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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

XVG-OURS-SEG-HR		95.38 72	95.00 89	96.51 48	98.10 77	94.07 20	92.46 175	98.13 45	90.69 135	93.75 191	96.25 152	98.03 2	97.02 288	92.08 96	95.55 287	98.45 119

pmmvs6		96.80 13	97.36 9	95.15 99	99.12 8	87.82 129	96.68 25	97.86 83	96.10 26	98.14 24	99.28 3	97.94 3	98.21 216	91.38 119	99.69 15	99.42 19

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 87	99.51 2	87.38 134	97.70 8	97.54 110	98.16 2	98.94 2	99.33 2	97.84 4	99.08 99	90.73 128	99.73 14	99.59 12

ACMH	88.36 12	96.59 27	97.43 5	94.07 142	98.56 38	85.33 179	96.33 43	98.30 23	94.66 40	98.72 8	98.30 30	97.51 5	98.00 234	94.87 14	99.59 27	98.86 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 29	97.16 12	98.17 40	93.11 70	96.48 79	97.36 75	96.92 6	99.34 62	94.31 23	99.38 55	98.92 67

ACMH+	88.43 11	96.48 30	96.82 16	95.47 84	98.54 43	89.06 100	95.65 71	98.61 9	96.10 26	98.16 23	97.52 63	96.90 7	98.62 178	90.30 141	99.60 25	98.72 91

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 19	93.53 39	97.51 9	98.44 12	92.35 82	95.95 107	96.41 136	96.71 8	99.42 29	93.99 33	99.36 56	99.13 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
abl_6		97.31 5	97.12 13	97.86 3	98.54 43	95.32 7	96.61 27	98.35 19	95.81 31	97.55 36	97.44 68	96.51 9	99.40 43	94.06 30	99.23 79	98.85 76

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 23	92.51 50	96.58 29	97.61 105	87.57 205	98.80 7	98.90 9	96.50 10	99.59 12	96.15 7	99.47 39	99.40 21

SED-MVS		96.00 52	96.41 32	94.76 112	98.51 47	86.97 144	95.21 86	98.10 49	91.95 92	97.63 32	97.25 83	96.48 11	99.35 59	93.29 63	99.29 68	97.95 160

test_241102_ONE					98.51 47	86.97 144		98.10 49	91.85 98	97.63 32	97.03 96	96.48 11	98.95 122

LPG-MVS_test		96.38 40	96.23 39	96.84 40	98.36 62	92.13 54	95.33 82	98.25 27	91.78 105	97.07 53	97.22 86	96.38 13	99.28 73	92.07 97	99.59 27	99.11 41

LGP-MVS_train				96.84 40	98.36 62	92.13 54		98.25 27	91.78 105	97.07 53	97.22 86	96.38 13	99.28 73	92.07 97	99.59 27	99.11 41

ACMM	88.83 9	96.30 43	96.07 50	96.97 35	98.39 58	92.95 46	94.74 105	98.03 65	90.82 132	97.15 51	96.85 107	96.25 15	99.00 114	93.10 72	99.33 60	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		87.83 269	90.79 206	78.96 350	90.46 339	88.63 109	92.72 163	90.67 312	91.65 113	98.68 11	97.64 57	96.06 16	77.53 371	59.84 366	99.41 52	70.73 369

ACMP	88.15 13	95.71 61	95.43 75	96.54 47	98.17 73	91.73 62	94.24 124	98.08 53	89.46 159	96.61 76	96.47 131	95.85 17	99.12 93	90.45 132	99.56 33	98.77 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TransMVSNet (Re)		95.27 80	96.04 52	92.97 178	98.37 61	81.92 219	95.07 94	96.76 173	93.97 54	97.77 28	98.57 19	95.72 18	97.90 240	88.89 179	99.23 79	99.08 45

ZNCC-MVS		96.42 36	96.20 41	97.07 30	98.80 27	92.79 48	96.08 54	98.16 43	91.74 109	95.34 135	96.36 144	95.68 19	99.44 24	94.41 21	99.28 73	98.97 59

ACMMP_NAP		96.21 45	96.12 47	96.49 50	98.90 18	91.42 64	94.57 113	98.03 65	90.42 143	96.37 82	97.35 78	95.68 19	99.25 77	94.44 20	99.34 58	98.80 80

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 92	93.82 34	96.31 45	98.25 27	95.51 35	96.99 60	97.05 95	95.63 21	99.39 48	93.31 62	98.88 120	98.75 85

DVP-MVS		95.82 58	96.18 42	94.72 114	98.51 47	86.69 151	95.20 88	97.00 151	91.85 98	97.40 46	97.35 78	95.58 22	99.34 62	93.44 55	99.31 63	98.13 141
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
test0726					98.51 47	86.69 151	95.34 81	98.18 36	91.85 98	97.63 32	97.37 72	95.58 22

MP-MVS-pluss		96.08 49	95.92 57	96.57 46	99.06 10	91.21 66	93.25 151	98.32 20	87.89 195	96.86 65	97.38 71	95.55 24	99.39 48	95.47 10	99.47 39	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 59	94.31 17	96.79 23	98.32 20	96.69 17	96.86 65	97.56 60	95.48 25	98.77 155	90.11 150	99.44 45	98.31 127
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SD-MVS		95.19 81	95.73 66	93.55 161	96.62 161	88.88 106	94.67 107	98.05 60	91.26 121	97.25 50	96.40 137	95.42 26	94.36 344	92.72 84	99.19 85	97.40 206
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
RE-MVS-def			96.66 20		98.07 79	95.27 8	96.37 40	98.12 46	95.66 33	97.00 58	97.03 96	95.40 27		93.49 48	98.84 125	98.00 152

test_241102_TWO								98.10 49	91.95 92	97.54 37	97.25 83	95.37 28	99.35 59	93.29 63	99.25 76	98.49 115

HFP-MVS		96.39 39	96.17 44	97.04 31	98.51 47	93.37 40	96.30 47	97.98 72	92.35 82	95.63 122	96.47 131	95.37 28	99.27 75	93.78 38	99.14 92	98.48 116

#test#		95.89 54	95.51 71	97.04 31	98.51 47	93.37 40	95.14 91	97.98 72	89.34 163	95.63 122	96.47 131	95.37 28	99.27 75	91.99 99	99.14 92	98.48 116

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 28	92.49 51	96.44 37	97.42 118	86.96 214	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 43	99.32 26

TranMVSNet+NR-MVSNet		96.07 50	96.26 38	95.50 83	98.26 67	87.69 130	93.75 140	97.86 83	95.96 30	97.48 41	97.14 90	95.33 32	99.44 24	90.79 127	99.76 11	99.38 22

PMVS	87.21 14	94.97 86	95.33 78	93.91 150	98.97 15	97.16 2	95.54 76	95.85 213	96.47 21	93.40 202	97.46 67	95.31 33	95.47 328	86.18 229	98.78 137	89.11 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pm-mvs1		95.43 70	95.94 55	93.93 148	98.38 59	85.08 182	95.46 79	97.12 145	91.84 101	97.28 48	98.46 25	95.30 34	97.71 260	90.17 148	99.42 47	98.99 53

PGM-MVS		96.32 41	95.94 55	97.43 19	98.59 37	93.84 33	95.33 82	98.30 23	91.40 118	95.76 116	96.87 106	95.26 35	99.45 23	92.77 80	99.21 82	99.00 51

PS-CasMVS		96.69 20	97.43 5	94.49 129	99.13 6	84.09 195	96.61 27	97.97 75	97.91 5	98.64 13	98.13 34	95.24 36	99.65 3	93.39 59	99.84 3	99.72 2

GST-MVS		96.24 44	95.99 54	97.00 34	98.65 30	92.71 49	95.69 70	98.01 69	92.08 90	95.74 118	96.28 149	95.22 37	99.42 29	93.17 69	99.06 98	98.88 72

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 25	93.86 32	99.07 2	98.98 4	97.01 13	98.92 4	98.78 14	95.22 37	98.61 179	96.85 2	99.77 10	99.31 27
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
DPE-MVS		95.89 54	95.88 58	95.92 63	97.93 93	89.83 86	93.46 147	98.30 23	92.37 80	97.75 29	96.95 99	95.14 39	99.51 18	91.74 108	99.28 73	98.41 122
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_one_0601					98.26 67	87.14 139		98.18 36	94.25 48	96.99 60	97.36 75	95.13 40

nrg030		96.32 41	96.55 26	95.62 78	97.83 96	88.55 113	95.77 66	98.29 26	92.68 73	98.03 26	97.91 46	95.13 40	98.95 122	93.85 36	99.49 38	99.36 24

APDe-MVS		96.46 32	96.64 22	95.93 61	97.68 109	89.38 97	96.90 19	98.41 16	92.52 77	97.43 43	97.92 45	95.11 42	99.50 19	94.45 19	99.30 65	98.92 67

ACMMP		96.61 24	96.34 34	97.43 19	98.61 34	93.88 30	96.95 18	98.18 36	92.26 85	96.33 85	96.84 110	95.10 43	99.40 43	93.47 52	99.33 60	99.02 50
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
SR-MVS		96.70 19	96.42 29	97.54 11	98.05 81	94.69 11	96.13 52	98.07 56	95.17 37	96.82 67	96.73 118	95.09 44	99.43 28	92.99 77	98.71 142	98.50 114

OPM-MVS		95.61 64	95.45 73	96.08 54	98.49 55	91.00 69	92.65 167	97.33 129	90.05 148	96.77 70	96.85 107	95.04 45	98.56 187	92.77 80	99.06 98	98.70 94
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DTE-MVSNet		96.74 17	97.43 5	94.67 116	99.13 6	84.68 185	96.51 31	97.94 81	98.14 3	98.67 12	98.32 29	95.04 45	99.69 2	93.27 65	99.82 8	99.62 10

region2R		96.41 37	96.09 48	97.38 23	98.62 32	93.81 36	96.32 44	97.96 76	92.26 85	95.28 139	96.57 128	95.02 47	99.41 36	93.63 42	99.11 96	98.94 62

PEN-MVS		96.69 20	97.39 8	94.61 118	99.16 4	84.50 186	96.54 30	98.05 60	98.06 4	98.64 13	98.25 31	95.01 48	99.65 3	92.95 78	99.83 6	99.68 4

SteuartSystems-ACMMP		96.40 38	96.30 36	96.71 42	98.63 31	91.96 57	95.70 68	98.01 69	93.34 67	96.64 74	96.57 128	94.99 49	99.36 58	93.48 51	99.34 58	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
canonicalmvs		94.59 103	94.69 100	94.30 136	95.60 236	87.03 143	95.59 72	98.24 30	91.56 115	95.21 145	92.04 298	94.95 50	98.66 174	91.45 117	97.57 238	97.20 216

ACMMPR		96.46 32	96.14 45	97.41 21	98.60 35	93.82 34	96.30 47	97.96 76	92.35 82	95.57 125	96.61 126	94.93 51	99.41 36	93.78 38	99.15 91	99.00 51

test1172		96.79 15	96.52 27	97.60 9	98.03 85	94.87 10	96.07 55	98.06 59	95.76 32	96.89 63	96.85 107	94.85 52	99.42 29	93.35 61	98.81 133	98.53 112

