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		86.90 1	88.67 1	81.57 25	91.50 1	63.30 118	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 66	68.08 89	97.05 1	96.93 1

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 63	74.51 48	96.15 2	92.88 7

abl_6		84.92 3	85.70 3	82.57 17	86.72 47	79.27 8	87.56 5	86.08 25	77.48 12	88.12 13	91.53 27	81.18 8	84.31 71	78.12 24	94.47 35	84.15 119

RE-MVS-def			85.50 4		86.19 53	79.18 9	87.23 7	86.27 20	77.51 10	87.65 18	90.73 45	81.38 7		78.11 25	94.46 36	84.89 91

test1172		84.85 4	85.39 5	83.21 3	88.34 37	80.50 6	85.12 28	85.22 39	81.06 2	87.20 27	90.28 67	79.20 14	85.58 46	78.04 27	94.08 54	83.55 129

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 34	77.71 16	86.81 15	85.25 38	77.42 14	86.15 36	90.24 68	81.69 5	85.94 31	77.77 29	93.58 65	83.09 142

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 53	79.18 9	87.23 7	86.27 20	77.51 10	87.65 18	90.73 45	79.20 14	85.58 46	78.11 25	94.46 36	84.89 91

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 33	75.83 26	86.27 23	86.89 16	73.69 22	86.17 35	91.70 24	78.23 20	85.20 56	79.45 13	94.91 25	88.15 45

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 75	75.40 29	91.60 1	87.80 7	73.52 23	88.90 10	93.06 6	71.39 74	81.53 115	81.53 3	92.15 86	88.91 37
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
ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 25	87.65 4	85.89 28	71.03 42	85.85 40	90.58 49	78.77 17	85.78 38	79.37 16	95.17 17	84.62 101
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
HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 38	74.40 34	85.24 27	87.21 14	70.69 45	85.14 52	90.42 57	78.99 16	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 3	84.43 60	71.96 39	84.70 59	90.56 50	77.12 26	86.18 25	79.24 18	95.36 13	82.49 161

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 11	82.79 87	72.41 33	85.11 53	90.85 41	76.65 29	84.89 60	79.30 17	94.63 32	82.35 164

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 105	73.53 43	85.50 26	87.45 12	74.11 19	86.45 33	90.52 53	80.02 11	84.48 67	77.73 30	94.34 47	85.93 72

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 42	70.41 62	82.04 57	86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 77	66.72 90	86.54 19	85.11 41	72.00 38	86.65 32	91.75 23	78.20 21	87.04 9	77.93 28	94.32 48	83.47 132

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 102	74.39 35	87.18 9	88.18 6	78.98 6	86.11 38	91.47 29	79.70 12	85.76 39	66.91 104	95.46 11	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 27	87.01 13	84.27 64	70.23 46	84.47 62	90.43 55	76.79 27	85.94 31	79.58 11	94.23 51	82.82 151

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 21	87.01 13	84.19 69	70.23 46	84.49 61	90.67 48	75.15 42	86.37 19	79.58 11	94.26 49	84.18 118

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 30	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 189

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 19	87.06 12	84.23 68	70.19 48	83.86 70	90.72 47	75.20 41	86.27 22	79.41 15	94.25 50	83.95 122

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	90.39 61	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 25	73.28 45	86.32 22	85.97 27	67.39 61	84.02 68	90.39 61	74.73 47	86.46 16	80.73 7	94.43 40	84.60 104

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 71	71.00 56	85.53 25	84.78 48	70.91 43	85.64 43	90.41 58	75.55 39	87.69 3	79.75 8	95.08 20	85.36 81
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 53	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 189

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 21	83.59 78	71.31 40	81.26 98	90.96 38	74.57 50	84.69 64	78.41 22	94.78 26	82.74 155
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SED-MVS		81.78 35	83.48 27	76.67 84	86.12 57	61.06 131	83.62 42	84.72 51	72.61 29	87.38 24	89.70 78	77.48 24	85.89 36	75.29 42	94.39 41	83.08 143

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 31	68.44 79	81.57 60	86.33 19	63.17 106	85.38 49	91.26 33	76.33 31	84.67 65	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 49	69.44 70	82.30 54	86.08 25	66.80 66	86.70 31	89.99 73	81.64 6	85.95 30	74.35 49	96.11 3	85.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	69.25 9	82.11 33	83.31 30	78.49 66	88.17 39	73.96 37	83.11 49	84.52 59	66.40 71	87.45 22	89.16 91	81.02 9	80.52 137	74.27 50	95.73 7	80.98 186
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 77	83.62 42	84.98 44	64.77 88	83.97 69	91.02 37	75.53 40	85.93 34	82.00 2	94.36 45	83.35 137

GST-MVS		82.79 27	83.27 32	81.34 32	88.99 27	73.29 44	85.94 24	85.13 40	68.58 57	84.14 67	90.21 70	73.37 60	86.41 17	79.09 19	93.98 58	84.30 117

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 23	82.04 57	85.40 35	67.96 59	84.91 57	90.88 39	75.59 37	86.57 15	78.16 23	94.71 30	83.82 123

PMVS	70.70 6	81.70 36	83.15 34	77.36 80	90.35 6	82.82 2	82.15 55	79.22 143	74.08 20	87.16 29	91.97 19	84.80 2	76.97 196	64.98 115	93.61 63	72.28 272
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS		81.15 41	83.12 35	75.24 104	86.16 55	60.78 136	83.77 40	80.58 124	72.48 31	85.83 41	90.41 58	78.57 18	85.69 41	75.86 39	94.39 41	79.24 213
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DPE-MVS		82.00 34	83.02 36	78.95 60	85.36 68	67.25 86	82.91 50	84.98 44	73.52 23	85.43 48	90.03 72	76.37 30	86.97 11	74.56 47	94.02 57	82.62 157

PEN-MVS		80.46 50	82.91 37	73.11 133	89.83 9	39.02 291	77.06 112	82.61 90	80.04 4	90.60 5	92.85 9	74.93 46	85.21 55	63.15 130	95.15 18	95.09 2

DTE-MVSNet		80.35 52	82.89 38	72.74 145	89.84 8	37.34 306	77.16 109	81.81 99	80.45 3	90.92 2	92.95 7	74.57 50	86.12 28	63.65 126	94.68 31	94.76 6

PS-CasMVS		80.41 51	82.86 39	73.07 134	89.93 7	39.21 288	77.15 110	81.28 109	79.74 5	90.87 3	92.73 11	75.03 44	84.93 59	63.83 125	95.19 16	95.07 3

#test#		82.40 30	82.71 40	81.48 27	89.25 21	75.69 27	84.47 34	84.27 64	64.45 91	84.47 62	90.43 55	76.79 27	85.94 31	76.01 36	94.23 51	82.82 151

SMA-MVS		82.12 32	82.68 41	80.43 42	88.90 30	69.52 68	85.12 28	84.76 49	63.53 101	84.23 66	91.47 29	72.02 66	87.16 7	79.74 10	94.36 45	84.61 102
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMH+	66.64 10	81.20 39	82.48 42	77.35 81	81.16 128	62.39 122	80.51 65	87.80 7	73.02 25	87.57 20	91.08 36	80.28 10	82.44 101	64.82 116	96.10 4	87.21 56

UA-Net		81.56 37	82.28 43	79.40 54	88.91 29	69.16 75	84.67 33	80.01 135	75.34 16	79.80 115	94.91 2	69.79 84	80.25 142	72.63 60	94.46 36	88.78 41

WR-MVS_H		80.22 55	82.17 44	74.39 111	89.46 15	42.69 265	78.24 97	82.24 93	78.21 9	89.57 8	92.10 18	68.05 99	85.59 45	66.04 107	95.62 9	94.88 5

SF-MVS		80.72 47	81.80 45	77.48 78	82.03 116	64.40 110	83.41 47	88.46 5	65.28 82	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

CPTT-MVS		81.51 38	81.76 46	80.76 40	89.20 24	78.75 12	86.48 20	82.03 96	68.80 53	80.92 103	88.52 101	72.00 67	82.39 102	74.80 44	93.04 71	81.14 181

APD-MVS		81.13 42	81.73 47	79.36 55	84.47 82	70.53 61	83.85 38	83.70 76	69.43 52	83.67 72	88.96 97	75.89 35	86.41 17	72.62 61	92.95 72	81.14 181
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		81.19 40	81.70 48	79.67 51	83.95 90	69.77 67	83.58 45	84.63 56	72.13 35	82.85 81	88.36 105	75.00 45	86.79 12	71.99 69	92.84 74	82.44 162

CP-MVSNet		79.48 59	81.65 49	72.98 137	89.66 13	39.06 290	76.76 113	80.46 126	78.91 7	90.32 6	91.70 24	68.49 94	84.89 60	63.40 129	95.12 19	95.01 4

OPM-MVS		80.99 45	81.63 50	79.07 58	86.86 46	69.39 71	79.41 81	84.00 74	65.64 75	85.54 46	89.28 84	76.32 32	83.47 83	74.03 51	93.57 66	84.35 114

SD-MVS		80.28 54	81.55 51	76.47 89	83.57 93	67.83 83	83.39 48	85.35 37	64.42 92	86.14 37	87.07 121	74.02 54	80.97 127	77.70 31	92.32 85	80.62 196
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		80.54 48	81.06 52	78.98 59	87.01 41	72.91 46	80.23 73	85.56 30	66.56 70	85.64 43	89.57 80	69.12 88	80.55 136	72.51 62	93.37 67	83.48 131

xxxxxxxxxxxxxcwj		80.31 53	80.94 53	78.42 68	87.00 42	67.23 87	79.24 82	88.61 4	56.65 161	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

LS3D		80.99 45	80.85 54	81.41 30	78.37 158	71.37 52	87.45 6	85.87 29	77.48 12	81.98 88	89.95 75	69.14 87	85.26 52	66.15 105	91.24 101	87.61 51

DeepC-MVS	72.44 4	81.00 44	80.83 55	81.50 26	86.70 48	70.03 66	82.06 56	87.00 15	59.89 128	80.91 104	90.53 51	72.19 64	88.56 1	73.67 54	94.52 34	85.92 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	73.19 2	81.08 43	80.48 56	82.87 9	81.41 125	72.03 47	84.38 35	86.23 23	77.28 15	80.65 106	90.18 71	59.80 171	87.58 4	73.06 57	91.34 99	89.01 33

v7n		79.37 61	80.41 57	76.28 91	78.67 157	55.81 169	79.22 84	82.51 92	70.72 44	87.54 21	92.44 14	68.00 101	81.34 116	72.84 58	91.72 88	91.69 10

