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.

9.14			88.26 15		92.84 60		91.52 45	94.75 1	73.93 127	88.57 22	94.67 18	75.57 22	95.79 57	86.77 35	95.76 23

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 49	92.40 24	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 48

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

test0726					95.27 5	71.25 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 62	94.67 26
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
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 42	94.10 8	75.90 88	92.29 7	95.66 10	81.67 6	97.38 11	87.44 33	96.34 15	93.95 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS		86.43 40	86.17 44	87.24 41	90.88 90	70.96 65	92.27 32	94.07 9	72.45 156	85.22 58	91.90 92	69.47 78	96.42 37	83.28 65	95.94 19	94.35 42

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 51	93.10 1	95.72 8	82.99 1	97.44 7	89.07 14	96.63 4	94.88 14

test_241102_TWO								94.06 10	77.24 51	92.78 4	95.72 8	81.26 8	97.44 7	89.07 14	96.58 6	94.26 47

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 54	93.10 1	95.39 11	82.99 1	97.27 12

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 92	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 36
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS	79.81 2	87.08 32	86.88 35	87.69 33	91.16 81	72.32 43	90.31 68	93.94 14	77.12 56	82.82 102	94.23 35	72.13 47	97.09 16	84.83 46	95.37 31	93.65 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 115	91.30 15

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 76	93.82 16	73.07 149	84.86 65	92.89 74	76.22 17	96.33 38	84.89 45	95.13 36	94.40 40

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 64	85.24 57	94.32 30	71.76 52	96.93 19	85.53 40	95.79 22	94.32 44

CS-MVS-test		86.29 43	86.48 38	85.71 69	91.02 86	67.21 156	92.36 29	93.78 18	78.97 28	83.51 93	91.20 116	70.65 68	95.15 81	81.96 80	94.89 42	94.77 23

3Dnovator+	77.84 4	85.48 58	84.47 75	88.51 7	91.08 84	73.49 16	93.18 11	93.78 18	80.79 8	76.66 198	93.37 62	60.40 195	96.75 26	77.20 121	93.73 66	95.29 5

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 16	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 110	92.29 7	95.97 2	74.28 29	97.24 13	88.58 21	96.91 1	94.87 16
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
EC-MVSNet		86.01 44	86.38 39	84.91 93	89.31 137	66.27 170	92.32 30	93.63 21	79.37 20	84.17 81	91.88 93	69.04 86	95.43 69	83.93 60	93.77 65	93.01 105

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 54	93.59 23	76.27 82	88.14 24	95.09 14	71.06 62	96.67 29	87.67 29	96.37 14	94.09 52

CSCG		86.41 42	86.19 43	87.07 45	92.91 58	72.48 37	90.81 55	93.56 24	73.95 125	83.16 96	91.07 121	75.94 18	95.19 79	79.94 100	94.38 58	93.55 82

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 78	93.50 25	75.17 103	86.34 48	95.29 12	70.86 64	96.00 53	88.78 19	96.04 16	94.58 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FIs		82.07 109	82.42 97	81.04 221	88.80 157	58.34 291	88.26 136	93.49 26	76.93 61	78.47 158	91.04 122	69.92 74	92.34 202	69.87 193	84.97 174	92.44 125

DELS-MVS		85.41 61	85.30 63	85.77 68	88.49 168	67.93 133	85.52 221	93.44 27	78.70 29	83.63 92	89.03 168	74.57 24	95.71 60	80.26 98	94.04 63	93.66 71
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
GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 62	84.68 66	93.99 48	70.67 67	96.82 22	84.18 59	95.01 37	93.90 61

FC-MVSNet-test		81.52 121	82.02 106	80.03 242	88.42 173	55.97 329	87.95 146	93.42 29	77.10 57	77.38 180	90.98 128	69.96 73	91.79 220	68.46 208	84.50 180	92.33 126

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 100	89.57 88	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 30

HPM-MVS		87.11 30	86.98 31	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 85	83.81 88	93.95 51	69.77 76	96.01 52	85.15 41	94.66 47	94.32 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 36	86.95 32	85.90 67	90.76 95	67.57 143	92.83 17	93.30 32	79.67 17	84.57 74	92.27 86	71.47 57	95.02 90	84.24 57	93.46 67	95.13 7

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 66	84.91 62	94.44 27	70.78 65	96.61 32	84.53 52	94.89 42	93.66 71

ACMMPR		87.44 23	87.23 27	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 66	84.66 69	94.52 20	68.81 88	96.65 30	84.53 52	94.90 41	94.00 56

SD-MVS		88.06 14	88.50 14	86.71 51	92.60 66	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 137	87.63 30	94.27 61	93.65 75
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
ACMMP		85.89 51	85.39 59	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 66	80.73 127	93.82 53	64.33 132	96.29 39	82.67 77	90.69 98	93.23 93
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
region2R		87.42 25	87.20 28	88.09 14	94.63 14	73.55 13	93.03 14	93.12 37	76.73 69	84.45 75	94.52 20	69.09 82	96.70 27	84.37 54	94.83 45	94.03 55

DPM-MVS		84.93 69	84.29 76	86.84 47	90.20 104	73.04 23	87.12 171	93.04 38	69.80 211	82.85 101	91.22 115	73.06 39	96.02 51	76.72 128	94.63 48	91.46 155

PGM-MVS		86.68 37	86.27 41	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 94	83.86 86	94.42 28	67.87 99	96.64 31	82.70 76	94.57 50	93.66 71

casdiffmvs_mvg		85.99 45	86.09 47	85.70 70	87.65 205	67.22 155	88.69 120	93.04 38	79.64 18	85.33 56	92.54 83	73.30 35	94.50 108	83.49 62	91.14 93	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS_fast	79.65 3	86.91 33	86.62 37	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 72	84.22 79	93.36 63	71.44 58	96.76 25	80.82 92	95.33 33	94.16 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		81.60 120	81.11 117	83.09 165	88.38 174	64.41 211	87.60 156	93.02 42	78.42 32	78.56 155	88.16 193	69.78 75	93.26 161	69.58 196	76.49 285	91.60 146

sasdasda		85.91 49	85.87 51	86.04 63	89.84 116	69.44 95	90.45 65	93.00 43	76.70 70	88.01 28	91.23 113	73.28 36	93.91 131	81.50 83	88.80 125	94.77 23

canonicalmvs		85.91 49	85.87 51	86.04 63	89.84 116	69.44 95	90.45 65	93.00 43	76.70 70	88.01 28	91.23 113	73.28 36	93.91 131	81.50 83	88.80 125	94.77 23

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 59	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 43

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 38

No_MVS				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 38

XVS		87.18 29	86.91 34	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 90	94.17 36	67.45 102	96.60 33	83.06 66	94.50 51	94.07 53

X-MVStestdata		80.37 150	77.83 186	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 90	12.47 412	67.45 102	96.60 33	83.06 66	94.50 51	94.07 53

APD-MVS_3200maxsize		85.97 47	85.88 50	86.22 57	92.69 63	69.53 89	91.93 37	92.99 46	73.54 137	85.94 49	94.51 23	65.80 122	95.61 61	83.04 68	92.51 75	93.53 84

test_prior				86.33 54	92.61 65	69.59 88		92.97 51					95.48 66			93.91 59

IU-MVS					95.30 2	71.25 57		92.95 52	66.81 257	92.39 6				88.94 16	96.63 4	94.85 19

balanced_conf03		86.78 34	86.99 30	86.15 58	91.24 80	67.61 141	90.51 59	92.90 53	77.26 50	87.44 37	91.63 101	71.27 61	96.06 47	85.62 39	95.01 37	94.78 22

baseline		84.93 69	84.98 67	84.80 97	87.30 217	65.39 190	87.30 166	92.88 54	77.62 39	84.04 84	92.26 87	71.81 51	93.96 124	81.31 86	90.30 103	95.03 9

MSLP-MVS++		85.43 60	85.76 53	84.45 106	91.93 72	70.24 76	90.71 56	92.86 55	77.46 47	84.22 79	92.81 78	67.16 106	92.94 182	80.36 96	94.35 59	90.16 199

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 56	80.26 11	87.78 30	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 102

casdiffmvs		85.11 66	85.14 65	85.01 87	87.20 219	65.77 182	87.75 153	92.83 57	77.84 37	84.36 78	92.38 85	72.15 46	93.93 130	81.27 88	90.48 100	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 40	92.83 57	73.01 151	88.58 21	94.52 20	73.36 34	96.49 36	84.26 55	95.01 37	92.70 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 51	92.83 57	81.50 5	85.79 52	93.47 60	73.02 40	97.00 18	84.90 43	94.94 40	94.10 51

CP-MVS		87.11 30	86.92 33	87.68 34	94.20 34	73.86 7	93.98 3	92.82 60	76.62 72	83.68 89	94.46 24	67.93 97	95.95 56	84.20 58	94.39 56	93.23 93

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 61	77.33 48	92.12 9	95.78 4	80.98 9	97.40 9	89.08 12	96.41 12	93.33 90
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
EIA-MVS		83.31 93	82.80 95	84.82 95	89.59 121	65.59 185	88.21 137	92.68 62	74.66 112	78.96 145	86.42 244	69.06 84	95.26 77	75.54 140	90.09 107	93.62 78

ZD-MVS					94.38 25	72.22 44		92.67 63	70.98 184	87.75 32	94.07 41	74.01 32	96.70 27	84.66 50	94.84 44

nrg030		83.88 77	83.53 81	84.96 89	86.77 227	69.28 99	90.46 64	92.67 63	74.79 109	82.95 97	91.33 112	72.70 43	93.09 176	80.79 94	79.28 255	92.50 121

WR-MVS_H		78.51 192	78.49 168	78.56 269	88.02 188	56.38 323	88.43 127	92.67 63	77.14 55	73.89 259	87.55 208	66.25 115	89.24 280	58.92 289	73.55 329	90.06 209

MVSMamba_PlusPlus		85.99 45	85.96 49	86.05 62	91.09 82	67.64 139	89.63 85	92.65 66	72.89 154	84.64 70	91.71 96	71.85 49	96.03 48	84.77 48	94.45 54	94.49 34

MonoMVSNet		85.32 63	85.07 66	86.07 61	90.86 91	67.64 139	89.63 85	92.65 66	72.35 161	84.64 70	90.81 130	68.76 90	96.09 46	81.45 85	94.45 54	94.49 34

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 66	77.57 41	83.84 87	94.40 29	72.24 45	96.28 40	85.65 38	95.30 35	93.62 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		84.90 71	84.67 71	85.59 72	89.39 132	68.66 117	88.74 118	92.64 69	79.97 15	84.10 82	85.71 257	69.32 80	95.38 73	80.82 92	91.37 90	92.72 111

MGCFI-Net		85.06 68	85.51 56	83.70 143	89.42 129	63.01 240	89.43 91	92.62 70	76.43 74	87.53 35	91.34 111	72.82 42	93.42 157	81.28 87	88.74 128	94.66 28

CANet		86.45 39	86.10 46	87.51 37	90.09 106	70.94 67	89.70 82	92.59 71	81.78 4	81.32 118	91.43 109	70.34 69	97.23 14	84.26 55	93.36 68	94.37 41

SR-MVS		86.73 35	86.67 36	86.91 46	94.11 37	72.11 47	92.37 28	92.56 72	74.50 114	86.84 45	94.65 19	67.31 104	95.77 58	84.80 47	92.85 71	92.84 110

alignmvs		85.48 58	85.32 62	85.96 66	89.51 125	69.47 92	89.74 80	92.47 73	76.17 83	87.73 34	91.46 108	70.32 70	93.78 137	81.51 82	88.95 122	94.63 29

原ACMM1				84.35 110	93.01 57	68.79 107		92.44 74	63.96 302	81.09 123	91.57 104	66.06 118	95.45 67	67.19 219	94.82 46	88.81 254