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 79	95.27 8	96.37 40	98.12 46	95.66 33	97.00 58	97.03 96	94.85 52	99.42 29	93.49 48	98.84 125	98.00 152

CP-MVS		96.44 35	96.08 49	97.54 11	98.29 64	94.62 14	96.80 22	98.08 53	92.67 75	95.08 150	96.39 141	94.77 54	99.42 29	93.17 69	99.44 45	98.58 110

test_0728_THIRD									93.26 68	97.40 46	97.35 78	94.69 55	99.34 62	93.88 34	99.42 47	98.89 70

9.14			94.81 94		97.49 120		94.11 129	98.37 17	87.56 206	95.38 132	96.03 161	94.66 56	99.08 99	90.70 129	98.97 113

GeoE		94.55 105	94.68 102	94.15 139	97.23 131	85.11 181	94.14 128	97.34 128	88.71 179	95.26 140	95.50 190	94.65 57	99.12 93	90.94 125	98.40 169	98.23 132

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 5	98.61 3	98.81 6	97.41 10	97.28 48	98.46 25	94.62 58	98.84 137	94.64 17	99.53 35	98.99 53

XVS		96.49 29	96.18 42	97.44 17	98.56 38	93.99 27	96.50 32	97.95 78	94.58 41	94.38 173	96.49 130	94.56 59	99.39 48	93.57 44	99.05 101	98.93 63

X-MVStestdata		90.70 207	88.45 249	97.44 17	98.56 38	93.99 27	96.50 32	97.95 78	94.58 41	94.38 173	26.89 372	94.56 59	99.39 48	93.57 44	99.05 101	98.93 63

mPP-MVS		96.46 32	96.05 51	97.69 5	98.62 32	94.65 13	96.45 35	97.74 96	92.59 76	95.47 128	96.68 121	94.50 61	99.42 29	93.10 72	99.26 75	98.99 53

DeepC-MVS	91.39 4	95.43 70	95.33 78	95.71 76	97.67 110	90.17 80	93.86 138	98.02 67	87.35 207	96.22 95	97.99 42	94.48 62	99.05 104	92.73 83	99.68 18	97.93 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		95.77 59	95.54 70	96.47 51	98.27 66	91.19 67	95.09 92	97.79 94	86.48 218	97.42 45	97.51 65	94.47 63	99.29 71	93.55 46	99.29 68	98.93 63
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
xxxxxxxxxxxxxcwj		95.03 83	94.93 90	95.33 89	97.46 123	88.05 123	92.04 196	98.42 15	87.63 203	96.36 83	96.68 121	94.37 64	99.32 68	92.41 91	99.05 101	98.64 101

SF-MVS		95.88 56	95.88 58	95.87 67	98.12 75	89.65 89	95.58 74	98.56 11	91.84 101	96.36 83	96.68 121	94.37 64	99.32 68	92.41 91	99.05 101	98.64 101

MP-MVS		96.14 47	95.68 67	97.51 13	98.81 25	94.06 21	96.10 53	97.78 95	92.73 72	93.48 199	96.72 119	94.23 66	99.42 29	91.99 99	99.29 68	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 88	94.03 26	96.97 17	97.61 105	87.68 202	98.45 18	98.77 15	94.20 67	99.50 19	96.70 3	99.40 53	99.53 14

test_0402		95.73 60	96.22 40	94.26 137	98.19 72	85.77 174	93.24 152	97.24 137	96.88 16	97.69 30	97.77 52	94.12 68	99.13 91	91.54 116	99.29 68	97.88 168

Effi-MVS+		92.79 160	92.74 158	92.94 181	95.10 249	83.30 203	94.00 133	97.53 112	91.36 119	89.35 293	90.65 322	94.01 69	98.66 174	87.40 208	95.30 295	96.88 227

DROMVSNet		95.44 69	95.62 69	94.89 106	96.93 147	87.69 130	96.48 34	99.14 3	93.93 55	92.77 224	94.52 231	93.95 70	99.49 22	93.62 43	99.22 81	97.51 197

OMC-MVS		94.22 120	93.69 132	95.81 68	97.25 130	91.27 65	92.27 187	97.40 119	87.10 213	94.56 168	95.42 195	93.74 71	98.11 225	86.62 220	98.85 124	98.06 144

LCM-MVSNet-Re		94.20 121	94.58 106	93.04 175	95.91 217	83.13 207	93.79 139	99.19 2	92.00 91	98.84 5	98.04 39	93.64 72	99.02 110	81.28 277	98.54 158	96.96 223

zzz-MVS		96.47 31	96.14 45	97.47 15	98.95 16	94.05 23	93.69 142	97.62 102	94.46 45	96.29 89	96.94 100	93.56 73	99.37 56	94.29 24	99.42 47	98.99 53

MTAPA		96.65 22	96.38 33	97.47 15	98.95 16	94.05 23	95.88 63	97.62 102	94.46 45	96.29 89	96.94 100	93.56 73	99.37 56	94.29 24	99.42 47	98.99 53

UA-Net		97.35 4	97.24 11	97.69 5	98.22 70	93.87 31	98.42 6	98.19 35	96.95 14	95.46 130	99.23 4	93.45 75	99.57 13	95.34 12	99.89 2	99.63 9

MVS_111021_HR		93.63 132	93.42 143	94.26 137	96.65 158	86.96 146	89.30 282	96.23 199	88.36 187	93.57 197	94.60 228	93.45 75	97.77 255	90.23 145	98.38 174	98.03 150

cdsmvs_eth3d_5k		23.35 341	31.13 344	0.00 359	0.00 382	0.00 383	0.00 370	95.58 225	0.00 377	0.00 378	91.15 311	93.43 77	0.00 378	0.00 376	0.00 376	0.00 374

APD-MVS		95.00 85	94.69 100	95.93 61	97.38 126	90.88 72	94.59 110	97.81 90	89.22 168	95.46 130	96.17 157	93.42 78	99.34 62	89.30 166	98.87 123	97.56 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ANet_high		94.83 95	96.28 37	90.47 261	96.65 158	73.16 329	94.33 122	98.74 8	96.39 23	98.09 25	98.93 8	93.37 79	98.70 167	90.38 135	99.68 18	99.53 14

casdiffmvs		94.32 115	94.80 95	92.85 185	96.05 206	81.44 226	92.35 183	98.05 60	91.53 116	95.75 117	96.80 111	93.35 80	98.49 193	91.01 124	98.32 183	98.64 101

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 41	91.77 61	97.15 13	97.37 120	88.98 172	98.26 22	98.86 10	93.35 80	99.60 8	96.41 4	99.45 43	99.66 6

VPA-MVSNet		95.14 82	95.67 68	93.58 160	97.76 100	83.15 206	94.58 112	97.58 107	93.39 66	97.05 56	98.04 39	93.25 82	98.51 192	89.75 160	99.59 27	99.08 45

Anonymous20240529		95.50 67	95.83 62	94.50 127	97.33 129	85.93 171	95.19 90	96.77 172	96.64 19	97.61 35	98.05 38	93.23 83	98.79 147	88.60 186	99.04 106	98.78 82

baseline		94.26 118	94.80 95	92.64 192	96.08 204	80.99 232	93.69 142	98.04 64	90.80 133	94.89 158	96.32 146	93.19 84	98.48 197	91.68 111	98.51 162	98.43 120

DeepPCF-MVS	90.46 6	94.20 121	93.56 138	96.14 52	95.96 213	92.96 45	89.48 276	97.46 116	85.14 241	96.23 94	95.42 195	93.19 84	98.08 226	90.37 136	98.76 139	97.38 209

Anonymous20231211		96.60 25	97.13 12	95.00 103	97.46 123	86.35 163	97.11 16	98.24 30	97.58 8	98.72 8	98.97 7	93.15 86	99.15 87	93.18 68	99.74 13	99.50 16

DVP-MVS++		95.93 53	96.34 34	94.70 115	96.54 167	86.66 153	98.45 4	98.22 32	93.26 68	97.54 37	97.36 75	93.12 87	99.38 54	93.88 34	98.68 146	98.04 147

OPU-MVS				95.15 99	96.84 151	89.43 94	95.21 86				95.66 180	93.12 87	98.06 227	86.28 228	98.61 151	97.95 160

LS3D		96.11 48	95.83 62	96.95 37	94.75 259	94.20 19	97.34 11	97.98 72	97.31 11	95.32 136	96.77 112	93.08 89	99.20 83	91.79 106	98.16 202	97.44 202

DP-MVS		95.62 63	95.84 61	94.97 104	97.16 136	88.62 110	94.54 118	97.64 101	96.94 15	96.58 77	97.32 81	93.07 90	98.72 161	90.45 132	98.84 125	97.57 192

EG-PatchMatch MVS		94.54 107	94.67 103	94.14 140	97.87 95	86.50 155	92.00 199	96.74 174	88.16 190	96.93 62	97.61 58	93.04 91	97.90 240	91.60 113	98.12 207	98.03 150

Fast-Effi-MVS+		91.28 199	90.86 203	92.53 199	95.45 240	82.53 214	89.25 285	96.52 187	85.00 246	89.91 282	88.55 342	92.94 92	98.84 137	84.72 247	95.44 291	96.22 251

PC_three_1452									75.31 321	95.87 113	95.75 176	92.93 93	96.34 313	87.18 211	98.68 146	98.04 147

v7n		96.82 10	97.31 10	95.33 89	98.54 43	86.81 148	96.83 20	98.07 56	96.59 20	98.46 17	98.43 27	92.91 94	99.52 17	96.25 6	99.76 11	99.65 8

XVG-ACMP-BASELINE		95.68 62	95.34 77	96.69 43	98.40 57	93.04 43	94.54 118	98.05 60	90.45 142	96.31 87	96.76 114	92.91 94	98.72 161	91.19 120	99.42 47	98.32 125

testgi		90.38 217	91.34 193	87.50 314	97.49 120	71.54 339	89.43 277	95.16 235	88.38 186	94.54 169	94.68 227	92.88 96	93.09 354	71.60 346	97.85 225	97.88 168

MVS_111021_LR		93.66 131	93.28 147	94.80 110	96.25 191	90.95 70	90.21 254	95.43 229	87.91 193	93.74 193	94.40 234	92.88 96	96.38 309	90.39 134	98.28 187	97.07 217

CNVR-MVS		94.58 104	94.29 116	95.46 85	96.94 145	89.35 98	91.81 215	96.80 169	89.66 155	93.90 188	95.44 194	92.80 98	98.72 161	92.74 82	98.52 160	98.32 125

ZD-MVS					97.23 131	90.32 79		97.54 110	84.40 253	94.78 162	95.79 172	92.76 99	99.39 48	88.72 184	98.40 169

XXY-MVS		92.58 168	93.16 150	90.84 252	97.75 101	79.84 249	91.87 209	96.22 201	85.94 228	95.53 127	97.68 54	92.69 100	94.48 340	83.21 258	97.51 239	98.21 135

CDPH-MVS		92.67 165	91.83 179	95.18 98	96.94 145	88.46 116	90.70 240	97.07 148	77.38 309	92.34 240	95.08 208	92.67 101	98.88 129	85.74 231	98.57 154	98.20 136

