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 46	99.27 1	99.54 1

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 21	93.73 60	85.72 28	96.79 1	95.51 5	88.86 12	95.63 7	96.99 8	84.81 64	93.16 128	91.10 1	97.53 66	96.58 28
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
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 40	96.29 16	88.16 31	94.17 86	86.07 37	98.48 17	97.22 17

abl_6		93.02 4	93.16 4	92.60 4	94.73 42	88.99 6	93.26 10	94.19 26	89.11 10	94.43 15	95.27 35	91.86 3	95.09 55	87.54 18	98.02 37	93.71 107

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 15	85.81 27	92.99 11	94.23 22	85.21 28	92.51 47	95.13 39	90.65 10	95.34 45	88.06 9	98.15 32	95.95 39

SR-MVS		92.23 6	92.34 6	91.91 15	94.89 38	87.85 8	92.51 20	93.87 41	88.20 17	93.24 34	94.02 79	90.15 16	95.67 28	86.82 26	97.34 72	92.19 161

APD-MVS_3200maxsize		92.05 8	92.24 7	91.48 22	93.02 75	85.17 31	92.47 22	95.05 12	87.65 20	93.21 35	94.39 65	90.09 17	95.08 56	86.67 27	97.60 63	94.18 87

HPM-MVS		92.13 7	92.20 8	91.91 15	95.58 25	84.67 38	93.51 6	94.85 14	82.88 51	91.77 61	93.94 88	90.55 12	95.73 26	88.50 7	98.23 28	95.33 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	83.01 3	91.97 9	91.95 9	92.04 10	93.68 61	86.15 18	93.37 8	95.10 11	90.28 8	92.11 53	95.03 41	89.75 20	94.93 60	79.95 98	98.27 26	95.04 60
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APDe-MVS		91.22 22	91.92 10	89.14 62	92.97 77	78.04 83	92.84 12	94.14 28	83.33 45	93.90 24	95.73 25	88.77 23	96.41 1	87.60 16	97.98 41	92.98 131

PS-CasMVS		90.06 40	91.92 10	84.47 139	96.56 7	58.83 266	89.04 71	92.74 87	91.40 4	96.12 3	96.06 22	87.23 43	95.57 31	79.42 106	98.74 5	99.00 2

DTE-MVSNet		89.98 44	91.91 12	84.21 145	96.51 8	57.84 271	88.93 74	92.84 84	91.92 2	96.16 2	96.23 18	86.95 46	95.99 7	79.05 108	98.57 14	98.80 6

PEN-MVS		90.03 42	91.88 13	84.48 138	96.57 6	58.88 263	88.95 72	93.19 68	91.62 3	96.01 5	96.16 20	87.02 45	95.60 30	78.69 111	98.72 8	98.97 3

ACMMP		91.91 10	91.87 14	92.03 11	95.53 26	85.91 22	93.35 9	94.16 27	82.52 55	92.39 50	94.14 74	89.15 22	95.62 29	87.35 20	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
LPG-MVS_test		91.47 17	91.68 15	90.82 36	94.75 40	81.69 53	90.00 48	94.27 19	82.35 56	93.67 31	94.82 47	91.18 5	95.52 36	85.36 44	98.73 6	95.23 56

SED-MVS		90.46 35	91.64 16	86.93 89	94.18 47	72.65 127	90.47 43	93.69 46	83.77 38	94.11 22	94.27 67	90.28 14	95.84 18	86.03 38	97.92 45	92.29 155

MTAPA		91.52 14	91.60 17	91.29 27	96.59 4	86.29 14	92.02 26	91.81 109	84.07 34	92.00 56	94.40 63	86.63 50	95.28 48	88.59 5	98.31 23	92.30 153

CP-MVS		91.67 12	91.58 18	91.96 12	95.29 31	87.62 9	93.38 7	93.36 56	83.16 47	91.06 73	94.00 80	88.26 28	95.71 27	87.28 23	98.39 20	92.55 146

UA-Net		91.49 15	91.53 19	91.39 24	94.98 35	82.95 51	93.52 5	92.79 85	88.22 16	88.53 121	97.64 2	83.45 76	94.55 75	86.02 40	98.60 12	96.67 25

ACMH+	77.89 11	90.73 28	91.50 20	88.44 73	93.00 76	76.26 109	89.65 59	95.55 4	87.72 19	93.89 26	94.94 43	91.62 4	93.44 118	78.35 114	98.76 3	95.61 46

mPP-MVS		91.69 11	91.47 21	92.37 5	96.04 12	88.48 7	92.72 14	92.60 90	83.09 48	91.54 64	94.25 71	87.67 39	95.51 39	87.21 24	98.11 33	93.12 127

HFP-MVS		91.30 19	91.39 22	91.02 31	95.43 28	84.66 39	92.58 18	93.29 64	81.99 59	91.47 65	93.96 84	88.35 26	95.56 32	87.74 11	97.74 55	92.85 135

XVS		91.54 13	91.36 23	92.08 8	95.64 23	86.25 16	92.64 15	93.33 58	85.07 29	89.99 88	94.03 78	86.57 52	95.80 20	87.35 20	97.62 61	94.20 85

SteuartSystems-ACMMP		91.16 24	91.36 23	90.55 40	93.91 56	80.97 60	91.49 32	93.48 54	82.82 52	92.60 46	93.97 81	88.19 29	96.29 4	87.61 15	98.20 31	94.39 80
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		91.49 15	91.35 25	91.92 14	95.74 19	85.88 24	92.58 18	93.25 66	81.99 59	91.40 67	94.17 73	87.51 40	95.87 14	87.74 11	97.76 53	93.99 93

ZNCC-MVS		91.26 21	91.34 26	91.01 33	95.73 20	83.05 49	92.18 24	94.22 23	80.14 81	91.29 70	93.97 81	87.93 36	95.87 14	88.65 4	97.96 44	94.12 90

MSP-MVS		90.06 40	91.32 27	86.29 102	94.16 50	72.56 132	90.54 40	91.01 129	83.61 41	93.75 28	94.65 52	89.76 18	95.78 23	86.42 28	97.97 42	90.55 201

WR-MVS_H		89.91 47	91.31 28	85.71 118	96.32 10	62.39 225	89.54 63	93.31 61	90.21 9	95.57 8	95.66 27	81.42 107	95.90 12	80.94 88	98.80 2	98.84 5

region2R		91.44 18	91.30 29	91.87 17	95.75 18	85.90 23	92.63 17	93.30 63	81.91 61	90.88 78	94.21 72	87.75 37	95.87 14	87.60 16	97.71 57	93.83 100

zzz-MVS		91.27 20	91.26 30	91.29 27	96.59 4	86.29 14	88.94 73	91.81 109	84.07 34	92.00 56	94.40 63	86.63 50	95.28 48	88.59 5	98.31 23	92.30 153

ACMH	76.49 14	89.34 57	91.14 31	83.96 151	92.50 89	70.36 157	89.55 61	93.84 42	81.89 62	94.70 12	95.44 32	90.69 9	88.31 242	83.33 65	98.30 25	93.20 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPE-MVS		90.53 33	91.08 32	88.88 64	93.38 67	78.65 79	89.15 70	94.05 31	84.68 32	93.90 24	94.11 76	88.13 32	96.30 3	84.51 54	97.81 51	91.70 176

MP-MVS-pluss		90.81 27	91.08 32	89.99 49	95.97 13	79.88 65	88.13 86	94.51 17	75.79 132	92.94 37	94.96 42	88.36 25	95.01 58	90.70 2	98.40 19	95.09 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 29	91.07 34	89.42 58	95.93 15	79.54 70	89.95 51	93.68 48	77.65 109	91.97 58	94.89 44	88.38 24	95.45 41	89.27 3	97.87 49	93.27 121

GST-MVS		90.96 26	91.01 35	90.82 36	95.45 27	82.73 52	91.75 30	93.74 44	80.98 72	91.38 68	93.80 91	87.20 44	95.80 20	87.10 25	97.69 58	93.93 96

ACMM	79.39 9	90.65 29	90.99 36	89.63 54	95.03 34	83.53 44	89.62 60	93.35 57	79.20 92	93.83 27	93.60 97	90.81 8	92.96 134	85.02 48	98.45 18	92.41 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 38	90.96 37	87.65 84	91.95 105	71.06 152	89.99 50	93.05 73	86.53 23	94.29 18	96.27 17	82.69 83	94.08 90	86.25 34	97.63 60	97.82 8

PGM-MVS		91.20 23	90.95 38	91.93 13	95.67 22	85.85 25	90.00 48	93.90 38	80.32 78	91.74 62	94.41 62	88.17 30	95.98 8	86.37 30	97.99 39	93.96 95

MP-MVS		91.14 25	90.91 39	91.83 19	96.18 11	86.88 11	92.20 23	93.03 76	82.59 54	88.52 122	94.37 66	86.74 49	95.41 43	86.32 31	98.21 29	93.19 125
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVSNet		89.27 58	90.91 39	84.37 140	96.34 9	58.61 268	88.66 81	92.06 101	90.78 5	95.67 6	95.17 38	81.80 103	95.54 35	79.00 109	98.69 9	98.95 4

SF-MVS		90.27 37	90.80 41	88.68 69	92.86 81	77.09 98	91.19 35	95.74 3	81.38 67	92.28 51	93.80 91	86.89 47	94.64 69	85.52 42	97.51 67	94.30 83

UniMVSNet_ETH3D		89.12 61	90.72 42	84.31 143	97.00 2	64.33 203	89.67 58	88.38 184	88.84 13	94.29 18	97.57 3	90.48 13	91.26 178	72.57 176	97.65 59	97.34 14

PMVS	80.48 6	90.08 39	90.66 43	88.34 75	96.71 3	92.97 1	90.31 45	89.57 168	88.51 15	90.11 85	95.12 40	90.98 7	88.92 232	77.55 125	97.07 79	83.13 295
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	79.16 10	90.54 32	90.60 44	90.35 44	94.36 44	80.98 59	89.16 69	94.05 31	79.03 95	92.87 39	93.74 95	90.60 11	95.21 52	82.87 70	98.76 3	94.87 61
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 36	90.48 45	89.83 50	95.31 30	79.52 71	90.98 36	93.24 67	75.37 139	92.84 41	95.28 34	85.58 61	96.09 6	87.92 10	97.76 53	93.88 98

LS3D		90.60 31	90.34 46	91.38 25	89.03 167	84.23 42	93.58 4	94.68 15	90.65 6	90.33 84	93.95 87	84.50 66	95.37 44	80.87 89	95.50 130	94.53 74