9.14			80.22 58		80.68 130		80.35 70	87.69 10	59.90 127	83.00 77	88.20 108	74.57 50	81.75 113	73.75 53	93.78 59

ACMH	63.62 14	77.50 78	80.11 59	69.68 183	79.61 140	56.28 166	78.81 87	83.62 77	63.41 104	87.14 30	90.23 69	76.11 33	73.32 231	67.58 96	94.44 39	79.44 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 66	86.46 50	74.79 33	77.15 110	85.39 36	66.73 67	80.39 110	88.85 99	74.43 53	78.33 177	74.73 46	85.79 186	82.35 164

XVG-OURS		79.51 58	79.82 61	78.58 65	86.11 60	74.96 32	76.33 119	84.95 46	66.89 63	82.75 82	88.99 96	66.82 110	78.37 175	74.80 44	90.76 117	82.40 163

ETH3D-3000-0.1		79.14 62	79.80 62	77.16 83	80.67 131	64.57 107	80.26 71	87.60 11	60.74 122	82.47 85	88.03 113	71.73 69	81.81 111	73.12 56	93.61 63	85.09 86

HPM-MVS++		79.89 56	79.80 62	80.18 45	89.02 26	78.44 13	83.49 46	80.18 132	64.71 90	78.11 135	88.39 103	65.46 123	83.14 90	77.64 32	91.20 102	78.94 216

MSP-MVS		80.49 49	79.67 64	82.96 7	89.70 12	77.46 22	87.16 10	85.10 42	64.94 87	81.05 100	88.38 104	57.10 198	87.10 8	79.75 8	83.87 215	84.31 115
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
test_0402		78.17 75	79.48 65	74.24 114	83.50 94	59.15 152	72.52 156	74.60 200	75.34 16	88.69 12	91.81 22	75.06 43	82.37 103	65.10 113	88.68 149	81.20 179

DP-MVS		78.44 72	79.29 66	75.90 95	81.86 119	65.33 100	79.05 86	84.63 56	74.83 18	80.41 109	86.27 148	71.68 70	83.45 84	62.45 134	92.40 83	78.92 217

UniMVSNet_ETH3D		76.74 83	79.02 67	69.92 182	89.27 20	43.81 253	74.47 143	71.70 220	72.33 34	85.50 47	93.65 3	77.98 22	76.88 199	54.60 196	91.64 91	89.08 31

TSAR-MVS + MP.		79.05 63	78.81 68	79.74 48	88.94 28	67.52 84	86.61 18	81.38 107	51.71 215	77.15 145	91.42 32	65.49 122	87.20 6	79.44 14	87.17 173	84.51 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OMC-MVS		79.41 60	78.79 69	81.28 34	80.62 132	70.71 60	80.91 63	84.76 49	62.54 110	81.77 90	86.65 138	71.46 72	83.53 82	67.95 94	92.44 82	89.60 23

HQP_MVS		78.77 66	78.78 70	78.72 62	85.18 69	65.18 102	82.74 51	85.49 32	65.45 77	78.23 132	89.11 92	60.83 162	86.15 26	71.09 71	90.94 109	84.82 93

ETH3D cwj APD-0.16		78.38 73	78.72 71	77.38 79	80.09 136	66.16 95	79.08 85	86.13 24	57.55 151	80.93 102	87.76 116	71.98 68	82.73 98	72.11 68	92.83 75	83.25 139

mvs_tets		78.93 64	78.67 72	79.72 49	84.81 76	73.93 38	80.65 64	76.50 181	51.98 213	87.40 23	91.86 21	76.09 34	78.53 167	68.58 84	90.20 123	86.69 64

CNVR-MVS		78.49 70	78.59 73	78.16 71	85.86 64	67.40 85	78.12 100	81.50 103	63.92 96	77.51 142	86.56 142	68.43 96	84.82 62	73.83 52	91.61 93	82.26 167

OurMVSNet-221017-0		78.57 68	78.53 74	78.67 63	80.48 133	64.16 111	80.24 72	82.06 95	61.89 114	88.77 11	93.32 4	57.15 196	82.60 100	70.08 77	92.80 76	89.25 27

test_djsdf		78.88 65	78.27 75	80.70 41	81.42 124	71.24 54	83.98 36	75.72 187	52.27 208	87.37 26	92.25 16	68.04 100	80.56 134	72.28 65	91.15 104	90.32 20

test_part1		76.97 81	78.21 76	73.25 130	77.87 165	45.76 242	78.27 96	87.26 13	66.69 68	85.31 50	91.43 31	55.95 207	84.24 73	65.71 109	95.43 12	89.75 22

jajsoiax		78.51 69	78.16 77	79.59 52	84.65 79	73.83 40	80.42 67	76.12 183	51.33 221	87.19 28	91.51 28	73.79 57	78.44 171	68.27 87	90.13 128	86.49 65

NCCC		78.25 74	78.04 78	78.89 61	85.61 65	69.45 69	79.80 78	80.99 118	65.77 74	75.55 174	86.25 150	67.42 104	85.42 48	70.10 76	90.88 115	81.81 174

anonymousdsp		78.60 67	77.80 79	81.00 37	78.01 163	74.34 36	80.09 74	76.12 183	50.51 230	89.19 9	90.88 39	71.45 73	77.78 188	73.38 55	90.60 119	90.90 16

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 159	84.61 80	42.57 267	70.98 181	78.29 161	68.67 56	83.04 76	89.26 85	72.99 62	80.75 133	55.58 189	95.47 10	91.35 11

AllTest		77.66 76	77.43 81	78.35 69	79.19 149	70.81 57	78.60 90	88.64 2	65.37 80	80.09 113	88.17 109	70.33 79	78.43 172	55.60 186	90.90 113	85.81 74

PS-MVSNAJss		77.54 77	77.35 82	78.13 73	84.88 74	66.37 93	78.55 91	79.59 140	53.48 199	86.29 34	92.43 15	62.39 144	80.25 142	67.90 95	90.61 118	87.77 48

Anonymous20231211		75.54 92	77.19 83	70.59 167	77.67 170	45.70 244	74.73 139	80.19 131	68.80 53	82.95 78	92.91 8	66.26 115	76.76 202	58.41 164	92.77 77	89.30 26

test_prior3		76.71 84	77.19 83	75.27 102	82.15 114	59.85 145	75.57 126	84.33 62	58.92 136	76.53 164	86.78 129	67.83 102	83.39 85	69.81 79	92.76 78	82.58 158

DeepPCF-MVS	71.07 5	78.48 71	77.14 85	82.52 19	84.39 86	77.04 24	76.35 117	84.05 72	56.66 160	80.27 111	85.31 167	68.56 93	87.03 10	67.39 99	91.26 100	83.50 130

CDPH-MVS		77.33 79	77.06 86	78.14 72	84.21 87	63.98 113	76.07 122	83.45 79	54.20 188	77.68 141	87.18 117	69.98 82	85.37 49	68.01 91	92.72 80	85.08 88

train_agg		76.38 85	76.55 87	75.86 96	85.47 66	69.32 73	76.42 115	78.69 152	54.00 192	76.97 148	86.74 132	66.60 112	81.10 122	72.50 63	91.56 94	77.15 234

agg_prior1		75.89 87	76.41 88	74.31 113	84.44 84	66.02 96	76.12 121	78.62 155	54.40 186	76.95 150	86.85 126	66.44 114	80.34 139	72.45 64	91.42 97	76.57 238

SixPastTwentyTwo		75.77 88	76.34 89	74.06 117	81.69 122	54.84 174	76.47 114	75.49 189	64.10 95	87.73 17	92.24 17	50.45 232	81.30 118	67.41 98	91.46 96	86.04 70

DeepC-MVS_fast	69.89 7	77.17 80	76.33 90	79.70 50	83.90 92	67.94 81	80.06 76	83.75 75	56.73 159	74.88 182	85.32 166	65.54 121	87.79 2	65.61 111	91.14 105	83.35 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v10		75.69 91	76.20 91	74.16 115	74.44 215	48.69 209	75.84 125	82.93 86	59.02 135	85.92 39	89.17 90	58.56 181	82.74 97	70.73 73	89.14 145	91.05 13

ETH3 D test6400		75.73 90	76.00 92	74.92 105	81.75 120	56.93 163	78.31 94	84.60 58	52.83 204	77.15 145	85.14 169	68.59 92	84.03 74	65.44 112	90.20 123	83.82 123

nrg030		74.87 104	75.99 93	71.52 160	74.90 202	49.88 205	74.10 146	82.58 91	54.55 185	83.50 74	89.21 87	71.51 71	75.74 211	61.24 140	92.34 84	88.94 36

MSLP-MVS++		74.48 107	75.78 94	70.59 167	84.66 78	62.40 121	78.65 89	84.24 67	60.55 124	77.71 140	81.98 201	63.12 137	77.64 190	62.95 131	88.14 151	71.73 277

UniMVSNet_NR-MVSNet		74.90 103	75.65 95	72.64 148	83.04 100	45.79 240	69.26 199	78.81 149	66.66 69	81.74 92	86.88 125	63.26 136	81.07 124	56.21 181	94.98 21	91.05 13

v8		75.07 99	75.64 96	73.35 126	73.42 227	47.46 227	75.20 130	81.45 105	60.05 126	85.64 43	89.26 85	58.08 188	81.80 112	69.71 81	87.97 156	90.79 17

DU-MVS		74.91 102	75.57 97	72.93 139	83.50 94	45.79 240	69.47 196	80.14 133	65.22 83	81.74 92	87.08 119	61.82 150	81.07 124	56.21 181	94.98 21	91.93 8

UniMVSNet (Re)		75.00 100	75.48 98	73.56 124	83.14 99	47.92 220	70.41 188	81.04 117	63.67 99	79.54 117	86.37 147	62.83 138	81.82 110	57.10 172	95.25 15	90.94 15

IS-MVSNet		75.10 98	75.42 99	74.15 116	79.23 147	48.05 218	79.43 79	78.04 164	70.09 49	79.17 122	88.02 114	53.04 217	83.60 80	58.05 166	93.76 62	90.79 17

HQP-MVS		75.24 96	75.01 100	75.94 94	82.37 109	58.80 155	77.32 106	84.12 70	59.08 132	71.58 223	85.96 160	58.09 186	85.30 51	67.38 100	89.16 142	83.73 127

X-MVStestdata		76.81 82	74.79 101	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	9.95 357	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

