High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 34	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 52	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 35	94.17 96	86.07 43	98.48 18	97.22 17

DROMVSNet		88.01 76	88.32 76	87.09 97	89.28 180	72.03 155	90.31 51	96.31 3	80.88 80	85.12 193	89.67 208	84.47 74	95.46 48	82.56 79	96.26 117	93.77 110

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

SF-MVS		90.27 39	90.80 45	88.68 75	92.86 87	77.09 109	91.19 41	95.74 5	81.38 74	92.28 58	93.80 100	86.89 51	94.64 78	85.52 48	97.51 74	94.30 87

CS-MVS-test		85.00 123	85.28 121	84.17 159	87.84 207	66.12 205	87.30 104	95.67 6	77.63 119	80.02 274	85.85 267	81.34 117	95.41 51	78.18 130	93.71 196	90.99 203

ACMH+	77.89 11	90.73 30	91.50 23	88.44 80	93.00 82	76.26 120	89.65 66	95.55 7	87.72 23	93.89 27	94.94 45	91.62 4	93.44 129	78.35 125	98.76 4	95.61 47

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 66	85.72 33	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 139	91.10 1	97.53 73	96.58 29
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
AllTest		87.97 78	87.40 87	89.68 54	91.59 126	83.40 50	89.50 71	95.44 9	79.47 95	88.00 140	93.03 114	82.66 91	91.47 185	70.81 202	96.14 121	94.16 92

TestCases				89.68 54	91.59 126	83.40 50		95.44 9	79.47 95	88.00 140	93.03 114	82.66 91	91.47 185	70.81 202	96.14 121	94.16 92

9.14			89.29 62		91.84 121		88.80 84	95.32 11	75.14 150	91.07 78	92.89 121	87.27 46	93.78 112	83.69 68	97.55 69

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 71	93.75 64	77.44 102	86.31 124	95.27 12	70.80 207	92.28 58	93.80 100	86.89 51	94.64 78	85.52 48	97.51 74	94.30 87

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 76	91.94 115	77.27 108	88.71 86	95.26 13	76.08 132	90.66 87	92.69 128	84.48 73	93.83 111	83.38 70	97.48 76	94.47 79

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 67	86.15 23	93.37 10	95.10 14	90.28 9	92.11 60	95.03 43	89.75 21	94.93 69	79.95 107	98.27 27	95.04 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 81	85.17 36	92.47 26	95.05 15	87.65 24	93.21 42	94.39 70	90.09 18	95.08 65	86.67 33	97.60 66	94.18 91

test_part1		87.15 87	87.82 80	85.15 137	88.88 189	63.04 231	87.98 94	94.85 16	82.52 61	93.61 37	95.73 26	67.51 234	95.71 31	80.48 104	98.83 2	96.69 25

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 43	93.51 8	94.85 16	82.88 57	91.77 68	93.94 97	90.55 13	95.73 30	88.50 7	98.23 29	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		82.02 179	82.63 168	80.19 233	84.80 260	57.56 291	82.39 209	94.72 18	71.24 203	80.22 272	84.89 287	75.85 170	94.56 84	76.08 155	93.49 201	88.46 252

LS3D		90.60 33	90.34 50	91.38 27	89.03 185	84.23 47	93.58 6	94.68 19	90.65 7	90.33 91	93.95 96	84.50 72	95.37 53	80.87 97	95.50 146	94.53 78

ETH3D cwj APD-0.16		87.83 81	87.62 83	88.47 78	91.21 142	78.20 90	87.26 105	94.54 20	72.05 194	88.89 122	92.31 140	83.86 77	94.24 90	81.59 90	96.87 91	92.97 141

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 93	94.51 21	75.79 141	92.94 44	94.96 44	88.36 29	95.01 67	90.70 2	98.40 20	95.09 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		85.50 111	86.14 105	83.58 172	87.97 204	67.13 195	87.55 100	94.32 22	73.44 168	88.47 132	87.54 240	86.45 58	91.06 200	75.76 160	93.76 193	92.54 157

LCM-MVSNet-Re		83.48 158	85.06 124	78.75 252	85.94 249	55.75 305	80.05 244	94.27 23	76.47 128	96.09 5	94.54 59	83.31 85	89.75 241	59.95 288	94.89 169	90.75 211

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 42	81.69 61	90.00 55	94.27 23	82.35 63	93.67 34	94.82 49	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 49	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 32	92.99 13	94.23 26	85.21 34	92.51 54	95.13 41	90.65 10	95.34 54	88.06 9	98.15 35	95.95 40

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 54	92.18 28	94.22 27	80.14 89	91.29 76	93.97 90	87.93 40	95.87 18	88.65 4	97.96 47	94.12 94

nrg030		87.85 80	88.49 74	85.91 122	90.07 171	69.73 174	87.86 97	94.20 28	74.04 160	92.70 52	94.66 53	85.88 65	91.50 184	79.72 110	97.32 80	96.50 30

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 61	93.64 68	79.07 83	88.54 89	94.20 28	73.53 166	89.71 106	94.82 49	85.09 67	95.77 29	84.17 63	98.03 39	93.26 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
abl_6		93.02 4	93.16 4	92.60 4	94.73 44	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 37	91.86 3	95.09 64	87.54 18	98.02 40	93.71 112

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 64	88.83 24	95.51 44	87.16 28	97.60 66	92.73 147

RE-MVS-def			92.61 5		94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 64	90.64 11		87.16 28	97.60 66	92.73 147

RPMNet		78.88 220	78.28 226	80.68 226	79.58 320	62.64 237	82.58 201	94.16 31	74.80 152	75.72 307	92.59 130	48.69 321	95.56 37	73.48 182	82.91 331	83.85 303

ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 27	93.35 11	94.16 31	82.52 61	92.39 57	94.14 83	89.15 23	95.62 34	87.35 23	98.24 28	94.56 75
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
APDe-MVS		91.22 24	91.92 13	89.14 65	92.97 83	78.04 92	92.84 16	94.14 35	83.33 51	93.90 25	95.73 26	88.77 26	96.41 1	87.60 16	97.98 44	92.98 138

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 141	81.66 64	91.25 39	94.13 36	88.89 13	88.83 125	94.26 75	77.55 151	95.86 21	84.88 55	95.87 134	95.24 57

test_one_0601					93.85 63	73.27 135		94.11 37	86.57 27	93.47 40	94.64 57	88.42 27

DVP-MVS++		90.07 42	91.09 35	87.00 98	91.55 132	72.64 141	96.19 2	94.10 38	85.33 32	93.49 38	94.64 57	81.12 119	95.88 16	87.41 21	95.94 130	92.48 159

test_0728_SECOND				86.79 102	94.25 49	72.45 149	90.54 46	94.10 38					95.88 16	86.42 34	97.97 45	92.02 180

DPE-MVS		90.53 35	91.08 36	88.88 67	93.38 73	78.65 88	89.15 77	94.05 40	84.68 38	93.90 25	94.11 85	88.13 36	96.30 3	84.51 60	97.81 54	91.70 191
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 76	94.05 40	79.03 103	92.87 46	93.74 104	90.60 12	95.21 61	82.87 76	98.76 4	94.87 64
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 46	89.49 72	93.98 42	79.68 93	92.09 61	93.89 98	83.80 79	93.10 143	82.67 78	98.04 37	93.64 117

baseline		85.20 116	85.93 108	83.02 182	86.30 241	62.37 242	84.55 147	93.96 43	74.48 157	87.12 151	92.03 146	82.30 96	91.94 174	78.39 123	94.21 184	94.74 71

casdiffmvs		85.21 115	85.85 110	83.31 177	86.17 246	62.77 235	83.03 190	93.93 44	74.69 154	88.21 138	92.68 129	82.29 97	91.89 177	77.87 137	93.75 195	95.27 56

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 112	93.91 45	80.07 90	86.75 162	93.26 110	93.64 2	90.93 203	84.60 59	90.75 259	93.97 98

test0726					94.16 53	72.56 145	90.63 45	93.90 46	83.61 47	93.75 31	94.49 61	89.76 19

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 24	92.75 17	93.90 46	78.43 111	89.16 120	92.25 143	72.03 215	96.36 2	88.21 8	90.93 254	92.98 138
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
PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 55	93.90 46	80.32 86	91.74 69	94.41 67	88.17 34	95.98 11	86.37 36	97.99 42	93.96 99

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 41	94.02 88	90.15 17	95.67 33	86.82 32	97.34 79	92.19 176

ACMH	76.49 14	89.34 60	91.14 34	83.96 163	92.50 95	70.36 171	89.55 68	93.84 50	81.89 69	94.70 13	95.44 34	90.69 9	88.31 261	83.33 71	98.30 26	93.20 130
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		88.96 67	89.88 53	86.22 115	91.63 125	77.07 110	89.82 61	93.77 51	78.90 104	92.88 45	92.29 141	86.11 62	90.22 225	86.24 41	97.24 82	91.36 199
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
GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 57	91.75 36	93.74 52	80.98 79	91.38 74	93.80 100	87.20 48	95.80 24	87.10 31	97.69 61	93.93 100

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 72	89.27 22	95.84 22	86.03 44	97.82 53	92.04 179

SED-MVS		90.46 37	91.64 19	86.93 99	94.18 50	72.65 139	90.47 49	93.69 54	83.77 44	94.11 23	94.27 72	90.28 15	95.84 22	86.03 44	97.92 48	92.29 170

test_241102_ONE					94.18 50	72.65 139		93.69 54	83.62 46	94.11 23	93.78 103	90.28 15	95.50 47

ACMMP_NAP		90.65 31	91.07 38	89.42 60	95.93 16	79.54 79	89.95 58	93.68 56	77.65 117	91.97 65	94.89 46	88.38 28	95.45 49	89.27 3	97.87 52	93.27 127

test1172		92.40 7	92.41 7	92.37 6	94.68 45	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 82	88.83 24	95.51 44	87.11 30	97.54 72	92.54 157

HQP_MVS		87.75 83	87.43 86	88.70 74	93.45 70	76.42 118	89.45 73	93.61 58	79.44 97	86.55 167	92.95 119	74.84 178	95.22 59	80.78 99	95.83 135	94.46 80

plane_prior5								93.61 58					95.22 59	80.78 99	95.83 135	94.46 80

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 127	93.60 60	80.16 88	89.13 121	93.44 108	83.82 78	90.98 201	83.86 66	95.30 154	93.60 119

testtj		89.51 57	89.48 60	89.59 58	92.26 102	80.80 69	90.14 54	93.54 61	83.37 50	90.57 88	92.55 133	84.99 68	96.15 5	81.26 91	96.61 101	91.83 187