HQP_MVS		83.64 83	83.14 87	85.14 82	90.08 107	68.71 113	91.25 49	92.44 74	79.12 23	78.92 147	91.00 126	60.42 193	95.38 73	78.71 106	86.32 158	91.33 156

plane_prior5								92.44 74					95.38 73	78.71 106	86.32 158	91.33 156

CDPH-MVS		85.76 53	85.29 64	87.17 43	93.49 47	71.08 61	88.58 124	92.42 77	68.32 246	84.61 72	93.48 58	72.32 44	96.15 45	79.00 102	95.43 30	94.28 46

UniMVSNet_NR-MVSNet		81.88 112	81.54 112	82.92 174	88.46 170	63.46 230	87.13 170	92.37 78	80.19 12	78.38 159	89.14 164	71.66 56	93.05 178	70.05 189	76.46 286	92.25 130

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 79	74.62 113	88.90 20	93.85 52	75.75 20	96.00 53	87.80 28	94.63 48	95.04 8
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CLD-MVS		82.31 105	81.65 111	84.29 113	88.47 169	67.73 137	85.81 212	92.35 79	75.78 89	78.33 161	86.58 239	64.01 135	94.35 111	76.05 133	87.48 142	90.79 173
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SR-MVS-dyc-post		85.77 52	85.61 55	86.23 56	93.06 55	70.63 73	91.88 38	92.27 81	73.53 138	85.69 53	94.45 25	65.00 130	95.56 62	82.75 72	91.87 83	92.50 121

RE-MVS-def			85.48 57		93.06 55	70.63 73	91.88 38	92.27 81	73.53 138	85.69 53	94.45 25	63.87 136		82.75 72	91.87 83	92.50 121

RPMNet		73.51 271	70.49 293	82.58 188	81.32 333	65.19 193	75.92 349	92.27 81	57.60 357	72.73 273	76.45 372	52.30 251	95.43 69	48.14 359	77.71 270	87.11 294

test11								92.23 84

mPP-MVS		86.67 38	86.32 40	87.72 30	94.41 22	73.55 13	92.74 20	92.22 85	76.87 63	82.81 103	94.25 34	66.44 112	96.24 41	82.88 71	94.28 60	93.38 87

DP-MVS Recon		83.11 97	82.09 104	86.15 58	94.44 19	70.92 68	88.79 114	92.20 86	70.53 194	79.17 143	91.03 124	64.12 134	96.03 48	68.39 209	90.14 106	91.50 151

HQP3-MVS								92.19 87							85.99 166

HQP-MVS		82.61 103	82.02 106	84.37 108	89.33 134	66.98 159	89.17 100	92.19 87	76.41 75	77.23 185	90.23 139	60.17 196	95.11 84	77.47 118	85.99 166	91.03 166

3Dnovator	76.31 5	83.38 91	82.31 101	86.59 52	87.94 191	72.94 28	90.64 57	92.14 89	77.21 53	75.47 223	92.83 76	58.56 202	94.72 102	73.24 162	92.71 73	92.13 137

iter_conf05		85.49 57	85.43 58	85.67 71	91.09 82	66.55 166	87.18 169	92.08 90	72.89 154	82.90 99	91.71 96	71.85 49	96.03 48	84.77 48	94.39 56	94.42 37

MTGPA								92.02 91

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 189	92.02 91	79.45 19	85.88 50	94.80 16	68.07 95	96.21 42	86.69 36	95.34 32	93.23 93

MVS_Test		83.15 94	83.06 89	83.41 152	86.86 223	63.21 236	86.11 202	92.00 93	74.31 118	82.87 100	89.44 161	70.03 72	93.21 165	77.39 120	88.50 133	93.81 66

PVSNet_BlendedMVS		80.60 142	80.02 135	82.36 192	88.85 152	65.40 188	86.16 201	92.00 93	69.34 221	78.11 166	86.09 252	66.02 119	94.27 114	71.52 174	82.06 221	87.39 284

PVSNet_Blended		80.98 129	80.34 130	82.90 175	88.85 152	65.40 188	84.43 244	92.00 93	67.62 252	78.11 166	85.05 276	66.02 119	94.27 114	71.52 174	89.50 115	89.01 244

QAPM		80.88 131	79.50 147	85.03 86	88.01 190	68.97 104	91.59 42	92.00 93	66.63 266	75.15 241	92.16 88	57.70 209	95.45 67	63.52 245	88.76 127	90.66 179

LPG-MVS_test		82.08 108	81.27 114	84.50 103	89.23 141	68.76 109	90.22 70	91.94 97	75.37 97	76.64 199	91.51 105	54.29 234	94.91 92	78.44 108	83.78 192	89.83 220

LGP-MVS_train				84.50 103	89.23 141	68.76 109		91.94 97	75.37 97	76.64 199	91.51 105	54.29 234	94.91 92	78.44 108	83.78 192	89.83 220

TEST9					93.26 50	72.96 25	88.75 116	91.89 99	68.44 244	85.00 60	93.10 67	74.36 28	95.41 71

train_agg		86.43 40	86.20 42	87.13 44	93.26 50	72.96 25	88.75 116	91.89 99	68.69 239	85.00 60	93.10 67	74.43 26	95.41 71	84.97 42	95.71 25	93.02 104

dcpmvs_2		85.63 55	86.15 45	84.06 129	91.71 75	64.94 199	86.47 192	91.87 101	73.63 133	86.60 47	93.02 72	76.57 15	91.87 219	83.36 63	92.15 79	95.35 3

DU-MVS		81.12 128	80.52 127	82.90 175	87.80 197	63.46 230	87.02 174	91.87 101	79.01 26	78.38 159	89.07 166	65.02 128	93.05 178	70.05 189	76.46 286	92.20 133

test_8					93.13 52	72.57 35	88.68 121	91.84 103	68.69 239	84.87 64	93.10 67	74.43 26	95.16 80

PAPM_NR		83.02 98	82.41 98	84.82 95	92.47 67	66.37 168	87.93 148	91.80 104	73.82 129	77.32 182	90.66 132	67.90 98	94.90 94	70.37 186	89.48 116	93.19 97

test12				86.80 49	92.63 64	70.70 72		91.79 105		82.71 104		71.67 55	96.16 44		94.50 51	93.54 83

agg_prior					92.85 59	71.94 50		91.78 106		84.41 76			94.93 91

PAPR		81.66 119	80.89 122	83.99 137	90.27 102	64.00 217	86.76 185	91.77 107	68.84 237	77.13 191	89.50 154	67.63 100	94.88 96	67.55 214	88.52 132	93.09 100

PVSNet_Blended_VisFu		82.62 102	81.83 110	84.96 89	90.80 93	69.76 87	88.74 118	91.70 108	69.39 219	78.96 145	88.46 184	65.47 124	94.87 97	74.42 148	88.57 130	90.24 197

HPM-MVS_fast		85.35 62	84.95 69	86.57 53	93.69 42	70.58 75	92.15 35	91.62 109	73.89 128	82.67 105	94.09 40	62.60 151	95.54 64	80.93 90	92.93 70	93.57 80

ACMM	73.20 8	80.78 139	79.84 140	83.58 146	89.31 137	68.37 122	89.99 73	91.60 110	70.28 199	77.25 183	89.66 149	53.37 244	93.53 150	74.24 151	82.85 211	88.85 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		80.60 142	80.55 126	80.76 228	88.07 186	60.80 268	86.86 179	91.58 111	75.67 93	80.24 131	89.45 160	63.34 139	90.25 262	70.51 185	79.22 256	91.23 159

OPM-MVS		83.50 87	82.95 92	85.14 82	88.79 158	70.95 66	89.13 105	91.52 112	77.55 44	80.96 125	91.75 95	60.71 186	94.50 108	79.67 101	86.51 156	89.97 215
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		78.97 182	77.69 194	82.81 179	90.54 98	64.29 213	90.11 72	91.51 113	65.01 286	76.16 214	88.13 198	50.56 277	93.03 181	69.68 195	77.56 273	91.11 162

PS-MVSNAJss		82.07 109	81.31 113	84.34 111	86.51 232	67.27 152	89.27 98	91.51 113	71.75 166	79.37 140	90.22 140	63.15 145	94.27 114	77.69 116	82.36 218	91.49 152

TAPA-MVS	73.13 9	79.15 176	77.94 182	82.79 182	89.59 121	62.99 244	88.16 140	91.51 113	65.77 275	77.14 190	91.09 120	60.91 184	93.21 165	50.26 346	87.05 147	92.17 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	74.13 6	81.51 123	80.57 125	84.36 109	89.42 129	68.69 116	89.97 74	91.50 116	74.46 116	75.04 245	90.41 136	53.82 239	94.54 105	77.56 117	82.91 210	89.86 219
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 148	78.84 163	85.01 87	87.71 202	68.99 103	83.65 257	91.46 117	63.00 309	77.77 174	90.28 137	66.10 116	95.09 88	61.40 269	88.22 136	90.94 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		80.84 132	80.31 131	82.42 190	87.85 195	62.33 249	87.74 154	91.33 118	80.55 9	77.99 170	89.86 144	65.23 126	92.62 188	67.05 221	75.24 313	92.30 128

PS-CasMVS		78.01 206	78.09 179	77.77 283	87.71 202	54.39 347	88.02 143	91.22 119	77.50 46	73.26 266	88.64 178	60.73 185	88.41 295	61.88 264	73.88 326	90.53 185

v7n		78.97 182	77.58 197	83.14 163	83.45 288	65.51 186	88.32 134	91.21 120	73.69 132	72.41 278	86.32 247	57.93 206	93.81 136	69.18 199	75.65 299	90.11 203

PEN-MVS		77.73 212	77.69 194	77.84 281	87.07 222	53.91 350	87.91 149	91.18 121	77.56 43	73.14 268	88.82 173	61.23 178	89.17 281	59.95 279	72.37 337	90.43 189

MM		89.16 6	89.23 7	88.97 4	90.79 94	73.65 10	92.66 23	91.17 122	86.57 1	87.39 38	94.97 15	71.70 54	97.68 1	92.19 1	95.63 28	95.57 1

save fliter					93.80 40	72.35 42	90.47 63	91.17 122	74.31 118

CP-MVSNet		78.22 197	78.34 173	77.84 281	87.83 196	54.54 345	87.94 147	91.17 122	77.65 38	73.48 264	88.49 183	62.24 160	88.43 294	62.19 260	74.07 322	90.55 184

114514_t		80.68 140	79.51 146	84.20 119	94.09 38	67.27 152	89.64 84	91.11 125	58.75 349	74.08 258	90.72 131	58.10 205	95.04 89	69.70 194	89.42 117	90.30 195

NR-MVSNet		80.23 152	79.38 149	82.78 183	87.80 197	63.34 233	86.31 196	91.09 126	79.01 26	72.17 281	89.07 166	67.20 105	92.81 187	66.08 228	75.65 299	92.20 133

OpenMVS	72.83 10	79.77 159	78.33 174	84.09 125	85.17 252	69.91 84	90.57 58	90.97 127	66.70 260	72.17 281	91.91 91	54.70 231	93.96 124	61.81 266	90.95 95	88.41 266

MAR-MVS		81.84 113	80.70 123	85.27 79	91.32 79	71.53 54	89.82 76	90.92 128	69.77 213	78.50 156	86.21 248	62.36 157	94.52 107	65.36 233	92.05 81	89.77 223
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
tt0805		78.73 186	77.83 186	81.43 208	85.17 252	60.30 276	89.41 94	90.90 129	71.21 178	77.17 189	88.73 174	46.38 313	93.21 165	72.57 169	78.96 257	90.79 173

Anonymous20240529		80.19 154	78.89 162	84.10 122	90.60 96	64.75 203	88.95 109	90.90 129	65.97 274	80.59 128	91.17 118	49.97 283	93.73 143	69.16 200	82.70 215	93.81 66

OMC-MVS		82.69 101	81.97 108	84.85 94	88.75 160	67.42 146	87.98 144	90.87 131	74.92 106	79.72 136	91.65 99	62.19 161	93.96 124	75.26 143	86.42 157	93.16 98

