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 32	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 13	85.07 50	99.27 2	99.54 1

test_part1		93.54 2	94.85 2	89.62 58	94.83 39	74.49 122	94.02 4	97.02 2	92.06 2	96.94 2	98.50 2	95.09 2	96.03 8	85.83 44	99.33 1	98.33 7

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 24	86.84 107	93.91 40	80.07 86	86.75 157	93.26 104	93.64 3	90.93 194	84.60 57	90.75 244	93.97 96

abl_6		93.02 5	93.16 5	92.60 4	94.73 43	88.99 7	93.26 11	94.19 27	89.11 12	94.43 16	95.27 36	91.86 4	95.09 60	87.54 18	98.02 38	93.71 109

ACMH+	77.89 11	90.73 31	91.50 24	88.44 76	93.00 80	76.26 114	89.65 63	95.55 5	87.72 23	93.89 27	94.94 44	91.62 5	93.44 123	78.35 119	98.76 4	95.61 47

LPG-MVS_test		91.47 20	91.68 19	90.82 38	94.75 41	81.69 57	90.00 52	94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 41	81.69 57		94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 58

PMVS	80.48 6	90.08 42	90.66 47	88.34 79	96.71 3	92.97 1	90.31 49	89.57 173	88.51 19	90.11 91	95.12 41	90.98 8	88.92 238	77.55 130	97.07 83	83.13 301
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 32	90.99 40	89.63 56	95.03 34	83.53 47	89.62 64	93.35 61	79.20 96	93.83 28	93.60 101	90.81 9	92.96 139	85.02 52	98.45 19	92.41 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 60	91.14 35	83.96 155	92.50 93	70.36 163	89.55 65	93.84 45	81.89 66	94.70 13	95.44 33	90.69 10	88.31 248	83.33 69	98.30 26	93.20 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 6	92.54 7	92.37 6	95.93 15	85.81 30	92.99 12	94.23 23	85.21 32	92.51 51	95.13 40	90.65 11	95.34 50	88.06 9	98.15 33	95.95 40

RE-MVS-def			92.61 6		94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	90.64 12		87.16 25	97.60 64	92.73 142

ACMP	79.16 10	90.54 35	90.60 48	90.35 46	94.36 46	80.98 63	89.16 73	94.05 35	79.03 99	92.87 43	93.74 99	90.60 13	95.21 57	82.87 74	98.76 4	94.87 63
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 10	92.20 12	91.91 17	95.58 25	84.67 41	93.51 7	94.85 15	82.88 55	91.77 65	93.94 92	90.55 14	95.73 28	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
UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 147	97.00 2	64.33 209	89.67 62	88.38 190	88.84 15	94.29 19	97.57 4	90.48 15	91.26 184	72.57 182	97.65 60	97.34 15

SED-MVS		90.46 38	91.64 20	86.93 93	94.18 49	72.65 133	90.47 47	93.69 49	83.77 42	94.11 23	94.27 70	90.28 16	95.84 20	86.03 41	97.92 46	92.29 160

test_241102_ONE					94.18 49	72.65 133		93.69 49	83.62 44	94.11 23	93.78 98	90.28 16	95.50 45

SR-MVS		92.23 9	92.34 10	91.91 17	94.89 38	87.85 11	92.51 23	93.87 44	88.20 21	93.24 38	94.02 83	90.15 18	95.67 30	86.82 29	97.34 76	92.19 166

APD-MVS_3200maxsize		92.05 11	92.24 11	91.48 24	93.02 79	85.17 34	92.47 25	95.05 13	87.65 24	93.21 39	94.39 68	90.09 19	95.08 61	86.67 30	97.60 64	94.18 89

DVP-MVS		90.06 43	91.32 31	86.29 106	94.16 52	72.56 138	90.54 44	91.01 134	83.61 45	93.75 31	94.65 53	89.76 20	95.78 25	86.42 31	97.97 43	90.55 207

test0726					94.16 52	72.56 138	90.63 43	93.90 41	83.61 45	93.75 31	94.49 59	89.76 20

COLMAP_ROB	83.01 3	91.97 12	91.95 13	92.04 12	93.68 65	86.15 21	93.37 9	95.10 12	90.28 9	92.11 57	95.03 42	89.75 22	94.93 65	79.95 103	98.27 27	95.04 62
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_241102_TWO								93.71 48	83.77 42	93.49 37	94.27 70	89.27 23	95.84 20	86.03 41	97.82 51	92.04 169

ACMMP		91.91 13	91.87 18	92.03 13	95.53 26	85.91 25	93.35 10	94.16 28	82.52 59	92.39 54	94.14 78	89.15 24	95.62 31	87.35 20	98.24 28	94.56 72
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
test1172		92.40 8	92.41 8	92.37 6	94.68 44	89.04 6	91.98 30	93.62 52	90.14 11	93.63 36	94.16 77	88.83 25	95.51 42	87.11 27	97.54 69	92.54 151

SR-MVS-dyc-post		92.41 7	92.41 8	92.39 5	94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	88.83 25	95.51 42	87.16 25	97.60 64	92.73 142

APDe-MVS		91.22 25	91.92 14	89.14 65	92.97 81	78.04 88	92.84 15	94.14 32	83.33 49	93.90 25	95.73 26	88.77 27	96.41 1	87.60 16	97.98 42	92.98 133

ACMMP_NAP		90.65 32	91.07 38	89.42 61	95.93 15	79.54 75	89.95 55	93.68 51	77.65 113	91.97 62	94.89 45	88.38 28	95.45 46	89.27 3	97.87 50	93.27 123

MP-MVS-pluss		90.81 30	91.08 36	89.99 51	95.97 13	79.88 70	88.13 90	94.51 18	75.79 136	92.94 41	94.96 43	88.36 29	95.01 63	90.70 2	98.40 20	95.09 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 22	91.39 26	91.02 33	95.43 28	84.66 42	92.58 21	93.29 68	81.99 63	91.47 69	93.96 88	88.35 30	95.56 34	87.74 11	97.74 56	92.85 137

#test#		90.49 37	90.31 51	91.02 33	95.43 28	84.66 42	90.65 42	93.29 68	77.00 120	91.47 69	93.96 88	88.35 30	95.56 34	84.88 53	97.74 56	92.85 137

CP-MVS		91.67 15	91.58 22	91.96 14	95.29 31	87.62 12	93.38 8	93.36 60	83.16 51	91.06 77	94.00 84	88.26 32	95.71 29	87.28 23	98.39 21	92.55 150

SteuartSystems-ACMMP		91.16 27	91.36 27	90.55 42	93.91 60	80.97 64	91.49 36	93.48 58	82.82 56	92.60 50	93.97 85	88.19 33	96.29 4	87.61 15	98.20 32	94.39 82
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 26	90.95 42	91.93 15	95.67 22	85.85 28	90.00 52	93.90 41	80.32 82	91.74 66	94.41 65	88.17 34	95.98 10	86.37 33	97.99 40	93.96 97

TDRefinement		93.52 3	93.39 4	93.88 1	95.94 14	90.26 3	95.70 2	96.46 3	90.58 8	92.86 44	96.29 17	88.16 35	94.17 91	86.07 40	98.48 18	97.22 18

DPE-MVS		90.53 36	91.08 36	88.88 67	93.38 71	78.65 84	89.15 74	94.05 35	84.68 36	93.90 25	94.11 80	88.13 36	96.30 3	84.51 58	97.81 52	91.70 181

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 71	86.76 111	92.78 90	78.78 102	92.51 51	93.64 100	88.13 36	93.84 104	84.83 55	97.55 67	94.10 93

pmmvs6		86.52 93	88.06 77	81.90 195	92.22 103	62.28 234	84.66 140	89.15 179	83.54 47	89.85 99	97.32 5	88.08 38	86.80 264	70.43 199	97.30 78	96.62 27

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 67	84.79 39	89.89 57	90.63 142	70.00 207	94.55 15	96.67 12	87.94 39	93.59 115	84.27 60	95.97 120	95.52 48

ZNCC-MVS		91.26 24	91.34 30	91.01 35	95.73 20	83.05 52	92.18 27	94.22 24	80.14 85	91.29 74	93.97 85	87.93 40	95.87 16	88.65 4	97.96 45	94.12 92

region2R		91.44 21	91.30 33	91.87 19	95.75 18	85.90 26	92.63 20	93.30 67	81.91 65	90.88 82	94.21 75	87.75 41	95.87 16	87.60 16	97.71 58	93.83 102

wuyk23d		75.13 250	79.30 208	62.63 324	75.56 333	75.18 119	80.89 224	73.10 312	75.06 146	94.76 12	95.32 34	87.73 42	52.85 352	34.16 351	97.11 82	59.85 348

mPP-MVS		91.69 14	91.47 25	92.37 6	96.04 12	88.48 10	92.72 17	92.60 94	83.09 52	91.54 68	94.25 74	87.67 43	95.51 42	87.21 24	98.11 34	93.12 129

ACMMPR		91.49 18	91.35 29	91.92 16	95.74 19	85.88 27	92.58 21	93.25 70	81.99 63	91.40 71	94.17 76	87.51 44	95.87 16	87.74 11	97.76 54	93.99 95

test_0728_THIRD									85.33 31	93.75 31	94.65 53	87.44 45	95.78 25	87.41 19	98.21 30	92.98 133

9.14			89.29 62		91.84 116		88.80 81	95.32 9	75.14 145	91.07 76	92.89 115	87.27 46	93.78 106	83.69 66	97.55 67

PS-CasMVS		90.06 43	91.92 14	84.47 143	96.56 7	58.83 272	89.04 75	92.74 91	91.40 5	96.12 4	96.06 23	87.23 47	95.57 33	79.42 111	98.74 6	99.00 2

GST-MVS		90.96 29	91.01 39	90.82 38	95.45 27	82.73 55	91.75 34	93.74 47	80.98 76	91.38 72	93.80 95	87.20 48	95.80 22	87.10 28	97.69 59	93.93 98

PEN-MVS		90.03 45	91.88 17	84.48 142	96.57 6	58.88 269	88.95 76	93.19 72	91.62 4	96.01 6	96.16 21	87.02 49	95.60 32	78.69 116	98.72 9	98.97 3

DTE-MVSNet		89.98 47	91.91 16	84.21 149	96.51 8	57.84 277	88.93 78	92.84 88	91.92 3	96.16 3	96.23 19	86.95 50	95.99 9	79.05 113	98.57 15	98.80 6