#test#		90.49 34	90.31 47	91.02 31	95.43 28	84.66 39	90.65 38	93.29 64	77.00 116	91.47 65	93.96 84	88.35 26	95.56 32	84.88 49	97.74 55	92.85 135

OPM-MVS		89.80 48	89.97 48	89.27 60	94.76 39	79.86 66	86.76 107	92.78 86	78.78 98	92.51 47	93.64 96	88.13 32	93.84 99	84.83 51	97.55 64	94.10 91

SD-MVS		88.96 64	89.88 49	86.22 105	91.63 114	77.07 99	89.82 54	93.77 43	78.90 96	92.88 38	92.29 129	86.11 58	90.22 210	86.24 35	97.24 75	91.36 185
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
XVG-ACMP-BASELINE		89.98 44	89.84 50	90.41 42	94.91 37	84.50 41	89.49 65	93.98 33	79.68 85	92.09 54	93.89 89	83.80 72	93.10 132	82.67 72	98.04 34	93.64 111

OurMVSNet-221017-0		90.01 43	89.74 51	90.83 35	93.16 73	80.37 62	91.91 29	93.11 70	81.10 70	95.32 9	97.24 5	72.94 192	94.85 63	85.07 46	97.78 52	97.26 15

3Dnovator+	83.92 2	89.97 46	89.66 52	90.92 34	91.27 127	81.66 56	91.25 33	94.13 29	88.89 11	88.83 116	94.26 70	77.55 141	95.86 17	84.88 49	95.87 118	95.24 55

APD-MVS		89.54 53	89.63 53	89.26 61	92.57 86	81.34 58	90.19 46	93.08 72	80.87 73	91.13 71	93.19 100	86.22 57	95.97 9	82.23 76	97.18 77	90.45 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 69	89.62 54	84.73 133	90.46 147	65.27 194	88.86 75	93.02 77	87.15 21	93.05 36	97.10 6	82.28 91	92.02 157	76.70 134	97.99 39	96.88 23

test_0402		88.65 67	89.58 55	85.88 114	92.55 87	72.22 140	84.01 149	89.44 170	88.63 14	94.38 17	95.77 24	86.38 56	93.59 110	79.84 99	95.21 139	91.82 173

testtj		89.51 54	89.48 56	89.59 56	92.26 96	80.80 61	90.14 47	93.54 52	83.37 44	90.57 82	92.55 121	84.99 63	96.15 5	81.26 83	96.61 92	91.83 172

XVG-OURS-SEG-HR		89.59 52	89.37 57	90.28 45	94.47 43	85.95 21	86.84 103	93.91 37	80.07 82	86.75 151	93.26 99	93.64 2	90.93 188	84.60 53	90.75 237	93.97 94

9.14			89.29 58		91.84 111		88.80 77	95.32 8	75.14 141	91.07 72	92.89 109	87.27 42	93.78 101	83.69 62	97.55 64

mvs_tets		89.78 49	89.27 59	91.30 26	93.51 63	84.79 36	89.89 53	90.63 137	70.00 201	94.55 14	96.67 11	87.94 35	93.59 110	84.27 56	95.97 114	95.52 47

xxxxxxxxxxxxxcwj		89.04 63	89.13 60	88.79 66	93.75 58	77.44 91	86.31 115	95.27 9	70.80 191	92.28 51	93.80 91	86.89 47	94.64 69	85.52 42	97.51 67	94.30 83

DeepC-MVS	82.31 4	89.15 60	89.08 61	89.37 59	93.64 62	79.07 74	88.54 82	94.20 24	73.53 157	89.71 97	94.82 47	85.09 62	95.77 25	84.17 57	98.03 36	93.26 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_djsdf		89.62 51	89.01 62	91.45 23	92.36 92	82.98 50	91.98 27	90.08 158	71.54 186	94.28 20	96.54 13	81.57 105	94.27 77	86.26 32	96.49 97	97.09 19

DP-MVS		88.60 68	89.01 62	87.36 87	91.30 125	77.50 90	87.55 92	92.97 79	87.95 18	89.62 101	92.87 110	84.56 65	93.89 96	77.65 123	96.62 91	90.70 196

CPTT-MVS		89.39 56	88.98 64	90.63 39	95.09 33	86.95 10	92.09 25	92.30 96	79.74 84	87.50 137	92.38 124	81.42 107	93.28 123	83.07 67	97.24 75	91.67 177

ETH3D-3000-0.1		88.85 66	88.96 65	88.52 70	91.94 107	77.27 97	88.71 79	95.26 10	76.08 123	90.66 81	92.69 116	84.48 67	93.83 100	83.38 64	97.48 69	94.47 75

anonymousdsp		89.73 50	88.88 66	92.27 7	89.82 158	86.67 12	90.51 42	90.20 155	69.87 202	95.06 10	96.14 21	84.28 68	93.07 133	87.68 13	96.34 102	97.09 19

XVG-OURS		89.18 59	88.83 67	90.23 46	94.28 45	86.11 20	85.91 118	93.60 51	80.16 80	89.13 112	93.44 98	83.82 71	90.98 186	83.86 60	95.30 138	93.60 113

jajsoiax		89.41 55	88.81 68	91.19 30	93.38 67	84.72 37	89.70 55	90.29 152	69.27 205	94.39 16	96.38 15	86.02 59	93.52 114	83.96 58	95.92 116	95.34 50

TranMVSNet+NR-MVSNet		87.86 75	88.76 69	85.18 126	94.02 53	64.13 204	84.38 144	91.29 121	84.88 31	92.06 55	93.84 90	86.45 54	93.73 102	73.22 166	98.66 10	97.69 9

nrg030		87.85 76	88.49 70	85.91 112	90.07 154	69.73 160	87.86 89	94.20 24	74.04 151	92.70 45	94.66 51	85.88 60	91.50 169	79.72 100	97.32 73	96.50 29

HPM-MVS++		88.93 65	88.45 71	90.38 43	94.92 36	85.85 25	89.70 55	91.27 122	78.20 105	86.69 152	92.28 130	80.36 118	95.06 57	86.17 36	96.49 97	90.22 206

DVP-MVS		89.08 62	88.16 72	91.83 19	95.76 17	86.14 19	92.75 13	93.90 38	78.43 103	89.16 111	92.25 131	72.03 204	96.36 2	88.21 8	90.93 232	92.98 131

pmmvs6		86.52 90	88.06 73	81.90 191	92.22 99	62.28 228	84.66 136	89.15 173	83.54 43	89.85 93	97.32 4	88.08 34	86.80 258	70.43 193	97.30 74	96.62 26

PS-MVSNAJss		88.31 70	87.90 74	89.56 57	93.31 69	77.96 84	87.94 88	91.97 104	70.73 193	94.19 21	96.67 11	76.94 150	94.57 73	83.07 67	96.28 104	96.15 31

TSAR-MVS + MP.		88.14 72	87.82 75	89.09 63	95.72 21	76.74 103	92.49 21	91.19 125	67.85 221	86.63 153	94.84 46	79.58 125	95.96 10	87.62 14	94.50 161	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
CNVR-MVS		87.81 78	87.68 76	88.21 77	92.87 79	77.30 96	85.25 128	91.23 123	77.31 113	87.07 145	91.47 150	82.94 81	94.71 66	84.67 52	96.27 106	92.62 145

ETH3D cwj APD-0.16		87.83 77	87.62 77	88.47 72	91.21 128	78.20 81	87.26 96	94.54 16	72.05 182	88.89 113	92.31 128	83.86 70	94.24 80	81.59 82	96.87 84	92.97 134

OMC-MVS		88.19 71	87.52 78	90.19 47	91.94 107	81.68 55	87.49 94	93.17 69	76.02 126	88.64 119	91.22 153	84.24 69	93.37 121	77.97 121	97.03 80	95.52 47

SixPastTwentyTwo		87.20 82	87.45 79	86.45 98	92.52 88	69.19 169	87.84 90	88.05 190	81.66 64	94.64 13	96.53 14	65.94 230	94.75 65	83.02 69	96.83 87	95.41 49

HQP_MVS		87.75 79	87.43 80	88.70 68	93.45 64	76.42 107	89.45 66	93.61 49	79.44 89	86.55 154	92.95 107	74.84 168	95.22 50	80.78 91	95.83 119	94.46 76

AllTest		87.97 74	87.40 81	89.68 52	91.59 115	83.40 45	89.50 64	95.44 6	79.47 87	88.00 131	93.03 103	82.66 84	91.47 170	70.81 185	96.14 110	94.16 88

Anonymous20240529		86.20 97	87.13 82	83.42 164	90.19 151	64.55 201	84.55 139	90.71 134	85.85 26	89.94 91	95.24 37	82.13 93	90.40 205	69.19 204	96.40 100	95.31 52

v10		86.54 89	87.10 83	84.84 130	88.16 184	63.28 213	86.64 110	92.20 98	75.42 138	92.81 43	94.50 57	74.05 177	94.06 91	83.88 59	96.28 104	97.17 18

UniMVSNet_NR-MVSNet		86.84 85	87.06 84	86.17 109	92.86 81	67.02 182	82.55 190	91.56 113	83.08 49	90.92 75	91.82 141	78.25 134	93.99 92	74.16 155	98.35 21	97.49 13

FC-MVSNet-test		85.93 102	87.05 85	82.58 182	92.25 97	56.44 282	85.75 121	93.09 71	77.33 112	91.94 59	94.65 52	74.78 170	93.41 120	75.11 150	98.58 13	97.88 7

DU-MVS		86.80 86	86.99 86	86.21 107	93.24 71	67.02 182	83.16 177	92.21 97	81.73 63	90.92 75	91.97 135	77.20 144	93.99 92	74.16 155	98.35 21	97.61 10

UniMVSNet (Re)		86.87 83	86.98 87	86.55 96	93.11 74	68.48 173	83.80 157	92.87 81	80.37 76	89.61 103	91.81 142	77.72 138	94.18 84	75.00 151	98.53 15	96.99 22

RPSCF		88.00 73	86.93 88	91.22 29	90.08 153	89.30 5	89.68 57	91.11 126	79.26 91	89.68 98	94.81 50	82.44 86	87.74 246	76.54 136	88.74 259	96.61 27

NCCC		87.36 80	86.87 89	88.83 65	92.32 95	78.84 77	86.58 111	91.09 127	78.77 99	84.85 184	90.89 167	80.85 112	95.29 46	81.14 85	95.32 135	92.34 151

v8		86.22 96	86.83 90	84.36 141	87.82 189	62.35 227	86.42 113	91.33 120	76.78 118	92.73 44	94.48 59	73.41 186	93.72 103	83.10 66	95.41 131	97.01 21

