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 56	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	93.16 144	91.10 1	97.53 77	96.58 33
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 49	96.29 16	88.16 35	94.17 101	86.07 45	98.48 18	97.22 20

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 40	91.86 3	95.09 65	87.54 18	98.02 41	93.71 119

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 69	90.64 11		87.16 28	97.60 70	92.73 155

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 58	95.13 46	90.65 10	95.34 55	88.06 9	98.15 36	95.95 44

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 87	88.83 24	95.51 46	87.11 30	97.54 76	92.54 165

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 69	88.83 24	95.51 46	87.16 28	97.60 70	92.73 155

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 43	94.02 93	90.15 17	95.67 34	86.82 32	97.34 83	92.19 184

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 44	94.39 75	90.09 18	95.08 66	86.67 35	97.60 70	94.18 96

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 73	93.94 102	90.55 13	95.73 31	88.50 7	98.23 29	95.33 57
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 65	95.03 48	89.75 21	94.93 70	79.95 112	98.27 27	95.04 67
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 69	92.97 86	78.04 93	92.84 16	94.14 35	83.33 51	93.90 25	95.73 28	88.77 26	96.41 1	87.60 16	97.98 45	92.98 146

PS-CasMVS		90.06 43	91.92 13	84.47 156	96.56 7	58.83 291	89.04 84	92.74 98	91.40 5	96.12 4	96.06 22	87.23 47	95.57 39	79.42 122	98.74 6	99.00 2

DTE-MVSNet		89.98 47	91.91 15	84.21 163	96.51 8	57.84 296	88.93 87	92.84 95	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 124	98.57 15	98.80 6

PEN-MVS		90.03 45	91.88 16	84.48 155	96.57 6	58.88 288	88.95 85	93.19 78	91.62 4	96.01 6	96.16 20	87.02 49	95.60 37	78.69 127	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 61	94.14 88	89.15 23	95.62 36	87.35 23	98.24 28	94.56 80
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 60	94.27 23	82.35 63	93.67 34	94.82 54	91.18 5	95.52 44	85.36 53	98.73 7	95.23 62

SED-MVS		90.46 37	91.64 19	86.93 104	94.18 50	72.65 145	90.47 53	93.69 54	83.77 44	94.11 23	94.27 77	90.28 15	95.84 23	86.03 46	97.92 49	92.29 178

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 31	91.81 122	84.07 40	92.00 68	94.40 73	86.63 54	95.28 58	88.59 5	98.31 24	92.30 176

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 85	94.00 94	88.26 32	95.71 32	87.28 26	98.39 21	92.55 164

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 96	88.22 20	88.53 136	97.64 2	83.45 84	94.55 85	86.02 48	98.60 13	96.67 30

ACMH+	77.89 11	90.73 30	91.50 23	88.44 85	93.00 85	76.26 124	89.65 71	95.55 7	87.72 23	93.89 27	94.94 50	91.62 4	93.44 134	78.35 130	98.76 4	95.61 51

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 101	83.09 54	91.54 75	94.25 81	87.67 43	95.51 46	87.21 27	98.11 37	93.12 141

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 76	93.96 98	88.35 30	95.56 40	87.74 11	97.74 62	92.85 150

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	89.99 103	94.03 92	86.57 56	95.80 25	87.35 23	97.62 68	94.20 94

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 57	93.97 95	88.19 33	96.29 4	87.61 15	98.20 33	94.39 89
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 76	81.99 66	91.40 78	94.17 86	87.51 44	95.87 19	87.74 11	97.76 60	93.99 103

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 81	93.97 95	87.93 40	95.87 19	88.65 4	97.96 48	94.12 100

DVP-MVS		90.06 43	91.32 30	86.29 117	94.16 53	72.56 151	90.54 50	91.01 143	83.61 47	93.75 31	94.65 59	89.76 19	95.78 28	86.42 36	97.97 46	90.55 227
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	85.71 134	96.32 10	62.39 247	89.54 75	93.31 70	90.21 10	95.57 9	95.66 31	81.42 117	95.90 15	80.94 101	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 90	94.21 82	87.75 41	95.87 19	87.60 16	97.71 64	93.83 111

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 86	91.81 122	84.07 40	92.00 68	94.40 73	86.63 54	95.28 58	88.59 5	98.31 24	92.30 176

ACMH	76.49 14	89.34 60	91.14 34	83.96 169	92.50 99	70.36 177	89.55 73	93.84 50	81.89 69	94.70 13	95.44 36	90.69 9	88.31 265	83.33 75	98.30 26	93.20 138
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		90.07 42	91.09 35	87.00 103	91.55 137	72.64 147	96.19 2	94.10 38	85.33 32	93.49 39	94.64 62	81.12 120	95.88 17	87.41 21	95.94 135	92.48 167

DPE-MVS		90.53 35	91.08 36	88.88 71	93.38 75	78.65 88	89.15 83	94.05 40	84.68 38	93.90 25	94.11 90	88.13 36	96.30 3	84.51 65	97.81 57	91.70 199
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	88.13 100	94.51 21	75.79 143	92.94 46	94.96 49	88.36 29	95.01 68	90.70 2	98.40 20	95.09 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 31	91.07 38	89.42 64	95.93 16	79.54 79	89.95 63	93.68 56	77.65 119	91.97 70	94.89 51	88.38 28	95.45 51	89.27 3	97.87 54	93.27 135

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 79	93.80 105	87.20 48	95.80 25	87.10 31	97.69 65	93.93 107

ACMM	79.39 9	90.65 31	90.99 40	89.63 59	95.03 35	83.53 49	89.62 72	93.35 66	79.20 101	93.83 28	93.60 112	90.81 8	92.96 151	85.02 58	98.45 19	92.41 170
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 40	90.96 41	87.65 97	91.95 118	71.06 172	89.99 62	93.05 84	86.53 28	94.29 19	96.27 17	82.69 91	94.08 105	86.25 42	97.63 67	97.82 8

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 60	93.90 46	80.32 86	91.74 74	94.41 72	88.17 34	95.98 11	86.37 38	97.99 43	93.96 106

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 28	93.03 87	82.59 60	88.52 137	94.37 76	86.74 53	95.41 53	86.32 39	98.21 31	93.19 139
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	84.37 157	96.34 9	58.61 293	88.66 94	92.06 112	90.78 6	95.67 7	95.17 45	81.80 113	95.54 43	79.00 125	98.69 10	98.95 4

SF-MVS		90.27 39	90.80 45	88.68 80	92.86 90	77.09 111	91.19 43	95.74 5	81.38 74	92.28 62	93.80 105	86.89 51	94.64 79	85.52 51	97.51 78	94.30 92

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 161	97.00 2	64.33 225	89.67 70	88.38 201	88.84 15	94.29 19	97.57 3	90.48 14	91.26 196	72.57 199	97.65 66	97.34 16

PMVS	80.48 6	90.08 41	90.66 47	88.34 88	96.71 3	92.97 1	90.31 56	89.57 183	88.51 19	90.11 99	95.12 47	90.98 7	88.92 255	77.55 144	97.07 91	83.13 322
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 82	94.05 40	79.03 104	92.87 48	93.74 109	90.60 12	95.21 62	82.87 80	98.76 4	94.87 70
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 38	90.48 49	89.83 53	95.31 31	79.52 80	90.98 46	93.24 77	75.37 150	92.84 50	95.28 39	85.58 66	96.09 7	87.92 10	97.76 60	93.88 109
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	89.03 192	84.23 47	93.58 6	94.68 19	90.65 7	90.33 97	93.95 101	84.50 72	95.37 54	80.87 102	95.50 152	94.53 83

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 44	90.65 48	93.29 73	77.00 127	91.47 76	93.96 98	88.35 30	95.56 40	84.88 60	97.74 62	92.85 150

OPM-MVS		89.80 51	89.97 52	89.27 66	94.76 41	79.86 75	86.76 123	92.78 97	78.78 107	92.51 58	93.64 111	88.13 36	93.84 115	84.83 62	97.55 73	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD-MVS		88.96 67	89.88 53	86.22 120	91.63 130	77.07 112	89.82 66	93.77 51	78.90 105	92.88 47	92.29 147	86.11 62	90.22 228	86.24 43	97.24 86	91.36 207
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 77	93.98 42	79.68 93	92.09 66	93.89 103	83.80 80	93.10 148	82.67 82	98.04 38	93.64 124

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 81	80.37 71	91.91 35	93.11 80	81.10 77	95.32 10	97.24 5	72.94 205	94.85 73	85.07 56	97.78 58	97.26 17

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 146	81.66 64	91.25 41	94.13 36	88.89 13	88.83 131	94.26 80	77.55 153	95.86 22	84.88 60	95.87 139	95.24 61

APD-MVS		89.54 56	89.63 57	89.26 67	92.57 95	81.34 66	90.19 58	93.08 83	80.87 81	91.13 83	93.19 116	86.22 61	95.97 12	82.23 88	97.18 88	90.45 229
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 73	89.62 58	84.73 151	90.46 168	65.27 216	88.86 88	93.02 88	87.15 25	93.05 45	97.10 6	82.28 100	92.02 176	76.70 153	97.99 43	96.88 26

test_0402		88.65 71	89.58 59	85.88 130	92.55 96	72.22 159	84.01 165	89.44 185	88.63 18	94.38 18	95.77 27	86.38 60	93.59 126	79.84 113	95.21 161	91.82 196

testtj		89.51 57	89.48 60	89.59 61	92.26 107	80.80 69	90.14 59	93.54 61	83.37 50	90.57 94	92.55 138	84.99 68	96.15 5	81.26 96	96.61 106	91.83 195

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 119	93.91 45	80.07 90	86.75 167	93.26 115	93.64 2	90.93 206	84.60 64	90.75 265	93.97 105

9.14			89.29 62		91.84 126		88.80 90	95.32 11	75.14 152	91.07 84	92.89 126	87.27 46	93.78 117	83.69 72	97.55 73

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 71	84.79 41	89.89 65	90.63 153	70.00 221	94.55 15	96.67 11	87.94 39	93.59 126	84.27 66	95.97 132	95.52 52

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 75	93.75 64	77.44 104	86.31 130	95.27 12	70.80 209	92.28 62	93.80 105	86.89 51	94.64 79	85.52 51	97.51 78	94.30 92

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 65	93.64 69	79.07 83	88.54 96	94.20 28	73.53 169	89.71 112	94.82 54	85.09 67	95.77 30	84.17 67	98.03 40	93.26 136
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	92.36 102	82.98 55	91.98 32	90.08 173	71.54 201	94.28 21	96.54 13	81.57 115	94.27 91	86.26 40	96.49 111	97.09 22

