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 99

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	92.19 144

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 81

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 68
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	93.12 115

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	92.55 132

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 79

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	92.30 138

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 86

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 92

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	92.85 121

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	92.30 138

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	92.98 118

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 88

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 77
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	93.19 113
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 89

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	93.27 109

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	92.41 135
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	95.50 120	94.53 72

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	91.70 158

#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	92.85 121

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 91

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	89.57 156	88.51 15	90.11 79	95.12 41	90.98 7	88.92 216	77.55 116	97.07 73	83.13 279
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	90.55 183

PS-CasMVS		90.06 38	91.92 10	84.47 131	96.56 7	58.83 251	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	58.88 248	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	57.84 255	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	93.64 101

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 84

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	90.08 147	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	86.75 142	93.26 94	93.64 2	90.93 175	84.60 48	90.75 220	93.97 87

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	90.45 185
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	91.83 154

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	91.67 159

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	88.31 226	83.33 58	98.30 25	93.20 112
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	58.61 253	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	90.98 173	83.86 54	95.30 127	93.60 103

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	93.26 110
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	88.38 170	88.84 13	94.29 19	97.57 4	90.48 13	91.26 166	72.57 159	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	90.93 215	92.98 118

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	90.22 196	86.24 34	97.24 69	91.36 167
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	86.69 143	92.28 124	80.36 109	95.06 52	86.17 35	96.49 91	90.22 189

test_0402		88.65 63	89.58 53	85.88 107	92.55 81	72.22 130	84.01 141	89.44 158	88.63 14	94.38 18	95.77 25	86.38 50	93.59 100	79.84 92	95.21 128	91.82 155

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	77.65 115	96.62 85	90.70 178

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	92.02 145	76.70 125	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	77.97 113	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	67.85 212	86.63 144	94.84 47	79.58 117	95.96 10	87.62 13	94.50 150	94.56 68
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	87.74 230	76.54 127	88.74 243	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	91.47 159	70.81 169	96.14 103	94.16 82

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	73.22 150	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	91.50 158	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	87.07 138	91.47 144	82.94 74	94.71 60	84.67 47	96.27 100	92.62 131

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	86.55 145	92.95 103	74.84 160	95.22 45	80.78 84	95.83 112	94.46 73

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	84.85 171	90.89 159	80.85 103	95.29 41	81.14 78	95.32 124	92.34 137

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	92.00 148

SixPastTwentyTwo		87.20 77	87.45 74	86.45 91	92.52 82	69.19 159	87.84 86	88.05 176	81.66 61	94.64 14	96.53 15	65.94 221	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	75.00 136	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	84.27 184	94.05 76	78.35 125	93.65 94	80.54 88	91.58 203	92.08 146
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	74.16 139	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	74.16 139	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	89.33 159	83.33 42	89.92 86	85.07 248	83.23 72	93.16 118	83.39 57	92.72 187	93.83 92

IS-MVSNet		86.66 83	86.82 86	86.17 102	92.05 97	66.87 174	91.21 34	88.64 166	86.30 25	89.60 97	92.59 113	69.22 207	94.91 56	73.89 143	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	81.90 182	92.22 93	62.28 218	84.66 128	89.15 161	83.54 40	89.85 87	97.32 5	88.08 31	86.80 242	70.43 177	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	88.00 178	82.82 49	89.84 88	85.48 238	82.06 88	92.77 130	83.83 55	91.04 209	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	84.76 172	87.70 209	78.87 121	94.18 74	80.67 86	96.29 97	92.73 126

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	84.58 174	91.57 140	81.92 94	94.17 76	79.54 96	96.97 75	92.80 123

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	88.01 203	79.09 119	92.13 141	75.51 131	95.06 135	90.41 186

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	84.96 167	90.69 165	80.01 112	95.14 48	78.37 106	95.78 114	91.82 155
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	95.41 121	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	90.40 191	69.19 188	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	65.91 223	86.19 152	91.75 138	83.77 66	94.98 54	77.43 119	96.71 84	93.73 98

Regformer-1		86.00 94	85.50 106	87.49 78	84.18 244	76.90 94	83.52 155	87.94 180	82.18 55	89.19 103	85.07 248	82.28 84	91.89 151	82.40 68	92.72 187	93.69 100

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	72.46 160	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	65.79 224	84.49 176	90.97 155	81.93 92	93.63 96	81.21 77	96.54 89	90.88 174

FC-MVSNet-test		85.93 97	87.05 80	82.58 173	92.25 91	56.44 266	85.75 113	93.09 63	77.33 108	91.94 55	94.65 53	74.78 162	93.41 110	75.11 135	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	88.42 168	74.57 141	83.56 191	85.65 235	78.49 123	94.21 72	72.04 163	92.88 182	94.05 85

agg_prior1		85.72 99	85.20 110	87.28 81	91.58 110	77.69 83	83.69 152	90.30 140	66.29 220	84.32 180	91.07 152	82.13 86	93.18 116	81.02 79	96.36 95	90.98 170

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	85.80 158	89.56 185	80.76 104	92.13 141	73.21 155	95.51 119	93.25 111
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	87.54 212	86.45 48	91.06 172	75.76 130	93.76 162	92.54 133

EPP-MVSNet		85.47 102	85.04 112	86.77 86	91.52 113	69.37 154	91.63 31	87.98 179	81.51 63	87.05 139	91.83 134	66.18 220	95.29 41	70.75 172	96.89 78	95.64 45

FIs		85.35 103	86.27 91	82.60 172	91.86 102	57.31 259	85.10 122	93.05 65	75.83 126	91.02 69	93.97 80	73.57 175	92.91 128	73.97 142	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	91.89 151	77.87 114	93.75 164	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	87.12 134	92.03 128	82.30 82	91.94 148	78.39 105	94.21 155	94.74 66

K. test v3		85.14 106	84.73 116	86.37 92	91.13 125	69.63 153	85.45 118	76.68 263	84.06 36	92.44 47	96.99 9	62.03 234	94.65 61	80.58 87	93.24 172	94.83 65

EI-MVSNet-Vis-set		85.12 107	84.53 125	86.88 83	84.01 247	72.76 119	83.91 145	85.18 214	80.44 71	88.75 110	85.49 237	80.08 111	91.92 149	82.02 71	90.85 218	95.97 38

Regformer-3		85.06 108	84.67 121	86.22 98	84.27 240	73.43 115	84.07 139	85.26 212	80.77 70	88.62 113	85.48 238	80.56 107	90.39 192	81.99 72	91.04 209	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	85.15 215	80.27 75	88.75 110	85.45 241	79.95 114	91.90 150	81.92 73	90.80 219	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	16.05 333	86.57 46	95.80 17	87.35 19	97.62 58	94.20 79

MSLP-MVS++		85.00 111	86.03 96	81.90 182	91.84 103	71.56 140	86.75 103	93.02 69	75.95 124	87.12 134	89.39 186	77.98 127	89.40 212	77.46 117	94.78 143	84.75 256

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	80.18 237	89.15 189	77.04 140	93.28 113	65.82 214	92.28 193	92.21 143

3Dnovator	80.37 7	84.80 113	84.71 119	85.06 120	86.36 211	74.71 108	88.77 75	90.00 149	75.65 129	84.96 167	93.17 96	74.06 168	91.19 167	78.28 109	91.09 207	89.29 203

