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 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 13	85.07 50	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 33	94.17 90	86.07 40	98.48 17	97.22 19

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 20	93.73 60	85.72 30	96.79 1	95.51 8	88.86 12	95.63 8	96.99 8	84.81 67	93.16 130	91.10 1	97.53 70	96.58 30
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
HPM-MVS_fast		92.50 4	92.54 5	92.37 5	95.93 15	85.81 29	92.99 12	94.23 22	85.21 34	92.51 55	95.13 44	90.65 9	95.34 50	88.06 8	98.15 34	95.95 41

SR-MVS-dyc-post		92.41 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 13	94.16 27	88.75 14	93.79 28	94.43 67	88.83 24	95.51 42	87.16 26	97.60 64	92.73 146

SR-MVS		92.23 6	92.34 7	91.91 15	94.89 37	87.85 8	92.51 23	93.87 45	88.20 19	93.24 39	94.02 89	90.15 16	95.67 32	86.82 29	97.34 74	92.19 173

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 14	82.88 56	91.77 68	93.94 97	90.55 12	95.73 30	88.50 6	98.23 27	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 78	85.17 35	92.47 25	95.05 13	87.65 22	93.21 40	94.39 72	90.09 17	95.08 59	86.67 30	97.60 64	94.18 92

COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 62	86.15 20	93.37 10	95.10 12	90.28 9	92.11 61	95.03 46	89.75 20	94.93 63	79.95 98	98.27 25	95.04 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 27	82.52 59	92.39 58	94.14 84	89.15 23	95.62 33	87.35 21	98.24 26	94.56 76
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 17	92.60 93	83.09 53	91.54 70	94.25 78	87.67 41	95.51 42	87.21 25	98.11 35	93.12 136

CP-MVS		91.67 12	91.58 19	91.96 12	95.29 30	87.62 9	93.38 9	93.36 59	83.16 52	91.06 80	94.00 90	88.26 30	95.71 31	87.28 24	98.39 20	92.55 155

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 35	89.99 98	94.03 88	86.57 52	95.80 24	87.35 21	97.62 62	94.20 90

MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 30	91.81 115	84.07 42	92.00 64	94.40 71	86.63 51	95.28 53	88.59 5	98.31 23	92.30 166

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 88	88.22 18	88.53 126	97.64 2	83.45 80	94.55 76	86.02 43	98.60 12	96.67 27

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 67	81.99 62	91.40 72	94.17 83	87.51 42	95.87 18	87.74 10	97.76 55	93.99 99

LPG-MVS_test		91.47 17	91.68 16	90.82 33	94.75 40	81.69 59	90.00 57	94.27 19	82.35 60	93.67 33	94.82 52	91.18 4	95.52 40	85.36 47	98.73 6	95.23 59

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 65	81.91 64	90.88 86	94.21 79	87.75 39	95.87 18	87.60 15	97.71 58	93.83 107

HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 21	93.29 66	81.99 62	91.47 71	93.96 94	88.35 29	95.56 37	87.74 10	97.74 57	92.85 143

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 28	94.22 24	80.14 84	91.29 75	93.97 91	87.93 38	95.87 18	88.65 4	97.96 45	94.12 96

APDe-MVS		91.22 21	91.92 11	89.14 64	92.97 80	78.04 86	92.84 15	94.14 31	83.33 51	93.90 24	95.73 27	88.77 25	96.41 1	87.60 15	97.98 42	92.98 140

PGM-MVS		91.20 22	90.95 39	91.93 13	95.67 22	85.85 27	90.00 57	93.90 42	80.32 81	91.74 69	94.41 70	88.17 32	95.98 10	86.37 33	97.99 40	93.96 102

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 37	93.48 57	82.82 57	92.60 54	93.97 91	88.19 31	96.29 4	87.61 14	98.20 31	94.39 86
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 24	90.91 40	91.83 18	96.18 10	86.88 13	92.20 27	93.03 80	82.59 58	88.52 127	94.37 73	86.74 50	95.41 48	86.32 34	98.21 29	93.19 133
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 48	80.98 75	91.38 73	93.80 101	87.20 46	95.80 24	87.10 28	97.69 59	93.93 103

MP-MVS-pluss		90.81 26	91.08 33	89.99 46	95.97 13	79.88 71	88.13 97	94.51 17	75.79 137	92.94 44	94.96 47	88.36 28	95.01 61	90.70 2	98.40 19	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 27	91.50 21	88.44 75	93.00 79	76.26 111	89.65 70	95.55 7	87.72 21	93.89 26	94.94 48	91.62 3	93.44 121	78.35 115	98.76 3	95.61 48

ACMMP_NAP		90.65 28	91.07 35	89.42 59	95.93 15	79.54 76	89.95 61	93.68 52	77.65 115	91.97 65	94.89 49	88.38 27	95.45 46	89.27 3	97.87 50	93.27 129

ACMM	79.39 9	90.65 28	90.99 37	89.63 55	95.03 33	83.53 47	89.62 71	93.35 60	79.20 96	93.83 27	93.60 108	90.81 7	92.96 136	85.02 52	98.45 18	92.41 160
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 30	90.34 47	91.38 24	89.03 180	84.23 45	93.58 6	94.68 16	90.65 7	90.33 92	93.95 96	84.50 69	95.37 49	80.87 88	95.50 141	94.53 79

ACMP	79.16 10	90.54 31	90.60 45	90.35 41	94.36 43	80.98 65	89.16 81	94.05 36	79.03 99	92.87 46	93.74 104	90.60 11	95.21 56	82.87 70	98.76 3	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 32	91.08 33	88.88 66	93.38 69	78.65 83	89.15 82	94.05 36	84.68 39	93.90 24	94.11 86	88.13 34	96.30 3	84.51 58	97.81 52	91.70 187
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 33	91.64 17	86.93 92	94.18 46	72.65 132	90.47 51	93.69 50	83.77 45	94.11 22	94.27 74	90.28 14	95.84 22	86.03 41	97.92 46	92.29 167

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 68	75.37 144	92.84 48	95.28 38	85.58 62	96.09 6	87.92 9	97.76 55	93.88 105
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SF-MVS		90.27 35	90.80 42	88.68 73	92.86 84	77.09 100	91.19 40	95.74 5	81.38 70	92.28 59	93.80 101	86.89 49	94.64 71	85.52 46	97.51 71	94.30 89

v7n		90.13 36	90.96 38	87.65 87	91.95 110	71.06 158	89.99 59	93.05 77	86.53 26	94.29 18	96.27 17	82.69 86	94.08 93	86.25 37	97.63 61	97.82 8

PMVS	80.48 6	90.08 37	90.66 44	88.34 78	96.71 3	92.97 1	90.31 54	89.57 176	88.51 17	90.11 94	95.12 45	90.98 6	88.92 242	77.55 129	97.07 82	83.13 313
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 38	91.09 32	87.00 91	91.55 127	72.64 134	96.19 2	94.10 34	85.33 32	93.49 36	94.64 60	81.12 112	95.88 16	87.41 19	95.94 124	92.48 157

DVP-MVS		90.06 39	91.32 28	86.29 104	94.16 49	72.56 138	90.54 48	91.01 135	83.61 48	93.75 30	94.65 57	89.76 18	95.78 27	86.42 31	97.97 43	90.55 215
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
PS-CasMVS		90.06 39	91.92 11	84.47 140	96.56 6	58.83 288	89.04 83	92.74 90	91.40 5	96.12 4	96.06 22	87.23 45	95.57 36	79.42 107	98.74 5	99.00 2

PEN-MVS		90.03 41	91.88 14	84.48 139	96.57 5	58.88 285	88.95 84	93.19 69	91.62 4	96.01 6	96.16 20	87.02 47	95.60 34	78.69 112	98.72 8	98.97 3

OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 76	80.37 68	91.91 33	93.11 73	81.10 73	95.32 10	97.24 5	72.94 194	94.85 65	85.07 50	97.78 53	97.26 16

DTE-MVSNet		89.98 43	91.91 13	84.21 148	96.51 7	57.84 293	88.93 85	92.84 87	91.92 3	96.16 3	96.23 18	86.95 48	95.99 9	79.05 109	98.57 14	98.80 6

XVG-ACMP-BASELINE		89.98 43	89.84 50	90.41 39	94.91 36	84.50 44	89.49 76	93.98 38	79.68 88	92.09 62	93.89 99	83.80 76	93.10 133	82.67 72	98.04 36	93.64 118

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 136	81.66 62	91.25 38	94.13 32	88.89 11	88.83 122	94.26 77	77.55 142	95.86 21	84.88 54	95.87 128	95.24 58

WR-MVS_H		89.91 46	91.31 29	85.71 119	96.32 9	62.39 243	89.54 74	93.31 64	90.21 10	95.57 9	95.66 29	81.42 109	95.90 14	80.94 87	98.80 2	98.84 5

OPM-MVS		89.80 47	89.97 48	89.27 61	94.76 39	79.86 72	86.76 119	92.78 89	78.78 102	92.51 55	93.64 107	88.13 34	93.84 102	84.83 55	97.55 67	94.10 97
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 48	89.27 59	91.30 25	93.51 65	84.79 40	89.89 63	90.63 145	70.00 212	94.55 15	96.67 11	87.94 37	93.59 113	84.27 59	95.97 121	95.52 49

anonymousdsp		89.73 49	88.88 66	92.27 7	89.82 167	86.67 14	90.51 50	90.20 161	69.87 213	95.06 11	96.14 21	84.28 72	93.07 134	87.68 12	96.34 105	97.09 21

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 95	82.98 53	91.98 31	90.08 164	71.54 193	94.28 20	96.54 13	81.57 107	94.27 82	86.26 35	96.49 99	97.09 21

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 115	93.91 41	80.07 85	86.75 159	93.26 111	93.64 2	90.93 191	84.60 57	90.75 253	93.97 101

APD-MVS		89.54 52	89.63 54	89.26 62	92.57 89	81.34 64	90.19 56	93.08 76	80.87 77	91.13 78	93.19 112	86.22 57	95.97 11	82.23 77	97.18 79	90.45 217
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 53	88.81 68	91.19 28	93.38 69	84.72 41	89.70 66	90.29 158	69.27 216	94.39 16	96.38 15	86.02 60	93.52 117	83.96 61	95.92 126	95.34 53

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 29	92.30 99	79.74 87	87.50 144	92.38 137	81.42 109	93.28 126	83.07 67	97.24 77	91.67 188

ACMH	76.49 14	89.34 55	91.14 31	83.96 153	92.50 92	70.36 164	89.55 72	93.84 46	81.89 65	94.70 13	95.44 34	90.69 8	88.31 252	83.33 65	98.30 24	93.20 132
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 56	89.12 60	89.84 48	88.67 186	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 233	74.12 165	96.10 116	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 186	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 233	74.12 165	96.10 116	94.45 82

