39 217

BH-untuned		90.68 216	90.90 210	90.05 286	95.98 221	79.57 266	90.04 270	94.94 248	87.91 200	94.07 187	93.00 288	87.76 194	97.78 261	79.19 311	95.17 307	92.80 346

MDA-MVSNet-bldmvs		91.04 208	90.88 211	91.55 236	94.68 275	80.16 246	85.49 345	92.14 307	90.41 149	94.93 164	95.79 184	85.10 229	96.93 300	85.15 247	94.19 329	97.57 202

Fast-Effi-MVS+		91.28 207	90.86 212	92.53 205	95.45 248	82.53 222	89.25 293	96.52 192	85.00 251	89.91 294	88.55 353	92.94 96	98.84 140	84.72 257	95.44 300	96.22 261

test20.03		90.80 212	90.85 213	90.63 269	95.63 242	79.24 273	89.81 278	92.87 289	89.90 156	94.39 180	96.40 148	85.77 224	95.27 342	73.86 343	99.05 106	97.39 217

PAPM_NR		91.03 209	90.81 214	91.68 232	96.73 164	81.10 239	93.72 151	96.35 199	88.19 196	88.77 315	92.12 310	85.09 230	97.25 288	82.40 277	93.90 330	96.68 243

new-patchmatchnet		88.97 260	90.79 215	83.50 351	94.28 285	55.83 385	85.34 346	93.56 278	86.18 229	95.47 136	95.73 189	83.10 243	96.51 311	85.40 244	98.06 218	98.16 145

wuyk23d		87.83 279	90.79 215	78.96 360	90.46 350	88.63 116	92.72 175	90.67 323	91.65 118	98.68 11	97.64 64	96.06 16	77.53 381	59.84 376	99.41 56	70.73 379

pmmvs-eth3d		91.54 200	90.73 217	93.99 149	95.76 234	87.86 134	90.83 245	93.98 274	78.23 314	94.02 191	96.22 165	82.62 252	96.83 303	86.57 230	98.33 188	97.29 223

MSDG		90.82 211	90.67 218	91.26 246	94.16 286	83.08 217	86.63 337	96.19 207	90.60 144	91.94 260	91.89 312	89.16 177	95.75 329	80.96 292	94.51 320	94.95 302

test1111		90.39 224	90.61 219	89.74 290	98.04 89	71.50 352	95.59 81	79.72 378	89.41 166	95.94 117	98.14 36	70.79 322	98.81 147	88.52 195	99.32 66	98.90 72

eth_miper_zixun_eth		90.72 214	90.61 219	91.05 253	92.04 328	76.84 310	86.91 328	96.67 183	85.21 245	94.41 179	93.92 265	79.53 276	98.26 218	89.76 164	97.02 261	98.06 152

cl____		90.65 217	90.56 221	90.91 261	91.85 330	76.98 307	86.75 333	95.36 241	85.53 241	94.06 188	94.89 229	77.36 296	97.98 242	90.27 148	98.98 115	97.76 189

DIV-MVS_self_test		90.65 217	90.56 221	90.91 261	91.85 330	76.99 306	86.75 333	95.36 241	85.52 243	94.06 188	94.89 229	77.37 295	97.99 241	90.28 147	98.97 119	97.76 189

BH-RMVSNet		90.47 221	90.44 223	90.56 271	95.21 256	78.65 285	89.15 294	93.94 275	88.21 195	92.74 235	94.22 253	86.38 218	97.88 249	78.67 314	95.39 302	95.14 297

miper_ehance_all_eth		90.48 220	90.42 224	90.69 267	91.62 335	76.57 313	86.83 331	96.18 208	83.38 265	94.06 188	92.66 299	82.20 255	98.04 233	89.79 163	97.02 261	97.45 210

UnsupCasMVSNet_eth		90.33 228	90.34 225	90.28 277	94.64 278	80.24 245	89.69 280	95.88 217	85.77 236	93.94 195	95.69 190	81.99 258	92.98 362	84.21 261	91.30 357	97.62 199

FMVSNet3		90.78 213	90.32 226	92.16 217	93.03 310	79.92 257	92.54 181	94.95 247	86.17 230	95.10 155	96.01 174	69.97 325	98.75 160	86.74 225	98.38 181	97.82 184

ECVR-MVS		90.12 235	90.16 227	90.00 287	97.81 102	72.68 346	95.76 76	78.54 380	89.04 176	95.36 142	98.10 39	70.51 323	98.64 181	87.10 221	99.18 93	98.67 100

IterMVS		90.18 233	90.16 227	90.21 281	93.15 306	75.98 319	87.56 316	92.97 288	86.43 226	94.09 185	96.40 148	78.32 286	97.43 280	87.87 209	94.69 317	97.23 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		90.42 222	90.16 227	91.20 250	97.66 116	77.32 301	94.33 130	87.66 339	91.20 129	92.99 226	95.13 217	75.40 308	98.28 214	77.86 317	99.19 91	97.99 163

MVS_0304		90.96 210	90.15 230	93.37 173	93.17 305	87.06 148	93.62 155	92.43 302	89.60 163	82.25 363	95.50 201	82.56 253	97.83 256	84.41 260	97.83 232	95.22 294

RPMNet		90.31 230	90.14 231	90.81 265	91.01 342	78.93 277	92.52 182	98.12 49	91.91 100	89.10 306	96.89 115	68.84 327	99.41 40	90.17 153	92.70 346	94.08 319