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 82	88.78 191	79.72 76	87.15 108	93.50 62	69.17 223	85.80 184	89.56 209	80.76 123	92.13 167	73.21 189	95.51 145	93.25 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 61	80.97 68	91.49 38	93.48 63	82.82 58	92.60 53	93.97 90	88.19 33	96.29 4	87.61 15	98.20 32	94.39 84
Skip Steuart: Steuart Systems R&D Blog.
ETV-MVS		84.31 137	83.91 151	85.52 131	88.58 193	70.40 170	84.50 151	93.37 64	78.76 108	84.07 216	78.72 342	80.39 128	95.13 63	73.82 178	92.98 214	91.04 202

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 79	94.00 89	88.26 32	95.71 31	87.28 26	98.39 21	92.55 156

ACMM	79.39 9	90.65 31	90.99 40	89.63 56	95.03 35	83.53 49	89.62 67	93.35 66	79.20 100	93.83 28	93.60 107	90.81 8	92.96 146	85.02 54	98.45 19	92.41 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EIA-MVS		82.19 176	81.23 189	85.10 138	87.95 205	69.17 184	83.22 187	93.33 67	70.42 211	78.58 285	79.77 339	77.29 153	94.20 93	71.51 199	88.96 277	91.93 185

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 97	94.03 87	86.57 56	95.80 24	87.35 23	97.62 64	94.20 89

X-MVStestdata		85.04 121	82.70 165	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 97	16.05 372	86.57 56	95.80 24	87.35 23	97.62 64	94.20 89

ETH3 D test6400		85.09 119	84.87 128	85.75 127	90.80 155	69.34 178	85.90 128	93.31 70	65.43 262	86.11 177	89.95 203	80.92 121	94.86 71	75.90 158	95.57 144	93.05 135

WR-MVS_H		89.91 50	91.31 31	85.71 128	96.32 10	62.39 241	89.54 70	93.31 70	90.21 10	95.57 9	95.66 29	81.42 115	95.90 15	80.94 96	98.80 3	98.84 5

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 28	92.63 21	93.30 72	81.91 68	90.88 84	94.21 77	87.75 41	95.87 18	87.60 16	97.71 60	93.83 104

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 44	92.58 22	93.29 73	81.99 66	91.47 71	93.96 93	88.35 30	95.56 37	87.74 11	97.74 58	92.85 142

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 44	90.65 44	93.29 73	77.00 125	91.47 71	93.96 93	88.35 30	95.56 37	84.88 55	97.74 58	92.85 142

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 29	92.58 22	93.25 75	81.99 66	91.40 73	94.17 81	87.51 44	95.87 18	87.74 11	97.76 56	93.99 97

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 80	90.98 42	93.24 76	75.37 148	92.84 48	95.28 36	85.58 66	96.09 7	87.92 10	97.76 56	93.88 102
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
PEN-MVS		90.03 45	91.88 16	84.48 148	96.57 6	58.88 280	88.95 79	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 121	98.72 9	98.97 3

OMC-MVS		88.19 74	87.52 84	90.19 49	91.94 115	81.68 63	87.49 102	93.17 78	76.02 135	88.64 128	91.22 166	84.24 76	93.37 132	77.97 136	97.03 87	95.52 48

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 79	80.37 71	91.91 34	93.11 79	81.10 77	95.32 10	97.24 5	72.94 203	94.85 72	85.07 52	97.78 55	97.26 15

FC-MVSNet-test		85.93 107	87.05 91	82.58 194	92.25 103	56.44 300	85.75 131	93.09 80	77.33 121	91.94 66	94.65 54	74.78 180	93.41 131	75.11 167	98.58 14	97.88 7

APD-MVS		89.54 56	89.63 57	89.26 63	92.57 92	81.34 66	90.19 53	93.08 81	80.87 81	91.13 77	93.19 111	86.22 61	95.97 12	82.23 83	97.18 84	90.45 221
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
FIs		85.35 114	86.27 102	82.60 193	91.86 118	57.31 293	85.10 139	93.05 82	75.83 140	91.02 80	93.97 90	73.57 193	92.91 150	73.97 175	98.02 40	97.58 12

v7n		90.13 40	90.96 41	87.65 92	91.95 113	71.06 166	89.99 57	93.05 82	86.53 28	94.29 19	96.27 17	82.69 90	94.08 100	86.25 40	97.63 63	97.82 8

PHI-MVS		86.38 97	85.81 111	88.08 86	88.44 197	77.34 105	89.35 75	93.05 82	73.15 177	84.76 200	87.70 237	78.87 139	94.18 94	80.67 101	96.29 113	92.73 147

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 27	93.03 85	82.59 60	88.52 131	94.37 71	86.74 53	95.41 51	86.32 37	98.21 30	93.19 131
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Anonymous20231211		88.40 72	89.62 58	84.73 144	90.46 163	65.27 210	88.86 82	93.02 86	87.15 25	93.05 43	97.10 6	82.28 98	92.02 173	76.70 148	97.99 42	96.88 23

MSLP-MVS++		85.00 123	86.03 107	81.90 203	91.84 121	71.56 164	86.75 117	93.02 86	75.95 138	87.12 151	89.39 210	77.98 145	89.40 247	77.46 141	94.78 171	84.75 293

DP-MVS		88.60 71	89.01 66	87.36 95	91.30 139	77.50 101	87.55 100	92.97 88	87.95 22	89.62 110	92.87 122	84.56 71	93.89 107	77.65 138	96.62 100	90.70 213

ANet_high		83.17 165	85.68 115	75.65 292	81.24 301	45.26 360	79.94 246	92.91 89	83.83 43	91.33 75	96.88 10	80.25 130	85.92 290	68.89 224	95.89 133	95.76 42

UniMVSNet (Re)		86.87 88	86.98 93	86.55 106	93.11 80	68.48 187	83.80 167	92.87 90	80.37 84	89.61 112	91.81 154	77.72 148	94.18 94	75.00 168	98.53 16	96.99 22

test_prior3		86.31 98	86.31 101	86.32 110	90.59 160	71.99 156	83.37 180	92.85 91	75.43 145	84.58 203	91.57 158	81.92 108	94.17 96	79.54 113	96.97 88	92.80 144

test_prior				86.32 110	90.59 160	71.99 156		92.85 91					94.17 96			92.80 144

DTE-MVSNet		89.98 47	91.91 15	84.21 156	96.51 8	57.84 288	88.93 81	92.84 93	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 118	98.57 15	98.80 6

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 94	88.22 20	88.53 130	97.64 2	83.45 83	94.55 85	86.02 46	98.60 13	96.67 26

OPM-MVS		89.80 51	89.97 52	89.27 62	94.76 41	79.86 75	86.76 116	92.78 95	78.78 106	92.51 54	93.64 106	88.13 36	93.84 110	84.83 57	97.55 69	94.10 95
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PS-CasMVS		90.06 43	91.92 13	84.47 149	96.56 7	58.83 283	89.04 78	92.74 96	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 116	98.74 6	99.00 2

HQP3-MVS								92.68 97							94.47 179

HQP-MVS		84.61 129	84.06 147	86.27 113	91.19 143	70.66 168	84.77 141	92.68 97	73.30 172	80.55 266	90.17 201	72.10 211	94.61 80	77.30 144	94.47 179	93.56 121

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 99	83.09 54	91.54 70	94.25 76	87.67 43	95.51 44	87.21 27	98.11 36	93.12 133

CLD-MVS		83.18 164	82.64 167	84.79 142	89.05 184	67.82 193	77.93 275	92.52 100	68.33 232	85.07 194	81.54 323	82.06 102	92.96 146	69.35 217	97.91 50	93.57 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DELS-MVS		81.44 187	81.25 187	82.03 201	84.27 270	62.87 234	76.47 297	92.49 101	70.97 206	81.64 253	83.83 297	75.03 175	92.70 153	74.29 170	92.22 230	90.51 220
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
Effi-MVS+		83.90 151	84.01 148	83.57 173	87.22 221	65.61 209	86.55 121	92.40 102	78.64 109	81.34 257	84.18 295	83.65 81	92.93 148	74.22 171	87.87 291	92.17 177

DP-MVS Recon		84.05 146	83.22 157	86.52 107	91.73 124	75.27 124	83.23 186	92.40 102	72.04 195	82.04 243	88.33 227	77.91 147	93.95 105	66.17 243	95.12 160	90.34 223

DeepPCF-MVS	81.24 5	87.28 85	86.21 104	90.49 43	91.48 136	84.90 39	83.41 179	92.38 104	70.25 215	89.35 118	90.68 187	82.85 89	94.57 82	79.55 112	95.95 129	92.00 181

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 29	92.30 105	79.74 92	87.50 147	92.38 136	81.42 115	93.28 134	83.07 73	97.24 82	91.67 192

DU-MVS		86.80 91	86.99 92	86.21 117	93.24 77	67.02 197	83.16 188	92.21 106	81.73 70	90.92 81	91.97 147	77.20 154	93.99 102	74.16 172	98.35 22	97.61 10

v10		86.54 94	87.10 89	84.84 141	88.16 203	63.28 228	86.64 119	92.20 107	75.42 147	92.81 50	94.50 60	74.05 188	94.06 101	83.88 65	96.28 114	97.17 18

MCST-MVS		84.36 135	83.93 150	85.63 129	91.59 126	71.58 163	83.52 174	92.13 108	61.82 283	83.96 217	89.75 207	79.93 134	93.46 128	78.33 126	94.34 182	91.87 186

Vis-MVSNet		86.86 89	86.58 98	87.72 90	92.09 109	77.43 104	87.35 103	92.09 109	78.87 105	84.27 214	94.05 86	78.35 143	93.65 115	80.54 103	91.58 242	92.08 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CP-MVSNet		89.27 61	90.91 43	84.37 150	96.34 9	58.61 285	88.66 88	92.06 110	90.78 6	95.67 7	95.17 40	81.80 111	95.54 41	79.00 119	98.69 10	98.95 4

CDPH-MVS		86.17 103	85.54 117	88.05 88	92.25 103	75.45 123	83.85 164	92.01 111	65.91 255	86.19 174	91.75 156	83.77 80	94.98 68	77.43 143	96.71 98	93.73 111

DeepC-MVS_fast	80.27 8	86.23 100	85.65 116	87.96 89	91.30 139	76.92 111	87.19 106	91.99 112	70.56 210	84.96 195	90.69 186	80.01 132	95.14 62	78.37 124	95.78 139	91.82 188
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 75	77.96 95	87.94 96	91.97 113	70.73 209	94.19 22	96.67 11	76.94 160	94.57 82	83.07 73	96.28 114	96.15 32

