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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 32	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 12	85.07 49	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 2	96.46 2	90.58 7	92.86 44	96.29 16	88.16 34	94.17 91	86.07 40	98.48 18	97.22 17

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 63	85.72 31	96.79 1	95.51 6	88.86 13	95.63 7	96.99 8	84.81 69	93.16 134	91.10 1	97.53 71	96.58 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
abl_6		93.02 4	93.16 4	92.60 4	94.73 42	88.99 7	93.26 10	94.19 27	89.11 11	94.43 15	95.27 36	91.86 3	95.09 60	87.54 18	98.02 38	93.71 108

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 15	85.81 30	92.99 11	94.23 23	85.21 31	92.51 51	95.13 40	90.65 10	95.34 49	88.06 9	98.15 33	95.95 40

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 53	88.95 8	92.87 12	94.16 28	88.75 15	93.79 28	94.43 61	88.83 24	95.51 41	87.16 25	97.60 64	92.73 143

test1172		92.40 7	92.41 7	92.37 6	94.68 43	89.04 6	91.98 29	93.62 52	90.14 10	93.63 35	94.16 76	88.83 24	95.51 41	87.11 27	97.54 70	92.54 153

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 38	87.85 11	92.51 22	93.87 44	88.20 20	93.24 38	94.02 82	90.15 17	95.67 30	86.82 29	97.34 77	92.19 168

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 25	84.67 41	93.51 6	94.85 14	82.88 54	91.77 65	93.94 91	90.55 13	95.73 27	88.50 7	98.23 29	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 78	85.17 34	92.47 24	95.05 13	87.65 23	93.21 39	94.39 67	90.09 18	95.08 61	86.67 30	97.60 64	94.18 88

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 64	86.15 21	93.37 8	95.10 12	90.28 8	92.11 57	95.03 42	89.75 21	94.93 65	79.95 103	98.27 27	95.04 62
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 12	91.87 17	92.03 13	95.53 26	85.91 25	93.35 9	94.16 28	82.52 58	92.39 54	94.14 77	89.15 23	95.62 31	87.35 20	98.24 28	94.56 72
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
mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 12	88.48 10	92.72 16	92.60 94	83.09 51	91.54 67	94.25 73	87.67 42	95.51 41	87.21 24	98.11 34	93.12 129

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 31	87.62 12	93.38 7	93.36 60	83.16 50	91.06 76	94.00 83	88.26 31	95.71 28	87.28 23	98.39 21	92.55 152

XVS		91.54 15	91.36 26	92.08 10	95.64 23	86.25 19	92.64 17	93.33 62	85.07 32	89.99 93	94.03 81	86.57 55	95.80 21	87.35 20	97.62 62	94.20 86

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 17	92.02 28	91.81 114	84.07 37	92.00 60	94.40 65	86.63 53	95.28 52	88.59 5	98.31 24	92.30 160

UA-Net		91.49 17	91.53 22	91.39 26	94.98 35	82.95 54	93.52 5	92.79 89	88.22 19	88.53 126	97.64 2	83.45 81	94.55 80	86.02 43	98.60 13	96.67 26

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 19	85.88 27	92.58 20	93.25 70	81.99 63	91.40 70	94.17 75	87.51 43	95.87 15	87.74 11	97.76 54	93.99 94

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 40	81.69 59	90.00 51	94.27 20	82.35 60	93.67 33	94.82 48	91.18 5	95.52 39	85.36 47	98.73 7	95.23 58

region2R		91.44 20	91.30 32	91.87 19	95.75 18	85.90 26	92.63 19	93.30 67	81.91 65	90.88 81	94.21 74	87.75 40	95.87 15	87.60 16	97.71 58	93.83 101

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 28	84.66 42	92.58 20	93.29 68	81.99 63	91.47 68	93.96 87	88.35 29	95.56 34	87.74 11	97.74 56	92.85 138

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 17	88.94 76	91.81 114	84.07 37	92.00 60	94.40 65	86.63 53	95.28 52	88.59 5	98.31 24	92.30 160

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 20	83.05 52	92.18 26	94.22 24	80.14 85	91.29 73	93.97 84	87.93 39	95.87 15	88.65 4	97.96 45	94.12 91

APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 80	78.04 90	92.84 14	94.14 32	83.33 48	93.90 24	95.73 25	88.77 26	96.41 1	87.60 16	97.98 42	92.98 134

PGM-MVS		91.20 25	90.95 41	91.93 15	95.67 22	85.85 28	90.00 51	93.90 41	80.32 82	91.74 66	94.41 64	88.17 33	95.98 9	86.37 33	97.99 40	93.96 96

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 59	80.97 66	91.49 35	93.48 58	82.82 55	92.60 50	93.97 84	88.19 32	96.29 4	87.61 15	98.20 32	94.39 81
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 27	90.91 42	91.83 21	96.18 11	86.88 14	92.20 25	93.03 80	82.59 57	88.52 127	94.37 68	86.74 52	95.41 47	86.32 34	98.21 30	93.19 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 28	91.01 38	90.82 38	95.45 27	82.73 55	91.75 33	93.74 47	80.98 76	91.38 71	93.80 94	87.20 47	95.80 21	87.10 28	97.69 59	93.93 97

MP-MVS-pluss		90.81 29	91.08 35	89.99 51	95.97 13	79.88 72	88.13 89	94.51 18	75.79 137	92.94 41	94.96 43	88.36 28	95.01 63	90.70 2	98.40 20	95.09 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 30	91.50 23	88.44 77	93.00 79	76.26 116	89.65 62	95.55 5	87.72 22	93.89 26	94.94 44	91.62 4	93.44 124	78.35 121	98.76 4	95.61 47

ACMMP_NAP		90.65 31	91.07 37	89.42 60	95.93 15	79.54 77	89.95 54	93.68 51	77.65 113	91.97 62	94.89 45	88.38 27	95.45 45	89.27 3	97.87 50	93.27 123

ACMM	79.39 9	90.65 31	90.99 39	89.63 56	95.03 34	83.53 47	89.62 63	93.35 61	79.20 96	93.83 27	93.60 101	90.81 8	92.96 140	85.02 51	98.45 19	92.41 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 33	90.34 49	91.38 27	89.03 175	84.23 45	93.58 4	94.68 16	90.65 6	90.33 88	93.95 90	84.50 71	95.37 48	80.87 93	95.50 137	94.53 75

ACMP	79.16 10	90.54 34	90.60 47	90.35 46	94.36 45	80.98 65	89.16 72	94.05 35	79.03 99	92.87 43	93.74 98	90.60 12	95.21 57	82.87 73	98.76 4	94.87 63
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 35	91.08 35	88.88 66	93.38 70	78.65 86	89.15 73	94.05 35	84.68 35	93.90 24	94.11 79	88.13 35	96.30 3	84.51 57	97.81 52	91.70 183
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
#test#		90.49 36	90.31 50	91.02 33	95.43 28	84.66 42	90.65 41	93.29 68	77.00 120	91.47 68	93.96 87	88.35 29	95.56 34	84.88 52	97.74 56	92.85 138

SED-MVS		90.46 37	91.64 19	86.93 94	94.18 48	72.65 134	90.47 46	93.69 49	83.77 41	94.11 22	94.27 69	90.28 15	95.84 19	86.03 41	97.92 46	92.29 162

SMA-MVS		90.31 38	90.48 48	89.83 52	95.31 30	79.52 78	90.98 39	93.24 71	75.37 144	92.84 45	95.28 35	85.58 65	96.09 7	87.92 10	97.76 54	93.88 99
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
SF-MVS		90.27 39	90.80 44	88.68 72	92.86 84	77.09 105	91.19 38	95.74 3	81.38 71	92.28 55	93.80 94	86.89 50	94.64 74	85.52 45	97.51 72	94.30 84

v7n		90.13 40	90.96 40	87.65 89	91.95 109	71.06 159	89.99 53	93.05 77	86.53 26	94.29 18	96.27 17	82.69 88	94.08 95	86.25 37	97.63 61	97.82 8

PMVS	80.48 6	90.08 41	90.66 46	88.34 80	96.71 3	92.97 1	90.31 48	89.57 173	88.51 18	90.11 90	95.12 41	90.98 7	88.92 241	77.55 135	97.07 84	83.13 306
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS		90.06 42	91.32 30	86.29 107	94.16 51	72.56 139	90.54 43	91.01 135	83.61 44	93.75 30	94.65 53	89.76 19	95.78 24	86.42 31	97.97 43	90.55 210
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
PS-CasMVS		90.06 42	91.92 13	84.47 144	96.56 7	58.83 276	89.04 74	92.74 91	91.40 4	96.12 3	96.06 22	87.23 46	95.57 33	79.42 111	98.74 6	99.00 2

PEN-MVS		90.03 44	91.88 16	84.48 143	96.57 6	58.88 273	88.95 75	93.19 72	91.62 3	96.01 5	96.16 20	87.02 48	95.60 32	78.69 117	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 45	89.74 54	90.83 37	93.16 76	80.37 69	91.91 32	93.11 74	81.10 74	95.32 9	97.24 5	72.94 197	94.85 68	85.07 49	97.78 53	97.26 15

DTE-MVSNet		89.98 46	91.91 15	84.21 151	96.51 8	57.84 281	88.93 77	92.84 88	91.92 2	96.16 2	96.23 18	86.95 49	95.99 8	79.05 114	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 46	89.84 53	90.41 44	94.91 37	84.50 44	89.49 68	93.98 37	79.68 89	92.09 58	93.89 92	83.80 77	93.10 138	82.67 75	98.04 35	93.64 113

3Dnovator+	83.92 2	89.97 48	89.66 55	90.92 36	91.27 132	81.66 62	91.25 36	94.13 33	88.89 12	88.83 121	94.26 72	77.55 146	95.86 18	84.88 52	95.87 125	95.24 57

WR-MVS_H		89.91 49	91.31 31	85.71 123	96.32 10	62.39 234	89.54 66	93.31 65	90.21 9	95.57 8	95.66 28	81.42 112	95.90 13	80.94 92	98.80 3	98.84 5

OPM-MVS		89.80 50	89.97 51	89.27 62	94.76 39	79.86 73	86.76 112	92.78 90	78.78 102	92.51 51	93.64 100	88.13 35	93.84 105	84.83 54	97.55 67	94.10 92

mvs_tets		89.78 51	89.27 62	91.30 28	93.51 66	84.79 39	89.89 56	90.63 143	70.00 210	94.55 14	96.67 11	87.94 38	93.59 116	84.27 59	95.97 121	95.52 48

anonymousdsp		89.73 52	88.88 69	92.27 9	89.82 166	86.67 15	90.51 45	90.20 160	69.87 211	95.06 10	96.14 21	84.28 73	93.07 139	87.68 13	96.34 109	97.09 19

test_djsdf		89.62 53	89.01 65	91.45 25	92.36 95	82.98 53	91.98 29	90.08 163	71.54 191	94.28 20	96.54 13	81.57 110	94.27 82	86.26 35	96.49 104	97.09 19