PVSNet_BlendedMVS		90.35 227	89.96 232	91.54 237	94.81 265	78.80 283	90.14 267	96.93 162	79.43 300	88.68 318	95.06 221	86.27 220	98.15 228	80.27 294	98.04 220	97.68 195

Patchmtry		90.11 236	89.92 233	90.66 268	90.35 351	77.00 305	92.96 168	92.81 290	90.25 151	94.74 172	96.93 112	67.11 332	97.52 274	85.17 245	98.98 115	97.46 209

CL-MVSNet_self_test		90.04 241	89.90 234	90.47 272	95.24 255	77.81 294	86.60 339	92.62 297	85.64 239	93.25 218	93.92 265	83.84 236	96.06 325	79.93 302	98.03 221	97.53 206

miper_lstm_enhance		89.90 243	89.80 235	90.19 283	91.37 338	77.50 298	83.82 360	95.00 245	84.84 255	93.05 224	94.96 226	76.53 305	95.20 343	89.96 160	98.67 155	97.86 179

114514_t		90.51 219	89.80 235	92.63 200	98.00 93	82.24 224	93.40 160	97.29 137	65.84 369	89.40 304	94.80 235	86.99 207	98.75 160	83.88 263	98.61 158	96.89 235

MG-MVS		89.54 248	89.80 235	88.76 306	94.88 261	72.47 348	89.60 281	92.44 301	85.82 235	89.48 303	95.98 175	82.85 247	97.74 266	81.87 281	95.27 305	96.08 266

test_yl		90.11 236	89.73 238	91.26 246	94.09 289	79.82 259	90.44 255	92.65 295	90.90 133	93.19 220	93.30 282	73.90 311	98.03 234	82.23 278	96.87 267	95.93 272

DCV-MVSNet		90.11 236	89.73 238	91.26 246	94.09 289	79.82 259	90.44 255	92.65 295	90.90 133	93.19 220	93.30 282	73.90 311	98.03 234	82.23 278	96.87 267	95.93 272

D2MVS		89.93 242	89.60 240	90.92 259	94.03 291	78.40 286	88.69 304	94.85 249	78.96 308	93.08 222	95.09 219	74.57 309	96.94 298	88.19 199	98.96 121	97.41 213

iter_conf_final		90.23 232	89.32 241	92.95 186	94.65 277	81.46 234	94.32 132	95.40 240	85.61 240	92.84 231	95.37 211	54.58 374	99.13 94	92.16 97	98.94 123	98.25 137

1121		90.26 231	89.23 242	93.34 174	97.15 144	87.40 139	91.94 213	94.39 263	67.88 364	91.02 275	94.91 228	86.91 211	98.59 187	81.17 289	97.71 237	94.02 324

xiu_mvs_v2_base		89.00 259	89.19 243	88.46 313	94.86 263	74.63 328	86.97 326	95.60 225	80.88 288	87.83 328	88.62 352	91.04 146	98.81 147	82.51 276	94.38 322	91.93 353

CANet_DTU		89.85 244	89.17 244	91.87 223	92.20 324	80.02 254	90.79 246	95.87 218	86.02 232	82.53 362	91.77 314	80.01 273	98.57 190	85.66 242	97.70 238	97.01 230

USDC		89.02 257	89.08 245	88.84 305	95.07 258	74.50 331	88.97 296	96.39 197	73.21 340	93.27 215	96.28 161	82.16 256	96.39 315	77.55 321	98.80 142	95.62 289

TAMVS		90.16 234	89.05 246	93.49 172	96.49 181	86.37 168	90.34 260	92.55 299	80.84 290	92.99 226	94.57 243	81.94 260	98.20 222	73.51 344	98.21 204	95.90 275

OpenMVS_ROB	85.12 16	89.52 249	89.05 246	90.92 259	94.58 279	81.21 238	91.10 240	93.41 282	77.03 321	93.41 208	93.99 263	83.23 242	97.80 258	79.93 302	94.80 314	93.74 331

PS-MVSNAJ		88.86 264	88.99 248	88.48 312	94.88 261	74.71 326	86.69 335	95.60 225	80.88 288	87.83 328	87.37 360	90.77 149	98.82 142	82.52 275	94.37 323	91.93 353

MVP-Stereo		90.07 239	88.92 249	93.54 169	96.31 194	86.49 163	90.93 243	95.59 229	79.80 294	91.48 265	95.59 193	80.79 269	97.39 284	78.57 315	91.19 358	96.76 241
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PLC	85.34 15	90.40 223	88.92 249	94.85 113	96.53 179	90.02 86	91.58 229	96.48 194	80.16 293	86.14 340	92.18 307	85.73 225	98.25 219	76.87 327	94.61 319	96.30 258
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		89.81 245	88.90 251	92.55 204	97.00 148	79.73 263	95.03 105	83.65 367	89.88 157	95.30 145	94.79 236	53.64 377	99.39 52	91.99 103	98.79 143	98.54 116

MAR-MVS		90.32 229	88.87 252	94.66 122	94.82 264	91.85 61	94.22 134	94.75 254	80.91 287	87.52 332	88.07 356	86.63 216	97.87 252	76.67 328	96.21 283	94.25 318
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVSTER		89.32 251	88.75 253	91.03 254	90.10 354	76.62 312	90.85 244	94.67 259	82.27 281	95.24 151	95.79 184	61.09 363	98.49 197	90.49 137	98.26 196	97.97 167

ppachtmachnet_test		88.61 269	88.64 254	88.50 311	91.76 332	70.99 355	84.59 353	92.98 287	79.30 305	92.38 248	93.53 278	79.57 275	97.45 279	86.50 234	97.17 256	97.07 226