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 69	93.75 62	77.44 96	86.31 119	95.27 10	70.80 196	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 85

SF-MVS		90.27 40	90.80 45	88.68 72	92.86 85	77.09 103	91.19 39	95.74 4	81.38 71	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 85

MP-MVS		91.14 28	90.91 43	91.83 21	96.18 11	86.88 14	92.20 26	93.03 80	82.59 58	88.52 128	94.37 69	86.74 53	95.41 48	86.32 34	98.21 30	93.19 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		91.27 23	91.26 34	91.29 29	96.59 4	86.29 17	88.94 77	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 158

MTAPA		91.52 17	91.60 21	91.29 29	96.59 4	86.29 17	92.02 29	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 158

XVS		91.54 16	91.36 27	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 94	94.03 82	86.57 56	95.80 22	87.35 20	97.62 62	94.20 87

X-MVStestdata		85.04 118	82.70 162	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 94	16.05 355	86.57 56	95.80 22	87.35 20	97.62 62	94.20 87

canonicalmvs		85.50 109	86.14 103	83.58 164	87.97 191	67.13 187	87.55 96	94.32 19	73.44 163	88.47 129	87.54 229	86.45 58	91.06 191	75.76 149	93.76 181	92.54 151

TranMVSNet+NR-MVSNet		87.86 78	88.76 73	85.18 130	94.02 57	64.13 210	84.38 148	91.29 126	84.88 35	92.06 59	93.84 94	86.45 58	93.73 107	73.22 172	98.66 11	97.69 10

test_0402		88.65 70	89.58 59	85.88 118	92.55 91	72.22 146	84.01 153	89.44 175	88.63 18	94.38 18	95.77 25	86.38 60	93.59 115	79.84 104	95.21 145	91.82 178

APD-MVS		89.54 56	89.63 57	89.26 64	92.57 90	81.34 62	90.19 50	93.08 76	80.87 77	91.13 75	93.19 105	86.22 61	95.97 11	82.23 80	97.18 81	90.45 209
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 67	89.88 53	86.22 109	91.63 119	77.07 104	89.82 58	93.77 46	78.90 100	92.88 42	92.29 135	86.11 62	90.22 216	86.24 38	97.24 79	91.36 190
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
ZD-MVS					92.22 103	80.48 66		91.85 111	71.22 193	90.38 87	92.98 110	86.06 63	96.11 6	81.99 82	96.75 94

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 71	84.72 40	89.70 59	90.29 157	69.27 211	94.39 17	96.38 16	86.02 64	93.52 119	83.96 62	95.92 122	95.34 52

nrg030		87.85 79	88.49 74	85.91 116	90.07 160	69.73 166	87.86 93	94.20 25	74.04 155	92.70 49	94.66 52	85.88 65	91.50 175	79.72 105	97.32 77	96.50 30

SMA-MVS		90.31 39	90.48 49	89.83 52	95.31 30	79.52 76	90.98 40	93.24 71	75.37 143	92.84 45	95.28 35	85.58 66	96.09 7	87.92 10	97.76 54	93.88 100
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
DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 62	93.64 66	79.07 79	88.54 86	94.20 25	73.53 161	89.71 103	94.82 48	85.09 67	95.77 27	84.17 61	98.03 37	93.26 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		89.51 57	89.48 60	89.59 59	92.26 100	80.80 65	90.14 51	93.54 56	83.37 48	90.57 86	92.55 127	84.99 68	96.15 5	81.26 88	96.61 98	91.83 177

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 23	93.73 64	85.72 31	96.79 1	95.51 6	88.86 14	95.63 8	96.99 9	84.81 69	93.16 133	91.10 1	97.53 70	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
DP-MVS		88.60 71	89.01 66	87.36 91	91.30 130	77.50 95	87.55 96	92.97 83	87.95 22	89.62 107	92.87 116	84.56 70	93.89 101	77.65 128	96.62 97	90.70 201

LS3D		90.60 34	90.34 50	91.38 27	89.03 173	84.23 45	93.58 5	94.68 16	90.65 7	90.33 89	93.95 91	84.50 71	95.37 49	80.87 94	95.50 136	94.53 76

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 73	91.94 112	77.27 102	88.71 83	95.26 11	76.08 127	90.66 85	92.69 122	84.48 72	93.83 105	83.38 68	97.48 73	94.47 77

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 164	86.67 15	90.51 46	90.20 160	69.87 208	95.06 11	96.14 22	84.28 73	93.07 138	87.68 13	96.34 108	97.09 20

OMC-MVS		88.19 74	87.52 82	90.19 49	91.94 112	81.68 59	87.49 98	93.17 73	76.02 130	88.64 125	91.22 159	84.24 74	93.37 126	77.97 126	97.03 84	95.52 48

ETH3D cwj APD-0.16		87.83 80	87.62 81	88.47 75	91.21 133	78.20 86	87.26 100	94.54 17	72.05 186	88.89 119	92.31 134	83.86 75	94.24 85	81.59 87	96.87 88	92.97 136

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 23	85.91 122	93.60 55	80.16 84	89.13 118	93.44 102	83.82 76	90.98 192	83.86 64	95.30 144	93.60 115

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 37	84.50 44	89.49 69	93.98 37	79.68 89	92.09 58	93.89 93	83.80 77	93.10 137	82.67 76	98.04 35	93.64 113

CDPH-MVS		86.17 101	85.54 114	88.05 84	92.25 101	75.45 117	83.85 158	92.01 106	65.91 242	86.19 167	91.75 150	83.77 78	94.98 64	77.43 133	96.71 95	93.73 108

Effi-MVS+		83.90 147	84.01 145	83.57 165	87.22 208	65.61 199	86.55 116	92.40 97	78.64 105	81.34 244	84.18 281	83.65 79	92.93 141	74.22 160	87.87 276	92.17 167

MVS_111021_HR		84.63 124	84.34 141	85.49 127	90.18 158	75.86 116	79.23 249	87.13 210	73.35 164	85.56 181	89.34 201	83.60 80	90.50 209	76.64 140	94.05 177	90.09 217

UA-Net		91.49 18	91.53 23	91.39 26	94.98 35	82.95 54	93.52 6	92.79 89	88.22 20	88.53 127	97.64 3	83.45 81	94.55 80	86.02 43	98.60 13	96.67 26

AdaColmap		83.66 150	83.69 150	83.57 165	90.05 161	72.26 145	86.29 121	90.00 165	78.19 110	81.65 239	87.16 236	83.40 82	94.24 85	61.69 262	94.76 163	84.21 283

LCM-MVSNet-Re		83.48 154	85.06 120	78.75 243	85.94 236	55.75 293	80.05 233	94.27 20	76.47 123	96.09 5	94.54 57	83.31 83	89.75 229	59.95 274	94.89 157	90.75 200

Regformer-2		86.74 90	86.08 104	88.73 70	84.18 259	79.20 78	83.52 167	89.33 177	83.33 49	89.92 98	85.07 270	83.23 84	93.16 133	83.39 67	92.72 207	93.83 102

TransMVSNet (Re)		84.02 143	85.74 110	78.85 241	91.00 140	55.20 298	82.29 201	87.26 206	79.65 90	88.38 132	95.52 32	83.00 85	86.88 262	67.97 222	96.60 99	94.45 80

CNVR-MVS		87.81 81	87.68 80	88.21 81	92.87 83	77.30 101	85.25 132	91.23 128	77.31 117	87.07 151	91.47 156	82.94 86	94.71 71	84.67 56	96.27 112	92.62 149

DeepPCF-MVS	81.24 5	87.28 84	86.21 102	90.49 43	91.48 127	84.90 37	83.41 172	92.38 99	70.25 204	89.35 115	90.68 180	82.85 87	94.57 78	79.55 107	95.95 121	92.00 171

v7n		90.13 41	90.96 41	87.65 88	91.95 110	71.06 158	89.99 54	93.05 77	86.53 27	94.29 19	96.27 18	82.69 88	94.08 95	86.25 37	97.63 61	97.82 9

AllTest		87.97 77	87.40 85	89.68 54	91.59 120	83.40 48	89.50 68	95.44 7	79.47 91	88.00 137	93.03 108	82.66 89	91.47 176	70.81 191	96.14 116	94.16 90

TestCases				89.68 54	91.59 120	83.40 48		95.44 7	79.47 91	88.00 137	93.03 108	82.66 89	91.47 176	70.81 191	96.14 116	94.16 90

RPSCF		88.00 76	86.93 92	91.22 31	90.08 159	89.30 5	89.68 61	91.11 131	79.26 95	89.68 104	94.81 51	82.44 91	87.74 252	76.54 141	88.74 266	96.61 28

ITE_SJBPF				90.11 50	90.72 147	84.97 36		90.30 154	81.56 69	90.02 93	91.20 161	82.40 92	90.81 200	73.58 169	94.66 164	94.56 72

Fast-Effi-MVS+		81.04 187	80.57 189	82.46 190	87.50 203	63.22 220	78.37 260	89.63 171	68.01 222	81.87 234	82.08 303	82.31 93	92.65 148	67.10 225	88.30 272	91.51 188

baseline		85.20 113	85.93 106	83.02 175	86.30 228	62.37 232	84.55 143	93.96 38	74.48 152	87.12 147	92.03 140	82.30 94	91.94 165	78.39 117	94.21 173	94.74 68

casdiffmvs		85.21 112	85.85 108	83.31 170	86.17 233	62.77 225	83.03 183	93.93 39	74.69 149	88.21 135	92.68 123	82.29 95	91.89 168	77.87 127	93.75 183	95.27 56

Anonymous20231211		88.40 72	89.62 58	84.73 137	90.46 153	65.27 200	88.86 79	93.02 81	87.15 25	93.05 40	97.10 7	82.28 96	92.02 163	76.70 139	97.99 40	96.88 24

Regformer-1		86.00 102	85.50 115	87.49 89	84.18 259	76.90 106	83.52 167	87.94 200	82.18 62	89.19 116	85.07 270	82.28 96	91.89 168	82.40 78	92.72 207	93.69 110

Anonymous20240529		86.20 100	87.13 86	83.42 168	90.19 157	64.55 207	84.55 143	90.71 139	85.85 30	89.94 97	95.24 38	82.13 98	90.40 211	69.19 210	96.40 106	95.31 54

agg_prior1		85.72 107	85.20 119	87.28 92	91.58 123	77.69 92	83.69 164	90.30 154	66.29 239	84.32 200	91.07 166	82.13 98	93.18 131	81.02 91	96.36 107	90.98 193

