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.

ETH3D-3000-0.1		78.58 16	78.91 14	77.61 48	83.06 79	57.86 92	84.14 45	88.31 1	60.37 101	79.14 22	90.35 29	57.76 28	87.00 65	77.16 22	89.90 18	87.97 20

9.14			78.75 16		83.10 78		84.15 43	88.26 2	59.90 113	78.57 27	90.36 28	57.51 31	86.86 68	77.39 18	89.52 25

SF-MVS		78.82 13	79.22 12	77.60 49	82.88 84	57.83 93	84.99 32	88.13 3	61.86 78	79.16 20	90.75 17	57.96 25	87.09 62	77.08 23	90.18 15	87.87 23

ETH3 D test6400		79.14 11	79.32 10	78.61 32	86.34 31	58.11 89	84.65 34	87.66 4	58.56 138	78.87 24	89.54 53	63.67 13	89.57 16	74.60 36	89.98 17	88.14 15

ETH3D cwj APD-0.16		78.02 25	78.13 25	77.71 47	82.10 89	58.65 82	82.72 70	87.55 5	58.33 143	78.05 30	90.06 40	58.35 24	87.65 51	76.15 28	89.86 19	86.82 59

DVP-MVS++.		81.67 1	82.40 1	79.47 9	87.24 14	59.15 68	88.18 1	87.15 6	65.04 20	84.26 5	91.86 6	67.01 1	90.84 3	79.48 4	91.38 2	88.42 7

test_0728_SECOND				79.19 15	87.82 3	59.11 71	87.85 5	87.15 6					90.84 3	78.66 13	90.61 11	87.62 36

test_part1		74.74 61	74.42 62	75.70 81	81.69 96	51.26 186	83.98 48	87.05 8	65.31 16	73.10 78	86.20 101	53.94 62	88.06 39	65.32 106	73.17 194	87.77 30

MCST-MVS		77.48 33	77.45 30	77.54 50	86.67 22	58.36 86	83.22 60	86.93 9	56.91 162	74.91 48	88.19 69	59.15 21	87.68 50	73.67 45	87.45 47	86.57 66

DeepC-MVS	69.38 2	78.56 18	78.14 24	79.83 6	83.60 72	61.62 26	84.17 42	86.85 10	63.23 49	73.84 67	90.25 35	57.68 29	89.96 13	74.62 35	89.03 26	87.89 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_one_0601					87.58 9	59.30 65		86.84 11	65.01 23	83.80 11	91.86 6	64.03 11

test0726					87.75 7	59.07 72	87.86 4	86.83 12	64.26 32	84.19 7	91.92 5	64.82 8

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 12	86.09 17	86.83 12	65.51 14	83.81 10	90.51 23	63.71 12	89.23 20	81.51 1	88.44 32	88.09 17
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
SED-MVS		81.56 2	82.30 2	79.32 12	87.77 4	58.90 77	87.82 7	86.78 14	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 15	90.87 5	88.23 12

test_241102_ONE					87.77 4	58.90 77		86.78 14	64.20 34	85.97 1	91.34 12	66.87 3	90.78 7

test_241102_TWO								86.73 16	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 15	90.70 7	87.65 34

CSCG		76.92 38	76.75 37	77.41 52	83.96 71	59.60 59	82.95 63	86.50 17	60.78 91	75.27 41	84.83 124	60.76 15	86.56 79	67.86 84	87.87 46	86.06 84

DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 48	85.71 26	86.42 18	63.28 47	83.27 13	91.83 10	64.96 7	90.47 10	76.41 27	89.67 22	86.84 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 23	60.02 53	88.12 3	86.42 18	62.94 54	82.40 14	92.12 2	59.64 18	89.76 14	78.70 11	88.32 36	86.79 61

3Dnovator+	66.72 4	75.84 50	74.57 60	79.66 8	82.40 87	59.92 56	85.83 22	86.32 20	66.92 8	67.80 157	89.24 58	42.03 194	89.38 18	64.07 115	86.50 61	89.69 1

CS-MVS-test		74.96 56	74.82 57	75.40 87	79.45 139	52.03 181	82.95 63	86.18 21	63.24 48	70.07 110	84.50 134	55.21 47	88.77 27	67.89 83	83.85 76	85.40 114

CS-MVS		74.01 72	74.24 65	73.32 143	76.47 211	48.51 227	79.19 124	86.17 22	60.56 95	71.62 98	83.71 150	55.16 48	87.94 43	69.21 74	86.11 63	83.51 177

DROMVSNet		75.84 50	75.87 47	75.74 78	78.86 150	52.65 166	83.73 53	86.08 23	63.47 45	72.77 84	87.25 81	53.13 74	87.93 44	71.97 56	85.57 67	86.66 64

ZNCC-MVS		78.82 13	78.67 17	79.30 13	86.43 30	62.05 21	86.62 11	86.01 24	63.32 46	75.08 43	90.47 27	53.96 61	88.68 28	76.48 26	89.63 24	87.16 51

SteuartSystems-ACMMP		79.48 10	79.31 11	79.98 2	83.01 82	62.18 19	87.60 9	85.83 25	66.69 10	78.03 31	90.98 14	54.26 57	90.06 12	78.42 17	89.02 27	87.69 32
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		75.87 49	75.36 50	77.41 52	80.62 116	55.91 127	84.28 39	85.78 26	56.08 183	73.41 72	86.58 94	50.94 99	88.54 29	70.79 63	89.71 21	87.79 29

SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 25	61.95 22	86.33 13	85.75 27	62.49 65	82.20 15	92.28 1	56.53 34	89.70 15	79.85 3	91.48 1	88.19 14
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
DPM-MVS		75.47 53	75.00 52	76.88 59	81.38 103	59.16 67	79.94 110	85.71 28	56.59 170	72.46 89	86.76 84	56.89 32	87.86 47	66.36 96	88.91 30	83.64 174

MSC_two_6792asdad				79.95 3	87.24 14	61.04 36		85.62 29					90.96 1	79.31 7	90.65 8	87.85 25

No_MVS				79.95 3	87.24 14	61.04 36		85.62 29					90.96 1	79.31 7	90.65 8	87.85 25

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 220	84.64 3				79.07 9	90.87 5	88.37 9

MP-MVS-pluss		78.35 22	78.46 18	78.03 43	84.96 60	59.52 61	82.93 65	85.39 32	62.15 70	76.41 36	91.51 11	52.47 79	86.78 71	80.66 2	89.64 23	87.80 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testtj		78.47 19	78.43 19	78.61 32	86.82 17	60.67 46	86.07 18	85.38 33	62.12 71	78.65 26	90.29 33	55.76 42	89.31 19	73.55 47	87.22 49	85.84 90

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 18	84.92 64	60.32 51	83.03 62	85.33 34	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 38	88.67 31	88.12 16

GST-MVS		78.14 24	77.85 27	78.99 25	86.05 43	61.82 25	85.84 21	85.21 35	63.56 44	74.29 59	90.03 43	52.56 76	88.53 30	74.79 34	88.34 34	86.63 65

ACMMP_NAP		78.77 15	78.78 15	78.74 30	85.44 52	61.04 36	83.84 52	85.16 36	62.88 56	78.10 28	91.26 13	52.51 77	88.39 31	79.34 6	90.52 13	86.78 62

HPM-MVS		77.28 34	76.85 36	78.54 34	85.00 59	60.81 43	82.91 66	85.08 37	62.57 63	73.09 79	89.97 46	50.90 100	87.48 53	75.30 30	86.85 56	87.33 48
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs		74.80 59	74.89 56	74.53 108	75.59 226	50.37 201	78.17 139	85.06 38	62.80 61	74.40 57	87.86 73	57.88 27	83.61 147	69.46 72	82.79 88	89.59 2

DVP-MVS		80.84 4	81.64 3	78.42 36	87.75 7	59.07 72	87.85 5	85.03 39	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 109
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
CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 20	84.37 36	85.03 39	66.96 5	77.58 32	90.06 40	59.47 20	89.13 22	78.67 12	89.73 20	87.03 54

ETV-MVS		74.46 66	73.84 70	76.33 70	79.27 142	55.24 139	79.22 123	85.00 41	64.97 24	72.65 86	79.46 240	53.65 70	87.87 46	67.45 89	82.91 84	85.89 89

test_prior3		76.89 40	76.96 35	76.69 62	84.20 69	57.27 101	81.75 85	84.88 42	60.37 101	75.01 44	89.06 59	56.22 38	86.43 84	72.19 53	88.96 28	86.38 68

test_prior				76.69 62	84.20 69	57.27 101		84.88 42					86.43 84			86.38 68

DeepC-MVS_fast	68.24 3	77.25 35	76.63 39	79.12 19	86.15 38	60.86 41	84.71 33	84.85 44	61.98 77	73.06 80	88.88 64	53.72 66	89.06 23	68.27 77	88.04 42	87.42 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CLD-MVS		73.33 78	72.68 82	75.29 92	78.82 152	53.33 158	78.23 138	84.79 45	61.30 85	70.41 105	81.04 203	52.41 80	87.12 60	64.61 114	82.49 92	85.41 113
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline		74.61 64	74.70 59	74.34 112	75.70 222	49.99 208	77.54 150	84.63 46	62.73 62	73.98 61	87.79 75	57.67 30	83.82 143	69.49 70	82.74 89	89.20 3

ACMMP		76.02 47	75.33 51	78.07 41	85.20 56	61.91 23	85.49 30	84.44 47	63.04 52	69.80 120	89.74 52	45.43 163	87.16 59	72.01 55	82.87 86	85.14 120
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
NCCC		78.58 16	78.31 20	79.39 11	87.51 12	62.61 16	85.20 31	84.42 48	66.73 9	74.67 54	89.38 56	55.30 46	89.18 21	74.19 39	87.34 48	86.38 68

APD-MVS		78.02 25	78.04 26	77.98 44	86.44 29	60.81 43	85.52 28	84.36 49	60.61 93	79.05 23	90.30 32	55.54 45	88.32 34	73.48 48	87.03 52	84.83 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 27	77.65 28	79.10 20	86.71 20	62.81 10	86.29 14	84.32 50	62.82 58	73.96 62	90.50 24	53.20 72	88.35 32	74.02 41	87.05 50	86.13 81

#test#		77.83 28	77.41 31	79.10 20	86.71 20	62.81 10	85.69 27	84.32 50	61.61 81	73.96 62	90.50 24	53.20 72	88.35 32	73.68 44	87.05 50	86.13 81

ACMMPR		77.71 29	77.23 33	79.16 16	86.75 19	62.93 9	86.29 14	84.24 52	62.82 58	73.55 71	90.56 22	49.80 106	88.24 35	74.02 41	87.03 52	86.32 76

DELS-MVS		74.76 60	74.46 61	75.65 83	77.84 179	52.25 175	75.59 190	84.17 53	63.76 41	73.15 75	82.79 163	59.58 19	86.80 69	67.24 90	86.04 64	87.89 21
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
region2R		77.67 31	77.18 34	79.15 17	86.76 18	62.95 8	86.29 14	84.16 54	62.81 60	73.30 73	90.58 21	49.90 104	88.21 36	73.78 43	87.03 52	86.29 79