Patchmatch-RL test		88.81 265	88.52 255	89.69 292	95.33 254	79.94 256	86.22 342	92.71 294	78.46 312	95.80 123	94.18 255	66.25 340	95.33 340	89.22 179	98.53 167	93.78 329

cl22		89.02 257	88.50 256	90.59 270	89.76 356	76.45 314	86.62 338	94.03 270	82.98 273	92.65 237	92.49 300	72.05 318	97.53 273	88.93 184	97.02 261	97.78 187

X-MVStestdata		90.70 215	88.45 257	97.44 19	98.56 41	93.99 29	96.50 36	97.95 81	94.58 43	94.38 181	26.89 382	94.56 61	99.39 52	93.57 47	99.05 106	98.93 66

DPM-MVS		89.35 250	88.40 258	92.18 216	96.13 211	84.20 201	86.96 327	96.15 210	75.40 330	87.36 333	91.55 319	83.30 241	98.01 238	82.17 280	96.62 276	94.32 317

jason		89.17 253	88.32 259	91.70 231	95.73 235	80.07 250	88.10 309	93.22 284	71.98 346	90.09 289	92.79 294	78.53 285	98.56 191	87.43 216	97.06 259	96.46 252
jason: jason.
AUN-MVS		90.05 240	88.30 260	95.32 98	96.09 212	90.52 81	92.42 189	92.05 310	82.08 283	88.45 320	92.86 291	65.76 342	98.69 173	88.91 186	96.07 284	96.75 242

FE-MVS		89.06 256	88.29 261	91.36 242	94.78 267	79.57 266	96.77 27	90.99 319	84.87 254	92.96 228	96.29 159	60.69 365	98.80 150	80.18 297	97.11 258	95.71 282

Anonymous20231206		88.77 266	88.29 261	90.20 282	96.31 194	78.81 282	89.56 283	93.49 280	74.26 335	92.38 248	95.58 196	82.21 254	95.43 337	72.07 352	98.75 148	96.34 256

EPNet		89.80 246	88.25 263	94.45 138	83.91 383	86.18 175	93.87 146	87.07 343	91.16 131	80.64 371	94.72 238	78.83 279	98.89 130	85.17 245	98.89 125	98.28 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
YYNet1		88.17 274	88.24 264	87.93 319	92.21 323	73.62 338	80.75 368	88.77 329	82.51 279	94.99 162	95.11 218	82.70 250	93.70 356	83.33 266	93.83 331	96.48 251

MDA-MVSNet_test_wron		88.16 275	88.23 265	87.93 319	92.22 322	73.71 337	80.71 369	88.84 328	82.52 278	94.88 167	95.14 216	82.70 250	93.61 357	83.28 267	93.80 332	96.46 252

CDS-MVSNet		89.55 247	88.22 266	93.53 170	95.37 252	86.49 163	89.26 291	93.59 277	79.76 296	91.15 272	92.31 306	77.12 297	98.38 207	77.51 322	97.92 228	95.71 282
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsany_test		89.11 255	88.21 267	91.83 224	91.30 339	90.25 84	88.09 310	78.76 379	76.37 325	96.43 86	98.39 30	83.79 237	90.43 372	86.57 230	94.20 327	94.80 304

PatchT		87.51 287	88.17 268	85.55 337	90.64 345	66.91 367	92.02 209	86.09 349	92.20 92	89.05 308	97.16 98	64.15 350	96.37 317	89.21 180	92.98 344	93.37 338

PVSNet_Blended		88.74 267	88.16 269	90.46 274	94.81 265	78.80 283	86.64 336	96.93 162	74.67 332	88.68 318	89.18 349	86.27 220	98.15 228	80.27 294	96.00 286	94.44 314

iter_conf05		88.94 262	88.09 270	91.50 238	92.74 317	76.97 308	92.80 173	95.92 215	82.82 275	93.65 202	95.37 211	49.41 381	99.13 94	90.82 131	99.28 77	98.40 127

UnsupCasMVSNet_bld		88.50 270	88.03 271	89.90 288	95.52 246	78.88 280	87.39 320	94.02 272	79.32 304	93.06 223	94.02 261	80.72 270	94.27 352	75.16 337	93.08 342	96.54 245

PatchMatch-RL		89.18 252	88.02 272	92.64 198	95.90 227	92.87 49	88.67 306	91.06 318	80.34 291	90.03 292	91.67 316	83.34 240	94.42 349	76.35 331	94.84 313	90.64 361

miper_enhance_ethall		88.42 271	87.87 273	90.07 284	88.67 368	75.52 323	85.10 347	95.59 229	75.68 326	92.49 241	89.45 346	78.96 278	97.88 249	87.86 210	97.02 261	96.81 238

MS-PatchMatch		88.05 276	87.75 274	88.95 302	93.28 302	77.93 291	87.88 312	92.49 300	75.42 329	92.57 240	93.59 276	80.44 271	94.24 354	81.28 286	92.75 345	94.69 310