Regformer-4		86.41 94	85.71 111	88.52 73	84.27 255	77.57 94	84.07 151	88.00 198	82.82 56	89.84 100	85.48 258	82.06 100	92.77 145	83.83 65	91.04 233	95.22 60

CLD-MVS		83.18 160	82.64 164	84.79 135	89.05 172	67.82 185	77.93 264	92.52 95	68.33 220	85.07 185	81.54 308	82.06 100	92.96 139	69.35 206	97.91 48	93.57 116
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TEST9					92.34 97	79.70 73	83.94 154	90.32 150	65.41 253	84.49 196	90.97 169	82.03 102	93.63 111

segment_acmp												81.94 103

train_agg		85.98 104	85.28 118	88.07 83	92.34 97	79.70 73	83.94 154	90.32 150	65.79 243	84.49 196	90.97 169	81.93 104	93.63 111	81.21 89	96.54 101	90.88 197

test_8					92.09 106	78.87 81	83.82 159	90.31 152	65.79 243	84.36 199	90.96 171	81.93 104	93.44 123

test_prior3		86.31 96	86.31 99	86.32 104	90.59 150	71.99 148	83.37 173	92.85 86	75.43 140	84.58 194	91.57 152	81.92 106	94.17 91	79.54 108	96.97 85	92.80 139

test_prior2							83.37 173		75.43 140	84.58 194	91.57 152	81.92 106		79.54 108	96.97 85

CP-MVSNet		89.27 61	90.91 43	84.37 144	96.34 9	58.61 274	88.66 85	92.06 105	90.78 6	95.67 7	95.17 39	81.80 108	95.54 38	79.00 114	98.69 10	98.95 4

MVS_111021_LR		84.28 136	83.76 149	85.83 120	89.23 170	83.07 51	80.99 223	83.56 250	72.71 178	86.07 171	89.07 208	81.75 109	86.19 274	77.11 136	93.36 187	88.24 238

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 96	82.98 53	91.98 30	90.08 163	71.54 190	94.28 21	96.54 14	81.57 110	94.27 82	86.26 35	96.49 103	97.09 20

cdsmvs_eth3d_5k		20.81 325	27.75 328	0.00 342	0.00 363	0.00 364	0.00 354	85.44 231	0.00 359	0.00 360	82.82 296	81.46 111	0.00 360	0.00 358	0.00 358	0.00 356

WR-MVS_H		89.91 50	91.31 32	85.71 122	96.32 10	62.39 231	89.54 67	93.31 65	90.21 10	95.57 9	95.66 28	81.42 112	95.90 14	80.94 93	98.80 3	98.84 5

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 33	86.95 13	92.09 28	92.30 100	79.74 88	87.50 143	92.38 130	81.42 112	93.28 128	83.07 71	97.24 79	91.67 182

pm-mvs1		83.69 149	84.95 123	79.91 227	90.04 162	59.66 259	82.43 197	87.44 203	75.52 139	87.85 139	95.26 37	81.25 114	85.65 281	68.74 215	96.04 119	94.42 81

OPU-MVS				88.27 80	91.89 114	77.83 90	90.47 47				91.22 159	81.12 115	94.68 72	74.48 158	95.35 139	92.29 160

ETH3 D test6400		85.09 116	84.87 124	85.75 121	90.80 145	69.34 170	85.90 123	93.31 65	65.43 249	86.11 170	89.95 194	80.92 116	94.86 67	75.90 148	95.57 134	93.05 130

NCCC		87.36 83	86.87 93	88.83 68	92.32 99	78.84 82	86.58 115	91.09 132	78.77 103	84.85 190	90.89 173	80.85 117	95.29 51	81.14 90	95.32 141	92.34 156

TAPA-MVS	77.73 12	85.71 108	84.83 125	88.37 78	88.78 178	79.72 72	87.15 103	93.50 57	69.17 212	85.80 177	89.56 199	80.76 118	92.13 158	73.21 177	95.51 135	93.25 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 167	81.41 181	85.90 117	85.60 237	76.53 111	83.07 182	89.62 172	73.02 174	79.11 268	83.51 286	80.74 119	90.24 215	68.76 214	89.29 257	90.94 195

VPA-MVSNet		83.47 155	84.73 126	79.69 232	90.29 155	57.52 280	81.30 219	88.69 185	76.29 124	87.58 142	94.44 61	80.60 120	87.20 257	66.60 230	96.82 92	94.34 84

Regformer-3		85.06 117	84.67 131	86.22 109	84.27 255	73.43 128	84.07 151	85.26 235	80.77 78	88.62 126	85.48 258	80.56 121	90.39 212	81.99 82	91.04 233	94.85 65

ETV-MVS		84.31 134	83.91 148	85.52 125	88.58 180	70.40 162	84.50 147	93.37 59	78.76 104	84.07 206	78.72 325	80.39 122	95.13 59	73.82 167	92.98 200	91.04 192

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 36	85.85 28	89.70 59	91.27 127	78.20 109	86.69 158	92.28 136	80.36 123	95.06 62	86.17 39	96.49 103	90.22 212

ANet_high		83.17 161	85.68 112	75.65 279	81.24 285	45.26 343	79.94 235	92.91 84	83.83 41	91.33 73	96.88 11	80.25 124	85.92 277	68.89 213	95.89 123	95.76 42

EI-MVSNet-Vis-set		85.12 115	84.53 135	86.88 94	84.01 262	72.76 132	83.91 157	85.18 237	80.44 79	88.75 123	85.49 257	80.08 125	91.92 166	82.02 81	90.85 242	95.97 38

DeepC-MVS_fast	80.27 8	86.23 98	85.65 113	87.96 85	91.30 130	76.92 105	87.19 101	91.99 107	70.56 199	84.96 186	90.69 179	80.01 126	95.14 58	78.37 118	95.78 129	91.82 178
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS		83.43 157	83.04 159	84.59 141	87.87 194	66.61 192	85.57 128	94.90 14	73.02 174	81.12 245	78.56 326	80.00 127	95.52 39	73.04 179	93.29 191	91.62 184

EI-MVSNet-UG-set		85.04 118	84.44 137	86.85 95	83.87 265	72.52 140	83.82 159	85.15 238	80.27 83	88.75 123	85.45 261	79.95 128	91.90 167	81.92 84	90.80 243	96.13 33

MCST-MVS		84.36 131	83.93 147	85.63 123	91.59 120	71.58 155	83.52 167	92.13 103	61.82 270	83.96 207	89.75 198	79.93 129	93.46 122	78.33 120	94.34 171	91.87 176

TSAR-MVS + MP.		88.14 75	87.82 79	89.09 66	95.72 21	76.74 108	92.49 24	91.19 130	67.85 227	86.63 159	94.84 47	79.58 130	95.96 12	87.62 14	94.50 167	94.56 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				86.57 99	90.74 146	72.63 136		90.69 140		82.76 222		79.20 131	94.80 69		95.32 141	92.27 162

CSCG		86.26 97	86.47 97	85.60 124	90.87 143	74.26 124	87.98 91	91.85 111	80.35 81	89.54 113	88.01 220	79.09 132	92.13 158	75.51 150	95.06 152	90.41 210

Test By Simon												79.09 132

PHI-MVS		86.38 95	85.81 109	88.08 82	88.44 184	77.34 99	89.35 72	93.05 77	73.15 172	84.76 191	87.70 226	78.87 134	94.18 89	80.67 98	96.29 109	92.73 142

EG-PatchMatch MVS		84.08 141	84.11 143	83.98 154	92.22 103	72.61 137	82.20 207	87.02 215	72.63 179	88.86 120	91.02 167	78.52 135	91.11 189	73.41 171	91.09 231	88.21 239

Effi-MVS+-dtu		85.82 106	83.38 152	93.14 3	87.13 210	91.15 2	87.70 95	88.42 188	74.57 150	83.56 213	85.65 255	78.49 136	94.21 87	72.04 185	92.88 202	94.05 94

mvs-test1		84.55 127	82.12 171	91.84 20	87.13 210	89.54 4	85.05 135	88.42 188	74.57 150	80.60 251	82.98 291	78.49 136	93.98 99	72.04 185	89.77 253	92.00 171

Vis-MVSNet		86.86 87	86.58 96	87.72 86	92.09 106	77.43 98	87.35 99	92.09 104	78.87 101	84.27 205	94.05 81	78.35 138	93.65 109	80.54 100	91.58 227	92.08 168
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 113	92.86 85	67.02 188	82.55 194	91.56 118	83.08 53	90.92 79	91.82 147	78.25 139	93.99 97	74.16 161	98.35 22	97.49 14

MSLP-MVS++		85.00 120	86.03 105	81.90 195	91.84 116	71.56 156	86.75 112	93.02 81	75.95 133	87.12 147	89.39 200	77.98 140	89.40 234	77.46 131	94.78 160	84.75 278

API-MVS		82.28 170	82.61 165	81.30 204	86.29 229	69.79 164	88.71 83	87.67 202	78.42 108	82.15 231	84.15 282	77.98 140	91.59 174	65.39 239	92.75 204	82.51 308

DP-MVS Recon		84.05 142	83.22 154	86.52 101	91.73 118	75.27 118	83.23 179	92.40 97	72.04 187	82.04 232	88.33 216	77.91 142	93.95 100	66.17 232	95.12 150	90.34 211

UniMVSNet (Re)		86.87 86	86.98 91	86.55 100	93.11 78	68.48 179	83.80 161	92.87 85	80.37 80	89.61 109	91.81 148	77.72 143	94.18 89	75.00 157	98.53 16	96.99 23

PCF-MVS	74.62 15	82.15 173	80.92 188	85.84 119	89.43 166	72.30 144	80.53 228	91.82 113	57.36 297	87.81 140	89.92 196	77.67 144	93.63 111	58.69 279	95.08 151	91.58 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 102	86.22 101	85.34 128	93.24 75	64.56 206	82.21 205	90.46 145	80.99 75	88.42 130	91.97 141	77.56 145	93.85 102	72.46 183	98.65 12	97.61 11

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 132	81.66 60	91.25 37	94.13 33	88.89 13	88.83 122	94.26 73	77.55 146	95.86 19	84.88 53	95.87 124	95.24 57

MVS_Test		82.47 168	83.22 154	80.22 224	82.62 275	57.75 279	82.54 195	91.96 109	71.16 194	82.89 221	92.52 129	77.41 147	90.50 209	80.04 102	87.84 277	92.40 155