IterMVS-LS		84.73 114	84.98 113	83.96 143	87.35 190	63.66 198	83.25 165	89.88 151	76.06 119	89.62 94	92.37 122	73.40 181	92.52 135	78.16 110	94.77 145	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	87.13 191	73.35 155	85.56 162	89.34 187	83.60 68	90.50 189	76.64 126	94.05 158	90.09 194

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	80.55 232	90.17 178	72.10 193	94.61 62	77.30 120	94.47 151	93.56 105

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	94.97 138	96.13 33

mvs-test1		84.55 118	82.12 160	91.84 18	87.13 195	89.54 4	85.05 123	88.42 168	74.57 141	80.60 229	82.98 269	78.49 123	93.98 84	72.04 163	89.77 230	92.00 148

FMVSNet1		84.55 118	85.45 107	81.85 184	90.27 141	61.05 230	86.83 99	88.27 173	78.57 98	89.66 93	95.64 29	75.43 154	90.68 184	69.09 189	95.33 123	93.82 94

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	94.65 148	95.97 38

Gipuma		84.44 121	86.33 89	78.78 222	84.20 243	73.57 114	89.55 58	90.44 132	84.24 33	84.38 178	94.89 45	76.35 151	80.40 288	76.14 128	96.80 83	82.36 287
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	66.22 221	91.62 59	94.55 56	75.88 152	91.95 147	77.02 124	94.89 140	94.56 68

MCST-MVS		84.36 122	83.93 137	85.63 111	91.59 107	71.58 139	83.52 155	92.13 91	61.82 248	83.96 186	89.75 183	79.93 115	93.46 107	78.33 108	94.34 153	91.87 153

VDDNet		84.35 124	85.39 108	81.25 192	95.13 31	59.32 246	85.42 119	81.11 241	86.41 24	87.41 132	96.21 20	73.61 174	90.61 187	66.33 208	96.85 79	93.81 97

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	84.07 185	78.72 302	80.39 108	95.13 49	73.82 145	92.98 180	91.04 169

v1240		84.30 126	84.51 126	83.65 150	87.65 185	61.26 227	82.85 176	91.54 106	67.94 210	90.68 75	90.65 168	71.71 199	93.64 95	82.84 65	94.78 143	96.07 35

MVS_111021_LR		84.28 127	83.76 139	85.83 109	89.23 155	83.07 48	80.99 211	83.56 226	72.71 169	86.07 153	89.07 191	81.75 97	86.19 251	77.11 122	93.36 167	88.24 216

v144192		84.24 128	84.41 128	83.71 149	87.59 187	61.57 223	82.95 174	91.03 120	67.82 213	89.80 89	90.49 170	73.28 182	93.51 105	81.88 74	94.89 140	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	67.94 210	90.06 80	90.34 172	72.04 196	93.59 100	82.32 69	94.91 139	96.07 35

VDD-MVS		84.23 129	84.58 124	83.20 160	91.17 124	65.16 186	83.25 165	84.97 221	79.79 78	87.18 133	94.27 68	74.77 163	90.89 178	69.24 185	96.54 89	93.55 107

v2v482		84.09 131	84.24 132	83.62 151	87.13 195	61.40 224	82.71 179	89.71 153	72.19 175	89.55 98	91.41 145	70.70 204	93.20 115	81.02 79	93.76 162	96.25 31

EG-PatchMatch MVS		84.08 132	84.11 133	83.98 142	92.22 93	72.61 121	82.20 194	87.02 195	72.63 170	88.86 107	91.02 153	78.52 122	91.11 170	73.41 149	91.09 207	88.21 217

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	82.04 210	88.33 199	77.91 129	93.95 85	66.17 209	95.12 133	90.34 188

TransMVSNet (Re)		84.02 134	85.74 101	78.85 221	91.00 127	55.20 276	82.29 188	87.26 186	79.65 81	88.38 119	95.52 32	83.00 73	86.88 240	67.97 199	96.60 87	94.45 75

Baseline_NR-MVSNet		84.00 135	85.90 98	78.29 232	91.47 115	53.44 285	82.29 188	87.00 196	79.06 89	89.55 98	95.72 27	77.20 136	86.14 252	72.30 161	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	81.58 240	74.73 139	85.66 159	86.06 232	72.56 191	92.69 132	75.44 133	95.21 128	89.01 211

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	87.96 204	72.33 192	90.83 179	76.02 129	94.11 156	92.69 128

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	81.34 222	84.18 259	83.65 67	92.93 126	74.22 138	87.87 253	92.17 145

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	74.39 278	87.45 214	75.36 155	95.42 37	77.03 123	92.83 183	92.25 142

pm-mvs1		83.69 140	84.95 114	79.91 208	90.04 147	59.66 243	82.43 184	87.44 183	75.52 130	87.85 126	95.26 37	81.25 102	85.65 258	68.74 193	96.04 106	94.42 76

AdaColmap		83.66 141	83.69 140	83.57 153	90.05 146	72.26 129	86.29 110	90.00 149	78.19 102	81.65 217	87.16 218	83.40 70	94.24 71	61.69 240	94.76 146	84.21 261

MIMVSNet1		83.63 142	84.59 123	80.74 198	94.06 48	62.77 209	82.72 178	84.53 224	77.57 107	90.34 77	95.92 24	76.88 148	85.83 256	61.88 238	97.42 64	93.62 102

WR-MVS		83.56 143	84.40 129	81.06 195	93.43 61	54.88 277	78.67 242	85.02 219	81.24 64	90.74 74	91.56 142	72.85 186	91.08 171	68.00 198	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	88.77 163	79.09 88	83.54 192	88.66 197	74.87 159	81.73 283	66.84 205	92.29 192	89.11 205

LCM-MVSNet-Re		83.48 145	85.06 111	78.75 223	85.94 221	55.75 271	80.05 219	94.27 15	76.47 115	96.09 5	94.54 57	83.31 71	89.75 207	59.95 252	94.89 140	90.75 177

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	92.29 139	80.06 89	90.22 227	95.62 46

VPA-MVSNet		83.47 146	84.73 116	79.69 212	90.29 140	57.52 258	81.30 207	88.69 165	76.29 116	87.58 129	94.44 61	80.60 106	87.20 235	66.60 207	96.82 82	94.34 78

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	81.12 223	78.56 303	80.00 113	95.52 32	73.04 156	93.29 171	91.62 161

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	80.87 227	87.92 207	73.49 178	92.42 136	70.07 179	88.40 244	91.60 162

CLD-MVS		83.18 150	82.64 153	84.79 123	89.05 157	67.82 169	77.93 249	92.52 83	68.33 205	85.07 166	81.54 286	82.06 88	92.96 124	69.35 184	97.91 45	93.57 104
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	75.65 259	81.24 269	45.26 320	79.94 221	92.91 72	83.83 37	91.33 65	96.88 11	80.25 110	85.92 254	68.89 191	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	54.66 285	81.46 219	90.81 162	76.98 141	94.38 67	72.62 158	96.18 101	90.82 176

UGNet		82.78 153	81.64 166	86.21 100	86.20 217	76.24 103	86.86 97	85.68 206	77.07 111	73.76 281	92.82 107	69.64 205	91.82 155	69.04 190	93.69 165	90.56 182
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	88.76 164	68.01 207	85.58 161	87.75 208	71.80 198	86.85 241	74.02 141	93.87 161	88.58 215