DP-MVS		88.60 72	89.01 66	87.36 100	91.30 144	77.50 102	87.55 107	92.97 90	87.95 22	89.62 116	92.87 127	84.56 71	93.89 112	77.65 142	96.62 105	90.70 221

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 30	92.30 107	79.74 92	87.50 153	92.38 142	81.42 117	93.28 139	83.07 77	97.24 86	91.67 200

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 81	91.94 120	77.27 110	88.71 92	95.26 13	76.08 134	90.66 93	92.69 133	84.48 73	93.83 116	83.38 74	97.48 80	94.47 84

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 180	86.67 17	90.51 52	90.20 170	69.87 223	95.06 11	96.14 21	84.28 75	93.07 149	87.68 13	96.34 118	97.09 22

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 133	93.60 60	80.16 88	89.13 127	93.44 113	83.82 79	90.98 204	83.86 70	95.30 160	93.60 126

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 75	84.72 42	89.70 67	90.29 167	69.27 226	94.39 17	96.38 15	86.02 64	93.52 130	83.96 68	95.92 137	95.34 56

TranMVSNet+NR-MVSNet		87.86 83	88.76 73	85.18 142	94.02 58	64.13 226	84.38 158	91.29 135	84.88 37	92.06 67	93.84 104	86.45 58	93.73 118	73.22 190	98.66 11	97.69 9

nrg030		87.85 84	88.49 74	85.91 128	90.07 176	69.73 180	87.86 104	94.20 28	74.04 163	92.70 55	94.66 58	85.88 65	91.50 187	79.72 116	97.32 84	96.50 34

bld_raw_conf005		88.83 70	88.48 75	89.85 52	92.53 97	76.54 117	91.30 40	93.28 75	74.96 154	93.26 42	96.02 23	70.41 225	95.63 35	86.73 34	97.87 54	97.39 15

HPM-MVS++		88.93 68	88.45 76	90.38 45	94.92 37	85.85 30	89.70 67	91.27 136	78.20 114	86.69 170	92.28 148	80.36 130	95.06 67	86.17 44	96.49 111	90.22 232

DROMVSNet		88.01 79	88.32 77	87.09 102	89.28 187	72.03 161	90.31 56	96.31 3	80.88 80	85.12 198	89.67 214	84.47 74	95.46 50	82.56 83	96.26 123	93.77 117

MSP-MVS		89.08 65	88.16 78	91.83 21	95.76 18	86.14 24	92.75 17	93.90 46	78.43 112	89.16 126	92.25 149	72.03 217	96.36 2	88.21 8	90.93 260	92.98 146
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		86.52 101	88.06 79	81.90 209	92.22 110	62.28 250	84.66 151	89.15 190	83.54 49	89.85 108	97.32 4	88.08 38	86.80 282	70.43 216	97.30 85	96.62 31

PS-MVSNAJss		88.31 74	87.90 80	89.56 62	93.31 77	77.96 96	87.94 103	91.97 115	70.73 211	94.19 22	96.67 11	76.94 162	94.57 83	83.07 77	96.28 120	96.15 36

RRT_MVS		88.30 75	87.83 81	89.70 56	93.62 70	75.70 127	92.36 27	89.06 192	77.34 122	93.63 36	95.83 26	65.40 251	95.90 15	85.01 59	98.23 29	97.49 13

test_part1		87.15 92	87.82 82	85.15 143	88.88 196	63.04 237	87.98 101	94.85 16	82.52 61	93.61 38	95.73 28	67.51 237	95.71 32	80.48 109	98.83 2	96.69 29

TSAR-MVS + MP.		88.14 77	87.82 82	89.09 70	95.72 22	76.74 116	92.49 25	91.19 139	67.85 246	86.63 171	94.84 53	79.58 136	95.96 13	87.62 14	94.50 184	94.56 80
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 86	87.68 84	88.21 90	92.87 88	77.30 109	85.25 143	91.23 137	77.31 124	87.07 161	91.47 168	82.94 89	94.71 76	84.67 63	96.27 122	92.62 162

CS-MVS		88.14 77	87.67 85	89.54 63	89.56 182	79.18 82	90.47 53	94.77 18	79.37 99	84.32 213	89.33 220	83.87 77	94.53 86	82.45 84	94.89 174	94.90 68

ETH3D cwj APD-0.16		87.83 85	87.62 86	88.47 83	91.21 147	78.20 90	87.26 111	94.54 20	72.05 197	88.89 128	92.31 146	83.86 78	94.24 94	81.59 95	96.87 96	92.97 149

OMC-MVS		88.19 76	87.52 87	90.19 49	91.94 120	81.68 63	87.49 109	93.17 79	76.02 137	88.64 134	91.22 172	84.24 76	93.37 137	77.97 140	97.03 92	95.52 52

SixPastTwentyTwo		87.20 91	87.45 88	86.45 113	92.52 98	69.19 189	87.84 105	88.05 208	81.66 71	94.64 14	96.53 14	65.94 248	94.75 75	83.02 79	96.83 99	95.41 54

HQP_MVS		87.75 87	87.43 89	88.70 79	93.45 72	76.42 122	89.45 78	93.61 58	79.44 97	86.55 172	92.95 124	74.84 180	95.22 60	80.78 104	95.83 140	94.46 85

AllTest		87.97 81	87.40 90	89.68 57	91.59 131	83.40 50	89.50 76	95.44 9	79.47 95	88.00 146	93.03 119	82.66 92	91.47 188	70.81 208	96.14 126	94.16 97

mvsmamba		87.87 82	87.23 91	89.78 54	92.31 106	76.51 121	91.09 45	91.87 118	72.61 187	92.16 64	95.23 43	66.01 247	95.59 38	86.02 48	97.78 58	97.24 18

Anonymous20240529		86.20 108	87.13 92	83.42 181	90.19 172	64.55 223	84.55 153	90.71 150	85.85 31	89.94 106	95.24 42	82.13 102	90.40 223	69.19 227	96.40 116	95.31 58

v10		86.54 100	87.10 93	84.84 147	88.16 211	63.28 234	86.64 126	92.20 109	75.42 149	92.81 52	94.50 65	74.05 190	94.06 106	83.88 69	96.28 120	97.17 21

UniMVSNet_NR-MVSNet		86.84 96	87.06 94	86.17 124	92.86 90	67.02 204	82.55 211	91.56 126	83.08 55	90.92 87	91.82 159	78.25 146	93.99 107	74.16 177	98.35 22	97.49 13

FC-MVSNet-test		85.93 113	87.05 95	82.58 200	92.25 108	56.44 307	85.75 137	93.09 82	77.33 123	91.94 71	94.65 59	74.78 182	93.41 136	75.11 172	98.58 14	97.88 7

test_low_dy_conf_001		87.67 88	86.99 96	89.72 55	93.02 83	78.17 92	91.15 44	89.33 187	69.89 222	92.70 55	95.39 37	66.77 243	94.23 96	86.77 33	97.90 52	96.76 27

DU-MVS		86.80 97	86.99 96	86.21 122	93.24 79	67.02 204	83.16 195	92.21 108	81.73 70	90.92 87	91.97 153	77.20 156	93.99 107	74.16 177	98.35 22	97.61 10

UniMVSNet (Re)		86.87 94	86.98 98	86.55 111	93.11 82	68.48 193	83.80 174	92.87 92	80.37 84	89.61 118	91.81 160	77.72 150	94.18 99	75.00 173	98.53 16	96.99 25

RPSCF		88.00 80	86.93 99	91.22 31	90.08 174	89.30 5	89.68 69	91.11 140	79.26 100	89.68 113	94.81 57	82.44 94	87.74 269	76.54 156	88.74 287	96.61 32

NCCC		87.36 89	86.87 100	88.83 72	92.32 105	78.84 86	86.58 127	91.09 141	78.77 108	84.85 204	90.89 185	80.85 123	95.29 56	81.14 98	95.32 157	92.34 174

v8		86.22 107	86.83 101	84.36 159	87.82 215	62.35 249	86.42 129	91.33 134	76.78 129	92.73 54	94.48 67	73.41 199	93.72 119	83.10 76	95.41 153	97.01 24

IS-MVSNet		86.66 99	86.82 102	86.17 124	92.05 116	66.87 206	91.21 42	88.64 197	86.30 30	89.60 119	92.59 135	69.22 229	94.91 71	73.89 181	97.89 53	96.72 28

Vis-MVSNet		86.86 95	86.58 103	87.72 95	92.09 114	77.43 106	87.35 110	92.09 111	78.87 106	84.27 219	94.05 91	78.35 145	93.65 120	80.54 108	91.58 248	92.08 186
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		86.26 105	86.47 104	85.60 136	90.87 158	74.26 135	87.98 101	91.85 119	80.35 85	89.54 122	88.01 239	79.09 138	92.13 172	75.51 166	95.06 168	90.41 230

CS-MVS-test		87.00 93	86.43 105	88.71 78	89.46 183	77.46 103	89.42 80	95.73 6	77.87 117	81.64 259	87.25 254	82.43 95	94.53 86	77.65 142	96.46 113	94.14 99

Gipuma		84.44 140	86.33 106	78.78 256	84.20 280	73.57 138	89.55 73	90.44 157	84.24 39	84.38 211	94.89 51	76.35 171	80.40 329	76.14 160	96.80 101	82.36 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_prior3		86.31 104	86.31 107	86.32 115	90.59 165	71.99 162	83.37 187	92.85 93	75.43 147	84.58 207	91.57 164	81.92 110	94.17 101	79.54 119	96.97 93	92.80 152

FIs		85.35 120	86.27 108	82.60 199	91.86 123	57.31 300	85.10 145	93.05 84	75.83 142	91.02 86	93.97 95	73.57 195	92.91 155	73.97 180	98.02 41	97.58 12

NR-MVSNet		86.00 110	86.22 109	85.34 140	93.24 79	64.56 222	82.21 223	90.46 156	80.99 78	88.42 139	91.97 153	77.56 152	93.85 113	72.46 200	98.65 12	97.61 10

DeepPCF-MVS	81.24 5	87.28 90	86.21 110	90.49 43	91.48 141	84.90 39	83.41 186	92.38 106	70.25 218	89.35 124	90.68 192	82.85 90	94.57 83	79.55 118	95.95 134	92.00 189

canonicalmvs		85.50 117	86.14 111	83.58 178	87.97 212	67.13 202	87.55 107	94.32 22	73.44 171	88.47 138	87.54 248	86.45 58	91.06 203	75.76 165	93.76 199	92.54 165

Regformer-2		86.74 98	86.08 112	88.73 76	84.18 281	79.20 81	83.52 181	89.33 187	83.33 51	89.92 107	85.07 291	83.23 87	93.16 144	83.39 73	92.72 225	93.83 111

