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 34	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 52	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 35	94.17 96	86.07 43	98.48 18	97.22 17

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 66	85.72 33	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 139	91.10 1	97.53 73	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 44	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 37	91.86 3	95.09 63	87.54 18	98.02 40	93.71 114

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 32	92.99 13	94.23 26	85.21 34	92.51 54	95.13 41	90.65 10	95.34 53	88.06 9	98.15 35	95.95 40

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 64	88.83 24	95.51 44	87.16 28	97.60 66	92.73 149

test1172		92.40 7	92.41 7	92.37 6	94.68 45	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 82	88.83 24	95.51 44	87.11 30	97.54 72	92.54 159

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 41	94.02 88	90.15 17	95.67 33	86.82 32	97.34 79	92.19 178

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 43	93.51 8	94.85 16	82.88 57	91.77 68	93.94 97	90.55 13	95.73 30	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 81	85.17 36	92.47 26	95.05 15	87.65 24	93.21 42	94.39 70	90.09 18	95.08 64	86.67 33	97.60 66	94.18 93

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 67	86.15 23	93.37 10	95.10 14	90.28 9	92.11 60	95.03 43	89.75 21	94.93 68	79.95 109	98.27 27	95.04 63
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 27	85.91 27	93.35 11	94.16 31	82.52 61	92.39 57	94.14 83	89.15 23	95.62 34	87.35 23	98.24 28	94.56 77
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 13	88.48 10	92.72 18	92.60 99	83.09 54	91.54 70	94.25 76	87.67 43	95.51 44	87.21 27	98.11 36	93.12 135

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 79	94.00 89	88.26 32	95.71 31	87.28 26	98.39 21	92.55 158

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 97	94.03 87	86.57 56	95.80 24	87.35 23	97.62 64	94.20 91

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 30	91.81 119	84.07 40	92.00 63	94.40 68	86.63 54	95.28 56	88.59 5	98.31 24	92.30 170

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 94	88.22 20	88.53 130	97.64 2	83.45 84	94.55 84	86.02 46	98.60 13	96.67 26

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 29	92.58 22	93.25 75	81.99 66	91.40 73	94.17 81	87.51 44	95.87 18	87.74 11	97.76 56	93.99 99

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 42	81.69 61	90.00 56	94.27 23	82.35 63	93.67 34	94.82 49	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 28	92.63 21	93.30 72	81.91 68	90.88 84	94.21 77	87.75 41	95.87 18	87.60 16	97.71 60	93.83 106

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 44	92.58 22	93.29 73	81.99 66	91.47 71	93.96 93	88.35 30	95.56 37	87.74 11	97.74 58	92.85 144

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 81	91.81 119	84.07 40	92.00 63	94.40 68	86.63 54	95.28 56	88.59 5	98.31 24	92.30 170

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 54	92.18 28	94.22 27	80.14 89	91.29 76	93.97 90	87.93 40	95.87 18	88.65 4	97.96 47	94.12 96

APDe-MVS		91.22 24	91.92 13	89.14 66	92.97 83	78.04 93	92.84 16	94.14 35	83.33 51	93.90 25	95.73 26	88.77 26	96.41 1	87.60 16	97.98 44	92.98 140

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 56	93.90 46	80.32 86	91.74 69	94.41 67	88.17 34	95.98 11	86.37 36	97.99 42	93.96 101

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 61	80.97 68	91.49 38	93.48 63	82.82 58	92.60 53	93.97 90	88.19 33	96.29 4	87.61 15	98.20 32	94.39 86
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 27	93.03 85	82.59 60	88.52 131	94.37 71	86.74 53	95.41 51	86.32 37	98.21 30	93.19 133
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 39	90.82 38	95.45 28	82.73 57	91.75 36	93.74 52	80.98 79	91.38 74	93.80 100	87.20 48	95.80 24	87.10 31	97.69 61	93.93 102

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 94	94.51 21	75.79 142	92.94 44	94.96 44	88.36 29	95.01 66	90.70 2	98.40 20	95.09 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 30	91.50 23	88.44 82	93.00 82	76.26 122	89.65 67	95.55 7	87.72 23	93.89 27	94.94 45	91.62 4	93.44 129	78.35 127	98.76 4	95.61 47

ACMMP_NAP		90.65 31	91.07 38	89.42 60	95.93 16	79.54 79	89.95 59	93.68 56	77.65 119	91.97 65	94.89 46	88.38 28	95.45 49	89.27 3	97.87 52	93.27 129

ACMM	79.39 9	90.65 31	90.99 40	89.63 56	95.03 35	83.53 49	89.62 68	93.35 66	79.20 102	93.83 28	93.60 107	90.81 8	92.96 146	85.02 54	98.45 19	92.41 164
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 33	90.34 50	91.38 27	89.03 187	84.23 47	93.58 6	94.68 19	90.65 7	90.33 91	93.95 96	84.50 72	95.37 52	80.87 99	95.50 146	94.53 80

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 77	94.05 40	79.03 105	92.87 46	93.74 104	90.60 12	95.21 60	82.87 76	98.76 4	94.87 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 35	91.08 36	88.88 69	93.38 73	78.65 88	89.15 78	94.05 40	84.68 38	93.90 25	94.11 85	88.13 36	96.30 3	84.51 60	97.81 54	91.70 193
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 51	91.02 33	95.43 29	84.66 44	90.65 44	93.29 73	77.00 126	91.47 71	93.96 93	88.35 30	95.56 37	84.88 55	97.74 58	92.85 144

SED-MVS		90.46 37	91.64 19	86.93 101	94.18 50	72.65 141	90.47 50	93.69 54	83.77 44	94.11 23	94.27 72	90.28 15	95.84 22	86.03 44	97.92 48	92.29 172

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 80	90.98 42	93.24 76	75.37 149	92.84 48	95.28 36	85.58 66	96.09 7	87.92 10	97.76 56	93.88 104
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 45	88.68 77	92.86 87	77.09 111	91.19 41	95.74 5	81.38 74	92.28 58	93.80 100	86.89 51	94.64 77	85.52 48	97.51 74	94.30 89

v7n		90.13 40	90.96 41	87.65 94	91.95 113	71.06 168	89.99 58	93.05 82	86.53 28	94.29 19	96.27 17	82.69 91	94.08 100	86.25 40	97.63 63	97.82 8

PMVS	80.48 6	90.08 41	90.66 47	88.34 85	96.71 3	92.97 1	90.31 52	89.57 180	88.51 19	90.11 93	95.12 42	90.98 7	88.92 251	77.55 141	97.07 86	83.13 317
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 42	91.09 35	87.00 100	91.55 132	72.64 143	96.19 2	94.10 38	85.33 32	93.49 38	94.64 57	81.12 120	95.88 16	87.41 21	95.94 130	92.48 161

DVP-MVS		90.06 43	91.32 30	86.29 114	94.16 53	72.56 147	90.54 46	91.01 140	83.61 47	93.75 31	94.65 54	89.76 19	95.78 27	86.42 34	97.97 45	90.55 221
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 43	91.92 13	84.47 151	96.56 7	58.83 285	89.04 79	92.74 96	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 118	98.74 6	99.00 2

PEN-MVS		90.03 45	91.88 16	84.48 150	96.57 6	58.88 282	88.95 80	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 123	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 79	80.37 71	91.91 34	93.11 79	81.10 77	95.32 10	97.24 5	72.94 204	94.85 71	85.07 52	97.78 55	97.26 15

DTE-MVSNet		89.98 47	91.91 15	84.21 158	96.51 8	57.84 290	88.93 82	92.84 93	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 120	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 46	89.49 73	93.98 42	79.68 93	92.09 61	93.89 98	83.80 80	93.10 143	82.67 78	98.04 37	93.64 119

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 141	81.66 64	91.25 39	94.13 36	88.89 13	88.83 125	94.26 75	77.55 152	95.86 21	84.88 55	95.87 134	95.24 57

WR-MVS_H		89.91 50	91.31 31	85.71 130	96.32 10	62.39 243	89.54 71	93.31 70	90.21 10	95.57 9	95.66 29	81.42 116	95.90 15	80.94 98	98.80 3	98.84 5

OPM-MVS		89.80 51	89.97 52	89.27 62	94.76 41	79.86 75	86.76 117	92.78 95	78.78 108	92.51 54	93.64 106	88.13 36	93.84 110	84.83 57	97.55 69	94.10 97
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 52	89.27 63	91.30 28	93.51 69	84.79 41	89.89 61	90.63 150	70.00 218	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 127	95.52 48

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 175	86.67 17	90.51 48	90.20 167	69.87 219	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 112	97.09 19

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 98	82.98 55	91.98 31	90.08 170	71.54 199	94.28 21	96.54 13	81.57 114	94.27 87	86.26 38	96.49 106	97.09 19

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 113	93.91 45	80.07 90	86.75 162	93.26 110	93.64 2	90.93 203	84.60 59	90.75 260	93.97 100

APD-MVS		89.54 56	89.63 57	89.26 63	92.57 92	81.34 66	90.19 54	93.08 81	80.87 81	91.13 77	93.19 111	86.22 61	95.97 12	82.23 85	97.18 84	90.45 223
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		89.51 57	89.48 60	89.59 58	92.26 102	80.80 69	90.14 55	93.54 61	83.37 50	90.57 88	92.55 133	84.99 68	96.15 5	81.26 93	96.61 101	91.83 189

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 73	84.72 42	89.70 63	90.29 164	69.27 222	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 132	95.34 52

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 29	92.30 105	79.74 92	87.50 147	92.38 136	81.42 116	93.28 134	83.07 73	97.24 82	91.67 194

ACMH	76.49 14	89.34 60	91.14 34	83.96 164	92.50 95	70.36 173	89.55 69	93.84 50	81.89 69	94.70 13	95.44 34	90.69 9	88.31 261	83.33 71	98.30 26	93.20 132
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 61	90.91 43	84.37 152	96.34 9	58.61 287	88.66 89	92.06 110	90.78 6	95.67 7	95.17 40	81.80 112	95.54 41	79.00 121	98.69 10	98.95 4

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 128	93.60 60	80.16 88	89.13 121	93.44 108	83.82 79	90.98 201	83.86 66	95.30 154	93.60 121

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 61	93.64 68	79.07 83	88.54 90	94.20 28	73.53 167	89.71 106	94.82 49	85.09 67	95.77 29	84.17 63	98.03 39	93.26 130
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 64	90.72 46	84.31 156	97.00 2	64.33 221	89.67 66	88.38 197	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 194	97.65 62	97.34 14

