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 54	99.27 1	99.54 1

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	93.16 142	91.10 1	97.53 75	96.58 31
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TDRefinement		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 48	96.29 16	88.16 35	94.17 99	86.07 43	98.48 18	97.22 19

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 38	91.86 3	95.09 64	87.54 18	98.02 41	93.71 117

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 67	90.64 11		87.16 28	97.60 68	92.73 153

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 56	95.13 44	90.65 10	95.34 54	88.06 9	98.15 36	95.95 42

test1172		92.40 7	92.41 7	92.37 6	94.68 45	89.04 6	91.98 32	93.62 57	90.14 11	93.63 36	94.16 85	88.83 24	95.51 45	87.11 30	97.54 74	92.54 163

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 67	88.83 24	95.51 45	87.16 28	97.60 68	92.73 153

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 42	94.02 91	90.15 17	95.67 34	86.82 32	97.34 81	92.19 182

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 83	85.17 36	92.47 26	95.05 15	87.65 24	93.21 43	94.39 73	90.09 18	95.08 65	86.67 33	97.60 68	94.18 94

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 71	93.94 100	90.55 13	95.73 31	88.50 7	98.23 29	95.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 68	86.15 23	93.37 10	95.10 14	90.28 9	92.11 63	95.03 46	89.75 21	94.93 69	79.95 110	98.27 27	95.04 65
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APDe-MVS		91.22 24	91.92 13	89.14 67	92.97 85	78.04 92	92.84 16	94.14 35	83.33 51	93.90 25	95.73 27	88.77 26	96.41 1	87.60 16	97.98 45	92.98 144

PS-CasMVS		90.06 43	91.92 13	84.47 154	96.56 7	58.83 289	89.04 82	92.74 97	91.40 5	96.12 4	96.06 22	87.23 47	95.57 38	79.42 120	98.74 6	99.00 2

DTE-MVSNet		89.98 47	91.91 15	84.21 161	96.51 8	57.84 294	88.93 85	92.84 94	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 122	98.57 15	98.80 6

PEN-MVS		90.03 45	91.88 16	84.48 153	96.57 6	58.88 286	88.95 83	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 36	78.69 125	98.72 9	98.97 3

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 59	94.14 86	89.15 23	95.62 35	87.35 23	98.24 28	94.56 78
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
LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 42	81.69 61	90.00 58	94.27 23	82.35 63	93.67 34	94.82 52	91.18 5	95.52 43	85.36 51	98.73 7	95.23 60

SED-MVS		90.46 37	91.64 19	86.93 102	94.18 50	72.65 143	90.47 51	93.69 54	83.77 44	94.11 23	94.27 75	90.28 15	95.84 23	86.03 44	97.92 49	92.29 176

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 31	91.81 121	84.07 40	92.00 66	94.40 71	86.63 54	95.28 57	88.59 5	98.31 24	92.30 174

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 83	94.00 92	88.26 32	95.71 32	87.28 26	98.39 21	92.55 162

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	88.53 134	97.64 2	83.45 84	94.55 84	86.02 46	98.60 13	96.67 28

ACMH+	77.89 11	90.73 30	91.50 23	88.44 83	93.00 84	76.26 122	89.65 69	95.55 7	87.72 23	93.89 27	94.94 48	91.62 4	93.44 132	78.35 128	98.76 4	95.61 49

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 73	94.25 79	87.67 43	95.51 45	87.21 27	98.11 37	93.12 139

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 74	93.96 96	88.35 30	95.56 39	87.74 11	97.74 60	92.85 148

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	89.99 101	94.03 90	86.57 56	95.80 25	87.35 23	97.62 66	94.20 92

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 61	80.97 68	91.49 39	93.48 63	82.82 58	92.60 55	93.97 93	88.19 33	96.29 4	87.61 15	98.20 33	94.39 87
Skip Steuart: Steuart Systems R&D Blog.
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 76	94.17 84	87.51 44	95.87 19	87.74 11	97.76 58	93.99 101

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 54	92.18 29	94.22 27	80.14 89	91.29 79	93.97 93	87.93 40	95.87 19	88.65 4	97.96 48	94.12 98

DVP-MVS		90.06 43	91.32 30	86.29 115	94.16 53	72.56 149	90.54 48	91.01 142	83.61 47	93.75 31	94.65 57	89.76 19	95.78 28	86.42 34	97.97 46	90.55 225
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
WR-MVS_H		89.91 50	91.31 31	85.71 132	96.32 10	62.39 245	89.54 73	93.31 70	90.21 10	95.57 9	95.66 30	81.42 117	95.90 15	80.94 99	98.80 3	98.84 5

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 88	94.21 80	87.75 41	95.87 19	87.60 16	97.71 62	93.83 109

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 84	91.81 121	84.07 40	92.00 66	94.40 71	86.63 54	95.28 57	88.59 5	98.31 24	92.30 174

ACMH	76.49 14	89.34 60	91.14 34	83.96 167	92.50 97	70.36 175	89.55 71	93.84 50	81.89 69	94.70 13	95.44 35	90.69 9	88.31 263	83.33 73	98.30 26	93.20 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		90.07 42	91.09 35	87.00 101	91.55 135	72.64 145	96.19 2	94.10 38	85.33 32	93.49 39	94.64 60	81.12 120	95.88 17	87.41 21	95.94 133	92.48 165

DPE-MVS		90.53 35	91.08 36	88.88 69	93.38 75	78.65 88	89.15 81	94.05 40	84.68 38	93.90 25	94.11 88	88.13 36	96.30 3	84.51 63	97.81 55	91.70 197
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 98	94.51 21	75.79 143	92.94 45	94.96 47	88.36 29	95.01 67	90.70 2	98.40 20	95.09 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 31	91.07 38	89.42 62	95.93 16	79.54 79	89.95 61	93.68 56	77.65 119	91.97 68	94.89 49	88.38 28	95.45 50	89.27 3	97.87 53	93.27 133

GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 57	91.75 37	93.74 52	80.98 79	91.38 77	93.80 103	87.20 48	95.80 25	87.10 31	97.69 63	93.93 105

ACMM	79.39 9	90.65 31	90.99 40	89.63 57	95.03 35	83.53 49	89.62 70	93.35 66	79.20 101	93.83 28	93.60 110	90.81 8	92.96 149	85.02 56	98.45 19	92.41 168
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 40	90.96 41	87.65 95	91.95 116	71.06 170	89.99 60	93.05 83	86.53 28	94.29 19	96.27 17	82.69 91	94.08 103	86.25 40	97.63 65	97.82 8

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 58	93.90 46	80.32 86	91.74 72	94.41 70	88.17 34	95.98 11	86.37 36	97.99 43	93.96 104

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 28	93.03 86	82.59 60	88.52 135	94.37 74	86.74 53	95.41 52	86.32 37	98.21 31	93.19 137
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVSNet		89.27 61	90.91 43	84.37 155	96.34 9	58.61 291	88.66 92	92.06 111	90.78 6	95.67 7	95.17 43	81.80 113	95.54 42	79.00 123	98.69 10	98.95 4

SF-MVS		90.27 39	90.80 45	88.68 78	92.86 89	77.09 110	91.19 42	95.74 5	81.38 74	92.28 60	93.80 103	86.89 51	94.64 78	85.52 49	97.51 76	94.30 90

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 159	97.00 2	64.33 223	89.67 68	88.38 199	88.84 15	94.29 19	97.57 3	90.48 14	91.26 194	72.57 197	97.65 64	97.34 15

PMVS	80.48 6	90.08 41	90.66 47	88.34 86	96.71 3	92.97 1	90.31 54	89.57 182	88.51 19	90.11 97	95.12 45	90.98 7	88.92 253	77.55 142	97.07 89	83.13 320
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 80	94.05 40	79.03 104	92.87 47	93.74 107	90.60 12	95.21 61	82.87 78	98.76 4	94.87 68
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 80	90.98 44	93.24 76	75.37 150	92.84 49	95.28 37	85.58 66	96.09 7	87.92 10	97.76 58	93.88 107
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
LS3D		90.60 33	90.34 50	91.38 27	89.03 190	84.23 47	93.58 6	94.68 19	90.65 7	90.33 95	93.95 99	84.50 72	95.37 53	80.87 100	95.50 150	94.53 81

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 44	90.65 46	93.29 73	77.00 127	91.47 74	93.96 96	88.35 30	95.56 39	84.88 58	97.74 60	92.85 148

OPM-MVS		89.80 51	89.97 52	89.27 64	94.76 41	79.86 75	86.76 121	92.78 96	78.78 107	92.51 56	93.64 109	88.13 36	93.84 113	84.83 60	97.55 71	94.10 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD-MVS		88.96 67	89.88 53	86.22 118	91.63 128	77.07 111	89.82 64	93.77 51	78.90 105	92.88 46	92.29 145	86.11 62	90.22 226	86.24 41	97.24 84	91.36 205
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
XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 46	89.49 75	93.98 42	79.68 93	92.09 64	93.89 101	83.80 80	93.10 146	82.67 80	98.04 38	93.64 122

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 81	80.37 71	91.91 35	93.11 79	81.10 77	95.32 10	97.24 5	72.94 205	94.85 72	85.07 54	97.78 56	97.26 16

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 144	81.66 64	91.25 40	94.13 36	88.89 13	88.83 129	94.26 78	77.55 153	95.86 22	84.88 58	95.87 137	95.24 59

APD-MVS		89.54 56	89.63 57	89.26 65	92.57 94	81.34 66	90.19 56	93.08 82	80.87 81	91.13 81	93.19 114	86.22 61	95.97 12	82.23 86	97.18 86	90.45 227
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 72	89.62 58	84.73 149	90.46 166	65.27 214	88.86 86	93.02 87	87.15 25	93.05 44	97.10 6	82.28 100	92.02 174	76.70 151	97.99 43	96.88 25

test_0402		88.65 70	89.58 59	85.88 128	92.55 95	72.22 157	84.01 163	89.44 184	88.63 18	94.38 18	95.77 26	86.38 60	93.59 124	79.84 111	95.21 159	91.82 194

testtj		89.51 57	89.48 60	89.59 59	92.26 105	80.80 69	90.14 57	93.54 61	83.37 50	90.57 92	92.55 136	84.99 68	96.15 5	81.26 94	96.61 104	91.83 193

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 117	93.91 45	80.07 90	86.75 165	93.26 113	93.64 2	90.93 204	84.60 62	90.75 263	93.97 103

9.14			89.29 62		91.84 124		88.80 88	95.32 11	75.14 152	91.07 82	92.89 124	87.27 46	93.78 115	83.69 70	97.55 71

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 71	84.79 41	89.89 63	90.63 152	70.00 220	94.55 15	96.67 11	87.94 39	93.59 124	84.27 64	95.97 130	95.52 50