MSLP-MVS++		85.00 129	86.03 113	81.90 209	91.84 126	71.56 170	86.75 124	93.02 88	75.95 140	87.12 157	89.39 218	77.98 147	89.40 250	77.46 145	94.78 177	84.75 299

baseline		85.20 122	85.93 114	83.02 189	86.30 248	62.37 248	84.55 153	93.96 43	74.48 160	87.12 157	92.03 152	82.30 98	91.94 177	78.39 128	94.21 191	94.74 77

Baseline_NR-MVSNet		84.00 155	85.90 115	78.29 267	91.47 142	53.44 326	82.29 219	87.00 230	79.06 103	89.55 120	95.72 30	77.20 156	86.14 293	72.30 201	98.51 17	95.28 59

casdiffmvs		85.21 121	85.85 116	83.31 183	86.17 254	62.77 241	83.03 197	93.93 44	74.69 157	88.21 144	92.68 134	82.29 99	91.89 180	77.87 141	93.75 201	95.27 60

GeoE		85.45 119	85.81 117	84.37 157	90.08 174	67.07 203	85.86 136	91.39 133	72.33 193	87.59 151	90.25 202	84.85 69	92.37 166	78.00 138	91.94 242	93.66 121

PHI-MVS		86.38 103	85.81 117	88.08 91	88.44 205	77.34 107	89.35 81	93.05 84	73.15 180	84.76 205	87.70 245	78.87 140	94.18 99	80.67 106	96.29 119	92.73 155

TransMVSNet (Re)		84.02 154	85.74 119	78.85 255	91.00 155	55.20 317	82.29 219	87.26 218	79.65 94	88.38 141	95.52 35	83.00 88	86.88 280	67.97 239	96.60 107	94.45 87

Regformer-4		86.41 102	85.71 120	88.52 81	84.27 277	77.57 101	84.07 162	88.00 210	82.82 58	89.84 109	85.48 279	82.06 104	92.77 157	83.83 71	91.04 254	95.22 64

ANet_high		83.17 171	85.68 121	75.65 298	81.24 309	45.26 365	79.94 252	92.91 91	83.83 43	91.33 80	96.88 10	80.25 131	85.92 295	68.89 230	95.89 138	95.76 46

DeepC-MVS_fast	80.27 8	86.23 106	85.65 122	87.96 94	91.30 144	76.92 113	87.19 112	91.99 114	70.56 212	84.96 200	90.69 191	80.01 133	95.14 63	78.37 129	95.78 145	91.82 196
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 109	85.54 123	88.05 93	92.25 108	75.45 128	83.85 171	92.01 113	65.91 259	86.19 179	91.75 162	83.77 81	94.98 69	77.43 147	96.71 103	93.73 118

Regformer-1		86.00 110	85.50 124	87.49 98	84.18 281	76.90 114	83.52 181	87.94 212	82.18 65	89.19 125	85.07 291	82.28 100	91.89 180	82.40 86	92.72 225	93.69 120

FMVSNet1		84.55 137	85.45 125	81.85 211	90.27 171	61.05 262	86.83 120	88.27 205	78.57 111	89.66 115	95.64 32	75.43 173	90.68 216	69.09 228	95.33 156	93.82 113

VDDNet		84.35 142	85.39 126	81.25 220	95.13 33	59.32 281	85.42 142	81.11 283	86.41 29	87.41 154	96.21 19	73.61 194	90.61 219	66.33 248	96.85 97	93.81 116

train_agg		85.98 112	85.28 127	88.07 92	92.34 103	79.70 77	83.94 167	90.32 161	65.79 260	84.49 209	90.97 181	81.93 108	93.63 122	81.21 97	96.54 109	90.88 216

agg_prior1		85.72 115	85.20 128	87.28 101	91.58 134	77.69 99	83.69 177	90.30 164	66.29 257	84.32 213	91.07 178	82.13 102	93.18 142	81.02 99	96.36 117	90.98 212

dcpmvs_2		84.23 148	85.14 129	81.50 217	88.61 200	61.98 253	82.90 202	93.11 80	68.66 235	92.77 53	92.39 141	78.50 142	87.63 271	76.99 152	92.30 230	94.90 68

LCM-MVSNet-Re		83.48 165	85.06 130	78.75 257	85.94 257	55.75 312	80.05 250	94.27 23	76.47 130	96.09 5	94.54 64	83.31 86	89.75 244	59.95 293	94.89 174	90.75 219

EPP-MVSNet		85.47 118	85.04 131	86.77 108	91.52 140	69.37 183	91.63 38	87.98 211	81.51 73	87.05 162	91.83 158	66.18 246	95.29 56	70.75 211	96.89 95	95.64 49

IterMVS-LS		84.73 133	84.98 132	83.96 169	87.35 225	63.66 229	83.25 191	89.88 177	76.06 135	89.62 116	92.37 145	73.40 201	92.52 162	78.16 135	94.77 179	95.69 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pm-mvs1		83.69 160	84.95 133	79.91 242	90.04 178	59.66 278	82.43 215	87.44 215	75.52 146	87.85 148	95.26 41	81.25 119	85.65 299	68.74 232	96.04 129	94.42 88

ETH3 D test6400		85.09 125	84.87 134	85.75 133	90.80 160	69.34 184	85.90 134	93.31 70	65.43 266	86.11 182	89.95 208	80.92 122	94.86 72	75.90 163	95.57 150	93.05 143

TAPA-MVS	77.73 12	85.71 116	84.83 135	88.37 87	88.78 198	79.72 76	87.15 114	93.50 62	69.17 227	85.80 189	89.56 215	80.76 124	92.13 172	73.21 195	95.51 151	93.25 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 166	84.73 136	79.69 246	90.29 170	57.52 299	81.30 237	88.69 196	76.29 131	87.58 152	94.44 68	80.60 127	87.20 275	66.60 247	96.82 100	94.34 91

K. test v3		85.14 123	84.73 136	86.37 114	91.13 152	69.63 182	85.45 141	76.68 308	84.06 42	92.44 60	96.99 8	62.03 268	94.65 78	80.58 107	93.24 210	94.83 76

v1144		84.54 139	84.72 138	84.00 167	87.67 219	62.55 245	82.97 199	90.93 146	70.32 217	89.80 110	90.99 180	73.50 196	93.48 132	81.69 94	94.65 182	95.97 42

3Dnovator	80.37 7	84.80 132	84.71 139	85.06 145	86.36 246	74.71 132	88.77 91	90.00 175	75.65 145	84.96 200	93.17 117	74.06 189	91.19 198	78.28 132	91.09 252	89.29 247

v1192		84.57 136	84.69 140	84.21 163	87.75 217	62.88 239	83.02 198	91.43 130	69.08 229	89.98 105	90.89 185	72.70 209	93.62 125	82.41 85	94.97 171	96.13 37

Regformer-3		85.06 126	84.67 141	86.22 120	84.27 277	73.43 139	84.07 162	85.26 249	80.77 82	88.62 135	85.48 279	80.56 128	90.39 224	81.99 90	91.04 254	94.85 74

MIMVSNet1		83.63 162	84.59 142	80.74 230	94.06 57	62.77 241	82.72 205	84.53 262	77.57 121	90.34 96	95.92 25	76.88 168	85.83 297	61.88 280	97.42 81	93.62 125

VDD-MVS		84.23 148	84.58 143	83.20 186	91.17 151	65.16 218	83.25 191	84.97 259	79.79 91	87.18 156	94.27 77	74.77 183	90.89 209	69.24 224	96.54 109	93.55 130

EI-MVSNet-Vis-set		85.12 124	84.53 144	86.88 105	84.01 284	72.76 144	83.91 170	85.18 251	80.44 83	88.75 132	85.49 278	80.08 132	91.92 178	82.02 89	90.85 263	95.97 42

v1240		84.30 144	84.51 145	83.65 176	87.65 220	61.26 259	82.85 203	91.54 127	67.94 244	90.68 92	90.65 194	71.71 219	93.64 121	82.84 81	94.78 177	96.07 39

bld_raw_dy_0_64		84.85 131	84.44 146	86.07 126	93.73 66	74.93 131	88.57 95	81.90 279	70.44 213	91.28 82	95.18 44	56.62 302	89.28 251	85.15 55	97.09 90	93.99 103

EI-MVSNet-UG-set		85.04 127	84.44 146	86.85 106	83.87 287	72.52 153	83.82 172	85.15 252	80.27 87	88.75 132	85.45 282	79.95 134	91.90 179	81.92 92	90.80 264	96.13 37

v144192		84.24 147	84.41 148	83.71 175	87.59 222	61.57 255	82.95 200	91.03 142	67.82 247	89.80 110	90.49 197	73.28 202	93.51 131	81.88 93	94.89 174	96.04 41

WR-MVS		83.56 163	84.40 149	81.06 225	93.43 74	54.88 318	78.67 273	85.02 256	81.24 75	90.74 91	91.56 166	72.85 206	91.08 202	68.00 238	98.04 38	97.23 19

v1921920		84.23 148	84.37 150	83.79 172	87.64 221	61.71 254	82.91 201	91.20 138	67.94 244	90.06 100	90.34 199	72.04 216	93.59 126	82.32 87	94.91 172	96.07 39

MVS_111021_HR		84.63 134	84.34 151	85.49 139	90.18 173	75.86 126	79.23 266	87.13 222	73.35 172	85.56 193	89.34 219	83.60 83	90.50 221	76.64 154	94.05 195	90.09 237

v2v482		84.09 151	84.24 152	83.62 177	87.13 230	61.40 256	82.71 206	89.71 179	72.19 196	89.55 120	91.41 169	70.70 224	93.20 141	81.02 99	93.76 199	96.25 35

EG-PatchMatch MVS		84.08 152	84.11 153	83.98 168	92.22 110	72.61 150	82.20 225	87.02 227	72.63 186	88.86 129	91.02 179	78.52 141	91.11 201	73.41 188	91.09 252	88.21 260

HQP-MVS		84.61 135	84.06 154	86.27 118	91.19 148	70.66 174	84.77 147	92.68 99	73.30 175	80.55 273	90.17 206	72.10 213	94.61 81	77.30 148	94.47 185	93.56 128

Effi-MVS+		83.90 158	84.01 155	83.57 179	87.22 228	65.61 215	86.55 128	92.40 104	78.64 110	81.34 264	84.18 302	83.65 82	92.93 153	74.22 176	87.87 297	92.17 185

alignmvs		83.94 157	83.98 156	83.80 171	87.80 216	67.88 199	84.54 155	91.42 132	73.27 178	88.41 140	87.96 240	72.33 212	90.83 211	76.02 162	94.11 193	92.69 159

MCST-MVS		84.36 141	83.93 157	85.63 135	91.59 131	71.58 169	83.52 181	92.13 110	61.82 288	83.96 223	89.75 213	79.93 135	93.46 133	78.33 131	94.34 188	91.87 194