IS-MVSNet		86.66 88	86.82 91	86.17 109	92.05 103	66.87 184	91.21 34	88.64 180	86.30 25	89.60 104	92.59 118	69.22 215	94.91 61	73.89 159	97.89 48	96.72 24

Vis-MVSNet		86.86 84	86.58 92	87.72 82	92.09 101	77.43 93	87.35 95	92.09 100	78.87 97	84.27 199	94.05 77	78.35 133	93.65 104	80.54 95	91.58 220	92.08 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		86.26 94	86.47 93	85.60 120	90.87 137	74.26 118	87.98 87	91.85 107	80.35 77	89.54 107	88.01 214	79.09 127	92.13 152	75.51 144	95.06 146	90.41 204

Gipuma		84.44 127	86.33 94	78.78 237	84.20 251	73.57 121	89.55 61	90.44 141	84.24 33	84.38 192	94.89 44	76.35 159	80.40 303	76.14 139	96.80 89	82.36 303
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_prior3		86.31 93	86.31 95	86.32 100	90.59 144	71.99 142	83.37 169	92.85 82	75.43 136	84.58 188	91.57 146	81.92 101	94.17 86	79.54 103	96.97 81	92.80 137

FIs		85.35 108	86.27 96	82.60 181	91.86 110	57.31 275	85.10 130	93.05 73	75.83 131	91.02 74	93.97 81	73.57 182	92.91 138	73.97 158	98.02 37	97.58 12

NR-MVSNet		86.00 99	86.22 97	85.34 124	93.24 71	64.56 200	82.21 200	90.46 140	80.99 71	88.42 124	91.97 135	77.56 140	93.85 97	72.46 177	98.65 11	97.61 10

DeepPCF-MVS	81.24 5	87.28 81	86.21 98	90.49 41	91.48 122	84.90 34	83.41 168	92.38 95	70.25 199	89.35 109	90.68 174	82.85 82	94.57 73	79.55 102	95.95 115	92.00 166

canonicalmvs		85.50 106	86.14 99	83.58 160	87.97 185	67.13 181	87.55 92	94.32 18	73.44 159	88.47 123	87.54 223	86.45 54	91.06 185	75.76 143	93.76 175	92.54 147

Regformer-2		86.74 87	86.08 100	88.73 67	84.18 252	79.20 73	83.52 163	89.33 171	83.33 45	89.92 92	85.07 263	83.23 79	93.16 128	83.39 63	92.72 200	93.83 100

MSLP-MVS++		85.00 117	86.03 101	81.90 191	91.84 111	71.56 150	86.75 108	93.02 77	75.95 129	87.12 141	89.39 194	77.98 135	89.40 228	77.46 126	94.78 154	84.75 272

baseline		85.20 110	85.93 102	83.02 171	86.30 222	62.37 226	84.55 139	93.96 34	74.48 148	87.12 141	92.03 134	82.30 89	91.94 159	78.39 112	94.21 167	94.74 66

Baseline_NR-MVSNet		84.00 141	85.90 103	78.29 247	91.47 123	53.44 301	82.29 196	87.00 212	79.06 94	89.55 105	95.72 26	77.20 144	86.14 268	72.30 178	98.51 16	95.28 53

casdiffmvs		85.21 109	85.85 104	83.31 166	86.17 227	62.77 219	83.03 179	93.93 35	74.69 145	88.21 129	92.68 117	82.29 90	91.89 162	77.87 122	93.75 177	95.27 54

PHI-MVS		86.38 92	85.81 105	88.08 78	88.44 178	77.34 94	89.35 68	93.05 73	73.15 168	84.76 185	87.70 220	78.87 129	94.18 84	80.67 93	96.29 103	92.73 140

TransMVSNet (Re)		84.02 140	85.74 106	78.85 236	91.00 135	55.20 292	82.29 196	87.26 200	79.65 86	88.38 126	95.52 31	83.00 80	86.88 256	67.97 215	96.60 93	94.45 78

Regformer-4		86.41 91	85.71 107	88.52 70	84.27 248	77.57 89	84.07 147	88.00 192	82.82 52	89.84 94	85.48 251	82.06 95	92.77 140	83.83 61	91.04 226	95.22 58

ANet_high		83.17 158	85.68 108	75.65 274	81.24 278	45.26 336	79.94 229	92.91 80	83.83 37	91.33 69	96.88 10	80.25 119	85.92 270	68.89 207	95.89 117	95.76 41

DeepC-MVS_fast	80.27 8	86.23 95	85.65 109	87.96 81	91.30 125	76.92 100	87.19 97	91.99 103	70.56 194	84.96 180	90.69 173	80.01 121	95.14 53	78.37 113	95.78 123	91.82 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 98	85.54 110	88.05 80	92.25 97	75.45 112	83.85 154	92.01 102	65.91 236	86.19 161	91.75 144	83.77 73	94.98 59	77.43 128	96.71 90	93.73 106

Regformer-1		86.00 99	85.50 111	87.49 85	84.18 252	76.90 101	83.52 163	87.94 194	82.18 58	89.19 110	85.07 263	82.28 91	91.89 162	82.40 74	92.72 200	93.69 108

FMVSNet1		84.55 124	85.45 112	81.85 193	90.27 150	61.05 239	86.83 104	88.27 187	78.57 102	89.66 100	95.64 28	75.43 162	90.68 198	69.09 205	95.33 134	93.82 102

VDDNet		84.35 130	85.39 113	81.25 201	95.13 32	59.32 256	85.42 127	81.11 258	86.41 24	87.41 138	96.21 19	73.61 181	90.61 201	66.33 224	96.85 85	93.81 105

train_agg		85.98 101	85.28 114	88.07 79	92.34 93	79.70 68	83.94 150	90.32 145	65.79 237	84.49 190	90.97 163	81.93 99	93.63 106	81.21 84	96.54 95	90.88 192

agg_prior1		85.72 104	85.20 115	87.28 88	91.58 118	77.69 87	83.69 160	90.30 149	66.29 233	84.32 194	91.07 160	82.13 93	93.18 126	81.02 86	96.36 101	90.98 188

LCM-MVSNet-Re		83.48 151	85.06 116	78.75 238	85.94 230	55.75 287	80.05 227	94.27 19	76.47 119	96.09 4	94.54 56	83.31 78	89.75 223	59.95 267	94.89 151	90.75 195

EPP-MVSNet		85.47 107	85.04 117	86.77 93	91.52 121	69.37 163	91.63 31	87.98 193	81.51 66	87.05 146	91.83 140	66.18 229	95.29 46	70.75 188	96.89 83	95.64 44

IterMVS-LS		84.73 120	84.98 118	83.96 151	87.35 199	63.66 208	83.25 173	89.88 162	76.06 124	89.62 101	92.37 127	73.40 188	92.52 145	78.16 117	94.77 156	95.69 42
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pm-mvs1		83.69 146	84.95 119	79.91 222	90.04 156	59.66 253	82.43 192	87.44 197	75.52 135	87.85 133	95.26 36	81.25 109	85.65 274	68.74 209	96.04 113	94.42 79

ETH3 D test6400		85.09 113	84.87 120	85.75 117	90.80 139	69.34 164	85.90 119	93.31 61	65.43 243	86.11 164	89.95 188	80.92 111	94.86 62	75.90 142	95.57 128	93.05 128

TAPA-MVS	77.73 12	85.71 105	84.83 121	88.37 74	88.78 172	79.72 67	87.15 99	93.50 53	69.17 206	85.80 171	89.56 193	80.76 113	92.13 152	73.21 171	95.51 129	93.25 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 152	84.73 122	79.69 227	90.29 149	57.52 274	81.30 214	88.69 179	76.29 120	87.58 136	94.44 60	80.60 115	87.20 251	66.60 223	96.82 88	94.34 82

K. test v3		85.14 111	84.73 122	86.37 99	91.13 133	69.63 162	85.45 126	76.68 280	84.06 36	92.44 49	96.99 8	62.03 247	94.65 68	80.58 94	93.24 185	94.83 65

v1144		84.54 126	84.72 124	84.00 149	87.67 193	62.55 223	82.97 181	90.93 130	70.32 198	89.80 95	90.99 162	73.50 183	93.48 116	81.69 81	94.65 159	95.97 37

3Dnovator	80.37 7	84.80 119	84.71 125	85.06 128	86.36 220	74.71 115	88.77 78	90.00 160	75.65 134	84.96 180	93.17 101	74.06 176	91.19 180	78.28 116	91.09 224	89.29 220

v1192		84.57 123	84.69 126	84.21 145	87.75 191	62.88 217	83.02 180	91.43 117	69.08 208	89.98 90	90.89 167	72.70 196	93.62 109	82.41 73	94.97 149	96.13 32

Regformer-3		85.06 114	84.67 127	86.22 105	84.27 248	73.43 122	84.07 147	85.26 229	80.77 74	88.62 120	85.48 251	80.56 116	90.39 206	81.99 78	91.04 226	94.85 63

testing_2		84.36 128	84.64 128	83.50 163	86.74 214	63.97 207	84.56 138	90.31 147	66.22 234	91.62 63	94.55 55	75.88 160	91.95 158	77.02 133	94.89 151	94.56 70

MIMVSNet1		83.63 148	84.59 129	80.74 211	94.06 52	62.77 219	82.72 186	84.53 241	77.57 111	90.34 83	95.92 23	76.88 156	85.83 272	61.88 253	97.42 70	93.62 112

VDD-MVS		84.23 135	84.58 130	83.20 168	91.17 132	65.16 196	83.25 173	84.97 238	79.79 83	87.18 140	94.27 67	74.77 171	90.89 191	69.24 201	96.54 95	93.55 117

EI-MVSNet-Vis-set		85.12 112	84.53 131	86.88 90	84.01 255	72.76 126	83.91 153	85.18 231	80.44 75	88.75 117	85.49 250	80.08 120	91.92 160	82.02 77	90.85 235	95.97 37

v1240		84.30 132	84.51 132	83.65 158	87.65 194	61.26 236	82.85 184	91.54 114	67.94 219	90.68 80	90.65 176	71.71 206	93.64 105	82.84 71	94.78 154	96.07 34

EI-MVSNet-UG-set		85.04 115	84.44 133	86.85 91	83.87 258	72.52 134	83.82 155	85.15 232	80.27 79	88.75 117	85.45 254	79.95 123	91.90 161	81.92 79	90.80 236	96.13 32

v144192		84.24 134	84.41 134	83.71 157	87.59 196	61.57 232	82.95 182	91.03 128	67.82 222	89.80 95	90.49 178	73.28 189	93.51 115	81.88 80	94.89 151	96.04 36