ETH3D-3000-0.1		94.86 92	94.55 107	95.81 68	97.61 113	89.72 87	94.05 131	98.37 17	88.09 191	95.06 151	95.85 167	92.58 102	99.10 97	90.33 140	98.99 108	98.62 105

Fast-Effi-MVS+-dtu		92.77 162	92.16 170	94.58 125	94.66 267	88.25 118	92.05 195	96.65 178	89.62 156	90.08 277	91.23 310	92.56 103	98.60 181	86.30 227	96.27 273	96.90 225

AllTest		94.88 91	94.51 110	96.00 56	98.02 86	92.17 52	95.26 85	98.43 13	90.48 140	95.04 152	96.74 116	92.54 104	97.86 246	85.11 240	98.98 109	97.98 156

TestCases				96.00 56	98.02 86	92.17 52		98.43 13	90.48 140	95.04 152	96.74 116	92.54 104	97.86 246	85.11 240	98.98 109	97.98 156

TinyColmap		92.00 183	92.76 157	89.71 280	95.62 235	77.02 294	90.72 239	96.17 204	87.70 201	95.26 140	96.29 148	92.54 104	96.45 306	81.77 272	98.77 138	95.66 276

Regformer-2		94.86 92	94.55 107	95.77 72	92.83 303	89.98 82	91.87 209	96.40 191	94.38 47	96.19 99	95.04 210	92.47 107	99.04 107	93.49 48	98.31 184	98.28 129

EGC-MVSNET		80.97 327	75.73 339	96.67 44	98.85 22	94.55 15	96.83 20	96.60 180	2.44 374	5.32 375	98.25 31	92.24 108	98.02 232	91.85 105	99.21 82	97.45 200

ETV-MVS		92.99 153	92.74 158	93.72 156	95.86 219	86.30 164	92.33 184	97.84 87	91.70 112	92.81 222	86.17 356	92.22 109	99.19 84	88.03 197	97.73 228	95.66 276

CLD-MVS		91.82 185	91.41 191	93.04 175	96.37 176	83.65 200	86.82 322	97.29 133	84.65 252	92.27 242	89.67 332	92.20 110	97.85 248	83.95 252	99.47 39	97.62 190
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
segment_acmp												92.14 111

Regformer-4		94.90 89	94.67 103	95.59 79	92.78 305	89.02 101	92.39 180	95.91 210	94.50 43	96.41 80	95.56 187	92.10 112	99.01 112	94.23 26	98.14 204	98.74 88

Vis-MVSNet		95.50 67	95.48 72	95.56 82	98.11 76	89.40 96	95.35 80	98.22 32	92.36 81	94.11 177	98.07 37	92.02 113	99.44 24	93.38 60	97.67 234	97.85 172
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		94.55 105	94.33 115	95.19 97	92.83 303	88.54 114	91.87 209	95.84 214	93.99 52	95.95 107	95.04 210	92.00 114	98.79 147	93.14 71	98.31 184	98.23 132

ITE_SJBPF				95.95 58	97.34 128	93.36 42		96.55 186	91.93 94	94.82 160	95.39 198	91.99 115	97.08 286	85.53 233	97.96 219	97.41 203

CP-MVSNet		96.19 46	96.80 17	94.38 135	98.99 14	83.82 198	96.31 45	97.53 112	97.60 7	98.34 19	97.52 63	91.98 116	99.63 6	93.08 74	99.81 9	99.70 3

CSCG		94.69 100	94.75 97	94.52 126	97.55 117	87.87 127	95.01 97	97.57 108	92.68 73	96.20 97	93.44 265	91.92 117	98.78 151	89.11 174	99.24 78	96.92 224

TSAR-MVS + MP.		94.96 87	94.75 97	95.57 81	98.86 21	88.69 107	96.37 40	96.81 168	85.23 238	94.75 163	97.12 91	91.85 118	99.40 43	93.45 53	98.33 181	98.62 105
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS-test		93.33 138	93.53 141	92.71 189	95.74 226	83.08 208	94.55 116	98.85 5	91.02 127	89.30 294	91.91 299	91.79 119	99.23 80	90.23 145	98.41 168	95.82 268

Gipuma		95.31 77	95.80 64	93.81 155	97.99 91	90.91 71	96.42 38	97.95 78	96.69 17	91.78 251	98.85 12	91.77 120	95.49 327	91.72 109	99.08 97	95.02 290
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		96.60 25	97.05 14	95.24 95	99.02 12	86.44 159	96.78 24	98.08 53	97.42 9	98.48 16	97.86 49	91.76 121	99.63 6	94.23 26	99.84 3	99.66 6

testtj		94.81 96	94.42 111	96.01 55	97.23 131	90.51 78	94.77 104	97.85 86	91.29 120	94.92 157	95.66 180	91.71 122	99.40 43	88.07 196	98.25 192	98.11 143

ETH3D cwj APD-0.16		93.99 126	93.38 144	95.80 70	96.82 152	89.92 83	92.72 163	98.02 67	84.73 251	93.65 195	95.54 189	91.68 123	99.22 81	88.78 181	98.49 165	98.26 131

AdaColmap		91.63 189	91.36 192	92.47 202	95.56 237	86.36 162	92.24 190	96.27 196	88.88 176	89.90 283	92.69 283	91.65 124	98.32 207	77.38 314	97.64 235	92.72 338

PHI-MVS		94.34 114	93.80 127	95.95 58	95.65 232	91.67 63	94.82 102	97.86 83	87.86 196	93.04 217	94.16 243	91.58 125	98.78 151	90.27 143	98.96 115	97.41 203

xiu_mvs_v1_base_debu		91.47 193	91.52 186	91.33 232	95.69 229	81.56 223	89.92 265	96.05 207	83.22 260	91.26 257	90.74 317	91.55 126	98.82 139	89.29 167	95.91 279	93.62 324

xiu_mvs_v1_base		91.47 193	91.52 186	91.33 232	95.69 229	81.56 223	89.92 265	96.05 207	83.22 260	91.26 257	90.74 317	91.55 126	98.82 139	89.29 167	95.91 279	93.62 324

xiu_mvs_v1_base_debi		91.47 193	91.52 186	91.33 232	95.69 229	81.56 223	89.92 265	96.05 207	83.22 260	91.26 257	90.74 317	91.55 126	98.82 139	89.29 167	95.91 279	93.62 324

tfpnnormal		94.27 117	94.87 93	92.48 201	97.71 105	80.88 234	94.55 116	95.41 230	93.70 60	96.67 73	97.72 53	91.40 129	98.18 220	87.45 206	99.18 87	98.36 123

Regformer-3		94.28 116	94.23 121	94.46 131	92.78 305	86.28 165	92.39 180	94.70 249	93.69 63	95.97 105	95.56 187	91.34 130	98.48 197	93.45 53	98.14 204	98.62 105

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 53	96.81 154	90.79 74	96.30 47	97.82 89	96.13 25	94.74 164	97.23 85	91.33 131	99.16 86	93.25 66	98.30 186	98.46 118

TEST9					96.45 174	89.46 92	90.60 242	96.92 158	79.09 298	90.49 269	94.39 235	91.31 132	98.88 129

agg_prior1		92.60 167	91.76 182	95.10 101	96.20 193	88.89 104	90.37 249	96.88 162	79.67 290	90.21 274	94.41 233	91.30 133	98.78 151	88.46 188	98.37 179	97.64 189

DeepC-MVS_fast	89.96 7	93.73 130	93.44 142	94.60 122	96.14 199	87.90 126	93.36 150	97.14 142	85.53 235	93.90 188	95.45 193	91.30 133	98.59 183	89.51 163	98.62 150	97.31 212
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		94.36 112	94.28 117	94.61 118	92.55 307	85.98 170	92.44 176	94.69 250	93.70 60	96.12 102	95.81 171	91.24 135	98.86 134	93.76 41	98.22 197	98.98 58

MCST-MVS		92.91 155	92.51 165	94.10 141	97.52 118	85.72 175	91.36 226	97.13 144	80.33 284	92.91 221	94.24 239	91.23 136	98.72 161	89.99 154	97.93 221	97.86 170

RPSCF		95.58 65	94.89 92	97.62 8	97.58 115	96.30 4	95.97 59	97.53 112	92.42 78	93.41 200	97.78 50	91.21 137	97.77 255	91.06 121	97.06 251	98.80 80

train_agg		92.71 164	91.83 179	95.35 87	96.45 174	89.46 92	90.60 242	96.92 158	79.37 293	90.49 269	94.39 235	91.20 138	98.88 129	88.66 185	98.43 167	97.72 183

test_8					96.37 176	89.14 99	90.51 245	96.89 161	79.37 293	90.42 271	94.36 237	91.20 138	98.82 139

EI-MVSNet-UG-set		94.35 113	94.27 119	94.59 123	92.46 308	85.87 172	92.42 178	94.69 250	93.67 64	96.13 101	95.84 170	91.20 138	98.86 134	93.78 38	98.23 195	99.03 49

EIA-MVS		92.35 174	92.03 173	93.30 171	95.81 222	83.97 196	92.80 162	98.17 40	87.71 200	89.79 287	87.56 346	91.17 141	99.18 85	87.97 198	97.27 246	96.77 231

xiu_mvs_v2_base		89.00 249	89.19 234	88.46 302	94.86 254	74.63 317	86.97 316	95.60 219	80.88 280	87.83 317	88.62 341	91.04 142	98.81 144	82.51 266	94.38 312	91.93 344

HPM-MVS++		95.02 84	94.39 112	96.91 38	97.88 94	93.58 38	94.09 130	96.99 153	91.05 126	92.40 235	95.22 202	91.03 143	99.25 77	92.11 94	98.69 145	97.90 166

TAPA-MVS	88.58 10	92.49 171	91.75 183	94.73 113	96.50 171	89.69 88	92.91 159	97.68 99	78.02 307	92.79 223	94.10 244	90.85 144	97.96 238	84.76 246	98.16 202	96.54 235
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		7.56 344	10.09 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	90.77 145	0.00 378	0.00 376	0.00 376	0.00 374

PS-MVSNAJss		96.01 51	96.04 52	95.89 66	98.82 24	88.51 115	95.57 75	97.88 82	88.72 178	98.81 6	98.86 10	90.77 145	99.60 8	95.43 11	99.53 35	99.57 13

PS-MVSNAJ		88.86 253	88.99 240	88.48 301	94.88 252	74.71 315	86.69 325	95.60 219	80.88 280	87.83 317	87.37 349	90.77 145	98.82 139	82.52 265	94.37 313	91.93 344

MVS_Test		92.57 170	93.29 145	90.40 264	93.53 290	75.85 309	92.52 170	96.96 154	88.73 177	92.35 238	96.70 120	90.77 145	98.37 205	92.53 88	95.49 289	96.99 222

MIMVSNet1		95.52 66	95.45 73	95.72 75	99.14 5	89.02 101	96.23 50	96.87 164	93.73 59	97.87 27	98.49 24	90.73 149	99.05 104	86.43 225	99.60 25	99.10 44

ab-mvs		92.40 172	92.62 162	91.74 221	97.02 141	81.65 222	95.84 64	95.50 228	86.95 215	92.95 220	97.56 60	90.70 150	97.50 268	79.63 295	97.43 242	96.06 257