FC-MVSNet-test		73.32 122	74.78 102	68.93 196	79.21 148	36.57 308	71.82 169	79.54 141	57.63 150	82.57 84	90.38 63	59.38 174	78.99 158	57.91 167	94.56 33	91.23 12

Vis-MVSNet		74.85 105	74.56 103	75.72 97	81.63 123	64.64 106	76.35 117	79.06 145	62.85 108	73.33 203	88.41 102	62.54 142	79.59 153	63.94 124	82.92 223	82.94 147
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AdaColmap		74.22 109	74.56 103	73.20 131	81.95 117	60.97 133	79.43 79	80.90 119	65.57 76	72.54 212	81.76 205	70.98 77	85.26 52	47.88 245	90.00 129	73.37 260

Regformer-2		75.32 94	74.47 105	77.88 74	74.22 217	66.65 91	72.77 154	77.54 170	68.47 58	80.44 108	72.08 298	70.60 78	80.97 127	70.08 77	84.02 213	86.01 71

CSCG		74.12 110	74.39 106	73.33 127	79.35 144	61.66 128	77.45 105	81.98 97	62.47 112	79.06 123	80.19 223	61.83 149	78.79 162	59.83 155	87.35 166	79.54 210

RPSCF		75.76 89	74.37 107	79.93 46	74.81 204	77.53 19	77.53 104	79.30 142	59.44 131	78.88 124	89.80 77	71.26 75	73.09 233	57.45 168	80.89 246	89.17 30

PHI-MVS		74.92 101	74.36 108	76.61 85	76.40 183	62.32 123	80.38 68	83.15 82	54.16 190	73.23 205	80.75 214	62.19 147	83.86 76	68.02 90	90.92 112	83.65 128

TAPA-MVS	65.27 12	75.16 97	74.29 109	77.77 75	74.86 203	68.08 80	77.89 101	84.04 73	55.15 174	76.19 170	83.39 185	66.91 108	80.11 147	60.04 153	90.14 127	85.13 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM_NR		73.91 111	74.16 110	73.16 132	81.90 118	53.50 184	81.28 61	81.40 106	66.17 72	73.30 204	83.31 189	59.96 167	83.10 92	58.45 163	81.66 238	82.87 149

NR-MVSNet		73.62 116	74.05 111	72.33 154	83.50 94	43.71 254	65.65 250	77.32 174	64.32 93	75.59 173	87.08 119	62.45 143	81.34 116	54.90 192	95.63 8	91.93 8

F-COLMAP		75.29 95	73.99 112	79.18 56	81.73 121	71.90 48	81.86 59	82.98 84	59.86 129	72.27 215	84.00 180	64.56 131	83.07 93	51.48 215	87.19 172	82.56 160

baseline		73.10 124	73.96 113	70.51 169	71.46 248	46.39 238	72.08 160	84.40 61	55.95 166	76.62 160	86.46 145	67.20 105	78.03 184	64.22 120	87.27 170	87.11 59

casdiffmvs		73.06 126	73.84 114	70.72 165	71.32 249	46.71 234	70.93 182	84.26 66	55.62 169	77.46 143	87.10 118	67.09 106	77.81 186	63.95 122	86.83 176	87.64 50

FIs		72.56 139	73.80 115	68.84 199	78.74 156	37.74 302	71.02 180	79.83 136	56.12 164	80.88 105	89.45 82	58.18 183	78.28 178	56.63 174	93.36 68	90.51 19

Anonymous20240529		72.56 139	73.79 116	68.86 198	76.89 180	45.21 246	68.80 207	77.25 176	67.16 62	76.89 153	90.44 54	65.95 118	74.19 228	50.75 221	90.00 129	87.18 58

Regformer-4		74.64 106	73.67 117	77.55 76	74.74 206	64.49 109	72.91 152	75.42 191	67.45 60	80.24 112	72.07 300	68.98 89	80.19 146	70.29 75	80.91 244	87.98 46

pmmvs6		71.82 145	73.66 118	66.31 228	75.94 190	42.01 269	66.99 233	72.53 214	63.45 103	76.43 167	92.78 10	72.95 63	69.69 265	51.41 216	90.46 120	87.22 55

Regformer-1		74.28 108	73.63 119	76.21 93	74.22 217	64.12 112	72.77 154	75.46 190	66.86 65	79.27 120	72.08 298	69.29 86	78.74 163	68.73 83	84.02 213	85.77 79

K. test v3		73.67 115	73.61 120	73.87 119	79.78 138	55.62 172	74.69 141	62.04 284	66.16 73	84.76 58	93.23 5	49.47 236	80.97 127	65.66 110	86.67 179	85.02 90

v1192		73.40 120	73.42 121	73.32 128	74.65 212	48.67 210	72.21 159	81.73 100	52.76 205	81.85 89	84.56 173	57.12 197	82.24 107	68.58 84	87.33 167	89.06 32

v1144		73.29 123	73.39 122	73.01 135	74.12 221	48.11 217	72.01 162	81.08 116	53.83 196	81.77 90	84.68 171	58.07 189	81.91 109	68.10 88	86.86 175	88.99 35

canonicalmvs		72.29 142	73.38 123	69.04 191	74.23 216	47.37 228	73.93 147	83.18 81	54.36 187	76.61 161	81.64 207	72.03 65	75.34 215	57.12 171	87.28 169	84.40 112

EPP-MVSNet		73.86 112	73.38 123	75.31 101	78.19 160	53.35 186	80.45 66	77.32 174	65.11 85	76.47 166	86.80 127	49.47 236	83.77 77	53.89 204	92.72 80	88.81 40

MCST-MVS		73.42 119	73.34 125	73.63 123	81.28 126	59.17 151	74.80 137	83.13 83	45.50 265	72.84 208	83.78 183	65.15 125	80.99 126	64.54 117	89.09 146	80.73 194

114514_t		73.40 120	73.33 126	73.64 122	84.15 89	57.11 162	78.20 98	80.02 134	43.76 279	72.55 211	86.07 158	64.00 134	83.35 87	60.14 151	91.03 108	80.45 199

Baseline_NR-MVSNet		70.62 156	73.19 127	62.92 257	76.97 176	34.44 324	68.84 203	70.88 236	60.25 125	79.50 118	90.53 51	61.82 150	69.11 269	54.67 195	95.27 14	85.22 82

v1240		73.06 126	73.14 128	72.84 142	74.74 206	47.27 230	71.88 168	81.11 113	51.80 214	82.28 87	84.21 177	56.22 205	82.34 104	68.82 82	87.17 173	88.91 37

VDDNet		71.60 147	73.13 129	67.02 221	86.29 51	41.11 274	69.97 190	66.50 256	68.72 55	74.74 184	91.70 24	59.90 168	75.81 209	48.58 239	91.72 88	84.15 119

IterMVS-LS		73.01 128	73.12 130	72.66 147	73.79 224	49.90 202	71.63 170	78.44 158	58.22 140	80.51 107	86.63 139	58.15 185	79.62 151	62.51 132	88.20 150	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		72.99 130	73.06 131	72.77 143	74.58 213	47.48 226	71.90 167	80.44 127	51.57 217	81.46 96	84.11 179	58.04 190	82.12 108	67.98 92	87.47 163	88.70 42

CNLPA		73.44 118	73.03 132	74.66 106	78.27 159	75.29 30	75.99 123	78.49 157	65.39 79	75.67 172	83.22 193	61.23 158	66.77 287	53.70 206	85.33 192	81.92 173

v1921920		72.96 132	72.98 133	72.89 141	74.67 209	47.58 225	71.92 166	80.69 120	51.70 216	81.69 94	83.89 181	56.58 203	82.25 106	68.34 86	87.36 165	88.82 39

MVS_111021_HR		72.98 131	72.97 134	72.99 136	80.82 129	65.47 99	68.81 205	72.77 211	57.67 148	75.76 171	82.38 198	71.01 76	77.17 194	61.38 139	86.15 182	76.32 239

Gipuma		69.55 169	72.83 135	59.70 280	63.63 310	53.97 180	80.08 75	75.93 185	64.24 94	73.49 200	88.93 98	57.89 192	62.46 304	59.75 156	91.55 95	62.67 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DP-MVS Recon		73.57 117	72.69 136	76.23 92	82.85 104	63.39 116	74.32 144	82.96 85	57.75 145	70.35 239	81.98 201	64.34 133	84.41 70	49.69 229	89.95 131	80.89 187

v2v482		72.55 141	72.58 137	72.43 151	72.92 239	46.72 233	71.41 173	79.13 144	55.27 172	81.17 99	85.25 168	55.41 208	81.13 121	67.25 103	85.46 188	89.43 25

WR-MVS		71.20 150	72.48 138	67.36 216	84.98 73	35.70 316	64.43 264	68.66 247	65.05 86	81.49 95	86.43 146	57.57 194	76.48 204	50.36 225	93.32 69	89.90 21

FMVSNet1		71.06 151	72.48 138	66.81 222	77.65 171	40.68 279	71.96 163	73.03 206	61.14 118	79.45 119	90.36 64	60.44 164	75.20 217	50.20 226	88.05 153	84.54 105

CLD-MVS		72.88 133	72.36 140	74.43 110	77.03 175	54.30 178	68.77 208	83.43 80	52.12 210	76.79 158	74.44 279	69.54 85	83.91 75	55.88 184	93.25 70	85.09 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+-dtu		75.43 93	72.28 141	84.91 2	77.05 173	83.58 1	78.47 92	77.70 168	57.68 146	74.89 181	78.13 250	64.80 128	84.26 72	56.46 178	85.32 193	86.88 61

Effi-MVS+		72.10 143	72.28 141	71.58 158	74.21 220	50.33 198	74.72 140	82.73 88	62.62 109	70.77 235	76.83 260	69.96 83	80.97 127	60.20 148	78.43 271	83.45 134

Regformer-3		72.86 134	72.28 141	74.62 107	74.74 206	60.18 142	72.91 152	71.76 219	64.74 89	78.42 130	72.07 300	67.00 107	76.28 206	67.97 93	80.91 244	87.39 53

ETV-MVS		72.72 136	72.16 144	74.38 112	76.90 179	55.95 167	73.34 150	84.67 53	62.04 113	72.19 218	70.81 310	65.90 119	85.24 54	58.64 161	84.96 200	81.95 172

EI-MVSNet-Vis-set		72.78 135	71.87 145	75.54 99	74.77 205	59.02 153	72.24 158	71.56 223	63.92 96	78.59 126	71.59 306	66.22 116	78.60 165	67.58 96	80.32 252	89.00 34