WR-MVS		83.56 149	84.40 135	81.06 206	93.43 66	54.88 293	78.67 250	85.02 236	81.24 68	90.74 79	91.56 148	72.85 193	91.08 184	68.00 214	98.04 34	97.23 16

v1921920		84.23 135	84.37 136	83.79 154	87.64 195	61.71 231	82.91 183	91.20 124	67.94 219	90.06 86	90.34 180	72.04 203	93.59 110	82.32 75	94.91 150	96.07 34

MVS_111021_HR		84.63 121	84.34 137	85.49 123	90.18 152	75.86 111	79.23 243	87.13 204	73.35 160	85.56 175	89.34 195	83.60 75	90.50 203	76.64 135	94.05 171	90.09 211

v2v482		84.09 137	84.24 138	83.62 159	87.13 204	61.40 233	82.71 187	89.71 164	72.19 181	89.55 105	91.41 151	70.70 211	93.20 125	81.02 86	93.76 175	96.25 30

EG-PatchMatch MVS		84.08 138	84.11 139	83.98 150	92.22 99	72.61 131	82.20 202	87.02 209	72.63 175	88.86 114	91.02 161	78.52 130	91.11 183	73.41 165	91.09 224	88.21 233

HQP-MVS		84.61 122	84.06 140	86.27 103	91.19 129	70.66 154	84.77 132	92.68 88	73.30 163	80.55 248	90.17 186	72.10 200	94.61 71	77.30 129	94.47 162	93.56 115

Effi-MVS+		83.90 144	84.01 141	83.57 161	87.22 202	65.61 193	86.55 112	92.40 93	78.64 101	81.34 238	84.18 274	83.65 74	92.93 136	74.22 154	87.87 269	92.17 162

alignmvs		83.94 143	83.98 142	83.80 153	87.80 190	67.88 178	84.54 141	91.42 119	73.27 166	88.41 125	87.96 215	72.33 199	90.83 193	76.02 141	94.11 169	92.69 142

MCST-MVS		84.36 128	83.93 143	85.63 119	91.59 115	71.58 149	83.52 163	92.13 99	61.82 263	83.96 201	89.75 192	79.93 124	93.46 117	78.33 115	94.34 165	91.87 171

ETV-MVS		84.31 131	83.91 144	85.52 121	88.58 174	70.40 156	84.50 143	93.37 55	78.76 100	84.07 200	78.72 318	80.39 117	95.13 54	73.82 161	92.98 193	91.04 187

MVS_111021_LR		84.28 133	83.76 145	85.83 116	89.23 164	83.07 48	80.99 218	83.56 243	72.71 174	86.07 165	89.07 202	81.75 104	86.19 267	77.11 131	93.36 180	88.24 232

AdaColmap		83.66 147	83.69 146	83.57 161	90.05 155	72.26 139	86.29 117	90.00 160	78.19 106	81.65 233	87.16 230	83.40 77	94.24 80	61.69 255	94.76 157	84.21 277

F-COLMAP		84.97 118	83.42 147	89.63 54	92.39 91	83.40 45	88.83 76	91.92 106	73.19 167	80.18 253	89.15 200	77.04 148	93.28 123	65.82 230	92.28 206	92.21 160

Effi-MVS+-dtu		85.82 103	83.38 148	93.14 3	87.13 204	91.15 2	87.70 91	88.42 182	74.57 146	83.56 207	85.65 248	78.49 131	94.21 82	72.04 179	92.88 195	94.05 92

V42		83.47 152	83.37 149	83.75 156	83.16 264	63.33 212	81.31 212	90.23 154	69.51 204	90.91 77	90.81 170	74.16 175	92.29 150	80.06 96	90.22 243	95.62 45

MVS_Test		82.47 165	83.22 150	80.22 219	82.62 268	57.75 273	82.54 191	91.96 105	71.16 189	82.89 215	92.52 123	77.41 142	90.50 203	80.04 97	87.84 270	92.40 150

DP-MVS Recon		84.05 139	83.22 150	86.52 97	91.73 113	75.27 113	83.23 175	92.40 93	72.04 183	82.04 226	88.33 210	77.91 137	93.95 95	66.17 225	95.12 144	90.34 205

PAPM_NR		83.23 156	83.19 152	83.33 165	90.90 136	65.98 190	88.19 85	90.78 133	78.13 107	80.87 243	87.92 218	73.49 185	92.42 146	70.07 195	88.40 260	91.60 180

CNLPA		83.55 150	83.10 153	84.90 129	89.34 162	83.87 43	84.54 141	88.77 177	79.09 93	83.54 208	88.66 208	74.87 167	81.73 298	66.84 221	92.29 205	89.11 222

CS-MVS		83.43 154	83.04 154	84.59 137	87.87 188	66.61 186	85.57 124	94.90 13	73.02 170	81.12 239	78.56 319	80.00 122	95.52 36	73.04 173	93.29 184	91.62 179

tfpnnormal		81.79 175	82.95 155	78.31 246	88.93 170	55.40 288	80.83 221	82.85 247	76.81 117	85.90 170	94.14 74	74.58 174	86.51 262	66.82 222	95.68 127	93.01 130

CANet		83.79 145	82.85 156	86.63 94	86.17 227	72.21 141	83.76 158	91.43 117	77.24 114	74.39 294	87.45 225	75.36 163	95.42 42	77.03 132	92.83 196	92.25 159

X-MVStestdata		85.04 115	82.70 157	92.08 8	95.64 23	86.25 16	92.64 15	93.33 58	85.07 29	89.99 88	16.05 348	86.57 52	95.80 20	87.35 20	97.62 61	94.20 85

TSAR-MVS + GP.		83.95 142	82.69 158	87.72 82	89.27 163	81.45 57	83.72 159	81.58 257	74.73 144	85.66 172	86.06 245	72.56 198	92.69 142	75.44 146	95.21 139	89.01 228

CLD-MVS		83.18 157	82.64 159	84.79 131	89.05 166	67.82 179	77.93 258	92.52 91	68.33 214	85.07 179	81.54 301	82.06 95	92.96 134	69.35 200	97.91 47	93.57 114
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 167	82.61 160	81.30 200	86.29 223	69.79 158	88.71 79	87.67 196	78.42 104	82.15 225	84.15 275	77.98 135	91.59 168	65.39 232	92.75 197	82.51 302

QAPM		82.59 163	82.59 161	82.58 182	86.44 215	66.69 185	89.94 52	90.36 144	67.97 218	84.94 182	92.58 120	72.71 195	92.18 151	70.63 191	87.73 271	88.85 229

114514_t		83.10 159	82.54 162	84.77 132	92.90 78	69.10 171	86.65 109	90.62 138	54.66 301	81.46 235	90.81 170	76.98 149	94.38 76	72.62 175	96.18 108	90.82 194

v148		82.31 166	82.48 163	81.81 196	85.59 232	59.66 253	81.47 210	86.02 220	72.85 172	88.05 130	90.65 176	70.73 210	90.91 190	75.15 149	91.79 215	94.87 61

EI-MVSNet		82.61 162	82.42 164	83.20 168	83.25 262	63.66 208	83.50 166	85.07 233	76.06 124	86.55 154	85.10 260	73.41 186	90.25 207	78.15 119	90.67 239	95.68 43

TinyColmap		81.25 180	82.34 165	77.99 252	85.33 235	60.68 245	82.32 195	88.33 185	71.26 188	86.97 148	92.22 133	77.10 147	86.98 255	62.37 248	95.17 141	86.31 256

mvs-test1		84.55 124	82.12 166	91.84 18	87.13 204	89.54 4	85.05 131	88.42 182	74.57 146	80.60 245	82.98 284	78.49 131	93.98 94	72.04 179	89.77 246	92.00 166

GBi-Net		82.02 172	82.07 167	81.85 193	86.38 217	61.05 239	86.83 104	88.27 187	72.43 176	86.00 166	95.64 28	63.78 239	90.68 198	65.95 226	93.34 181	93.82 102

test1		82.02 172	82.07 167	81.85 193	86.38 217	61.05 239	86.83 104	88.27 187	72.43 176	86.00 166	95.64 28	63.78 239	90.68 198	65.95 226	93.34 181	93.82 102

OpenMVS	76.72 13	81.98 174	82.00 169	81.93 190	84.42 244	68.22 175	88.50 83	89.48 169	66.92 228	81.80 232	91.86 137	72.59 197	90.16 212	71.19 184	91.25 223	87.40 245

LF4IMVS		82.75 161	81.93 170	85.19 125	82.08 269	80.15 64	85.53 125	88.76 178	68.01 216	85.58 174	87.75 219	71.80 205	86.85 257	74.02 157	93.87 174	88.58 231

VPNet		80.25 198	81.68 171	75.94 273	92.46 90	47.98 330	76.70 275	81.67 256	73.45 158	84.87 183	92.82 111	74.66 173	86.51 262	61.66 256	96.85 85	93.33 118

UGNet		82.78 160	81.64 172	86.21 107	86.20 226	76.24 110	86.86 102	85.68 223	77.07 115	73.76 297	92.82 111	69.64 212	91.82 165	69.04 206	93.69 178	90.56 200
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
FMVSNet2		81.31 179	81.61 173	80.41 216	86.38 217	58.75 267	83.93 152	86.58 215	72.43 176	87.65 135	92.98 105	63.78 239	90.22 210	66.86 219	93.92 173	92.27 157

cl_fuxian		81.64 176	81.59 174	81.79 197	80.86 284	59.15 260	78.61 251	90.18 156	68.36 213	87.20 139	87.11 232	69.39 213	91.62 167	78.16 117	94.43 164	94.60 69

MVSFormer		82.23 168	81.57 175	84.19 147	85.54 233	69.26 166	91.98 27	90.08 158	71.54 186	76.23 278	85.07 263	58.69 267	94.27 77	86.26 32	88.77 257	89.03 226

Fast-Effi-MVS+-dtu		82.54 164	81.41 176	85.90 113	85.60 231	76.53 106	83.07 178	89.62 167	73.02 170	79.11 261	83.51 279	80.74 114	90.24 209	68.76 208	89.29 250	90.94 190

DELS-MVS		81.44 178	81.25 177	82.03 189	84.27 248	62.87 218	76.47 280	92.49 92	70.97 190	81.64 234	83.83 276	75.03 165	92.70 141	74.29 153	92.22 209	90.51 202
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
EIA-MVS		82.19 169	81.23 178	85.10 127	87.95 186	69.17 170	83.22 176	93.33 58	70.42 195	78.58 264	79.77 315	77.29 143	94.20 83	71.51 182	88.96 255	91.93 170