UA-Net		85.08 67	84.96 68	85.45 75	92.07 70	68.07 130	89.78 79	90.86 132	82.48 2	84.60 73	93.20 66	69.35 79	95.22 78	71.39 177	90.88 96	93.07 101

test_fmvsm_n_1920		85.29 64	85.34 60	85.13 84	86.12 237	69.93 83	88.65 122	90.78 133	69.97 207	88.27 23	93.98 49	71.39 59	91.54 231	88.49 23	90.45 101	93.91 59

EPP-MVSNet		83.40 90	83.02 90	84.57 101	90.13 105	64.47 209	92.32 30	90.73 134	74.45 117	79.35 141	91.10 119	69.05 85	95.12 82	72.78 166	87.22 145	94.13 50

DTE-MVSNet		76.99 226	76.80 212	77.54 288	86.24 234	53.06 359	87.52 158	90.66 135	77.08 58	72.50 276	88.67 177	60.48 192	89.52 275	57.33 306	70.74 348	90.05 210

v10		79.74 160	78.67 164	82.97 173	84.06 276	64.95 198	87.88 151	90.62 136	73.11 148	75.11 242	86.56 240	61.46 172	94.05 123	73.68 154	75.55 301	89.90 217

test_fmvsmconf_n		85.92 48	86.04 48	85.57 73	85.03 258	69.51 90	89.62 87	90.58 137	73.42 140	87.75 32	94.02 44	72.85 41	93.24 162	90.37 3	90.75 97	93.96 57

v1192		79.59 163	78.43 171	83.07 167	83.55 286	64.52 205	86.93 177	90.58 137	70.83 185	77.78 173	85.90 253	59.15 199	93.94 127	73.96 153	77.19 276	90.76 175

v1144		80.03 156	79.03 159	83.01 170	83.78 282	64.51 206	87.11 172	90.57 139	71.96 165	78.08 168	86.20 249	61.41 173	93.94 127	74.93 144	77.23 274	90.60 182

XVG-OURS-SEG-HR		80.81 134	79.76 141	83.96 139	85.60 245	68.78 108	83.54 262	90.50 140	70.66 192	76.71 197	91.66 98	60.69 187	91.26 242	76.94 124	81.58 226	91.83 142

MVS		78.19 200	76.99 208	81.78 200	85.66 243	66.99 158	84.66 234	90.47 141	55.08 369	72.02 283	85.27 268	63.83 137	94.11 122	66.10 227	89.80 113	84.24 339

XVG-OURS		80.41 147	79.23 155	83.97 138	85.64 244	69.02 102	83.03 273	90.39 142	71.09 181	77.63 176	91.49 107	54.62 233	91.35 240	75.71 136	83.47 203	91.54 149

MVSFormer		82.85 100	82.05 105	85.24 80	87.35 211	70.21 77	90.50 61	90.38 143	68.55 241	81.32 118	89.47 156	61.68 166	93.46 154	78.98 103	90.26 104	92.05 139

test_djsdf		80.30 151	79.32 152	83.27 156	83.98 278	65.37 191	90.50 61	90.38 143	68.55 241	76.19 210	88.70 175	56.44 220	93.46 154	78.98 103	80.14 245	90.97 169

CPTT-MVS		83.73 80	83.33 86	84.92 92	93.28 49	70.86 69	92.09 36	90.38 143	68.75 238	79.57 138	92.83 76	60.60 191	93.04 180	80.92 91	91.56 88	90.86 172

v144192		79.47 166	78.37 172	82.78 183	83.35 289	63.96 218	86.96 175	90.36 146	69.99 206	77.50 177	85.67 260	60.66 188	93.77 139	74.27 150	76.58 284	90.62 180

v1921920		79.22 174	78.03 180	82.80 180	83.30 291	63.94 219	86.80 181	90.33 147	69.91 209	77.48 178	85.53 263	58.44 203	93.75 141	73.60 155	76.85 281	90.71 178

MVS_111021_HR		85.14 65	84.75 70	86.32 55	91.65 76	72.70 30	85.98 204	90.33 147	76.11 84	82.08 108	91.61 103	71.36 60	94.17 120	81.02 89	92.58 74	92.08 138

v1240		78.99 181	77.78 189	82.64 186	83.21 293	63.54 227	86.62 188	90.30 149	69.74 216	77.33 181	85.68 259	57.04 217	93.76 140	73.13 163	76.92 278	90.62 180

test_fmvsmconf0.1_n		85.61 56	85.65 54	85.50 74	82.99 303	69.39 97	89.65 83	90.29 150	73.31 143	87.77 31	94.15 38	71.72 53	93.23 163	90.31 4	90.67 99	93.89 62

v8		79.97 158	79.02 160	82.80 180	84.09 275	64.50 208	87.96 145	90.29 150	74.13 124	75.24 238	86.81 226	62.88 150	93.89 134	74.39 149	75.40 308	90.00 211

mvs_tets		79.13 177	77.77 190	83.22 160	84.70 262	66.37 168	89.17 100	90.19 152	69.38 220	75.40 228	89.46 158	44.17 334	93.15 172	76.78 127	80.70 237	90.14 200

jajsoiax		79.29 173	77.96 181	83.27 156	84.68 263	66.57 165	89.25 99	90.16 153	69.20 227	75.46 225	89.49 155	45.75 324	93.13 174	76.84 125	80.80 235	90.11 203

Vis-MVSNet		83.46 88	82.80 95	85.43 76	90.25 103	68.74 111	90.30 69	90.13 154	76.33 81	80.87 126	92.89 74	61.00 183	94.20 118	72.45 171	90.97 94	93.35 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ		81.69 117	81.02 119	83.70 143	89.51 125	68.21 127	84.28 248	90.09 155	70.79 186	81.26 122	85.62 262	63.15 145	94.29 112	75.62 138	88.87 124	88.59 262

xiu_mvs_v2_base		81.69 117	81.05 118	83.60 145	89.15 144	68.03 132	84.46 242	90.02 156	70.67 189	81.30 121	86.53 242	63.17 144	94.19 119	75.60 139	88.54 131	88.57 263

FA-MVS(test-final)		80.96 130	79.91 138	84.10 122	88.30 177	65.01 197	84.55 239	90.01 157	73.25 146	79.61 137	87.57 206	58.35 204	94.72 102	71.29 178	86.25 160	92.56 118

v2v482		80.23 152	79.29 153	83.05 168	83.62 284	64.14 215	87.04 173	89.97 158	73.61 134	78.18 165	87.22 217	61.10 181	93.82 135	76.11 131	76.78 283	91.18 160

test_yl		81.17 126	80.47 128	83.24 158	89.13 145	63.62 223	86.21 199	89.95 159	72.43 159	81.78 114	89.61 151	57.50 212	93.58 145	70.75 181	86.90 149	92.52 119

DCV-MVSNet		81.17 126	80.47 128	83.24 158	89.13 145	63.62 223	86.21 199	89.95 159	72.43 159	81.78 114	89.61 151	57.50 212	93.58 145	70.75 181	86.90 149	92.52 119

V42		79.38 172	78.24 176	82.83 177	81.10 335	65.50 187	85.55 217	89.82 161	71.57 172	78.21 163	86.12 251	60.66 188	93.18 171	75.64 137	75.46 305	89.81 222

VNet		82.21 106	82.41 98	81.62 203	90.82 92	60.93 265	84.47 240	89.78 162	76.36 80	84.07 83	91.88 93	64.71 131	90.26 261	70.68 183	88.89 123	93.66 71

diffmvs		82.10 107	81.88 109	82.76 185	83.00 301	63.78 222	83.68 256	89.76 163	72.94 152	82.02 109	89.85 145	65.96 121	90.79 255	82.38 78	87.30 144	93.71 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XVG-ACMP-BASELINE		76.11 243	74.27 252	81.62 203	83.20 294	64.67 204	83.60 260	89.75 164	69.75 214	71.85 284	87.09 222	32.78 377	92.11 209	69.99 191	80.43 241	88.09 270

EI-MVSNet-Vis-set		84.19 74	83.81 79	85.31 78	88.18 179	67.85 134	87.66 155	89.73 165	80.05 14	82.95 97	89.59 153	70.74 66	94.82 98	80.66 95	84.72 177	93.28 92

EI-MVSNet-UG-set		83.81 78	83.38 84	85.09 85	87.87 194	67.53 144	87.44 162	89.66 166	79.74 16	82.23 107	89.41 162	70.24 71	94.74 101	79.95 99	83.92 191	92.99 107

test_fmvsmconf0.01_n		84.73 72	84.52 74	85.34 77	80.25 343	69.03 100	89.47 89	89.65 167	73.24 147	86.98 43	94.27 32	66.62 108	93.23 163	90.26 5	89.95 111	93.78 68

PAPM		77.68 216	76.40 223	81.51 206	87.29 218	61.85 256	83.78 255	89.59 168	64.74 288	71.23 289	88.70 175	62.59 152	93.66 144	52.66 331	87.03 148	89.01 244

MVS_0304		87.69 20	87.55 22	88.12 13	89.45 128	71.76 51	91.47 46	89.54 169	82.14 3	86.65 46	94.28 31	68.28 94	97.46 6	90.81 2	95.31 34	95.15 6

anonymousdsp		78.60 190	77.15 204	82.98 172	80.51 341	67.08 157	87.24 168	89.53 170	65.66 277	75.16 240	87.19 219	52.52 247	92.25 205	77.17 122	79.34 254	89.61 227

MG-MVS		83.41 89	83.45 82	83.28 155	92.74 62	62.28 251	88.17 139	89.50 171	75.22 99	81.49 117	92.74 82	66.75 107	95.11 84	72.85 165	91.58 87	92.45 124

PLC	70.83 11	78.05 204	76.37 224	83.08 166	91.88 74	67.80 135	88.19 138	89.46 172	64.33 294	69.87 306	88.38 186	53.66 240	93.58 145	58.86 290	82.73 213	87.86 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SDMVSNet		80.38 148	80.18 134	80.99 222	89.03 150	64.94 199	80.45 305	89.40 173	75.19 101	76.61 201	89.98 142	60.61 190	87.69 303	76.83 126	83.55 201	90.33 193

Fast-Effi-MVS+		80.81 134	79.92 137	83.47 148	88.85 152	64.51 206	85.53 219	89.39 174	70.79 186	78.49 157	85.06 275	67.54 101	93.58 145	67.03 222	86.58 154	92.32 127

IterMVS-LS		80.06 155	79.38 149	82.11 194	85.89 240	63.20 237	86.79 182	89.34 175	74.19 121	75.45 226	86.72 229	66.62 108	92.39 198	72.58 168	76.86 280	90.75 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
API-MVS		81.99 111	81.23 115	84.26 118	90.94 88	70.18 82	91.10 52	89.32 176	71.51 173	78.66 152	88.28 189	65.26 125	95.10 87	64.74 239	91.23 92	87.51 282

GBi-Net		78.40 193	77.40 199	81.40 210	87.60 206	63.01 240	88.39 129	89.28 177	71.63 168	75.34 231	87.28 213	54.80 227	91.11 245	62.72 252	79.57 249	90.09 205

test1		78.40 193	77.40 199	81.40 210	87.60 206	63.01 240	88.39 129	89.28 177	71.63 168	75.34 231	87.28 213	54.80 227	91.11 245	62.72 252	79.57 249	90.09 205