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 24	92.75 17	93.90 46	78.43 113	89.16 120	92.25 143	72.03 216	96.36 2	88.21 8	90.93 255	92.98 140
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 66	89.13 64	88.79 73	93.75 64	77.44 104	86.31 125	95.27 12	70.80 207	92.28 58	93.80 100	86.89 51	94.64 77	85.52 48	97.51 74	94.30 89

SD-MVS		88.96 67	89.88 53	86.22 117	91.63 125	77.07 112	89.82 62	93.77 51	78.90 106	92.88 45	92.29 141	86.11 62	90.22 225	86.24 41	97.24 82	91.36 201
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 68	88.45 75	90.38 45	94.92 37	85.85 30	89.70 63	91.27 133	78.20 115	86.69 165	92.28 142	80.36 130	95.06 65	86.17 42	96.49 106	90.22 226

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 78	91.94 115	77.27 110	88.71 87	95.26 13	76.08 133	90.66 87	92.69 128	84.48 73	93.83 111	83.38 70	97.48 76	94.47 81

test_0402		88.65 70	89.58 59	85.88 126	92.55 93	72.22 155	84.01 159	89.44 182	88.63 18	94.38 18	95.77 25	86.38 60	93.59 121	79.84 110	95.21 155	91.82 190

DP-MVS		88.60 71	89.01 66	87.36 97	91.30 139	77.50 103	87.55 101	92.97 88	87.95 22	89.62 110	92.87 122	84.56 71	93.89 107	77.65 139	96.62 100	90.70 215

Anonymous20231211		88.40 72	89.62 58	84.73 146	90.46 163	65.27 212	88.86 83	93.02 86	87.15 25	93.05 43	97.10 6	82.28 99	92.02 173	76.70 149	97.99 42	96.88 23

PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 75	77.96 96	87.94 97	91.97 113	70.73 209	94.19 22	96.67 11	76.94 161	94.57 81	83.07 73	96.28 114	96.15 32

OMC-MVS		88.19 74	87.52 85	90.19 49	91.94 115	81.68 63	87.49 103	93.17 78	76.02 136	88.64 128	91.22 166	84.24 76	93.37 132	77.97 137	97.03 87	95.52 48

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 68	95.72 22	76.74 116	92.49 25	91.19 136	67.85 240	86.63 166	94.84 48	79.58 136	95.96 13	87.62 14	94.50 180	94.56 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS		88.03 76	87.67 83	89.10 67	89.60 177	77.89 97	90.49 49	94.78 18	79.37 100	84.25 217	89.32 213	83.84 78	94.49 85	82.47 81	94.93 167	94.93 65

DROMVSNet		88.01 77	88.32 76	87.09 99	89.28 182	72.03 157	90.31 52	96.31 3	80.88 80	85.12 193	89.67 209	84.47 74	95.46 48	82.56 79	96.26 117	93.77 112

RPSCF		88.00 78	86.93 95	91.22 31	90.08 169	89.30 5	89.68 65	91.11 137	79.26 101	89.68 107	94.81 52	82.44 94	87.74 265	76.54 151	88.74 282	96.61 28

AllTest		87.97 79	87.40 88	89.68 54	91.59 126	83.40 50	89.50 72	95.44 9	79.47 96	88.00 140	93.03 114	82.66 92	91.47 185	70.81 203	96.14 121	94.16 94

TranMVSNet+NR-MVSNet		87.86 80	88.76 73	85.18 138	94.02 58	64.13 222	84.38 153	91.29 132	84.88 37	92.06 62	93.84 99	86.45 58	93.73 113	73.22 185	98.66 11	97.69 9

nrg030		87.85 81	88.49 74	85.91 124	90.07 171	69.73 176	87.86 98	94.20 28	74.04 161	92.70 52	94.66 53	85.88 65	91.50 184	79.72 112	97.32 80	96.50 30

ETH3D cwj APD-0.16		87.83 82	87.62 84	88.47 80	91.21 142	78.20 90	87.26 106	94.54 20	72.05 195	88.89 122	92.31 140	83.86 77	94.24 90	81.59 92	96.87 91	92.97 143

CNVR-MVS		87.81 83	87.68 82	88.21 87	92.87 85	77.30 109	85.25 138	91.23 134	77.31 123	87.07 155	91.47 162	82.94 89	94.71 74	84.67 58	96.27 116	92.62 156

HQP_MVS		87.75 84	87.43 87	88.70 76	93.45 70	76.42 120	89.45 74	93.61 58	79.44 98	86.55 167	92.95 119	74.84 179	95.22 58	80.78 101	95.83 135	94.46 82

CS-MVS-test		87.40 85	86.81 99	89.20 64	89.70 176	78.05 92	87.30 105	95.67 6	79.64 95	84.28 213	85.85 269	81.34 118	94.57 81	82.55 80	94.93 167	94.97 64

NCCC		87.36 86	86.87 96	88.83 70	92.32 101	78.84 86	86.58 121	91.09 138	78.77 109	84.85 199	90.89 180	80.85 123	95.29 54	81.14 95	95.32 151	92.34 168

DeepPCF-MVS	81.24 5	87.28 87	86.21 106	90.49 43	91.48 136	84.90 39	83.41 180	92.38 104	70.25 215	89.35 118	90.68 187	82.85 90	94.57 81	79.55 114	95.95 129	92.00 183

SixPastTwentyTwo		87.20 88	87.45 86	86.45 110	92.52 94	69.19 185	87.84 99	88.05 203	81.66 71	94.64 14	96.53 14	65.94 244	94.75 73	83.02 75	96.83 94	95.41 50

test_part1		87.15 89	87.82 80	85.15 139	88.88 191	63.04 233	87.98 95	94.85 16	82.52 61	93.61 37	95.73 26	67.51 235	95.71 31	80.48 106	98.83 2	96.69 25

UniMVSNet (Re)		86.87 90	86.98 94	86.55 108	93.11 80	68.48 189	83.80 168	92.87 90	80.37 84	89.61 112	91.81 154	77.72 149	94.18 94	75.00 168	98.53 16	96.99 22

Vis-MVSNet		86.86 91	86.58 100	87.72 92	92.09 109	77.43 106	87.35 104	92.09 109	78.87 107	84.27 215	94.05 86	78.35 144	93.65 115	80.54 105	91.58 243	92.08 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 92	87.06 91	86.17 121	92.86 87	67.02 200	82.55 204	91.56 123	83.08 55	90.92 81	91.82 153	78.25 145	93.99 102	74.16 172	98.35 22	97.49 13

DU-MVS		86.80 93	86.99 93	86.21 119	93.24 77	67.02 200	83.16 189	92.21 106	81.73 70	90.92 81	91.97 147	77.20 155	93.99 102	74.16 172	98.35 22	97.61 10

Regformer-2		86.74 94	86.08 108	88.73 74	84.18 275	79.20 82	83.52 175	89.33 184	83.33 51	89.92 101	85.07 285	83.23 87	93.16 139	83.39 69	92.72 221	93.83 106

IS-MVSNet		86.66 95	86.82 98	86.17 121	92.05 111	66.87 202	91.21 40	88.64 193	86.30 30	89.60 113	92.59 130	69.22 227	94.91 69	73.89 176	97.89 51	96.72 24

v10		86.54 96	87.10 90	84.84 143	88.16 205	63.28 230	86.64 120	92.20 107	75.42 148	92.81 50	94.50 60	74.05 189	94.06 101	83.88 65	96.28 114	97.17 18

pmmvs6		86.52 97	88.06 78	81.90 204	92.22 105	62.28 246	84.66 146	89.15 186	83.54 49	89.85 102	97.32 4	88.08 38	86.80 277	70.43 211	97.30 81	96.62 27

Regformer-4		86.41 98	85.71 116	88.52 78	84.27 271	77.57 102	84.07 156	88.00 205	82.82 58	89.84 103	85.48 273	82.06 103	92.77 152	83.83 67	91.04 249	95.22 60

PHI-MVS		86.38 99	85.81 113	88.08 88	88.44 199	77.34 107	89.35 76	93.05 82	73.15 178	84.76 200	87.70 239	78.87 140	94.18 94	80.67 103	96.29 113	92.73 149

test_prior3		86.31 100	86.31 103	86.32 112	90.59 160	71.99 158	83.37 181	92.85 91	75.43 146	84.58 203	91.57 158	81.92 109	94.17 96	79.54 115	96.97 88	92.80 146

CSCG		86.26 101	86.47 101	85.60 132	90.87 153	74.26 131	87.98 95	91.85 116	80.35 85	89.54 116	88.01 233	79.09 138	92.13 167	75.51 161	95.06 162	90.41 224

DeepC-MVS_fast	80.27 8	86.23 102	85.65 118	87.96 91	91.30 139	76.92 113	87.19 107	91.99 112	70.56 210	84.96 195	90.69 186	80.01 133	95.14 61	78.37 126	95.78 139	91.82 190
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 103	86.83 97	84.36 154	87.82 209	62.35 245	86.42 123	91.33 131	76.78 128	92.73 51	94.48 62	73.41 198	93.72 114	83.10 72	95.41 147	97.01 21

Anonymous20240529		86.20 104	87.13 89	83.42 176	90.19 167	64.55 219	84.55 148	90.71 147	85.85 31	89.94 100	95.24 39	82.13 101	90.40 220	69.19 222	96.40 110	95.31 54

CDPH-MVS		86.17 105	85.54 119	88.05 90	92.25 103	75.45 125	83.85 165	92.01 111	65.91 255	86.19 174	91.75 156	83.77 81	94.98 67	77.43 144	96.71 98	93.73 113

Regformer-1		86.00 106	85.50 120	87.49 95	84.18 275	76.90 114	83.52 175	87.94 207	82.18 65	89.19 119	85.07 285	82.28 99	91.89 177	82.40 83	92.72 221	93.69 115

NR-MVSNet		86.00 106	86.22 105	85.34 136	93.24 77	64.56 218	82.21 216	90.46 153	80.99 78	88.42 133	91.97 147	77.56 151	93.85 108	72.46 195	98.65 12	97.61 10

train_agg		85.98 108	85.28 123	88.07 89	92.34 99	79.70 77	83.94 161	90.32 158	65.79 256	84.49 205	90.97 176	81.93 107	93.63 117	81.21 94	96.54 104	90.88 210

FC-MVSNet-test		85.93 109	87.05 92	82.58 195	92.25 103	56.44 301	85.75 132	93.09 80	77.33 122	91.94 66	94.65 54	74.78 181	93.41 131	75.11 167	98.58 14	97.88 7