CDPH-MVS		76.31 44	75.67 49	78.22 40	85.35 55	59.14 70	81.31 94	84.02 55	56.32 175	74.05 60	88.98 62	53.34 71	87.92 45	69.23 73	88.42 33	87.59 37

HQP_MVS		74.31 68	73.73 71	76.06 72	81.41 101	56.31 116	84.22 40	84.01 56	64.52 28	69.27 128	86.10 104	45.26 167	87.21 57	68.16 80	80.58 107	84.65 136

plane_prior5								84.01 56					87.21 57	68.16 80	80.58 107	84.65 136

XVS		77.17 36	76.56 40	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 92	90.01 45	47.95 127	88.01 41	71.55 59	86.74 58	86.37 71

X-MVStestdata		70.21 124	67.28 165	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 92	6.49 367	47.95 127	88.01 41	71.55 59	86.74 58	86.37 71

HQP3-MVS								83.90 60							80.35 113

HQP-MVS		73.45 77	72.80 81	75.40 87	80.66 113	54.94 140	82.31 78	83.90 60	62.10 72	67.85 152	85.54 118	45.46 161	86.93 66	67.04 92	80.35 113	84.32 144

canonicalmvs		74.67 63	74.98 54	73.71 127	78.94 149	50.56 199	80.23 105	83.87 62	60.30 107	77.15 33	86.56 95	59.65 17	82.00 183	66.01 99	82.12 93	88.58 6

SD-MVS		77.70 30	77.62 29	77.93 45	84.47 67	61.88 24	84.55 35	83.87 62	60.37 101	79.89 18	89.38 56	54.97 50	85.58 103	76.12 29	84.94 69	86.33 74
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
TSAR-MVS + MP.		78.44 20	78.28 21	78.90 26	84.96 60	61.41 29	84.03 46	83.82 64	59.34 126	79.37 19	89.76 51	59.84 16	87.62 52	76.69 25	86.74 58	87.68 33
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		76.77 41	76.06 43	78.88 27	86.14 39	62.73 12	82.55 74	83.74 65	61.71 79	72.45 91	90.34 31	48.48 123	88.13 37	72.32 52	86.85 56	85.78 92

HPM-MVS++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 66	65.37 15	78.78 25	90.64 19	58.63 23	87.24 55	79.00 10	90.37 14	85.26 119

OPM-MVS		74.73 62	74.25 64	76.19 71	80.81 112	59.01 75	82.60 73	83.64 67	63.74 42	72.52 88	87.49 76	47.18 141	85.88 96	69.47 71	80.78 103	83.66 172
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FOURS1					86.12 40	60.82 42	88.18 1	83.61 68	60.87 88	81.50 16

FIs		70.82 112	71.43 93	68.98 225	78.33 165	38.14 318	76.96 164	83.59 69	61.02 87	67.33 164	86.73 86	55.07 49	81.64 189	54.61 186	79.22 129	87.14 52

MP-MVS		78.35 22	78.26 22	78.64 31	86.54 27	63.47 5	86.02 20	83.55 70	63.89 40	73.60 70	90.60 20	54.85 53	86.72 72	77.20 21	88.06 41	85.74 98
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
QAPM		70.05 126	68.81 134	73.78 121	76.54 209	53.43 156	83.23 59	83.48 71	52.89 229	65.90 188	86.29 100	41.55 205	86.49 83	51.01 211	78.40 143	81.42 214

test11								83.47 72

CP-MVS		77.12 37	76.68 38	78.43 35	86.05 43	63.18 7	87.55 10	83.45 73	62.44 67	72.68 85	90.50 24	48.18 125	87.34 54	73.59 46	85.71 65	84.76 135

原ACMM1				74.69 99	85.39 54	59.40 62		83.42 74	51.47 244	70.27 108	86.61 92	48.61 121	86.51 82	53.85 191	87.96 43	78.16 260

LPG-MVS_test		72.74 84	71.74 89	75.76 76	80.22 122	57.51 99	82.55 74	83.40 75	61.32 83	66.67 173	87.33 79	39.15 225	86.59 77	67.70 85	77.30 153	83.19 186

LGP-MVS_train				75.76 76	80.22 122	57.51 99		83.40 75	61.32 83	66.67 173	87.33 79	39.15 225	86.59 77	67.70 85	77.30 153	83.19 186

test12				77.76 46	84.52 66	58.41 85		83.36 77		72.93 82		54.61 55	88.05 40		88.12 40	86.81 60

PAPR		71.72 101	70.82 104	74.41 111	81.20 108	51.17 187	79.55 119	83.33 78	55.81 188	66.93 169	84.61 129	50.95 98	86.06 89	55.79 174	79.20 130	86.00 85

CANet		76.46 43	75.93 45	78.06 42	81.29 104	57.53 98	82.35 76	83.31 79	67.78 3	70.09 109	86.34 99	54.92 51	88.90 25	72.68 51	84.55 71	87.76 31

APD-MVS_3200maxsize		74.96 56	74.39 63	76.67 64	82.20 88	58.24 88	83.67 54	83.29 80	58.41 140	73.71 68	90.14 37	45.62 156	85.99 92	69.64 69	82.85 87	85.78 92

PAPM_NR		72.63 86	71.80 88	75.13 93	81.72 95	53.42 157	79.91 112	83.28 81	59.14 128	66.31 181	85.90 110	51.86 87	86.06 89	57.45 163	80.62 105	85.91 88

EIA-MVS		71.78 99	70.60 106	75.30 91	79.85 130	53.54 154	77.27 158	83.26 82	57.92 150	66.49 176	79.39 241	52.07 85	86.69 73	60.05 150	79.14 132	85.66 100

FC-MVSNet-test		69.80 131	70.58 108	67.46 239	77.61 189	34.73 341	76.05 184	83.19 83	60.84 89	65.88 189	86.46 96	54.52 56	80.76 212	52.52 201	78.12 144	86.91 55

3Dnovator	64.47 5	72.49 88	71.39 95	75.79 75	77.70 181	58.99 76	80.66 101	83.15 84	62.24 69	65.46 195	86.59 93	42.38 192	85.52 105	59.59 155	84.72 70	82.85 195

MVS_Test		72.45 89	72.46 84	72.42 162	74.88 234	48.50 228	76.28 178	83.14 85	59.40 124	72.46 89	84.68 126	55.66 44	81.12 200	65.98 100	79.66 121	87.63 35

DP-MVS Recon		72.15 96	70.73 105	76.40 68	86.57 26	57.99 91	81.15 96	82.96 86	57.03 159	66.78 170	85.56 116	44.50 174	88.11 38	51.77 207	80.23 116	83.10 190

Regformer-2		75.63 52	74.99 53	77.54 50	80.43 118	58.32 87	79.50 120	82.92 87	67.84 1	75.94 37	80.75 213	55.73 43	86.80 69	71.44 61	80.38 111	87.50 40

UniMVSNet (Re)		70.63 115	70.20 112	71.89 166	78.55 158	45.29 264	75.94 187	82.92 87	63.68 43	68.16 146	83.59 153	53.89 64	83.49 150	53.97 189	71.12 219	86.89 56

MAR-MVS		71.51 103	70.15 114	75.60 85	81.84 94	59.39 63	81.38 93	82.90 89	54.90 210	68.08 149	78.70 248	47.73 129	85.51 106	51.68 209	84.17 74	81.88 210
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
nrg030		72.96 82	73.01 79	72.84 151	75.41 229	50.24 202	80.02 108	82.89 90	58.36 142	74.44 56	86.73 86	58.90 22	80.83 208	65.84 101	74.46 173	87.44 43

ACMP	63.53 6	72.30 91	71.20 100	75.59 86	80.28 120	57.54 97	82.74 69	82.84 91	60.58 94	65.24 202	86.18 102	39.25 223	86.03 91	66.95 94	76.79 160	83.22 184
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ZD-MVS					86.64 23	60.38 50		82.70 92	57.95 149	78.10 28	90.06 40	56.12 40	88.84 26	74.05 40	87.00 55

UniMVSNet_NR-MVSNet		71.11 107	71.00 102	71.44 177	79.20 143	44.13 273	76.02 186	82.60 93	66.48 13	68.20 143	84.60 130	56.82 33	82.82 168	54.62 184	70.43 226	87.36 47

alignmvs		73.86 75	73.99 67	73.45 137	78.20 168	50.50 200	78.57 132	82.43 94	59.40 124	76.57 34	86.71 88	56.42 37	81.23 199	65.84 101	81.79 95	88.62 4

Anonymous20231211		69.28 143	68.47 140	71.73 170	80.28 120	47.18 244	79.98 109	82.37 95	54.61 212	67.24 165	84.01 142	39.43 221	82.41 178	55.45 178	72.83 198	85.62 103

mPP-MVS		76.54 42	75.93 45	78.34 39	86.47 28	63.50 4	85.74 25	82.28 96	62.90 55	71.77 95	90.26 34	46.61 150	86.55 80	71.71 57	85.66 66	84.97 127

SR-MVS		76.13 46	75.70 48	77.40 54	85.87 45	61.20 33	85.52 28	82.19 97	59.99 112	75.10 42	90.35 29	47.66 131	86.52 81	71.64 58	82.99 81	84.47 141

Regformer-1		75.47 53	74.93 55	77.09 57	80.43 118	57.70 96	79.50 120	82.13 98	67.84 1	75.73 40	80.75 213	56.50 35	86.07 88	71.07 62	80.38 111	87.50 40

PS-MVSNAJss		72.24 92	71.21 99	75.31 90	78.50 159	55.93 126	81.63 87	82.12 99	56.24 178	70.02 114	85.68 115	47.05 143	84.34 131	65.27 107	74.41 175	85.67 99

WR-MVS_H		67.02 190	66.92 173	67.33 242	77.95 177	37.75 321	77.57 148	82.11 100	62.03 76	62.65 233	82.48 172	50.57 101	79.46 227	42.91 276	64.01 293	84.79 133

ACMM	61.98 7	70.80 113	69.73 119	74.02 116	80.59 117	58.59 83	82.68 71	82.02 101	55.46 196	67.18 166	84.39 136	38.51 230	83.17 155	60.65 145	76.10 164	80.30 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSLP-MVS++		73.77 76	73.47 75	74.66 101	83.02 81	59.29 66	82.30 81	81.88 102	59.34 126	71.59 99	86.83 83	45.94 154	83.65 146	65.09 109	85.22 68	81.06 225

abl_6		74.34 67	73.50 73	76.86 60	82.43 86	60.16 52	83.48 57	81.86 103	58.81 133	73.95 64	89.86 48	41.87 197	86.62 76	67.98 82	81.23 102	83.80 165

MVS		67.37 181	66.33 186	70.51 201	75.46 228	50.94 189	73.95 220	81.85 104	41.57 332	62.54 236	78.57 253	47.98 126	85.47 109	52.97 199	82.05 94	75.14 292

RRT_test8_iter05		68.17 170	66.86 174	72.07 165	75.81 220	46.33 250	76.41 175	81.81 105	56.43 173	66.52 175	81.30 199	31.90 296	84.25 132	63.77 122	67.83 266	85.64 102

