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 14		92.84 60		91.52 46	94.75 1	73.93 125	88.57 22	94.67 19	75.57 22	95.79 53	86.77 35	95.76 23

SF-MVS		88.46 11	88.74 11	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 87	89.16 19	95.10 14	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 42

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 58	94.67 24
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 43	94.10 8	75.90 85	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 52
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS		86.43 38	86.17 42	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 152	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

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 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 41

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

APDe-MVS		89.15 6	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 89	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 31
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 31	86.88 33	87.69 33	91.16 80	72.32 43	90.31 68	93.94 14	77.12 55	82.82 96	94.23 35	72.13 45	97.09 16	84.83 45	95.37 32	93.65 69
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	74.49 113	91.30 15

MCST-MVS		87.37 26	87.25 25	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 147	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

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 63	85.24 53	94.32 31	71.76 48	96.93 19	85.53 39	95.79 22	94.32 38

CS-MVS-test		86.29 41	86.48 36	85.71 65	91.02 83	67.21 152	92.36 29	93.78 18	78.97 28	83.51 88	91.20 111	70.65 63	95.15 77	81.96 76	94.89 41	94.77 22

3Dnovator+	77.84 4	85.48 53	84.47 68	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 195	93.37 62	60.40 188	96.75 26	77.20 117	93.73 62	95.29 5

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	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 7	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 107	92.29 7	95.97 2	74.28 29	97.24 12	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 42	86.38 37	84.91 88	89.31 130	66.27 166	92.32 30	93.63 21	79.37 20	84.17 76	91.88 93	69.04 82	95.43 65	83.93 57	93.77 61	93.01 99

ACMMP_NAP		88.05 16	88.08 16	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 79	88.14 24	95.09 15	71.06 57	96.67 29	87.67 29	96.37 14	94.09 46

CSCG		86.41 40	86.19 41	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 123	83.16 91	91.07 116	75.94 18	95.19 75	79.94 94	94.38 54	93.55 76

MP-MVS-pluss		87.67 20	87.72 20	87.54 36	93.64 44	72.04 48	89.80 79	93.50 25	75.17 100	86.34 44	95.29 12	70.86 59	96.00 49	88.78 19	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FIs		82.07 102	82.42 90	81.04 219	88.80 150	58.34 288	88.26 135	93.49 26	76.93 60	78.47 153	91.04 117	69.92 70	92.34 199	69.87 190	84.97 166	92.44 118

DELS-MVS		85.41 56	85.30 57	85.77 64	88.49 161	67.93 132	85.52 219	93.44 27	78.70 29	83.63 87	89.03 164	74.57 24	95.71 56	80.26 92	94.04 59	93.66 65
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 24	87.26 24	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 63	93.99 48	70.67 62	96.82 22	84.18 56	95.01 37	93.90 55

FC-MVSNet-test		81.52 115	82.02 99	80.03 240	88.42 165	55.97 325	87.95 146	93.42 29	77.10 56	77.38 177	90.98 122	69.96 68	91.79 217	68.46 205	84.50 172	92.33 119

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 50	92.24 68	69.03 99	89.57 87	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 27

HPM-MVS		87.11 29	86.98 29	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 82	83.81 83	93.95 51	69.77 72	96.01 48	85.15 40	94.66 46	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 34	86.95 30	85.90 63	90.76 90	67.57 140	92.83 17	93.30 32	79.67 17	84.57 69	92.27 86	71.47 53	95.02 86	84.24 54	93.46 63	95.13 6

HFP-MVS		87.58 21	87.47 23	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 59	94.44 28	70.78 60	96.61 32	84.53 49	94.89 41	93.66 65

ACMMPR		87.44 22	87.23 26	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 66	94.52 21	68.81 84	96.65 30	84.53 49	94.90 40	94.00 50

SD-MVS		88.06 14	88.50 13	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 136	87.63 30	94.27 57	93.65 69
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 47	85.39 53	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 121	93.82 53	64.33 125	96.29 39	82.67 73	90.69 94	93.23 87
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 24	87.20 27	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 70	94.52 21	69.09 78	96.70 27	84.37 51	94.83 44	94.03 49

DPM-MVS		84.93 62	84.29 69	86.84 47	90.20 99	73.04 23	87.12 169	93.04 38	69.80 205	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 148

PGM-MVS		86.68 35	86.27 39	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 91	83.86 81	94.42 29	67.87 92	96.64 31	82.70 72	94.57 49	93.66 65

casdiffmvs_mvg		85.99 43	86.09 45	85.70 66	87.65 193	67.22 151	88.69 119	93.04 38	79.64 18	85.33 52	92.54 83	73.30 35	94.50 107	83.49 59	91.14 89	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 32	86.62 35	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 70	84.22 74	93.36 63	71.44 54	96.76 25	80.82 85	95.33 34	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		81.60 114	81.11 110	83.09 162	88.38 166	64.41 209	87.60 156	93.02 42	78.42 32	78.56 150	88.16 190	69.78 71	93.26 159	69.58 193	76.49 275	91.60 140

canonicalmvs		85.91 46	85.87 48	86.04 60	89.84 111	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 131	81.50 79	88.80 120	94.77 22

CNVR-MVS		88.93 9	89.13 9	88.33 8	94.77 12	73.82 8	90.51 61	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 37

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

XVS		87.18 28	86.91 32	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	94.17 36	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

X-MVStestdata		80.37 144	77.83 181	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 398	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

APD-MVS_3200maxsize		85.97 44	85.88 47	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 135	85.94 45	94.51 24	65.80 115	95.61 57	83.04 65	92.51 71	93.53 78

test_prior				86.33 54	92.61 65	69.59 88		92.97 50					95.48 62			93.91 53

IU-MVS					95.30 2	71.25 57		92.95 51	66.81 251	92.39 6				88.94 16	96.63 4	94.85 19

baseline		84.93 62	84.98 60	84.80 92	87.30 207	65.39 188	87.30 165	92.88 52	77.62 39	84.04 79	92.26 87	71.81 47	93.96 124	81.31 80	90.30 99	95.03 8

MSLP-MVS++		85.43 55	85.76 49	84.45 103	91.93 72	70.24 76	90.71 58	92.86 53	77.46 47	84.22 74	92.81 78	67.16 99	92.94 180	80.36 90	94.35 55	90.16 194

HPM-MVS++		89.02 8	89.15 8	88.63 5	95.01 9	76.03 1	92.38 27	92.85 54	80.26 11	87.78 29	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 96

casdiffmvs		85.11 60	85.14 59	85.01 82	87.20 209	65.77 179	87.75 153	92.83 55	77.84 37	84.36 73	92.38 85	72.15 44	93.93 130	81.27 81	90.48 96	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 22	87.52 22	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 149	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 105
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 52	92.83 55	81.50 5	85.79 48	93.47 60	73.02 39	97.00 18	84.90 42	94.94 39	94.10 45

CP-MVS		87.11 29	86.92 31	87.68 34	94.20 34	73.86 7	93.98 3	92.82 58	76.62 70	83.68 84	94.46 25	67.93 90	95.95 52	84.20 55	94.39 53	93.23 87

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 59	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 84
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 86	82.80 88	84.82 90	89.59 115	65.59 181	88.21 136	92.68 60	74.66 109	78.96 139	86.42 241	69.06 80	95.26 73	75.54 138	90.09 103	93.62 72

ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 180	87.75 31	94.07 41	74.01 32	96.70 27	84.66 47	94.84 43

nrg030		83.88 70	83.53 74	84.96 84	86.77 217	69.28 98	90.46 65	92.67 61	74.79 106	82.95 92	91.33 108	72.70 41	93.09 174	80.79 87	79.28 246	92.50 114