FMVSNet1		77.44 219	76.12 226	81.40 210	86.81 226	63.01 240	88.39 129	89.28 177	70.49 195	74.39 255	87.28 213	49.06 297	91.11 245	60.91 273	78.52 260	90.09 205

cdsmvs_eth3d_5k		19.96 380	26.61 382	0.00 400	0.00 423	0.00 425	0.00 411	89.26 180	0.00 418	0.00 419	88.61 179	61.62 168	0.00 419	0.00 418	0.00 417	0.00 415

ab-mvs		79.51 164	78.97 161	81.14 218	88.46 170	60.91 266	83.84 254	89.24 181	70.36 196	79.03 144	88.87 172	63.23 143	90.21 263	65.12 235	82.57 216	92.28 129

cascas		76.72 232	74.64 245	82.99 171	85.78 242	65.88 178	82.33 277	89.21 182	60.85 330	72.74 272	81.02 336	47.28 306	93.75 141	67.48 215	85.02 173	89.34 234

eth_miper_zixun_eth		77.92 208	76.69 217	81.61 205	83.00 301	61.98 254	83.15 267	89.20 183	69.52 218	74.86 248	84.35 288	61.76 165	92.56 191	71.50 176	72.89 335	90.28 196

h-mvs33		83.15 94	82.19 102	86.02 65	90.56 97	70.85 70	88.15 141	89.16 184	76.02 86	84.67 67	91.39 110	61.54 169	95.50 65	82.71 74	75.48 303	91.72 145

miper_ehance_all_eth		78.59 191	77.76 191	81.08 220	82.66 310	61.56 260	83.65 257	89.15 185	68.87 236	75.55 222	83.79 300	66.49 111	92.03 211	73.25 161	76.39 288	89.64 226

Effi-MVS+		83.62 85	83.08 88	85.24 80	88.38 174	67.45 145	88.89 111	89.15 185	75.50 95	82.27 106	88.28 189	69.61 77	94.45 110	77.81 115	87.84 137	93.84 65

c3_l		78.75 185	77.91 183	81.26 214	82.89 305	61.56 260	84.09 252	89.13 187	69.97 207	75.56 221	84.29 289	66.36 113	92.09 210	73.47 158	75.48 303	90.12 202

LTVRE_ROB	69.57 13	76.25 241	74.54 248	81.41 209	88.60 165	64.38 212	79.24 319	89.12 188	70.76 188	69.79 308	87.86 200	49.09 296	93.20 168	56.21 316	80.16 243	86.65 304
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
F-COLMAP		76.38 240	74.33 251	82.50 189	89.28 139	66.95 162	88.41 128	89.03 189	64.05 299	66.83 334	88.61 179	46.78 310	92.89 183	57.48 303	78.55 259	87.67 277

FMVSNet2		78.20 199	77.21 203	81.20 216	87.60 206	62.89 245	87.47 160	89.02 190	71.63 168	75.29 237	87.28 213	54.80 227	91.10 248	62.38 257	79.38 253	89.61 227

ACMH	67.68 16	75.89 246	73.93 255	81.77 201	88.71 162	66.61 164	88.62 123	89.01 191	69.81 210	66.78 335	86.70 233	41.95 349	91.51 234	55.64 317	78.14 266	87.17 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall		77.87 210	76.86 210	80.92 225	81.65 324	61.38 262	82.68 274	88.98 192	65.52 279	75.47 223	82.30 325	65.76 123	92.00 213	72.95 164	76.39 288	89.39 232

无先验							87.48 159	88.98 192	60.00 336				94.12 121	67.28 217		88.97 247

AdaColmap		80.58 145	79.42 148	84.06 129	93.09 54	68.91 105	89.36 96	88.97 194	69.27 222	75.70 219	89.69 148	57.20 216	95.77 58	63.06 250	88.41 134	87.50 283

EI-MVSNet		80.52 146	79.98 136	82.12 193	84.28 270	63.19 238	86.41 193	88.95 195	74.18 122	78.69 150	87.54 209	66.62 108	92.43 196	72.57 169	80.57 239	90.74 177

MVSTER		79.01 180	77.88 185	82.38 191	83.07 298	64.80 202	84.08 253	88.95 195	69.01 234	78.69 150	87.17 220	54.70 231	92.43 196	74.69 145	80.57 239	89.89 218

1314		76.53 234	75.30 240	80.21 239	83.93 279	62.32 250	84.66 234	88.81 197	60.23 334	70.16 300	84.07 295	55.30 224	90.73 257	67.37 216	83.21 207	87.59 281

UniMVSNet_ETH3D		79.10 178	78.24 176	81.70 202	86.85 224	60.24 277	87.28 167	88.79 198	74.25 120	76.84 192	90.53 135	49.48 289	91.56 229	67.98 210	82.15 219	93.29 91

xiu_mvs_v1_base_debu		80.80 136	79.72 142	84.03 134	87.35 211	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

xiu_mvs_v1_base		80.80 136	79.72 142	84.03 134	87.35 211	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

xiu_mvs_v1_base_debi		80.80 136	79.72 142	84.03 134	87.35 211	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

FMVSNet3		77.88 209	76.85 211	80.97 224	86.84 225	62.36 248	86.52 191	88.77 199	71.13 179	75.34 231	86.66 235	54.07 237	91.10 248	62.72 252	79.57 249	89.45 231

patch_mono-2		83.65 82	84.54 72	80.99 222	90.06 111	65.83 179	84.21 249	88.74 203	71.60 171	85.01 59	92.44 84	74.51 25	83.50 337	82.15 79	92.15 79	93.64 77

GeoE		81.71 116	81.01 120	83.80 142	89.51 125	64.45 210	88.97 108	88.73 204	71.27 177	78.63 153	89.76 147	66.32 114	93.20 168	69.89 192	86.02 165	93.74 69

CANet_DTU		80.61 141	79.87 139	82.83 177	85.60 245	63.17 239	87.36 163	88.65 205	76.37 79	75.88 216	88.44 185	53.51 242	93.07 177	73.30 160	89.74 114	92.25 130

HyFIR lowres test		77.53 218	75.40 236	83.94 140	89.59 121	66.62 163	80.36 306	88.64 206	56.29 365	76.45 203	85.17 272	57.64 210	93.28 160	61.34 271	83.10 209	91.91 141

WR-MVS		79.49 165	79.22 156	80.27 238	88.79 158	58.35 290	85.06 226	88.61 207	78.56 30	77.65 175	88.34 187	63.81 138	90.66 258	64.98 237	77.22 275	91.80 144

BH-untuned		79.47 166	78.60 166	82.05 195	89.19 143	65.91 177	86.07 203	88.52 208	72.18 162	75.42 227	87.69 203	61.15 180	93.54 149	60.38 276	86.83 151	86.70 303

IS-MVSNet		83.15 94	82.81 94	84.18 120	89.94 114	63.30 234	91.59 42	88.46 209	79.04 25	79.49 139	92.16 88	65.10 127	94.28 113	67.71 212	91.86 85	94.95 10

pm-mvs1		77.25 224	76.68 218	78.93 263	84.22 272	58.62 289	86.41 193	88.36 210	71.37 175	73.31 265	88.01 199	61.22 179	89.15 282	64.24 243	73.01 334	89.03 243

UGNet		80.83 133	79.59 145	84.54 102	88.04 187	68.09 129	89.42 93	88.16 211	76.95 60	76.22 209	89.46 158	49.30 293	93.94 127	68.48 207	90.31 102	91.60 146
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
VDD-MVS		83.01 99	82.36 100	84.96 89	91.02 86	66.40 167	88.91 110	88.11 212	77.57 41	84.39 77	93.29 64	52.19 253	93.91 131	77.05 123	88.70 129	94.57 32

Effi-MVS+-dtu		80.03 156	78.57 167	84.42 107	85.13 256	68.74 111	88.77 115	88.10 213	74.99 105	74.97 246	83.49 306	57.27 215	93.36 158	73.53 156	80.88 233	91.18 160

v148		78.72 187	77.80 188	81.47 207	82.73 308	61.96 255	86.30 197	88.08 214	73.26 145	76.18 211	85.47 265	62.46 155	92.36 200	71.92 173	73.82 327	90.09 205

EG-PatchMatch MVS		74.04 265	71.82 277	80.71 229	84.92 259	67.42 146	85.86 209	88.08 214	66.04 272	64.22 356	83.85 297	35.10 374	92.56 191	57.44 304	80.83 234	82.16 364

cl22		78.07 203	77.01 206	81.23 215	82.37 317	61.83 257	83.55 261	87.98 216	68.96 235	75.06 244	83.87 296	61.40 174	91.88 218	73.53 156	76.39 288	89.98 214

test_fmvsmvis_n_1920		84.02 76	83.87 78	84.49 105	84.12 274	69.37 98	88.15 141	87.96 217	70.01 205	83.95 85	93.23 65	68.80 89	91.51 234	88.61 20	89.96 110	92.57 117

pmmvs6		74.69 259	73.39 261	78.61 267	81.38 330	57.48 306	86.64 187	87.95 218	64.99 287	70.18 298	86.61 236	50.43 279	89.52 275	62.12 262	70.18 350	88.83 253

MVP-Stereo		76.12 242	74.46 250	81.13 219	85.37 250	69.79 86	84.42 245	87.95 218	65.03 285	67.46 327	85.33 267	53.28 245	91.73 224	58.01 300	83.27 206	81.85 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____		77.72 213	76.76 214	80.58 231	82.49 314	60.48 273	83.09 269	87.87 220	69.22 225	74.38 256	85.22 271	62.10 162	91.53 232	71.09 179	75.41 307	89.73 225

DIV-MVS_self_test		77.72 213	76.76 214	80.58 231	82.48 315	60.48 273	83.09 269	87.86 221	69.22 225	74.38 256	85.24 269	62.10 162	91.53 232	71.09 179	75.40 308	89.74 224

BH-w/o		78.21 198	77.33 202	80.84 226	88.81 156	65.13 195	84.87 230	87.85 222	69.75 214	74.52 253	84.74 282	61.34 175	93.11 175	58.24 298	85.84 168	84.27 338

FE-MVS		77.78 211	75.68 229	84.08 126	88.09 185	66.00 174	83.13 268	87.79 223	68.42 245	78.01 169	85.23 270	45.50 327	95.12 82	59.11 287	85.83 169	91.11 162

HY-MVS	69.67 12	77.95 207	77.15 204	80.36 235	87.57 210	60.21 278	83.37 264	87.78 224	66.11 270	75.37 230	87.06 224	63.27 141	90.48 260	61.38 270	82.43 217	90.40 191

1112_ss		77.40 221	76.43 222	80.32 237	89.11 149	60.41 275	83.65 257	87.72 225	62.13 322	73.05 269	86.72 229	62.58 153	89.97 267	62.11 263	80.80 235	90.59 183

mvs_anonymous		79.42 169	79.11 158	80.34 236	84.45 269	57.97 297	82.59 275	87.62 226	67.40 256	76.17 213	88.56 182	68.47 91	89.59 274	70.65 184	86.05 164	93.47 85

ACMH+	68.96 14	76.01 245	74.01 253	82.03 196	88.60 165	65.31 192	88.86 112	87.55 227	70.25 201	67.75 323	87.47 211	41.27 350	93.19 170	58.37 296	75.94 296	87.60 279

tfpnnormal		74.39 260	73.16 264	78.08 278	86.10 239	58.05 294	84.65 236	87.53 228	70.32 198	71.22 290	85.63 261	54.97 225	89.86 268	43.03 378	75.02 315	86.32 307

CHOSEN 1792x2688		77.63 217	75.69 228	83.44 149	89.98 113	68.58 119	78.70 328	87.50 229	56.38 364	75.80 218	86.84 225	58.67 201	91.40 239	61.58 268	85.75 170	90.34 192

ambc				75.24 311	73.16 386	50.51 376	63.05 400	87.47 230		64.28 355	77.81 366	17.80 402	89.73 272	57.88 301	60.64 378	85.49 322

Fast-Effi-MVS+-dtu		78.02 205	76.49 220	82.62 187	83.16 297	66.96 161	86.94 176	87.45 231	72.45 156	71.49 288	84.17 293	54.79 230	91.58 227	67.61 213	80.31 242	89.30 235

D2MVS		74.82 258	73.21 263	79.64 252	79.81 350	62.56 247	80.34 307	87.35 232	64.37 293	68.86 315	82.66 320	46.37 314	90.10 264	67.91 211	81.24 229	86.25 308