114514_t		70.83 111	69.56 121	74.64 103	86.21 34	54.63 144	82.34 77	81.81 105	48.22 275	63.01 228	85.83 112	40.92 213	87.10 61	57.91 161	79.79 118	82.18 204

PCF-MVS	61.88 8	70.95 110	69.49 123	75.35 89	77.63 184	55.71 129	76.04 185	81.81 105	50.30 256	69.66 121	85.40 121	52.51 77	84.89 120	51.82 206	80.24 115	85.45 109
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPP-MVSNet		72.16 95	71.31 98	74.71 98	78.68 156	49.70 211	82.10 82	81.65 108	60.40 98	65.94 186	85.84 111	51.74 89	86.37 86	55.93 171	79.55 124	88.07 19

test1172		75.36 55	74.81 58	77.02 58	85.47 51	60.79 45	83.94 51	81.63 109	59.52 123	74.66 55	90.18 36	44.74 170	85.84 97	70.63 65	82.52 90	84.42 142

PVSNet_BlendedMVS		68.56 160	67.72 150	71.07 191	77.03 199	50.57 197	74.50 212	81.52 110	53.66 223	64.22 220	79.72 234	49.13 114	82.87 164	55.82 172	73.92 179	79.77 247

PVSNet_Blended		68.59 156	67.72 150	71.19 186	77.03 199	50.57 197	72.51 242	81.52 110	51.91 237	64.22 220	77.77 265	49.13 114	82.87 164	55.82 172	79.58 122	80.14 238

DU-MVS		70.01 127	69.53 122	71.44 177	78.05 174	44.13 273	75.01 202	81.51 112	64.37 31	68.20 143	84.52 131	49.12 116	82.82 168	54.62 184	70.43 226	87.37 45

Regformer-4		74.25 70	73.48 74	76.57 67	79.75 131	56.54 115	78.54 134	81.49 113	66.93 7	73.90 65	80.30 221	53.84 65	85.98 93	69.76 68	76.84 158	87.17 50

v1144		70.42 120	69.31 126	73.76 123	73.22 253	50.64 196	77.83 143	81.43 114	58.58 136	69.40 126	81.16 200	47.53 134	85.29 114	64.01 117	70.64 222	85.34 115

v10		70.21 124	69.02 131	73.81 120	73.51 252	50.92 191	78.74 128	81.39 115	60.05 111	66.39 179	81.83 188	47.58 133	85.41 112	62.80 128	68.86 257	85.09 123

SR-MVS-dyc-post		74.57 65	73.90 68	76.58 66	83.49 74	59.87 57	84.29 37	81.36 116	58.07 146	73.14 76	90.07 38	44.74 170	85.84 97	68.20 78	81.76 97	84.03 152

RE-MVS-def			73.71 72		83.49 74	59.87 57	84.29 37	81.36 116	58.07 146	73.14 76	90.07 38	43.06 186		68.20 78	81.76 97	84.03 152

v1192		69.97 129	68.68 136	73.85 119	73.19 254	50.94 189	77.68 146	81.36 116	57.51 154	68.95 134	80.85 210	45.28 166	85.33 113	62.97 127	70.37 228	85.27 118

RPMNet		61.53 253	58.42 263	70.86 193	69.96 303	52.07 178	65.31 303	81.36 116	43.20 322	59.36 265	70.15 328	35.37 257	85.47 109	36.42 315	64.65 290	75.06 293

OpenMVS	61.03 9	68.85 149	67.56 153	72.70 155	74.26 247	53.99 148	81.21 95	81.34 120	52.70 230	62.75 231	85.55 117	38.86 228	84.14 134	48.41 231	83.01 80	79.97 241

v7n		69.01 148	67.36 162	73.98 117	72.51 268	52.65 166	78.54 134	81.30 121	60.26 108	62.67 232	81.62 191	43.61 181	84.49 128	57.01 165	68.70 259	84.79 133

MG-MVS		73.96 73	73.89 69	74.16 115	85.65 47	49.69 213	81.59 90	81.29 122	61.45 82	71.05 101	88.11 70	51.77 88	87.73 49	61.05 143	83.09 79	85.05 124

TEST9					85.58 49	61.59 27	81.62 88	81.26 123	55.65 193	74.93 46	88.81 65	53.70 67	84.68 124

train_agg		76.27 45	76.15 42	76.64 65	85.58 49	61.59 27	81.62 88	81.26 123	55.86 185	74.93 46	88.81 65	53.70 67	84.68 124	75.24 32	88.33 35	83.65 173

PAPM		67.92 174	66.69 176	71.63 174	78.09 172	49.02 221	77.09 161	81.24 125	51.04 250	60.91 252	83.98 143	47.71 130	84.99 115	40.81 289	79.32 128	80.90 227

test_8					85.40 53	60.96 39	81.54 91	81.18 126	55.86 185	74.81 49	88.80 67	53.70 67	84.45 129

TranMVSNet+NR-MVSNet		70.36 121	70.10 116	71.17 188	78.64 157	42.97 284	76.53 172	81.16 127	66.95 6	68.53 139	85.42 120	51.61 90	83.07 156	52.32 202	69.70 243	87.46 42

HPM-MVS_fast		74.30 69	73.46 76	76.80 61	84.45 68	59.04 74	83.65 55	81.05 128	60.15 109	70.43 104	89.84 49	41.09 212	85.59 102	67.61 87	82.90 85	85.77 95

agg_prior1		75.94 48	76.01 44	75.72 79	85.04 57	59.96 54	81.44 92	81.04 129	56.14 181	74.68 52	88.90 63	53.91 63	84.04 136	75.01 33	87.92 45	83.16 189

agg_prior					85.04 57	59.96 54		81.04 129		74.68 52			84.04 136

Anonymous20240529		69.91 130	69.02 131	72.56 157	80.19 125	47.65 238	77.56 149	80.99 131	55.45 197	69.88 118	86.76 84	39.24 224	82.18 181	54.04 188	77.10 155	87.85 25

zzz-MVS		77.61 32	77.36 32	78.35 37	86.08 41	63.57 2	83.37 58	80.97 132	65.13 18	75.77 38	90.88 15	48.63 119	86.66 74	77.23 19	88.17 38	84.81 131

MTGPA								80.97 132

MTAPA		76.90 39	76.42 41	78.35 37	86.08 41	63.57 2	74.92 205	80.97 132	65.13 18	75.77 38	90.88 15	48.63 119	86.66 74	77.23 19	88.17 38	84.81 131

NR-MVSNet		69.54 139	68.85 133	71.59 175	78.05 174	43.81 277	74.20 215	80.86 135	65.18 17	62.76 230	84.52 131	52.35 82	83.59 148	50.96 212	70.78 221	87.37 45

v8		70.33 122	69.28 127	73.49 135	73.15 255	50.22 203	78.62 131	80.78 136	60.79 90	66.45 178	82.11 183	49.35 109	84.98 117	63.58 123	68.71 258	85.28 117

v144192		69.71 132	68.51 138	73.33 142	73.10 256	50.13 205	77.54 150	80.64 137	56.65 164	68.57 138	80.55 215	46.87 148	84.96 119	62.98 126	69.66 244	84.89 129

v1921920		69.47 141	68.17 146	73.36 141	73.06 257	50.10 206	77.39 153	80.56 138	56.58 171	68.59 136	80.37 217	44.72 172	84.98 117	62.47 132	69.82 239	85.00 125

v1240		69.24 145	67.91 148	73.25 146	73.02 259	49.82 209	77.21 159	80.54 139	56.43 173	68.34 142	80.51 216	43.33 184	84.99 115	62.03 136	69.77 242	84.95 128

v2v482		70.50 118	69.45 125	73.66 129	72.62 265	50.03 207	77.58 147	80.51 140	59.90 113	69.52 122	82.14 182	47.53 134	84.88 122	65.07 110	70.17 232	86.09 83

PEN-MVS		66.60 199	66.45 179	67.04 243	77.11 197	36.56 331	77.03 163	80.42 141	62.95 53	62.51 238	84.03 141	46.69 149	79.07 238	44.22 261	63.08 302	85.51 106

API-MVS		72.17 94	71.41 94	74.45 110	81.95 93	57.22 103	84.03 46	80.38 142	59.89 116	68.40 140	82.33 175	49.64 107	87.83 48	51.87 205	84.16 75	78.30 258

PVSNet_Blended_VisFu		71.45 105	70.39 110	74.65 102	82.01 90	58.82 79	79.93 111	80.35 143	55.09 203	65.82 191	82.16 181	49.17 113	82.64 173	60.34 148	78.62 141	82.50 200

test_yl		69.69 133	69.13 128	71.36 181	78.37 163	45.74 258	74.71 208	80.20 144	57.91 151	70.01 115	83.83 146	42.44 190	82.87 164	54.97 180	79.72 119	85.48 107

DCV-MVSNet		69.69 133	69.13 128	71.36 181	78.37 163	45.74 258	74.71 208	80.20 144	57.91 151	70.01 115	83.83 146	42.44 190	82.87 164	54.97 180	79.72 119	85.48 107

TAPA-MVS	59.36 10	66.60 199	65.20 205	70.81 194	76.63 206	48.75 225	76.52 173	80.04 146	50.64 254	65.24 202	84.93 123	39.15 225	78.54 245	36.77 308	76.88 157	85.14 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
xxxxxxxxxxxxxcwj		78.37 21	78.25 23	78.76 29	86.17 36	61.30 31	83.98 48	79.95 147	59.00 129	79.16 20	90.75 17	57.96 25	87.09 62	77.08 23	90.18 15	87.87 23

Regformer-3		73.89 74	73.28 78	75.71 80	79.75 131	55.48 136	78.54 134	79.93 148	66.58 11	73.62 69	80.30 221	54.87 52	84.54 127	69.09 75	76.84 158	87.10 53

OMC-MVS		71.40 106	70.60 106	73.78 121	76.60 207	53.15 160	79.74 116	79.78 149	58.37 141	68.75 135	86.45 97	45.43 163	80.60 213	62.58 129	77.73 147	87.58 38

ACMH	55.70 15	65.20 217	63.57 219	70.07 207	78.07 173	52.01 182	79.48 122	79.69 150	55.75 190	56.59 288	80.98 205	27.12 326	80.94 205	42.90 277	71.58 215	77.25 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		69.02 147	69.47 124	67.69 238	77.42 192	41.00 301	74.04 217	79.68 151	60.06 110	69.26 130	84.81 125	51.06 97	77.58 258	54.44 187	74.43 174	84.48 140

save fliter					86.17 36	61.30 31	83.98 48	79.66 152	59.00 129

Effi-MVS+		73.31 79	72.54 83	75.62 84	77.87 178	53.64 151	79.62 118	79.61 153	61.63 80	72.02 94	82.61 168	56.44 36	85.97 94	63.99 118	79.07 133	87.25 49

PS-CasMVS		66.42 203	66.32 187	66.70 247	77.60 190	36.30 336	76.94 165	79.61 153	62.36 68	62.43 240	83.66 151	45.69 155	78.37 246	45.35 258	63.26 300	85.42 112