Test By Simon												90.61 151

3Dnovator	92.54 3	94.80 97	94.90 91	94.47 130	95.47 239	87.06 141	96.63 26	97.28 135	91.82 104	94.34 175	97.41 69	90.60 152	98.65 176	92.47 89	98.11 208	97.70 184

NCCC		94.08 124	93.54 139	95.70 77	96.49 172	89.90 85	92.39 180	96.91 160	90.64 137	92.33 241	94.60 228	90.58 153	98.96 120	90.21 147	97.70 232	98.23 132

UniMVSNet_NR-MVSNet		95.35 73	95.21 83	95.76 73	97.69 108	88.59 111	92.26 188	97.84 87	94.91 38	96.80 68	95.78 175	90.42 154	99.41 36	91.60 113	99.58 31	99.29 28

test_prior3		93.29 140	92.85 154	94.61 118	95.95 214	87.23 136	90.21 254	97.36 125	89.33 164	90.77 264	94.81 220	90.41 155	98.68 171	88.21 189	98.55 155	97.93 162

test_prior2							90.21 254		89.33 164	90.77 264	94.81 220	90.41 155		88.21 189	98.55 155

KD-MVS_self_test		94.10 123	94.73 99	92.19 207	97.66 111	79.49 258	94.86 101	97.12 145	89.59 158	96.87 64	97.65 56	90.40 157	98.34 206	89.08 175	99.35 57	98.75 85

MSLP-MVS++		93.25 145	93.88 125	91.37 231	96.34 182	82.81 212	93.11 153	97.74 96	89.37 162	94.08 179	95.29 201	90.40 157	96.35 311	90.35 137	98.25 192	94.96 291

UniMVSNet (Re)		95.32 75	95.15 85	95.80 70	97.79 99	88.91 103	92.91 159	98.07 56	93.46 65	96.31 87	95.97 164	90.14 159	99.34 62	92.11 94	99.64 23	99.16 36

Effi-MVS+-dtu		93.90 128	92.60 164	97.77 4	94.74 261	96.67 3	94.00 133	95.41 230	89.94 149	91.93 249	92.13 296	90.12 160	98.97 119	87.68 203	97.48 240	97.67 187

mvs-test1		93.07 151	91.80 181	96.89 39	94.74 261	95.83 6	92.17 191	95.41 230	89.94 149	89.85 284	90.59 323	90.12 160	98.88 129	87.68 203	95.66 285	95.97 260

FMVSNet1		94.84 94	95.13 86	93.97 145	97.60 114	84.29 188	95.99 56	96.56 183	92.38 79	97.03 57	98.53 21	90.12 160	98.98 115	88.78 181	99.16 90	98.65 97

DU-MVS		95.28 78	95.12 87	95.75 74	97.75 101	88.59 111	92.58 168	97.81 90	93.99 52	96.80 68	95.90 165	90.10 163	99.41 36	91.60 113	99.58 31	99.26 29

NR-MVSNet		95.28 78	95.28 81	95.26 94	97.75 101	87.21 138	95.08 93	97.37 120	93.92 57	97.65 31	95.90 165	90.10 163	99.33 67	90.11 150	99.66 21	99.26 29

Baseline_NR-MVSNet		94.47 109	95.09 88	92.60 196	98.50 54	80.82 235	92.08 194	96.68 176	93.82 58	96.29 89	98.56 20	90.10 163	97.75 258	90.10 152	99.66 21	99.24 31

API-MVS		91.52 192	91.61 184	91.26 235	94.16 277	86.26 166	94.66 108	94.82 244	91.17 124	92.13 245	91.08 313	90.03 166	97.06 287	79.09 302	97.35 245	90.45 353

CS-MVS		92.12 180	92.62 162	90.60 258	94.57 270	78.12 279	92.00 199	98.58 10	87.75 199	90.08 277	91.88 301	89.79 167	99.10 97	90.35 137	98.60 153	94.58 300

test12				94.43 133	95.95 214	86.75 149		96.24 198		89.76 288		89.79 167	98.79 147		97.95 220	97.75 182

旧先验1					96.20 193	84.17 193		94.82 244			95.57 186	89.57 169			97.89 223	96.32 247

DELS-MVS		92.05 182	92.16 170	91.72 222	94.44 272	80.13 241	87.62 303	97.25 136	87.34 208	92.22 243	93.18 272	89.54 170	98.73 160	89.67 161	98.20 200	96.30 248
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
VPNet		93.08 149	93.76 129	91.03 243	98.60 35	75.83 311	91.51 221	95.62 218	91.84 101	95.74 118	97.10 92	89.31 171	98.32 207	85.07 242	99.06 98	98.93 63

QAPM		92.88 157	92.77 156	93.22 173	95.82 220	83.31 202	96.45 35	97.35 127	83.91 256	93.75 191	96.77 112	89.25 172	98.88 129	84.56 248	97.02 253	97.49 198

MSDG		90.82 203	90.67 209	91.26 235	94.16 277	83.08 208	86.63 327	96.19 202	90.60 139	91.94 248	91.89 300	89.16 173	95.75 322	80.96 283	94.51 311	94.95 292

CPTT-MVS		94.74 98	94.12 122	96.60 45	98.15 74	93.01 44	95.84 64	97.66 100	89.21 169	93.28 206	95.46 192	88.89 174	98.98 115	89.80 157	98.82 131	97.80 177

ETH3 D test6400		91.91 184	91.25 195	93.89 151	96.59 162	84.41 187	92.10 193	97.72 98	78.52 303	91.82 250	93.78 258	88.70 175	99.13 91	83.61 254	98.39 172	98.14 139

DP-MVS Recon		92.31 175	91.88 178	93.60 159	97.18 135	86.87 147	91.10 231	97.37 120	84.92 248	92.08 246	94.08 245	88.59 176	98.20 217	83.50 255	98.14 204	95.73 272

FC-MVSNet-test		95.32 75	95.88 58	93.62 158	98.49 55	81.77 220	95.90 62	98.32 20	93.93 55	97.53 39	97.56 60	88.48 177	99.40 43	92.91 79	99.83 6	99.68 4

OpenMVS	89.45 8	92.27 177	92.13 172	92.68 191	94.53 271	84.10 194	95.70 68	97.03 149	82.44 272	91.14 261	96.42 135	88.47 178	98.38 202	85.95 230	97.47 241	95.55 280

F-COLMAP		92.28 176	91.06 200	95.95 58	97.52 118	91.90 58	93.53 145	97.18 140	83.98 255	88.70 306	94.04 246	88.41 179	98.55 189	80.17 288	95.99 278	97.39 207

ambc				92.98 177	96.88 149	83.01 210	95.92 61	96.38 193		96.41 80	97.48 66	88.26 180	97.80 251	89.96 155	98.93 117	98.12 142

v10		94.68 101	95.27 82	92.90 183	96.57 164	80.15 239	94.65 109	97.57 108	90.68 136	97.43 43	98.00 41	88.18 181	99.15 87	94.84 15	99.55 34	99.41 20

v8		94.65 102	95.29 80	92.74 188	96.65 158	79.77 253	94.59 110	97.17 141	91.86 97	97.47 42	97.93 44	88.16 182	99.08 99	94.32 22	99.47 39	99.38 22

TSAR-MVS + GP.		93.07 151	92.41 168	95.06 102	95.82 220	90.87 73	90.97 233	92.61 289	88.04 192	94.61 167	93.79 257	88.08 183	97.81 250	89.41 165	98.39 172	96.50 240

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 11	91.85 59	97.98 7	98.01 69	94.15 50	98.93 3	99.07 5	88.07 184	99.57 13	95.86 9	99.69 15	99.46 18

diffmvs		91.74 186	91.93 177	91.15 241	93.06 298	78.17 278	88.77 293	97.51 115	86.28 222	92.42 234	93.96 251	88.04 185	97.46 271	90.69 130	96.67 266	97.82 175

原ACMM1				92.87 184	96.91 148	84.22 191		97.01 150	76.84 314	89.64 290	94.46 232	88.00 186	98.70 167	81.53 275	98.01 217	95.70 274

VDD-MVS		94.37 111	94.37 113	94.40 134	97.49 120	86.07 169	93.97 135	93.28 274	94.49 44	96.24 93	97.78 50	87.99 187	98.79 147	88.92 177	99.14 92	98.34 124

XVG-OURS		94.72 99	94.12 122	96.50 49	98.00 88	94.23 18	91.48 222	98.17 40	90.72 134	95.30 137	96.47 131	87.94 188	96.98 289	91.41 118	97.61 237	98.30 128

CANet		92.38 173	91.99 175	93.52 165	93.82 288	83.46 201	91.14 229	97.00 151	89.81 153	86.47 327	94.04 246	87.90 189	99.21 82	89.50 164	98.27 188	97.90 166

BH-untuned		90.68 208	90.90 201	90.05 275	95.98 212	79.57 257	90.04 261	94.94 240	87.91 193	94.07 180	93.00 274	87.76 190	97.78 254	79.19 301	95.17 298	92.80 336

FIs		94.90 89	95.35 76	93.55 161	98.28 65	81.76 221	95.33 82	98.14 44	93.05 71	97.07 53	97.18 88	87.65 191	99.29 71	91.72 109	99.69 15	99.61 11

v1144		93.50 133	93.81 126	92.57 197	96.28 187	79.61 256	91.86 213	96.96 154	86.95 215	95.91 111	96.32 146	87.65 191	98.96 120	93.51 47	98.88 120	99.13 39

mvs_anonymous		90.37 218	91.30 194	87.58 313	92.17 314	68.00 353	89.84 269	94.73 248	83.82 257	93.22 211	97.40 70	87.54 193	97.40 276	87.94 199	95.05 300	97.34 210

PCF-MVS	84.52 17	89.12 246	87.71 265	93.34 168	96.06 205	85.84 173	86.58 330	97.31 130	68.46 352	93.61 196	93.89 254	87.51 194	98.52 191	67.85 357	98.11 208	95.66 276
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		92.67 165	92.96 151	91.79 219	96.27 188	80.15 239	91.95 201	94.98 238	92.19 88	94.52 170	96.07 159	87.43 195	97.39 277	84.83 244	98.38 174	97.83 173

v148		92.87 158	93.29 145	91.62 225	96.25 191	77.72 286	91.28 227	95.05 236	89.69 154	95.93 110	96.04 160	87.34 196	98.38 202	90.05 153	97.99 218	98.78 82

V42		93.43 136	93.58 136	92.97 178	95.34 245	81.22 229	92.67 166	96.49 188	87.25 209	96.20 97	96.37 143	87.32 197	98.85 136	92.39 93	98.21 198	98.85 76

v1192		93.49 134	93.78 128	92.62 195	96.16 197	79.62 255	91.83 214	97.22 139	86.07 226	96.10 103	96.38 142	87.22 198	99.02 110	94.14 29	98.88 120	99.22 32

WR-MVS		93.49 134	93.72 130	92.80 187	97.57 116	80.03 245	90.14 258	95.68 217	93.70 60	96.62 75	95.39 198	87.21 199	99.04 107	87.50 205	99.64 23	99.33 25

