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.

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 67	74.51 51	96.15 2	92.88 7

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

AllTest		77.66 77	77.43 82	78.35 71	79.19 157	70.81 61	78.60 94	88.64 3	65.37 83	80.09 117	88.17 115	70.33 81	78.43 180	55.60 196	90.90 116	85.81 75

TestCases				78.35 71	79.19 157	70.81 61		88.64 3	65.37 83	80.09 117	88.17 115	70.33 81	78.43 180	55.60 196	90.90 116	85.81 75

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 70	87.00 43	67.23 91	79.24 86	88.61 5	56.65 166	84.29 67	89.18 91	73.73 59	83.22 94	76.01 39	93.77 62	84.81 98

SF-MVS		80.72 48	81.80 46	77.48 80	82.03 124	64.40 116	83.41 50	88.46 6	65.28 85	84.29 67	89.18 91	73.73 59	83.22 94	76.01 39	93.77 62	84.81 98

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 110	74.39 37	87.18 11	88.18 7	78.98 7	86.11 39	91.47 30	79.70 12	85.76 42	66.91 108	95.46 11	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	66.64 10	81.20 40	82.48 43	77.35 84	81.16 136	62.39 128	80.51 68	87.80 8	73.02 28	87.57 21	91.08 37	80.28 10	82.44 107	64.82 120	96.10 4	87.21 56

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 78	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 121	81.53 3	92.15 89	88.91 37
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
LCM-MVSNet		86.90 1	88.67 1	81.57 25	91.50 1	63.30 124	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 70	68.08 93	97.05 1	96.93 1

9.14			80.22 59		80.68 138		80.35 73	87.69 11	59.90 132	83.00 80	88.20 114	74.57 51	81.75 119	73.75 56	93.78 61

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 87	80.67 139	64.57 113	80.26 75	87.60 12	60.74 127	82.47 88	88.03 119	71.73 70	81.81 117	73.12 59	93.61 65	85.09 87

DROMVSNet		77.08 82	77.39 83	76.14 99	76.86 193	56.87 173	80.32 74	87.52 13	63.45 106	74.66 195	84.52 182	69.87 86	84.94 62	69.76 84	89.59 145	86.60 65

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 113	73.53 45	85.50 28	87.45 14	74.11 21	86.45 34	90.52 54	80.02 11	84.48 71	77.73 30	94.34 49	85.93 73

test_part1		76.97 83	78.21 77	73.25 137	77.87 177	45.76 252	78.27 100	87.26 15	66.69 71	85.31 52	91.43 32	55.95 214	84.24 78	65.71 113	95.43 12	89.75 22

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 39	74.40 36	85.24 29	87.21 16	70.69 48	85.14 54	90.42 58	78.99 16	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 50	70.03 70	82.06 59	87.00 17	59.89 133	80.91 108	90.53 52	72.19 65	88.56 1	73.67 57	94.52 36	85.92 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 36	91.70 24	78.23 20	85.20 58	79.45 13	94.91 25	88.15 45

CS-MVS-test		73.63 118	73.74 120	73.30 136	75.80 206	51.70 200	77.02 117	86.83 19	61.29 121	68.47 267	79.23 247	65.42 129	85.14 61	64.04 125	85.55 200	83.07 154

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 43	70.41 66	82.04 60	86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 69

LGP-MVS_train				80.90 38	87.00 43	70.41 66		86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 69

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 32	68.44 83	81.57 63	86.33 22	63.17 110	85.38 51	91.26 34	76.33 32	84.67 69	83.30 1	94.96 23	86.17 68
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	79.20 14	85.58 48	78.11 25	94.46 38	84.89 92

RE-MVS-def			85.50 4		86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	81.38 7		78.11 25	94.46 38	84.89 92

RPMNet		65.77 218	65.08 230	67.84 220	66.37 302	48.24 225	70.93 187	86.27 23	54.66 187	61.35 308	86.77 137	33.29 323	85.67 46	55.93 193	70.17 331	69.62 309

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 133	72.03 50	84.38 37	86.23 26	77.28 16	80.65 110	90.18 74	59.80 177	87.58 4	73.06 60	91.34 102	89.01 33

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 82	80.09 144	66.16 101	79.08 89	86.13 27	57.55 156	80.93 106	87.76 122	71.98 69	82.73 104	72.11 71	92.83 77	83.25 147

abl_6		84.92 3	85.70 3	82.57 17	86.72 49	79.27 8	87.56 7	86.08 28	77.48 13	88.12 14	91.53 28	81.18 8	84.31 76	78.12 24	94.47 37	84.15 123

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 51	69.44 74	82.30 57	86.08 28	66.80 69	86.70 32	89.99 76	81.64 6	85.95 32	74.35 52	96.11 3	85.81 75
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 26	73.28 47	86.32 24	85.97 30	67.39 64	84.02 71	90.39 62	74.73 48	86.46 16	80.73 7	94.43 42	84.60 107

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 31	71.03 45	85.85 41	90.58 50	78.77 17	85.78 41	79.37 16	95.17 17	84.62 104
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
LS3D		80.99 46	80.85 55	81.41 30	78.37 169	71.37 56	87.45 8	85.87 32	77.48 13	81.98 91	89.95 78	69.14 90	85.26 54	66.15 109	91.24 104	87.61 51

test_one_0601					85.84 67	61.45 135		85.63 33	75.27 19	85.62 47	90.38 64	76.72 29

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 61	87.01 42	72.91 48	80.23 77	85.56 34	66.56 73	85.64 44	89.57 83	69.12 91	80.55 143	72.51 65	93.37 69	83.48 139

DVP-MVS++		81.24 39	82.74 40	76.76 88	83.14 103	60.90 142	91.64 1	85.49 35	74.03 23	84.93 56	90.38 64	66.82 113	85.90 37	77.43 33	90.78 120	83.49 137

test_0728_SECOND				76.57 91	86.20 54	60.57 147	83.77 43	85.49 35					85.90 37	75.86 42	94.39 43	83.25 147

HQP_MVS		78.77 67	78.78 71	78.72 64	85.18 72	65.18 108	82.74 54	85.49 35	65.45 80	78.23 136	89.11 95	60.83 168	86.15 28	71.09 74	90.94 112	84.82 96

plane_prior5								85.49 35					86.15 28	71.09 74	90.94 112	84.82 96

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 60	85.40 39	67.96 62	84.91 60	90.88 40	75.59 38	86.57 15	78.16 23	94.71 32	83.82 127

XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 68	86.46 52	74.79 34	77.15 114	85.39 40	66.73 70	80.39 114	88.85 102	74.43 54	78.33 185	74.73 49	85.79 198	82.35 174

SD-MVS		80.28 55	81.55 52	76.47 94	83.57 97	67.83 87	83.39 51	85.35 41	64.42 95	86.14 38	87.07 127	74.02 55	80.97 134	77.70 31	92.32 87	80.62 206
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
SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 42	77.42 15	86.15 37	90.24 71	81.69 5	85.94 33	77.77 29	93.58 67	83.09 151

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 43	81.06 3	87.20 28	90.28 70	79.20 14	85.58 48	78.04 27	94.08 56	83.55 135

GST-MVS		82.79 27	83.27 32	81.34 32	88.99 28	73.29 46	85.94 26	85.13 44	68.58 60	84.14 70	90.21 73	73.37 61	86.41 17	79.09 19	93.98 60	84.30 121

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 80	66.72 96	86.54 21	85.11 45	72.00 41	86.65 33	91.75 23	78.20 21	87.04 9	77.93 28	94.32 50	83.47 140

test0726					86.16 57	60.78 144	83.81 42	85.10 46	72.48 34	85.27 53	89.96 77	78.57 18

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 46	64.94 90	81.05 104	88.38 110	57.10 205	87.10 8	79.75 8	83.87 228	84.31 119
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
DPE-MVS		82.00 34	83.02 36	78.95 62	85.36 71	67.25 90	82.91 53	84.98 48	73.52 26	85.43 50	90.03 75	76.37 31	86.97 11	74.56 50	94.02 59	82.62 167
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 81	83.62 45	84.98 48	64.77 91	83.97 72	91.02 38	75.53 41	85.93 36	82.00 2	94.36 47	83.35 145

XVG-OURS		79.51 59	79.82 62	78.58 67	86.11 62	74.96 33	76.33 124	84.95 50	66.89 66	82.75 85	88.99 99	66.82 113	78.37 183	74.80 47	90.76 123	82.40 173

test_241102_TWO								84.80 51	72.61 32	84.93 56	89.70 81	77.73 23	85.89 39	75.29 45	94.22 55	83.25 147

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 74	71.00 60	85.53 27	84.78 52	70.91 46	85.64 44	90.41 59	75.55 40	87.69 3	79.75 8	95.08 20	85.36 82
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.12 32	82.68 42	80.43 42	88.90 31	69.52 72	85.12 30	84.76 53	63.53 104	84.23 69	91.47 30	72.02 67	87.16 7	79.74 10	94.36 47	84.61 105
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
OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 140	70.71 64	80.91 66	84.76 53	62.54 114	81.77 94	86.65 144	71.46 73	83.53 88	67.95 98	92.44 84	89.60 23

SED-MVS		81.78 35	83.48 27	76.67 89	86.12 59	61.06 138	83.62 45	84.72 55	72.61 32	87.38 25	89.70 81	77.48 24	85.89 39	75.29 45	94.39 43	83.08 152

test_241102_ONE					86.12 59	61.06 138		84.72 55	72.64 31	87.38 25	89.47 84	77.48 24	85.74 43

ETV-MVS		72.72 141	72.16 148	74.38 119	76.90 191	55.95 176	73.34 155	84.67 57	62.04 117	72.19 227	70.81 324	65.90 124	85.24 56	58.64 172	84.96 213	81.95 181

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	90.39 62	73.86 56	86.31 20	78.84 20	94.03 57	84.64 102

X-MVStestdata		76.81 84	74.79 103	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	9.95 371	73.86 56	86.31 20	78.84 20	94.03 57	84.64 102

testtj		81.19 41	81.70 49	79.67 51	83.95 93	69.77 71	83.58 48	84.63 60	72.13 38	82.85 84	88.36 111	75.00 46	86.79 12	71.99 72	92.84 76	82.44 172

DP-MVS		78.44 73	79.29 67	75.90 101	81.86 127	65.33 106	79.05 90	84.63 60	74.83 20	80.41 113	86.27 155	71.68 71	83.45 90	62.45 144	92.40 85	78.92 227

ETH3 D test6400		75.73 92	76.00 94	74.92 112	81.75 128	56.93 171	78.31 98	84.60 62	52.83 215	77.15 151	85.14 177	68.59 95	84.03 79	65.44 116	90.20 129	83.82 127