WR-MVS_H		78.51 188	78.49 164	78.56 265	88.02 178	56.38 320	88.43 126	92.67 61	77.14 54	73.89 252	87.55 205	66.25 108	89.24 274	58.92 283	73.55 319	90.06 204

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 64	77.57 41	83.84 82	94.40 30	72.24 43	96.28 40	85.65 38	95.30 35	93.62 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		84.90 64	84.67 64	85.59 67	89.39 124	68.66 116	88.74 117	92.64 65	79.97 15	84.10 77	85.71 254	69.32 76	95.38 69	80.82 85	91.37 86	92.72 104

CANet		86.45 37	86.10 44	87.51 37	90.09 101	70.94 67	89.70 83	92.59 66	81.78 4	81.32 112	91.43 106	70.34 64	97.23 13	84.26 52	93.36 64	94.37 35

SR-MVS		86.73 33	86.67 34	86.91 46	94.11 37	72.11 47	92.37 28	92.56 67	74.50 112	86.84 42	94.65 20	67.31 97	95.77 54	84.80 46	92.85 67	92.84 103

alignmvs		85.48 53	85.32 56	85.96 62	89.51 119	69.47 92	89.74 81	92.47 68	76.17 80	87.73 33	91.46 105	70.32 65	93.78 136	81.51 78	88.95 117	94.63 26

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 293	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 248

HQP_MVS		83.64 76	83.14 80	85.14 77	90.08 102	68.71 112	91.25 50	92.44 69	79.12 23	78.92 141	91.00 120	60.42 186	95.38 69	78.71 102	86.32 151	91.33 149

plane_prior5								92.44 69					95.38 69	78.71 102	86.32 151	91.33 149

CDPH-MVS		85.76 49	85.29 58	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 240	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

UniMVSNet_NR-MVSNet		81.88 105	81.54 105	82.92 171	88.46 163	63.46 228	87.13 168	92.37 73	80.19 12	78.38 154	89.14 160	71.66 52	93.05 176	70.05 186	76.46 276	92.25 123

TSAR-MVS + MP.		88.02 17	88.11 15	87.72 30	93.68 43	72.13 46	91.41 47	92.35 74	74.62 111	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 47	95.04 7
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 98	81.65 104	84.29 110	88.47 162	67.73 136	85.81 210	92.35 74	75.78 86	78.33 156	86.58 236	64.01 128	94.35 110	76.05 131	87.48 135	90.79 168
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 48	85.61 51	86.23 56	93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	65.00 123	95.56 58	82.75 68	91.87 79	92.50 114

RE-MVS-def			85.48 52		93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	63.87 129		82.75 68	91.87 79	92.50 114

RPMNet		73.51 264	70.49 282	82.58 185	81.32 322	65.19 191	75.92 339	92.27 76	57.60 347	72.73 264	76.45 360	52.30 245	95.43 65	48.14 350	77.71 260	87.11 285

test11								92.23 79

mPP-MVS		86.67 36	86.32 38	87.72 30	94.41 22	73.55 13	92.74 20	92.22 80	76.87 62	82.81 97	94.25 34	66.44 105	96.24 41	82.88 67	94.28 56	93.38 81

DP-MVS Recon		83.11 90	82.09 97	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 189	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 145

HQP3-MVS								92.19 82							85.99 158

HQP-MVS		82.61 96	82.02 99	84.37 105	89.33 127	66.98 155	89.17 98	92.19 82	76.41 72	77.23 182	90.23 133	60.17 189	95.11 80	77.47 114	85.99 158	91.03 161

3Dnovator	76.31 5	83.38 84	82.31 94	86.59 52	87.94 180	72.94 28	90.64 59	92.14 84	77.21 52	75.47 220	92.83 76	58.56 195	94.72 99	73.24 159	92.71 69	92.13 130

MTGPA								92.02 85

MTAPA		87.23 27	87.00 28	87.90 22	94.18 35	74.25 5	86.58 187	92.02 85	79.45 19	85.88 46	94.80 17	68.07 89	96.21 42	86.69 36	95.34 33	93.23 87

MVS_Test		83.15 87	83.06 82	83.41 149	86.86 213	63.21 234	86.11 200	92.00 87	74.31 116	82.87 94	89.44 156	70.03 67	93.21 163	77.39 116	88.50 126	93.81 60

PVSNet_BlendedMVS		80.60 137	80.02 129	82.36 189	88.85 145	65.40 186	86.16 199	92.00 87	69.34 215	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 211	87.39 275

PVSNet_Blended		80.98 123	80.34 124	82.90 172	88.85 145	65.40 186	84.43 242	92.00 87	67.62 246	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 238

QAPM		80.88 125	79.50 141	85.03 81	88.01 179	68.97 103	91.59 43	92.00 87	66.63 260	75.15 235	92.16 88	57.70 202	95.45 63	63.52 240	88.76 121	90.66 174

LPG-MVS_test		82.08 101	81.27 107	84.50 99	89.23 134	68.76 108	90.22 70	91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 182	89.83 215

LGP-MVS_train				84.50 99	89.23 134	68.76 108		91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 182	89.83 215

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 238	85.00 57	93.10 67	74.36 28	95.41 67

train_agg		86.43 38	86.20 40	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 233	85.00 57	93.10 67	74.43 26	95.41 67	84.97 41	95.71 25	93.02 98

dcpmvs_2		85.63 51	86.15 43	84.06 125	91.71 75	64.94 197	86.47 190	91.87 95	73.63 131	86.60 43	93.02 72	76.57 15	91.87 216	83.36 60	92.15 75	95.35 3

DU-MVS		81.12 122	80.52 121	82.90 172	87.80 186	63.46 228	87.02 172	91.87 95	79.01 26	78.38 154	89.07 162	65.02 121	93.05 176	70.05 186	76.46 276	92.20 126

test_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 233	84.87 61	93.10 67	74.43 26	95.16 76

PAPM_NR		83.02 91	82.41 91	84.82 90	92.47 67	66.37 164	87.93 148	91.80 98	73.82 127	77.32 179	90.66 125	67.90 91	94.90 91	70.37 183	89.48 112	93.19 91

test12				86.80 49	92.63 64	70.70 72		91.79 99		82.71 98		71.67 51	96.16 44		94.50 50	93.54 77

agg_prior					92.85 59	71.94 51		91.78 100		84.41 71			94.93 87

PAPR		81.66 113	80.89 115	83.99 133	90.27 97	64.00 215	86.76 183	91.77 101	68.84 231	77.13 188	89.50 149	67.63 93	94.88 93	67.55 211	88.52 125	93.09 94

PVSNet_Blended_VisFu		82.62 95	81.83 103	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 213	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 192

HPM-MVS_fast		85.35 57	84.95 62	86.57 53	93.69 42	70.58 75	92.15 36	91.62 103	73.89 126	82.67 99	94.09 40	62.60 144	95.54 60	80.93 83	92.93 66	93.57 74

ACMM	73.20 8	80.78 133	79.84 134	83.58 143	89.31 130	68.37 121	89.99 73	91.60 104	70.28 194	77.25 180	89.66 144	53.37 238	93.53 149	74.24 148	82.85 201	88.85 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		80.60 137	80.55 120	80.76 226	88.07 176	60.80 265	86.86 177	91.58 105	75.67 90	80.24 125	89.45 155	63.34 132	90.25 259	70.51 182	79.22 247	91.23 153

OPM-MVS		83.50 80	82.95 85	85.14 77	88.79 151	70.95 66	89.13 103	91.52 106	77.55 44	80.96 119	91.75 95	60.71 179	94.50 107	79.67 95	86.51 149	89.97 210
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		78.97 178	77.69 189	82.81 176	90.54 93	64.29 211	90.11 72	91.51 107	65.01 278	76.16 212	88.13 195	50.56 270	93.03 179	69.68 192	77.56 263	91.11 156