IterMVS-LS		93.78 129	94.28 117	92.27 204	96.27 188	79.21 265	91.87 209	96.78 170	91.77 107	96.57 78	97.07 93	87.15 200	98.74 159	91.99 99	99.03 107	98.86 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		92.99 153	93.26 149	92.19 207	92.12 315	79.21 265	92.32 185	94.67 252	91.77 107	95.24 143	95.85 167	87.14 201	98.49 193	91.99 99	98.26 189	98.86 73

v144192		93.20 148	93.54 139	92.16 211	96.05 206	78.26 277	91.95 201	97.14 142	84.98 247	95.96 106	96.11 158	87.08 202	99.04 107	93.79 37	98.84 125	99.17 35

114514_t		90.51 211	89.80 227	92.63 194	98.00 88	82.24 216	93.40 149	97.29 133	65.84 359	89.40 292	94.80 223	86.99 203	98.75 156	83.88 253	98.61 151	96.89 226

新几何1				93.17 174	97.16 136	87.29 135		94.43 254	67.95 353	91.29 256	94.94 215	86.97 204	98.23 215	81.06 282	97.75 227	93.98 315

HQP_MVS		94.26 118	93.93 124	95.23 96	97.71 105	88.12 121	94.56 114	97.81 90	91.74 109	93.31 203	95.59 182	86.93 205	98.95 122	89.26 170	98.51 162	98.60 108

plane_prior6					97.21 134	88.23 119						86.93 205

1121		90.26 223	89.23 233	93.34 168	97.15 138	87.40 133	91.94 203	94.39 255	67.88 354	91.02 262	94.91 216	86.91 207	98.59 183	81.17 280	97.71 231	94.02 314

UGNet		93.08 149	92.50 166	94.79 111	93.87 286	87.99 125	95.07 94	94.26 259	90.64 137	87.33 323	97.67 55	86.89 208	98.49 193	88.10 194	98.71 142	97.91 165
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		92.72 163	92.02 174	94.84 109	95.65 232	91.99 56	92.92 158	96.60 180	85.08 245	92.44 233	93.62 260	86.80 209	96.35 311	86.81 215	98.25 192	96.18 253

v1921920		93.26 143	93.61 135	92.19 207	96.04 210	78.31 276	91.88 208	97.24 137	85.17 240	96.19 99	96.19 154	86.76 210	99.05 104	94.18 28	98.84 125	99.22 32

v1240		93.29 140	93.71 131	92.06 214	96.01 211	77.89 283	91.81 215	97.37 120	85.12 243	96.69 72	96.40 137	86.67 211	99.07 103	94.51 18	98.76 139	99.22 32

MAR-MVS		90.32 221	88.87 244	94.66 117	94.82 255	91.85 59	94.22 125	94.75 247	80.91 279	87.52 321	88.07 345	86.63 212	97.87 245	76.67 318	96.21 274	94.25 308
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
MSP-MVS		95.34 74	94.63 105	97.48 14	98.67 29	94.05 23	96.41 39	98.18 36	91.26 121	95.12 146	95.15 203	86.60 213	99.50 19	93.43 57	96.81 261	98.89 70
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
BH-RMVSNet		90.47 213	90.44 214	90.56 260	95.21 248	78.65 274	89.15 286	93.94 267	88.21 188	92.74 225	94.22 240	86.38 214	97.88 242	78.67 304	95.39 293	95.14 287

CNLPA		91.72 187	91.20 196	93.26 172	96.17 196	91.02 68	91.14 229	95.55 226	90.16 147	90.87 263	93.56 263	86.31 215	94.40 343	79.92 294	97.12 250	94.37 305

PVSNet_BlendedMVS		90.35 219	89.96 224	91.54 228	94.81 256	78.80 272	90.14 258	96.93 156	79.43 292	88.68 307	95.06 209	86.27 216	98.15 223	80.27 285	98.04 214	97.68 186

PVSNet_Blended		88.74 256	88.16 260	90.46 263	94.81 256	78.80 272	86.64 326	96.93 156	74.67 322	88.68 307	89.18 338	86.27 216	98.15 223	80.27 285	96.00 277	94.44 304

PAPR		87.65 274	86.77 282	90.27 267	92.85 302	77.38 290	88.56 298	96.23 199	76.82 315	84.98 335	89.75 331	86.08 218	97.16 284	72.33 341	93.35 325	96.26 250

v2v482		93.29 140	93.63 134	92.29 203	96.35 181	78.82 270	91.77 217	96.28 195	88.45 184	95.70 121	96.26 151	86.02 219	98.90 126	93.02 75	98.81 133	99.14 38

test20.03		90.80 204	90.85 204	90.63 257	95.63 234	79.24 263	89.81 270	92.87 280	89.90 151	94.39 172	96.40 137	85.77 220	95.27 335	73.86 333	99.05 101	97.39 207

PLC	85.34 15	90.40 215	88.92 241	94.85 108	96.53 170	90.02 81	91.58 220	96.48 189	80.16 285	86.14 329	92.18 294	85.73 221	98.25 214	76.87 317	94.61 310	96.30 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		84.98 302	84.30 302	87.01 317	91.03 330	77.69 287	91.94 203	94.16 260	59.36 367	84.23 341	87.50 348	85.66 222	96.80 296	71.79 343	93.05 332	86.54 360

testdata				91.03 243	96.87 150	82.01 217		94.28 258	71.55 338	92.46 232	95.42 195	85.65 223	97.38 279	82.64 263	97.27 246	93.70 322

PM-MVS		93.33 138	92.67 161	95.33 89	96.58 163	94.06 21	92.26 188	92.18 295	85.92 229	96.22 95	96.61 126	85.64 224	95.99 320	90.35 137	98.23 195	95.93 262

MDA-MVSNet-bldmvs		91.04 200	90.88 202	91.55 227	94.68 266	80.16 238	85.49 334	92.14 298	90.41 144	94.93 156	95.79 172	85.10 225	96.93 292	85.15 237	94.19 318	97.57 192

PAPM_NR		91.03 201	90.81 205	91.68 224	96.73 156	81.10 231	93.72 141	96.35 194	88.19 189	88.77 304	92.12 297	85.09 226	97.25 281	82.40 267	93.90 319	96.68 234

HQP2-MVS												84.76 227

HQP-MVS		92.09 181	91.49 189	93.88 152	96.36 178	84.89 183	91.37 223	97.31 130	87.16 210	88.81 300	93.40 266	84.76 227	98.60 181	86.55 222	97.73 228	98.14 139

test222					96.95 144	85.27 180	88.83 291	93.61 268	65.09 361	90.74 266	94.85 219	84.62 229			97.36 244	93.91 316

VDDNet		94.03 125	94.27 119	93.31 170	98.87 20	82.36 215	95.51 78	91.78 304	97.19 12	96.32 86	98.60 18	84.24 230	98.75 156	87.09 213	98.83 130	98.81 79

PVSNet_Blended_VisFu		91.63 189	91.20 196	92.94 181	97.73 104	83.95 197	92.14 192	97.46 116	78.85 302	92.35 238	94.98 213	84.16 231	99.08 99	86.36 226	96.77 263	95.79 270

CL-MVSNet_self_test		90.04 232	89.90 226	90.47 261	95.24 247	77.81 284	86.60 329	92.62 288	85.64 234	93.25 210	93.92 252	83.84 232	96.06 318	79.93 292	98.03 215	97.53 196

BH-w/o		87.21 284	87.02 278	87.79 312	94.77 258	77.27 292	87.90 301	93.21 277	81.74 277	89.99 281	88.39 344	83.47 233	96.93 292	71.29 347	92.43 339	89.15 354

PatchMatch-RL		89.18 244	88.02 262	92.64 192	95.90 218	92.87 47	88.67 297	91.06 308	80.34 283	90.03 280	91.67 305	83.34 234	94.42 342	76.35 321	94.84 304	90.64 352

DPM-MVS		89.35 242	88.40 250	92.18 210	96.13 202	84.20 192	86.96 317	96.15 205	75.40 320	87.36 322	91.55 308	83.30 235	98.01 233	82.17 270	96.62 267	94.32 307

OpenMVS_ROB	85.12 16	89.52 241	89.05 238	90.92 248	94.58 269	81.21 230	91.10 231	93.41 273	77.03 313	93.41 200	93.99 250	83.23 236	97.80 251	79.93 292	94.80 305	93.74 321

new-patchmatchnet		88.97 250	90.79 206	83.50 341	94.28 276	55.83 375	85.34 335	93.56 270	86.18 224	95.47 128	95.73 177	83.10 237	96.51 304	85.40 234	98.06 212	98.16 137

1314		86.46 293	86.33 290	86.87 319	91.65 323	74.54 318	91.94 203	94.10 261	74.28 324	84.78 337	87.33 350	83.03 238	95.00 337	78.72 303	91.16 348	91.06 350

IS-MVSNet		94.49 108	94.35 114	94.92 105	98.25 69	86.46 158	97.13 15	94.31 257	96.24 24	96.28 92	96.36 144	82.88 239	99.35 59	88.19 191	99.52 37	98.96 60

MG-MVS		89.54 240	89.80 227	88.76 295	94.88 252	72.47 336	89.60 273	92.44 292	85.82 230	89.48 291	95.98 163	82.85 240	97.74 259	81.87 271	95.27 296	96.08 256

TR-MVS		87.70 271	87.17 274	89.27 288	94.11 279	79.26 262	88.69 295	91.86 303	81.94 276	90.69 267	89.79 329	82.82 241	97.42 274	72.65 340	91.98 343	91.14 349

c3_l		91.32 198	91.42 190	91.00 246	92.29 310	76.79 300	87.52 309	96.42 190	85.76 232	94.72 166	93.89 254	82.73 242	98.16 222	90.93 126	98.55 155	98.04 147

YYNet1		88.17 264	88.24 255	87.93 309	92.21 312	73.62 326	80.75 358	88.77 318	82.51 271	94.99 154	95.11 206	82.70 243	93.70 349	83.33 256	93.83 320	96.48 241

MDA-MVSNet_test_wron		88.16 265	88.23 256	87.93 309	92.22 311	73.71 325	80.71 359	88.84 317	82.52 270	94.88 159	95.14 204	82.70 243	93.61 350	83.28 257	93.80 321	96.46 242

pmmvs-eth3d		91.54 191	90.73 208	93.99 143	95.76 225	87.86 128	90.83 236	93.98 266	78.23 306	94.02 184	96.22 153	82.62 245	96.83 295	86.57 221	98.33 181	97.29 213

MVS_0304		90.96 202	90.15 221	93.37 167	93.17 295	87.06 141	93.62 144	92.43 293	89.60 157	82.25 352	95.50 190	82.56 246	97.83 249	84.41 250	97.83 226	95.22 284

Anonymous20231206		88.77 255	88.29 253	90.20 271	96.31 185	78.81 271	89.56 275	93.49 272	74.26 325	92.38 236	95.58 185	82.21 247	95.43 330	72.07 342	98.75 141	96.34 246

miper_ehance_all_eth		90.48 212	90.42 215	90.69 255	91.62 324	76.57 302	86.83 321	96.18 203	83.38 258	94.06 181	92.66 285	82.20 248	98.04 228	89.79 158	97.02 253	97.45 200