EIA-MVS		82.19 172	81.23 183	85.10 131	87.95 192	69.17 176	83.22 180	93.33 62	70.42 200	78.58 271	79.77 322	77.29 148	94.20 88	71.51 188	88.96 262	91.93 175

xiu_mvs_v2_base		77.19 231	76.75 232	78.52 247	87.01 216	61.30 241	75.55 295	87.12 213	61.24 276	74.45 300	78.79 324	77.20 149	90.93 194	64.62 245	84.80 304	83.32 297

DU-MVS		86.80 89	86.99 90	86.21 111	93.24 75	67.02 188	83.16 181	92.21 101	81.73 67	90.92 79	91.97 141	77.20 149	93.99 97	74.16 161	98.35 22	97.61 11

Baseline_NR-MVSNet		84.00 144	85.90 107	78.29 253	91.47 128	53.44 307	82.29 201	87.00 218	79.06 98	89.55 111	95.72 27	77.20 149	86.14 275	72.30 184	98.51 17	95.28 55

TinyColmap		81.25 184	82.34 170	77.99 258	85.33 241	60.68 251	82.32 200	88.33 191	71.26 192	86.97 154	92.22 139	77.10 152	86.98 261	62.37 255	95.17 147	86.31 262

F-COLMAP		84.97 121	83.42 151	89.63 56	92.39 95	83.40 48	88.83 80	91.92 110	73.19 171	80.18 260	89.15 206	77.04 153	93.28 128	65.82 237	92.28 213	92.21 165

114514_t		83.10 162	82.54 167	84.77 136	92.90 82	69.10 177	86.65 113	90.62 143	54.66 308	81.46 241	90.81 176	76.98 154	94.38 81	72.62 181	96.18 114	90.82 199

xiu_mvs_v1_base_debu		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 249	79.59 262	82.73 298	76.94 155	90.14 221	73.22 172	88.33 268	86.90 257

xiu_mvs_v1_base		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 249	79.59 262	82.73 298	76.94 155	90.14 221	73.22 172	88.33 268	86.90 257

xiu_mvs_v1_base_debi		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 249	79.59 262	82.73 298	76.94 155	90.14 221	73.22 172	88.33 268	86.90 257

pcd_1.5k_mvsjas		6.41 328	8.55 331	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	76.94 155	0.00 360	0.00 358	0.00 358	0.00 356

PS-MVSNAJss		88.31 73	87.90 78	89.56 60	93.31 73	77.96 89	87.94 92	91.97 108	70.73 198	94.19 22	96.67 12	76.94 155	94.57 78	83.07 71	96.28 110	96.15 32

PS-MVSNAJ		77.04 233	76.53 234	78.56 246	87.09 215	61.40 239	75.26 297	87.13 210	61.25 275	74.38 302	77.22 334	76.94 155	90.94 193	64.63 244	84.83 303	83.35 296

MIMVSNet1		83.63 151	84.59 133	80.74 215	94.06 56	62.77 225	82.72 190	84.53 247	77.57 115	90.34 88	95.92 24	76.88 161	85.83 279	61.88 260	97.42 74	93.62 114

原ACMM1				84.60 140	92.81 88	74.01 125		91.50 120	62.59 264	82.73 223	90.67 181	76.53 162	94.25 84	69.24 207	95.69 132	85.55 269

MSDG		80.06 208	79.99 204	80.25 223	83.91 264	68.04 183	77.51 272	89.19 178	77.65 113	81.94 233	83.45 288	76.37 163	86.31 272	63.31 251	86.59 286	86.41 260

Gipuma		84.44 130	86.33 98	78.78 242	84.20 258	73.57 127	89.55 65	90.44 146	84.24 37	84.38 198	94.89 45	76.35 164	80.40 309	76.14 145	96.80 93	82.36 309
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing_2		84.36 131	84.64 132	83.50 167	86.74 220	63.97 213	84.56 142	90.31 152	66.22 240	91.62 67	94.55 56	75.88 165	91.95 164	77.02 138	94.89 157	94.56 72

XXY-MVS		74.44 260	76.19 237	69.21 305	84.61 247	52.43 316	71.70 315	77.18 283	60.73 279	80.60 251	90.96 171	75.44 166	69.35 333	56.13 292	88.33 268	85.86 267

FMVSNet1		84.55 127	85.45 116	81.85 197	90.27 156	61.05 245	86.83 108	88.27 193	78.57 106	89.66 106	95.64 29	75.43 167	90.68 204	69.09 211	95.33 140	93.82 104

CANet		83.79 148	82.85 161	86.63 98	86.17 233	72.21 147	83.76 162	91.43 122	77.24 118	74.39 301	87.45 231	75.36 168	95.42 47	77.03 137	92.83 203	92.25 164

ab-mvs		79.67 209	80.56 190	76.99 266	88.48 182	56.93 284	84.70 139	86.06 225	68.95 216	80.78 250	93.08 107	75.30 169	84.62 290	56.78 288	90.90 240	89.43 222

DELS-MVS		81.44 182	81.25 182	82.03 193	84.27 255	62.87 224	76.47 286	92.49 96	70.97 195	81.64 240	83.83 283	75.03 170	92.70 146	74.29 159	92.22 216	90.51 208
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
PAPR		78.84 214	78.10 220	81.07 209	85.17 242	60.22 254	82.21 205	90.57 144	62.51 265	75.32 296	84.61 277	74.99 171	92.30 155	59.48 277	88.04 274	90.68 202

CNLPA		83.55 153	83.10 158	84.90 133	89.34 168	83.87 46	84.54 145	88.77 183	79.09 97	83.54 214	88.66 214	74.87 172	81.73 304	66.84 228	92.29 212	89.11 228

HQP_MVS		87.75 82	87.43 84	88.70 71	93.45 68	76.42 112	89.45 70	93.61 53	79.44 93	86.55 160	92.95 113	74.84 173	95.22 55	80.78 96	95.83 125	94.46 78

plane_prior6					92.61 89	76.54 109						74.84 173

FC-MVSNet-test		85.93 105	87.05 89	82.58 186	92.25 101	56.44 288	85.75 125	93.09 75	77.33 116	91.94 63	94.65 53	74.78 175	93.41 125	75.11 156	98.58 14	97.88 8

VDD-MVS		84.23 138	84.58 134	83.20 172	91.17 137	65.16 202	83.25 177	84.97 244	79.79 87	87.18 146	94.27 70	74.77 176	90.89 197	69.24 207	96.54 101	93.55 119

BH-untuned		80.96 188	80.99 186	80.84 214	88.55 181	68.23 180	80.33 231	88.46 187	72.79 177	86.55 160	86.76 241	74.72 177	91.77 172	61.79 261	88.99 261	82.52 307

VPNet		80.25 203	81.68 176	75.94 278	92.46 94	47.98 337	76.70 281	81.67 263	73.45 162	84.87 189	92.82 117	74.66 178	86.51 269	61.66 263	96.85 89	93.33 120

tfpnnormal		81.79 179	82.95 160	78.31 251	88.93 176	55.40 294	80.83 226	82.85 254	76.81 121	85.90 176	94.14 78	74.58 179	86.51 269	66.82 229	95.68 133	93.01 132

CL-MVSNet_2432*1600		81.93 178	83.14 157	78.30 252	84.75 246	52.75 311	80.37 230	89.42 176	70.24 205	90.26 90	93.39 103	74.55 180	86.77 265	68.61 217	96.64 96	95.38 51

V42		83.47 155	83.37 153	83.75 160	83.16 271	63.33 218	81.31 217	90.23 159	69.51 210	90.91 81	90.81 176	74.16 181	92.29 156	80.06 101	90.22 250	95.62 46

3Dnovator	80.37 7	84.80 122	84.71 129	85.06 132	86.36 226	74.71 120	88.77 82	90.00 165	75.65 138	84.96 186	93.17 106	74.06 182	91.19 186	78.28 121	91.09 231	89.29 226

v10		86.54 92	87.10 87	84.84 134	88.16 190	63.28 219	86.64 114	92.20 102	75.42 142	92.81 47	94.50 58	74.05 183	94.06 96	83.88 63	96.28 110	97.17 19

旧先验1					91.97 109	71.77 150		81.78 262			91.84 145	73.92 184			93.65 185	83.61 291

mvs_anonymous		78.13 221	78.76 212	76.23 277	79.24 310	50.31 331	78.69 255	84.82 245	61.60 274	83.09 220	92.82 117	73.89 185	87.01 258	68.33 220	86.41 288	91.37 189

MAR-MVS		80.24 204	78.74 213	84.73 137	86.87 219	78.18 87	85.75 125	87.81 201	65.67 248	77.84 274	78.50 327	73.79 186	90.53 208	61.59 265	90.87 241	85.49 271
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
VDDNet		84.35 133	85.39 117	81.25 205	95.13 32	59.32 262	85.42 131	81.11 265	86.41 28	87.41 144	96.21 20	73.61 187	90.61 207	66.33 231	96.85 89	93.81 107

FIs		85.35 111	86.27 100	82.60 185	91.86 115	57.31 281	85.10 134	93.05 77	75.83 135	91.02 78	93.97 85	73.57 188	92.91 143	73.97 164	98.02 38	97.58 13

v1144		84.54 129	84.72 128	84.00 153	87.67 199	62.55 229	82.97 185	90.93 135	70.32 203	89.80 101	90.99 168	73.50 189	93.48 121	81.69 86	94.65 165	95.97 38

diffmvs		80.40 199	80.48 193	80.17 225	79.02 313	60.04 255	77.54 271	90.28 158	66.65 237	82.40 226	87.33 234	73.50 189	87.35 256	77.98 125	89.62 255	93.13 128

PAPM_NR		83.23 159	83.19 156	83.33 169	90.90 142	65.98 196	88.19 89	90.78 138	78.13 111	80.87 249	87.92 224	73.49 191	92.42 151	70.07 201	88.40 267	91.60 185

v8		86.22 99	86.83 94	84.36 145	87.82 195	62.35 233	86.42 117	91.33 125	76.78 122	92.73 48	94.48 60	73.41 192	93.72 108	83.10 70	95.41 137	97.01 22

EI-MVSNet		82.61 165	82.42 169	83.20 172	83.25 269	63.66 214	83.50 170	85.07 239	76.06 128	86.55 160	85.10 267	73.41 192	90.25 213	78.15 124	90.67 246	95.68 44