PS-MVSNAJss		82.07 102	81.31 106	84.34 108	86.51 221	67.27 149	89.27 96	91.51 107	71.75 161	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 208	91.49 146

TAPA-MVS	73.13 9	79.15 172	77.94 177	82.79 179	89.59 115	62.99 241	88.16 139	91.51 107	65.77 269	77.14 187	91.09 115	60.91 177	93.21 163	50.26 338	87.05 140	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	74.13 6	81.51 117	80.57 119	84.36 106	89.42 122	68.69 115	89.97 74	91.50 110	74.46 114	75.04 239	90.41 130	53.82 233	94.54 104	77.56 113	82.91 200	89.86 214
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 142	78.84 158	85.01 82	87.71 190	68.99 102	83.65 255	91.46 111	63.00 299	77.77 171	90.28 131	66.10 109	95.09 84	61.40 263	88.22 129	90.94 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		80.84 126	80.31 125	82.42 187	87.85 183	62.33 246	87.74 154	91.33 112	80.55 9	77.99 167	89.86 139	65.23 119	92.62 186	67.05 218	75.24 303	92.30 121

PS-CasMVS		78.01 202	78.09 174	77.77 278	87.71 190	54.39 341	88.02 143	91.22 113	77.50 46	73.26 258	88.64 175	60.73 178	88.41 290	61.88 258	73.88 316	90.53 180

v7n		78.97 178	77.58 192	83.14 160	83.45 277	65.51 182	88.32 133	91.21 114	73.69 130	72.41 268	86.32 244	57.93 199	93.81 135	69.18 196	75.65 289	90.11 198

PEN-MVS		77.73 208	77.69 189	77.84 276	87.07 212	53.91 344	87.91 149	91.18 115	77.56 43	73.14 260	88.82 170	61.23 171	89.17 275	59.95 273	72.37 327	90.43 184

MM				88.97 4		73.65 10	92.66 23	91.17 116	86.57 1	87.39 35	94.97 16	71.70 50	97.68 1	92.19 1	95.63 28	95.57 1

save fliter					93.80 40	72.35 42	90.47 64	91.17 116	74.31 116

CP-MVSNet		78.22 193	78.34 169	77.84 276	87.83 185	54.54 339	87.94 147	91.17 116	77.65 38	73.48 256	88.49 180	62.24 153	88.43 289	62.19 254	74.07 312	90.55 179

114514_t		80.68 134	79.51 140	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 339	74.08 251	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 190

NR-MVSNet		80.23 147	79.38 143	82.78 180	87.80 186	63.34 231	86.31 194	91.09 120	79.01 26	72.17 271	89.07 162	67.20 98	92.81 185	66.08 225	75.65 289	92.20 126

OpenMVS	72.83 10	79.77 155	78.33 170	84.09 121	85.17 240	69.91 84	90.57 60	90.97 121	66.70 254	72.17 271	91.91 91	54.70 224	93.96 124	61.81 260	90.95 91	88.41 258

MAR-MVS		81.84 106	80.70 117	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 207	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 218
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		78.73 182	77.83 181	81.43 205	85.17 240	60.30 273	89.41 92	90.90 123	71.21 174	77.17 186	88.73 171	46.38 306	93.21 163	72.57 166	78.96 249	90.79 168

Anonymous20240529		80.19 149	78.89 157	84.10 118	90.60 91	64.75 201	88.95 107	90.90 123	65.97 268	80.59 122	91.17 113	49.97 276	93.73 142	69.16 197	82.70 205	93.81 60

OMC-MVS		82.69 94	81.97 101	84.85 89	88.75 153	67.42 143	87.98 144	90.87 125	74.92 103	79.72 130	91.65 97	62.19 154	93.96 124	75.26 140	86.42 150	93.16 92

UA-Net		85.08 61	84.96 61	85.45 70	92.07 70	68.07 129	89.78 80	90.86 126	82.48 3	84.60 68	93.20 66	69.35 75	95.22 74	71.39 174	90.88 92	93.07 95

test_fmvsm_n_1920		85.29 58	85.34 54	85.13 79	86.12 226	69.93 83	88.65 121	90.78 127	69.97 201	88.27 23	93.98 49	71.39 55	91.54 228	88.49 23	90.45 97	93.91 53

EPP-MVSNet		83.40 83	83.02 83	84.57 96	90.13 100	64.47 207	92.32 30	90.73 128	74.45 115	79.35 135	91.10 114	69.05 81	95.12 78	72.78 163	87.22 138	94.13 44

DTE-MVSNet		76.99 223	76.80 207	77.54 283	86.24 223	53.06 352	87.52 158	90.66 129	77.08 57	72.50 266	88.67 174	60.48 185	89.52 269	57.33 299	70.74 338	90.05 205

v10		79.74 156	78.67 160	82.97 170	84.06 265	64.95 196	87.88 151	90.62 130	73.11 146	75.11 236	86.56 237	61.46 165	94.05 123	73.68 151	75.55 291	89.90 212

test_fmvsmconf_n		85.92 45	86.04 46	85.57 68	85.03 247	69.51 90	89.62 86	90.58 131	73.42 138	87.75 31	94.02 44	72.85 40	93.24 160	90.37 3	90.75 93	93.96 51

v1192		79.59 159	78.43 167	83.07 164	83.55 275	64.52 203	86.93 175	90.58 131	70.83 181	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 266	90.76 170

v1144		80.03 151	79.03 154	83.01 167	83.78 271	64.51 204	87.11 170	90.57 133	71.96 160	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 264	90.60 177

XVG-OURS-SEG-HR		80.81 128	79.76 135	83.96 135	85.60 233	68.78 107	83.54 260	90.50 134	70.66 187	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 217	91.83 136

MVS		78.19 196	76.99 203	81.78 197	85.66 231	66.99 154	84.66 232	90.47 135	55.08 359	72.02 273	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 330

XVG-OURS		80.41 141	79.23 149	83.97 134	85.64 232	69.02 101	83.03 271	90.39 136	71.09 177	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 193	91.54 142

MVSFormer		82.85 93	82.05 98	85.24 75	87.35 201	70.21 77	90.50 62	90.38 137	68.55 235	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

test_djsdf		80.30 146	79.32 146	83.27 153	83.98 267	65.37 189	90.50 62	90.38 137	68.55 235	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 236	90.97 164

CPTT-MVS		83.73 73	83.33 79	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 232	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 167

v144192		79.47 162	78.37 168	82.78 180	83.35 278	63.96 216	86.96 173	90.36 140	69.99 200	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 274	90.62 175

v1921920		79.22 170	78.03 175	82.80 177	83.30 280	63.94 217	86.80 179	90.33 141	69.91 203	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 271	90.71 173

MVS_111021_HR		85.14 59	84.75 63	86.32 55	91.65 76	72.70 30	85.98 202	90.33 141	76.11 81	82.08 102	91.61 100	71.36 56	94.17 120	81.02 82	92.58 70	92.08 131

v1240		78.99 177	77.78 184	82.64 183	83.21 282	63.54 225	86.62 186	90.30 143	69.74 210	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 268	90.62 175

test_fmvsmconf0.1_n		85.61 52	85.65 50	85.50 69	82.99 292	69.39 96	89.65 84	90.29 144	73.31 141	87.77 30	94.15 38	71.72 49	93.23 161	90.31 4	90.67 95	93.89 56

v8		79.97 154	79.02 155	82.80 177	84.09 264	64.50 206	87.96 145	90.29 144	74.13 122	75.24 233	86.81 223	62.88 143	93.89 133	74.39 146	75.40 298	90.00 206

mvs_tets		79.13 173	77.77 185	83.22 157	84.70 251	66.37 164	89.17 98	90.19 146	69.38 214	75.40 225	89.46 153	44.17 325	93.15 170	76.78 124	80.70 228	90.14 195

jajsoiax		79.29 169	77.96 176	83.27 153	84.68 252	66.57 162	89.25 97	90.16 147	69.20 220	75.46 222	89.49 150	45.75 317	93.13 172	76.84 121	80.80 226	90.11 198