XVG-OURS-SEG-HR		89.59 54	89.37 60	90.28 47	94.47 44	85.95 24	86.84 108	93.91 40	80.07 86	86.75 158	93.26 104	93.64 2	90.93 197	84.60 56	90.75 249	93.97 95

APD-MVS		89.54 55	89.63 56	89.26 63	92.57 89	81.34 64	90.19 49	93.08 76	80.87 77	91.13 74	93.19 105	86.22 60	95.97 10	82.23 79	97.18 82	90.45 212
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		89.51 56	89.48 59	89.59 58	92.26 99	80.80 67	90.14 50	93.54 56	83.37 47	90.57 85	92.55 127	84.99 67	96.15 5	81.26 87	96.61 99	91.83 179

jajsoiax		89.41 57	88.81 71	91.19 32	93.38 70	84.72 40	89.70 58	90.29 157	69.27 214	94.39 16	96.38 15	86.02 63	93.52 120	83.96 61	95.92 123	95.34 52

CPTT-MVS		89.39 58	88.98 67	90.63 41	95.09 33	86.95 13	92.09 27	92.30 100	79.74 88	87.50 143	92.38 130	81.42 112	93.28 129	83.07 70	97.24 80	91.67 184

ACMH	76.49 14	89.34 59	91.14 34	83.96 157	92.50 92	70.36 164	89.55 64	93.84 45	81.89 66	94.70 12	95.44 33	90.69 9	88.31 251	83.33 68	98.30 26	93.20 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 60	90.91 42	84.37 145	96.34 9	58.61 278	88.66 84	92.06 105	90.78 5	95.67 6	95.17 39	81.80 108	95.54 38	79.00 115	98.69 10	98.95 4

XVG-OURS		89.18 61	88.83 70	90.23 48	94.28 46	86.11 23	85.91 123	93.60 55	80.16 84	89.13 117	93.44 102	83.82 76	90.98 195	83.86 63	95.30 145	93.60 115

DeepC-MVS	82.31 4	89.15 62	89.08 64	89.37 61	93.64 65	79.07 81	88.54 85	94.20 25	73.53 162	89.71 102	94.82 48	85.09 66	95.77 26	84.17 60	98.03 37	93.26 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 63	90.72 45	84.31 149	97.00 2	64.33 212	89.67 61	88.38 190	88.84 14	94.29 18	97.57 3	90.48 14	91.26 187	72.57 186	97.65 60	97.34 14

MSP-MVS		89.08 64	88.16 75	91.83 21	95.76 17	86.14 22	92.75 15	93.90 41	78.43 107	89.16 116	92.25 137	72.03 209	96.36 2	88.21 8	90.93 244	92.98 134
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
xxxxxxxxxxxxxcwj		89.04 65	89.13 63	88.79 68	93.75 61	77.44 98	86.31 120	95.27 10	70.80 199	92.28 55	93.80 94	86.89 50	94.64 74	85.52 45	97.51 72	94.30 84

SD-MVS		88.96 66	89.88 52	86.22 110	91.63 119	77.07 106	89.82 57	93.77 46	78.90 100	92.88 42	92.29 135	86.11 61	90.22 219	86.24 38	97.24 80	91.36 191
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
HPM-MVS++		88.93 67	88.45 74	90.38 45	94.92 36	85.85 28	89.70 58	91.27 128	78.20 109	86.69 161	92.28 136	80.36 124	95.06 62	86.17 39	96.49 104	90.22 215

ETH3D-3000-0.1		88.85 68	88.96 68	88.52 73	91.94 111	77.27 104	88.71 82	95.26 11	76.08 128	90.66 84	92.69 122	84.48 72	93.83 106	83.38 67	97.48 74	94.47 76

test_0402		88.65 69	89.58 58	85.88 119	92.55 90	72.22 147	84.01 153	89.44 175	88.63 17	94.38 17	95.77 24	86.38 59	93.59 116	79.84 104	95.21 146	91.82 180

DP-MVS		88.60 70	89.01 65	87.36 92	91.30 130	77.50 97	87.55 96	92.97 83	87.95 21	89.62 106	92.87 116	84.56 70	93.89 102	77.65 133	96.62 98	90.70 204

Anonymous20231211		88.40 71	89.62 57	84.73 139	90.46 154	65.27 203	88.86 78	93.02 81	87.15 24	93.05 40	97.10 6	82.28 96	92.02 167	76.70 143	97.99 40	96.88 23

PS-MVSNAJss		88.31 72	87.90 77	89.56 59	93.31 72	77.96 91	87.94 92	91.97 108	70.73 201	94.19 21	96.67 11	76.94 155	94.57 78	83.07 70	96.28 111	96.15 32

OMC-MVS		88.19 73	87.52 82	90.19 49	91.94 111	81.68 61	87.49 99	93.17 73	76.02 131	88.64 124	91.22 159	84.24 74	93.37 127	77.97 131	97.03 85	95.52 48

TSAR-MVS + MP.		88.14 74	87.82 78	89.09 65	95.72 21	76.74 110	92.49 23	91.19 131	67.85 232	86.63 162	94.84 47	79.58 130	95.96 11	87.62 14	94.50 169	94.56 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RPSCF		88.00 75	86.93 92	91.22 31	90.08 160	89.30 5	89.68 60	91.11 132	79.26 95	89.68 103	94.81 51	82.44 91	87.74 255	76.54 145	88.74 271	96.61 28

AllTest		87.97 76	87.40 85	89.68 54	91.59 120	83.40 48	89.50 67	95.44 7	79.47 91	88.00 136	93.03 108	82.66 89	91.47 179	70.81 195	96.14 117	94.16 89

TranMVSNet+NR-MVSNet		87.86 77	88.76 72	85.18 131	94.02 56	64.13 213	84.38 148	91.29 127	84.88 34	92.06 59	93.84 93	86.45 57	93.73 108	73.22 177	98.66 11	97.69 9

nrg030		87.85 78	88.49 73	85.91 117	90.07 162	69.73 167	87.86 93	94.20 25	74.04 156	92.70 49	94.66 52	85.88 64	91.50 178	79.72 105	97.32 78	96.50 30

ETH3D cwj APD-0.16		87.83 79	87.62 81	88.47 75	91.21 133	78.20 88	87.26 101	94.54 17	72.05 187	88.89 118	92.31 134	83.86 75	94.24 85	81.59 86	96.87 89	92.97 137

CNVR-MVS		87.81 80	87.68 80	88.21 82	92.87 82	77.30 103	85.25 133	91.23 129	77.31 117	87.07 151	91.47 156	82.94 86	94.71 71	84.67 55	96.27 113	92.62 150

HQP_MVS		87.75 81	87.43 84	88.70 71	93.45 67	76.42 114	89.45 69	93.61 53	79.44 93	86.55 163	92.95 113	74.84 172	95.22 55	80.78 95	95.83 126	94.46 77

NCCC		87.36 82	86.87 93	88.83 67	92.32 98	78.84 84	86.58 116	91.09 133	78.77 103	84.85 193	90.89 173	80.85 118	95.29 50	81.14 89	95.32 142	92.34 158

DeepPCF-MVS	81.24 5	87.28 83	86.21 102	90.49 43	91.48 127	84.90 37	83.41 173	92.38 99	70.25 207	89.35 114	90.68 180	82.85 87	94.57 78	79.55 107	95.95 122	92.00 173

SixPastTwentyTwo		87.20 84	87.45 83	86.45 103	92.52 91	69.19 176	87.84 94	88.05 196	81.66 68	94.64 13	96.53 14	65.94 237	94.75 70	83.02 72	96.83 92	95.41 50

test_part1		87.15 85	87.82 78	85.15 132	88.88 179	63.04 224	87.98 90	94.85 14	82.52 58	93.61 36	95.73 25	67.51 228	95.71 28	80.48 100	98.83 2	96.69 25

UniMVSNet (Re)		86.87 86	86.98 91	86.55 101	93.11 77	68.48 180	83.80 162	92.87 85	80.37 80	89.61 108	91.81 148	77.72 143	94.18 89	75.00 161	98.53 16	96.99 22

Vis-MVSNet		86.86 87	86.58 96	87.72 87	92.09 105	77.43 100	87.35 100	92.09 104	78.87 101	84.27 208	94.05 80	78.35 138	93.65 110	80.54 99	91.58 232	92.08 170
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 114	92.86 84	67.02 190	82.55 196	91.56 118	83.08 52	90.92 78	91.82 147	78.25 139	93.99 97	74.16 165	98.35 22	97.49 13

DU-MVS		86.80 89	86.99 90	86.21 112	93.24 74	67.02 190	83.16 182	92.21 101	81.73 67	90.92 78	91.97 141	77.20 149	93.99 97	74.16 165	98.35 22	97.61 10

Regformer-2		86.74 90	86.08 104	88.73 69	84.18 263	79.20 80	83.52 168	89.33 177	83.33 48	89.92 97	85.07 275	83.23 84	93.16 134	83.39 66	92.72 211	93.83 101

IS-MVSNet		86.66 91	86.82 95	86.17 114	92.05 107	66.87 192	91.21 37	88.64 186	86.30 28	89.60 109	92.59 124	69.22 220	94.91 66	73.89 169	97.89 49	96.72 24

v10		86.54 92	87.10 87	84.84 136	88.16 193	63.28 221	86.64 115	92.20 102	75.42 143	92.81 47	94.50 57	74.05 182	94.06 96	83.88 62	96.28 111	97.17 18

pmmvs6		86.52 93	88.06 76	81.90 198	92.22 102	62.28 237	84.66 141	89.15 179	83.54 46	89.85 98	97.32 4	88.08 37	86.80 267	70.43 203	97.30 79	96.62 27

Regformer-4		86.41 94	85.71 112	88.52 73	84.27 259	77.57 96	84.07 151	88.00 198	82.82 55	89.84 99	85.48 263	82.06 100	92.77 146	83.83 64	91.04 238	95.22 60

PHI-MVS		86.38 95	85.81 109	88.08 83	88.44 187	77.34 101	89.35 71	93.05 77	73.15 173	84.76 194	87.70 229	78.87 134	94.18 89	80.67 97	96.29 110	92.73 143

test_prior3		86.31 96	86.31 99	86.32 105	90.59 151	71.99 149	83.37 174	92.85 86	75.43 141	84.58 197	91.57 152	81.92 106	94.17 91	79.54 108	96.97 86	92.80 140

CSCG		86.26 97	86.47 97	85.60 125	90.87 144	74.26 125	87.98 90	91.85 111	80.35 81	89.54 112	88.01 223	79.09 132	92.13 161	75.51 154	95.06 153	90.41 213