IterMVS-LS		84.73 123	84.98 122	83.96 155	87.35 205	63.66 214	83.25 177	89.88 167	76.06 128	89.62 107	92.37 133	73.40 194	92.52 150	78.16 122	94.77 162	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		84.24 137	84.41 138	83.71 161	87.59 202	61.57 238	82.95 186	91.03 133	67.82 228	89.80 101	90.49 184	73.28 195	93.51 120	81.88 85	94.89 157	96.04 37

BH-RMVSNet		80.53 195	80.22 198	81.49 203	87.19 209	66.21 195	77.79 267	86.23 223	74.21 154	83.69 209	88.50 215	73.25 196	90.75 201	63.18 252	87.90 275	87.52 249

PLC	73.85 16	82.09 174	80.31 194	87.45 90	90.86 144	80.29 68	85.88 124	90.65 141	68.17 221	76.32 284	86.33 246	73.12 197	92.61 149	61.40 266	90.02 252	89.44 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 77	80.37 67	91.91 33	93.11 74	81.10 74	95.32 10	97.24 6	72.94 198	94.85 68	85.07 50	97.78 53	97.26 16

WR-MVS		83.56 152	84.40 139	81.06 210	93.43 70	54.88 299	78.67 256	85.02 242	81.24 72	90.74 83	91.56 154	72.85 199	91.08 190	68.00 221	98.04 35	97.23 17

VNet		79.31 210	80.27 195	76.44 273	87.92 193	53.95 303	75.58 294	84.35 248	74.39 153	82.23 229	90.72 178	72.84 200	84.39 292	60.38 273	93.98 178	90.97 194

QAPM		82.59 166	82.59 166	82.58 186	86.44 221	66.69 191	89.94 56	90.36 149	67.97 224	84.94 188	92.58 126	72.71 201	92.18 157	70.63 197	87.73 278	88.85 235

v1192		84.57 126	84.69 130	84.21 149	87.75 197	62.88 223	83.02 184	91.43 122	69.08 214	89.98 96	90.89 173	72.70 202	93.62 114	82.41 77	94.97 155	96.13 33

OpenMVS	76.72 13	81.98 177	82.00 174	81.93 194	84.42 251	68.22 181	88.50 87	89.48 174	66.92 234	81.80 238	91.86 143	72.59 203	90.16 218	71.19 190	91.25 230	87.40 251

TSAR-MVS + GP.		83.95 145	82.69 163	87.72 86	89.27 169	81.45 61	83.72 163	81.58 264	74.73 148	85.66 178	86.06 251	72.56 204	92.69 147	75.44 152	95.21 145	89.01 234

alignmvs		83.94 146	83.98 146	83.80 157	87.80 196	67.88 184	84.54 145	91.42 124	73.27 170	88.41 131	87.96 221	72.33 205	90.83 199	76.02 147	94.11 175	92.69 146

HQP2-MVS												72.10 206

HQP-MVS		84.61 125	84.06 144	86.27 107	91.19 134	70.66 160	84.77 136	92.68 92	73.30 167	80.55 254	90.17 192	72.10 206	94.61 76	77.30 134	94.47 168	93.56 117

testgi		72.36 273	74.61 249	65.59 318	80.56 297	42.82 349	68.29 326	73.35 309	66.87 235	81.84 235	89.93 195	72.08 208	66.92 341	46.05 336	92.54 209	87.01 256

v1921920		84.23 138	84.37 140	83.79 158	87.64 201	61.71 237	82.91 187	91.20 129	67.94 225	90.06 92	90.34 186	72.04 209	93.59 115	82.32 79	94.91 156	96.07 35

MSP-MVS		89.08 65	88.16 76	91.83 21	95.76 17	86.14 22	92.75 16	93.90 41	78.43 107	89.16 117	92.25 137	72.03 210	96.36 2	88.21 8	90.93 239	92.98 133

LF4IMVS		82.75 164	81.93 175	85.19 129	82.08 276	80.15 69	85.53 129	88.76 184	68.01 222	85.58 180	87.75 225	71.80 211	86.85 263	74.02 163	93.87 180	88.58 237

v1240		84.30 135	84.51 136	83.65 162	87.65 200	61.26 242	82.85 188	91.54 119	67.94 225	90.68 84	90.65 182	71.71 212	93.64 110	82.84 75	94.78 160	96.07 35

ambc				82.98 176	90.55 152	64.86 203	88.20 88	89.15 179		89.40 114	93.96 88	71.67 213	91.38 183	78.83 115	96.55 100	92.71 145

1121		80.86 189	79.81 205	84.02 152	93.93 59	78.70 83	81.64 212	80.18 271	55.43 305	83.67 210	91.15 162	71.29 214	91.41 181	67.95 223	93.06 197	81.96 313

新几何1				82.95 177	93.96 58	78.56 85		80.24 270	55.45 304	83.93 208	91.08 164	71.19 215	88.33 247	65.84 236	93.07 196	81.95 314

v148		82.31 169	82.48 168	81.81 200	85.59 238	59.66 259	81.47 215	86.02 226	72.85 176	88.05 136	90.65 182	70.73 216	90.91 196	75.15 155	91.79 222	94.87 63

v2v482		84.09 140	84.24 142	83.62 163	87.13 210	61.40 239	82.71 191	89.71 169	72.19 185	89.55 111	91.41 157	70.70 217	93.20 130	81.02 91	93.76 181	96.25 31

UGNet		82.78 163	81.64 177	86.21 111	86.20 232	76.24 115	86.86 106	85.68 229	77.07 119	73.76 304	92.82 117	69.64 218	91.82 171	69.04 212	93.69 184	90.56 206
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
cl_fuxian		81.64 180	81.59 179	81.79 201	80.86 291	59.15 266	78.61 257	90.18 161	68.36 219	87.20 145	87.11 238	69.39 219	91.62 173	78.16 122	94.43 170	94.60 71

MG-MVS		80.32 202	80.94 187	78.47 249	88.18 188	52.62 314	82.29 201	85.01 243	72.01 188	79.24 267	92.54 128	69.36 220	93.36 127	70.65 196	89.19 260	89.45 220

IS-MVSNet		86.66 91	86.82 95	86.17 113	92.05 108	66.87 190	91.21 38	88.64 186	86.30 29	89.60 110	92.59 124	69.22 221	94.91 66	73.89 165	97.89 49	96.72 25

PVSNet_BlendedMVS		78.80 215	77.84 221	81.65 202	84.43 249	63.41 216	79.49 243	90.44 146	61.70 273	75.43 294	87.07 239	69.11 222	91.44 178	60.68 271	92.24 214	90.11 216

PVSNet_Blended		76.49 240	75.40 244	79.76 229	84.43 249	63.41 216	75.14 298	90.44 146	57.36 297	75.43 294	78.30 328	69.11 222	91.44 178	60.68 271	87.70 279	84.42 281

BH-w/o		76.57 238	76.07 239	78.10 256	86.88 218	65.92 197	77.63 269	86.33 222	65.69 247	80.89 248	79.95 319	68.97 224	90.74 202	53.01 310	85.25 298	77.62 331

MVS		73.21 267	72.59 270	75.06 282	80.97 288	60.81 250	81.64 212	85.92 227	46.03 342	71.68 313	77.54 330	68.47 225	89.77 228	55.70 295	85.39 295	74.60 336

miper_ehance_all_eth		80.34 201	80.04 203	81.24 207	79.82 303	58.95 268	77.66 268	89.66 170	65.75 246	85.99 175	85.11 266	68.29 226	91.42 180	76.03 146	92.03 218	93.33 120

Anonymous202405211		80.51 196	81.19 184	78.49 248	88.48 182	57.26 282	76.63 282	82.49 256	81.21 73	84.30 203	92.24 138	67.99 227	86.24 273	62.22 256	95.13 148	91.98 174

testdata				79.54 235	92.87 83	72.34 143		80.14 272	59.91 284	85.47 183	91.75 150	67.96 228	85.24 283	68.57 219	92.18 217	81.06 327

DPM-MVS		80.10 207	79.18 209	82.88 181	90.71 148	69.74 165	78.87 253	90.84 136	60.29 282	75.64 293	85.92 253	67.28 229	93.11 136	71.24 189	91.79 222	85.77 268

PVSNet_Blended_VisFu		81.55 181	80.49 192	84.70 139	91.58 123	73.24 130	84.21 149	91.67 117	62.86 263	80.94 247	87.16 236	67.27 230	92.87 144	69.82 203	88.94 263	87.99 243

MDA-MVSNet-bldmvs		77.47 228	76.90 231	79.16 239	79.03 312	64.59 204	66.58 333	75.67 293	73.15 172	88.86 120	88.99 209	66.94 231	81.23 306	64.71 242	88.22 273	91.64 183

test222					93.31 73	76.54 109	79.38 244	77.79 280	52.59 318	82.36 227	90.84 175	66.83 232			91.69 224	81.25 322

TR-MVS		76.77 236	75.79 240	79.72 231	86.10 235	65.79 198	77.14 275	83.02 252	65.20 254	81.40 242	82.10 302	66.30 233	90.73 203	55.57 296	85.27 297	82.65 303

OpenMVS_ROB	70.19 17	77.77 227	77.46 224	78.71 244	84.39 252	61.15 243	81.18 221	82.52 255	62.45 267	83.34 215	87.37 232	66.20 234	88.66 244	64.69 243	85.02 300	86.32 261

EPP-MVSNet		85.47 110	85.04 121	86.77 97	91.52 126	69.37 169	91.63 35	87.98 199	81.51 70	87.05 152	91.83 146	66.18 235	95.29 51	70.75 194	96.89 87	95.64 45

SixPastTwentyTwo		87.20 85	87.45 83	86.45 102	92.52 92	69.19 175	87.84 94	88.05 196	81.66 68	94.64 14	96.53 15	65.94 236	94.75 70	83.02 73	96.83 91	95.41 50

PatchMatch-RL		74.48 258	73.22 263	78.27 254	87.70 198	85.26 33	75.92 291	70.09 327	64.34 258	76.09 287	81.25 310	65.87 237	78.07 315	53.86 306	83.82 308	71.48 339

