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	93.71 115

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 55	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	92.73 150

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	92.54 160

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	92.19 179

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 69	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 61	95.03 43	89.75 21	94.93 68	79.95 108	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 58	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	92.60 100	83.09 54	91.54 71	94.25 76	87.67 43	95.51 44	87.21 27	98.11 36	93.12 136

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 80	94.00 89	88.26 32	95.71 31	87.28 26	98.39 21	92.55 159

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 98	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	91.81 120	84.07 40	92.00 64	94.40 68	86.63 54	95.28 56	88.59 5	98.31 24	92.30 171

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 95	88.22 20	88.53 131	97.64 2	83.45 84	94.55 83	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 74	94.17 81	87.51 44	95.87 18	87.74 11	97.76 56	93.99 100

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 85	94.21 77	87.75 41	95.87 18	87.60 16	97.71 60	93.83 107

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 72	93.96 93	88.35 30	95.56 37	87.74 11	97.74 58	92.85 145

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 82	91.81 120	84.07 40	92.00 64	94.40 68	86.63 54	95.28 56	88.59 5	98.31 24	92.30 171

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 77	93.97 90	87.93 40	95.87 18	88.65 4	97.96 47	94.12 97

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	92.98 141

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 70	94.41 67	88.17 34	95.98 11	86.37 36	97.99 42	93.96 102

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 54	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 86	82.59 60	88.52 132	94.37 71	86.74 53	95.41 51	86.32 37	98.21 30	93.19 134
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 75	93.80 100	87.20 48	95.80 24	87.10 31	97.69 61	93.93 103

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 95	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	78.35 126	98.76 4	95.61 47

ACMMP_NAP		90.65 31	91.07 38	89.42 61	95.93 16	79.54 79	89.95 59	93.68 56	77.65 119	91.97 66	94.89 46	88.38 28	95.45 49	89.27 3	97.87 52	93.27 130

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	79.20 101	93.83 28	93.60 107	90.81 8	92.96 146	85.02 54	98.45 19	92.41 165
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 92	93.95 96	84.50 72	95.37 52	80.87 98	95.50 147	94.53 80

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 78	94.05 40	79.03 104	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 68	93.38 73	78.65 89	89.15 79	94.05 40	84.68 38	93.90 25	94.11 85	88.13 36	96.30 3	84.51 60	97.81 54	91.70 194
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 72	93.96 93	88.35 30	95.56 37	84.88 55	97.74 58	92.85 145

SED-MVS		90.46 37	91.64 19	86.93 101	94.18 50	72.65 141	90.47 49	93.69 54	83.77 44	94.11 23	94.27 72	90.28 15	95.84 22	86.03 44	97.92 48	92.29 173

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	93.88 105
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 59	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 83	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	89.57 181	88.51 19	90.11 94	95.12 42	90.98 7	88.92 251	77.55 141	97.07 86	83.13 318
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	95.94 131	92.48 162

DVP-MVS		90.06 43	91.32 30	86.29 114	94.16 53	72.56 147	90.54 46	91.01 141	83.61 47	93.75 31	94.65 54	89.76 19	95.78 27	86.42 34	97.97 45	90.55 222
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	58.83 286	89.04 80	92.74 97	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 117	98.74 6	99.00 2

PEN-MVS		90.03 45	91.88 16	84.48 150	96.57 6	58.88 283	88.95 81	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 122	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	72.94 205	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	57.84 291	88.93 83	92.84 94	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 119	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 62	93.89 98	83.80 80	93.10 143	82.67 78	98.04 37	93.64 120

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	88.83 126	94.26 75	77.55 153	95.86 21	84.88 55	95.87 135	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	81.42 117	95.90 15	80.94 97	98.80 3	98.84 5

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 41	79.86 75	86.76 117	92.78 96	78.78 107	92.51 55	93.64 106	88.13 36	93.84 110	84.83 57	97.55 69	94.10 98
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	90.63 151	70.00 218	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 128	95.52 48

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 175	86.67 17	90.51 48	90.20 168	69.87 219	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 113	97.09 19

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 98	82.98 55	91.98 31	90.08 171	71.54 199	94.28 21	96.54 13	81.57 115	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	86.75 163	93.26 110	93.64 2	90.93 203	84.60 59	90.75 261	93.97 101

APD-MVS		89.54 56	89.63 57	89.26 64	92.57 92	81.34 66	90.19 54	93.08 82	80.87 81	91.13 78	93.19 111	86.22 61	95.97 12	82.23 84	97.18 84	90.45 224
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 89	92.55 133	84.99 68	96.15 5	81.26 92	96.61 101	91.83 190

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 73	84.72 42	89.70 63	90.29 165	69.27 222	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 133	95.34 52

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 29	92.30 106	79.74 92	87.50 148	92.38 137	81.42 117	93.28 134	83.07 73	97.24 82	91.67 195

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	93.20 133
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	58.61 288	88.66 90	92.06 111	90.78 6	95.67 7	95.17 40	81.80 113	95.54 41	79.00 120	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	89.13 122	93.44 108	83.82 79	90.98 201	83.86 66	95.30 155	93.60 122

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 62	93.64 68	79.07 84	88.54 91	94.20 28	73.53 167	89.71 107	94.82 49	85.09 67	95.77 29	84.17 63	98.03 39	93.26 131
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	88.38 198	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 195	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	78.43 112	89.16 121	92.25 144	72.03 217	96.36 2	88.21 8	90.93 256	92.98 141
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 72	93.75 64	77.44 104	86.31 125	95.27 12	70.80 207	92.28 59	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	78.90 105	92.88 45	92.29 142	86.11 62	90.22 225	86.24 41	97.24 82	91.36 202
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	91.27 134	78.20 114	86.69 166	92.28 143	80.36 130	95.06 65	86.17 42	96.49 106	90.22 227

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 78	91.94 115	77.27 110	88.71 88	95.26 13	76.08 133	90.66 88	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	89.44 183	88.63 18	94.38 18	95.77 25	86.38 60	93.59 121	79.84 109	95.21 156	91.82 191

DP-MVS		88.60 71	89.01 66	87.36 97	91.30 139	77.50 102	87.55 102	92.97 89	87.95 22	89.62 111	92.87 122	84.56 71	93.89 107	77.65 138	96.62 100	90.70 216

Anonymous20231211		88.40 72	89.62 58	84.73 146	90.46 163	65.27 212	88.86 84	93.02 87	87.15 25	93.05 43	97.10 6	82.28 100	92.02 173	76.70 150	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 98	91.97 114	70.73 209	94.19 22	96.67 11	76.94 162	94.57 81	83.07 73	96.28 115	96.15 32

OMC-MVS		88.19 74	87.52 85	90.19 49	91.94 115	81.68 63	87.49 104	93.17 78	76.02 136	88.64 129	91.22 167	84.24 76	93.37 132	77.97 136	97.03 87	95.52 48

CS-MVS		88.14 75	87.67 83	89.54 60	89.56 177	79.18 83	90.47 49	94.77 18	79.37 99	84.32 210	89.33 214	83.87 77	94.53 84	82.45 80	94.89 170	94.90 64

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 67	95.72 22	76.74 116	92.49 25	91.19 137	67.85 241	86.63 167	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
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	85.12 194	89.67 210	84.47 74	95.46 48	82.56 79	96.26 118	93.77 113

RPSCF		88.00 78	86.93 95	91.22 31	90.08 169	89.30 5	89.68 65	91.11 138	79.26 100	89.68 108	94.81 52	82.44 94	87.74 265	76.54 152	88.74 283	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 95	88.00 141	93.03 114	82.66 92	91.47 185	70.81 204	96.14 122	94.16 94

TranMVSNet+NR-MVSNet		87.86 80	88.76 73	85.18 138	94.02 58	64.13 222	84.38 153	91.29 133	84.88 37	92.06 63	93.84 99	86.45 58	93.73 113	73.22 186	98.66 11	97.69 9

nrg030		87.85 81	88.49 74	85.91 124	90.07 171	69.73 176	87.86 99	94.20 28	74.04 161	92.70 53	94.66 53	85.88 65	91.50 184	79.72 111	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 91	87.26 106	94.54 20	72.05 195	88.89 123	92.31 141	83.86 78	94.24 90	81.59 91	96.87 91	92.97 144

CNVR-MVS		87.81 83	87.68 82	88.21 87	92.87 85	77.30 109	85.25 138	91.23 135	77.31 123	87.07 156	91.47 163	82.94 89	94.71 74	84.67 58	96.27 117	92.62 157

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 97	86.55 168	92.95 119	74.84 180	95.22 58	80.78 100	95.83 136	94.46 82

NCCC		87.36 85	86.87 96	88.83 69	92.32 101	78.84 87	86.58 121	91.09 139	78.77 108	84.85 200	90.89 181	80.85 123	95.29 54	81.14 94	95.32 152	92.34 169

DeepPCF-MVS	81.24 5	87.28 86	86.21 106	90.49 43	91.48 136	84.90 39	83.41 180	92.38 105	70.25 215	89.35 119	90.68 188	82.85 90	94.57 81	79.55 113	95.95 130	92.00 184

SixPastTwentyTwo		87.20 87	87.45 86	86.45 110	92.52 94	69.19 185	87.84 100	88.05 204	81.66 71	94.64 14	96.53 14	65.94 245	94.75 73	83.02 75	96.83 94	95.41 50

test_part1		87.15 88	87.82 80	85.15 139	88.88 191	63.04 233	87.98 96	94.85 16	82.52 61	93.61 37	95.73 26	67.51 236	95.71 31	80.48 105	98.83 2	96.69 25