EI-MVSNet		82.61 155	82.42 158	83.20 160	83.25 254	63.66 198	83.50 158	85.07 216	76.06 119	86.55 145	85.10 246	73.41 179	90.25 193	78.15 111	90.67 222	95.68 44

QAPM		82.59 156	82.59 155	82.58 173	86.44 206	66.69 175	89.94 49	90.36 135	67.97 209	84.94 169	92.58 115	72.71 188	92.18 140	70.63 175	87.73 255	88.85 213

Fast-Effi-MVS+-dtu		82.54 157	81.41 169	85.90 106	85.60 222	76.53 99	83.07 170	89.62 155	73.02 165	79.11 245	83.51 264	80.74 105	90.24 195	68.76 192	89.29 234	90.94 172

MVS_Test		82.47 158	83.22 144	80.22 205	82.62 260	57.75 257	82.54 183	91.96 97	71.16 182	82.89 199	92.52 118	77.41 134	90.50 189	80.04 90	87.84 254	92.40 136

v148		82.31 159	82.48 157	81.81 187	85.59 223	59.66 243	81.47 203	86.02 203	72.85 167	88.05 123	90.65 168	70.73 203	90.91 177	75.15 134	91.79 199	94.87 62

API-MVS		82.28 160	82.61 154	81.30 190	86.29 214	69.79 148	88.71 76	87.67 182	78.42 100	82.15 209	84.15 260	77.98 127	91.59 157	65.39 216	92.75 184	82.51 286

MVSFormer		82.23 161	81.57 168	84.19 139	85.54 224	69.26 156	91.98 27	90.08 147	71.54 179	76.23 262	85.07 248	58.69 253	94.27 68	86.26 31	88.77 241	89.03 209

ETV-MVS		82.19 162	81.23 172	85.10 119	87.95 177	69.17 160	83.22 168	93.33 51	70.42 187	78.58 248	79.77 299	77.29 135	94.20 73	71.51 166	88.96 239	91.93 152

PCF-MVS	74.62 15	82.15 163	80.92 176	85.84 108	89.43 151	72.30 128	80.53 215	91.82 100	57.36 274	87.81 127	89.92 181	77.67 131	93.63 96	58.69 257	95.08 134	91.58 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 164	80.31 182	87.45 79	90.86 130	80.29 62	85.88 112	90.65 127	68.17 206	76.32 261	86.33 227	73.12 184	92.61 134	61.40 244	90.02 229	89.44 198
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	81.85 184	86.38 208	61.05 230	86.83 99	88.27 173	72.43 171	86.00 154	95.64 29	63.78 228	90.68 184	65.95 210	93.34 168	93.82 94

test1		82.02 165	82.07 161	81.85 184	86.38 208	61.05 230	86.83 99	88.27 173	72.43 171	86.00 154	95.64 29	63.78 228	90.68 184	65.95 210	93.34 168	93.82 94

OpenMVS	76.72 13	81.98 167	82.00 163	81.93 181	84.42 236	68.22 165	88.50 79	89.48 157	66.92 215	81.80 216	91.86 131	72.59 190	90.16 198	71.19 168	91.25 206	87.40 229

tfpnnormal		81.79 168	82.95 149	78.31 231	88.93 161	55.40 272	80.83 214	82.85 230	76.81 113	85.90 157	94.14 73	74.58 166	86.51 246	66.82 206	95.68 118	93.01 117

PVSNet_Blended_VisFu		81.55 169	80.49 180	84.70 127	91.58 110	73.24 117	84.21 137	91.67 104	62.86 241	80.94 225	87.16 218	67.27 215	92.87 129	69.82 181	88.94 240	87.99 221

DELS-MVS		81.44 170	81.25 171	82.03 180	84.27 240	62.87 208	76.47 266	92.49 84	70.97 183	81.64 218	83.83 261	75.03 157	92.70 131	74.29 137	92.22 196	90.51 184
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		81.31 171	81.61 167	80.41 202	86.38 208	58.75 252	83.93 144	86.58 198	72.43 171	87.65 128	92.98 100	63.78 228	90.22 196	66.86 203	93.92 160	92.27 140

DI_MVS_plusplus_test		81.27 172	81.26 170	81.29 191	84.98 228	61.65 222	81.98 196	87.25 187	63.56 237	87.56 130	89.60 184	73.62 173	91.83 154	72.20 162	90.59 226	90.38 187

TinyColmap		81.25 173	82.34 159	77.99 237	85.33 226	60.68 235	82.32 187	88.33 171	71.26 181	86.97 140	92.22 127	77.10 139	86.98 239	62.37 233	95.17 130	86.31 240

tttt0517		81.07 174	79.58 190	85.52 113	88.99 160	66.45 178	87.03 96	75.51 271	73.76 150	88.32 121	90.20 175	37.96 327	94.16 79	79.36 100	95.13 131	95.93 41

Fast-Effi-MVS+		81.04 175	80.57 177	82.46 177	87.50 188	63.22 204	78.37 245	89.63 154	68.01 207	81.87 212	82.08 281	82.31 81	92.65 133	67.10 202	88.30 249	91.51 165

BH-untuned		80.96 176	80.99 174	80.84 197	88.55 166	68.23 164	80.33 217	88.46 167	72.79 168	86.55 145	86.76 222	74.72 164	91.77 156	61.79 239	88.99 238	82.52 285

1121		80.86 177	79.81 189	84.02 140	93.93 51	78.70 76	81.64 200	80.18 247	55.43 282	83.67 189	91.15 149	71.29 201	91.41 163	67.95 200	93.06 177	81.96 291

xiu_mvs_v1_base_debu		80.84 178	80.14 185	82.93 166	88.31 170	71.73 135	79.53 226	87.17 188	65.43 228	79.59 239	82.73 276	76.94 142	90.14 199	73.22 150	88.33 245	86.90 235

xiu_mvs_v1_base		80.84 178	80.14 185	82.93 166	88.31 170	71.73 135	79.53 226	87.17 188	65.43 228	79.59 239	82.73 276	76.94 142	90.14 199	73.22 150	88.33 245	86.90 235

xiu_mvs_v1_base_debi		80.84 178	80.14 185	82.93 166	88.31 170	71.73 135	79.53 226	87.17 188	65.43 228	79.59 239	82.73 276	76.94 142	90.14 199	73.22 150	88.33 245	86.90 235

IterMVS-SCA-FT		80.64 181	79.41 191	84.34 134	83.93 248	69.66 152	76.28 268	81.09 242	72.43 171	86.47 151	90.19 176	60.46 240	93.15 120	77.45 118	86.39 266	90.22 189

BH-RMVSNet		80.53 182	80.22 184	81.49 189	87.19 194	66.21 179	77.79 251	86.23 200	74.21 145	83.69 188	88.50 198	73.25 183	90.75 181	63.18 230	87.90 252	87.52 227

Anonymous202405211		80.51 183	81.19 173	78.49 228	88.48 167	57.26 260	76.63 262	82.49 232	81.21 65	84.30 182	92.24 126	67.99 212	86.24 250	62.22 234	95.13 131	91.98 151

diffmvs		80.40 184	80.48 181	80.17 206	79.02 291	60.04 239	77.54 254	90.28 144	66.65 218	82.40 204	87.33 217	73.50 176	87.35 234	77.98 112	89.62 232	93.13 114

