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 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 11	85.07 41	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 2	96.46 2	90.58 7	92.86 38	96.29 17	88.16 28	94.17 76	86.07 36	98.48 17	97.22 18

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 21	93.73 55	85.72 28	96.79 1	95.51 4	88.86 12	95.63 8	96.99 9	84.81 59	93.16 118	91.10 1	97.53 63	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
abl_6		93.02 4	93.16 4	92.60 4	94.73 41	88.99 6	93.26 11	94.19 21	89.11 10	94.43 16	95.27 36	91.86 3	95.09 50	87.54 17	98.02 38	93.71 101

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 15	85.81 27	92.99 12	94.23 18	85.21 28	92.51 45	95.13 40	90.65 10	95.34 40	88.06 8	98.15 32	95.95 40

SR-MVS		92.23 6	92.34 6	91.91 15	94.89 37	87.85 8	92.51 21	93.87 36	88.20 17	93.24 32	94.02 78	90.15 14	95.67 25	86.82 25	97.34 66	92.19 148

HPM-MVS		92.13 7	92.20 8	91.91 15	95.58 24	84.67 38	93.51 7	94.85 11	82.88 48	91.77 57	93.94 86	90.55 12	95.73 23	88.50 6	98.23 28	95.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 8	92.24 7	91.48 22	93.02 70	85.17 31	92.47 23	95.05 9	87.65 20	93.21 33	94.39 66	90.09 15	95.08 51	86.67 26	97.60 60	94.18 83

COLMAP_ROB	83.01 3	91.97 9	91.95 9	92.04 10	93.68 56	86.15 18	93.37 9	95.10 8	90.28 8	92.11 49	95.03 42	89.75 18	94.93 55	79.95 91	98.27 26	95.04 61
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 10	91.87 14	92.03 11	95.53 25	85.91 22	93.35 10	94.16 22	82.52 52	92.39 48	94.14 73	89.15 19	95.62 26	87.35 19	98.24 27	94.56 70
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 11	91.47 20	92.37 5	96.04 12	88.48 7	92.72 15	92.60 82	83.09 45	91.54 60	94.25 70	87.67 35	95.51 35	87.21 23	98.11 33	93.12 119

CP-MVS		91.67 12	91.58 17	91.96 12	95.29 30	87.62 9	93.38 8	93.36 49	83.16 44	91.06 68	94.00 79	88.26 25	95.71 24	87.28 22	98.39 20	92.55 136

XVS		91.54 13	91.36 22	92.08 8	95.64 22	86.25 16	92.64 16	93.33 51	85.07 29	89.99 82	94.03 77	86.57 46	95.80 17	87.35 19	97.62 58	94.20 81

MTAPA		91.52 14	91.60 16	91.29 27	96.59 4	86.29 14	92.02 26	91.81 101	84.07 34	92.00 52	94.40 64	86.63 44	95.28 43	88.59 4	98.31 23	92.30 142

UA-Net		91.49 15	91.53 18	91.39 24	94.98 34	82.95 50	93.52 6	92.79 77	88.22 16	88.53 114	97.64 3	83.45 69	94.55 66	86.02 37	98.60 12	96.67 26

ACMMPR		91.49 15	91.35 24	91.92 14	95.74 19	85.88 24	92.58 19	93.25 58	81.99 56	91.40 63	94.17 72	87.51 36	95.87 14	87.74 10	97.76 50	93.99 88

LPG-MVS_test		91.47 17	91.68 15	90.82 35	94.75 39	81.69 52	90.00 45	94.27 15	82.35 53	93.67 30	94.82 48	91.18 5	95.52 32	85.36 39	98.73 6	95.23 57

region2R		91.44 18	91.30 28	91.87 17	95.75 18	85.90 23	92.63 18	93.30 55	81.91 58	90.88 73	94.21 71	87.75 33	95.87 14	87.60 15	97.71 54	93.83 94

HFP-MVS		91.30 19	91.39 21	91.02 31	95.43 27	84.66 39	92.58 19	93.29 56	81.99 56	91.47 61	93.96 82	88.35 23	95.56 29	87.74 10	97.74 52	92.85 125

zzz-MVS		91.27 20	91.26 29	91.29 27	96.59 4	86.29 14	88.94 70	91.81 101	84.07 34	92.00 52	94.40 64	86.63 44	95.28 43	88.59 4	98.31 23	92.30 142

APDe-MVS		91.22 21	91.92 10	89.14 60	92.97 72	78.04 80	92.84 13	94.14 23	83.33 42	93.90 23	95.73 26	88.77 20	96.41 1	87.60 15	97.98 42	92.98 122

PGM-MVS		91.20 22	90.95 37	91.93 13	95.67 21	85.85 25	90.00 45	93.90 33	80.32 74	91.74 58	94.41 63	88.17 27	95.98 8	86.37 29	97.99 40	93.96 90

SteuartSystems-ACMMP		91.16 23	91.36 22	90.55 39	93.91 52	80.97 59	91.49 32	93.48 47	82.82 49	92.60 44	93.97 80	88.19 26	96.29 4	87.61 14	98.20 31	94.39 79
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 24	90.91 38	91.83 19	96.18 11	86.88 11	92.20 24	93.03 68	82.59 51	88.52 115	94.37 67	86.74 43	95.41 38	86.32 30	98.21 29	93.19 117
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 25	91.01 34	90.82 35	95.45 26	82.73 51	91.75 30	93.74 39	80.98 68	91.38 64	93.80 89	87.20 40	95.80 17	87.10 24	97.69 55	93.93 91

MP-MVS-pluss		90.81 26	91.08 31	89.99 48	95.97 13	79.88 64	88.13 82	94.51 13	75.79 127	92.94 35	94.96 43	88.36 22	95.01 53	90.70 2	98.40 19	95.09 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 27	91.50 19	88.44 67	93.00 71	76.26 102	89.65 56	95.55 3	87.72 19	93.89 25	94.94 44	91.62 4	93.44 108	78.35 107	98.76 3	95.61 47

ACMMP_NAP		90.65 28	91.07 33	89.42 56	95.93 15	79.54 69	89.95 48	93.68 40	77.65 105	91.97 54	94.89 45	88.38 21	95.45 36	89.27 3	97.87 47	93.27 113

ACMM	79.39 9	90.65 28	90.99 35	89.63 52	95.03 33	83.53 44	89.62 57	93.35 50	79.20 87	93.83 26	93.60 92	90.81 8	92.96 124	85.02 43	98.45 18	92.41 139
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 30	90.34 44	91.38 25	89.03 158	84.23 42	93.58 5	94.68 12	90.65 6	90.33 78	93.95 85	84.50 61	95.37 39	80.87 82	95.50 122	94.53 74

ACMP	79.16 10	90.54 31	90.60 42	90.35 43	94.36 43	80.98 58	89.16 66	94.05 26	79.03 90	92.87 37	93.74 90	90.60 11	95.21 47	82.87 64	98.76 3	94.87 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 32	91.08 31	88.88 62	93.38 62	78.65 77	89.15 67	94.05 26	84.68 32	93.90 23	94.11 75	88.13 29	96.30 3	84.51 49	97.81 48	91.70 162

#test#		90.49 33	90.31 45	91.02 31	95.43 27	84.66 39	90.65 37	93.29 56	77.00 112	91.47 61	93.96 82	88.35 23	95.56 29	84.88 44	97.74 52	92.85 125

SMA-MVS		90.31 34	90.48 43	89.83 49	95.31 29	79.52 70	90.98 35	93.24 59	75.37 134	92.84 39	95.28 35	85.58 56	96.09 6	87.92 9	97.76 50	93.88 93

test_normal		90.27 35	91.34 25	87.07 82	91.28 119	69.70 151	93.91 4	93.67 41	78.72 96	96.99 2	98.27 2	85.59 55	93.79 90	85.51 38	98.07 34	97.72 9

v7n		90.13 36	90.96 36	87.65 77	91.95 99	71.06 142	89.99 47	93.05 65	86.53 23	94.29 19	96.27 18	82.69 76	94.08 80	86.25 33	97.63 57	97.82 8

PMVS	80.48 6	90.08 37	90.66 41	88.34 69	96.71 3	92.97 1	90.31 42	89.57 158	88.51 15	90.11 79	95.12 41	90.98 7	88.92 220	77.55 117	97.07 73	83.13 283
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS		90.06 38	91.32 26	86.29 95	94.16 46	72.56 122	90.54 39	91.01 121	83.61 38	93.75 27	94.65 53	89.76 16	95.78 20	86.42 27	97.97 43	90.55 187

PS-CasMVS		90.06 38	91.92 10	84.47 131	96.56 7	58.83 255	89.04 68	92.74 79	91.40 4	96.12 4	96.06 23	87.23 39	95.57 28	79.42 99	98.74 5	99.00 2

PEN-MVS		90.03 40	91.88 13	84.48 130	96.57 6	58.88 252	88.95 69	93.19 60	91.62 3	96.01 6	96.16 21	87.02 41	95.60 27	78.69 104	98.72 8	98.97 3

OurMVSNet-221017-0		90.01 41	89.74 49	90.83 34	93.16 68	80.37 61	91.91 29	93.11 62	81.10 66	95.32 10	97.24 6	72.94 185	94.85 57	85.07 41	97.78 49	97.26 16

DTE-MVSNet		89.98 42	91.91 12	84.21 137	96.51 8	57.84 259	88.93 71	92.84 76	91.92 2	96.16 3	96.23 19	86.95 42	95.99 7	79.05 101	98.57 14	98.80 6