TSAR-MVS + GP.		85.71 54	85.33 61	86.84 47	91.34 78	72.50 36	89.07 106	87.28 233	76.41 75	85.80 51	90.22 140	74.15 31	95.37 76	81.82 81	91.88 82	92.65 116

fmvsm_l_conf0.5_n		84.47 73	84.54 72	84.27 116	85.42 248	68.81 106	88.49 126	87.26 234	68.08 248	88.03 27	93.49 57	72.04 48	91.77 221	88.90 17	89.14 121	92.24 132

hse-mvs2		81.72 115	80.94 121	84.07 127	88.72 161	67.68 138	85.87 208	87.26 234	76.02 86	84.67 67	88.22 192	61.54 169	93.48 152	82.71 74	73.44 331	91.06 164

AUN-MVS		79.21 175	77.60 196	84.05 132	88.71 162	67.61 141	85.84 210	87.26 234	69.08 230	77.23 185	88.14 197	53.20 246	93.47 153	75.50 141	73.45 330	91.06 164

BH-RMVSNet		79.61 161	78.44 170	83.14 163	89.38 133	65.93 176	84.95 229	87.15 237	73.56 136	78.19 164	89.79 146	56.67 219	93.36 158	59.53 283	86.74 152	90.13 201

Test_1112_low_res		76.40 239	75.44 234	79.27 257	89.28 139	58.09 293	81.69 284	87.07 238	59.53 341	72.48 277	86.67 234	61.30 176	89.33 278	60.81 275	80.15 244	90.41 190

KD-MVS_self_test		68.81 316	67.59 323	72.46 337	74.29 378	45.45 388	77.93 339	87.00 239	63.12 306	63.99 358	78.99 358	42.32 344	84.77 329	56.55 314	64.09 371	87.16 292

mvsmamba		80.60 142	79.38 149	84.27 116	89.74 119	67.24 154	87.47 160	86.95 240	70.02 204	75.38 229	88.93 169	51.24 269	92.56 191	75.47 142	89.22 119	93.00 106

LS3D		76.95 228	74.82 244	83.37 153	90.45 99	67.36 149	89.15 104	86.94 241	61.87 324	69.52 309	90.61 133	51.71 265	94.53 106	46.38 367	86.71 153	88.21 268

miper_lstm_enhance		74.11 264	73.11 265	77.13 293	80.11 345	59.62 283	72.23 368	86.92 242	66.76 259	70.40 295	82.92 315	56.93 218	82.92 341	69.06 201	72.63 336	88.87 251

fmvsm_l_conf0.5_n_a		84.13 75	84.16 77	84.06 129	85.38 249	68.40 121	88.34 133	86.85 243	67.48 255	87.48 36	93.40 61	70.89 63	91.61 225	88.38 25	89.22 119	92.16 136

jason		81.39 124	80.29 132	84.70 99	86.63 231	69.90 85	85.95 205	86.77 244	63.24 305	81.07 124	89.47 156	61.08 182	92.15 208	78.33 111	90.07 109	92.05 139
jason: jason.
OurMVSNet-221017-0		74.26 262	72.42 272	79.80 247	83.76 283	59.59 284	85.92 207	86.64 245	66.39 268	66.96 332	87.58 205	39.46 358	91.60 226	65.76 231	69.27 353	88.22 267

VPNet		78.69 188	78.66 165	78.76 265	88.31 176	55.72 332	84.45 243	86.63 246	76.79 65	78.26 162	90.55 134	59.30 198	89.70 273	66.63 223	77.05 277	90.88 171

USDC		70.33 305	68.37 307	76.21 299	80.60 339	56.23 326	79.19 321	86.49 247	60.89 329	61.29 368	85.47 265	31.78 380	89.47 277	53.37 328	76.21 294	82.94 357

lupinMVS		81.39 124	80.27 133	84.76 98	87.35 211	70.21 77	85.55 217	86.41 248	62.85 312	81.32 118	88.61 179	61.68 166	92.24 206	78.41 110	90.26 104	91.83 142

TR-MVS		77.44 219	76.18 225	81.20 216	88.24 178	63.24 235	84.61 237	86.40 249	67.55 253	77.81 172	86.48 243	54.10 236	93.15 172	57.75 302	82.72 214	87.20 289

旧先验1					91.96 71	65.79 181		86.37 250			93.08 71	69.31 81			92.74 72	88.74 259

GA-MVS		76.87 229	75.17 241	81.97 198	82.75 307	62.58 246	81.44 289	86.35 251	72.16 164	74.74 249	82.89 316	46.20 318	92.02 212	68.85 204	81.09 231	91.30 158

CDS-MVSNet		79.07 179	77.70 193	83.17 162	87.60 206	68.23 126	84.40 246	86.20 252	67.49 254	76.36 206	86.54 241	61.54 169	90.79 255	61.86 265	87.33 143	90.49 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.61 103	82.11 103	84.11 121	88.82 155	71.58 53	85.15 224	86.16 253	74.69 111	80.47 129	91.04 122	62.29 158	90.55 259	80.33 97	90.08 108	90.20 198

MSDG		73.36 275	70.99 288	80.49 233	84.51 268	65.80 180	80.71 300	86.13 254	65.70 276	65.46 347	83.74 301	44.60 330	90.91 253	51.13 339	76.89 279	84.74 334

TransMVSNet (Re)		75.39 255	74.56 247	77.86 280	85.50 247	57.10 311	86.78 183	86.09 255	72.17 163	71.53 287	87.34 212	63.01 149	89.31 279	56.84 311	61.83 374	87.17 290

VDDNet		81.52 121	80.67 124	84.05 132	90.44 100	64.13 216	89.73 81	85.91 256	71.11 180	83.18 95	93.48 58	50.54 278	93.49 151	73.40 159	88.25 135	94.54 33

sd_testset		77.70 215	77.40 199	78.60 268	89.03 150	60.02 279	79.00 323	85.83 257	75.19 101	76.61 201	89.98 142	54.81 226	85.46 323	62.63 256	83.55 201	90.33 193

Baseline_NR-MVSNet		78.15 201	78.33 174	77.61 286	85.79 241	56.21 327	86.78 183	85.76 258	73.60 135	77.93 171	87.57 206	65.02 128	88.99 284	67.14 220	75.33 310	87.63 278

Anonymous20240521		68.80 317	67.22 326	73.55 326	74.33 377	54.11 348	83.18 266	85.61 259	58.15 352	61.68 367	80.94 338	30.71 383	81.27 350	57.00 309	73.34 333	85.28 325

test_vis1_n_1920		75.52 251	75.78 227	74.75 317	79.84 349	57.44 307	83.26 265	85.52 260	62.83 313	79.34 142	86.17 250	45.10 329	79.71 356	78.75 105	81.21 230	87.10 296

新几何1				83.42 150	93.13 52	70.71 71		85.48 261	57.43 359	81.80 113	91.98 90	63.28 140	92.27 204	64.60 240	92.99 69	87.27 288

EPNet		83.72 81	82.92 93	86.14 60	84.22 272	69.48 91	91.05 53	85.27 262	81.30 6	76.83 193	91.65 99	66.09 117	95.56 62	76.00 134	93.85 64	93.38 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		67.33 328	65.99 332	71.37 343	73.48 383	51.47 369	75.16 356	85.19 263	65.20 282	60.78 370	80.93 340	42.35 343	77.20 367	57.12 307	53.69 389	85.44 323

IB-MVS	68.01 15	75.85 247	73.36 262	83.31 154	84.76 261	66.03 172	83.38 263	85.06 264	70.21 202	69.40 310	81.05 335	45.76 323	94.66 104	65.10 236	75.49 302	89.25 236
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
TAMVS		78.89 184	77.51 198	83.03 169	87.80 197	67.79 136	84.72 233	85.05 265	67.63 251	76.75 196	87.70 202	62.25 159	90.82 254	58.53 294	87.13 146	90.49 187

CL-MVSNet_self_test		72.37 286	71.46 281	75.09 312	79.49 356	53.53 352	80.76 298	85.01 266	69.12 229	70.51 293	82.05 329	57.92 207	84.13 332	52.27 333	66.00 366	87.60 279

testdata				79.97 243	90.90 89	64.21 214		84.71 267	59.27 343	85.40 55	92.91 73	62.02 164	89.08 283	68.95 202	91.37 90	86.63 305

MS-PatchMatch		73.83 268	72.67 268	77.30 291	83.87 280	66.02 173	81.82 281	84.66 268	61.37 328	68.61 318	82.82 318	47.29 305	88.21 296	59.27 284	84.32 187	77.68 380

ET-MVSNet_ETH3D		78.63 189	76.63 219	84.64 100	86.73 228	69.47 92	85.01 227	84.61 269	69.54 217	66.51 342	86.59 237	50.16 281	91.75 222	76.26 130	84.24 188	92.69 114

CNLPA		78.08 202	76.79 213	81.97 198	90.40 101	71.07 62	87.59 157	84.55 270	66.03 273	72.38 279	89.64 150	57.56 211	86.04 316	59.61 282	83.35 205	88.79 255

MIMVSNet1		68.58 319	66.78 329	73.98 324	80.07 346	51.82 365	80.77 297	84.37 271	64.40 292	59.75 375	82.16 328	36.47 370	83.63 336	42.73 379	70.33 349	86.48 306

KD-MVS_2432*1600		66.22 337	63.89 339	73.21 328	75.47 375	53.42 354	70.76 375	84.35 272	64.10 297	66.52 340	78.52 360	34.55 375	84.98 326	50.40 342	50.33 393	81.23 368

miper_refine_blended		66.22 337	63.89 339	73.21 328	75.47 375	53.42 354	70.76 375	84.35 272	64.10 297	66.52 340	78.52 360	34.55 375	84.98 326	50.40 342	50.33 393	81.23 368

test_0402		72.79 283	70.44 294	79.84 246	88.13 182	65.99 175	85.93 206	84.29 274	65.57 278	67.40 329	85.49 264	46.92 309	92.61 189	35.88 392	74.38 321	80.94 370

EU-MVSNet		68.53 321	67.61 322	71.31 346	78.51 362	47.01 385	84.47 240	84.27 275	42.27 392	66.44 343	84.79 281	40.44 355	83.76 334	58.76 292	68.54 358	83.17 351

thisisatest0530		79.40 170	77.76 191	84.31 112	87.69 204	65.10 196	87.36 163	84.26 276	70.04 203	77.42 179	88.26 191	49.94 284	94.79 100	70.20 187	84.70 178	93.03 103

COLMAP_ROB	66.92 17	73.01 280	70.41 295	80.81 227	87.13 221	65.63 184	88.30 135	84.19 277	62.96 310	63.80 360	87.69 203	38.04 366	92.56 191	46.66 364	74.91 316	84.24 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt0517		79.40 170	77.91 183	83.90 141	88.10 184	63.84 220	88.37 132	84.05 278	71.45 174	76.78 195	89.12 165	49.93 286	94.89 95	70.18 188	83.18 208	92.96 108

CMPMVS	51.72 21	70.19 307	68.16 310	76.28 298	73.15 387	57.55 305	79.47 316	83.92 279	48.02 385	56.48 385	84.81 280	43.13 339	86.42 313	62.67 255	81.81 225	84.89 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous202405211		78.25 196	77.01 206	81.99 197	91.03 85	60.67 270	84.77 232	83.90 280	70.65 193	80.00 134	91.20 116	41.08 352	91.43 238	65.21 234	85.26 172	93.85 63

XXY-MVS		75.41 254	75.56 232	74.96 313	83.59 285	57.82 301	80.59 302	83.87 281	66.54 267	74.93 247	88.31 188	63.24 142	80.09 355	62.16 261	76.85 281	86.97 297