CP-MVSNet		66.49 202	66.41 183	66.72 245	77.67 183	36.33 334	76.83 169	79.52 155	62.45 66	62.54 236	83.47 157	46.32 151	78.37 246	45.47 256	63.43 299	85.45 109

V42		68.65 155	67.35 163	72.56 157	68.93 314	50.18 204	72.90 236	79.47 156	56.92 161	69.45 125	80.26 223	46.29 152	82.99 157	64.07 115	67.82 267	84.53 138

Fast-Effi-MVS+		70.28 123	69.12 130	73.73 126	78.50 159	51.50 185	75.01 202	79.46 157	56.16 180	68.59 136	79.55 238	53.97 60	84.05 135	53.34 196	77.53 149	85.65 101

DTE-MVSNet		65.58 210	65.34 202	66.31 250	76.06 217	34.79 339	76.43 174	79.38 158	62.55 64	61.66 247	83.83 146	45.60 157	79.15 236	41.64 288	60.88 316	85.00 125

EI-MVSNet-Vis-set		72.42 90	71.59 90	74.91 95	78.47 161	54.02 147	77.05 162	79.33 159	65.03 22	71.68 97	79.35 243	52.75 75	84.89 120	66.46 95	74.23 176	85.83 91

EI-MVSNet-UG-set		71.92 97	71.06 101	74.52 109	77.98 176	53.56 153	76.62 170	79.16 160	64.40 30	71.18 100	78.95 247	52.19 83	84.66 126	65.47 105	73.57 184	85.32 116

XVG-OURS-SEG-HR		68.81 150	67.47 158	72.82 153	74.40 245	56.87 113	70.59 269	79.04 161	54.77 211	66.99 168	86.01 107	39.57 220	78.21 249	62.54 130	73.33 189	83.37 179

PS-MVSNAJ		70.51 117	69.70 120	72.93 149	81.52 98	55.79 128	74.92 205	79.00 162	55.04 208	69.88 118	78.66 249	47.05 143	82.19 180	61.61 139	79.58 122	80.83 228

xiu_mvs_v2_base		70.52 116	69.75 118	72.84 151	81.21 107	55.63 132	75.11 199	78.92 163	54.92 209	69.96 117	79.68 235	47.00 147	82.09 182	61.60 140	79.37 125	80.81 229

EG-PatchMatch MVS		64.71 221	62.87 226	70.22 203	77.68 182	53.48 155	77.99 141	78.82 164	53.37 224	56.03 291	77.41 268	24.75 340	84.04 136	46.37 244	73.42 188	73.14 312

XVG-OURS		68.76 154	67.37 161	72.90 150	74.32 246	57.22 103	70.09 275	78.81 165	55.24 199	67.79 158	85.81 114	36.54 253	78.28 248	62.04 135	75.74 167	83.19 186

cl_fuxian		68.33 164	67.56 153	70.62 198	70.87 288	46.21 253	74.47 213	78.80 166	56.22 179	66.19 182	78.53 254	51.88 86	81.40 194	62.08 133	69.04 253	84.25 146

ambc				65.13 269	63.72 342	37.07 326	47.66 352	78.78 167		54.37 310	71.42 317	11.24 361	80.94 205	45.64 251	53.85 339	77.38 269

AdaColmap		69.99 128	68.66 137	73.97 118	84.94 62	57.83 93	82.63 72	78.71 168	56.28 177	64.34 215	84.14 138	41.57 202	87.06 64	46.45 243	78.88 134	77.02 275

IS-MVSNet		71.57 102	71.00 102	73.27 144	78.86 150	45.63 262	80.22 106	78.69 169	64.14 38	66.46 177	87.36 78	49.30 110	85.60 101	50.26 216	83.71 77	88.59 5

miper_ehance_all_eth		68.03 171	67.24 169	70.40 202	70.54 292	46.21 253	73.98 218	78.68 170	55.07 206	66.05 184	77.80 263	52.16 84	81.31 196	61.53 142	69.32 247	83.67 170

cdsmvs_eth3d_5k		17.50 335	23.34 334	0.00 355	0.00 378	0.00 378	0.00 366	78.63 171	0.00 373	0.00 374	82.18 178	49.25 112	0.00 372	0.00 372	0.00 370	0.00 370

TSAR-MVS + GP.		74.90 58	74.15 66	77.17 56	82.00 91	58.77 80	81.80 84	78.57 172	58.58 136	74.32 58	84.51 133	55.94 41	87.22 56	67.11 91	84.48 73	85.52 105

mvs_tets		68.18 168	66.36 185	73.63 132	75.61 225	55.35 138	80.77 99	78.56 173	52.48 233	64.27 218	84.10 140	27.45 324	81.84 186	63.45 125	70.56 225	83.69 169

MVP-Stereo		65.41 214	63.80 215	70.22 203	77.62 188	55.53 134	76.30 177	78.53 174	50.59 255	56.47 289	78.65 250	39.84 217	82.68 171	44.10 265	72.12 210	72.44 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		68.25 166	66.45 179	73.66 129	75.62 224	55.49 135	80.82 98	78.51 175	52.33 234	64.33 216	84.11 139	28.28 318	81.81 187	63.48 124	70.62 223	83.67 170

MVSFormer		71.50 104	70.38 111	74.88 96	78.76 153	57.15 108	82.79 67	78.48 176	51.26 248	69.49 123	83.22 158	43.99 179	83.24 153	66.06 97	79.37 125	84.23 147

test_djsdf		69.45 142	67.74 149	74.58 106	74.57 241	54.92 142	82.79 67	78.48 176	51.26 248	65.41 196	83.49 156	38.37 232	83.24 153	66.06 97	69.25 250	85.56 104

diffmvs		70.69 114	70.43 109	71.46 176	69.45 309	48.95 223	72.93 235	78.46 178	57.27 156	71.69 96	83.97 144	51.48 91	77.92 253	70.70 64	77.95 146	87.53 39

EI-MVSNet		69.27 144	68.44 142	71.73 170	74.47 242	49.39 218	75.20 197	78.45 179	59.60 119	69.16 132	76.51 279	51.29 92	82.50 175	59.86 154	71.45 217	83.30 181

XVG-ACMP-BASELINE		64.36 225	62.23 234	70.74 196	72.35 270	52.45 173	70.80 268	78.45 179	53.84 221	59.87 260	81.10 202	16.24 354	79.32 230	55.64 177	71.76 212	80.47 232

MVSTER		67.16 187	65.58 200	71.88 167	70.37 296	49.70 211	70.25 274	78.45 179	51.52 242	69.16 132	80.37 217	38.45 231	82.50 175	60.19 149	71.46 216	83.44 178

miper_enhance_ethall		67.11 188	66.09 192	70.17 206	69.21 311	45.98 256	72.85 237	78.41 182	51.38 245	65.65 192	75.98 288	51.17 95	81.25 197	60.82 144	69.32 247	83.29 183

MVS_111021_HR		74.02 71	73.46 76	75.69 82	83.01 82	60.63 47	77.29 157	78.40 183	61.18 86	70.58 103	85.97 108	54.18 59	84.00 140	67.52 88	82.98 83	82.45 201

1314		64.61 223	63.21 223	68.80 227	71.87 278	47.46 241	73.95 220	78.39 184	42.88 325	59.97 258	76.60 278	38.11 236	79.39 229	54.84 182	72.32 207	79.55 248

Vis-MVSNet		72.18 93	71.37 96	74.61 104	81.29 104	55.41 137	80.90 97	78.28 185	60.73 92	69.23 131	88.09 71	44.36 176	82.65 172	57.68 162	81.75 99	85.77 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		71.01 109	70.15 114	73.60 133	79.57 137	52.17 176	78.93 126	78.12 186	58.02 148	67.76 160	83.87 145	52.36 81	82.72 170	56.90 166	75.79 166	85.92 87

ACMH+	57.40 11	66.12 205	64.06 210	72.30 164	77.79 180	52.83 164	80.39 104	78.03 187	57.30 155	57.47 283	82.55 170	27.68 322	84.17 133	45.54 253	69.78 240	79.90 242

eth_miper_zixun_eth		67.63 177	66.28 189	71.67 172	71.60 280	48.33 230	73.68 228	77.88 188	55.80 189	65.91 187	78.62 252	47.35 140	82.88 163	59.45 156	66.25 278	83.81 161

CPTT-MVS		72.78 83	72.08 87	74.87 97	84.88 65	61.41 29	84.15 43	77.86 189	55.27 198	67.51 162	88.08 72	41.93 196	81.85 185	69.04 76	80.01 117	81.35 218

GBi-Net		67.21 183	66.55 177	69.19 221	77.63 184	43.33 280	77.31 154	77.83 190	56.62 167	65.04 206	82.70 164	41.85 198	80.33 218	47.18 237	72.76 200	83.92 156

test1		67.21 183	66.55 177	69.19 221	77.63 184	43.33 280	77.31 154	77.83 190	56.62 167	65.04 206	82.70 164	41.85 198	80.33 218	47.18 237	72.76 200	83.92 156

FMVSNet1		66.70 197	65.87 194	69.19 221	77.49 191	43.33 280	77.31 154	77.83 190	56.45 172	64.60 214	82.70 164	38.08 237	80.33 218	46.08 246	72.31 208	83.92 156

UA-Net		73.13 80	72.93 80	73.76 123	83.58 73	51.66 184	78.75 127	77.66 193	67.75 4	72.61 87	89.42 54	49.82 105	83.29 152	53.61 194	83.14 78	86.32 76

VDD-MVS		72.50 87	72.09 86	73.75 125	81.58 97	49.69 213	77.76 145	77.63 194	63.21 50	73.21 74	89.02 61	42.14 193	83.32 151	61.72 138	82.50 91	88.25 11

IterMVS-LS		69.22 146	68.48 139	71.43 179	74.44 244	49.40 217	76.23 179	77.55 195	59.60 119	65.85 190	81.59 194	51.28 93	81.58 192	59.87 153	69.90 238	83.30 181
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		66.93 192	66.31 188	68.79 228	77.63 184	42.98 283	76.11 181	77.47 196	56.62 167	65.22 204	82.17 180	41.85 198	80.18 221	47.05 240	72.72 203	83.20 185

PLC	56.13 14	65.09 218	63.21 223	70.72 197	81.04 110	54.87 143	78.57 132	77.47 196	48.51 271	55.71 292	81.89 186	33.71 273	79.71 223	41.66 286	70.37 228	77.58 267
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		68.27 165	67.29 164	71.21 185	79.74 133	53.22 159	76.06 183	77.46 198	57.19 157	66.10 183	81.61 192	45.37 165	83.50 149	45.42 257	76.68 162	76.91 279

VNet		69.68 135	70.19 113	68.16 234	79.73 134	41.63 297	70.53 270	77.38 199	60.37 101	70.69 102	86.63 91	51.08 96	77.09 264	53.61 194	81.69 101	85.75 97

cl-mvsnet2		67.47 180	66.45 179	70.54 200	69.85 305	46.49 248	73.85 225	77.35 200	55.07 206	65.51 194	77.92 259	47.64 132	81.10 201	61.58 141	69.32 247	84.01 154

anonymousdsp		67.00 191	64.82 208	73.57 134	70.09 300	56.13 121	76.35 176	77.35 200	48.43 273	64.99 209	80.84 211	33.01 281	80.34 217	64.66 112	67.64 269	84.23 147

cascas		65.98 206	63.42 221	73.64 131	77.26 195	52.58 169	72.26 246	77.21 202	48.56 270	61.21 251	74.60 300	32.57 292	85.82 99	50.38 215	76.75 161	82.52 199