Anonymous202405211		80.51 191	81.19 179	78.49 243	88.48 176	57.26 276	76.63 276	82.49 249	81.21 69	84.30 197	92.24 132	67.99 221	86.24 266	62.22 249	95.13 142	91.98 169

RRT_MVS		83.25 155	81.08 180	89.74 51	80.55 291	79.32 72	86.41 114	86.69 213	72.33 180	87.00 147	91.08 158	44.98 325	95.55 34	84.47 55	96.24 107	94.36 81

BH-untuned		80.96 183	80.99 181	80.84 210	88.55 175	68.23 174	80.33 225	88.46 181	72.79 173	86.55 154	86.76 235	74.72 172	91.77 166	61.79 254	88.99 254	82.52 301

MG-MVS		80.32 197	80.94 182	78.47 244	88.18 182	52.62 307	82.29 196	85.01 237	72.01 184	79.24 260	92.54 122	69.36 214	93.36 122	70.65 190	89.19 253	89.45 214

PCF-MVS	74.62 15	82.15 170	80.92 183	85.84 115	89.43 160	72.30 138	80.53 223	91.82 108	57.36 290	87.81 134	89.92 190	77.67 139	93.63 106	58.69 272	95.08 145	91.58 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.04 182	80.57 184	82.46 186	87.50 197	63.22 214	78.37 254	89.63 166	68.01 216	81.87 228	82.08 296	82.31 88	92.65 143	67.10 218	88.30 265	91.51 183

LFMVS		80.15 201	80.56 185	78.89 235	89.19 165	55.93 284	85.22 129	73.78 299	82.96 50	84.28 198	92.72 115	57.38 276	90.07 219	63.80 240	95.75 124	90.68 197

ab-mvs		79.67 204	80.56 185	76.99 260	88.48 176	56.93 278	84.70 135	86.06 219	68.95 210	80.78 244	93.08 102	75.30 164	84.62 283	56.78 281	90.90 233	89.43 216

PVSNet_Blended_VisFu		81.55 177	80.49 187	84.70 135	91.58 118	73.24 124	84.21 145	91.67 112	62.86 256	80.94 241	87.16 230	67.27 224	92.87 139	69.82 197	88.94 256	87.99 237

diffmvs		80.40 194	80.48 188	80.17 220	79.02 306	60.04 249	77.54 265	90.28 153	66.65 231	82.40 220	87.33 228	73.50 183	87.35 250	77.98 120	89.62 248	93.13 126

PLC	73.85 16	82.09 171	80.31 189	87.45 86	90.86 138	80.29 63	85.88 120	90.65 136	68.17 215	76.32 277	86.33 240	73.12 191	92.61 144	61.40 259	90.02 245	89.44 215
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 205	80.27 190	76.44 268	87.92 187	53.95 297	75.58 288	84.35 242	74.39 149	82.23 223	90.72 172	72.84 194	84.39 285	60.38 266	93.98 172	90.97 189

cl-mvsnet_		80.42 193	80.23 191	81.02 207	79.99 294	59.25 257	77.07 271	87.02 209	67.37 225	86.18 163	89.21 198	63.08 244	90.16 212	76.31 138	95.80 121	93.65 110

cl-mvsnet1		80.43 192	80.23 191	81.02 207	79.99 294	59.25 257	77.07 271	87.02 209	67.38 224	86.19 161	89.22 197	63.09 243	90.16 212	76.32 137	95.80 121	93.66 109

eth_miper_zixun_eth		80.84 185	80.22 193	82.71 179	81.41 276	60.98 242	77.81 260	90.14 157	67.31 226	86.95 149	87.24 229	64.26 235	92.31 148	75.23 148	91.61 218	94.85 63

BH-RMVSNet		80.53 190	80.22 193	81.49 199	87.19 203	66.21 189	77.79 261	86.23 217	74.21 150	83.69 203	88.50 209	73.25 190	90.75 195	63.18 245	87.90 268	87.52 243

xiu_mvs_v1_base_debu		80.84 185	80.14 195	82.93 174	88.31 179	71.73 145	79.53 234	87.17 201	65.43 243	79.59 255	82.73 291	76.94 150	90.14 215	73.22 166	88.33 261	86.90 251

xiu_mvs_v1_base		80.84 185	80.14 195	82.93 174	88.31 179	71.73 145	79.53 234	87.17 201	65.43 243	79.59 255	82.73 291	76.94 150	90.14 215	73.22 166	88.33 261	86.90 251

xiu_mvs_v1_base_debi		80.84 185	80.14 195	82.93 174	88.31 179	71.73 145	79.53 234	87.17 201	65.43 243	79.59 255	82.73 291	76.94 150	90.14 215	73.22 166	88.33 261	86.90 251

miper_ehance_all_eth		80.34 196	80.04 198	81.24 203	79.82 296	58.95 262	77.66 262	89.66 165	65.75 240	85.99 169	85.11 259	68.29 220	91.42 174	76.03 140	92.03 211	93.33 118

MSDG		80.06 203	79.99 199	80.25 218	83.91 257	68.04 177	77.51 266	89.19 172	77.65 109	81.94 227	83.45 281	76.37 158	86.31 265	63.31 244	86.59 279	86.41 254

1121		80.86 184	79.81 200	84.02 148	93.93 55	78.70 78	81.64 207	80.18 264	55.43 298	83.67 204	91.15 156	71.29 208	91.41 175	67.95 216	93.06 190	81.96 307

tttt0517		81.07 181	79.58 201	85.52 121	88.99 169	66.45 188	87.03 101	75.51 288	73.76 155	88.32 128	90.20 183	37.96 341	94.16 89	79.36 107	95.13 142	95.93 40

IterMVS-SCA-FT		80.64 189	79.41 202	84.34 142	83.93 256	69.66 161	76.28 282	81.09 259	72.43 176	86.47 160	90.19 184	60.46 253	93.15 130	77.45 127	86.39 282	90.22 206

wuyk23d		75.13 245	79.30 203	62.63 319	75.56 326	75.18 114	80.89 219	73.10 305	75.06 142	94.76 11	95.32 33	87.73 38	52.85 346	34.16 344	97.11 78	59.85 342

DPM-MVS		80.10 202	79.18 204	82.88 177	90.71 142	69.74 159	78.87 247	90.84 131	60.29 275	75.64 286	85.92 247	67.28 223	93.11 131	71.24 183	91.79 215	85.77 262

PM-MVS		80.20 200	79.00 205	83.78 155	88.17 183	86.66 13	81.31 212	66.81 331	69.64 203	88.33 127	90.19 184	64.58 233	83.63 292	71.99 181	90.03 244	81.06 321

mvs_anonymous		78.13 215	78.76 206	76.23 272	79.24 303	50.31 324	78.69 249	84.82 239	61.60 267	83.09 214	92.82 111	73.89 179	87.01 252	68.33 213	86.41 281	91.37 184

MAR-MVS		80.24 199	78.74 207	84.73 133	86.87 213	78.18 82	85.75 121	87.81 195	65.67 242	77.84 267	78.50 320	73.79 180	90.53 202	61.59 258	90.87 234	85.49 265
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
FMVSNet3		78.80 209	78.55 208	79.57 229	82.89 267	56.89 280	81.76 204	85.77 222	69.04 209	86.00 166	90.44 179	51.75 294	90.09 218	65.95 226	93.34 181	91.72 175

test_yl		78.71 211	78.51 209	79.32 232	84.32 246	58.84 264	78.38 252	85.33 227	75.99 127	82.49 218	86.57 236	58.01 270	90.02 220	62.74 246	92.73 198	89.10 223

DCV-MVSNet		78.71 211	78.51 209	79.32 232	84.32 246	58.84 264	78.38 252	85.33 227	75.99 127	82.49 218	86.57 236	58.01 270	90.02 220	62.74 246	92.73 198	89.10 223

EPNet		80.37 195	78.41 211	86.23 104	76.75 317	73.28 123	87.18 98	77.45 275	76.24 122	68.14 317	88.93 204	65.41 232	93.85 97	69.47 199	96.12 112	91.55 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cl-mvsnet2		78.97 207	78.21 212	81.24 203	77.74 310	59.01 261	77.46 268	87.13 204	65.79 237	84.32 194	85.10 260	58.96 266	90.88 192	75.36 147	92.03 211	93.84 99

PAPR		78.84 208	78.10 213	81.07 205	85.17 236	60.22 248	82.21 200	90.57 139	62.51 258	75.32 289	84.61 270	74.99 166	92.30 149	59.48 270	88.04 267	90.68 197

PVSNet_BlendedMVS		78.80 209	77.84 214	81.65 198	84.43 242	63.41 210	79.49 237	90.44 141	61.70 266	75.43 287	87.07 233	69.11 216	91.44 172	60.68 264	92.24 207	90.11 210

Vis-MVSNet (Re-imp)		77.82 219	77.79 215	77.92 253	88.82 171	51.29 317	83.28 171	71.97 312	74.04 151	82.23 223	89.78 191	57.38 276	89.41 227	57.22 280	95.41 131	93.05 128

RRT_test8_iter05		78.08 216	77.52 216	79.75 225	80.84 285	52.54 308	80.61 222	88.96 175	67.77 223	84.62 187	89.29 196	33.89 346	92.10 155	77.59 124	94.15 168	94.62 67

Patchmtry		76.56 233	77.46 217	73.83 282	79.37 302	46.60 333	82.41 193	76.90 277	73.81 154	85.56 175	92.38 124	48.07 303	83.98 289	63.36 243	95.31 137	90.92 191

OpenMVS_ROB	70.19 17	77.77 221	77.46 217	78.71 239	84.39 245	61.15 237	81.18 216	82.52 248	62.45 260	83.34 209	87.37 226	66.20 228	88.66 238	64.69 236	85.02 293	86.32 255

thisisatest0530		79.07 206	77.33 219	84.26 144	87.13 204	64.58 199	83.66 161	75.95 283	68.86 211	85.22 178	87.36 227	38.10 339	93.57 113	75.47 145	94.28 166	94.62 67

MVS_0304		78.17 214	77.23 220	80.99 209	84.13 254	69.07 172	81.39 211	80.81 261	76.28 121	67.53 322	89.11 201	62.87 245	86.77 259	60.90 263	92.01 214	87.13 248