DP-MVS		76.78 231	74.57 246	83.42 150	93.29 48	69.46 94	88.55 125	83.70 282	63.98 301	70.20 297	88.89 171	54.01 238	94.80 99	46.66 364	81.88 224	86.01 315

tfpn200view9		76.42 238	75.37 238	79.55 255	89.13 145	57.65 303	85.17 222	83.60 283	73.41 141	76.45 203	86.39 245	52.12 254	91.95 214	48.33 355	83.75 195	89.07 237

thres400		76.50 235	75.37 238	79.86 245	89.13 145	57.65 303	85.17 222	83.60 283	73.41 141	76.45 203	86.39 245	52.12 254	91.95 214	48.33 355	83.75 195	90.00 211

SixPastTwentyTwo		73.37 273	71.26 286	79.70 249	85.08 257	57.89 299	85.57 213	83.56 285	71.03 183	65.66 346	85.88 254	42.10 347	92.57 190	59.11 287	63.34 372	88.65 261

thres200		75.55 250	74.47 249	78.82 264	87.78 200	57.85 300	83.07 271	83.51 286	72.44 158	75.84 217	84.42 284	52.08 257	91.75 222	47.41 362	83.64 200	86.86 299

IterMVS-SCA-FT		75.43 253	73.87 257	80.11 241	82.69 309	64.85 201	81.57 286	83.47 287	69.16 228	70.49 294	84.15 294	51.95 260	88.15 297	69.23 198	72.14 340	87.34 286

CVMVSNet		72.99 281	72.58 270	74.25 321	84.28 270	50.85 374	86.41 193	83.45 288	44.56 389	73.23 267	87.54 209	49.38 291	85.70 318	65.90 229	78.44 262	86.19 310

ITE_SJBPF				78.22 275	81.77 323	60.57 271		83.30 289	69.25 224	67.54 325	87.20 218	36.33 371	87.28 306	54.34 323	74.62 319	86.80 300

thisisatest0515		77.33 222	75.38 237	83.18 161	85.27 251	63.80 221	82.11 280	83.27 290	65.06 284	75.91 215	83.84 298	49.54 288	94.27 114	67.24 218	86.19 161	91.48 153

thres100view900		76.50 235	75.55 233	79.33 256	89.52 124	56.99 312	85.83 211	83.23 291	73.94 126	76.32 207	87.12 221	51.89 262	91.95 214	48.33 355	83.75 195	89.07 237

thres600view7		76.50 235	75.44 234	79.68 250	89.40 131	57.16 309	85.53 219	83.23 291	73.79 130	76.26 208	87.09 222	51.89 262	91.89 217	48.05 360	83.72 198	90.00 211

test222					91.50 77	68.26 125	84.16 250	83.20 293	54.63 370	79.74 135	91.63 101	58.97 200			91.42 89	86.77 301

EPNet_dtu		75.46 252	74.86 243	77.23 292	82.57 312	54.60 344	86.89 178	83.09 294	71.64 167	66.25 344	85.86 255	55.99 221	88.04 299	54.92 320	86.55 155	89.05 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		83.80 79	83.71 80	84.07 127	86.69 229	67.31 150	89.46 90	83.07 295	71.09 181	86.96 44	93.70 55	69.02 87	91.47 236	88.79 18	84.62 179	93.44 86

fmvsm_s_conf0.1_n		83.56 86	83.38 84	84.10 122	84.86 260	67.28 151	89.40 95	83.01 296	70.67 189	87.08 41	93.96 50	68.38 92	91.45 237	88.56 22	84.50 180	93.56 81

testing91		76.54 233	75.66 231	79.18 260	88.43 172	55.89 330	81.08 292	83.00 297	73.76 131	75.34 231	84.29 289	46.20 318	90.07 265	64.33 241	84.50 180	91.58 148

TDRefinement		67.49 326	64.34 336	76.92 294	73.47 384	61.07 264	84.86 231	82.98 298	59.77 338	58.30 379	85.13 273	26.06 388	87.89 300	47.92 361	60.59 379	81.81 366

OpenMVS_ROB	64.09 19	70.56 303	68.19 309	77.65 285	80.26 342	59.41 286	85.01 227	82.96 299	58.76 348	65.43 348	82.33 324	37.63 368	91.23 244	45.34 374	76.03 295	82.32 361

fmvsm_s_conf0.5_n_a		83.63 84	83.41 83	84.28 114	86.14 236	68.12 128	89.43 91	82.87 300	70.27 200	87.27 40	93.80 54	69.09 82	91.58 227	88.21 26	83.65 199	93.14 99

fmvsm_s_conf0.1_n_a		83.32 92	82.99 91	84.28 114	83.79 281	68.07 130	89.34 97	82.85 301	69.80 211	87.36 39	94.06 42	68.34 93	91.56 229	87.95 27	83.46 204	93.21 96

RPSCF		73.23 277	71.46 281	78.54 270	82.50 313	59.85 280	82.18 279	82.84 302	58.96 346	71.15 291	89.41 162	45.48 328	84.77 329	58.82 291	71.83 342	91.02 168

CostFormer		75.24 256	73.90 256	79.27 257	82.65 311	58.27 292	80.80 295	82.73 303	61.57 325	75.33 235	83.13 311	55.52 222	91.07 251	64.98 237	78.34 265	88.45 264

IterMVS		74.29 261	72.94 266	78.35 274	81.53 327	63.49 229	81.58 285	82.49 304	68.06 249	69.99 303	83.69 303	51.66 266	85.54 321	65.85 230	71.64 343	86.01 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_cas_vis1_n_1920		73.76 269	73.74 259	73.81 325	75.90 370	59.77 281	80.51 303	82.40 305	58.30 351	81.62 116	85.69 258	44.35 333	76.41 374	76.29 129	78.61 258	85.23 326

WTY-MVS		75.65 249	75.68 229	75.57 305	86.40 233	56.82 314	77.92 340	82.40 305	65.10 283	76.18 211	87.72 201	63.13 148	80.90 352	60.31 277	81.96 222	89.00 246

pmmvs4		74.03 267	71.91 276	80.39 234	81.96 320	68.32 123	81.45 288	82.14 307	59.32 342	69.87 306	85.13 273	52.40 250	88.13 298	60.21 278	74.74 318	84.73 335

FMVSNet5		69.50 312	67.96 313	74.15 322	82.97 304	55.35 337	80.01 311	82.12 308	62.56 317	63.02 361	81.53 332	36.92 369	81.92 346	48.42 354	74.06 323	85.17 329

mamv4		76.81 230	78.23 178	72.54 336	86.12 237	65.75 183	78.76 327	82.07 309	64.12 296	72.97 270	91.02 125	67.97 96	68.08 400	83.04 68	78.02 267	83.80 346

baseline1		76.98 227	76.75 216	77.66 284	88.13 182	55.66 333	85.12 225	81.89 310	73.04 150	76.79 194	88.90 170	62.43 156	87.78 302	63.30 249	71.18 346	89.55 229

UnsupCasMVSNet_bld		63.70 345	61.53 351	70.21 352	73.69 381	51.39 370	72.82 366	81.89 310	55.63 367	57.81 381	71.80 386	38.67 362	78.61 360	49.26 351	52.21 391	80.63 372

LFMVS		81.82 114	81.23 115	83.57 147	91.89 73	63.43 232	89.84 75	81.85 312	77.04 59	83.21 94	93.10 67	52.26 252	93.43 156	71.98 172	89.95 111	93.85 63

sss		73.60 270	73.64 260	73.51 327	82.80 306	55.01 341	76.12 347	81.69 313	62.47 318	74.68 250	85.85 256	57.32 214	78.11 363	60.86 274	80.93 232	87.39 284

pmmvs-eth3d		70.50 304	67.83 317	78.52 272	77.37 366	66.18 171	81.82 281	81.51 314	58.90 347	63.90 359	80.42 343	42.69 342	86.28 314	58.56 293	65.30 368	83.11 353

TinyColmap		67.30 329	64.81 334	74.76 316	81.92 322	56.68 318	80.29 308	81.49 315	60.33 332	56.27 386	83.22 308	24.77 392	87.66 304	45.52 372	69.47 352	79.95 375

testing99		76.09 244	75.12 242	79.00 261	88.16 180	55.50 335	80.79 296	81.40 316	73.30 144	75.17 239	84.27 291	44.48 332	90.02 266	64.28 242	84.22 189	91.48 153

tpmvs		71.09 296	69.29 301	76.49 297	82.04 319	56.04 328	78.92 325	81.37 317	64.05 299	67.18 331	78.28 362	49.74 287	89.77 270	49.67 349	72.37 337	83.67 347

WBMVS		73.43 272	72.81 267	75.28 310	87.91 192	50.99 373	78.59 331	81.31 318	65.51 281	74.47 254	84.83 279	46.39 312	86.68 309	58.41 295	77.86 268	88.17 269

pmmvs5		71.55 292	70.20 298	75.61 304	77.83 363	56.39 322	81.74 283	80.89 319	57.76 355	67.46 327	84.49 283	49.26 294	85.32 325	57.08 308	75.29 311	85.11 330

ANet_high		50.57 367	46.10 371	63.99 370	48.67 415	39.13 404	70.99 374	80.85 320	61.39 327	31.18 404	57.70 400	17.02 403	73.65 391	31.22 397	15.89 412	79.18 377

LCM-MVSNet		54.25 358	49.68 368	67.97 365	53.73 412	45.28 391	66.85 390	80.78 321	35.96 401	39.45 402	62.23 395	8.70 412	78.06 364	48.24 358	51.20 392	80.57 373

PVSNet	64.34 18	72.08 290	70.87 290	75.69 303	86.21 235	56.44 321	74.37 362	80.73 322	62.06 323	70.17 299	82.23 327	42.86 341	83.31 339	54.77 321	84.45 184	87.32 287

baseline2		75.70 248	73.83 258	81.30 213	83.26 292	61.79 258	82.57 276	80.65 323	66.81 257	66.88 333	83.42 307	57.86 208	92.19 207	63.47 246	79.57 249	89.91 216

ppachtmachnet_test		70.04 308	67.34 325	78.14 277	79.80 351	61.13 263	79.19 321	80.59 324	59.16 344	65.27 349	79.29 353	46.75 311	87.29 305	49.33 350	66.72 361	86.00 317

Gipuma		45.18 372	41.86 375	55.16 384	77.03 368	51.52 368	32.50 408	80.52 325	32.46 404	27.12 407	35.02 408	9.52 411	75.50 381	22.31 405	60.21 380	38.45 407
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		68.60 318	67.80 318	71.02 348	80.23 344	50.75 375	78.30 336	80.47 326	56.79 362	66.11 345	82.63 321	46.35 315	78.95 359	43.62 377	75.70 298	83.36 350

LCM-MVSNet-Re		77.05 225	76.94 209	77.36 289	87.20 219	51.60 367	80.06 309	80.46 327	75.20 100	67.69 324	86.72 229	62.48 154	88.98 285	63.44 247	89.25 118	91.51 150

testing11		75.14 257	74.01 253	78.53 271	88.16 180	56.38 323	80.74 299	80.42 328	70.67 189	72.69 275	83.72 302	43.61 337	89.86 268	62.29 259	83.76 194	89.36 233

tpm2		73.26 276	71.46 281	78.63 266	83.34 290	56.71 317	80.65 301	80.40 329	56.63 363	73.55 263	82.02 330	51.80 264	91.24 243	56.35 315	78.42 263	87.95 271

CR-MVSNet		73.37 273	71.27 285	79.67 251	81.32 333	65.19 193	75.92 349	80.30 330	59.92 337	72.73 273	81.19 333	52.50 248	86.69 308	59.84 280	77.71 270	87.11 294

Patchmtry		70.74 300	69.16 303	75.49 308	80.72 337	54.07 349	74.94 360	80.30 330	58.34 350	70.01 301	81.19 333	52.50 248	86.54 310	53.37 328	71.09 347	85.87 319

tpm cat1		70.57 302	68.31 308	77.35 290	82.41 316	57.95 298	78.08 337	80.22 332	52.04 376	68.54 319	77.66 367	52.00 259	87.84 301	51.77 334	72.07 341	86.25 308