CS-MVS-test		87.00 89	86.43 101	88.71 75	89.46 178	77.46 103	89.42 76	95.73 6	77.87 117	81.64 255	87.25 249	82.43 95	94.53 84	77.65 138	96.46 108	94.14 96

UniMVSNet (Re)		86.87 90	86.98 94	86.55 108	93.11 80	68.48 189	83.80 168	92.87 91	80.37 84	89.61 113	91.81 155	77.72 150	94.18 94	75.00 169	98.53 16	96.99 22

Vis-MVSNet		86.86 91	86.58 99	87.72 92	92.09 109	77.43 106	87.35 105	92.09 110	78.87 106	84.27 216	94.05 86	78.35 145	93.65 115	80.54 104	91.58 244	92.08 181
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	82.55 205	91.56 124	83.08 55	90.92 82	91.82 154	78.25 146	93.99 102	74.16 173	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	92.21 107	81.73 70	90.92 82	91.97 148	77.20 156	93.99 102	74.16 173	98.35 22	97.61 10

Regformer-2		86.74 94	86.08 108	88.73 73	84.18 276	79.20 82	83.52 175	89.33 185	83.33 51	89.92 102	85.07 286	83.23 87	93.16 139	83.39 69	92.72 221	93.83 107

IS-MVSNet		86.66 95	86.82 98	86.17 121	92.05 111	66.87 202	91.21 40	88.64 194	86.30 30	89.60 114	92.59 130	69.22 228	94.91 69	73.89 177	97.89 51	96.72 24

v10		86.54 96	87.10 90	84.84 143	88.16 206	63.28 230	86.64 120	92.20 108	75.42 148	92.81 50	94.50 60	74.05 190	94.06 101	83.88 65	96.28 115	97.17 18

pmmvs6		86.52 97	88.06 78	81.90 204	92.22 105	62.28 246	84.66 146	89.15 187	83.54 49	89.85 103	97.32 4	88.08 38	86.80 278	70.43 212	97.30 81	96.62 27

Regformer-4		86.41 98	85.71 116	88.52 78	84.27 272	77.57 101	84.07 156	88.00 206	82.82 58	89.84 104	85.48 274	82.06 104	92.77 152	83.83 67	91.04 250	95.22 60

PHI-MVS		86.38 99	85.81 113	88.08 88	88.44 200	77.34 107	89.35 77	93.05 83	73.15 178	84.76 201	87.70 240	78.87 140	94.18 94	80.67 102	96.29 114	92.73 150

test_prior3		86.31 100	86.31 103	86.32 112	90.59 160	71.99 158	83.37 181	92.85 92	75.43 146	84.58 204	91.57 159	81.92 110	94.17 96	79.54 114	96.97 88	92.80 147

CSCG		86.26 101	86.47 100	85.60 132	90.87 153	74.26 131	87.98 96	91.85 117	80.35 85	89.54 117	88.01 234	79.09 138	92.13 167	75.51 162	95.06 163	90.41 225

DeepC-MVS_fast	80.27 8	86.23 102	85.65 118	87.96 91	91.30 139	76.92 113	87.19 107	91.99 113	70.56 210	84.96 196	90.69 187	80.01 133	95.14 61	78.37 125	95.78 140	91.82 191
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	87.82 210	62.35 245	86.42 123	91.33 132	76.78 128	92.73 52	94.48 62	73.41 199	93.72 114	83.10 72	95.41 148	97.01 21

Anonymous20240529		86.20 104	87.13 89	83.42 176	90.19 167	64.55 219	84.55 148	90.71 148	85.85 31	89.94 101	95.24 39	82.13 102	90.40 220	69.19 223	96.40 111	95.31 54

CDPH-MVS		86.17 105	85.54 119	88.05 90	92.25 103	75.45 125	83.85 165	92.01 112	65.91 256	86.19 175	91.75 157	83.77 81	94.98 67	77.43 144	96.71 98	93.73 114

Regformer-1		86.00 106	85.50 120	87.49 95	84.18 276	76.90 114	83.52 175	87.94 208	82.18 65	89.19 120	85.07 286	82.28 100	91.89 177	82.40 82	92.72 221	93.69 116

NR-MVSNet		86.00 106	86.22 105	85.34 136	93.24 77	64.56 218	82.21 217	90.46 154	80.99 78	88.42 134	91.97 148	77.56 152	93.85 108	72.46 196	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	90.32 159	65.79 257	84.49 206	90.97 177	81.93 108	93.63 117	81.21 93	96.54 104	90.88 211

FC-MVSNet-test		85.93 109	87.05 92	82.58 195	92.25 103	56.44 302	85.75 132	93.09 81	77.33 122	91.94 67	94.65 54	74.78 182	93.41 131	75.11 168	98.58 14	97.88 7

Effi-MVS+-dtu		85.82 110	83.38 157	93.14 3	87.13 225	91.15 2	87.70 101	88.42 196	74.57 156	83.56 225	85.65 271	78.49 143	94.21 92	72.04 198	92.88 216	94.05 99

agg_prior1		85.72 111	85.20 124	87.28 98	91.58 129	77.69 99	83.69 171	90.30 162	66.29 253	84.32 210	91.07 174	82.13 102	93.18 137	81.02 95	96.36 112	90.98 207

TAPA-MVS	77.73 12	85.71 112	84.83 131	88.37 84	88.78 193	79.72 76	87.15 109	93.50 62	69.17 223	85.80 185	89.56 211	80.76 124	92.13 167	73.21 191	95.51 146	93.25 132
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 113	86.14 107	83.58 173	87.97 207	67.13 198	87.55 102	94.32 22	73.44 169	88.47 133	87.54 243	86.45 58	91.06 200	75.76 161	93.76 195	92.54 160

EPP-MVSNet		85.47 114	85.04 127	86.77 105	91.52 135	69.37 179	91.63 37	87.98 207	81.51 73	87.05 157	91.83 153	66.18 244	95.29 54	70.75 207	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	91.39 131	72.33 190	87.59 146	90.25 198	84.85 69	92.37 161	78.00 134	91.94 238	93.66 117

FIs		85.35 116	86.27 104	82.60 194	91.86 118	57.31 295	85.10 140	93.05 83	75.83 141	91.02 81	93.97 90	73.57 195	92.91 150	73.97 176	98.02 40	97.58 12

casdiffmvs		85.21 117	85.85 112	83.31 178	86.17 248	62.77 237	83.03 191	93.93 44	74.69 155	88.21 139	92.68 129	82.29 99	91.89 177	77.87 137	93.75 197	95.27 56

baseline		85.20 118	85.93 110	83.02 183	86.30 243	62.37 244	84.55 148	93.96 43	74.48 158	87.12 152	92.03 147	82.30 98	91.94 174	78.39 124	94.21 186	94.74 73

K. test v3		85.14 119	84.73 132	86.37 111	91.13 147	69.63 178	85.45 136	76.68 304	84.06 42	92.44 57	96.99 8	62.03 264	94.65 76	80.58 103	93.24 206	94.83 72

EI-MVSNet-Vis-set		85.12 120	84.53 140	86.88 102	84.01 279	72.76 140	83.91 164	85.18 247	80.44 83	88.75 127	85.49 273	80.08 132	91.92 175	82.02 85	90.85 259	95.97 38

ETH3 D test6400		85.09 121	84.87 130	85.75 129	90.80 155	69.34 180	85.90 129	93.31 70	65.43 263	86.11 178	89.95 204	80.92 122	94.86 70	75.90 159	95.57 145	93.05 138

Regformer-3		85.06 122	84.67 137	86.22 117	84.27 272	73.43 135	84.07 156	85.26 245	80.77 82	88.62 130	85.48 274	80.56 128	90.39 221	81.99 86	91.04 250	94.85 70

EI-MVSNet-UG-set		85.04 123	84.44 142	86.85 103	83.87 282	72.52 149	83.82 166	85.15 248	80.27 87	88.75 127	85.45 277	79.95 134	91.90 176	81.92 88	90.80 260	96.13 33

X-MVStestdata		85.04 123	82.70 167	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 98	16.05 374	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 87	75.95 139	87.12 152	89.39 212	77.98 147	89.40 247	77.46 142	94.78 173	84.75 295

F-COLMAP		84.97 126	83.42 156	89.63 56	92.39 97	83.40 50	88.83 85	91.92 116	73.19 177	80.18 275	89.15 219	77.04 160	93.28 134	65.82 250	92.28 229	92.21 178

3Dnovator	80.37 7	84.80 127	84.71 135	85.06 141	86.36 241	74.71 128	88.77 87	90.00 173	75.65 144	84.96 196	93.17 112	74.06 189	91.19 195	78.28 128	91.09 248	89.29 243

IterMVS-LS		84.73 128	84.98 128	83.96 164	87.35 220	63.66 225	83.25 185	89.88 175	76.06 134	89.62 111	92.37 140	73.40 201	92.52 157	78.16 131	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	84.34 146	85.49 135	90.18 168	75.86 124	79.23 261	87.13 218	73.35 170	85.56 189	89.34 213	83.60 83	90.50 218	76.64 151	94.05 191	90.09 232

HQP-MVS		84.61 130	84.06 149	86.27 115	91.19 143	70.66 170	84.77 142	92.68 98	73.30 173	80.55 269	90.17 202	72.10 213	94.61 79	77.30 145	94.47 181	93.56 124

v1192		84.57 131	84.69 136	84.21 158	87.75 212	62.88 235	83.02 192	91.43 128	69.08 225	89.98 100	90.89 181	72.70 209	93.62 120	82.41 81	94.97 167	96.13 33

mvs-test1		84.55 132	82.12 176	91.84 20	87.13 225	89.54 4	85.05 141	88.42 196	74.57 156	80.60 266	82.98 308	78.49 143	93.98 104	72.04 198	89.77 270	92.00 184