CP-MVSNet		89.27 58	90.91 40	84.37 141	96.34 8	58.61 290	88.66 91	92.06 104	90.78 6	95.67 7	95.17 43	81.80 105	95.54 39	79.00 110	98.69 9	98.95 4

XVG-OURS		89.18 59	88.83 67	90.23 43	94.28 44	86.11 22	85.91 128	93.60 55	80.16 83	89.13 119	93.44 109	83.82 75	90.98 189	83.86 63	95.30 149	93.60 120

DeepC-MVS	82.31 4	89.15 60	89.08 62	89.37 60	93.64 63	79.07 79	88.54 93	94.20 25	73.53 161	89.71 105	94.82 52	85.09 63	95.77 29	84.17 60	98.03 38	93.26 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 61	90.72 43	84.31 146	97.00 2	64.33 221	89.67 69	88.38 191	88.84 13	94.29 18	97.57 3	90.48 13	91.26 181	72.57 189	97.65 60	97.34 15

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 42	78.43 107	89.16 118	92.25 143	72.03 206	96.36 2	88.21 7	90.93 248	92.98 140
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SD-MVS		88.96 63	89.88 49	86.22 107	91.63 121	77.07 101	89.82 64	93.77 47	78.90 100	92.88 45	92.29 141	86.11 58	90.22 212	86.24 38	97.24 77	91.36 195
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		88.93 64	88.45 71	90.38 40	94.92 35	85.85 27	89.70 66	91.27 128	78.20 109	86.69 162	92.28 142	80.36 120	95.06 60	86.17 39	96.49 99	90.22 221

test_0402		88.65 65	89.58 56	85.88 116	92.55 90	72.22 146	84.01 157	89.44 178	88.63 16	94.38 17	95.77 26	86.38 56	93.59 113	79.84 99	95.21 150	91.82 183

DP-MVS		88.60 66	89.01 63	87.36 89	91.30 134	77.50 93	87.55 104	92.97 83	87.95 20	89.62 109	92.87 123	84.56 68	93.89 99	77.65 127	96.62 94	90.70 209

APD_test1		88.40 67	87.91 75	89.88 47	89.50 170	86.65 16	89.98 60	91.91 110	84.26 40	90.87 87	93.92 98	82.18 96	89.29 237	73.75 172	94.81 167	93.70 114

Anonymous20231211		88.40 67	89.62 55	84.73 135	90.46 155	65.27 211	88.86 86	93.02 81	87.15 23	93.05 43	97.10 6	82.28 95	92.02 162	76.70 139	97.99 40	96.88 25

PS-MVSNAJss		88.31 69	87.90 76	89.56 57	93.31 71	77.96 89	87.94 100	91.97 107	70.73 202	94.19 21	96.67 11	76.94 151	94.57 74	83.07 67	96.28 107	96.15 33

RRT_MVS		88.30 70	87.83 77	89.70 52	93.62 64	75.70 115	92.36 26	89.06 183	77.34 118	93.63 35	95.83 25	65.40 236	95.90 14	85.01 53	98.23 27	97.49 13

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 112	81.68 61	87.49 106	93.17 70	76.02 131	88.64 125	91.22 166	84.24 73	93.37 124	77.97 125	97.03 83	95.52 49

CS-MVS		88.14 72	87.67 80	89.54 58	89.56 169	79.18 78	90.47 51	94.77 15	79.37 94	84.32 205	89.33 212	83.87 74	94.53 77	82.45 74	94.89 163	94.90 65

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 65	95.72 21	76.74 104	92.49 24	91.19 131	67.85 236	86.63 163	94.84 51	79.58 126	95.96 12	87.62 13	94.50 175	94.56 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 74	89.79 51	82.98 176	93.26 73	63.94 225	91.10 41	89.64 173	85.07 35	90.91 84	91.09 170	89.16 22	91.87 167	82.03 78	95.87 128	93.13 134

DROMVSNet		88.01 75	88.32 72	87.09 90	89.28 175	72.03 148	90.31 54	96.31 3	80.88 76	85.12 189	89.67 206	84.47 70	95.46 45	82.56 73	96.26 110	93.77 112

RPSCF		88.00 76	86.93 93	91.22 27	90.08 161	89.30 4	89.68 68	91.11 132	79.26 95	89.68 106	94.81 55	82.44 89	87.74 256	76.54 142	88.74 274	96.61 29

AllTest		87.97 77	87.40 85	89.68 53	91.59 122	83.40 48	89.50 75	95.44 9	79.47 90	88.00 137	93.03 115	82.66 87	91.47 174	70.81 197	96.14 113	94.16 93

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 99	76.51 108	91.09 42	91.87 111	72.61 180	92.16 60	95.23 41	66.01 232	95.59 35	86.02 43	97.78 53	97.24 17

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 127	94.02 54	64.13 222	84.38 151	91.29 127	84.88 38	92.06 63	93.84 100	86.45 54	93.73 104	73.22 180	98.66 10	97.69 9

nrg030		87.85 80	88.49 70	85.91 114	90.07 163	69.73 168	87.86 101	94.20 25	74.04 155	92.70 53	94.66 56	85.88 61	91.50 173	79.72 102	97.32 75	96.50 31

CNVR-MVS		87.81 81	87.68 79	88.21 80	92.87 82	77.30 99	85.25 137	91.23 129	77.31 120	87.07 153	91.47 161	82.94 84	94.71 68	84.67 56	96.27 109	92.62 153

HQP_MVS		87.75 82	87.43 84	88.70 72	93.45 66	76.42 109	89.45 77	93.61 53	79.44 92	86.55 164	92.95 120	74.84 169	95.22 54	80.78 90	95.83 130	94.46 80

NCCC		87.36 83	86.87 94	88.83 67	92.32 98	78.84 82	86.58 123	91.09 133	78.77 103	84.85 196	90.89 178	80.85 114	95.29 51	81.14 85	95.32 146	92.34 164

DeepPCF-MVS	81.24 5	87.28 84	86.21 103	90.49 38	91.48 131	84.90 38	83.41 175	92.38 98	70.25 209	89.35 117	90.68 185	82.85 85	94.57 74	79.55 104	95.95 123	92.00 178

SixPastTwentyTwo		87.20 85	87.45 83	86.45 101	92.52 91	69.19 177	87.84 102	88.05 199	81.66 67	94.64 14	96.53 14	65.94 233	94.75 67	83.02 69	96.83 88	95.41 51

CS-MVS-test		87.00 86	86.43 99	88.71 71	89.46 171	77.46 94	89.42 79	95.73 6	77.87 113	81.64 251	87.25 247	82.43 90	94.53 77	77.65 127	96.46 101	94.14 95

UniMVSNet (Re)		86.87 87	86.98 92	86.55 99	93.11 77	68.48 182	83.80 166	92.87 85	80.37 79	89.61 111	91.81 154	77.72 139	94.18 88	75.00 158	98.53 15	96.99 24

Vis-MVSNet		86.86 88	86.58 97	87.72 85	92.09 106	77.43 96	87.35 107	92.09 103	78.87 101	84.27 210	94.05 87	78.35 134	93.65 106	80.54 94	91.58 236	92.08 175
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 89	87.06 89	86.17 110	92.86 84	67.02 194	82.55 199	91.56 118	83.08 54	90.92 82	91.82 153	78.25 135	93.99 95	74.16 163	98.35 21	97.49 13

DU-MVS		86.80 90	86.99 91	86.21 108	93.24 74	67.02 194	83.16 183	92.21 100	81.73 66	90.92 82	91.97 147	77.20 145	93.99 95	74.16 163	98.35 21	97.61 10

casdiffmvs_mvg		86.72 91	87.51 82	84.36 143	87.09 223	65.22 212	84.16 153	94.23 22	77.89 112	91.28 76	93.66 106	84.35 71	92.71 142	80.07 95	94.87 166	95.16 61
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IS-MVSNet		86.66 92	86.82 96	86.17 110	92.05 108	66.87 197	91.21 39	88.64 188	86.30 28	89.60 112	92.59 131	69.22 216	94.91 64	73.89 169	97.89 49	96.72 26

v10		86.54 93	87.10 88	84.84 131	88.16 200	63.28 231	86.64 122	92.20 101	75.42 143	92.81 50	94.50 63	74.05 179	94.06 94	83.88 62	96.28 107	97.17 20

pmmvs6		86.52 94	88.06 74	81.90 196	92.22 102	62.28 246	84.66 144	89.15 181	83.54 50	89.85 102	97.32 4	88.08 36	86.80 269	70.43 205	97.30 76	96.62 28

PHI-MVS		86.38 95	85.81 109	88.08 81	88.44 194	77.34 97	89.35 80	93.05 77	73.15 172	84.76 197	87.70 238	78.87 130	94.18 88	80.67 92	96.29 106	92.73 146

CSCG		86.26 96	86.47 98	85.60 121	90.87 147	74.26 123	87.98 99	91.85 112	80.35 80	89.54 115	88.01 231	79.09 128	92.13 158	75.51 151	95.06 157	90.41 218

DeepC-MVS_fast	80.27 8	86.23 97	85.65 113	87.96 84	91.30 134	76.92 102	87.19 108	91.99 106	70.56 203	84.96 192	90.69 184	80.01 123	95.14 57	78.37 114	95.78 135	91.82 183
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 98	86.83 95	84.36 143	87.82 204	62.35 245	86.42 125	91.33 126	76.78 124	92.73 52	94.48 65	73.41 188	93.72 105	83.10 66	95.41 142	97.01 23

Anonymous20240529		86.20 99	87.13 87	83.42 166	90.19 159	64.55 219	84.55 146	90.71 142	85.85 31	89.94 101	95.24 40	82.13 97	90.40 208	69.19 216	96.40 104	95.31 55

CDPH-MVS		86.17 100	85.54 114	88.05 83	92.25 100	75.45 116	83.85 163	92.01 105	65.91 248	86.19 171	91.75 156	83.77 77	94.98 62	77.43 132	96.71 92	93.73 113

NR-MVSNet		86.00 101	86.22 102	85.34 125	93.24 74	64.56 218	82.21 211	90.46 148	80.99 74	88.42 129	91.97 147	77.56 141	93.85 100	72.46 190	98.65 11	97.61 10

train_agg		85.98 102	85.28 117	88.07 82	92.34 96	79.70 74	83.94 159	90.32 153	65.79 249	84.49 200	90.97 174	81.93 101	93.63 108	81.21 84	96.54 97	90.88 203

FC-MVSNet-test		85.93 103	87.05 90	82.58 187	92.25 100	56.44 304	85.75 131	93.09 75	77.33 119	91.94 66	94.65 57	74.78 171	93.41 123	75.11 157	98.58 13	97.88 7