CANet		73.00 129	71.84 146	76.48 88	75.82 191	61.28 129	74.81 135	80.37 129	63.17 106	62.43 291	80.50 218	61.10 160	85.16 58	64.00 121	84.34 209	83.01 146

MVS_111021_LR		72.10 143	71.82 147	72.95 138	79.53 142	73.90 39	70.45 187	66.64 255	56.87 156	76.81 157	81.76 205	68.78 90	71.76 251	61.81 135	83.74 217	73.18 262

PCF-MVS	63.80 13	72.70 137	71.69 148	75.72 97	78.10 161	60.01 144	73.04 151	81.50 103	45.34 269	79.66 116	84.35 176	65.15 125	82.65 99	48.70 237	89.38 141	84.50 110
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 138	71.68 149	75.47 100	74.67 209	58.64 158	72.02 161	71.50 224	63.53 101	78.58 128	71.39 309	65.98 117	78.53 167	67.30 102	80.18 254	89.23 28

TransMVSNet (Re)		69.62 167	71.63 150	63.57 247	76.51 182	35.93 314	65.75 249	71.29 229	61.05 119	75.02 179	89.90 76	65.88 120	70.41 263	49.79 228	89.48 139	84.38 113

TSAR-MVS + GP.		73.08 125	71.60 151	77.54 77	78.99 155	70.73 59	74.96 132	69.38 244	60.73 123	74.39 191	78.44 246	57.72 193	82.78 96	60.16 150	89.60 137	79.11 215

LCM-MVSNet-Re		69.10 177	71.57 152	61.70 264	70.37 259	34.30 326	61.45 287	79.62 137	56.81 157	89.59 7	88.16 111	68.44 95	72.94 234	42.30 272	87.33 167	77.85 231

API-MVS		70.97 153	71.51 153	69.37 185	75.20 196	55.94 168	80.99 62	76.84 178	62.48 111	71.24 230	77.51 256	61.51 154	80.96 131	52.04 211	85.76 187	71.22 281

VDD-MVS		70.81 154	71.44 154	68.91 197	79.07 154	46.51 235	67.82 220	70.83 237	61.23 117	74.07 195	88.69 100	59.86 169	75.62 212	51.11 218	90.28 122	84.61 102

MG-MVS		70.47 158	71.34 155	67.85 210	79.26 146	40.42 283	74.67 142	75.15 196	58.41 139	68.74 256	88.14 112	56.08 206	83.69 78	59.90 154	81.71 237	79.43 212

3Dnovator	65.95 11	71.50 148	71.22 156	72.34 153	73.16 231	63.09 119	78.37 93	78.32 159	57.67 148	72.22 217	84.61 172	54.77 209	78.47 169	60.82 146	81.07 243	75.45 245

RRT_MVS		73.80 114	71.19 157	81.60 24	71.04 250	70.33 64	78.78 88	74.91 197	56.96 155	77.83 137	85.56 164	32.82 314	87.39 5	71.16 70	91.68 90	87.07 60

alignmvs		70.54 157	71.00 158	69.15 190	73.50 225	48.04 219	69.85 193	79.62 137	53.94 195	76.54 163	82.00 200	59.00 177	74.68 223	57.32 169	87.21 171	84.72 97

EG-PatchMatch MVS		70.70 155	70.88 159	70.16 175	82.64 108	58.80 155	71.48 171	73.64 204	54.98 175	76.55 162	81.77 204	61.10 160	78.94 159	54.87 193	80.84 247	72.74 267

V42		71.06 151	70.83 160	71.72 157	67.25 283	47.14 231	65.94 244	80.35 130	51.35 220	83.40 75	83.23 191	59.25 175	78.80 161	65.91 108	80.81 248	89.23 28

MVS_Test		69.84 165	70.71 161	67.24 217	67.49 282	43.25 261	69.87 192	81.22 112	52.69 206	71.57 226	86.68 135	62.09 148	74.51 225	66.05 106	78.74 267	83.96 121

mvs-test1		73.81 113	70.69 162	83.18 5	77.05 173	81.39 3	75.39 128	77.70 168	57.68 146	71.19 232	74.72 275	64.80 128	83.66 79	56.46 178	81.19 242	84.50 110

CS-MVS		71.24 149	70.57 163	73.26 129	74.93 199	52.00 190	73.59 148	85.55 31	55.58 170	68.88 252	70.17 317	64.37 132	85.62 44	57.19 170	84.83 202	82.17 168

VPA-MVSNet		68.71 182	70.37 164	63.72 246	76.13 187	38.06 300	64.10 266	71.48 225	56.60 163	74.10 194	88.31 106	64.78 130	69.72 264	47.69 247	90.15 126	83.37 136

PLC	62.01 16	71.79 146	70.28 165	76.33 90	80.31 135	68.63 78	78.18 99	81.24 110	54.57 184	67.09 266	80.63 216	59.44 172	81.74 114	46.91 252	84.17 210	78.63 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ANet_high		67.08 202	69.94 166	58.51 288	57.55 339	27.09 348	58.43 305	76.80 179	63.56 100	82.40 86	91.93 20	59.82 170	64.98 295	50.10 227	88.86 148	83.46 133

cl_fuxian		69.82 166	69.89 167	69.61 184	66.24 291	43.48 257	68.12 217	79.61 139	51.43 219	77.72 139	80.18 224	54.61 212	78.15 183	63.62 127	87.50 162	87.20 57

pm-mvs1		68.40 185	69.85 168	64.04 244	73.10 235	39.94 285	64.61 262	70.50 238	55.52 171	73.97 196	89.33 83	63.91 135	68.38 273	49.68 230	88.02 154	83.81 125

BH-untuned		69.39 172	69.46 169	69.18 189	77.96 164	56.88 164	68.47 214	77.53 171	56.77 158	77.79 138	79.63 230	60.30 166	80.20 145	46.04 256	80.65 249	70.47 286

v148		69.38 173	69.39 170	69.36 186	69.14 269	44.56 249	68.83 204	72.70 212	54.79 179	78.59 126	84.12 178	54.69 210	76.74 203	59.40 158	82.20 229	86.79 62

TinyColmap		67.98 191	69.28 171	64.08 242	67.98 277	46.82 232	70.04 189	75.26 194	53.05 201	77.36 144	86.79 128	59.39 173	72.59 241	45.64 258	88.01 155	72.83 265

QAPM		69.18 176	69.26 172	68.94 195	71.61 247	52.58 189	80.37 69	78.79 151	49.63 239	73.51 199	85.14 169	53.66 215	79.12 156	55.11 191	75.54 287	75.11 249

MIMVSNet1		66.57 206	69.23 173	58.59 287	81.26 127	37.73 303	64.06 267	57.62 296	57.02 154	78.40 131	90.75 44	62.65 139	58.10 317	41.77 277	89.58 138	79.95 205

DPM-MVS		69.98 163	69.22 174	72.26 155	82.69 107	58.82 154	70.53 186	81.23 111	47.79 256	64.16 280	80.21 221	51.32 228	83.12 91	60.14 151	84.95 201	74.83 251

UGNet		70.20 160	69.05 175	73.65 121	76.24 185	63.64 114	75.87 124	72.53 214	61.48 116	60.93 301	86.14 154	52.37 221	77.12 195	50.67 222	85.21 194	80.17 204
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
MVSFormer		69.93 164	69.03 176	72.63 149	74.93 199	59.19 149	83.98 36	75.72 187	52.27 208	63.53 287	76.74 261	43.19 268	80.56 134	72.28 65	78.67 269	78.14 226

EI-MVSNet		69.61 168	69.01 177	71.41 162	73.94 222	49.90 202	71.31 176	71.32 227	58.22 140	75.40 177	70.44 312	58.16 184	75.85 207	62.51 132	79.81 258	88.48 43

PVSNet_Blended_VisFu		70.04 161	68.88 178	73.53 125	82.71 106	63.62 115	74.81 135	81.95 98	48.53 249	67.16 265	79.18 238	51.42 227	78.38 174	54.39 200	79.72 261	78.60 219

GBi-Net		68.30 187	68.79 179	66.81 222	73.14 232	40.68 279	71.96 163	73.03 206	54.81 176	74.72 185	90.36 64	48.63 243	75.20 217	47.12 249	85.37 189	84.54 105

test1		68.30 187	68.79 179	66.81 222	73.14 232	40.68 279	71.96 163	73.03 206	54.81 176	74.72 185	90.36 64	48.63 243	75.20 217	47.12 249	85.37 189	84.54 105

OpenMVS	62.51 15	68.76 181	68.75 181	68.78 200	70.56 257	53.91 181	78.29 95	77.35 173	48.85 246	70.22 241	83.52 184	52.65 220	76.93 197	55.31 190	81.99 231	75.49 244

Fast-Effi-MVS+-dtu		70.00 162	68.74 182	73.77 120	73.47 226	64.53 108	71.36 174	78.14 163	55.81 168	68.84 255	74.71 276	65.36 124	75.75 210	52.00 212	79.00 265	81.03 183

eth_miper_zixun_eth		69.42 171	68.73 183	71.50 161	67.99 276	46.42 236	67.58 222	78.81 149	50.72 228	78.13 134	80.34 220	50.15 234	80.34 139	60.18 149	84.65 203	87.74 49

PAPR		69.20 175	68.66 184	70.82 164	75.15 198	47.77 222	75.31 129	81.11 113	49.62 240	66.33 268	79.27 235	61.53 153	82.96 94	48.12 243	81.50 240	81.74 175

DELS-MVS		68.83 179	68.31 185	70.38 170	70.55 258	48.31 213	63.78 271	82.13 94	54.00 192	68.96 251	75.17 271	58.95 178	80.06 148	58.55 162	82.74 225	82.76 153
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
Fast-Effi-MVS+		68.81 180	68.30 186	70.35 171	74.66 211	48.61 211	66.06 243	78.32 159	50.62 229	71.48 229	75.54 267	68.75 91	79.59 153	50.55 224	78.73 268	82.86 150

cl-mvsnet_		68.26 190	68.26 187	68.29 205	64.98 302	43.67 255	65.89 245	74.67 198	50.04 235	76.86 155	82.42 197	48.74 241	75.38 213	60.92 145	89.81 133	85.80 78

cl-mvsnet1		68.27 189	68.26 187	68.29 205	64.98 302	43.67 255	65.89 245	74.67 198	50.04 235	76.86 155	82.43 196	48.74 241	75.38 213	60.94 144	89.81 133	85.81 74