Effi-MVS+-dtu		85.82 110	83.38 156	93.14 3	87.13 224	91.15 2	87.70 100	88.42 195	74.57 156	83.56 225	85.65 270	78.49 142	94.21 92	72.04 197	92.88 216	94.05 98

agg_prior1		85.72 111	85.20 124	87.28 98	91.58 129	77.69 100	83.69 171	90.30 161	66.29 252	84.32 209	91.07 173	82.13 101	93.18 137	81.02 96	96.36 111	90.98 206

TAPA-MVS	77.73 12	85.71 112	84.83 130	88.37 84	88.78 193	79.72 76	87.15 109	93.50 62	69.17 223	85.80 184	89.56 210	80.76 124	92.13 167	73.21 190	95.51 145	93.25 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 113	86.14 107	83.58 173	87.97 206	67.13 198	87.55 101	94.32 22	73.44 169	88.47 132	87.54 242	86.45 58	91.06 200	75.76 160	93.76 195	92.54 159

EPP-MVSNet		85.47 114	85.04 126	86.77 105	91.52 135	69.37 179	91.63 37	87.98 206	81.51 73	87.05 156	91.83 152	66.18 243	95.29 54	70.75 206	96.89 90	95.64 45

GeoE		85.45 115	85.81 113	84.37 152	90.08 169	67.07 199	85.86 131	91.39 130	72.33 190	87.59 145	90.25 197	84.85 69	92.37 161	78.00 135	91.94 237	93.66 116

FIs		85.35 116	86.27 104	82.60 194	91.86 118	57.31 294	85.10 140	93.05 82	75.83 141	91.02 80	93.97 90	73.57 194	92.91 150	73.97 175	98.02 40	97.58 12

casdiffmvs		85.21 117	85.85 112	83.31 178	86.17 247	62.77 237	83.03 191	93.93 44	74.69 155	88.21 138	92.68 129	82.29 98	91.89 177	77.87 138	93.75 197	95.27 56

baseline		85.20 118	85.93 110	83.02 183	86.30 242	62.37 244	84.55 148	93.96 43	74.48 158	87.12 151	92.03 146	82.30 97	91.94 174	78.39 125	94.21 186	94.74 73

K. test v3		85.14 119	84.73 131	86.37 111	91.13 147	69.63 178	85.45 136	76.68 303	84.06 42	92.44 56	96.99 8	62.03 263	94.65 76	80.58 104	93.24 206	94.83 72

EI-MVSNet-Vis-set		85.12 120	84.53 139	86.88 102	84.01 278	72.76 140	83.91 164	85.18 246	80.44 83	88.75 126	85.49 272	80.08 132	91.92 175	82.02 86	90.85 258	95.97 38

ETH3 D test6400		85.09 121	84.87 129	85.75 129	90.80 155	69.34 180	85.90 129	93.31 70	65.43 262	86.11 177	89.95 203	80.92 122	94.86 70	75.90 158	95.57 144	93.05 137

Regformer-3		85.06 122	84.67 136	86.22 117	84.27 271	73.43 135	84.07 156	85.26 244	80.77 82	88.62 129	85.48 273	80.56 128	90.39 221	81.99 87	91.04 249	94.85 70

EI-MVSNet-UG-set		85.04 123	84.44 141	86.85 103	83.87 281	72.52 149	83.82 166	85.15 247	80.27 87	88.75 126	85.45 276	79.95 134	91.90 176	81.92 89	90.80 259	96.13 33

X-MVStestdata		85.04 123	82.70 166	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 97	16.05 373	86.57 56	95.80 24	87.35 23	97.62 64	94.20 91

MSLP-MVS++		85.00 125	86.03 109	81.90 204	91.84 121	71.56 166	86.75 118	93.02 86	75.95 139	87.12 151	89.39 211	77.98 146	89.40 247	77.46 142	94.78 173	84.75 294

F-COLMAP		84.97 126	83.42 155	89.63 56	92.39 97	83.40 50	88.83 84	91.92 115	73.19 177	80.18 274	89.15 218	77.04 159	93.28 134	65.82 249	92.28 228	92.21 177

3Dnovator	80.37 7	84.80 127	84.71 134	85.06 141	86.36 240	74.71 128	88.77 86	90.00 172	75.65 144	84.96 195	93.17 112	74.06 188	91.19 195	78.28 129	91.09 247	89.29 242

IterMVS-LS		84.73 128	84.98 127	83.96 164	87.35 219	63.66 225	83.25 185	89.88 174	76.06 134	89.62 110	92.37 139	73.40 200	92.52 157	78.16 132	94.77 175	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 129	84.34 145	85.49 135	90.18 168	75.86 124	79.23 260	87.13 217	73.35 170	85.56 188	89.34 212	83.60 83	90.50 218	76.64 150	94.05 191	90.09 231

HQP-MVS		84.61 130	84.06 148	86.27 115	91.19 143	70.66 170	84.77 142	92.68 97	73.30 173	80.55 268	90.17 201	72.10 212	94.61 79	77.30 145	94.47 181	93.56 123

v1192		84.57 131	84.69 135	84.21 158	87.75 211	62.88 235	83.02 192	91.43 127	69.08 225	89.98 99	90.89 180	72.70 208	93.62 120	82.41 82	94.97 166	96.13 33

mvs-test1		84.55 132	82.12 175	91.84 20	87.13 224	89.54 4	85.05 141	88.42 195	74.57 156	80.60 265	82.98 307	78.49 142	93.98 104	72.04 197	89.77 269	92.00 183

FMVSNet1		84.55 132	85.45 121	81.85 206	90.27 166	61.05 257	86.83 114	88.27 200	78.57 112	89.66 109	95.64 30	75.43 172	90.68 213	69.09 223	95.33 150	93.82 108

v1144		84.54 134	84.72 133	84.00 162	87.67 213	62.55 241	82.97 193	90.93 143	70.32 214	89.80 104	90.99 175	73.50 195	93.48 127	81.69 91	94.65 178	95.97 38

Gipuma		84.44 135	86.33 102	78.78 251	84.20 274	73.57 134	89.55 69	90.44 154	84.24 39	84.38 207	94.89 46	76.35 170	80.40 325	76.14 155	96.80 96	82.36 325
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 136	83.93 151	85.63 131	91.59 126	71.58 165	83.52 175	92.13 108	61.82 283	83.96 219	89.75 208	79.93 135	93.46 128	78.33 128	94.34 184	91.87 188

VDDNet		84.35 137	85.39 122	81.25 214	95.13 33	59.32 275	85.42 137	81.11 277	86.41 29	87.41 148	96.21 19	73.61 193	90.61 216	66.33 243	96.85 92	93.81 111

ETV-MVS		84.31 138	83.91 152	85.52 133	88.58 195	70.40 172	84.50 152	93.37 64	78.76 110	84.07 218	78.72 343	80.39 129	95.13 62	73.82 178	92.98 214	91.04 205

v1240		84.30 139	84.51 140	83.65 171	87.65 214	61.26 254	82.85 196	91.54 124	67.94 238	90.68 86	90.65 189	71.71 218	93.64 116	82.84 77	94.78 173	96.07 35

MVS_111021_LR		84.28 140	83.76 153	85.83 128	89.23 184	83.07 53	80.99 234	83.56 261	72.71 183	86.07 178	89.07 220	81.75 113	86.19 287	77.11 147	93.36 202	88.24 254

h-mvs33		84.25 141	82.76 165	88.72 75	91.82 123	82.60 58	84.00 160	84.98 253	71.27 201	86.70 163	90.55 191	63.04 259	93.92 106	78.26 130	94.20 187	89.63 233

v144192		84.24 142	84.41 142	83.71 170	87.59 216	61.57 250	82.95 194	91.03 139	67.82 241	89.80 104	90.49 192	73.28 201	93.51 126	81.88 90	94.89 171	96.04 37

v1921920		84.23 143	84.37 144	83.79 167	87.64 215	61.71 249	82.91 195	91.20 135	67.94 238	90.06 94	90.34 194	72.04 215	93.59 121	82.32 84	94.91 169	96.07 35

VDD-MVS		84.23 143	84.58 138	83.20 180	91.17 146	65.16 214	83.25 185	84.97 254	79.79 91	87.18 150	94.27 72	74.77 182	90.89 206	69.24 219	96.54 104	93.55 125

v2v482		84.09 145	84.24 146	83.62 172	87.13 224	61.40 251	82.71 199	89.71 176	72.19 194	89.55 114	91.41 163	70.70 223	93.20 136	81.02 96	93.76 195	96.25 31

EG-PatchMatch MVS		84.08 146	84.11 147	83.98 163	92.22 105	72.61 146	82.20 218	87.02 222	72.63 184	88.86 123	91.02 174	78.52 141	91.11 198	73.41 183	91.09 247	88.21 255

DP-MVS Recon		84.05 147	83.22 158	86.52 109	91.73 124	75.27 126	83.23 187	92.40 102	72.04 196	82.04 245	88.33 229	77.91 148	93.95 105	66.17 244	95.12 160	90.34 225

TransMVSNet (Re)		84.02 148	85.74 115	78.85 250	91.00 150	55.20 311	82.29 212	87.26 213	79.65 94	88.38 135	95.52 33	83.00 88	86.88 275	67.97 234	96.60 102	94.45 84

Baseline_NR-MVSNet		84.00 149	85.90 111	78.29 262	91.47 137	53.44 320	82.29 212	87.00 225	79.06 104	89.55 114	95.72 28	77.20 155	86.14 288	72.30 196	98.51 17	95.28 55

TSAR-MVS + GP.		83.95 150	82.69 167	87.72 92	89.27 183	81.45 65	83.72 170	81.58 276	74.73 154	85.66 185	86.06 264	72.56 210	92.69 154	75.44 163	95.21 155	89.01 250

alignmvs		83.94 151	83.98 150	83.80 166	87.80 210	67.88 195	84.54 150	91.42 129	73.27 176	88.41 134	87.96 234	72.33 211	90.83 208	76.02 157	94.11 189	92.69 153

Effi-MVS+		83.90 152	84.01 149	83.57 174	87.22 222	65.61 211	86.55 122	92.40 102	78.64 111	81.34 259	84.18 296	83.65 82	92.93 148	74.22 171	87.87 292	92.17 179

CANet		83.79 153	82.85 164	86.63 106	86.17 247	72.21 156	83.76 169	91.43 127	77.24 124	74.39 318	87.45 244	75.36 173	95.42 50	77.03 148	92.83 217	92.25 176

pm-mvs1		83.69 154	84.95 128	79.91 236	90.04 173	59.66 272	82.43 208	87.44 210	75.52 145	87.85 142	95.26 38	81.25 119	85.65 294	68.74 227	96.04 124	94.42 85