XVG-ACMP-BASELINE		89.98 42	89.84 48	90.41 41	94.91 36	84.50 41	89.49 62	93.98 28	79.68 80	92.09 50	93.89 87	83.80 65	93.10 122	82.67 66	98.04 35	93.64 105

3Dnovator+	83.92 2	89.97 44	89.66 50	90.92 33	91.27 120	81.66 55	91.25 33	94.13 24	88.89 11	88.83 109	94.26 69	77.55 133	95.86 16	84.88 44	95.87 111	95.24 56

WR-MVS_H		89.91 45	91.31 27	85.71 110	96.32 10	62.39 215	89.54 60	93.31 54	90.21 9	95.57 9	95.66 28	81.42 100	95.90 12	80.94 81	98.80 2	98.84 5

OPM-MVS		89.80 46	89.97 46	89.27 58	94.76 38	79.86 65	86.76 102	92.78 78	78.78 93	92.51 45	93.64 91	88.13 29	93.84 89	84.83 46	97.55 61	94.10 86

mvs_tets		89.78 47	89.27 57	91.30 26	93.51 58	84.79 36	89.89 50	90.63 128	70.00 193	94.55 15	96.67 12	87.94 32	93.59 100	84.27 50	95.97 107	95.52 48

anonymousdsp		89.73 48	88.88 62	92.27 7	89.82 149	86.67 12	90.51 41	90.20 146	69.87 194	95.06 11	96.14 22	84.28 62	93.07 123	87.68 12	96.34 96	97.09 20

test_djsdf		89.62 49	89.01 59	91.45 23	92.36 86	82.98 49	91.98 27	90.08 149	71.54 179	94.28 21	96.54 14	81.57 98	94.27 68	86.26 31	96.49 91	97.09 20

XVG-OURS-SEG-HR		89.59 50	89.37 55	90.28 44	94.47 42	85.95 21	86.84 98	93.91 32	80.07 77	86.75 144	93.26 94	93.64 2	90.93 177	84.60 48	90.75 224	93.97 89

APD-MVS		89.54 51	89.63 51	89.26 59	92.57 80	81.34 57	90.19 43	93.08 64	80.87 69	91.13 66	93.19 95	86.22 51	95.97 9	82.23 70	97.18 71	90.45 189
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		89.51 52	89.48 54	89.59 54	92.26 90	80.80 60	90.14 44	93.54 45	83.37 41	90.57 76	92.55 116	84.99 58	96.15 5	81.26 76	96.61 86	91.83 158

jajsoiax		89.41 53	88.81 64	91.19 30	93.38 62	84.72 37	89.70 52	90.29 143	69.27 197	94.39 17	96.38 16	86.02 53	93.52 104	83.96 52	95.92 109	95.34 51

CPTT-MVS		89.39 54	88.98 61	90.63 38	95.09 32	86.95 10	92.09 25	92.30 88	79.74 79	87.50 131	92.38 119	81.42 100	93.28 113	83.07 61	97.24 69	91.67 163

ACMH	76.49 14	89.34 55	91.14 30	83.96 143	92.50 83	70.36 147	89.55 58	93.84 37	81.89 59	94.70 13	95.44 33	90.69 9	88.31 230	83.33 58	98.30 25	93.20 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 56	90.91 38	84.37 132	96.34 9	58.61 257	88.66 77	92.06 93	90.78 5	95.67 7	95.17 39	81.80 96	95.54 31	79.00 102	98.69 9	98.95 4

XVG-OURS		89.18 57	88.83 63	90.23 45	94.28 44	86.11 20	85.91 111	93.60 44	80.16 76	89.13 105	93.44 93	83.82 64	90.98 175	83.86 54	95.30 129	93.60 107

DeepC-MVS	82.31 4	89.15 58	89.08 58	89.37 57	93.64 57	79.07 72	88.54 78	94.20 19	73.53 152	89.71 91	94.82 48	85.09 57	95.77 22	84.17 51	98.03 37	93.26 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 59	90.72 40	84.31 135	97.00 2	64.33 193	89.67 55	88.38 172	88.84 13	94.29 19	97.57 4	90.48 13	91.26 168	72.57 163	97.65 56	97.34 15

MSP-MVS		89.08 60	88.16 68	91.83 19	95.76 17	86.14 19	92.75 14	93.90 33	78.43 99	89.16 104	92.25 125	72.03 197	96.36 2	88.21 7	90.93 219	92.98 122

SD-MVS		88.96 61	89.88 47	86.22 98	91.63 106	77.07 92	89.82 51	93.77 38	78.90 91	92.88 36	92.29 123	86.11 52	90.22 198	86.24 34	97.24 69	91.36 171
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		88.93 62	88.45 67	90.38 42	94.92 35	85.85 25	89.70 52	91.27 114	78.20 101	86.69 145	92.28 124	80.36 109	95.06 52	86.17 35	96.49 91	90.22 193

test_0402		88.65 63	89.58 53	85.88 107	92.55 81	72.22 130	84.01 141	89.44 160	88.63 14	94.38 18	95.77 25	86.38 50	93.59 100	79.84 92	95.21 130	91.82 159

DP-MVS		88.60 64	89.01 59	87.36 80	91.30 117	77.50 86	87.55 88	92.97 71	87.95 18	89.62 94	92.87 106	84.56 60	93.89 86	77.65 116	96.62 85	90.70 182

Anonymous20231211		88.40 65	89.62 52	84.73 125	90.46 138	65.27 184	88.86 72	93.02 69	87.15 21	93.05 34	97.10 7	82.28 84	92.02 146	76.70 126	97.99 40	96.88 24

PS-MVSNAJss		88.31 66	87.90 70	89.56 55	93.31 64	77.96 81	87.94 84	91.97 96	70.73 185	94.19 22	96.67 12	76.94 142	94.57 64	83.07 61	96.28 98	96.15 32

OMC-MVS		88.19 67	87.52 73	90.19 46	91.94 101	81.68 54	87.49 90	93.17 61	76.02 121	88.64 112	91.22 147	84.24 63	93.37 111	77.97 114	97.03 74	95.52 48

TSAR-MVS + MP.		88.14 68	87.82 71	89.09 61	95.72 20	76.74 96	92.49 22	91.19 117	67.85 213	86.63 146	94.84 47	79.58 117	95.96 10	87.62 13	94.50 152	94.56 70
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RPSCF		88.00 69	86.93 83	91.22 29	90.08 144	89.30 5	89.68 54	91.11 118	79.26 86	89.68 92	94.81 51	82.44 79	87.74 234	76.54 128	88.74 247	96.61 28

AllTest		87.97 70	87.40 76	89.68 50	91.59 107	83.40 45	89.50 61	95.44 5	79.47 82	88.00 124	93.03 98	82.66 77	91.47 161	70.81 173	96.14 103	94.16 84

TranMVSNet+NR-MVSNet		87.86 71	88.76 65	85.18 118	94.02 49	64.13 194	84.38 136	91.29 113	84.88 31	92.06 51	93.84 88	86.45 48	93.73 92	73.22 154	98.66 10	97.69 10

nrg030		87.85 72	88.49 66	85.91 105	90.07 145	69.73 150	87.86 85	94.20 19	74.04 146	92.70 43	94.66 52	85.88 54	91.50 160	79.72 93	97.32 67	96.50 30

CNVR-MVS		87.81 73	87.68 72	88.21 70	92.87 74	77.30 91	85.25 120	91.23 115	77.31 109	87.07 139	91.47 144	82.94 74	94.71 60	84.67 47	96.27 100	92.62 135

HQP_MVS		87.75 74	87.43 75	88.70 65	93.45 59	76.42 100	89.45 63	93.61 42	79.44 84	86.55 147	92.95 103	74.84 160	95.22 45	80.78 84	95.83 112	94.46 75

NCCC		87.36 75	86.87 84	88.83 63	92.32 89	78.84 75	86.58 106	91.09 119	78.77 94	84.85 175	90.89 159	80.85 103	95.29 41	81.14 78	95.32 126	92.34 141

DeepPCF-MVS	81.24 5	87.28 76	86.21 93	90.49 40	91.48 114	84.90 34	83.41 160	92.38 87	70.25 191	89.35 102	90.68 166	82.85 75	94.57 64	79.55 95	95.95 108	92.00 152

SixPastTwentyTwo		87.20 77	87.45 74	86.45 91	92.52 82	69.19 159	87.84 86	88.05 178	81.66 61	94.64 14	96.53 15	65.94 222	94.75 59	83.02 63	96.83 81	95.41 50

UniMVSNet (Re)		86.87 78	86.98 82	86.55 89	93.11 69	68.48 163	83.80 149	92.87 73	80.37 72	89.61 96	91.81 136	77.72 130	94.18 74	75.00 140	98.53 15	96.99 23

Vis-MVSNet		86.86 79	86.58 87	87.72 75	92.09 95	77.43 88	87.35 91	92.09 92	78.87 92	84.27 188	94.05 76	78.35 125	93.65 94	80.54 88	91.58 207	92.08 150
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 102	92.86 76	67.02 172	82.55 182	91.56 105	83.08 46	90.92 70	91.82 135	78.25 126	93.99 82	74.16 143	98.35 21	97.49 14

DU-MVS		86.80 81	86.99 81	86.21 100	93.24 66	67.02 172	83.16 169	92.21 89	81.73 60	90.92 70	91.97 129	77.20 136	93.99 82	74.16 143	98.35 21	97.61 11