EPNet		80.37 185	78.41 200	86.23 97	76.75 301	73.28 116	87.18 93	77.45 258	76.24 118	68.14 302	88.93 193	65.41 223	93.85 87	69.47 183	96.12 105	91.55 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		80.32 186	80.94 175	78.47 229	88.18 173	52.62 291	82.29 188	85.01 220	72.01 177	79.24 244	92.54 117	69.36 206	93.36 112	70.65 174	89.19 237	89.45 197

VPNet		80.25 187	81.68 165	75.94 258	92.46 84	47.98 314	76.70 261	81.67 239	73.45 153	84.87 170	92.82 107	74.66 165	86.51 246	61.66 241	96.85 79	93.33 108

MAR-MVS		80.24 188	78.74 196	84.73 125	86.87 204	78.18 79	85.75 113	87.81 181	65.67 227	77.84 251	78.50 304	73.79 172	90.53 188	61.59 243	90.87 217	85.49 249
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		80.20 189	79.00 194	83.78 147	88.17 174	86.66 13	81.31 205	66.81 315	69.64 195	88.33 120	90.19 176	64.58 224	83.63 276	71.99 165	90.03 228	81.06 306

LFMVS		80.15 190	80.56 178	78.89 220	89.19 156	55.93 268	85.22 121	73.78 282	82.96 47	84.28 183	92.72 111	57.38 262	90.07 203	63.80 225	95.75 115	90.68 179

DPM-MVS		80.10 191	79.18 193	82.88 169	90.71 133	69.74 149	78.87 239	90.84 123	60.29 260	75.64 270	85.92 234	67.28 214	93.11 121	71.24 167	91.79 199	85.77 246

MSDG		80.06 192	79.99 188	80.25 204	83.91 249	68.04 167	77.51 255	89.19 160	77.65 105	81.94 211	83.45 266	76.37 150	86.31 249	63.31 229	86.59 263	86.41 238

ab-mvs		79.67 193	80.56 178	76.99 245	88.48 167	56.93 262	84.70 127	86.06 202	68.95 202	80.78 228	93.08 97	75.30 156	84.62 267	56.78 266	90.90 216	89.43 199

VNet		79.31 194	80.27 183	76.44 253	87.92 178	53.95 281	75.58 275	84.35 225	74.39 144	82.23 207	90.72 164	72.84 187	84.39 269	60.38 251	93.98 159	90.97 171

thisisatest0530		79.07 195	77.33 206	84.26 136	87.13 195	64.58 189	83.66 153	75.95 266	68.86 203	85.22 165	87.36 216	38.10 325	93.57 103	75.47 132	94.28 154	94.62 67

PAPR		78.84 196	78.10 201	81.07 194	85.17 227	60.22 238	82.21 192	90.57 130	62.51 243	75.32 273	84.61 255	74.99 158	92.30 138	59.48 255	88.04 251	90.68 179

PVSNet_BlendedMVS		78.80 197	77.84 202	81.65 188	84.43 234	63.41 200	79.49 229	90.44 132	61.70 251	75.43 271	87.07 220	69.11 208	91.44 161	60.68 249	92.24 194	90.11 193

FMVSNet3		78.80 197	78.55 197	79.57 214	82.89 259	56.89 264	81.76 197	85.77 205	69.04 201	86.00 154	90.44 171	51.75 280	90.09 202	65.95 210	93.34 168	91.72 157

test_yl		78.71 199	78.51 198	79.32 217	84.32 238	58.84 249	78.38 243	85.33 210	75.99 122	82.49 202	86.57 223	58.01 256	90.02 204	62.74 231	92.73 185	89.10 206

DCV-MVSNet		78.71 199	78.51 198	79.32 217	84.32 238	58.84 249	78.38 243	85.33 210	75.99 122	82.49 202	86.57 223	58.01 256	90.02 204	62.74 231	92.73 185	89.10 206

pmmvs-eth3d		78.42 201	77.04 209	82.57 175	87.44 189	74.41 110	80.86 213	79.67 250	55.68 280	84.69 173	90.31 174	60.91 238	85.42 259	62.20 235	91.59 202	87.88 224

MVS_0304		78.17 202	77.23 207	80.99 196	84.13 246	69.07 162	81.39 204	80.81 244	76.28 117	67.53 307	89.11 190	62.87 232	86.77 243	60.90 248	92.01 198	87.13 232

mvs_anonymous		78.13 203	78.76 195	76.23 257	79.24 288	50.31 308	78.69 241	84.82 222	61.60 252	83.09 198	92.82 107	73.89 171	87.01 236	68.33 197	86.41 265	91.37 166

TAMVS		78.08 204	76.36 214	83.23 159	90.62 134	72.87 118	79.08 236	80.01 249	61.72 250	81.35 221	86.92 221	63.96 227	88.78 220	50.61 296	93.01 179	88.04 220

Vis-MVSNet (Re-imp)		77.82 205	77.79 203	77.92 238	88.82 162	51.29 301	83.28 163	71.97 296	74.04 146	82.23 207	89.78 182	57.38 262	89.41 211	57.22 265	95.41 121	93.05 116

CANet_DTU		77.81 206	77.05 208	80.09 207	81.37 268	59.90 241	83.26 164	88.29 172	69.16 199	67.83 305	83.72 262	60.93 237	89.47 208	69.22 187	89.70 231	90.88 174

OpenMVS_ROB	70.19 17	77.77 207	77.46 204	78.71 224	84.39 237	61.15 228	81.18 209	82.52 231	62.45 245	83.34 193	87.37 215	66.20 219	88.66 222	64.69 220	85.02 277	86.32 239

MDA-MVSNet-bldmvs		77.47 208	76.90 210	79.16 219	79.03 290	64.59 188	66.58 313	75.67 269	73.15 163	88.86 107	88.99 192	66.94 216	81.23 285	64.71 219	88.22 250	91.64 160

jason		77.42 209	75.75 221	82.43 178	87.10 199	69.27 155	77.99 248	81.94 237	51.47 304	77.84 251	85.07 248	60.32 242	89.00 214	70.74 173	89.27 236	89.03 209
jason: jason.
CDS-MVSNet		77.32 210	75.40 224	83.06 162	89.00 159	72.48 125	77.90 250	82.17 235	60.81 255	78.94 246	83.49 265	59.30 250	88.76 221	54.64 282	92.37 191	87.93 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 211	76.75 211	78.52 227	87.01 201	61.30 226	75.55 276	87.12 193	61.24 254	74.45 277	78.79 301	77.20 136	90.93 175	64.62 222	84.80 281	83.32 275

MVSTER		77.09 212	75.70 222	81.25 192	75.27 314	61.08 229	77.49 256	85.07 216	60.78 256	86.55 145	88.68 196	43.14 316	90.25 193	73.69 146	90.67 222	92.42 134

PS-MVSNAJ		77.04 213	76.53 213	78.56 226	87.09 200	61.40 224	75.26 277	87.13 191	61.25 253	74.38 279	77.22 311	76.94 142	90.94 174	64.63 221	84.83 280	83.35 274

IterMVS		76.91 214	76.34 215	78.64 225	80.91 273	64.03 195	76.30 267	79.03 251	64.88 234	83.11 196	89.16 188	59.90 246	84.46 268	68.61 195	85.15 276	87.42 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 215	75.67 223	80.34 203	80.48 280	62.16 220	73.50 288	84.80 223	57.61 272	82.24 206	87.54 212	51.31 281	87.65 231	70.40 178	93.19 174	91.23 168