Effi-MVS+-dtu		85.82 104	83.38 149	93.14 3	87.13 219	91.15 2	87.70 103	88.42 190	74.57 151	83.56 220	85.65 270	78.49 133	94.21 86	72.04 192	92.88 210	94.05 98

TAPA-MVS	77.73 12	85.71 105	84.83 123	88.37 77	88.78 185	79.72 73	87.15 110	93.50 56	69.17 217	85.80 180	89.56 207	80.76 115	92.13 158	73.21 185	95.51 140	93.25 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 106	86.14 104	83.58 162	87.97 201	67.13 192	87.55 104	94.32 18	73.44 163	88.47 128	87.54 241	86.45 54	91.06 188	75.76 150	93.76 190	92.54 156

EPP-MVSNet		85.47 107	85.04 120	86.77 96	91.52 130	69.37 172	91.63 36	87.98 201	81.51 69	87.05 154	91.83 152	66.18 231	95.29 51	70.75 200	96.89 85	95.64 46

GeoE		85.45 108	85.81 109	84.37 141	90.08 161	67.07 193	85.86 130	91.39 125	72.33 186	87.59 142	90.25 195	84.85 66	92.37 152	78.00 123	91.94 230	93.66 115

FIs		85.35 109	86.27 101	82.60 186	91.86 114	57.31 297	85.10 139	93.05 77	75.83 136	91.02 81	93.97 91	73.57 184	92.91 140	73.97 168	98.02 39	97.58 12

casdiffmvs		85.21 110	85.85 108	83.31 169	86.17 244	62.77 237	83.03 185	93.93 40	74.69 150	88.21 134	92.68 130	82.29 94	91.89 166	77.87 126	93.75 192	95.27 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		85.20 111	85.93 106	83.02 175	86.30 238	62.37 244	84.55 146	93.96 39	74.48 152	87.12 148	92.03 146	82.30 93	91.94 163	78.39 113	94.21 182	94.74 73

K. test v3		85.14 112	84.73 124	86.37 102	91.13 141	69.63 170	85.45 135	76.68 302	84.06 43	92.44 57	96.99 8	62.03 254	94.65 70	80.58 93	93.24 201	94.83 72

EI-MVSNet-Vis-set		85.12 113	84.53 131	86.88 93	84.01 271	72.76 131	83.91 162	85.18 239	80.44 78	88.75 123	85.49 272	80.08 122	91.92 164	82.02 79	90.85 251	95.97 39

EI-MVSNet-UG-set		85.04 114	84.44 133	86.85 94	83.87 274	72.52 140	83.82 164	85.15 240	80.27 82	88.75 123	85.45 274	79.95 124	91.90 165	81.92 81	90.80 252	96.13 34

X-MVStestdata		85.04 114	82.70 160	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 35	89.99 98	16.05 374	86.57 52	95.80 24	87.35 21	97.62 62	94.20 90

MSLP-MVS++		85.00 116	86.03 105	81.90 196	91.84 117	71.56 156	86.75 120	93.02 81	75.95 134	87.12 148	89.39 210	77.98 136	89.40 236	77.46 130	94.78 168	84.75 290

F-COLMAP		84.97 117	83.42 148	89.63 55	92.39 94	83.40 48	88.83 87	91.92 109	73.19 171	80.18 271	89.15 216	77.04 149	93.28 126	65.82 245	92.28 221	92.21 172

bld_raw_dy_0_64		84.85 118	84.44 133	86.07 112	93.73 60	74.93 119	88.57 92	81.90 272	70.44 204	91.28 76	95.18 42	56.62 289	89.28 238	85.15 49	97.09 81	93.99 99

3Dnovator	80.37 7	84.80 119	84.71 127	85.06 129	86.36 236	74.71 120	88.77 89	90.00 166	75.65 139	84.96 192	93.17 113	74.06 178	91.19 183	78.28 117	91.09 242	89.29 237

IterMVS-LS		84.73 120	84.98 121	83.96 153	87.35 214	63.66 226	83.25 179	89.88 168	76.06 129	89.62 109	92.37 140	73.40 190	92.52 147	78.16 120	94.77 170	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 121	84.34 138	85.49 124	90.18 160	75.86 114	79.23 253	87.13 211	73.35 164	85.56 184	89.34 211	83.60 79	90.50 206	76.64 140	94.05 186	90.09 226

HQP-MVS		84.61 122	84.06 141	86.27 105	91.19 137	70.66 160	84.77 140	92.68 91	73.30 167	80.55 264	90.17 199	72.10 202	94.61 72	77.30 134	94.47 176	93.56 122

v1192		84.57 123	84.69 128	84.21 148	87.75 206	62.88 235	83.02 186	91.43 122	69.08 219	89.98 100	90.89 178	72.70 198	93.62 111	82.41 75	94.97 160	96.13 34

FMVSNet1		84.55 124	85.45 115	81.85 198	90.27 158	61.05 259	86.83 116	88.27 196	78.57 106	89.66 108	95.64 30	75.43 162	90.68 201	69.09 217	95.33 145	93.82 108

v1144		84.54 125	84.72 126	84.00 151	87.67 208	62.55 241	82.97 187	90.93 138	70.32 208	89.80 103	90.99 173	73.50 185	93.48 119	81.69 83	94.65 173	95.97 39

Gipuma		84.44 126	86.33 100	78.78 243	84.20 269	73.57 126	89.55 72	90.44 149	84.24 41	84.38 202	94.89 49	76.35 160	80.40 316	76.14 146	96.80 90	82.36 321
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 127	83.93 144	85.63 120	91.59 122	71.58 155	83.52 172	92.13 102	61.82 276	83.96 215	89.75 205	79.93 125	93.46 120	78.33 116	94.34 179	91.87 182

VDDNet		84.35 128	85.39 116	81.25 207	95.13 31	59.32 278	85.42 136	81.11 277	86.41 27	87.41 145	96.21 19	73.61 183	90.61 204	66.33 239	96.85 86	93.81 111

ETV-MVS		84.31 129	83.91 145	85.52 122	88.58 190	70.40 163	84.50 150	93.37 58	78.76 104	84.07 214	78.72 341	80.39 119	95.13 58	73.82 171	92.98 208	91.04 199

v1240		84.30 130	84.51 132	83.65 160	87.65 209	61.26 256	82.85 191	91.54 119	67.94 234	90.68 89	90.65 187	71.71 208	93.64 107	82.84 71	94.78 168	96.07 36

MVS_111021_LR		84.28 131	83.76 146	85.83 118	89.23 177	83.07 51	80.99 228	83.56 258	72.71 178	86.07 174	89.07 218	81.75 106	86.19 279	77.11 136	93.36 197	88.24 250

h-mvs33		84.25 132	82.76 159	88.72 70	91.82 119	82.60 56	84.00 158	84.98 246	71.27 195	86.70 160	90.55 189	63.04 250	93.92 98	78.26 118	94.20 183	89.63 229

v144192		84.24 133	84.41 135	83.71 159	87.59 211	61.57 252	82.95 188	91.03 134	67.82 237	89.80 103	90.49 190	73.28 191	93.51 118	81.88 82	94.89 163	96.04 38

dcpmvs_2		84.23 134	85.14 118	81.50 204	88.61 189	61.98 250	82.90 190	93.11 73	68.66 225	92.77 51	92.39 136	78.50 132	87.63 258	76.99 138	92.30 218	94.90 65

v1921920		84.23 134	84.37 137	83.79 156	87.64 210	61.71 251	82.91 189	91.20 130	67.94 234	90.06 95	90.34 192	72.04 205	93.59 113	82.32 76	94.91 161	96.07 36

VDD-MVS		84.23 134	84.58 130	83.20 172	91.17 140	65.16 214	83.25 179	84.97 247	79.79 86	87.18 147	94.27 74	74.77 172	90.89 194	69.24 213	96.54 97	93.55 124

v2v482		84.09 137	84.24 139	83.62 161	87.13 219	61.40 253	82.71 194	89.71 171	72.19 189	89.55 113	91.41 162	70.70 212	93.20 128	81.02 86	93.76 190	96.25 32

EG-PatchMatch MVS		84.08 138	84.11 140	83.98 152	92.22 102	72.61 137	82.20 213	87.02 216	72.63 179	88.86 120	91.02 172	78.52 131	91.11 186	73.41 177	91.09 242	88.21 251

DP-MVS Recon		84.05 139	83.22 151	86.52 100	91.73 120	75.27 117	83.23 181	92.40 96	72.04 190	82.04 241	88.33 227	77.91 138	93.95 97	66.17 240	95.12 155	90.34 220

TransMVSNet (Re)		84.02 140	85.74 111	78.85 242	91.00 144	55.20 314	82.29 207	87.26 207	79.65 89	88.38 131	95.52 33	83.00 83	86.88 267	67.97 230	96.60 95	94.45 82

Baseline_NR-MVSNet		84.00 141	85.90 107	78.29 254	91.47 132	53.44 323	82.29 207	87.00 219	79.06 98	89.55 113	95.72 28	77.20 145	86.14 280	72.30 191	98.51 16	95.28 56

TSAR-MVS + GP.		83.95 142	82.69 161	87.72 85	89.27 176	81.45 63	83.72 168	81.58 276	74.73 149	85.66 181	86.06 265	72.56 200	92.69 144	75.44 153	95.21 150	89.01 245

alignmvs		83.94 143	83.98 143	83.80 155	87.80 205	67.88 189	84.54 148	91.42 124	73.27 170	88.41 130	87.96 232	72.33 201	90.83 196	76.02 148	94.11 184	92.69 150

Effi-MVS+		83.90 144	84.01 142	83.57 163	87.22 217	65.61 210	86.55 124	92.40 96	78.64 105	81.34 256	84.18 293	83.65 78	92.93 138	74.22 162	87.87 285	92.17 174

CANet		83.79 145	82.85 158	86.63 97	86.17 244	72.21 147	83.76 167	91.43 122	77.24 121	74.39 318	87.45 243	75.36 163	95.42 47	77.03 137	92.83 211	92.25 171

pm-mvs1		83.69 146	84.95 122	79.91 229	90.04 165	59.66 275	82.43 203	87.44 204	75.52 141	87.85 139	95.26 39	81.25 111	85.65 286	68.74 222	96.04 118	94.42 85

AdaColmap		83.66 147	83.69 147	83.57 163	90.05 164	72.26 145	86.29 127	90.00 166	78.19 110	81.65 250	87.16 249	83.40 81	94.24 85	61.69 276	94.76 171	84.21 295

MIMVSNet1		83.63 148	84.59 129	80.74 217	94.06 53	62.77 237	82.72 193	84.53 252	77.57 117	90.34 91	95.92 24	76.88 157	85.83 284	61.88 274	97.42 72	93.62 119

WR-MVS		83.56 149	84.40 136	81.06 212	93.43 68	54.88 315	78.67 261	85.02 244	81.24 71	90.74 88	91.56 159	72.85 195	91.08 187	68.00 229	98.04 36	97.23 18