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 73	93.75 64	77.44 103	86.31 128	95.27 12	70.80 208	92.28 60	93.80 103	86.89 51	94.64 78	85.52 49	97.51 76	94.30 90

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 63	93.64 69	79.07 83	88.54 94	94.20 28	73.53 168	89.71 110	94.82 52	85.09 67	95.77 30	84.17 65	98.03 40	93.26 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_djsdf		89.62 54	89.01 66	91.45 25	92.36 100	82.98 55	91.98 32	90.08 172	71.54 200	94.28 21	96.54 13	81.57 115	94.27 90	86.26 38	96.49 109	97.09 21

DP-MVS		88.60 71	89.01 66	87.36 98	91.30 142	77.50 101	87.55 105	92.97 89	87.95 22	89.62 114	92.87 125	84.56 71	93.89 110	77.65 140	96.62 103	90.70 219

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 30	92.30 106	79.74 92	87.50 151	92.38 140	81.42 117	93.28 137	83.07 75	97.24 84	91.67 198

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 79	91.94 118	77.27 109	88.71 90	95.26 13	76.08 134	90.66 91	92.69 131	84.48 73	93.83 114	83.38 72	97.48 78	94.47 82

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 178	86.67 17	90.51 50	90.20 169	69.87 221	95.06 11	96.14 21	84.28 75	93.07 147	87.68 13	96.34 116	97.09 21

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 131	93.60 60	80.16 88	89.13 125	93.44 111	83.82 79	90.98 202	83.86 68	95.30 158	93.60 124

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 75	84.72 42	89.70 65	90.29 166	69.27 224	94.39 17	96.38 15	86.02 64	93.52 128	83.96 66	95.92 135	95.34 54

TranMVSNet+NR-MVSNet		87.86 82	88.76 73	85.18 140	94.02 58	64.13 224	84.38 156	91.29 134	84.88 37	92.06 65	93.84 102	86.45 58	93.73 116	73.22 188	98.66 11	97.69 9

nrg030		87.85 83	88.49 74	85.91 126	90.07 174	69.73 178	87.86 102	94.20 28	74.04 162	92.70 54	94.66 56	85.88 65	91.50 185	79.72 114	97.32 82	96.50 32

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 30	89.70 65	91.27 135	78.20 114	86.69 168	92.28 146	80.36 130	95.06 66	86.17 42	96.49 109	90.22 230

DROMVSNet		88.01 78	88.32 76	87.09 100	89.28 185	72.03 159	90.31 54	96.31 3	80.88 80	85.12 196	89.67 212	84.47 74	95.46 49	82.56 81	96.26 121	93.77 115

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	89.16 124	92.25 147	72.03 217	96.36 2	88.21 8	90.93 258	92.98 144
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
pmmvs6		86.52 99	88.06 78	81.90 207	92.22 108	62.28 248	84.66 149	89.15 188	83.54 49	89.85 106	97.32 4	88.08 38	86.80 280	70.43 214	97.30 83	96.62 29

PS-MVSNAJss		88.31 73	87.90 79	89.56 60	93.31 77	77.96 95	87.94 101	91.97 114	70.73 210	94.19 22	96.67 11	76.94 162	94.57 82	83.07 75	96.28 118	96.15 34

RRT_MVS		88.30 74	87.83 80	89.70 54	93.62 70	75.70 125	92.36 27	89.06 190	77.34 122	93.63 36	95.83 25	65.40 249	95.90 15	85.01 57	98.23 29	97.49 13

test_part1		87.15 90	87.82 81	85.15 141	88.88 194	63.04 235	87.98 99	94.85 16	82.52 61	93.61 38	95.73 27	67.51 236	95.71 32	80.48 107	98.83 2	96.69 27

TSAR-MVS + MP.		88.14 76	87.82 81	89.09 68	95.72 22	76.74 115	92.49 25	91.19 138	67.85 244	86.63 169	94.84 51	79.58 136	95.96 13	87.62 14	94.50 182	94.56 78
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		87.81 85	87.68 83	88.21 88	92.87 87	77.30 108	85.25 141	91.23 136	77.31 124	87.07 159	91.47 166	82.94 89	94.71 75	84.67 61	96.27 120	92.62 160

CS-MVS		88.14 76	87.67 84	89.54 61	89.56 180	79.18 82	90.47 51	94.77 18	79.37 99	84.32 211	89.33 218	83.87 77	94.53 85	82.45 82	94.89 172	94.90 66

ETH3D cwj APD-0.16		87.83 84	87.62 85	88.47 81	91.21 145	78.20 90	87.26 109	94.54 20	72.05 196	88.89 126	92.31 144	83.86 78	94.24 93	81.59 93	96.87 94	92.97 147

OMC-MVS		88.19 75	87.52 86	90.19 49	91.94 118	81.68 63	87.49 107	93.17 78	76.02 137	88.64 132	91.22 170	84.24 76	93.37 135	77.97 138	97.03 90	95.52 50

SixPastTwentyTwo		87.20 89	87.45 87	86.45 111	92.52 96	69.19 187	87.84 103	88.05 206	81.66 71	94.64 14	96.53 14	65.94 246	94.75 74	83.02 77	96.83 97	95.41 52

HQP_MVS		87.75 86	87.43 88	88.70 77	93.45 72	76.42 120	89.45 76	93.61 58	79.44 97	86.55 170	92.95 122	74.84 180	95.22 59	80.78 102	95.83 138	94.46 83

AllTest		87.97 80	87.40 89	89.68 55	91.59 129	83.40 50	89.50 74	95.44 9	79.47 95	88.00 144	93.03 117	82.66 92	91.47 186	70.81 206	96.14 124	94.16 95

mvsmamba		87.87 81	87.23 90	89.78 53	92.31 104	76.51 119	91.09 43	91.87 117	72.61 186	92.16 62	95.23 41	66.01 245	95.59 37	86.02 46	97.78 56	97.24 17

Anonymous20240529		86.20 106	87.13 91	83.42 179	90.19 170	64.55 221	84.55 151	90.71 149	85.85 31	89.94 104	95.24 40	82.13 102	90.40 221	69.19 225	96.40 114	95.31 56

v10		86.54 98	87.10 92	84.84 145	88.16 209	63.28 232	86.64 124	92.20 108	75.42 149	92.81 51	94.50 63	74.05 190	94.06 104	83.88 67	96.28 118	97.17 20

UniMVSNet_NR-MVSNet		86.84 94	87.06 93	86.17 122	92.86 89	67.02 202	82.55 209	91.56 125	83.08 55	90.92 85	91.82 157	78.25 146	93.99 105	74.16 175	98.35 22	97.49 13

FC-MVSNet-test		85.93 111	87.05 94	82.58 198	92.25 106	56.44 305	85.75 135	93.09 81	77.33 123	91.94 69	94.65 57	74.78 182	93.41 134	75.11 170	98.58 14	97.88 7

DU-MVS		86.80 95	86.99 95	86.21 120	93.24 79	67.02 202	83.16 193	92.21 107	81.73 70	90.92 85	91.97 151	77.20 156	93.99 105	74.16 175	98.35 22	97.61 10

UniMVSNet (Re)		86.87 92	86.98 96	86.55 109	93.11 82	68.48 191	83.80 172	92.87 91	80.37 84	89.61 116	91.81 158	77.72 150	94.18 97	75.00 171	98.53 16	96.99 24

RPSCF		88.00 79	86.93 97	91.22 31	90.08 172	89.30 5	89.68 67	91.11 139	79.26 100	89.68 111	94.81 55	82.44 94	87.74 267	76.54 154	88.74 285	96.61 30

NCCC		87.36 87	86.87 98	88.83 70	92.32 103	78.84 86	86.58 125	91.09 140	78.77 108	84.85 202	90.89 183	80.85 123	95.29 55	81.14 96	95.32 155	92.34 172

v8		86.22 105	86.83 99	84.36 157	87.82 213	62.35 247	86.42 127	91.33 133	76.78 129	92.73 53	94.48 65	73.41 199	93.72 117	83.10 74	95.41 151	97.01 23

IS-MVSNet		86.66 97	86.82 100	86.17 122	92.05 114	66.87 204	91.21 41	88.64 195	86.30 30	89.60 117	92.59 133	69.22 228	94.91 70	73.89 179	97.89 52	96.72 26

Vis-MVSNet		86.86 93	86.58 101	87.72 93	92.09 112	77.43 105	87.35 108	92.09 110	78.87 106	84.27 217	94.05 89	78.35 145	93.65 118	80.54 106	91.58 246	92.08 184
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		86.26 103	86.47 102	85.60 134	90.87 156	74.26 133	87.98 99	91.85 118	80.35 85	89.54 120	88.01 237	79.09 138	92.13 170	75.51 164	95.06 166	90.41 228

CS-MVS-test		87.00 91	86.43 103	88.71 76	89.46 181	77.46 102	89.42 78	95.73 6	77.87 117	81.64 257	87.25 252	82.43 95	94.53 85	77.65 140	96.46 111	94.14 97

Gipuma		84.44 138	86.33 104	78.78 254	84.20 278	73.57 136	89.55 71	90.44 156	84.24 39	84.38 209	94.89 49	76.35 171	80.40 327	76.14 158	96.80 99	82.36 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_prior3		86.31 102	86.31 105	86.32 113	90.59 163	71.99 160	83.37 185	92.85 92	75.43 147	84.58 205	91.57 162	81.92 110	94.17 99	79.54 117	96.97 91	92.80 150

FIs		85.35 118	86.27 106	82.60 197	91.86 121	57.31 298	85.10 143	93.05 83	75.83 142	91.02 84	93.97 93	73.57 195	92.91 153	73.97 178	98.02 41	97.58 12

NR-MVSNet		86.00 108	86.22 107	85.34 138	93.24 79	64.56 220	82.21 221	90.46 155	80.99 78	88.42 137	91.97 151	77.56 152	93.85 111	72.46 198	98.65 12	97.61 10

DeepPCF-MVS	81.24 5	87.28 88	86.21 108	90.49 43	91.48 139	84.90 39	83.41 184	92.38 105	70.25 217	89.35 122	90.68 190	82.85 90	94.57 82	79.55 116	95.95 132	92.00 187

canonicalmvs		85.50 115	86.14 109	83.58 176	87.97 210	67.13 200	87.55 105	94.32 22	73.44 170	88.47 136	87.54 246	86.45 58	91.06 201	75.76 163	93.76 197	92.54 163

Regformer-2		86.74 96	86.08 110	88.73 74	84.18 279	79.20 81	83.52 179	89.33 186	83.33 51	89.92 105	85.07 289	83.23 87	93.16 142	83.39 71	92.72 223	93.83 109