MVS_Test		82.47 172	83.22 157	80.22 232	82.62 291	57.75 290	82.54 204	91.96 114	71.16 205	82.89 232	92.52 135	77.41 152	90.50 218	80.04 106	87.84 292	92.40 163

F-COLMAP		84.97 125	83.42 154	89.63 56	92.39 97	83.40 50	88.83 83	91.92 115	73.19 176	80.18 273	89.15 216	77.04 158	93.28 134	65.82 248	92.28 227	92.21 175

ZD-MVS					92.22 105	80.48 70		91.85 116	71.22 204	90.38 89	92.98 116	86.06 63	96.11 6	81.99 85	96.75 97

CSCG		86.26 99	86.47 99	85.60 130	90.87 153	74.26 129	87.98 94	91.85 116	80.35 85	89.54 116	88.01 231	79.09 137	92.13 167	75.51 161	95.06 162	90.41 222

PCF-MVS	74.62 15	82.15 177	80.92 194	85.84 125	89.43 177	72.30 151	80.53 239	91.82 118	57.36 313	87.81 143	89.92 205	77.67 149	93.63 117	58.69 293	95.08 161	91.58 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 80	91.81 119	84.07 40	92.00 63	94.40 68	86.63 54	95.28 57	88.59 5	98.31 24	92.30 168

MTGPA								91.81 119

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 30	91.81 119	84.07 40	92.00 63	94.40 68	86.63 54	95.28 57	88.59 5	98.31 24	92.30 168

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 146	91.58 129	73.24 136	84.21 153	91.67 122	62.86 276	80.94 259	87.16 247	67.27 236	92.87 151	69.82 214	88.94 278	87.99 258

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 119	92.86 87	67.02 197	82.55 203	91.56 123	83.08 55	90.92 81	91.82 153	78.25 144	93.99 102	74.16 172	98.35 22	97.49 13

v1240		84.30 138	84.51 139	83.65 170	87.65 213	61.26 252	82.85 195	91.54 124	67.94 238	90.68 86	90.65 189	71.71 217	93.64 116	82.84 77	94.78 171	96.07 35

原ACMM1				84.60 147	92.81 90	74.01 130		91.50 125	62.59 277	82.73 234	90.67 188	76.53 167	94.25 89	69.24 218	95.69 142	85.55 284

test11								91.46 126

CANet		83.79 152	82.85 163	86.63 104	86.17 246	72.21 154	83.76 168	91.43 127	77.24 123	74.39 317	87.45 242	75.36 173	95.42 50	77.03 147	92.83 217	92.25 174

v1192		84.57 130	84.69 134	84.21 156	87.75 210	62.88 233	83.02 191	91.43 127	69.08 225	89.98 99	90.89 180	72.70 207	93.62 120	82.41 80	94.97 166	96.13 33

alignmvs		83.94 150	83.98 149	83.80 165	87.80 209	67.88 192	84.54 149	91.42 129	73.27 175	88.41 134	87.96 232	72.33 210	90.83 208	76.02 157	94.11 187	92.69 151

GeoE		85.45 113	85.81 111	84.37 150	90.08 169	67.07 196	85.86 130	91.39 130	72.33 189	87.59 145	90.25 197	84.85 69	92.37 161	78.00 134	91.94 236	93.66 114

v8		86.22 101	86.83 96	84.36 152	87.82 208	62.35 243	86.42 122	91.33 131	76.78 127	92.73 51	94.48 62	73.41 197	93.72 114	83.10 72	95.41 147	97.01 21

TranMVSNet+NR-MVSNet		87.86 79	88.76 73	85.18 136	94.02 58	64.13 220	84.38 152	91.29 132	84.88 37	92.06 62	93.84 99	86.45 58	93.73 113	73.22 184	98.66 11	97.69 9

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 30	89.70 62	91.27 133	78.20 113	86.69 165	92.28 142	80.36 129	95.06 66	86.17 42	96.49 106	90.22 224

CNVR-MVS		87.81 82	87.68 82	88.21 85	92.87 85	77.30 107	85.25 137	91.23 134	77.31 122	87.07 155	91.47 162	82.94 88	94.71 75	84.67 58	96.27 116	92.62 154

v1921920		84.23 142	84.37 143	83.79 166	87.64 214	61.71 247	82.91 194	91.20 135	67.94 238	90.06 94	90.34 194	72.04 214	93.59 121	82.32 82	94.91 167	96.07 35

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 66	95.72 22	76.74 114	92.49 25	91.19 136	67.85 240	86.63 166	94.84 48	79.58 135	95.96 13	87.62 14	94.50 178	94.56 75
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RPSCF		88.00 77	86.93 94	91.22 31	90.08 169	89.30 5	89.68 64	91.11 137	79.26 99	89.68 107	94.81 52	82.44 93	87.74 265	76.54 150	88.74 281	96.61 28

NCCC		87.36 84	86.87 95	88.83 68	92.32 101	78.84 86	86.58 120	91.09 138	78.77 107	84.85 199	90.89 180	80.85 122	95.29 55	81.14 93	95.32 151	92.34 166

v144192		84.24 141	84.41 141	83.71 169	87.59 215	61.57 248	82.95 193	91.03 139	67.82 241	89.80 104	90.49 192	73.28 200	93.51 126	81.88 88	94.89 169	96.04 37

MSC_two_6792asdad				88.81 69	91.55 132	77.99 93		91.01 140					96.05 8	87.45 19	98.17 33	92.40 163

No_MVS				88.81 69	91.55 132	77.99 93		91.01 140					96.05 8	87.45 19	98.17 33	92.40 163

DVP-MVS		90.06 43	91.32 30	86.29 112	94.16 53	72.56 145	90.54 46	91.01 140	83.61 47	93.75 31	94.65 54	89.76 19	95.78 27	86.42 34	97.97 45	90.55 219
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
v1144		84.54 133	84.72 132	84.00 161	87.67 212	62.55 239	82.97 192	90.93 143	70.32 214	89.80 104	90.99 175	73.50 194	93.48 127	81.69 89	94.65 176	95.97 38

DPM-MVS		80.10 213	79.18 215	82.88 188	90.71 158	69.74 173	78.87 264	90.84 144	60.29 297	75.64 309	85.92 265	67.28 235	93.11 142	71.24 200	91.79 237	85.77 283

IU-MVS					94.18 50	72.64 141		90.82 145	56.98 315	89.67 108				85.78 47	97.92 48	93.28 126

PAPM_NR		83.23 163	83.19 159	83.33 176	90.90 152	65.98 206	88.19 92	90.78 146	78.13 115	80.87 261	87.92 235	73.49 196	92.42 158	70.07 212	88.40 282	91.60 194

Anonymous20240529		86.20 102	87.13 88	83.42 175	90.19 167	64.55 217	84.55 147	90.71 147	85.85 31	89.94 100	95.24 39	82.13 100	90.40 220	69.19 221	96.40 110	95.31 54

test12				86.57 105	90.74 156	72.63 143		90.69 148		82.76 233		79.20 136	94.80 73		95.32 151	92.27 172

PLC	73.85 16	82.09 178	80.31 200	87.45 94	90.86 154	80.29 72	85.88 129	90.65 149	68.17 234	76.32 300	86.33 257	73.12 202	92.61 156	61.40 280	90.02 267	89.44 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs_tets		89.78 52	89.27 63	91.30 28	93.51 69	84.79 41	89.89 60	90.63 150	70.00 218	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 127	95.52 48

114514_t		83.10 166	82.54 171	84.77 143	92.90 84	69.10 185	86.65 118	90.62 151	54.66 324	81.46 254	90.81 183	76.98 159	94.38 86	72.62 192	96.18 119	90.82 210

PAPR		78.84 221	78.10 228	81.07 217	85.17 256	60.22 265	82.21 216	90.57 152	62.51 278	75.32 312	84.61 291	74.99 176	92.30 164	59.48 291	88.04 289	90.68 214

NR-MVSNet		86.00 104	86.22 103	85.34 134	93.24 77	64.56 216	82.21 216	90.46 153	80.99 78	88.42 133	91.97 147	77.56 150	93.85 108	72.46 194	98.65 12	97.61 10

PVSNet_BlendedMVS		78.80 222	77.84 230	81.65 210	84.43 264	63.41 225	79.49 254	90.44 154	61.70 286	75.43 310	87.07 250	69.11 227	91.44 187	60.68 285	92.24 228	90.11 228

PVSNet_Blended		76.49 250	75.40 254	79.76 238	84.43 264	63.41 225	75.14 309	90.44 154	57.36 313	75.43 310	78.30 344	69.11 227	91.44 187	60.68 285	87.70 294	84.42 296

Gipuma		84.44 134	86.33 100	78.78 251	84.20 273	73.57 132	89.55 68	90.44 154	84.24 39	84.38 207	94.89 46	76.35 169	80.40 325	76.14 154	96.80 96	82.36 324
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
QAPM		82.59 170	82.59 170	82.58 194	86.44 234	66.69 201	89.94 59	90.36 157	67.97 237	84.94 197	92.58 132	72.71 206	92.18 166	70.63 208	87.73 293	88.85 249

TEST9					92.34 99	79.70 77	83.94 160	90.32 158	65.41 266	84.49 205	90.97 176	82.03 104	93.63 117

train_agg		85.98 106	85.28 121	88.07 87	92.34 99	79.70 77	83.94 160	90.32 158	65.79 256	84.49 205	90.97 176	81.93 106	93.63 117	81.21 92	96.54 104	90.88 208

test_8					92.09 109	78.87 85	83.82 165	90.31 160	65.79 256	84.36 208	90.96 178	81.93 106	93.44 129

agg_prior1		85.72 109	85.20 123	87.28 96	91.58 129	77.69 98	83.69 170	90.30 161	66.29 252	84.32 209	91.07 173	82.13 100	93.18 137	81.02 94	96.36 111	90.98 204

agg_prior					91.58 129	77.69 98		90.30 161		84.32 209			93.18 137

ITE_SJBPF				90.11 50	90.72 157	84.97 38		90.30 161	81.56 72	90.02 96	91.20 168	82.40 94	90.81 209	73.58 181	94.66 175	94.56 75

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 73	84.72 42	89.70 62	90.29 164	69.27 222	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 132	95.34 52

diffmvs		80.40 204	80.48 199	80.17 234	79.02 329	60.04 266	77.54 282	90.28 165	66.65 250	82.40 237	87.33 245	73.50 194	87.35 269	77.98 135	89.62 270	93.13 132