AdaColmap		83.66 155	83.69 154	83.57 174	90.05 172	72.26 154	86.29 127	90.00 172	78.19 116	81.65 254	87.16 249	83.40 85	94.24 90	61.69 277	94.76 176	84.21 299

MIMVSNet1		83.63 156	84.59 137	80.74 224	94.06 57	62.77 237	82.72 198	84.53 257	77.57 121	90.34 90	95.92 24	76.88 167	85.83 292	61.88 275	97.42 77	93.62 120

WR-MVS		83.56 157	84.40 143	81.06 219	93.43 72	54.88 312	78.67 267	85.02 251	81.24 75	90.74 85	91.56 160	72.85 205	91.08 199	68.00 233	98.04 37	97.23 16

CNLPA		83.55 158	83.10 162	84.90 142	89.34 181	83.87 48	84.54 150	88.77 190	79.09 103	83.54 226	88.66 226	74.87 178	81.73 319	66.84 240	92.29 227	89.11 244

LCM-MVSNet-Re		83.48 159	85.06 125	78.75 252	85.94 250	55.75 306	80.05 244	94.27 23	76.47 129	96.09 5	94.54 59	83.31 86	89.75 241	59.95 289	94.89 171	90.75 213

hse-mvs2		83.47 160	81.81 180	88.47 80	91.03 149	82.27 59	82.61 200	83.69 259	71.27 201	86.70 163	86.05 265	63.04 259	92.41 159	78.26 130	93.62 201	90.71 214

V42		83.47 160	83.37 157	83.75 169	83.16 287	63.33 229	81.31 228	90.23 166	69.51 221	90.91 83	90.81 183	74.16 187	92.29 165	80.06 107	90.22 266	95.62 46

VPA-MVSNet		83.47 160	84.73 131	79.69 241	90.29 165	57.52 293	81.30 230	88.69 192	76.29 130	87.58 146	94.44 63	80.60 127	87.20 270	66.60 242	96.82 95	94.34 88

RRT_MVS		83.25 163	81.08 191	89.74 53	80.55 315	79.32 81	86.41 124	86.69 226	72.33 190	87.00 157	91.08 171	44.98 346	95.55 40	84.47 61	96.24 118	94.36 87

PAPM_NR		83.23 164	83.19 160	83.33 177	90.90 152	65.98 208	88.19 93	90.78 146	78.13 117	80.87 263	87.92 237	73.49 197	92.42 158	70.07 213	88.40 283	91.60 196

CLD-MVS		83.18 165	82.64 168	84.79 144	89.05 186	67.82 196	77.93 275	92.52 100	68.33 232	85.07 194	81.54 324	82.06 103	92.96 146	69.35 218	97.91 50	93.57 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 166	85.68 117	75.65 293	81.24 302	45.26 361	79.94 246	92.91 89	83.83 43	91.33 75	96.88 10	80.25 131	85.92 290	68.89 225	95.89 133	95.76 42

114514_t		83.10 167	82.54 171	84.77 145	92.90 84	69.10 187	86.65 119	90.62 151	54.66 325	81.46 256	90.81 183	76.98 160	94.38 86	72.62 193	96.18 119	90.82 212

UGNet		82.78 168	81.64 182	86.21 119	86.20 246	76.24 123	86.86 112	85.68 238	77.07 125	73.76 321	92.82 123	69.64 224	91.82 180	69.04 224	93.69 198	90.56 220
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		82.75 169	81.93 179	85.19 137	82.08 293	80.15 73	85.53 135	88.76 191	68.01 235	85.58 187	87.75 238	71.80 217	86.85 276	74.02 174	93.87 194	88.58 253

EI-MVSNet		82.61 170	82.42 173	83.20 180	83.25 285	63.66 225	83.50 178	85.07 248	76.06 134	86.55 167	85.10 282	73.41 198	90.25 222	78.15 134	90.67 262	95.68 44

QAPM		82.59 171	82.59 170	82.58 195	86.44 235	66.69 204	89.94 60	90.36 157	67.97 237	84.94 197	92.58 132	72.71 207	92.18 166	70.63 209	87.73 294	88.85 251

Fast-Effi-MVS+-dtu		82.54 172	81.41 186	85.90 125	85.60 252	76.53 119	83.07 190	89.62 179	73.02 180	79.11 283	83.51 301	80.74 125	90.24 224	68.76 226	89.29 273	90.94 208

MVS_Test		82.47 173	83.22 158	80.22 233	82.62 292	57.75 292	82.54 205	91.96 114	71.16 205	82.89 234	92.52 135	77.41 153	90.50 218	80.04 108	87.84 293	92.40 165

v148		82.31 174	82.48 172	81.81 209	85.59 253	59.66 272	81.47 226	86.02 234	72.85 181	88.05 139	90.65 189	70.73 222	90.91 205	75.15 166	91.79 238	94.87 66

API-MVS		82.28 175	82.61 169	81.30 213	86.29 243	69.79 174	88.71 87	87.67 209	78.42 114	82.15 244	84.15 297	77.98 146	91.59 183	65.39 251	92.75 218	82.51 324

MVSFormer		82.23 176	81.57 185	84.19 160	85.54 254	69.26 182	91.98 31	90.08 170	71.54 199	76.23 302	85.07 285	58.69 284	94.27 87	86.26 38	88.77 280	89.03 248

EIA-MVS		82.19 177	81.23 189	85.10 140	87.95 207	69.17 186	83.22 188	93.33 67	70.42 211	78.58 286	79.77 340	77.29 154	94.20 93	71.51 200	88.96 278	91.93 187

PCF-MVS	74.62 15	82.15 178	80.92 194	85.84 127	89.43 179	72.30 153	80.53 239	91.82 118	57.36 314	87.81 143	89.92 205	77.67 150	93.63 117	58.69 294	95.08 161	91.58 197
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 179	80.31 200	87.45 96	90.86 154	80.29 72	85.88 130	90.65 149	68.17 234	76.32 301	86.33 259	73.12 203	92.61 156	61.40 281	90.02 268	89.44 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 180	82.07 176	81.85 206	86.38 237	61.05 257	86.83 114	88.27 200	72.43 185	86.00 179	95.64 30	63.78 253	90.68 213	65.95 245	93.34 203	93.82 108

test1		82.02 180	82.07 176	81.85 206	86.38 237	61.05 257	86.83 114	88.27 200	72.43 185	86.00 179	95.64 30	63.78 253	90.68 213	65.95 245	93.34 203	93.82 108

OpenMVS	76.72 13	81.98 182	82.00 178	81.93 203	84.42 267	68.22 191	88.50 91	89.48 181	66.92 247	81.80 252	91.86 149	72.59 209	90.16 227	71.19 202	91.25 246	87.40 267

KD-MVS_self_test		81.93 183	83.14 161	78.30 261	84.75 262	52.75 324	80.37 241	89.42 183	70.24 216	90.26 92	93.39 109	74.55 186	86.77 278	68.61 229	96.64 99	95.38 51

tfpnnormal		81.79 184	82.95 163	78.31 260	88.93 190	55.40 307	80.83 237	82.85 266	76.81 127	85.90 183	94.14 83	74.58 185	86.51 282	66.82 241	95.68 143	93.01 139

c3_l		81.64 185	81.59 184	81.79 210	80.86 308	59.15 279	78.61 268	90.18 168	68.36 231	87.20 149	87.11 251	69.39 225	91.62 182	78.16 132	94.43 183	94.60 76

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 148	91.58 129	73.24 138	84.21 154	91.67 122	62.86 276	80.94 261	87.16 249	67.27 237	92.87 151	69.82 215	88.94 279	87.99 259

DELS-MVS		81.44 187	81.25 187	82.03 202	84.27 271	62.87 236	76.47 297	92.49 101	70.97 206	81.64 255	83.83 298	75.03 176	92.70 153	74.29 170	92.22 231	90.51 222
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		81.31 188	81.61 183	80.41 230	86.38 237	58.75 286	83.93 163	86.58 228	72.43 185	87.65 144	92.98 116	63.78 253	90.22 225	66.86 238	93.92 193	92.27 174

TinyColmap		81.25 189	82.34 174	77.99 267	85.33 256	60.68 264	82.32 211	88.33 198	71.26 203	86.97 158	92.22 145	77.10 158	86.98 274	62.37 270	95.17 157	86.31 278

AUN-MVS		81.18 190	78.78 219	88.39 83	90.93 151	82.14 60	82.51 206	83.67 260	64.69 270	80.29 271	85.91 268	51.07 316	92.38 160	76.29 154	93.63 200	90.65 218

tttt0517		81.07 191	79.58 212	85.52 133	88.99 189	66.45 206	87.03 111	75.51 311	73.76 165	88.32 137	90.20 198	37.96 362	94.16 99	79.36 119	95.13 158	95.93 41

Fast-Effi-MVS+		81.04 192	80.57 195	82.46 199	87.50 217	63.22 231	78.37 271	89.63 178	68.01 235	81.87 248	82.08 319	82.31 96	92.65 155	67.10 237	88.30 288	91.51 199

BH-untuned		80.96 193	80.99 192	80.84 223	88.55 196	68.23 190	80.33 242	88.46 194	72.79 182	86.55 167	86.76 254	74.72 183	91.77 181	61.79 276	88.99 277	82.52 323

1121		80.86 194	79.81 211	84.02 161	93.93 60	78.70 87	81.64 223	80.18 284	55.43 322	83.67 222	91.15 169	71.29 220	91.41 190	67.95 235	93.06 211	81.96 329

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 192	81.41 300	60.98 260	77.81 277	90.14 169	67.31 245	86.95 159	87.24 248	64.26 249	92.31 163	75.23 165	91.61 241	94.85 70

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 186	88.31 200	71.73 161	79.53 251	87.17 214	65.43 262	79.59 276	82.73 314	76.94 161	90.14 230	73.22 185	88.33 284	86.90 273

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 186	88.31 200	71.73 161	79.53 251	87.17 214	65.43 262	79.59 276	82.73 314	76.94 161	90.14 230	73.22 185	88.33 284	86.90 273

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 186	88.31 200	71.73 161	79.53 251	87.17 214	65.43 262	79.59 276	82.73 314	76.94 161	90.14 230	73.22 185	88.33 284	86.90 273

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 155	83.93 279	69.66 177	76.28 299	81.09 278	72.43 185	86.47 173	90.19 199	60.46 269	93.15 141	77.45 143	86.39 305	90.22 226