PCF-MVS	84.52 17	89.12 254	87.71 275	93.34 174	96.06 214	85.84 182	86.58 340	97.31 134	68.46 362	93.61 204	93.89 267	87.51 198	98.52 195	67.85 367	98.11 214	95.66 286
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs4		88.95 261	87.70 276	92.70 196	94.30 284	85.60 185	87.22 322	92.16 306	74.62 333	89.75 301	94.19 254	77.97 289	96.41 314	82.71 272	96.36 281	96.09 265

our_test_3		87.55 286	87.59 277	87.44 325	91.76 332	70.48 356	83.83 359	90.55 324	79.79 295	92.06 259	92.17 308	78.63 284	95.63 330	84.77 255	94.73 315	96.22 261

thisisatest0530		88.69 268	87.52 278	92.20 212	96.33 192	79.36 270	92.81 172	84.01 366	86.44 225	93.67 201	92.68 298	53.62 378	99.25 80	89.65 167	98.45 174	98.00 160

1112_ss		88.42 271	87.41 279	91.45 239	96.69 165	80.99 240	89.72 279	96.72 181	73.37 339	87.00 336	90.69 331	77.38 294	98.20 222	81.38 285	93.72 333	95.15 296

baseline1		87.62 285	87.31 280	88.54 310	94.71 274	74.27 334	93.10 165	88.20 335	86.20 228	92.18 256	93.04 287	73.21 314	95.52 332	79.32 309	85.82 370	95.83 277

lupinMVS		88.34 273	87.31 280	91.45 239	94.74 270	80.06 251	87.23 321	92.27 303	71.10 350	88.83 309	91.15 322	77.02 298	98.53 194	86.67 228	96.75 273	95.76 280

N_pmnet		88.90 263	87.25 282	93.83 160	94.40 283	93.81 38	84.73 350	87.09 342	79.36 303	93.26 216	92.43 304	79.29 277	91.68 366	77.50 323	97.22 255	96.00 269

SCA		87.43 289	87.21 283	88.10 317	92.01 329	71.98 350	89.43 285	88.11 337	82.26 282	88.71 316	92.83 292	78.65 282	97.59 271	79.61 306	93.30 337	94.75 307

TR-MVS		87.70 281	87.17 284	89.27 299	94.11 288	79.26 272	88.69 304	91.86 312	81.94 284	90.69 280	89.79 340	82.82 248	97.42 281	72.65 350	91.98 354	91.14 358

pmmvs5		87.87 278	87.14 285	90.07 284	93.26 304	76.97 308	88.89 298	92.18 304	73.71 338	88.36 321	93.89 267	76.86 303	96.73 306	80.32 293	96.81 270	96.51 247

FMVS		86.65 303	87.13 286	85.19 341	90.28 352	86.11 177	86.52 341	91.66 314	69.76 357	95.73 128	97.21 96	69.51 326	81.28 380	89.15 181	94.40 321	88.17 368

CR-MVSNet		87.89 277	87.12 287	90.22 280	91.01 342	78.93 277	92.52 182	92.81 290	73.08 341	89.10 306	96.93 112	67.11 332	97.64 270	88.80 188	92.70 346	94.08 319

thres600view7		87.66 283	87.10 288	89.36 297	96.05 215	73.17 340	92.72 175	85.31 358	91.89 101	93.29 213	90.97 325	63.42 354	98.39 205	73.23 346	96.99 266	96.51 247

BH-w/o		87.21 294	87.02 289	87.79 322	94.77 268	77.27 302	87.90 311	93.21 286	81.74 285	89.99 293	88.39 355	83.47 239	96.93 300	71.29 357	92.43 350	89.15 363

thres100view900		87.35 291	86.89 290	88.72 307	96.14 208	73.09 342	93.00 167	85.31 358	92.13 94	93.26 216	90.96 326	63.42 354	98.28 214	71.27 358	96.54 277	94.79 305

GA-MVS		87.70 281	86.82 291	90.31 276	93.27 303	77.22 303	84.72 352	92.79 292	85.11 249	89.82 297	90.07 335	66.80 335	97.76 264	84.56 258	94.27 326	95.96 271

sss		87.23 293	86.82 291	88.46 313	93.96 292	77.94 290	86.84 330	92.78 293	77.59 316	87.61 331	91.83 313	78.75 280	91.92 365	77.84 318	94.20 327	95.52 291

PAPR		87.65 284	86.77 293	90.27 278	92.85 312	77.38 300	88.56 307	96.23 204	76.82 324	84.98 346	89.75 342	86.08 222	97.16 291	72.33 351	93.35 336	96.26 260

EU-MVSNet		87.39 290	86.71 294	89.44 294	93.40 301	76.11 317	94.93 109	90.00 326	57.17 378	95.71 129	97.37 79	64.77 348	97.68 269	92.67 89	94.37 323	94.52 312

Test_1112_low_res		87.50 288	86.58 295	90.25 279	96.80 163	77.75 295	87.53 318	96.25 202	69.73 358	86.47 338	93.61 275	75.67 307	97.88 249	79.95 300	93.20 338	95.11 298

FMVSNet5		87.82 280	86.56 296	91.62 234	92.31 320	79.81 261	93.49 157	94.81 253	83.26 266	91.36 267	96.93 112	52.77 379	97.49 277	76.07 332	98.03 221	97.55 205

MIMVSNet		87.13 298	86.54 297	88.89 304	96.05 215	76.11 317	94.39 128	88.51 331	81.37 286	88.27 323	96.75 126	72.38 316	95.52 332	65.71 372	95.47 299	95.03 299

tfpn200view9		87.05 299	86.52 298	88.67 308	95.77 232	72.94 343	91.89 216	86.00 350	90.84 135	92.61 238	89.80 338	63.93 351	98.28 214	71.27 358	96.54 277	94.79 305

thres400		87.20 295	86.52 298	89.24 301	95.77 232	72.94 343	91.89 216	86.00 350	90.84 135	92.61 238	89.80 338	63.93 351	98.28 214	71.27 358	96.54 277	96.51 247