Vis-MVSNet		83.46 81	82.80 88	85.43 71	90.25 98	68.74 110	90.30 69	90.13 148	76.33 78	80.87 120	92.89 74	61.00 176	94.20 118	72.45 168	90.97 90	93.35 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ		81.69 110	81.02 112	83.70 141	89.51 119	68.21 126	84.28 246	90.09 149	70.79 182	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 254

xiu_mvs_v2_base		81.69 110	81.05 111	83.60 142	89.15 137	68.03 131	84.46 240	90.02 150	70.67 185	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 255

FA-MVS(test-final)		80.96 124	79.91 132	84.10 118	88.30 169	65.01 195	84.55 237	90.01 151	73.25 143	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

v2v482		80.23 147	79.29 147	83.05 165	83.62 273	64.14 213	87.04 171	89.97 152	73.61 132	78.18 162	87.22 214	61.10 174	93.82 134	76.11 129	76.78 273	91.18 154

test_yl		81.17 120	80.47 122	83.24 155	89.13 138	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DCV-MVSNet		81.17 120	80.47 122	83.24 155	89.13 138	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

V42		79.38 168	78.24 172	82.83 174	81.10 324	65.50 183	85.55 215	89.82 155	71.57 168	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 295	89.81 217

VNet		82.21 99	82.41 91	81.62 200	90.82 88	60.93 262	84.47 238	89.78 156	76.36 77	84.07 78	91.88 93	64.71 124	90.26 258	70.68 180	88.89 118	93.66 65

diffmvs		82.10 100	81.88 102	82.76 182	83.00 290	63.78 220	83.68 254	89.76 157	72.94 150	82.02 103	89.85 140	65.96 114	90.79 252	82.38 74	87.30 137	93.71 64
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		76.11 238	74.27 246	81.62 200	83.20 283	64.67 202	83.60 258	89.75 158	69.75 208	71.85 274	87.09 219	32.78 367	92.11 206	69.99 188	80.43 232	88.09 261

EI-MVSNet-Vis-set		84.19 67	83.81 72	85.31 73	88.18 171	67.85 133	87.66 155	89.73 159	80.05 14	82.95 92	89.59 148	70.74 61	94.82 95	80.66 89	84.72 169	93.28 86

EI-MVSNet-UG-set		83.81 71	83.38 77	85.09 80	87.87 182	67.53 141	87.44 161	89.66 160	79.74 16	82.23 101	89.41 157	70.24 66	94.74 98	79.95 93	83.92 181	92.99 100

test_fmvsmconf0.01_n		84.73 65	84.52 67	85.34 72	80.25 332	69.03 99	89.47 88	89.65 161	73.24 144	86.98 40	94.27 32	66.62 101	93.23 161	90.26 5	89.95 107	93.78 62

PAPM		77.68 212	76.40 218	81.51 203	87.29 208	61.85 253	83.78 253	89.59 162	64.74 280	71.23 279	88.70 172	62.59 145	93.66 143	52.66 323	87.03 141	89.01 238

anonymousdsp		78.60 186	77.15 199	82.98 169	80.51 330	67.08 153	87.24 167	89.53 163	65.66 271	75.16 234	87.19 216	52.52 241	92.25 202	77.17 118	79.34 245	89.61 222

MG-MVS		83.41 82	83.45 75	83.28 152	92.74 62	62.28 248	88.17 138	89.50 164	75.22 96	81.49 111	92.74 82	66.75 100	95.11 80	72.85 162	91.58 83	92.45 117

PLC	70.83 11	78.05 200	76.37 219	83.08 163	91.88 74	67.80 134	88.19 137	89.46 165	64.33 286	69.87 296	88.38 183	53.66 234	93.58 144	58.86 284	82.73 203	87.86 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SDMVSNet		80.38 142	80.18 128	80.99 220	89.03 143	64.94 197	80.45 299	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 298	76.83 123	83.55 190	90.33 188

Fast-Effi-MVS+		80.81 128	79.92 131	83.47 145	88.85 145	64.51 204	85.53 217	89.39 167	70.79 182	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

IterMVS-LS		80.06 150	79.38 143	82.11 191	85.89 228	63.20 235	86.79 180	89.34 168	74.19 119	75.45 223	86.72 226	66.62 101	92.39 195	72.58 165	76.86 270	90.75 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
API-MVS		81.99 104	81.23 108	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 169	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 273

GBi-Net		78.40 189	77.40 194	81.40 207	87.60 195	63.01 238	88.39 128	89.28 170	71.63 163	75.34 227	87.28 210	54.80 220	91.11 242	62.72 247	79.57 240	90.09 200

test1		78.40 189	77.40 194	81.40 207	87.60 195	63.01 238	88.39 128	89.28 170	71.63 163	75.34 227	87.28 210	54.80 220	91.11 242	62.72 247	79.57 240	90.09 200

FMVSNet1		77.44 215	76.12 221	81.40 207	86.81 216	63.01 238	88.39 128	89.28 170	70.49 190	74.39 248	87.28 210	49.06 290	91.11 242	60.91 267	78.52 252	90.09 200

MVS_0304		88.08 13	88.08 16	88.08 14	89.67 113	72.04 48	92.26 33	89.26 173	84.19 2	85.01 55	95.18 13	69.93 69	97.20 14	91.63 2	95.60 29	94.99 9

cdsmvs_eth3d_5k		19.96 365	26.61 367	0.00 386	0.00 408	0.00 411	0.00 397	89.26 173	0.00 404	0.00 405	88.61 176	61.62 161	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs		79.51 160	78.97 156	81.14 216	88.46 163	60.91 263	83.84 252	89.24 175	70.36 191	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 206	92.28 122

cascas		76.72 228	74.64 239	82.99 168	85.78 230	65.88 174	82.33 275	89.21 176	60.85 320	72.74 263	81.02 324	47.28 300	93.75 140	67.48 212	85.02 165	89.34 227

eth_miper_zixun_eth		77.92 204	76.69 212	81.61 202	83.00 290	61.98 251	83.15 265	89.20 177	69.52 212	74.86 242	84.35 283	61.76 158	92.56 189	71.50 173	72.89 325	90.28 191

h-mvs33		83.15 87	82.19 95	86.02 61	90.56 92	70.85 70	88.15 140	89.16 178	76.02 83	84.67 64	91.39 107	61.54 162	95.50 61	82.71 70	75.48 293	91.72 139

miper_ehance_all_eth		78.59 187	77.76 186	81.08 218	82.66 299	61.56 257	83.65 255	89.15 179	68.87 230	75.55 219	83.79 292	66.49 104	92.03 208	73.25 158	76.39 278	89.64 221

Effi-MVS+		83.62 78	83.08 81	85.24 75	88.38 166	67.45 142	88.89 109	89.15 179	75.50 92	82.27 100	88.28 186	69.61 73	94.45 109	77.81 111	87.84 130	93.84 59

c3_l		78.75 181	77.91 178	81.26 211	82.89 294	61.56 257	84.09 250	89.13 181	69.97 201	75.56 218	84.29 284	66.36 106	92.09 207	73.47 155	75.48 293	90.12 197