V42		83.47 159	83.37 156	83.75 168	83.16 286	63.33 227	81.31 228	90.23 166	69.51 221	90.91 83	90.81 183	74.16 186	92.29 165	80.06 105	90.22 265	95.62 46

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 175	86.67 17	90.51 48	90.20 167	69.87 219	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 112	97.09 19

c3_l		81.64 185	81.59 184	81.79 209	80.86 307	59.15 277	78.61 268	90.18 168	68.36 231	87.20 149	87.11 249	69.39 224	91.62 182	78.16 131	94.43 181	94.60 74

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 191	81.41 299	60.98 258	77.81 277	90.14 169	67.31 245	86.95 159	87.24 246	64.26 248	92.31 163	75.23 165	91.61 240	94.85 68

MVSFormer		82.23 175	81.57 185	84.19 158	85.54 253	69.26 180	91.98 31	90.08 170	71.54 198	76.23 301	85.07 283	58.69 283	94.27 87	86.26 38	88.77 279	89.03 246

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 98	82.98 55	91.98 31	90.08 170	71.54 198	94.28 21	96.54 13	81.57 113	94.27 87	86.26 38	96.49 106	97.09 19

AdaColmap		83.66 154	83.69 153	83.57 173	90.05 172	72.26 152	86.29 126	90.00 172	78.19 114	81.65 252	87.16 247	83.40 84	94.24 90	61.69 276	94.76 174	84.21 298

3Dnovator	80.37 7	84.80 126	84.71 133	85.06 139	86.36 239	74.71 126	88.77 85	90.00 172	75.65 143	84.96 195	93.17 112	74.06 187	91.19 195	78.28 127	91.09 246	89.29 240

IterMVS-LS		84.73 127	84.98 126	83.96 163	87.35 218	63.66 223	83.25 184	89.88 174	76.06 133	89.62 110	92.37 139	73.40 199	92.52 157	78.16 131	94.77 173	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
save fliter					93.75 64	77.44 102	86.31 124	89.72 175	70.80 207

v2v482		84.09 144	84.24 145	83.62 171	87.13 223	61.40 249	82.71 198	89.71 176	72.19 193	89.55 114	91.41 163	70.70 222	93.20 136	81.02 94	93.76 193	96.25 31

miper_ehance_all_eth		80.34 206	80.04 209	81.24 215	79.82 319	58.95 279	77.66 279	89.66 177	65.75 259	85.99 182	85.11 279	68.29 231	91.42 189	76.03 156	92.03 232	93.33 124

Fast-Effi-MVS+		81.04 192	80.57 195	82.46 198	87.50 216	63.22 229	78.37 271	89.63 178	68.01 235	81.87 246	82.08 318	82.31 95	92.65 155	67.10 236	88.30 287	91.51 197

Fast-Effi-MVS+-dtu		82.54 171	81.41 186	85.90 123	85.60 251	76.53 117	83.07 189	89.62 179	73.02 179	79.11 282	83.51 300	80.74 124	90.24 224	68.76 225	89.29 272	90.94 206

PMVS	80.48 6	90.08 41	90.66 47	88.34 83	96.71 3	92.97 1	90.31 51	89.57 180	88.51 19	90.11 93	95.12 42	90.98 7	88.92 251	77.55 140	97.07 86	83.13 316
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OpenMVS	76.72 13	81.98 182	82.00 178	81.93 202	84.42 266	68.22 189	88.50 90	89.48 181	66.92 247	81.80 250	91.86 149	72.59 208	90.16 227	71.19 201	91.25 245	87.40 266

test_0402		88.65 70	89.58 59	85.88 124	92.55 93	72.22 153	84.01 158	89.44 182	88.63 18	94.38 18	95.77 25	86.38 60	93.59 121	79.84 108	95.21 155	91.82 188

KD-MVS_self_test		81.93 183	83.14 160	78.30 261	84.75 261	52.75 323	80.37 241	89.42 183	70.24 216	90.26 92	93.39 109	74.55 185	86.77 278	68.61 228	96.64 99	95.38 51

Regformer-2		86.74 92	86.08 106	88.73 72	84.18 274	79.20 82	83.52 174	89.33 184	83.33 51	89.92 101	85.07 283	83.23 86	93.16 139	83.39 69	92.72 221	93.83 104

MSDG		80.06 214	79.99 210	80.25 231	83.91 279	68.04 191	77.51 283	89.19 185	77.65 117	81.94 244	83.45 302	76.37 168	86.31 285	63.31 265	86.59 301	86.41 275

ambc				82.98 183	90.55 162	64.86 213	88.20 91	89.15 186		89.40 117	93.96 93	71.67 218	91.38 192	78.83 120	96.55 103	92.71 150

pmmvs6		86.52 95	88.06 78	81.90 203	92.22 105	62.28 244	84.66 145	89.15 186	83.54 49	89.85 102	97.32 4	88.08 38	86.80 277	70.43 210	97.30 81	96.62 27

RRT_test8_iter05		78.08 231	77.52 232	79.75 239	80.84 308	52.54 327	80.61 238	88.96 188	67.77 242	84.62 202	89.29 212	33.89 366	92.10 170	77.59 139	94.15 186	94.62 72

miper_enhance_ethall		77.83 233	76.93 240	80.51 227	76.15 346	58.01 287	75.47 307	88.82 189	58.05 307	83.59 222	80.69 327	64.41 247	91.20 194	73.16 190	92.03 232	92.33 167

CNLPA		83.55 157	83.10 161	84.90 140	89.34 179	83.87 48	84.54 149	88.77 190	79.09 101	83.54 224	88.66 224	74.87 177	81.73 319	66.84 239	92.29 226	89.11 242

LF4IMVS		82.75 168	81.93 179	85.19 135	82.08 292	80.15 73	85.53 134	88.76 191	68.01 235	85.58 187	87.75 236	71.80 216	86.85 276	74.02 174	93.87 192	88.58 251

VPA-MVSNet		83.47 159	84.73 130	79.69 241	90.29 165	57.52 292	81.30 230	88.69 192	76.29 129	87.58 146	94.44 63	80.60 126	87.20 270	66.60 241	96.82 95	94.34 86

IS-MVSNet		86.66 93	86.82 97	86.17 119	92.05 111	66.87 199	91.21 40	88.64 193	86.30 30	89.60 113	92.59 130	69.22 226	94.91 70	73.89 176	97.89 51	96.72 24

BH-untuned		80.96 193	80.99 192	80.84 222	88.55 194	68.23 188	80.33 242	88.46 194	72.79 181	86.55 167	86.76 252	74.72 182	91.77 181	61.79 275	88.99 276	82.52 322

Effi-MVS+-dtu		85.82 108	83.38 155	93.14 3	87.13 223	91.15 2	87.70 99	88.42 195	74.57 155	83.56 223	85.65 268	78.49 141	94.21 92	72.04 196	92.88 216	94.05 96

mvs-test1		84.55 131	82.12 175	91.84 20	87.13 223	89.54 4	85.05 140	88.42 195	74.57 155	80.60 263	82.98 306	78.49 141	93.98 104	72.04 196	89.77 268	92.00 181

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 154	97.00 2	64.33 219	89.67 65	88.38 197	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 193	97.65 62	97.34 14

TinyColmap		81.25 189	82.34 174	77.99 267	85.33 255	60.68 262	82.32 211	88.33 198	71.26 202	86.97 158	92.22 145	77.10 157	86.98 274	62.37 269	95.17 157	86.31 277

CANet_DTU		77.81 235	77.05 238	80.09 235	81.37 300	59.90 268	83.26 183	88.29 199	69.16 224	67.83 343	83.72 298	60.93 265	89.47 243	69.22 220	89.70 269	90.88 208

GBi-Net		82.02 179	82.07 176	81.85 205	86.38 236	61.05 255	86.83 113	88.27 200	72.43 184	86.00 179	95.64 30	63.78 252	90.68 213	65.95 244	93.34 203	93.82 106

test1		82.02 179	82.07 176	81.85 205	86.38 236	61.05 255	86.83 113	88.27 200	72.43 184	86.00 179	95.64 30	63.78 252	90.68 213	65.95 244	93.34 203	93.82 106

FMVSNet1		84.55 131	85.45 119	81.85 205	90.27 166	61.05 255	86.83 113	88.27 200	78.57 110	89.66 109	95.64 30	75.43 172	90.68 213	69.09 222	95.33 150	93.82 106

SixPastTwentyTwo		87.20 86	87.45 85	86.45 108	92.52 94	69.19 183	87.84 98	88.05 203	81.66 71	94.64 14	96.53 14	65.94 243	94.75 74	83.02 75	96.83 94	95.41 50

USDC		76.63 247	76.73 243	76.34 287	83.46 282	57.20 295	80.02 245	88.04 204	52.14 339	83.65 221	91.25 165	63.24 255	86.65 281	54.66 318	94.11 187	85.17 288

Regformer-4		86.41 96	85.71 114	88.52 76	84.27 270	77.57 100	84.07 155	88.00 205	82.82 58	89.84 103	85.48 271	82.06 102	92.77 152	83.83 67	91.04 248	95.22 60

EPP-MVSNet		85.47 112	85.04 125	86.77 103	91.52 135	69.37 177	91.63 37	87.98 206	81.51 73	87.05 156	91.83 152	66.18 242	95.29 55	70.75 205	96.89 90	95.64 45

Regformer-1		86.00 104	85.50 118	87.49 93	84.18 274	76.90 112	83.52 174	87.94 207	82.18 65	89.19 119	85.07 283	82.28 98	91.89 177	82.40 81	92.72 221	93.69 113

MAR-MVS		80.24 209	78.74 220	84.73 144	86.87 233	78.18 91	85.75 131	87.81 208	65.67 261	77.84 290	78.50 343	73.79 191	90.53 217	61.59 279	90.87 256	85.49 286
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
API-MVS		82.28 174	82.61 169	81.30 212	86.29 242	69.79 172	88.71 86	87.67 209	78.42 112	82.15 242	84.15 296	77.98 145	91.59 183	65.39 250	92.75 218	82.51 323

pm-mvs1		83.69 153	84.95 127	79.91 236	90.04 173	59.66 270	82.43 207	87.44 210	75.52 144	87.85 142	95.26 38	81.25 118	85.65 294	68.74 226	96.04 124	94.42 83

cascas		76.29 253	74.81 258	80.72 225	84.47 263	62.94 232	73.89 318	87.34 211	55.94 318	75.16 314	76.53 353	63.97 250	91.16 196	65.00 252	90.97 253	88.06 256