TR-MVS		76.77 216	75.79 219	79.72 211	86.10 220	65.79 182	77.14 257	83.02 228	65.20 232	81.40 220	82.10 280	66.30 218	90.73 183	55.57 274	85.27 274	82.65 281

USDC		76.63 217	76.73 212	76.34 255	83.46 252	57.20 261	80.02 220	88.04 177	52.14 300	83.65 190	91.25 146	63.24 231	86.65 245	54.66 281	94.11 156	85.17 251

BH-w/o		76.57 218	76.07 218	78.10 235	86.88 203	65.92 181	77.63 252	86.33 199	65.69 226	80.89 226	79.95 296	68.97 210	90.74 182	53.01 288	85.25 275	77.62 310

Patchmtry		76.56 219	77.46 204	73.83 267	79.37 287	46.60 317	82.41 185	76.90 260	73.81 149	85.56 162	92.38 119	48.07 289	83.98 273	63.36 228	95.31 126	90.92 173

PVSNet_Blended		76.49 220	75.40 224	79.76 210	84.43 234	63.41 200	75.14 278	90.44 132	57.36 274	75.43 271	78.30 305	69.11 208	91.44 161	60.68 249	87.70 256	84.42 259

miper_lstm_enhance		76.45 221	76.10 217	77.51 242	76.72 302	60.97 233	64.69 316	85.04 218	63.98 236	83.20 195	88.22 200	56.67 265	78.79 293	73.22 150	93.12 175	92.78 125

lupinMVS		76.37 222	74.46 232	82.09 179	85.54 224	69.26 156	76.79 259	80.77 245	50.68 310	76.23 262	82.82 274	58.69 253	88.94 215	69.85 180	88.77 241	88.07 218

cascas		76.29 223	74.81 228	80.72 200	84.47 233	62.94 206	73.89 286	87.34 184	55.94 279	75.16 275	76.53 314	63.97 226	91.16 168	65.00 217	90.97 214	88.06 219

RPMNet		76.06 224	75.79 219	76.85 249	79.58 282	62.64 211	82.58 180	71.75 299	74.80 138	75.72 268	92.59 113	48.69 287	84.07 271	73.48 148	82.91 291	83.85 266

thres600view7		75.97 225	75.35 226	77.85 240	87.01 201	51.84 298	80.45 216	73.26 287	75.20 135	83.10 197	86.31 229	45.54 302	89.05 213	55.03 279	92.24 194	92.66 129

GA-MVS		75.83 226	74.61 229	79.48 216	81.87 263	59.25 247	73.42 289	82.88 229	68.68 204	79.75 238	81.80 283	50.62 283	89.46 209	66.85 204	85.64 271	89.72 195

MVP-Stereo		75.81 227	73.51 241	82.71 171	89.35 152	73.62 113	80.06 218	85.20 213	60.30 259	73.96 280	87.94 205	57.89 260	89.45 210	52.02 291	74.87 317	85.06 253
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 228	75.05 227	76.66 252	87.27 191	51.88 296	81.07 210	73.26 287	75.68 128	83.25 194	86.37 226	45.54 302	88.80 217	51.98 292	90.99 211	89.31 201

ET-MVSNet_ETH3D		75.28 229	72.77 247	82.81 170	83.03 258	68.11 166	77.09 258	76.51 264	60.67 258	77.60 255	80.52 291	38.04 326	91.15 169	70.78 171	90.68 221	89.17 204

thres400		75.14 230	74.23 234	77.86 239	86.24 215	52.12 293	79.24 233	73.87 280	73.34 156	81.82 214	84.60 256	46.02 296	88.80 217	51.98 292	90.99 211	92.66 129

wuyk23d		75.13 231	79.30 192	62.63 305	75.56 309	75.18 107	80.89 212	73.10 289	75.06 137	94.76 12	95.32 34	87.73 34	52.85 331	34.16 329	97.11 72	59.85 327

EU-MVSNet		75.12 232	74.43 233	77.18 244	83.11 257	59.48 245	85.71 115	82.43 233	39.76 328	85.64 160	88.76 194	44.71 312	87.88 229	73.86 144	85.88 270	84.16 262

HyFIR lowres test		75.12 232	72.66 249	82.50 176	91.44 116	65.19 185	72.47 292	87.31 185	46.79 316	80.29 235	84.30 258	52.70 279	92.10 144	51.88 295	86.73 262	90.22 189

CMPMVS	59.41 20	75.12 232	73.57 239	79.77 209	75.84 308	67.22 170	81.21 208	82.18 234	50.78 308	76.50 258	87.66 210	55.20 274	82.99 278	62.17 237	90.64 225	89.09 208
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 235	72.98 246	80.73 199	84.95 229	71.71 138	76.23 269	77.59 257	52.83 294	77.73 254	86.38 225	56.35 268	84.97 263	57.72 264	87.05 260	85.51 248

tfpn200view9		74.86 236	74.23 234	76.74 251	86.24 215	52.12 293	79.24 233	73.87 280	73.34 156	81.82 214	84.60 256	46.02 296	88.80 217	51.98 292	90.99 211	89.31 201

1112_ss		74.82 237	73.74 237	78.04 236	89.57 150	60.04 239	76.49 265	87.09 194	54.31 286	73.66 282	79.80 297	60.25 243	86.76 244	58.37 258	84.15 284	87.32 230

ppachtmachnet_test		74.73 238	74.00 236	76.90 247	80.71 277	56.89 264	71.53 296	78.42 253	58.24 267	79.32 243	82.92 273	57.91 259	84.26 270	65.60 215	91.36 205	89.56 196

Patchmatch-RL test		74.48 239	73.68 238	76.89 248	84.83 231	66.54 177	72.29 293	69.16 308	57.70 270	86.76 141	86.33 227	45.79 301	82.59 280	69.63 182	90.65 224	81.54 297

PatchMatch-RL		74.48 239	73.22 243	78.27 233	87.70 183	85.26 30	75.92 271	70.09 305	64.34 235	76.09 264	81.25 288	65.87 222	78.07 294	53.86 284	83.82 285	71.48 318

XXY-MVS		74.44 241	76.19 216	69.21 286	84.61 232	52.43 292	71.70 295	77.18 259	60.73 257	80.60 229	90.96 157	75.44 153	69.35 312	56.13 270	88.33 245	85.86 245

CR-MVSNet		74.00 242	73.04 245	76.85 249	79.58 282	62.64 211	82.58 180	76.90 260	50.50 311	75.72 268	92.38 119	48.07 289	84.07 271	68.72 194	82.91 291	83.85 266

Test_1112_low_res		73.90 243	73.08 244	76.35 254	90.35 139	55.95 267	73.40 290	86.17 201	50.70 309	73.14 283	85.94 233	58.31 255	85.90 255	56.51 268	83.22 288	87.20 231

test20.03		73.75 244	74.59 231	71.22 281	81.11 271	51.12 303	70.15 301	72.10 295	70.42 187	80.28 236	91.50 143	64.21 225	74.72 304	46.96 312	94.58 149	87.82 226