EPNet		80.37 200	78.41 217	86.23 108	76.75 324	73.28 129	87.18 102	77.45 282	76.24 126	68.14 324	88.93 210	65.41 238	93.85 102	69.47 205	96.12 118	91.55 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PM-MVS		80.20 205	79.00 210	83.78 159	88.17 189	86.66 16	81.31 217	66.81 337	69.64 209	88.33 133	90.19 190	64.58 239	83.63 298	71.99 187	90.03 251	81.06 327

miper_enhance_ethall		77.83 224	76.93 230	80.51 219	76.15 330	58.01 276	75.47 296	88.82 182	58.05 291	83.59 212	80.69 312	64.41 240	91.20 185	73.16 178	92.03 218	92.33 157

eth_miper_zixun_eth		80.84 190	80.22 198	82.71 183	81.41 283	60.98 248	77.81 266	90.14 162	67.31 232	86.95 155	87.24 235	64.26 241	92.31 154	75.23 154	91.61 225	94.85 65

test20.03		73.75 263	74.59 251	71.22 300	81.11 287	51.12 326	70.15 321	72.10 318	70.42 200	80.28 259	91.50 155	64.21 242	74.72 325	46.96 334	94.58 166	87.82 248

cascas		76.29 243	74.81 248	80.72 217	84.47 248	62.94 222	73.89 306	87.34 204	55.94 302	75.16 298	76.53 337	63.97 243	91.16 187	65.00 240	90.97 238	88.06 241

TAMVS		78.08 222	76.36 235	83.23 171	90.62 149	72.87 131	79.08 250	80.01 273	61.72 272	81.35 243	86.92 240	63.96 244	88.78 242	50.61 318	93.01 199	88.04 242

GBi-Net		82.02 175	82.07 172	81.85 197	86.38 223	61.05 245	86.83 108	88.27 193	72.43 180	86.00 172	95.64 29	63.78 245	90.68 204	65.95 233	93.34 188	93.82 104

test1		82.02 175	82.07 172	81.85 197	86.38 223	61.05 245	86.83 108	88.27 193	72.43 180	86.00 172	95.64 29	63.78 245	90.68 204	65.95 233	93.34 188	93.82 104

FMVSNet2		81.31 183	81.61 178	80.41 221	86.38 223	58.75 273	83.93 156	86.58 221	72.43 180	87.65 141	92.98 110	63.78 245	90.22 216	66.86 226	93.92 179	92.27 162

USDC		76.63 237	76.73 233	76.34 275	83.46 267	57.20 283	80.02 234	88.04 197	52.14 323	83.65 211	91.25 158	63.24 248	86.65 268	54.66 303	94.11 175	85.17 273

cl-mvsnet1		80.43 197	80.23 196	81.02 211	79.99 301	59.25 263	77.07 277	87.02 215	67.38 230	86.19 167	89.22 203	63.09 249	90.16 218	76.32 142	95.80 127	93.66 111

cl-mvsnet_		80.42 198	80.23 196	81.02 211	79.99 301	59.25 263	77.07 277	87.02 215	67.37 231	86.18 169	89.21 204	63.08 250	90.16 218	76.31 143	95.80 127	93.65 112

MVS_0304		78.17 220	77.23 227	80.99 213	84.13 261	69.07 178	81.39 216	80.81 268	76.28 125	67.53 329	89.11 207	62.87 251	86.77 265	60.90 270	92.01 221	87.13 254

new-patchmatchnet		70.10 288	73.37 262	60.29 331	81.23 286	16.95 359	59.54 342	74.62 298	62.93 262	80.97 246	87.93 223	62.83 252	71.90 328	55.24 299	95.01 154	92.00 171

K. test v3		85.14 114	84.73 126	86.37 103	91.13 138	69.63 168	85.45 130	76.68 287	84.06 40	92.44 53	96.99 9	62.03 253	94.65 73	80.58 99	93.24 192	94.83 67

lessismore_v0				85.95 115	91.10 139	70.99 159		70.91 325		91.79 64	94.42 64	61.76 254	92.93 141	79.52 110	93.03 198	93.93 98

1314		73.22 266	72.56 272	75.20 281	80.41 300	57.84 277	81.64 212	85.36 232	51.68 326	73.10 307	76.65 336	61.45 255	85.19 284	63.54 248	79.21 330	82.59 304

CANet_DTU		77.81 226	77.05 228	80.09 226	81.37 284	59.90 257	83.26 176	88.29 192	69.16 213	67.83 327	83.72 284	60.93 256	89.47 230	69.22 209	89.70 254	90.88 197

pmmvs-eth3d		78.42 219	77.04 229	82.57 188	87.44 204	74.41 123	80.86 225	79.67 274	55.68 303	84.69 192	90.31 188	60.91 257	85.42 282	62.20 257	91.59 226	87.88 246

UnsupCasMVSNet_eth		71.63 280	72.30 274	69.62 303	76.47 327	52.70 313	70.03 322	80.97 267	59.18 285	79.36 265	88.21 218	60.50 258	69.12 334	58.33 282	77.62 335	87.04 255

IterMVS-SCA-FT		80.64 194	79.41 207	84.34 146	83.93 263	69.66 167	76.28 288	81.09 266	72.43 180	86.47 166	90.19 190	60.46 259	93.15 135	77.45 132	86.39 289	90.22 212

SCA		73.32 264	72.57 271	75.58 280	81.62 280	55.86 291	78.89 252	71.37 324	61.73 271	74.93 299	83.42 289	60.46 259	87.01 258	58.11 284	82.63 318	83.88 285

jason		77.42 229	75.75 241	82.43 191	87.10 214	69.27 171	77.99 263	81.94 261	51.47 327	77.84 274	85.07 270	60.32 261	89.00 236	70.74 195	89.27 259	89.03 232
jason: jason.
1112_ss		74.82 256	73.74 257	78.04 257	89.57 165	60.04 255	76.49 285	87.09 214	54.31 309	73.66 305	79.80 320	60.25 262	86.76 267	58.37 280	84.15 307	87.32 252

HY-MVS	64.64 18	73.03 268	72.47 273	74.71 283	83.36 268	54.19 301	82.14 208	81.96 260	56.76 301	69.57 321	86.21 250	60.03 263	84.83 289	49.58 323	82.65 316	85.11 274

Anonymous20231206		71.38 281	71.88 276	69.88 301	86.31 227	54.37 300	70.39 320	74.62 298	52.57 319	76.73 280	88.76 211	59.94 264	72.06 327	44.35 339	93.23 193	83.23 299

IterMVS		76.91 234	76.34 236	78.64 245	80.91 289	64.03 211	76.30 287	79.03 275	64.88 256	83.11 218	89.16 205	59.90 265	84.46 291	68.61 217	85.15 299	87.42 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 289	70.44 285	68.90 306	73.76 343	53.42 308	58.99 345	67.20 333	58.42 288	87.10 149	85.39 263	59.82 266	67.32 338	59.79 275	83.50 310	85.96 264

MDA-MVSNet_test_wron		70.05 290	70.44 285	68.88 307	73.84 342	53.47 306	58.93 346	67.28 332	58.43 287	87.09 150	85.40 262	59.80 267	67.25 339	59.66 276	83.54 309	85.92 266

PMMVS		61.65 313	60.38 319	65.47 320	65.40 356	69.26 172	63.97 338	61.73 345	36.80 353	60.11 347	68.43 346	59.42 268	66.35 343	48.97 325	78.57 332	60.81 347

CDS-MVSNet		77.32 230	75.40 244	83.06 174	89.00 174	72.48 141	77.90 265	82.17 259	60.81 277	78.94 269	83.49 287	59.30 269	88.76 243	54.64 304	92.37 211	87.93 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 295	69.68 290	67.82 312	79.42 307	51.15 325	67.82 330	75.79 291	54.15 310	77.47 279	85.36 265	59.26 270	70.64 330	48.46 327	79.35 328	81.66 316

WTY-MVS		67.91 299	68.35 297	66.58 316	80.82 293	48.12 336	65.96 334	72.60 314	53.67 313	71.20 315	81.68 307	58.97 271	69.06 335	48.57 326	81.67 319	82.55 305

cl-mvsnet2		78.97 212	78.21 219	81.24 207	77.74 317	59.01 267	77.46 274	87.13 210	65.79 243	84.32 200	85.10 267	58.96 272	90.88 198	75.36 153	92.03 218	93.84 101

MVSFormer		82.23 171	81.57 180	84.19 151	85.54 239	69.26 172	91.98 30	90.08 163	71.54 190	76.23 285	85.07 270	58.69 273	94.27 82	86.26 35	88.77 264	89.03 232

lupinMVS		76.37 242	74.46 252	82.09 192	85.54 239	69.26 172	76.79 279	80.77 269	50.68 333	76.23 285	82.82 296	58.69 273	88.94 237	69.85 202	88.77 264	88.07 240

Test_1112_low_res		73.90 262	73.08 264	76.35 274	90.35 154	55.95 289	73.40 310	86.17 224	50.70 332	73.14 306	85.94 252	58.31 275	85.90 278	56.51 290	83.22 311	87.20 253

test_yl		78.71 217	78.51 215	79.32 237	84.32 253	58.84 270	78.38 258	85.33 233	75.99 131	82.49 224	86.57 242	58.01 276	90.02 226	62.74 253	92.73 205	89.10 229

DCV-MVSNet		78.71 217	78.51 215	79.32 237	84.32 253	58.84 270	78.38 258	85.33 233	75.99 131	82.49 224	86.57 242	58.01 276	90.02 226	62.74 253	92.73 205	89.10 229

sss		66.92 301	67.26 301	65.90 317	77.23 320	51.10 327	64.79 335	71.72 322	52.12 324	70.13 319	80.18 317	57.96 278	65.36 346	50.21 319	81.01 324	81.25 322

ppachtmachnet_test		74.73 257	74.00 256	76.90 268	80.71 295	56.89 286	71.53 316	78.42 277	58.24 289	79.32 266	82.92 295	57.91 279	84.26 293	65.60 238	91.36 229	89.56 219

MVP-Stereo		75.81 246	73.51 261	82.71 183	89.35 167	73.62 126	80.06 232	85.20 236	60.30 281	73.96 303	87.94 222	57.89 280	89.45 232	52.02 313	74.87 340	85.06 275
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 278	70.06 289	76.92 267	86.39 222	53.97 302	76.62 283	86.62 220	53.44 314	63.97 342	84.73 276	57.79 281	92.34 153	39.65 346	81.33 322	84.45 280

LFMVS		80.15 206	80.56 190	78.89 240	89.19 171	55.93 290	85.22 133	73.78 306	82.96 54	84.28 204	92.72 121	57.38 282	90.07 225	63.80 247	95.75 130	90.68 202

Vis-MVSNet (Re-imp)		77.82 225	77.79 222	77.92 259	88.82 177	51.29 324	83.28 175	71.97 319	74.04 155	82.23 229	89.78 197	57.38 282	89.41 233	57.22 287	95.41 137	93.05 130