FMVSNet1		84.55 132	85.45 121	81.85 206	90.27 166	61.05 258	86.83 114	88.27 201	78.57 111	89.66 110	95.64 30	75.43 173	90.68 213	69.09 224	95.33 151	93.82 109

v1144		84.54 134	84.72 134	84.00 162	87.67 214	62.55 241	82.97 193	90.93 144	70.32 214	89.80 105	90.99 176	73.50 196	93.48 127	81.69 90	94.65 178	95.97 38

Gipuma		84.44 135	86.33 102	78.78 252	84.20 275	73.57 134	89.55 69	90.44 155	84.24 39	84.38 208	94.89 46	76.35 171	80.40 326	76.14 156	96.80 96	82.36 326
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 136	83.93 152	85.63 131	91.59 126	71.58 165	83.52 175	92.13 109	61.82 284	83.96 219	89.75 209	79.93 135	93.46 128	78.33 127	94.34 184	91.87 189

VDDNet		84.35 137	85.39 122	81.25 215	95.13 33	59.32 276	85.42 137	81.11 278	86.41 29	87.41 149	96.21 19	73.61 194	90.61 216	66.33 244	96.85 92	93.81 112

ETV-MVS		84.31 138	83.91 153	85.52 133	88.58 196	70.40 172	84.50 152	93.37 64	78.76 109	84.07 218	78.72 344	80.39 129	95.13 62	73.82 179	92.98 214	91.04 206

v1240		84.30 139	84.51 141	83.65 171	87.65 215	61.26 255	82.85 197	91.54 125	67.94 239	90.68 87	90.65 190	71.71 219	93.64 116	82.84 77	94.78 173	96.07 35

MVS_111021_LR		84.28 140	83.76 154	85.83 128	89.23 184	83.07 53	80.99 235	83.56 262	72.71 183	86.07 179	89.07 221	81.75 114	86.19 288	77.11 147	93.36 202	88.24 255

h-mvs33		84.25 141	82.76 166	88.72 74	91.82 123	82.60 58	84.00 160	84.98 254	71.27 201	86.70 164	90.55 192	63.04 260	93.92 106	78.26 129	94.20 187	89.63 234

v144192		84.24 142	84.41 143	83.71 170	87.59 217	61.57 251	82.95 194	91.03 140	67.82 242	89.80 105	90.49 193	73.28 202	93.51 126	81.88 89	94.89 170	96.04 37

dcpmvs_2		84.23 143	85.14 125	81.50 212	88.61 195	61.98 249	82.90 196	93.11 79	68.66 231	92.77 51	92.39 136	78.50 142	87.63 267	76.99 149	92.30 226	94.90 64

v1921920		84.23 143	84.37 145	83.79 167	87.64 216	61.71 250	82.91 195	91.20 136	67.94 239	90.06 95	90.34 195	72.04 216	93.59 121	82.32 83	94.91 168	96.07 35

VDD-MVS		84.23 143	84.58 139	83.20 180	91.17 146	65.16 214	83.25 185	84.97 255	79.79 91	87.18 151	94.27 72	74.77 183	90.89 206	69.24 220	96.54 104	93.55 126

v2v482		84.09 146	84.24 147	83.62 172	87.13 225	61.40 252	82.71 200	89.71 177	72.19 194	89.55 115	91.41 164	70.70 224	93.20 136	81.02 95	93.76 195	96.25 31

EG-PatchMatch MVS		84.08 147	84.11 148	83.98 163	92.22 105	72.61 146	82.20 219	87.02 223	72.63 184	88.86 124	91.02 175	78.52 141	91.11 198	73.41 184	91.09 248	88.21 256

DP-MVS Recon		84.05 148	83.22 159	86.52 109	91.73 124	75.27 126	83.23 187	92.40 103	72.04 196	82.04 245	88.33 230	77.91 149	93.95 105	66.17 245	95.12 161	90.34 226

TransMVSNet (Re)		84.02 149	85.74 115	78.85 251	91.00 150	55.20 312	82.29 213	87.26 214	79.65 94	88.38 136	95.52 33	83.00 88	86.88 276	67.97 235	96.60 102	94.45 84

Baseline_NR-MVSNet		84.00 150	85.90 111	78.29 263	91.47 137	53.44 321	82.29 213	87.00 226	79.06 103	89.55 115	95.72 28	77.20 156	86.14 289	72.30 197	98.51 17	95.28 55

TSAR-MVS + GP.		83.95 151	82.69 168	87.72 92	89.27 183	81.45 65	83.72 170	81.58 277	74.73 154	85.66 186	86.06 266	72.56 211	92.69 154	75.44 164	95.21 156	89.01 251

alignmvs		83.94 152	83.98 151	83.80 166	87.80 211	67.88 195	84.54 150	91.42 130	73.27 176	88.41 135	87.96 235	72.33 212	90.83 208	76.02 158	94.11 189	92.69 154

Effi-MVS+		83.90 153	84.01 150	83.57 174	87.22 223	65.61 211	86.55 122	92.40 103	78.64 110	81.34 260	84.18 297	83.65 82	92.93 148	74.22 172	87.87 293	92.17 180

CANet		83.79 154	82.85 165	86.63 106	86.17 248	72.21 156	83.76 169	91.43 128	77.24 124	74.39 319	87.45 245	75.36 174	95.42 50	77.03 148	92.83 217	92.25 177

pm-mvs1		83.69 155	84.95 129	79.91 237	90.04 173	59.66 273	82.43 209	87.44 211	75.52 145	87.85 143	95.26 38	81.25 119	85.65 295	68.74 228	96.04 125	94.42 85

AdaColmap		83.66 156	83.69 155	83.57 174	90.05 172	72.26 154	86.29 127	90.00 173	78.19 115	81.65 254	87.16 251	83.40 85	94.24 90	61.69 278	94.76 176	84.21 300

MIMVSNet1		83.63 157	84.59 138	80.74 225	94.06 57	62.77 237	82.72 199	84.53 258	77.57 121	90.34 91	95.92 24	76.88 168	85.83 293	61.88 276	97.42 77	93.62 121

WR-MVS		83.56 158	84.40 144	81.06 220	93.43 72	54.88 313	78.67 268	85.02 252	81.24 75	90.74 86	91.56 161	72.85 206	91.08 199	68.00 234	98.04 37	97.23 16

CNLPA		83.55 159	83.10 163	84.90 142	89.34 181	83.87 48	84.54 150	88.77 191	79.09 102	83.54 226	88.66 227	74.87 179	81.73 320	66.84 241	92.29 228	89.11 245

LCM-MVSNet-Re		83.48 160	85.06 126	78.75 253	85.94 251	55.75 307	80.05 245	94.27 23	76.47 129	96.09 5	94.54 59	83.31 86	89.75 241	59.95 290	94.89 170	90.75 214

hse-mvs2		83.47 161	81.81 181	88.47 80	91.03 149	82.27 59	82.61 201	83.69 260	71.27 201	86.70 164	86.05 267	63.04 260	92.41 159	78.26 129	93.62 201	90.71 215

V42		83.47 161	83.37 158	83.75 169	83.16 288	63.33 229	81.31 229	90.23 167	69.51 221	90.91 84	90.81 184	74.16 188	92.29 165	80.06 106	90.22 267	95.62 46

VPA-MVSNet		83.47 161	84.73 132	79.69 242	90.29 165	57.52 294	81.30 231	88.69 193	76.29 130	87.58 147	94.44 63	80.60 127	87.20 271	66.60 243	96.82 95	94.34 88

RRT_MVS		83.25 164	81.08 192	89.74 53	80.55 316	79.32 81	86.41 124	86.69 227	72.33 190	87.00 158	91.08 172	44.98 347	95.55 40	84.47 61	96.24 119	94.36 87

PAPM_NR		83.23 165	83.19 161	83.33 177	90.90 152	65.98 208	88.19 94	90.78 147	78.13 116	80.87 264	87.92 238	73.49 198	92.42 158	70.07 214	88.40 284	91.60 197

CLD-MVS		83.18 166	82.64 169	84.79 144	89.05 186	67.82 196	77.93 276	92.52 101	68.33 233	85.07 195	81.54 325	82.06 104	92.96 146	69.35 219	97.91 50	93.57 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 167	85.68 117	75.65 294	81.24 303	45.26 362	79.94 247	92.91 90	83.83 43	91.33 76	96.88 10	80.25 131	85.92 291	68.89 226	95.89 134	95.76 42

114514_t		83.10 168	82.54 172	84.77 145	92.90 84	69.10 187	86.65 119	90.62 152	54.66 326	81.46 257	90.81 184	76.98 161	94.38 86	72.62 194	96.18 120	90.82 213

UGNet		82.78 169	81.64 183	86.21 119	86.20 247	76.24 123	86.86 112	85.68 239	77.07 125	73.76 322	92.82 123	69.64 225	91.82 180	69.04 225	93.69 198	90.56 221
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		82.75 170	81.93 180	85.19 137	82.08 294	80.15 73	85.53 135	88.76 192	68.01 236	85.58 188	87.75 239	71.80 218	86.85 277	74.02 175	93.87 194	88.58 254

EI-MVSNet		82.61 171	82.42 174	83.20 180	83.25 286	63.66 225	83.50 178	85.07 249	76.06 134	86.55 168	85.10 283	73.41 199	90.25 222	78.15 133	90.67 263	95.68 44

QAPM		82.59 172	82.59 171	82.58 195	86.44 236	66.69 204	89.94 60	90.36 158	67.97 238	84.94 198	92.58 132	72.71 208	92.18 166	70.63 210	87.73 295	88.85 252