CS-MVS		69.29 179	69.70 174	68.07 217	70.59 268	42.36 280	69.70 201	84.56 63	53.13 211	67.96 270	76.74 274	59.41 179	83.56 86	60.33 158	84.84 215	78.28 235

ACMM	69.25 9	82.11 33	83.31 30	78.49 68	88.17 40	73.96 39	83.11 52	84.52 64	66.40 74	87.45 23	89.16 94	81.02 9	80.52 144	74.27 53	95.73 7	80.98 195
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 65	71.96 42	84.70 62	90.56 51	77.12 26	86.18 27	79.24 18	95.36 13	82.49 171

baseline		73.10 128	73.96 115	70.51 177	71.46 261	46.39 248	72.08 165	84.40 66	55.95 172	76.62 166	86.46 151	67.20 108	78.03 192	64.22 124	87.27 182	87.11 59

test_prior3		76.71 86	77.19 85	75.27 109	82.15 122	59.85 153	75.57 131	84.33 67	58.92 141	76.53 170	86.78 135	67.83 105	83.39 91	69.81 82	92.76 80	82.58 168

test_prior				75.27 109	82.15 122	59.85 153		84.33 67					83.39 91			82.58 168

HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 28	87.01 15	84.27 69	70.23 49	84.47 65	90.43 56	76.79 27	85.94 33	79.58 11	94.23 53	82.82 161

#test#		82.40 30	82.71 41	81.48 27	89.25 21	75.69 28	84.47 36	84.27 69	64.45 94	84.47 65	90.43 56	76.79 27	85.94 33	76.01 39	94.23 53	82.82 161

casdiffmvs		73.06 131	73.84 116	70.72 173	71.32 262	46.71 244	70.93 187	84.26 71	55.62 175	77.46 149	87.10 124	67.09 109	77.81 195	63.95 128	86.83 188	87.64 50

MSLP-MVS++		74.48 109	75.78 96	70.59 175	84.66 81	62.40 127	78.65 93	84.24 72	60.55 129	77.71 146	81.98 212	63.12 143	77.64 199	62.95 141	88.14 163	71.73 291

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 73	70.19 51	83.86 73	90.72 48	75.20 42	86.27 22	79.41 15	94.25 52	83.95 126

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 74	70.23 49	84.49 64	90.67 49	75.15 43	86.37 19	79.58 11	94.26 51	84.18 122

HQP3-MVS								84.12 75							89.16 152

HQP-MVS		75.24 98	75.01 102	75.94 100	82.37 117	58.80 163	77.32 110	84.12 75	59.08 137	71.58 232	85.96 167	58.09 193	85.30 53	67.38 104	89.16 152	83.73 133

DeepPCF-MVS	71.07 5	78.48 72	77.14 87	82.52 19	84.39 89	77.04 25	76.35 122	84.05 77	56.66 165	80.27 115	85.31 175	68.56 96	87.03 10	67.39 103	91.26 103	83.50 136

TAPA-MVS	65.27 12	75.16 99	74.29 111	77.77 77	74.86 216	68.08 84	77.89 105	84.04 78	55.15 179	76.19 177	83.39 195	66.91 111	80.11 154	60.04 164	90.14 133	85.13 86
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 48	69.39 75	79.41 85	84.00 79	65.64 78	85.54 48	89.28 87	76.32 33	83.47 89	74.03 54	93.57 68	84.35 118
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS_fast	69.89 7	77.17 81	76.33 92	79.70 50	83.90 95	67.94 85	80.06 80	83.75 80	56.73 164	74.88 189	85.32 174	65.54 126	87.79 2	65.61 115	91.14 108	83.35 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		81.13 43	81.73 48	79.36 55	84.47 85	70.53 65	83.85 41	83.70 81	69.43 55	83.67 75	88.96 100	75.89 36	86.41 17	72.62 64	92.95 74	81.14 190
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH	63.62 14	77.50 79	80.11 60	69.68 191	79.61 148	56.28 175	78.81 91	83.62 82	63.41 108	87.14 31	90.23 72	76.11 34	73.32 244	67.58 100	94.44 41	79.44 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 83	71.31 43	81.26 102	90.96 39	74.57 51	84.69 68	78.41 22	94.78 28	82.74 165
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		77.33 80	77.06 88	78.14 74	84.21 90	63.98 119	76.07 127	83.45 84	54.20 197	77.68 147	87.18 123	69.98 84	85.37 51	68.01 95	92.72 82	85.08 89

CLD-MVS		72.88 138	72.36 144	74.43 117	77.03 187	54.30 187	68.77 216	83.43 85	52.12 221	76.79 164	74.44 293	69.54 88	83.91 80	55.88 194	93.25 72	85.09 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		72.29 148	73.38 127	69.04 199	74.23 229	47.37 238	73.93 153	83.18 86	54.36 192	76.61 167	81.64 218	72.03 66	75.34 224	57.12 181	87.28 181	84.40 116

PHI-MVS		74.92 103	74.36 110	76.61 90	76.40 196	62.32 129	80.38 71	83.15 87	54.16 199	73.23 213	80.75 225	62.19 153	83.86 81	68.02 94	90.92 115	83.65 134

MCST-MVS		73.42 122	73.34 129	73.63 130	81.28 134	59.17 159	74.80 142	83.13 88	45.50 278	72.84 216	83.78 192	65.15 132	80.99 132	64.54 121	89.09 157	80.73 204

F-COLMAP		75.29 97	73.99 114	79.18 56	81.73 129	71.90 51	81.86 62	82.98 89	59.86 134	72.27 224	84.00 189	64.56 138	83.07 99	51.48 226	87.19 184	82.56 170

DP-MVS Recon		73.57 120	72.69 140	76.23 97	82.85 112	63.39 122	74.32 149	82.96 90	57.75 150	70.35 249	81.98 212	64.34 139	84.41 75	49.69 240	89.95 137	80.89 197

v10		75.69 93	76.20 93	74.16 122	74.44 228	48.69 219	75.84 130	82.93 91	59.02 140	85.92 40	89.17 93	58.56 188	82.74 103	70.73 76	89.14 155	91.05 13

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 92	72.41 36	85.11 55	90.85 42	76.65 30	84.89 64	79.30 17	94.63 34	82.35 174

Effi-MVS+		72.10 149	72.28 145	71.58 165	74.21 233	50.33 208	74.72 145	82.73 93	62.62 113	70.77 245	76.83 273	69.96 85	80.97 134	60.20 159	78.43 285	83.45 142

test11								82.71 94

PEN-MVS		80.46 51	82.91 37	73.11 140	89.83 9	39.02 303	77.06 116	82.61 95	80.04 5	90.60 6	92.85 9	74.93 47	85.21 57	63.15 140	95.15 18	95.09 2

nrg030		74.87 106	75.99 95	71.52 167	74.90 215	49.88 215	74.10 152	82.58 96	54.55 190	83.50 77	89.21 90	71.51 72	75.74 220	61.24 150	92.34 86	88.94 36

v7n		79.37 62	80.41 58	76.28 96	78.67 168	55.81 178	79.22 88	82.51 97	70.72 47	87.54 22	92.44 14	68.00 104	81.34 122	72.84 61	91.72 91	91.69 10

WR-MVS_H		80.22 56	82.17 45	74.39 118	89.46 15	42.69 276	78.24 101	82.24 98	78.21 10	89.57 9	92.10 18	68.05 102	85.59 47	66.04 111	95.62 9	94.88 5

DELS-MVS		68.83 185	68.31 191	70.38 178	70.55 272	48.31 223	63.78 283	82.13 99	54.00 202	68.96 262	75.17 285	58.95 185	80.06 155	58.55 173	82.74 238	82.76 163
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
OurMVSNet-221017-0		78.57 69	78.53 75	78.67 65	80.48 141	64.16 117	80.24 76	82.06 100	61.89 118	88.77 12	93.32 4	57.15 203	82.60 106	70.08 80	92.80 78	89.25 27

CPTT-MVS		81.51 38	81.76 47	80.76 40	89.20 24	78.75 12	86.48 22	82.03 101	68.80 56	80.92 107	88.52 106	72.00 68	82.39 108	74.80 47	93.04 73	81.14 190

CSCG		74.12 112	74.39 108	73.33 134	79.35 152	61.66 134	77.45 109	81.98 102	62.47 116	79.06 127	80.19 234	61.83 155	78.79 170	59.83 166	87.35 178	79.54 220

PVSNet_Blended_VisFu		70.04 166	68.88 184	73.53 132	82.71 114	63.62 121	74.81 140	81.95 103	48.53 260	67.16 278	79.18 250	51.42 234	78.38 182	54.39 210	79.72 275	78.60 229

DTE-MVSNet		80.35 53	82.89 38	72.74 152	89.84 8	37.34 318	77.16 113	81.81 104	80.45 4	90.92 3	92.95 7	74.57 51	86.12 30	63.65 132	94.68 33	94.76 6

v1192		73.40 123	73.42 125	73.32 135	74.65 225	48.67 220	72.21 164	81.73 105	52.76 216	81.85 92	84.56 181	57.12 204	82.24 113	68.58 88	87.33 179	89.06 32

原ACMM1				73.90 125	85.90 63	65.15 110		81.67 106	50.97 236	74.25 200	86.16 160	61.60 158	83.54 87	56.75 183	91.08 110	73.00 277

test12				76.51 92	82.28 120	60.94 141		81.64 107		73.60 206		64.88 134	85.19 59		90.42 127	83.38 143

CNVR-MVS		78.49 71	78.59 74	78.16 73	85.86 66	67.40 89	78.12 104	81.50 108	63.92 99	77.51 148	86.56 148	68.43 99	84.82 66	73.83 55	91.61 96	82.26 177

PCF-MVS	63.80 13	72.70 142	71.69 152	75.72 103	78.10 172	60.01 152	73.04 156	81.50 108	45.34 282	79.66 120	84.35 185	65.15 132	82.65 105	48.70 248	89.38 151	84.50 114
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		75.07 101	75.64 98	73.35 133	73.42 240	47.46 237	75.20 135	81.45 110	60.05 131	85.64 44	89.26 88	58.08 195	81.80 118	69.71 85	87.97 168	90.79 17

PAPM_NR		73.91 113	74.16 112	73.16 139	81.90 126	53.50 193	81.28 64	81.40 111	66.17 75	73.30 212	83.31 200	59.96 173	83.10 98	58.45 174	81.66 252	82.87 159

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 29	67.52 88	86.61 20	81.38 112	51.71 226	77.15 151	91.42 33	65.49 127	87.20 6	79.44 14	87.17 185	84.51 113
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EIA-MVS		68.59 190	67.16 209	72.90 147	75.18 211	55.64 180	69.39 204	81.29 113	52.44 218	64.53 289	70.69 325	60.33 171	82.30 111	54.27 212	76.31 296	80.75 203