MSLP-MVS++		85.00 127	86.03 111	81.90 207	91.84 124	71.56 168	86.75 122	93.02 87	75.95 140	87.12 155	89.39 216	77.98 147	89.40 248	77.46 143	94.78 175	84.75 297

baseline		85.20 120	85.93 112	83.02 187	86.30 246	62.37 246	84.55 151	93.96 43	74.48 159	87.12 155	92.03 150	82.30 98	91.94 175	78.39 126	94.21 189	94.74 75

Baseline_NR-MVSNet		84.00 153	85.90 113	78.29 265	91.47 140	53.44 324	82.29 217	87.00 228	79.06 103	89.55 118	95.72 29	77.20 156	86.14 291	72.30 199	98.51 17	95.28 57

casdiffmvs		85.21 119	85.85 114	83.31 181	86.17 252	62.77 239	83.03 195	93.93 44	74.69 156	88.21 142	92.68 132	82.29 99	91.89 178	77.87 139	93.75 199	95.27 58

GeoE		85.45 117	85.81 115	84.37 155	90.08 172	67.07 201	85.86 134	91.39 132	72.33 192	87.59 149	90.25 200	84.85 69	92.37 164	78.00 136	91.94 240	93.66 119

PHI-MVS		86.38 101	85.81 115	88.08 89	88.44 203	77.34 106	89.35 79	93.05 83	73.15 179	84.76 203	87.70 243	78.87 140	94.18 97	80.67 104	96.29 117	92.73 153

TransMVSNet (Re)		84.02 152	85.74 117	78.85 253	91.00 153	55.20 315	82.29 217	87.26 216	79.65 94	88.38 139	95.52 34	83.00 88	86.88 278	67.97 237	96.60 105	94.45 85

Regformer-4		86.41 100	85.71 118	88.52 79	84.27 275	77.57 100	84.07 160	88.00 208	82.82 58	89.84 107	85.48 277	82.06 104	92.77 155	83.83 69	91.04 252	95.22 62

ANet_high		83.17 169	85.68 119	75.65 296	81.24 307	45.26 363	79.94 250	92.91 90	83.83 43	91.33 78	96.88 10	80.25 131	85.92 293	68.89 228	95.89 136	95.76 44

DeepC-MVS_fast	80.27 8	86.23 104	85.65 120	87.96 92	91.30 142	76.92 112	87.19 110	91.99 113	70.56 211	84.96 198	90.69 189	80.01 133	95.14 62	78.37 127	95.78 143	91.82 194
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 107	85.54 121	88.05 91	92.25 106	75.45 126	83.85 169	92.01 112	65.91 257	86.19 177	91.75 160	83.77 81	94.98 68	77.43 145	96.71 101	93.73 116

Regformer-1		86.00 108	85.50 122	87.49 96	84.18 279	76.90 113	83.52 179	87.94 210	82.18 65	89.19 123	85.07 289	82.28 100	91.89 178	82.40 84	92.72 223	93.69 118

FMVSNet1		84.55 135	85.45 123	81.85 209	90.27 169	61.05 260	86.83 118	88.27 203	78.57 111	89.66 113	95.64 31	75.43 173	90.68 214	69.09 226	95.33 154	93.82 111

VDDNet		84.35 140	85.39 124	81.25 218	95.13 33	59.32 279	85.42 140	81.11 281	86.41 29	87.41 152	96.21 19	73.61 194	90.61 217	66.33 246	96.85 95	93.81 114

train_agg		85.98 110	85.28 125	88.07 90	92.34 101	79.70 77	83.94 165	90.32 160	65.79 258	84.49 207	90.97 179	81.93 108	93.63 120	81.21 95	96.54 107	90.88 214

agg_prior1		85.72 113	85.20 126	87.28 99	91.58 132	77.69 98	83.69 175	90.30 163	66.29 255	84.32 211	91.07 176	82.13 102	93.18 140	81.02 97	96.36 115	90.98 210

dcpmvs_2		84.23 146	85.14 127	81.50 215	88.61 198	61.98 251	82.90 200	93.11 79	68.66 233	92.77 52	92.39 139	78.50 142	87.63 269	76.99 150	92.30 228	94.90 66

LCM-MVSNet-Re		83.48 163	85.06 128	78.75 255	85.94 255	55.75 310	80.05 248	94.27 23	76.47 130	96.09 5	94.54 62	83.31 86	89.75 242	59.95 291	94.89 172	90.75 217

EPP-MVSNet		85.47 116	85.04 129	86.77 106	91.52 138	69.37 181	91.63 38	87.98 209	81.51 73	87.05 160	91.83 156	66.18 244	95.29 55	70.75 209	96.89 93	95.64 47

IterMVS-LS		84.73 131	84.98 130	83.96 167	87.35 223	63.66 227	83.25 189	89.88 176	76.06 135	89.62 114	92.37 143	73.40 201	92.52 160	78.16 133	94.77 177	95.69 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pm-mvs1		83.69 158	84.95 131	79.91 240	90.04 176	59.66 276	82.43 213	87.44 213	75.52 146	87.85 146	95.26 39	81.25 119	85.65 297	68.74 230	96.04 127	94.42 86

ETH3 D test6400		85.09 123	84.87 132	85.75 131	90.80 158	69.34 182	85.90 132	93.31 70	65.43 264	86.11 180	89.95 206	80.92 122	94.86 71	75.90 161	95.57 148	93.05 141

TAPA-MVS	77.73 12	85.71 114	84.83 133	88.37 85	88.78 196	79.72 76	87.15 112	93.50 62	69.17 225	85.80 187	89.56 213	80.76 124	92.13 170	73.21 193	95.51 149	93.25 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 164	84.73 134	79.69 244	90.29 168	57.52 297	81.30 235	88.69 194	76.29 131	87.58 150	94.44 66	80.60 127	87.20 273	66.60 245	96.82 98	94.34 89

K. test v3		85.14 121	84.73 134	86.37 112	91.13 150	69.63 180	85.45 139	76.68 306	84.06 42	92.44 58	96.99 8	62.03 266	94.65 77	80.58 105	93.24 208	94.83 74

v1144		84.54 137	84.72 136	84.00 165	87.67 217	62.55 243	82.97 197	90.93 145	70.32 216	89.80 108	90.99 178	73.50 196	93.48 130	81.69 92	94.65 180	95.97 40

3Dnovator	80.37 7	84.80 130	84.71 137	85.06 143	86.36 244	74.71 130	88.77 89	90.00 174	75.65 145	84.96 198	93.17 115	74.06 189	91.19 196	78.28 130	91.09 250	89.29 245

v1192		84.57 134	84.69 138	84.21 161	87.75 215	62.88 237	83.02 196	91.43 129	69.08 227	89.98 103	90.89 183	72.70 209	93.62 123	82.41 83	94.97 169	96.13 35

Regformer-3		85.06 124	84.67 139	86.22 118	84.27 275	73.43 137	84.07 160	85.26 247	80.77 82	88.62 133	85.48 277	80.56 128	90.39 222	81.99 88	91.04 252	94.85 72

MIMVSNet1		83.63 160	84.59 140	80.74 228	94.06 57	62.77 239	82.72 203	84.53 260	77.57 121	90.34 94	95.92 24	76.88 168	85.83 295	61.88 278	97.42 79	93.62 123

VDD-MVS		84.23 146	84.58 141	83.20 184	91.17 149	65.16 216	83.25 189	84.97 257	79.79 91	87.18 154	94.27 75	74.77 183	90.89 207	69.24 222	96.54 107	93.55 128

EI-MVSNet-Vis-set		85.12 122	84.53 142	86.88 103	84.01 282	72.76 142	83.91 168	85.18 249	80.44 83	88.75 130	85.49 276	80.08 132	91.92 176	82.02 87	90.85 261	95.97 40

v1240		84.30 142	84.51 143	83.65 174	87.65 218	61.26 257	82.85 201	91.54 126	67.94 242	90.68 90	90.65 192	71.71 219	93.64 119	82.84 79	94.78 175	96.07 37

bld_raw_dy_0_64		84.85 129	84.44 144	86.07 124	93.73 66	74.93 129	88.57 93	81.90 277	70.44 212	91.28 80	95.18 42	56.62 300	89.28 249	85.15 53	97.09 88	93.99 101

EI-MVSNet-UG-set		85.04 125	84.44 144	86.85 104	83.87 285	72.52 151	83.82 170	85.15 250	80.27 87	88.75 130	85.45 280	79.95 134	91.90 177	81.92 90	90.80 262	96.13 35

v144192		84.24 145	84.41 146	83.71 173	87.59 220	61.57 253	82.95 198	91.03 141	67.82 245	89.80 108	90.49 195	73.28 202	93.51 129	81.88 91	94.89 172	96.04 39

WR-MVS		83.56 161	84.40 147	81.06 223	93.43 74	54.88 316	78.67 271	85.02 254	81.24 75	90.74 89	91.56 164	72.85 206	91.08 200	68.00 236	98.04 38	97.23 18

v1921920		84.23 146	84.37 148	83.79 170	87.64 219	61.71 252	82.91 199	91.20 137	67.94 242	90.06 98	90.34 197	72.04 216	93.59 124	82.32 85	94.91 170	96.07 37

MVS_111021_HR		84.63 132	84.34 149	85.49 137	90.18 171	75.86 124	79.23 264	87.13 220	73.35 171	85.56 191	89.34 217	83.60 83	90.50 219	76.64 152	94.05 193	90.09 235

v2v482		84.09 149	84.24 150	83.62 175	87.13 228	61.40 254	82.71 204	89.71 178	72.19 195	89.55 118	91.41 167	70.70 224	93.20 139	81.02 97	93.76 197	96.25 33

EG-PatchMatch MVS		84.08 150	84.11 151	83.98 166	92.22 108	72.61 148	82.20 223	87.02 225	72.63 185	88.86 127	91.02 177	78.52 141	91.11 199	73.41 186	91.09 250	88.21 258

HQP-MVS		84.61 133	84.06 152	86.27 116	91.19 146	70.66 172	84.77 145	92.68 98	73.30 174	80.55 271	90.17 204	72.10 213	94.61 80	77.30 146	94.47 183	93.56 126

Effi-MVS+		83.90 156	84.01 153	83.57 177	87.22 226	65.61 213	86.55 126	92.40 103	78.64 110	81.34 262	84.18 300	83.65 82	92.93 151	74.22 174	87.87 295	92.17 183

alignmvs		83.94 155	83.98 154	83.80 169	87.80 214	67.88 197	84.54 153	91.42 131	73.27 177	88.41 138	87.96 238	72.33 212	90.83 209	76.02 160	94.11 191	92.69 157

MCST-MVS		84.36 139	83.93 155	85.63 133	91.59 129	71.58 167	83.52 179	92.13 109	61.82 286	83.96 221	89.75 211	79.93 135	93.46 131	78.33 129	94.34 186	91.87 192