Fast-Effi-MVS+-dtu		82.54 173	81.41 187	85.90 125	85.60 253	76.53 119	83.07 190	89.62 180	73.02 180	79.11 284	83.51 302	80.74 125	90.24 224	68.76 227	89.29 274	90.94 209

MVS_Test		82.47 174	83.22 159	80.22 234	82.62 293	57.75 293	82.54 206	91.96 115	71.16 205	82.89 234	92.52 135	77.41 154	90.50 218	80.04 107	87.84 294	92.40 166

v148		82.31 175	82.48 173	81.81 209	85.59 254	59.66 273	81.47 227	86.02 235	72.85 181	88.05 140	90.65 190	70.73 223	90.91 205	75.15 167	91.79 239	94.87 66

API-MVS		82.28 176	82.61 170	81.30 214	86.29 244	69.79 174	88.71 88	87.67 210	78.42 113	82.15 244	84.15 298	77.98 147	91.59 183	65.39 252	92.75 218	82.51 325

MVSFormer		82.23 177	81.57 186	84.19 160	85.54 255	69.26 182	91.98 31	90.08 171	71.54 199	76.23 303	85.07 286	58.69 285	94.27 87	86.26 38	88.77 281	89.03 249

EIA-MVS		82.19 178	81.23 190	85.10 140	87.95 208	69.17 186	83.22 188	93.33 67	70.42 211	78.58 287	79.77 341	77.29 155	94.20 93	71.51 201	88.96 279	91.93 188

PCF-MVS	74.62 15	82.15 179	80.92 195	85.84 127	89.43 179	72.30 153	80.53 240	91.82 119	57.36 315	87.81 144	89.92 206	77.67 151	93.63 117	58.69 295	95.08 162	91.58 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 180	80.31 201	87.45 96	90.86 154	80.29 72	85.88 130	90.65 150	68.17 235	76.32 302	86.33 261	73.12 204	92.61 156	61.40 282	90.02 269	89.44 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 181	82.07 177	81.85 206	86.38 238	61.05 258	86.83 114	88.27 201	72.43 185	86.00 180	95.64 30	63.78 254	90.68 213	65.95 246	93.34 203	93.82 109

test1		82.02 181	82.07 177	81.85 206	86.38 238	61.05 258	86.83 114	88.27 201	72.43 185	86.00 180	95.64 30	63.78 254	90.68 213	65.95 246	93.34 203	93.82 109

OpenMVS	76.72 13	81.98 183	82.00 179	81.93 203	84.42 268	68.22 191	88.50 92	89.48 182	66.92 248	81.80 252	91.86 150	72.59 210	90.16 227	71.19 203	91.25 247	87.40 268

KD-MVS_self_test		81.93 184	83.14 162	78.30 262	84.75 263	52.75 325	80.37 242	89.42 184	70.24 216	90.26 93	93.39 109	74.55 187	86.77 279	68.61 230	96.64 99	95.38 51

tfpnnormal		81.79 185	82.95 164	78.31 261	88.93 190	55.40 308	80.83 238	82.85 267	76.81 127	85.90 184	94.14 83	74.58 186	86.51 283	66.82 242	95.68 144	93.01 140

c3_l		81.64 186	81.59 185	81.79 210	80.86 309	59.15 280	78.61 269	90.18 169	68.36 232	87.20 150	87.11 253	69.39 226	91.62 182	78.16 131	94.43 183	94.60 76

PVSNet_Blended_VisFu		81.55 187	80.49 199	84.70 148	91.58 129	73.24 138	84.21 154	91.67 123	62.86 277	80.94 262	87.16 251	67.27 238	92.87 151	69.82 216	88.94 280	87.99 260

DELS-MVS		81.44 188	81.25 188	82.03 202	84.27 272	62.87 236	76.47 298	92.49 102	70.97 206	81.64 255	83.83 299	75.03 177	92.70 153	74.29 171	92.22 232	90.51 223
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		81.31 189	81.61 184	80.41 231	86.38 238	58.75 287	83.93 163	86.58 229	72.43 185	87.65 145	92.98 116	63.78 254	90.22 225	66.86 239	93.92 193	92.27 175

TinyColmap		81.25 190	82.34 175	77.99 268	85.33 257	60.68 265	82.32 212	88.33 199	71.26 203	86.97 159	92.22 146	77.10 159	86.98 275	62.37 271	95.17 158	86.31 279

AUN-MVS		81.18 191	78.78 220	88.39 83	90.93 151	82.14 60	82.51 207	83.67 261	64.69 271	80.29 272	85.91 270	51.07 317	92.38 160	76.29 155	93.63 200	90.65 219

tttt0517		81.07 192	79.58 213	85.52 133	88.99 189	66.45 206	87.03 111	75.51 312	73.76 165	88.32 138	90.20 199	37.96 363	94.16 99	79.36 118	95.13 159	95.93 41

Fast-Effi-MVS+		81.04 193	80.57 196	82.46 199	87.50 218	63.22 231	78.37 272	89.63 179	68.01 236	81.87 248	82.08 320	82.31 97	92.65 155	67.10 238	88.30 289	91.51 200

BH-untuned		80.96 194	80.99 193	80.84 224	88.55 197	68.23 190	80.33 243	88.46 195	72.79 182	86.55 168	86.76 256	74.72 184	91.77 181	61.79 277	88.99 278	82.52 324

1121		80.86 195	79.81 212	84.02 161	93.93 60	78.70 88	81.64 224	80.18 285	55.43 323	83.67 222	91.15 170	71.29 221	91.41 190	67.95 236	93.06 211	81.96 330

eth_miper_zixun_eth		80.84 196	80.22 205	82.71 192	81.41 301	60.98 261	77.81 278	90.14 170	67.31 246	86.95 160	87.24 250	64.26 250	92.31 163	75.23 166	91.61 242	94.85 70

xiu_mvs_v1_base_debu		80.84 196	80.14 207	82.93 186	88.31 201	71.73 161	79.53 252	87.17 215	65.43 263	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

xiu_mvs_v1_base		80.84 196	80.14 207	82.93 186	88.31 201	71.73 161	79.53 252	87.17 215	65.43 263	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

xiu_mvs_v1_base_debi		80.84 196	80.14 207	82.93 186	88.31 201	71.73 161	79.53 252	87.17 215	65.43 263	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

IterMVS-SCA-FT		80.64 200	79.41 214	84.34 155	83.93 280	69.66 177	76.28 300	81.09 279	72.43 185	86.47 174	90.19 200	60.46 270	93.15 141	77.45 143	86.39 306	90.22 227

BH-RMVSNet		80.53 201	80.22 205	81.49 213	87.19 224	66.21 207	77.79 279	86.23 232	74.21 160	83.69 221	88.50 228	73.25 203	90.75 210	63.18 268	87.90 292	87.52 266

Anonymous202405211		80.51 202	81.19 191	78.49 258	88.48 198	57.26 296	76.63 294	82.49 269	81.21 76	84.30 214	92.24 145	67.99 234	86.24 287	62.22 272	95.13 159	91.98 187

DIV-MVS_self_test		80.43 203	80.23 203	81.02 221	79.99 319	59.25 277	77.07 289	87.02 223	67.38 244	86.19 175	89.22 216	63.09 258	90.16 227	76.32 153	95.80 138	93.66 117

cl____		80.42 204	80.23 203	81.02 221	79.99 319	59.25 277	77.07 289	87.02 223	67.37 245	86.18 177	89.21 217	63.08 259	90.16 227	76.31 154	95.80 138	93.65 119

diffmvs		80.40 205	80.48 200	80.17 235	79.02 331	60.04 269	77.54 283	90.28 166	66.65 251	82.40 239	87.33 248	73.50 196	87.35 270	77.98 135	89.62 272	93.13 135

EPNet		80.37 206	78.41 227	86.23 116	76.75 342	73.28 136	87.18 108	77.45 299	76.24 132	68.14 342	88.93 223	65.41 247	93.85 108	69.47 218	96.12 124	91.55 199
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 207	80.04 210	81.24 217	79.82 321	58.95 282	77.66 280	89.66 178	65.75 260	85.99 183	85.11 282	68.29 233	91.42 189	76.03 157	92.03 234	93.33 127

MG-MVS		80.32 208	80.94 194	78.47 259	88.18 204	52.62 328	82.29 213	85.01 253	72.01 197	79.24 283	92.54 134	69.36 227	93.36 133	70.65 209	89.19 277	89.45 237

VPNet		80.25 209	81.68 182	75.94 293	92.46 96	47.98 353	76.70 293	81.67 276	73.45 168	84.87 199	92.82 123	74.66 185	86.51 283	61.66 279	96.85 92	93.33 127

MAR-MVS		80.24 210	78.74 222	84.73 146	86.87 235	78.18 92	85.75 132	87.81 209	65.67 262	77.84 292	78.50 345	73.79 193	90.53 217	61.59 281	90.87 258	85.49 288
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		80.20 211	79.00 218	83.78 168	88.17 205	86.66 18	81.31 229	66.81 357	69.64 220	88.33 137	90.19 200	64.58 248	83.63 312	71.99 200	90.03 268	81.06 344

Anonymous20240521		80.18 212	81.25 188	76.95 280	83.15 289	60.84 263	82.46 208	85.99 236	68.76 229	86.78 161	93.73 105	59.13 282	77.44 333	73.71 180	97.55 69	92.56 158

LFMVS		80.15 213	80.56 197	78.89 250	89.19 185	55.93 304	85.22 139	73.78 324	82.96 56	84.28 215	92.72 127	57.38 294	90.07 234	63.80 262	95.75 141	90.68 217