PS-CasMVS		80.41 52	82.86 39	73.07 141	89.93 7	39.21 300	77.15 114	81.28 114	79.74 6	90.87 4	92.73 11	75.03 45	84.93 63	63.83 131	95.19 16	95.07 3

PLC	62.01 16	71.79 152	70.28 170	76.33 95	80.31 143	68.63 82	78.18 103	81.24 115	54.57 189	67.09 279	80.63 227	59.44 178	81.74 120	46.91 263	84.17 223	78.63 228
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		69.98 168	69.22 180	72.26 162	82.69 115	58.82 162	70.53 191	81.23 116	47.79 267	64.16 293	80.21 232	51.32 235	83.12 97	60.14 162	84.95 214	74.83 264

MVS_Test		69.84 170	70.71 166	67.24 226	67.49 296	43.25 272	69.87 198	81.22 117	52.69 217	71.57 235	86.68 141	62.09 154	74.51 235	66.05 110	78.74 281	83.96 125

v1240		73.06 131	73.14 132	72.84 149	74.74 219	47.27 240	71.88 173	81.11 118	51.80 225	82.28 90	84.21 186	56.22 212	82.34 110	68.82 86	87.17 185	88.91 37

PAPR		69.20 181	68.66 190	70.82 172	75.15 212	47.77 232	75.31 134	81.11 118	49.62 251	66.33 281	79.27 246	61.53 159	82.96 100	48.12 254	81.50 254	81.74 184

ZD-MVS					83.91 94	69.36 76		81.09 120	58.91 143	82.73 86	89.11 95	75.77 37	86.63 13	72.73 62	92.93 75

v1144		73.29 126	73.39 126	73.01 142	74.12 234	48.11 227	72.01 167	81.08 121	53.83 206	81.77 94	84.68 179	58.07 196	81.91 115	68.10 92	86.86 187	88.99 35

UniMVSNet (Re)		75.00 102	75.48 100	73.56 131	83.14 103	47.92 230	70.41 194	81.04 122	63.67 102	79.54 121	86.37 153	62.83 144	81.82 116	57.10 182	95.25 15	90.94 15

NCCC		78.25 75	78.04 79	78.89 63	85.61 68	69.45 73	79.80 82	80.99 123	65.77 77	75.55 181	86.25 157	67.42 107	85.42 50	70.10 79	90.88 118	81.81 183

AdaColmap		74.22 111	74.56 105	73.20 138	81.95 125	60.97 140	79.43 83	80.90 124	65.57 79	72.54 221	81.76 216	70.98 78	85.26 54	47.88 256	90.00 135	73.37 274

MSC_two_6792asdad				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 130

No_MVS				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 130

v1921920		72.96 137	72.98 137	72.89 148	74.67 222	47.58 235	71.92 171	80.69 127	51.70 227	81.69 98	83.89 190	56.58 210	82.25 112	68.34 90	87.36 177	88.82 39

zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 56	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 73	80.98 4	94.75 29	80.82 199

MTGPA								80.63 128

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 32	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 73	80.98 4	94.75 29	80.82 199

DVP-MVS		81.15 42	83.12 35	75.24 111	86.16 57	60.78 144	83.77 43	80.58 131	72.48 34	85.83 42	90.41 59	78.57 18	85.69 44	75.86 42	94.39 43	79.24 223
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
ITE_SJBPF				80.35 44	76.94 189	73.60 43		80.48 132	66.87 67	83.64 76	86.18 158	70.25 83	79.90 156	61.12 153	88.95 158	87.56 52

CP-MVSNet		79.48 60	81.65 50	72.98 144	89.66 13	39.06 302	76.76 118	80.46 133	78.91 8	90.32 7	91.70 24	68.49 97	84.89 64	63.40 137	95.12 19	95.01 4

v144192		72.99 135	73.06 135	72.77 150	74.58 226	47.48 236	71.90 172	80.44 134	51.57 228	81.46 100	84.11 188	58.04 197	82.12 114	67.98 96	87.47 175	88.70 42

IU-MVS					86.12 59	60.90 142		80.38 135	45.49 280	81.31 101				75.64 44	94.39 43	84.65 101

CANet		73.00 134	71.84 150	76.48 93	75.82 204	61.28 136	74.81 140	80.37 136	63.17 110	62.43 304	80.50 229	61.10 166	85.16 60	64.00 127	84.34 222	83.01 156

V42		71.06 156	70.83 165	71.72 164	67.25 297	47.14 241	65.94 255	80.35 137	51.35 231	83.40 78	83.23 202	59.25 182	78.80 169	65.91 112	80.81 262	89.23 28

Anonymous20231211		75.54 94	77.19 85	70.59 175	77.67 182	45.70 254	74.73 144	80.19 138	68.80 56	82.95 81	92.91 8	66.26 120	76.76 211	58.41 175	92.77 79	89.30 26

HPM-MVS++		79.89 57	79.80 63	80.18 45	89.02 27	78.44 13	83.49 49	80.18 139	64.71 93	78.11 139	88.39 109	65.46 128	83.14 96	77.64 32	91.20 105	78.94 226

DU-MVS		74.91 104	75.57 99	72.93 146	83.50 98	45.79 250	69.47 203	80.14 140	65.22 86	81.74 96	87.08 125	61.82 156	81.07 130	56.21 191	94.98 21	91.93 8

114514_t		73.40 123	73.33 130	73.64 129	84.15 92	57.11 170	78.20 102	80.02 141	43.76 292	72.55 220	86.07 165	64.00 140	83.35 93	60.14 162	91.03 111	80.45 209

UA-Net		81.56 37	82.28 44	79.40 54	88.91 30	69.16 79	84.67 35	80.01 142	75.34 17	79.80 119	94.91 2	69.79 87	80.25 149	72.63 63	94.46 38	88.78 41

FIs		72.56 144	73.80 117	68.84 207	78.74 167	37.74 314	71.02 185	79.83 143	56.12 170	80.88 109	89.45 85	58.18 190	78.28 186	56.63 184	93.36 70	90.51 19

alignmvs		70.54 162	71.00 163	69.15 198	73.50 238	48.04 229	69.85 199	79.62 144	53.94 205	76.54 169	82.00 211	59.00 184	74.68 233	57.32 180	87.21 183	84.72 100

LCM-MVSNet-Re		69.10 183	71.57 157	61.70 276	70.37 273	34.30 338	61.45 299	79.62 144	56.81 162	89.59 8	88.16 117	68.44 98	72.94 247	42.30 286	87.33 179	77.85 244

c3_l		69.82 171	69.89 172	69.61 192	66.24 305	43.48 268	68.12 226	79.61 146	51.43 230	77.72 145	80.18 235	54.61 219	78.15 191	63.62 133	87.50 174	87.20 57

PS-MVSNAJss		77.54 78	77.35 84	78.13 75	84.88 77	66.37 99	78.55 95	79.59 147	53.48 209	86.29 35	92.43 15	62.39 150	80.25 149	67.90 99	90.61 124	87.77 48

GeoE		73.14 127	73.77 119	71.26 170	78.09 173	52.64 198	74.32 149	79.56 148	56.32 169	76.35 175	83.36 199	70.76 79	77.96 193	63.32 138	81.84 247	83.18 150

FC-MVSNet-test		73.32 125	74.78 104	68.93 204	79.21 156	36.57 320	71.82 174	79.54 149	57.63 155	82.57 87	90.38 64	59.38 181	78.99 166	57.91 178	94.56 35	91.23 12

RPSCF		75.76 91	74.37 109	79.93 46	74.81 217	77.53 20	77.53 108	79.30 150	59.44 136	78.88 128	89.80 80	71.26 76	73.09 246	57.45 179	80.89 260	89.17 30

PMVS	70.70 6	81.70 36	83.15 34	77.36 83	90.35 6	82.82 2	82.15 58	79.22 151	74.08 22	87.16 30	91.97 19	84.80 2	76.97 205	64.98 119	93.61 65	72.28 286
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v2v482		72.55 146	72.58 141	72.43 158	72.92 252	46.72 243	71.41 178	79.13 152	55.27 177	81.17 103	85.25 176	55.41 215	81.13 127	67.25 107	85.46 201	89.43 25

Vis-MVSNet		74.85 107	74.56 105	75.72 103	81.63 131	64.64 112	76.35 122	79.06 153	62.85 112	73.33 211	88.41 108	62.54 148	79.59 160	63.94 130	82.92 236	82.94 157
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet_BlendedMVS		65.38 220	64.30 232	68.61 209	69.81 276	49.36 216	65.60 263	78.96 154	45.50 278	59.98 317	78.61 256	51.82 230	78.20 188	44.30 276	84.11 224	78.27 236

PVSNet_Blended		62.90 249	61.64 253	66.69 234	69.81 276	49.36 216	61.23 302	78.96 154	42.04 303	59.98 317	68.86 342	51.82 230	78.20 188	44.30 276	77.77 292	72.52 282

miper_ehance_all_eth		68.36 192	68.16 198	68.98 201	65.14 316	43.34 270	67.07 242	78.92 156	49.11 255	76.21 176	77.72 266	53.48 223	77.92 194	61.16 152	84.59 218	85.68 81

eth_miper_zixun_eth		69.42 176	68.73 189	71.50 168	67.99 290	46.42 246	67.58 231	78.81 157	50.72 239	78.13 138	80.34 231	50.15 241	80.34 146	60.18 160	84.65 216	87.74 49

UniMVSNet_NR-MVSNet		74.90 105	75.65 97	72.64 155	83.04 108	45.79 250	69.26 206	78.81 157	66.66 72	81.74 96	86.88 131	63.26 142	81.07 130	56.21 191	94.98 21	91.05 13

QAPM		69.18 182	69.26 178	68.94 203	71.61 260	52.58 199	80.37 72	78.79 159	49.63 250	73.51 207	85.14 177	53.66 222	79.12 164	55.11 201	75.54 301	75.11 262

TEST9					85.47 69	69.32 77	76.42 120	78.69 160	53.73 207	76.97 154	86.74 138	66.84 112	81.10 128

train_agg		76.38 87	76.55 89	75.86 102	85.47 69	69.32 77	76.42 120	78.69 160	54.00 202	76.97 154	86.74 138	66.60 116	81.10 128	72.50 66	91.56 97	77.15 247

test_8					85.09 75	67.89 86	76.26 125	78.66 162	54.00 202	76.89 159	86.72 140	66.60 116	80.89 139

agg_prior1		75.89 89	76.41 90	74.31 120	84.44 87	66.02 102	76.12 126	78.62 163	54.40 191	76.95 156	86.85 132	66.44 119	80.34 146	72.45 67	91.42 100	76.57 251

agg_prior					84.44 87	66.02 102		78.62 163		76.95 156			80.34 146

CNLPA		73.44 121	73.03 136	74.66 113	78.27 170	75.29 31	75.99 128	78.49 165	65.39 82	75.67 179	83.22 204	61.23 164	66.77 300	53.70 216	85.33 205	81.92 182