1121		69.23 174	68.26 187	72.12 156	88.36 36	71.40 51	68.59 209	62.06 282	43.80 278	74.75 183	86.18 151	52.92 218	76.85 200	54.47 197	83.27 221	68.12 303

FMVSNet2		67.48 198	68.21 190	65.29 233	73.14 232	38.94 292	68.81 205	71.21 233	54.81 176	76.73 159	86.48 144	48.63 243	74.60 224	47.98 244	86.11 184	82.35 164

BH-RMVSNet		68.69 183	68.20 191	70.14 176	76.40 183	53.90 182	64.62 261	73.48 205	58.01 142	73.91 197	81.78 203	59.09 176	78.22 179	48.59 238	77.96 276	78.31 222

miper_ehance_all_eth		68.36 186	68.16 192	68.98 193	65.14 301	43.34 259	67.07 232	78.92 148	49.11 244	76.21 169	77.72 253	53.48 216	77.92 185	61.16 142	84.59 205	85.68 80

tfpnnormal		66.48 207	67.93 193	62.16 262	73.40 228	36.65 307	63.45 274	64.99 264	55.97 165	72.82 209	87.80 115	57.06 199	69.10 270	48.31 242	87.54 160	80.72 195

LFMVS		67.06 203	67.89 194	64.56 238	78.02 162	38.25 297	70.81 185	59.60 290	65.18 84	71.06 233	86.56 142	43.85 264	75.22 216	46.35 255	89.63 136	80.21 203

AUN-MVS		70.22 159	67.88 195	77.22 82	82.96 103	71.61 49	69.08 201	71.39 226	49.17 243	71.70 221	78.07 251	37.62 301	79.21 155	61.81 135	89.15 144	80.82 189

tttt0517		69.46 170	67.79 196	74.46 108	75.34 194	52.72 188	75.05 131	63.27 275	54.69 181	78.87 125	84.37 175	26.63 343	81.15 120	63.95 122	87.93 157	89.51 24

VPNet		65.58 213	67.56 197	59.65 281	79.72 139	30.17 342	60.27 296	62.14 279	54.19 189	71.24 230	86.63 139	58.80 179	67.62 278	44.17 265	90.87 116	81.18 180

DIV-MVS_2432*1600		66.38 208	67.51 198	62.97 256	61.76 317	34.39 325	58.11 307	75.30 193	50.84 227	77.12 147	85.42 165	56.84 201	69.44 266	51.07 219	91.16 103	85.08 88

diffmvs		67.42 200	67.50 199	67.20 218	62.26 315	45.21 246	64.87 258	77.04 177	48.21 250	71.74 220	79.70 229	58.40 182	71.17 255	64.99 114	80.27 253	85.22 82

MSDG		67.47 199	67.48 200	67.46 215	70.70 254	54.69 176	66.90 235	78.17 162	60.88 121	70.41 238	74.76 273	61.22 159	73.18 232	47.38 248	76.87 279	74.49 253

EPNet		69.10 177	67.32 201	74.46 108	68.33 273	61.27 130	77.56 103	63.57 273	60.95 120	56.62 317	82.75 194	51.53 226	81.24 119	54.36 201	90.20 123	80.88 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LF4IMVS		67.50 197	67.31 202	68.08 208	58.86 334	61.93 124	71.43 172	75.90 186	44.67 274	72.42 214	80.20 222	57.16 195	70.44 261	58.99 160	86.12 183	71.88 275

EIA-MVS		68.59 184	67.16 203	72.90 140	75.18 197	55.64 171	69.39 197	81.29 108	52.44 207	64.53 276	70.69 311	60.33 165	82.30 105	54.27 202	76.31 282	80.75 193

xiu_mvs_v1_base_debu		67.87 192	67.07 204	70.26 172	79.13 151	61.90 125	67.34 226	71.25 230	47.98 252	67.70 260	74.19 285	61.31 155	72.62 238	56.51 175	78.26 273	76.27 240

xiu_mvs_v1_base		67.87 192	67.07 204	70.26 172	79.13 151	61.90 125	67.34 226	71.25 230	47.98 252	67.70 260	74.19 285	61.31 155	72.62 238	56.51 175	78.26 273	76.27 240

xiu_mvs_v1_base_debi		67.87 192	67.07 204	70.26 172	79.13 151	61.90 125	67.34 226	71.25 230	47.98 252	67.70 260	74.19 285	61.31 155	72.62 238	56.51 175	78.26 273	76.27 240

Anonymous202405211		66.02 209	66.89 207	63.43 250	74.22 217	38.14 298	59.00 302	66.13 257	63.33 105	69.76 245	85.95 161	51.88 222	70.50 260	44.23 264	87.52 161	81.64 176

cl-mvsnet2		67.14 201	66.51 208	69.03 192	63.20 311	43.46 258	66.88 236	76.25 182	49.22 242	74.48 189	77.88 252	45.49 254	77.40 192	60.64 147	84.59 205	86.24 66

wuyk23d		61.97 246	66.25 209	49.12 314	58.19 338	60.77 138	66.32 241	52.97 319	55.93 167	90.62 4	86.91 124	73.07 61	35.98 353	20.63 355	91.63 92	50.62 344

MAR-MVS		67.72 195	66.16 210	72.40 152	74.45 214	64.99 105	74.87 133	77.50 172	48.67 247	65.78 272	68.58 331	57.01 200	77.79 187	46.68 254	81.92 232	74.42 254
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
IterMVS-SCA-FT		67.68 196	66.07 211	72.49 150	73.34 229	58.20 160	63.80 270	65.55 261	48.10 251	76.91 152	82.64 195	45.20 255	78.84 160	61.20 141	77.89 277	80.44 200

bset_n11_16_dypcd		66.91 205	65.84 212	70.12 177	72.95 238	53.54 183	63.64 272	68.65 248	48.54 248	72.54 212	74.28 282	40.58 285	78.54 166	63.52 128	87.82 158	78.29 223

mvs_anonymous		65.08 218	65.49 213	63.83 245	63.79 308	37.60 304	66.52 240	69.82 242	43.44 283	73.46 201	86.08 157	58.79 180	71.75 252	51.90 213	75.63 286	82.15 169

thisisatest0530		67.05 204	65.16 214	72.73 146	73.10 235	50.55 197	71.26 178	63.91 271	50.22 232	74.46 190	80.75 214	26.81 342	80.25 142	59.43 157	86.50 180	87.37 54

FMVSNet3		65.00 219	65.16 214	64.52 239	69.47 265	37.56 305	66.63 238	70.38 239	51.55 218	74.72 185	83.27 190	37.89 300	74.44 226	47.12 249	85.37 189	81.57 177

VNet		64.01 231	65.15 216	60.57 275	73.28 230	35.61 317	57.60 309	67.08 253	54.61 183	66.76 267	83.37 187	56.28 204	66.87 283	42.19 273	85.20 195	79.23 214

RRT_test8_iter05		65.80 211	65.13 217	67.80 213	67.02 286	40.85 278	67.13 231	75.33 192	49.73 237	72.69 210	81.32 208	24.45 353	77.37 193	61.69 138	86.82 177	85.18 84

ab-mvs		64.11 229	65.13 217	61.05 271	71.99 245	38.03 301	67.59 221	68.79 246	49.08 245	65.32 273	86.26 149	58.02 191	66.85 285	39.33 288	79.79 260	78.27 224

test_yl		65.11 216	65.09 219	65.18 234	70.59 255	40.86 276	63.22 279	72.79 209	57.91 143	68.88 252	79.07 241	42.85 271	74.89 221	45.50 259	84.97 197	79.81 206

DCV-MVSNet		65.11 216	65.09 219	65.18 234	70.59 255	40.86 276	63.22 279	72.79 209	57.91 143	68.88 252	79.07 241	42.85 271	74.89 221	45.50 259	84.97 197	79.81 206

RPMNet		65.77 212	65.08 221	67.84 211	66.37 288	48.24 215	70.93 182	86.27 20	54.66 182	61.35 295	86.77 131	33.29 311	85.67 43	55.93 183	70.17 317	69.62 295

miper_enhance_ethall		65.86 210	65.05 222	68.28 207	61.62 319	42.62 266	64.74 259	77.97 165	42.52 288	73.42 202	72.79 295	49.66 235	77.68 189	58.12 165	84.59 205	84.54 105

PVSNet_BlendedMVS		65.38 214	64.30 223	68.61 201	69.81 262	49.36 206	65.60 252	78.96 146	45.50 265	59.98 304	78.61 244	51.82 223	78.20 180	44.30 262	84.11 211	78.27 224

BH-w/o		64.81 220	64.29 224	66.36 227	76.08 189	54.71 175	65.61 251	75.23 195	50.10 234	71.05 234	71.86 305	54.33 213	79.02 157	38.20 298	76.14 283	65.36 318

xiu_mvs_v2_base		64.43 225	63.96 225	65.85 232	77.72 169	51.32 194	63.63 273	72.31 217	45.06 273	61.70 292	69.66 321	62.56 140	73.93 230	49.06 234	73.91 299	72.31 271

CANet_DTU		64.04 230	63.83 226	64.66 237	68.39 270	42.97 263	73.45 149	74.50 201	52.05 212	54.78 324	75.44 270	43.99 263	70.42 262	53.49 208	78.41 272	80.59 197

TAMVS		65.31 215	63.75 227	69.97 181	82.23 113	59.76 147	66.78 237	63.37 274	45.20 270	69.79 244	79.37 234	47.42 249	72.17 244	34.48 320	85.15 196	77.99 230

PS-MVSNAJ		64.27 228	63.73 228	65.90 231	77.82 167	51.42 193	63.33 276	72.33 216	45.09 272	61.60 293	68.04 332	62.39 144	73.95 229	49.07 233	73.87 300	72.34 270

PM-MVS		64.49 223	63.61 229	67.14 220	76.68 181	75.15 31	68.49 213	42.85 342	51.17 224	77.85 136	80.51 217	45.76 250	66.31 290	52.83 210	76.35 281	59.96 336

TR-MVS		64.59 221	63.54 230	67.73 214	75.75 193	50.83 196	63.39 275	70.29 240	49.33 241	71.55 227	74.55 277	50.94 229	78.46 170	40.43 284	75.69 285	73.89 258

CL-MVSNet_2432*1600		62.44 244	63.40 231	59.55 282	72.34 242	32.38 333	56.39 311	64.84 265	51.21 223	67.46 263	81.01 213	50.75 230	63.51 302	38.47 296	88.12 152	82.75 154