CANet_DTU		77.81 220	77.05 221	80.09 221	81.37 277	59.90 251	83.26 172	88.29 186	69.16 207	67.83 320	83.72 277	60.93 250	89.47 224	69.22 203	89.70 247	90.88 192

pmmvs-eth3d		78.42 213	77.04 222	82.57 184	87.44 198	74.41 117	80.86 220	79.67 267	55.68 296	84.69 186	90.31 182	60.91 251	85.42 275	62.20 250	91.59 219	87.88 240

miper_enhance_ethall		77.83 218	76.93 223	80.51 214	76.15 323	58.01 270	75.47 290	88.82 176	58.05 284	83.59 206	80.69 305	64.41 234	91.20 179	73.16 172	92.03 211	92.33 152

MDA-MVSNet-bldmvs		77.47 222	76.90 224	79.16 234	79.03 305	64.59 198	66.58 327	75.67 286	73.15 168	88.86 114	88.99 203	66.94 225	81.23 300	64.71 235	88.22 266	91.64 178

xiu_mvs_v2_base		77.19 225	76.75 225	78.52 242	87.01 210	61.30 235	75.55 289	87.12 207	61.24 269	74.45 293	78.79 317	77.20 144	90.93 188	64.62 238	84.80 297	83.32 291

USDC		76.63 231	76.73 226	76.34 270	83.46 260	57.20 277	80.02 228	88.04 191	52.14 316	83.65 205	91.25 152	63.24 242	86.65 261	54.66 296	94.11 169	85.17 267

PS-MVSNAJ		77.04 227	76.53 227	78.56 241	87.09 209	61.40 233	75.26 291	87.13 204	61.25 268	74.38 295	77.22 327	76.94 150	90.94 187	64.63 237	84.83 296	83.35 290

TAMVS		78.08 216	76.36 228	83.23 167	90.62 143	72.87 125	79.08 244	80.01 266	61.72 265	81.35 237	86.92 234	63.96 238	88.78 236	50.61 311	93.01 192	88.04 236

IterMVS		76.91 228	76.34 229	78.64 240	80.91 282	64.03 205	76.30 281	79.03 268	64.88 250	83.11 212	89.16 199	59.90 259	84.46 284	68.61 211	85.15 292	87.42 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 255	76.19 230	69.21 300	84.61 240	52.43 309	71.70 309	77.18 276	60.73 272	80.60 245	90.96 165	75.44 161	69.35 327	56.13 285	88.33 261	85.86 261

miper_lstm_enhance		76.45 235	76.10 231	77.51 257	76.72 318	60.97 243	64.69 330	85.04 235	63.98 252	83.20 211	88.22 211	56.67 279	78.79 308	73.22 166	93.12 188	92.78 139

BH-w/o		76.57 232	76.07 232	78.10 250	86.88 212	65.92 191	77.63 263	86.33 216	65.69 241	80.89 242	79.95 312	68.97 218	90.74 196	53.01 303	85.25 291	77.62 325

RPMNet		76.06 238	75.79 233	76.85 264	79.58 297	62.64 221	82.58 188	71.75 315	74.80 143	75.72 284	92.59 118	48.69 301	84.07 287	73.48 164	82.91 307	83.85 282

TR-MVS		76.77 230	75.79 233	79.72 226	86.10 229	65.79 192	77.14 269	83.02 245	65.20 248	81.40 236	82.10 295	66.30 227	90.73 197	55.57 289	85.27 290	82.65 297

jason		77.42 223	75.75 235	82.43 187	87.10 208	69.27 165	77.99 257	81.94 254	51.47 320	77.84 267	85.07 263	60.32 255	89.00 230	70.74 189	89.27 252	89.03 226
jason: jason.
MVSTER		77.09 226	75.70 236	81.25 201	75.27 330	61.08 238	77.49 267	85.07 233	60.78 271	86.55 154	88.68 207	43.14 331	90.25 207	73.69 162	90.67 239	92.42 148

D2MVS		76.84 229	75.67 237	80.34 217	80.48 292	62.16 230	73.50 302	84.80 240	57.61 288	82.24 222	87.54 223	51.31 295	87.65 247	70.40 194	93.19 187	91.23 186

PVSNet_Blended		76.49 234	75.40 238	79.76 224	84.43 242	63.41 210	75.14 292	90.44 141	57.36 290	75.43 287	78.30 321	69.11 216	91.44 172	60.68 264	87.70 272	84.42 275

CDS-MVSNet		77.32 224	75.40 238	83.06 170	89.00 168	72.48 135	77.90 259	82.17 252	60.81 270	78.94 262	83.49 280	59.30 263	88.76 237	54.64 297	92.37 204	87.93 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 239	75.35 240	77.85 255	87.01 210	51.84 314	80.45 224	73.26 303	75.20 140	83.10 213	86.31 242	45.54 316	89.05 229	55.03 294	92.24 207	92.66 143

thres100view900		75.45 242	75.05 241	76.66 267	87.27 200	51.88 313	81.07 217	73.26 303	75.68 133	83.25 210	86.37 239	45.54 316	88.80 233	51.98 307	90.99 228	89.31 218

cascas		76.29 237	74.81 242	80.72 213	84.47 241	62.94 216	73.89 300	87.34 198	55.94 295	75.16 291	76.53 330	63.97 237	91.16 181	65.00 233	90.97 231	88.06 235

GA-MVS		75.83 240	74.61 243	79.48 231	81.87 271	59.25 257	73.42 303	82.88 246	68.68 212	79.75 254	81.80 298	50.62 297	89.46 225	66.85 220	85.64 287	89.72 212

testgi		72.36 268	74.61 243	65.59 313	80.56 290	42.82 342	68.29 320	73.35 302	66.87 229	81.84 229	89.93 189	72.08 202	66.92 335	46.05 329	92.54 202	87.01 250

test20.03		73.75 258	74.59 245	71.22 295	81.11 280	51.12 319	70.15 315	72.10 311	70.42 195	80.28 252	91.50 149	64.21 236	74.72 319	46.96 327	94.58 160	87.82 242

lupinMVS		76.37 236	74.46 246	82.09 188	85.54 233	69.26 166	76.79 273	80.77 262	50.68 326	76.23 278	82.82 289	58.69 267	88.94 231	69.85 196	88.77 257	88.07 234

EU-MVSNet		75.12 246	74.43 247	77.18 259	83.11 265	59.48 255	85.71 123	82.43 250	39.76 344	85.64 173	88.76 205	44.71 327	87.88 245	73.86 160	85.88 286	84.16 278

tfpn200view9		74.86 250	74.23 248	76.74 266	86.24 224	52.12 310	79.24 241	73.87 297	73.34 161	81.82 230	84.60 271	46.02 310	88.80 233	51.98 307	90.99 228	89.31 218

thres400		75.14 244	74.23 248	77.86 254	86.24 224	52.12 310	79.24 241	73.87 297	73.34 161	81.82 230	84.60 271	46.02 310	88.80 233	51.98 307	90.99 228	92.66 143

ppachtmachnet_test		74.73 252	74.00 250	76.90 262	80.71 288	56.89 280	71.53 310	78.42 270	58.24 282	79.32 259	82.92 288	57.91 273	84.26 286	65.60 231	91.36 222	89.56 213

1112_ss		74.82 251	73.74 251	78.04 251	89.57 159	60.04 249	76.49 279	87.09 208	54.31 302	73.66 298	79.80 313	60.25 256	86.76 260	58.37 273	84.15 300	87.32 246

Patchmatch-RL test		74.48 253	73.68 252	76.89 263	84.83 239	66.54 187	72.29 307	69.16 324	57.70 286	86.76 150	86.33 240	45.79 315	82.59 295	69.63 198	90.65 241	81.54 312

CMPMVS	59.41 20	75.12 246	73.57 253	79.77 223	75.84 325	67.22 180	81.21 215	82.18 251	50.78 324	76.50 274	87.66 221	55.20 288	82.99 294	62.17 252	90.64 242	89.09 225
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 260	73.54 254	72.43 291	84.92 238	47.79 331	79.89 230	74.00 296	65.93 235	78.81 263	86.28 243	56.36 281	81.63 299	56.63 282	79.04 324	87.87 241

MVP-Stereo		75.81 241	73.51 255	82.71 179	89.35 161	73.62 120	80.06 226	85.20 230	60.30 274	73.96 296	87.94 216	57.89 274	89.45 226	52.02 306	74.87 333	85.06 269
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
new-patchmatchnet		70.10 283	73.37 256	60.29 326	81.23 279	16.95 352	59.54 336	74.62 291	62.93 255	80.97 240	87.93 217	62.83 246	71.90 322	55.24 292	95.01 148	92.00 166

PatchMatch-RL		74.48 253	73.22 257	78.27 248	87.70 192	85.26 30	75.92 285	70.09 321	64.34 251	76.09 280	81.25 303	65.87 231	78.07 309	53.86 299	83.82 301	71.48 333

Test_1112_low_res		73.90 257	73.08 258	76.35 269	90.35 148	55.95 283	73.40 304	86.17 218	50.70 325	73.14 299	85.94 246	58.31 269	85.90 271	56.51 283	83.22 304	87.20 247

CR-MVSNet		74.00 256	73.04 259	76.85 264	79.58 297	62.64 221	82.58 188	76.90 277	50.50 327	75.72 284	92.38 124	48.07 303	84.07 287	68.72 210	82.91 307	83.85 282

pmmvs4		74.92 249	72.98 260	80.73 212	84.95 237	71.71 148	76.23 283	77.59 274	52.83 310	77.73 270	86.38 238	56.35 282	84.97 279	57.72 279	87.05 276	85.51 264

ET-MVSNet_ETH3D		75.28 243	72.77 261	82.81 178	83.03 266	68.11 176	77.09 270	76.51 281	60.67 273	77.60 271	80.52 307	38.04 340	91.15 182	70.78 187	90.68 238	89.17 221

PatchT		70.52 280	72.76 262	63.79 318	79.38 301	33.53 348	77.63 263	65.37 333	73.61 156	71.77 305	92.79 114	44.38 328	75.65 316	64.53 239	85.37 289	82.18 305

HyFIR lowres test		75.12 246	72.66 263	82.50 185	91.44 124	65.19 195	72.47 306	87.31 199	46.79 332	80.29 251	84.30 273	52.70 293	92.10 155	51.88 310	86.73 278	90.22 206

MVS		73.21 262	72.59 264	75.06 277	80.97 281	60.81 244	81.64 207	85.92 221	46.03 335	71.68 306	77.54 323	68.47 219	89.77 222	55.70 288	85.39 288	74.60 330

SCA		73.32 259	72.57 265	75.58 275	81.62 273	55.86 285	78.89 246	71.37 318	61.73 264	74.93 292	83.42 282	60.46 253	87.01 252	58.11 277	82.63 311	83.88 279