IterMVS-LS		73.01 133	73.12 134	72.66 154	73.79 237	49.90 212	71.63 175	78.44 166	58.22 145	80.51 111	86.63 145	58.15 192	79.62 158	62.51 142	88.20 162	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		68.81 186	68.30 192	70.35 179	74.66 224	48.61 221	66.06 254	78.32 167	50.62 240	71.48 238	75.54 281	68.75 94	79.59 160	50.55 235	78.73 282	82.86 160

3Dnovator	65.95 11	71.50 154	71.22 161	72.34 160	73.16 244	63.09 125	78.37 97	78.32 167	57.67 153	72.22 226	84.61 180	54.77 216	78.47 177	60.82 156	81.07 257	75.45 258

TranMVSNet+NR-MVSNet		76.13 88	77.66 81	71.56 166	84.61 83	42.57 278	70.98 186	78.29 169	68.67 59	83.04 79	89.26 88	72.99 63	80.75 140	55.58 199	95.47 10	91.35 11

MSDG		67.47 205	67.48 206	67.46 224	70.70 267	54.69 185	66.90 246	78.17 170	60.88 126	70.41 248	74.76 287	61.22 165	73.18 245	47.38 259	76.87 293	74.49 266

Fast-Effi-MVS+-dtu		70.00 167	68.74 188	73.77 127	73.47 239	64.53 114	71.36 179	78.14 171	55.81 174	68.84 265	74.71 290	65.36 130	75.75 219	52.00 223	79.00 279	81.03 192

IS-MVSNet		75.10 100	75.42 101	74.15 123	79.23 155	48.05 228	79.43 83	78.04 172	70.09 52	79.17 126	88.02 120	53.04 224	83.60 85	58.05 177	93.76 64	90.79 17

miper_enhance_ethall		65.86 216	65.05 231	68.28 215	61.62 334	42.62 277	64.74 271	77.97 173	42.52 301	73.42 210	72.79 309	49.66 242	77.68 198	58.12 176	84.59 218	84.54 109

save fliter					87.00 43	67.23 91	79.24 86	77.94 174	56.65 166

ambc				70.10 186	77.74 180	50.21 210	74.28 151	77.93 175		79.26 125	88.29 113	54.11 221	79.77 157	64.43 122	91.10 109	80.30 211

Effi-MVS+-dtu		75.43 95	72.28 145	84.91 2	77.05 185	83.58 1	78.47 96	77.70 176	57.68 151	74.89 188	78.13 263	64.80 135	84.26 77	56.46 188	85.32 206	86.88 61

mvs-test1		73.81 115	70.69 167	83.18 5	77.05 185	81.39 3	75.39 133	77.70 176	57.68 151	71.19 242	74.72 289	64.80 135	83.66 84	56.46 188	81.19 256	84.50 114

Regformer-2		75.32 96	74.47 107	77.88 76	74.22 230	66.65 97	72.77 159	77.54 178	68.47 61	80.44 112	72.08 312	70.60 80	80.97 134	70.08 80	84.02 226	86.01 72

BH-untuned		69.39 177	69.46 175	69.18 197	77.96 176	56.88 172	68.47 223	77.53 179	56.77 163	77.79 144	79.63 241	60.30 172	80.20 152	46.04 269	80.65 263	70.47 300

MAR-MVS		67.72 201	66.16 216	72.40 159	74.45 227	64.99 111	74.87 138	77.50 180	48.67 258	65.78 285	68.58 344	57.01 207	77.79 196	46.68 266	81.92 245	74.42 267
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
OpenMVS	62.51 15	68.76 187	68.75 187	68.78 208	70.56 271	53.91 190	78.29 99	77.35 181	48.85 257	70.22 251	83.52 193	52.65 227	76.93 206	55.31 200	81.99 244	75.49 257

NR-MVSNet		73.62 119	74.05 113	72.33 161	83.50 98	43.71 265	65.65 261	77.32 182	64.32 96	75.59 180	87.08 125	62.45 149	81.34 122	54.90 202	95.63 8	91.93 8

EPP-MVSNet		73.86 114	73.38 127	75.31 108	78.19 171	53.35 195	80.45 69	77.32 182	65.11 88	76.47 172	86.80 133	49.47 243	83.77 82	53.89 214	92.72 82	88.81 40

Anonymous20240529		72.56 144	73.79 118	68.86 206	76.89 192	45.21 256	68.80 215	77.25 184	67.16 65	76.89 159	90.44 55	65.95 123	74.19 240	50.75 232	90.00 135	87.18 58

diffmvs		67.42 206	67.50 205	67.20 227	62.26 330	45.21 256	64.87 270	77.04 185	48.21 261	71.74 229	79.70 240	58.40 189	71.17 268	64.99 118	80.27 267	85.22 83

API-MVS		70.97 158	71.51 158	69.37 193	75.20 210	55.94 177	80.99 65	76.84 186	62.48 115	71.24 240	77.51 269	61.51 160	80.96 138	52.04 222	85.76 199	71.22 295

ANet_high		67.08 208	69.94 171	58.51 300	57.55 354	27.09 363	58.43 317	76.80 187	63.56 103	82.40 89	91.93 20	59.82 176	64.98 308	50.10 238	88.86 159	83.46 141

PAPM		61.79 259	60.37 266	66.05 238	76.09 201	41.87 282	69.30 205	76.79 188	40.64 313	53.80 343	79.62 242	44.38 270	82.92 101	29.64 352	73.11 317	73.36 275

mvs_tets		78.93 65	78.67 73	79.72 49	84.81 79	73.93 40	80.65 67	76.50 189	51.98 224	87.40 24	91.86 21	76.09 35	78.53 175	68.58 88	90.20 129	86.69 64

cl22		67.14 207	66.51 214	69.03 200	63.20 326	43.46 269	66.88 247	76.25 190	49.22 253	74.48 197	77.88 265	45.49 263	77.40 201	60.64 157	84.59 218	86.24 67

anonymousdsp		78.60 68	77.80 80	81.00 37	78.01 175	74.34 38	80.09 78	76.12 191	50.51 241	89.19 10	90.88 40	71.45 74	77.78 197	73.38 58	90.60 125	90.90 16

jajsoiax		78.51 70	78.16 78	79.59 52	84.65 82	73.83 42	80.42 70	76.12 191	51.33 232	87.19 29	91.51 29	73.79 58	78.44 179	68.27 91	90.13 134	86.49 66

Gipuma		69.55 174	72.83 139	59.70 292	63.63 325	53.97 189	80.08 79	75.93 193	64.24 97	73.49 208	88.93 101	57.89 199	62.46 317	59.75 167	91.55 98	62.67 344
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		67.50 203	67.31 208	68.08 216	58.86 349	61.93 130	71.43 177	75.90 194	44.67 287	72.42 223	80.20 233	57.16 202	70.44 274	58.99 171	86.12 195	71.88 289

MVSFormer		69.93 169	69.03 182	72.63 156	74.93 213	59.19 157	83.98 39	75.72 195	52.27 219	63.53 300	76.74 274	43.19 277	80.56 141	72.28 68	78.67 283	78.14 239

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 132	71.24 58	83.98 39	75.72 195	52.27 219	87.37 27	92.25 16	68.04 103	80.56 141	72.28 68	91.15 107	90.32 20

SixPastTwentyTwo		75.77 90	76.34 91	74.06 124	81.69 130	54.84 183	76.47 119	75.49 197	64.10 98	87.73 18	92.24 17	50.45 239	81.30 124	67.41 102	91.46 99	86.04 71

Regformer-1		74.28 110	73.63 123	76.21 98	74.22 230	64.12 118	72.77 159	75.46 198	66.86 68	79.27 124	72.08 312	69.29 89	78.74 171	68.73 87	84.02 226	85.77 80

Regformer-4		74.64 108	73.67 121	77.55 78	74.74 219	64.49 115	72.91 157	75.42 199	67.45 63	80.24 116	72.07 314	68.98 92	80.19 153	70.29 78	80.91 258	87.98 46

RRT_test8_iter05		65.80 217	65.13 226	67.80 222	67.02 300	40.85 290	67.13 241	75.33 200	49.73 248	72.69 218	81.32 219	24.45 365	77.37 202	61.69 148	86.82 189	85.18 85

KD-MVS_self_test		66.38 214	67.51 204	62.97 266	61.76 332	34.39 337	58.11 319	75.30 201	50.84 238	77.12 153	85.42 173	56.84 208	69.44 279	51.07 230	91.16 106	85.08 89

TinyColmap		67.98 197	69.28 177	64.08 251	67.98 291	46.82 242	70.04 195	75.26 202	53.05 212	77.36 150	86.79 134	59.39 180	72.59 254	45.64 271	88.01 167	72.83 279

BH-w/o		64.81 227	64.29 233	66.36 236	76.08 202	54.71 184	65.61 262	75.23 203	50.10 245	71.05 244	71.86 319	54.33 220	79.02 165	38.20 312	76.14 297	65.36 332

MG-MVS		70.47 163	71.34 160	67.85 219	79.26 154	40.42 295	74.67 147	75.15 204	58.41 144	68.74 266	88.14 118	56.08 213	83.69 83	59.90 165	81.71 251	79.43 222

RRT_MVS		73.80 116	71.19 162	81.60 24	71.04 263	70.33 68	78.78 92	74.91 205	56.96 160	77.83 143	85.56 172	32.82 326	87.39 5	71.16 73	91.68 93	87.07 60

cl____		68.26 196	68.26 193	68.29 213	64.98 317	43.67 266	65.89 256	74.67 206	50.04 246	76.86 161	82.42 208	48.74 250	75.38 222	60.92 155	89.81 140	85.80 79

DIV-MVS_self_test		68.27 195	68.26 193	68.29 213	64.98 317	43.67 266	65.89 256	74.67 206	50.04 246	76.86 161	82.43 207	48.74 250	75.38 222	60.94 154	89.81 140	85.81 75

test_0402		78.17 76	79.48 66	74.24 121	83.50 98	59.15 160	72.52 161	74.60 208	75.34 17	88.69 13	91.81 22	75.06 44	82.37 109	65.10 117	88.68 160	81.20 188

CANet_DTU		64.04 239	63.83 235	64.66 246	68.39 284	42.97 274	73.45 154	74.50 209	52.05 223	54.78 337	75.44 284	43.99 272	70.42 275	53.49 218	78.41 286	80.59 207

USDC		62.80 250	63.10 244	61.89 275	65.19 313	43.30 271	67.42 234	74.20 210	35.80 335	72.25 225	84.48 183	45.67 261	71.95 262	37.95 314	84.97 210	70.42 302