CNLPA		83.55 150	83.10 155	84.90 130	89.34 174	83.87 46	84.54 148	88.77 185	79.09 97	83.54 221	88.66 224	74.87 168	81.73 310	66.84 235	92.29 220	89.11 239

LCM-MVSNet-Re		83.48 151	85.06 119	78.75 244	85.94 248	55.75 309	80.05 237	94.27 19	76.47 125	96.09 5	94.54 62	83.31 82	89.75 228	59.95 287	94.89 163	90.75 206

hse-mvs2		83.47 152	81.81 173	88.47 74	91.03 143	82.27 57	82.61 195	83.69 256	71.27 195	86.70 160	86.05 266	63.04 250	92.41 150	78.26 118	93.62 196	90.71 208

V42		83.47 152	83.37 150	83.75 158	83.16 280	63.33 230	81.31 222	90.23 160	69.51 215	90.91 84	90.81 181	74.16 177	92.29 156	80.06 96	90.22 259	95.62 47

VPA-MVSNet		83.47 152	84.73 124	79.69 233	90.29 157	57.52 296	81.30 224	88.69 187	76.29 126	87.58 143	94.44 66	80.60 118	87.20 262	66.60 238	96.82 89	94.34 88

PAPM_NR		83.23 155	83.19 153	83.33 168	90.90 146	65.98 206	88.19 96	90.78 141	78.13 111	80.87 260	87.92 235	73.49 187	92.42 149	70.07 207	88.40 276	91.60 190

CLD-MVS		83.18 156	82.64 162	84.79 132	89.05 179	67.82 190	77.93 269	92.52 94	68.33 227	85.07 190	81.54 321	82.06 98	92.96 136	69.35 212	97.91 48	93.57 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 157	85.68 112	75.65 287	81.24 296	45.26 362	79.94 239	92.91 84	83.83 44	91.33 74	96.88 10	80.25 121	85.92 282	68.89 220	95.89 127	95.76 43

FA-MVS(test-final)		83.13 158	83.02 156	83.43 165	86.16 246	66.08 205	88.00 98	88.36 192	75.55 140	85.02 191	92.75 128	65.12 237	92.50 148	74.94 159	91.30 240	91.72 185

114514_t		83.10 159	82.54 165	84.77 134	92.90 81	69.10 179	86.65 121	90.62 146	54.66 317	81.46 253	90.81 181	76.98 150	94.38 81	72.62 188	96.18 111	90.82 205

UGNet		82.78 160	81.64 175	86.21 108	86.20 243	76.24 112	86.86 114	85.68 232	77.07 122	73.76 321	92.82 124	69.64 213	91.82 169	69.04 219	93.69 193	90.56 214
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 161	81.93 172	85.19 126	82.08 287	80.15 70	85.53 134	88.76 186	68.01 231	85.58 183	87.75 237	71.80 207	86.85 268	74.02 167	93.87 189	88.58 248

EI-MVSNet		82.61 162	82.42 167	83.20 172	83.25 278	63.66 226	83.50 173	85.07 241	76.06 129	86.55 164	85.10 280	73.41 188	90.25 209	78.15 122	90.67 255	95.68 45

QAPM		82.59 163	82.59 164	82.58 187	86.44 231	66.69 198	89.94 62	90.36 152	67.97 233	84.94 194	92.58 133	72.71 197	92.18 157	70.63 203	87.73 287	88.85 246

Fast-Effi-MVS+-dtu		82.54 164	81.41 179	85.90 115	85.60 249	76.53 107	83.07 184	89.62 175	73.02 174	79.11 281	83.51 298	80.74 116	90.24 211	68.76 221	89.29 265	90.94 201

MVS_Test		82.47 165	83.22 151	80.22 226	82.62 286	57.75 295	82.54 200	91.96 108	71.16 199	82.89 228	92.52 135	77.41 143	90.50 206	80.04 97	87.84 286	92.40 161

v148		82.31 166	82.48 166	81.81 201	85.59 250	59.66 275	81.47 220	86.02 228	72.85 175	88.05 136	90.65 187	70.73 211	90.91 193	75.15 156	91.79 231	94.87 67

API-MVS		82.28 167	82.61 163	81.30 206	86.29 239	69.79 166	88.71 90	87.67 203	78.42 108	82.15 239	84.15 294	77.98 136	91.59 172	65.39 247	92.75 212	82.51 320

MVSFormer		82.23 168	81.57 178	84.19 150	85.54 251	69.26 174	91.98 31	90.08 164	71.54 193	76.23 301	85.07 283	58.69 275	94.27 82	86.26 35	88.77 272	89.03 243

EIA-MVS		82.19 169	81.23 182	85.10 128	87.95 202	69.17 178	83.22 182	93.33 61	70.42 205	78.58 284	79.77 337	77.29 144	94.20 87	71.51 194	88.96 270	91.93 181

PCF-MVS	74.62 15	82.15 170	80.92 186	85.84 117	89.43 172	72.30 144	80.53 232	91.82 114	57.36 307	87.81 140	89.92 202	77.67 140	93.63 108	58.69 292	95.08 156	91.58 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 171	80.31 192	87.45 88	90.86 148	80.29 69	85.88 129	90.65 144	68.17 229	76.32 300	86.33 260	73.12 193	92.61 146	61.40 279	90.02 261	89.44 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 172	82.07 169	81.85 198	86.38 233	61.05 259	86.83 116	88.27 196	72.43 181	86.00 175	95.64 30	63.78 244	90.68 201	65.95 241	93.34 198	93.82 108

test1		82.02 172	82.07 169	81.85 198	86.38 233	61.05 259	86.83 116	88.27 196	72.43 181	86.00 175	95.64 30	63.78 244	90.68 201	65.95 241	93.34 198	93.82 108

OpenMVS	76.72 13	81.98 174	82.00 171	81.93 195	84.42 264	68.22 184	88.50 94	89.48 177	66.92 242	81.80 248	91.86 149	72.59 199	90.16 214	71.19 196	91.25 241	87.40 263

KD-MVS_self_test		81.93 175	83.14 154	78.30 253	84.75 259	52.75 327	80.37 234	89.42 179	70.24 210	90.26 93	93.39 110	74.55 176	86.77 270	68.61 224	96.64 93	95.38 52

tfpnnormal		81.79 176	82.95 157	78.31 252	88.93 183	55.40 310	80.83 231	82.85 264	76.81 123	85.90 179	94.14 84	74.58 175	86.51 274	66.82 236	95.68 139	93.01 139

c3_l		81.64 177	81.59 177	81.79 202	80.86 302	59.15 282	78.61 262	90.18 162	68.36 226	87.20 146	87.11 251	69.39 214	91.62 171	78.16 120	94.43 178	94.60 75

PVSNet_Blended_VisFu		81.55 178	80.49 190	84.70 137	91.58 125	73.24 129	84.21 152	91.67 117	62.86 269	80.94 258	87.16 249	67.27 225	92.87 141	69.82 209	88.94 271	87.99 255

DELS-MVS		81.44 179	81.25 180	82.03 194	84.27 268	62.87 236	76.47 292	92.49 95	70.97 200	81.64 251	83.83 295	75.03 166	92.70 143	74.29 161	92.22 224	90.51 216
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
FMVSNet2		81.31 180	81.61 176	80.41 223	86.38 233	58.75 289	83.93 161	86.58 221	72.43 181	87.65 141	92.98 117	63.78 244	90.22 212	66.86 233	93.92 188	92.27 169

TinyColmap		81.25 181	82.34 168	77.99 259	85.33 253	60.68 267	82.32 206	88.33 194	71.26 197	86.97 155	92.22 145	77.10 148	86.98 266	62.37 268	95.17 152	86.31 274

AUN-MVS		81.18 182	78.78 212	88.39 76	90.93 145	82.14 58	82.51 201	83.67 257	64.69 262	80.29 267	85.91 269	51.07 310	92.38 151	76.29 145	93.63 195	90.65 212

tttt0517		81.07 183	79.58 203	85.52 122	88.99 182	66.45 201	87.03 112	75.51 310	73.76 159	88.32 133	90.20 196	37.96 363	94.16 92	79.36 108	95.13 153	95.93 42

Fast-Effi-MVS+		81.04 184	80.57 187	82.46 191	87.50 212	63.22 232	78.37 265	89.63 174	68.01 231	81.87 244	82.08 316	82.31 92	92.65 145	67.10 232	88.30 281	91.51 193

BH-untuned		80.96 185	80.99 184	80.84 216	88.55 191	68.23 183	80.33 235	88.46 189	72.79 177	86.55 164	86.76 255	74.72 173	91.77 170	61.79 275	88.99 269	82.52 319

eth_miper_zixun_eth		80.84 186	80.22 196	82.71 184	81.41 294	60.98 262	77.81 271	90.14 163	67.31 240	86.95 156	87.24 248	64.26 240	92.31 154	75.23 155	91.61 234	94.85 71

xiu_mvs_v1_base_debu		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

xiu_mvs_v1_base		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

xiu_mvs_v1_base_debi		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

IterMVS-SCA-FT		80.64 190	79.41 204	84.34 145	83.93 272	69.66 169	76.28 294	81.09 278	72.43 181	86.47 170	90.19 197	60.46 260	93.15 131	77.45 131	86.39 300	90.22 221

BH-RMVSNet		80.53 191	80.22 196	81.49 205	87.19 218	66.21 204	77.79 272	86.23 225	74.21 154	83.69 217	88.50 225	73.25 192	90.75 198	63.18 265	87.90 284	87.52 261

Anonymous202405211		80.51 192	81.19 183	78.49 249	88.48 192	57.26 298	76.63 289	82.49 266	81.21 72	84.30 208	92.24 144	67.99 222	86.24 278	62.22 269	95.13 153	91.98 180

DIV-MVS_self_test		80.43 193	80.23 194	81.02 213	79.99 310	59.25 279	77.07 282	87.02 216	67.38 238	86.19 171	89.22 213	63.09 248	90.16 214	76.32 143	95.80 133	93.66 115

cl____		80.42 194	80.23 194	81.02 213	79.99 310	59.25 279	77.07 282	87.02 216	67.37 239	86.18 173	89.21 214	63.08 249	90.16 214	76.31 144	95.80 133	93.65 117

diffmvs		80.40 195	80.48 191	80.17 227	79.02 322	60.04 271	77.54 276	90.28 159	66.65 245	82.40 234	87.33 246	73.50 185	87.35 261	77.98 124	89.62 263	93.13 134
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet		80.37 196	78.41 219	86.23 106	76.75 333	73.28 127	87.18 109	77.45 297	76.24 128	68.14 342	88.93 220	65.41 235	93.85 100	69.47 211	96.12 115	91.55 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
iter_conf_final		80.36 197	78.88 209	84.79 132	86.29 239	66.36 203	86.95 113	86.25 224	68.16 230	82.09 240	89.48 208	36.59 366	94.51 79	79.83 100	94.30 180	93.50 125

miper_ehance_all_eth		80.34 198	80.04 201	81.24 209	79.82 312	58.95 284	77.66 273	89.66 172	65.75 252	85.99 178	85.11 279	68.29 221	91.42 178	76.03 147	92.03 226	93.33 126