DeepC-MVS_fast	80.27 8	86.23 98	85.65 114	87.96 86	91.30 130	76.92 107	87.19 102	91.99 107	70.56 202	84.96 189	90.69 179	80.01 127	95.14 58	78.37 120	95.78 130	91.82 180
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 99	86.83 94	84.36 147	87.82 197	62.35 236	86.42 118	91.33 126	76.78 122	92.73 48	94.48 59	73.41 191	93.72 109	83.10 69	95.41 138	97.01 21

Anonymous20240529		86.20 100	87.13 86	83.42 170	90.19 158	64.55 210	84.55 143	90.71 140	85.85 29	89.94 96	95.24 38	82.13 98	90.40 214	69.19 214	96.40 107	95.31 54

CDPH-MVS		86.17 101	85.54 115	88.05 85	92.25 100	75.45 119	83.85 159	92.01 106	65.91 247	86.19 170	91.75 150	83.77 78	94.98 64	77.43 138	96.71 96	93.73 107

Regformer-1		86.00 102	85.50 116	87.49 90	84.18 263	76.90 108	83.52 168	87.94 200	82.18 62	89.19 115	85.07 275	82.28 96	91.89 171	82.40 77	92.72 211	93.69 109

NR-MVSNet		86.00 102	86.22 101	85.34 129	93.24 74	64.56 209	82.21 208	90.46 146	80.99 75	88.42 129	91.97 141	77.56 145	93.85 103	72.46 187	98.65 12	97.61 10

train_agg		85.98 104	85.28 120	88.07 84	92.34 96	79.70 75	83.94 155	90.32 151	65.79 248	84.49 199	90.97 169	81.93 104	93.63 112	81.21 88	96.54 102	90.88 199

FC-MVSNet-test		85.93 105	87.05 89	82.58 189	92.25 100	56.44 292	85.75 127	93.09 75	77.33 116	91.94 63	94.65 53	74.78 174	93.41 126	75.11 160	98.58 14	97.88 7

Effi-MVS+-dtu		85.82 106	83.38 153	93.14 3	87.13 212	91.15 2	87.70 95	88.42 188	74.57 151	83.56 216	85.65 260	78.49 136	94.21 87	72.04 189	92.88 206	94.05 93

agg_prior1		85.72 107	85.20 121	87.28 93	91.58 123	77.69 94	83.69 165	90.30 154	66.29 244	84.32 203	91.07 166	82.13 98	93.18 132	81.02 90	96.36 108	90.98 195

TAPA-MVS	77.73 12	85.71 108	84.83 127	88.37 79	88.78 181	79.72 74	87.15 104	93.50 57	69.17 215	85.80 180	89.56 201	80.76 119	92.13 161	73.21 182	95.51 136	93.25 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 109	86.14 103	83.58 167	87.97 194	67.13 188	87.55 96	94.32 19	73.44 164	88.47 128	87.54 232	86.45 57	91.06 194	75.76 153	93.76 185	92.54 153

EPP-MVSNet		85.47 110	85.04 123	86.77 98	91.52 126	69.37 170	91.63 34	87.98 199	81.51 70	87.05 152	91.83 146	66.18 236	95.29 50	70.75 198	96.89 88	95.64 45

GeoE		85.45 111	85.81 109	84.37 145	90.08 160	67.07 189	85.86 126	91.39 125	72.33 184	87.59 141	90.25 190	84.85 68	92.37 155	78.00 129	91.94 226	93.66 110

FIs		85.35 112	86.27 100	82.60 188	91.86 114	57.31 285	85.10 135	93.05 77	75.83 136	91.02 77	93.97 84	73.57 187	92.91 144	73.97 168	98.02 38	97.58 12

casdiffmvs		85.21 113	85.85 108	83.31 172	86.17 236	62.77 228	83.03 184	93.93 39	74.69 150	88.21 134	92.68 123	82.29 95	91.89 171	77.87 132	93.75 187	95.27 56

baseline		85.20 114	85.93 106	83.02 177	86.30 231	62.37 235	84.55 143	93.96 38	74.48 153	87.12 147	92.03 140	82.30 94	91.94 168	78.39 119	94.21 176	94.74 68

K. test v3		85.14 115	84.73 128	86.37 104	91.13 138	69.63 169	85.45 131	76.68 294	84.06 39	92.44 53	96.99 8	62.03 256	94.65 73	80.58 98	93.24 196	94.83 67

EI-MVSNet-Vis-set		85.12 116	84.53 136	86.88 95	84.01 266	72.76 133	83.91 158	85.18 238	80.44 79	88.75 122	85.49 262	80.08 126	91.92 169	82.02 80	90.85 247	95.97 38

ETH3 D test6400		85.09 117	84.87 126	85.75 122	90.80 146	69.34 171	85.90 124	93.31 65	65.43 254	86.11 173	89.95 196	80.92 117	94.86 67	75.90 152	95.57 135	93.05 131

CS-MVS		85.09 117	85.49 117	83.90 159	86.75 223	65.28 202	87.53 98	95.66 4	76.39 124	81.90 238	86.80 244	80.98 116	95.23 54	79.09 113	94.36 173	91.17 193

Regformer-3		85.06 119	84.67 133	86.22 110	84.27 259	73.43 129	84.07 151	85.26 236	80.77 78	88.62 125	85.48 263	80.56 122	90.39 215	81.99 81	91.04 238	94.85 65

EI-MVSNet-UG-set		85.04 120	84.44 138	86.85 96	83.87 269	72.52 141	83.82 160	85.15 239	80.27 83	88.75 122	85.45 266	79.95 128	91.90 170	81.92 83	90.80 248	96.13 33

X-MVStestdata		85.04 120	82.70 163	92.08 10	95.64 23	86.25 19	92.64 17	93.33 62	85.07 32	89.99 93	16.05 363	86.57 55	95.80 21	87.35 20	97.62 62	94.20 86

MSLP-MVS++		85.00 122	86.03 105	81.90 198	91.84 115	71.56 157	86.75 113	93.02 81	75.95 134	87.12 147	89.39 202	77.98 140	89.40 237	77.46 136	94.78 162	84.75 283

F-COLMAP		84.97 123	83.42 152	89.63 56	92.39 94	83.40 48	88.83 79	91.92 110	73.19 172	80.18 265	89.15 208	77.04 153	93.28 129	65.82 241	92.28 217	92.21 167

3Dnovator	80.37 7	84.80 124	84.71 131	85.06 134	86.36 229	74.71 122	88.77 81	90.00 165	75.65 139	84.96 189	93.17 106	74.06 181	91.19 189	78.28 123	91.09 236	89.29 231

IterMVS-LS		84.73 125	84.98 124	83.96 157	87.35 207	63.66 216	83.25 178	89.88 167	76.06 129	89.62 106	92.37 133	73.40 193	92.52 151	78.16 126	94.77 164	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 126	84.34 142	85.49 128	90.18 159	75.86 118	79.23 252	87.13 210	73.35 165	85.56 184	89.34 203	83.60 80	90.50 212	76.64 144	94.05 181	90.09 220

HQP-MVS		84.61 127	84.06 145	86.27 108	91.19 134	70.66 161	84.77 137	92.68 92	73.30 168	80.55 259	90.17 194	72.10 205	94.61 76	77.30 139	94.47 170	93.56 117

v1192		84.57 128	84.69 132	84.21 151	87.75 199	62.88 226	83.02 185	91.43 122	69.08 217	89.98 95	90.89 173	72.70 201	93.62 115	82.41 76	94.97 157	96.13 33

mvs-test1		84.55 129	82.12 172	91.84 20	87.13 212	89.54 4	85.05 136	88.42 188	74.57 151	80.60 256	82.98 297	78.49 136	93.98 99	72.04 189	89.77 258	92.00 173

FMVSNet1		84.55 129	85.45 118	81.85 200	90.27 157	61.05 248	86.83 109	88.27 193	78.57 106	89.66 105	95.64 29	75.43 166	90.68 207	69.09 215	95.33 141	93.82 103

v1144		84.54 131	84.72 130	84.00 155	87.67 201	62.55 232	82.97 186	90.93 136	70.32 206	89.80 100	90.99 168	73.50 188	93.48 122	81.69 85	94.65 167	95.97 38

Gipuma		84.44 132	86.33 98	78.78 245	84.20 262	73.57 128	89.55 64	90.44 147	84.24 36	84.38 201	94.89 45	76.35 164	80.40 315	76.14 149	96.80 94	82.36 314
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 133	83.93 148	85.63 124	91.59 120	71.58 156	83.52 168	92.13 103	61.82 275	83.96 210	89.75 200	79.93 129	93.46 123	78.33 122	94.34 174	91.87 178

VDDNet		84.35 134	85.39 119	81.25 208	95.13 32	59.32 266	85.42 132	81.11 268	86.41 27	87.41 144	96.21 19	73.61 186	90.61 210	66.33 235	96.85 90	93.81 106

ETV-MVS		84.31 135	83.91 149	85.52 126	88.58 183	70.40 163	84.50 147	93.37 59	78.76 104	84.07 209	78.72 333	80.39 123	95.13 59	73.82 171	92.98 204	91.04 194

v1240		84.30 136	84.51 137	83.65 165	87.65 202	61.26 245	82.85 189	91.54 119	67.94 230	90.68 83	90.65 182	71.71 211	93.64 111	82.84 74	94.78 162	96.07 35

MVS_111021_LR		84.28 137	83.76 150	85.83 121	89.23 172	83.07 51	80.99 226	83.56 253	72.71 178	86.07 174	89.07 210	81.75 109	86.19 277	77.11 141	93.36 192	88.24 243

hse-mvs3		84.25 138	82.76 162	88.72 70	91.82 117	82.60 56	84.00 154	84.98 245	71.27 193	86.70 159	90.55 184	63.04 252	93.92 101	78.26 124	94.20 177	89.63 222

v144192		84.24 139	84.41 139	83.71 164	87.59 204	61.57 241	82.95 187	91.03 134	67.82 233	89.80 100	90.49 185	73.28 194	93.51 121	81.88 84	94.89 160	96.04 37

v1921920		84.23 140	84.37 141	83.79 161	87.64 203	61.71 240	82.91 188	91.20 130	67.94 230	90.06 91	90.34 187	72.04 208	93.59 116	82.32 78	94.91 158	96.07 35

VDD-MVS		84.23 140	84.58 135	83.20 174	91.17 137	65.16 205	83.25 178	84.97 246	79.79 87	87.18 146	94.27 69	74.77 175	90.89 200	69.24 211	96.54 102	93.55 119

v2v482		84.09 142	84.24 143	83.62 166	87.13 212	61.40 242	82.71 192	89.71 169	72.19 186	89.55 110	91.41 157	70.70 216	93.20 131	81.02 90	93.76 185	96.25 31