LTVRE_ROB	69.57 13	76.25 236	74.54 242	81.41 206	88.60 158	64.38 210	79.24 312	89.12 182	70.76 184	69.79 298	87.86 197	49.09 289	93.20 166	56.21 309	80.16 234	86.65 295
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		76.38 235	74.33 245	82.50 186	89.28 132	66.95 158	88.41 127	89.03 183	64.05 290	66.83 324	88.61 176	46.78 304	92.89 181	57.48 296	78.55 251	87.67 268

FMVSNet2		78.20 195	77.21 198	81.20 214	87.60 195	62.89 242	87.47 160	89.02 184	71.63 163	75.29 232	87.28 210	54.80 220	91.10 245	62.38 252	79.38 244	89.61 222

ACMH	67.68 16	75.89 240	73.93 248	81.77 198	88.71 155	66.61 161	88.62 122	89.01 185	69.81 204	66.78 325	86.70 230	41.95 341	91.51 231	55.64 310	78.14 258	87.17 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall		77.87 206	76.86 205	80.92 223	81.65 313	61.38 259	82.68 272	88.98 186	65.52 273	75.47 220	82.30 313	65.76 116	92.00 210	72.95 161	76.39 278	89.39 226

无先验							87.48 159	88.98 186	60.00 326				94.12 121	67.28 214		88.97 241

AdaColmap		80.58 139	79.42 142	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 216	75.70 217	89.69 143	57.20 209	95.77 54	63.06 245	88.41 127	87.50 274

EI-MVSNet		80.52 140	79.98 130	82.12 190	84.28 259	63.19 236	86.41 191	88.95 189	74.18 120	78.69 145	87.54 206	66.62 101	92.43 193	72.57 166	80.57 230	90.74 172

MVSTER		79.01 176	77.88 180	82.38 188	83.07 287	64.80 200	84.08 251	88.95 189	69.01 228	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 230	89.89 213

RRT_MVS		80.35 145	79.22 150	83.74 140	87.63 194	65.46 185	91.08 54	88.92 191	73.82 127	76.44 203	90.03 136	49.05 291	94.25 117	76.84 121	79.20 248	91.51 143

1314		76.53 229	75.30 235	80.21 237	83.93 268	62.32 247	84.66 232	88.81 192	60.23 324	70.16 290	84.07 287	55.30 217	90.73 254	67.37 213	83.21 197	87.59 272

UniMVSNet_ETH3D		79.10 174	78.24 172	81.70 199	86.85 214	60.24 274	87.28 166	88.79 193	74.25 118	76.84 189	90.53 129	49.48 282	91.56 226	67.98 207	82.15 209	93.29 85

xiu_mvs_v1_base_debu		80.80 130	79.72 136	84.03 130	87.35 201	70.19 79	85.56 212	88.77 194	69.06 225	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 233

xiu_mvs_v1_base		80.80 130	79.72 136	84.03 130	87.35 201	70.19 79	85.56 212	88.77 194	69.06 225	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 233

xiu_mvs_v1_base_debi		80.80 130	79.72 136	84.03 130	87.35 201	70.19 79	85.56 212	88.77 194	69.06 225	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 233

FMVSNet3		77.88 205	76.85 206	80.97 222	86.84 215	62.36 245	86.52 189	88.77 194	71.13 175	75.34 227	86.66 232	54.07 231	91.10 245	62.72 247	79.57 240	89.45 225

patch_mono-2		83.65 75	84.54 65	80.99 220	90.06 106	65.83 175	84.21 247	88.74 198	71.60 167	85.01 55	92.44 84	74.51 25	83.50 328	82.15 75	92.15 75	93.64 71

GeoE		81.71 109	81.01 113	83.80 139	89.51 119	64.45 208	88.97 106	88.73 199	71.27 173	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 157	93.74 63

CANet_DTU		80.61 136	79.87 133	82.83 174	85.60 233	63.17 237	87.36 162	88.65 200	76.37 76	75.88 214	88.44 182	53.51 236	93.07 175	73.30 157	89.74 110	92.25 123

HyFIR lowres test		77.53 214	75.40 231	83.94 136	89.59 115	66.62 160	80.36 300	88.64 201	56.29 355	76.45 200	85.17 269	57.64 203	93.28 158	61.34 265	83.10 199	91.91 135

WR-MVS		79.49 161	79.22 150	80.27 236	88.79 151	58.35 287	85.06 224	88.61 202	78.56 30	77.65 172	88.34 184	63.81 131	90.66 255	64.98 234	77.22 265	91.80 138

BH-untuned		79.47 162	78.60 162	82.05 192	89.19 136	65.91 173	86.07 201	88.52 203	72.18 157	75.42 224	87.69 200	61.15 173	93.54 148	60.38 270	86.83 144	86.70 294

IS-MVSNet		83.15 87	82.81 87	84.18 116	89.94 109	63.30 232	91.59 43	88.46 204	79.04 25	79.49 133	92.16 88	65.10 120	94.28 112	67.71 209	91.86 81	94.95 10

pm-mvs1		77.25 221	76.68 213	78.93 259	84.22 261	58.62 286	86.41 191	88.36 205	71.37 171	73.31 257	88.01 196	61.22 172	89.15 276	64.24 238	73.01 324	89.03 237

UGNet		80.83 127	79.59 139	84.54 98	88.04 177	68.09 128	89.42 91	88.16 206	76.95 59	76.22 207	89.46 153	49.30 286	93.94 127	68.48 204	90.31 98	91.60 140
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 92	82.36 93	84.96 84	91.02 83	66.40 163	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 247	93.91 131	77.05 119	88.70 122	94.57 29

Effi-MVS+-dtu		80.03 151	78.57 163	84.42 104	85.13 244	68.74 110	88.77 114	88.10 208	74.99 102	74.97 240	83.49 297	57.27 208	93.36 156	73.53 153	80.88 224	91.18 154

v148		78.72 183	77.80 183	81.47 204	82.73 297	61.96 252	86.30 195	88.08 209	73.26 142	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 317	90.09 200

EG-PatchMatch MVS		74.04 258	71.82 268	80.71 227	84.92 248	67.42 143	85.86 207	88.08 209	66.04 266	64.22 346	83.85 289	35.10 364	92.56 189	57.44 297	80.83 225	82.16 353

cl22		78.07 199	77.01 201	81.23 212	82.37 306	61.83 254	83.55 259	87.98 211	68.96 229	75.06 238	83.87 288	61.40 167	91.88 215	73.53 153	76.39 278	89.98 209

test_fmvsmvis_n_1920		84.02 69	83.87 71	84.49 101	84.12 263	69.37 97	88.15 140	87.96 212	70.01 199	83.95 80	93.23 65	68.80 85	91.51 231	88.61 20	89.96 106	92.57 110

pmmvs6		74.69 252	73.39 254	78.61 263	81.38 319	57.48 303	86.64 185	87.95 213	64.99 279	70.18 288	86.61 233	50.43 272	89.52 269	62.12 256	70.18 340	88.83 247

MVP-Stereo		76.12 237	74.46 244	81.13 217	85.37 238	69.79 86	84.42 243	87.95 213	65.03 277	67.46 317	85.33 264	53.28 239	91.73 221	58.01 293	83.27 196	81.85 354
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____		77.72 209	76.76 209	80.58 229	82.49 303	60.48 270	83.09 267	87.87 215	69.22 218	74.38 249	85.22 268	62.10 155	91.53 229	71.09 176	75.41 297	89.73 220

DIV-MVS_self_test		77.72 209	76.76 209	80.58 229	82.48 304	60.48 270	83.09 267	87.86 216	69.22 218	74.38 249	85.24 266	62.10 155	91.53 229	71.09 176	75.40 298	89.74 219