DPM-MVS		80.10 214	79.18 217	82.88 189	90.71 158	69.74 175	78.87 265	90.84 145	60.29 299	75.64 311	85.92 269	67.28 237	93.11 142	71.24 202	91.79 239	85.77 285

MSDG		80.06 215	79.99 211	80.25 233	83.91 281	68.04 194	77.51 284	89.19 186	77.65 119	81.94 246	83.45 304	76.37 170	86.31 286	63.31 267	86.59 303	86.41 277

ab-mvs		79.67 216	80.56 197	76.99 279	88.48 198	56.93 298	84.70 145	86.06 234	68.95 227	80.78 265	93.08 113	75.30 175	84.62 304	56.78 304	90.90 257	89.43 239

VNet		79.31 217	80.27 202	76.44 287	87.92 209	53.95 317	75.58 307	84.35 259	74.39 159	82.23 242	90.72 186	72.84 207	84.39 306	60.38 289	93.98 192	90.97 208

bset_n11_16_dypcd		79.19 218	77.97 231	82.86 190	85.81 252	66.85 203	75.02 312	79.31 289	66.07 254	83.50 227	83.37 307	55.04 308	92.10 170	78.63 123	94.99 166	89.63 234

thisisatest0530		79.07 219	77.33 238	84.26 157	87.13 225	64.58 217	83.66 173	75.95 307	68.86 228	85.22 193	87.36 247	38.10 361	93.57 124	75.47 163	94.28 185	94.62 74

cl22		78.97 220	78.21 229	81.24 217	77.74 335	59.01 281	77.46 286	87.13 218	65.79 257	84.32 210	85.10 283	58.96 284	90.88 207	75.36 165	92.03 234	93.84 106

patch_mono-2		78.89 221	79.39 215	77.41 277	84.78 262	68.11 192	75.60 305	83.11 264	60.96 293	79.36 280	89.89 207	75.18 176	72.97 344	73.32 185	92.30 226	91.15 204

RPMNet		78.88 222	78.28 228	80.68 228	79.58 322	62.64 239	82.58 203	94.16 31	74.80 153	75.72 309	92.59 130	48.69 323	95.56 37	73.48 183	82.91 333	83.85 305

PAPR		78.84 223	78.10 230	81.07 219	85.17 258	60.22 268	82.21 217	90.57 153	62.51 279	75.32 314	84.61 293	74.99 178	92.30 164	59.48 293	88.04 291	90.68 217

PVSNet_BlendedMVS		78.80 224	77.84 232	81.65 211	84.43 266	63.41 227	79.49 255	90.44 155	61.70 287	75.43 312	87.07 254	69.11 229	91.44 187	60.68 287	92.24 230	90.11 231

FMVSNet3		78.80 224	78.55 224	79.57 244	82.89 292	56.89 300	81.76 221	85.77 238	69.04 226	86.00 180	90.44 194	51.75 315	90.09 233	65.95 246	93.34 203	91.72 193

test_yl		78.71 226	78.51 225	79.32 247	84.32 270	58.84 284	78.38 270	85.33 243	75.99 137	82.49 237	86.57 257	58.01 288	90.02 236	62.74 269	92.73 219	89.10 246

DCV-MVSNet		78.71 226	78.51 225	79.32 247	84.32 270	58.84 284	78.38 270	85.33 243	75.99 137	82.49 237	86.57 257	58.01 288	90.02 236	62.74 269	92.73 219	89.10 246

test1111		78.53 228	78.85 219	77.56 274	92.22 105	47.49 355	82.61 201	69.24 349	72.43 185	85.28 192	94.20 78	51.91 313	90.07 234	65.36 253	96.45 109	95.11 61

ECVR-MVS		78.44 229	78.63 223	77.88 270	91.85 119	48.95 349	83.68 172	69.91 347	72.30 192	84.26 217	94.20 78	51.89 314	89.82 238	63.58 263	96.02 126	94.87 66

pmmvs-eth3d		78.42 230	77.04 241	82.57 197	87.44 219	74.41 130	80.86 237	79.67 288	55.68 321	84.69 202	90.31 197	60.91 268	85.42 296	62.20 273	91.59 243	87.88 263

MVS_0304		78.17 231	77.23 239	80.99 223	84.13 278	69.07 188	81.39 228	80.81 281	76.28 131	67.53 347	89.11 220	62.87 262	86.77 279	60.90 286	92.01 237	87.13 271

mvs_anonymous		78.13 232	78.76 221	76.23 292	79.24 328	50.31 346	78.69 267	84.82 256	61.60 288	83.09 233	92.82 123	73.89 192	87.01 272	68.33 233	86.41 305	91.37 201

RRT_test8_iter05		78.08 233	77.52 234	79.75 240	80.84 310	52.54 329	80.61 239	88.96 189	67.77 243	84.62 203	89.29 215	33.89 368	92.10 170	77.59 140	94.15 188	94.62 74

TAMVS		78.08 233	76.36 247	83.23 179	90.62 159	72.87 139	79.08 262	80.01 287	61.72 286	81.35 259	86.92 255	63.96 253	88.78 255	50.61 337	93.01 213	88.04 259

miper_enhance_ethall		77.83 235	76.93 242	80.51 229	76.15 348	58.01 290	75.47 309	88.82 190	58.05 309	83.59 224	80.69 329	64.41 249	91.20 194	73.16 192	92.03 234	92.33 170

Vis-MVSNet (Re-imp)		77.82 236	77.79 233	77.92 269	88.82 192	51.29 339	83.28 183	71.97 337	74.04 161	82.23 242	89.78 208	57.38 294	89.41 246	57.22 303	95.41 148	93.05 138

CANet_DTU		77.81 237	77.05 240	80.09 236	81.37 302	59.90 271	83.26 184	88.29 200	69.16 224	67.83 345	83.72 300	60.93 267	89.47 243	69.22 222	89.70 271	90.88 211

OpenMVS_ROB	70.19 17	77.77 238	77.46 235	78.71 254	84.39 269	61.15 256	81.18 233	82.52 268	62.45 281	83.34 228	87.37 246	66.20 243	88.66 257	64.69 258	85.02 317	86.32 278

MDA-MVSNet-bldmvs		77.47 239	76.90 243	79.16 249	79.03 330	64.59 216	66.58 348	75.67 310	73.15 178	88.86 124	88.99 222	66.94 239	81.23 322	64.71 257	88.22 290	91.64 196

jason		77.42 240	75.75 253	82.43 200	87.10 229	69.27 181	77.99 275	81.94 274	51.47 345	77.84 292	85.07 286	60.32 272	89.00 249	70.74 208	89.27 276	89.03 249
jason: jason.
CDS-MVSNet		77.32 241	75.40 256	83.06 182	89.00 188	72.48 150	77.90 277	82.17 272	60.81 294	78.94 285	83.49 303	59.30 280	88.76 256	54.64 321	92.37 225	87.93 262
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 242	76.75 244	78.52 257	87.01 231	61.30 254	75.55 308	87.12 221	61.24 290	74.45 318	78.79 343	77.20 156	90.93 203	64.62 260	84.80 323	83.32 314

MVSTER		77.09 243	75.70 254	81.25 215	75.27 355	61.08 257	77.49 285	85.07 249	60.78 295	86.55 168	88.68 226	43.14 353	90.25 222	73.69 181	90.67 263	92.42 164

PS-MVSNAJ		77.04 244	76.53 246	78.56 256	87.09 230	61.40 252	75.26 310	87.13 218	61.25 289	74.38 320	77.22 352	76.94 162	90.94 202	64.63 259	84.83 322	83.35 313

IterMVS		76.91 245	76.34 248	78.64 255	80.91 307	64.03 223	76.30 299	79.03 292	64.88 270	83.11 231	89.16 218	59.90 276	84.46 305	68.61 230	85.15 316	87.42 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 246	75.67 255	80.34 232	80.48 317	62.16 248	73.50 322	84.80 257	57.61 313	82.24 241	87.54 243	51.31 316	87.65 266	70.40 213	93.19 208	91.23 203

CL-MVSNet_self_test		76.81 247	77.38 237	75.12 297	86.90 233	51.34 337	73.20 325	80.63 283	68.30 234	81.80 252	88.40 229	66.92 240	80.90 323	55.35 315	94.90 169	93.12 136

TR-MVS		76.77 248	75.79 252	79.72 241	86.10 250	65.79 210	77.14 287	83.02 265	65.20 268	81.40 258	82.10 319	66.30 242	90.73 212	55.57 312	85.27 314	82.65 320

USDC		76.63 249	76.73 245	76.34 289	83.46 284	57.20 297	80.02 246	88.04 205	52.14 341	83.65 223	91.25 166	63.24 257	86.65 282	54.66 320	94.11 189	85.17 290

BH-w/o		76.57 250	76.07 251	78.10 266	86.88 234	65.92 209	77.63 281	86.33 230	65.69 261	80.89 263	79.95 338	68.97 231	90.74 211	53.01 329	85.25 315	77.62 350

Patchmtry		76.56 251	77.46 235	73.83 303	79.37 327	46.60 359	82.41 210	76.90 301	73.81 164	85.56 189	92.38 137	48.07 325	83.98 309	63.36 266	95.31 154	90.92 210