WTY-MVS		86.93 301	86.50 300	88.24 315	94.96 259	74.64 327	87.19 323	92.07 309	78.29 313	88.32 322	91.59 318	78.06 288	94.27 352	74.88 338	93.15 340	95.80 278

1314		86.46 304	86.33 301	86.87 329	91.65 334	74.54 329	91.94 213	94.10 269	74.28 334	84.78 348	87.33 361	83.03 244	95.00 344	78.72 313	91.16 359	91.06 359

cascas		87.02 300	86.28 302	89.25 300	91.56 336	76.45 314	84.33 356	96.78 176	71.01 351	86.89 337	85.91 368	81.35 263	96.94 298	83.09 269	95.60 295	94.35 316

Patchmatch-test		86.10 306	86.01 303	86.38 334	90.63 346	74.22 335	89.57 282	86.69 344	85.73 238	89.81 298	92.83 292	65.24 346	91.04 369	77.82 320	95.78 292	93.88 328

HY-MVS	82.50 18	86.81 302	85.93 304	89.47 293	93.63 299	77.93 291	94.02 141	91.58 316	75.68 326	83.64 355	93.64 273	77.40 293	97.42 281	71.70 355	92.07 353	93.05 343

CHOSEN 1792x2688		87.19 296	85.92 305	91.00 257	97.13 145	79.41 269	84.51 354	95.60 225	64.14 372	90.07 291	94.81 232	78.26 287	97.14 292	73.34 345	95.38 303	96.46 252

CMPMVS	68.83 22	87.28 292	85.67 306	92.09 219	88.77 367	85.42 187	90.31 261	94.38 264	70.02 356	88.00 326	93.30 282	73.78 313	94.03 355	75.96 334	96.54 277	96.83 237
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HyFIR lowres test		87.19 296	85.51 307	92.24 211	97.12 146	80.51 244	85.03 348	96.06 211	66.11 368	91.66 264	92.98 290	70.12 324	99.14 92	75.29 336	95.23 306	97.07 226

thres200		85.85 307	85.18 308	87.88 321	94.44 281	72.52 347	89.08 295	86.21 347	88.57 189	91.44 266	88.40 354	64.22 349	98.00 239	68.35 366	95.88 291	93.12 340

CVMVSNet		85.16 311	84.72 309	86.48 330	92.12 326	70.19 357	92.32 196	88.17 336	56.15 379	90.64 281	95.85 179	67.97 330	96.69 307	88.78 189	90.52 361	92.56 348

PatchmatchNet		85.22 310	84.64 310	86.98 328	89.51 361	69.83 362	90.52 253	87.34 341	78.87 309	87.22 335	92.74 296	66.91 334	96.53 309	81.77 282	86.88 369	94.58 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		85.42 309	84.57 311	87.96 318	97.81 102	66.53 370	96.14 58	56.35 387	89.04 176	93.55 206	98.10 39	42.88 389	98.68 175	88.09 203	99.18 93	98.67 100

EPNet_dtu		85.63 308	84.37 312	89.40 296	86.30 377	74.33 333	91.64 228	88.26 333	84.84 255	72.96 380	89.85 336	71.27 321	97.69 268	76.60 329	97.62 242	96.18 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS		84.98 313	84.30 313	87.01 327	91.03 341	77.69 297	91.94 213	94.16 268	59.36 377	84.23 352	87.50 359	85.66 226	96.80 304	71.79 353	93.05 343	86.54 370

ET-MVSNet_ETH3D		86.15 305	84.27 314	91.79 226	93.04 309	81.28 236	87.17 324	86.14 348	79.57 299	83.65 354	88.66 351	57.10 369	98.18 225	87.74 211	95.40 301	95.90 275

tpm		84.38 316	84.08 315	85.30 340	90.47 349	63.43 380	89.34 288	85.63 354	77.24 320	87.62 330	95.03 224	61.00 364	97.30 287	79.26 310	91.09 360	95.16 295

tpmvs		84.22 317	83.97 316	84.94 342	87.09 374	65.18 373	91.21 237	88.35 332	82.87 274	85.21 343	90.96 326	65.24 346	96.75 305	79.60 308	85.25 371	92.90 345

MDTV_nov1_ep13			83.88 317		89.42 362	61.52 381	88.74 303	87.41 340	73.99 336	84.96 347	94.01 262	65.25 345	95.53 331	78.02 316	93.16 339

PMMVS2		81.31 334	83.44 318	74.92 362	90.52 348	46.49 387	69.19 376	85.23 361	84.30 260	87.95 327	94.71 239	76.95 300	84.36 379	64.07 373	98.09 216	93.89 327

FPMVS		84.50 315	83.28 319	88.16 316	96.32 193	94.49 18	85.76 343	85.47 356	83.09 270	85.20 344	94.26 251	63.79 353	86.58 377	63.72 374	91.88 356	83.40 373

test-LLR		83.58 319	83.17 320	84.79 344	89.68 358	66.86 368	83.08 361	84.52 363	83.07 271	82.85 360	84.78 371	62.86 357	93.49 358	82.85 270	94.86 311	94.03 322