FMVSNet3		66.32 204	65.61 199	68.46 231	76.48 210	42.34 287	74.98 204	77.15 203	55.83 187	65.04 206	81.16 200	39.91 216	80.14 222	47.18 237	72.76 200	82.90 194

v148		68.24 167	67.19 170	71.40 180	70.43 294	47.77 237	75.76 189	77.03 204	58.91 131	67.36 163	80.10 226	48.60 122	81.89 184	60.01 151	66.52 277	84.53 138

Fast-Effi-MVS+-dtu		67.37 181	65.33 203	73.48 136	72.94 260	57.78 95	77.47 152	76.88 205	57.60 153	61.97 243	76.85 273	39.31 222	80.49 216	54.72 183	70.28 231	82.17 206

CANet_DTU		68.18 168	67.71 152	69.59 216	74.83 235	46.24 252	78.66 130	76.85 206	59.60 119	63.45 224	82.09 184	35.25 258	77.41 260	59.88 152	78.76 138	85.14 120

cl-mvsnet____		67.18 185	66.26 190	69.94 209	70.20 297	45.74 258	73.30 230	76.83 207	55.10 201	65.27 198	79.57 237	47.39 138	80.53 214	59.41 158	69.22 251	83.53 176

cl-mvsnet1		67.18 185	66.26 190	69.94 209	70.20 297	45.74 258	73.29 231	76.83 207	55.10 201	65.27 198	79.58 236	47.38 139	80.53 214	59.43 157	69.22 251	83.54 175

hse-mvs3		72.71 85	71.49 92	76.40 68	81.99 92	59.58 60	76.92 166	76.74 209	60.40 98	74.81 49	85.95 109	45.54 159	85.76 100	70.41 66	70.61 224	83.86 160

BH-w/o		66.85 193	65.83 195	69.90 212	79.29 140	52.46 172	74.66 210	76.65 210	54.51 216	64.85 210	78.12 255	45.59 158	82.95 160	43.26 272	75.54 169	74.27 305

LTVRE_ROB	55.42 16	63.15 237	61.23 246	68.92 226	76.57 208	47.80 235	59.92 325	76.39 211	54.35 218	58.67 273	82.46 173	29.44 311	81.49 193	42.12 282	71.14 218	77.46 268
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
BH-RMVSNet		68.81 150	67.42 159	72.97 148	80.11 127	52.53 170	74.26 214	76.29 212	58.48 139	68.38 141	84.20 137	42.59 188	83.83 142	46.53 242	75.91 165	82.56 197

F-COLMAP		63.05 238	60.87 251	69.58 218	76.99 201	53.63 152	78.12 140	76.16 213	47.97 279	52.41 322	81.61 192	27.87 320	78.11 250	40.07 292	66.66 275	77.00 276

ab-mvs		66.65 198	66.42 182	67.37 240	76.17 214	41.73 294	70.41 273	76.14 214	53.99 219	65.98 185	83.51 155	49.48 108	76.24 273	48.60 229	73.46 187	84.14 150

WR-MVS		68.47 162	68.47 140	68.44 232	80.20 124	39.84 304	73.75 227	76.07 215	64.68 25	68.11 148	83.63 152	50.39 103	79.14 237	49.78 217	69.66 244	86.34 73

Effi-MVS+-dtu		69.64 137	67.53 156	75.95 73	76.10 215	62.29 18	80.20 107	76.06 216	59.83 117	65.26 201	77.09 269	41.56 203	84.02 139	60.60 146	71.09 220	81.53 213

mvs-test1		70.44 119	68.19 145	77.18 55	76.10 215	63.22 6	80.59 102	76.06 216	59.83 117	66.32 180	79.87 229	41.56 203	85.53 104	60.60 146	72.77 199	82.80 196

RRT_MVS		68.77 153	66.71 175	74.95 94	75.93 219	58.55 84	80.50 103	75.84 218	56.09 182	68.17 145	83.74 149	28.50 316	82.98 158	65.67 103	65.91 280	83.33 180

MSDG		61.81 251	59.23 257	69.55 219	72.64 264	52.63 168	70.45 272	75.81 219	51.38 245	53.70 314	76.11 284	29.52 309	81.08 203	37.70 303	65.79 283	74.93 297

miper_lstm_enhance		62.03 247	60.88 250	65.49 265	66.71 327	46.25 251	56.29 337	75.70 220	50.68 252	61.27 250	75.48 293	40.21 215	68.03 307	56.31 169	65.25 286	82.18 204

pm-mvs1		65.24 216	64.97 207	66.04 257	72.38 269	39.40 309	72.62 240	75.63 221	55.53 195	62.35 242	83.18 160	47.45 136	76.47 270	49.06 226	66.54 276	82.24 203

UniMVSNet_ETH3D		67.60 178	67.07 172	69.18 224	77.39 193	42.29 288	74.18 216	75.59 222	60.37 101	66.77 171	86.06 106	37.64 239	78.93 244	52.16 204	73.49 186	86.32 76

HyFIR lowres test		65.67 209	63.01 225	73.67 128	79.97 129	55.65 131	69.07 283	75.52 223	42.68 326	63.53 223	77.95 257	40.43 214	81.64 189	46.01 247	71.91 211	83.73 168

pmmvs6		63.69 229	62.82 228	66.27 252	70.63 291	39.27 310	73.13 233	75.47 224	52.69 231	59.75 263	82.30 176	39.71 219	77.03 265	47.40 235	64.35 292	82.53 198

UGNet		68.81 150	67.39 160	73.06 147	78.33 165	54.47 145	79.77 114	75.40 225	60.45 97	63.22 225	84.40 135	32.71 288	80.91 207	51.71 208	80.56 109	83.81 161
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
VDDNet		71.81 98	71.33 97	73.26 145	82.80 85	47.60 240	78.74 128	75.27 226	59.59 122	72.94 81	89.40 55	41.51 206	83.91 141	58.75 159	82.99 81	88.26 10

hse-mvs2		71.04 108	69.86 117	74.60 105	79.58 136	57.12 110	73.96 219	75.25 227	60.40 98	74.81 49	81.95 185	45.54 159	82.90 161	70.41 66	66.83 274	83.77 166

AUN-MVS		68.45 163	66.41 183	74.57 107	79.53 138	57.08 111	73.93 223	75.23 228	54.44 217	66.69 172	81.85 187	37.10 248	82.89 162	62.07 134	66.84 273	83.75 167

mvs_anonymous		68.03 171	67.51 157	69.59 216	72.08 273	44.57 271	71.99 249	75.23 228	51.67 238	67.06 167	82.57 169	54.68 54	77.94 252	56.56 167	75.71 168	86.26 80

TR-MVS		66.59 201	65.07 206	71.17 188	79.18 144	49.63 215	73.48 229	75.20 230	52.95 227	67.90 150	80.33 220	39.81 218	83.68 145	43.20 273	73.56 185	80.20 236

IB-MVS	56.42 12	65.40 215	62.73 229	73.40 140	74.89 233	52.78 165	73.09 234	75.13 231	55.69 191	58.48 277	73.73 306	32.86 283	86.32 87	50.63 213	70.11 233	81.10 224
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
xiu_mvs_v1_base_debu		68.58 157	67.28 165	72.48 159	78.19 169	57.19 105	75.28 194	75.09 232	51.61 239	70.04 111	81.41 196	32.79 284	79.02 239	63.81 119	77.31 150	81.22 220

xiu_mvs_v1_base		68.58 157	67.28 165	72.48 159	78.19 169	57.19 105	75.28 194	75.09 232	51.61 239	70.04 111	81.41 196	32.79 284	79.02 239	63.81 119	77.31 150	81.22 220

xiu_mvs_v1_base_debi		68.58 157	67.28 165	72.48 159	78.19 169	57.19 105	75.28 194	75.09 232	51.61 239	70.04 111	81.41 196	32.79 284	79.02 239	63.81 119	77.31 150	81.22 220

TransMVSNet (Re)		64.72 220	64.33 209	65.87 261	75.22 231	38.56 315	74.66 210	75.08 235	58.90 132	61.79 246	82.63 167	51.18 94	78.07 251	43.63 269	55.87 332	80.99 226

ET-MVSNet_ETH3D		67.96 173	65.72 197	74.68 100	76.67 205	55.62 133	75.11 199	74.74 236	52.91 228	60.03 257	80.12 225	33.68 274	82.64 173	61.86 137	76.34 163	85.78 92

LS3D		64.71 221	62.50 231	71.34 183	79.72 135	55.71 129	79.82 113	74.72 237	48.50 272	56.62 287	84.62 128	33.59 276	82.34 179	29.65 346	75.23 171	75.97 283

Baseline_NR-MVSNet		67.05 189	67.56 153	65.50 264	75.65 223	37.70 322	75.42 192	74.65 238	59.90 113	68.14 147	83.15 161	49.12 116	77.20 262	52.23 203	69.78 240	81.60 212

HY-MVS	56.14 13	64.55 224	63.89 212	66.55 248	74.73 238	41.02 299	69.96 276	74.43 239	49.29 264	61.66 247	80.92 207	47.43 137	76.68 268	44.91 260	71.69 213	81.94 208

GA-MVS		65.53 212	63.70 216	71.02 192	70.87 288	48.10 232	70.48 271	74.40 240	56.69 163	64.70 212	76.77 274	33.66 275	81.10 201	55.42 179	70.32 230	83.87 159

DIV-MVS_2432*1600		55.22 292	53.89 298	59.21 300	57.80 360	27.47 360	57.75 332	74.32 241	47.38 285	50.90 328	70.00 329	28.45 317	70.30 298	40.44 291	57.92 326	79.87 243

无先验							79.66 117	74.30 242	48.40 274				80.78 210	53.62 192		79.03 254

thisisatest0530		67.92 174	65.78 196	74.33 113	76.29 212	51.03 188	76.89 167	74.25 243	53.67 222	65.59 193	81.76 189	35.15 259	85.50 107	55.94 170	72.47 204	86.47 67

CHOSEN 1792x2688		65.08 219	62.84 227	71.82 168	81.49 100	56.26 119	66.32 294	74.20 244	40.53 337	63.16 227	78.65 250	41.30 208	77.80 255	45.80 249	74.09 177	81.40 215

MS-PatchMatch		62.42 242	61.46 242	65.31 268	75.21 232	52.10 177	72.05 248	74.05 245	46.41 294	57.42 284	74.36 301	34.35 268	77.57 259	45.62 252	73.67 181	66.26 345

tttt0517		67.83 176	65.66 198	74.33 113	76.69 204	50.82 193	77.86 142	73.99 246	54.54 215	64.64 213	82.53 171	35.06 260	85.50 107	55.71 175	69.91 237	86.67 63