EG-PatchMatch MVS		84.08 143	84.11 144	83.98 156	92.22 102	72.61 138	82.20 210	87.02 215	72.63 179	88.86 119	91.02 167	78.52 135	91.11 192	73.41 176	91.09 236	88.21 244

DP-MVS Recon		84.05 144	83.22 155	86.52 102	91.73 118	75.27 120	83.23 180	92.40 97	72.04 188	82.04 236	88.33 219	77.91 142	93.95 100	66.17 236	95.12 151	90.34 214

TransMVSNet (Re)		84.02 145	85.74 111	78.85 244	91.00 141	55.20 302	82.29 204	87.26 206	79.65 90	88.38 131	95.52 32	83.00 85	86.88 265	67.97 226	96.60 100	94.45 79

Baseline_NR-MVSNet		84.00 146	85.90 107	78.29 256	91.47 128	53.44 311	82.29 204	87.00 218	79.06 98	89.55 110	95.72 27	77.20 149	86.14 278	72.30 188	98.51 17	95.28 55

TSAR-MVS + GP.		83.95 147	82.69 164	87.72 87	89.27 171	81.45 63	83.72 164	81.58 267	74.73 149	85.66 181	86.06 255	72.56 203	92.69 148	75.44 156	95.21 146	89.01 239

alignmvs		83.94 148	83.98 147	83.80 160	87.80 198	67.88 185	84.54 145	91.42 124	73.27 171	88.41 130	87.96 224	72.33 204	90.83 202	76.02 151	94.11 179	92.69 147

Effi-MVS+		83.90 149	84.01 146	83.57 168	87.22 210	65.61 201	86.55 117	92.40 97	78.64 105	81.34 250	84.18 286	83.65 79	92.93 142	74.22 164	87.87 281	92.17 169

CANet		83.79 150	82.85 161	86.63 99	86.17 236	72.21 148	83.76 163	91.43 122	77.24 118	74.39 308	87.45 234	75.36 167	95.42 46	77.03 142	92.83 207	92.25 166

pm-mvs1		83.69 151	84.95 125	79.91 230	90.04 164	59.66 263	82.43 200	87.44 203	75.52 140	87.85 138	95.26 37	81.25 114	85.65 284	68.74 219	96.04 120	94.42 80

AdaColmap		83.66 152	83.69 151	83.57 168	90.05 163	72.26 146	86.29 122	90.00 165	78.19 110	81.65 245	87.16 239	83.40 82	94.24 85	61.69 266	94.76 165	84.21 288

MIMVSNet1		83.63 153	84.59 134	80.74 218	94.06 55	62.77 228	82.72 191	84.53 249	77.57 115	90.34 87	95.92 23	76.88 161	85.83 282	61.88 264	97.42 75	93.62 114

WR-MVS		83.56 154	84.40 140	81.06 213	93.43 69	54.88 303	78.67 259	85.02 243	81.24 72	90.74 82	91.56 154	72.85 198	91.08 193	68.00 225	98.04 35	97.23 16

CNLPA		83.55 155	83.10 159	84.90 135	89.34 170	83.87 46	84.54 145	88.77 183	79.09 97	83.54 217	88.66 216	74.87 171	81.73 309	66.84 232	92.29 216	89.11 233

LCM-MVSNet-Re		83.48 156	85.06 122	78.75 246	85.94 239	55.75 297	80.05 236	94.27 20	76.47 123	96.09 4	94.54 56	83.31 83	89.75 232	59.95 278	94.89 160	90.75 202

hse-mvs2		83.47 157	81.81 177	88.47 75	91.03 140	82.27 57	82.61 193	83.69 251	71.27 193	86.70 159	86.05 256	63.04 252	92.41 153	78.26 124	93.62 191	90.71 203

V42		83.47 157	83.37 154	83.75 163	83.16 275	63.33 220	81.31 220	90.23 159	69.51 213	90.91 80	90.81 176	74.16 180	92.29 159	80.06 101	90.22 255	95.62 46

VPA-MVSNet		83.47 157	84.73 128	79.69 235	90.29 156	57.52 284	81.30 222	88.69 185	76.29 125	87.58 142	94.44 60	80.60 121	87.20 260	66.60 234	96.82 93	94.34 83

RRT_MVS		83.25 160	81.08 188	89.74 53	80.55 303	79.32 79	86.41 119	86.69 219	72.33 184	87.00 153	91.08 164	44.98 337	95.55 37	84.47 58	96.24 114	94.36 82

PAPM_NR		83.23 161	83.19 157	83.33 171	90.90 143	65.98 198	88.19 88	90.78 139	78.13 111	80.87 254	87.92 227	73.49 190	92.42 152	70.07 205	88.40 272	91.60 186

CLD-MVS		83.18 162	82.64 165	84.79 137	89.05 174	67.82 186	77.93 267	92.52 95	68.33 224	85.07 188	81.54 314	82.06 100	92.96 140	69.35 210	97.91 48	93.57 116
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 163	85.68 113	75.65 283	81.24 290	45.26 350	79.94 238	92.91 84	83.83 40	91.33 72	96.88 10	80.25 125	85.92 280	68.89 217	95.89 124	95.76 42

114514_t		83.10 164	82.54 168	84.77 138	92.90 81	69.10 178	86.65 114	90.62 144	54.66 315	81.46 247	90.81 176	76.98 154	94.38 81	72.62 185	96.18 115	90.82 201

UGNet		82.78 165	81.64 179	86.21 112	86.20 235	76.24 117	86.86 107	85.68 230	77.07 119	73.76 311	92.82 117	69.64 217	91.82 174	69.04 216	93.69 188	90.56 209
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 166	81.93 176	85.19 130	82.08 281	80.15 71	85.53 130	88.76 184	68.01 227	85.58 183	87.75 228	71.80 210	86.85 266	74.02 167	93.87 184	88.58 242

EI-MVSNet		82.61 167	82.42 170	83.20 174	83.25 273	63.66 216	83.50 171	85.07 240	76.06 129	86.55 163	85.10 272	73.41 191	90.25 216	78.15 128	90.67 251	95.68 44

QAPM		82.59 168	82.59 167	82.58 189	86.44 224	66.69 194	89.94 55	90.36 150	67.97 229	84.94 191	92.58 126	72.71 200	92.18 160	70.63 201	87.73 283	88.85 240

Fast-Effi-MVS+-dtu		82.54 169	81.41 183	85.90 118	85.60 241	76.53 113	83.07 183	89.62 172	73.02 175	79.11 273	83.51 291	80.74 120	90.24 218	68.76 218	89.29 262	90.94 197

MVS_Test		82.47 170	83.22 155	80.22 227	82.62 280	57.75 283	82.54 197	91.96 109	71.16 197	82.89 225	92.52 129	77.41 147	90.50 212	80.04 102	87.84 282	92.40 157

v148		82.31 171	82.48 169	81.81 203	85.59 242	59.66 263	81.47 218	86.02 226	72.85 176	88.05 135	90.65 182	70.73 215	90.91 199	75.15 159	91.79 227	94.87 63

API-MVS		82.28 172	82.61 166	81.30 207	86.29 232	69.79 165	88.71 82	87.67 202	78.42 108	82.15 235	84.15 287	77.98 140	91.59 177	65.39 243	92.75 208	82.51 313

MVSFormer		82.23 173	81.57 182	84.19 153	85.54 243	69.26 173	91.98 29	90.08 163	71.54 191	76.23 292	85.07 275	58.69 277	94.27 82	86.26 35	88.77 269	89.03 237

EIA-MVS		82.19 174	81.23 186	85.10 133	87.95 195	69.17 177	83.22 181	93.33 62	70.42 203	78.58 276	79.77 330	77.29 148	94.20 88	71.51 192	88.96 267	91.93 177

PCF-MVS	74.62 15	82.15 175	80.92 191	85.84 120	89.43 168	72.30 145	80.53 231	91.82 113	57.36 304	87.81 139	89.92 198	77.67 144	93.63 112	58.69 283	95.08 152	91.58 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 176	80.31 197	87.45 91	90.86 145	80.29 70	85.88 125	90.65 142	68.17 226	76.32 291	86.33 250	73.12 196	92.61 150	61.40 270	90.02 257	89.44 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 177	82.07 173	81.85 200	86.38 226	61.05 248	86.83 109	88.27 193	72.43 180	86.00 175	95.64 29	63.78 246	90.68 207	65.95 237	93.34 193	93.82 103

test1		82.02 177	82.07 173	81.85 200	86.38 226	61.05 248	86.83 109	88.27 193	72.43 180	86.00 175	95.64 29	63.78 246	90.68 207	65.95 237	93.34 193	93.82 103

OpenMVS	76.72 13	81.98 179	82.00 175	81.93 197	84.42 255	68.22 182	88.50 86	89.48 174	66.92 239	81.80 243	91.86 143	72.59 202	90.16 221	71.19 194	91.25 235	87.40 256

DIV-MVS_2432*1600		81.93 180	83.14 158	78.30 255	84.75 250	52.75 315	80.37 233	89.42 176	70.24 208	90.26 89	93.39 103	74.55 179	86.77 268	68.61 221	96.64 97	95.38 51

tfpnnormal		81.79 181	82.95 160	78.31 254	88.93 178	55.40 298	80.83 229	82.85 257	76.81 121	85.90 179	94.14 77	74.58 178	86.51 272	66.82 233	95.68 134	93.01 133

cl_fuxian		81.64 182	81.59 181	81.79 204	80.86 296	59.15 270	78.61 260	90.18 161	68.36 223	87.20 145	87.11 241	69.39 218	91.62 176	78.16 126	94.43 172	94.60 71

PVSNet_Blended_VisFu		81.55 183	80.49 195	84.70 141	91.58 123	73.24 131	84.21 149	91.67 117	62.86 268	80.94 252	87.16 239	67.27 230	92.87 145	69.82 207	88.94 268	87.99 248

DELS-MVS		81.44 184	81.25 184	82.03 196	84.27 259	62.87 227	76.47 289	92.49 96	70.97 198	81.64 246	83.83 288	75.03 169	92.70 147	74.29 163	92.22 220	90.51 211
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
FMVSNet2		81.31 185	81.61 180	80.41 224	86.38 226	58.75 277	83.93 157	86.58 221	72.43 180	87.65 140	92.98 110	63.78 246	90.22 219	66.86 230	93.92 183	92.27 164

TinyColmap		81.25 186	82.34 171	77.99 261	85.33 245	60.68 255	82.32 203	88.33 191	71.26 195	86.97 154	92.22 139	77.10 152	86.98 264	62.37 259	95.17 148	86.31 267

AUN-MVS		81.18 187	78.78 214	88.39 78	90.93 142	82.14 58	82.51 198	83.67 252	64.69 262	80.29 262	85.91 259	51.07 307	92.38 154	76.29 148	93.63 190	90.65 207

tttt0517		81.07 188	79.58 209	85.52 126	88.99 177	66.45 196	87.03 106	75.51 302	73.76 160	88.32 133	90.20 191	37.96 353	94.16 94	79.36 112	95.13 149	95.93 41