SCA		73.32 245	72.57 251	75.58 260	81.62 265	55.86 269	78.89 238	71.37 302	61.73 249	74.93 276	83.42 267	60.46 240	87.01 236	58.11 262	82.63 295	83.88 263

baseline1		73.26 246	73.54 240	72.43 276	84.92 230	47.79 315	79.89 222	74.00 279	65.93 222	78.81 247	86.28 230	56.36 267	81.63 284	56.63 267	79.04 308	87.87 225

1314		73.22 247	72.56 252	75.20 261	80.41 281	57.84 255	81.64 200	85.36 209	51.68 303	73.10 284	76.65 313	61.45 236	85.19 261	63.54 226	79.21 307	82.59 282

MVS		73.21 248	72.59 250	75.06 262	80.97 272	60.81 234	81.64 200	85.92 204	46.03 319	71.68 290	77.54 307	68.47 211	89.77 206	55.70 273	85.39 272	74.60 315

HY-MVS	64.64 18	73.03 249	72.47 253	74.71 263	83.36 253	54.19 279	82.14 195	81.96 236	56.76 278	69.57 298	86.21 231	60.03 244	84.83 266	49.58 301	82.65 293	85.11 252

thisisatest0515		73.00 250	70.52 264	80.46 201	81.45 267	59.90 241	73.16 291	74.31 278	57.86 269	76.08 265	77.78 306	37.60 328	92.12 143	65.00 217	91.45 204	89.35 200

EPNet_dtu		72.87 251	71.33 262	77.49 243	77.72 295	60.55 236	82.35 186	75.79 267	66.49 219	58.39 330	81.06 289	53.68 277	85.98 253	53.55 285	92.97 181	85.95 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 252	71.41 261	76.28 256	83.25 254	60.34 237	83.50 158	79.02 252	37.77 329	76.33 260	85.10 246	49.60 286	87.41 233	70.54 176	77.54 313	81.08 304

CHOSEN 1792x2688		72.45 253	70.56 263	78.13 234	90.02 148	63.08 205	68.72 305	83.16 227	42.99 325	75.92 266	85.46 240	57.22 264	85.18 262	49.87 300	81.67 296	86.14 241

testgi		72.36 254	74.61 229	65.59 299	80.56 279	42.82 326	68.29 306	73.35 286	66.87 216	81.84 213	89.93 180	72.08 195	66.92 320	46.05 314	92.54 189	87.01 234

thres200		72.34 255	71.55 260	74.70 264	83.48 251	51.60 299	75.02 279	73.71 283	70.14 192	78.56 249	80.57 290	46.20 294	88.20 227	46.99 311	89.29 234	84.32 260

FPMVS		72.29 256	72.00 255	73.14 270	88.63 164	85.00 32	74.65 282	67.39 309	71.94 178	77.80 253	87.66 210	50.48 284	75.83 300	49.95 298	79.51 303	58.58 329

FMVSNet5		72.10 257	71.69 257	73.32 268	81.57 266	53.02 288	76.77 260	78.37 254	63.31 238	76.37 259	91.85 132	36.68 329	78.98 291	47.87 308	92.45 190	87.95 222

our_test_3		71.85 258	71.59 258	72.62 274	80.71 277	53.78 282	69.72 303	71.71 301	58.80 264	78.03 250	80.51 292	56.61 266	78.84 292	62.20 235	86.04 269	85.23 250

PAPM		71.77 259	70.06 269	76.92 246	86.39 207	53.97 280	76.62 263	86.62 197	53.44 291	63.97 320	84.73 254	57.79 261	92.34 137	39.65 324	81.33 299	84.45 258

IB-MVS	62.13 19	71.64 260	68.97 273	79.66 213	80.80 276	62.26 219	73.94 285	76.90 260	63.27 239	68.63 301	76.79 312	33.83 332	91.84 153	59.28 256	87.26 258	84.88 254
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		71.63 261	72.30 254	69.62 284	76.47 304	52.70 290	70.03 302	80.97 243	59.18 263	79.36 242	88.21 201	60.50 239	69.12 313	58.33 260	77.62 312	87.04 233

Anonymous20231206		71.38 262	71.88 256	69.88 282	86.31 212	54.37 278	70.39 300	74.62 274	52.57 296	76.73 257	88.76 194	59.94 245	72.06 306	44.35 317	93.23 173	83.23 277

MIMVSNet		71.09 263	71.59 258	69.57 285	87.23 192	50.07 309	78.91 237	71.83 297	60.20 261	71.26 291	91.76 137	55.08 275	76.09 298	41.06 322	87.02 261	82.54 284

MS-PatchMatch		70.93 264	70.22 267	73.06 271	81.85 264	62.50 214	73.82 287	77.90 255	52.44 297	75.92 266	81.27 287	55.67 271	81.75 282	55.37 276	77.70 311	74.94 314

pmmvs5		70.73 265	70.07 268	72.72 272	77.03 300	52.73 289	74.14 283	75.65 270	50.36 312	72.17 288	85.37 244	55.42 273	80.67 287	52.86 289	87.59 257	84.77 255

PatchT		70.52 266	72.76 248	63.79 304	79.38 286	33.53 332	77.63 252	65.37 317	73.61 151	71.77 289	92.79 110	44.38 313	75.65 301	64.53 223	85.37 273	82.18 289

N_pmnet		70.20 267	68.80 275	74.38 265	80.91 273	84.81 35	59.12 324	76.45 265	55.06 283	75.31 274	82.36 279	55.74 270	54.82 330	47.02 310	87.24 259	83.52 270

tpmvs		70.16 268	69.56 271	71.96 279	74.71 318	48.13 312	79.63 224	75.45 272	65.02 233	70.26 295	81.88 282	45.34 307	85.68 257	58.34 259	75.39 316	82.08 290

new-patchmatchnet		70.10 269	73.37 242	60.29 312	81.23 270	16.95 336	59.54 322	74.62 274	62.93 240	80.97 224	87.93 206	62.83 233	71.90 307	55.24 277	95.01 137	92.00 148

YYNet1		70.06 270	70.44 265	68.90 287	73.76 320	53.42 286	58.99 325	67.20 311	58.42 266	87.10 136	85.39 243	59.82 247	67.32 317	59.79 253	83.50 287	85.96 242

MDA-MVSNet_test_wron		70.05 271	70.44 265	68.88 288	73.84 319	53.47 284	58.93 326	67.28 310	58.43 265	87.09 137	85.40 242	59.80 248	67.25 318	59.66 254	83.54 286	85.92 244

CostFormer		69.98 272	68.68 276	73.87 266	77.14 298	50.72 306	79.26 232	74.51 276	51.94 302	70.97 294	84.75 253	45.16 310	87.49 232	55.16 278	79.23 306	83.40 273

baseline2		69.77 273	66.89 282	78.41 230	79.51 284	58.09 254	76.23 269	69.57 307	57.50 273	64.82 318	77.45 309	46.02 296	88.44 223	53.08 287	77.83 310	88.70 214

PatchmatchNet		69.71 274	68.83 274	72.33 277	77.66 296	53.60 283	79.29 231	69.99 306	57.66 271	72.53 286	82.93 272	46.45 293	80.08 290	60.91 247	72.09 321	83.31 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 275	66.64 286	77.70 241	73.19 322	71.24 141	75.67 273	65.56 316	70.42 187	65.18 314	92.97 102	33.64 333	83.06 277	53.52 286	69.61 327	78.79 309