MG-MVS		80.32 199	80.94 185	78.47 250	88.18 198	52.62 330	82.29 207	85.01 245	72.01 191	79.24 280	92.54 134	69.36 215	93.36 125	70.65 202	89.19 268	89.45 231

VPNet		80.25 200	81.68 174	75.94 285	92.46 93	47.98 353	76.70 287	81.67 274	73.45 162	84.87 195	92.82 124	74.66 174	86.51 274	61.66 277	96.85 86	93.33 126

MAR-MVS		80.24 201	78.74 214	84.73 135	86.87 229	78.18 85	85.75 131	87.81 202	65.67 254	77.84 289	78.50 342	73.79 182	90.53 205	61.59 278	90.87 250	85.49 283
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PM-MVS		80.20 202	79.00 208	83.78 157	88.17 199	86.66 15	81.31 222	66.81 355	69.64 214	88.33 132	90.19 197	64.58 238	83.63 302	71.99 193	90.03 260	81.06 338

Anonymous20240521		80.18 203	81.25 180	76.95 272	83.15 281	60.84 264	82.46 202	85.99 229	68.76 223	86.78 157	93.73 105	59.13 272	77.44 323	73.71 173	97.55 67	92.56 154

LFMVS		80.15 204	80.56 188	78.89 241	89.19 178	55.93 306	85.22 138	73.78 322	82.96 55	84.28 209	92.72 129	57.38 284	90.07 221	63.80 259	95.75 136	90.68 210

DPM-MVS		80.10 205	79.18 207	82.88 182	90.71 151	69.74 167	78.87 258	90.84 139	60.29 291	75.64 309	85.92 268	67.28 224	93.11 132	71.24 195	91.79 231	85.77 280

MSDG		80.06 206	79.99 202	80.25 225	83.91 273	68.04 188	77.51 277	89.19 180	77.65 115	81.94 242	83.45 300	76.37 159	86.31 277	63.31 264	86.59 297	86.41 272

FE-MVS		79.98 207	78.86 210	83.36 167	86.47 230	66.45 201	89.73 65	84.74 251	72.80 176	84.22 213	91.38 163	44.95 344	93.60 112	63.93 258	91.50 237	90.04 227

ab-mvs		79.67 208	80.56 188	76.99 271	88.48 192	56.93 300	84.70 143	86.06 227	68.95 221	80.78 261	93.08 114	75.30 164	84.62 294	56.78 301	90.90 249	89.43 233

VNet		79.31 209	80.27 193	76.44 279	87.92 203	53.95 319	75.58 302	84.35 253	74.39 153	82.23 237	90.72 183	72.84 196	84.39 296	60.38 286	93.98 187	90.97 200

thisisatest0530		79.07 210	77.33 229	84.26 147	87.13 219	64.58 217	83.66 170	75.95 305	68.86 222	85.22 188	87.36 245	38.10 361	93.57 116	75.47 152	94.28 181	94.62 74

cl22		78.97 211	78.21 221	81.24 209	77.74 326	59.01 283	77.46 279	87.13 211	65.79 249	84.32 205	85.10 280	58.96 274	90.88 195	75.36 154	92.03 226	93.84 106

patch_mono-2		78.89 212	79.39 205	77.41 268	84.78 258	68.11 186	75.60 300	83.11 261	60.96 285	79.36 277	89.89 203	75.18 165	72.97 334	73.32 179	92.30 218	91.15 197

RPMNet		78.88 213	78.28 220	80.68 220	79.58 313	62.64 239	82.58 197	94.16 27	74.80 148	75.72 307	92.59 131	48.69 318	95.56 37	73.48 176	82.91 329	83.85 300

PAPR		78.84 214	78.10 222	81.07 211	85.17 254	60.22 270	82.21 211	90.57 147	62.51 271	75.32 313	84.61 288	74.99 167	92.30 155	59.48 290	88.04 283	90.68 210

iter_conf05		78.81 215	77.35 228	83.21 171	82.98 284	60.75 266	84.09 155	88.34 193	63.12 267	84.25 212	89.48 208	31.41 371	94.51 79	76.64 140	95.83 130	94.38 87

PVSNet_BlendedMVS		78.80 216	77.84 223	81.65 203	84.43 262	63.41 228	79.49 247	90.44 149	61.70 279	75.43 310	87.07 252	69.11 217	91.44 176	60.68 284	92.24 222	90.11 225

FMVSNet3		78.80 216	78.55 216	79.57 235	82.89 285	56.89 302	81.76 215	85.77 231	69.04 220	86.00 175	90.44 191	51.75 308	90.09 220	65.95 241	93.34 198	91.72 185

test_yl		78.71 218	78.51 217	79.32 238	84.32 266	58.84 286	78.38 263	85.33 236	75.99 132	82.49 232	86.57 256	58.01 278	90.02 223	62.74 266	92.73 213	89.10 240

DCV-MVSNet		78.71 218	78.51 217	79.32 238	84.32 266	58.84 286	78.38 263	85.33 236	75.99 132	82.49 232	86.57 256	58.01 278	90.02 223	62.74 266	92.73 213	89.10 240

test1111		78.53 220	78.85 211	77.56 265	92.22 102	47.49 355	82.61 195	69.24 347	72.43 181	85.28 187	94.20 80	51.91 306	90.07 221	65.36 248	96.45 102	95.11 62

ECVR-MVS		78.44 221	78.63 215	77.88 261	91.85 115	48.95 349	83.68 169	69.91 345	72.30 187	84.26 211	94.20 80	51.89 307	89.82 225	63.58 260	96.02 119	94.87 67

pmmvs-eth3d		78.42 222	77.04 232	82.57 189	87.44 213	74.41 122	80.86 230	79.67 287	55.68 313	84.69 198	90.31 194	60.91 258	85.42 287	62.20 270	91.59 235	87.88 258

MVS_0304		78.17 223	77.23 230	80.99 215	84.13 270	69.07 180	81.39 221	80.81 280	76.28 127	67.53 347	89.11 217	62.87 252	86.77 270	60.90 283	92.01 229	87.13 266

mvs_anonymous		78.13 224	78.76 213	76.23 284	79.24 319	50.31 346	78.69 260	84.82 249	61.60 280	83.09 227	92.82 124	73.89 181	87.01 263	68.33 228	86.41 299	91.37 194

TAMVS		78.08 225	76.36 238	83.23 170	90.62 152	72.87 130	79.08 254	80.01 286	61.72 278	81.35 255	86.92 254	63.96 243	88.78 246	50.61 336	93.01 207	88.04 254

miper_enhance_ethall		77.83 226	76.93 233	80.51 221	76.15 339	58.01 292	75.47 304	88.82 184	58.05 301	83.59 219	80.69 325	64.41 239	91.20 182	73.16 186	92.03 226	92.33 165

Vis-MVSNet (Re-imp)		77.82 227	77.79 224	77.92 260	88.82 184	51.29 340	83.28 177	71.97 334	74.04 155	82.23 237	89.78 204	57.38 284	89.41 235	57.22 300	95.41 142	93.05 138

CANet_DTU		77.81 228	77.05 231	80.09 228	81.37 295	59.90 273	83.26 178	88.29 195	69.16 218	67.83 345	83.72 296	60.93 257	89.47 230	69.22 215	89.70 262	90.88 203

OpenMVS_ROB	70.19 17	77.77 229	77.46 225	78.71 245	84.39 265	61.15 257	81.18 226	82.52 265	62.45 273	83.34 222	87.37 244	66.20 230	88.66 248	64.69 254	85.02 311	86.32 273

MDA-MVSNet-bldmvs		77.47 230	76.90 234	79.16 240	79.03 321	64.59 216	66.58 344	75.67 308	73.15 172	88.86 120	88.99 219	66.94 226	81.23 312	64.71 253	88.22 282	91.64 189

jason		77.42 231	75.75 244	82.43 192	87.10 222	69.27 173	77.99 268	81.94 271	51.47 335	77.84 289	85.07 283	60.32 262	89.00 240	70.74 201	89.27 267	89.03 243
jason: jason.
CDS-MVSNet		77.32 232	75.40 247	83.06 174	89.00 181	72.48 141	77.90 270	82.17 269	60.81 286	78.94 282	83.49 299	59.30 270	88.76 247	54.64 319	92.37 217	87.93 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 233	76.75 235	78.52 248	87.01 225	61.30 255	75.55 303	87.12 214	61.24 282	74.45 317	78.79 340	77.20 145	90.93 191	64.62 256	84.80 318	83.32 309

MVSTER		77.09 234	75.70 245	81.25 207	75.27 347	61.08 258	77.49 278	85.07 241	60.78 287	86.55 164	88.68 223	43.14 353	90.25 209	73.69 174	90.67 255	92.42 159

PS-MVSNAJ		77.04 235	76.53 237	78.56 247	87.09 223	61.40 253	75.26 305	87.13 211	61.25 281	74.38 319	77.22 351	76.94 151	90.94 190	64.63 255	84.83 317	83.35 308

IterMVS		76.91 236	76.34 239	78.64 246	80.91 300	64.03 223	76.30 293	79.03 290	64.88 261	83.11 225	89.16 215	59.90 266	84.46 295	68.61 224	85.15 310	87.42 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 237	75.67 246	80.34 224	80.48 308	62.16 249	73.50 317	84.80 250	57.61 305	82.24 236	87.54 241	51.31 309	87.65 257	70.40 206	93.19 203	91.23 196

CL-MVSNet_self_test		76.81 238	77.38 227	75.12 290	86.90 227	51.34 338	73.20 320	80.63 283	68.30 228	81.80 248	88.40 226	66.92 227	80.90 313	55.35 313	94.90 162	93.12 136

TR-MVS		76.77 239	75.79 243	79.72 232	86.10 247	65.79 208	77.14 280	83.02 262	65.20 259	81.40 254	82.10 314	66.30 229	90.73 200	55.57 310	85.27 308	82.65 315

USDC		76.63 240	76.73 236	76.34 281	83.46 276	57.20 299	80.02 238	88.04 200	52.14 331	83.65 218	91.25 165	63.24 247	86.65 273	54.66 318	94.11 184	85.17 285

BH-w/o		76.57 241	76.07 242	78.10 257	86.88 228	65.92 207	77.63 274	86.33 222	65.69 253	80.89 259	79.95 334	68.97 219	90.74 199	53.01 327	85.25 309	77.62 348