USDC		89.02 247	89.08 237	88.84 294	95.07 250	74.50 320	88.97 288	96.39 192	73.21 331	93.27 207	96.28 149	82.16 249	96.39 308	77.55 311	98.80 135	95.62 279

EPP-MVSNet		93.91 127	93.68 133	94.59 123	98.08 78	85.55 177	97.44 10	94.03 262	94.22 49	94.94 155	96.19 154	82.07 250	99.57 13	87.28 210	98.89 118	98.65 97

UnsupCasMVSNet_eth		90.33 220	90.34 216	90.28 266	94.64 268	80.24 237	89.69 272	95.88 211	85.77 231	93.94 187	95.69 178	81.99 251	92.98 355	84.21 251	91.30 346	97.62 190

alignmvs		93.26 143	92.85 154	94.50 127	95.70 228	87.45 132	93.45 148	95.76 215	91.58 114	95.25 142	92.42 292	81.96 252	98.72 161	91.61 112	97.87 224	97.33 211

TAMVS		90.16 225	89.05 238	93.49 166	96.49 172	86.37 161	90.34 251	92.55 290	80.84 282	92.99 218	94.57 230	81.94 253	98.20 217	73.51 334	98.21 198	95.90 265

Anonymous202405211		92.58 168	92.50 166	92.83 186	96.55 166	83.22 204	92.43 177	91.64 305	94.10 51	95.59 124	96.64 124	81.88 254	97.50 268	85.12 239	98.52 160	97.77 179

SixPastTwentyTwo		94.91 88	95.21 83	93.98 144	98.52 46	83.19 205	95.93 60	94.84 243	94.86 39	98.49 15	98.74 16	81.45 255	99.60 8	94.69 16	99.39 54	99.15 37

cascas		87.02 290	86.28 291	89.25 289	91.56 326	76.45 303	84.33 345	96.78 170	71.01 342	86.89 326	85.91 357	81.35 256	96.94 290	83.09 259	95.60 286	94.35 306

GBi-Net		93.21 146	92.96 151	93.97 145	95.40 241	84.29 188	95.99 56	96.56 183	88.63 180	95.10 147	98.53 21	81.31 257	98.98 115	86.74 216	98.38 174	98.65 97

test1		93.21 146	92.96 151	93.97 145	95.40 241	84.29 188	95.99 56	96.56 183	88.63 180	95.10 147	98.53 21	81.31 257	98.98 115	86.74 216	98.38 174	98.65 97

FMVSNet2		92.78 161	92.73 160	92.95 180	95.40 241	81.98 218	94.18 126	95.53 227	88.63 180	96.05 104	97.37 72	81.31 257	98.81 144	87.38 209	98.67 148	98.06 144

MVE	59.87 23	73.86 338	72.65 341	77.47 351	87.00 366	74.35 321	61.37 368	60.93 376	67.27 355	69.69 371	86.49 354	81.24 260	72.33 372	56.45 369	83.45 363	85.74 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MVP-Stereo		90.07 230	88.92 241	93.54 163	96.31 185	86.49 156	90.93 234	95.59 223	79.80 286	91.48 253	95.59 182	80.79 261	97.39 277	78.57 305	91.19 347	96.76 232
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		88.50 259	88.03 261	89.90 277	95.52 238	78.88 269	87.39 310	94.02 264	79.32 296	93.06 215	94.02 248	80.72 262	94.27 345	75.16 327	93.08 331	96.54 235

MS-PatchMatch		88.05 266	87.75 264	88.95 291	93.28 292	77.93 281	87.88 302	92.49 291	75.42 319	92.57 230	93.59 262	80.44 263	94.24 347	81.28 277	92.75 334	94.69 299

Anonymous20240521		92.86 159	93.57 137	90.74 254	96.57 164	75.50 313	94.15 127	95.60 219	89.38 161	95.90 112	97.90 48	80.39 264	97.96 238	92.60 87	99.68 18	98.75 85

CANet_DTU		89.85 236	89.17 235	91.87 217	92.20 313	80.02 246	90.79 237	95.87 212	86.02 227	82.53 351	91.77 303	80.01 265	98.57 186	85.66 232	97.70 232	97.01 221

PMMVS		83.00 312	81.11 320	88.66 298	83.81 374	86.44 159	82.24 355	85.65 344	61.75 366	82.07 354	85.64 358	79.75 266	91.59 360	75.99 323	93.09 330	87.94 359

ppachtmachnet_test		88.61 258	88.64 246	88.50 300	91.76 321	70.99 343	84.59 342	92.98 278	79.30 297	92.38 236	93.53 264	79.57 267	97.45 272	86.50 224	97.17 249	97.07 217

eth_miper_zixun_eth		90.72 206	90.61 210	91.05 242	92.04 317	76.84 299	86.91 318	96.67 177	85.21 239	94.41 171	93.92 252	79.53 268	98.26 213	89.76 159	97.02 253	98.06 144

N_pmnet		88.90 252	87.25 272	93.83 154	94.40 274	93.81 36	84.73 339	87.09 332	79.36 295	93.26 208	92.43 291	79.29 269	91.68 359	77.50 313	97.22 248	96.00 259

miper_enhance_ethall		88.42 260	87.87 263	90.07 273	88.67 357	75.52 312	85.10 336	95.59 223	75.68 316	92.49 231	89.45 335	78.96 270	97.88 242	87.86 201	97.02 253	96.81 229

EPNet		89.80 238	88.25 254	94.45 132	83.91 373	86.18 167	93.87 137	87.07 333	91.16 125	80.64 361	94.72 225	78.83 271	98.89 128	85.17 235	98.89 118	98.28 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		87.23 283	86.82 280	88.46 302	93.96 283	77.94 280	86.84 320	92.78 284	77.59 308	87.61 320	91.83 302	78.75 272	91.92 358	77.84 308	94.20 317	95.52 281

IterMVS-SCA-FT		91.65 188	91.55 185	91.94 216	93.89 285	79.22 264	87.56 306	93.51 271	91.53 116	95.37 133	96.62 125	78.65 273	98.90 126	91.89 104	94.95 301	97.70 184

SCA		87.43 279	87.21 273	88.10 307	92.01 318	71.98 338	89.43 277	88.11 326	82.26 274	88.71 305	92.83 278	78.65 273	97.59 264	79.61 296	93.30 326	94.75 296

our_test_3		87.55 276	87.59 267	87.44 315	91.76 321	70.48 344	83.83 349	90.55 313	79.79 287	92.06 247	92.17 295	78.63 275	95.63 323	84.77 245	94.73 306	96.22 251

jason		89.17 245	88.32 251	91.70 223	95.73 227	80.07 242	88.10 300	93.22 275	71.98 337	90.09 276	92.79 280	78.53 276	98.56 187	87.43 207	97.06 251	96.46 242
jason: jason.
IterMVS		90.18 224	90.16 218	90.21 270	93.15 296	75.98 308	87.56 306	92.97 279	86.43 220	94.09 178	96.40 137	78.32 277	97.43 273	87.87 200	94.69 308	97.23 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		87.19 286	85.92 294	91.00 246	97.13 139	79.41 259	84.51 343	95.60 219	64.14 362	90.07 279	94.81 220	78.26 278	97.14 285	73.34 335	95.38 294	96.46 242

WTY-MVS		86.93 291	86.50 289	88.24 305	94.96 251	74.64 316	87.19 313	92.07 300	78.29 305	88.32 311	91.59 307	78.06 279	94.27 345	74.88 328	93.15 329	95.80 269

pmmvs4		88.95 251	87.70 266	92.70 190	94.30 275	85.60 176	87.22 312	92.16 297	74.62 323	89.75 289	94.19 241	77.97 280	96.41 307	82.71 262	96.36 272	96.09 255

DSMNet-mixed		82.21 317	81.56 316	84.16 338	89.57 348	70.00 349	90.65 241	77.66 372	54.99 370	83.30 347	97.57 59	77.89 281	90.50 363	66.86 360	95.54 288	91.97 343

lessismore_v0				93.87 153	98.05 81	83.77 199		80.32 367		97.13 52	97.91 46	77.49 282	99.11 95	92.62 86	98.08 211	98.74 88

HY-MVS	82.50 18	86.81 292	85.93 293	89.47 282	93.63 289	77.93 281	94.02 132	91.58 306	75.68 316	83.64 344	93.64 259	77.40 283	97.42 274	71.70 345	92.07 342	93.05 333

1112_ss		88.42 260	87.41 269	91.45 229	96.69 157	80.99 232	89.72 271	96.72 175	73.37 330	87.00 325	90.69 320	77.38 284	98.20 217	81.38 276	93.72 322	95.15 286

DIV-MVS_self_test		90.65 209	90.56 212	90.91 250	91.85 319	76.99 296	86.75 323	95.36 233	85.52 237	94.06 181	94.89 217	77.37 285	97.99 236	90.28 142	98.97 113	97.76 180

cl____		90.65 209	90.56 212	90.91 250	91.85 319	76.98 297	86.75 323	95.36 233	85.53 235	94.06 181	94.89 217	77.36 286	97.98 237	90.27 143	98.98 109	97.76 180

CDS-MVSNet		89.55 239	88.22 257	93.53 164	95.37 244	86.49 156	89.26 283	93.59 269	79.76 288	91.15 260	92.31 293	77.12 287	98.38 202	77.51 312	97.92 222	95.71 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		92.18 179	92.23 169	92.04 215	94.74 261	80.06 243	97.15 13	97.37 120	88.98 172	88.83 298	92.79 280	77.02 288	99.60 8	96.41 4	96.75 264	96.46 242

lupinMVS		88.34 262	87.31 270	91.45 229	94.74 261	80.06 243	87.23 311	92.27 294	71.10 341	88.83 298	91.15 311	77.02 288	98.53 190	86.67 219	96.75 264	95.76 271

PMMVS2		81.31 323	83.44 307	74.92 352	90.52 337	46.49 377	69.19 366	85.23 352	84.30 254	87.95 316	94.71 226	76.95 290	84.36 370	64.07 363	98.09 210	93.89 317

h-mvs33		92.89 156	91.99 175	95.58 80	96.97 143	90.55 76	93.94 136	94.01 265	89.23 166	93.95 185	96.19 154	76.88 291	99.14 89	91.02 122	95.71 284	97.04 220

hse-mvs2		92.24 178	91.20 196	95.38 86	96.16 197	90.65 75	92.52 170	92.01 302	89.23 166	93.95 185	92.99 275	76.88 291	98.69 169	91.02 122	96.03 276	96.81 229

pmmvs5		87.87 268	87.14 275	90.07 273	93.26 294	76.97 298	88.89 290	92.18 295	73.71 329	88.36 310	93.89 254	76.86 293	96.73 298	80.32 284	96.81 261	96.51 237

K. test v3		93.37 137	93.27 148	93.66 157	98.05 81	82.62 213	94.35 121	86.62 335	96.05 28	97.51 40	98.85 12	76.59 294	99.65 3	93.21 67	98.20 200	98.73 90

miper_lstm_enhance		89.90 235	89.80 227	90.19 272	91.37 328	77.50 288	83.82 350	95.00 237	84.84 249	93.05 216	94.96 214	76.53 295	95.20 336	89.96 155	98.67 148	97.86 170

test_part1		94.39 110	94.55 107	93.92 149	96.14 199	82.86 211	95.54 76	98.09 52	95.36 36	98.27 20	98.36 28	75.91 296	99.44 24	93.41 58	99.84 3	99.47 17