USDC		56.35 284	54.24 295	62.69 284	64.74 337	40.31 302	65.05 305	73.83 247	43.93 317	47.58 338	77.71 266	15.36 356	75.05 277	38.19 302	61.81 311	72.70 316

tfpnnormal		62.47 241	61.63 240	64.99 270	74.81 236	39.01 311	71.22 259	73.72 248	55.22 200	60.21 255	80.09 227	41.26 211	76.98 266	30.02 344	68.09 263	78.97 255

jason		69.65 136	68.39 143	73.43 139	78.27 167	56.88 112	77.12 160	73.71 249	46.53 293	69.34 127	83.22 158	43.37 183	79.18 232	64.77 111	79.20 130	84.23 147
jason: jason.
D2MVS		62.30 244	60.29 253	68.34 233	66.46 329	48.42 229	65.70 297	73.42 250	47.71 281	58.16 279	75.02 296	30.51 301	77.71 256	53.96 190	71.68 214	78.90 256

COLMAP_ROB	52.97 17	61.27 257	58.81 259	68.64 229	74.63 239	52.51 171	78.42 137	73.30 251	49.92 261	50.96 327	81.51 195	23.06 342	79.40 228	31.63 336	65.85 281	74.01 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
lupinMVS		69.57 138	68.28 144	73.44 138	78.76 153	57.15 108	76.57 171	73.29 252	46.19 296	69.49 123	82.18 178	43.99 179	79.23 231	64.66 112	79.37 125	83.93 155

DP-MVS		65.68 208	63.66 218	71.75 169	84.93 63	56.87 113	80.74 100	73.16 253	53.06 226	59.09 269	82.35 174	36.79 252	85.94 95	32.82 328	69.96 236	72.45 320

thisisatest0515		65.83 207	63.50 220	72.82 153	73.75 250	49.50 216	71.32 257	73.12 254	49.39 263	63.82 222	76.50 281	34.95 262	84.84 123	53.20 198	75.49 170	84.13 151

VPNet		67.52 179	68.11 147	65.74 262	79.18 144	36.80 329	72.17 247	72.83 255	62.04 75	67.79 158	85.83 112	48.88 118	76.60 269	51.30 210	72.97 197	83.81 161

CL-MVSNet_2432*1600		61.53 253	60.94 249	63.30 279	68.95 313	36.93 328	67.60 289	72.80 256	55.67 192	59.95 259	76.63 275	45.01 169	72.22 289	39.74 296	62.09 309	80.74 230

OurMVSNet-221017-0		61.37 256	58.63 262	69.61 215	72.05 274	48.06 233	73.93 223	72.51 257	47.23 289	54.74 304	80.92 207	21.49 349	81.24 198	48.57 230	56.22 331	79.53 249

EPNet		73.09 81	72.16 85	75.90 74	75.95 218	56.28 118	83.05 61	72.39 258	66.53 12	65.27 198	87.00 82	50.40 102	85.47 109	62.48 131	86.32 62	85.94 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		64.00 227	63.36 222	65.93 259	79.28 141	42.58 286	71.35 256	72.36 259	46.41 294	60.55 254	77.89 261	46.27 153	73.28 283	46.18 245	69.97 235	81.92 209

test_0402		63.25 235	61.01 248	69.96 208	80.00 128	54.37 146	76.86 168	72.02 260	54.58 214	58.71 272	80.79 212	35.00 261	84.36 130	26.41 354	64.71 289	71.15 332

EU-MVSNet		55.61 289	54.41 292	59.19 301	65.41 335	33.42 347	72.44 243	71.91 261	28.81 354	51.27 325	73.87 305	24.76 339	69.08 303	43.04 274	58.20 325	75.06 293

KD-MVS_2432*1600		53.45 300	51.50 307	59.30 297	62.82 343	37.14 324	55.33 338	71.79 262	47.34 287	55.09 300	70.52 324	21.91 347	70.45 296	35.72 318	42.97 354	70.31 335

miper_refine_blended		53.45 300	51.50 307	59.30 297	62.82 343	37.14 324	55.33 338	71.79 262	47.34 287	55.09 300	70.52 324	21.91 347	70.45 296	35.72 318	42.97 354	70.31 335

Anonymous202405211		66.84 194	65.99 193	69.40 220	80.19 125	42.21 289	71.11 263	71.31 264	58.80 134	67.90 150	86.39 98	29.83 308	79.65 224	49.60 223	78.78 137	86.33 74

LFMVS		71.78 99	71.59 90	72.32 163	83.40 76	46.38 249	79.75 115	71.08 265	64.18 35	72.80 83	88.64 68	42.58 189	83.72 144	57.41 164	84.49 72	86.86 57

CDS-MVSNet		66.80 195	65.37 201	71.10 190	78.98 148	53.13 162	73.27 232	71.07 266	52.15 236	64.72 211	80.23 224	43.56 182	77.10 263	45.48 255	78.88 134	83.05 191
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20240521		55.30 290	54.41 292	57.96 309	60.92 355	41.73 294	71.09 264	71.06 267	41.18 333	48.65 336	73.31 308	16.93 353	59.25 337	42.54 278	64.01 293	72.90 314

OpenMVS_ROB	52.78 18	60.03 261	58.14 267	65.69 263	70.47 293	44.82 266	75.33 193	70.86 268	45.04 304	56.06 290	76.00 285	26.89 329	79.65 224	35.36 320	67.29 270	72.60 317

CNLPA		65.43 213	64.02 211	69.68 214	78.73 155	58.07 90	77.82 144	70.71 269	51.49 243	61.57 249	83.58 154	38.23 235	70.82 293	43.90 266	70.10 234	80.16 237

CostFormer		64.04 226	62.51 230	68.61 230	71.88 277	45.77 257	71.30 258	70.60 270	47.55 283	64.31 217	76.61 277	41.63 201	79.62 226	49.74 219	69.00 255	80.42 233

bset_n11_16_dypcd		65.57 211	63.69 217	71.19 186	70.84 290	51.79 183	71.37 255	70.48 271	53.33 225	65.19 205	76.41 282	31.46 298	81.76 188	65.12 108	69.04 253	80.01 240

Test_1112_low_res		62.32 243	61.77 238	64.00 275	79.08 147	39.53 308	68.17 285	70.17 272	43.25 321	59.03 270	79.90 228	44.08 177	71.24 292	43.79 268	68.42 261	81.25 219

MVS_111021_LR		69.50 140	68.78 135	71.65 173	78.38 162	59.33 64	74.82 207	70.11 273	58.08 145	67.83 156	84.68 126	41.96 195	76.34 272	65.62 104	77.54 148	79.30 252

DWT-MVSNet_test		61.90 248	59.93 255	67.83 236	71.98 276	46.09 255	71.03 266	69.71 274	50.09 258	58.51 276	70.62 322	30.21 305	77.63 257	49.28 224	67.91 264	79.78 246

ANet_high		41.38 323	37.47 328	53.11 327	39.73 369	24.45 365	56.94 334	69.69 275	47.65 282	26.04 361	52.32 354	12.44 357	62.38 327	21.80 357	10.61 367	72.49 319

SixPastTwentyTwo		61.65 252	58.80 260	70.20 205	75.80 221	47.22 243	75.59 190	69.68 276	54.61 212	54.11 311	79.26 244	27.07 327	82.96 159	43.27 271	49.79 346	80.41 234

IterMVS-SCA-FT		62.49 240	61.52 241	65.40 266	71.99 275	50.80 194	71.15 262	69.63 277	45.71 302	60.61 253	77.93 258	37.45 241	65.99 317	55.67 176	63.50 298	79.42 250

TAMVS		66.78 196	65.27 204	71.33 184	79.16 146	53.67 150	73.84 226	69.59 278	52.32 235	65.28 197	81.72 190	44.49 175	77.40 261	42.32 280	78.66 140	82.92 192

CMPMVS	42.80 21	57.81 276	55.97 281	63.32 278	60.98 353	47.38 242	64.66 307	69.50 279	32.06 351	46.83 342	77.80 263	29.50 310	71.36 291	48.68 228	73.75 180	71.21 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn200view9		63.18 236	62.18 235	66.21 253	76.85 202	39.62 306	71.96 250	69.44 280	56.63 165	62.61 234	79.83 230	37.18 244	79.17 233	31.84 332	73.25 191	79.83 244

thres400		63.31 232	62.18 235	66.72 245	76.85 202	39.62 306	71.96 250	69.44 280	56.63 165	62.61 234	79.83 230	37.18 244	79.17 233	31.84 332	73.25 191	81.36 216

thres200		62.20 245	61.16 247	65.34 267	75.38 230	39.99 303	69.60 278	69.29 282	55.64 194	61.87 245	76.99 270	37.07 249	78.96 243	31.28 340	73.28 190	77.06 274

UnsupCasMVSNet_eth		53.16 305	52.47 303	55.23 318	59.45 357	33.39 348	59.43 327	69.13 283	45.98 298	50.35 334	72.32 312	29.30 312	58.26 340	42.02 284	44.30 352	74.05 307

thres100view900		63.28 234	62.41 232	65.89 260	77.31 194	38.66 314	72.65 238	69.11 284	57.07 158	62.45 239	81.03 204	37.01 250	79.17 233	31.84 332	73.25 191	79.83 244

thres600view7		63.30 233	62.27 233	66.41 249	77.18 196	38.87 312	72.35 244	69.11 284	56.98 160	62.37 241	80.96 206	37.01 250	79.00 242	31.43 339	73.05 196	81.36 216

CVMVSNet		59.63 265	59.14 258	61.08 295	74.47 242	38.84 313	75.20 197	68.74 286	31.15 352	58.24 278	76.51 279	32.39 293	68.58 305	49.77 218	65.84 282	75.81 286

TinyColmap		54.14 295	51.72 305	61.40 293	66.84 326	41.97 290	66.52 292	68.51 287	44.81 305	42.69 352	75.77 289	11.66 359	72.94 284	31.96 330	56.77 329	69.27 341

baseline2		63.42 231	61.26 245	69.89 213	72.55 267	47.62 239	71.54 253	68.38 288	50.11 257	54.82 303	75.55 292	43.06 186	80.96 204	48.13 232	67.16 272	81.11 223

IterMVS		62.79 239	61.27 244	67.35 241	69.37 310	52.04 180	71.17 260	68.24 289	52.63 232	59.82 261	76.91 272	37.32 243	72.36 286	52.80 200	63.19 301	77.66 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
旧先验1					83.04 80	53.15 160		67.52 290			87.85 74	44.08 177			80.76 104	78.03 265

AllTest		57.08 280	54.65 289	64.39 273	71.44 282	49.03 219	69.92 277	67.30 291	45.97 299	47.16 340	79.77 232	17.47 351	67.56 309	33.65 325	59.16 322	76.57 280

TestCases				64.39 273	71.44 282	49.03 219		67.30 291	45.97 299	47.16 340	79.77 232	17.47 351	67.56 309	33.65 325	59.16 322	76.57 280

baseline1		63.81 228	63.87 214	63.62 276	76.29 212	36.36 332	71.78 252	67.29 293	56.05 184	64.23 219	82.95 162	47.11 142	74.41 280	47.30 236	61.85 310	80.10 239

tpmvs		58.47 269	56.95 275	63.03 283	70.20 297	41.21 298	67.90 288	67.23 294	49.62 262	54.73 305	70.84 320	34.14 269	76.24 273	36.64 312	61.29 314	71.64 328