HyFIR lowres test		75.12 261	72.66 280	82.50 197	91.44 138	65.19 211	72.47 325	87.31 212	46.79 355	80.29 269	84.30 294	52.70 310	92.10 170	51.88 334	86.73 300	90.22 224

TransMVSNet (Re)		84.02 147	85.74 113	78.85 250	91.00 150	55.20 310	82.29 212	87.26 213	79.65 94	88.38 135	95.52 33	83.00 87	86.88 275	67.97 233	96.60 102	94.45 82

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 185	88.31 198	71.73 159	79.53 251	87.17 214	65.43 262	79.59 276	82.73 313	76.94 160	90.14 230	73.22 184	88.33 283	86.90 272

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 185	88.31 198	71.73 159	79.53 251	87.17 214	65.43 262	79.59 276	82.73 313	76.94 160	90.14 230	73.22 184	88.33 283	86.90 272

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 185	88.31 198	71.73 159	79.53 251	87.17 214	65.43 262	79.59 276	82.73 313	76.94 160	90.14 230	73.22 184	88.33 283	86.90 272

cl22		78.97 219	78.21 227	81.24 215	77.74 333	59.01 278	77.46 285	87.13 217	65.79 256	84.32 209	85.10 280	58.96 282	90.88 207	75.36 164	92.03 232	93.84 103

PS-MVSNAJ		77.04 242	76.53 244	78.56 255	87.09 228	61.40 249	75.26 308	87.13 217	61.25 288	74.38 318	77.22 350	76.94 160	90.94 202	64.63 257	84.83 320	83.35 311

MVS_111021_HR		84.63 128	84.34 144	85.49 133	90.18 168	75.86 122	79.23 260	87.13 217	73.35 169	85.56 188	89.34 211	83.60 82	90.50 218	76.64 149	94.05 189	90.09 229

xiu_mvs_v2_base		77.19 240	76.75 242	78.52 256	87.01 229	61.30 251	75.55 306	87.12 220	61.24 289	74.45 316	78.79 341	77.20 154	90.93 203	64.62 258	84.80 321	83.32 312

1112_ss		74.82 266	73.74 267	78.04 266	89.57 176	60.04 266	76.49 296	87.09 221	54.31 325	73.66 321	79.80 337	60.25 271	86.76 280	58.37 294	84.15 324	87.32 267

cl____		80.42 203	80.23 202	81.02 219	79.99 317	59.25 274	77.07 288	87.02 222	67.37 244	86.18 176	89.21 214	63.08 257	90.16 227	76.31 152	95.80 137	93.65 116

DIV-MVS_self_test		80.43 202	80.23 202	81.02 219	79.99 317	59.25 274	77.07 288	87.02 222	67.38 243	86.19 174	89.22 213	63.09 256	90.16 227	76.32 151	95.80 137	93.66 114

EG-PatchMatch MVS		84.08 145	84.11 146	83.98 162	92.22 105	72.61 144	82.20 218	87.02 222	72.63 183	88.86 123	91.02 174	78.52 140	91.11 198	73.41 183	91.09 246	88.21 254

Baseline_NR-MVSNet		84.00 148	85.90 109	78.29 262	91.47 137	53.44 319	82.29 212	87.00 225	79.06 102	89.55 114	95.72 28	77.20 154	86.14 288	72.30 195	98.51 17	95.28 55

RRT_MVS		83.25 162	81.08 191	89.74 53	80.55 314	79.32 81	86.41 123	86.69 226	72.33 189	87.00 157	91.08 171	44.98 345	95.55 40	84.47 61	96.24 118	94.36 85

PAPM		71.77 290	70.06 300	76.92 279	86.39 235	53.97 314	76.62 294	86.62 227	53.44 330	63.97 358	84.73 290	57.79 291	92.34 162	39.65 363	81.33 339	84.45 295

FMVSNet2		81.31 188	81.61 183	80.41 229	86.38 236	58.75 284	83.93 162	86.58 228	72.43 184	87.65 144	92.98 116	63.78 252	90.22 225	66.86 237	93.92 191	92.27 172

BH-w/o		76.57 248	76.07 249	78.10 265	86.88 232	65.92 207	77.63 280	86.33 229	65.69 260	80.89 260	79.95 336	68.97 229	90.74 211	53.01 327	85.25 313	77.62 348

EGC-MVSNET		74.79 267	69.99 301	89.19 64	94.89 39	87.00 14	91.89 35	86.28 230	1.09 373	2.23 375	95.98 23	81.87 110	89.48 242	79.76 109	95.96 128	91.10 201

BH-RMVSNet		80.53 200	80.22 204	81.49 211	87.19 222	66.21 204	77.79 278	86.23 231	74.21 159	83.69 219	88.50 225	73.25 201	90.75 210	63.18 266	87.90 290	87.52 264

Test_1112_low_res		73.90 274	73.08 275	76.35 286	90.35 164	55.95 301	73.40 322	86.17 232	50.70 348	73.14 322	85.94 264	58.31 285	85.90 291	56.51 304	83.22 328	87.20 268

ab-mvs		79.67 215	80.56 196	76.99 277	88.48 195	56.93 296	84.70 144	86.06 233	68.95 227	80.78 262	93.08 113	75.30 174	84.62 303	56.78 302	90.90 255	89.43 236

v148		82.31 173	82.48 172	81.81 208	85.59 252	59.66 270	81.47 226	86.02 234	72.85 180	88.05 139	90.65 189	70.73 221	90.91 205	75.15 166	91.79 237	94.87 64

Anonymous20240521		80.18 211	81.25 187	76.95 278	83.15 287	60.84 260	82.46 206	85.99 235	68.76 229	86.78 160	93.73 105	59.13 280	77.44 332	73.71 179	97.55 69	92.56 155

MVS		73.21 279	72.59 281	75.06 296	80.97 304	60.81 261	81.64 223	85.92 236	46.03 358	71.68 329	77.54 346	68.47 230	89.77 239	55.70 309	85.39 310	74.60 353

FMVSNet3		78.80 222	78.55 222	79.57 243	82.89 290	56.89 298	81.76 220	85.77 237	69.04 226	86.00 179	90.44 193	51.75 313	90.09 233	65.95 244	93.34 203	91.72 190

UGNet		82.78 167	81.64 182	86.21 117	86.20 245	76.24 121	86.86 111	85.68 238	77.07 124	73.76 320	92.82 123	69.64 223	91.82 180	69.04 223	93.69 197	90.56 218
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
无先验							82.81 196	85.62 239	58.09 306				91.41 190	67.95 234		84.48 294

cdsmvs_eth3d_5k		20.81 340	27.75 343	0.00 359	0.00 382	0.00 383	0.00 370	85.44 240	0.00 377	0.00 378	82.82 311	81.46 114	0.00 378	0.00 376	0.00 376	0.00 374

1314		73.22 278	72.56 283	75.20 294	80.41 316	57.84 288	81.64 223	85.36 241	51.68 342	73.10 323	76.65 352	61.45 264	85.19 297	63.54 262	79.21 347	82.59 319

test_yl		78.71 224	78.51 223	79.32 246	84.32 268	58.84 281	78.38 269	85.33 242	75.99 136	82.49 235	86.57 253	58.01 286	90.02 236	62.74 267	92.73 219	89.10 243

DCV-MVSNet		78.71 224	78.51 223	79.32 246	84.32 268	58.84 281	78.38 269	85.33 242	75.99 136	82.49 235	86.57 253	58.01 286	90.02 236	62.74 267	92.73 219	89.10 243

Regformer-3		85.06 120	84.67 135	86.22 115	84.27 270	73.43 133	84.07 155	85.26 244	80.77 82	88.62 129	85.48 271	80.56 127	90.39 221	81.99 85	91.04 248	94.85 68

MVP-Stereo		75.81 256	73.51 271	82.71 191	89.35 178	73.62 131	80.06 243	85.20 245	60.30 296	73.96 319	87.94 233	57.89 290	89.45 245	52.02 330	74.87 357	85.06 290
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EI-MVSNet-Vis-set		85.12 118	84.53 138	86.88 100	84.01 277	72.76 138	83.91 163	85.18 246	80.44 83	88.75 126	85.49 270	80.08 131	91.92 175	82.02 84	90.85 257	95.97 38

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 101	83.87 280	72.52 147	83.82 165	85.15 247	80.27 87	88.75 126	85.45 274	79.95 133	91.90 176	81.92 87	90.80 258	96.13 33

EI-MVSNet		82.61 169	82.42 173	83.20 179	83.25 284	63.66 223	83.50 177	85.07 248	76.06 133	86.55 167	85.10 280	73.41 197	90.25 222	78.15 133	90.67 261	95.68 44

MVSTER		77.09 241	75.70 252	81.25 213	75.27 353	61.08 254	77.49 284	85.07 248	60.78 293	86.55 167	88.68 223	43.14 351	90.25 222	73.69 180	90.67 261	92.42 161

miper_lstm_enhance		76.45 251	76.10 248	77.51 274	76.72 341	60.97 259	64.69 349	85.04 250	63.98 272	83.20 228	88.22 228	56.67 295	78.79 330	73.22 184	93.12 209	92.78 146

WR-MVS		83.56 156	84.40 142	81.06 218	93.43 72	54.88 311	78.67 267	85.02 251	81.24 75	90.74 85	91.56 160	72.85 204	91.08 199	68.00 232	98.04 37	97.23 16

MG-MVS		80.32 207	80.94 193	78.47 258	88.18 201	52.62 326	82.29 212	85.01 252	72.01 196	79.24 281	92.54 134	69.36 225	93.36 133	70.65 207	89.19 275	89.45 234

h-mvs33		84.25 140	82.76 164	88.72 73	91.82 123	82.60 58	84.00 159	84.98 253	71.27 200	86.70 163	90.55 191	63.04 258	93.92 106	78.26 128	94.20 185	89.63 231

VDD-MVS		84.23 142	84.58 137	83.20 179	91.17 146	65.16 212	83.25 184	84.97 254	79.79 91	87.18 150	94.27 72	74.77 181	90.89 206	69.24 218	96.54 104	93.55 123

mvs_anonymous		78.13 230	78.76 219	76.23 290	79.24 326	50.31 344	78.69 266	84.82 255	61.60 287	83.09 231	92.82 123	73.89 190	87.01 271	68.33 231	86.41 303	91.37 198

D2MVS		76.84 244	75.67 253	80.34 230	80.48 315	62.16 246	73.50 320	84.80 256	57.61 311	82.24 239	87.54 240	51.31 314	87.65 266	70.40 211	93.19 208	91.23 200

MIMVSNet1		83.63 155	84.59 136	80.74 223	94.06 57	62.77 235	82.72 197	84.53 257	77.57 120	90.34 90	95.92 24	76.88 166	85.83 292	61.88 274	97.42 77	93.62 118