1314		73.22 261	72.56 266	75.20 276	80.41 293	57.84 271	81.64 207	85.36 226	51.68 319	73.10 300	76.65 329	61.45 249	85.19 277	63.54 241	79.21 323	82.59 298

HY-MVS	64.64 18	73.03 263	72.47 267	74.71 278	83.36 261	54.19 295	82.14 203	81.96 253	56.76 294	69.57 314	86.21 244	60.03 257	84.83 282	49.58 316	82.65 309	85.11 268

UnsupCasMVSNet_eth		71.63 275	72.30 268	69.62 298	76.47 320	52.70 306	70.03 316	80.97 260	59.18 278	79.36 258	88.21 212	60.50 252	69.12 328	58.33 275	77.62 328	87.04 249

FPMVS		72.29 270	72.00 269	73.14 285	88.63 173	85.00 32	74.65 296	67.39 325	71.94 185	77.80 269	87.66 221	50.48 298	75.83 315	49.95 313	79.51 319	58.58 344

Anonymous20231206		71.38 276	71.88 270	69.88 296	86.31 221	54.37 294	70.39 314	74.62 291	52.57 312	76.73 273	88.76 205	59.94 258	72.06 321	44.35 332	93.23 186	83.23 293

FMVSNet5		72.10 271	71.69 271	73.32 283	81.57 274	53.02 304	76.77 274	78.37 271	63.31 253	76.37 275	91.85 138	36.68 343	78.98 306	47.87 323	92.45 203	87.95 238

our_test_3		71.85 272	71.59 272	72.62 289	80.71 288	53.78 298	69.72 317	71.71 317	58.80 279	78.03 266	80.51 308	56.61 280	78.84 307	62.20 250	86.04 285	85.23 266

MIMVSNet		71.09 277	71.59 272	69.57 299	87.23 201	50.07 325	78.91 245	71.83 313	60.20 276	71.26 307	91.76 143	55.08 289	76.09 313	41.06 337	87.02 277	82.54 300

thres200		72.34 269	71.55 274	74.70 279	83.48 259	51.60 315	75.02 293	73.71 300	70.14 200	78.56 265	80.57 306	46.20 308	88.20 243	46.99 326	89.29 250	84.32 276

CVMVSNet		72.62 266	71.41 275	76.28 271	83.25 262	60.34 247	83.50 166	79.02 269	37.77 345	76.33 276	85.10 260	49.60 300	87.41 249	70.54 192	77.54 329	81.08 319

EPNet_dtu		72.87 265	71.33 276	77.49 258	77.72 311	60.55 246	82.35 194	75.79 284	66.49 232	58.39 345	81.06 304	53.68 291	85.98 269	53.55 300	92.97 194	85.95 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		72.45 267	70.56 277	78.13 249	90.02 157	63.08 215	68.72 319	83.16 244	42.99 341	75.92 282	85.46 253	57.22 278	85.18 278	49.87 315	81.67 312	86.14 257

thisisatest0515		73.00 264	70.52 278	80.46 215	81.45 275	59.90 251	73.16 305	74.31 295	57.86 285	76.08 281	77.78 322	37.60 342	92.12 154	65.00 233	91.45 221	89.35 217

YYNet1		70.06 284	70.44 279	68.90 301	73.76 336	53.42 302	58.99 339	67.20 327	58.42 281	87.10 143	85.39 256	59.82 260	67.32 332	59.79 268	83.50 303	85.96 258

MDA-MVSNet_test_wron		70.05 285	70.44 279	68.88 302	73.84 335	53.47 300	58.93 340	67.28 326	58.43 280	87.09 144	85.40 255	59.80 261	67.25 333	59.66 269	83.54 302	85.92 260

MS-PatchMatch		70.93 278	70.22 281	73.06 286	81.85 272	62.50 224	73.82 301	77.90 272	52.44 313	75.92 282	81.27 302	55.67 285	81.75 297	55.37 291	77.70 327	74.94 329

pmmvs5		70.73 279	70.07 282	72.72 287	77.03 316	52.73 305	74.14 297	75.65 287	50.36 328	72.17 304	85.37 257	55.42 287	80.67 302	52.86 304	87.59 273	84.77 271

PAPM		71.77 273	70.06 283	76.92 261	86.39 216	53.97 296	76.62 277	86.62 214	53.44 307	63.97 335	84.73 269	57.79 275	92.34 147	39.65 339	81.33 315	84.45 274

UnsupCasMVSNet_bld		69.21 290	69.68 284	67.82 307	79.42 300	51.15 318	67.82 324	75.79 284	54.15 303	77.47 272	85.36 258	59.26 264	70.64 324	48.46 320	79.35 321	81.66 310

tpmvs		70.16 282	69.56 285	71.96 293	74.71 334	48.13 328	79.63 232	75.45 289	65.02 249	70.26 311	81.88 297	45.34 321	85.68 273	58.34 274	75.39 332	82.08 306

gg-mvs-nofinetune		68.96 291	69.11 286	68.52 306	76.12 324	45.32 335	83.59 162	55.88 346	86.68 22	64.62 334	97.01 7	30.36 351	83.97 290	44.78 331	82.94 306	76.26 327

IB-MVS	62.13 19	71.64 274	68.97 287	79.66 228	80.80 287	62.26 229	73.94 299	76.90 277	63.27 254	68.63 316	76.79 328	33.83 347	91.84 164	59.28 271	87.26 274	84.88 270
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
PatchmatchNet		69.71 288	68.83 288	72.33 292	77.66 312	53.60 299	79.29 239	69.99 322	57.66 287	72.53 302	82.93 287	46.45 307	80.08 305	60.91 262	72.09 336	83.31 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 281	68.80 289	74.38 280	80.91 282	84.81 35	59.12 338	76.45 282	55.06 299	75.31 290	82.36 294	55.74 284	54.82 345	47.02 325	87.24 275	83.52 286

CostFormer		69.98 286	68.68 290	73.87 281	77.14 314	50.72 322	79.26 240	74.51 293	51.94 318	70.97 310	84.75 268	45.16 324	87.49 248	55.16 293	79.23 322	83.40 289

WTY-MVS		67.91 294	68.35 291	66.58 311	80.82 286	48.12 329	65.96 328	72.60 307	53.67 306	71.20 308	81.68 300	58.97 265	69.06 329	48.57 319	81.67 312	82.55 299

MDTV_nov1_ep13			68.29 292		78.03 309	43.87 339	74.12 298	72.22 310	52.17 314	67.02 323	85.54 249	45.36 320	80.85 301	55.73 286	84.42 299

tpm		67.95 293	68.08 293	67.55 308	78.74 308	43.53 340	75.60 287	67.10 330	54.92 300	72.23 303	88.10 213	42.87 332	75.97 314	52.21 305	80.95 318	83.15 294

Patchmatch-test		65.91 301	67.38 294	61.48 324	75.51 327	43.21 341	68.84 318	63.79 335	62.48 259	72.80 301	83.42 282	44.89 326	59.52 344	48.27 322	86.45 280	81.70 309

sss		66.92 296	67.26 295	65.90 312	77.23 313	51.10 320	64.79 329	71.72 316	52.12 317	70.13 312	80.18 310	57.96 272	65.36 340	50.21 312	81.01 317	81.25 316

baseline2		69.77 287	66.89 296	78.41 245	79.51 299	58.09 269	76.23 283	69.57 323	57.50 289	64.82 333	77.45 325	46.02 310	88.44 239	53.08 302	77.83 326	88.70 230

tpm2		68.45 292	66.83 297	73.30 284	78.93 307	48.50 327	79.76 231	71.76 314	47.50 331	69.92 313	83.60 278	42.07 333	88.40 240	48.44 321	79.51 319	83.01 296

test-LLR		67.21 295	66.74 298	68.63 304	76.45 321	55.21 290	67.89 321	67.14 328	62.43 261	65.08 330	72.39 335	43.41 329	69.37 325	61.00 260	84.89 294	81.31 314

tpmrst		66.28 300	66.69 299	65.05 316	72.82 342	39.33 343	78.20 255	70.69 320	53.16 309	67.88 319	80.36 309	48.18 302	74.75 318	58.13 276	70.79 338	81.08 319

JIA-IIPM		69.41 289	66.64 300	77.70 256	73.19 338	71.24 151	75.67 286	65.56 332	70.42 195	65.18 329	92.97 106	33.64 348	83.06 293	53.52 301	69.61 342	78.79 324

PVSNet	58.17 21	66.41 299	65.63 301	68.75 303	81.96 270	49.88 326	62.19 334	72.51 309	51.03 322	68.04 318	75.34 333	50.84 296	74.77 317	45.82 330	82.96 305	81.60 311

tpm cat1		66.76 297	65.21 302	71.42 294	77.09 315	50.62 323	78.01 256	73.68 301	44.89 337	68.64 315	79.00 316	45.51 318	82.42 296	49.91 314	70.15 339	81.23 318

DWT-MVSNet_test		66.43 298	64.37 303	72.63 288	74.86 333	50.86 321	76.52 278	72.74 306	54.06 304	65.50 327	68.30 341	32.13 349	84.84 281	61.63 257	73.59 334	82.19 304

test0.0.03 1		64.66 304	64.36 304	65.57 314	75.03 332	46.89 332	64.69 330	61.58 340	62.43 261	71.18 309	77.54 323	43.41 329	68.47 330	40.75 338	82.65 309	81.35 313

test-mter		65.00 303	63.79 305	68.63 304	76.45 321	55.21 290	67.89 321	67.14 328	50.98 323	65.08 330	72.39 335	28.27 353	69.37 325	61.00 260	84.89 294	81.31 314

ADS-MVSNet2		65.87 302	63.64 306	72.55 290	73.16 339	56.92 279	67.10 325	74.81 290	49.74 329	66.04 325	82.97 285	46.71 305	77.26 310	42.29 334	69.96 340	83.46 287

MVS-HIRNet		61.16 311	62.92 307	55.87 329	79.09 304	35.34 347	71.83 308	57.98 345	46.56 333	59.05 342	91.14 157	49.95 299	76.43 312	38.74 340	71.92 337	55.84 345

EPMVS		62.47 305	62.63 308	62.01 320	70.63 346	38.74 344	74.76 294	52.86 348	53.91 305	67.71 321	80.01 311	39.40 337	66.60 336	55.54 290	68.81 343	80.68 323

ADS-MVSNet		61.90 307	62.19 309	61.03 325	73.16 339	36.42 346	67.10 325	61.75 338	49.74 329	66.04 325	82.97 285	46.71 305	63.21 342	42.29 334	69.96 340	83.46 287