Regformer-2		86.74 82	86.08 95	88.73 64	84.18 244	79.20 71	83.52 155	89.33 161	83.33 42	89.92 86	85.07 252	83.23 72	93.16 118	83.39 57	92.72 190	93.83 94

IS-MVSNet		86.66 83	86.82 86	86.17 102	92.05 97	66.87 174	91.21 34	88.64 168	86.30 25	89.60 97	92.59 113	69.22 208	94.91 56	73.89 147	97.89 46	96.72 25

v10		86.54 84	87.10 78	84.84 122	88.16 175	63.28 203	86.64 105	92.20 90	75.42 133	92.81 41	94.50 58	74.05 169	94.06 81	83.88 53	96.28 98	97.17 19

pmmvs6		86.52 85	88.06 69	81.90 183	92.22 93	62.28 218	84.66 128	89.15 163	83.54 40	89.85 87	97.32 5	88.08 31	86.80 246	70.43 181	97.30 68	96.62 27

Regformer-4		86.41 86	85.71 102	88.52 66	84.27 240	77.57 85	84.07 139	88.00 180	82.82 49	89.84 88	85.48 242	82.06 88	92.77 130	83.83 55	91.04 213	95.22 59

PHI-MVS		86.38 87	85.81 100	88.08 71	88.44 169	77.34 89	89.35 65	93.05 65	73.15 163	84.76 176	87.70 211	78.87 121	94.18 74	80.67 86	96.29 97	92.73 130

test_prior3		86.31 88	86.31 90	86.32 93	90.59 135	71.99 132	83.37 161	92.85 74	75.43 131	84.58 178	91.57 140	81.92 94	94.17 76	79.54 96	96.97 75	92.80 127

CSCG		86.26 89	86.47 88	85.60 112	90.87 129	74.26 111	87.98 83	91.85 99	80.35 73	89.54 100	88.01 205	79.09 119	92.13 142	75.51 134	95.06 137	90.41 190

DeepC-MVS_fast	80.27 8	86.23 90	85.65 104	87.96 74	91.30 117	76.92 93	87.19 92	91.99 95	70.56 186	84.96 171	90.69 165	80.01 112	95.14 48	78.37 106	95.78 116	91.82 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 91	86.83 85	84.36 133	87.82 180	62.35 217	86.42 108	91.33 112	76.78 114	92.73 42	94.48 60	73.41 179	93.72 93	83.10 60	95.41 123	97.01 22

Anonymous20240529		86.20 92	87.13 77	83.42 156	90.19 142	64.55 191	84.55 131	90.71 125	85.85 26	89.94 85	95.24 38	82.13 86	90.40 193	69.19 192	96.40 94	95.31 53

CDPH-MVS		86.17 93	85.54 105	88.05 73	92.25 91	75.45 105	83.85 146	92.01 94	65.91 227	86.19 154	91.75 138	83.77 66	94.98 54	77.43 120	96.71 84	93.73 100

Regformer-1		86.00 94	85.50 106	87.49 78	84.18 244	76.90 94	83.52 155	87.94 182	82.18 55	89.19 103	85.07 252	82.28 84	91.89 152	82.40 68	92.72 190	93.69 102

NR-MVSNet		86.00 94	86.22 92	85.34 116	93.24 66	64.56 190	82.21 192	90.46 131	80.99 67	88.42 117	91.97 129	77.56 132	93.85 87	72.46 164	98.65 11	97.61 11

train_agg		85.98 96	85.28 109	88.07 72	92.34 87	79.70 67	83.94 142	90.32 136	65.79 228	84.49 180	90.97 155	81.93 92	93.63 96	81.21 77	96.54 89	90.88 178

FC-MVSNet-test		85.93 97	87.05 80	82.58 174	92.25 91	56.44 270	85.75 113	93.09 63	77.33 108	91.94 55	94.65 53	74.78 162	93.41 110	75.11 139	98.58 13	97.88 7

Effi-MVS+-dtu		85.82 98	83.38 142	93.14 3	87.13 195	91.15 2	87.70 87	88.42 170	74.57 141	83.56 195	85.65 239	78.49 123	94.21 72	72.04 167	92.88 185	94.05 87

agg_prior1		85.72 99	85.20 110	87.28 81	91.58 110	77.69 83	83.69 152	90.30 140	66.29 224	84.32 184	91.07 152	82.13 86	93.18 116	81.02 79	96.36 95	90.98 174

TAPA-MVS	77.73 12	85.71 100	84.83 115	88.37 68	88.78 163	79.72 66	87.15 94	93.50 46	69.17 198	85.80 162	89.56 185	80.76 104	92.13 142	73.21 159	95.51 121	93.25 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 101	86.14 94	83.58 152	87.97 176	67.13 171	87.55 88	94.32 14	73.44 154	88.47 116	87.54 214	86.45 48	91.06 174	75.76 133	93.76 165	92.54 137

EPP-MVSNet		85.47 102	85.04 112	86.77 86	91.52 113	69.37 154	91.63 31	87.98 181	81.51 63	87.05 140	91.83 134	66.18 221	95.29 41	70.75 176	96.89 78	95.64 45

FIs		85.35 103	86.27 91	82.60 173	91.86 102	57.31 263	85.10 122	93.05 65	75.83 126	91.02 69	93.97 80	73.57 175	92.91 128	73.97 146	98.02 38	97.58 13

casdiffmvs		85.21 104	85.85 99	83.31 158	86.17 218	62.77 209	83.03 171	93.93 30	74.69 140	88.21 122	92.68 112	82.29 83	91.89 152	77.87 115	93.75 167	95.27 55

baseline		85.20 105	85.93 97	83.02 163	86.30 213	62.37 216	84.55 131	93.96 29	74.48 143	87.12 135	92.03 128	82.30 82	91.94 149	78.39 105	94.21 158	94.74 67

K. test v3		85.14 106	84.73 116	86.37 92	91.13 125	69.63 153	85.45 118	76.68 267	84.06 36	92.44 47	96.99 9	62.03 238	94.65 61	80.58 87	93.24 175	94.83 66

EI-MVSNet-Vis-set		85.12 107	84.53 125	86.88 83	84.01 247	72.76 119	83.91 145	85.18 218	80.44 71	88.75 110	85.49 241	80.08 111	91.92 150	82.02 71	90.85 222	95.97 38

Regformer-3		85.06 108	84.67 121	86.22 98	84.27 240	73.43 115	84.07 139	85.26 216	80.77 70	88.62 113	85.48 242	80.56 107	90.39 194	81.99 72	91.04 213	94.85 64

EI-MVSNet-UG-set		85.04 109	84.44 127	86.85 84	83.87 250	72.52 124	83.82 147	85.15 219	80.27 75	88.75 110	85.45 245	79.95 114	91.90 151	81.92 73	90.80 223	96.13 33

X-MVStestdata		85.04 109	82.70 151	92.08 8	95.64 22	86.25 16	92.64 16	93.33 51	85.07 29	89.99 82	16.05 337	86.57 46	95.80 17	87.35 19	97.62 58	94.20 81

MSLP-MVS++		85.00 111	86.03 96	81.90 183	91.84 103	71.56 140	86.75 103	93.02 69	75.95 124	87.12 135	89.39 186	77.98 127	89.40 216	77.46 118	94.78 145	84.75 260

F-COLMAP		84.97 112	83.42 141	89.63 52	92.39 85	83.40 45	88.83 73	91.92 98	73.19 162	80.18 241	89.15 191	77.04 140	93.28 113	65.82 218	92.28 196	92.21 147

3Dnovator	80.37 7	84.80 113	84.71 119	85.06 120	86.36 211	74.71 108	88.77 75	90.00 151	75.65 129	84.96 171	93.17 96	74.06 168	91.19 169	78.28 109	91.09 211	89.29 207

IterMVS-LS		84.73 114	84.98 113	83.96 143	87.35 190	63.66 198	83.25 165	89.88 153	76.06 119	89.62 94	92.37 122	73.40 181	92.52 135	78.16 110	94.77 147	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 115	84.34 131	85.49 115	90.18 143	75.86 104	79.23 235	87.13 193	73.35 155	85.56 166	89.34 187	83.60 68	90.50 191	76.64 127	94.05 161	90.09 198

HQP-MVS		84.61 116	84.06 134	86.27 96	91.19 121	70.66 144	84.77 124	92.68 80	73.30 158	80.55 236	90.17 178	72.10 193	94.61 62	77.30 121	94.47 153	93.56 109

v1192		84.57 117	84.69 120	84.21 137	87.75 182	62.88 207	83.02 172	91.43 109	69.08 200	89.98 84	90.89 159	72.70 189	93.62 99	82.41 67	94.97 140	96.13 33

mvs-test1		84.55 118	82.12 160	91.84 18	87.13 195	89.54 4	85.05 123	88.42 170	74.57 141	80.60 233	82.98 273	78.49 123	93.98 84	72.04 167	89.77 234	92.00 152

FMVSNet1		84.55 118	85.45 107	81.85 185	90.27 141	61.05 230	86.83 99	88.27 175	78.57 98	89.66 93	95.64 29	75.43 154	90.68 186	69.09 193	95.33 125	93.82 96

v1144		84.54 120	84.72 118	84.00 141	87.67 184	62.55 213	82.97 173	90.93 122	70.32 190	89.80 89	90.99 154	73.50 176	93.48 106	81.69 75	94.65 150	95.97 38