MDTV_nov1_ep13			69.97 299		83.18 295	53.48 353	77.10 345	80.18 333	60.45 331	69.33 312	80.44 342	48.89 300	86.90 307	51.60 336	78.51 261

AllTest		70.96 297	68.09 312	79.58 253	85.15 254	63.62 223	84.58 238	79.83 334	62.31 319	60.32 372	86.73 227	32.02 378	88.96 287	50.28 344	71.57 344	86.15 311

TestCases				79.58 253	85.15 254	63.62 223		79.83 334	62.31 319	60.32 372	86.73 227	32.02 378	88.96 287	50.28 344	71.57 344	86.15 311

test_fmvs1_n		70.86 299	70.24 297	72.73 334	72.51 391	55.28 338	81.27 291	79.71 336	51.49 380	78.73 149	84.87 278	27.54 387	77.02 368	76.06 132	79.97 247	85.88 318

Vis-MVSNet (Re-imp)		78.36 195	78.45 169	78.07 279	88.64 164	51.78 366	86.70 186	79.63 337	74.14 123	75.11 242	90.83 129	61.29 177	89.75 271	58.10 299	91.60 86	92.69 114

MIMVSNet		70.69 301	69.30 300	74.88 314	84.52 267	56.35 325	75.87 351	79.42 338	64.59 289	67.76 322	82.41 322	41.10 351	81.54 348	46.64 366	81.34 227	86.75 302

dmvs_re		71.14 295	70.58 291	72.80 333	81.96 320	59.68 282	75.60 353	79.34 339	68.55 241	69.27 313	80.72 341	49.42 290	76.54 371	52.56 332	77.79 269	82.19 363

SCA		74.22 263	72.33 273	79.91 244	84.05 277	62.17 252	79.96 312	79.29 340	66.30 269	72.38 279	80.13 345	51.95 260	88.60 292	59.25 285	77.67 272	88.96 248

testing222		74.04 265	72.66 269	78.19 276	87.89 193	55.36 336	81.06 293	79.20 341	71.30 176	74.65 251	83.57 305	39.11 361	88.67 291	51.43 338	85.75 170	90.53 185

tpmrst		72.39 284	72.13 275	73.18 331	80.54 340	49.91 378	79.91 313	79.08 342	63.11 307	71.69 286	79.95 347	55.32 223	82.77 342	65.66 232	73.89 325	86.87 298

test_fmvs1		70.93 298	70.52 292	72.16 338	73.71 380	55.05 340	80.82 294	78.77 343	51.21 381	78.58 154	84.41 285	31.20 382	76.94 369	75.88 135	80.12 246	84.47 337

PatchmatchNet		73.12 278	71.33 284	78.49 273	83.18 295	60.85 267	79.63 314	78.57 344	64.13 295	71.73 285	79.81 350	51.20 270	85.97 317	57.40 305	76.36 293	88.66 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet-bldmvs		66.68 332	63.66 341	75.75 302	79.28 358	60.56 272	73.92 364	78.35 345	64.43 291	50.13 394	79.87 349	44.02 335	83.67 335	46.10 369	56.86 382	83.03 355

new-patchmatchnet		61.73 349	61.73 350	61.70 373	72.74 389	24.50 416	69.16 382	78.03 346	61.40 326	56.72 384	75.53 378	38.42 363	76.48 373	45.95 370	57.67 381	84.13 341

our_test_3		69.14 314	67.00 327	75.57 305	79.80 351	58.80 287	77.96 338	77.81 347	59.55 340	62.90 364	78.25 363	47.43 304	83.97 333	51.71 335	67.58 360	83.93 344

test20.03		67.45 327	66.95 328	68.94 356	75.48 374	44.84 393	77.50 341	77.67 348	66.66 261	63.01 362	83.80 299	47.02 308	78.40 361	42.53 381	68.86 357	83.58 348

WB-MVSnew		71.96 291	71.65 279	72.89 332	84.67 266	51.88 364	82.29 278	77.57 349	62.31 319	73.67 262	83.00 313	53.49 243	81.10 351	45.75 371	82.13 220	85.70 320

test-LLR		72.94 282	72.43 271	74.48 318	81.35 331	58.04 295	78.38 332	77.46 350	66.66 261	69.95 304	79.00 356	48.06 302	79.24 357	66.13 225	84.83 175	86.15 311

test-mter		71.41 293	70.39 296	74.48 318	81.35 331	58.04 295	78.38 332	77.46 350	60.32 333	69.95 304	79.00 356	36.08 372	79.24 357	66.13 225	84.83 175	86.15 311

ECVR-MVS		79.61 161	79.26 154	80.67 230	90.08 107	54.69 343	87.89 150	77.44 352	74.88 107	80.27 130	92.79 79	48.96 299	92.45 195	68.55 206	92.50 76	94.86 17

UBG		73.08 279	72.27 274	75.51 307	88.02 188	51.29 371	78.35 335	77.38 353	65.52 279	73.87 260	82.36 323	45.55 325	86.48 312	55.02 319	84.39 186	88.75 257

tpm		72.37 286	71.71 278	74.35 320	82.19 318	52.00 361	79.22 320	77.29 354	64.56 290	72.95 271	83.68 304	51.35 267	83.26 340	58.33 297	75.80 297	87.81 275

LF4IMVS		64.02 344	62.19 348	69.50 354	70.90 392	53.29 357	76.13 346	77.18 355	52.65 375	58.59 377	80.98 337	23.55 395	76.52 372	53.06 330	66.66 362	78.68 378

test1111		79.43 168	79.18 157	80.15 240	89.99 112	53.31 356	87.33 165	77.05 356	75.04 104	80.23 132	92.77 81	48.97 298	92.33 203	68.87 203	92.40 78	94.81 20

K. test v3		71.19 294	68.51 306	79.21 259	83.04 300	57.78 302	84.35 247	76.91 357	72.90 153	62.99 363	82.86 317	39.27 359	91.09 250	61.65 267	52.66 390	88.75 257

UWE-MVS		72.13 289	71.49 280	74.03 323	86.66 230	47.70 382	81.40 290	76.89 358	63.60 304	75.59 220	84.22 292	39.94 357	85.62 320	48.98 352	86.13 163	88.77 256

testgi		66.67 333	66.53 330	67.08 367	75.62 373	41.69 402	75.93 348	76.50 359	66.11 270	65.20 352	86.59 237	35.72 373	74.71 386	43.71 376	73.38 332	84.84 333

test_fmvs2		68.35 323	67.48 324	70.98 349	69.50 394	51.95 362	80.05 310	76.38 360	49.33 383	74.65 251	84.38 286	23.30 396	75.40 384	74.51 147	75.17 314	85.60 321

test_vis1_n		69.85 311	69.21 302	71.77 340	72.66 390	55.27 339	81.48 287	76.21 361	52.03 377	75.30 236	83.20 310	28.97 385	76.22 376	74.60 146	78.41 264	83.81 345

PatchMatch-RL		72.38 285	70.90 289	76.80 296	88.60 165	67.38 148	79.53 315	76.17 362	62.75 315	69.36 311	82.00 331	45.51 326	84.89 328	53.62 326	80.58 238	78.12 379

JIA-IIPM		66.32 336	62.82 347	76.82 295	77.09 367	61.72 259	65.34 395	75.38 363	58.04 354	64.51 354	62.32 394	42.05 348	86.51 311	51.45 337	69.22 354	82.21 362

ADS-MVSNet2		66.20 339	63.33 342	74.82 315	79.92 347	58.75 288	67.55 387	75.19 364	53.37 373	65.25 350	75.86 375	42.32 344	80.53 354	41.57 382	68.91 355	85.18 327

ETVMVS		72.25 288	71.05 287	75.84 301	87.77 201	51.91 363	79.39 317	74.98 365	69.26 223	73.71 261	82.95 314	40.82 354	86.14 315	46.17 368	84.43 185	89.47 230

PatchT		68.46 322	67.85 315	70.29 351	80.70 338	43.93 395	72.47 367	74.88 366	60.15 335	70.55 292	76.57 371	49.94 284	81.59 347	50.58 340	74.83 317	85.34 324

dp		66.80 331	65.43 333	70.90 350	79.74 353	48.82 381	75.12 358	74.77 367	59.61 339	64.08 357	77.23 368	42.89 340	80.72 353	48.86 353	66.58 363	83.16 352

MDA-MVSNet_test_wron		65.03 340	62.92 344	71.37 343	75.93 369	56.73 315	69.09 384	74.73 368	57.28 360	54.03 389	77.89 364	45.88 320	74.39 388	49.89 348	61.55 375	82.99 356

TESTMET0.1,1		69.89 310	69.00 304	72.55 335	79.27 359	56.85 313	78.38 332	74.71 369	57.64 356	68.09 321	77.19 369	37.75 367	76.70 370	63.92 244	84.09 190	84.10 342

YYNet1		65.03 340	62.91 345	71.38 342	75.85 371	56.60 319	69.12 383	74.66 370	57.28 360	54.12 388	77.87 365	45.85 321	74.48 387	49.95 347	61.52 376	83.05 354

test_fmvs3		63.36 346	61.82 349	67.98 364	62.51 403	46.96 386	77.37 343	74.03 371	45.24 388	67.50 326	78.79 359	12.16 408	72.98 392	72.77 167	66.02 365	83.99 343

PMMVS		69.34 313	68.67 305	71.35 345	75.67 372	62.03 253	75.17 355	73.46 372	50.00 382	68.68 316	79.05 354	52.07 258	78.13 362	61.16 272	82.77 212	73.90 386

PVSNet_0	57.27 20	61.67 350	59.27 353	68.85 358	79.61 354	57.44 307	68.01 385	73.44 373	55.93 366	58.54 378	70.41 389	44.58 331	77.55 366	47.01 363	35.91 401	71.55 389

Syy-MVS		68.05 324	67.85 315	68.67 360	84.68 263	40.97 403	78.62 329	73.08 374	66.65 264	66.74 336	79.46 351	52.11 256	82.30 344	32.89 395	76.38 291	82.75 358

myMVS_eth3d		67.02 330	66.29 331	69.21 355	84.68 263	42.58 398	78.62 329	73.08 374	66.65 264	66.74 336	79.46 351	31.53 381	82.30 344	39.43 387	76.38 291	82.75 358

test0.0.03 1		68.00 325	67.69 320	68.90 357	77.55 364	47.43 383	75.70 352	72.95 376	66.66 261	66.56 338	82.29 326	48.06 302	75.87 379	44.97 375	74.51 320	83.41 349

testing3		68.56 320	67.67 321	71.22 347	87.33 216	42.87 397	83.06 272	71.54 377	70.36 196	69.08 314	84.38 286	30.33 384	85.69 319	37.50 390	75.45 306	85.09 331

ADS-MVSNet		64.36 343	62.88 346	68.78 359	79.92 347	47.17 384	67.55 387	71.18 378	53.37 373	65.25 350	75.86 375	42.32 344	73.99 389	41.57 382	68.91 355	85.18 327

Patchmatch-RL test		70.24 306	67.78 319	77.61 286	77.43 365	59.57 285	71.16 372	70.33 379	62.94 311	68.65 317	72.77 384	50.62 276	85.49 322	69.58 196	66.58 363	87.77 276

gg-mvs-nofinetune		69.95 309	67.96 313	75.94 300	83.07 298	54.51 346	77.23 344	70.29 380	63.11 307	70.32 296	62.33 393	43.62 336	88.69 290	53.88 325	87.76 138	84.62 336

door-mid								69.98 381

GG-mvs-BLEND				75.38 309	81.59 326	55.80 331	79.32 318	69.63 382		67.19 330	73.67 382	43.24 338	88.90 289	50.41 341	84.50 180	81.45 367