MVS		60.62 268	59.97 268	62.58 270	68.13 289	47.28 239	68.59 218	73.96 211	32.19 350	59.94 319	68.86 342	50.48 238	77.64 199	41.85 290	75.74 298	62.83 342

EG-PatchMatch MVS		70.70 160	70.88 164	70.16 183	82.64 116	58.80 163	71.48 176	73.64 212	54.98 180	76.55 168	81.77 215	61.10 166	78.94 167	54.87 203	80.84 261	72.74 281

BH-RMVSNet		68.69 189	68.20 197	70.14 184	76.40 196	53.90 191	64.62 273	73.48 213	58.01 147	73.91 205	81.78 214	59.09 183	78.22 187	48.59 249	77.96 290	78.31 233

GBi-Net		68.30 193	68.79 185	66.81 231	73.14 245	40.68 291	71.96 168	73.03 214	54.81 181	74.72 192	90.36 67	48.63 252	75.20 226	47.12 260	85.37 202	84.54 109

test1		68.30 193	68.79 185	66.81 231	73.14 245	40.68 291	71.96 168	73.03 214	54.81 181	74.72 192	90.36 67	48.63 252	75.20 226	47.12 260	85.37 202	84.54 109

FMVSNet1		71.06 156	72.48 142	66.81 231	77.65 183	40.68 291	71.96 168	73.03 214	61.14 123	79.45 123	90.36 67	60.44 170	75.20 226	50.20 237	88.05 165	84.54 109

test_yl		65.11 222	65.09 228	65.18 243	70.59 268	40.86 288	63.22 291	72.79 217	57.91 148	68.88 263	79.07 253	42.85 280	74.89 230	45.50 272	84.97 210	79.81 216

DCV-MVSNet		65.11 222	65.09 228	65.18 243	70.59 268	40.86 288	63.22 291	72.79 217	57.91 148	68.88 263	79.07 253	42.85 280	74.89 230	45.50 272	84.97 210	79.81 216

MVS_111021_HR		72.98 136	72.97 138	72.99 143	80.82 137	65.47 105	68.81 213	72.77 219	57.67 153	75.76 178	82.38 209	71.01 77	77.17 203	61.38 149	86.15 194	76.32 252

v148		69.38 178	69.39 176	69.36 194	69.14 283	44.56 259	68.83 212	72.70 220	54.79 184	78.59 130	84.12 187	54.69 217	76.74 212	59.40 169	82.20 242	86.79 62

1314		59.83 273	58.86 276	62.74 269	65.71 310	44.78 258	68.59 218	72.63 221	33.54 348	61.05 312	67.29 350	43.62 275	71.26 267	49.49 242	67.84 342	72.19 287

pmmvs6		71.82 151	73.66 122	66.31 237	75.94 203	42.01 281	66.99 243	72.53 222	63.45 106	76.43 173	92.78 10	72.95 64	69.69 278	51.41 227	90.46 126	87.22 55

UGNet		70.20 165	69.05 181	73.65 128	76.24 198	63.64 120	75.87 129	72.53 222	61.48 120	60.93 314	86.14 161	52.37 228	77.12 204	50.67 233	85.21 207	80.17 214
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
PS-MVSNAJ		64.27 237	63.73 237	65.90 240	77.82 179	51.42 203	63.33 288	72.33 224	45.09 285	61.60 306	68.04 345	62.39 150	73.95 242	49.07 244	73.87 314	72.34 284

xiu_mvs_v2_base		64.43 234	63.96 234	65.85 241	77.72 181	51.32 204	63.63 285	72.31 225	45.06 286	61.70 305	69.66 334	62.56 146	73.93 243	49.06 245	73.91 313	72.31 285

HyFIR lowres test		63.01 247	60.47 265	70.61 174	83.04 108	54.10 188	59.93 310	72.24 226	33.67 346	69.00 260	75.63 280	38.69 304	76.93 206	36.60 323	75.45 303	80.81 202

Regformer-3		72.86 139	72.28 145	74.62 114	74.74 219	60.18 150	72.91 157	71.76 227	64.74 92	78.42 134	72.07 314	67.00 110	76.28 215	67.97 97	80.91 258	87.39 53

UniMVSNet_ETH3D		76.74 85	79.02 68	69.92 190	89.27 20	43.81 264	74.47 148	71.70 228	72.33 37	85.50 49	93.65 3	77.98 22	76.88 208	54.60 206	91.64 94	89.08 31

cascas		64.59 230	62.77 247	70.05 187	75.27 209	50.02 211	61.79 298	71.61 229	42.46 302	63.68 298	68.89 341	49.33 245	80.35 145	47.82 257	84.05 225	79.78 218

MVP-Stereo		61.56 260	59.22 272	68.58 210	79.28 153	60.44 148	69.20 207	71.57 230	43.58 295	56.42 331	78.37 259	39.57 301	76.46 214	34.86 333	60.16 355	68.86 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EI-MVSNet-Vis-set		72.78 140	71.87 149	75.54 106	74.77 218	59.02 161	72.24 163	71.56 231	63.92 99	78.59 130	71.59 320	66.22 121	78.60 173	67.58 100	80.32 266	89.00 34

EI-MVSNet-UG-set		72.63 143	71.68 153	75.47 107	74.67 222	58.64 166	72.02 166	71.50 232	63.53 104	78.58 132	71.39 323	65.98 122	78.53 175	67.30 106	80.18 268	89.23 28

VPA-MVSNet		68.71 188	70.37 169	63.72 255	76.13 200	38.06 312	64.10 278	71.48 233	56.60 168	74.10 202	88.31 112	64.78 137	69.72 277	47.69 258	90.15 132	83.37 144

hse-mvs2		72.32 147	70.66 168	77.31 85	83.10 107	71.77 52	69.19 208	71.45 234	54.28 193	77.89 140	78.26 260	49.04 246	79.23 162	63.62 133	89.13 156	80.92 196

AUN-MVS		70.22 164	67.88 201	77.22 86	82.96 111	71.61 53	69.08 209	71.39 235	49.17 254	71.70 230	78.07 264	37.62 311	79.21 163	61.81 145	89.15 154	80.82 199

EI-MVSNet		69.61 173	69.01 183	71.41 169	73.94 235	49.90 212	71.31 181	71.32 236	58.22 145	75.40 184	70.44 326	58.16 191	75.85 216	62.51 142	79.81 272	88.48 43

MVSTER		63.29 244	61.60 255	68.36 211	59.77 346	46.21 249	60.62 305	71.32 236	41.83 304	75.40 184	79.12 251	30.25 347	75.85 216	56.30 190	79.81 272	83.03 155

TransMVSNet (Re)		69.62 172	71.63 154	63.57 257	76.51 195	35.93 326	65.75 260	71.29 238	61.05 124	75.02 186	89.90 79	65.88 125	70.41 276	49.79 239	89.48 147	84.38 117

xiu_mvs_v1_base_debu		67.87 198	67.07 210	70.26 180	79.13 159	61.90 131	67.34 235	71.25 239	47.98 263	67.70 272	74.19 299	61.31 161	72.62 251	56.51 185	78.26 287	76.27 253

xiu_mvs_v1_base		67.87 198	67.07 210	70.26 180	79.13 159	61.90 131	67.34 235	71.25 239	47.98 263	67.70 272	74.19 299	61.31 161	72.62 251	56.51 185	78.26 287	76.27 253

xiu_mvs_v1_base_debi		67.87 198	67.07 210	70.26 180	79.13 159	61.90 131	67.34 235	71.25 239	47.98 263	67.70 272	74.19 299	61.31 161	72.62 251	56.51 185	78.26 287	76.27 253

FMVSNet2		67.48 204	68.21 196	65.29 242	73.14 245	38.94 304	68.81 213	71.21 242	54.81 181	76.73 165	86.48 150	48.63 252	74.60 234	47.98 255	86.11 196	82.35 174

h-mvs33		73.08 129	71.61 155	77.48 80	83.89 96	72.89 49	70.47 192	71.12 243	54.28 193	77.89 140	83.41 194	49.04 246	80.98 133	63.62 133	90.77 122	78.58 230

miper_lstm_enhance		61.97 256	61.63 254	62.98 265	60.04 342	45.74 253	47.53 347	70.95 244	44.04 289	73.06 214	78.84 255	39.72 299	60.33 323	55.82 195	84.64 217	82.88 158

无先验							74.82 139	70.94 245	47.75 268				76.85 209	54.47 207		72.09 288

Baseline_NR-MVSNet		70.62 161	73.19 131	62.92 268	76.97 188	34.44 336	68.84 211	70.88 246	60.25 130	79.50 122	90.53 52	61.82 156	69.11 282	54.67 205	95.27 14	85.22 83

VDD-MVS		70.81 159	71.44 159	68.91 205	79.07 162	46.51 245	67.82 229	70.83 247	61.23 122	74.07 203	88.69 103	59.86 175	75.62 221	51.11 229	90.28 128	84.61 105

pm-mvs1		68.40 191	69.85 173	64.04 253	73.10 248	39.94 297	64.61 274	70.50 248	55.52 176	73.97 204	89.33 86	63.91 141	68.38 286	49.68 241	88.02 166	83.81 129

FMVSNet3		65.00 225	65.16 223	64.52 248	69.47 279	37.56 317	66.63 249	70.38 249	51.55 229	74.72 192	83.27 201	37.89 310	74.44 236	47.12 260	85.37 202	81.57 186

TR-MVS		64.59 230	63.54 239	67.73 223	75.75 207	50.83 206	63.39 287	70.29 250	49.33 252	71.55 236	74.55 291	50.94 236	78.46 178	40.43 298	75.69 299	73.89 271

cdsmvs_eth3d_5k		17.71 340	23.62 342	0.00 359	0.00 382	0.00 383	0.00 370	70.17 251	0.00 377	0.00 378	74.25 297	68.16 101	0.00 378	0.00 376	0.00 376	0.00 374

mvs_anonymous		65.08 224	65.49 220	63.83 254	63.79 323	37.60 316	66.52 251	69.82 252	43.44 296	73.46 209	86.08 164	58.79 187	71.75 265	51.90 224	75.63 300	82.15 178

D2MVS		62.58 252	61.05 260	67.20 227	63.85 322	47.92 230	56.29 324	69.58 253	39.32 316	70.07 252	78.19 261	34.93 319	72.68 249	53.44 219	83.74 230	81.00 194

TSAR-MVS + GP.		73.08 129	71.60 156	77.54 79	78.99 164	70.73 63	74.96 137	69.38 254	60.73 128	74.39 199	78.44 258	57.72 200	82.78 102	60.16 161	89.60 144	79.11 225

GA-MVS		62.91 248	61.66 252	66.66 235	67.09 299	44.49 260	61.18 303	69.36 255	51.33 232	69.33 258	74.47 292	36.83 314	74.94 229	50.60 234	74.72 308	80.57 208