CHOSEN 1792x2688		72.45 272	70.56 283	78.13 255	90.02 163	63.08 221	68.72 325	83.16 251	42.99 348	75.92 289	85.46 260	57.22 284	85.18 285	49.87 322	81.67 319	86.14 263

miper_lstm_enhance		76.45 241	76.10 238	77.51 263	76.72 325	60.97 249	64.69 336	85.04 241	63.98 259	83.20 217	88.22 217	56.67 285	78.79 314	73.22 172	93.12 195	92.78 141

our_test_3		71.85 277	71.59 278	72.62 294	80.71 295	53.78 304	69.72 323	71.71 323	58.80 286	78.03 273	80.51 315	56.61 286	78.84 313	62.20 257	86.04 292	85.23 272

baseline1		73.26 265	73.54 260	72.43 296	84.92 244	47.79 338	79.89 236	74.00 303	65.93 241	78.81 270	86.28 249	56.36 287	81.63 305	56.63 289	79.04 331	87.87 247

pmmvs4		74.92 254	72.98 266	80.73 216	84.95 243	71.71 154	76.23 289	77.59 281	52.83 317	77.73 277	86.38 244	56.35 288	84.97 286	57.72 286	87.05 283	85.51 270

MVE	40.22 23	51.82 324	50.47 327	55.87 334	62.66 358	51.91 319	31.61 353	39.28 358	40.65 349	50.76 355	74.98 341	56.24 289	44.67 355	33.94 352	64.11 351	71.04 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		70.20 286	68.80 295	74.38 285	80.91 289	84.81 38	59.12 344	76.45 289	55.06 306	75.31 297	82.36 301	55.74 290	54.82 351	47.02 332	87.24 282	83.52 292

MS-PatchMatch		70.93 283	70.22 287	73.06 291	81.85 279	62.50 230	73.82 307	77.90 279	52.44 320	75.92 289	81.27 309	55.67 291	81.75 303	55.37 298	77.70 334	74.94 335

DSMNet-mixed		60.98 318	61.61 317	59.09 333	72.88 348	45.05 344	74.70 301	46.61 357	26.20 354	65.34 335	90.32 187	55.46 292	63.12 349	41.72 343	81.30 323	69.09 343

pmmvs5		70.73 284	70.07 288	72.72 292	77.03 323	52.73 312	74.14 303	75.65 294	50.36 335	72.17 311	85.37 264	55.42 293	80.67 308	52.86 311	87.59 280	84.77 277

CMPMVS	59.41 20	75.12 251	73.57 259	79.77 228	75.84 332	67.22 186	81.21 220	82.18 258	50.78 331	76.50 281	87.66 227	55.20 294	82.99 300	62.17 259	90.64 249	89.09 231
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		71.09 282	71.59 278	69.57 304	87.23 207	50.07 332	78.91 251	71.83 320	60.20 283	71.26 314	91.76 149	55.08 295	76.09 319	41.06 344	87.02 284	82.54 306

PVSNet_0	51.08 22	56.10 321	54.97 326	59.48 332	75.12 338	53.28 309	55.16 347	61.89 343	44.30 345	59.16 348	62.48 351	54.22 296	65.91 345	35.40 350	47.01 353	59.25 349

EPNet_dtu		72.87 270	71.33 282	77.49 264	77.72 318	60.55 252	82.35 199	75.79 291	66.49 238	58.39 352	81.06 311	53.68 297	85.98 276	53.55 307	92.97 201	85.95 265
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 323	59.27 323	44.74 337	64.30 357	12.32 360	40.60 351	49.79 356	53.19 315	65.06 339	84.81 274	53.60 298	49.76 353	32.68 353	89.41 256	72.15 338

HyFIR lowres test		75.12 251	72.66 269	82.50 189	91.44 129	65.19 201	72.47 312	87.31 205	46.79 339	80.29 257	84.30 280	52.70 299	92.10 161	51.88 317	86.73 285	90.22 212

FMVSNet3		78.80 215	78.55 214	79.57 234	82.89 274	56.89 286	81.76 209	85.77 228	69.04 215	86.00 172	90.44 185	51.75 300	90.09 224	65.95 233	93.34 188	91.72 180

D2MVS		76.84 235	75.67 243	80.34 222	80.48 299	62.16 236	73.50 308	84.80 246	57.61 295	82.24 228	87.54 229	51.31 301	87.65 253	70.40 200	93.19 194	91.23 191

AUN-MVS		81.18 185	78.78 211	88.39 77	90.93 141	82.14 56	82.51 196	83.67 249	64.69 257	80.29 257	85.91 254	51.07 302	92.38 152	76.29 144	93.63 186	90.65 204

PVSNet	58.17 21	66.41 304	65.63 307	68.75 308	81.96 277	49.88 333	62.19 340	72.51 316	51.03 329	68.04 325	75.34 340	50.84 303	74.77 323	45.82 337	82.96 312	81.60 317

GA-MVS		75.83 245	74.61 249	79.48 236	81.87 278	59.25 263	73.42 309	82.88 253	68.68 218	79.75 261	81.80 305	50.62 304	89.46 231	66.85 227	85.64 294	89.72 218

FPMVS		72.29 275	72.00 275	73.14 290	88.63 179	85.00 35	74.65 302	67.39 331	71.94 189	77.80 276	87.66 227	50.48 305	75.83 321	49.95 320	79.51 326	58.58 350

MVS-HIRNet		61.16 316	62.92 313	55.87 334	79.09 311	35.34 354	71.83 314	57.98 351	46.56 340	59.05 349	91.14 163	49.95 306	76.43 318	38.74 347	71.92 344	55.84 351

CVMVSNet		72.62 271	71.41 281	76.28 276	83.25 269	60.34 253	83.50 170	79.02 276	37.77 352	76.33 283	85.10 267	49.60 307	87.41 255	70.54 198	77.54 336	81.08 325

RPMNet		78.88 213	78.28 218	80.68 218	79.58 304	62.64 227	82.58 192	94.16 28	74.80 147	75.72 291	92.59 124	48.69 308	95.56 34	73.48 170	82.91 314	83.85 288

tpmrst		66.28 305	66.69 305	65.05 321	72.82 349	39.33 350	78.20 261	70.69 326	53.16 316	67.88 326	80.36 316	48.18 309	74.75 324	58.13 283	70.79 345	81.08 325

CR-MVSNet		74.00 261	73.04 265	76.85 270	79.58 304	62.64 227	82.58 192	76.90 284	50.50 334	75.72 291	92.38 130	48.07 310	84.07 294	68.72 216	82.91 314	83.85 288

Patchmtry		76.56 239	77.46 224	73.83 287	79.37 309	46.60 340	82.41 198	76.90 284	73.81 158	85.56 181	92.38 130	48.07 310	83.98 295	63.36 250	95.31 143	90.92 196

ADS-MVSNet2		65.87 307	63.64 312	72.55 295	73.16 346	56.92 285	67.10 331	74.81 297	49.74 336	66.04 332	82.97 292	46.71 312	77.26 316	42.29 341	69.96 347	83.46 293

ADS-MVSNet		61.90 312	62.19 315	61.03 330	73.16 346	36.42 353	67.10 331	61.75 344	49.74 336	66.04 332	82.97 292	46.71 312	63.21 348	42.29 341	69.96 347	83.46 293

PatchmatchNet		69.71 293	68.83 294	72.33 297	77.66 319	53.60 305	79.29 245	69.99 328	57.66 294	72.53 309	82.93 294	46.45 314	80.08 311	60.91 269	72.09 343	83.31 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 274	71.55 280	74.70 284	83.48 266	51.60 322	75.02 299	73.71 307	70.14 206	78.56 272	80.57 313	46.20 315	88.20 249	46.99 333	89.29 257	84.32 282

sam_mvs1												46.11 316				83.88 285

tfpn200view9		74.86 255	74.23 254	76.74 271	86.24 230	52.12 317	79.24 247	73.87 304	73.34 165	81.82 236	84.60 278	46.02 317	88.80 239	51.98 314	90.99 235	89.31 224

thres400		75.14 249	74.23 254	77.86 260	86.24 230	52.12 317	79.24 247	73.87 304	73.34 165	81.82 236	84.60 278	46.02 317	88.80 239	51.98 314	90.99 235	92.66 147

baseline2		69.77 292	66.89 302	78.41 250	79.51 306	58.09 275	76.23 289	69.57 329	57.50 296	64.82 340	77.45 332	46.02 317	88.44 245	53.08 309	77.83 333	88.70 236

patchmatchnet-post											81.71 306	45.93 320	87.01 258

sam_mvs												45.92 321

Patchmatch-RL test		74.48 258	73.68 258	76.89 269	84.83 245	66.54 193	72.29 313	69.16 330	57.70 293	86.76 156	86.33 246	45.79 322	82.59 301	69.63 204	90.65 248	81.54 318

thres100view900		75.45 247	75.05 247	76.66 272	87.27 206	51.88 320	81.07 222	73.26 310	75.68 137	83.25 216	86.37 245	45.54 323	88.80 239	51.98 314	90.99 235	89.31 224

thres600view7		75.97 244	75.35 246	77.85 261	87.01 216	51.84 321	80.45 229	73.26 310	75.20 144	83.10 219	86.31 248	45.54 323	89.05 235	55.03 301	92.24 214	92.66 147

tpm cat1		66.76 302	65.21 308	71.42 299	77.09 322	50.62 330	78.01 262	73.68 308	44.89 344	68.64 322	79.00 323	45.51 325	82.42 302	49.91 321	70.15 346	81.23 324

test_post											3.10 357	45.43 326	77.22 317

MDTV_nov1_ep13			68.29 298		78.03 316	43.87 346	74.12 304	72.22 317	52.17 321	67.02 330	85.54 256	45.36 327	80.85 307	55.73 293	84.42 306

tpmvs		70.16 287	69.56 291	71.96 298	74.71 341	48.13 335	79.63 238	75.45 296	65.02 255	70.26 318	81.88 304	45.34 328	85.68 280	58.34 281	75.39 339	82.08 312

MDTV_nov1_ep13_2view						27.60 358	70.76 318		46.47 341	61.27 344		45.20 329		49.18 324		83.75 290

test_post1							78.85 254				3.13 356	45.19 330	80.13 310	58.11 284

CostFormer		69.98 291	68.68 296	73.87 286	77.14 321	50.72 329	79.26 246	74.51 300	51.94 325	70.97 317	84.75 275	45.16 331	87.49 254	55.16 300	79.23 329	83.40 295