Gipuma		84.44 121	86.33 89	78.78 226	84.20 243	73.57 114	89.55 58	90.44 132	84.24 33	84.38 182	94.89 45	76.35 151	80.40 292	76.14 131	96.80 83	82.36 291
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing_2		84.36 122	84.64 122	83.50 155	86.74 205	63.97 197	84.56 130	90.31 138	66.22 225	91.62 59	94.55 56	75.88 152	91.95 148	77.02 125	94.89 142	94.56 70

MCST-MVS		84.36 122	83.93 137	85.63 111	91.59 107	71.58 139	83.52 155	92.13 91	61.82 252	83.96 190	89.75 183	79.93 115	93.46 107	78.33 108	94.34 156	91.87 157

VDDNet		84.35 124	85.39 108	81.25 194	95.13 31	59.32 247	85.42 119	81.11 245	86.41 24	87.41 132	96.21 20	73.61 174	90.61 189	66.33 212	96.85 79	93.81 99

EIA-MVS		84.31 125	83.91 138	85.52 113	88.58 165	70.40 146	84.50 135	93.37 48	78.76 95	84.07 189	78.72 306	80.39 108	95.13 49	73.82 149	92.98 183	91.04 173

v1240		84.30 126	84.51 126	83.65 150	87.65 185	61.26 227	82.85 176	91.54 106	67.94 211	90.68 75	90.65 168	71.71 199	93.64 95	82.84 65	94.78 145	96.07 35

MVS_111021_LR		84.28 127	83.76 139	85.83 109	89.23 155	83.07 48	80.99 211	83.56 230	72.71 169	86.07 157	89.07 193	81.75 97	86.19 255	77.11 123	93.36 170	88.24 220

v144192		84.24 128	84.41 128	83.71 149	87.59 187	61.57 223	82.95 174	91.03 120	67.82 214	89.80 89	90.49 170	73.28 182	93.51 105	81.88 74	94.89 142	96.04 37

v1921920		84.23 129	84.37 130	83.79 146	87.64 186	61.71 221	82.91 175	91.20 116	67.94 211	90.06 80	90.34 172	72.04 196	93.59 100	82.32 69	94.91 141	96.07 35

VDD-MVS		84.23 129	84.58 124	83.20 160	91.17 124	65.16 186	83.25 165	84.97 225	79.79 78	87.18 134	94.27 68	74.77 163	90.89 180	69.24 189	96.54 89	93.55 111

v2v482		84.09 131	84.24 132	83.62 151	87.13 195	61.40 224	82.71 179	89.71 155	72.19 175	89.55 98	91.41 145	70.70 204	93.20 115	81.02 79	93.76 165	96.25 31

EG-PatchMatch MVS		84.08 132	84.11 133	83.98 142	92.22 93	72.61 121	82.20 194	87.02 197	72.63 170	88.86 107	91.02 153	78.52 122	91.11 172	73.41 153	91.09 211	88.21 221

DP-MVS Recon		84.05 133	83.22 144	86.52 90	91.73 105	75.27 106	83.23 167	92.40 85	72.04 176	82.04 214	88.33 201	77.91 129	93.95 85	66.17 213	95.12 135	90.34 192

TransMVSNet (Re)		84.02 134	85.74 101	78.85 225	91.00 127	55.20 280	82.29 188	87.26 188	79.65 81	88.38 119	95.52 32	83.00 73	86.88 244	67.97 203	96.60 87	94.45 77

Baseline_NR-MVSNet		84.00 135	85.90 98	78.29 236	91.47 115	53.44 289	82.29 188	87.00 200	79.06 89	89.55 98	95.72 27	77.20 136	86.14 256	72.30 165	98.51 16	95.28 54

TSAR-MVS + GP.		83.95 136	82.69 152	87.72 75	89.27 154	81.45 56	83.72 151	81.58 244	74.73 139	85.66 163	86.06 236	72.56 191	92.69 132	75.44 136	95.21 130	89.01 215

alignmvs		83.94 137	83.98 136	83.80 145	87.80 181	67.88 168	84.54 133	91.42 111	73.27 161	88.41 118	87.96 206	72.33 192	90.83 181	76.02 132	94.11 159	92.69 132

Effi-MVS+		83.90 138	84.01 135	83.57 153	87.22 193	65.61 183	86.55 107	92.40 85	78.64 97	81.34 226	84.18 263	83.65 67	92.93 126	74.22 142	87.87 257	92.17 149

CANet		83.79 139	82.85 150	86.63 87	86.17 218	72.21 131	83.76 150	91.43 109	77.24 110	74.39 282	87.45 216	75.36 155	95.42 37	77.03 124	92.83 186	92.25 146

pm-mvs1		83.69 140	84.95 114	79.91 212	90.04 147	59.66 244	82.43 184	87.44 185	75.52 130	87.85 126	95.26 37	81.25 102	85.65 262	68.74 197	96.04 106	94.42 78

AdaColmap		83.66 141	83.69 140	83.57 153	90.05 146	72.26 129	86.29 110	90.00 151	78.19 102	81.65 221	87.16 221	83.40 70	94.24 71	61.69 244	94.76 148	84.21 265

MIMVSNet1		83.63 142	84.59 123	80.74 202	94.06 48	62.77 209	82.72 178	84.53 228	77.57 107	90.34 77	95.92 24	76.88 148	85.83 260	61.88 242	97.42 64	93.62 106

WR-MVS		83.56 143	84.40 129	81.06 197	93.43 61	54.88 281	78.67 242	85.02 223	81.24 64	90.74 74	91.56 142	72.85 186	91.08 173	68.00 202	98.04 35	97.23 17

CNLPA		83.55 144	83.10 147	84.90 121	89.34 153	83.87 43	84.54 133	88.77 165	79.09 88	83.54 196	88.66 199	74.87 159	81.73 287	66.84 209	92.29 195	89.11 209

LCM-MVSNet-Re		83.48 145	85.06 111	78.75 227	85.94 221	55.75 275	80.05 219	94.27 15	76.47 115	96.09 5	94.54 57	83.31 71	89.75 211	59.95 256	94.89 142	90.75 181

V42		83.47 146	83.37 143	83.75 148	83.16 256	63.33 202	81.31 205	90.23 145	69.51 196	90.91 72	90.81 162	74.16 167	92.29 140	80.06 89	90.22 231	95.62 46

VPA-MVSNet		83.47 146	84.73 116	79.69 216	90.29 140	57.52 262	81.30 207	88.69 167	76.29 116	87.58 129	94.44 61	80.60 106	87.20 239	66.60 211	96.82 82	94.34 80

CS-MVS		83.43 148	83.04 148	84.59 129	87.87 179	66.61 176	85.57 116	94.90 10	73.02 165	81.12 227	78.56 307	80.00 113	95.52 32	73.04 160	93.29 174	91.62 165

PAPM_NR		83.23 149	83.19 146	83.33 157	90.90 128	65.98 180	88.19 81	90.78 124	78.13 103	80.87 231	87.92 209	73.49 178	92.42 136	70.07 183	88.40 248	91.60 166

CLD-MVS		83.18 150	82.64 153	84.79 123	89.05 157	67.82 169	77.93 250	92.52 83	68.33 206	85.07 170	81.54 290	82.06 88	92.96 124	69.35 188	97.91 45	93.57 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 151	85.68 103	75.65 263	81.24 270	45.26 324	79.94 221	92.91 72	83.83 37	91.33 65	96.88 11	80.25 110	85.92 258	68.89 195	95.89 110	95.76 42

114514_t		83.10 152	82.54 156	84.77 124	92.90 73	69.10 161	86.65 104	90.62 129	54.66 289	81.46 223	90.81 162	76.98 141	94.38 67	72.62 162	96.18 101	90.82 180

UGNet		82.78 153	81.64 166	86.21 100	86.20 217	76.24 103	86.86 97	85.68 210	77.07 111	73.76 285	92.82 107	69.64 205	91.82 156	69.04 194	93.69 168	90.56 186
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 154	81.93 164	85.19 117	82.08 261	80.15 63	85.53 117	88.76 166	68.01 208	85.58 165	87.75 210	71.80 198	86.85 245	74.02 145	93.87 164	88.58 219

EI-MVSNet		82.61 155	82.42 158	83.20 160	83.25 254	63.66 198	83.50 158	85.07 220	76.06 119	86.55 147	85.10 250	73.41 179	90.25 195	78.15 112	90.67 226	95.68 44

QAPM		82.59 156	82.59 155	82.58 174	86.44 206	66.69 175	89.94 49	90.36 135	67.97 210	84.94 173	92.58 115	72.71 188	92.18 141	70.63 179	87.73 259	88.85 217

Fast-Effi-MVS+-dtu		82.54 157	81.41 170	85.90 106	85.60 222	76.53 99	83.07 170	89.62 157	73.02 165	79.11 249	83.51 268	80.74 105	90.24 197	68.76 196	89.29 238	90.94 176

MVS_Test		82.47 158	83.22 144	80.22 209	82.62 260	57.75 261	82.54 183	91.96 97	71.16 182	82.89 203	92.52 118	77.41 134	90.50 191	80.04 90	87.84 258	92.40 140

v148		82.31 159	82.48 157	81.81 188	85.59 223	59.66 244	81.47 203	86.02 207	72.85 167	88.05 123	90.65 168	70.73 203	90.91 179	75.15 138	91.79 202	94.87 62

API-MVS		82.28 160	82.61 154	81.30 192	86.29 214	69.79 148	88.71 76	87.67 184	78.42 100	82.15 213	84.15 264	77.98 127	91.59 159	65.39 220	92.75 187	82.51 290