OpenMVS_ROB	54.93 17	63.23 235	63.28 232	63.07 254	69.81 262	45.34 245	68.52 212	67.14 252	43.74 280	70.61 237	79.22 236	47.90 247	72.66 237	48.75 236	73.84 301	71.21 282

pmmvs-eth3d		64.41 226	63.27 233	67.82 212	75.81 192	60.18 142	69.49 195	62.05 283	38.81 308	74.13 193	82.23 199	43.76 265	68.65 271	42.53 271	80.63 251	74.63 252

Vis-MVSNet (Re-imp)		62.74 241	63.21 234	61.34 269	72.19 243	31.56 337	67.31 229	53.87 313	53.60 198	69.88 243	83.37 187	40.52 286	70.98 256	41.40 278	86.78 178	81.48 178

USDC		62.80 240	63.10 235	61.89 263	65.19 298	43.30 260	67.42 225	74.20 202	35.80 322	72.25 216	84.48 174	45.67 252	71.95 249	37.95 300	84.97 197	70.42 288

Patchmtry		60.91 253	63.01 236	54.62 297	66.10 293	26.27 351	67.47 224	56.40 306	54.05 191	72.04 219	86.66 136	33.19 312	60.17 311	43.69 266	87.45 164	77.42 232

jason		64.47 224	62.84 237	69.34 188	76.91 178	59.20 148	67.15 230	65.67 258	35.29 323	65.16 274	76.74 261	44.67 259	70.68 257	54.74 194	79.28 264	78.14 226
jason: jason.
cascas		64.59 221	62.77 238	70.05 179	75.27 195	50.02 201	61.79 286	71.61 221	42.46 289	63.68 285	68.89 328	49.33 238	80.35 138	47.82 246	84.05 212	79.78 208

CDS-MVSNet		64.33 227	62.66 239	69.35 187	80.44 134	58.28 159	65.26 254	65.66 259	44.36 275	67.30 264	75.54 267	43.27 267	71.77 250	37.68 301	84.44 208	78.01 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS		63.12 236	62.48 240	65.02 236	66.34 290	52.86 187	63.81 269	62.25 278	46.57 262	71.51 228	80.40 219	44.60 260	66.82 286	51.38 217	75.47 288	75.38 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_0304		62.51 243	62.27 241	63.25 251	69.39 266	48.47 212	64.05 268	62.48 277	59.69 130	54.10 329	81.04 212	45.71 251	66.31 290	41.38 279	82.58 227	74.96 250

MDA-MVSNet-bldmvs		62.34 245	61.73 242	64.16 240	61.64 318	49.90 202	48.11 333	57.24 302	53.31 200	80.95 101	79.39 233	49.00 239	61.55 308	45.92 257	80.05 255	81.03 183

GA-MVS		62.91 238	61.66 243	66.66 226	67.09 285	44.49 250	61.18 291	69.36 245	51.33 221	69.33 247	74.47 278	36.83 302	74.94 220	50.60 223	74.72 294	80.57 198

PVSNet_Blended		62.90 239	61.64 244	66.69 225	69.81 262	49.36 206	61.23 290	78.96 146	42.04 290	59.98 304	68.86 329	51.82 223	78.20 180	44.30 262	77.77 278	72.52 268

miper_lstm_enhance		61.97 246	61.63 245	62.98 255	60.04 327	45.74 243	47.53 335	70.95 234	44.04 276	73.06 206	78.84 243	39.72 289	60.33 310	55.82 185	84.64 204	82.88 148

MVSTER		63.29 234	61.60 246	68.36 203	59.77 331	46.21 239	60.62 293	71.32 227	41.83 291	75.40 177	79.12 239	30.25 335	75.85 207	56.30 180	79.81 258	83.03 145

lupinMVS		63.36 232	61.49 247	68.97 194	74.93 199	59.19 149	65.80 248	64.52 269	34.68 328	63.53 287	74.25 283	43.19 268	70.62 258	53.88 205	78.67 269	77.10 235

thres600view7		61.82 248	61.38 248	63.12 253	71.81 246	34.93 321	64.64 260	56.99 303	54.78 180	70.33 240	79.74 228	32.07 321	72.42 243	38.61 294	83.46 219	82.02 170

thres100view900		61.17 252	61.09 249	61.39 268	72.14 244	35.01 320	65.42 253	56.99 303	55.23 173	70.71 236	79.90 226	32.07 321	72.09 245	35.61 316	81.73 234	77.08 236

D2MVS		62.58 242	61.05 250	67.20 218	63.85 307	47.92 220	56.29 312	69.58 243	39.32 303	70.07 242	78.19 248	34.93 307	72.68 236	53.44 209	83.74 217	81.00 185

CMPMVS	48.73 20	61.54 251	60.89 251	63.52 248	61.08 322	51.55 192	68.07 218	68.00 251	33.88 330	65.87 270	81.25 210	37.91 299	67.71 276	49.32 232	82.60 226	71.31 280
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		60.82 254	60.80 252	60.86 274	68.37 271	41.16 273	72.27 157	68.27 250	26.96 351	69.08 248	75.71 265	32.09 320	67.44 279	55.59 188	78.90 266	73.97 256

ET-MVSNet_ETH3D		63.32 233	60.69 253	71.20 163	70.15 261	55.66 170	65.02 257	64.32 270	43.28 287	68.99 250	72.05 304	25.46 349	78.19 182	54.16 203	82.80 224	79.74 209

HyFIR lowres test		63.01 237	60.47 254	70.61 166	83.04 100	54.10 179	59.93 298	72.24 218	33.67 333	69.00 249	75.63 266	38.69 294	76.93 197	36.60 309	75.45 289	80.81 192

PAPM		61.79 249	60.37 255	66.05 229	76.09 188	41.87 270	69.30 198	76.79 180	40.64 300	53.80 330	79.62 231	44.38 261	82.92 95	29.64 338	73.11 303	73.36 261

FPMVS		59.43 265	60.07 256	57.51 291	77.62 172	71.52 50	62.33 283	50.92 324	57.40 152	69.40 246	80.00 225	39.14 292	61.92 307	37.47 304	66.36 330	39.09 353

tfpn200view9		60.35 259	59.97 257	61.51 266	70.78 252	35.35 318	63.27 277	57.47 297	53.00 202	68.31 257	77.09 258	32.45 318	72.09 245	35.61 316	81.73 234	77.08 236

MVS		60.62 257	59.97 257	62.58 259	68.13 275	47.28 229	68.59 209	73.96 203	32.19 337	59.94 306	68.86 329	50.48 231	77.64 190	41.85 276	75.74 284	62.83 328

thres400		60.77 256	59.97 257	63.15 252	70.78 252	35.35 318	63.27 277	57.47 297	53.00 202	68.31 257	77.09 258	32.45 318	72.09 245	35.61 316	81.73 234	82.02 170

ppachtmachnet_test		60.26 260	59.61 260	62.20 261	67.70 280	44.33 251	58.18 306	60.96 287	40.75 299	65.80 271	72.57 296	41.23 278	63.92 299	46.87 253	82.42 228	78.33 221

MVP-Stereo		61.56 250	59.22 261	68.58 202	79.28 145	60.44 140	69.20 200	71.57 222	43.58 282	56.42 318	78.37 247	39.57 291	76.46 205	34.86 319	60.16 341	68.86 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-RL test		59.95 261	59.12 262	62.44 260	72.46 241	54.61 177	59.63 299	47.51 335	41.05 297	74.58 188	74.30 281	31.06 329	65.31 292	51.61 214	79.85 257	67.39 306

pmmvs4		60.78 255	59.04 263	66.00 230	73.06 237	57.67 161	64.53 263	60.22 288	36.91 317	65.96 269	77.27 257	39.66 290	68.54 272	38.87 291	74.89 293	71.80 276

1112_ss		59.48 264	58.99 264	60.96 273	77.84 166	42.39 268	61.42 288	68.45 249	37.96 312	59.93 307	67.46 334	45.11 257	65.07 294	40.89 282	71.81 308	75.41 246

1314		59.83 262	58.86 265	62.74 258	65.71 295	44.78 248	68.59 209	72.63 213	33.54 335	61.05 299	67.29 337	43.62 266	71.26 254	49.49 231	67.84 328	72.19 273

Test_1112_low_res		58.78 269	58.69 266	59.04 285	79.41 143	38.13 299	57.62 308	66.98 254	34.74 326	59.62 308	77.56 255	42.92 270	63.65 301	38.66 293	70.73 314	75.35 248

EPNet_dtu		58.93 268	58.52 267	60.16 279	67.91 278	47.70 224	69.97 190	58.02 294	49.73 237	47.28 345	73.02 294	38.14 296	62.34 305	36.57 310	85.99 185	70.43 287
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		58.96 267	58.49 268	60.36 277	66.37 288	48.24 215	70.93 182	56.40 306	32.87 336	61.35 295	86.66 136	33.19 312	63.22 303	48.50 240	70.17 317	69.62 295

CVMVSNet		59.21 266	58.44 269	61.51 266	73.94 222	47.76 223	71.31 176	64.56 268	26.91 352	60.34 303	70.44 312	36.24 304	67.65 277	53.57 207	68.66 325	69.12 300

baseline1		57.82 274	58.36 270	56.19 294	69.17 268	30.76 341	62.94 281	55.21 309	46.04 264	63.83 283	78.47 245	41.20 279	63.68 300	39.44 287	68.99 323	74.13 255

SCA		58.57 271	58.04 271	60.17 278	70.17 260	41.07 275	65.19 255	53.38 317	43.34 286	61.00 300	73.48 289	45.20 255	69.38 267	40.34 285	70.31 316	70.05 290

thisisatest0515		60.48 258	57.86 272	68.34 204	67.25 283	46.42 236	60.58 294	62.14 279	40.82 298	63.58 286	69.12 324	26.28 345	78.34 176	48.83 235	82.13 230	80.26 202

PatchMatch-RL		58.68 270	57.72 273	61.57 265	76.21 186	73.59 42	61.83 285	49.00 331	47.30 260	61.08 297	68.97 326	50.16 233	59.01 314	36.06 315	68.84 324	52.10 343

HY-MVS	49.31 19	57.96 273	57.59 274	59.10 284	66.85 287	36.17 311	65.13 256	65.39 262	39.24 305	54.69 326	78.14 249	44.28 262	67.18 282	33.75 324	70.79 313	73.95 257

test20.03		55.74 281	57.51 275	50.42 307	59.89 330	32.09 335	50.63 327	49.01 330	50.11 233	65.07 275	83.23 191	45.61 253	48.11 329	30.22 334	83.82 216	71.07 284