ETV-MVS		84.31 143	83.91 158	85.52 137	88.58 201	70.40 176	84.50 157	93.37 64	78.76 109	84.07 222	78.72 348	80.39 129	95.13 64	73.82 183	92.98 218	91.04 211

MVS_111021_LR		84.28 145	83.76 159	85.83 132	89.23 189	83.07 53	80.99 241	83.56 266	72.71 185	86.07 183	89.07 226	81.75 114	86.19 292	77.11 150	93.36 206	88.24 259

AdaColmap		83.66 161	83.69 160	83.57 179	90.05 177	72.26 158	86.29 132	90.00 175	78.19 115	81.65 258	87.16 256	83.40 85	94.24 94	61.69 282	94.76 180	84.21 304

F-COLMAP		84.97 130	83.42 161	89.63 59	92.39 101	83.40 50	88.83 89	91.92 117	73.19 179	80.18 279	89.15 224	77.04 160	93.28 139	65.82 254	92.28 233	92.21 183

Effi-MVS+-dtu		85.82 114	83.38 162	93.14 3	87.13 230	91.15 2	87.70 106	88.42 199	74.57 158	83.56 229	85.65 276	78.49 143	94.21 97	72.04 202	92.88 220	94.05 102

V42		83.47 166	83.37 163	83.75 174	83.16 293	63.33 233	81.31 235	90.23 169	69.51 225	90.91 89	90.81 188	74.16 188	92.29 170	80.06 110	90.22 271	95.62 50

MVS_Test		82.47 178	83.22 164	80.22 239	82.62 299	57.75 298	82.54 212	91.96 116	71.16 207	82.89 237	92.52 140	77.41 154	90.50 221	80.04 111	87.84 298	92.40 171

DP-MVS Recon		84.05 153	83.22 164	86.52 112	91.73 129	75.27 129	83.23 193	92.40 104	72.04 198	82.04 249	88.33 235	77.91 149	93.95 110	66.17 249	95.12 166	90.34 231

PAPM_NR		83.23 169	83.19 166	83.33 182	90.90 157	65.98 212	88.19 99	90.78 149	78.13 116	80.87 268	87.92 243	73.49 198	92.42 163	70.07 218	88.40 288	91.60 202

KD-MVS_self_test		81.93 188	83.14 167	78.30 266	84.75 268	52.75 330	80.37 247	89.42 186	70.24 219	90.26 98	93.39 114	74.55 187	86.77 283	68.61 234	96.64 104	95.38 55

CNLPA		83.55 164	83.10 168	84.90 146	89.34 186	83.87 48	84.54 155	88.77 194	79.09 102	83.54 230	88.66 232	74.87 179	81.73 323	66.84 245	92.29 232	89.11 249

tfpnnormal		81.79 189	82.95 169	78.31 265	88.93 195	55.40 313	80.83 244	82.85 271	76.81 128	85.90 188	94.14 88	74.58 186	86.51 287	66.82 246	95.68 149	93.01 145

CANet		83.79 159	82.85 170	86.63 109	86.17 254	72.21 160	83.76 175	91.43 130	77.24 125	74.39 323	87.45 250	75.36 174	95.42 52	77.03 151	92.83 221	92.25 182

h-mvs33		84.25 146	82.76 171	88.72 77	91.82 128	82.60 58	84.00 166	84.98 258	71.27 203	86.70 168	90.55 196	63.04 264	93.92 111	78.26 133	94.20 192	89.63 239

X-MVStestdata		85.04 127	82.70 172	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 103	16.05 377	86.57 56	95.80 25	87.35 23	97.62 68	94.20 94

TSAR-MVS + GP.		83.95 156	82.69 173	87.72 95	89.27 188	81.45 65	83.72 176	81.58 282	74.73 156	85.66 190	86.06 271	72.56 211	92.69 159	75.44 168	95.21 161	89.01 255

CLD-MVS		83.18 170	82.64 174	84.79 148	89.05 191	67.82 200	77.93 281	92.52 102	68.33 237	85.07 199	81.54 329	82.06 104	92.96 151	69.35 223	97.91 51	93.57 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 180	82.61 175	81.30 219	86.29 249	69.79 178	88.71 92	87.67 214	78.42 113	82.15 247	84.15 303	77.98 147	91.59 186	65.39 256	92.75 222	82.51 329

QAPM		82.59 176	82.59 176	82.58 200	86.44 241	66.69 207	89.94 64	90.36 160	67.97 243	84.94 202	92.58 137	72.71 208	92.18 171	70.63 214	87.73 299	88.85 256

114514_t		83.10 172	82.54 177	84.77 150	92.90 87	69.10 191	86.65 125	90.62 154	54.66 330	81.46 261	90.81 188	76.98 161	94.38 90	72.62 198	96.18 124	90.82 218

v148		82.31 179	82.48 178	81.81 214	85.59 259	59.66 278	81.47 233	86.02 239	72.85 183	88.05 145	90.65 194	70.73 223	90.91 208	75.15 171	91.79 243	94.87 70

EI-MVSNet		82.61 175	82.42 179	83.20 186	83.25 291	63.66 229	83.50 184	85.07 253	76.06 135	86.55 172	85.10 288	73.41 199	90.25 225	78.15 137	90.67 267	95.68 48

TinyColmap		81.25 194	82.34 180	77.99 272	85.33 262	60.68 270	82.32 218	88.33 203	71.26 205	86.97 163	92.22 151	77.10 159	86.98 279	62.37 275	95.17 163	86.31 283

mvs-test1		84.55 137	82.12 181	91.84 20	87.13 230	89.54 4	85.05 146	88.42 199	74.57 158	80.60 270	82.98 312	78.49 143	93.98 109	72.04 202	89.77 274	92.00 189

GBi-Net		82.02 185	82.07 182	81.85 211	86.38 243	61.05 262	86.83 120	88.27 205	72.43 188	86.00 184	95.64 32	63.78 258	90.68 216	65.95 250	93.34 207	93.82 113

test1		82.02 185	82.07 182	81.85 211	86.38 243	61.05 262	86.83 120	88.27 205	72.43 188	86.00 184	95.64 32	63.78 258	90.68 216	65.95 250	93.34 207	93.82 113

OpenMVS	76.72 13	81.98 187	82.00 184	81.93 208	84.42 273	68.22 195	88.50 97	89.48 184	66.92 252	81.80 256	91.86 155	72.59 210	90.16 230	71.19 207	91.25 251	87.40 272

LF4IMVS		82.75 174	81.93 185	85.19 141	82.08 300	80.15 73	85.53 140	88.76 195	68.01 241	85.58 192	87.75 244	71.80 218	86.85 281	74.02 179	93.87 198	88.58 258

hse-mvs2		83.47 166	81.81 186	88.47 83	91.03 154	82.27 59	82.61 207	83.69 264	71.27 203	86.70 168	86.05 272	63.04 264	92.41 164	78.26 133	93.62 205	90.71 220

VPNet		80.25 214	81.68 187	75.94 297	92.46 100	47.98 356	76.70 298	81.67 281	73.45 170	84.87 203	92.82 128	74.66 185	86.51 287	61.66 283	96.85 97	93.33 132

UGNet		82.78 173	81.64 188	86.21 122	86.20 253	76.24 125	86.86 118	85.68 243	77.07 126	73.76 326	92.82 128	69.64 226	91.82 183	69.04 229	93.69 202	90.56 226
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		81.31 193	81.61 189	80.41 236	86.38 243	58.75 292	83.93 169	86.58 232	72.43 188	87.65 150	92.98 121	63.78 258	90.22 228	66.86 243	93.92 197	92.27 180

c3_l		81.64 190	81.59 190	81.79 215	80.86 315	59.15 285	78.61 274	90.18 171	68.36 236	87.20 155	87.11 258	69.39 227	91.62 185	78.16 135	94.43 187	94.60 79

MVSFormer		82.23 181	81.57 191	84.19 165	85.54 260	69.26 186	91.98 32	90.08 173	71.54 201	76.23 307	85.07 291	58.69 289	94.27 91	86.26 40	88.77 285	89.03 253

Fast-Effi-MVS+-dtu		82.54 177	81.41 192	85.90 129	85.60 258	76.53 120	83.07 196	89.62 182	73.02 182	79.11 288	83.51 307	80.74 125	90.24 227	68.76 231	89.29 278	90.94 214

Anonymous20240521		80.18 217	81.25 193	76.95 284	83.15 294	60.84 267	82.46 214	85.99 240	68.76 233	86.78 165	93.73 110	59.13 286	77.44 336	73.71 184	97.55 73	92.56 163

DELS-MVS		81.44 192	81.25 193	82.03 207	84.27 277	62.87 240	76.47 302	92.49 103	70.97 208	81.64 259	83.83 304	75.03 177	92.70 158	74.29 175	92.22 236	90.51 228
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		82.19 182	81.23 195	85.10 144	87.95 213	69.17 190	83.22 194	93.33 67	70.42 214	78.58 291	79.77 345	77.29 155	94.20 98	71.51 205	88.96 283	91.93 193

Anonymous202405211		80.51 206	81.19 196	78.49 262	88.48 203	57.26 301	76.63 299	82.49 273	81.21 76	84.30 217	92.24 150	67.99 235	86.24 291	62.22 276	95.13 164	91.98 192

BH-untuned		80.96 198	80.99 197	80.84 229	88.55 202	68.23 194	80.33 248	88.46 198	72.79 184	86.55 172	86.76 261	74.72 184	91.77 184	61.79 281	88.99 282	82.52 328

MG-MVS		80.32 213	80.94 198	78.47 263	88.18 209	52.62 333	82.29 219	85.01 257	72.01 199	79.24 287	92.54 139	69.36 228	93.36 138	70.65 213	89.19 281	89.45 241

PCF-MVS	74.62 15	82.15 183	80.92 199	85.84 131	89.43 184	72.30 157	80.53 245	91.82 121	57.36 319	87.81 149	89.92 210	77.67 151	93.63 122	58.69 298	95.08 167	91.58 203
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.04 197	80.57 200	82.46 204	87.50 223	63.22 235	78.37 277	89.63 181	68.01 241	81.87 252	82.08 324	82.31 97	92.65 160	67.10 242	88.30 293	91.51 205

LFMVS		80.15 218	80.56 201	78.89 254	89.19 190	55.93 309	85.22 144	73.78 328	82.96 56	84.28 218	92.72 132	57.38 298	90.07 237	63.80 266	95.75 146	90.68 222

ab-mvs		79.67 221	80.56 201	76.99 283	88.48 203	56.93 303	84.70 150	86.06 238	68.95 231	80.78 269	93.08 118	75.30 175	84.62 307	56.78 307	90.90 261	89.43 243