Fast-Effi-MVS+		81.04 189	80.57 192	82.46 193	87.50 205	63.22 222	78.37 263	89.63 171	68.01 227	81.87 239	82.08 309	82.31 93	92.65 149	67.10 229	88.30 277	91.51 189

BH-untuned		80.96 190	80.99 189	80.84 217	88.55 184	68.23 181	80.33 234	88.46 187	72.79 177	86.55 163	86.76 245	74.72 176	91.77 175	61.79 265	88.99 266	82.52 312

1121		80.86 191	79.81 208	84.02 154	93.93 58	78.70 85	81.64 215	80.18 275	55.43 312	83.67 213	91.15 162	71.29 213	91.41 184	67.95 227	93.06 201	81.96 318

eth_miper_zixun_eth		80.84 192	80.22 201	82.71 186	81.41 288	60.98 251	77.81 269	90.14 162	67.31 237	86.95 155	87.24 238	64.26 242	92.31 157	75.23 158	91.61 230	94.85 65

xiu_mvs_v1_base_debu		80.84 192	80.14 203	82.93 180	88.31 188	71.73 152	79.53 243	87.17 207	65.43 254	79.59 267	82.73 304	76.94 155	90.14 224	73.22 177	88.33 273	86.90 262

xiu_mvs_v1_base		80.84 192	80.14 203	82.93 180	88.31 188	71.73 152	79.53 243	87.17 207	65.43 254	79.59 267	82.73 304	76.94 155	90.14 224	73.22 177	88.33 273	86.90 262

xiu_mvs_v1_base_debi		80.84 192	80.14 203	82.93 180	88.31 188	71.73 152	79.53 243	87.17 207	65.43 254	79.59 267	82.73 304	76.94 155	90.14 224	73.22 177	88.33 273	86.90 262

IterMVS-SCA-FT		80.64 196	79.41 210	84.34 148	83.93 267	69.66 168	76.28 291	81.09 269	72.43 180	86.47 169	90.19 192	60.46 262	93.15 136	77.45 137	86.39 294	90.22 215

BH-RMVSNet		80.53 197	80.22 201	81.49 206	87.19 211	66.21 197	77.79 270	86.23 223	74.21 155	83.69 212	88.50 217	73.25 195	90.75 204	63.18 256	87.90 280	87.52 254

Anonymous202405211		80.51 198	81.19 187	78.49 251	88.48 185	57.26 286	76.63 285	82.49 259	81.21 73	84.30 206	92.24 138	67.99 226	86.24 276	62.22 260	95.13 149	91.98 176

cl-mvsnet1		80.43 199	80.23 199	81.02 214	79.99 306	59.25 267	77.07 280	87.02 215	67.38 235	86.19 170	89.22 205	63.09 250	90.16 221	76.32 146	95.80 128	93.66 110

cl-mvsnet____		80.42 200	80.23 199	81.02 214	79.99 306	59.25 267	77.07 280	87.02 215	67.37 236	86.18 172	89.21 206	63.08 251	90.16 221	76.31 147	95.80 128	93.65 112

diffmvs		80.40 201	80.48 196	80.17 228	79.02 318	60.04 259	77.54 274	90.28 158	66.65 242	82.40 230	87.33 237	73.50 188	87.35 259	77.98 130	89.62 260	93.13 128

EPNet		80.37 202	78.41 220	86.23 109	76.75 329	73.28 130	87.18 103	77.45 289	76.24 127	68.14 331	88.93 212	65.41 239	93.85 103	69.47 209	96.12 119	91.55 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 203	80.04 206	81.24 210	79.82 308	58.95 272	77.66 271	89.66 170	65.75 251	85.99 178	85.11 271	68.29 225	91.42 183	76.03 150	92.03 222	93.33 120

MG-MVS		80.32 204	80.94 190	78.47 252	88.18 191	52.62 318	82.29 204	85.01 244	72.01 189	79.24 272	92.54 128	69.36 219	93.36 128	70.65 200	89.19 265	89.45 225

VPNet		80.25 205	81.68 178	75.94 282	92.46 93	47.98 342	76.70 284	81.67 266	73.45 163	84.87 192	92.82 117	74.66 177	86.51 272	61.66 267	96.85 90	93.33 120

MAR-MVS		80.24 206	78.74 216	84.73 139	86.87 222	78.18 89	85.75 127	87.81 201	65.67 253	77.84 281	78.50 334	73.79 185	90.53 211	61.59 269	90.87 246	85.49 276
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
PM-MVS		80.20 207	79.00 213	83.78 162	88.17 192	86.66 16	81.31 220	66.81 345	69.64 212	88.33 132	90.19 192	64.58 240	83.63 301	71.99 191	90.03 256	81.06 332

Anonymous20240521		80.18 208	81.25 184	76.95 270	83.15 276	60.84 253	82.46 199	85.99 227	68.76 221	86.78 156	93.73 99	59.13 274	77.44 322	73.71 172	97.55 67	92.56 151

LFMVS		80.15 209	80.56 193	78.89 243	89.19 173	55.93 294	85.22 134	73.78 314	82.96 53	84.28 207	92.72 121	57.38 286	90.07 228	63.80 251	95.75 131	90.68 205

DPM-MVS		80.10 210	79.18 212	82.88 183	90.71 149	69.74 166	78.87 256	90.84 137	60.29 289	75.64 300	85.92 258	67.28 229	93.11 137	71.24 193	91.79 227	85.77 273

MSDG		80.06 211	79.99 207	80.25 226	83.91 268	68.04 184	77.51 275	89.19 178	77.65 113	81.94 237	83.45 293	76.37 163	86.31 275	63.31 255	86.59 291	86.41 265

ab-mvs		79.67 212	80.56 193	76.99 269	88.48 185	56.93 288	84.70 140	86.06 225	68.95 219	80.78 255	93.08 107	75.30 168	84.62 293	56.78 292	90.90 245	89.43 227

VNet		79.31 213	80.27 198	76.44 277	87.92 196	53.95 307	75.58 297	84.35 250	74.39 154	82.23 233	90.72 178	72.84 199	84.39 295	60.38 277	93.98 182	90.97 196

bset_n11_16_dypcd		79.19 214	77.97 224	82.86 184	85.81 240	66.85 193	75.02 302	79.31 279	66.07 245	83.50 218	83.37 296	55.04 300	92.10 164	78.63 118	94.99 156	89.63 222

thisisatest0530		79.07 215	77.33 231	84.26 150	87.13 212	64.58 208	83.66 166	75.95 297	68.86 220	85.22 187	87.36 236	38.10 351	93.57 119	75.47 155	94.28 175	94.62 69

cl-mvsnet2		78.97 216	78.21 222	81.24 210	77.74 322	59.01 271	77.46 277	87.13 210	65.79 248	84.32 203	85.10 272	58.96 276	90.88 201	75.36 157	92.03 222	93.84 100

RPMNet		78.88 217	78.28 221	80.68 221	79.58 309	62.64 230	82.58 194	94.16 28	74.80 148	75.72 298	92.59 124	48.69 313	95.56 34	73.48 175	82.91 321	83.85 293

PAPR		78.84 218	78.10 223	81.07 212	85.17 246	60.22 258	82.21 208	90.57 145	62.51 270	75.32 303	84.61 282	74.99 170	92.30 158	59.48 281	88.04 279	90.68 205

PVSNet_BlendedMVS		78.80 219	77.84 225	81.65 205	84.43 253	63.41 218	79.49 246	90.44 147	61.70 278	75.43 301	87.07 242	69.11 221	91.44 181	60.68 275	92.24 218	90.11 219

FMVSNet3		78.80 219	78.55 217	79.57 237	82.89 279	56.89 290	81.76 212	85.77 229	69.04 218	86.00 175	90.44 186	51.75 305	90.09 227	65.95 237	93.34 193	91.72 182

test_yl		78.71 221	78.51 218	79.32 240	84.32 257	58.84 274	78.38 261	85.33 234	75.99 132	82.49 228	86.57 246	58.01 280	90.02 229	62.74 257	92.73 209	89.10 234

DCV-MVSNet		78.71 221	78.51 218	79.32 240	84.32 257	58.84 274	78.38 261	85.33 234	75.99 132	82.49 228	86.57 246	58.01 280	90.02 229	62.74 257	92.73 209	89.10 234

pmmvs-eth3d		78.42 223	77.04 234	82.57 191	87.44 206	74.41 124	80.86 228	79.67 278	55.68 310	84.69 195	90.31 189	60.91 260	85.42 285	62.20 261	91.59 231	87.88 251

MVS_0304		78.17 224	77.23 232	80.99 216	84.13 265	69.07 179	81.39 219	80.81 271	76.28 126	67.53 336	89.11 209	62.87 254	86.77 268	60.90 274	92.01 225	87.13 259

mvs_anonymous		78.13 225	78.76 215	76.23 281	79.24 315	50.31 336	78.69 258	84.82 247	61.60 279	83.09 224	92.82 117	73.89 184	87.01 261	68.33 224	86.41 293	91.37 190

RRT_test8_iter05		78.08 226	77.52 227	79.75 233	80.84 297	52.54 319	80.61 230	88.96 181	67.77 234	84.62 196	89.29 204	33.89 358	92.10 164	77.59 134	94.15 178	94.62 69

TAMVS		78.08 226	76.36 240	83.23 173	90.62 150	72.87 132	79.08 253	80.01 277	61.72 277	81.35 249	86.92 243	63.96 245	88.78 245	50.61 325	93.01 203	88.04 247

miper_enhance_ethall		77.83 228	76.93 235	80.51 222	76.15 335	58.01 280	75.47 299	88.82 182	58.05 298	83.59 215	80.69 318	64.41 241	91.20 188	73.16 183	92.03 222	92.33 159

Vis-MVSNet (Re-imp)		77.82 229	77.79 226	77.92 262	88.82 180	51.29 329	83.28 176	71.97 327	74.04 156	82.23 233	89.78 199	57.38 286	89.41 236	57.22 291	95.41 138	93.05 131

CANet_DTU		77.81 230	77.05 233	80.09 229	81.37 289	59.90 261	83.26 177	88.29 192	69.16 216	67.83 334	83.72 289	60.93 259	89.47 233	69.22 213	89.70 259	90.88 199

OpenMVS_ROB	70.19 17	77.77 231	77.46 228	78.71 247	84.39 256	61.15 246	81.18 224	82.52 258	62.45 272	83.34 219	87.37 235	66.20 235	88.66 247	64.69 247	85.02 305	86.32 266