Gipuma		34.77 328	31.91 332	43.33 341	62.05 348	37.87 319	20.39 362	67.03 295	23.23 360	18.41 364	25.84 363	4.24 369	62.73 325	14.71 361	51.32 343	29.38 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tpm2		62.07 246	60.10 254	67.99 235	72.79 262	43.86 276	71.05 265	66.85 296	43.14 323	62.77 229	75.39 294	38.32 233	80.80 209	41.69 285	68.88 256	79.32 251

XXY-MVS		60.68 258	61.67 239	57.70 312	70.43 294	38.45 316	64.19 309	66.47 297	48.05 278	63.22 225	80.86 209	49.28 111	60.47 332	45.25 259	67.28 271	74.19 306

1121		68.53 161	67.16 171	72.63 156	85.64 48	61.14 34	73.95 220	66.46 298	44.61 308	70.28 107	86.68 89	41.42 207	80.78 210	53.62 192	81.79 95	75.97 283

新几何1				70.76 195	85.66 46	61.13 35		66.43 299	44.68 307	70.29 106	86.64 90	41.29 209	75.23 276	49.72 220	81.75 99	75.93 285

ppachtmachnet_test		58.06 274	55.38 285	66.10 256	69.51 307	48.99 222	68.01 287	66.13 300	44.50 310	54.05 312	70.74 321	32.09 295	72.34 287	36.68 311	56.71 330	76.99 278

tpm cat1		59.25 266	56.95 275	66.15 254	72.19 272	46.96 245	68.09 286	65.76 301	40.03 340	57.81 281	70.56 323	38.32 233	74.51 279	38.26 301	61.50 313	77.00 276

EPNet_dtu		61.90 248	61.97 237	61.68 290	72.89 261	39.78 305	75.85 188	65.62 302	55.09 203	54.56 307	79.36 242	37.59 240	67.02 312	39.80 295	76.95 156	78.25 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		58.51 268	57.36 271	61.96 289	70.04 301	41.83 292	69.40 281	65.46 303	50.73 251	53.30 320	74.06 304	22.65 343	70.18 300	42.16 281	68.44 260	73.86 310

pmmvs4		61.48 255	59.39 256	67.76 237	71.57 281	53.86 149	71.42 254	65.34 304	44.20 313	59.46 264	77.92 259	35.90 254	74.71 278	43.87 267	64.87 288	74.71 301

testdata				64.66 271	81.52 98	52.93 163		65.29 305	46.09 297	73.88 66	87.46 77	38.08 237	66.26 316	53.31 197	78.48 142	74.78 300

TDRefinement		53.44 302	50.72 310	61.60 291	64.31 340	46.96 245	70.89 267	65.27 306	41.78 328	44.61 348	77.98 256	11.52 360	66.36 315	28.57 349	51.59 342	71.49 329

MIMVSNet1		55.17 293	54.31 294	57.77 311	70.03 302	32.01 351	65.68 298	64.81 307	49.19 265	46.75 343	76.00 285	25.53 336	64.04 322	28.65 348	62.13 308	77.26 272

pmmvs-eth3d		58.81 267	56.31 280	66.30 251	67.61 321	52.42 174	72.30 245	64.76 308	43.55 319	54.94 302	74.19 303	28.95 313	72.60 285	43.31 270	57.21 327	73.88 309

MDTV_nov1_ep13			57.00 274		72.73 263	38.26 317	65.02 306	64.73 309	44.74 306	55.46 294	72.48 311	32.61 291	70.47 295	37.47 304	67.75 268

UnsupCasMVSNet_bld		50.07 313	48.87 314	53.66 324	60.97 354	33.67 346	57.62 333	64.56 310	39.47 342	47.38 339	64.02 347	27.47 323	59.32 336	34.69 322	43.68 353	67.98 344

ITE_SJBPF				62.09 288	66.16 331	44.55 272		64.32 311	47.36 286	55.31 297	80.34 219	19.27 350	62.68 326	36.29 316	62.39 307	79.04 253

WTY-MVS		59.75 264	60.39 252	57.85 310	72.32 271	37.83 320	61.05 323	64.18 312	45.95 301	61.91 244	79.11 246	47.01 146	60.88 331	42.50 279	69.49 246	74.83 298

MDA-MVSNet-bldmvs		53.87 298	50.81 309	63.05 282	66.25 330	48.58 226	56.93 335	63.82 313	48.09 277	41.22 353	70.48 326	30.34 303	68.00 308	34.24 323	45.92 351	72.57 318

Vis-MVSNet (Re-imp)		63.69 229	63.88 213	63.14 281	74.75 237	31.04 354	71.16 261	63.64 314	56.32 175	59.80 262	84.99 122	44.51 173	75.46 275	39.12 297	80.62 105	82.92 192

test222					83.14 77	58.68 81	72.57 241	63.45 315	41.78 328	67.56 161	86.12 103	37.13 247			78.73 139	74.98 296

PVSNet	50.76 19	58.40 270	57.39 270	61.42 292	75.53 227	44.04 275	61.43 318	63.45 315	47.04 291	56.91 285	73.61 307	27.00 328	64.76 320	39.12 297	72.40 205	75.47 290

SCA		60.49 259	58.38 264	66.80 244	74.14 249	48.06 233	63.35 311	63.23 317	49.13 266	59.33 268	72.10 313	37.45 241	74.27 281	44.17 262	62.57 305	78.05 262

CR-MVSNet		59.91 262	57.90 269	65.96 258	69.96 303	52.07 178	65.31 303	63.15 318	42.48 327	59.36 265	74.84 297	35.83 255	70.75 294	45.50 254	64.65 290	75.06 293

Patchmtry		57.16 279	56.47 278	59.23 299	69.17 312	34.58 342	62.98 312	63.15 318	44.53 309	56.83 286	74.84 297	35.83 255	68.71 304	40.03 293	60.91 315	74.39 304

pmmvs5		56.47 282	55.68 283	58.86 303	61.41 350	36.71 330	66.37 293	62.75 320	40.38 338	53.70 314	76.62 276	34.56 264	67.05 311	40.02 294	65.27 285	72.83 315

K. test v3		60.47 260	57.11 272	70.56 199	73.74 251	48.22 231	75.10 201	62.55 321	58.27 144	53.62 316	76.31 283	27.81 321	81.59 191	47.42 234	39.18 357	81.88 210

FMVSNet5		55.86 287	54.93 287	58.66 305	71.05 287	36.35 333	64.18 310	62.48 322	46.76 292	50.66 332	74.73 299	25.80 334	64.04 322	33.11 327	65.57 284	75.59 289

PatchmatchNet		59.84 263	58.24 265	64.65 272	73.05 258	46.70 247	69.42 280	62.18 323	47.55 283	58.88 271	71.96 315	34.49 266	69.16 302	42.99 275	63.60 297	78.07 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231206		55.10 294	55.30 286	54.48 322	69.81 306	33.94 345	62.91 313	62.13 324	41.08 334	55.18 299	75.65 290	32.75 287	56.59 347	30.32 343	67.86 265	72.91 313

sss		56.17 286	56.57 277	54.96 319	66.93 325	36.32 335	57.94 331	61.69 325	41.67 330	58.64 274	75.32 295	38.72 229	56.25 348	42.04 283	66.19 279	72.31 325

our_test_3		56.49 281	54.42 291	62.68 285	69.51 307	45.48 263	66.08 295	61.49 326	44.11 316	50.73 331	69.60 332	33.05 280	68.15 306	38.38 300	56.86 328	74.40 303

tpmrst		58.24 271	58.70 261	56.84 313	66.97 324	34.32 343	69.57 279	61.14 327	47.17 290	58.58 275	71.60 316	41.28 210	60.41 333	49.20 225	62.84 303	75.78 287

MIMVSNet		57.35 277	57.07 273	58.22 306	74.21 248	37.18 323	62.46 314	60.88 328	48.88 268	55.29 298	75.99 287	31.68 297	62.04 328	31.87 331	72.35 206	75.43 291

LCM-MVSNet		40.30 324	35.88 329	53.57 325	42.24 366	29.15 358	45.21 355	60.53 329	22.23 362	28.02 360	50.98 357	3.72 371	61.78 329	31.22 341	38.76 358	69.78 338

ADS-MVSNet2		51.33 310	48.76 315	59.07 302	66.02 333	44.60 270	50.90 346	59.76 330	36.90 344	50.74 329	66.18 342	26.38 330	63.11 324	27.17 350	54.76 335	69.50 339

new-patchmatchnet		47.56 317	47.73 318	47.06 337	58.81 358	9.37 372	48.78 350	59.21 331	43.28 320	44.22 349	68.66 334	25.67 335	57.20 344	31.57 338	49.35 347	74.62 302

test20.03		53.87 298	54.02 297	53.41 326	61.47 349	28.11 359	61.30 320	59.21 331	51.34 247	52.09 323	77.43 267	33.29 279	58.55 339	29.76 345	60.27 319	73.58 311

JIA-IIPM		51.56 309	47.68 319	63.21 280	64.61 338	50.73 195	47.71 351	58.77 333	42.90 324	48.46 337	51.72 355	24.97 338	70.24 299	36.06 317	53.89 338	68.64 343

testgi		51.90 307	52.37 304	50.51 335	60.39 356	23.55 366	58.42 329	58.15 334	49.03 267	51.83 324	79.21 245	22.39 344	55.59 351	29.24 347	62.64 304	72.40 324

LCM-MVSNet-Re		61.88 250	61.35 243	63.46 277	74.58 240	31.48 353	61.42 319	58.14 335	58.71 135	53.02 321	79.55 238	43.07 185	76.80 267	45.69 250	77.96 145	82.11 207

test-LLR		58.15 273	58.13 268	58.22 306	68.57 315	44.80 267	65.46 300	57.92 336	50.08 259	55.44 295	69.82 330	32.62 289	57.44 342	49.66 221	73.62 182	72.41 322

test-mter		56.42 283	55.82 282	58.22 306	68.57 315	44.80 267	65.46 300	57.92 336	39.94 341	55.44 295	69.82 330	21.92 346	57.44 342	49.66 221	73.62 182	72.41 322

RPSCF		55.80 288	54.22 296	60.53 296	65.13 336	42.91 285	64.30 308	57.62 338	36.84 346	58.05 280	82.28 177	28.01 319	56.24 349	37.14 306	58.61 324	82.44 202

GG-mvs-BLEND				62.34 286	71.36 286	37.04 327	69.20 282	57.33 339		54.73 305	65.48 344	30.37 302	77.82 254	34.82 321	74.93 172	72.17 326

MDA-MVSNet_test_wron		50.71 312	48.95 313	56.00 317	61.17 351	41.84 291	51.90 345	56.45 340	40.96 335	44.79 347	67.84 336	30.04 307	55.07 354	36.71 310	50.69 345	71.11 333