PVSNet_Blended_VisFu		81.55 191	80.49 203	84.70 153	91.58 134	73.24 142	84.21 159	91.67 125	62.86 281	80.94 266	87.16 256	67.27 239	92.87 156	69.82 220	88.94 284	87.99 264

diffmvs		80.40 209	80.48 204	80.17 240	79.02 335	60.04 274	77.54 288	90.28 168	66.65 255	82.40 242	87.33 253	73.50 196	87.35 274	77.98 139	89.62 276	93.13 140

PLC	73.85 16	82.09 184	80.31 205	87.45 99	90.86 159	80.29 72	85.88 135	90.65 152	68.17 239	76.32 306	86.33 266	73.12 204	92.61 161	61.40 285	90.02 273	89.44 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 222	80.27 206	76.44 291	87.92 214	53.95 322	75.58 311	84.35 263	74.39 161	82.23 245	90.72 190	72.84 207	84.39 309	60.38 292	93.98 196	90.97 213

cl____		80.42 208	80.23 207	81.02 226	79.99 323	59.25 282	77.07 294	87.02 227	67.37 249	86.18 181	89.21 222	63.08 263	90.16 230	76.31 158	95.80 143	93.65 123

DIV-MVS_self_test		80.43 207	80.23 207	81.02 226	79.99 323	59.25 282	77.07 294	87.02 227	67.38 248	86.19 179	89.22 221	63.09 262	90.16 230	76.32 157	95.80 143	93.66 121

eth_miper_zixun_eth		80.84 200	80.22 209	82.71 197	81.41 307	60.98 265	77.81 283	90.14 172	67.31 250	86.95 164	87.24 255	64.26 254	92.31 168	75.23 170	91.61 246	94.85 74

BH-RMVSNet		80.53 205	80.22 209	81.49 218	87.19 229	66.21 211	77.79 284	86.23 236	74.21 162	83.69 225	88.50 233	73.25 203	90.75 213	63.18 272	87.90 296	87.52 270

xiu_mvs_v1_base_debu		80.84 200	80.14 211	82.93 192	88.31 206	71.73 165	79.53 257	87.17 219	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 190	88.33 289	86.90 278

xiu_mvs_v1_base		80.84 200	80.14 211	82.93 192	88.31 206	71.73 165	79.53 257	87.17 219	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 190	88.33 289	86.90 278

xiu_mvs_v1_base_debi		80.84 200	80.14 211	82.93 192	88.31 206	71.73 165	79.53 257	87.17 219	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 190	88.33 289	86.90 278

miper_ehance_all_eth		80.34 212	80.04 214	81.24 222	79.82 325	58.95 287	77.66 285	89.66 180	65.75 263	85.99 187	85.11 287	68.29 234	91.42 192	76.03 161	92.03 238	93.33 132

MSDG		80.06 220	79.99 215	80.25 238	83.91 286	68.04 198	77.51 289	89.19 189	77.65 119	81.94 250	83.45 309	76.37 170	86.31 290	63.31 271	86.59 307	86.41 281

1121		80.86 199	79.81 216	84.02 166	93.93 60	78.70 87	81.64 230	80.18 290	55.43 327	83.67 226	91.15 175	71.29 221	91.41 193	67.95 240	93.06 215	81.96 333

tttt0517		81.07 196	79.58 217	85.52 137	88.99 194	66.45 209	87.03 116	75.51 316	73.76 167	88.32 143	90.20 203	37.96 366	94.16 104	79.36 123	95.13 164	95.93 45

IterMVS-SCA-FT		80.64 204	79.41 218	84.34 160	83.93 285	69.66 181	76.28 304	81.09 284	72.43 188	86.47 178	90.19 204	60.46 274	93.15 146	77.45 146	86.39 310	90.22 232

patch_mono-2		78.89 225	79.39 219	77.41 281	84.78 267	68.11 196	75.60 309	83.11 268	60.96 297	79.36 284	89.89 211	75.18 176	72.97 347	73.32 189	92.30 230	91.15 209

wuyk23d		75.13 266	79.30 220	62.63 345	75.56 355	75.18 130	80.89 242	73.10 334	75.06 153	94.76 12	95.32 38	87.73 42	52.85 374	34.16 373	97.11 89	59.85 370

DPM-MVS		80.10 219	79.18 221	82.88 195	90.71 163	69.74 179	78.87 270	90.84 147	60.29 303	75.64 315	85.92 274	67.28 238	93.11 147	71.24 206	91.79 243	85.77 289

PM-MVS		80.20 216	79.00 222	83.78 173	88.17 210	86.66 18	81.31 235	66.81 360	69.64 224	88.33 142	90.19 204	64.58 252	83.63 315	71.99 204	90.03 272	81.06 347

iter_conf_final		80.36 211	78.88 223	84.79 148	86.29 249	66.36 210	86.95 117	86.25 235	68.16 240	82.09 248	89.48 216	36.59 369	94.51 88	79.83 114	94.30 189	93.50 131

test1111		78.53 233	78.85 224	77.56 278	92.22 110	47.49 358	82.61 207	69.24 352	72.43 188	85.28 196	94.20 83	51.91 317	90.07 237	65.36 257	96.45 114	95.11 65

AUN-MVS		81.18 195	78.78 225	88.39 86	90.93 156	82.14 60	82.51 213	83.67 265	64.69 274	80.29 276	85.91 275	51.07 321	92.38 165	76.29 159	93.63 204	90.65 224

mvs_anonymous		78.13 237	78.76 226	76.23 296	79.24 332	50.31 349	78.69 272	84.82 260	61.60 292	83.09 236	92.82 128	73.89 192	87.01 276	68.33 237	86.41 309	91.37 206

MAR-MVS		80.24 215	78.74 227	84.73 151	86.87 240	78.18 91	85.75 137	87.81 213	65.67 265	77.84 296	78.50 349	73.79 193	90.53 220	61.59 284	90.87 262	85.49 292
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		78.44 234	78.63 228	77.88 274	91.85 124	48.95 352	83.68 178	69.91 350	72.30 194	84.26 220	94.20 83	51.89 318	89.82 241	63.58 267	96.02 130	94.87 70

FMVSNet3		78.80 229	78.55 229	79.57 248	82.89 298	56.89 305	81.76 227	85.77 242	69.04 230	86.00 184	90.44 198	51.75 319	90.09 236	65.95 250	93.34 207	91.72 198

test_yl		78.71 231	78.51 230	79.32 251	84.32 275	58.84 289	78.38 275	85.33 247	75.99 138	82.49 240	86.57 262	58.01 292	90.02 239	62.74 273	92.73 223	89.10 250

DCV-MVSNet		78.71 231	78.51 230	79.32 251	84.32 275	58.84 289	78.38 275	85.33 247	75.99 138	82.49 240	86.57 262	58.01 292	90.02 239	62.74 273	92.73 223	89.10 250

EPNet		80.37 210	78.41 232	86.23 119	76.75 346	73.28 140	87.18 113	77.45 303	76.24 133	68.14 346	88.93 228	65.41 250	93.85 113	69.47 222	96.12 128	91.55 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		78.88 226	78.28 233	80.68 233	79.58 326	62.64 243	82.58 209	94.16 31	74.80 155	75.72 313	92.59 135	48.69 327	95.56 40	73.48 187	82.91 337	83.85 309

cl22		78.97 224	78.21 234	81.24 222	77.74 339	59.01 286	77.46 291	87.13 222	65.79 260	84.32 213	85.10 288	58.96 288	90.88 210	75.36 169	92.03 238	93.84 110

PAPR		78.84 227	78.10 235	81.07 224	85.17 263	60.22 273	82.21 223	90.57 155	62.51 283	75.32 318	84.61 298	74.99 178	92.30 169	59.48 296	88.04 295	90.68 222

PVSNet_BlendedMVS		78.80 229	77.84 236	81.65 216	84.43 271	63.41 231	79.49 260	90.44 157	61.70 291	75.43 316	87.07 259	69.11 230	91.44 190	60.68 290	92.24 234	90.11 236

Vis-MVSNet (Re-imp)		77.82 240	77.79 237	77.92 273	88.82 197	51.29 343	83.28 189	71.97 340	74.04 163	82.23 245	89.78 212	57.38 298	89.41 249	57.22 306	95.41 153	93.05 143

Patchmtry		76.56 255	77.46 238	73.83 307	79.37 331	46.60 362	82.41 216	76.90 305	73.81 166	85.56 193	92.38 142	48.07 329	83.98 312	63.36 270	95.31 159	90.92 215

OpenMVS_ROB	70.19 17	77.77 242	77.46 238	78.71 258	84.39 274	61.15 260	81.18 239	82.52 272	62.45 285	83.34 231	87.37 251	66.20 245	88.66 261	64.69 262	85.02 321	86.32 282

CL-MVSNet_self_test		76.81 251	77.38 240	75.12 301	86.90 238	51.34 341	73.20 328	80.63 288	68.30 238	81.80 256	88.40 234	66.92 241	80.90 326	55.35 318	94.90 173	93.12 141

iter_conf05		78.81 228	77.35 241	83.21 185	82.98 297	60.75 269	84.09 161	88.34 202	63.12 279	84.25 221	89.48 216	31.41 374	94.51 88	76.64 154	95.83 140	94.38 90

thisisatest0530		79.07 223	77.33 242	84.26 162	87.13 230	64.58 221	83.66 179	75.95 311	68.86 232	85.22 197	87.36 252	38.10 364	93.57 129	75.47 167	94.28 190	94.62 78

MVS_0304		78.17 236	77.23 243	80.99 228	84.13 283	69.07 192	81.39 234	80.81 286	76.28 132	67.53 351	89.11 225	62.87 266	86.77 283	60.90 289	92.01 241	87.13 275

CANet_DTU		77.81 241	77.05 244	80.09 241	81.37 308	59.90 276	83.26 190	88.29 204	69.16 228	67.83 349	83.72 305	60.93 271	89.47 246	69.22 226	89.70 275	90.88 216

pmmvs-eth3d		78.42 235	77.04 245	82.57 202	87.44 224	74.41 134	80.86 243	79.67 293	55.68 325	84.69 206	90.31 201	60.91 272	85.42 300	62.20 277	91.59 247	87.88 267

miper_enhance_ethall		77.83 239	76.93 246	80.51 234	76.15 352	58.01 295	75.47 313	88.82 193	58.05 313	83.59 228	80.69 333	64.41 253	91.20 197	73.16 196	92.03 238	92.33 175