MDA-MVSNet-bldmvs		77.47 232	76.90 236	79.16 242	79.03 317	64.59 207	66.58 338	75.67 300	73.15 173	88.86 119	88.99 211	66.94 231	81.23 311	64.71 246	88.22 278	91.64 185

jason		77.42 233	75.75 246	82.43 194	87.10 216	69.27 172	77.99 266	81.94 264	51.47 334	77.84 281	85.07 275	60.32 264	89.00 239	70.74 199	89.27 264	89.03 237
jason: jason.
CDS-MVSNet		77.32 234	75.40 249	83.06 176	89.00 176	72.48 142	77.90 268	82.17 262	60.81 284	78.94 274	83.49 292	59.30 272	88.76 246	54.64 309	92.37 215	87.93 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 235	76.75 237	78.52 250	87.01 218	61.30 244	75.55 298	87.12 213	61.24 281	74.45 307	78.79 332	77.20 149	90.93 197	64.62 249	84.80 311	83.32 302

MVSTER		77.09 236	75.70 247	81.25 208	75.27 342	61.08 247	77.49 276	85.07 240	60.78 285	86.55 163	88.68 215	43.14 343	90.25 216	73.69 173	90.67 251	92.42 155

PS-MVSNAJ		77.04 237	76.53 239	78.56 249	87.09 217	61.40 242	75.26 300	87.13 210	61.25 280	74.38 309	77.22 341	76.94 155	90.94 196	64.63 248	84.83 310	83.35 301

IterMVS		76.91 238	76.34 241	78.64 248	80.91 294	64.03 214	76.30 290	79.03 282	64.88 261	83.11 222	89.16 207	59.90 268	84.46 294	68.61 221	85.15 304	87.42 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 239	75.67 248	80.34 225	80.48 304	62.16 239	73.50 312	84.80 248	57.61 302	82.24 232	87.54 232	51.31 306	87.65 256	70.40 204	93.19 198	91.23 192

CL-MVSNet_2432*1600		76.81 240	77.38 230	75.12 286	86.90 220	51.34 327	73.20 315	80.63 273	68.30 225	81.80 243	88.40 218	66.92 232	80.90 312	55.35 303	94.90 159	93.12 129

TR-MVS		76.77 241	75.79 245	79.72 234	86.10 238	65.79 200	77.14 278	83.02 255	65.20 259	81.40 248	82.10 308	66.30 234	90.73 206	55.57 300	85.27 302	82.65 308

USDC		76.63 242	76.73 238	76.34 279	83.46 271	57.20 287	80.02 237	88.04 197	52.14 330	83.65 214	91.25 158	63.24 249	86.65 271	54.66 308	94.11 179	85.17 278

BH-w/o		76.57 243	76.07 244	78.10 259	86.88 221	65.92 199	77.63 272	86.33 222	65.69 252	80.89 253	79.95 327	68.97 223	90.74 205	53.01 317	85.25 303	77.62 338

Patchmtry		76.56 244	77.46 228	73.83 292	79.37 314	46.60 347	82.41 201	76.90 291	73.81 159	85.56 184	92.38 130	48.07 315	83.98 298	63.36 254	95.31 144	90.92 198

PVSNet_Blended		76.49 245	75.40 249	79.76 232	84.43 253	63.41 218	75.14 301	90.44 147	57.36 304	75.43 301	78.30 335	69.11 221	91.44 181	60.68 275	87.70 284	84.42 286

miper_lstm_enhance		76.45 246	76.10 243	77.51 266	76.72 330	60.97 252	64.69 341	85.04 242	63.98 264	83.20 221	88.22 220	56.67 289	78.79 320	73.22 177	93.12 199	92.78 142

lupinMVS		76.37 247	74.46 257	82.09 195	85.54 243	69.26 173	76.79 282	80.77 272	50.68 340	76.23 292	82.82 302	58.69 277	88.94 240	69.85 206	88.77 269	88.07 245

cascas		76.29 248	74.81 253	80.72 220	84.47 252	62.94 225	73.89 310	87.34 204	55.94 309	75.16 305	76.53 344	63.97 244	91.16 190	65.00 244	90.97 243	88.06 246

thres600view7		75.97 249	75.35 251	77.85 264	87.01 218	51.84 325	80.45 232	73.26 318	75.20 145	83.10 223	86.31 252	45.54 328	89.05 238	55.03 306	92.24 218	92.66 148

GA-MVS		75.83 250	74.61 254	79.48 239	81.87 283	59.25 267	73.42 313	82.88 256	68.68 222	79.75 266	81.80 311	50.62 309	89.46 234	66.85 231	85.64 299	89.72 221

MVP-Stereo		75.81 251	73.51 266	82.71 186	89.35 169	73.62 127	80.06 235	85.20 237	60.30 288	73.96 310	87.94 225	57.89 284	89.45 235	52.02 320	74.87 347	85.06 280
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 252	75.05 252	76.66 276	87.27 208	51.88 324	81.07 225	73.26 318	75.68 138	83.25 220	86.37 249	45.54 328	88.80 242	51.98 321	90.99 240	89.31 229

ET-MVSNet_ETH3D		75.28 253	72.77 272	82.81 185	83.03 278	68.11 183	77.09 279	76.51 295	60.67 287	77.60 285	80.52 322	38.04 352	91.15 191	70.78 197	90.68 250	89.17 232

thres400		75.14 254	74.23 259	77.86 263	86.24 233	52.12 321	79.24 250	73.87 312	73.34 166	81.82 241	84.60 283	46.02 322	88.80 242	51.98 321	90.99 240	92.66 148

wuyk23d		75.13 255	79.30 211	62.63 331	75.56 338	75.18 121	80.89 227	73.10 320	75.06 147	94.76 11	95.32 34	87.73 41	52.85 359	34.16 358	97.11 83	59.85 355

EU-MVSNet		75.12 256	74.43 258	77.18 268	83.11 277	59.48 265	85.71 129	82.43 260	39.76 358	85.64 182	88.76 213	44.71 339	87.88 254	73.86 170	85.88 298	84.16 289

HyFIR lowres test		75.12 256	72.66 274	82.50 192	91.44 129	65.19 204	72.47 317	87.31 205	46.79 346	80.29 262	84.30 285	52.70 304	92.10 164	51.88 324	86.73 290	90.22 215

CMPMVS	59.41 20	75.12 256	73.57 264	79.77 231	75.84 337	67.22 187	81.21 223	82.18 261	50.78 338	76.50 288	87.66 230	55.20 298	82.99 303	62.17 263	90.64 254	89.09 236
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 259	72.98 271	80.73 219	84.95 247	71.71 155	76.23 292	77.59 288	52.83 324	77.73 284	86.38 248	56.35 292	84.97 289	57.72 290	87.05 288	85.51 275

tfpn200view9		74.86 260	74.23 259	76.74 275	86.24 233	52.12 321	79.24 250	73.87 312	73.34 166	81.82 241	84.60 283	46.02 322	88.80 242	51.98 321	90.99 240	89.31 229

1112_ss		74.82 261	73.74 262	78.04 260	89.57 167	60.04 259	76.49 288	87.09 214	54.31 316	73.66 312	79.80 328	60.25 265	86.76 270	58.37 284	84.15 314	87.32 257

ppachtmachnet_test		74.73 262	74.00 261	76.90 272	80.71 300	56.89 290	71.53 321	78.42 284	58.24 296	79.32 271	82.92 301	57.91 283	84.26 296	65.60 242	91.36 234	89.56 224

Patchmatch-RL test		74.48 263	73.68 263	76.89 273	84.83 249	66.54 195	72.29 318	69.16 338	57.70 300	86.76 157	86.33 250	45.79 327	82.59 304	69.63 208	90.65 253	81.54 323

PatchMatch-RL		74.48 263	73.22 268	78.27 257	87.70 200	85.26 33	75.92 294	70.09 335	64.34 263	76.09 294	81.25 316	65.87 238	78.07 321	53.86 311	83.82 315	71.48 346

XXY-MVS		74.44 265	76.19 242	69.21 312	84.61 251	52.43 320	71.70 320	77.18 290	60.73 286	80.60 256	90.96 171	75.44 165	69.35 340	56.13 296	88.33 273	85.86 272

CR-MVSNet		74.00 266	73.04 270	76.85 274	79.58 309	62.64 230	82.58 194	76.90 291	50.50 341	75.72 298	92.38 130	48.07 315	84.07 297	68.72 220	82.91 321	83.85 293

Test_1112_low_res		73.90 267	73.08 269	76.35 278	90.35 155	55.95 293	73.40 314	86.17 224	50.70 339	73.14 313	85.94 257	58.31 279	85.90 281	56.51 294	83.22 318	87.20 258

test20.03		73.75 268	74.59 256	71.22 305	81.11 292	51.12 331	70.15 326	72.10 326	70.42 203	80.28 264	91.50 155	64.21 243	74.72 332	46.96 341	94.58 168	87.82 253

SCA		73.32 269	72.57 276	75.58 284	81.62 285	55.86 295	78.89 255	71.37 332	61.73 276	74.93 306	83.42 294	60.46 262	87.01 261	58.11 288	82.63 325	83.88 290

baseline1		73.26 270	73.54 265	72.43 301	84.92 248	47.79 343	79.89 239	74.00 310	65.93 246	78.81 275	86.28 253	56.36 291	81.63 310	56.63 293	79.04 338	87.87 252

1314		73.22 271	72.56 277	75.20 285	80.41 305	57.84 281	81.64 215	85.36 233	51.68 333	73.10 314	76.65 343	61.45 258	85.19 287	63.54 252	79.21 337	82.59 309

MVS		73.21 272	72.59 275	75.06 287	80.97 293	60.81 254	81.64 215	85.92 228	46.03 349	71.68 320	77.54 337	68.47 224	89.77 231	55.70 299	85.39 300	74.60 343

HY-MVS	64.64 18	73.03 273	72.47 278	74.71 288	83.36 272	54.19 305	82.14 211	81.96 263	56.76 308	69.57 328	86.21 254	60.03 266	84.83 292	49.58 330	82.65 323	85.11 279

thisisatest0515		73.00 274	70.52 289	80.46 223	81.45 287	59.90 261	73.16 316	74.31 309	57.86 299	76.08 295	77.78 336	37.60 354	92.12 163	65.00 244	91.45 233	89.35 228

EPNet_dtu		72.87 275	71.33 287	77.49 267	77.72 323	60.55 256	82.35 202	75.79 298	66.49 243	58.39 359	81.06 317	53.68 302	85.98 279	53.55 312	92.97 205	85.95 270
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 276	71.41 286	76.28 280	83.25 273	60.34 257	83.50 171	79.02 283	37.77 359	76.33 290	85.10 272	49.60 312	87.41 258	70.54 202	77.54 343	81.08 330