XXY-MVS		55.19 284	57.40 276	48.56 316	64.45 305	34.84 323	51.54 326	53.59 315	38.99 307	63.79 284	79.43 232	56.59 202	45.57 333	36.92 308	71.29 310	65.25 319

thres200		57.55 275	57.02 277	59.17 283	67.89 279	34.93 321	58.91 304	57.25 301	50.24 231	64.01 281	71.46 308	32.49 317	71.39 253	31.31 330	79.57 262	71.19 283

IB-MVS	49.67 18	59.69 263	56.96 278	67.90 209	68.19 274	50.30 199	61.42 288	65.18 263	47.57 258	55.83 321	67.15 338	23.77 354	79.60 152	43.56 268	79.97 256	73.79 259
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
testgi		54.00 291	56.86 279	45.45 324	58.20 337	25.81 352	49.05 329	49.50 329	45.43 268	67.84 259	81.17 211	51.81 225	43.20 345	29.30 339	79.41 263	67.34 308

gg-mvs-nofinetune		55.75 280	56.75 280	52.72 303	62.87 312	28.04 347	68.92 202	41.36 350	71.09 41	50.80 337	92.63 12	20.74 357	66.86 284	29.97 336	72.41 305	63.25 327

our_test_3		56.46 277	56.51 281	56.30 293	67.70 280	39.66 287	55.36 317	52.34 322	40.57 301	63.85 282	69.91 320	40.04 288	58.22 316	43.49 269	75.29 292	71.03 285

PatchT		53.35 292	56.47 282	43.99 330	64.19 306	17.46 359	59.15 300	43.10 341	52.11 211	54.74 325	86.95 123	29.97 338	49.98 326	43.62 267	74.40 297	64.53 326

CHOSEN 1792x2688		58.09 272	56.30 283	63.45 249	79.95 137	50.93 195	54.07 320	65.59 260	28.56 348	61.53 294	74.33 280	41.09 281	66.52 289	33.91 323	67.69 329	72.92 264

CostFormer		57.35 276	56.14 284	60.97 272	63.76 309	38.43 294	67.50 223	60.22 288	37.14 316	59.12 309	76.34 263	32.78 315	71.99 248	39.12 290	69.27 322	72.47 269

MIMVSNet		54.39 287	56.12 285	49.20 312	72.57 240	30.91 340	59.98 297	48.43 333	41.66 292	55.94 320	83.86 182	41.19 280	50.42 325	26.05 345	75.38 290	66.27 314

Anonymous20231206		54.13 288	55.82 286	49.04 315	70.89 251	35.96 313	51.73 325	50.87 325	34.86 324	62.49 290	79.22 236	42.52 274	44.29 341	27.95 343	81.88 233	66.88 310

new-patchmatchnet		52.89 294	55.76 287	44.26 329	59.94 329	6.31 362	37.36 352	50.76 326	41.10 295	64.28 279	79.82 227	44.77 258	48.43 328	36.24 313	87.61 159	78.03 228

FMVSNet5		55.08 285	55.54 288	53.71 298	65.80 294	33.50 330	56.22 313	52.50 321	43.72 281	61.06 298	83.38 186	25.46 349	54.87 320	30.11 335	81.64 239	72.75 266

tpmvs		55.84 279	55.45 289	57.01 292	60.33 326	33.20 331	65.89 245	59.29 292	47.52 259	56.04 319	73.60 288	31.05 330	68.06 275	40.64 283	64.64 333	69.77 293

MS-PatchMatch		55.59 282	54.89 290	57.68 290	69.18 267	49.05 208	61.00 292	62.93 276	35.98 320	58.36 311	68.93 327	36.71 303	66.59 288	37.62 303	63.30 337	57.39 339

tpm2		56.12 278	54.64 291	60.55 276	66.24 291	36.01 312	68.14 216	56.77 305	33.60 334	58.25 312	75.52 269	30.25 335	74.33 227	33.27 325	69.76 321	71.32 279

PatchmatchNet		54.60 286	54.27 292	55.59 295	65.17 300	39.08 289	66.92 234	51.80 323	39.89 302	58.39 310	73.12 293	31.69 323	58.33 315	43.01 270	58.38 347	69.38 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			54.05 293		65.54 296	29.30 344	59.00 302	55.22 308	35.96 321	52.44 332	75.98 264	30.77 332	59.62 312	38.21 297	73.33 302

YYNet1		52.58 295	53.50 294	49.85 308	54.15 353	36.45 310	40.53 346	46.55 337	38.09 311	75.52 175	73.31 292	41.08 282	43.88 342	41.10 280	71.14 312	69.21 299

MDA-MVSNet_test_wron		52.57 296	53.49 295	49.81 309	54.24 352	36.47 309	40.48 347	46.58 336	38.13 310	75.47 176	73.32 291	41.05 283	43.85 343	40.98 281	71.20 311	69.10 301

UnsupCasMVSNet_eth		52.26 298	53.29 296	49.16 313	55.08 349	33.67 329	50.03 328	58.79 293	37.67 313	63.43 289	74.75 274	41.82 276	45.83 332	38.59 295	59.42 343	67.98 305

baseline2		55.57 283	52.74 297	64.05 243	65.26 297	44.11 252	62.38 282	54.43 312	39.03 306	51.21 335	67.35 336	33.66 310	72.45 242	37.14 306	64.22 335	75.60 243

tpm cat1		54.02 290	52.63 298	58.19 289	64.85 304	39.86 286	66.26 242	57.28 300	32.16 338	56.90 315	70.39 314	32.75 316	65.30 293	34.29 321	58.79 344	69.41 297

pmmvs5		52.49 297	52.58 299	52.21 305	54.99 350	32.38 333	55.45 316	53.84 314	32.15 339	55.49 323	74.81 272	38.08 297	57.37 318	34.02 322	74.40 297	66.88 310

tpm		50.60 303	52.42 300	45.14 326	65.18 299	26.29 350	60.30 295	43.50 340	37.41 314	57.01 314	79.09 240	30.20 337	42.32 346	32.77 327	66.36 330	66.81 312

JIA-IIPM		54.03 289	51.62 301	61.25 270	59.14 333	55.21 173	59.10 301	47.72 334	50.85 226	50.31 341	85.81 162	20.10 359	63.97 298	36.16 314	55.41 352	64.55 325

KD-MVS_2432*1600		52.05 300	51.58 302	53.44 299	52.11 355	31.20 338	44.88 342	64.83 266	41.53 293	64.37 277	70.03 318	15.61 364	64.20 296	36.25 311	74.61 295	64.93 322

miper_refine_blended		52.05 300	51.58 302	53.44 299	52.11 355	31.20 338	44.88 342	64.83 266	41.53 293	64.37 277	70.03 318	15.61 364	64.20 296	36.25 311	74.61 295	64.93 322

tpmrst		50.15 305	51.38 304	46.45 321	56.05 344	24.77 353	64.40 265	49.98 327	36.14 319	53.32 331	69.59 322	35.16 306	48.69 327	39.24 289	58.51 346	65.89 315

PVSNet	43.83 21	51.56 302	51.17 305	52.73 302	68.34 272	38.27 296	48.22 332	53.56 316	36.41 318	54.29 327	64.94 341	34.60 308	54.20 323	30.34 333	69.87 319	65.71 317

DWT-MVSNet_test		53.04 293	51.12 306	58.77 286	61.23 320	38.67 293	62.16 284	57.74 295	38.24 309	51.76 334	59.07 348	21.36 356	67.40 280	44.80 261	63.76 336	70.25 289

N_pmnet		52.06 299	51.11 307	54.92 296	59.64 332	71.03 55	37.42 351	61.62 286	33.68 332	57.12 313	72.10 297	37.94 298	31.03 355	29.13 342	71.35 309	62.70 329

UnsupCasMVSNet_bld		50.01 306	51.03 308	46.95 317	58.61 335	32.64 332	48.31 331	53.27 318	34.27 329	60.47 302	71.53 307	41.40 277	47.07 330	30.68 332	60.78 340	61.13 334

test-LLR		50.43 304	50.69 309	49.64 310	60.76 323	41.87 270	53.18 322	45.48 338	43.41 284	49.41 342	60.47 346	29.22 340	44.73 339	42.09 274	72.14 306	62.33 332

WTY-MVS		49.39 307	50.31 310	46.62 320	61.22 321	32.00 336	46.61 338	49.77 328	33.87 331	54.12 328	69.55 323	41.96 275	45.40 335	31.28 331	64.42 334	62.47 331

Patchmatch-test		47.93 310	49.96 311	41.84 333	57.42 340	24.26 354	48.75 330	41.49 349	39.30 304	56.79 316	73.48 289	30.48 334	33.87 354	29.29 340	72.61 304	67.39 306

sss		47.59 312	48.32 312	45.40 325	56.73 343	33.96 327	45.17 341	48.51 332	32.11 341	52.37 333	65.79 339	40.39 287	41.91 349	31.85 328	61.97 338	60.35 335

test0.0.03 1		47.72 311	48.31 313	45.93 322	55.53 348	29.39 343	46.40 339	41.21 351	43.41 284	55.81 322	67.65 333	29.22 340	43.77 344	25.73 348	69.87 319	64.62 324

test-mter		48.56 309	48.20 314	49.64 310	60.76 323	41.87 270	53.18 322	45.48 338	31.91 342	49.41 342	60.47 346	18.34 360	44.73 339	42.09 274	72.14 306	62.33 332

MVS-HIRNet		45.53 315	47.29 315	40.24 336	62.29 314	26.82 349	56.02 314	37.41 355	29.74 347	43.69 354	81.27 209	33.96 309	55.48 319	24.46 351	56.79 348	38.43 354

ADS-MVSNet2		48.76 308	47.25 316	53.29 301	55.90 346	40.54 282	47.34 336	54.99 311	31.41 344	50.48 338	72.06 302	31.23 326	54.26 322	25.93 346	55.93 349	65.07 320

EPMVS		45.74 314	46.53 317	43.39 331	54.14 354	22.33 356	55.02 318	35.00 357	34.69 327	51.09 336	70.20 316	25.92 347	42.04 348	37.19 305	55.50 351	65.78 316

ADS-MVSNet		44.62 319	45.58 318	41.73 334	55.90 346	20.83 357	47.34 336	39.94 353	31.41 344	50.48 338	72.06 302	31.23 326	39.31 351	25.93 346	55.93 349	65.07 320