ETV-MVS		84.31 141	83.91 156	85.52 135	88.58 199	70.40 174	84.50 155	93.37 64	78.76 109	84.07 220	78.72 346	80.39 129	95.13 63	73.82 181	92.98 216	91.04 209

MVS_111021_LR		84.28 143	83.76 157	85.83 130	89.23 187	83.07 53	80.99 239	83.56 264	72.71 184	86.07 181	89.07 224	81.75 114	86.19 290	77.11 148	93.36 204	88.24 257

AdaColmap		83.66 159	83.69 158	83.57 177	90.05 175	72.26 156	86.29 130	90.00 174	78.19 115	81.65 256	87.16 254	83.40 85	94.24 93	61.69 280	94.76 178	84.21 302

F-COLMAP		84.97 128	83.42 159	89.63 57	92.39 99	83.40 50	88.83 87	91.92 116	73.19 178	80.18 277	89.15 222	77.04 160	93.28 137	65.82 252	92.28 231	92.21 181

Effi-MVS+-dtu		85.82 112	83.38 160	93.14 3	87.13 228	91.15 2	87.70 104	88.42 197	74.57 157	83.56 227	85.65 274	78.49 143	94.21 95	72.04 200	92.88 218	94.05 100

V42		83.47 164	83.37 161	83.75 172	83.16 291	63.33 231	81.31 233	90.23 168	69.51 223	90.91 87	90.81 186	74.16 188	92.29 168	80.06 108	90.22 269	95.62 48

MVS_Test		82.47 176	83.22 162	80.22 237	82.62 297	57.75 296	82.54 210	91.96 115	71.16 206	82.89 235	92.52 138	77.41 154	90.50 219	80.04 109	87.84 296	92.40 169

DP-MVS Recon		84.05 151	83.22 162	86.52 110	91.73 127	75.27 127	83.23 191	92.40 103	72.04 197	82.04 247	88.33 233	77.91 149	93.95 108	66.17 247	95.12 164	90.34 229

PAPM_NR		83.23 167	83.19 164	83.33 180	90.90 155	65.98 210	88.19 97	90.78 148	78.13 116	80.87 266	87.92 241	73.49 198	92.42 161	70.07 216	88.40 286	91.60 200

KD-MVS_self_test		81.93 186	83.14 165	78.30 264	84.75 266	52.75 328	80.37 245	89.42 185	70.24 218	90.26 96	93.39 112	74.55 187	86.77 281	68.61 232	96.64 102	95.38 53

CNLPA		83.55 162	83.10 166	84.90 144	89.34 184	83.87 48	84.54 153	88.77 192	79.09 102	83.54 228	88.66 230	74.87 179	81.73 321	66.84 243	92.29 230	89.11 247

tfpnnormal		81.79 187	82.95 167	78.31 263	88.93 193	55.40 311	80.83 242	82.85 269	76.81 128	85.90 186	94.14 86	74.58 186	86.51 285	66.82 244	95.68 147	93.01 143

CANet		83.79 157	82.85 168	86.63 107	86.17 252	72.21 158	83.76 173	91.43 129	77.24 125	74.39 321	87.45 248	75.36 174	95.42 51	77.03 149	92.83 219	92.25 180

h-mvs33		84.25 144	82.76 169	88.72 75	91.82 126	82.60 58	84.00 164	84.98 256	71.27 202	86.70 166	90.55 194	63.04 262	93.92 109	78.26 131	94.20 190	89.63 237

X-MVStestdata		85.04 125	82.70 170	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 101	16.05 375	86.57 56	95.80 25	87.35 23	97.62 66	94.20 92

TSAR-MVS + GP.		83.95 154	82.69 171	87.72 93	89.27 186	81.45 65	83.72 174	81.58 280	74.73 155	85.66 188	86.06 269	72.56 211	92.69 157	75.44 166	95.21 159	89.01 253

CLD-MVS		83.18 168	82.64 172	84.79 146	89.05 189	67.82 198	77.93 279	92.52 101	68.33 235	85.07 197	81.54 327	82.06 104	92.96 149	69.35 221	97.91 51	93.57 125
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 178	82.61 173	81.30 217	86.29 247	69.79 176	88.71 90	87.67 212	78.42 113	82.15 245	84.15 301	77.98 147	91.59 184	65.39 254	92.75 220	82.51 327

QAPM		82.59 174	82.59 174	82.58 198	86.44 239	66.69 205	89.94 62	90.36 159	67.97 241	84.94 200	92.58 135	72.71 208	92.18 169	70.63 212	87.73 297	88.85 254

114514_t		83.10 170	82.54 175	84.77 148	92.90 86	69.10 189	86.65 123	90.62 153	54.66 328	81.46 259	90.81 186	76.98 161	94.38 89	72.62 196	96.18 122	90.82 216

v148		82.31 177	82.48 176	81.81 212	85.59 257	59.66 276	81.47 231	86.02 237	72.85 182	88.05 143	90.65 192	70.73 223	90.91 206	75.15 169	91.79 241	94.87 68

EI-MVSNet		82.61 173	82.42 177	83.20 184	83.25 289	63.66 227	83.50 182	85.07 251	76.06 135	86.55 170	85.10 286	73.41 199	90.25 223	78.15 135	90.67 265	95.68 46

TinyColmap		81.25 192	82.34 178	77.99 270	85.33 260	60.68 268	82.32 216	88.33 201	71.26 204	86.97 161	92.22 149	77.10 159	86.98 277	62.37 273	95.17 161	86.31 281

mvs-test1		84.55 135	82.12 179	91.84 20	87.13 228	89.54 4	85.05 144	88.42 197	74.57 157	80.60 268	82.98 310	78.49 143	93.98 107	72.04 200	89.77 272	92.00 187

GBi-Net		82.02 183	82.07 180	81.85 209	86.38 241	61.05 260	86.83 118	88.27 203	72.43 187	86.00 182	95.64 31	63.78 256	90.68 214	65.95 248	93.34 205	93.82 111

test1		82.02 183	82.07 180	81.85 209	86.38 241	61.05 260	86.83 118	88.27 203	72.43 187	86.00 182	95.64 31	63.78 256	90.68 214	65.95 248	93.34 205	93.82 111

OpenMVS	76.72 13	81.98 185	82.00 182	81.93 206	84.42 271	68.22 193	88.50 95	89.48 183	66.92 250	81.80 254	91.86 153	72.59 210	90.16 228	71.19 205	91.25 249	87.40 270

LF4IMVS		82.75 172	81.93 183	85.19 139	82.08 298	80.15 73	85.53 138	88.76 193	68.01 239	85.58 190	87.75 242	71.80 218	86.85 279	74.02 177	93.87 196	88.58 256

hse-mvs2		83.47 164	81.81 184	88.47 81	91.03 152	82.27 59	82.61 205	83.69 262	71.27 202	86.70 166	86.05 270	63.04 262	92.41 162	78.26 131	93.62 203	90.71 218

VPNet		80.25 212	81.68 185	75.94 295	92.46 98	47.98 354	76.70 296	81.67 279	73.45 169	84.87 201	92.82 126	74.66 185	86.51 285	61.66 281	96.85 95	93.33 130

UGNet		82.78 171	81.64 186	86.21 120	86.20 251	76.24 123	86.86 116	85.68 241	77.07 126	73.76 324	92.82 126	69.64 225	91.82 181	69.04 227	93.69 200	90.56 224
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
FMVSNet2		81.31 191	81.61 187	80.41 234	86.38 241	58.75 290	83.93 167	86.58 230	72.43 187	87.65 148	92.98 119	63.78 256	90.22 226	66.86 241	93.92 195	92.27 178

c3_l		81.64 188	81.59 188	81.79 213	80.86 313	59.15 283	78.61 272	90.18 170	68.36 234	87.20 153	87.11 256	69.39 226	91.62 183	78.16 133	94.43 185	94.60 77

MVSFormer		82.23 179	81.57 189	84.19 163	85.54 258	69.26 184	91.98 32	90.08 172	71.54 200	76.23 305	85.07 289	58.69 287	94.27 90	86.26 38	88.77 283	89.03 251

Fast-Effi-MVS+-dtu		82.54 175	81.41 190	85.90 127	85.60 256	76.53 118	83.07 194	89.62 181	73.02 181	79.11 286	83.51 305	80.74 125	90.24 225	68.76 229	89.29 276	90.94 212

Anonymous20240521		80.18 215	81.25 191	76.95 282	83.15 292	60.84 265	82.46 212	85.99 238	68.76 231	86.78 163	93.73 108	59.13 284	77.44 334	73.71 182	97.55 71	92.56 161

DELS-MVS		81.44 190	81.25 191	82.03 205	84.27 275	62.87 238	76.47 300	92.49 102	70.97 207	81.64 257	83.83 302	75.03 177	92.70 156	74.29 173	92.22 234	90.51 226
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
EIA-MVS		82.19 180	81.23 193	85.10 142	87.95 211	69.17 188	83.22 192	93.33 67	70.42 213	78.58 289	79.77 343	77.29 155	94.20 96	71.51 203	88.96 281	91.93 191

Anonymous202405211		80.51 204	81.19 194	78.49 260	88.48 201	57.26 299	76.63 297	82.49 271	81.21 76	84.30 215	92.24 148	67.99 234	86.24 289	62.22 274	95.13 162	91.98 190

BH-untuned		80.96 196	80.99 195	80.84 227	88.55 200	68.23 192	80.33 246	88.46 196	72.79 183	86.55 170	86.76 259	74.72 184	91.77 182	61.79 279	88.99 280	82.52 326

MG-MVS		80.32 211	80.94 196	78.47 261	88.18 207	52.62 331	82.29 217	85.01 255	72.01 198	79.24 285	92.54 137	69.36 227	93.36 136	70.65 211	89.19 279	89.45 239

PCF-MVS	74.62 15	82.15 181	80.92 197	85.84 129	89.43 182	72.30 155	80.53 243	91.82 120	57.36 317	87.81 147	89.92 208	77.67 151	93.63 120	58.69 296	95.08 165	91.58 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.04 195	80.57 198	82.46 202	87.50 221	63.22 233	78.37 275	89.63 180	68.01 239	81.87 250	82.08 322	82.31 97	92.65 158	67.10 240	88.30 291	91.51 203

LFMVS		80.15 216	80.56 199	78.89 252	89.19 188	55.93 307	85.22 142	73.78 326	82.96 56	84.28 216	92.72 130	57.38 296	90.07 235	63.80 264	95.75 144	90.68 220

ab-mvs		79.67 219	80.56 199	76.99 281	88.48 201	56.93 301	84.70 148	86.06 236	68.95 229	80.78 267	93.08 116	75.30 175	84.62 305	56.78 305	90.90 259	89.43 241