MVSFormer		82.23 161	81.57 169	84.19 139	85.54 224	69.26 156	91.98 27	90.08 149	71.54 179	76.23 266	85.07 252	58.69 257	94.27 68	86.26 31	88.77 245	89.03 213

ETV-MVS		82.19 162	81.23 173	85.10 119	87.95 177	69.17 160	83.22 168	93.33 51	70.42 187	78.58 252	79.77 303	77.29 135	94.20 73	71.51 170	88.96 243	91.93 156

PCF-MVS	74.62 15	82.15 163	80.92 177	85.84 108	89.43 151	72.30 128	80.53 215	91.82 100	57.36 278	87.81 127	89.92 181	77.67 131	93.63 96	58.69 261	95.08 136	91.58 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 164	80.31 183	87.45 79	90.86 130	80.29 62	85.88 112	90.65 127	68.17 207	76.32 265	86.33 231	73.12 184	92.61 134	61.40 248	90.02 233	89.44 202
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 165	82.07 161	81.85 185	86.38 208	61.05 230	86.83 99	88.27 175	72.43 171	86.00 158	95.64 29	63.78 230	90.68 186	65.95 214	93.34 171	93.82 96

test1		82.02 165	82.07 161	81.85 185	86.38 208	61.05 230	86.83 99	88.27 175	72.43 171	86.00 158	95.64 29	63.78 230	90.68 186	65.95 214	93.34 171	93.82 96

OpenMVS	76.72 13	81.98 167	82.00 163	81.93 182	84.42 236	68.22 165	88.50 79	89.48 159	66.92 219	81.80 220	91.86 131	72.59 190	90.16 200	71.19 172	91.25 210	87.40 233

tfpnnormal		81.79 168	82.95 149	78.31 235	88.93 161	55.40 276	80.83 214	82.85 234	76.81 113	85.90 161	94.14 73	74.58 166	86.51 250	66.82 210	95.68 120	93.01 121

cl_fuxian		81.64 169	81.59 168	81.79 189	80.86 276	59.15 251	78.61 243	90.18 147	68.36 205	87.20 133	87.11 223	69.39 206	91.62 158	78.16 110	94.43 155	94.60 69

PVSNet_Blended_VisFu		81.55 170	80.49 181	84.70 127	91.58 110	73.24 117	84.21 137	91.67 104	62.86 245	80.94 229	87.16 221	67.27 216	92.87 129	69.82 185	88.94 244	87.99 225

DELS-MVS		81.44 171	81.25 172	82.03 181	84.27 240	62.87 208	76.47 270	92.49 84	70.97 183	81.64 222	83.83 265	75.03 157	92.70 131	74.29 141	92.22 199	90.51 188
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
FMVSNet2		81.31 172	81.61 167	80.41 206	86.38 208	58.75 256	83.93 144	86.58 202	72.43 171	87.65 128	92.98 100	63.78 230	90.22 198	66.86 207	93.92 163	92.27 144

DI_MVS_plusplus_test		81.27 173	81.26 171	81.29 193	84.98 228	61.65 222	81.98 196	87.25 189	63.56 241	87.56 130	89.60 184	73.62 173	91.83 155	72.20 166	90.59 230	90.38 191

TinyColmap		81.25 174	82.34 159	77.99 241	85.33 226	60.68 236	82.32 187	88.33 173	71.26 181	86.97 141	92.22 127	77.10 139	86.98 243	62.37 237	95.17 132	86.31 244

tttt0517		81.07 175	79.58 194	85.52 113	88.99 160	66.45 178	87.03 96	75.51 275	73.76 150	88.32 121	90.20 175	37.96 331	94.16 79	79.36 100	95.13 133	95.93 41

Fast-Effi-MVS+		81.04 176	80.57 178	82.46 178	87.50 188	63.22 204	78.37 246	89.63 156	68.01 208	81.87 216	82.08 285	82.31 81	92.65 133	67.10 206	88.30 253	91.51 169

BH-untuned		80.96 177	80.99 175	80.84 201	88.55 166	68.23 164	80.33 217	88.46 169	72.79 168	86.55 147	86.76 226	74.72 164	91.77 157	61.79 243	88.99 242	82.52 289

1121		80.86 178	79.81 193	84.02 140	93.93 51	78.70 76	81.64 200	80.18 251	55.43 286	83.67 193	91.15 149	71.29 201	91.41 165	67.95 204	93.06 180	81.96 295

eth_miper_zixun_eth		80.84 179	80.22 187	82.71 171	81.41 268	60.98 233	77.81 252	90.14 148	67.31 217	86.95 142	87.24 220	64.26 226	92.31 138	75.23 137	91.61 205	94.85 64

xiu_mvs_v1_base_debu		80.84 179	80.14 189	82.93 166	88.31 170	71.73 135	79.53 226	87.17 190	65.43 232	79.59 243	82.73 280	76.94 142	90.14 203	73.22 154	88.33 249	86.90 239

xiu_mvs_v1_base		80.84 179	80.14 189	82.93 166	88.31 170	71.73 135	79.53 226	87.17 190	65.43 232	79.59 243	82.73 280	76.94 142	90.14 203	73.22 154	88.33 249	86.90 239

xiu_mvs_v1_base_debi		80.84 179	80.14 189	82.93 166	88.31 170	71.73 135	79.53 226	87.17 190	65.43 232	79.59 243	82.73 280	76.94 142	90.14 203	73.22 154	88.33 249	86.90 239

IterMVS-SCA-FT		80.64 183	79.41 195	84.34 134	83.93 248	69.66 152	76.28 272	81.09 246	72.43 171	86.47 153	90.19 176	60.46 244	93.15 120	77.45 119	86.39 270	90.22 193

BH-RMVSNet		80.53 184	80.22 187	81.49 191	87.19 194	66.21 179	77.79 253	86.23 204	74.21 145	83.69 192	88.50 200	73.25 183	90.75 183	63.18 234	87.90 256	87.52 231

Anonymous202405211		80.51 185	81.19 174	78.49 232	88.48 167	57.26 264	76.63 266	82.49 236	81.21 65	84.30 186	92.24 126	67.99 213	86.24 254	62.22 238	95.13 133	91.98 155

cl-mvsnet1		80.43 186	80.23 185	81.02 198	79.99 284	59.25 248	77.07 261	87.02 197	67.38 215	86.19 154	89.22 188	63.09 234	90.16 200	76.32 129	95.80 114	93.66 103

cl-mvsnet_		80.42 187	80.23 185	81.02 198	79.99 284	59.25 248	77.07 261	87.02 197	67.37 216	86.18 156	89.21 189	63.08 235	90.16 200	76.31 130	95.80 114	93.65 104

diffmvs		80.40 188	80.48 182	80.17 210	79.02 295	60.04 240	77.54 256	90.28 144	66.65 222	82.40 208	87.33 219	73.50 176	87.35 238	77.98 113	89.62 236	93.13 118

EPNet		80.37 189	78.41 204	86.23 97	76.75 305	73.28 116	87.18 93	77.45 262	76.24 118	68.14 306	88.93 195	65.41 224	93.85 87	69.47 187	96.12 105	91.55 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		80.32 190	80.94 176	78.47 233	88.18 173	52.62 295	82.29 188	85.01 224	72.01 177	79.24 248	92.54 117	69.36 207	93.36 112	70.65 178	89.19 241	89.45 201

VPNet		80.25 191	81.68 165	75.94 262	92.46 84	47.98 318	76.70 265	81.67 243	73.45 153	84.87 174	92.82 107	74.66 165	86.51 250	61.66 245	96.85 79	93.33 112

MAR-MVS		80.24 192	78.74 200	84.73 125	86.87 204	78.18 79	85.75 113	87.81 183	65.67 231	77.84 255	78.50 308	73.79 172	90.53 190	61.59 247	90.87 221	85.49 253
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PM-MVS		80.20 193	79.00 198	83.78 147	88.17 174	86.66 13	81.31 205	66.81 319	69.64 195	88.33 120	90.19 176	64.58 225	83.63 280	71.99 169	90.03 232	81.06 310

LFMVS		80.15 194	80.56 179	78.89 224	89.19 156	55.93 272	85.22 121	73.78 286	82.96 47	84.28 187	92.72 111	57.38 266	90.07 207	63.80 229	95.75 117	90.68 183

DPM-MVS		80.10 195	79.18 197	82.88 169	90.71 133	69.74 149	78.87 239	90.84 123	60.29 264	75.64 274	85.92 238	67.28 215	93.11 121	71.24 171	91.79 202	85.77 250

MSDG		80.06 196	79.99 192	80.25 208	83.91 249	68.04 167	77.51 257	89.19 162	77.65 105	81.94 215	83.45 270	76.37 150	86.31 253	63.31 233	86.59 267	86.41 242

ab-mvs		79.67 197	80.56 179	76.99 249	88.48 167	56.93 266	84.70 127	86.06 206	68.95 202	80.78 232	93.08 97	75.30 156	84.62 271	56.78 270	90.90 220	89.43 203

VNet		79.31 198	80.27 184	76.44 257	87.92 178	53.95 285	75.58 279	84.35 229	74.39 144	82.23 211	90.72 164	72.84 187	84.39 273	60.38 255	93.98 162	90.97 175

thisisatest0530		79.07 199	77.33 210	84.26 136	87.13 195	64.58 189	83.66 153	75.95 270	68.86 203	85.22 169	87.36 218	38.10 329	93.57 103	75.47 135	94.28 157	94.62 68