JIA-IIPM		85.08 312	83.04 321	91.19 251	87.56 370	86.14 176	89.40 287	84.44 365	88.98 178	82.20 364	97.95 48	56.82 371	96.15 321	76.55 330	83.45 374	91.30 357

thisisatest0515		84.72 314	82.99 322	89.90 288	92.96 311	75.33 325	84.36 355	83.42 368	77.37 318	88.27 323	86.65 362	53.94 376	98.72 165	82.56 274	97.40 250	95.67 285

tpmrst		82.85 325	82.93 323	82.64 353	87.65 369	58.99 383	90.14 267	87.90 338	75.54 328	83.93 353	91.63 317	66.79 337	95.36 338	81.21 288	81.54 377	93.57 337

PVSNet	76.22 20	82.89 324	82.37 324	84.48 346	93.96 292	64.38 378	78.60 371	88.61 330	71.50 348	84.43 351	86.36 366	74.27 310	94.60 346	69.87 364	93.69 334	94.46 313

CostFormer		83.09 322	82.21 325	85.73 336	89.27 363	67.01 366	90.35 259	86.47 346	70.42 354	83.52 357	93.23 285	61.18 362	96.85 302	77.21 325	88.26 367	93.34 339

ADS-MVSNet2		84.01 318	82.20 326	89.41 295	89.04 364	76.37 316	87.57 314	90.98 320	72.71 344	84.46 349	92.45 301	68.08 328	96.48 312	70.58 362	83.97 372	95.38 292

DSMNet-mixed		82.21 328	81.56 327	84.16 348	89.57 360	70.00 361	90.65 250	77.66 382	54.99 380	83.30 358	97.57 66	77.89 290	90.50 371	66.86 370	95.54 297	91.97 352

ADS-MVSNet		82.25 327	81.55 328	84.34 347	89.04 364	65.30 372	87.57 314	85.13 362	72.71 344	84.46 349	92.45 301	68.08 328	92.33 364	70.58 362	83.97 372	95.38 292

baseline2		83.38 320	81.54 329	88.90 303	91.38 337	72.84 345	88.78 301	81.22 373	78.97 307	79.82 373	87.56 357	61.73 361	97.80 258	74.30 341	90.05 363	96.05 268

test0.0.03 1		82.48 326	81.47 330	85.48 338	89.70 357	73.57 339	84.73 350	81.64 372	83.07 271	88.13 325	86.61 363	62.86 357	89.10 376	66.24 371	90.29 362	93.77 330

PMMVS		83.00 323	81.11 331	88.66 309	83.81 384	86.44 166	82.24 365	85.65 353	61.75 376	82.07 365	85.64 369	79.75 274	91.59 367	75.99 333	93.09 341	87.94 369

IB-MVS	77.21 19	83.11 321	81.05 332	89.29 298	91.15 340	75.85 320	85.66 344	86.00 350	79.70 297	82.02 367	86.61 363	48.26 382	98.39 205	77.84 318	92.22 351	93.63 333
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
gg-mvs-nofinetune		82.10 331	81.02 333	85.34 339	87.46 372	71.04 353	94.74 114	67.56 384	96.44 22	79.43 374	98.99 6	45.24 383	96.15 321	67.18 369	92.17 352	88.85 365

new_pmnet		81.22 335	81.01 334	81.86 355	90.92 344	70.15 358	84.03 357	80.25 377	70.83 352	85.97 341	89.78 341	67.93 331	84.65 378	67.44 368	91.90 355	90.78 360

E-PMN		80.72 340	80.86 335	80.29 358	85.11 380	68.77 364	72.96 373	81.97 371	87.76 206	83.25 359	83.01 375	62.22 360	89.17 375	77.15 326	94.31 325	82.93 374

KD-MVS_2432*1600		82.17 329	80.75 336	86.42 332	82.04 385	70.09 359	81.75 366	90.80 321	82.56 276	90.37 285	89.30 347	42.90 387	96.11 323	74.47 339	92.55 348	93.06 341

miper_refine_blended		82.17 329	80.75 336	86.42 332	82.04 385	70.09 359	81.75 366	90.80 321	82.56 276	90.37 285	89.30 347	42.90 387	96.11 323	74.47 339	92.55 348	93.06 341

MVS-HIRNet		78.83 346	80.60 338	73.51 363	93.07 307	47.37 386	87.10 325	78.00 381	68.94 360	77.53 376	97.26 90	71.45 320	94.62 345	63.28 375	88.74 365	78.55 378

EPMVS		81.17 337	80.37 339	83.58 350	85.58 379	65.08 375	90.31 261	71.34 383	77.31 319	85.80 342	91.30 320	59.38 366	92.70 363	79.99 299	82.34 376	92.96 344

tpm2		81.46 333	80.35 340	84.80 343	89.90 355	65.14 374	90.44 255	85.36 357	65.82 370	82.05 366	92.44 303	57.94 368	96.69 307	70.71 361	88.49 366	92.56 348

EMVS		80.35 342	80.28 341	80.54 357	84.73 382	69.07 363	72.54 375	80.73 374	87.80 204	81.66 369	81.73 376	62.89 356	89.84 373	75.79 335	94.65 318	82.71 375