BH-w/o		78.21 194	77.33 197	80.84 224	88.81 149	65.13 193	84.87 228	87.85 217	69.75 208	74.52 247	84.74 277	61.34 168	93.11 173	58.24 291	85.84 160	84.27 329

FE-MVS		77.78 207	75.68 225	84.08 122	88.09 175	66.00 170	83.13 266	87.79 218	68.42 239	78.01 166	85.23 267	45.50 319	95.12 78	59.11 281	85.83 161	91.11 156

HY-MVS	69.67 12	77.95 203	77.15 199	80.36 233	87.57 199	60.21 275	83.37 262	87.78 219	66.11 264	75.37 226	87.06 221	63.27 134	90.48 257	61.38 264	82.43 207	90.40 186

1112_ss		77.40 217	76.43 217	80.32 235	89.11 142	60.41 272	83.65 255	87.72 220	62.13 312	73.05 261	86.72 226	62.58 146	89.97 262	62.11 257	80.80 226	90.59 178

mvs_anonymous		79.42 165	79.11 153	80.34 234	84.45 258	57.97 294	82.59 273	87.62 221	67.40 250	76.17 211	88.56 179	68.47 86	89.59 268	70.65 181	86.05 156	93.47 79

ACMH+	68.96 14	76.01 239	74.01 247	82.03 193	88.60 158	65.31 190	88.86 110	87.55 222	70.25 196	67.75 313	87.47 208	41.27 342	93.19 168	58.37 289	75.94 286	87.60 270

tfpnnormal		74.39 253	73.16 257	78.08 273	86.10 227	58.05 291	84.65 234	87.53 223	70.32 193	71.22 280	85.63 258	54.97 218	89.86 263	43.03 368	75.02 305	86.32 298

CHOSEN 1792x2688		77.63 213	75.69 224	83.44 146	89.98 108	68.58 118	78.70 320	87.50 224	56.38 354	75.80 216	86.84 222	58.67 194	91.40 236	61.58 262	85.75 162	90.34 187

ambc				75.24 303	73.16 374	50.51 366	63.05 386	87.47 225		64.28 345	77.81 354	17.80 388	89.73 266	57.88 294	60.64 368	85.49 313

Fast-Effi-MVS+-dtu		78.02 201	76.49 215	82.62 184	83.16 286	66.96 157	86.94 174	87.45 226	72.45 152	71.49 278	84.17 285	54.79 223	91.58 224	67.61 210	80.31 233	89.30 229

D2MVS		74.82 251	73.21 256	79.64 250	79.81 339	62.56 244	80.34 301	87.35 227	64.37 285	68.86 305	82.66 309	46.37 307	90.10 261	67.91 208	81.24 220	86.25 299

TSAR-MVS + GP.		85.71 50	85.33 55	86.84 47	91.34 78	72.50 36	89.07 104	87.28 228	76.41 72	85.80 47	90.22 134	74.15 31	95.37 72	81.82 77	91.88 78	92.65 109

fmvsm_l_conf0.5_n		84.47 66	84.54 65	84.27 113	85.42 236	68.81 105	88.49 125	87.26 229	68.08 242	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

hse-mvs2		81.72 108	80.94 114	84.07 123	88.72 154	67.68 138	85.87 206	87.26 229	76.02 83	84.67 64	88.22 189	61.54 162	93.48 151	82.71 70	73.44 321	91.06 159

AUN-MVS		79.21 171	77.60 191	84.05 128	88.71 155	67.61 139	85.84 208	87.26 229	69.08 224	77.23 182	88.14 194	53.20 240	93.47 152	75.50 139	73.45 320	91.06 159

BH-RMVSNet		79.61 157	78.44 166	83.14 160	89.38 125	65.93 172	84.95 227	87.15 232	73.56 134	78.19 161	89.79 141	56.67 212	93.36 156	59.53 277	86.74 145	90.13 196

Test_1112_low_res		76.40 234	75.44 229	79.27 255	89.28 132	58.09 290	81.69 282	87.07 233	59.53 331	72.48 267	86.67 231	61.30 169	89.33 272	60.81 269	80.15 235	90.41 185

KD-MVS_self_test		68.81 305	67.59 312	72.46 327	74.29 367	45.45 376	77.93 329	87.00 234	63.12 296	63.99 348	78.99 346	42.32 335	84.77 320	56.55 307	64.09 361	87.16 283

LS3D		76.95 225	74.82 238	83.37 150	90.45 94	67.36 146	89.15 102	86.94 235	61.87 314	69.52 299	90.61 126	51.71 259	94.53 105	46.38 358	86.71 146	88.21 260

miper_lstm_enhance		74.11 257	73.11 258	77.13 288	80.11 334	59.62 280	72.23 357	86.92 236	66.76 253	70.40 285	82.92 304	56.93 211	82.92 332	69.06 198	72.63 326	88.87 245

fmvsm_l_conf0.5_n_a		84.13 68	84.16 70	84.06 125	85.38 237	68.40 120	88.34 132	86.85 237	67.48 249	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

jason		81.39 118	80.29 126	84.70 94	86.63 220	69.90 85	85.95 203	86.77 238	63.24 295	81.07 118	89.47 151	61.08 175	92.15 205	78.33 107	90.07 105	92.05 132
jason: jason.
OurMVSNet-221017-0		74.26 255	72.42 264	79.80 245	83.76 272	59.59 281	85.92 205	86.64 239	66.39 262	66.96 322	87.58 202	39.46 348	91.60 223	65.76 228	69.27 343	88.22 259

VPNet		78.69 184	78.66 161	78.76 261	88.31 168	55.72 327	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 267	66.63 220	77.05 267	90.88 166

USDC		70.33 294	68.37 296	76.21 294	80.60 328	56.23 322	79.19 314	86.49 241	60.89 319	61.29 357	85.47 262	31.78 370	89.47 271	53.37 320	76.21 284	82.94 347

lupinMVS		81.39 118	80.27 127	84.76 93	87.35 201	70.21 77	85.55 215	86.41 242	62.85 302	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

TR-MVS		77.44 215	76.18 220	81.20 214	88.24 170	63.24 233	84.61 235	86.40 243	67.55 247	77.81 169	86.48 240	54.10 230	93.15 170	57.75 295	82.72 204	87.20 280

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 251

GA-MVS		76.87 226	75.17 236	81.97 195	82.75 296	62.58 243	81.44 287	86.35 245	72.16 159	74.74 243	82.89 305	46.20 311	92.02 209	68.85 201	81.09 222	91.30 152

CDS-MVSNet		79.07 175	77.70 188	83.17 159	87.60 195	68.23 125	84.40 244	86.20 246	67.49 248	76.36 204	86.54 238	61.54 162	90.79 252	61.86 259	87.33 136	90.49 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.61 96	82.11 96	84.11 117	88.82 148	71.58 53	85.15 222	86.16 247	74.69 108	80.47 123	91.04 117	62.29 151	90.55 256	80.33 91	90.08 104	90.20 193

MSDG		73.36 267	70.99 277	80.49 231	84.51 257	65.80 177	80.71 294	86.13 248	65.70 270	65.46 337	83.74 293	44.60 322	90.91 250	51.13 331	76.89 269	84.74 325

TransMVSNet (Re)		75.39 249	74.56 241	77.86 275	85.50 235	57.10 308	86.78 181	86.09 249	72.17 158	71.53 277	87.34 209	63.01 142	89.31 273	56.84 304	61.83 364	87.17 281