PVSNet_Blended		76.49 252	75.40 256	79.76 239	84.43 266	63.41 227	75.14 311	90.44 155	57.36 315	75.43 312	78.30 346	69.11 229	91.44 187	60.68 287	87.70 296	84.42 298

miper_lstm_enhance		76.45 253	76.10 250	77.51 275	76.72 343	60.97 262	64.69 351	85.04 251	63.98 273	83.20 230	88.22 231	56.67 297	78.79 331	73.22 186	93.12 209	92.78 149

lupinMVS		76.37 254	74.46 264	82.09 201	85.54 255	69.26 182	76.79 291	80.77 282	50.68 351	76.23 303	82.82 313	58.69 285	88.94 250	69.85 215	88.77 281	88.07 257

cascas		76.29 255	74.81 260	80.72 227	84.47 265	62.94 234	73.89 320	87.34 212	55.94 320	75.16 316	76.53 355	63.97 252	91.16 196	65.00 254	90.97 255	88.06 258

thres600view7		75.97 256	75.35 258	77.85 272	87.01 231	51.84 335	80.45 241	73.26 328	75.20 150	83.10 232	86.31 263	45.54 338	89.05 248	55.03 318	92.24 230	92.66 155

GA-MVS		75.83 257	74.61 261	79.48 246	81.87 296	59.25 277	73.42 323	82.88 266	68.68 230	79.75 276	81.80 322	50.62 319	89.46 244	66.85 240	85.64 311	89.72 233

MVP-Stereo		75.81 258	73.51 273	82.71 192	89.35 180	73.62 133	80.06 244	85.20 246	60.30 298	73.96 321	87.94 236	57.89 292	89.45 245	52.02 332	74.87 359	85.06 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 259	75.05 259	76.66 286	87.27 221	51.88 334	81.07 234	73.26 328	75.68 143	83.25 229	86.37 260	45.54 338	88.80 252	51.98 333	90.99 252	89.31 241

ET-MVSNet_ETH3D		75.28 260	72.77 280	82.81 191	83.03 291	68.11 192	77.09 288	76.51 305	60.67 297	77.60 296	80.52 333	38.04 362	91.15 197	70.78 206	90.68 262	89.17 244

thres400		75.14 261	74.23 266	77.86 271	86.24 245	52.12 331	79.24 259	73.87 322	73.34 171	81.82 250	84.60 294	46.02 332	88.80 252	51.98 333	90.99 252	92.66 155

wuyk23d		75.13 262	79.30 216	62.63 342	75.56 351	75.18 127	80.89 236	73.10 330	75.06 152	94.76 12	95.32 35	87.73 42	52.85 371	34.16 370	97.11 85	59.85 367

EU-MVSNet		75.12 263	74.43 265	77.18 278	83.11 290	59.48 275	85.71 134	82.43 270	39.76 369	85.64 187	88.76 224	44.71 349	87.88 264	73.86 178	85.88 310	84.16 301

HyFIR lowres test		75.12 263	72.66 282	82.50 198	91.44 138	65.19 213	72.47 327	87.31 213	46.79 357	80.29 272	84.30 296	52.70 312	92.10 170	51.88 336	86.73 302	90.22 227

CMPMVS	59.41 20	75.12 263	73.57 271	79.77 238	75.84 350	67.22 197	81.21 232	82.18 271	50.78 349	76.50 299	87.66 241	55.20 306	82.99 314	62.17 275	90.64 266	89.09 248
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 266	72.98 279	80.73 226	84.95 259	71.71 164	76.23 301	77.59 298	52.83 335	77.73 295	86.38 259	56.35 300	84.97 300	57.72 302	87.05 300	85.51 287

tfpn200view9		74.86 267	74.23 266	76.74 285	86.24 245	52.12 331	79.24 259	73.87 322	73.34 171	81.82 250	84.60 294	46.02 332	88.80 252	51.98 333	90.99 252	89.31 241

1112_ss		74.82 268	73.74 269	78.04 267	89.57 176	60.04 269	76.49 297	87.09 222	54.31 327	73.66 323	79.80 339	60.25 273	86.76 281	58.37 296	84.15 326	87.32 269

EGC-MVSNET		74.79 269	69.99 303	89.19 65	94.89 39	87.00 14	91.89 35	86.28 231	1.09 375	2.23 377	95.98 23	81.87 112	89.48 242	79.76 110	95.96 129	91.10 205

ppachtmachnet_test		74.73 270	74.00 268	76.90 282	80.71 313	56.89 300	71.53 331	78.42 294	58.24 307	79.32 282	82.92 312	57.91 291	84.26 307	65.60 251	91.36 246	89.56 236

Patchmatch-RL test		74.48 271	73.68 270	76.89 283	84.83 261	66.54 205	72.29 328	69.16 350	57.70 311	86.76 162	86.33 261	45.79 337	82.59 315	69.63 217	90.65 265	81.54 335

PatchMatch-RL		74.48 271	73.22 276	78.27 264	87.70 213	85.26 35	75.92 303	70.09 345	64.34 272	76.09 305	81.25 327	65.87 246	78.07 332	53.86 323	83.82 327	71.48 358

XXY-MVS		74.44 273	76.19 249	69.21 323	84.61 264	52.43 330	71.70 330	77.18 300	60.73 296	80.60 266	90.96 179	75.44 172	69.35 352	56.13 308	88.33 285	85.86 284

test2506		74.12 274	73.39 274	76.28 290	91.85 119	44.20 365	84.06 158	48.20 377	72.30 192	81.90 247	94.20 78	27.22 378	89.77 239	64.81 256	96.02 126	94.87 66

CR-MVSNet		74.00 275	73.04 278	76.85 284	79.58 322	62.64 239	82.58 203	76.90 301	50.50 352	75.72 309	92.38 137	48.07 325	84.07 308	68.72 229	82.91 333	83.85 305

Test_1112_low_res		73.90 276	73.08 277	76.35 288	90.35 164	55.95 303	73.40 324	86.17 233	50.70 350	73.14 324	85.94 268	58.31 287	85.90 292	56.51 306	83.22 330	87.20 270

test20.03		73.75 277	74.59 263	71.22 316	81.11 305	51.12 341	70.15 336	72.10 336	70.42 211	80.28 274	91.50 162	64.21 251	74.72 343	46.96 353	94.58 179	87.82 265

SCA		73.32 278	72.57 284	75.58 295	81.62 298	55.86 305	78.89 264	71.37 342	61.73 285	74.93 317	83.42 305	60.46 270	87.01 272	58.11 300	82.63 337	83.88 302

baseline1		73.26 279	73.54 272	72.43 312	84.92 260	47.79 354	79.89 248	74.00 320	65.93 255	78.81 286	86.28 264	56.36 299	81.63 321	56.63 305	79.04 350	87.87 264

1314		73.22 280	72.56 285	75.20 296	80.41 318	57.84 291	81.64 224	85.36 242	51.68 344	73.10 325	76.65 354	61.45 266	85.19 298	63.54 264	79.21 349	82.59 321

MVS		73.21 281	72.59 283	75.06 298	80.97 306	60.81 264	81.64 224	85.92 237	46.03 360	71.68 331	77.54 348	68.47 232	89.77 239	55.70 311	85.39 312	74.60 355

HY-MVS	64.64 18	73.03 282	72.47 286	74.71 299	83.36 285	54.19 315	82.14 220	81.96 273	56.76 319	69.57 339	86.21 265	60.03 274	84.83 303	49.58 342	82.65 335	85.11 291

thisisatest0515		73.00 283	70.52 297	80.46 230	81.45 300	59.90 271	73.16 326	74.31 319	57.86 310	76.08 306	77.78 347	37.60 364	92.12 169	65.00 254	91.45 245	89.35 240

EPNet_dtu		72.87 284	71.33 295	77.49 276	77.72 336	60.55 266	82.35 211	75.79 308	66.49 252	58.39 370	81.06 328	53.68 310	85.98 290	53.55 324	92.97 215	85.95 282
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 285	71.41 294	76.28 290	83.25 286	60.34 267	83.50 178	79.02 293	37.77 370	76.33 301	85.10 283	49.60 322	87.41 269	70.54 211	77.54 355	81.08 342

CHOSEN 1792x2688		72.45 286	70.56 296	78.13 265	90.02 174	63.08 232	68.72 340	83.16 263	42.99 366	75.92 307	85.46 276	57.22 296	85.18 299	49.87 341	81.67 338	86.14 280

testgi		72.36 287	74.61 261	65.59 336	80.56 315	42.82 369	68.29 341	73.35 327	66.87 249	81.84 249	89.93 205	72.08 215	66.92 360	46.05 355	92.54 223	87.01 273

thres200		72.34 288	71.55 293	74.70 300	83.48 283	51.60 336	75.02 312	73.71 325	70.14 217	78.56 288	80.57 332	46.20 330	88.20 262	46.99 352	89.29 274	84.32 299

FPMVS		72.29 289	72.00 288	73.14 306	88.63 194	85.00 37	74.65 316	67.39 351	71.94 198	77.80 294	87.66 241	50.48 320	75.83 339	49.95 339	79.51 345	58.58 369

FMVSNet5		72.10 290	71.69 290	73.32 304	81.57 299	53.02 324	76.77 292	78.37 295	63.31 274	76.37 300	91.85 151	36.68 365	78.98 329	47.87 349	92.45 224	87.95 261

our_test_3		71.85 291	71.59 291	72.62 310	80.71 313	53.78 318	69.72 338	71.71 341	58.80 304	78.03 289	80.51 334	56.61 298	78.84 330	62.20 273	86.04 309	85.23 289

PAPM		71.77 292	70.06 302	76.92 281	86.39 237	53.97 316	76.62 295	86.62 228	53.44 332	63.97 360	84.73 292	57.79 293	92.34 162	39.65 365	81.33 341	84.45 297