Anonymous20240521		63.55 241	66.07 217	55.99 307	66.18 307	44.04 263	68.77 216	68.80 256	46.99 272	72.57 219	85.84 169	39.87 298	50.22 339	53.40 220	92.23 88	73.71 273

ab-mvs		64.11 238	65.13 226	61.05 283	71.99 258	38.03 313	67.59 230	68.79 257	49.08 256	65.32 286	86.26 156	58.02 198	66.85 298	39.33 302	79.79 274	78.27 236

WR-MVS		71.20 155	72.48 142	67.36 225	84.98 76	35.70 328	64.43 276	68.66 258	65.05 89	81.49 99	86.43 152	57.57 201	76.48 213	50.36 236	93.32 71	89.90 21

bset_n11_16_dypcd		66.91 211	65.84 219	70.12 185	72.95 251	53.54 192	63.64 284	68.65 259	48.54 259	72.54 221	74.28 296	40.58 294	78.54 174	63.52 136	87.82 170	78.29 234

EGC-MVSNET		64.77 228	61.17 258	75.60 105	86.90 47	74.47 35	84.04 38	68.62 260	0.60 373	1.13 375	91.61 27	65.32 131	74.15 241	64.01 126	88.28 161	78.17 238

1112_ss		59.48 275	58.99 275	60.96 285	77.84 178	42.39 279	61.42 300	68.45 261	37.96 325	59.93 320	67.46 347	45.11 266	65.07 307	40.89 296	71.81 322	75.41 259

EU-MVSNet		60.82 265	60.80 263	60.86 286	68.37 285	41.16 285	72.27 162	68.27 262	26.96 364	69.08 259	75.71 279	32.09 332	67.44 292	55.59 198	78.90 280	73.97 269

CMPMVS	48.73 20	61.54 261	60.89 261	63.52 258	61.08 337	51.55 202	68.07 227	68.00 263	33.88 343	65.87 283	81.25 221	37.91 309	67.71 289	49.32 243	82.60 239	71.31 294
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	54.93 17	63.23 245	63.28 241	63.07 264	69.81 276	45.34 255	68.52 221	67.14 264	43.74 293	70.61 247	79.22 248	47.90 256	72.66 250	48.75 247	73.84 315	71.21 296

VNet		64.01 240	65.15 225	60.57 287	73.28 243	35.61 329	57.60 321	67.08 265	54.61 188	66.76 280	83.37 197	56.28 211	66.87 296	42.19 287	85.20 208	79.23 224

Test_1112_low_res		58.78 280	58.69 277	59.04 297	79.41 151	38.13 311	57.62 320	66.98 266	34.74 339	59.62 321	77.56 268	42.92 279	63.65 314	38.66 307	70.73 328	75.35 261

MVS_111021_LR		72.10 149	71.82 151	72.95 145	79.53 150	73.90 41	70.45 193	66.64 267	56.87 161	76.81 163	81.76 216	68.78 93	71.76 264	61.81 145	83.74 230	73.18 276

VDDNet		71.60 153	73.13 133	67.02 230	86.29 53	41.11 286	69.97 196	66.50 268	68.72 58	74.74 191	91.70 24	59.90 174	75.81 218	48.58 250	91.72 91	84.15 123

Anonymous202405211		66.02 215	66.89 213	63.43 260	74.22 230	38.14 310	59.00 314	66.13 269	63.33 109	69.76 256	85.95 168	51.88 229	70.50 273	44.23 278	87.52 173	81.64 185

jason		64.47 233	62.84 246	69.34 196	76.91 190	59.20 156	67.15 240	65.67 270	35.29 336	65.16 287	76.74 274	44.67 268	70.68 270	54.74 204	79.28 278	78.14 239
jason: jason.
CDS-MVSNet		64.33 236	62.66 248	69.35 195	80.44 142	58.28 167	65.26 266	65.66 271	44.36 288	67.30 277	75.54 281	43.27 276	71.77 263	37.68 315	84.44 221	78.01 242
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 1792x2688		58.09 283	56.30 294	63.45 259	79.95 145	50.93 205	54.07 332	65.59 272	28.56 361	61.53 307	74.33 294	41.09 290	66.52 302	33.91 337	67.69 343	72.92 278

IterMVS-SCA-FT		67.68 202	66.07 217	72.49 157	73.34 242	58.20 168	63.80 282	65.55 273	48.10 262	76.91 158	82.64 206	45.20 264	78.84 168	61.20 151	77.89 291	80.44 210

HY-MVS	49.31 19	57.96 284	57.59 285	59.10 296	66.85 301	36.17 323	65.13 268	65.39 274	39.24 318	54.69 339	78.14 262	44.28 271	67.18 295	33.75 338	70.79 327	73.95 270

IB-MVS	49.67 18	59.69 274	56.96 289	67.90 218	68.19 288	50.30 209	61.42 300	65.18 275	47.57 269	55.83 334	67.15 351	23.77 366	79.60 159	43.56 282	79.97 270	73.79 272
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
tfpnnormal		66.48 213	67.93 199	62.16 274	73.40 241	36.65 319	63.45 286	64.99 276	55.97 171	72.82 217	87.80 121	57.06 206	69.10 283	48.31 253	87.54 172	80.72 205

CL-MVSNet_self_test		62.44 254	63.40 240	59.55 294	72.34 255	32.38 345	56.39 323	64.84 277	51.21 234	67.46 276	81.01 224	50.75 237	63.51 315	38.47 310	88.12 164	82.75 164

KD-MVS_2432*1600		52.05 311	51.58 313	53.44 312	52.11 370	31.20 350	44.88 354	64.83 278	41.53 306	64.37 290	70.03 331	15.61 378	64.20 309	36.25 325	74.61 309	64.93 336

miper_refine_blended		52.05 311	51.58 313	53.44 312	52.11 370	31.20 350	44.88 354	64.83 278	41.53 306	64.37 290	70.03 331	15.61 378	64.20 309	36.25 325	74.61 309	64.93 336

CVMVSNet		59.21 277	58.44 280	61.51 278	73.94 235	47.76 233	71.31 181	64.56 280	26.91 365	60.34 316	70.44 326	36.24 316	67.65 290	53.57 217	68.66 339	69.12 314

lupinMVS		63.36 242	61.49 256	68.97 202	74.93 213	59.19 157	65.80 259	64.52 281	34.68 341	63.53 300	74.25 297	43.19 277	70.62 271	53.88 215	78.67 283	77.10 248

ET-MVSNet_ETH3D		63.32 243	60.69 264	71.20 171	70.15 275	55.66 179	65.02 269	64.32 282	43.28 300	68.99 261	72.05 318	25.46 361	78.19 190	54.16 213	82.80 237	79.74 219

thisisatest0530		67.05 210	65.16 223	72.73 153	73.10 248	50.55 207	71.26 183	63.91 283	50.22 243	74.46 198	80.75 225	26.81 354	80.25 149	59.43 168	86.50 192	87.37 54

旧先验1					84.55 84	60.36 149		63.69 284			87.05 128	54.65 218			83.34 233	69.66 308

EPNet		69.10 183	67.32 207	74.46 115	68.33 287	61.27 137	77.56 107	63.57 285	60.95 125	56.62 330	82.75 205	51.53 233	81.24 125	54.36 211	90.20 129	80.88 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAMVS		65.31 221	63.75 236	69.97 189	82.23 121	59.76 155	66.78 248	63.37 286	45.20 283	69.79 255	79.37 245	47.42 258	72.17 257	34.48 334	85.15 209	77.99 243

tttt0517		69.46 175	67.79 202	74.46 115	75.34 208	52.72 197	75.05 136	63.27 287	54.69 186	78.87 129	84.37 184	26.63 355	81.15 126	63.95 128	87.93 169	89.51 24

MS-PatchMatch		55.59 293	54.89 301	57.68 302	69.18 281	49.05 218	61.00 304	62.93 288	35.98 333	58.36 324	68.93 340	36.71 315	66.59 301	37.62 317	63.30 351	57.39 353

MVS_0304		62.51 253	62.27 250	63.25 261	69.39 280	48.47 222	64.05 280	62.48 289	59.69 135	54.10 342	81.04 223	45.71 260	66.31 303	41.38 293	82.58 240	74.96 263

IterMVS		63.12 246	62.48 249	65.02 245	66.34 304	52.86 196	63.81 281	62.25 290	46.57 275	71.51 237	80.40 230	44.60 269	66.82 299	51.38 228	75.47 302	75.38 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thisisatest0515		60.48 269	57.86 283	68.34 212	67.25 297	46.42 246	60.58 306	62.14 291	40.82 311	63.58 299	69.12 337	26.28 357	78.34 184	48.83 246	82.13 243	80.26 212

VPNet		65.58 219	67.56 203	59.65 293	79.72 147	30.17 355	60.27 308	62.14 291	54.19 198	71.24 240	86.63 145	58.80 186	67.62 291	44.17 279	90.87 119	81.18 189

新几何1				69.99 188	88.37 36	71.34 57		62.08 293	43.85 290	74.99 187	86.11 163	52.85 226	70.57 272	50.99 231	83.23 235	68.05 318

1121		69.23 180	68.26 193	72.12 163	88.36 37	71.40 55	68.59 218	62.06 294	43.80 291	74.75 190	86.18 158	52.92 225	76.85 209	54.47 207	83.27 234	68.12 317

pmmvs-eth3d		64.41 235	63.27 242	67.82 221	75.81 205	60.18 150	69.49 202	62.05 295	38.81 321	74.13 201	82.23 210	43.76 274	68.65 284	42.53 285	80.63 265	74.63 265

K. test v3		73.67 117	73.61 124	73.87 126	79.78 146	55.62 181	74.69 146	62.04 296	66.16 76	84.76 61	93.23 5	49.47 243	80.97 134	65.66 114	86.67 191	85.02 91

testdata				64.13 250	85.87 65	63.34 123		61.80 297	47.83 266	76.42 174	86.60 147	48.83 249	62.31 319	54.46 209	81.26 255	66.74 327

N_pmnet		52.06 310	51.11 318	54.92 309	59.64 347	71.03 59	37.42 363	61.62 298	33.68 345	57.12 326	72.10 311	37.94 308	31.03 369	29.13 356	71.35 323	62.70 343

ppachtmachnet_test		60.26 271	59.61 271	62.20 273	67.70 294	44.33 261	58.18 318	60.96 299	40.75 312	65.80 284	72.57 310	41.23 287	63.92 312	46.87 264	82.42 241	78.33 232

pmmvs4		60.78 266	59.04 274	66.00 239	73.06 250	57.67 169	64.53 275	60.22 300	36.91 330	65.96 282	77.27 270	39.66 300	68.54 285	38.87 305	74.89 307	71.80 290