UnsupCasMVSNet_bld		69.21 276	69.68 270	67.82 293	79.42 285	51.15 302	67.82 310	75.79 267	54.15 287	77.47 256	85.36 245	59.26 251	70.64 309	48.46 305	79.35 305	81.66 295

gg-mvs-nofinetune		68.96 277	69.11 272	68.52 292	76.12 307	45.32 319	83.59 154	55.88 330	86.68 22	64.62 319	97.01 8	30.36 336	83.97 274	44.78 316	82.94 290	76.26 312

tpm2		68.45 278	66.83 283	73.30 269	78.93 292	48.50 311	79.76 223	71.76 298	47.50 315	69.92 297	83.60 263	42.07 319	88.40 224	48.44 306	79.51 303	83.01 280

tpm		67.95 279	68.08 279	67.55 294	78.74 293	43.53 324	75.60 274	67.10 314	54.92 284	72.23 287	88.10 202	42.87 317	75.97 299	52.21 290	80.95 302	83.15 278

PatchFormer-LS_test		67.91 280	66.49 287	72.17 278	75.29 313	51.85 297	75.68 272	73.62 285	57.23 276	68.64 299	68.13 326	42.19 318	82.76 279	64.06 224	73.51 319	81.89 293

WTY-MVS		67.91 280	68.35 277	66.58 297	80.82 275	48.12 313	65.96 314	72.60 291	53.67 290	71.20 292	81.68 285	58.97 252	69.06 314	48.57 304	81.67 296	82.55 283

test-LLR		67.21 282	66.74 284	68.63 290	76.45 305	55.21 274	67.89 307	67.14 312	62.43 246	65.08 315	72.39 319	43.41 314	69.37 310	61.00 245	84.89 278	81.31 299

sss		66.92 283	67.26 281	65.90 298	77.23 297	51.10 304	64.79 315	71.72 300	52.12 301	70.13 296	80.18 294	57.96 258	65.36 325	50.21 297	81.01 301	81.25 301

tpm cat1		66.76 284	65.21 289	71.42 280	77.09 299	50.62 307	78.01 247	73.68 284	44.89 321	68.64 299	79.00 300	45.51 304	82.42 281	49.91 299	70.15 324	81.23 303

DWT-MVSNet_test		66.43 285	64.37 290	72.63 273	74.86 317	50.86 305	76.52 264	72.74 290	54.06 288	65.50 312	68.30 325	32.13 334	84.84 265	61.63 242	73.59 318	82.19 288

PVSNet	58.17 21	66.41 286	65.63 288	68.75 289	81.96 262	49.88 310	62.19 320	72.51 293	51.03 306	68.04 303	75.34 317	50.84 282	74.77 302	45.82 315	82.96 289	81.60 296

tpmrst		66.28 287	66.69 285	65.05 302	72.82 326	39.33 327	78.20 246	70.69 304	53.16 293	67.88 304	80.36 293	48.18 288	74.75 303	58.13 261	70.79 323	81.08 304

Patchmatch-test		65.91 288	67.38 280	61.48 310	75.51 310	43.21 325	68.84 304	63.79 319	62.48 244	72.80 285	83.42 267	44.89 311	59.52 329	48.27 307	86.45 264	81.70 294

ADS-MVSNet2		65.87 289	63.64 293	72.55 275	73.16 323	56.92 263	67.10 311	74.81 273	49.74 313	66.04 310	82.97 270	46.71 291	77.26 295	42.29 319	69.96 325	83.46 271

test-mter		65.00 290	63.79 292	68.63 290	76.45 305	55.21 274	67.89 307	67.14 312	50.98 307	65.08 315	72.39 319	28.27 338	69.37 310	61.00 245	84.89 278	81.31 299

test0.0.03 1		64.66 291	64.36 291	65.57 300	75.03 316	46.89 316	64.69 316	61.58 324	62.43 246	71.18 293	77.54 307	43.41 314	68.47 315	40.75 323	82.65 293	81.35 298

pmmvs3		62.47 292	60.02 303	69.80 283	71.58 329	64.00 196	70.52 299	58.44 328	39.77 327	66.05 309	75.84 315	27.10 340	72.28 305	46.15 313	84.77 282	73.11 316

EPMVS		62.47 292	62.63 295	62.01 306	70.63 330	38.74 328	74.76 280	52.86 332	53.91 289	67.71 306	80.01 295	39.40 323	66.60 321	55.54 275	68.81 328	80.68 308

ADS-MVSNet		61.90 294	62.19 296	61.03 311	73.16 323	36.42 330	67.10 311	61.75 322	49.74 313	66.04 310	82.97 270	46.71 291	63.21 327	42.29 319	69.96 325	83.46 271

PMMVS		61.65 295	60.38 300	65.47 301	65.40 333	69.26 156	63.97 318	61.73 323	36.80 330	60.11 325	68.43 323	59.42 249	66.35 322	48.97 303	78.57 309	60.81 326

E-PMN		61.59 296	61.62 297	61.49 309	66.81 331	55.40 272	53.77 328	60.34 325	66.80 217	58.90 328	65.50 327	40.48 322	66.12 323	55.72 272	86.25 267	62.95 325

TESTMET0.1,1		61.29 297	60.32 301	64.19 303	72.06 327	51.30 300	67.89 307	62.09 320	45.27 320	60.65 324	69.01 322	27.93 339	64.74 326	56.31 269	81.65 298	76.53 311

MVS-HIRNet		61.16 298	62.92 294	55.87 315	79.09 289	35.34 331	71.83 294	57.98 329	46.56 317	59.05 327	91.14 150	49.95 285	76.43 297	38.74 325	71.92 322	55.84 330

EMVS		61.10 299	60.81 299	61.99 307	65.96 332	55.86 269	53.10 329	58.97 327	67.06 214	56.89 331	63.33 328	40.98 320	67.03 319	54.79 280	86.18 268	63.08 324

DSMNet-mixed		60.98 300	61.61 298	59.09 314	72.88 325	45.05 321	74.70 281	46.61 335	26.20 331	65.34 313	90.32 173	55.46 272	63.12 328	41.72 321	81.30 300	69.09 322

dp		60.70 301	60.29 302	61.92 308	72.04 328	38.67 329	70.83 297	64.08 318	51.28 305	60.75 323	77.28 310	36.59 330	71.58 308	47.41 309	62.34 330	75.52 313

CHOSEN 280x420		59.08 302	56.52 306	66.76 296	76.51 303	64.39 192	49.62 330	59.00 326	43.86 323	55.66 332	68.41 324	35.55 331	68.21 316	43.25 318	76.78 315	67.69 323

PVSNet_0	51.08 22	56.10 303	54.97 307	59.48 313	75.12 315	53.28 287	55.16 327	61.89 321	44.30 322	59.16 326	62.48 329	54.22 276	65.91 324	35.40 328	47.01 331	59.25 328

new_pmnet		55.69 304	57.66 305	49.76 317	75.47 311	30.59 333	59.56 321	51.45 333	43.62 324	62.49 321	75.48 316	40.96 321	49.15 333	37.39 327	72.52 320	69.55 321

PMMVS2		55.64 305	59.27 304	44.74 318	64.30 334	12.32 337	40.60 331	49.79 334	53.19 292	65.06 317	84.81 252	53.60 278	49.76 332	32.68 331	89.41 233	72.15 317