BH-RMVSNet		80.53 200	80.22 204	81.49 212	87.19 223	66.21 207	77.79 278	86.23 231	74.21 160	83.69 221	88.50 227	73.25 202	90.75 210	63.18 267	87.90 291	87.52 265

Anonymous202405211		80.51 201	81.19 190	78.49 257	88.48 197	57.26 295	76.63 293	82.49 268	81.21 76	84.30 212	92.24 144	67.99 233	86.24 286	62.22 271	95.13 158	91.98 186

DIV-MVS_self_test		80.43 202	80.23 202	81.02 220	79.99 318	59.25 276	77.07 288	87.02 222	67.38 243	86.19 174	89.22 215	63.09 257	90.16 227	76.32 152	95.80 137	93.66 116

cl____		80.42 203	80.23 202	81.02 220	79.99 318	59.25 276	77.07 288	87.02 222	67.37 244	86.18 176	89.21 216	63.08 258	90.16 227	76.31 153	95.80 137	93.65 118

diffmvs		80.40 204	80.48 199	80.17 234	79.02 330	60.04 268	77.54 282	90.28 165	66.65 250	82.40 239	87.33 247	73.50 195	87.35 269	77.98 136	89.62 271	93.13 134

EPNet		80.37 205	78.41 226	86.23 116	76.75 341	73.28 136	87.18 108	77.45 298	76.24 132	68.14 341	88.93 222	65.41 246	93.85 108	69.47 217	96.12 123	91.55 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 206	80.04 209	81.24 216	79.82 320	58.95 281	77.66 279	89.66 177	65.75 259	85.99 182	85.11 281	68.29 232	91.42 189	76.03 156	92.03 233	93.33 126

MG-MVS		80.32 207	80.94 193	78.47 258	88.18 203	52.62 327	82.29 212	85.01 252	72.01 197	79.24 282	92.54 134	69.36 226	93.36 133	70.65 208	89.19 276	89.45 236

VPNet		80.25 208	81.68 181	75.94 292	92.46 96	47.98 352	76.70 292	81.67 275	73.45 168	84.87 198	92.82 123	74.66 184	86.51 282	61.66 278	96.85 92	93.33 126

MAR-MVS		80.24 209	78.74 221	84.73 146	86.87 234	78.18 91	85.75 132	87.81 208	65.67 261	77.84 291	78.50 344	73.79 192	90.53 217	61.59 280	90.87 257	85.49 287
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		80.20 210	79.00 217	83.78 168	88.17 204	86.66 18	81.31 228	66.81 356	69.64 220	88.33 136	90.19 199	64.58 247	83.63 311	71.99 199	90.03 267	81.06 343

Anonymous20240521		80.18 211	81.25 187	76.95 279	83.15 288	60.84 262	82.46 207	85.99 235	68.76 229	86.78 160	93.73 105	59.13 281	77.44 332	73.71 179	97.55 69	92.56 157

LFMVS		80.15 212	80.56 196	78.89 249	89.19 185	55.93 303	85.22 139	73.78 323	82.96 56	84.28 213	92.72 127	57.38 293	90.07 234	63.80 261	95.75 140	90.68 216

DPM-MVS		80.10 213	79.18 216	82.88 189	90.71 158	69.74 175	78.87 264	90.84 144	60.29 298	75.64 310	85.92 267	67.28 236	93.11 142	71.24 201	91.79 238	85.77 284

MSDG		80.06 214	79.99 210	80.25 232	83.91 280	68.04 194	77.51 283	89.19 185	77.65 119	81.94 246	83.45 303	76.37 169	86.31 285	63.31 266	86.59 302	86.41 276

ab-mvs		79.67 215	80.56 196	76.99 278	88.48 197	56.93 297	84.70 145	86.06 233	68.95 227	80.78 264	93.08 113	75.30 174	84.62 303	56.78 303	90.90 256	89.43 238

VNet		79.31 216	80.27 201	76.44 286	87.92 208	53.95 316	75.58 306	84.35 258	74.39 159	82.23 242	90.72 185	72.84 206	84.39 305	60.38 288	93.98 192	90.97 207

bset_n11_16_dypcd		79.19 217	77.97 230	82.86 190	85.81 251	66.85 203	75.02 311	79.31 288	66.07 253	83.50 227	83.37 306	55.04 307	92.10 170	78.63 124	94.99 165	89.63 233

thisisatest0530		79.07 218	77.33 237	84.26 157	87.13 224	64.58 217	83.66 173	75.95 306	68.86 228	85.22 192	87.36 246	38.10 360	93.57 124	75.47 162	94.28 185	94.62 74

cl22		78.97 219	78.21 228	81.24 216	77.74 334	59.01 280	77.46 285	87.13 217	65.79 256	84.32 209	85.10 282	58.96 283	90.88 207	75.36 164	92.03 233	93.84 105

patch_mono-2		78.89 220	79.39 214	77.41 276	84.78 261	68.11 192	75.60 304	83.11 263	60.96 292	79.36 279	89.89 206	75.18 175	72.97 343	73.32 184	92.30 226	91.15 203

RPMNet		78.88 221	78.28 227	80.68 227	79.58 321	62.64 239	82.58 202	94.16 31	74.80 153	75.72 308	92.59 130	48.69 322	95.56 37	73.48 182	82.91 332	83.85 304

PAPR		78.84 222	78.10 229	81.07 218	85.17 257	60.22 267	82.21 216	90.57 152	62.51 278	75.32 313	84.61 292	74.99 177	92.30 164	59.48 292	88.04 290	90.68 216

PVSNet_BlendedMVS		78.80 223	77.84 231	81.65 211	84.43 265	63.41 227	79.49 254	90.44 154	61.70 286	75.43 311	87.07 252	69.11 228	91.44 187	60.68 286	92.24 229	90.11 230

FMVSNet3		78.80 223	78.55 223	79.57 243	82.89 291	56.89 299	81.76 220	85.77 237	69.04 226	86.00 179	90.44 193	51.75 314	90.09 233	65.95 245	93.34 203	91.72 192

test_yl		78.71 225	78.51 224	79.32 246	84.32 269	58.84 283	78.38 269	85.33 242	75.99 137	82.49 237	86.57 255	58.01 287	90.02 236	62.74 268	92.73 219	89.10 245

DCV-MVSNet		78.71 225	78.51 224	79.32 246	84.32 269	58.84 283	78.38 269	85.33 242	75.99 137	82.49 237	86.57 255	58.01 287	90.02 236	62.74 268	92.73 219	89.10 245

test1111		78.53 227	78.85 218	77.56 273	92.22 105	47.49 354	82.61 200	69.24 348	72.43 185	85.28 191	94.20 78	51.91 312	90.07 234	65.36 252	96.45 108	95.11 61

ECVR-MVS		78.44 228	78.63 222	77.88 269	91.85 119	48.95 348	83.68 172	69.91 346	72.30 192	84.26 216	94.20 78	51.89 313	89.82 238	63.58 262	96.02 125	94.87 66

pmmvs-eth3d		78.42 229	77.04 240	82.57 197	87.44 218	74.41 130	80.86 236	79.67 287	55.68 320	84.69 201	90.31 196	60.91 267	85.42 295	62.20 272	91.59 242	87.88 262

MVS_0304		78.17 230	77.23 238	80.99 222	84.13 277	69.07 188	81.39 227	80.81 280	76.28 131	67.53 346	89.11 219	62.87 261	86.77 278	60.90 285	92.01 236	87.13 270

mvs_anonymous		78.13 231	78.76 220	76.23 291	79.24 327	50.31 345	78.69 266	84.82 255	61.60 287	83.09 233	92.82 123	73.89 191	87.01 271	68.33 232	86.41 304	91.37 200

RRT_test8_iter05		78.08 232	77.52 233	79.75 239	80.84 309	52.54 328	80.61 238	88.96 188	67.77 242	84.62 202	89.29 214	33.89 367	92.10 170	77.59 140	94.15 188	94.62 74

TAMVS		78.08 232	76.36 246	83.23 179	90.62 159	72.87 139	79.08 261	80.01 286	61.72 285	81.35 258	86.92 253	63.96 252	88.78 255	50.61 336	93.01 213	88.04 258

miper_enhance_ethall		77.83 234	76.93 241	80.51 228	76.15 347	58.01 289	75.47 308	88.82 189	58.05 308	83.59 224	80.69 328	64.41 248	91.20 194	73.16 191	92.03 233	92.33 169

Vis-MVSNet (Re-imp)		77.82 235	77.79 232	77.92 268	88.82 192	51.29 338	83.28 183	71.97 336	74.04 161	82.23 242	89.78 207	57.38 293	89.41 246	57.22 302	95.41 147	93.05 137

CANet_DTU		77.81 236	77.05 239	80.09 235	81.37 301	59.90 270	83.26 184	88.29 199	69.16 224	67.83 344	83.72 299	60.93 266	89.47 243	69.22 221	89.70 270	90.88 210

OpenMVS_ROB	70.19 17	77.77 237	77.46 234	78.71 253	84.39 268	61.15 255	81.18 232	82.52 267	62.45 280	83.34 228	87.37 245	66.20 242	88.66 257	64.69 257	85.02 316	86.32 277

MDA-MVSNet-bldmvs		77.47 238	76.90 242	79.16 248	79.03 329	64.59 216	66.58 347	75.67 309	73.15 178	88.86 123	88.99 221	66.94 238	81.23 321	64.71 256	88.22 289	91.64 195

jason		77.42 239	75.75 252	82.43 200	87.10 228	69.27 181	77.99 274	81.94 273	51.47 344	77.84 291	85.07 285	60.32 271	89.00 249	70.74 207	89.27 275	89.03 248
jason: jason.
CDS-MVSNet		77.32 240	75.40 255	83.06 182	89.00 188	72.48 150	77.90 276	82.17 271	60.81 293	78.94 284	83.49 302	59.30 279	88.76 256	54.64 320	92.37 225	87.93 261
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 241	76.75 243	78.52 256	87.01 230	61.30 253	75.55 307	87.12 220	61.24 289	74.45 317	78.79 342	77.20 155	90.93 203	64.62 259	84.80 322	83.32 313

MVSTER		77.09 242	75.70 253	81.25 214	75.27 354	61.08 256	77.49 284	85.07 248	60.78 294	86.55 167	88.68 225	43.14 352	90.25 222	73.69 180	90.67 262	92.42 163

PS-MVSNAJ		77.04 243	76.53 245	78.56 255	87.09 229	61.40 251	75.26 309	87.13 217	61.25 288	74.38 319	77.22 351	76.94 161	90.94 202	64.63 258	84.83 321	83.35 312