E-PMN		61.59 309	61.62 310	61.49 323	66.81 347	55.40 288	53.77 342	60.34 341	66.80 230	58.90 343	65.50 342	40.48 336	66.12 338	55.72 287	86.25 283	62.95 340

DSMNet-mixed		60.98 313	61.61 311	59.09 328	72.88 341	45.05 337	74.70 295	46.61 351	26.20 347	65.34 328	90.32 181	55.46 286	63.12 343	41.72 336	81.30 316	69.09 337

EMVS		61.10 312	60.81 312	61.99 321	65.96 348	55.86 285	53.10 343	58.97 343	67.06 227	56.89 346	63.33 343	40.98 334	67.03 334	54.79 295	86.18 284	63.08 339

PMMVS		61.65 308	60.38 313	65.47 315	65.40 349	69.26 166	63.97 332	61.73 339	36.80 346	60.11 340	68.43 339	59.42 262	66.35 337	48.97 318	78.57 325	60.81 341

TESTMET0.1,1		61.29 310	60.32 314	64.19 317	72.06 343	51.30 316	67.89 321	62.09 336	45.27 336	60.65 339	69.01 338	27.93 354	64.74 341	56.31 284	81.65 314	76.53 326

dp		60.70 314	60.29 315	61.92 322	72.04 344	38.67 345	70.83 311	64.08 334	51.28 321	60.75 338	77.28 326	36.59 344	71.58 323	47.41 324	62.34 345	75.52 328

pmmvs3		62.47 305	60.02 316	69.80 297	71.58 345	64.00 206	70.52 313	58.44 344	39.77 343	66.05 324	75.84 331	27.10 355	72.28 320	46.15 328	84.77 298	73.11 331

PMMVS2		55.64 318	59.27 317	44.74 332	64.30 350	12.32 353	40.60 345	49.79 350	53.19 308	65.06 332	84.81 267	53.60 292	49.76 347	32.68 346	89.41 249	72.15 332

new_pmnet		55.69 317	57.66 318	49.76 331	75.47 328	30.59 349	59.56 335	51.45 349	43.62 340	62.49 336	75.48 332	40.96 335	49.15 348	37.39 342	72.52 335	69.55 336

CHOSEN 280x420		59.08 315	56.52 319	66.76 310	76.51 319	64.39 202	49.62 344	59.00 342	43.86 339	55.66 347	68.41 340	35.55 345	68.21 331	43.25 333	76.78 331	67.69 338

PVSNet_0	51.08 22	56.10 316	54.97 320	59.48 327	75.12 331	53.28 303	55.16 341	61.89 337	44.30 338	59.16 341	62.48 344	54.22 290	65.91 339	35.40 343	47.01 346	59.25 343

MVE	40.22 23	51.82 319	50.47 321	55.87 329	62.66 351	51.91 312	31.61 347	39.28 352	40.65 342	50.76 348	74.98 334	56.24 283	44.67 349	33.94 345	64.11 344	71.04 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		20.81 320	27.75 322	0.00 337	0.00 356	0.00 357	0.00 348	85.44 225	0.00 352	0.00 353	82.82 289	81.46 106	0.00 354	0.00 351	0.00 351	0.00 350

tmp_tt		20.25 321	24.50 323	7.49 334	4.47 353	8.70 354	34.17 346	25.16 354	1.00 349	32.43 350	18.49 347	39.37 338	9.21 351	21.64 347	43.75 347	4.57 347

ab-mvs-re		6.65 322	8.87 324	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	79.80 313	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		6.41 323	8.55 325	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	76.94 150	0.00 354	0.00 351	0.00 351	0.00 350

test123		6.27 324	8.08 326	0.84 335	1.11 355	0.57 355	62.90 333	0.82 356	0.54 350	1.07 352	2.75 352	1.26 356	0.30 352	1.04 349	1.26 350	1.66 348

testmvs		5.91 325	7.65 327	0.72 336	1.20 354	0.37 356	59.14 337	0.67 357	0.49 351	1.11 351	2.76 351	0.94 357	0.24 353	1.02 350	1.47 349	1.55 349

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

IU-MVS					94.18 47	72.64 129		90.82 132	56.98 292	89.67 99				85.78 41	97.92 45	93.28 120

OPU-MVS				88.27 76	91.89 109	77.83 85	90.47 43				91.22 153	81.12 110	94.68 67	74.48 152	95.35 133	92.29 155

test_241102_TWO								93.71 45	83.77 38	93.49 33	94.27 67	89.27 21	95.84 18	86.03 38	97.82 50	92.04 164

test_241102_ONE					94.18 47	72.65 127		93.69 46	83.62 40	94.11 22	93.78 94	90.28 14	95.50 40

save fliter					93.75 58	77.44 91	86.31 115	89.72 163	70.80 191

test_0728_THIRD									85.33 27	93.75 28	94.65 52	87.44 41	95.78 23	87.41 19	98.21 29	92.98 131

test_0728_SECOND				86.79 92	94.25 46	72.45 136	90.54 40	94.10 30					95.88 13	86.42 28	97.97 42	92.02 165

test0726					94.16 50	72.56 132	90.63 39	93.90 38	83.61 41	93.75 28	94.49 58	89.76 18

GSMVS																83.88 279

test_part2					93.86 57	77.77 86				92.84 41

test_part1				0.00 337		0.00 357	0.00 348	93.93 35				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sam_mvs1												46.11 309				83.88 279

sam_mvs												45.92 314

ambc				82.98 172	90.55 146	64.86 197	88.20 84	89.15 173		89.40 108	93.96 84	71.67 207	91.38 177	78.83 110	96.55 94	92.71 141

MTGPA								91.81 109

test_post1							78.85 248				3.13 349	45.19 323	80.13 304	58.11 277

test_post											3.10 350	45.43 319	77.22 311

patchmatchnet-post											81.71 299	45.93 313	87.01 252

GG-mvs-BLEND				67.16 309	73.36 337	46.54 334	84.15 146	55.04 347		58.64 344	61.95 345	29.93 352	83.87 291	38.71 341	76.92 330	71.07 334

MTMP							90.66 37	33.14 353

gm-plane-assit					75.42 329	44.97 338			52.17 314		72.36 337		87.90 244	54.10 298

test9_res														80.83 90	96.45 99	90.57 199

TEST9					92.34 93	79.70 68	83.94 150	90.32 145	65.41 247	84.49 190	90.97 163	82.03 97	93.63 106

test_8					92.09 101	78.87 76	83.82 155	90.31 147	65.79 237	84.36 193	90.96 165	81.93 99	93.44 118

agg_prior2														79.68 101	96.16 109	90.22 206

agg_prior					91.58 118	77.69 87		90.30 149		84.32 194			93.18 126

TestCases				89.68 52	91.59 115	83.40 45		95.44 6	79.47 87	88.00 131	93.03 103	82.66 84	91.47 170	70.81 185	96.14 110	94.16 88

test_prior4						78.97 75	84.59 137

test_prior2							83.37 169		75.43 136	84.58 188	91.57 146	81.92 101		79.54 103	96.97 81

test_prior				86.32 100	90.59 144	71.99 142		92.85 82					94.17 86			92.80 137

旧先验2							81.73 205		56.88 293	86.54 159			84.90 280	72.81 174

新几何2							81.72 206

新几何1				82.95 173	93.96 54	78.56 80		80.24 263	55.45 297	83.93 202	91.08 158	71.19 209	88.33 241	65.84 229	93.07 189	81.95 308

旧先验1					91.97 104	71.77 144		81.78 255			91.84 139	73.92 178			93.65 179	83.61 285

无先验							82.81 185	85.62 224	58.09 283				91.41 175	67.95 216		84.48 273

原ACMM2							82.26 199

原ACMM1				84.60 136	92.81 84	74.01 119		91.50 115	62.59 257	82.73 217	90.67 175	76.53 157	94.25 79	69.24 201	95.69 126	85.55 263

test222					93.31 69	76.54 104	79.38 238	77.79 273	52.59 311	82.36 221	90.84 169	66.83 226			91.69 217	81.25 316

testdata2													86.43 264	63.52 242

segment_acmp												81.94 98

testdata				79.54 230	92.87 79	72.34 137		80.14 265	59.91 277	85.47 177	91.75 144	67.96 222	85.24 276	68.57 212	92.18 210	81.06 321

testdata1							79.62 233		73.95 153

test12				86.57 95	90.74 140	72.63 130		90.69 135		82.76 216		79.20 126	94.80 64		95.32 135	92.27 157

plane_prior7					93.45 64	77.31 95

plane_prior6					92.61 85	76.54 104						74.84 168

plane_prior5								93.61 49					95.22 50	80.78 91	95.83 119	94.46 76

plane_prior4											92.95 107

plane_prior3						76.85 102			77.79 108	86.55 154

plane_prior2							89.45 66		79.44 89

plane_prior1					92.83 83

plane_prior						76.42 107	87.15 99		75.94 130						95.03 147

n2								0.00 358

nn								0.00 358

door-mid								74.45 294

lessismore_v0				85.95 111	91.10 134	70.99 153		70.91 319		91.79 60	94.42 61	61.76 248	92.93 136	79.52 105	93.03 191	93.93 96

LGP-MVS_train				90.82 36	94.75 40	81.69 53		94.27 19	82.35 56	93.67 31	94.82 47	91.18 5	95.52 36	85.36 44	98.73 6	95.23 56

test11								91.46 116

door								72.57 308

HQP5-MVS						70.66 154

HQP-NCC					91.19 129		84.77 132		73.30 163	80.55 248

ACMP_Plane					91.19 129		84.77 132		73.30 163	80.55 248

BP-MVS														77.30 129

HQP4-MVS										80.56 247			94.61 71			93.56 115

HQP3-MVS								92.68 88							94.47 162

HQP2-MVS												72.10 200

NP-MVS					91.95 105	74.55 116					90.17 186

MDTV_nov1_ep13_2view						27.60 351	70.76 312		46.47 334	61.27 337		45.20 322		49.18 317		83.75 284

ACMMP++_ref															95.74 125

ACMMP++															97.35 71

Test By Simon												79.09 127

ITE_SJBPF				90.11 48	90.72 141	84.97 33		90.30 149	81.56 65	90.02 87	91.20 155	82.40 87	90.81 194	73.58 163	94.66 158	94.56 70

DeepMVS_CX				24.13 333	32.95 352	29.49 350		21.63 355	12.07 348	37.95 349	45.07 346	30.84 350	19.21 350	17.94 348	33.06 348	23.69 346

High-resolution multi-view benchmark