E-PMN		45.17 316	45.36 319	44.60 328	50.07 357	42.75 264	38.66 349	42.29 346	46.39 263	39.55 355	51.15 353	26.00 346	45.37 336	37.68 301	76.41 280	45.69 350

pmmvs3		46.71 313	45.09 320	51.55 306	56.76 342	48.25 214	55.78 315	39.53 354	24.13 355	50.35 340	63.40 342	15.90 363	51.08 324	29.29 340	70.69 315	55.33 342

TESTMET0.1,1		45.17 316	44.93 321	45.89 323	56.02 345	38.31 295	53.18 322	41.94 348	27.85 349	44.86 350	56.47 349	17.93 361	41.50 350	38.08 299	68.06 326	57.85 338

dp		44.09 321	44.88 322	41.72 335	58.53 336	23.18 355	54.70 319	42.38 345	34.80 325	44.25 352	65.61 340	24.48 352	44.80 338	29.77 337	49.42 354	57.18 340

DSMNet-mixed		43.18 322	44.66 323	38.75 338	54.75 351	28.88 346	57.06 310	27.42 360	13.47 357	47.27 346	77.67 254	38.83 293	39.29 352	25.32 350	60.12 342	48.08 346

EMVS		44.61 320	44.45 324	45.10 327	48.91 359	43.00 262	37.92 350	41.10 352	46.75 261	38.00 357	48.43 355	26.42 344	46.27 331	37.11 307	75.38 290	46.03 349

PMMVS		44.69 318	43.95 325	46.92 318	50.05 358	53.47 185	48.08 334	42.40 344	22.36 356	44.01 353	53.05 351	42.60 273	45.49 334	31.69 329	61.36 339	41.79 351

PMMVS2		37.74 325	40.87 326	28.36 340	42.41 361	5.35 363	24.61 354	27.75 359	32.15 339	47.85 344	70.27 315	35.85 305	29.51 356	19.08 356	67.85 327	50.22 345

PVSNet_0	36.71 22	41.12 324	40.78 327	42.14 332	59.97 328	40.13 284	40.97 345	42.24 347	30.81 346	44.86 350	49.41 354	40.70 284	45.12 337	23.15 352	34.96 356	41.16 352

CHOSEN 280x420		41.62 323	39.89 328	46.80 319	61.81 316	51.59 191	33.56 353	35.74 356	27.48 350	37.64 358	53.53 350	23.24 355	42.09 347	27.39 344	58.64 345	46.72 348

new_pmnet		37.55 326	39.80 329	30.79 339	56.83 341	16.46 360	39.35 348	30.65 358	25.59 353	45.26 348	61.60 345	24.54 351	28.02 357	21.60 353	52.80 353	47.90 347

MVE	27.91 23	36.69 327	35.64 330	39.84 337	43.37 360	35.85 315	19.49 355	24.61 361	24.68 354	39.05 356	62.63 344	38.67 295	27.10 358	21.04 354	47.25 355	56.56 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		17.71 328	23.62 331	0.00 345	0.00 366	0.00 367	0.00 357	70.17 241	0.00 362	0.00 363	74.25 283	68.16 98	0.00 363	0.00 361	0.00 361	0.00 359

tmp_tt		11.98 329	14.73 332	3.72 342	2.28 363	4.62 364	19.44 356	14.50 363	0.47 359	21.55 359	9.58 358	25.78 348	4.57 360	11.61 357	27.37 357	1.96 356

ab-mvs-re		5.62 330	7.50 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	67.46 334	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		5.20 331	6.93 334	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	62.39 144	0.00 363	0.00 361	0.00 361	0.00 359

test123		4.43 332	5.78 335	0.39 344	0.97 364	0.28 365	46.33 340	0.45 365	0.31 360	0.62 361	1.50 361	0.61 367	0.11 362	0.56 359	0.63 359	0.77 358

testmvs		4.06 333	5.28 336	0.41 343	0.64 365	0.16 366	42.54 344	0.31 366	0.26 361	0.50 362	1.40 362	0.77 366	0.17 361	0.56 359	0.55 360	0.90 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					83.91 91	69.36 72		81.09 115	58.91 138	82.73 83	89.11 92	75.77 36	86.63 13	72.73 59	92.93 73

IU-MVS					86.12 57	60.90 135		80.38 128	45.49 267	81.31 97				75.64 41	94.39 41	84.65 98

OPU-MVS				78.65 64	83.44 97	66.85 89	83.62 42				86.12 155	66.82 110	86.01 29	61.72 137	89.79 135	83.08 143

test_241102_TWO								84.80 47	72.61 29	84.93 54	89.70 78	77.73 23	85.89 36	75.29 42	94.22 53	83.25 139

test_241102_ONE					86.12 57	61.06 131		84.72 51	72.64 28	87.38 24	89.47 81	77.48 24	85.74 40

save fliter					87.00 42	67.23 87	79.24 82	77.94 166	56.65 161

test_0728_THIRD									74.03 21	85.83 41	90.41 58	75.58 38	85.69 41	77.43 33	94.74 29	84.31 115

test_0728_SECOND				76.57 86	86.20 52	60.57 139	83.77 40	85.49 32					85.90 35	75.86 39	94.39 41	83.25 139

test0726					86.16 55	60.78 136	83.81 39	85.10 42	72.48 31	85.27 51	89.96 74	78.57 18

GSMVS																70.05 290

test_part2					85.90 61	66.44 92				84.61 60

sam_mvs1												31.41 324				70.05 290

sam_mvs												31.21 328

ambc				70.10 178	77.74 168	50.21 200	74.28 145	77.93 167		79.26 121	88.29 107	54.11 214	79.77 150	64.43 118	91.10 106	80.30 201

MTGPA								80.63 121

test_post1							66.63 238				2.08 359	30.66 333	59.33 313	40.34 285

test_post											1.99 360	30.91 331	54.76 321

patchmatchnet-post											68.99 325	31.32 325	69.38 267

GG-mvs-BLEND				52.24 304	60.64 325	29.21 345	69.73 194	42.41 343		45.47 347	52.33 352	20.43 358	68.16 274	25.52 349	65.42 332	59.36 337

MTMP							84.83 31	19.26 362

gm-plane-assit					62.51 313	33.91 328			37.25 315		62.71 343		72.74 235	38.70 292

test9_res														72.12 67	91.37 98	77.40 233

TEST9					85.47 66	69.32 73	76.42 115	78.69 152	53.73 197	76.97 148	86.74 132	66.84 109	81.10 122

test_8					85.09 72	67.89 82	76.26 120	78.66 154	54.00 192	76.89 153	86.72 134	66.60 112	80.89 132

agg_prior2														70.70 74	90.93 111	78.55 220

agg_prior					84.44 84	66.02 96		78.62 155		76.95 150			80.34 139

TestCases				78.35 69	79.19 149	70.81 57		88.64 2	65.37 80	80.09 113	88.17 109	70.33 79	78.43 172	55.60 186	90.90 113	85.81 74

test_prior4						70.14 65	77.57 102

test_prior2							75.57 126		58.92 136	76.53 164	86.78 129	67.83 102		69.81 79	92.76 78

test_prior				75.27 102	82.15 114	59.85 145		84.33 62					83.39 85			82.58 158

旧先验2							71.17 179		45.11 271	78.54 129			61.28 309	59.19 159

新几何2							71.33 175

新几何1				69.99 180	88.37 35	71.34 53		62.08 281	43.85 277	74.99 180	86.11 156	52.85 219	70.57 259	50.99 220	83.23 222	68.05 304

旧先验1					84.55 81	60.36 141		63.69 272			87.05 122	54.65 211			83.34 220	69.66 294

无先验							74.82 134	70.94 235	47.75 257				76.85 200	54.47 197		72.09 274

原ACMM2							74.78 138

原ACMM1				73.90 118	85.90 61	65.15 104		81.67 101	50.97 225	74.25 192	86.16 153	61.60 152	83.54 81	56.75 173	91.08 107	73.00 263

test222					87.30 40	69.15 76	67.85 219	59.59 291	41.06 296	73.05 207	85.72 163	48.03 246			80.65 249	66.92 309

testdata2													67.30 281	48.34 241

segment_acmp												68.30 97

testdata				64.13 241	85.87 63	63.34 117		61.80 285	47.83 255	76.42 168	86.60 141	48.83 240	62.31 306	54.46 199	81.26 241	66.74 313

testdata1							68.34 215		57.24 153

test12				76.51 87	82.28 112	60.94 134		81.64 102		73.60 198		64.88 127	85.19 57		90.42 121	83.38 135

plane_prior7					85.18 69	66.21 94

plane_prior6					84.18 88	65.31 101						60.83 162

plane_prior5								85.49 32					86.15 26	71.09 71	90.94 109	84.82 93

plane_prior4											89.11 92

plane_prior3						65.67 98			63.82 98	78.23 132

plane_prior2							82.74 51		65.45 77

plane_prior1					84.46 83

plane_prior						65.18 102	80.06 76		61.88 115						89.91 132

n2								0.00 367

nn								0.00 367

door-mid								55.02 310

lessismore_v0				72.75 144	79.60 141	56.83 165		57.37 299		83.80 71	89.01 95	47.45 248	78.74 163	64.39 119	86.49 181	82.69 156

LGP-MVS_train				80.90 38	87.00 42	70.41 62		86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

test11								82.71 89

door								52.91 320

HQP5-MVS						58.80 155

HQP-NCC					82.37 109		77.32 106		59.08 132	71.58 223

ACMP_Plane					82.37 109		77.32 106		59.08 132	71.58 223

BP-MVS														67.38 100

HQP4-MVS										71.59 222			85.31 50			83.74 126

HQP3-MVS								84.12 70							89.16 142

HQP2-MVS												58.09 186

NP-MVS					83.34 98	63.07 120					85.97 159

MDTV_nov1_ep13_2view						18.41 358	53.74 321		31.57 343	44.89 349		29.90 339		32.93 326		71.48 278

ACMMP++_ref															89.47 140

ACMMP++															91.96 87

Test By Simon												62.56 140

ITE_SJBPF				80.35 44	76.94 177	73.60 41		80.48 125	66.87 64	83.64 73	86.18 151	70.25 81	79.90 149	61.12 143	88.95 147	87.56 52

DeepMVS_CX				11.83 341	15.51 362	13.86 361		11.25 364	5.76 358	20.85 360	26.46 356	17.06 362	9.22 359	9.69 358	13.82 358	12.42 355

High-resolution multi-view benchmark