PVSNet_Blended_VisFu		81.55 189	80.49 201	84.70 151	91.58 132	73.24 140	84.21 157	91.67 124	62.86 279	80.94 264	87.16 254	67.27 238	92.87 154	69.82 218	88.94 282	87.99 262

diffmvs		80.40 207	80.48 202	80.17 238	79.02 333	60.04 272	77.54 286	90.28 167	66.65 253	82.40 240	87.33 251	73.50 196	87.35 272	77.98 137	89.62 274	93.13 138

PLC	73.85 16	82.09 182	80.31 203	87.45 97	90.86 157	80.29 72	85.88 133	90.65 151	68.17 237	76.32 304	86.33 264	73.12 204	92.61 159	61.40 283	90.02 271	89.44 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 220	80.27 204	76.44 289	87.92 212	53.95 320	75.58 309	84.35 261	74.39 160	82.23 243	90.72 188	72.84 207	84.39 307	60.38 290	93.98 194	90.97 211

cl____		80.42 206	80.23 205	81.02 224	79.99 321	59.25 280	77.07 292	87.02 225	67.37 247	86.18 179	89.21 220	63.08 261	90.16 228	76.31 156	95.80 141	93.65 121

DIV-MVS_self_test		80.43 205	80.23 205	81.02 224	79.99 321	59.25 280	77.07 292	87.02 225	67.38 246	86.19 177	89.22 219	63.09 260	90.16 228	76.32 155	95.80 141	93.66 119

eth_miper_zixun_eth		80.84 198	80.22 207	82.71 195	81.41 305	60.98 263	77.81 281	90.14 171	67.31 248	86.95 162	87.24 253	64.26 252	92.31 166	75.23 168	91.61 244	94.85 72

BH-RMVSNet		80.53 203	80.22 207	81.49 216	87.19 227	66.21 209	77.79 282	86.23 234	74.21 161	83.69 223	88.50 231	73.25 203	90.75 211	63.18 270	87.90 294	87.52 268

xiu_mvs_v1_base_debu		80.84 198	80.14 209	82.93 190	88.31 204	71.73 163	79.53 255	87.17 217	65.43 264	79.59 279	82.73 317	76.94 162	90.14 231	73.22 188	88.33 287	86.90 276

xiu_mvs_v1_base		80.84 198	80.14 209	82.93 190	88.31 204	71.73 163	79.53 255	87.17 217	65.43 264	79.59 279	82.73 317	76.94 162	90.14 231	73.22 188	88.33 287	86.90 276

xiu_mvs_v1_base_debi		80.84 198	80.14 209	82.93 190	88.31 204	71.73 163	79.53 255	87.17 217	65.43 264	79.59 279	82.73 317	76.94 162	90.14 231	73.22 188	88.33 287	86.90 276

miper_ehance_all_eth		80.34 210	80.04 212	81.24 220	79.82 323	58.95 285	77.66 283	89.66 179	65.75 261	85.99 185	85.11 285	68.29 233	91.42 190	76.03 159	92.03 236	93.33 130

MSDG		80.06 218	79.99 213	80.25 236	83.91 284	68.04 196	77.51 287	89.19 187	77.65 119	81.94 248	83.45 307	76.37 170	86.31 288	63.31 269	86.59 305	86.41 279

1121		80.86 197	79.81 214	84.02 164	93.93 60	78.70 87	81.64 228	80.18 288	55.43 325	83.67 224	91.15 173	71.29 221	91.41 191	67.95 238	93.06 213	81.96 331

tttt0517		81.07 194	79.58 215	85.52 135	88.99 192	66.45 207	87.03 114	75.51 314	73.76 166	88.32 141	90.20 201	37.96 364	94.16 102	79.36 121	95.13 162	95.93 43

IterMVS-SCA-FT		80.64 202	79.41 216	84.34 158	83.93 283	69.66 179	76.28 302	81.09 282	72.43 187	86.47 176	90.19 202	60.46 272	93.15 144	77.45 144	86.39 308	90.22 230

patch_mono-2		78.89 223	79.39 217	77.41 279	84.78 265	68.11 194	75.60 307	83.11 266	60.96 295	79.36 282	89.89 209	75.18 176	72.97 345	73.32 187	92.30 228	91.15 207

wuyk23d		75.13 264	79.30 218	62.63 343	75.56 353	75.18 128	80.89 240	73.10 332	75.06 153	94.76 12	95.32 36	87.73 42	52.85 372	34.16 371	97.11 87	59.85 368

DPM-MVS		80.10 217	79.18 219	82.88 193	90.71 161	69.74 177	78.87 268	90.84 146	60.29 301	75.64 313	85.92 272	67.28 237	93.11 145	71.24 204	91.79 241	85.77 287

PM-MVS		80.20 214	79.00 220	83.78 171	88.17 208	86.66 18	81.31 233	66.81 358	69.64 222	88.33 140	90.19 202	64.58 250	83.63 313	71.99 202	90.03 270	81.06 345

iter_conf_final		80.36 209	78.88 221	84.79 146	86.29 247	66.36 208	86.95 115	86.25 233	68.16 238	82.09 246	89.48 214	36.59 367	94.51 87	79.83 112	94.30 187	93.50 129

test1111		78.53 231	78.85 222	77.56 276	92.22 108	47.49 356	82.61 205	69.24 350	72.43 187	85.28 194	94.20 81	51.91 315	90.07 235	65.36 255	96.45 112	95.11 63

AUN-MVS		81.18 193	78.78 223	88.39 84	90.93 154	82.14 60	82.51 211	83.67 263	64.69 272	80.29 274	85.91 273	51.07 319	92.38 163	76.29 157	93.63 202	90.65 222

mvs_anonymous		78.13 235	78.76 224	76.23 294	79.24 330	50.31 347	78.69 270	84.82 258	61.60 290	83.09 234	92.82 126	73.89 192	87.01 274	68.33 235	86.41 307	91.37 204

MAR-MVS		80.24 213	78.74 225	84.73 149	86.87 238	78.18 91	85.75 135	87.81 211	65.67 263	77.84 294	78.50 347	73.79 193	90.53 218	61.59 282	90.87 260	85.49 290
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
ECVR-MVS		78.44 232	78.63 226	77.88 272	91.85 122	48.95 350	83.68 176	69.91 348	72.30 193	84.26 218	94.20 81	51.89 316	89.82 239	63.58 265	96.02 128	94.87 68

FMVSNet3		78.80 227	78.55 227	79.57 246	82.89 296	56.89 303	81.76 225	85.77 240	69.04 228	86.00 182	90.44 196	51.75 317	90.09 234	65.95 248	93.34 205	91.72 196

test_yl		78.71 229	78.51 228	79.32 249	84.32 273	58.84 287	78.38 273	85.33 245	75.99 138	82.49 238	86.57 260	58.01 290	90.02 237	62.74 271	92.73 221	89.10 248

DCV-MVSNet		78.71 229	78.51 228	79.32 249	84.32 273	58.84 287	78.38 273	85.33 245	75.99 138	82.49 238	86.57 260	58.01 290	90.02 237	62.74 271	92.73 221	89.10 248

EPNet		80.37 208	78.41 230	86.23 117	76.75 344	73.28 138	87.18 111	77.45 301	76.24 133	68.14 344	88.93 226	65.41 248	93.85 111	69.47 220	96.12 126	91.55 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		78.88 224	78.28 231	80.68 231	79.58 324	62.64 241	82.58 207	94.16 31	74.80 154	75.72 311	92.59 133	48.69 325	95.56 39	73.48 185	82.91 335	83.85 307

cl22		78.97 222	78.21 232	81.24 220	77.74 337	59.01 284	77.46 289	87.13 220	65.79 258	84.32 211	85.10 286	58.96 286	90.88 208	75.36 167	92.03 236	93.84 108

PAPR		78.84 225	78.10 233	81.07 222	85.17 261	60.22 271	82.21 221	90.57 154	62.51 281	75.32 316	84.61 296	74.99 178	92.30 167	59.48 294	88.04 293	90.68 220

PVSNet_BlendedMVS		78.80 227	77.84 234	81.65 214	84.43 269	63.41 229	79.49 258	90.44 156	61.70 289	75.43 314	87.07 257	69.11 229	91.44 188	60.68 288	92.24 232	90.11 234

Vis-MVSNet (Re-imp)		77.82 238	77.79 235	77.92 271	88.82 195	51.29 341	83.28 187	71.97 338	74.04 162	82.23 243	89.78 210	57.38 296	89.41 247	57.22 304	95.41 151	93.05 141

Patchmtry		76.56 253	77.46 236	73.83 305	79.37 329	46.60 360	82.41 214	76.90 303	73.81 165	85.56 191	92.38 140	48.07 327	83.98 310	63.36 268	95.31 157	90.92 213

OpenMVS_ROB	70.19 17	77.77 240	77.46 236	78.71 256	84.39 272	61.15 258	81.18 237	82.52 270	62.45 283	83.34 229	87.37 249	66.20 243	88.66 259	64.69 260	85.02 319	86.32 280

CL-MVSNet_self_test		76.81 249	77.38 238	75.12 299	86.90 236	51.34 339	73.20 326	80.63 286	68.30 236	81.80 254	88.40 232	66.92 240	80.90 324	55.35 316	94.90 171	93.12 139

iter_conf05		78.81 226	77.35 239	83.21 183	82.98 295	60.75 267	84.09 159	88.34 200	63.12 277	84.25 219	89.48 214	31.41 372	94.51 87	76.64 152	95.83 138	94.38 88

thisisatest0530		79.07 221	77.33 240	84.26 160	87.13 228	64.58 219	83.66 177	75.95 309	68.86 230	85.22 195	87.36 250	38.10 362	93.57 127	75.47 165	94.28 188	94.62 76

MVS_0304		78.17 234	77.23 241	80.99 226	84.13 281	69.07 190	81.39 232	80.81 284	76.28 132	67.53 349	89.11 223	62.87 264	86.77 281	60.90 287	92.01 239	87.13 273

CANet_DTU		77.81 239	77.05 242	80.09 239	81.37 306	59.90 274	83.26 188	88.29 202	69.16 226	67.83 347	83.72 303	60.93 269	89.47 244	69.22 224	89.70 273	90.88 214

pmmvs-eth3d		78.42 233	77.04 243	82.57 200	87.44 222	74.41 132	80.86 241	79.67 291	55.68 323	84.69 204	90.31 199	60.91 270	85.42 298	62.20 275	91.59 245	87.88 265

miper_enhance_ethall		77.83 237	76.93 244	80.51 232	76.15 350	58.01 293	75.47 311	88.82 191	58.05 311	83.59 226	80.69 331	64.41 251	91.20 195	73.16 194	92.03 236	92.33 173