PAPR		78.84 200	78.10 205	81.07 196	85.17 227	60.22 239	82.21 192	90.57 130	62.51 247	75.32 277	84.61 259	74.99 158	92.30 139	59.48 259	88.04 255	90.68 183

PVSNet_BlendedMVS		78.80 201	77.84 206	81.65 190	84.43 234	63.41 200	79.49 229	90.44 132	61.70 255	75.43 275	87.07 224	69.11 209	91.44 163	60.68 253	92.24 197	90.11 197

FMVSNet3		78.80 201	78.55 201	79.57 218	82.89 259	56.89 268	81.76 197	85.77 209	69.04 201	86.00 158	90.44 171	51.75 284	90.09 206	65.95 214	93.34 171	91.72 161

test_yl		78.71 203	78.51 202	79.32 221	84.32 238	58.84 253	78.38 244	85.33 214	75.99 122	82.49 206	86.57 227	58.01 260	90.02 208	62.74 235	92.73 188	89.10 210

DCV-MVSNet		78.71 203	78.51 202	79.32 221	84.32 238	58.84 253	78.38 244	85.33 214	75.99 122	82.49 206	86.57 227	58.01 260	90.02 208	62.74 235	92.73 188	89.10 210

pmmvs-eth3d		78.42 205	77.04 213	82.57 176	87.44 189	74.41 110	80.86 213	79.67 254	55.68 284	84.69 177	90.31 174	60.91 242	85.42 263	62.20 239	91.59 206	87.88 228

MVS_0304		78.17 206	77.23 211	80.99 200	84.13 246	69.07 162	81.39 204	80.81 248	76.28 117	67.53 311	89.11 192	62.87 236	86.77 247	60.90 252	92.01 201	87.13 236

mvs_anonymous		78.13 207	78.76 199	76.23 261	79.24 292	50.31 312	78.69 241	84.82 226	61.60 256	83.09 202	92.82 107	73.89 171	87.01 240	68.33 201	86.41 269	91.37 170

TAMVS		78.08 208	76.36 218	83.23 159	90.62 134	72.87 118	79.08 236	80.01 253	61.72 254	81.35 225	86.92 225	63.96 229	88.78 224	50.61 300	93.01 182	88.04 224

Vis-MVSNet (Re-imp)		77.82 209	77.79 207	77.92 242	88.82 162	51.29 305	83.28 163	71.97 300	74.04 146	82.23 211	89.78 182	57.38 266	89.41 215	57.22 269	95.41 123	93.05 120

CANet_DTU		77.81 210	77.05 212	80.09 211	81.37 269	59.90 242	83.26 164	88.29 174	69.16 199	67.83 309	83.72 266	60.93 241	89.47 212	69.22 191	89.70 235	90.88 178

OpenMVS_ROB	70.19 17	77.77 211	77.46 208	78.71 228	84.39 237	61.15 228	81.18 209	82.52 235	62.45 249	83.34 197	87.37 217	66.20 220	88.66 226	64.69 224	85.02 281	86.32 243

MDA-MVSNet-bldmvs		77.47 212	76.90 214	79.16 223	79.03 294	64.59 188	66.58 317	75.67 273	73.15 163	88.86 107	88.99 194	66.94 217	81.23 289	64.71 223	88.22 254	91.64 164

jason		77.42 213	75.75 225	82.43 179	87.10 199	69.27 155	77.99 249	81.94 241	51.47 308	77.84 255	85.07 252	60.32 246	89.00 218	70.74 177	89.27 240	89.03 213
jason: jason.
CDS-MVSNet		77.32 214	75.40 228	83.06 162	89.00 159	72.48 125	77.90 251	82.17 239	60.81 259	78.94 250	83.49 269	59.30 254	88.76 225	54.64 286	92.37 194	87.93 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 215	76.75 215	78.52 231	87.01 201	61.30 226	75.55 280	87.12 195	61.24 258	74.45 281	78.79 305	77.20 136	90.93 177	64.62 226	84.80 285	83.32 279

MVSTER		77.09 216	75.70 226	81.25 194	75.27 318	61.08 229	77.49 258	85.07 220	60.78 260	86.55 147	88.68 198	43.14 320	90.25 195	73.69 150	90.67 226	92.42 138

PS-MVSNAJ		77.04 217	76.53 217	78.56 230	87.09 200	61.40 224	75.26 281	87.13 193	61.25 257	74.38 283	77.22 315	76.94 142	90.94 176	64.63 225	84.83 284	83.35 278

IterMVS		76.91 218	76.34 219	78.64 229	80.91 274	64.03 195	76.30 271	79.03 255	64.88 238	83.11 200	89.16 190	59.90 250	84.46 272	68.61 199	85.15 280	87.42 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 219	75.67 227	80.34 207	80.48 282	62.16 220	73.50 292	84.80 227	57.61 276	82.24 210	87.54 214	51.31 285	87.65 235	70.40 182	93.19 177	91.23 172

TR-MVS		76.77 220	75.79 223	79.72 215	86.10 220	65.79 182	77.14 259	83.02 232	65.20 236	81.40 224	82.10 284	66.30 219	90.73 185	55.57 278	85.27 278	82.65 285

USDC		76.63 221	76.73 216	76.34 259	83.46 252	57.20 265	80.02 220	88.04 179	52.14 304	83.65 194	91.25 146	63.24 233	86.65 249	54.66 285	94.11 159	85.17 255

BH-w/o		76.57 222	76.07 222	78.10 239	86.88 203	65.92 181	77.63 254	86.33 203	65.69 230	80.89 230	79.95 300	68.97 211	90.74 184	53.01 292	85.25 279	77.62 314

Patchmtry		76.56 223	77.46 208	73.83 271	79.37 291	46.60 321	82.41 185	76.90 264	73.81 149	85.56 166	92.38 119	48.07 293	83.98 277	63.36 232	95.31 128	90.92 177

PVSNet_Blended		76.49 224	75.40 228	79.76 214	84.43 234	63.41 200	75.14 282	90.44 132	57.36 278	75.43 275	78.30 309	69.11 209	91.44 163	60.68 253	87.70 260	84.42 263

miper_lstm_enhance		76.45 225	76.10 221	77.51 246	76.72 306	60.97 234	64.69 320	85.04 222	63.98 240	83.20 199	88.22 202	56.67 269	78.79 297	73.22 154	93.12 178	92.78 129

lupinMVS		76.37 226	74.46 236	82.09 180	85.54 224	69.26 156	76.79 263	80.77 249	50.68 314	76.23 266	82.82 278	58.69 257	88.94 219	69.85 184	88.77 245	88.07 222

cascas		76.29 227	74.81 232	80.72 204	84.47 233	62.94 206	73.89 290	87.34 186	55.94 283	75.16 279	76.53 318	63.97 228	91.16 170	65.00 221	90.97 218	88.06 223

RPMNet		76.06 228	75.79 223	76.85 253	79.58 286	62.64 211	82.58 180	71.75 303	74.80 138	75.72 272	92.59 113	48.69 291	84.07 275	73.48 152	82.91 295	83.85 270

thres600view7		75.97 229	75.35 230	77.85 244	87.01 201	51.84 302	80.45 216	73.26 291	75.20 135	83.10 201	86.31 233	45.54 306	89.05 217	55.03 283	92.24 197	92.66 133

GA-MVS		75.83 230	74.61 233	79.48 220	81.87 263	59.25 248	73.42 293	82.88 233	68.68 204	79.75 242	81.80 287	50.62 287	89.46 213	66.85 208	85.64 275	89.72 199

MVP-Stereo		75.81 231	73.51 245	82.71 171	89.35 152	73.62 113	80.06 218	85.20 217	60.30 263	73.96 284	87.94 207	57.89 264	89.45 214	52.02 295	74.87 321	85.06 257
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 232	75.05 231	76.66 256	87.27 191	51.88 300	81.07 210	73.26 291	75.68 128	83.25 198	86.37 230	45.54 306	88.80 221	51.98 296	90.99 215	89.31 205

ET-MVSNet_ETH3D		75.28 233	72.77 251	82.81 170	83.03 258	68.11 166	77.09 260	76.51 268	60.67 262	77.60 259	80.52 295	38.04 330	91.15 171	70.78 175	90.68 225	89.17 208

thres400		75.14 234	74.23 238	77.86 243	86.24 215	52.12 297	79.24 233	73.87 284	73.34 156	81.82 218	84.60 260	46.02 300	88.80 221	51.98 296	90.99 215	92.66 133

wuyk23d		75.13 235	79.30 196	62.63 309	75.56 313	75.18 107	80.89 212	73.10 293	75.06 137	94.76 12	95.32 34	87.73 34	52.85 335	34.16 333	97.11 72	59.85 331

EU-MVSNet		75.12 236	74.43 237	77.18 248	83.11 257	59.48 246	85.71 115	82.43 237	39.76 332	85.64 164	88.76 196	44.71 316	87.88 233	73.86 148	85.88 274	84.16 266

HyFIR lowres test		75.12 236	72.66 253	82.50 177	91.44 116	65.19 185	72.47 296	87.31 187	46.79 320	80.29 239	84.30 262	52.70 283	92.10 145	51.88 299	86.73 266	90.22 193

CMPMVS	59.41 20	75.12 236	73.57 243	79.77 213	75.84 312	67.22 170	81.21 208	82.18 238	50.78 312	76.50 262	87.66 212	55.20 278	82.99 282	62.17 241	90.64 229	89.09 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 239	72.98 250	80.73 203	84.95 229	71.71 138	76.23 273	77.59 261	52.83 298	77.73 258	86.38 229	56.35 272	84.97 267	57.72 268	87.05 264	85.51 252

tfpn200view9		74.86 240	74.23 238	76.74 255	86.24 215	52.12 297	79.24 233	73.87 284	73.34 156	81.82 218	84.60 260	46.02 300	88.80 221	51.98 296	90.99 215	89.31 205