PAPM		81.91 332	80.11 342	87.31 326	93.87 295	72.32 349	84.02 358	93.22 284	69.47 359	76.13 378	89.84 337	72.15 317	97.23 289	53.27 380	89.02 364	92.37 350

test-mter		81.21 336	80.01 343	84.79 344	89.68 358	66.86 368	83.08 361	84.52 363	73.85 337	82.85 360	84.78 371	43.66 386	93.49 358	82.85 270	94.86 311	94.03 322

tpm cat1		80.61 341	79.46 344	84.07 349	88.78 366	65.06 376	89.26 291	88.23 334	62.27 375	81.90 368	89.66 344	62.70 359	95.29 341	71.72 354	80.60 378	91.86 355

pmmvs3		80.83 339	78.96 345	86.45 331	87.23 373	77.48 299	84.87 349	82.31 370	63.83 373	85.03 345	89.50 345	49.66 380	93.10 360	73.12 348	95.10 308	88.78 367

dp		79.28 344	78.62 346	81.24 356	85.97 378	56.45 384	86.91 328	85.26 360	72.97 342	81.45 370	89.17 350	56.01 373	95.45 336	73.19 347	76.68 379	91.82 356

TESTMET0.1,1		79.09 345	78.04 347	82.25 354	87.52 371	64.03 379	83.08 361	80.62 375	70.28 355	80.16 372	83.22 374	44.13 385	90.56 370	79.95 300	93.36 335	92.15 351

CHOSEN 280x420		80.04 343	77.97 348	86.23 335	90.13 353	74.53 330	72.87 374	89.59 327	66.38 367	76.29 377	85.32 370	56.96 370	95.36 338	69.49 365	94.72 316	88.79 366

EGC-MVSNET		80.97 338	75.73 349	96.67 46	98.85 25	94.55 17	96.83 22	96.60 186	2.44 384	5.32 385	98.25 34	92.24 112	98.02 237	91.85 109	99.21 89	97.45 210

PVSNet_0	70.34 21	74.58 347	72.96 350	79.47 359	90.63 346	66.24 371	73.26 372	83.40 369	63.67 374	78.02 375	78.35 378	72.53 315	89.59 374	56.68 378	60.05 382	82.57 376

MVE	59.87 23	73.86 348	72.65 351	77.47 361	87.00 376	74.35 332	61.37 378	60.93 386	67.27 365	69.69 381	86.49 365	81.24 267	72.33 382	56.45 379	83.45 374	85.74 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 349	48.94 352	54.93 364	39.68 388	12.38 390	28.59 379	90.09 325	6.82 382	41.10 384	78.41 377	54.41 375	70.69 383	50.12 381	51.26 383	81.72 377

tmp_tt		37.97 350	44.33 353	18.88 366	11.80 389	21.54 389	63.51 377	45.66 390	4.23 383	51.34 383	50.48 381	59.08 367	22.11 385	44.50 382	68.35 381	13.00 381

cdsmvs_eth3d_5k		23.35 351	31.13 354	0.00 369	0.00 392	0.00 393	0.00 380	95.58 231	0.00 387	0.00 388	91.15 322	93.43 81	0.00 388	0.00 386	0.00 386	0.00 384

test123		9.49 352	12.01 355	1.91 367	2.87 390	1.30 391	82.38 364	1.34 392	1.36 385	2.84 386	6.56 384	2.45 390	0.97 386	2.73 384	5.56 384	3.47 382

testmvs		9.02 353	11.42 356	1.81 368	2.77 391	1.13 392	79.44 370	1.90 391	1.18 386	2.65 387	6.80 383	1.95 391	0.87 387	2.62 385	3.45 385	3.44 383

pcd_1.5k_mvsjas		7.56 354	10.09 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	90.77 149	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		7.56 354	10.08 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	90.69 331	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

FOURS1					99.21 3	94.68 14	98.45 4	98.81 8	97.73 6	98.27 20

MSC_two_6792asdad				95.90 70	96.54 176	89.57 96		96.87 170					99.41 40	94.06 33	99.30 69	98.72 96

PC_three_1452									75.31 331	95.87 121	95.75 188	92.93 97	96.34 320	87.18 220	98.68 153	98.04 155

No_MVS				95.90 70	96.54 176	89.57 96		96.87 170					99.41 40	94.06 33	99.30 69	98.72 96

test_one_0601					98.26 72	87.14 146		98.18 39	94.25 50	96.99 66	97.36 82	95.13 42

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					97.23 137	90.32 83		97.54 113	84.40 259	94.78 170	95.79 184	92.76 103	99.39 52	88.72 192	98.40 176

IU-MVS					98.51 51	86.66 160		96.83 173	72.74 343	95.83 122				93.00 80	99.29 72	98.64 106

OPU-MVS				95.15 104	96.84 158	89.43 100	95.21 95				95.66 191	93.12 91	98.06 232	86.28 238	98.61 158	97.95 168

test_241102_TWO								98.10 52	91.95 97	97.54 39	97.25 91	95.37 30	99.35 63	93.29 67	99.25 81	98.49 120

test_241102_ONE					98.51 51	86.97 151		98.10 52	91.85 103	97.63 34	97.03 105	96.48 11	98.95 124

save fliter					97.46 129	88.05 129	92.04 207	97.08 152	87.63 210

test_0728_THIRD									93.26 72	97.40 49	97.35 85	94.69 57	99.34 66	93.88 37	99.42 51	98.89 73

test_0728_SECOND				94.88 112	98.55 44	86.72 157	95.20 97	98.22 35					99.38 58	93.44 59	99.31 67	98.53 117

test0726					98.51 51	86.69 158	95.34 90	98.18 39	91.85 103	97.63 34	97.37 79	95.58 24