MDA-MVSNet-bldmvs		77.47 243	76.90 247	79.16 253	79.03 334	64.59 220	66.58 351	75.67 314	73.15 180	88.86 129	88.99 227	66.94 240	81.23 325	64.71 261	88.22 294	91.64 201

xiu_mvs_v2_base		77.19 246	76.75 248	78.52 261	87.01 236	61.30 258	75.55 312	87.12 225	61.24 294	74.45 322	78.79 347	77.20 156	90.93 206	64.62 264	84.80 327	83.32 318

USDC		76.63 253	76.73 249	76.34 293	83.46 289	57.20 302	80.02 251	88.04 209	52.14 344	83.65 227	91.25 171	63.24 261	86.65 286	54.66 323	94.11 193	85.17 294

PS-MVSNAJ		77.04 248	76.53 250	78.56 260	87.09 235	61.40 256	75.26 314	87.13 222	61.25 293	74.38 324	77.22 356	76.94 162	90.94 205	64.63 263	84.83 326	83.35 317

TAMVS		78.08 238	76.36 251	83.23 184	90.62 164	72.87 143	79.08 267	80.01 292	61.72 290	81.35 263	86.92 260	63.96 257	88.78 259	50.61 340	93.01 217	88.04 263

IterMVS		76.91 249	76.34 252	78.64 259	80.91 313	64.03 227	76.30 303	79.03 296	64.88 273	83.11 234	89.16 223	59.90 280	84.46 308	68.61 234	85.15 320	87.42 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 277	76.19 253	69.21 326	84.61 269	52.43 334	71.70 333	77.18 304	60.73 300	80.60 270	90.96 183	75.44 172	69.35 355	56.13 311	88.33 289	85.86 288

miper_lstm_enhance		76.45 257	76.10 254	77.51 279	76.72 347	60.97 266	64.69 354	85.04 255	63.98 276	83.20 233	88.22 236	56.67 301	78.79 334	73.22 190	93.12 213	92.78 154

BH-w/o		76.57 254	76.07 255	78.10 270	86.88 239	65.92 213	77.63 286	86.33 233	65.69 264	80.89 267	79.95 342	68.97 232	90.74 214	53.01 332	85.25 319	77.62 353

TR-MVS		76.77 252	75.79 256	79.72 245	86.10 256	65.79 214	77.14 292	83.02 269	65.20 271	81.40 262	82.10 323	66.30 244	90.73 215	55.57 315	85.27 318	82.65 324

jason		77.42 244	75.75 257	82.43 205	87.10 234	69.27 185	77.99 280	81.94 278	51.47 348	77.84 296	85.07 291	60.32 276	89.00 253	70.74 212	89.27 280	89.03 253
jason: jason.
MVSTER		77.09 247	75.70 258	81.25 220	75.27 359	61.08 261	77.49 290	85.07 253	60.78 299	86.55 172	88.68 231	43.14 356	90.25 225	73.69 185	90.67 267	92.42 169

D2MVS		76.84 250	75.67 259	80.34 237	80.48 321	62.16 252	73.50 325	84.80 261	57.61 317	82.24 244	87.54 248	51.31 320	87.65 270	70.40 217	93.19 212	91.23 208

PVSNet_Blended		76.49 256	75.40 260	79.76 244	84.43 271	63.41 231	75.14 315	90.44 157	57.36 319	75.43 316	78.30 350	69.11 230	91.44 190	60.68 290	87.70 300	84.42 302

CDS-MVSNet		77.32 245	75.40 260	83.06 188	89.00 193	72.48 154	77.90 282	82.17 276	60.81 298	78.94 289	83.49 308	59.30 284	88.76 260	54.64 324	92.37 229	87.93 266
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 260	75.35 262	77.85 276	87.01 236	51.84 339	80.45 246	73.26 332	75.20 151	83.10 235	86.31 268	45.54 342	89.05 252	55.03 321	92.24 234	92.66 160

thres100view900		75.45 263	75.05 263	76.66 290	87.27 226	51.88 338	81.07 240	73.26 332	75.68 144	83.25 232	86.37 265	45.54 342	88.80 256	51.98 336	90.99 256	89.31 245

cascas		76.29 259	74.81 264	80.72 232	84.47 270	62.94 238	73.89 323	87.34 216	55.94 324	75.16 320	76.53 359	63.97 256	91.16 199	65.00 258	90.97 259	88.06 262

GA-MVS		75.83 261	74.61 265	79.48 250	81.87 302	59.25 282	73.42 326	82.88 270	68.68 234	79.75 280	81.80 326	50.62 323	89.46 247	66.85 244	85.64 315	89.72 238

testgi		72.36 291	74.61 265	65.59 339	80.56 320	42.82 372	68.29 344	73.35 331	66.87 253	81.84 253	89.93 209	72.08 215	66.92 363	46.05 358	92.54 227	87.01 277

test20.03		73.75 281	74.59 267	71.22 319	81.11 311	51.12 345	70.15 339	72.10 339	70.42 214	80.28 278	91.50 167	64.21 255	74.72 346	46.96 356	94.58 183	87.82 269

lupinMVS		76.37 258	74.46 268	82.09 206	85.54 260	69.26 186	76.79 296	80.77 287	50.68 354	76.23 307	82.82 317	58.69 289	88.94 254	69.85 219	88.77 285	88.07 261

EU-MVSNet		75.12 267	74.43 269	77.18 282	83.11 295	59.48 280	85.71 139	82.43 274	39.76 372	85.64 191	88.76 229	44.71 352	87.88 268	73.86 182	85.88 314	84.16 305

tfpn200view9		74.86 271	74.23 270	76.74 289	86.24 251	52.12 335	79.24 264	73.87 326	73.34 173	81.82 254	84.60 299	46.02 336	88.80 256	51.98 336	90.99 256	89.31 245

thres400		75.14 265	74.23 270	77.86 275	86.24 251	52.12 335	79.24 264	73.87 326	73.34 173	81.82 254	84.60 299	46.02 336	88.80 256	51.98 336	90.99 256	92.66 160

ppachtmachnet_test		74.73 274	74.00 272	76.90 286	80.71 318	56.89 305	71.53 334	78.42 298	58.24 311	79.32 286	82.92 316	57.91 295	84.26 310	65.60 255	91.36 250	89.56 240

1112_ss		74.82 272	73.74 273	78.04 271	89.57 181	60.04 274	76.49 301	87.09 226	54.31 331	73.66 327	79.80 343	60.25 277	86.76 285	58.37 299	84.15 330	87.32 273

Patchmatch-RL test		74.48 275	73.68 274	76.89 287	84.83 266	66.54 208	72.29 331	69.16 353	57.70 315	86.76 166	86.33 266	45.79 341	82.59 318	69.63 221	90.65 269	81.54 338

CMPMVS	59.41 20	75.12 267	73.57 275	79.77 243	75.84 354	67.22 201	81.21 238	82.18 275	50.78 352	76.50 303	87.66 246	55.20 311	82.99 317	62.17 279	90.64 270	89.09 252
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 283	73.54 276	72.43 315	84.92 265	47.79 357	79.89 253	74.00 324	65.93 258	78.81 290	86.28 269	56.36 304	81.63 324	56.63 308	79.04 354	87.87 268

MVP-Stereo		75.81 262	73.51 277	82.71 197	89.35 185	73.62 137	80.06 249	85.20 250	60.30 302	73.96 325	87.94 241	57.89 296	89.45 248	52.02 335	74.87 363	85.06 296
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test2506		74.12 278	73.39 278	76.28 294	91.85 124	44.20 368	84.06 164	48.20 380	72.30 194	81.90 251	94.20 83	27.22 381	89.77 242	64.81 260	96.02 130	94.87 70

new-patchmatchnet		70.10 306	73.37 279	60.29 352	81.23 310	16.95 382	59.54 362	74.62 319	62.93 280	80.97 265	87.93 242	62.83 267	71.90 350	55.24 319	95.01 170	92.00 189

PatchMatch-RL		74.48 275	73.22 280	78.27 268	87.70 218	85.26 35	75.92 307	70.09 348	64.34 275	76.09 309	81.25 331	65.87 249	78.07 335	53.86 326	83.82 331	71.48 361

Test_1112_low_res		73.90 280	73.08 281	76.35 292	90.35 169	55.95 308	73.40 327	86.17 237	50.70 353	73.14 328	85.94 273	58.31 291	85.90 296	56.51 309	83.22 334	87.20 274

CR-MVSNet		74.00 279	73.04 282	76.85 288	79.58 326	62.64 243	82.58 209	76.90 305	50.50 355	75.72 313	92.38 142	48.07 329	84.07 311	68.72 233	82.91 337	83.85 309

pmmvs4		74.92 270	72.98 283	80.73 231	84.95 264	71.71 168	76.23 305	77.59 302	52.83 338	77.73 299	86.38 264	56.35 305	84.97 304	57.72 305	87.05 304	85.51 291

ET-MVSNet_ETH3D		75.28 264	72.77 284	82.81 196	83.03 296	68.11 196	77.09 293	76.51 309	60.67 301	77.60 300	80.52 337	38.04 365	91.15 200	70.78 210	90.68 266	89.17 248

PatchT		70.52 303	72.76 285	63.79 344	79.38 330	33.53 378	77.63 286	65.37 362	73.61 168	71.77 334	92.79 131	44.38 353	75.65 343	64.53 265	85.37 317	82.18 331

HyFIR lowres test		75.12 267	72.66 286	82.50 203	91.44 143	65.19 217	72.47 330	87.31 217	46.79 360	80.29 276	84.30 301	52.70 316	92.10 175	51.88 339	86.73 306	90.22 232

MVS		73.21 285	72.59 287	75.06 302	80.97 312	60.81 268	81.64 230	85.92 241	46.03 363	71.68 335	77.54 352	68.47 233	89.77 242	55.70 314	85.39 316	74.60 358

SCA		73.32 282	72.57 288	75.58 299	81.62 304	55.86 310	78.89 269	71.37 345	61.73 289	74.93 321	83.42 310	60.46 274	87.01 276	58.11 303	82.63 341	83.88 306

1314		73.22 284	72.56 289	75.20 300	80.41 322	57.84 296	81.64 230	85.36 246	51.68 347	73.10 329	76.65 358	61.45 270	85.19 302	63.54 268	79.21 353	82.59 325

HY-MVS	64.64 18	73.03 286	72.47 290	74.71 303	83.36 290	54.19 320	82.14 226	81.96 277	56.76 323	69.57 343	86.21 270	60.03 278	84.83 306	49.58 345	82.65 339	85.11 295

UnsupCasMVSNet_eth		71.63 298	72.30 291	69.62 324	76.47 349	52.70 332	70.03 340	80.97 285	59.18 306	79.36 284	88.21 237	60.50 273	69.12 356	58.33 301	77.62 358	87.04 276

FPMVS		72.29 293	72.00 292	73.14 310	88.63 199	85.00 37	74.65 319	67.39 354	71.94 200	77.80 298	87.66 246	50.48 324	75.83 342	49.95 342	79.51 349	58.58 372