CHOSEN 1792x2688		72.45 277	70.56 288	78.13 258	90.02 165	63.08 223	68.72 330	83.16 254	42.99 355	75.92 296	85.46 265	57.22 288	85.18 288	49.87 329	81.67 326	86.14 268

testgi		72.36 278	74.61 254	65.59 325	80.56 302	42.82 356	68.29 331	73.35 317	66.87 240	81.84 240	89.93 197	72.08 207	66.92 348	46.05 343	92.54 213	87.01 261

thres200		72.34 279	71.55 285	74.70 289	83.48 270	51.60 326	75.02 302	73.71 315	70.14 209	78.56 277	80.57 321	46.20 320	88.20 252	46.99 340	89.29 262	84.32 287

FPMVS		72.29 280	72.00 280	73.14 295	88.63 182	85.00 35	74.65 306	67.39 339	71.94 190	77.80 283	87.66 230	50.48 310	75.83 328	49.95 327	79.51 333	58.58 357

FMVSNet5		72.10 281	71.69 282	73.32 293	81.57 286	53.02 314	76.77 283	78.37 285	63.31 265	76.37 289	91.85 144	36.68 355	78.98 318	47.87 337	92.45 214	87.95 249

our_test_3		71.85 282	71.59 283	72.62 299	80.71 300	53.78 308	69.72 328	71.71 331	58.80 293	78.03 278	80.51 323	56.61 290	78.84 319	62.20 261	86.04 297	85.23 277

PAPM		71.77 283	70.06 294	76.92 271	86.39 225	53.97 306	76.62 286	86.62 220	53.44 321	63.97 349	84.73 281	57.79 285	92.34 156	39.65 353	81.33 329	84.45 285

IB-MVS	62.13 19	71.64 284	68.97 298	79.66 236	80.80 299	62.26 238	73.94 309	76.90 291	63.27 266	68.63 330	76.79 342	33.83 359	91.84 173	59.28 282	87.26 286	84.88 281
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
UnsupCasMVSNet_eth		71.63 285	72.30 279	69.62 310	76.47 332	52.70 317	70.03 327	80.97 270	59.18 292	79.36 270	88.21 221	60.50 261	69.12 341	58.33 286	77.62 342	87.04 260

Anonymous20231206		71.38 286	71.88 281	69.88 308	86.31 230	54.37 304	70.39 325	74.62 305	52.57 326	76.73 287	88.76 213	59.94 267	72.06 334	44.35 346	93.23 197	83.23 304

MIMVSNet		71.09 287	71.59 283	69.57 311	87.23 209	50.07 337	78.91 254	71.83 328	60.20 290	71.26 321	91.76 149	55.08 299	76.09 326	41.06 351	87.02 289	82.54 311

MS-PatchMatch		70.93 288	70.22 292	73.06 296	81.85 284	62.50 233	73.82 311	77.90 286	52.44 327	75.92 296	81.27 315	55.67 295	81.75 308	55.37 302	77.70 341	74.94 342

pmmvs5		70.73 289	70.07 293	72.72 297	77.03 328	52.73 316	74.14 307	75.65 301	50.36 342	72.17 318	85.37 269	55.42 297	80.67 314	52.86 318	87.59 285	84.77 282

PatchT		70.52 290	72.76 273	63.79 330	79.38 313	33.53 362	77.63 272	65.37 347	73.61 161	71.77 319	92.79 120	44.38 340	75.65 329	64.53 250	85.37 301	82.18 316

N_pmnet		70.20 291	68.80 300	74.38 290	80.91 294	84.81 38	59.12 351	76.45 296	55.06 313	75.31 304	82.36 307	55.74 294	54.82 358	47.02 339	87.24 287	83.52 297

tpmvs		70.16 292	69.56 296	71.96 303	74.71 346	48.13 340	79.63 241	75.45 303	65.02 260	70.26 325	81.88 310	45.34 333	85.68 283	58.34 285	75.39 346	82.08 317

new-patchmatchnet		70.10 293	73.37 267	60.29 338	81.23 291	16.95 366	59.54 349	74.62 305	62.93 267	80.97 251	87.93 226	62.83 255	71.90 335	55.24 304	95.01 155	92.00 173

YYNet1		70.06 294	70.44 290	68.90 313	73.76 348	53.42 312	58.99 352	67.20 341	58.42 295	87.10 149	85.39 268	59.82 269	67.32 345	59.79 279	83.50 317	85.96 269

MDA-MVSNet_test_wron		70.05 295	70.44 290	68.88 314	73.84 347	53.47 310	58.93 353	67.28 340	58.43 294	87.09 150	85.40 267	59.80 270	67.25 346	59.66 280	83.54 316	85.92 271

CostFormer		69.98 296	68.68 301	73.87 291	77.14 326	50.72 334	79.26 249	74.51 307	51.94 332	70.97 324	84.75 280	45.16 336	87.49 257	55.16 305	79.23 336	83.40 300

baseline2		69.77 297	66.89 307	78.41 253	79.51 311	58.09 279	76.23 292	69.57 337	57.50 303	64.82 347	77.45 339	46.02 322	88.44 248	53.08 314	77.83 340	88.70 241

PatchmatchNet		69.71 298	68.83 299	72.33 302	77.66 324	53.60 309	79.29 248	69.99 336	57.66 301	72.53 316	82.93 300	46.45 319	80.08 317	60.91 273	72.09 350	83.31 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 299	66.64 311	77.70 265	73.19 350	71.24 158	75.67 295	65.56 346	70.42 203	65.18 343	92.97 112	33.64 360	83.06 302	53.52 313	69.61 356	78.79 337

UnsupCasMVSNet_bld		69.21 300	69.68 295	67.82 319	79.42 312	51.15 330	67.82 335	75.79 298	54.15 317	77.47 286	85.36 270	59.26 273	70.64 337	48.46 334	79.35 335	81.66 321

gg-mvs-nofinetune		68.96 301	69.11 297	68.52 318	76.12 336	45.32 349	83.59 167	55.88 360	86.68 25	64.62 348	97.01 7	30.36 363	83.97 299	44.78 345	82.94 320	76.26 340

tpm2		68.45 302	66.83 308	73.30 294	78.93 319	48.50 339	79.76 240	71.76 329	47.50 345	69.92 327	83.60 290	42.07 345	88.40 249	48.44 335	79.51 333	83.01 307

tpm		67.95 303	68.08 304	67.55 320	78.74 320	43.53 354	75.60 296	67.10 344	54.92 314	72.23 317	88.10 222	42.87 344	75.97 327	52.21 319	80.95 332	83.15 305

WTY-MVS		67.91 304	68.35 302	66.58 323	80.82 298	48.12 341	65.96 339	72.60 322	53.67 320	71.20 322	81.68 313	58.97 275	69.06 342	48.57 333	81.67 326	82.55 310

test-LLR		67.21 305	66.74 309	68.63 316	76.45 333	55.21 300	67.89 332	67.14 342	62.43 273	65.08 344	72.39 349	43.41 341	69.37 338	61.00 271	84.89 308	81.31 325

sss		66.92 306	67.26 306	65.90 324	77.23 325	51.10 332	64.79 340	71.72 330	52.12 331	70.13 326	80.18 325	57.96 282	65.36 353	50.21 326	81.01 331	81.25 327

KD-MVS_2432*1600		66.87 307	65.81 312	70.04 306	67.50 359	47.49 344	62.56 345	79.16 280	61.21 282	77.98 279	80.61 319	25.29 369	82.48 305	53.02 315	84.92 306	80.16 335

miper_refine_blended		66.87 307	65.81 312	70.04 306	67.50 359	47.49 344	62.56 345	79.16 280	61.21 282	77.98 279	80.61 319	25.29 369	82.48 305	53.02 315	84.92 306	80.16 335

tpm cat1		66.76 309	65.21 315	71.42 304	77.09 327	50.62 335	78.01 265	73.68 316	44.89 351	68.64 329	79.00 331	45.51 330	82.42 307	49.91 328	70.15 353	81.23 329

DWT-MVSNet_test		66.43 310	64.37 316	72.63 298	74.86 345	50.86 333	76.52 287	72.74 321	54.06 318	65.50 341	68.30 355	32.13 361	84.84 291	61.63 268	73.59 348	82.19 315

PVSNet	58.17 21	66.41 311	65.63 314	68.75 315	81.96 282	49.88 338	62.19 347	72.51 324	51.03 336	68.04 332	75.34 347	50.84 308	74.77 330	45.82 344	82.96 319	81.60 322

tpmrst		66.28 312	66.69 310	65.05 328	72.82 354	39.33 357	78.20 264	70.69 334	53.16 323	67.88 333	80.36 324	48.18 314	74.75 331	58.13 287	70.79 352	81.08 330

Patchmatch-test		65.91 313	67.38 305	61.48 336	75.51 339	43.21 355	68.84 329	63.79 349	62.48 271	72.80 315	83.42 294	44.89 338	59.52 357	48.27 336	86.45 292	81.70 320

ADS-MVSNet2		65.87 314	63.64 319	72.55 300	73.16 351	56.92 289	67.10 336	74.81 304	49.74 343	66.04 339	82.97 298	46.71 317	77.26 323	42.29 348	69.96 354	83.46 298

test-mter		65.00 315	63.79 318	68.63 316	76.45 333	55.21 300	67.89 332	67.14 342	50.98 337	65.08 344	72.39 349	28.27 366	69.37 338	61.00 271	84.89 308	81.31 325

test0.0.03 1		64.66 316	64.36 317	65.57 326	75.03 344	46.89 346	64.69 341	61.58 354	62.43 273	71.18 323	77.54 337	43.41 341	68.47 343	40.75 352	82.65 323	81.35 324

pmmvs3		62.47 317	60.02 329	69.80 309	71.58 357	64.00 215	70.52 324	58.44 358	39.77 357	66.05 338	75.84 345	27.10 368	72.28 333	46.15 342	84.77 312	73.11 344

EPMVS		62.47 317	62.63 321	62.01 332	70.63 358	38.74 358	74.76 304	52.86 362	53.91 319	67.71 335	80.01 326	39.40 349	66.60 349	55.54 301	68.81 357	80.68 334

ADS-MVSNet		61.90 319	62.19 322	61.03 337	73.16 351	36.42 360	67.10 336	61.75 352	49.74 343	66.04 339	82.97 298	46.71 317	63.21 355	42.29 348	69.96 354	83.46 298

PMMVS		61.65 320	60.38 326	65.47 327	65.40 363	69.26 173	63.97 343	61.73 353	36.80 360	60.11 354	68.43 353	59.42 271	66.35 350	48.97 332	78.57 339	60.81 354

E-PMN		61.59 321	61.62 323	61.49 335	66.81 361	55.40 298	53.77 355	60.34 355	66.80 241	58.90 357	65.50 356	40.48 348	66.12 351	55.72 298	86.25 295	62.95 353

TESTMET0.1,1		61.29 322	60.32 327	64.19 329	72.06 355	51.30 328	67.89 332	62.09 350	45.27 350	60.65 353	69.01 352	27.93 367	64.74 354	56.31 295	81.65 328	76.53 339