1112_ss		74.82 241	73.74 241	78.04 240	89.57 150	60.04 240	76.49 269	87.09 196	54.31 290	73.66 286	79.80 301	60.25 247	86.76 248	58.37 262	84.15 288	87.32 234

ppachtmachnet_test		74.73 242	74.00 240	76.90 251	80.71 279	56.89 268	71.53 300	78.42 257	58.24 271	79.32 247	82.92 277	57.91 263	84.26 274	65.60 219	91.36 209	89.56 200

Patchmatch-RL test		74.48 243	73.68 242	76.89 252	84.83 231	66.54 177	72.29 297	69.16 312	57.70 274	86.76 143	86.33 231	45.79 305	82.59 284	69.63 186	90.65 228	81.54 301

PatchMatch-RL		74.48 243	73.22 247	78.27 237	87.70 183	85.26 30	75.92 275	70.09 309	64.34 239	76.09 268	81.25 292	65.87 223	78.07 298	53.86 288	83.82 289	71.48 322

XXY-MVS		74.44 245	76.19 220	69.21 290	84.61 232	52.43 296	71.70 299	77.18 263	60.73 261	80.60 233	90.96 157	75.44 153	69.35 316	56.13 274	88.33 249	85.86 249

CR-MVSNet		74.00 246	73.04 249	76.85 253	79.58 286	62.64 211	82.58 180	76.90 264	50.50 315	75.72 272	92.38 119	48.07 293	84.07 275	68.72 198	82.91 295	83.85 270

Test_1112_low_res		73.90 247	73.08 248	76.35 258	90.35 139	55.95 271	73.40 294	86.17 205	50.70 313	73.14 287	85.94 237	58.31 259	85.90 259	56.51 272	83.22 292	87.20 235

test20.03		73.75 248	74.59 235	71.22 285	81.11 272	51.12 307	70.15 305	72.10 299	70.42 187	80.28 240	91.50 143	64.21 227	74.72 308	46.96 316	94.58 151	87.82 230

SCA		73.32 249	72.57 255	75.58 264	81.62 265	55.86 273	78.89 238	71.37 306	61.73 253	74.93 280	83.42 271	60.46 244	87.01 240	58.11 266	82.63 299	83.88 267

baseline1		73.26 250	73.54 244	72.43 280	84.92 230	47.79 319	79.89 222	74.00 283	65.93 226	78.81 251	86.28 234	56.36 271	81.63 288	56.63 271	79.04 312	87.87 229

1314		73.22 251	72.56 256	75.20 265	80.41 283	57.84 259	81.64 200	85.36 213	51.68 307	73.10 288	76.65 317	61.45 240	85.19 265	63.54 230	79.21 311	82.59 286

MVS		73.21 252	72.59 254	75.06 266	80.97 273	60.81 235	81.64 200	85.92 208	46.03 323	71.68 294	77.54 311	68.47 212	89.77 210	55.70 277	85.39 276	74.60 319

HY-MVS	64.64 18	73.03 253	72.47 257	74.71 267	83.36 253	54.19 283	82.14 195	81.96 240	56.76 282	69.57 302	86.21 235	60.03 248	84.83 270	49.58 305	82.65 297	85.11 256

thisisatest0515		73.00 254	70.52 268	80.46 205	81.45 267	59.90 242	73.16 295	74.31 282	57.86 273	76.08 269	77.78 310	37.60 332	92.12 144	65.00 221	91.45 208	89.35 204

EPNet_dtu		72.87 255	71.33 266	77.49 247	77.72 299	60.55 237	82.35 186	75.79 271	66.49 223	58.39 334	81.06 293	53.68 281	85.98 257	53.55 289	92.97 184	85.95 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 256	71.41 265	76.28 260	83.25 254	60.34 238	83.50 158	79.02 256	37.77 333	76.33 264	85.10 250	49.60 290	87.41 237	70.54 180	77.54 317	81.08 308

CHOSEN 1792x2688		72.45 257	70.56 267	78.13 238	90.02 148	63.08 205	68.72 309	83.16 231	42.99 329	75.92 270	85.46 244	57.22 268	85.18 266	49.87 304	81.67 300	86.14 245

testgi		72.36 258	74.61 233	65.59 303	80.56 281	42.82 330	68.29 310	73.35 290	66.87 220	81.84 217	89.93 180	72.08 195	66.92 324	46.05 318	92.54 192	87.01 238

thres200		72.34 259	71.55 264	74.70 268	83.48 251	51.60 303	75.02 283	73.71 287	70.14 192	78.56 253	80.57 294	46.20 298	88.20 231	46.99 315	89.29 238	84.32 264

FPMVS		72.29 260	72.00 259	73.14 274	88.63 164	85.00 32	74.65 286	67.39 313	71.94 178	77.80 257	87.66 212	50.48 288	75.83 304	49.95 302	79.51 307	58.58 333

FMVSNet5		72.10 261	71.69 261	73.32 272	81.57 266	53.02 292	76.77 264	78.37 258	63.31 242	76.37 263	91.85 132	36.68 333	78.98 295	47.87 312	92.45 193	87.95 226

our_test_3		71.85 262	71.59 262	72.62 278	80.71 279	53.78 286	69.72 307	71.71 305	58.80 268	78.03 254	80.51 296	56.61 270	78.84 296	62.20 239	86.04 273	85.23 254

PAPM		71.77 263	70.06 273	76.92 250	86.39 207	53.97 284	76.62 267	86.62 201	53.44 295	63.97 324	84.73 258	57.79 265	92.34 137	39.65 328	81.33 303	84.45 262

IB-MVS	62.13 19	71.64 264	68.97 277	79.66 217	80.80 278	62.26 219	73.94 289	76.90 264	63.27 243	68.63 305	76.79 316	33.83 336	91.84 154	59.28 260	87.26 262	84.88 258
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UnsupCasMVSNet_eth		71.63 265	72.30 258	69.62 288	76.47 308	52.70 294	70.03 306	80.97 247	59.18 267	79.36 246	88.21 203	60.50 243	69.12 317	58.33 264	77.62 316	87.04 237

Anonymous20231206		71.38 266	71.88 260	69.88 286	86.31 212	54.37 282	70.39 304	74.62 278	52.57 300	76.73 261	88.76 196	59.94 249	72.06 310	44.35 321	93.23 176	83.23 281

MIMVSNet		71.09 267	71.59 262	69.57 289	87.23 192	50.07 313	78.91 237	71.83 301	60.20 265	71.26 295	91.76 137	55.08 279	76.09 302	41.06 326	87.02 265	82.54 288

MS-PatchMatch		70.93 268	70.22 271	73.06 275	81.85 264	62.50 214	73.82 291	77.90 259	52.44 301	75.92 270	81.27 291	55.67 275	81.75 286	55.37 280	77.70 315	74.94 318

pmmvs5		70.73 269	70.07 272	72.72 276	77.03 304	52.73 293	74.14 287	75.65 274	50.36 316	72.17 292	85.37 248	55.42 277	80.67 291	52.86 293	87.59 261	84.77 259

PatchT		70.52 270	72.76 252	63.79 308	79.38 290	33.53 336	77.63 254	65.37 321	73.61 151	71.77 293	92.79 110	44.38 317	75.65 305	64.53 227	85.37 277	82.18 293

N_pmnet		70.20 271	68.80 279	74.38 269	80.91 274	84.81 35	59.12 328	76.45 269	55.06 287	75.31 278	82.36 283	55.74 274	54.82 334	47.02 314	87.24 263	83.52 274

tpmvs		70.16 272	69.56 275	71.96 283	74.71 322	48.13 316	79.63 224	75.45 276	65.02 237	70.26 299	81.88 286	45.34 311	85.68 261	58.34 263	75.39 320	82.08 294

new-patchmatchnet		70.10 273	73.37 246	60.29 316	81.23 271	16.95 340	59.54 326	74.62 278	62.93 244	80.97 228	87.93 208	62.83 237	71.90 311	55.24 281	95.01 139	92.00 152

YYNet1		70.06 274	70.44 269	68.90 291	73.76 324	53.42 290	58.99 329	67.20 315	58.42 270	87.10 137	85.39 247	59.82 251	67.32 321	59.79 257	83.50 291	85.96 246

MDA-MVSNet_test_wron		70.05 275	70.44 269	68.88 292	73.84 323	53.47 288	58.93 330	67.28 314	58.43 269	87.09 138	85.40 246	59.80 252	67.25 322	59.66 258	83.54 290	85.92 248

CostFormer		69.98 276	68.68 280	73.87 270	77.14 302	50.72 310	79.26 232	74.51 280	51.94 306	70.97 298	84.75 257	45.16 314	87.49 236	55.16 282	79.23 310	83.40 277

baseline2		69.77 277	66.89 286	78.41 234	79.51 288	58.09 258	76.23 273	69.57 311	57.50 277	64.82 322	77.45 313	46.02 300	88.44 227	53.08 291	77.83 314	88.70 218

PatchmatchNet		69.71 278	68.83 278	72.33 281	77.66 300	53.60 287	79.29 231	69.99 310	57.66 275	72.53 290	82.93 276	46.45 297	80.08 294	60.91 251	72.09 325	83.31 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 279	66.64 290	77.70 245	73.19 326	71.24 141	75.67 277	65.56 320	70.42 187	65.18 318	92.97 102	33.64 337	83.06 281	53.52 290	69.61 331	78.79 313