MDA-MVSNet-bldmvs		77.47 241	76.90 245	79.16 251	79.03 332	64.59 218	66.58 349	75.67 312	73.15 179	88.86 127	88.99 225	66.94 239	81.23 323	64.71 259	88.22 292	91.64 199

xiu_mvs_v2_base		77.19 244	76.75 246	78.52 259	87.01 234	61.30 256	75.55 310	87.12 223	61.24 292	74.45 320	78.79 345	77.20 156	90.93 204	64.62 262	84.80 325	83.32 316

USDC		76.63 251	76.73 247	76.34 291	83.46 287	57.20 300	80.02 249	88.04 207	52.14 342	83.65 225	91.25 169	63.24 259	86.65 284	54.66 321	94.11 191	85.17 292

PS-MVSNAJ		77.04 246	76.53 248	78.56 258	87.09 233	61.40 254	75.26 312	87.13 220	61.25 291	74.38 322	77.22 354	76.94 162	90.94 203	64.63 261	84.83 324	83.35 315

TAMVS		78.08 236	76.36 249	83.23 182	90.62 162	72.87 141	79.08 265	80.01 290	61.72 288	81.35 261	86.92 258	63.96 255	88.78 257	50.61 338	93.01 215	88.04 261

IterMVS		76.91 247	76.34 250	78.64 257	80.91 311	64.03 225	76.30 301	79.03 294	64.88 271	83.11 232	89.16 221	59.90 278	84.46 306	68.61 232	85.15 318	87.42 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 275	76.19 251	69.21 324	84.61 267	52.43 332	71.70 331	77.18 302	60.73 298	80.60 268	90.96 181	75.44 172	69.35 353	56.13 309	88.33 287	85.86 286

miper_lstm_enhance		76.45 255	76.10 252	77.51 277	76.72 345	60.97 264	64.69 352	85.04 253	63.98 274	83.20 231	88.22 234	56.67 299	78.79 332	73.22 188	93.12 211	92.78 152

BH-w/o		76.57 252	76.07 253	78.10 268	86.88 237	65.92 211	77.63 284	86.33 231	65.69 262	80.89 265	79.95 340	68.97 231	90.74 212	53.01 330	85.25 317	77.62 351

TR-MVS		76.77 250	75.79 254	79.72 243	86.10 254	65.79 212	77.14 290	83.02 267	65.20 269	81.40 260	82.10 321	66.30 242	90.73 213	55.57 313	85.27 316	82.65 322

jason		77.42 242	75.75 255	82.43 203	87.10 232	69.27 183	77.99 278	81.94 276	51.47 346	77.84 294	85.07 289	60.32 274	89.00 251	70.74 210	89.27 278	89.03 251
jason: jason.
MVSTER		77.09 245	75.70 256	81.25 218	75.27 357	61.08 259	77.49 288	85.07 251	60.78 297	86.55 170	88.68 229	43.14 354	90.25 223	73.69 183	90.67 265	92.42 167

D2MVS		76.84 248	75.67 257	80.34 235	80.48 319	62.16 250	73.50 323	84.80 259	57.61 315	82.24 242	87.54 246	51.31 318	87.65 268	70.40 215	93.19 210	91.23 206

PVSNet_Blended		76.49 254	75.40 258	79.76 242	84.43 269	63.41 229	75.14 313	90.44 156	57.36 317	75.43 314	78.30 348	69.11 229	91.44 188	60.68 288	87.70 298	84.42 300

CDS-MVSNet		77.32 243	75.40 258	83.06 186	89.00 191	72.48 152	77.90 280	82.17 274	60.81 296	78.94 287	83.49 306	59.30 282	88.76 258	54.64 322	92.37 227	87.93 264
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 258	75.35 260	77.85 274	87.01 234	51.84 337	80.45 244	73.26 330	75.20 151	83.10 233	86.31 266	45.54 340	89.05 250	55.03 319	92.24 232	92.66 158

thres100view900		75.45 261	75.05 261	76.66 288	87.27 224	51.88 336	81.07 238	73.26 330	75.68 144	83.25 230	86.37 263	45.54 340	88.80 254	51.98 334	90.99 254	89.31 243

cascas		76.29 257	74.81 262	80.72 230	84.47 268	62.94 236	73.89 321	87.34 214	55.94 322	75.16 318	76.53 357	63.97 254	91.16 197	65.00 256	90.97 257	88.06 260

GA-MVS		75.83 259	74.61 263	79.48 248	81.87 300	59.25 280	73.42 324	82.88 268	68.68 232	79.75 278	81.80 324	50.62 321	89.46 245	66.85 242	85.64 313	89.72 236

testgi		72.36 289	74.61 263	65.59 337	80.56 318	42.82 370	68.29 342	73.35 329	66.87 251	81.84 251	89.93 207	72.08 215	66.92 361	46.05 356	92.54 225	87.01 275

test20.03		73.75 279	74.59 265	71.22 317	81.11 309	51.12 343	70.15 337	72.10 337	70.42 213	80.28 276	91.50 165	64.21 253	74.72 344	46.96 354	94.58 181	87.82 267

lupinMVS		76.37 256	74.46 266	82.09 204	85.54 258	69.26 184	76.79 294	80.77 285	50.68 352	76.23 305	82.82 315	58.69 287	88.94 252	69.85 217	88.77 283	88.07 259

EU-MVSNet		75.12 265	74.43 267	77.18 280	83.11 293	59.48 278	85.71 137	82.43 272	39.76 370	85.64 189	88.76 227	44.71 350	87.88 266	73.86 180	85.88 312	84.16 303

tfpn200view9		74.86 269	74.23 268	76.74 287	86.24 249	52.12 333	79.24 262	73.87 324	73.34 172	81.82 252	84.60 297	46.02 334	88.80 254	51.98 334	90.99 254	89.31 243

thres400		75.14 263	74.23 268	77.86 273	86.24 249	52.12 333	79.24 262	73.87 324	73.34 172	81.82 252	84.60 297	46.02 334	88.80 254	51.98 334	90.99 254	92.66 158

ppachtmachnet_test		74.73 272	74.00 270	76.90 284	80.71 316	56.89 303	71.53 332	78.42 296	58.24 309	79.32 284	82.92 314	57.91 293	84.26 308	65.60 253	91.36 248	89.56 238

1112_ss		74.82 270	73.74 271	78.04 269	89.57 179	60.04 272	76.49 299	87.09 224	54.31 329	73.66 325	79.80 341	60.25 275	86.76 283	58.37 297	84.15 328	87.32 271

Patchmatch-RL test		74.48 273	73.68 272	76.89 285	84.83 264	66.54 206	72.29 329	69.16 351	57.70 313	86.76 164	86.33 264	45.79 339	82.59 316	69.63 219	90.65 267	81.54 336

CMPMVS	59.41 20	75.12 265	73.57 273	79.77 241	75.84 352	67.22 199	81.21 236	82.18 273	50.78 350	76.50 301	87.66 244	55.20 309	82.99 315	62.17 277	90.64 268	89.09 250
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 281	73.54 274	72.43 313	84.92 263	47.79 355	79.89 251	74.00 322	65.93 256	78.81 288	86.28 267	56.36 302	81.63 322	56.63 306	79.04 352	87.87 266

MVP-Stereo		75.81 260	73.51 275	82.71 195	89.35 183	73.62 135	80.06 247	85.20 248	60.30 300	73.96 323	87.94 239	57.89 294	89.45 246	52.02 333	74.87 361	85.06 294
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test2506		74.12 276	73.39 276	76.28 292	91.85 122	44.20 366	84.06 162	48.20 378	72.30 193	81.90 249	94.20 81	27.22 379	89.77 240	64.81 258	96.02 128	94.87 68

new-patchmatchnet		70.10 304	73.37 277	60.29 350	81.23 308	16.95 380	59.54 360	74.62 317	62.93 278	80.97 263	87.93 240	62.83 265	71.90 348	55.24 317	95.01 168	92.00 187

PatchMatch-RL		74.48 273	73.22 278	78.27 266	87.70 216	85.26 35	75.92 305	70.09 346	64.34 273	76.09 307	81.25 329	65.87 247	78.07 333	53.86 324	83.82 329	71.48 359

Test_1112_low_res		73.90 278	73.08 279	76.35 290	90.35 167	55.95 306	73.40 325	86.17 235	50.70 351	73.14 326	85.94 271	58.31 289	85.90 294	56.51 307	83.22 332	87.20 272

CR-MVSNet		74.00 277	73.04 280	76.85 286	79.58 324	62.64 241	82.58 207	76.90 303	50.50 353	75.72 311	92.38 140	48.07 327	84.07 309	68.72 231	82.91 335	83.85 307

pmmvs4		74.92 268	72.98 281	80.73 229	84.95 262	71.71 166	76.23 303	77.59 300	52.83 336	77.73 297	86.38 262	56.35 303	84.97 302	57.72 303	87.05 302	85.51 289

ET-MVSNet_ETH3D		75.28 262	72.77 282	82.81 194	83.03 294	68.11 194	77.09 291	76.51 307	60.67 299	77.60 298	80.52 335	38.04 363	91.15 198	70.78 208	90.68 264	89.17 246

PatchT		70.52 301	72.76 283	63.79 342	79.38 328	33.53 376	77.63 284	65.37 360	73.61 167	71.77 332	92.79 129	44.38 351	75.65 341	64.53 263	85.37 315	82.18 329

HyFIR lowres test		75.12 265	72.66 284	82.50 201	91.44 141	65.19 215	72.47 328	87.31 215	46.79 358	80.29 274	84.30 299	52.70 314	92.10 173	51.88 337	86.73 304	90.22 230

MVS		73.21 283	72.59 285	75.06 300	80.97 310	60.81 266	81.64 228	85.92 239	46.03 361	71.68 333	77.54 350	68.47 232	89.77 240	55.70 312	85.39 314	74.60 356

SCA		73.32 280	72.57 286	75.58 297	81.62 302	55.86 308	78.89 267	71.37 343	61.73 287	74.93 319	83.42 308	60.46 272	87.01 274	58.11 301	82.63 339	83.88 304

1314		73.22 282	72.56 287	75.20 298	80.41 320	57.84 294	81.64 228	85.36 244	51.68 345	73.10 327	76.65 356	61.45 268	85.19 300	63.54 266	79.21 351	82.59 323

HY-MVS	64.64 18	73.03 284	72.47 288	74.71 301	83.36 288	54.19 318	82.14 224	81.96 275	56.76 321	69.57 341	86.21 268	60.03 276	84.83 304	49.58 343	82.65 337	85.11 293

UnsupCasMVSNet_eth		71.63 296	72.30 289	69.62 322	76.47 347	52.70 330	70.03 338	80.97 283	59.18 304	79.36 282	88.21 235	60.50 271	69.12 354	58.33 299	77.62 356	87.04 274

FPMVS		72.29 291	72.00 290	73.14 308	88.63 197	85.00 37	74.65 317	67.39 352	71.94 199	77.80 296	87.66 244	50.48 322	75.83 340	49.95 340	79.51 347	58.58 370