MVE	40.22 23	51.82 306	50.47 308	55.87 315	62.66 335	51.91 295	31.61 333	39.28 336	40.65 326	50.76 333	74.98 318	56.24 269	44.67 334	33.94 330	64.11 329	71.04 320
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		20.81 307	27.75 309	0.00 323	0.00 340	0.00 341	0.00 334	85.44 208	0.00 336	0.00 338	82.82 274	81.46 99	0.00 339	0.00 336	0.00 336	0.00 335

tmp_tt		20.25 308	24.50 310	7.49 320	4.47 337	8.70 338	34.17 332	25.16 338	1.00 333	32.43 335	18.49 332	39.37 324	9.21 336	21.64 332	43.75 332	4.57 332

ab-mvs-re		6.65 309	8.87 311	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	79.80 297	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		6.41 310	8.55 312	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	76.94 142	0.00 339	0.00 336	0.00 336	0.00 335

test123		6.27 311	8.08 313	0.84 321	1.11 339	0.57 339	62.90 319	0.82 340	0.54 334	1.07 337	2.75 337	1.26 341	0.30 337	1.04 334	1.26 335	1.66 333

testmvs		5.91 312	7.65 314	0.72 322	1.20 338	0.37 340	59.14 323	0.67 341	0.49 335	1.11 336	2.76 336	0.94 342	0.24 338	1.02 335	1.47 334	1.55 334

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

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	92.00 146	83.25 59	96.93 77	88.98 212

save fliter					93.75 54	77.44 87	86.31 109	89.72 152	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	92.98 118

test_0728_SECOND				86.79 85	94.25 45	72.45 126	90.54 39	94.10 25					95.88 13	86.42 27	97.97 43	92.02 147

test0726					94.16 46	72.56 122	90.63 38	93.90 33	83.61 38	93.75 27	94.49 59	89.76 16

GSMVS																83.88 263

test_part2					93.86 53	77.77 82				92.84 39

test_part1				0.00 323		0.00 341	0.00 334	93.93 30				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												46.11 295				83.88 263

sam_mvs												45.92 300

ambc				82.98 164	90.55 137	64.86 187	88.20 80	89.15 161		89.40 101	93.96 82	71.67 200	91.38 165	78.83 103	96.55 88	92.71 127

MTGPA								91.81 101

test_post1							78.85 240				3.13 334	45.19 309	80.13 289	58.11 262

test_post											3.10 335	45.43 305	77.22 296

patchmatchnet-post											81.71 284	45.93 299	87.01 236

GG-mvs-BLEND				67.16 295	73.36 321	46.54 318	84.15 138	55.04 331		58.64 329	61.95 330	29.93 337	83.87 275	38.71 326	76.92 314	71.07 319

MTMP							90.66 36	33.14 337

gm-plane-assit					75.42 312	44.97 322			52.17 298		72.36 321		87.90 228	54.10 283

test9_res														80.83 83	96.45 93	90.57 181

TEST9					92.34 87	79.70 67	83.94 142	90.32 136	65.41 231	84.49 176	90.97 155	82.03 90	93.63 96

test_8					92.09 95	78.87 74	83.82 147	90.31 138	65.79 224	84.36 179	90.96 157	81.93 92	93.44 108

agg_prior2														79.68 94	96.16 102	90.22 189

agg_prior					91.58 110	77.69 83		90.30 140		84.32 180			93.18 116

TestCases				89.68 50	91.59 107	83.40 45		95.44 5	79.47 82	88.00 124	93.03 98	82.66 77	91.47 159	70.81 169	96.14 103	94.16 82

test_prior4						78.97 73	84.59 129

test_prior2							83.37 161		75.43 131	84.58 174	91.57 140	81.92 94		79.54 96	96.97 75

test_prior				86.32 93	90.59 135	71.99 132		92.85 74					94.17 76			92.80 123

旧先验2							81.73 198		56.88 277	86.54 150			84.90 264	72.81 157

新几何2							81.72 199

新几何1				82.95 165	93.96 50	78.56 78		80.24 246	55.45 281	83.93 187	91.08 151	71.19 202	88.33 225	65.84 213	93.07 176	81.95 292

旧先验1					91.97 98	71.77 134		81.78 238			91.84 133	73.92 170			93.65 166	83.61 269

无先验							82.81 177	85.62 207	58.09 268				91.41 163	67.95 200		84.48 257

原ACMM2							82.26 191

原ACMM1				84.60 128	92.81 78	74.01 112		91.50 107	62.59 242	82.73 201	90.67 167	76.53 149	94.25 70	69.24 185	95.69 117	85.55 247

test222					93.31 64	76.54 97	79.38 230	77.79 256	52.59 295	82.36 205	90.84 161	66.83 217			91.69 201	81.25 301

testdata2													86.43 248	63.52 227

segment_acmp												81.94 91

testdata				79.54 215	92.87 74	72.34 127		80.14 248	59.91 262	85.47 164	91.75 138	67.96 213	85.24 260	68.57 196	92.18 197	81.06 306

testdata1							79.62 225		73.95 148

test12				86.57 88	90.74 131	72.63 120		90.69 126		82.76 200		79.20 118	94.80 58		95.32 124	92.27 140

plane_prior7					93.45 59	77.31 90

plane_prior6					92.61 79	76.54 97						74.84 160

plane_prior5								93.61 42					95.22 45	80.78 84	95.83 112	94.46 73

plane_prior4											92.95 103

plane_prior3						76.85 95			77.79 104	86.55 145

plane_prior2							89.45 63		79.44 84

plane_prior1					92.83 77

plane_prior						76.42 100	87.15 94		75.94 125						95.03 136

n2								0.00 342

nn								0.00 342

door-mid								74.45 277

lessismore_v0				85.95 104	91.10 126	70.99 143		70.91 303		91.79 56	94.42 62	61.76 235	92.93 126	79.52 98	93.03 178	93.93 89

LGP-MVS_train				90.82 35	94.75 39	81.69 52		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

test11								91.46 108

door								72.57 292

HQP5-MVS						70.66 144

HQP-NCC					91.19 121		84.77 124		73.30 158	80.55 232

ACMP_Plane					91.19 121		84.77 124		73.30 158	80.55 232

BP-MVS														77.30 120

HQP4-MVS										80.56 231			94.61 62			93.56 105

HQP3-MVS								92.68 80							94.47 151

HQP2-MVS												72.10 193

NP-MVS					91.95 99	74.55 109					90.17 178

MDTV_nov1_ep13_2view						27.60 335	70.76 298		46.47 318	61.27 322		45.20 308		49.18 302		83.75 268

MDTV_nov1_ep13			68.29 278		78.03 294	43.87 323	74.12 284	72.22 294	52.17 298	67.02 308	85.54 236	45.36 306	80.85 286	55.73 271	84.42 283

ACMMP++_ref															95.74 116

ACMMP++															97.35 65

Test By Simon												79.09 119

ITE_SJBPF				90.11 47	90.72 132	84.97 33		90.30 140	81.56 62	90.02 81	91.20 148	82.40 80	90.81 180	73.58 147	94.66 147	94.56 68

DeepMVS_CX				24.13 319	32.95 336	29.49 334		21.63 339	12.07 332	37.95 334	45.07 331	30.84 335	19.21 335	17.94 333	33.06 333	23.69 331

High-resolution multi-view benchmark