YYNet1		50.73 311	48.96 312	56.03 316	61.10 352	41.78 293	51.94 344	56.44 341	40.94 336	44.84 346	67.80 337	30.08 306	55.08 353	36.77 308	50.71 344	71.22 330

gg-mvs-nofinetune		57.86 275	56.43 279	62.18 287	72.62 265	35.35 338	66.57 291	56.33 342	50.65 253	57.64 282	57.10 352	30.65 300	76.36 271	37.38 305	78.88 134	74.82 299

TESTMET0.1,1		55.28 291	54.90 288	56.42 314	66.56 328	43.67 278	65.46 300	56.27 343	39.18 343	53.83 313	67.44 338	24.21 341	55.46 352	48.04 233	73.11 195	70.13 337

PMMVS		53.96 296	53.26 302	56.04 315	62.60 346	50.92 191	61.17 322	56.09 344	32.81 350	53.51 318	66.84 340	34.04 270	59.93 335	44.14 264	68.18 262	57.27 353

tpm		57.34 278	58.16 266	54.86 320	71.80 279	34.77 340	67.47 290	56.04 345	48.20 276	60.10 256	76.92 271	37.17 246	53.41 355	40.76 290	65.01 287	76.40 282

PVSNet_0	43.31 20	47.46 318	45.64 321	52.92 328	67.60 322	44.65 269	54.06 342	54.64 346	41.59 331	46.15 344	58.75 351	30.99 299	58.66 338	32.18 329	24.81 360	55.46 354

dp		51.89 308	51.60 306	52.77 329	68.44 318	32.45 350	62.36 315	54.57 347	44.16 314	49.31 335	67.91 335	28.87 315	56.61 346	33.89 324	54.89 334	69.24 342

PatchT		53.17 304	53.44 301	52.33 331	68.29 319	25.34 364	58.21 330	54.41 348	44.46 311	54.56 307	69.05 333	33.32 278	60.94 330	36.93 307	61.76 312	70.73 334

test0.0.03 1		53.32 303	53.59 300	52.50 330	62.81 345	29.45 357	59.51 326	54.11 349	50.08 259	54.40 309	74.31 302	32.62 289	55.92 350	30.50 342	63.95 295	72.15 327

PatchMatch-RL		56.25 285	54.55 290	61.32 294	77.06 198	56.07 123	65.57 299	54.10 350	44.13 315	53.49 319	71.27 319	25.20 337	66.78 313	36.52 314	63.66 296	61.12 348

FPMVS		42.18 322	41.11 325	45.39 338	58.03 359	41.01 300	49.50 348	53.81 351	30.07 353	33.71 358	64.03 345	11.69 358	52.08 357	14.01 362	55.11 333	43.09 358

Patchmatch-RL test		58.16 272	55.49 284	66.15 254	67.92 320	48.89 224	60.66 324	51.07 352	47.86 280	59.36 265	62.71 349	34.02 271	72.27 288	56.41 168	59.40 321	77.30 270

lessismore_v0				69.91 211	71.42 284	47.80 235		50.90 353		50.39 333	75.56 291	27.43 325	81.33 195	45.91 248	34.10 359	80.59 231

ADS-MVSNet		48.48 315	47.77 317	50.63 334	66.02 333	29.92 356	50.90 346	50.87 354	36.90 344	50.74 329	66.18 342	26.38 330	52.47 356	27.17 350	54.76 335	69.50 339

EPMVS		53.96 296	53.69 299	54.79 321	66.12 332	31.96 352	62.34 316	49.05 355	44.42 312	55.54 293	71.33 318	30.22 304	56.70 345	41.65 287	62.54 306	75.71 288

PMVS	28.69 22	36.22 327	33.29 331	45.02 340	36.82 371	35.98 337	54.68 341	48.74 356	26.31 357	21.02 362	51.61 356	2.88 373	60.10 334	9.99 366	47.58 349	38.99 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LF4IMVS		42.95 321	42.26 323	45.04 339	48.30 364	32.50 349	54.80 340	48.49 357	28.03 355	40.51 355	70.16 327	9.24 364	43.89 361	31.63 336	49.18 348	58.72 350

Patchmatch-test		49.08 314	48.28 316	51.50 333	64.40 339	30.85 355	45.68 353	48.46 358	35.60 347	46.10 345	72.10 313	34.47 267	46.37 359	27.08 352	60.65 318	77.27 271

door								47.60 359

door-mid								47.19 360

pmmvs3		44.92 320	41.95 324	53.86 323	52.58 362	43.55 279	62.11 317	46.90 361	26.05 358	40.63 354	60.19 350	11.08 362	57.91 341	31.83 335	46.15 350	60.11 349

MVS-HIRNet		45.52 319	44.48 322	48.65 336	68.49 317	34.05 344	59.41 328	44.50 362	27.03 356	37.96 357	50.47 358	26.16 333	64.10 321	26.74 353	59.52 320	47.82 356

CHOSEN 280x420		47.83 316	46.36 320	52.24 332	67.37 323	49.78 210	38.91 359	43.11 363	35.00 348	43.27 351	63.30 348	28.95 313	49.19 358	36.53 313	60.80 317	57.76 352

test_method		19.68 334	18.10 337	24.41 349	13.68 374	3.11 375	12.06 365	42.37 364	2.00 369	11.97 367	36.38 360	5.77 368	29.35 368	15.06 360	23.65 361	40.76 359

PM-MVS		52.33 306	50.19 311	58.75 304	62.10 347	45.14 265	65.75 296	40.38 365	43.60 318	53.52 317	72.65 310	9.16 365	65.87 318	50.41 214	54.18 337	65.24 347

E-PMN		23.77 331	22.73 335	26.90 347	42.02 367	20.67 367	42.66 357	35.70 366	17.43 363	10.28 369	25.05 364	6.42 367	42.39 363	10.28 365	14.71 364	17.63 363

EMVS		22.97 332	21.84 336	26.36 348	40.20 368	19.53 369	41.95 358	34.64 367	17.09 364	9.73 370	22.83 365	7.29 366	42.22 364	9.18 367	13.66 365	17.32 364

new_pmnet		34.13 329	34.29 330	33.64 344	52.63 361	18.23 370	44.43 356	33.90 368	22.81 361	30.89 359	53.18 353	10.48 363	35.72 366	20.77 358	39.51 356	46.98 357

DSMNet-mixed		39.30 326	38.72 327	41.03 342	51.22 363	19.66 368	45.53 354	31.35 369	15.83 365	39.80 356	67.42 339	22.19 345	45.13 360	22.43 356	52.69 340	58.31 351

PMMVS2		27.40 330	25.91 333	31.87 346	39.46 370	6.57 373	31.17 360	28.52 370	23.96 359	20.45 363	48.94 359	4.20 370	37.94 365	16.51 359	19.97 362	51.09 355

MVE	17.77 23	21.41 333	17.77 338	32.34 345	34.34 372	25.44 363	16.11 363	24.11 371	11.19 366	13.22 366	31.92 361	1.58 374	30.95 367	10.47 364	17.03 363	40.62 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							86.03 19	17.08 372

tmp_tt		9.43 337	11.14 340	4.30 352	2.38 375	4.40 374	13.62 364	16.08 373	0.39 370	15.89 365	13.06 366	15.80 355	5.54 371	12.63 363	10.46 368	2.95 366

DeepMVS_CX				12.03 351	17.97 373	10.91 371		10.60 374	7.46 367	11.07 368	28.36 362	3.28 372	11.29 370	8.01 368	9.74 369	13.89 365

wuyk23d		13.32 336	12.52 339	15.71 350	47.54 365	26.27 361	31.06 361	1.98 375	4.93 368	5.18 371	1.94 370	0.45 375	18.54 369	6.81 369	12.83 366	2.33 367

N_pmnet		39.35 325	40.28 326	36.54 343	63.76 341	1.62 376	49.37 349	0.76 376	34.62 349	43.61 350	66.38 341	26.25 332	42.57 362	26.02 355	51.77 341	65.44 346

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

pcd_1.5k_mvsjas		3.92 341	5.23 344	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	47.05 143	0.00 372	0.00 372	0.00 370	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

testmvs		4.52 340	6.03 343	0.01 354	0.01 376	0.00 378	53.86 343	0.00 377	0.01 371	0.04 372	0.27 371	0.00 377	0.00 372	0.04 370	0.00 370	0.03 369

test123		4.73 339	6.30 342	0.02 353	0.01 376	0.01 377	56.36 336	0.00 377	0.01 371	0.04 372	0.21 372	0.01 376	0.00 372	0.03 371	0.00 370	0.04 368

n2								0.00 377

nn								0.00 377

ab-mvs-re		6.49 338	8.65 341	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	77.89 261	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 378	0.00 366	0.00 377	0.00 373	0.00 374	0.00 373	0.00 377	0.00 372	0.00 372	0.00 370	0.00 370

PC_three_1452									55.09 203	84.46 4	89.84 49	66.68 5	89.41 17	74.24 37	91.38 2	88.42 7

eth-test2					0.00 378

eth-test					0.00 378

OPU-MVS				79.83 6	87.54 11	60.93 40	87.82 7				89.89 47	67.01 1	90.33 11	73.16 49	91.15 4	88.23 12

test_0728_THIRD									65.04 20	83.82 8	92.00 3	64.69 10	90.75 8	79.48 4	90.63 10	88.09 17

GSMVS																78.05 262

test_part2					87.58 9	60.47 49				83.42 12

sam_mvs1												34.74 263				78.05 262

sam_mvs												33.43 277

test_post1							68.67 284				3.64 368	32.39 293	69.49 301	44.17 262

test_post											3.55 369	33.90 272	66.52 314

patchmatchnet-post											64.03 345	34.50 265	74.27 281

gm-plane-assit					71.40 285	41.72 296			48.85 269		73.31 308		82.48 177	48.90 227

test9_res														75.28 31	88.31 37	83.81 161

agg_prior2														73.09 50	87.93 44	84.33 143

test_prior4						62.51 17	82.08 83

test_prior2							81.75 85		60.37 101	75.01 44	89.06 59	56.22 38		72.19 53	88.96 28

旧先验2							76.08 182		45.32 303	76.55 35			65.56 319	58.75 159

新几何2							76.12 180

原ACMM2							79.02 125

testdata2													72.18 290	46.95 241

segment_acmp												54.23 58

testdata1							72.65 238		60.50 96

plane_prior7					81.41 101	55.96 125

plane_prior6					81.20 108	56.24 120						45.26 167

plane_prior4											86.10 104

plane_prior3						56.09 122			63.92 39	69.27 128

plane_prior2							84.22 40		64.52 28

plane_prior1					81.27 106

plane_prior						56.31 116	83.58 56		63.19 51						80.48 110

HQP5-MVS						54.94 140

HQP-NCC					80.66 113		82.31 78		62.10 72	67.85 152

ACMP_Plane					80.66 113		82.31 78		62.10 72	67.85 152

BP-MVS														67.04 92

HQP4-MVS										67.85 152			86.93 66			84.32 144

HQP2-MVS												45.46 161

NP-MVS					80.98 111	56.05 124					85.54 118

MDTV_nov1_ep13_2view						25.89 362	61.22 321		40.10 339	51.10 326		32.97 282		38.49 299		78.61 257

ACMMP++_ref															74.07 178

ACMMP++															72.16 209

Test By Simon												48.33 124

High-resolution multi-view benchmark