IB-MVS	62.13 19	71.64 293	68.97 307	79.66 243	80.80 312	62.26 247	73.94 319	76.90 301	63.27 275	68.63 341	76.79 353	33.83 369	91.84 179	59.28 294	87.26 298	84.88 293
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		71.63 294	72.30 287	69.62 321	76.47 345	52.70 327	70.03 337	80.97 280	59.18 302	79.36 280	88.21 232	60.50 269	69.12 353	58.33 298	77.62 354	87.04 272

Anonymous20231206		71.38 295	71.88 289	69.88 319	86.31 242	54.37 314	70.39 335	74.62 315	52.57 337	76.73 298	88.76 224	59.94 275	72.06 346	44.35 358	93.23 207	83.23 316

MIMVSNet		71.09 296	71.59 291	69.57 322	87.23 222	50.07 347	78.91 263	71.83 338	60.20 300	71.26 332	91.76 156	55.08 307	76.09 337	41.06 363	87.02 301	82.54 323

MS-PatchMatch		70.93 297	70.22 300	73.06 307	81.85 297	62.50 242	73.82 321	77.90 296	52.44 338	75.92 307	81.27 326	55.67 303	81.75 319	55.37 314	77.70 353	74.94 354

pmmvs5		70.73 298	70.07 301	72.72 308	77.03 341	52.73 326	74.14 317	75.65 311	50.36 353	72.17 329	85.37 280	55.42 305	80.67 325	52.86 330	87.59 297	84.77 294

PatchT		70.52 299	72.76 281	63.79 341	79.38 326	33.53 375	77.63 281	65.37 359	73.61 166	71.77 330	92.79 126	44.38 350	75.65 340	64.53 261	85.37 313	82.18 328

N_pmnet		70.20 300	68.80 309	74.38 301	80.91 307	84.81 40	59.12 361	76.45 306	55.06 324	75.31 315	82.36 318	55.74 302	54.82 370	47.02 351	87.24 299	83.52 309

tpmvs		70.16 301	69.56 305	71.96 314	74.71 359	48.13 351	79.63 250	75.45 313	65.02 269	70.26 336	81.88 321	45.34 343	85.68 294	58.34 297	75.39 358	82.08 329

new-patchmatchnet		70.10 302	73.37 275	60.29 349	81.23 304	16.95 379	59.54 359	74.62 315	62.93 276	80.97 261	87.93 237	62.83 263	71.90 347	55.24 316	95.01 165	92.00 184

YYNet1		70.06 303	70.44 298	68.90 324	73.76 361	53.42 322	58.99 362	67.20 353	58.42 306	87.10 154	85.39 279	59.82 277	67.32 357	59.79 291	83.50 329	85.96 281

MDA-MVSNet_test_wron		70.05 304	70.44 298	68.88 325	73.84 360	53.47 320	58.93 363	67.28 352	58.43 305	87.09 155	85.40 278	59.80 278	67.25 358	59.66 292	83.54 328	85.92 283

CostFormer		69.98 305	68.68 310	73.87 302	77.14 339	50.72 344	79.26 258	74.51 317	51.94 343	70.97 335	84.75 291	45.16 346	87.49 268	55.16 317	79.23 348	83.40 312

baseline2		69.77 306	66.89 316	78.41 260	79.51 324	58.09 289	76.23 301	69.57 348	57.50 314	64.82 358	77.45 350	46.02 332	88.44 258	53.08 326	77.83 352	88.70 253

PatchmatchNet		69.71 307	68.83 308	72.33 313	77.66 337	53.60 319	79.29 257	69.99 346	57.66 312	72.53 327	82.93 311	46.45 329	80.08 328	60.91 285	72.09 362	83.31 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 308	66.64 320	77.70 273	73.19 363	71.24 167	75.67 304	65.56 358	70.42 211	65.18 354	92.97 118	33.64 370	83.06 313	53.52 325	69.61 368	78.79 349

UnsupCasMVSNet_bld		69.21 309	69.68 304	67.82 330	79.42 325	51.15 340	67.82 345	75.79 308	54.15 328	77.47 297	85.36 281	59.26 281	70.64 349	48.46 346	79.35 347	81.66 333

gg-mvs-nofinetune		68.96 310	69.11 306	68.52 329	76.12 349	45.32 361	83.59 174	55.88 372	86.68 26	64.62 359	97.01 7	30.36 373	83.97 310	44.78 357	82.94 332	76.26 352

tpm2		68.45 311	66.83 317	73.30 305	78.93 332	48.50 350	79.76 249	71.76 339	47.50 356	69.92 338	83.60 301	42.07 355	88.40 259	48.44 347	79.51 345	83.01 319

tpm		67.95 312	68.08 313	67.55 331	78.74 333	43.53 367	75.60 305	67.10 356	54.92 325	72.23 328	88.10 233	42.87 354	75.97 338	52.21 331	80.95 344	83.15 317

WTY-MVS		67.91 313	68.35 311	66.58 334	80.82 311	48.12 352	65.96 349	72.60 332	53.67 331	71.20 333	81.68 324	58.97 283	69.06 354	48.57 345	81.67 338	82.55 322

test-LLR		67.21 314	66.74 318	68.63 327	76.45 346	55.21 310	67.89 342	67.14 354	62.43 282	65.08 355	72.39 360	43.41 351	69.37 350	61.00 283	84.89 320	81.31 337

sss		66.92 315	67.26 315	65.90 335	77.23 338	51.10 342	64.79 350	71.72 340	52.12 342	70.13 337	80.18 336	57.96 290	65.36 365	50.21 338	81.01 343	81.25 339

KD-MVS_2432*1600		66.87 316	65.81 321	70.04 317	67.50 372	47.49 355	62.56 355	79.16 290	61.21 291	77.98 290	80.61 330	25.29 380	82.48 316	53.02 327	84.92 318	80.16 347

miper_refine_blended		66.87 316	65.81 321	70.04 317	67.50 372	47.49 355	62.56 355	79.16 290	61.21 291	77.98 290	80.61 330	25.29 380	82.48 316	53.02 327	84.92 318	80.16 347

tpm cat1		66.76 318	65.21 324	71.42 315	77.09 340	50.62 345	78.01 274	73.68 326	44.89 362	68.64 340	79.00 342	45.51 340	82.42 318	49.91 340	70.15 365	81.23 341

DWT-MVSNet_test		66.43 319	64.37 325	72.63 309	74.86 358	50.86 343	76.52 296	72.74 331	54.06 329	65.50 352	68.30 366	32.13 371	84.84 302	61.63 280	73.59 360	82.19 327

PVSNet	58.17 21	66.41 320	65.63 323	68.75 326	81.96 295	49.88 348	62.19 357	72.51 334	51.03 347	68.04 343	75.34 358	50.84 318	74.77 341	45.82 356	82.96 331	81.60 334

tpmrst		66.28 321	66.69 319	65.05 339	72.82 367	39.33 370	78.20 273	70.69 344	53.16 334	67.88 344	80.36 335	48.18 324	74.75 342	58.13 299	70.79 364	81.08 342

Patchmatch-test		65.91 322	67.38 314	61.48 347	75.51 352	43.21 368	68.84 339	63.79 361	62.48 280	72.80 326	83.42 305	44.89 348	59.52 369	48.27 348	86.45 304	81.70 332

ADS-MVSNet2		65.87 323	63.64 328	72.55 311	73.16 364	56.92 299	67.10 346	74.81 314	49.74 354	66.04 350	82.97 309	46.71 327	77.26 334	42.29 360	69.96 366	83.46 310

test-mter		65.00 324	63.79 327	68.63 327	76.45 346	55.21 310	67.89 342	67.14 354	50.98 348	65.08 355	72.39 360	28.27 376	69.37 350	61.00 283	84.89 320	81.31 337

test0.0.03 1		64.66 325	64.36 326	65.57 337	75.03 357	46.89 358	64.69 351	61.58 366	62.43 282	71.18 334	77.54 348	43.41 351	68.47 355	40.75 364	82.65 335	81.35 336

pmmvs3		62.47 326	60.02 338	69.80 320	71.58 370	64.00 224	70.52 334	58.44 370	39.77 368	66.05 349	75.84 356	27.10 379	72.28 345	46.15 354	84.77 324	73.11 356

EPMVS		62.47 326	62.63 330	62.01 343	70.63 371	38.74 371	74.76 314	52.86 374	53.91 330	67.71 346	80.01 337	39.40 359	66.60 361	55.54 313	68.81 369	80.68 346

ADS-MVSNet		61.90 328	62.19 331	61.03 348	73.16 364	36.42 373	67.10 346	61.75 364	49.74 354	66.04 350	82.97 309	46.71 327	63.21 367	42.29 360	69.96 366	83.46 310

PMMVS		61.65 329	60.38 335	65.47 338	65.40 376	69.26 182	63.97 353	61.73 365	36.80 371	60.11 365	68.43 364	59.42 279	66.35 362	48.97 344	78.57 351	60.81 366

E-PMN		61.59 330	61.62 332	61.49 346	66.81 374	55.40 308	53.77 365	60.34 367	66.80 250	58.90 368	65.50 367	40.48 358	66.12 363	55.72 310	86.25 307	62.95 365

TESTMET0.1,1		61.29 331	60.32 336	64.19 340	72.06 368	51.30 338	67.89 342	62.09 362	45.27 361	60.65 364	69.01 363	27.93 377	64.74 366	56.31 307	81.65 340	76.53 351

MVS-HIRNet		61.16 332	62.92 329	55.87 352	79.09 329	35.34 374	71.83 329	57.98 371	46.56 358	59.05 367	91.14 171	49.95 321	76.43 336	38.74 366	71.92 363	55.84 370