Patchmtry		76.56 242	77.46 225	73.83 296	79.37 318	46.60 359	82.41 204	76.90 299	73.81 158	85.56 184	92.38 137	48.07 321	83.98 299	63.36 263	95.31 148	90.92 202

PVSNet_Blended		76.49 243	75.40 247	79.76 231	84.43 262	63.41 228	75.14 306	90.44 149	57.36 307	75.43 310	78.30 343	69.11 217	91.44 176	60.68 284	87.70 288	84.42 293

miper_lstm_enhance		76.45 244	76.10 241	77.51 266	76.72 334	60.97 263	64.69 348	85.04 243	63.98 264	83.20 224	88.22 228	56.67 288	78.79 321	73.22 180	93.12 204	92.78 145

lupinMVS		76.37 245	74.46 256	82.09 193	85.54 251	69.26 174	76.79 285	80.77 282	50.68 341	76.23 301	82.82 308	58.69 275	88.94 241	69.85 208	88.77 272	88.07 252

cascas		76.29 246	74.81 252	80.72 219	84.47 261	62.94 234	73.89 315	87.34 205	55.94 312	75.16 315	76.53 355	63.97 242	91.16 184	65.00 250	90.97 247	88.06 253

thres600view7		75.97 247	75.35 249	77.85 263	87.01 225	51.84 336	80.45 233	73.26 326	75.20 145	83.10 226	86.31 262	45.54 335	89.05 239	55.03 316	92.24 222	92.66 151

GA-MVS		75.83 248	74.61 253	79.48 237	81.87 289	59.25 279	73.42 318	82.88 263	68.68 224	79.75 272	81.80 318	50.62 312	89.46 231	66.85 234	85.64 305	89.72 228

MVP-Stereo		75.81 249	73.51 265	82.71 184	89.35 173	73.62 125	80.06 236	85.20 238	60.30 290	73.96 320	87.94 233	57.89 282	89.45 232	52.02 330	74.87 358	85.06 287
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 250	75.20 250	77.27 269	75.01 350	69.47 171	78.93 255	84.88 248	46.67 348	87.08 152	87.84 236	50.44 314	71.62 338	77.42 133	88.53 275	90.72 207

thres100view900		75.45 251	75.05 251	76.66 278	87.27 215	51.88 335	81.07 227	73.26 326	75.68 138	83.25 223	86.37 259	45.54 335	88.80 243	51.98 331	90.99 244	89.31 235

ET-MVSNet_ETH3D		75.28 252	72.77 273	82.81 183	83.03 283	68.11 186	77.09 281	76.51 303	60.67 289	77.60 294	80.52 329	38.04 362	91.15 185	70.78 199	90.68 254	89.17 238

thres400		75.14 253	74.23 258	77.86 262	86.24 241	52.12 332	79.24 251	73.87 320	73.34 165	81.82 246	84.60 289	46.02 329	88.80 243	51.98 331	90.99 244	92.66 151

wuyk23d		75.13 254	79.30 206	62.63 341	75.56 343	75.18 118	80.89 229	73.10 328	75.06 147	94.76 12	95.32 35	87.73 40	52.85 370	34.16 370	97.11 80	59.85 367

EU-MVSNet		75.12 255	74.43 257	77.18 270	83.11 282	59.48 277	85.71 133	82.43 267	39.76 367	85.64 182	88.76 221	44.71 346	87.88 255	73.86 170	85.88 304	84.16 296

HyFIR lowres test		75.12 255	72.66 275	82.50 190	91.44 133	65.19 213	72.47 322	87.31 206	46.79 347	80.29 267	84.30 291	52.70 305	92.10 161	51.88 335	86.73 295	90.22 221

CMPMVS	59.41 20	75.12 255	73.57 263	79.77 230	75.84 342	67.22 191	81.21 225	82.18 268	50.78 339	76.50 297	87.66 239	55.20 298	82.99 304	62.17 272	90.64 258	89.09 242
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 258	72.98 271	80.73 218	84.95 255	71.71 154	76.23 295	77.59 296	52.83 325	77.73 293	86.38 258	56.35 292	84.97 291	57.72 299	87.05 292	85.51 282

tfpn200view9		74.86 259	74.23 258	76.74 277	86.24 241	52.12 332	79.24 251	73.87 320	73.34 165	81.82 246	84.60 289	46.02 329	88.80 243	51.98 331	90.99 244	89.31 235

1112_ss		74.82 260	73.74 261	78.04 258	89.57 168	60.04 271	76.49 291	87.09 215	54.31 318	73.66 322	79.80 335	60.25 263	86.76 272	58.37 293	84.15 322	87.32 264

EGC-MVSNET		74.79 261	69.99 298	89.19 63	94.89 37	87.00 11	91.89 34	86.28 223	1.09 375	2.23 377	95.98 23	81.87 104	89.48 229	79.76 101	95.96 122	91.10 198

ppachtmachnet_test		74.73 262	74.00 260	76.90 274	80.71 305	56.89 302	71.53 326	78.42 292	58.24 299	79.32 279	82.92 307	57.91 281	84.26 297	65.60 246	91.36 239	89.56 230

Patchmatch-RL test		74.48 263	73.68 262	76.89 275	84.83 257	66.54 199	72.29 323	69.16 348	57.70 303	86.76 158	86.33 260	45.79 334	82.59 305	69.63 210	90.65 257	81.54 329

PatchMatch-RL		74.48 263	73.22 268	78.27 255	87.70 207	85.26 34	75.92 298	70.09 343	64.34 263	76.09 303	81.25 323	65.87 234	78.07 322	53.86 321	83.82 323	71.48 356

XXY-MVS		74.44 265	76.19 240	69.21 321	84.61 260	52.43 331	71.70 325	77.18 298	60.73 288	80.60 262	90.96 176	75.44 161	69.35 344	56.13 306	88.33 277	85.86 279

test2506		74.12 266	73.39 266	76.28 282	91.85 115	44.20 365	84.06 156	48.20 375	72.30 187	81.90 243	94.20 80	27.22 378	89.77 226	64.81 252	96.02 119	94.87 67

CR-MVSNet		74.00 267	73.04 270	76.85 276	79.58 313	62.64 239	82.58 197	76.90 299	50.50 342	75.72 307	92.38 137	48.07 321	84.07 298	68.72 223	82.91 329	83.85 300

Test_1112_low_res		73.90 268	73.08 269	76.35 280	90.35 156	55.95 305	73.40 319	86.17 226	50.70 340	73.14 323	85.94 267	58.31 277	85.90 283	56.51 303	83.22 326	87.20 265

test20.03		73.75 269	74.59 255	71.22 311	81.11 298	51.12 342	70.15 331	72.10 333	70.42 205	80.28 269	91.50 160	64.21 241	74.72 333	46.96 352	94.58 174	87.82 260

test_fmvs2		73.57 270	72.80 272	75.90 286	72.74 361	68.84 181	77.07 282	84.32 254	45.14 353	82.89 228	84.22 292	48.37 319	70.36 341	73.40 178	87.03 293	88.52 249

SCA		73.32 271	72.57 277	75.58 288	81.62 291	55.86 307	78.89 257	71.37 339	61.73 277	74.93 316	83.42 301	60.46 260	87.01 263	58.11 297	82.63 334	83.88 297

baseline1		73.26 272	73.54 264	72.43 307	84.92 256	47.79 354	79.89 240	74.00 318	65.93 247	78.81 283	86.28 263	56.36 291	81.63 311	56.63 302	79.04 348	87.87 259

1314		73.22 273	72.56 278	75.20 289	80.41 309	57.84 293	81.64 218	85.36 235	51.68 334	73.10 324	76.65 354	61.45 256	85.19 289	63.54 261	79.21 347	82.59 316

MVS		73.21 274	72.59 276	75.06 291	80.97 299	60.81 265	81.64 218	85.92 230	46.03 351	71.68 331	77.54 346	68.47 220	89.77 226	55.70 309	85.39 306	74.60 353

HY-MVS	64.64 18	73.03 275	72.47 279	74.71 292	83.36 277	54.19 317	82.14 214	81.96 270	56.76 311	69.57 339	86.21 264	60.03 264	84.83 293	49.58 341	82.65 332	85.11 286

thisisatest0515		73.00 276	70.52 291	80.46 222	81.45 293	59.90 273	73.16 321	74.31 317	57.86 302	76.08 304	77.78 345	37.60 364	92.12 160	65.00 250	91.45 238	89.35 234

EPNet_dtu		72.87 277	71.33 288	77.49 267	77.72 327	60.55 268	82.35 205	75.79 306	66.49 246	58.39 370	81.06 324	53.68 301	85.98 281	53.55 322	92.97 209	85.95 277
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 278	71.41 287	76.28 282	83.25 278	60.34 269	83.50 173	79.02 291	37.77 370	76.33 299	85.10 280	49.60 317	87.41 260	70.54 204	77.54 354	81.08 336

CHOSEN 1792x2688		72.45 279	70.56 290	78.13 256	90.02 166	63.08 233	68.72 335	83.16 260	42.99 361	75.92 305	85.46 273	57.22 286	85.18 290	49.87 340	81.67 336	86.14 275

testgi		72.36 280	74.61 253	65.59 334	80.56 307	42.82 369	68.29 336	73.35 325	66.87 243	81.84 245	89.93 201	72.08 204	66.92 355	46.05 355	92.54 215	87.01 268

thres200		72.34 281	71.55 286	74.70 293	83.48 275	51.60 337	75.02 307	73.71 323	70.14 211	78.56 285	80.57 328	46.20 327	88.20 253	46.99 351	89.29 265	84.32 294

FPMVS		72.29 282	72.00 281	73.14 300	88.63 188	85.00 36	74.65 310	67.39 349	71.94 192	77.80 291	87.66 239	50.48 313	75.83 329	49.95 338	79.51 343	58.58 369

FMVSNet5		72.10 283	71.69 283	73.32 298	81.57 292	53.02 326	76.77 286	78.37 293	63.31 265	76.37 298	91.85 150	36.68 365	78.98 319	47.87 348	92.45 216	87.95 256

our_test_3		71.85 284	71.59 284	72.62 304	80.71 305	53.78 320	69.72 333	71.71 338	58.80 296	78.03 286	80.51 330	56.61 290	78.84 320	62.20 270	86.04 303	85.23 284

PAPM		71.77 285	70.06 297	76.92 273	86.39 232	53.97 318	76.62 290	86.62 220	53.44 322	63.97 360	84.73 287	57.79 283	92.34 153	39.65 365	81.33 339	84.45 292