GSMVS																94.75 307

test_part2					98.21 76	89.41 101				96.72 77

sam_mvs1												66.64 338				94.75 307

sam_mvs												66.41 339

ambc				92.98 183	96.88 155	83.01 218	95.92 69	96.38 198		96.41 87	97.48 73	88.26 184	97.80 258	89.96 160	98.93 124	98.12 150

MTGPA								97.62 105

test_post1							90.21 263				5.85 386	65.36 344	96.00 326	79.61 306

test_post											6.07 385	65.74 343	95.84 328

patchmatchnet-post											91.71 315	66.22 341	97.59 271

GG-mvs-BLEND				83.24 352	85.06 381	71.03 354	94.99 108	65.55 385		74.09 379	75.51 379	44.57 384	94.46 348	59.57 377	87.54 368	84.24 372

MTMP							94.82 111	54.62 388

gm-plane-assit					87.08 375	59.33 382			71.22 349		83.58 373		97.20 290	73.95 342

test9_res														88.16 201	98.40 176	97.83 182

TEST9					96.45 183	89.46 98	90.60 251	96.92 164	79.09 306	90.49 282	94.39 248	91.31 135	98.88 131

test_8					96.37 185	89.14 105	90.51 254	96.89 167	79.37 301	90.42 284	94.36 250	91.20 141	98.82 142

agg_prior2														87.06 223	98.36 187	97.98 164

agg_prior					96.20 202	88.89 111		96.88 168		90.21 287			98.78 155

TestCases				96.00 61	98.02 91	92.17 54		98.43 14	90.48 145	95.04 160	96.74 127	92.54 108	97.86 253	85.11 250	98.98 115	97.98 164

test_prior4						89.91 89	90.74 247

test_prior2							90.21 263		89.33 170	90.77 277	94.81 232	90.41 159		88.21 197	98.55 163

test_prior				94.61 124	95.95 223	87.23 143		97.36 129					98.68 175			97.93 170

旧先验2							90.00 272		68.65 361	92.71 236			96.52 310	85.15 247

新几何2							90.02 271

新几何1				93.17 180	97.16 142	87.29 141		94.43 262	67.95 363	91.29 268	94.94 227	86.97 208	98.23 220	81.06 291	97.75 233	93.98 325

旧先验1					96.20 202	84.17 202		94.82 251			95.57 197	89.57 173			97.89 229	96.32 257

无先验							89.94 273	95.75 222	70.81 353				98.59 187	81.17 289		94.81 303

原ACMM2							89.34 288

原ACMM1				92.87 191	96.91 154	84.22 200		97.01 156	76.84 323	89.64 302	94.46 245	88.00 190	98.70 171	81.53 284	98.01 223	95.70 284

test222					96.95 150	85.27 189	88.83 300	93.61 276	65.09 371	90.74 279	94.85 231	84.62 233			97.36 251	93.91 326

testdata2													98.03 234	80.24 296

segment_acmp												92.14 115

testdata				91.03 254	96.87 156	82.01 225		94.28 266	71.55 347	92.46 243	95.42 206	85.65 227	97.38 286	82.64 273	97.27 253	93.70 332

testdata1							88.96 297		88.44 191

test12				94.43 139	95.95 223	86.75 156		96.24 203		89.76 300		89.79 172	98.79 151		97.95 226	97.75 191

plane_prior7					97.71 110	88.68 115

plane_prior6					97.21 140	88.23 126						86.93 209

plane_prior5								97.81 93					98.95 124	89.26 177	98.51 170	98.60 113

plane_prior4											95.59 193

plane_prior3						88.43 124			90.35 150	93.31 211

plane_prior2							94.56 123		91.74 114

plane_prior1					97.38 132

plane_prior						88.12 127	93.01 166		88.98 178						98.06 218

n2								0.00 393

nn								0.00 393

door-mid								92.13 308

lessismore_v0				93.87 159	98.05 86	83.77 208		80.32 376		97.13 57	97.91 53	77.49 292	99.11 100	92.62 90	98.08 217	98.74 93

LGP-MVS_train				96.84 42	98.36 67	92.13 56		98.25 30	91.78 110	97.07 59	97.22 94	96.38 13	99.28 76	92.07 101	99.59 29	99.11 45

test11								96.65 184

door								91.26 317

HQP5-MVS						84.89 192

HQP-NCC					96.36 187		91.37 232		87.16 217	88.81 311

ACMP_Plane					96.36 187		91.37 232		87.16 217	88.81 311

BP-MVS														86.55 232

HQP4-MVS										88.81 311			98.61 183			98.15 146

HQP3-MVS								97.31 134							97.73 234

HQP2-MVS												84.76 231

NP-MVS					96.82 160	87.10 147					93.40 280

MDTV_nov1_ep13_2view						42.48 388	88.45 308		67.22 366	83.56 356		66.80 335		72.86 349		94.06 321

ACMMP++_ref															98.82 138

ACMMP++															99.25 81

Test By Simon												90.61 155

ITE_SJBPF				95.95 64	97.34 134	93.36 44		96.55 191	91.93 99	94.82 168	95.39 209	91.99 119	97.08 294	85.53 243	97.96 225	97.41 213

DeepMVS_CX				53.83 365	70.38 387	64.56 377		48.52 389	33.01 381	65.50 382	74.21 380	56.19 372	46.64 384	38.45 383	70.07 380	50.30 380

High-resolution multi-view benchmark