EMVS		61.10 333	60.81 334	61.99 344	65.96 375	55.86 305	53.10 366	58.97 369	67.06 247	56.89 371	63.33 368	40.98 356	67.03 359	54.79 319	86.18 308	63.08 364

DSMNet-mixed		60.98 334	61.61 333	59.09 351	72.88 366	45.05 363	74.70 315	46.61 378	26.20 372	65.34 353	90.32 196	55.46 304	63.12 368	41.72 362	81.30 342	69.09 362

dp		60.70 335	60.29 337	61.92 345	72.04 369	38.67 372	70.83 332	64.08 360	51.28 346	60.75 363	77.28 351	36.59 366	71.58 348	47.41 350	62.34 371	75.52 353

CHOSEN 280x420		59.08 336	56.52 341	66.76 333	76.51 344	64.39 220	49.62 367	59.00 368	43.86 364	55.66 372	68.41 365	35.55 367	68.21 356	43.25 359	76.78 357	67.69 363

PVSNet_0	51.08 22	56.10 337	54.97 342	59.48 350	75.12 356	53.28 323	55.16 364	61.89 363	44.30 363	59.16 366	62.48 369	54.22 309	65.91 364	35.40 369	47.01 372	59.25 368

new_pmnet		55.69 338	57.66 340	49.76 354	75.47 353	30.59 376	59.56 358	51.45 375	43.62 365	62.49 361	75.48 357	40.96 357	49.15 373	37.39 368	72.52 361	69.55 361

PMMVS2		55.64 339	59.27 339	44.74 355	64.30 377	12.32 380	40.60 368	49.79 376	53.19 333	65.06 357	84.81 290	53.60 311	49.76 372	32.68 372	89.41 273	72.15 357

MVE	40.22 23	51.82 340	50.47 343	55.87 352	62.66 378	51.91 333	31.61 370	39.28 379	40.65 367	50.76 373	74.98 359	56.24 301	44.67 374	33.94 371	64.11 370	71.04 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 341	29.60 344	33.06 356	17.99 380	3.84 382	13.62 371	73.92 321	2.79 374	18.29 376	53.41 371	28.53 375	43.25 375	22.56 373	35.27 374	52.11 371

cdsmvs_eth3d_5k		20.81 342	27.75 345	0.00 361	0.00 384	0.00 385	0.00 372	85.44 241	0.00 379	0.00 380	82.82 313	81.46 116	0.00 380	0.00 378	0.00 378	0.00 376

tmp_tt		20.25 343	24.50 346	7.49 358	4.47 381	8.70 381	34.17 369	25.16 381	1.00 376	32.43 375	18.49 373	39.37 360	9.21 377	21.64 374	43.75 373	4.57 373

ab-mvs-re		6.65 344	8.87 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	79.80 339	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.41 345	8.55 348	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	76.94 162	0.00 380	0.00 378	0.00 378	0.00 376

test123		6.27 346	8.08 349	0.84 359	1.11 383	0.57 383	62.90 354	0.82 383	0.54 377	1.07 379	2.75 378	1.26 382	0.30 378	1.04 376	1.26 377	1.66 374

testmvs		5.91 347	7.65 350	0.72 360	1.20 382	0.37 384	59.14 360	0.67 384	0.49 378	1.11 378	2.76 377	0.94 383	0.24 379	1.02 377	1.47 376	1.55 375

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

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

MSC_two_6792asdad				88.81 70	91.55 132	77.99 94		91.01 141					96.05 8	87.45 19	98.17 33	92.40 166

PC_three_1452									58.96 303	90.06 95	91.33 165	80.66 126	93.03 145	75.78 160	95.94 131	92.48 162

No_MVS				88.81 70	91.55 132	77.99 94		91.01 141					96.05 8	87.45 19	98.17 33	92.40 166

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					0.00 384

eth-test					0.00 384

ZD-MVS					92.22 105	80.48 70		91.85 117	71.22 204	90.38 90	92.98 116	86.06 63	96.11 6	81.99 86	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	92.73 150

IU-MVS					94.18 50	72.64 143		90.82 146	56.98 317	89.67 109				85.78 47	97.92 48	93.28 129

OPU-MVS				88.27 86	91.89 117	77.83 97	90.47 49				91.22 167	81.12 120	94.68 75	74.48 170	95.35 150	92.29 173

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	92.04 182

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 86	95.32 11	75.14 151	91.07 79	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	89.72 176	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	92.98 141

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	92.02 183

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																83.88 302

test_part2					93.86 62	77.77 98				92.84 48

sam_mvs1												46.11 331				83.88 302

sam_mvs												45.92 336

ambc				82.98 184	90.55 162	64.86 215	88.20 93	89.15 187		89.40 118	93.96 93	71.67 220	91.38 192	78.83 121	96.55 103	92.71 153

MTGPA								91.81 120

test_post1							78.85 266				3.13 375	45.19 345	80.13 327	58.11 300

test_post											3.10 376	45.43 341	77.22 335

patchmatchnet-post											81.71 323	45.93 335	87.01 272

GG-mvs-BLEND				67.16 332	73.36 362	46.54 360	84.15 155	55.04 373		58.64 369	61.95 370	29.93 374	83.87 311	38.71 367	76.92 356	71.07 359

MTMP							90.66 43	33.14 380

gm-plane-assit					75.42 354	44.97 364			52.17 339		72.36 362		87.90 263	54.10 322

test9_res														80.83 99	96.45 109	90.57 220

TEST9					92.34 99	79.70 77	83.94 161	90.32 159	65.41 267	84.49 206	90.97 177	82.03 106	93.63 117

test_8					92.09 109	78.87 86	83.82 166	90.31 161	65.79 257	84.36 209	90.96 179	81.93 108	93.44 129

agg_prior2														79.68 112	96.16 121	90.22 227

agg_prior					91.58 129	77.69 99		90.30 162		84.32 210			93.18 137

TestCases				89.68 54	91.59 126	83.40 50		95.44 9	79.47 95	88.00 141	93.03 114	82.66 92	91.47 185	70.81 204	96.14 122	94.16 94

test_prior4						78.97 85	84.59 147

test_prior2							83.37 181		75.43 146	84.58 204	91.57 159	81.92 110		79.54 114	96.97 88

test_prior				86.32 112	90.59 160	71.99 158		92.85 92					94.17 96			92.80 147

旧先验2							81.73 222		56.88 318	86.54 173			84.90 301	72.81 193

新几何2							81.72 223

新几何1				82.95 185	93.96 59	78.56 90		80.24 284	55.45 322	83.93 220	91.08 172	71.19 222	88.33 260	65.84 249	93.07 210	81.95 331

旧先验1					91.97 112	71.77 160		81.78 275			91.84 152	73.92 191			93.65 199	83.61 308

无先验							82.81 198	85.62 240	58.09 308				91.41 190	67.95 236		84.48 296

原ACMM2							82.26 216

原ACMM1				84.60 149	92.81 90	74.01 132		91.50 126	62.59 278	82.73 236	90.67 189	76.53 169	94.25 89	69.24 220	95.69 143	85.55 286

test222					93.31 75	76.54 117	79.38 256	77.79 297	52.59 336	82.36 240	90.84 183	66.83 241			91.69 241	81.25 339

testdata2													86.43 285	63.52 265

segment_acmp												81.94 107

testdata				79.54 245	92.87 85	72.34 152		80.14 286	59.91 301	85.47 191	91.75 157	67.96 235	85.24 297	68.57 232	92.18 233	81.06 344

testdata1							79.62 251		73.95 163

test12				86.57 107	90.74 156	72.63 145		90.69 149		82.76 235		79.20 137	94.80 72		95.32 152	92.27 175

plane_prior7					93.45 70	77.31 108

plane_prior6					92.61 91	76.54 117						74.84 180

plane_prior5								93.61 58					95.22 58	80.78 100	95.83 136	94.46 82

plane_prior4											92.95 119

plane_prior3						76.85 115			77.79 118	86.55 168

plane_prior2							89.45 74		79.44 97

plane_prior1					92.83 89

plane_prior						76.42 120	87.15 109		75.94 140						95.03 164

n2								0.00 385

nn								0.00 385

door-mid								74.45 318

lessismore_v0				85.95 123	91.10 148	70.99 169		70.91 343		91.79 68	94.42 66	61.76 265	92.93 148	79.52 116	93.03 212	93.93 103

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								91.46 127

door								72.57 333

HQP5-MVS						70.66 170

HQP-NCC					91.19 143		84.77 142		73.30 173	80.55 269

ACMP_Plane					91.19 143		84.77 142		73.30 173	80.55 269

BP-MVS														77.30 145

HQP4-MVS										80.56 268			94.61 79			93.56 124

HQP3-MVS								92.68 98							94.47 181

HQP2-MVS												72.10 213

NP-MVS					91.95 113	74.55 129					90.17 202

MDTV_nov1_ep13_2view						27.60 378	70.76 333		46.47 359	61.27 362		45.20 344		49.18 343		83.75 307

MDTV_nov1_ep13			68.29 312		78.03 334	43.87 366	74.12 318	72.22 335	52.17 339	67.02 348	85.54 272	45.36 342	80.85 324	55.73 309	84.42 325

ACMMP++_ref															95.74 142

ACMMP++															97.35 78

Test By Simon												79.09 138

ITE_SJBPF				90.11 50	90.72 157	84.97 38		90.30 162	81.56 72	90.02 97	91.20 169	82.40 96	90.81 209	73.58 182	94.66 177	94.56 77

DeepMVS_CX				24.13 357	32.95 379	29.49 377		21.63 382	12.07 373	37.95 374	45.07 372	30.84 372	19.21 376	17.94 375	33.06 375	23.69 372

High-resolution multi-view benchmark