VNet		79.31 216	80.27 201	76.44 285	87.92 206	53.95 315	75.58 305	84.35 258	74.39 158	82.23 240	90.72 185	72.84 205	84.39 305	60.38 287	93.98 190	90.97 205

hse-mvs2		83.47 159	81.81 180	88.47 78	91.03 149	82.27 59	82.61 199	83.69 259	71.27 200	86.70 163	86.05 263	63.04 258	92.41 159	78.26 128	93.62 200	90.71 212

AUN-MVS		81.18 190	78.78 218	88.39 81	90.93 151	82.14 60	82.51 205	83.67 260	64.69 270	80.29 269	85.91 266	51.07 315	92.38 160	76.29 153	93.63 199	90.65 216

MVS_111021_LR		84.28 139	83.76 152	85.83 126	89.23 182	83.07 53	80.99 234	83.56 261	72.71 182	86.07 178	89.07 218	81.75 112	86.19 287	77.11 146	93.36 202	88.24 253

CHOSEN 1792x2688		72.45 284	70.56 294	78.13 264	90.02 174	63.08 230	68.72 338	83.16 262	42.99 364	75.92 305	85.46 273	57.22 294	85.18 298	49.87 339	81.67 336	86.14 278

TR-MVS		76.77 246	75.79 250	79.72 240	86.10 248	65.79 208	77.14 286	83.02 263	65.20 267	81.40 255	82.10 317	66.30 240	90.73 212	55.57 310	85.27 312	82.65 318

GA-MVS		75.83 255	74.61 259	79.48 245	81.87 294	59.25 274	73.42 321	82.88 264	68.68 230	79.75 275	81.80 320	50.62 317	89.46 244	66.85 238	85.64 309	89.72 230

tfpnnormal		81.79 184	82.95 162	78.31 260	88.93 188	55.40 306	80.83 237	82.85 265	76.81 126	85.90 183	94.14 83	74.58 184	86.51 282	66.82 240	95.68 143	93.01 137

OpenMVS_ROB	70.19 17	77.77 236	77.46 233	78.71 253	84.39 267	61.15 253	81.18 232	82.52 266	62.45 280	83.34 226	87.37 243	66.20 241	88.66 257	64.69 256	85.02 315	86.32 276

Anonymous202405211		80.51 201	81.19 190	78.49 257	88.48 195	57.26 294	76.63 293	82.49 267	81.21 76	84.30 212	92.24 144	67.99 232	86.24 286	62.22 270	95.13 158	91.98 184

EU-MVSNet		75.12 261	74.43 263	77.18 276	83.11 288	59.48 272	85.71 133	82.43 268	39.76 367	85.64 186	88.76 221	44.71 347	87.88 264	73.86 177	85.88 308	84.16 299

CMPMVS	59.41 20	75.12 261	73.57 269	79.77 237	75.84 348	67.22 194	81.21 231	82.18 269	50.78 347	76.50 297	87.66 238	55.20 304	82.99 313	62.17 273	90.64 264	89.09 245
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CDS-MVSNet		77.32 239	75.40 254	83.06 181	89.00 186	72.48 148	77.90 276	82.17 270	60.81 292	78.94 283	83.49 301	59.30 278	88.76 256	54.64 319	92.37 225	87.93 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	64.64 18	73.03 280	72.47 284	74.71 297	83.36 283	54.19 313	82.14 219	81.96 271	56.76 317	69.57 337	86.21 261	60.03 272	84.83 302	49.58 340	82.65 333	85.11 289

jason		77.42 238	75.75 251	82.43 199	87.10 227	69.27 179	77.99 274	81.94 272	51.47 343	77.84 290	85.07 283	60.32 270	89.00 249	70.74 206	89.27 274	89.03 246
jason: jason.
旧先验1					91.97 112	71.77 158		81.78 273			91.84 151	73.92 189			93.65 198	83.61 306

VPNet		80.25 208	81.68 181	75.94 291	92.46 96	47.98 351	76.70 292	81.67 274	73.45 167	84.87 198	92.82 123	74.66 183	86.51 282	61.66 277	96.85 92	93.33 124

TSAR-MVS + GP.		83.95 149	82.69 166	87.72 90	89.27 181	81.45 65	83.72 169	81.58 275	74.73 153	85.66 185	86.06 262	72.56 209	92.69 154	75.44 163	95.21 155	89.01 248

VDDNet		84.35 136	85.39 120	81.25 213	95.13 33	59.32 273	85.42 136	81.11 276	86.41 29	87.41 148	96.21 19	73.61 192	90.61 216	66.33 242	96.85 92	93.81 109

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 153	83.93 278	69.66 175	76.28 299	81.09 277	72.43 184	86.47 173	90.19 199	60.46 268	93.15 141	77.45 142	86.39 304	90.22 224

UnsupCasMVSNet_eth		71.63 292	72.30 285	69.62 319	76.47 343	52.70 325	70.03 335	80.97 278	59.18 300	79.36 279	88.21 229	60.50 267	69.12 351	58.33 296	77.62 352	87.04 270

MVS_0304		78.17 229	77.23 237	80.99 221	84.13 276	69.07 186	81.39 227	80.81 279	76.28 130	67.53 345	89.11 217	62.87 260	86.77 278	60.90 284	92.01 235	87.13 269

lupinMVS		76.37 252	74.46 262	82.09 200	85.54 253	69.26 180	76.79 290	80.77 280	50.68 349	76.23 301	82.82 311	58.69 283	88.94 250	69.85 213	88.77 279	88.07 255

CL-MVSNet_self_test		76.81 245	77.38 235	75.12 295	86.90 231	51.34 335	73.20 323	80.63 281	68.30 233	81.80 250	88.40 226	66.92 238	80.90 322	55.35 313	94.90 168	93.12 133

新几何1				82.95 184	93.96 59	78.56 89		80.24 282	55.45 320	83.93 218	91.08 171	71.19 220	88.33 260	65.84 247	93.07 210	81.95 329

1121		80.86 194	79.81 211	84.02 160	93.93 60	78.70 87	81.64 223	80.18 283	55.43 321	83.67 220	91.15 169	71.29 219	91.41 190	67.95 234	93.06 211	81.96 328

testdata				79.54 244	92.87 85	72.34 150		80.14 284	59.91 299	85.47 190	91.75 156	67.96 233	85.24 296	68.57 230	92.18 231	81.06 342

TAMVS		78.08 231	76.36 245	83.23 178	90.62 159	72.87 137	79.08 261	80.01 285	61.72 285	81.35 256	86.92 251	63.96 251	88.78 255	50.61 335	93.01 213	88.04 257

pmmvs-eth3d		78.42 228	77.04 239	82.57 196	87.44 217	74.41 128	80.86 236	79.67 286	55.68 319	84.69 201	90.31 196	60.91 266	85.42 295	62.20 271	91.59 241	87.88 261

bset_n11_16_dypcd		79.19 217	77.97 229	82.86 189	85.81 250	66.85 200	75.02 310	79.31 287	66.07 253	83.50 225	83.37 305	55.04 306	92.10 170	78.63 122	94.99 165	89.63 231

KD-MVS_2432*1600		66.87 314	65.81 319	70.04 315	67.50 370	47.49 353	62.56 353	79.16 288	61.21 290	77.98 288	80.61 328	25.29 378	82.48 315	53.02 325	84.92 316	80.16 345

miper_refine_blended		66.87 314	65.81 319	70.04 315	67.50 370	47.49 353	62.56 353	79.16 288	61.21 290	77.98 288	80.61 328	25.29 378	82.48 315	53.02 325	84.92 316	80.16 345

IterMVS		76.91 243	76.34 246	78.64 254	80.91 305	64.03 221	76.30 298	79.03 290	64.88 269	83.11 229	89.16 215	59.90 274	84.46 304	68.61 228	85.15 314	87.42 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		72.62 283	71.41 292	76.28 288	83.25 284	60.34 264	83.50 177	79.02 291	37.77 368	76.33 299	85.10 280	49.60 320	87.41 268	70.54 209	77.54 353	81.08 340

ppachtmachnet_test		74.73 268	74.00 266	76.90 280	80.71 311	56.89 298	71.53 329	78.42 292	58.24 305	79.32 280	82.92 310	57.91 289	84.26 306	65.60 249	91.36 244	89.56 233

FMVSNet5		72.10 288	71.69 288	73.32 302	81.57 297	53.02 322	76.77 291	78.37 293	63.31 273	76.37 298	91.85 150	36.68 363	78.98 328	47.87 347	92.45 224	87.95 259

MS-PatchMatch		70.93 295	70.22 298	73.06 305	81.85 295	62.50 240	73.82 319	77.90 294	52.44 336	75.92 305	81.27 324	55.67 301	81.75 318	55.37 312	77.70 351	74.94 352

test222					93.31 75	76.54 115	79.38 255	77.79 295	52.59 334	82.36 238	90.84 182	66.83 239			91.69 239	81.25 337

pmmvs4		74.92 264	72.98 277	80.73 224	84.95 257	71.71 162	76.23 300	77.59 296	52.83 333	77.73 293	86.38 255	56.35 298	84.97 299	57.72 300	87.05 298	85.51 285

EPNet		80.37 205	78.41 225	86.23 114	76.75 340	73.28 134	87.18 107	77.45 297	76.24 131	68.14 340	88.93 220	65.41 245	93.85 108	69.47 216	96.12 123	91.55 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		74.44 271	76.19 247	69.21 321	84.61 262	52.43 328	71.70 328	77.18 298	60.73 294	80.60 263	90.96 178	75.44 171	69.35 350	56.13 306	88.33 283	85.86 282

CR-MVSNet		74.00 273	73.04 276	76.85 282	79.58 320	62.64 237	82.58 201	76.90 299	50.50 350	75.72 307	92.38 136	48.07 323	84.07 307	68.72 227	82.91 331	83.85 303

Patchmtry		76.56 249	77.46 233	73.83 301	79.37 325	46.60 357	82.41 208	76.90 299	73.81 163	85.56 188	92.38 136	48.07 323	83.98 308	63.36 264	95.31 153	90.92 207

IB-MVS	62.13 19	71.64 291	68.97 305	79.66 242	80.80 310	62.26 245	73.94 317	76.90 299	63.27 274	68.63 339	76.79 351	33.83 367	91.84 179	59.28 292	87.26 296	84.88 291
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
K. test v3		85.14 117	84.73 130	86.37 109	91.13 147	69.63 176	85.45 135	76.68 302	84.06 42	92.44 56	96.99 8	62.03 262	94.65 77	80.58 102	93.24 206	94.83 70