IterMVS		76.91 244	76.34 247	78.64 254	80.91 306	64.03 223	76.30 298	79.03 291	64.88 269	83.11 231	89.16 217	59.90 275	84.46 304	68.61 229	85.15 315	87.42 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 245	75.67 254	80.34 231	80.48 316	62.16 248	73.50 321	84.80 256	57.61 312	82.24 241	87.54 242	51.31 315	87.65 266	70.40 212	93.19 208	91.23 202

CL-MVSNet_self_test		76.81 246	77.38 236	75.12 296	86.90 232	51.34 336	73.20 324	80.63 282	68.30 233	81.80 252	88.40 228	66.92 239	80.90 322	55.35 314	94.90 170	93.12 135

TR-MVS		76.77 247	75.79 251	79.72 240	86.10 249	65.79 210	77.14 286	83.02 264	65.20 267	81.40 257	82.10 318	66.30 241	90.73 212	55.57 311	85.27 313	82.65 319

USDC		76.63 248	76.73 244	76.34 288	83.46 283	57.20 296	80.02 245	88.04 204	52.14 340	83.65 223	91.25 165	63.24 256	86.65 281	54.66 319	94.11 189	85.17 289

BH-w/o		76.57 249	76.07 250	78.10 265	86.88 233	65.92 209	77.63 280	86.33 229	65.69 260	80.89 262	79.95 337	68.97 230	90.74 211	53.01 328	85.25 314	77.62 349

Patchmtry		76.56 250	77.46 234	73.83 302	79.37 326	46.60 358	82.41 209	76.90 300	73.81 164	85.56 188	92.38 136	48.07 324	83.98 308	63.36 265	95.31 153	90.92 209

PVSNet_Blended		76.49 251	75.40 255	79.76 238	84.43 265	63.41 227	75.14 310	90.44 154	57.36 314	75.43 311	78.30 345	69.11 228	91.44 187	60.68 286	87.70 295	84.42 297

miper_lstm_enhance		76.45 252	76.10 249	77.51 274	76.72 342	60.97 261	64.69 350	85.04 250	63.98 272	83.20 230	88.22 230	56.67 296	78.79 330	73.22 185	93.12 209	92.78 148

lupinMVS		76.37 253	74.46 263	82.09 201	85.54 254	69.26 182	76.79 290	80.77 281	50.68 350	76.23 302	82.82 312	58.69 284	88.94 250	69.85 214	88.77 280	88.07 256

cascas		76.29 254	74.81 259	80.72 226	84.47 264	62.94 234	73.89 319	87.34 211	55.94 319	75.16 315	76.53 354	63.97 251	91.16 196	65.00 253	90.97 254	88.06 257

thres600view7		75.97 255	75.35 257	77.85 271	87.01 230	51.84 334	80.45 240	73.26 327	75.20 150	83.10 232	86.31 261	45.54 337	89.05 248	55.03 317	92.24 229	92.66 154

GA-MVS		75.83 256	74.61 260	79.48 245	81.87 295	59.25 276	73.42 322	82.88 265	68.68 230	79.75 275	81.80 321	50.62 318	89.46 244	66.85 239	85.64 310	89.72 232

MVP-Stereo		75.81 257	73.51 272	82.71 192	89.35 180	73.62 133	80.06 243	85.20 245	60.30 297	73.96 320	87.94 235	57.89 291	89.45 245	52.02 331	74.87 358	85.06 291
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 258	75.05 258	76.66 285	87.27 220	51.88 333	81.07 233	73.26 327	75.68 143	83.25 229	86.37 258	45.54 337	88.80 252	51.98 332	90.99 251	89.31 240

ET-MVSNet_ETH3D		75.28 259	72.77 279	82.81 191	83.03 290	68.11 192	77.09 287	76.51 304	60.67 296	77.60 295	80.52 332	38.04 361	91.15 197	70.78 205	90.68 261	89.17 243

thres400		75.14 260	74.23 265	77.86 270	86.24 244	52.12 330	79.24 258	73.87 321	73.34 171	81.82 250	84.60 293	46.02 331	88.80 252	51.98 332	90.99 251	92.66 154

wuyk23d		75.13 261	79.30 215	62.63 341	75.56 350	75.18 127	80.89 235	73.10 329	75.06 152	94.76 12	95.32 35	87.73 42	52.85 370	34.16 369	97.11 85	59.85 366

EU-MVSNet		75.12 262	74.43 264	77.18 277	83.11 289	59.48 274	85.71 134	82.43 269	39.76 368	85.64 186	88.76 223	44.71 348	87.88 264	73.86 177	85.88 309	84.16 300

HyFIR lowres test		75.12 262	72.66 281	82.50 198	91.44 138	65.19 213	72.47 326	87.31 212	46.79 356	80.29 271	84.30 295	52.70 311	92.10 170	51.88 335	86.73 301	90.22 226

CMPMVS	59.41 20	75.12 262	73.57 270	79.77 237	75.84 349	67.22 197	81.21 231	82.18 270	50.78 348	76.50 298	87.66 240	55.20 305	82.99 313	62.17 274	90.64 265	89.09 247
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 265	72.98 278	80.73 225	84.95 258	71.71 164	76.23 300	77.59 297	52.83 334	77.73 294	86.38 257	56.35 299	84.97 299	57.72 301	87.05 299	85.51 286

tfpn200view9		74.86 266	74.23 265	76.74 284	86.24 244	52.12 330	79.24 258	73.87 321	73.34 171	81.82 250	84.60 293	46.02 331	88.80 252	51.98 332	90.99 251	89.31 240

1112_ss		74.82 267	73.74 268	78.04 266	89.57 178	60.04 268	76.49 296	87.09 221	54.31 326	73.66 322	79.80 338	60.25 272	86.76 280	58.37 295	84.15 325	87.32 268

EGC-MVSNET		74.79 268	69.99 302	89.19 65	94.89 39	87.00 14	91.89 35	86.28 230	1.09 374	2.23 376	95.98 23	81.87 111	89.48 242	79.76 111	95.96 128	91.10 204

ppachtmachnet_test		74.73 269	74.00 267	76.90 281	80.71 312	56.89 299	71.53 330	78.42 293	58.24 306	79.32 281	82.92 311	57.91 290	84.26 306	65.60 250	91.36 245	89.56 235

Patchmatch-RL test		74.48 270	73.68 269	76.89 282	84.83 260	66.54 205	72.29 327	69.16 349	57.70 310	86.76 161	86.33 259	45.79 336	82.59 314	69.63 216	90.65 264	81.54 334

PatchMatch-RL		74.48 270	73.22 275	78.27 263	87.70 212	85.26 35	75.92 302	70.09 344	64.34 271	76.09 304	81.25 326	65.87 245	78.07 331	53.86 322	83.82 326	71.48 357

XXY-MVS		74.44 272	76.19 248	69.21 322	84.61 263	52.43 329	71.70 329	77.18 299	60.73 295	80.60 265	90.96 178	75.44 171	69.35 351	56.13 307	88.33 284	85.86 283

test2506		74.12 273	73.39 273	76.28 289	91.85 119	44.20 364	84.06 158	48.20 376	72.30 192	81.90 247	94.20 78	27.22 377	89.77 239	64.81 255	96.02 125	94.87 66

CR-MVSNet		74.00 274	73.04 277	76.85 283	79.58 321	62.64 239	82.58 202	76.90 300	50.50 351	75.72 308	92.38 136	48.07 324	84.07 307	68.72 228	82.91 332	83.85 304

Test_1112_low_res		73.90 275	73.08 276	76.35 287	90.35 164	55.95 302	73.40 323	86.17 232	50.70 349	73.14 323	85.94 266	58.31 286	85.90 291	56.51 305	83.22 329	87.20 269

test20.03		73.75 276	74.59 262	71.22 315	81.11 304	51.12 340	70.15 335	72.10 335	70.42 211	80.28 273	91.50 161	64.21 250	74.72 342	46.96 352	94.58 179	87.82 264

SCA		73.32 277	72.57 283	75.58 294	81.62 297	55.86 304	78.89 263	71.37 341	61.73 284	74.93 316	83.42 304	60.46 269	87.01 271	58.11 299	82.63 336	83.88 301

baseline1		73.26 278	73.54 271	72.43 311	84.92 259	47.79 353	79.89 247	74.00 319	65.93 254	78.81 285	86.28 262	56.36 298	81.63 320	56.63 304	79.04 349	87.87 263

1314		73.22 279	72.56 284	75.20 295	80.41 317	57.84 290	81.64 223	85.36 241	51.68 343	73.10 324	76.65 353	61.45 265	85.19 297	63.54 263	79.21 348	82.59 320

MVS		73.21 280	72.59 282	75.06 297	80.97 305	60.81 263	81.64 223	85.92 236	46.03 359	71.68 330	77.54 347	68.47 231	89.77 239	55.70 310	85.39 311	74.60 354

HY-MVS	64.64 18	73.03 281	72.47 285	74.71 298	83.36 284	54.19 314	82.14 219	81.96 272	56.76 318	69.57 338	86.21 263	60.03 273	84.83 302	49.58 341	82.65 334	85.11 290

thisisatest0515		73.00 282	70.52 296	80.46 229	81.45 299	59.90 270	73.16 325	74.31 318	57.86 309	76.08 305	77.78 346	37.60 363	92.12 169	65.00 253	91.45 244	89.35 239

EPNet_dtu		72.87 283	71.33 294	77.49 275	77.72 335	60.55 265	82.35 210	75.79 307	66.49 251	58.39 369	81.06 327	53.68 309	85.98 289	53.55 323	92.97 215	85.95 281
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 284	71.41 293	76.28 289	83.25 285	60.34 266	83.50 178	79.02 292	37.77 369	76.33 300	85.10 282	49.60 321	87.41 268	70.54 210	77.54 354	81.08 341

CHOSEN 1792x2688		72.45 285	70.56 295	78.13 264	90.02 174	63.08 232	68.72 339	83.16 262	42.99 365	75.92 306	85.46 275	57.22 295	85.18 298	49.87 340	81.67 337	86.14 279

testgi		72.36 286	74.61 260	65.59 335	80.56 314	42.82 368	68.29 340	73.35 326	66.87 248	81.84 249	89.93 204	72.08 214	66.92 359	46.05 354	92.54 223	87.01 272

thres200		72.34 287	71.55 292	74.70 299	83.48 282	51.60 335	75.02 311	73.71 324	70.14 217	78.56 287	80.57 331	46.20 329	88.20 262	46.99 351	89.29 273	84.32 298

FPMVS		72.29 288	72.00 287	73.14 305	88.63 194	85.00 37	74.65 315	67.39 350	71.94 198	77.80 293	87.66 240	50.48 319	75.83 338	49.95 338	79.51 344	58.58 368