IB-MVS	62.13 19	71.64 286	68.97 304	79.66 234	80.80 304	62.26 247	73.94 314	76.90 299	63.27 266	68.63 341	76.79 353	33.83 369	91.84 168	59.28 291	87.26 290	84.88 288
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UnsupCasMVSNet_eth		71.63 287	72.30 280	69.62 319	76.47 336	52.70 329	70.03 332	80.97 279	59.18 294	79.36 277	88.21 229	60.50 259	69.12 345	58.33 295	77.62 353	87.04 267

test_vis3_rt		71.42 288	70.67 289	73.64 297	69.66 367	70.46 162	66.97 343	89.73 169	42.68 363	88.20 135	83.04 303	43.77 348	60.07 365	65.35 249	86.66 296	90.39 219

Anonymous20231206		71.38 289	71.88 282	69.88 317	86.31 237	54.37 316	70.39 330	74.62 313	52.57 327	76.73 296	88.76 221	59.94 265	72.06 336	44.35 358	93.23 202	83.23 311

MIMVSNet		71.09 290	71.59 284	69.57 320	87.23 216	50.07 347	78.91 256	71.83 335	60.20 292	71.26 332	91.76 155	55.08 299	76.09 327	41.06 363	87.02 294	82.54 318

test_fmvs1_n		70.94 291	70.41 294	72.53 306	73.92 352	66.93 196	75.99 297	84.21 255	43.31 360	79.40 276	79.39 338	43.47 349	68.55 348	69.05 218	84.91 314	82.10 323

MS-PatchMatch		70.93 292	70.22 295	73.06 301	81.85 290	62.50 242	73.82 316	77.90 294	52.44 328	75.92 305	81.27 322	55.67 295	81.75 309	55.37 312	77.70 352	74.94 352

pmmvs5		70.73 293	70.07 296	72.72 303	77.03 332	52.73 328	74.14 311	75.65 309	50.36 343	72.17 329	85.37 277	55.42 297	80.67 315	52.86 328	87.59 289	84.77 289

PatchT		70.52 294	72.76 274	63.79 340	79.38 317	33.53 375	77.63 274	65.37 357	73.61 160	71.77 330	92.79 127	44.38 347	75.65 330	64.53 257	85.37 307	82.18 322

test_vis1_n		70.29 295	69.99 298	71.20 312	75.97 341	66.50 200	76.69 288	80.81 280	44.22 356	75.43 310	77.23 350	50.00 315	68.59 347	66.71 237	82.85 331	78.52 347

N_pmnet		70.20 296	68.80 306	74.38 294	80.91 300	84.81 39	59.12 359	76.45 304	55.06 315	75.31 314	82.36 313	55.74 294	54.82 369	47.02 350	87.24 291	83.52 304

tpmvs		70.16 297	69.56 301	71.96 309	74.71 351	48.13 351	79.63 242	75.45 311	65.02 260	70.26 336	81.88 317	45.34 340	85.68 285	58.34 294	75.39 357	82.08 324

new-patchmatchnet		70.10 298	73.37 267	60.29 348	81.23 297	16.95 379	59.54 357	74.62 313	62.93 268	80.97 257	87.93 234	62.83 253	71.90 337	55.24 314	95.01 159	92.00 178

YYNet1		70.06 299	70.44 292	68.90 322	73.76 354	53.42 324	58.99 360	67.20 351	58.42 298	87.10 150	85.39 276	59.82 267	67.32 352	59.79 288	83.50 325	85.96 276

MDA-MVSNet_test_wron		70.05 300	70.44 292	68.88 323	73.84 353	53.47 322	58.93 361	67.28 350	58.43 297	87.09 151	85.40 275	59.80 268	67.25 353	59.66 289	83.54 324	85.92 278

CostFormer		69.98 301	68.68 307	73.87 295	77.14 330	50.72 344	79.26 250	74.51 315	51.94 333	70.97 335	84.75 286	45.16 343	87.49 259	55.16 315	79.23 346	83.40 307

baseline2		69.77 302	66.89 313	78.41 251	79.51 315	58.09 291	76.23 295	69.57 346	57.50 306	64.82 358	77.45 348	46.02 329	88.44 249	53.08 324	77.83 350	88.70 247

PatchmatchNet		69.71 303	68.83 305	72.33 308	77.66 328	53.60 321	79.29 249	69.99 344	57.66 304	72.53 327	82.93 306	46.45 326	80.08 318	60.91 282	72.09 361	83.31 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 304	69.05 303	71.14 313	69.15 368	65.77 209	73.98 313	83.32 259	42.83 362	77.77 292	78.27 344	43.39 352	68.50 349	68.39 227	84.38 321	79.15 345

JIA-IIPM		69.41 305	66.64 317	77.70 264	73.19 356	71.24 157	75.67 299	65.56 356	70.42 205	65.18 354	92.97 119	33.64 370	83.06 303	53.52 323	69.61 367	78.79 346

UnsupCasMVSNet_bld		69.21 306	69.68 300	67.82 328	79.42 316	51.15 341	67.82 340	75.79 306	54.15 319	77.47 295	85.36 278	59.26 271	70.64 340	48.46 345	79.35 345	81.66 327

gg-mvs-nofinetune		68.96 307	69.11 302	68.52 327	76.12 340	45.32 361	83.59 171	55.88 370	86.68 24	64.62 359	97.01 7	30.36 373	83.97 300	44.78 357	82.94 328	76.26 350

tpm2		68.45 308	66.83 314	73.30 299	78.93 323	48.50 350	79.76 241	71.76 336	47.50 346	69.92 338	83.60 297	42.07 355	88.40 250	48.44 346	79.51 343	83.01 314

tpm		67.95 309	68.08 310	67.55 329	78.74 324	43.53 367	75.60 300	67.10 354	54.92 316	72.23 328	88.10 230	42.87 354	75.97 328	52.21 329	80.95 342	83.15 312

WTY-MVS		67.91 310	68.35 308	66.58 332	80.82 303	48.12 352	65.96 345	72.60 329	53.67 321	71.20 333	81.68 320	58.97 273	69.06 346	48.57 344	81.67 336	82.55 317

test-LLR		67.21 311	66.74 315	68.63 325	76.45 337	55.21 312	67.89 337	67.14 352	62.43 274	65.08 355	72.39 360	43.41 350	69.37 342	61.00 280	84.89 315	81.31 331

sss		66.92 312	67.26 312	65.90 333	77.23 329	51.10 343	64.79 347	71.72 337	52.12 332	70.13 337	80.18 332	57.96 280	65.36 360	50.21 337	81.01 341	81.25 333

KD-MVS_2432*1600		66.87 313	65.81 319	70.04 315	67.50 369	47.49 355	62.56 352	79.16 288	61.21 283	77.98 287	80.61 326	25.29 380	82.48 306	53.02 325	84.92 312	80.16 342

miper_refine_blended		66.87 313	65.81 319	70.04 315	67.50 369	47.49 355	62.56 352	79.16 288	61.21 283	77.98 287	80.61 326	25.29 380	82.48 306	53.02 325	84.92 312	80.16 342

tpm cat1		66.76 315	65.21 322	71.42 310	77.09 331	50.62 345	78.01 267	73.68 324	44.89 354	68.64 340	79.00 339	45.51 337	82.42 308	49.91 339	70.15 364	81.23 335

PVSNet	58.17 21	66.41 316	65.63 321	68.75 324	81.96 288	49.88 348	62.19 354	72.51 331	51.03 337	68.04 343	75.34 358	50.84 311	74.77 331	45.82 356	82.96 327	81.60 328

tpmrst		66.28 317	66.69 316	65.05 337	72.82 360	39.33 370	78.20 266	70.69 342	53.16 324	67.88 344	80.36 331	48.18 320	74.75 332	58.13 296	70.79 363	81.08 336

Patchmatch-test		65.91 318	67.38 311	61.48 346	75.51 344	43.21 368	68.84 334	63.79 359	62.48 272	72.80 326	83.42 301	44.89 345	59.52 367	48.27 347	86.45 298	81.70 326

ADS-MVSNet2		65.87 319	63.64 326	72.55 305	73.16 357	56.92 301	67.10 341	74.81 312	49.74 344	66.04 350	82.97 304	46.71 324	77.26 324	42.29 360	69.96 365	83.46 305

test_vis1_rt		65.64 320	64.09 324	70.31 314	66.09 373	70.20 165	61.16 355	81.60 275	38.65 368	72.87 325	69.66 363	52.84 303	60.04 366	56.16 305	77.77 351	80.68 340

mvsany_test3		65.48 321	62.97 327	73.03 302	69.99 366	76.17 113	64.83 346	43.71 377	43.68 358	80.25 270	87.05 253	52.83 304	63.09 364	51.92 334	72.44 360	79.84 344

test-mter		65.00 322	63.79 325	68.63 325	76.45 337	55.21 312	67.89 337	67.14 352	50.98 338	65.08 355	72.39 360	28.27 376	69.37 342	61.00 280	84.89 315	81.31 331

test0.0.03 1		64.66 323	64.36 323	65.57 335	75.03 349	46.89 358	64.69 348	61.58 364	62.43 274	71.18 334	77.54 346	43.41 350	68.47 350	40.75 364	82.65 332	81.35 330

test_f		64.31 324	65.85 318	59.67 349	66.54 372	62.24 248	57.76 362	70.96 340	40.13 365	84.36 203	82.09 315	46.93 323	51.67 371	61.99 273	81.89 335	65.12 363

pmmvs3		62.47 325	60.02 337	69.80 318	71.58 364	64.00 224	70.52 329	58.44 368	39.77 366	66.05 349	75.84 356	27.10 379	72.28 335	46.15 354	84.77 319	73.11 354

EPMVS		62.47 325	62.63 329	62.01 342	70.63 365	38.74 371	74.76 308	52.86 372	53.91 320	67.71 346	80.01 333	39.40 359	66.60 356	55.54 311	68.81 368	80.68 340

ADS-MVSNet		61.90 327	62.19 330	61.03 347	73.16 357	36.42 373	67.10 341	61.75 362	49.74 344	66.04 350	82.97 304	46.71 324	63.21 362	42.29 360	69.96 365	83.46 305

PMMVS		61.65 328	60.38 334	65.47 336	65.40 376	69.26 174	63.97 350	61.73 363	36.80 371	60.11 365	68.43 365	59.42 269	66.35 357	48.97 343	78.57 349	60.81 366

E-PMN		61.59 329	61.62 331	61.49 345	66.81 371	55.40 310	53.77 365	60.34 365	66.80 244	58.90 368	65.50 367	40.48 358	66.12 358	55.72 308	86.25 301	62.95 365

TESTMET0.1,1		61.29 330	60.32 335	64.19 339	72.06 362	51.30 339	67.89 337	62.09 360	45.27 352	60.65 364	69.01 364	27.93 377	64.74 361	56.31 304	81.65 338	76.53 349

MVS-HIRNet		61.16 331	62.92 328	55.87 352	79.09 320	35.34 374	71.83 324	57.98 369	46.56 349	59.05 367	91.14 169	49.95 316	76.43 326	38.74 366	71.92 362	55.84 370