Test_1112_low_res		87.50 278	86.58 284	90.25 268	96.80 155	77.75 285	87.53 308	96.25 197	69.73 348	86.47 327	93.61 261	75.67 297	97.88 242	79.95 290	93.20 327	95.11 288

Vis-MVSNet (Re-imp)		90.42 214	90.16 218	91.20 239	97.66 111	77.32 291	94.33 122	87.66 328	91.20 123	92.99 218	95.13 205	75.40 298	98.28 209	77.86 307	99.19 85	97.99 155

D2MVS		89.93 234	89.60 232	90.92 248	94.03 282	78.40 275	88.69 295	94.85 242	78.96 300	93.08 214	95.09 207	74.57 299	96.94 290	88.19 191	98.96 115	97.41 203

PVSNet	76.22 20	82.89 313	82.37 313	84.48 336	93.96 283	64.38 368	78.60 361	88.61 319	71.50 339	84.43 340	86.36 355	74.27 300	94.60 339	69.87 354	93.69 323	94.46 303

test_yl		90.11 227	89.73 230	91.26 235	94.09 280	79.82 250	90.44 246	92.65 286	90.90 128	93.19 212	93.30 268	73.90 301	98.03 229	82.23 268	96.87 259	95.93 262

DCV-MVSNet		90.11 227	89.73 230	91.26 235	94.09 280	79.82 250	90.44 246	92.65 286	90.90 128	93.19 212	93.30 268	73.90 301	98.03 229	82.23 268	96.87 259	95.93 262

CMPMVS	68.83 22	87.28 282	85.67 295	92.09 213	88.77 356	85.42 178	90.31 252	94.38 256	70.02 347	88.00 315	93.30 268	73.78 303	94.03 348	75.96 324	96.54 268	96.83 228
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		87.62 275	87.31 270	88.54 299	94.71 265	74.27 323	93.10 154	88.20 324	86.20 223	92.18 244	93.04 273	73.21 304	95.52 325	79.32 299	85.82 359	95.83 267

bset_n11_16_dypcd		89.99 233	89.15 236	92.53 199	94.75 259	81.34 227	84.19 346	87.56 329	85.13 242	93.77 190	92.46 287	72.82 305	99.01 112	92.46 90	99.21 82	97.23 214

PVSNet_0	70.34 21	74.58 337	72.96 340	79.47 349	90.63 335	66.24 361	73.26 362	83.40 360	63.67 364	78.02 365	78.35 368	72.53 306	89.59 365	56.68 368	60.05 372	82.57 366

MIMVSNet		87.13 288	86.54 286	88.89 293	96.05 206	76.11 306	94.39 120	88.51 320	81.37 278	88.27 312	96.75 115	72.38 307	95.52 325	65.71 362	95.47 290	95.03 289

PAPM		81.91 321	80.11 331	87.31 316	93.87 286	72.32 337	84.02 348	93.22 275	69.47 349	76.13 368	89.84 326	72.15 308	97.23 282	53.27 370	89.02 353	92.37 341

cl22		89.02 247	88.50 248	90.59 259	89.76 344	76.45 303	86.62 328	94.03 262	82.98 266	92.65 227	92.49 286	72.05 309	97.53 266	88.93 176	97.02 253	97.78 178

LFMVS		91.33 197	91.16 199	91.82 218	96.27 188	79.36 260	95.01 97	85.61 346	96.04 29	94.82 160	97.06 94	72.03 310	98.46 199	84.96 243	98.70 144	97.65 188

MVS-HIRNet		78.83 336	80.60 327	73.51 353	93.07 297	47.37 376	87.10 315	78.00 371	68.94 350	77.53 366	97.26 82	71.45 311	94.62 338	63.28 365	88.74 354	78.55 368

EPNet_dtu		85.63 297	84.37 301	89.40 285	86.30 367	74.33 322	91.64 219	88.26 322	84.84 249	72.96 370	89.85 325	71.27 312	97.69 261	76.60 319	97.62 236	96.18 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1111		90.39 216	90.61 210	89.74 279	98.04 84	71.50 340	95.59 72	79.72 369	89.41 160	95.94 109	98.14 33	70.79 313	98.81 144	88.52 187	99.32 62	98.90 69

ECVR-MVS		90.12 226	90.16 218	90.00 276	97.81 97	72.68 334	95.76 67	78.54 370	89.04 170	95.36 134	98.10 35	70.51 314	98.64 177	87.10 212	99.18 87	98.67 95

HyFIR lowres test		87.19 286	85.51 296	92.24 205	97.12 140	80.51 236	85.03 337	96.06 206	66.11 358	91.66 252	92.98 276	70.12 315	99.14 89	75.29 326	95.23 297	97.07 217

FMVSNet3		90.78 205	90.32 217	92.16 211	93.03 300	79.92 248	92.54 169	94.95 239	86.17 225	95.10 147	96.01 162	69.97 316	98.75 156	86.74 216	98.38 174	97.82 175

RPMNet		90.31 222	90.14 222	90.81 253	91.01 331	78.93 267	92.52 170	98.12 46	91.91 95	89.10 295	96.89 105	68.84 317	99.41 36	90.17 148	92.70 335	94.08 309

ADS-MVSNet2		84.01 307	82.20 315	89.41 284	89.04 353	76.37 305	87.57 304	90.98 309	72.71 335	84.46 338	92.45 288	68.08 318	96.48 305	70.58 352	83.97 361	95.38 282

ADS-MVSNet		82.25 316	81.55 317	84.34 337	89.04 353	65.30 362	87.57 304	85.13 353	72.71 335	84.46 338	92.45 288	68.08 318	92.33 357	70.58 352	83.97 361	95.38 282

CVMVSNet		85.16 300	84.72 298	86.48 320	92.12 315	70.19 345	92.32 185	88.17 325	56.15 369	90.64 268	95.85 167	67.97 320	96.69 299	88.78 181	90.52 350	92.56 339

new_pmnet		81.22 324	81.01 323	81.86 345	90.92 333	70.15 346	84.03 347	80.25 368	70.83 343	85.97 330	89.78 330	67.93 321	84.65 369	67.44 358	91.90 344	90.78 351

CR-MVSNet		87.89 267	87.12 276	90.22 269	91.01 331	78.93 267	92.52 170	92.81 281	73.08 332	89.10 295	96.93 102	67.11 322	97.64 263	88.80 180	92.70 335	94.08 309

Patchmtry		90.11 227	89.92 225	90.66 256	90.35 340	77.00 295	92.96 157	92.81 281	90.25 146	94.74 164	96.93 102	67.11 322	97.52 267	85.17 235	98.98 109	97.46 199

PatchmatchNet		85.22 299	84.64 299	86.98 318	89.51 349	69.83 350	90.52 244	87.34 331	78.87 301	87.22 324	92.74 282	66.91 324	96.53 302	81.77 272	86.88 358	94.58 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GA-MVS		87.70 271	86.82 280	90.31 265	93.27 293	77.22 293	84.72 341	92.79 283	85.11 244	89.82 285	90.07 324	66.80 325	97.76 257	84.56 248	94.27 316	95.96 261

MDTV_nov1_ep13_2view						42.48 378	88.45 299		67.22 356	83.56 345		66.80 325		72.86 339		94.06 311

tpmrst		82.85 314	82.93 312	82.64 343	87.65 358	58.99 373	90.14 258	87.90 327	75.54 318	83.93 342	91.63 306	66.79 327	95.36 331	81.21 279	81.54 367	93.57 327

sam_mvs1												66.64 328				94.75 296

sam_mvs												66.41 329

Patchmatch-RL test		88.81 254	88.52 247	89.69 281	95.33 246	79.94 247	86.22 331	92.71 285	78.46 304	95.80 115	94.18 242	66.25 330	95.33 333	89.22 172	98.53 159	93.78 319

patchmatchnet-post											91.71 304	66.22 331	97.59 264

AUN-MVS		90.05 231	88.30 252	95.32 92	96.09 203	90.52 77	92.42 178	92.05 301	82.08 275	88.45 309	92.86 277	65.76 332	98.69 169	88.91 178	96.07 275	96.75 233

test_post											6.07 375	65.74 333	95.84 321

test_post1							90.21 254				5.85 376	65.36 334	96.00 319	79.61 296

MDTV_nov1_ep13			83.88 306		89.42 350	61.52 371	88.74 294	87.41 330	73.99 327	84.96 336	94.01 249	65.25 335	95.53 324	78.02 306	93.16 328

Patchmatch-test		86.10 295	86.01 292	86.38 324	90.63 335	74.22 324	89.57 274	86.69 334	85.73 233	89.81 286	92.83 278	65.24 336	91.04 361	77.82 310	95.78 283	93.88 318

tpmvs		84.22 306	83.97 305	84.94 332	87.09 364	65.18 363	91.21 228	88.35 321	82.87 267	85.21 332	90.96 315	65.24 336	96.75 297	79.60 298	85.25 360	92.90 335

EU-MVSNet		87.39 280	86.71 283	89.44 283	93.40 291	76.11 306	94.93 100	90.00 315	57.17 368	95.71 120	97.37 72	64.77 338	97.68 262	92.67 85	94.37 313	94.52 302

thres200		85.85 296	85.18 297	87.88 311	94.44 272	72.52 335	89.08 287	86.21 337	88.57 183	91.44 254	88.40 343	64.22 339	98.00 234	68.35 356	95.88 282	93.12 330

PatchT		87.51 277	88.17 258	85.55 327	90.64 334	66.91 355	92.02 198	86.09 339	92.20 87	89.05 297	97.16 89	64.15 340	96.37 310	89.21 173	92.98 333	93.37 328

tfpn200view9		87.05 289	86.52 287	88.67 297	95.77 223	72.94 331	91.89 206	86.00 341	90.84 130	92.61 228	89.80 327	63.93 341	98.28 209	71.27 348	96.54 268	94.79 294

thres400		87.20 285	86.52 287	89.24 290	95.77 223	72.94 331	91.89 206	86.00 341	90.84 130	92.61 228	89.80 327	63.93 341	98.28 209	71.27 348	96.54 268	96.51 237

FPMVS		84.50 304	83.28 308	88.16 306	96.32 184	94.49 16	85.76 332	85.47 347	83.09 263	85.20 333	94.26 238	63.79 343	86.58 368	63.72 364	91.88 345	83.40 363

thres100view900		87.35 281	86.89 279	88.72 296	96.14 199	73.09 330	93.00 156	85.31 349	92.13 89	93.26 208	90.96 315	63.42 344	98.28 209	71.27 348	96.54 268	94.79 294

thres600view7		87.66 273	87.10 277	89.36 286	96.05 206	73.17 328	92.72 163	85.31 349	91.89 96	93.29 205	90.97 314	63.42 344	98.39 200	73.23 336	96.99 258	96.51 237