ET-MVSNet_ETH3D		75.28 258	72.77 278	82.81 190	83.03 289	68.11 190	77.09 287	76.51 303	60.67 295	77.60 294	80.52 331	38.04 360	91.15 197	70.78 204	90.68 260	89.17 241

N_pmnet		70.20 298	68.80 307	74.38 299	80.91 305	84.81 40	59.12 359	76.45 304	55.06 322	75.31 313	82.36 316	55.74 300	54.82 368	47.02 349	87.24 297	83.52 307

thisisatest0530		79.07 218	77.33 236	84.26 155	87.13 223	64.58 215	83.66 172	75.95 305	68.86 228	85.22 192	87.36 244	38.10 359	93.57 124	75.47 162	94.28 183	94.62 72

EPNet_dtu		72.87 282	71.33 293	77.49 275	77.72 334	60.55 263	82.35 210	75.79 306	66.49 251	58.39 368	81.06 326	53.68 308	85.98 289	53.55 322	92.97 215	85.95 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_bld		69.21 307	69.68 302	67.82 328	79.42 323	51.15 338	67.82 343	75.79 306	54.15 326	77.47 295	85.36 278	59.26 279	70.64 347	48.46 344	79.35 345	81.66 331

MDA-MVSNet-bldmvs		77.47 237	76.90 241	79.16 248	79.03 328	64.59 214	66.58 346	75.67 308	73.15 177	88.86 123	88.99 219	66.94 237	81.23 321	64.71 255	88.22 288	91.64 193

pmmvs5		70.73 296	70.07 299	72.72 306	77.03 339	52.73 324	74.14 315	75.65 309	50.36 351	72.17 327	85.37 277	55.42 303	80.67 324	52.86 328	87.59 295	84.77 292

tttt0517		81.07 191	79.58 212	85.52 131	88.99 187	66.45 203	87.03 110	75.51 310	73.76 164	88.32 137	90.20 198	37.96 361	94.16 99	79.36 117	95.13 158	95.93 41

tpmvs		70.16 299	69.56 303	71.96 312	74.71 357	48.13 349	79.63 249	75.45 311	65.02 268	70.26 334	81.88 319	45.34 341	85.68 293	58.34 295	75.39 356	82.08 327

ADS-MVSNet2		65.87 321	63.64 326	72.55 309	73.16 362	56.92 297	67.10 344	74.81 312	49.74 352	66.04 348	82.97 307	46.71 325	77.26 333	42.29 358	69.96 364	83.46 308

new-patchmatchnet		70.10 300	73.37 273	60.29 347	81.23 302	16.95 377	59.54 357	74.62 313	62.93 275	80.97 258	87.93 234	62.83 261	71.90 345	55.24 314	95.01 164	92.00 181

Anonymous20231206		71.38 293	71.88 287	69.88 317	86.31 240	54.37 312	70.39 333	74.62 313	52.57 335	76.73 296	88.76 221	59.94 273	72.06 344	44.35 356	93.23 207	83.23 314

CostFormer		69.98 303	68.68 308	73.87 300	77.14 337	50.72 342	79.26 257	74.51 315	51.94 341	70.97 333	84.75 289	45.16 344	87.49 267	55.16 315	79.23 346	83.40 310

door-mid								74.45 316

thisisatest0515		73.00 281	70.52 295	80.46 228	81.45 298	59.90 268	73.16 324	74.31 317	57.86 308	76.08 304	77.78 345	37.60 362	92.12 169	65.00 252	91.45 243	89.35 237

baseline1		73.26 277	73.54 270	72.43 310	84.92 258	47.79 352	79.89 247	74.00 318	65.93 254	78.81 284	86.28 260	56.36 297	81.63 320	56.63 303	79.04 348	87.87 262

test_method		30.46 339	29.60 342	33.06 354	17.99 378	3.84 380	13.62 369	73.92 319	2.79 372	18.29 374	53.41 369	28.53 373	43.25 373	22.56 371	35.27 372	52.11 369

tfpn200view9		74.86 265	74.23 264	76.74 283	86.24 243	52.12 329	79.24 258	73.87 320	73.34 170	81.82 248	84.60 292	46.02 330	88.80 252	51.98 331	90.99 250	89.31 238

thres400		75.14 259	74.23 264	77.86 270	86.24 243	52.12 329	79.24 258	73.87 320	73.34 170	81.82 248	84.60 292	46.02 330	88.80 252	51.98 331	90.99 250	92.66 152

LFMVS		80.15 212	80.56 196	78.89 249	89.19 183	55.93 302	85.22 138	73.78 322	82.96 56	84.28 213	92.72 127	57.38 292	90.07 234	63.80 260	95.75 140	90.68 214

thres200		72.34 286	71.55 291	74.70 298	83.48 281	51.60 334	75.02 310	73.71 323	70.14 217	78.56 286	80.57 330	46.20 328	88.20 262	46.99 350	89.29 272	84.32 297

tpm cat1		66.76 316	65.21 322	71.42 313	77.09 338	50.62 343	78.01 273	73.68 324	44.89 360	68.64 338	79.00 340	45.51 338	82.42 317	49.91 338	70.15 363	81.23 339

testgi		72.36 285	74.61 259	65.59 334	80.56 313	42.82 367	68.29 339	73.35 325	66.87 248	81.84 247	89.93 204	72.08 213	66.92 358	46.05 353	92.54 223	87.01 271

thres100view900		75.45 257	75.05 257	76.66 284	87.27 219	51.88 332	81.07 233	73.26 326	75.68 142	83.25 227	86.37 256	45.54 336	88.80 252	51.98 331	90.99 250	89.31 238

thres600view7		75.97 254	75.35 256	77.85 271	87.01 229	51.84 333	80.45 240	73.26 326	75.20 149	83.10 230	86.31 259	45.54 336	89.05 248	55.03 316	92.24 228	92.66 152

wuyk23d		75.13 260	79.30 214	62.63 340	75.56 349	75.18 125	80.89 235	73.10 328	75.06 151	94.76 12	95.32 35	87.73 42	52.85 369	34.16 368	97.11 85	59.85 365

DWT-MVSNet_test		66.43 317	64.37 323	72.63 307	74.86 356	50.86 341	76.52 295	72.74 329	54.06 327	65.50 350	68.30 364	32.13 369	84.84 301	61.63 278	73.59 358	82.19 325

WTY-MVS		67.91 311	68.35 309	66.58 332	80.82 309	48.12 350	65.96 347	72.60 330	53.67 329	71.20 331	81.68 322	58.97 281	69.06 352	48.57 343	81.67 336	82.55 320

door								72.57 331

PVSNet	58.17 21	66.41 318	65.63 321	68.75 324	81.96 293	49.88 346	62.19 355	72.51 332	51.03 345	68.04 341	75.34 356	50.84 316	74.77 340	45.82 354	82.96 329	81.60 332

MDTV_nov1_ep13			68.29 310		78.03 332	43.87 364	74.12 316	72.22 333	52.17 337	67.02 346	85.54 269	45.36 340	80.85 323	55.73 307	84.42 323

test20.03		73.75 275	74.59 261	71.22 314	81.11 303	51.12 339	70.15 334	72.10 334	70.42 211	80.28 271	91.50 161	64.21 249	74.72 342	46.96 351	94.58 177	87.82 263

Vis-MVSNet (Re-imp)		77.82 234	77.79 231	77.92 268	88.82 190	51.29 337	83.28 182	71.97 335	74.04 160	82.23 240	89.78 206	57.38 292	89.41 246	57.22 301	95.41 147	93.05 135

MIMVSNet		71.09 294	71.59 289	69.57 320	87.23 220	50.07 345	78.91 262	71.83 336	60.20 298	71.26 330	91.76 155	55.08 305	76.09 336	41.06 361	87.02 299	82.54 321

tpm2		68.45 309	66.83 315	73.30 303	78.93 330	48.50 348	79.76 248	71.76 337	47.50 354	69.92 336	83.60 299	42.07 353	88.40 259	48.44 345	79.51 343	83.01 317

sss		66.92 313	67.26 313	65.90 333	77.23 336	51.10 340	64.79 348	71.72 338	52.12 340	70.13 335	80.18 334	57.96 288	65.36 363	50.21 336	81.01 341	81.25 337

our_test_3		71.85 289	71.59 289	72.62 308	80.71 311	53.78 316	69.72 336	71.71 339	58.80 302	78.03 287	80.51 332	56.61 296	78.84 329	62.20 271	86.04 307	85.23 287

SCA		73.32 276	72.57 282	75.58 293	81.62 296	55.86 303	78.89 263	71.37 340	61.73 284	74.93 315	83.42 303	60.46 268	87.01 271	58.11 298	82.63 335	83.88 300

lessismore_v0				85.95 121	91.10 148	70.99 167		70.91 341		91.79 67	94.42 66	61.76 263	92.93 148	79.52 115	93.03 212	93.93 100

tpmrst		66.28 319	66.69 317	65.05 337	72.82 365	39.33 368	78.20 272	70.69 342	53.16 332	67.88 342	80.36 333	48.18 322	74.75 341	58.13 297	70.79 362	81.08 340

PatchMatch-RL		74.48 269	73.22 274	78.27 263	87.70 211	85.26 35	75.92 302	70.09 343	64.34 271	76.09 303	81.25 325	65.87 244	78.07 331	53.86 321	83.82 325	71.48 356

PatchmatchNet		69.71 305	68.83 306	72.33 311	77.66 335	53.60 317	79.29 256	69.99 344	57.66 310	72.53 325	82.93 309	46.45 327	80.08 327	60.91 283	72.09 360	83.31 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ECVR-MVS		78.44 227	78.63 221	77.88 269	91.85 119	48.95 347	83.68 171	69.91 345	72.30 191	84.26 215	94.20 78	51.89 312	89.82 238	63.58 261	96.02 125	94.87 64

baseline2		69.77 304	66.89 314	78.41 259	79.51 322	58.09 286	76.23 300	69.57 346	57.50 312	64.82 356	77.45 348	46.02 330	88.44 258	53.08 324	77.83 350	88.70 250

test1111		78.53 226	78.85 217	77.56 273	92.22 105	47.49 353	82.61 199	69.24 347	72.43 184	85.28 191	94.20 78	51.91 311	90.07 234	65.36 251	96.45 108	95.11 61

Patchmatch-RL test		74.48 269	73.68 268	76.89 281	84.83 259	66.54 202	72.29 326	69.16 348	57.70 309	86.76 161	86.33 257	45.79 335	82.59 314	69.63 215	90.65 263	81.54 333