EMVS		61.10 332	60.81 333	61.99 343	65.96 374	55.86 307	53.10 366	58.97 367	67.06 241	56.89 371	63.33 368	40.98 356	67.03 354	54.79 317	86.18 302	63.08 364

DSMNet-mixed		60.98 333	61.61 332	59.09 351	72.88 359	45.05 363	74.70 309	46.61 376	26.20 372	65.34 353	90.32 193	55.46 296	63.12 363	41.72 362	81.30 340	69.09 360

dp		60.70 334	60.29 336	61.92 344	72.04 363	38.67 372	70.83 327	64.08 358	51.28 336	60.75 363	77.28 349	36.59 366	71.58 339	47.41 349	62.34 370	75.52 351

CHOSEN 280x420		59.08 335	56.52 340	66.76 331	76.51 335	64.39 220	49.62 367	59.00 366	43.86 357	55.66 372	68.41 366	35.55 368	68.21 351	43.25 359	76.78 356	67.69 361

mvsany_test1		58.48 336	56.47 341	64.50 338	65.90 375	68.21 185	56.95 363	42.11 378	38.30 369	65.69 352	77.19 352	56.96 287	59.35 368	46.16 353	58.96 371	65.93 362

PVSNet_0	51.08 22	56.10 337	54.97 342	59.48 350	75.12 348	53.28 325	55.16 364	61.89 361	44.30 355	59.16 366	62.48 369	54.22 300	65.91 359	35.40 369	47.01 372	59.25 368

new_pmnet		55.69 338	57.66 339	49.76 354	75.47 345	30.59 376	59.56 356	51.45 373	43.62 359	62.49 361	75.48 357	40.96 357	49.15 373	37.39 368	72.52 359	69.55 359

PMMVS2		55.64 339	59.27 338	44.74 355	64.30 377	12.32 380	40.60 368	49.79 374	53.19 323	65.06 357	84.81 285	53.60 302	49.76 372	32.68 372	89.41 264	72.15 355

MVE	40.22 23	51.82 340	50.47 343	55.87 352	62.66 378	51.91 334	31.61 370	39.28 379	40.65 364	50.76 373	74.98 359	56.24 293	44.67 374	33.94 371	64.11 369	71.04 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 341	29.60 344	33.06 356	17.99 380	3.84 382	13.62 371	73.92 319	2.79 374	18.29 376	53.41 371	28.53 375	43.25 375	22.56 373	35.27 374	52.11 371

cdsmvs_eth3d_5k		20.81 342	27.75 345	0.00 361	0.00 384	0.00 385	0.00 372	85.44 234	0.00 379	0.00 380	82.82 308	81.46 108	0.00 380	0.00 378	0.00 378	0.00 376

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

ab-mvs-re		6.65 344	8.87 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	79.80 335	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.41 345	8.55 348	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	76.94 151	0.00 380	0.00 378	0.00 378	0.00 376

test123		6.27 346	8.08 349	0.84 359	1.11 383	0.57 383	62.90 351	0.82 383	0.54 377	1.07 379	2.75 378	1.26 382	0.30 378	1.04 376	1.26 377	1.66 374

testmvs		5.91 347	7.65 350	0.72 360	1.20 382	0.37 384	59.14 358	0.67 384	0.49 378	1.11 378	2.76 377	0.94 383	0.24 379	1.02 377	1.47 376	1.55 375

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

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

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

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

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

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

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

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

FOURS1					96.08 11	87.41 10	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 68	91.55 127	77.99 87		91.01 135					96.05 7	87.45 17	98.17 32	92.40 161

PC_three_1452									58.96 295	90.06 95	91.33 164	80.66 117	93.03 135	75.78 149	95.94 124	92.48 157

No_MVS				88.81 68	91.55 127	77.99 87		91.01 135					96.05 7	87.45 17	98.17 32	92.40 161

test_one_0601					93.85 58	73.27 128		94.11 33	86.57 25	93.47 38	94.64 60	88.42 26

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					92.22 102	80.48 67		91.85 112	71.22 198	90.38 90	92.98 117	86.06 59	96.11 5	81.99 80	96.75 91

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 27	88.75 14	93.79 28	94.43 67	90.64 10		87.16 26	97.60 64	92.73 146

IU-MVS					94.18 46	72.64 134		90.82 140	56.98 309	89.67 107				85.78 45	97.92 46	93.28 128

OPU-MVS				88.27 79	91.89 113	77.83 90	90.47 51				91.22 166	81.12 112	94.68 69	74.48 160	95.35 144	92.29 167

test_241102_TWO								93.71 49	83.77 45	93.49 36	94.27 74	89.27 21	95.84 22	86.03 41	97.82 51	92.04 176

test_241102_ONE					94.18 46	72.65 132		93.69 50	83.62 47	94.11 22	93.78 103	90.28 14	95.50 44

9.14			89.29 58		91.84 117		88.80 88	95.32 11	75.14 146	91.07 79	92.89 122	87.27 44	93.78 103	83.69 64	97.55 67

save fliter					93.75 59	77.44 95	86.31 126	89.72 170	70.80 201

test_0728_THIRD									85.33 32	93.75 30	94.65 57	87.44 43	95.78 27	87.41 19	98.21 29	92.98 140

test_0728_SECOND				86.79 95	94.25 45	72.45 142	90.54 48	94.10 34					95.88 16	86.42 31	97.97 43	92.02 177

test0726					94.16 49	72.56 138	90.63 45	93.90 42	83.61 48	93.75 30	94.49 64	89.76 18

GSMVS																83.88 297

test_part2					93.86 57	77.77 91				92.84 48

sam_mvs1												46.11 328				83.88 297

sam_mvs												45.92 333

ambc				82.98 176	90.55 154	64.86 215	88.20 95	89.15 181		89.40 116	93.96 94	71.67 209	91.38 180	78.83 111	96.55 96	92.71 149

MTGPA								91.81 115

test_post1							78.85 259				3.13 375	45.19 342	80.13 317	58.11 297

test_post											3.10 376	45.43 338	77.22 325

patchmatchnet-post											81.71 319	45.93 332	87.01 263

GG-mvs-BLEND				67.16 330	73.36 355	46.54 360	84.15 154	55.04 371		58.64 369	61.95 370	29.93 374	83.87 301	38.71 367	76.92 355	71.07 357

MTMP							90.66 44	33.14 380

gm-plane-assit					75.42 346	44.97 364			52.17 329		72.36 362		87.90 254	54.10 320

test9_res														80.83 89	96.45 102	90.57 213

TEST9					92.34 96	79.70 74	83.94 159	90.32 153	65.41 258	84.49 200	90.97 174	82.03 99	93.63 108

test_8					92.09 106	78.87 81	83.82 164	90.31 155	65.79 249	84.36 203	90.96 176	81.93 101	93.44 121

agg_prior2														79.68 103	96.16 112	90.22 221

agg_prior					91.58 125	77.69 92		90.30 156		84.32 205			93.18 129

TestCases				89.68 53	91.59 122	83.40 48		95.44 9	79.47 90	88.00 137	93.03 115	82.66 87	91.47 174	70.81 197	96.14 113	94.16 93

test_prior4						78.97 80	84.59 145

test_prior2							83.37 176		75.43 142	84.58 199	91.57 158	81.92 103		79.54 105	96.97 84

test_prior				86.32 103	90.59 153	71.99 149		92.85 86					94.17 90			92.80 144

旧先验2							81.73 216		56.88 310	86.54 169			84.90 292	72.81 187

新几何2							81.72 217

新几何1				82.95 178	93.96 55	78.56 84		80.24 284	55.45 314	83.93 216	91.08 171	71.19 210	88.33 251	65.84 244	93.07 205	81.95 325

旧先验1					91.97 109	71.77 150		81.78 273			91.84 151	73.92 180			93.65 194	83.61 303

无先验							82.81 192	85.62 233	58.09 300				91.41 179	67.95 231		84.48 291

原ACMM2							82.26 210

原ACMM1				84.60 138	92.81 87	74.01 124		91.50 120	62.59 270	82.73 231	90.67 186	76.53 158	94.25 84	69.24 213	95.69 138	85.55 281

test222					93.31 71	76.54 105	79.38 248	77.79 295	52.59 326	82.36 235	90.84 180	66.83 228			91.69 233	81.25 333

testdata2													86.43 276	63.52 262

segment_acmp												81.94 100

testdata				79.54 236	92.87 82	72.34 143		80.14 285	59.91 293	85.47 186	91.75 156	67.96 223	85.24 288	68.57 226	92.18 225	81.06 338

testdata1							79.62 243		73.95 157

test12				86.57 98	90.74 149	72.63 136		90.69 143		82.76 230		79.20 127	94.80 66		95.32 146	92.27 169

plane_prior7					93.45 66	77.31 98

plane_prior6					92.61 88	76.54 105						74.84 169

plane_prior5								93.61 53					95.22 54	80.78 90	95.83 130	94.46 80

plane_prior4											92.95 120

plane_prior3						76.85 103			77.79 114	86.55 164

plane_prior2							89.45 77		79.44 92

plane_prior1					92.83 86

plane_prior						76.42 109	87.15 110		75.94 135						95.03 158

n2								0.00 385

nn								0.00 385

door-mid								74.45 316

lessismore_v0				85.95 113	91.10 142	70.99 159		70.91 341		91.79 67	94.42 69	61.76 255	92.93 138	79.52 106	93.03 206	93.93 103

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 19	82.35 60	93.67 33	94.82 52	91.18 4	95.52 40	85.36 47	98.73 6	95.23 59

test11								91.46 121

door								72.57 330

HQP5-MVS						70.66 160

HQP-NCC					91.19 137		84.77 140		73.30 167	80.55 264

ACMP_Plane					91.19 137		84.77 140		73.30 167	80.55 264

BP-MVS														77.30 134

HQP4-MVS										80.56 263			94.61 72			93.56 122

HQP3-MVS								92.68 91							94.47 176

HQP2-MVS												72.10 202

NP-MVS					91.95 110	74.55 121					90.17 199

MDTV_nov1_ep13_2view						27.60 378	70.76 328		46.47 350	61.27 362		45.20 341		49.18 342		83.75 302

MDTV_nov1_ep13			68.29 309		78.03 325	43.87 366	74.12 312	72.22 332	52.17 329	67.02 348	85.54 271	45.36 339	80.85 314	55.73 307	84.42 320

ACMMP++_ref															95.74 137

ACMMP++															97.35 73

Test By Simon												79.09 128

ITE_SJBPF				90.11 45	90.72 150	84.97 37		90.30 156	81.56 68	90.02 97	91.20 168	82.40 91	90.81 197	73.58 175	94.66 172	94.56 76

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

High-resolution multi-view benchmark