FPMVS		53.68 361	51.64 363	59.81 376	65.08 400	51.03 372	69.48 380	69.58 383	41.46 393	40.67 400	72.32 385	16.46 404	70.00 397	24.24 404	65.42 367	58.40 400

door								69.44 384

Patchmatch-test		64.82 342	63.24 343	69.57 353	79.42 357	49.82 379	63.49 399	69.05 385	51.98 378	59.95 374	80.13 345	50.91 272	70.98 393	40.66 384	73.57 328	87.90 273

CHOSEN 280x420		66.51 334	64.71 335	71.90 339	81.45 328	63.52 228	57.98 402	68.95 386	53.57 372	62.59 365	76.70 370	46.22 317	75.29 385	55.25 318	79.68 248	76.88 382

MVStest1		56.63 356	52.76 362	68.25 363	61.67 404	53.25 358	71.67 370	68.90 387	38.59 397	50.59 393	83.05 312	25.08 390	70.66 394	36.76 391	38.56 400	80.83 371

EGC-MVSNET		52.07 365	47.05 369	67.14 366	83.51 287	60.71 269	80.50 304	67.75 388	0.07 415	0.43 416	75.85 377	24.26 393	81.54 348	28.82 398	62.25 373	59.16 398

m2depth		59.91 352	57.10 356	68.34 362	67.13 398	46.65 387	74.64 361	67.41 389	48.30 384	62.52 366	85.04 277	20.40 398	75.93 378	42.55 380	45.90 399	82.44 360

EPMVS		69.02 315	68.16 310	71.59 341	79.61 354	49.80 380	77.40 342	66.93 390	62.82 314	70.01 301	79.05 354	45.79 322	77.86 365	56.58 313	75.26 312	87.13 293

APD_test1		53.31 362	49.93 367	63.42 372	65.68 399	50.13 377	71.59 371	66.90 391	34.43 402	40.58 401	71.56 387	8.65 413	76.27 375	34.64 394	55.36 387	63.86 396

lessismore_v0				78.97 262	81.01 336	57.15 310		65.99 392		61.16 369	82.82 318	39.12 360	91.34 241	59.67 281	46.92 396	88.43 265

dmvs_testset		62.63 347	64.11 338	58.19 377	78.55 361	24.76 415	75.28 354	65.94 393	67.91 250	60.34 371	76.01 374	53.56 241	73.94 390	31.79 396	67.65 359	75.88 384

pmmvs3		57.79 354	54.26 359	68.37 361	64.02 402	56.72 316	75.12 358	65.17 394	40.20 394	52.93 390	69.86 390	20.36 399	75.48 382	45.45 373	55.25 388	72.90 388

MVS-HIRNet		59.14 353	57.67 355	63.57 371	81.65 324	43.50 396	71.73 369	65.06 395	39.59 396	51.43 391	57.73 399	38.34 364	82.58 343	39.53 385	73.95 324	64.62 395

PM-MVS		66.41 335	64.14 337	73.20 330	73.92 379	56.45 320	78.97 324	64.96 396	63.88 303	64.72 353	80.24 344	19.84 400	83.44 338	66.24 224	64.52 370	79.71 376

PMVS	37.38 22	44.16 373	40.28 377	55.82 382	40.82 417	42.54 400	65.12 396	63.99 397	34.43 402	24.48 408	57.12 401	3.92 418	76.17 377	17.10 409	55.52 386	48.75 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2506		77.30 223	76.49 220	79.74 248	90.08 107	52.02 360	87.86 152	63.10 398	74.88 107	80.16 133	92.79 79	38.29 365	92.35 201	68.74 205	92.50 76	94.86 17

test_method		31.52 377	29.28 381	38.23 391	27.03 419	6.50 422	20.94 410	62.21 399	4.05 413	22.35 411	52.50 404	13.33 405	47.58 411	27.04 401	34.04 403	60.62 397

WB-MVS		54.94 357	54.72 358	55.60 383	73.50 382	20.90 417	74.27 363	61.19 400	59.16 344	50.61 392	74.15 380	47.19 307	75.78 380	17.31 408	35.07 402	70.12 390

test_vis1_rt		60.28 351	58.42 354	65.84 368	67.25 397	55.60 334	70.44 377	60.94 401	44.33 390	59.00 376	66.64 391	24.91 391	68.67 398	62.80 251	69.48 351	73.25 387

SSC-MVS		53.88 360	53.59 360	54.75 385	72.87 388	19.59 418	73.84 365	60.53 402	57.58 358	49.18 396	73.45 383	46.34 316	75.47 383	16.20 411	32.28 404	69.20 391

testf1		45.72 369	41.96 373	57.00 378	56.90 406	45.32 389	66.14 392	59.26 403	26.19 406	30.89 405	60.96 397	4.14 416	70.64 395	26.39 402	46.73 397	55.04 401

APD_test2		45.72 369	41.96 373	57.00 378	56.90 406	45.32 389	66.14 392	59.26 403	26.19 406	30.89 405	60.96 397	4.14 416	70.64 395	26.39 402	46.73 397	55.04 401

test_f		52.09 364	50.82 365	55.90 381	53.82 411	42.31 401	59.42 401	58.31 405	36.45 400	56.12 387	70.96 388	12.18 407	57.79 407	53.51 327	56.57 384	67.60 392

new_pmnet		50.91 366	50.29 366	52.78 386	68.58 395	34.94 408	63.71 397	56.63 406	39.73 395	44.95 397	65.47 392	21.93 397	58.48 406	34.98 393	56.62 383	64.92 394

DSMNet-mixed		57.77 355	56.90 357	60.38 375	67.70 396	35.61 406	69.18 381	53.97 407	32.30 405	57.49 382	79.88 348	40.39 356	68.57 399	38.78 388	72.37 337	76.97 381

PMMVS2		40.82 374	38.86 378	46.69 388	53.84 410	16.45 419	48.61 405	49.92 408	37.49 398	31.67 403	60.97 396	8.14 414	56.42 408	28.42 399	30.72 405	67.19 393

mvsany_test1		62.30 348	61.26 352	65.41 369	69.52 393	54.86 342	66.86 389	49.78 409	46.65 386	68.50 320	83.21 309	49.15 295	66.28 401	56.93 310	60.77 377	75.11 385

test_vis3_rt		49.26 368	47.02 370	56.00 380	54.30 409	45.27 392	66.76 391	48.08 410	36.83 399	44.38 398	53.20 403	7.17 415	64.07 403	56.77 312	55.66 385	58.65 399

E-PMN		31.77 376	30.64 379	35.15 393	52.87 413	27.67 410	57.09 403	47.86 411	24.64 408	16.40 413	33.05 409	11.23 409	54.90 409	14.46 412	18.15 410	22.87 409

EMVS		30.81 378	29.65 380	34.27 394	50.96 414	25.95 414	56.58 404	46.80 412	24.01 409	15.53 414	30.68 410	12.47 406	54.43 410	12.81 413	17.05 411	22.43 410

mvsany_test3		53.99 359	51.45 364	61.61 374	55.51 408	44.74 394	63.52 398	45.41 413	43.69 391	58.11 380	76.45 372	17.99 401	63.76 404	54.77 321	47.59 395	76.34 383

MVE	26.22 23	30.37 379	25.89 383	43.81 390	44.55 416	35.46 407	28.87 409	39.07 414	18.20 410	18.58 412	40.18 407	2.68 419	47.37 412	17.07 410	23.78 409	48.60 404
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 371	45.38 372	45.55 389	73.36 385	26.85 413	67.72 386	34.19 415	54.15 371	49.65 395	56.41 402	25.43 389	62.94 405	19.45 406	28.09 406	46.86 405

kuosan		39.70 375	40.40 376	37.58 392	64.52 401	26.98 411	65.62 394	33.02 416	46.12 387	42.79 399	48.99 405	24.10 394	46.56 413	12.16 414	26.30 407	39.20 406

MTMP							92.18 34	32.83 417

tmp_tt		18.61 381	21.40 384	10.23 397	4.82 420	10.11 420	34.70 407	30.74 418	1.48 414	23.91 410	26.07 411	28.42 386	13.41 416	27.12 400	15.35 413	7.17 411

DeepMVS_CX				27.40 395	40.17 418	26.90 412		24.59 419	17.44 411	23.95 409	48.61 406	9.77 410	26.48 414	18.06 407	24.47 408	28.83 408

N_pmnet		52.79 363	53.26 361	51.40 387	78.99 360	7.68 421	69.52 379	3.89 420	51.63 379	57.01 383	74.98 379	40.83 353	65.96 402	37.78 389	64.67 369	80.56 374

wuyk23d		16.82 382	15.94 385	19.46 396	58.74 405	31.45 409	39.22 406	3.74 421	6.84 412	6.04 415	2.70 415	1.27 420	24.29 415	10.54 415	14.40 414	2.63 412

testmvs		6.04 385	8.02 388	0.10 399	0.08 421	0.03 424	69.74 378	0.04 422	0.05 416	0.31 417	1.68 416	0.02 422	0.04 417	0.24 416	0.02 415	0.25 414

test123		6.12 384	8.11 387	0.14 398	0.06 422	0.09 423	71.05 373	0.03 423	0.04 417	0.25 418	1.30 417	0.05 421	0.03 418	0.21 417	0.01 416	0.29 413

test_blank		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uanet_test		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

DCPMVS		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

pcd_1.5k_mvsjas		5.26 386	7.02 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	63.15 145	0.00 419	0.00 418	0.00 417	0.00 415

sosnet-low-res		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

sosnet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uncertanet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

Regformer		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

n2								0.00 424

nn								0.00 424

ab-mvs-re		7.23 383	9.64 386	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	86.72 229	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uanet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	0.00 418	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

WAC-MVS						42.58 398								39.46 386

PC_three_1452									68.21 247	92.02 12	94.00 46	82.09 5	95.98 55	84.58 51	96.68 2	94.95 10

eth-test2					0.00 423

eth-test					0.00 423

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 61	96.48 8	94.88 14

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 9	96.57 7	94.67 26

GSMVS																88.96 248

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 268				88.96 248

sam_mvs												50.01 282

test_post1							78.90 326				5.43 414	48.81 301	85.44 324	59.25 285

test_post											5.46 413	50.36 280	84.24 331

patchmatchnet-post											74.00 381	51.12 271	88.60 292

gm-plane-assit					81.40 329	53.83 351			62.72 316		80.94 338		92.39 198	63.40 248

test9_res														84.90 43	95.70 26	92.87 109

agg_prior2														82.91 70	95.45 29	92.70 112

test_prior4						72.60 34	89.01 107

test_prior2							88.85 113		75.41 96	84.91 62	93.54 56	74.28 29		83.31 64	95.86 20

旧先验2							86.56 190		58.10 353	87.04 42			88.98 285	74.07 152

新几何2							86.29 198

原ACMM2							86.86 179

testdata2													91.01 252	62.37 258

segment_acmp												73.08 38

testdata1							84.14 251		75.71 90

plane_prior7					90.08 107	68.51 120

plane_prior6					89.84 116	68.70 115						60.42 193

plane_prior4											91.00 126

plane_prior3						68.60 118			78.44 31	78.92 147

plane_prior2							91.25 49		79.12 23

plane_prior1					89.90 115

plane_prior						68.71 113	90.38 67		77.62 39						86.16 162

HQP5-MVS						66.98 159

HQP-NCC					89.33 134		89.17 100		76.41 75	77.23 185

ACMP_Plane					89.33 134		89.17 100		76.41 75	77.23 185

BP-MVS														77.47 118

HQP4-MVS										77.24 184			95.11 84			91.03 166

HQP2-MVS												60.17 196

NP-MVS					89.62 120	68.32 123					90.24 138

MDTV_nov1_ep13_2view						37.79 405	75.16 356		55.10 368	66.53 339		49.34 292		53.98 324		87.94 272

ACMMP++_ref															81.95 223

ACMMP++															81.25 228

Test By Simon												64.33 132

High-resolution multi-view benchmark