CostFormer		57.35 287	56.14 295	60.97 284	63.76 324	38.43 306	67.50 232	60.22 300	37.14 329	59.12 322	76.34 277	32.78 327	71.99 261	39.12 304	69.27 336	72.47 283

LFMVS		67.06 209	67.89 200	64.56 247	78.02 174	38.25 309	70.81 190	59.60 302	65.18 87	71.06 243	86.56 148	43.85 273	75.22 225	46.35 267	89.63 143	80.21 213

test222					87.30 41	69.15 80	67.85 228	59.59 303	41.06 309	73.05 215	85.72 171	48.03 255			80.65 263	66.92 323

tpmvs		55.84 290	55.45 300	57.01 304	60.33 341	33.20 343	65.89 256	59.29 304	47.52 270	56.04 332	73.60 302	31.05 342	68.06 288	40.64 297	64.64 347	69.77 307

UnsupCasMVSNet_eth		52.26 309	53.29 307	49.16 326	55.08 364	33.67 341	50.03 340	58.79 305	37.67 326	63.43 302	74.75 288	41.82 285	45.83 346	38.59 309	59.42 357	67.98 319

EPNet_dtu		58.93 279	58.52 278	60.16 291	67.91 292	47.70 234	69.97 196	58.02 306	49.73 248	47.28 358	73.02 308	38.14 306	62.34 318	36.57 324	85.99 197	70.43 301
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		53.04 304	51.12 317	58.77 298	61.23 335	38.67 305	62.16 296	57.74 307	38.24 322	51.76 347	59.07 361	21.36 368	67.40 293	44.80 275	63.76 350	70.25 303

MIMVSNet1		66.57 212	69.23 179	58.59 299	81.26 135	37.73 315	64.06 279	57.62 308	57.02 159	78.40 135	90.75 45	62.65 145	58.10 330	41.77 291	89.58 146	79.95 215

tfpn200view9		60.35 270	59.97 268	61.51 278	70.78 265	35.35 330	63.27 289	57.47 309	53.00 213	68.31 268	77.09 271	32.45 330	72.09 258	35.61 330	81.73 248	77.08 249

thres400		60.77 267	59.97 268	63.15 262	70.78 265	35.35 330	63.27 289	57.47 309	53.00 213	68.31 268	77.09 271	32.45 330	72.09 258	35.61 330	81.73 248	82.02 179

lessismore_v0				72.75 151	79.60 149	56.83 174		57.37 311		83.80 74	89.01 98	47.45 257	78.74 171	64.39 123	86.49 193	82.69 166

tpm cat1		54.02 301	52.63 309	58.19 301	64.85 319	39.86 298	66.26 253	57.28 312	32.16 351	56.90 328	70.39 328	32.75 328	65.30 306	34.29 335	58.79 358	69.41 311

thres200		57.55 286	57.02 288	59.17 295	67.89 293	34.93 333	58.91 316	57.25 313	50.24 242	64.01 294	71.46 322	32.49 329	71.39 266	31.31 344	79.57 276	71.19 297

MDA-MVSNet-bldmvs		62.34 255	61.73 251	64.16 249	61.64 333	49.90 212	48.11 345	57.24 314	53.31 210	80.95 105	79.39 244	49.00 248	61.55 321	45.92 270	80.05 269	81.03 192

thres100view900		61.17 263	61.09 259	61.39 280	72.14 257	35.01 332	65.42 265	56.99 315	55.23 178	70.71 246	79.90 237	32.07 333	72.09 258	35.61 330	81.73 248	77.08 249

thres600view7		61.82 258	61.38 257	63.12 263	71.81 259	34.93 333	64.64 272	56.99 315	54.78 185	70.33 250	79.74 239	32.07 333	72.42 256	38.61 308	83.46 232	82.02 179

tpm2		56.12 289	54.64 302	60.55 288	66.24 305	36.01 324	68.14 225	56.77 317	33.60 347	58.25 325	75.52 283	30.25 347	74.33 238	33.27 339	69.76 335	71.32 293

ECVR-MVS		64.82 226	65.22 221	63.60 256	78.80 165	31.14 352	66.97 244	56.47 318	54.23 195	69.94 253	88.68 104	37.23 312	74.81 232	45.28 274	89.41 149	84.86 94

CR-MVSNet		58.96 278	58.49 279	60.36 289	66.37 302	48.24 225	70.93 187	56.40 319	32.87 349	61.35 308	86.66 142	33.19 324	63.22 316	48.50 251	70.17 331	69.62 309

Patchmtry		60.91 264	63.01 245	54.62 310	66.10 308	26.27 366	67.47 233	56.40 319	54.05 201	72.04 228	86.66 142	33.19 324	60.17 324	43.69 280	87.45 176	77.42 245

MDTV_nov1_ep13			54.05 304		65.54 311	29.30 358	59.00 314	55.22 321	35.96 334	52.44 345	75.98 278	30.77 344	59.62 325	38.21 311	73.33 316

baseline1		57.82 285	58.36 281	56.19 306	69.17 282	30.76 354	62.94 293	55.21 322	46.04 277	63.83 296	78.47 257	41.20 288	63.68 313	39.44 301	68.99 337	74.13 268

door-mid								55.02 323

ADS-MVSNet2		48.76 319	47.25 327	53.29 314	55.90 361	40.54 294	47.34 348	54.99 324	31.41 357	50.48 351	72.06 316	31.23 338	54.26 335	25.93 360	55.93 363	65.07 334

baseline2		55.57 294	52.74 308	64.05 252	65.26 312	44.11 262	62.38 294	54.43 325	39.03 319	51.21 348	67.35 349	33.66 322	72.45 255	37.14 320	64.22 349	75.60 256

test1111		64.62 229	65.19 222	62.93 267	79.01 163	29.91 356	65.45 264	54.41 326	54.09 200	71.47 239	88.48 107	37.02 313	74.29 239	46.83 265	89.94 138	84.58 108

Vis-MVSNet (Re-imp)		62.74 251	63.21 243	61.34 281	72.19 256	31.56 349	67.31 239	53.87 327	53.60 208	69.88 254	83.37 197	40.52 295	70.98 269	41.40 292	86.78 190	81.48 187

pmmvs5		52.49 308	52.58 310	52.21 318	54.99 365	32.38 345	55.45 328	53.84 328	32.15 352	55.49 336	74.81 286	38.08 307	57.37 331	34.02 336	74.40 311	66.88 324

XXY-MVS		55.19 295	57.40 287	48.56 329	64.45 320	34.84 335	51.54 338	53.59 329	38.99 320	63.79 297	79.43 243	56.59 209	45.57 347	36.92 322	71.29 324	65.25 333

PVSNet	43.83 21	51.56 313	51.17 316	52.73 315	68.34 286	38.27 308	48.22 344	53.56 330	36.41 331	54.29 340	64.94 354	34.60 320	54.20 336	30.34 347	69.87 333	65.71 331

test_method		19.26 339	19.12 343	19.71 354	9.09 378	1.91 380	7.79 369	53.44 331	1.42 372	10.27 374	35.80 369	17.42 375	25.11 373	12.44 371	24.38 372	32.10 369

SCA		58.57 282	58.04 282	60.17 290	70.17 274	41.07 287	65.19 267	53.38 332	43.34 299	61.00 313	73.48 303	45.20 264	69.38 280	40.34 299	70.31 330	70.05 304

UnsupCasMVSNet_bld		50.01 317	51.03 319	46.95 330	58.61 350	32.64 344	48.31 343	53.27 333	34.27 342	60.47 315	71.53 321	41.40 286	47.07 344	30.68 346	60.78 354	61.13 348

wuyk23d		61.97 256	66.25 215	49.12 327	58.19 353	60.77 146	66.32 252	52.97 334	55.93 173	90.62 5	86.91 130	73.07 62	35.98 367	20.63 369	91.63 95	50.62 358

door								52.91 335

FMVSNet5		55.08 296	55.54 299	53.71 311	65.80 309	33.50 342	56.22 325	52.50 336	43.72 294	61.06 311	83.38 196	25.46 361	54.87 333	30.11 349	81.64 253	72.75 280

our_test_3		56.46 288	56.51 292	56.30 305	67.70 294	39.66 299	55.36 329	52.34 337	40.57 314	63.85 295	69.91 333	40.04 297	58.22 329	43.49 283	75.29 306	71.03 299

PatchmatchNet		54.60 297	54.27 303	55.59 308	65.17 315	39.08 301	66.92 245	51.80 338	39.89 315	58.39 323	73.12 307	31.69 335	58.33 328	43.01 284	58.38 361	69.38 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FPMVS		59.43 276	60.07 267	57.51 303	77.62 184	71.52 54	62.33 295	50.92 339	57.40 157	69.40 257	80.00 236	39.14 302	61.92 320	37.47 318	66.36 344	39.09 367

Anonymous20231206		54.13 299	55.82 297	49.04 328	70.89 264	35.96 325	51.73 337	50.87 340	34.86 337	62.49 303	79.22 248	42.52 283	44.29 355	27.95 357	81.88 246	66.88 324

new-patchmatchnet		52.89 305	55.76 298	44.26 342	59.94 344	6.31 377	37.36 364	50.76 341	41.10 308	64.28 292	79.82 238	44.77 267	48.43 342	36.24 327	87.61 171	78.03 241

tpmrst		50.15 316	51.38 315	46.45 334	56.05 359	24.77 368	64.40 277	49.98 342	36.14 332	53.32 344	69.59 335	35.16 318	48.69 341	39.24 303	58.51 360	65.89 329

WTY-MVS		49.39 318	50.31 321	46.62 333	61.22 336	32.00 348	46.61 350	49.77 343	33.87 344	54.12 341	69.55 336	41.96 284	45.40 349	31.28 345	64.42 348	62.47 345

testgi		54.00 302	56.86 290	45.45 337	58.20 352	25.81 367	49.05 341	49.50 344	45.43 281	67.84 271	81.17 222	51.81 232	43.20 359	29.30 353	79.41 277	67.34 322

test20.03		55.74 292	57.51 286	50.42 320	59.89 345	32.09 347	50.63 339	49.01 345	50.11 244	65.07 288	83.23 202	45.61 262	48.11 343	30.22 348	83.82 229	71.07 298

PatchMatch-RL		58.68 281	57.72 284	61.57 277	76.21 199	73.59 44	61.83 297	49.00 346	47.30 271	61.08 310	68.97 339	50.16 240	59.01 327	36.06 329	68.84 338	52.10 357

sss		47.59 323	48.32 323	45.40 338	56.73 358	33.96 339	45.17 353	48.51 347	32.11 354	52.37 346	65.79 352	40.39 296	41.91 363	31.85 342	61.97 352	60.35 349