Anonymous20231206		71.38 297	71.88 291	69.88 320	86.31 245	54.37 317	70.39 336	74.62 317	52.57 338	76.73 300	88.76 227	59.94 277	72.06 347	44.35 359	93.23 209	83.23 318

FMVSNet5		72.10 292	71.69 292	73.32 306	81.57 303	53.02 327	76.77 295	78.37 297	63.31 275	76.37 302	91.85 154	36.68 366	78.98 330	47.87 350	92.45 226	87.95 263

our_test_3		71.85 293	71.59 293	72.62 311	80.71 316	53.78 321	69.72 339	71.71 342	58.80 306	78.03 291	80.51 336	56.61 301	78.84 331	62.20 275	86.04 311	85.23 291

MIMVSNet		71.09 298	71.59 293	69.57 323	87.23 225	50.07 348	78.91 266	71.83 339	60.20 302	71.26 334	91.76 159	55.08 310	76.09 338	41.06 364	87.02 303	82.54 325

thres200		72.34 290	71.55 295	74.70 302	83.48 286	51.60 338	75.02 314	73.71 327	70.14 219	78.56 290	80.57 334	46.20 332	88.20 264	46.99 353	89.29 276	84.32 301

CVMVSNet		72.62 287	71.41 296	76.28 292	83.25 289	60.34 270	83.50 182	79.02 295	37.77 371	76.33 303	85.10 286	49.60 324	87.41 271	70.54 213	77.54 357	81.08 343

EPNet_dtu		72.87 286	71.33 297	77.49 278	77.72 338	60.55 269	82.35 215	75.79 310	66.49 254	58.39 371	81.06 330	53.68 312	85.98 292	53.55 325	92.97 217	85.95 284
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		72.45 288	70.56 298	78.13 267	90.02 177	63.08 234	68.72 341	83.16 265	42.99 367	75.92 309	85.46 279	57.22 298	85.18 301	49.87 342	81.67 340	86.14 282

thisisatest0515		73.00 285	70.52 299	80.46 233	81.45 304	59.90 274	73.16 327	74.31 321	57.86 312	76.08 308	77.78 349	37.60 365	92.12 172	65.00 256	91.45 247	89.35 242

YYNet1		70.06 305	70.44 300	68.90 325	73.76 362	53.42 325	58.99 363	67.20 354	58.42 308	87.10 157	85.39 282	59.82 279	67.32 358	59.79 292	83.50 331	85.96 283

MDA-MVSNet_test_wron		70.05 306	70.44 300	68.88 326	73.84 361	53.47 323	58.93 364	67.28 353	58.43 307	87.09 158	85.40 281	59.80 280	67.25 359	59.66 293	83.54 330	85.92 285

MS-PatchMatch		70.93 299	70.22 302	73.06 309	81.85 301	62.50 244	73.82 322	77.90 298	52.44 339	75.92 309	81.27 328	55.67 306	81.75 320	55.37 315	77.70 355	74.94 355

pmmvs5		70.73 300	70.07 303	72.72 310	77.03 343	52.73 329	74.14 318	75.65 313	50.36 354	72.17 331	85.37 283	55.42 308	80.67 326	52.86 331	87.59 299	84.77 296

PAPM		71.77 294	70.06 304	76.92 283	86.39 240	53.97 319	76.62 298	86.62 229	53.44 333	63.97 361	84.73 295	57.79 295	92.34 165	39.65 366	81.33 343	84.45 299

EGC-MVSNET		74.79 271	69.99 305	89.19 66	94.89 39	87.00 14	91.89 36	86.28 232	1.09 376	2.23 378	95.98 23	81.87 112	89.48 243	79.76 113	95.96 131	91.10 208

UnsupCasMVSNet_bld		69.21 311	69.68 306	67.82 331	79.42 327	51.15 342	67.82 346	75.79 310	54.15 330	77.47 299	85.36 284	59.26 283	70.64 350	48.46 347	79.35 349	81.66 334

tpmvs		70.16 303	69.56 307	71.96 315	74.71 360	48.13 352	79.63 253	75.45 315	65.02 270	70.26 338	81.88 323	45.34 345	85.68 296	58.34 298	75.39 360	82.08 330

gg-mvs-nofinetune		68.96 312	69.11 308	68.52 330	76.12 351	45.32 362	83.59 178	55.88 373	86.68 26	64.62 360	97.01 7	30.36 374	83.97 311	44.78 358	82.94 334	76.26 353

IB-MVS	62.13 19	71.64 295	68.97 309	79.66 245	80.80 315	62.26 249	73.94 320	76.90 303	63.27 276	68.63 343	76.79 355	33.83 370	91.84 180	59.28 295	87.26 300	84.88 295
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
PatchmatchNet		69.71 309	68.83 310	72.33 314	77.66 339	53.60 322	79.29 260	69.99 347	57.66 314	72.53 329	82.93 313	46.45 331	80.08 329	60.91 286	72.09 363	83.31 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 302	68.80 311	74.38 303	80.91 311	84.81 40	59.12 362	76.45 308	55.06 326	75.31 317	82.36 320	55.74 305	54.82 371	47.02 352	87.24 301	83.52 311

CostFormer		69.98 307	68.68 312	73.87 304	77.14 341	50.72 345	79.26 261	74.51 319	51.94 344	70.97 337	84.75 294	45.16 348	87.49 270	55.16 318	79.23 350	83.40 314

WTY-MVS		67.91 315	68.35 313	66.58 335	80.82 314	48.12 353	65.96 350	72.60 333	53.67 332	71.20 335	81.68 326	58.97 285	69.06 355	48.57 346	81.67 340	82.55 324

MDTV_nov1_ep13			68.29 314		78.03 336	43.87 367	74.12 319	72.22 336	52.17 340	67.02 350	85.54 275	45.36 344	80.85 325	55.73 310	84.42 327

tpm		67.95 314	68.08 315	67.55 332	78.74 335	43.53 368	75.60 307	67.10 357	54.92 327	72.23 330	88.10 236	42.87 355	75.97 339	52.21 332	80.95 346	83.15 319

Patchmatch-test		65.91 323	67.38 316	61.48 348	75.51 354	43.21 369	68.84 340	63.79 362	62.48 282	72.80 328	83.42 308	44.89 349	59.52 370	48.27 349	86.45 306	81.70 333

sss		66.92 317	67.26 317	65.90 336	77.23 340	51.10 344	64.79 351	71.72 341	52.12 343	70.13 339	80.18 338	57.96 292	65.36 366	50.21 339	81.01 345	81.25 340

baseline2		69.77 308	66.89 318	78.41 262	79.51 326	58.09 292	76.23 303	69.57 349	57.50 316	64.82 359	77.45 352	46.02 334	88.44 260	53.08 327	77.83 354	88.70 255

tpm2		68.45 313	66.83 319	73.30 307	78.93 334	48.50 351	79.76 252	71.76 340	47.50 357	69.92 340	83.60 304	42.07 356	88.40 261	48.44 348	79.51 347	83.01 321

test-LLR		67.21 316	66.74 320	68.63 328	76.45 348	55.21 313	67.89 343	67.14 355	62.43 284	65.08 356	72.39 362	43.41 352	69.37 351	61.00 284	84.89 322	81.31 338

tpmrst		66.28 322	66.69 321	65.05 340	72.82 368	39.33 371	78.20 276	70.69 345	53.16 335	67.88 346	80.36 337	48.18 326	74.75 343	58.13 300	70.79 365	81.08 343

JIA-IIPM		69.41 310	66.64 322	77.70 275	73.19 364	71.24 169	75.67 306	65.56 359	70.42 213	65.18 355	92.97 121	33.64 371	83.06 314	53.52 326	69.61 369	78.79 350

KD-MVS_2432*1600		66.87 318	65.81 323	70.04 318	67.50 373	47.49 356	62.56 356	79.16 292	61.21 293	77.98 292	80.61 332	25.29 381	82.48 317	53.02 328	84.92 320	80.16 348

miper_refine_blended		66.87 318	65.81 323	70.04 318	67.50 373	47.49 356	62.56 356	79.16 292	61.21 293	77.98 292	80.61 332	25.29 381	82.48 317	53.02 328	84.92 320	80.16 348

PVSNet	58.17 21	66.41 321	65.63 325	68.75 327	81.96 299	49.88 349	62.19 358	72.51 335	51.03 348	68.04 345	75.34 360	50.84 320	74.77 342	45.82 357	82.96 333	81.60 335

tpm cat1		66.76 320	65.21 326	71.42 316	77.09 342	50.62 346	78.01 277	73.68 328	44.89 363	68.64 342	79.00 344	45.51 342	82.42 319	49.91 341	70.15 366	81.23 342

test0.0.03 1		64.66 326	64.36 327	65.57 338	75.03 359	46.89 359	64.69 352	61.58 367	62.43 284	71.18 336	77.54 350	43.41 352	68.47 356	40.75 365	82.65 337	81.35 337

test-mter		65.00 325	63.79 328	68.63 328	76.45 348	55.21 313	67.89 343	67.14 355	50.98 349	65.08 356	72.39 362	28.27 377	69.37 351	61.00 284	84.89 322	81.31 338

ADS-MVSNet2		65.87 324	63.64 329	72.55 312	73.16 365	56.92 302	67.10 347	74.81 316	49.74 355	66.04 352	82.97 311	46.71 329	77.26 335	42.29 361	69.96 367	83.46 312

MVS-HIRNet		61.16 333	62.92 330	55.87 353	79.09 331	35.34 375	71.83 330	57.98 372	46.56 359	59.05 368	91.14 174	49.95 323	76.43 337	38.74 367	71.92 364	55.84 371

EPMVS		62.47 327	62.63 331	62.01 344	70.63 372	38.74 372	74.76 315	52.86 375	53.91 331	67.71 348	80.01 339	39.40 360	66.60 362	55.54 314	68.81 370	80.68 347

ADS-MVSNet		61.90 329	62.19 332	61.03 349	73.16 365	36.42 374	67.10 347	61.75 365	49.74 355	66.04 352	82.97 311	46.71 329	63.21 368	42.29 361	69.96 367	83.46 312

E-PMN		61.59 331	61.62 333	61.49 347	66.81 375	55.40 311	53.77 366	60.34 368	66.80 252	58.90 369	65.50 368	40.48 359	66.12 364	55.72 311	86.25 309	62.95 366

DSMNet-mixed		60.98 335	61.61 334	59.09 352	72.88 367	45.05 364	74.70 316	46.61 379	26.20 373	65.34 354	90.32 198	55.46 307	63.12 369	41.72 363	81.30 344	69.09 363