EMVS		80.35 332	80.28 330	80.54 347	84.73 372	69.07 351	72.54 365	80.73 365	87.80 197	81.66 358	81.73 365	62.89 346	89.84 364	75.79 325	94.65 309	82.71 365

test-LLR		83.58 308	83.17 309	84.79 334	89.68 346	66.86 357	83.08 351	84.52 354	83.07 264	82.85 349	84.78 360	62.86 347	93.49 351	82.85 260	94.86 302	94.03 312

test0.0.03 1		82.48 315	81.47 319	85.48 328	89.70 345	73.57 327	84.73 339	81.64 363	83.07 264	88.13 314	86.61 352	62.86 347	89.10 367	66.24 361	90.29 351	93.77 320

tpm cat1		80.61 331	79.46 333	84.07 339	88.78 355	65.06 366	89.26 283	88.23 323	62.27 365	81.90 357	89.66 333	62.70 349	95.29 334	71.72 344	80.60 368	91.86 346

E-PMN		80.72 330	80.86 324	80.29 348	85.11 370	68.77 352	72.96 363	81.97 362	87.76 198	83.25 348	83.01 364	62.22 350	89.17 366	77.15 316	94.31 315	82.93 364

RRT_MVS		91.36 196	90.05 223	95.29 93	89.21 352	88.15 120	92.51 174	94.89 241	86.73 217	95.54 126	95.68 179	61.82 351	99.30 70	94.91 13	99.13 95	98.43 120

baseline2		83.38 309	81.54 318	88.90 292	91.38 327	72.84 333	88.78 292	81.22 364	78.97 299	79.82 363	87.56 346	61.73 352	97.80 251	74.30 331	90.05 352	96.05 258

CostFormer		83.09 311	82.21 314	85.73 326	89.27 351	67.01 354	90.35 250	86.47 336	70.42 345	83.52 346	93.23 271	61.18 353	96.85 294	77.21 315	88.26 356	93.34 329

MVSTER		89.32 243	88.75 245	91.03 243	90.10 342	76.62 301	90.85 235	94.67 252	82.27 273	95.24 143	95.79 172	61.09 354	98.49 193	90.49 131	98.26 189	97.97 159

tpm		84.38 305	84.08 304	85.30 331	90.47 338	63.43 370	89.34 280	85.63 345	77.24 312	87.62 319	95.03 212	61.00 355	97.30 280	79.26 300	91.09 349	95.16 285

EPMVS		81.17 326	80.37 328	83.58 340	85.58 369	65.08 365	90.31 252	71.34 373	77.31 311	85.80 331	91.30 309	59.38 356	92.70 356	79.99 289	82.34 366	92.96 334

tmp_tt		37.97 340	44.33 343	18.88 356	11.80 379	21.54 379	63.51 367	45.66 380	4.23 373	51.34 373	50.48 371	59.08 357	22.11 375	44.50 372	68.35 371	13.00 371

tpm2		81.46 322	80.35 329	84.80 333	89.90 343	65.14 364	90.44 246	85.36 348	65.82 360	82.05 355	92.44 290	57.94 358	96.69 299	70.71 351	88.49 355	92.56 339

ET-MVSNet_ETH3D		86.15 294	84.27 303	91.79 219	93.04 299	81.28 228	87.17 314	86.14 338	79.57 291	83.65 343	88.66 340	57.10 359	98.18 220	87.74 202	95.40 292	95.90 265

CHOSEN 280x420		80.04 333	77.97 338	86.23 325	90.13 341	74.53 319	72.87 364	89.59 316	66.38 357	76.29 367	85.32 359	56.96 360	95.36 331	69.49 355	94.72 307	88.79 357

JIA-IIPM		85.08 301	83.04 310	91.19 240	87.56 359	86.14 168	89.40 279	84.44 356	88.98 172	82.20 353	97.95 43	56.82 361	96.15 314	76.55 320	83.45 363	91.30 348

DeepMVS_CX				53.83 355	70.38 377	64.56 367		48.52 379	33.01 371	65.50 372	74.21 370	56.19 362	46.64 374	38.45 373	70.07 370	50.30 370

dp		79.28 334	78.62 336	81.24 346	85.97 368	56.45 374	86.91 318	85.26 351	72.97 333	81.45 359	89.17 339	56.01 363	95.45 329	73.19 337	76.68 369	91.82 347

test_method		50.44 339	48.94 342	54.93 354	39.68 378	12.38 380	28.59 369	90.09 314	6.82 372	41.10 374	78.41 367	54.41 364	70.69 373	50.12 371	51.26 373	81.72 367

thisisatest0515		84.72 303	82.99 311	89.90 277	92.96 301	75.33 314	84.36 344	83.42 359	77.37 310	88.27 312	86.65 351	53.94 365	98.72 161	82.56 264	97.40 243	95.67 275

tttt0517		89.81 237	88.90 243	92.55 198	97.00 142	79.73 254	95.03 96	83.65 358	89.88 152	95.30 137	94.79 224	53.64 366	99.39 48	91.99 99	98.79 136	98.54 111

thisisatest0530		88.69 257	87.52 268	92.20 206	96.33 183	79.36 260	92.81 161	84.01 357	86.44 219	93.67 194	92.68 284	53.62 367	99.25 77	89.65 162	98.45 166	98.00 152

FMVSNet5		87.82 270	86.56 285	91.62 225	92.31 309	79.81 252	93.49 146	94.81 246	83.26 259	91.36 255	96.93 102	52.77 368	97.49 270	76.07 322	98.03 215	97.55 195

pmmvs3		80.83 328	78.96 335	86.45 321	87.23 363	77.48 289	84.87 338	82.31 361	63.83 363	85.03 334	89.50 334	49.66 369	93.10 353	73.12 338	95.10 299	88.78 358

DWT-MVSNet_test		80.74 329	79.18 334	85.43 329	87.51 361	66.87 356	89.87 268	86.01 340	74.20 326	80.86 360	80.62 366	48.84 370	96.68 301	81.54 274	83.14 365	92.75 337

IB-MVS	77.21 19	83.11 310	81.05 321	89.29 287	91.15 329	75.85 309	85.66 333	86.00 341	79.70 289	82.02 356	86.61 352	48.26 371	98.39 200	77.84 308	92.22 340	93.63 323
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
RRT_test8_iter05		88.21 263	88.17 258	88.33 304	91.62 324	66.82 359	91.73 218	96.60 180	86.34 221	94.14 176	95.38 200	47.72 372	99.11 95	91.78 107	98.26 189	99.06 47

gg-mvs-nofinetune		82.10 320	81.02 322	85.34 330	87.46 362	71.04 341	94.74 105	67.56 374	96.44 22	79.43 364	98.99 6	45.24 373	96.15 314	67.18 359	92.17 341	88.85 356

GG-mvs-BLEND				83.24 342	85.06 371	71.03 342	94.99 99	65.55 375		74.09 369	75.51 369	44.57 374	94.46 341	59.57 367	87.54 357	84.24 362

TESTMET0.1,1		79.09 335	78.04 337	82.25 344	87.52 360	64.03 369	83.08 351	80.62 366	70.28 346	80.16 362	83.22 363	44.13 375	90.56 362	79.95 290	93.36 324	92.15 342

test-mter		81.21 325	80.01 332	84.79 334	89.68 346	66.86 357	83.08 351	84.52 354	73.85 328	82.85 349	84.78 360	43.66 376	93.49 351	82.85 260	94.86 302	94.03 312

KD-MVS_2432*1600		82.17 318	80.75 325	86.42 322	82.04 375	70.09 347	81.75 356	90.80 310	82.56 268	90.37 272	89.30 336	42.90 377	96.11 316	74.47 329	92.55 337	93.06 331

miper_refine_blended		82.17 318	80.75 325	86.42 322	82.04 375	70.09 347	81.75 356	90.80 310	82.56 268	90.37 272	89.30 336	42.90 377	96.11 316	74.47 329	92.55 337	93.06 331

test2506		85.42 298	84.57 300	87.96 308	97.81 97	66.53 360	96.14 51	56.35 377	89.04 170	93.55 198	98.10 35	42.88 379	98.68 171	88.09 195	99.18 87	98.67 95

test123		9.49 342	12.01 345	1.91 357	2.87 380	1.30 381	82.38 354	1.34 382	1.36 375	2.84 376	6.56 374	2.45 380	0.97 376	2.73 374	5.56 374	3.47 372

testmvs		9.02 343	11.42 346	1.81 358	2.77 381	1.13 382	79.44 360	1.90 381	1.18 376	2.65 377	6.80 373	1.95 381	0.87 377	2.62 375	3.45 375	3.44 373

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

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

ab-mvs-re		7.56 344	10.08 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	90.69 320	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

FOURS1					99.21 3	94.68 12	98.45 4	98.81 6	97.73 6	98.27 20

MSC_two_6792asdad				95.90 64	96.54 167	89.57 90		96.87 164					99.41 36	94.06 30	99.30 65	98.72 91

No_MVS				95.90 64	96.54 167	89.57 90		96.87 164					99.41 36	94.06 30	99.30 65	98.72 91

eth-test2					0.00 382

eth-test					0.00 382

IU-MVS					98.51 47	86.66 153		96.83 167	72.74 334	95.83 114				93.00 76	99.29 68	98.64 101

save fliter					97.46 123	88.05 123	92.04 196	97.08 147	87.63 203

test_0728_SECOND				94.88 107	98.55 41	86.72 150	95.20 88	98.22 32					99.38 54	93.44 55	99.31 63	98.53 112

GSMVS																94.75 296

test_part2					98.21 71	89.41 95				96.72 71

MTGPA								97.62 102

MTMP							94.82 102	54.62 378

gm-plane-assit					87.08 365	59.33 372			71.22 340		83.58 362		97.20 283	73.95 332

test9_res														88.16 193	98.40 169	97.83 173

agg_prior2														87.06 214	98.36 180	97.98 156

agg_prior					96.20 193	88.89 104		96.88 162		90.21 274			98.78 151

test_prior4						89.91 84	90.74 238

test_prior				94.61 118	95.95 214	87.23 136		97.36 125					98.68 171			97.93 162

旧先验2							90.00 263		68.65 351	92.71 226			96.52 303	85.15 237

新几何2							90.02 262

无先验							89.94 264	95.75 216	70.81 344				98.59 183	81.17 280		94.81 293

原ACMM2							89.34 280

testdata2													98.03 229	80.24 287

testdata1							88.96 289		88.44 185

plane_prior7					97.71 105	88.68 108

plane_prior5								97.81 90					98.95 122	89.26 170	98.51 162	98.60 108

plane_prior4											95.59 182

plane_prior3						88.43 117			90.35 145	93.31 203

plane_prior2							94.56 114		91.74 109

plane_prior1					97.38 126

plane_prior						88.12 121	93.01 155		88.98 172						98.06 212

n2								0.00 383

nn								0.00 383

door-mid								92.13 299

test11								96.65 178

door								91.26 307

HQP5-MVS						84.89 183

HQP-NCC					96.36 178		91.37 223		87.16 210	88.81 300

ACMP_Plane					96.36 178		91.37 223		87.16 210	88.81 300

BP-MVS														86.55 222

HQP4-MVS										88.81 300			98.61 179			98.15 138

HQP3-MVS								97.31 130							97.73 228

NP-MVS					96.82 152	87.10 140					93.40 266

ACMMP++_ref															98.82 131

ACMMP++															99.25 76

High-resolution multi-view benchmark