FPMVS		72.29 287	72.00 286	73.14 304	88.63 192	85.00 37	74.65 314	67.39 349	71.94 197	77.80 292	87.66 238	50.48 318	75.83 338	49.95 337	79.51 343	58.58 367

MDA-MVSNet_test_wron		70.05 302	70.44 296	68.88 323	73.84 358	53.47 318	58.93 361	67.28 350	58.43 303	87.09 154	85.40 275	59.80 276	67.25 356	59.66 290	83.54 326	85.92 281

YYNet1		70.06 301	70.44 296	68.90 322	73.76 359	53.42 320	58.99 360	67.20 351	58.42 304	87.10 153	85.39 276	59.82 275	67.32 355	59.79 289	83.50 327	85.96 279

test-LLR		67.21 312	66.74 316	68.63 325	76.45 344	55.21 308	67.89 340	67.14 352	62.43 281	65.08 353	72.39 358	43.41 349	69.37 348	61.00 281	84.89 318	81.31 335

test-mter		65.00 322	63.79 325	68.63 325	76.45 344	55.21 308	67.89 340	67.14 352	50.98 346	65.08 353	72.39 358	28.27 374	69.37 348	61.00 281	84.89 318	81.31 335

tpm		67.95 310	68.08 311	67.55 329	78.74 331	43.53 365	75.60 304	67.10 354	54.92 323	72.23 326	88.10 230	42.87 352	75.97 337	52.21 329	80.95 342	83.15 315

PM-MVS		80.20 210	79.00 216	83.78 167	88.17 202	86.66 18	81.31 228	66.81 355	69.64 220	88.33 136	90.19 199	64.58 246	83.63 311	71.99 198	90.03 266	81.06 342

JIA-IIPM		69.41 306	66.64 318	77.70 272	73.19 361	71.24 165	75.67 303	65.56 356	70.42 211	65.18 352	92.97 118	33.64 368	83.06 312	53.52 323	69.61 366	78.79 347

PatchT		70.52 297	72.76 279	63.79 339	79.38 324	33.53 373	77.63 280	65.37 357	73.61 165	71.77 328	92.79 126	44.38 348	75.65 339	64.53 259	85.37 311	82.18 326

dp		60.70 333	60.29 335	61.92 343	72.04 367	38.67 370	70.83 330	64.08 358	51.28 344	60.75 361	77.28 349	36.59 364	71.58 346	47.41 348	62.34 369	75.52 351

Patchmatch-test		65.91 320	67.38 312	61.48 345	75.51 350	43.21 366	68.84 337	63.79 359	62.48 279	72.80 324	83.42 303	44.89 346	59.52 367	48.27 346	86.45 302	81.70 330

TESTMET0.1,1		61.29 329	60.32 334	64.19 338	72.06 366	51.30 336	67.89 340	62.09 360	45.27 359	60.65 362	69.01 361	27.93 375	64.74 364	56.31 305	81.65 338	76.53 349

PVSNet_0	51.08 22	56.10 335	54.97 340	59.48 348	75.12 354	53.28 321	55.16 362	61.89 361	44.30 361	59.16 364	62.48 367	54.22 307	65.91 362	35.40 367	47.01 370	59.25 366

ADS-MVSNet		61.90 326	62.19 329	61.03 346	73.16 362	36.42 371	67.10 344	61.75 362	49.74 352	66.04 348	82.97 307	46.71 325	63.21 365	42.29 358	69.96 364	83.46 308

PMMVS		61.65 327	60.38 333	65.47 336	65.40 374	69.26 180	63.97 351	61.73 363	36.80 369	60.11 363	68.43 362	59.42 277	66.35 360	48.97 342	78.57 349	60.81 364

test0.0.03 1		64.66 323	64.36 324	65.57 335	75.03 355	46.89 356	64.69 349	61.58 364	62.43 281	71.18 332	77.54 346	43.41 349	68.47 353	40.75 362	82.65 333	81.35 334

E-PMN		61.59 328	61.62 330	61.49 344	66.81 372	55.40 306	53.77 363	60.34 365	66.80 249	58.90 366	65.50 365	40.48 356	66.12 361	55.72 308	86.25 305	62.95 363

CHOSEN 280x420		59.08 334	56.52 339	66.76 331	76.51 342	64.39 218	49.62 365	59.00 366	43.86 362	55.66 370	68.41 363	35.55 365	68.21 354	43.25 357	76.78 355	67.69 361

EMVS		61.10 331	60.81 332	61.99 342	65.96 373	55.86 303	53.10 364	58.97 367	67.06 246	56.89 369	63.33 366	40.98 354	67.03 357	54.79 317	86.18 306	63.08 362

pmmvs3		62.47 324	60.02 336	69.80 318	71.58 368	64.00 222	70.52 332	58.44 368	39.77 366	66.05 347	75.84 354	27.10 377	72.28 343	46.15 352	84.77 322	73.11 354

MVS-HIRNet		61.16 330	62.92 327	55.87 350	79.09 327	35.34 372	71.83 327	57.98 369	46.56 356	59.05 365	91.14 170	49.95 319	76.43 335	38.74 364	71.92 361	55.84 368

gg-mvs-nofinetune		68.96 308	69.11 304	68.52 327	76.12 347	45.32 359	83.59 173	55.88 370	86.68 26	64.62 357	97.01 7	30.36 371	83.97 309	44.78 355	82.94 330	76.26 350

GG-mvs-BLEND				67.16 330	73.36 360	46.54 358	84.15 154	55.04 371		58.64 367	61.95 368	29.93 372	83.87 310	38.71 365	76.92 354	71.07 357

EPMVS		62.47 324	62.63 328	62.01 341	70.63 369	38.74 369	74.76 312	52.86 372	53.91 328	67.71 344	80.01 335	39.40 357	66.60 359	55.54 311	68.81 367	80.68 344

new_pmnet		55.69 336	57.66 338	49.76 352	75.47 351	30.59 374	59.56 356	51.45 373	43.62 363	62.49 359	75.48 355	40.96 355	49.15 371	37.39 366	72.52 359	69.55 359

PMMVS2		55.64 337	59.27 337	44.74 353	64.30 375	12.32 378	40.60 366	49.79 374	53.19 331	65.06 355	84.81 288	53.60 309	49.76 370	32.68 370	89.41 271	72.15 355

test2506		74.12 272	73.39 272	76.28 288	91.85 119	44.20 363	84.06 157	48.20 375	72.30 191	81.90 245	94.20 78	27.22 376	89.77 239	64.81 254	96.02 125	94.87 64

DSMNet-mixed		60.98 332	61.61 331	59.09 349	72.88 364	45.05 361	74.70 313	46.61 376	26.20 370	65.34 351	90.32 195	55.46 302	63.12 366	41.72 360	81.30 340	69.09 360

MVE	40.22 23	51.82 338	50.47 341	55.87 350	62.66 376	51.91 331	31.61 368	39.28 377	40.65 365	50.76 371	74.98 357	56.24 299	44.67 372	33.94 369	64.11 368	71.04 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							90.66 43	33.14 378

tmp_tt		20.25 341	24.50 344	7.49 356	4.47 379	8.70 379	34.17 367	25.16 379	1.00 374	32.43 373	18.49 371	39.37 358	9.21 375	21.64 372	43.75 371	4.57 371

DeepMVS_CX				24.13 355	32.95 377	29.49 375		21.63 380	12.07 371	37.95 372	45.07 370	30.84 370	19.21 374	17.94 373	33.06 373	23.69 370

test123		6.27 344	8.08 347	0.84 357	1.11 381	0.57 381	62.90 352	0.82 381	0.54 375	1.07 377	2.75 376	1.26 380	0.30 376	1.04 374	1.26 375	1.66 372

testmvs		5.91 345	7.65 348	0.72 358	1.20 380	0.37 382	59.14 358	0.67 382	0.49 376	1.11 376	2.76 375	0.94 381	0.24 377	1.02 375	1.47 374	1.55 373

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		6.41 343	8.55 346	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	76.94 160	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		6.65 342	8.87 345	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	79.80 337	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									58.96 301	90.06 94	91.33 164	80.66 125	93.03 145	75.78 159	95.94 130	92.48 159

eth-test2					0.00 382

eth-test					0.00 382

OPU-MVS				88.27 84	91.89 117	77.83 96	90.47 49				91.22 166	81.12 119	94.68 76	74.48 169	95.35 149	92.29 170

test_0728_THIRD									85.33 32	93.75 31	94.65 54	87.44 45	95.78 27	87.41 21	98.21 30	92.98 138

GSMVS																83.88 300

test_part2					93.86 62	77.77 97				92.84 48

sam_mvs1												46.11 329				83.88 300

sam_mvs												45.92 334

test_post1							78.85 265				3.13 373	45.19 343	80.13 326	58.11 298

test_post											3.10 374	45.43 339	77.22 334

patchmatchnet-post											81.71 321	45.93 333	87.01 271

gm-plane-assit					75.42 352	44.97 362			52.17 337		72.36 360		87.90 263	54.10 320

test9_res														80.83 98	96.45 108	90.57 217

agg_prior2														79.68 111	96.16 120	90.22 224

test_prior4						78.97 84	84.59 146

test_prior2							83.37 180		75.43 145	84.58 203	91.57 158	81.92 108		79.54 113	96.97 88

旧先验2							81.73 221		56.88 316	86.54 172			84.90 300	72.81 191

新几何2							81.72 222

原ACMM2							82.26 215

testdata2													86.43 284	63.52 263

segment_acmp												81.94 105

testdata1							79.62 250		73.95 162

plane_prior7					93.45 70	77.31 106

plane_prior6					92.61 91	76.54 115						74.84 178

plane_prior4											92.95 119

plane_prior3						76.85 113			77.79 116	86.55 167

plane_prior2							89.45 73		79.44 97

plane_prior1					92.83 89

plane_prior						76.42 118	87.15 108		75.94 139						95.03 163

HQP5-MVS						70.66 168

HQP-NCC					91.19 143		84.77 141		73.30 172	80.55 266

ACMP_Plane					91.19 143		84.77 141		73.30 172	80.55 266

BP-MVS														77.30 144

HQP4-MVS										80.56 265			94.61 80			93.56 121

HQP2-MVS												72.10 211

NP-MVS					91.95 113	74.55 127					90.17 201

MDTV_nov1_ep13_2view						27.60 376	70.76 331		46.47 357	61.27 360		45.20 342		49.18 341		83.75 305

ACMMP++_ref															95.74 141

ACMMP++															97.35 78

Test By Simon												79.09 137

High-resolution multi-view benchmark