UnsupCasMVSNet_bld		69.21 280	69.68 274	67.82 297	79.42 289	51.15 306	67.82 314	75.79 271	54.15 291	77.47 260	85.36 249	59.26 255	70.64 313	48.46 309	79.35 309	81.66 299

gg-mvs-nofinetune		68.96 281	69.11 276	68.52 296	76.12 311	45.32 323	83.59 154	55.88 334	86.68 22	64.62 323	97.01 8	30.36 340	83.97 278	44.78 320	82.94 294	76.26 316

tpm2		68.45 282	66.83 287	73.30 273	78.93 296	48.50 315	79.76 223	71.76 302	47.50 319	69.92 301	83.60 267	42.07 323	88.40 228	48.44 310	79.51 307	83.01 284

tpm		67.95 283	68.08 283	67.55 298	78.74 297	43.53 328	75.60 278	67.10 318	54.92 288	72.23 291	88.10 204	42.87 321	75.97 303	52.21 294	80.95 306	83.15 282

PatchFormer-LS_test		67.91 284	66.49 291	72.17 282	75.29 317	51.85 301	75.68 276	73.62 289	57.23 280	68.64 303	68.13 330	42.19 322	82.76 283	64.06 228	73.51 323	81.89 297

WTY-MVS		67.91 284	68.35 281	66.58 301	80.82 277	48.12 317	65.96 318	72.60 295	53.67 294	71.20 296	81.68 289	58.97 256	69.06 318	48.57 308	81.67 300	82.55 287

test-LLR		67.21 286	66.74 288	68.63 294	76.45 309	55.21 278	67.89 311	67.14 316	62.43 250	65.08 319	72.39 323	43.41 318	69.37 314	61.00 249	84.89 282	81.31 303

sss		66.92 287	67.26 285	65.90 302	77.23 301	51.10 308	64.79 319	71.72 304	52.12 305	70.13 300	80.18 298	57.96 262	65.36 329	50.21 301	81.01 305	81.25 305

tpm cat1		66.76 288	65.21 293	71.42 284	77.09 303	50.62 311	78.01 248	73.68 288	44.89 325	68.64 303	79.00 304	45.51 308	82.42 285	49.91 303	70.15 328	81.23 307

DWT-MVSNet_test		66.43 289	64.37 294	72.63 277	74.86 321	50.86 309	76.52 268	72.74 294	54.06 292	65.50 316	68.30 329	32.13 338	84.84 269	61.63 246	73.59 322	82.19 292

PVSNet	58.17 21	66.41 290	65.63 292	68.75 293	81.96 262	49.88 314	62.19 324	72.51 297	51.03 310	68.04 307	75.34 321	50.84 286	74.77 306	45.82 319	82.96 293	81.60 300

tpmrst		66.28 291	66.69 289	65.05 306	72.82 330	39.33 331	78.20 247	70.69 308	53.16 297	67.88 308	80.36 297	48.18 292	74.75 307	58.13 265	70.79 327	81.08 308

Patchmatch-test		65.91 292	67.38 284	61.48 314	75.51 314	43.21 329	68.84 308	63.79 323	62.48 248	72.80 289	83.42 271	44.89 315	59.52 333	48.27 311	86.45 268	81.70 298

ADS-MVSNet2		65.87 293	63.64 297	72.55 279	73.16 327	56.92 267	67.10 315	74.81 277	49.74 317	66.04 314	82.97 274	46.71 295	77.26 299	42.29 323	69.96 329	83.46 275

test-mter		65.00 294	63.79 296	68.63 294	76.45 309	55.21 278	67.89 311	67.14 316	50.98 311	65.08 319	72.39 323	28.27 342	69.37 314	61.00 249	84.89 282	81.31 303

test0.0.03 1		64.66 295	64.36 295	65.57 304	75.03 320	46.89 320	64.69 320	61.58 328	62.43 250	71.18 297	77.54 311	43.41 318	68.47 319	40.75 327	82.65 297	81.35 302

pmmvs3		62.47 296	60.02 307	69.80 287	71.58 333	64.00 196	70.52 303	58.44 332	39.77 331	66.05 313	75.84 319	27.10 344	72.28 309	46.15 317	84.77 286	73.11 320

EPMVS		62.47 296	62.63 299	62.01 310	70.63 334	38.74 332	74.76 284	52.86 336	53.91 293	67.71 310	80.01 299	39.40 327	66.60 325	55.54 279	68.81 332	80.68 312

ADS-MVSNet		61.90 298	62.19 300	61.03 315	73.16 327	36.42 334	67.10 315	61.75 326	49.74 317	66.04 314	82.97 274	46.71 295	63.21 331	42.29 323	69.96 329	83.46 275

PMMVS		61.65 299	60.38 304	65.47 305	65.40 337	69.26 156	63.97 322	61.73 327	36.80 334	60.11 329	68.43 327	59.42 253	66.35 326	48.97 307	78.57 313	60.81 330

E-PMN		61.59 300	61.62 301	61.49 313	66.81 335	55.40 276	53.77 332	60.34 329	66.80 221	58.90 332	65.50 331	40.48 326	66.12 327	55.72 276	86.25 271	62.95 329

TESTMET0.1,1		61.29 301	60.32 305	64.19 307	72.06 331	51.30 304	67.89 311	62.09 324	45.27 324	60.65 328	69.01 326	27.93 343	64.74 330	56.31 273	81.65 302	76.53 315

MVS-HIRNet		61.16 302	62.92 298	55.87 319	79.09 293	35.34 335	71.83 298	57.98 333	46.56 321	59.05 331	91.14 150	49.95 289	76.43 301	38.74 329	71.92 326	55.84 334

EMVS		61.10 303	60.81 303	61.99 311	65.96 336	55.86 273	53.10 333	58.97 331	67.06 218	56.89 335	63.33 332	40.98 324	67.03 323	54.79 284	86.18 272	63.08 328

DSMNet-mixed		60.98 304	61.61 302	59.09 318	72.88 329	45.05 325	74.70 285	46.61 339	26.20 335	65.34 317	90.32 173	55.46 276	63.12 332	41.72 325	81.30 304	69.09 326

dp		60.70 305	60.29 306	61.92 312	72.04 332	38.67 333	70.83 301	64.08 322	51.28 309	60.75 327	77.28 314	36.59 334	71.58 312	47.41 313	62.34 334	75.52 317

CHOSEN 280x420		59.08 306	56.52 310	66.76 300	76.51 307	64.39 192	49.62 334	59.00 330	43.86 327	55.66 336	68.41 328	35.55 335	68.21 320	43.25 322	76.78 319	67.69 327

PVSNet_0	51.08 22	56.10 307	54.97 311	59.48 317	75.12 319	53.28 291	55.16 331	61.89 325	44.30 326	59.16 330	62.48 333	54.22 280	65.91 328	35.40 332	47.01 335	59.25 332

new_pmnet		55.69 308	57.66 309	49.76 321	75.47 315	30.59 337	59.56 325	51.45 337	43.62 328	62.49 325	75.48 320	40.96 325	49.15 337	37.39 331	72.52 324	69.55 325

PMMVS2		55.64 309	59.27 308	44.74 322	64.30 338	12.32 341	40.60 335	49.79 338	53.19 296	65.06 321	84.81 256	53.60 282	49.76 336	32.68 335	89.41 237	72.15 321

MVE	40.22 23	51.82 310	50.47 312	55.87 319	62.66 339	51.91 299	31.61 337	39.28 340	40.65 330	50.76 337	74.98 322	56.24 273	44.67 338	33.94 334	64.11 333	71.04 324
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		20.81 311	27.75 313	0.00 327	0.00 344	0.00 345	0.00 338	85.44 212	0.00 340	0.00 342	82.82 278	81.46 99	0.00 343	0.00 340	0.00 340	0.00 339

tmp_tt		20.25 312	24.50 314	7.49 324	4.47 341	8.70 342	34.17 336	25.16 342	1.00 337	32.43 339	18.49 336	39.37 328	9.21 340	21.64 336	43.75 336	4.57 336

ab-mvs-re		6.65 313	8.87 315	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	79.80 301	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

pcd_1.5k_mvsjas		6.41 314	8.55 316	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	76.94 142	0.00 343	0.00 340	0.00 340	0.00 339

test123		6.27 315	8.08 317	0.84 325	1.11 343	0.57 343	62.90 323	0.82 344	0.54 338	1.07 341	2.75 341	1.26 345	0.30 341	1.04 338	1.26 339	1.66 337

testmvs		5.91 316	7.65 318	0.72 326	1.20 342	0.37 344	59.14 327	0.67 345	0.49 339	1.11 340	2.76 340	0.94 346	0.24 342	1.02 339	1.47 338	1.55 338

uanet_test		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet-low-res		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uncertanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

Regformer		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

9.14			89.29 56		91.84 103		88.80 74	95.32 7	75.14 136	91.07 67	92.89 105	87.27 38	93.78 91	83.69 56	97.55 61

save filter2										89.01 106	92.98 100	79.84 116	92.00 147	83.25 59	96.93 77	88.98 216

save fliter					93.75 54	77.44 87	86.31 109	89.72 154	70.80 184

test_0728_THIRD									85.33 27	93.75 27	94.65 53	87.44 37	95.78 20	87.41 18	98.21 29

High-resolution multi-view benchmark