MIMVSNet		54.39 298	56.12 296	49.20 325	72.57 253	30.91 353	59.98 309	48.43 348	41.66 305	55.94 333	83.86 191	41.19 289	50.42 338	26.05 359	75.38 304	66.27 328

JIA-IIPM		54.03 300	51.62 312	61.25 282	59.14 348	55.21 182	59.10 313	47.72 349	50.85 237	50.31 354	85.81 170	20.10 371	63.97 311	36.16 328	55.41 366	64.55 339

Patchmatch-RL test		59.95 272	59.12 273	62.44 271	72.46 254	54.61 186	59.63 311	47.51 350	41.05 310	74.58 196	74.30 295	31.06 341	65.31 305	51.61 225	79.85 271	67.39 320

MDA-MVSNet_test_wron		52.57 307	53.49 306	49.81 322	54.24 367	36.47 321	40.48 359	46.58 351	38.13 323	75.47 183	73.32 305	41.05 292	43.85 357	40.98 295	71.20 325	69.10 315

YYNet1		52.58 306	53.50 305	49.85 321	54.15 368	36.45 322	40.53 358	46.55 352	38.09 324	75.52 182	73.31 306	41.08 291	43.88 356	41.10 294	71.14 326	69.21 313

test-LLR		50.43 315	50.69 320	49.64 323	60.76 338	41.87 282	53.18 334	45.48 353	43.41 297	49.41 355	60.47 359	29.22 352	44.73 353	42.09 288	72.14 320	62.33 346

test-mter		48.56 320	48.20 325	49.64 323	60.76 338	41.87 282	53.18 334	45.48 353	31.91 355	49.41 355	60.47 359	18.34 372	44.73 353	42.09 288	72.14 320	62.33 346

tpm		50.60 314	52.42 311	45.14 339	65.18 314	26.29 365	60.30 307	43.50 355	37.41 327	57.01 327	79.09 252	30.20 349	42.32 360	32.77 341	66.36 344	66.81 326

PatchT		53.35 303	56.47 293	43.99 343	64.19 321	17.46 374	59.15 312	43.10 356	52.11 222	54.74 338	86.95 129	29.97 350	49.98 340	43.62 281	74.40 311	64.53 340

PM-MVS		64.49 232	63.61 238	67.14 229	76.68 194	75.15 32	68.49 222	42.85 357	51.17 235	77.85 142	80.51 228	45.76 259	66.31 303	52.83 221	76.35 295	59.96 350

GG-mvs-BLEND				52.24 317	60.64 340	29.21 359	69.73 200	42.41 358		45.47 360	52.33 365	20.43 370	68.16 287	25.52 363	65.42 346	59.36 351

PMMVS		44.69 329	43.95 336	46.92 331	50.05 373	53.47 194	48.08 346	42.40 359	22.36 369	44.01 366	53.05 364	42.60 282	45.49 348	31.69 343	61.36 353	41.79 365

dp		44.09 332	44.88 333	41.72 348	58.53 351	23.18 370	54.70 331	42.38 360	34.80 338	44.25 365	65.61 353	24.48 364	44.80 352	29.77 351	49.42 368	57.18 354

E-PMN		45.17 327	45.36 330	44.60 341	50.07 372	42.75 275	38.66 361	42.29 361	46.39 276	39.55 368	51.15 366	26.00 358	45.37 350	37.68 315	76.41 294	45.69 364

PVSNet_0	36.71 22	41.12 335	40.78 338	42.14 345	59.97 343	40.13 296	40.97 357	42.24 362	30.81 359	44.86 363	49.41 367	40.70 293	45.12 351	23.15 366	34.96 370	41.16 366

TESTMET0.1,1		45.17 327	44.93 332	45.89 336	56.02 360	38.31 307	53.18 334	41.94 363	27.85 362	44.86 363	56.47 362	17.93 373	41.50 364	38.08 313	68.06 340	57.85 352

Patchmatch-test		47.93 321	49.96 322	41.84 346	57.42 355	24.26 369	48.75 342	41.49 364	39.30 317	56.79 329	73.48 303	30.48 346	33.87 368	29.29 354	72.61 318	67.39 320

gg-mvs-nofinetune		55.75 291	56.75 291	52.72 316	62.87 327	28.04 361	68.92 210	41.36 365	71.09 44	50.80 350	92.63 12	20.74 369	66.86 297	29.97 350	72.41 319	63.25 341

test0.0.03 1		47.72 322	48.31 324	45.93 335	55.53 363	29.39 357	46.40 351	41.21 366	43.41 297	55.81 335	67.65 346	29.22 352	43.77 358	25.73 362	69.87 333	64.62 338

EMVS		44.61 331	44.45 335	45.10 340	48.91 374	43.00 273	37.92 362	41.10 367	46.75 274	38.00 370	48.43 368	26.42 356	46.27 345	37.11 321	75.38 304	46.03 363

ADS-MVSNet		44.62 330	45.58 329	41.73 347	55.90 361	20.83 372	47.34 348	39.94 368	31.41 357	50.48 351	72.06 316	31.23 338	39.31 365	25.93 360	55.93 363	65.07 334

pmmvs3		46.71 324	45.09 331	51.55 319	56.76 357	48.25 224	55.78 327	39.53 369	24.13 368	50.35 353	63.40 355	15.90 377	51.08 337	29.29 354	70.69 329	55.33 356

test2506		61.23 262	60.85 262	62.38 272	78.80 165	27.88 362	67.33 238	37.42 370	54.23 195	67.55 275	88.68 104	17.87 374	74.39 237	46.33 268	89.41 149	84.86 94

MVS-HIRNet		45.53 326	47.29 326	40.24 349	62.29 329	26.82 364	56.02 326	37.41 371	29.74 360	43.69 367	81.27 220	33.96 321	55.48 332	24.46 365	56.79 362	38.43 368

CHOSEN 280x420		41.62 334	39.89 339	46.80 332	61.81 331	51.59 201	33.56 365	35.74 372	27.48 363	37.64 371	53.53 363	23.24 367	42.09 361	27.39 358	58.64 359	46.72 362

EPMVS		45.74 325	46.53 328	43.39 344	54.14 369	22.33 371	55.02 330	35.00 373	34.69 340	51.09 349	70.20 330	25.92 359	42.04 362	37.19 319	55.50 365	65.78 330

new_pmnet		37.55 337	39.80 340	30.79 352	56.83 356	16.46 375	39.35 360	30.65 374	25.59 366	45.26 361	61.60 358	24.54 363	28.02 371	21.60 367	52.80 367	47.90 361

PMMVS2		37.74 336	40.87 337	28.36 353	42.41 376	5.35 378	24.61 366	27.75 375	32.15 352	47.85 357	70.27 329	35.85 317	29.51 370	19.08 370	67.85 341	50.22 359

DSMNet-mixed		43.18 333	44.66 334	38.75 351	54.75 366	28.88 360	57.06 322	27.42 376	13.47 370	47.27 359	77.67 267	38.83 303	39.29 366	25.32 364	60.12 356	48.08 360

MVE	27.91 23	36.69 338	35.64 341	39.84 350	43.37 375	35.85 327	19.49 367	24.61 377	24.68 367	39.05 369	62.63 357	38.67 305	27.10 372	21.04 368	47.25 369	56.56 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							84.83 33	19.26 378

tmp_tt		11.98 341	14.73 344	3.72 356	2.28 379	4.62 379	19.44 368	14.50 379	0.47 374	21.55 372	9.58 372	25.78 360	4.57 375	11.61 372	27.37 371	1.96 371

DeepMVS_CX				11.83 355	15.51 377	13.86 376		11.25 380	5.76 371	20.85 373	26.46 370	17.06 376	9.22 374	9.69 373	13.82 373	12.42 370

test123		4.43 344	5.78 347	0.39 358	0.97 380	0.28 381	46.33 352	0.45 381	0.31 375	0.62 376	1.50 375	0.61 381	0.11 377	0.56 374	0.63 374	0.77 373

testmvs		4.06 345	5.28 348	0.41 357	0.64 381	0.16 382	42.54 356	0.31 382	0.26 376	0.50 377	1.40 376	0.77 380	0.17 376	0.56 374	0.55 375	0.90 372

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		5.20 343	6.93 346	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	62.39 150	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

n2								0.00 383

nn								0.00 383

ab-mvs-re		5.62 342	7.50 345	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	67.46 347	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

PC_three_1452									46.98 273	81.83 93	86.28 154	66.55 118	84.47 72	63.31 139	90.78 120	83.49 137

eth-test2					0.00 382

eth-test					0.00 382

OPU-MVS				78.65 66	83.44 101	66.85 95	83.62 45				86.12 162	66.82 113	86.01 31	61.72 147	89.79 142	83.08 152

test_0728_THIRD									74.03 23	85.83 42	90.41 59	75.58 39	85.69 44	77.43 33	94.74 31	84.31 119

GSMVS																70.05 304

test_part2					85.90 63	66.44 98				84.61 63

sam_mvs1												31.41 336				70.05 304

sam_mvs												31.21 340

test_post1							66.63 249				2.08 373	30.66 345	59.33 326	40.34 299

test_post											1.99 374	30.91 343	54.76 334

patchmatchnet-post											68.99 338	31.32 337	69.38 280

gm-plane-assit					62.51 328	33.91 340			37.25 328		62.71 356		72.74 248	38.70 306

test9_res														72.12 70	91.37 101	77.40 246

agg_prior2														70.70 77	90.93 114	78.55 231

test_prior4						70.14 69	77.57 106

test_prior2							75.57 131		58.92 141	76.53 170	86.78 135	67.83 105		69.81 82	92.76 80

旧先验2							71.17 184		45.11 284	78.54 133			61.28 322	59.19 170

新几何2							71.33 180

原ACMM2							74.78 143

testdata2													67.30 294	48.34 252

segment_acmp												68.30 100

testdata1							68.34 224		57.24 158

plane_prior7					85.18 72	66.21 100

plane_prior6					84.18 91	65.31 107						60.83 168

plane_prior4											89.11 95

plane_prior3						65.67 104			63.82 101	78.23 136

plane_prior2							82.74 54		65.45 80

plane_prior1					84.46 86

plane_prior						65.18 108	80.06 80		61.88 119						89.91 139

HQP5-MVS						58.80 163

HQP-NCC					82.37 117		77.32 110		59.08 137	71.58 232

ACMP_Plane					82.37 117		77.32 110		59.08 137	71.58 232

BP-MVS														67.38 104

HQP4-MVS										71.59 231			85.31 52			83.74 132

HQP2-MVS												58.09 193

NP-MVS					83.34 102	63.07 126					85.97 166

MDTV_nov1_ep13_2view						18.41 373	53.74 333		31.57 356	44.89 362		29.90 351		32.93 340		71.48 292

ACMMP++_ref															89.47 148

ACMMP++															91.96 90

Test By Simon												62.56 146

High-resolution multi-view benchmark