FMVSNet5		72.10 289	71.69 289	73.32 303	81.57 298	53.02 323	76.77 291	78.37 294	63.31 273	76.37 299	91.85 150	36.68 364	78.98 328	47.87 348	92.45 224	87.95 260

our_test_3		71.85 290	71.59 290	72.62 309	80.71 312	53.78 317	69.72 337	71.71 340	58.80 303	78.03 288	80.51 333	56.61 297	78.84 329	62.20 272	86.04 308	85.23 288

PAPM		71.77 291	70.06 301	76.92 280	86.39 236	53.97 315	76.62 294	86.62 227	53.44 331	63.97 359	84.73 291	57.79 292	92.34 162	39.65 364	81.33 340	84.45 296

IB-MVS	62.13 19	71.64 292	68.97 306	79.66 242	80.80 311	62.26 247	73.94 318	76.90 300	63.27 274	68.63 340	76.79 352	33.83 368	91.84 179	59.28 293	87.26 297	84.88 292
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		71.63 293	72.30 286	69.62 320	76.47 344	52.70 326	70.03 336	80.97 279	59.18 301	79.36 279	88.21 231	60.50 268	69.12 352	58.33 297	77.62 353	87.04 271

Anonymous20231206		71.38 294	71.88 288	69.88 318	86.31 241	54.37 313	70.39 334	74.62 314	52.57 336	76.73 297	88.76 223	59.94 274	72.06 345	44.35 357	93.23 207	83.23 315

MIMVSNet		71.09 295	71.59 290	69.57 321	87.23 221	50.07 346	78.91 262	71.83 337	60.20 299	71.26 331	91.76 155	55.08 306	76.09 336	41.06 362	87.02 300	82.54 322

MS-PatchMatch		70.93 296	70.22 299	73.06 306	81.85 296	62.50 242	73.82 320	77.90 295	52.44 337	75.92 306	81.27 325	55.67 302	81.75 318	55.37 313	77.70 352	74.94 353

pmmvs5		70.73 297	70.07 300	72.72 307	77.03 340	52.73 325	74.14 316	75.65 310	50.36 352	72.17 328	85.37 279	55.42 304	80.67 324	52.86 329	87.59 296	84.77 293

PatchT		70.52 298	72.76 280	63.79 340	79.38 325	33.53 374	77.63 280	65.37 358	73.61 166	71.77 329	92.79 126	44.38 349	75.65 339	64.53 260	85.37 312	82.18 327

N_pmnet		70.20 299	68.80 308	74.38 300	80.91 306	84.81 40	59.12 360	76.45 305	55.06 323	75.31 314	82.36 317	55.74 301	54.82 369	47.02 350	87.24 298	83.52 308

tpmvs		70.16 300	69.56 304	71.96 313	74.71 358	48.13 350	79.63 249	75.45 312	65.02 268	70.26 335	81.88 320	45.34 342	85.68 293	58.34 296	75.39 357	82.08 328

new-patchmatchnet		70.10 301	73.37 274	60.29 348	81.23 303	16.95 378	59.54 358	74.62 314	62.93 275	80.97 260	87.93 236	62.83 262	71.90 346	55.24 315	95.01 164	92.00 183

YYNet1		70.06 302	70.44 297	68.90 323	73.76 360	53.42 321	58.99 361	67.20 352	58.42 305	87.10 153	85.39 278	59.82 276	67.32 356	59.79 290	83.50 328	85.96 280

MDA-MVSNet_test_wron		70.05 303	70.44 297	68.88 324	73.84 359	53.47 319	58.93 362	67.28 351	58.43 304	87.09 154	85.40 277	59.80 277	67.25 357	59.66 291	83.54 327	85.92 282

CostFormer		69.98 304	68.68 309	73.87 301	77.14 338	50.72 343	79.26 257	74.51 316	51.94 342	70.97 334	84.75 290	45.16 345	87.49 267	55.16 316	79.23 347	83.40 311

baseline2		69.77 305	66.89 315	78.41 259	79.51 323	58.09 288	76.23 300	69.57 347	57.50 313	64.82 357	77.45 349	46.02 331	88.44 258	53.08 325	77.83 351	88.70 252

PatchmatchNet		69.71 306	68.83 307	72.33 312	77.66 336	53.60 318	79.29 256	69.99 345	57.66 311	72.53 326	82.93 310	46.45 328	80.08 327	60.91 284	72.09 361	83.31 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 307	66.64 319	77.70 272	73.19 362	71.24 167	75.67 303	65.56 357	70.42 211	65.18 353	92.97 118	33.64 369	83.06 312	53.52 324	69.61 367	78.79 348

UnsupCasMVSNet_bld		69.21 308	69.68 303	67.82 329	79.42 324	51.15 339	67.82 344	75.79 307	54.15 327	77.47 296	85.36 280	59.26 280	70.64 348	48.46 345	79.35 346	81.66 332

gg-mvs-nofinetune		68.96 309	69.11 305	68.52 328	76.12 348	45.32 360	83.59 174	55.88 371	86.68 26	64.62 358	97.01 7	30.36 372	83.97 309	44.78 356	82.94 331	76.26 351

tpm2		68.45 310	66.83 316	73.30 304	78.93 331	48.50 349	79.76 248	71.76 338	47.50 355	69.92 337	83.60 300	42.07 354	88.40 259	48.44 346	79.51 344	83.01 318

tpm		67.95 311	68.08 312	67.55 330	78.74 332	43.53 366	75.60 304	67.10 355	54.92 324	72.23 327	88.10 232	42.87 353	75.97 337	52.21 330	80.95 343	83.15 316

WTY-MVS		67.91 312	68.35 310	66.58 333	80.82 310	48.12 351	65.96 348	72.60 331	53.67 330	71.20 332	81.68 323	58.97 282	69.06 353	48.57 344	81.67 337	82.55 321

test-LLR		67.21 313	66.74 317	68.63 326	76.45 345	55.21 309	67.89 341	67.14 353	62.43 281	65.08 354	72.39 359	43.41 350	69.37 349	61.00 282	84.89 319	81.31 336

sss		66.92 314	67.26 314	65.90 334	77.23 337	51.10 341	64.79 349	71.72 339	52.12 341	70.13 336	80.18 335	57.96 289	65.36 364	50.21 337	81.01 342	81.25 338

KD-MVS_2432*1600		66.87 315	65.81 320	70.04 316	67.50 371	47.49 354	62.56 354	79.16 289	61.21 290	77.98 289	80.61 329	25.29 379	82.48 315	53.02 326	84.92 317	80.16 346

miper_refine_blended		66.87 315	65.81 320	70.04 316	67.50 371	47.49 354	62.56 354	79.16 289	61.21 290	77.98 289	80.61 329	25.29 379	82.48 315	53.02 326	84.92 317	80.16 346

tpm cat1		66.76 317	65.21 323	71.42 314	77.09 339	50.62 344	78.01 273	73.68 325	44.89 361	68.64 339	79.00 341	45.51 339	82.42 317	49.91 339	70.15 364	81.23 340

DWT-MVSNet_test		66.43 318	64.37 324	72.63 308	74.86 357	50.86 342	76.52 295	72.74 330	54.06 328	65.50 351	68.30 365	32.13 370	84.84 301	61.63 279	73.59 359	82.19 326

PVSNet	58.17 21	66.41 319	65.63 322	68.75 325	81.96 294	49.88 347	62.19 356	72.51 333	51.03 346	68.04 342	75.34 357	50.84 317	74.77 340	45.82 355	82.96 330	81.60 333

tpmrst		66.28 320	66.69 318	65.05 338	72.82 366	39.33 369	78.20 272	70.69 343	53.16 333	67.88 343	80.36 334	48.18 323	74.75 341	58.13 298	70.79 363	81.08 341

Patchmatch-test		65.91 321	67.38 313	61.48 346	75.51 351	43.21 367	68.84 338	63.79 360	62.48 279	72.80 325	83.42 304	44.89 347	59.52 368	48.27 347	86.45 303	81.70 331

ADS-MVSNet2		65.87 322	63.64 327	72.55 310	73.16 363	56.92 298	67.10 345	74.81 313	49.74 353	66.04 349	82.97 308	46.71 326	77.26 333	42.29 359	69.96 365	83.46 309

test-mter		65.00 323	63.79 326	68.63 326	76.45 345	55.21 309	67.89 341	67.14 353	50.98 347	65.08 354	72.39 359	28.27 375	69.37 349	61.00 282	84.89 319	81.31 336

test0.0.03 1		64.66 324	64.36 325	65.57 336	75.03 356	46.89 357	64.69 350	61.58 365	62.43 281	71.18 333	77.54 347	43.41 350	68.47 354	40.75 363	82.65 334	81.35 335

pmmvs3		62.47 325	60.02 337	69.80 319	71.58 369	64.00 224	70.52 333	58.44 369	39.77 367	66.05 348	75.84 355	27.10 378	72.28 344	46.15 353	84.77 323	73.11 355

EPMVS		62.47 325	62.63 329	62.01 342	70.63 370	38.74 370	74.76 313	52.86 373	53.91 329	67.71 345	80.01 336	39.40 358	66.60 360	55.54 312	68.81 368	80.68 345

ADS-MVSNet		61.90 327	62.19 330	61.03 347	73.16 363	36.42 372	67.10 345	61.75 363	49.74 353	66.04 349	82.97 308	46.71 326	63.21 366	42.29 359	69.96 365	83.46 309

PMMVS		61.65 328	60.38 334	65.47 337	65.40 375	69.26 182	63.97 352	61.73 364	36.80 370	60.11 364	68.43 363	59.42 278	66.35 361	48.97 343	78.57 350	60.81 365

E-PMN		61.59 329	61.62 331	61.49 345	66.81 373	55.40 307	53.77 364	60.34 366	66.80 249	58.90 367	65.50 366	40.48 357	66.12 362	55.72 309	86.25 306	62.95 364

TESTMET0.1,1		61.29 330	60.32 335	64.19 339	72.06 367	51.30 337	67.89 341	62.09 361	45.27 360	60.65 363	69.01 362	27.93 376	64.74 365	56.31 306	81.65 339	76.53 350

MVS-HIRNet		61.16 331	62.92 328	55.87 351	79.09 328	35.34 373	71.83 328	57.98 370	46.56 357	59.05 366	91.14 170	49.95 320	76.43 335	38.74 365	71.92 362	55.84 369

EMVS		61.10 332	60.81 333	61.99 343	65.96 374	55.86 304	53.10 365	58.97 368	67.06 246	56.89 370	63.33 367	40.98 355	67.03 358	54.79 318	86.18 307	63.08 363