Anonymous20231206		71.38 299	71.88 293	69.88 322	86.31 247	54.37 319	70.39 338	74.62 319	52.57 340	76.73 302	88.76 229	59.94 279	72.06 349	44.35 361	93.23 211	83.23 320

FMVSNet5		72.10 294	71.69 294	73.32 308	81.57 305	53.02 329	76.77 297	78.37 299	63.31 277	76.37 304	91.85 156	36.68 368	78.98 332	47.87 352	92.45 228	87.95 265

our_test_3		71.85 295	71.59 295	72.62 313	80.71 318	53.78 323	69.72 341	71.71 344	58.80 308	78.03 293	80.51 338	56.61 303	78.84 333	62.20 277	86.04 313	85.23 293

MIMVSNet		71.09 300	71.59 295	69.57 325	87.23 227	50.07 350	78.91 268	71.83 341	60.20 304	71.26 336	91.76 161	55.08 312	76.09 340	41.06 366	87.02 305	82.54 327

thres200		72.34 292	71.55 297	74.70 304	83.48 288	51.60 340	75.02 316	73.71 329	70.14 220	78.56 292	80.57 336	46.20 334	88.20 266	46.99 355	89.29 278	84.32 303

CVMVSNet		72.62 289	71.41 298	76.28 294	83.25 291	60.34 272	83.50 184	79.02 297	37.77 373	76.33 305	85.10 288	49.60 326	87.41 273	70.54 215	77.54 359	81.08 345

EPNet_dtu		72.87 288	71.33 299	77.49 280	77.72 340	60.55 271	82.35 217	75.79 312	66.49 256	58.39 373	81.06 332	53.68 314	85.98 294	53.55 327	92.97 219	85.95 286
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		72.45 290	70.56 300	78.13 269	90.02 179	63.08 236	68.72 343	83.16 267	42.99 369	75.92 311	85.46 281	57.22 300	85.18 303	49.87 344	81.67 342	86.14 284

thisisatest0515		73.00 287	70.52 301	80.46 235	81.45 306	59.90 276	73.16 329	74.31 323	57.86 314	76.08 310	77.78 351	37.60 367	92.12 174	65.00 258	91.45 249	89.35 244

YYNet1		70.06 307	70.44 302	68.90 327	73.76 364	53.42 327	58.99 365	67.20 356	58.42 310	87.10 159	85.39 284	59.82 281	67.32 360	59.79 294	83.50 333	85.96 285

MDA-MVSNet_test_wron		70.05 308	70.44 302	68.88 328	73.84 363	53.47 325	58.93 366	67.28 355	58.43 309	87.09 160	85.40 283	59.80 282	67.25 361	59.66 295	83.54 332	85.92 287

MS-PatchMatch		70.93 301	70.22 304	73.06 311	81.85 303	62.50 246	73.82 324	77.90 300	52.44 341	75.92 311	81.27 330	55.67 308	81.75 322	55.37 317	77.70 357	74.94 357

pmmvs5		70.73 302	70.07 305	72.72 312	77.03 345	52.73 331	74.14 320	75.65 315	50.36 356	72.17 333	85.37 285	55.42 310	80.67 328	52.86 333	87.59 301	84.77 298

PAPM		71.77 296	70.06 306	76.92 285	86.39 242	53.97 321	76.62 300	86.62 231	53.44 335	63.97 363	84.73 297	57.79 297	92.34 167	39.65 368	81.33 345	84.45 301

EGC-MVSNET		74.79 273	69.99 307	89.19 68	94.89 39	87.00 14	91.89 36	86.28 234	1.09 378	2.23 380	95.98 24	81.87 112	89.48 245	79.76 115	95.96 133	91.10 210

UnsupCasMVSNet_bld		69.21 313	69.68 308	67.82 333	79.42 329	51.15 344	67.82 348	75.79 312	54.15 332	77.47 301	85.36 286	59.26 285	70.64 352	48.46 349	79.35 351	81.66 336

tpmvs		70.16 305	69.56 309	71.96 317	74.71 362	48.13 354	79.63 255	75.45 317	65.02 272	70.26 340	81.88 325	45.34 347	85.68 298	58.34 300	75.39 362	82.08 332

gg-mvs-nofinetune		68.96 314	69.11 310	68.52 332	76.12 353	45.32 364	83.59 180	55.88 375	86.68 26	64.62 362	97.01 7	30.36 376	83.97 313	44.78 360	82.94 336	76.26 355

IB-MVS	62.13 19	71.64 297	68.97 311	79.66 247	80.80 317	62.26 251	73.94 322	76.90 305	63.27 278	68.63 345	76.79 357	33.83 372	91.84 182	59.28 297	87.26 302	84.88 297
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		69.71 311	68.83 312	72.33 316	77.66 341	53.60 324	79.29 262	69.99 349	57.66 316	72.53 331	82.93 315	46.45 333	80.08 331	60.91 288	72.09 365	83.31 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 304	68.80 313	74.38 305	80.91 313	84.81 40	59.12 364	76.45 310	55.06 328	75.31 319	82.36 322	55.74 307	54.82 373	47.02 354	87.24 303	83.52 313

CostFormer		69.98 309	68.68 314	73.87 306	77.14 343	50.72 347	79.26 263	74.51 321	51.94 346	70.97 339	84.75 296	45.16 350	87.49 272	55.16 320	79.23 352	83.40 316

WTY-MVS		67.91 317	68.35 315	66.58 337	80.82 316	48.12 355	65.96 352	72.60 335	53.67 334	71.20 337	81.68 328	58.97 287	69.06 357	48.57 348	81.67 342	82.55 326

MDTV_nov1_ep13			68.29 316		78.03 338	43.87 369	74.12 321	72.22 338	52.17 342	67.02 352	85.54 277	45.36 346	80.85 327	55.73 312	84.42 329

tpm		67.95 316	68.08 317	67.55 334	78.74 337	43.53 370	75.60 309	67.10 359	54.92 329	72.23 332	88.10 238	42.87 357	75.97 341	52.21 334	80.95 348	83.15 321

Patchmatch-test		65.91 325	67.38 318	61.48 350	75.51 356	43.21 371	68.84 342	63.79 364	62.48 284	72.80 330	83.42 310	44.89 351	59.52 372	48.27 351	86.45 308	81.70 335

sss		66.92 319	67.26 319	65.90 338	77.23 342	51.10 346	64.79 353	71.72 343	52.12 345	70.13 341	80.18 340	57.96 294	65.36 368	50.21 341	81.01 347	81.25 342

baseline2		69.77 310	66.89 320	78.41 264	79.51 328	58.09 294	76.23 305	69.57 351	57.50 318	64.82 361	77.45 354	46.02 336	88.44 262	53.08 329	77.83 356	88.70 257

tpm2		68.45 315	66.83 321	73.30 309	78.93 336	48.50 353	79.76 254	71.76 342	47.50 359	69.92 342	83.60 306	42.07 358	88.40 263	48.44 350	79.51 349	83.01 323

test-LLR		67.21 318	66.74 322	68.63 330	76.45 350	55.21 315	67.89 345	67.14 357	62.43 286	65.08 358	72.39 364	43.41 354	69.37 353	61.00 286	84.89 324	81.31 340

tpmrst		66.28 324	66.69 323	65.05 342	72.82 370	39.33 373	78.20 278	70.69 347	53.16 337	67.88 348	80.36 339	48.18 328	74.75 345	58.13 302	70.79 367	81.08 345

JIA-IIPM		69.41 312	66.64 324	77.70 277	73.19 366	71.24 171	75.67 308	65.56 361	70.42 214	65.18 357	92.97 123	33.64 373	83.06 316	53.52 328	69.61 371	78.79 352

KD-MVS_2432*1600		66.87 320	65.81 325	70.04 320	67.50 375	47.49 358	62.56 358	79.16 294	61.21 295	77.98 294	80.61 334	25.29 383	82.48 319	53.02 330	84.92 322	80.16 350

miper_refine_blended		66.87 320	65.81 325	70.04 320	67.50 375	47.49 358	62.56 358	79.16 294	61.21 295	77.98 294	80.61 334	25.29 383	82.48 319	53.02 330	84.92 322	80.16 350

PVSNet	58.17 21	66.41 323	65.63 327	68.75 329	81.96 301	49.88 351	62.19 360	72.51 337	51.03 350	68.04 347	75.34 362	50.84 322	74.77 344	45.82 359	82.96 335	81.60 337

tpm cat1		66.76 322	65.21 328	71.42 318	77.09 344	50.62 348	78.01 279	73.68 330	44.89 365	68.64 344	79.00 346	45.51 344	82.42 321	49.91 343	70.15 368	81.23 344

test0.0.03 1		64.66 328	64.36 329	65.57 340	75.03 361	46.89 361	64.69 354	61.58 369	62.43 286	71.18 338	77.54 352	43.41 354	68.47 358	40.75 367	82.65 339	81.35 339

test-mter		65.00 327	63.79 330	68.63 330	76.45 350	55.21 315	67.89 345	67.14 357	50.98 351	65.08 358	72.39 364	28.27 379	69.37 353	61.00 286	84.89 324	81.31 340

ADS-MVSNet2		65.87 326	63.64 331	72.55 314	73.16 367	56.92 304	67.10 349	74.81 318	49.74 357	66.04 354	82.97 313	46.71 331	77.26 337	42.29 363	69.96 369	83.46 314

MVS-HIRNet		61.16 335	62.92 332	55.87 355	79.09 333	35.34 377	71.83 332	57.98 374	46.56 361	59.05 370	91.14 176	49.95 325	76.43 339	38.74 369	71.92 366	55.84 373

EPMVS		62.47 329	62.63 333	62.01 346	70.63 374	38.74 374	74.76 317	52.86 377	53.91 333	67.71 350	80.01 341	39.40 362	66.60 364	55.54 316	68.81 372	80.68 349

ADS-MVSNet		61.90 331	62.19 334	61.03 351	73.16 367	36.42 376	67.10 349	61.75 367	49.74 357	66.04 354	82.97 313	46.71 331	63.21 370	42.29 363	69.96 369	83.46 314

E-PMN		61.59 333	61.62 335	61.49 349	66.81 377	55.40 313	53.77 368	60.34 370	66.80 254	58.90 371	65.50 370	40.48 361	66.12 366	55.72 313	86.25 311	62.95 368

DSMNet-mixed		60.98 337	61.61 336	59.09 354	72.88 369	45.05 366	74.70 318	46.61 381	26.20 375	65.34 356	90.32 200	55.46 309	63.12 371	41.72 365	81.30 346	69.09 365