MVS-HIRNet		61.16 323	62.92 320	55.87 341	79.09 316	35.34 361	71.83 319	57.98 359	46.56 347	59.05 356	91.14 163	49.95 311	76.43 325	38.74 354	71.92 351	55.84 358

EMVS		61.10 324	60.81 325	61.99 333	65.96 362	55.86 295	53.10 356	58.97 357	67.06 238	56.89 360	63.33 357	40.98 346	67.03 347	54.79 307	86.18 296	63.08 352

DSMNet-mixed		60.98 325	61.61 324	59.09 340	72.88 353	45.05 351	74.70 305	46.61 365	26.20 361	65.34 342	90.32 188	55.46 296	63.12 356	41.72 350	81.30 330	69.09 350

dp		60.70 326	60.29 328	61.92 334	72.04 356	38.67 359	70.83 322	64.08 348	51.28 335	60.75 352	77.28 340	36.59 356	71.58 336	47.41 338	62.34 359	75.52 341

CHOSEN 280x420		59.08 327	56.52 332	66.76 322	76.51 331	64.39 211	49.62 357	59.00 356	43.86 353	55.66 361	68.41 354	35.55 357	68.21 344	43.25 347	76.78 345	67.69 351

PVSNet_0	51.08 22	56.10 328	54.97 333	59.48 339	75.12 343	53.28 313	55.16 354	61.89 351	44.30 352	59.16 355	62.48 358	54.22 301	65.91 352	35.40 357	47.01 360	59.25 356

new_pmnet		55.69 329	57.66 331	49.76 343	75.47 340	30.59 363	59.56 348	51.45 363	43.62 354	62.49 350	75.48 346	40.96 347	49.15 361	37.39 356	72.52 349	69.55 349

PMMVS2		55.64 330	59.27 330	44.74 344	64.30 364	12.32 367	40.60 358	49.79 364	53.19 322	65.06 346	84.81 279	53.60 303	49.76 360	32.68 360	89.41 261	72.15 345

MVE	40.22 23	51.82 331	50.47 334	55.87 341	62.66 365	51.91 323	31.61 360	39.28 366	40.65 356	50.76 362	74.98 348	56.24 293	44.67 362	33.94 359	64.11 358	71.04 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 332	29.60 335	33.06 345	17.99 367	3.84 369	13.62 361	73.92 311	2.79 363	18.29 365	53.41 360	28.53 365	43.25 363	22.56 361	35.27 362	52.11 359

cdsmvs_eth3d_5k		20.81 333	27.75 336	0.00 350	0.00 371	0.00 372	0.00 362	85.44 232	0.00 367	0.00 368	82.82 302	81.46 111	0.00 368	0.00 366	0.00 366	0.00 364

tmp_tt		20.25 334	24.50 337	7.49 347	4.47 368	8.70 368	34.17 359	25.16 368	1.00 364	32.43 364	18.49 362	39.37 350	9.21 365	21.64 362	43.75 361	4.57 361

ab-mvs-re		6.65 335	8.87 338	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	79.80 328	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		6.41 336	8.55 339	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	76.94 155	0.00 368	0.00 366	0.00 366	0.00 364

test123		6.27 337	8.08 340	0.84 348	1.11 370	0.57 370	62.90 344	0.82 370	0.54 365	1.07 367	2.75 367	1.26 371	0.30 366	1.04 364	1.26 365	1.66 362

testmvs		5.91 338	7.65 341	0.72 349	1.20 369	0.37 371	59.14 350	0.67 371	0.49 366	1.11 366	2.76 366	0.94 372	0.24 367	1.02 365	1.47 364	1.55 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ZD-MVS					92.22 102	80.48 68		91.85 111	71.22 196	90.38 86	92.98 110	86.06 62	96.11 6	81.99 81	96.75 95

RE-MVS-def			92.61 5		94.13 53	88.95 8	92.87 12	94.16 28	88.75 15	93.79 28	94.43 61	90.64 11		87.16 25	97.60 64	92.73 143

IU-MVS					94.18 48	72.64 136		90.82 138	56.98 306	89.67 104				85.78 44	97.92 46	93.28 122

OPU-MVS				88.27 81	91.89 113	77.83 92	90.47 46				91.22 159	81.12 115	94.68 72	74.48 162	95.35 140	92.29 162

test_241102_TWO								93.71 48	83.77 41	93.49 37	94.27 69	89.27 22	95.84 19	86.03 41	97.82 51	92.04 171

test_241102_ONE					94.18 48	72.65 134		93.69 49	83.62 43	94.11 22	93.78 97	90.28 15	95.50 44

9.14			89.29 61		91.84 115		88.80 80	95.32 9	75.14 146	91.07 75	92.89 115	87.27 45	93.78 107	83.69 65	97.55 67

save fliter					93.75 61	77.44 98	86.31 120	89.72 168	70.80 199

test_0728_THIRD									85.33 30	93.75 30	94.65 53	87.44 44	95.78 24	87.41 19	98.21 30	92.98 134

test_0728_SECOND				86.79 97	94.25 47	72.45 143	90.54 43	94.10 34					95.88 14	86.42 31	97.97 43	92.02 172

test0726					94.16 51	72.56 139	90.63 42	93.90 41	83.61 44	93.75 30	94.49 58	89.76 19

GSMVS																83.88 290

test_part2					93.86 60	77.77 93				92.84 45

sam_mvs1												46.11 321				83.88 290

sam_mvs												45.92 326

ambc				82.98 178	90.55 153	64.86 206	88.20 87	89.15 179		89.40 113	93.96 87	71.67 212	91.38 186	78.83 116	96.55 101	92.71 146

MTGPA								91.81 114

test_post1							78.85 257				3.13 364	45.19 335	80.13 316	58.11 288

test_post											3.10 365	45.43 331	77.22 324

patchmatchnet-post											81.71 312	45.93 325	87.01 261

GG-mvs-BLEND				67.16 321	73.36 349	46.54 348	84.15 150	55.04 361		58.64 358	61.95 359	29.93 364	83.87 300	38.71 355	76.92 344	71.07 347

MTMP							90.66 40	33.14 367

gm-plane-assit					75.42 341	44.97 352			52.17 328		72.36 351		87.90 253	54.10 310

test9_res														80.83 94	96.45 106	90.57 208

TEST9					92.34 96	79.70 75	83.94 155	90.32 151	65.41 258	84.49 199	90.97 169	82.03 102	93.63 112

test_8					92.09 105	78.87 83	83.82 160	90.31 153	65.79 248	84.36 202	90.96 171	81.93 104	93.44 124

agg_prior2														79.68 106	96.16 116	90.22 215

agg_prior					91.58 123	77.69 94		90.30 154		84.32 203			93.18 132

TestCases				89.68 54	91.59 120	83.40 48		95.44 7	79.47 91	88.00 136	93.03 108	82.66 89	91.47 179	70.81 195	96.14 117	94.16 89

test_prior4						78.97 82	84.59 142

test_prior2							83.37 174		75.43 141	84.58 197	91.57 152	81.92 106		79.54 108	96.97 86

test_prior				86.32 105	90.59 151	71.99 149		92.85 86					94.17 91			92.80 140

旧先验2							81.73 213		56.88 307	86.54 168			84.90 290	72.81 184

新几何2							81.72 214

新几何1				82.95 179	93.96 57	78.56 87		80.24 274	55.45 311	83.93 211	91.08 164	71.19 214	88.33 250	65.84 240	93.07 200	81.95 319

旧先验1					91.97 108	71.77 151		81.78 265			91.84 145	73.92 183			93.65 189	83.61 296

无先验							82.81 190	85.62 231	58.09 297				91.41 184	67.95 227		84.48 284

原ACMM2							82.26 207

原ACMM1				84.60 142	92.81 87	74.01 126		91.50 120	62.59 269	82.73 227	90.67 181	76.53 162	94.25 84	69.24 211	95.69 133	85.55 274

test222					93.31 72	76.54 111	79.38 247	77.79 287	52.59 325	82.36 231	90.84 175	66.83 233			91.69 229	81.25 327

testdata2													86.43 274	63.52 253

segment_acmp												81.94 103

testdata				79.54 238	92.87 82	72.34 144		80.14 276	59.91 291	85.47 186	91.75 150	67.96 227	85.24 286	68.57 223	92.18 221	81.06 332

testdata1							79.62 242		73.95 158

test12				86.57 100	90.74 147	72.63 137		90.69 141		82.76 226		79.20 131	94.80 69		95.32 142	92.27 164

plane_prior7					93.45 67	77.31 102

plane_prior6					92.61 88	76.54 111						74.84 172

plane_prior5								93.61 53					95.22 55	80.78 95	95.83 126	94.46 77

plane_prior4											92.95 113

plane_prior3						76.85 109			77.79 112	86.55 163

plane_prior2							89.45 69		79.44 93

plane_prior1					92.83 86

plane_prior						76.42 114	87.15 104		75.94 135						95.03 154

n2								0.00 372

nn								0.00 372

door-mid								74.45 308

lessismore_v0				85.95 116	91.10 139	70.99 160		70.91 333		91.79 64	94.42 63	61.76 257	92.93 142	79.52 110	93.03 202	93.93 97

LGP-MVS_train				90.82 38	94.75 40	81.69 59		94.27 20	82.35 60	93.67 33	94.82 48	91.18 5	95.52 39	85.36 47	98.73 7	95.23 58

test11								91.46 121

door								72.57 323

HQP5-MVS						70.66 161

HQP-NCC					91.19 134		84.77 137		73.30 168	80.55 259

ACMP_Plane					91.19 134		84.77 137		73.30 168	80.55 259

BP-MVS														77.30 139

HQP4-MVS										80.56 258			94.61 76			93.56 117

HQP3-MVS								92.68 92							94.47 170

HQP2-MVS												72.10 205

NP-MVS					91.95 109	74.55 123					90.17 194

MDTV_nov1_ep13_2view						27.60 365	70.76 323		46.47 348	61.27 351		45.20 334		49.18 331		83.75 295

MDTV_nov1_ep13			68.29 303		78.03 321	43.87 353	74.12 308	72.22 325	52.17 328	67.02 337	85.54 261	45.36 332	80.85 313	55.73 297	84.42 313

ACMMP++_ref															95.74 132

ACMMP++															97.35 76

Test By Simon												79.09 132

ITE_SJBPF				90.11 50	90.72 148	84.97 36		90.30 154	81.56 69	90.02 92	91.20 161	82.40 92	90.81 203	73.58 174	94.66 166	94.56 72

DeepMVS_CX				24.13 346	32.95 366	29.49 364		21.63 369	12.07 362	37.95 363	45.07 361	30.84 362	19.21 364	17.94 363	33.06 363	23.69 360

High-resolution multi-view benchmark