RRT_MVS		83.25 158	81.08 185	89.74 53	80.55 298	79.32 77	86.41 118	86.69 219	72.33 184	87.00 153	91.08 164	44.98 332	95.55 37	84.47 59	96.24 113	94.36 83

Patchmatch-test		65.91 306	67.38 300	61.48 329	75.51 334	43.21 348	68.84 324	63.79 341	62.48 266	72.80 308	83.42 289	44.89 333	59.52 350	48.27 329	86.45 287	81.70 315

EU-MVSNet		75.12 251	74.43 253	77.18 265	83.11 272	59.48 261	85.71 127	82.43 257	39.76 351	85.64 179	88.76 211	44.71 334	87.88 251	73.86 166	85.88 293	84.16 284

PatchT		70.52 285	72.76 268	63.79 323	79.38 308	33.53 355	77.63 269	65.37 339	73.61 160	71.77 312	92.79 120	44.38 335	75.65 322	64.53 246	85.37 296	82.18 311

test-LLR		67.21 300	66.74 304	68.63 309	76.45 328	55.21 296	67.89 327	67.14 334	62.43 268	65.08 337	72.39 342	43.41 336	69.37 331	61.00 267	84.89 301	81.31 320

test0.0.03 1		64.66 309	64.36 310	65.57 319	75.03 339	46.89 339	64.69 336	61.58 346	62.43 268	71.18 316	77.54 330	43.41 336	68.47 336	40.75 345	82.65 316	81.35 319

MVSTER		77.09 232	75.70 242	81.25 205	75.27 337	61.08 244	77.49 273	85.07 239	60.78 278	86.55 160	88.68 213	43.14 338	90.25 213	73.69 168	90.67 246	92.42 153

tpm		67.95 298	68.08 299	67.55 313	78.74 315	43.53 347	75.60 293	67.10 336	54.92 307	72.23 310	88.10 219	42.87 339	75.97 320	52.21 312	80.95 325	83.15 300

tpm2		68.45 297	66.83 303	73.30 289	78.93 314	48.50 334	79.76 237	71.76 321	47.50 338	69.92 320	83.60 285	42.07 340	88.40 246	48.44 328	79.51 326	83.01 302

EMVS		61.10 317	60.81 318	61.99 326	65.96 355	55.86 291	53.10 349	58.97 349	67.06 233	56.89 353	63.33 350	40.98 341	67.03 340	54.79 302	86.18 291	63.08 345

new_pmnet		55.69 322	57.66 324	49.76 336	75.47 335	30.59 356	59.56 341	51.45 355	43.62 347	62.49 343	75.48 339	40.96 342	49.15 354	37.39 349	72.52 342	69.55 342

E-PMN		61.59 314	61.62 316	61.49 328	66.81 354	55.40 294	53.77 348	60.34 347	66.80 236	58.90 350	65.50 349	40.48 343	66.12 344	55.72 294	86.25 290	62.95 346

EPMVS		62.47 310	62.63 314	62.01 325	70.63 353	38.74 351	74.76 300	52.86 354	53.91 312	67.71 328	80.01 318	39.40 344	66.60 342	55.54 297	68.81 350	80.68 329

tmp_tt		20.25 326	24.50 329	7.49 339	4.47 360	8.70 361	34.17 352	25.16 360	1.00 356	32.43 357	18.49 354	39.37 345	9.21 357	21.64 354	43.75 354	4.57 353

thisisatest0530		79.07 211	77.33 226	84.26 148	87.13 210	64.58 205	83.66 165	75.95 290	68.86 217	85.22 184	87.36 233	38.10 346	93.57 118	75.47 151	94.28 172	94.62 69

ET-MVSNet_ETH3D		75.28 248	72.77 267	82.81 182	83.03 273	68.11 182	77.09 276	76.51 288	60.67 280	77.60 278	80.52 314	38.04 347	91.15 188	70.78 193	90.68 245	89.17 227

tttt0517		81.07 186	79.58 206	85.52 125	88.99 175	66.45 194	87.03 105	75.51 295	73.76 159	88.32 134	90.20 189	37.96 348	94.16 94	79.36 112	95.13 148	95.93 41

thisisatest0515		73.00 269	70.52 284	80.46 220	81.45 282	59.90 257	73.16 311	74.31 302	57.86 292	76.08 288	77.78 329	37.60 349	92.12 160	65.00 240	91.45 228	89.35 223

FMVSNet5		72.10 276	71.69 277	73.32 288	81.57 281	53.02 310	76.77 280	78.37 278	63.31 260	76.37 282	91.85 144	36.68 350	78.98 312	47.87 330	92.45 210	87.95 244

dp		60.70 319	60.29 321	61.92 327	72.04 351	38.67 352	70.83 317	64.08 340	51.28 328	60.75 345	77.28 333	36.59 351	71.58 329	47.41 331	62.34 352	75.52 334

CHOSEN 280x420		59.08 320	56.52 325	66.76 315	76.51 326	64.39 208	49.62 350	59.00 348	43.86 346	55.66 354	68.41 347	35.55 352	68.21 337	43.25 340	76.78 338	67.69 344

RRT_test8_iter05		78.08 222	77.52 223	79.75 230	80.84 292	52.54 315	80.61 227	88.96 181	67.77 229	84.62 193	89.29 202	33.89 353	92.10 161	77.59 129	94.15 174	94.62 69

IB-MVS	62.13 19	71.64 279	68.97 293	79.66 233	80.80 294	62.26 235	73.94 305	76.90 284	63.27 261	68.63 323	76.79 335	33.83 354	91.84 170	59.28 278	87.26 281	84.88 276
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
JIA-IIPM		69.41 294	66.64 306	77.70 262	73.19 345	71.24 157	75.67 292	65.56 338	70.42 200	65.18 336	92.97 112	33.64 355	83.06 299	53.52 308	69.61 349	78.79 330

DWT-MVSNet_test		66.43 303	64.37 309	72.63 293	74.86 340	50.86 328	76.52 284	72.74 313	54.06 311	65.50 334	68.30 348	32.13 356	84.84 288	61.63 264	73.59 341	82.19 310

DeepMVS_CX				24.13 338	32.95 359	29.49 357		21.63 361	12.07 355	37.95 356	45.07 353	30.84 357	19.21 356	17.94 355	33.06 355	23.69 352

gg-mvs-nofinetune		68.96 296	69.11 292	68.52 311	76.12 331	45.32 342	83.59 166	55.88 352	86.68 26	64.62 341	97.01 8	30.36 358	83.97 296	44.78 338	82.94 313	76.26 333

GG-mvs-BLEND				67.16 314	73.36 344	46.54 341	84.15 150	55.04 353		58.64 351	61.95 352	29.93 359	83.87 297	38.71 348	76.92 337	71.07 340

test-mter		65.00 308	63.79 311	68.63 309	76.45 328	55.21 296	67.89 327	67.14 334	50.98 330	65.08 337	72.39 342	28.27 360	69.37 331	61.00 267	84.89 301	81.31 320

TESTMET0.1,1		61.29 315	60.32 320	64.19 322	72.06 350	51.30 323	67.89 327	62.09 342	45.27 343	60.65 346	69.01 345	27.93 361	64.74 347	56.31 291	81.65 321	76.53 332

pmmvs3		62.47 310	60.02 322	69.80 302	71.58 352	64.00 212	70.52 319	58.44 350	39.77 350	66.05 331	75.84 338	27.10 362	72.28 326	46.15 335	84.77 305	73.11 337

test123		6.27 329	8.08 332	0.84 340	1.11 362	0.57 362	62.90 339	0.82 362	0.54 357	1.07 359	2.75 359	1.26 363	0.30 358	1.04 356	1.26 357	1.66 354

testmvs		5.91 330	7.65 333	0.72 341	1.20 361	0.37 363	59.14 343	0.67 363	0.49 358	1.11 358	2.76 358	0.94 364	0.24 359	1.02 357	1.47 356	1.55 355

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		6.65 327	8.87 330	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	79.80 320	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

IU-MVS					94.18 49	72.64 135		90.82 137	56.98 299	89.67 105				85.78 45	97.92 46	93.28 122

save fliter					93.75 62	77.44 96	86.31 119	89.72 168	70.80 196

test_0728_SECOND				86.79 96	94.25 48	72.45 142	90.54 44	94.10 34					95.88 15	86.42 31	97.97 43	92.02 170

GSMVS																83.88 285

test_part2					93.86 61	77.77 91				92.84 45

MTGPA								91.81 114

MTMP							90.66 41	33.14 359

gm-plane-assit					75.42 336	44.97 345			52.17 321		72.36 344		87.90 250	54.10 305

test9_res														80.83 95	96.45 105	90.57 205

agg_prior2														79.68 106	96.16 115	90.22 212

agg_prior					91.58 123	77.69 92		90.30 154		84.32 200			93.18 131

test_prior4						78.97 80	84.59 141

test_prior				86.32 104	90.59 150	71.99 148		92.85 86					94.17 91			92.80 139

旧先验2							81.73 210		56.88 300	86.54 165			84.90 287	72.81 180

新几何2							81.72 211

无先验							82.81 189	85.62 230	58.09 290				91.41 181	67.95 223		84.48 279

原ACMM2							82.26 204

testdata2													86.43 271	63.52 249

testdata1							79.62 239		73.95 157

plane_prior7					93.45 68	77.31 100

plane_prior5								93.61 53					95.22 55	80.78 96	95.83 125	94.46 78

plane_prior4											92.95 113

plane_prior3						76.85 107			77.79 112	86.55 160

plane_prior2							89.45 70		79.44 93

plane_prior1					92.83 87

plane_prior						76.42 112	87.15 103		75.94 134						95.03 153

n2								0.00 364

nn								0.00 364

door-mid								74.45 301

test11								91.46 121

door								72.57 315

HQP5-MVS						70.66 160

HQP-NCC					91.19 134		84.77 136		73.30 167	80.55 254

ACMP_Plane					91.19 134		84.77 136		73.30 167	80.55 254

BP-MVS														77.30 134

HQP4-MVS										80.56 253			94.61 76			93.56 117

HQP3-MVS								92.68 92							94.47 168

NP-MVS					91.95 110	74.55 121					90.17 192

ACMMP++_ref															95.74 131

ACMMP++															97.35 75

High-resolution multi-view benchmark