VDDNet		81.52 115	80.67 118	84.05 128	90.44 95	64.13 214	89.73 82	85.91 250	71.11 176	83.18 90	93.48 58	50.54 271	93.49 150	73.40 156	88.25 128	94.54 30

mvsmamba		81.69 110	80.74 116	84.56 97	87.45 200	66.72 159	91.26 48	85.89 251	74.66 109	78.23 159	90.56 127	54.33 227	94.91 88	80.73 88	83.54 192	92.04 134

sd_testset		77.70 211	77.40 194	78.60 264	89.03 143	60.02 276	79.00 316	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 314	62.63 251	83.55 190	90.33 188

Baseline_NR-MVSNet		78.15 197	78.33 170	77.61 281	85.79 229	56.21 323	86.78 181	85.76 253	73.60 133	77.93 168	87.57 203	65.02 121	88.99 278	67.14 217	75.33 300	87.63 269

Anonymous20240521		68.80 306	67.22 315	73.55 317	74.33 366	54.11 342	83.18 264	85.61 254	58.15 342	61.68 356	80.94 326	30.71 373	81.27 341	57.00 302	73.34 323	85.28 316

test_vis1_n_1920		75.52 245	75.78 223	74.75 309	79.84 338	57.44 304	83.26 263	85.52 255	62.83 303	79.34 136	86.17 247	45.10 321	79.71 347	78.75 101	81.21 221	87.10 287

新几何1				83.42 147	93.13 52	70.71 71		85.48 256	57.43 349	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 279

EPNet		83.72 74	82.92 86	86.14 59	84.22 261	69.48 91	91.05 55	85.27 257	81.30 6	76.83 190	91.65 97	66.09 110	95.56 58	76.00 132	93.85 60	93.38 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		67.33 317	65.99 321	71.37 333	73.48 372	51.47 361	75.16 346	85.19 258	65.20 274	60.78 359	80.93 328	42.35 334	77.20 358	57.12 300	53.69 379	85.44 314

IB-MVS	68.01 15	75.85 241	73.36 255	83.31 151	84.76 250	66.03 168	83.38 261	85.06 259	70.21 197	69.40 300	81.05 323	45.76 316	94.66 101	65.10 233	75.49 292	89.25 230
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		78.89 180	77.51 193	83.03 166	87.80 186	67.79 135	84.72 231	85.05 260	67.63 245	76.75 193	87.70 199	62.25 152	90.82 251	58.53 288	87.13 139	90.49 182

CL-MVSNet_self_test		72.37 277	71.46 271	75.09 304	79.49 345	53.53 346	80.76 293	85.01 261	69.12 223	70.51 283	82.05 317	57.92 200	84.13 323	52.27 325	66.00 356	87.60 270

iter_conf05		80.00 153	78.70 159	83.91 137	87.84 184	65.83 175	88.84 112	84.92 262	71.61 166	78.70 144	88.94 165	43.88 327	94.56 102	79.28 97	84.28 178	91.33 149

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 333	85.40 51	92.91 73	62.02 157	89.08 277	68.95 199	91.37 86	86.63 296

MS-PatchMatch		73.83 261	72.67 260	77.30 286	83.87 269	66.02 169	81.82 279	84.66 264	61.37 318	68.61 308	82.82 307	47.29 299	88.21 291	59.27 278	84.32 177	77.68 368

ET-MVSNet_ETH3D		78.63 185	76.63 214	84.64 95	86.73 218	69.47 92	85.01 225	84.61 265	69.54 211	66.51 332	86.59 234	50.16 274	91.75 219	76.26 128	84.24 179	92.69 107

CNLPA		78.08 198	76.79 208	81.97 195	90.40 96	71.07 62	87.59 157	84.55 266	66.03 267	72.38 269	89.64 145	57.56 204	86.04 308	59.61 276	83.35 195	88.79 249

iter_conf_final		80.63 135	79.35 145	84.46 102	89.36 126	67.70 137	89.85 75	84.49 267	73.19 145	78.30 157	88.94 165	45.98 312	94.56 102	79.59 96	84.48 175	91.11 156

MIMVSNet1		68.58 308	66.78 318	73.98 315	80.07 335	51.82 357	80.77 292	84.37 268	64.40 284	59.75 364	82.16 316	36.47 360	83.63 327	42.73 369	70.33 339	86.48 297

KD-MVS_2432*1600		66.22 326	63.89 328	73.21 319	75.47 364	53.42 348	70.76 363	84.35 269	64.10 288	66.52 330	78.52 348	34.55 365	84.98 317	50.40 334	50.33 383	81.23 357

miper_refine_blended		66.22 326	63.89 328	73.21 319	75.47 364	53.42 348	70.76 363	84.35 269	64.10 288	66.52 330	78.52 348	34.55 365	84.98 317	50.40 334	50.33 383	81.23 357

test_0402		72.79 274	70.44 283	79.84 244	88.13 172	65.99 171	85.93 204	84.29 271	65.57 272	67.40 319	85.49 261	46.92 303	92.61 187	35.88 380	74.38 311	80.94 359

EU-MVSNet		68.53 310	67.61 311	71.31 336	78.51 351	47.01 374	84.47 238	84.27 272	42.27 379	66.44 333	84.79 276	40.44 346	83.76 325	58.76 286	68.54 348	83.17 341

thisisatest0530		79.40 166	77.76 186	84.31 109	87.69 192	65.10 194	87.36 162	84.26 273	70.04 198	77.42 176	88.26 188	49.94 277	94.79 97	70.20 184	84.70 170	93.03 97

COLMAP_ROB	66.92 17	73.01 271	70.41 284	80.81 225	87.13 211	65.63 180	88.30 134	84.19 274	62.96 300	63.80 350	87.69 200	38.04 356	92.56 189	46.66 355	74.91 306	84.24 330
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt0517		79.40 166	77.91 178	83.90 138	88.10 174	63.84 218	88.37 131	84.05 275	71.45 170	76.78 192	89.12 161	49.93 279	94.89 92	70.18 185	83.18 198	92.96 101

CMPMVS	51.72 21	70.19 296	68.16 299	76.28 293	73.15 375	57.55 302	79.47 310	83.92 276	48.02 373	56.48 374	84.81 275	43.13 330	86.42 306	62.67 250	81.81 215	84.89 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous202405211		78.25 192	77.01 201	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 188	80.00 128	91.20 111	41.08 344	91.43 235	65.21 231	85.26 164	93.85 57

XXY-MVS		75.41 248	75.56 227	74.96 305	83.59 274	57.82 298	80.59 296	83.87 278	66.54 261	74.93 241	88.31 185	63.24 135	80.09 346	62.16 255	76.85 271	86.97 288

DP-MVS		76.78 227	74.57 240	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 292	70.20 287	88.89 168	54.01 232	94.80 96	46.66 355	81.88 214	86.01 306

tfpn200view9		76.42 233	75.37 233	79.55 253	89.13 138	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 346	83.75 184	89.07 231

thres400		76.50 230	75.37 233	79.86 243	89.13 138	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 346	83.75 184	90.00 206

SixPastTwentyTwo		73.37 265	71.26 276	79.70 247	85.08 245	57.89 296	85.57 211	83.56 282	71.03 179	65.66 336	85.88 251	42.10 339	92.57 188	59.11 281	63.34 362	88.65 253

thres200		75.55 244	74.47 243	78.82 260	87.78 189	57.85 297	83.07 269	83.51 283	72.44 154	75.84 215	84.42 279	52.08 251	91.75 219	47.41 353	83.64 189	86.86 290

IterMVS-SCA-FT		75.43 247	73.87 250	80.11 239	82.69 298	64.85 199	81.57 284	83.47 284	69.16 222	70.49 284	84.15 286	51.95 254	88.15 292	69.23 195	72.14 330	87.34 277