EMVS		61.10 334	60.81 335	61.99 345	65.96 376	55.86 308	53.10 367	58.97 370	67.06 249	56.89 372	63.33 369	40.98 357	67.03 360	54.79 320	86.18 310	63.08 365

PMMVS		61.65 330	60.38 336	65.47 339	65.40 377	69.26 184	63.97 354	61.73 366	36.80 372	60.11 366	68.43 366	59.42 281	66.35 363	48.97 345	78.57 353	60.81 367

TESTMET0.1,1		61.29 332	60.32 337	64.19 341	72.06 369	51.30 340	67.89 343	62.09 363	45.27 362	60.65 365	69.01 365	27.93 378	64.74 367	56.31 308	81.65 342	76.53 352

dp		60.70 336	60.29 338	61.92 346	72.04 370	38.67 373	70.83 333	64.08 361	51.28 347	60.75 364	77.28 353	36.59 367	71.58 349	47.41 351	62.34 372	75.52 354

pmmvs3		62.47 327	60.02 339	69.80 321	71.58 371	64.00 226	70.52 335	58.44 371	39.77 369	66.05 351	75.84 358	27.10 380	72.28 346	46.15 355	84.77 326	73.11 357

PMMVS2		55.64 340	59.27 340	44.74 356	64.30 378	12.32 381	40.60 369	49.79 377	53.19 334	65.06 358	84.81 293	53.60 313	49.76 373	32.68 373	89.41 275	72.15 358

new_pmnet		55.69 339	57.66 341	49.76 355	75.47 355	30.59 377	59.56 359	51.45 376	43.62 366	62.49 362	75.48 359	40.96 358	49.15 374	37.39 369	72.52 362	69.55 362

CHOSEN 280x420		59.08 337	56.52 342	66.76 334	76.51 346	64.39 222	49.62 368	59.00 369	43.86 365	55.66 373	68.41 367	35.55 369	68.21 357	43.25 360	76.78 359	67.69 364

PVSNet_0	51.08 22	56.10 338	54.97 343	59.48 351	75.12 358	53.28 326	55.16 365	61.89 364	44.30 364	59.16 367	62.48 370	54.22 311	65.91 365	35.40 370	47.01 373	59.25 369

MVE	40.22 23	51.82 341	50.47 344	55.87 353	62.66 379	51.91 335	31.61 371	39.28 380	40.65 368	50.76 374	74.98 361	56.24 304	44.67 375	33.94 372	64.11 371	71.04 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 342	29.60 345	33.06 357	17.99 381	3.84 383	13.62 372	73.92 323	2.79 375	18.29 377	53.41 372	28.53 376	43.25 376	22.56 374	35.27 375	52.11 372

cdsmvs_eth3d_5k		20.81 343	27.75 346	0.00 362	0.00 385	0.00 386	0.00 373	85.44 243	0.00 380	0.00 381	82.82 315	81.46 116	0.00 381	0.00 379	0.00 379	0.00 377

tmp_tt		20.25 344	24.50 347	7.49 359	4.47 382	8.70 382	34.17 370	25.16 382	1.00 377	32.43 376	18.49 374	39.37 361	9.21 378	21.64 375	43.75 374	4.57 374

ab-mvs-re		6.65 345	8.87 348	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	79.80 341	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

pcd_1.5k_mvsjas		6.41 346	8.55 349	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	76.94 162	0.00 381	0.00 379	0.00 379	0.00 377

test123		6.27 347	8.08 350	0.84 360	1.11 384	0.57 384	62.90 355	0.82 384	0.54 378	1.07 380	2.75 379	1.26 383	0.30 379	1.04 377	1.26 378	1.66 375

testmvs		5.91 348	7.65 351	0.72 361	1.20 383	0.37 385	59.14 361	0.67 385	0.49 379	1.11 379	2.76 378	0.94 384	0.24 380	1.02 378	1.47 377	1.55 376

test_blank		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

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	91.55 135	77.99 93		91.01 142					96.05 8	87.45 19	98.17 34	92.40 169

PC_three_1452									58.96 305	90.06 98	91.33 168	80.66 126	93.03 148	75.78 162	95.94 133	92.48 165

No_MVS				88.81 71	91.55 135	77.99 93		91.01 142					96.05 8	87.45 19	98.17 34	92.40 169

test_one_0601					93.85 63	73.27 139		94.11 37	86.57 27	93.47 41	94.64 60	88.42 27

eth-test2					0.00 385

eth-test					0.00 385

ZD-MVS					92.22 108	80.48 70		91.85 118	71.22 205	90.38 93	92.98 119	86.06 63	96.11 6	81.99 88	96.75 100

IU-MVS					94.18 50	72.64 145		90.82 147	56.98 319	89.67 112				85.78 48	97.92 49	93.28 132

OPU-MVS				88.27 87	91.89 120	77.83 96	90.47 51				91.22 170	81.12 120	94.68 76	74.48 172	95.35 153	92.29 176

test_241102_TWO								93.71 53	83.77 44	93.49 39	94.27 75	89.27 22	95.84 23	86.03 44	97.82 54	92.04 185

test_241102_ONE					94.18 50	72.65 143		93.69 54	83.62 46	94.11 23	93.78 106	90.28 15	95.50 48

save fliter					93.75 64	77.44 103	86.31 128	89.72 177	70.80 208

test_0728_THIRD									85.33 32	93.75 31	94.65 57	87.44 45	95.78 28	87.41 21	98.21 31	92.98 144

test_0728_SECOND				86.79 105	94.25 49	72.45 153	90.54 48	94.10 38					95.88 17	86.42 34	97.97 46	92.02 186

test0726					94.16 53	72.56 149	90.63 47	93.90 46	83.61 47	93.75 31	94.49 64	89.76 19

GSMVS																83.88 304

test_part2					93.86 62	77.77 97				92.84 49

sam_mvs1												46.11 333				83.88 304

sam_mvs												45.92 338

ambc				82.98 188	90.55 165	64.86 217	88.20 96	89.15 188		89.40 121	93.96 96	71.67 220	91.38 193	78.83 124	96.55 106	92.71 156

MTGPA								91.81 121

test_post1							78.85 269				3.13 376	45.19 347	80.13 328	58.11 301

test_post											3.10 377	45.43 343	77.22 336

patchmatchnet-post											81.71 325	45.93 337	87.01 274

GG-mvs-BLEND				67.16 333	73.36 363	46.54 361	84.15 158	55.04 374		58.64 370	61.95 371	29.93 375	83.87 312	38.71 368	76.92 358	71.07 360

MTMP							90.66 45	33.14 381

gm-plane-assit					75.42 356	44.97 365			52.17 340		72.36 364		87.90 265	54.10 323

test9_res														80.83 101	96.45 112	90.57 223

TEST9					92.34 101	79.70 77	83.94 165	90.32 160	65.41 268	84.49 207	90.97 179	82.03 106	93.63 120

test_8					92.09 112	78.87 85	83.82 170	90.31 162	65.79 258	84.36 210	90.96 181	81.93 108	93.44 132

agg_prior2														79.68 115	96.16 123	90.22 230

agg_prior					91.58 132	77.69 98		90.30 163		84.32 211			93.18 140

TestCases				89.68 55	91.59 129	83.40 50		95.44 9	79.47 95	88.00 144	93.03 117	82.66 92	91.47 186	70.81 206	96.14 124	94.16 95

test_prior4						78.97 84	84.59 150

test_prior2							83.37 185		75.43 147	84.58 205	91.57 162	81.92 110		79.54 117	96.97 91

test_prior				86.32 113	90.59 163	71.99 160		92.85 92					94.17 99			92.80 150

旧先验2							81.73 226		56.88 320	86.54 175			84.90 303	72.81 195

新几何2							81.72 227

新几何1				82.95 189	93.96 59	78.56 89		80.24 287	55.45 324	83.93 222	91.08 175	71.19 222	88.33 262	65.84 251	93.07 212	81.95 332

旧先验1					91.97 115	71.77 162		81.78 278			91.84 155	73.92 191			93.65 201	83.61 310

无先验							82.81 202	85.62 242	58.09 310				91.41 191	67.95 238		84.48 298

原ACMM2							82.26 220

原ACMM1				84.60 152	92.81 92	74.01 134		91.50 127	62.59 280	82.73 237	90.67 191	76.53 169	94.25 92	69.24 222	95.69 146	85.55 288

test222					93.31 77	76.54 116	79.38 259	77.79 299	52.59 337	82.36 241	90.84 185	66.83 241			91.69 243	81.25 340

testdata2													86.43 287	63.52 267

segment_acmp												81.94 107

testdata				79.54 247	92.87 87	72.34 154		80.14 289	59.91 303	85.47 193	91.75 160	67.96 235	85.24 299	68.57 234	92.18 235	81.06 345

testdata1							79.62 254		73.95 164

test12				86.57 108	90.74 159	72.63 147		90.69 150		82.76 236		79.20 137	94.80 73		95.32 155	92.27 178

plane_prior7					93.45 72	77.31 107

plane_prior6					92.61 93	76.54 116						74.84 180

plane_prior5								93.61 58					95.22 59	80.78 102	95.83 138	94.46 83

plane_prior4											92.95 122

plane_prior3						76.85 114			77.79 118	86.55 170

plane_prior2							89.45 76		79.44 97

plane_prior1					92.83 91

plane_prior						76.42 120	87.15 112		75.94 141						95.03 167

n2								0.00 386

nn								0.00 386

door-mid								74.45 320

lessismore_v0				85.95 125	91.10 151	70.99 171		70.91 344		91.79 70	94.42 69	61.76 267	92.93 151	79.52 119	93.03 214	93.93 105

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 52	91.18 5	95.52 43	85.36 51	98.73 7	95.23 60

test11								91.46 128

door								72.57 334

HQP5-MVS						70.66 172

HQP-NCC					91.19 146		84.77 145		73.30 174	80.55 271

ACMP_Plane					91.19 146		84.77 145		73.30 174	80.55 271

BP-MVS														77.30 146

HQP4-MVS										80.56 270			94.61 80			93.56 126

HQP3-MVS								92.68 98							94.47 183

HQP2-MVS												72.10 213

NP-MVS					91.95 116	74.55 131					90.17 204

MDTV_nov1_ep13_2view						27.60 379	70.76 334		46.47 360	61.27 363		45.20 346		49.18 344		83.75 309

ACMMP++_ref															95.74 145

ACMMP++															97.35 80

Test By Simon												79.09 138

ITE_SJBPF				90.11 50	90.72 160	84.97 38		90.30 163	81.56 72	90.02 100	91.20 172	82.40 96	90.81 210	73.58 184	94.66 179	94.56 78

DeepMVS_CX				24.13 358	32.95 380	29.49 378		21.63 383	12.07 374	37.95 375	45.07 373	30.84 373	19.21 377	17.94 376	33.06 376	23.69 373

High-resolution multi-view benchmark