EMVS		61.10 336	60.81 337	61.99 347	65.96 378	55.86 310	53.10 369	58.97 372	67.06 251	56.89 374	63.33 371	40.98 359	67.03 362	54.79 322	86.18 312	63.08 367

PMMVS		61.65 332	60.38 338	65.47 341	65.40 379	69.26 186	63.97 356	61.73 368	36.80 374	60.11 368	68.43 368	59.42 283	66.35 365	48.97 347	78.57 355	60.81 369

TESTMET0.1,1		61.29 334	60.32 339	64.19 343	72.06 371	51.30 342	67.89 345	62.09 365	45.27 364	60.65 367	69.01 367	27.93 380	64.74 369	56.31 310	81.65 344	76.53 354

dp		60.70 338	60.29 340	61.92 348	72.04 372	38.67 375	70.83 335	64.08 363	51.28 349	60.75 366	77.28 355	36.59 369	71.58 351	47.41 353	62.34 374	75.52 356

pmmvs3		62.47 329	60.02 341	69.80 323	71.58 373	64.00 228	70.52 337	58.44 373	39.77 371	66.05 353	75.84 360	27.10 382	72.28 348	46.15 357	84.77 328	73.11 359

PMMVS2		55.64 342	59.27 342	44.74 358	64.30 380	12.32 383	40.60 371	49.79 379	53.19 336	65.06 360	84.81 295	53.60 315	49.76 375	32.68 375	89.41 277	72.15 360

new_pmnet		55.69 341	57.66 343	49.76 357	75.47 357	30.59 379	59.56 361	51.45 378	43.62 368	62.49 364	75.48 361	40.96 360	49.15 376	37.39 371	72.52 364	69.55 364

CHOSEN 280x420		59.08 339	56.52 344	66.76 336	76.51 348	64.39 224	49.62 370	59.00 371	43.86 367	55.66 375	68.41 369	35.55 371	68.21 359	43.25 362	76.78 361	67.69 366

PVSNet_0	51.08 22	56.10 340	54.97 345	59.48 353	75.12 360	53.28 328	55.16 367	61.89 366	44.30 366	59.16 369	62.48 372	54.22 313	65.91 367	35.40 372	47.01 375	59.25 371

MVE	40.22 23	51.82 343	50.47 346	55.87 355	62.66 381	51.91 337	31.61 373	39.28 382	40.65 370	50.76 376	74.98 363	56.24 306	44.67 377	33.94 374	64.11 373	71.04 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 344	29.60 347	33.06 359	17.99 383	3.84 385	13.62 374	73.92 325	2.79 377	18.29 379	53.41 374	28.53 378	43.25 378	22.56 376	35.27 377	52.11 374

cdsmvs_eth3d_5k		20.81 345	27.75 348	0.00 364	0.00 387	0.00 388	0.00 375	85.44 245	0.00 382	0.00 383	82.82 317	81.46 116	0.00 383	0.00 381	0.00 381	0.00 379

tmp_tt		20.25 346	24.50 349	7.49 361	4.47 384	8.70 384	34.17 372	25.16 384	1.00 379	32.43 378	18.49 376	39.37 363	9.21 380	21.64 377	43.75 376	4.57 376

ab-mvs-re		6.65 347	8.87 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	79.80 343	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		6.41 348	8.55 351	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	76.94 162	0.00 383	0.00 381	0.00 381	0.00 379

test123		6.27 349	8.08 352	0.84 362	1.11 386	0.57 386	62.90 357	0.82 386	0.54 380	1.07 382	2.75 381	1.26 385	0.30 381	1.04 379	1.26 380	1.66 377

testmvs		5.91 350	7.65 353	0.72 363	1.20 385	0.37 387	59.14 363	0.67 387	0.49 381	1.11 381	2.76 380	0.94 386	0.24 382	1.02 380	1.47 379	1.55 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

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

MSC_two_6792asdad				88.81 73	91.55 137	77.99 94		91.01 143					96.05 8	87.45 19	98.17 34	92.40 171

PC_three_1452									58.96 307	90.06 100	91.33 170	80.66 126	93.03 150	75.78 164	95.94 135	92.48 167

No_MVS				88.81 73	91.55 137	77.99 94		91.01 143					96.05 8	87.45 19	98.17 34	92.40 171

test_one_0601					93.85 63	73.27 141		94.11 37	86.57 27	93.47 41	94.64 62	88.42 27

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					92.22 110	80.48 70		91.85 119	71.22 206	90.38 95	92.98 121	86.06 63	96.11 6	81.99 90	96.75 102

IU-MVS					94.18 50	72.64 147		90.82 148	56.98 321	89.67 114				85.78 50	97.92 49	93.28 134

OPU-MVS				88.27 89	91.89 122	77.83 97	90.47 53				91.22 172	81.12 120	94.68 77	74.48 174	95.35 155	92.29 178

test_241102_TWO								93.71 53	83.77 44	93.49 39	94.27 77	89.27 22	95.84 23	86.03 46	97.82 56	92.04 187

test_241102_ONE					94.18 50	72.65 145		93.69 54	83.62 46	94.11 23	93.78 108	90.28 15	95.50 49

save fliter					93.75 64	77.44 104	86.31 130	89.72 178	70.80 209

test_0728_THIRD									85.33 32	93.75 31	94.65 59	87.44 45	95.78 28	87.41 21	98.21 31	92.98 146

test_0728_SECOND				86.79 107	94.25 49	72.45 155	90.54 50	94.10 38					95.88 17	86.42 36	97.97 46	92.02 188

test0726					94.16 53	72.56 151	90.63 49	93.90 46	83.61 47	93.75 31	94.49 66	89.76 19

GSMVS																83.88 306

test_part2					93.86 62	77.77 98				92.84 50

sam_mvs1												46.11 335				83.88 306

sam_mvs												45.92 340

ambc				82.98 190	90.55 167	64.86 219	88.20 98	89.15 190		89.40 123	93.96 98	71.67 220	91.38 195	78.83 126	96.55 108	92.71 158

MTGPA								91.81 122

test_post1							78.85 271				3.13 378	45.19 349	80.13 330	58.11 303

test_post											3.10 379	45.43 345	77.22 338

patchmatchnet-post											81.71 327	45.93 339	87.01 276

GG-mvs-BLEND				67.16 335	73.36 365	46.54 363	84.15 160	55.04 376		58.64 372	61.95 373	29.93 377	83.87 314	38.71 370	76.92 360	71.07 362

MTMP							90.66 47	33.14 383

gm-plane-assit					75.42 358	44.97 367			52.17 342		72.36 366		87.90 267	54.10 325

test9_res														80.83 103	96.45 114	90.57 225

TEST9					92.34 103	79.70 77	83.94 167	90.32 161	65.41 270	84.49 209	90.97 181	82.03 106	93.63 122

test_8					92.09 114	78.87 85	83.82 172	90.31 163	65.79 260	84.36 212	90.96 183	81.93 108	93.44 134

agg_prior2														79.68 117	96.16 125	90.22 232

agg_prior					91.58 134	77.69 99		90.30 164		84.32 213			93.18 142

TestCases				89.68 57	91.59 131	83.40 50		95.44 9	79.47 95	88.00 146	93.03 119	82.66 92	91.47 188	70.81 208	96.14 126	94.16 97

test_prior4						78.97 84	84.59 152

test_prior2							83.37 187		75.43 147	84.58 207	91.57 164	81.92 110		79.54 119	96.97 93

test_prior				86.32 115	90.59 165	71.99 162		92.85 93					94.17 101			92.80 152

旧先验2							81.73 228		56.88 322	86.54 177			84.90 305	72.81 197

新几何2							81.72 229

新几何1				82.95 191	93.96 59	78.56 89		80.24 289	55.45 326	83.93 224	91.08 177	71.19 222	88.33 264	65.84 253	93.07 214	81.95 334

旧先验1					91.97 117	71.77 164		81.78 280			91.84 157	73.92 191			93.65 203	83.61 312

无先验							82.81 204	85.62 244	58.09 312				91.41 193	67.95 240		84.48 300

原ACMM2							82.26 222

原ACMM1				84.60 154	92.81 93	74.01 136		91.50 128	62.59 282	82.73 239	90.67 193	76.53 169	94.25 93	69.24 224	95.69 148	85.55 290

test222					93.31 77	76.54 117	79.38 261	77.79 301	52.59 339	82.36 243	90.84 187	66.83 242			91.69 245	81.25 342

testdata2													86.43 289	63.52 269

segment_acmp												81.94 107

testdata				79.54 249	92.87 88	72.34 156		80.14 291	59.91 305	85.47 195	91.75 162	67.96 236	85.24 301	68.57 236	92.18 237	81.06 347

testdata1							79.62 256		73.95 165

test12				86.57 110	90.74 161	72.63 149		90.69 151		82.76 238		79.20 137	94.80 74		95.32 157	92.27 180

plane_prior7					93.45 72	77.31 108

plane_prior6					92.61 94	76.54 117						74.84 180

plane_prior5								93.61 58					95.22 60	80.78 104	95.83 140	94.46 85

plane_prior4											92.95 124

plane_prior3						76.85 115			77.79 118	86.55 172

plane_prior2							89.45 78		79.44 97

plane_prior1					92.83 92

plane_prior						76.42 122	87.15 114		75.94 141						95.03 169

n2								0.00 388

nn								0.00 388

door-mid								74.45 322

lessismore_v0				85.95 127	91.10 153	70.99 173		70.91 346		91.79 72	94.42 71	61.76 269	92.93 153	79.52 121	93.03 216	93.93 107

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 54	91.18 5	95.52 44	85.36 53	98.73 7	95.23 62

test11								91.46 129

door								72.57 336

HQP5-MVS						70.66 174

HQP-NCC					91.19 148		84.77 147		73.30 175	80.55 273

ACMP_Plane					91.19 148		84.77 147		73.30 175	80.55 273

BP-MVS														77.30 148

HQP4-MVS										80.56 272			94.61 81			93.56 128

HQP3-MVS								92.68 99							94.47 185

HQP2-MVS												72.10 213

NP-MVS					91.95 118	74.55 133					90.17 206

MDTV_nov1_ep13_2view						27.60 381	70.76 336		46.47 362	61.27 365		45.20 348		49.18 346		83.75 311

ACMMP++_ref															95.74 147

ACMMP++															97.35 82

Test By Simon												79.09 138

ITE_SJBPF				90.11 50	90.72 162	84.97 38		90.30 164	81.56 72	90.02 102	91.20 174	82.40 96	90.81 212	73.58 186	94.66 181	94.56 80

DeepMVS_CX				24.13 360	32.95 382	29.49 380		21.63 385	12.07 376	37.95 377	45.07 375	30.84 375	19.21 379	17.94 378	33.06 378	23.69 375

High-resolution multi-view benchmark