DSMNet-mixed		60.98 333	61.61 332	59.09 350	72.88 365	45.05 362	74.70 314	46.61 377	26.20 371	65.34 352	90.32 195	55.46 303	63.12 367	41.72 361	81.30 341	69.09 361

dp		60.70 334	60.29 336	61.92 344	72.04 368	38.67 371	70.83 331	64.08 359	51.28 345	60.75 362	77.28 350	36.59 365	71.58 347	47.41 349	62.34 370	75.52 352

CHOSEN 280x420		59.08 335	56.52 340	66.76 332	76.51 343	64.39 220	49.62 366	59.00 367	43.86 363	55.66 371	68.41 364	35.55 366	68.21 355	43.25 358	76.78 356	67.69 362

PVSNet_0	51.08 22	56.10 336	54.97 341	59.48 349	75.12 355	53.28 322	55.16 363	61.89 362	44.30 362	59.16 365	62.48 368	54.22 308	65.91 363	35.40 368	47.01 371	59.25 367

new_pmnet		55.69 337	57.66 339	49.76 353	75.47 352	30.59 375	59.56 357	51.45 374	43.62 364	62.49 360	75.48 356	40.96 356	49.15 372	37.39 367	72.52 360	69.55 360

PMMVS2		55.64 338	59.27 338	44.74 354	64.30 376	12.32 379	40.60 367	49.79 375	53.19 332	65.06 356	84.81 289	53.60 310	49.76 371	32.68 371	89.41 272	72.15 356

MVE	40.22 23	51.82 339	50.47 342	55.87 351	62.66 377	51.91 332	31.61 369	39.28 378	40.65 366	50.76 372	74.98 358	56.24 300	44.67 373	33.94 370	64.11 369	71.04 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 340	29.60 343	33.06 355	17.99 379	3.84 381	13.62 370	73.92 320	2.79 373	18.29 375	53.41 370	28.53 374	43.25 374	22.56 372	35.27 373	52.11 370

cdsmvs_eth3d_5k		20.81 341	27.75 344	0.00 360	0.00 383	0.00 384	0.00 371	85.44 240	0.00 378	0.00 379	82.82 312	81.46 115	0.00 379	0.00 377	0.00 377	0.00 375

tmp_tt		20.25 342	24.50 345	7.49 357	4.47 380	8.70 380	34.17 368	25.16 380	1.00 375	32.43 374	18.49 372	39.37 359	9.21 376	21.64 373	43.75 372	4.57 372

ab-mvs-re		6.65 343	8.87 346	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	79.80 338	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		6.41 344	8.55 347	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	76.94 161	0.00 379	0.00 377	0.00 377	0.00 375

test123		6.27 345	8.08 348	0.84 358	1.11 382	0.57 382	62.90 353	0.82 382	0.54 376	1.07 378	2.75 377	1.26 381	0.30 377	1.04 375	1.26 376	1.66 373

testmvs		5.91 346	7.65 349	0.72 359	1.20 381	0.37 383	59.14 359	0.67 383	0.49 377	1.11 377	2.76 376	0.94 382	0.24 378	1.02 376	1.47 375	1.55 374

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 71	91.55 132	77.99 94		91.01 140					96.05 8	87.45 19	98.17 33	92.40 165

PC_three_1452									58.96 302	90.06 94	91.33 164	80.66 126	93.03 145	75.78 159	95.94 130	92.48 161

No_MVS				88.81 71	91.55 132	77.99 94		91.01 140					96.05 8	87.45 19	98.17 33	92.40 165

test_one_0601					93.85 63	73.27 137		94.11 37	86.57 27	93.47 40	94.64 57	88.42 27

eth-test2					0.00 383

eth-test					0.00 383

ZD-MVS					92.22 105	80.48 70		91.85 116	71.22 204	90.38 89	92.98 116	86.06 63	96.11 6	81.99 87	96.75 97

RE-MVS-def			92.61 5		94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 64	90.64 11		87.16 28	97.60 66	92.73 149

IU-MVS					94.18 50	72.64 143		90.82 145	56.98 316	89.67 108				85.78 47	97.92 48	93.28 128

OPU-MVS				88.27 86	91.89 117	77.83 98	90.47 50				91.22 166	81.12 120	94.68 75	74.48 169	95.35 149	92.29 172

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 72	89.27 22	95.84 22	86.03 44	97.82 53	92.04 181

test_241102_ONE					94.18 50	72.65 141		93.69 54	83.62 46	94.11 23	93.78 103	90.28 15	95.50 47

9.14			89.29 62		91.84 121		88.80 85	95.32 11	75.14 151	91.07 78	92.89 121	87.27 46	93.78 112	83.69 68	97.55 69

save fliter					93.75 64	77.44 104	86.31 125	89.72 175	70.80 207

test_0728_THIRD									85.33 32	93.75 31	94.65 54	87.44 45	95.78 27	87.41 21	98.21 30	92.98 140

test_0728_SECOND				86.79 104	94.25 49	72.45 151	90.54 46	94.10 38					95.88 16	86.42 34	97.97 45	92.02 182

test0726					94.16 53	72.56 147	90.63 45	93.90 46	83.61 47	93.75 31	94.49 61	89.76 19

GSMVS																83.88 301

test_part2					93.86 62	77.77 99				92.84 48

sam_mvs1												46.11 330				83.88 301

sam_mvs												45.92 335

ambc				82.98 184	90.55 162	64.86 215	88.20 92	89.15 186		89.40 117	93.96 93	71.67 219	91.38 192	78.83 122	96.55 103	92.71 152

MTGPA								91.81 119

test_post1							78.85 265				3.13 374	45.19 344	80.13 326	58.11 299

test_post											3.10 375	45.43 340	77.22 334

patchmatchnet-post											81.71 322	45.93 334	87.01 271

GG-mvs-BLEND				67.16 331	73.36 361	46.54 359	84.15 155	55.04 372		58.64 368	61.95 369	29.93 373	83.87 310	38.71 366	76.92 355	71.07 358

MTMP							90.66 43	33.14 379

gm-plane-assit					75.42 353	44.97 363			52.17 338		72.36 361		87.90 263	54.10 321

test9_res														80.83 100	96.45 108	90.57 219

TEST9					92.34 99	79.70 77	83.94 161	90.32 158	65.41 266	84.49 205	90.97 176	82.03 105	93.63 117

test_8					92.09 109	78.87 85	83.82 166	90.31 160	65.79 256	84.36 208	90.96 178	81.93 107	93.44 129

agg_prior2														79.68 113	96.16 120	90.22 226

agg_prior					91.58 129	77.69 100		90.30 161		84.32 209			93.18 137

TestCases				89.68 54	91.59 126	83.40 50		95.44 9	79.47 96	88.00 140	93.03 114	82.66 92	91.47 185	70.81 203	96.14 121	94.16 94

test_prior4						78.97 84	84.59 147

test_prior2							83.37 181		75.43 146	84.58 203	91.57 158	81.92 109		79.54 115	96.97 88

test_prior				86.32 112	90.59 160	71.99 158		92.85 91					94.17 96			92.80 146

旧先验2							81.73 221		56.88 317	86.54 172			84.90 300	72.81 192

新几何2							81.72 222

新几何1				82.95 185	93.96 59	78.56 89		80.24 283	55.45 321	83.93 220	91.08 171	71.19 221	88.33 260	65.84 248	93.07 210	81.95 330

旧先验1					91.97 112	71.77 160		81.78 274			91.84 151	73.92 190			93.65 199	83.61 307

无先验							82.81 197	85.62 239	58.09 307				91.41 190	67.95 235		84.48 295

原ACMM2							82.26 215

原ACMM1				84.60 149	92.81 90	74.01 132		91.50 125	62.59 277	82.73 236	90.67 188	76.53 168	94.25 89	69.24 219	95.69 142	85.55 285

test222					93.31 75	76.54 117	79.38 255	77.79 296	52.59 335	82.36 240	90.84 182	66.83 240			91.69 240	81.25 338

testdata2													86.43 284	63.52 264

segment_acmp												81.94 106

testdata				79.54 244	92.87 85	72.34 152		80.14 285	59.91 300	85.47 190	91.75 156	67.96 234	85.24 296	68.57 231	92.18 232	81.06 343

testdata1							79.62 250		73.95 163

test12				86.57 107	90.74 156	72.63 145		90.69 148		82.76 235		79.20 137	94.80 72		95.32 151	92.27 174

plane_prior7					93.45 70	77.31 108

plane_prior6					92.61 91	76.54 117						74.84 179

plane_prior5								93.61 58					95.22 58	80.78 101	95.83 135	94.46 82

plane_prior4											92.95 119

plane_prior3						76.85 115			77.79 118	86.55 167

plane_prior2							89.45 74		79.44 98

plane_prior1					92.83 89

plane_prior						76.42 120	87.15 109		75.94 140						95.03 163

n2								0.00 384

nn								0.00 384

door-mid								74.45 317

lessismore_v0				85.95 123	91.10 148	70.99 169		70.91 342		91.79 67	94.42 66	61.76 264	92.93 148	79.52 117	93.03 212	93.93 102

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 49	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

test11								91.46 126

door								72.57 332

HQP5-MVS						70.66 170

HQP-NCC					91.19 143		84.77 142		73.30 173	80.55 268

ACMP_Plane					91.19 143		84.77 142		73.30 173	80.55 268

BP-MVS														77.30 145

HQP4-MVS										80.56 267			94.61 79			93.56 123

HQP3-MVS								92.68 97							94.47 181

HQP2-MVS												72.10 212

NP-MVS					91.95 113	74.55 129					90.17 201

MDTV_nov1_ep13_2view						27.60 377	70.76 332		46.47 358	61.27 361		45.20 343		49.18 342		83.75 306

MDTV_nov1_ep13			68.29 311		78.03 333	43.87 365	74.12 317	72.22 334	52.17 338	67.02 347	85.54 271	45.36 341	80.85 323	55.73 308	84.42 324

ACMMP++_ref															95.74 141

ACMMP++															97.35 78

Test By Simon												79.09 138

ITE_SJBPF				90.11 50	90.72 157	84.97 38		90.30 161	81.56 72	90.02 96	91.20 168	82.40 95	90.81 209	73.58 181	94.66 177	94.56 77

DeepMVS_CX				24.13 356	32.95 378	29.49 376		21.63 381	12.07 372	37.95 373	45.07 371	30.84 371	19.21 375	17.94 374	33.06 374	23.69 371

High-resolution multi-view benchmark