CVMVSNet		72.99 272	72.58 262	74.25 313	84.28 259	50.85 364	86.41 191	83.45 285	44.56 376	73.23 259	87.54 206	49.38 284	85.70 310	65.90 226	78.44 254	86.19 301

ITE_SJBPF				78.22 270	81.77 312	60.57 268		83.30 286	69.25 217	67.54 315	87.20 215	36.33 361	87.28 301	54.34 315	74.62 309	86.80 291

thisisatest0515		77.33 218	75.38 232	83.18 158	85.27 239	63.80 219	82.11 278	83.27 287	65.06 276	75.91 213	83.84 290	49.54 281	94.27 113	67.24 215	86.19 154	91.48 147

thres100view900		76.50 230	75.55 228	79.33 254	89.52 118	56.99 309	85.83 209	83.23 288	73.94 124	76.32 205	87.12 218	51.89 256	91.95 211	48.33 346	83.75 184	89.07 231

thres600view7		76.50 230	75.44 229	79.68 248	89.40 123	57.16 306	85.53 217	83.23 288	73.79 129	76.26 206	87.09 219	51.89 256	91.89 214	48.05 351	83.72 187	90.00 206

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 360	79.74 129	91.63 99	58.97 193			91.42 85	86.77 292

EPNet_dtu		75.46 246	74.86 237	77.23 287	82.57 301	54.60 338	86.89 176	83.09 291	71.64 162	66.25 334	85.86 252	55.99 214	88.04 294	54.92 312	86.55 148	89.05 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		83.80 72	83.71 73	84.07 123	86.69 219	67.31 147	89.46 89	83.07 292	71.09 177	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 171	93.44 80

fmvsm_s_conf0.1_n		83.56 79	83.38 77	84.10 118	84.86 249	67.28 148	89.40 93	83.01 293	70.67 185	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 172	93.56 75

TDRefinement		67.49 315	64.34 325	76.92 289	73.47 373	61.07 261	84.86 229	82.98 294	59.77 328	58.30 368	85.13 270	26.06 378	87.89 295	47.92 352	60.59 369	81.81 355

OpenMVS_ROB	64.09 19	70.56 292	68.19 298	77.65 280	80.26 331	59.41 283	85.01 225	82.96 295	58.76 338	65.43 338	82.33 312	37.63 358	91.23 241	45.34 364	76.03 285	82.32 350

fmvsm_s_conf0.5_n_a		83.63 77	83.41 76	84.28 111	86.14 225	68.12 127	89.43 90	82.87 296	70.27 195	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 188	93.14 93

fmvsm_s_conf0.1_n_a		83.32 85	82.99 84	84.28 111	83.79 270	68.07 129	89.34 95	82.85 297	69.80 205	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 194	93.21 90

RPSCF		73.23 269	71.46 271	78.54 266	82.50 302	59.85 277	82.18 277	82.84 298	58.96 336	71.15 281	89.41 157	45.48 320	84.77 320	58.82 285	71.83 332	91.02 163

CostFormer		75.24 250	73.90 249	79.27 255	82.65 300	58.27 289	80.80 291	82.73 299	61.57 315	75.33 230	83.13 302	55.52 215	91.07 248	64.98 234	78.34 257	88.45 256

IterMVS		74.29 254	72.94 259	78.35 269	81.53 316	63.49 227	81.58 283	82.49 300	68.06 243	69.99 293	83.69 294	51.66 260	85.54 312	65.85 227	71.64 333	86.01 306
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_cas_vis1_n_1920		73.76 262	73.74 252	73.81 316	75.90 359	59.77 278	80.51 297	82.40 301	58.30 341	81.62 110	85.69 255	44.35 324	76.41 365	76.29 127	78.61 250	85.23 317

WTY-MVS		75.65 243	75.68 225	75.57 299	86.40 222	56.82 311	77.92 330	82.40 301	65.10 275	76.18 209	87.72 198	63.13 141	80.90 343	60.31 271	81.96 212	89.00 240

pmmvs4		74.03 260	71.91 267	80.39 232	81.96 309	68.32 122	81.45 286	82.14 303	59.32 332	69.87 296	85.13 270	52.40 244	88.13 293	60.21 272	74.74 308	84.73 326

FMVSNet5		69.50 301	67.96 302	74.15 314	82.97 293	55.35 331	80.01 305	82.12 304	62.56 307	63.02 351	81.53 320	36.92 359	81.92 337	48.42 345	74.06 313	85.17 320

baseline1		76.98 224	76.75 211	77.66 279	88.13 172	55.66 328	85.12 223	81.89 305	73.04 148	76.79 191	88.90 167	62.43 149	87.78 297	63.30 244	71.18 336	89.55 224

UnsupCasMVSNet_bld		63.70 334	61.53 340	70.21 342	73.69 370	51.39 362	72.82 355	81.89 305	55.63 357	57.81 370	71.80 374	38.67 352	78.61 351	49.26 343	52.21 381	80.63 360

LFMVS		81.82 107	81.23 108	83.57 144	91.89 73	63.43 230	89.84 76	81.85 307	77.04 58	83.21 89	93.10 67	52.26 246	93.43 155	71.98 169	89.95 107	93.85 57

sss		73.60 263	73.64 253	73.51 318	82.80 295	55.01 335	76.12 337	81.69 308	62.47 308	74.68 244	85.85 253	57.32 207	78.11 354	60.86 268	80.93 223	87.39 275

pmmvs-eth3d		70.50 293	67.83 306	78.52 267	77.37 355	66.18 167	81.82 279	81.51 309	58.90 337	63.90 349	80.42 331	42.69 333	86.28 307	58.56 287	65.30 358	83.11 343

TinyColmap		67.30 318	64.81 323	74.76 308	81.92 311	56.68 315	80.29 302	81.49 310	60.33 322	56.27 375	83.22 299	24.77 380	87.66 299	45.52 362	69.47 342	79.95 363

tpmvs		71.09 285	69.29 290	76.49 292	82.04 308	56.04 324	78.92 318	81.37 311	64.05 290	67.18 321	78.28 350	49.74 280	89.77 264	49.67 341	72.37 327	83.67 337

pmmvs5		71.55 281	70.20 287	75.61 298	77.83 352	56.39 319	81.74 281	80.89 312	57.76 345	67.46 317	84.49 278	49.26 287	85.32 316	57.08 301	75.29 301	85.11 321

ANet_high		50.57 354	46.10 358	63.99 358	48.67 400	39.13 392	70.99 362	80.85 313	61.39 317	31.18 390	57.70 388	17.02 389	73.65 381	31.22 385	15.89 398	79.18 365

LCM-MVSNet		54.25 345	49.68 355	67.97 353	53.73 397	45.28 379	66.85 377	80.78 314	35.96 387	39.45 388	62.23 383	8.70 398	78.06 355	48.24 349	51.20 382	80.57 361

PVSNet	64.34 18	72.08 279	70.87 279	75.69 297	86.21 224	56.44 318	74.37 351	80.73 315	62.06 313	70.17 289	82.23 315	42.86 332	83.31 330	54.77 313	84.45 176	87.32 278

baseline2		75.70 242	73.83 251	81.30 210	83.26 281	61.79 255	82.57 274	80.65 316	66.81 251	66.88 323	83.42 298	57.86 201	92.19 204	63.47 241	79.57 240	89.91 211

ppachtmachnet_test		70.04 297	67.34 314	78.14 272	79.80 340	61.13 260	79.19 314	80.59 317	59.16 334	65.27 339	79.29 341	46.75 305	87.29 300	49.33 342	66.72 351	86.00 308

Gipuma		45.18 358	41.86 361	55.16 372	77.03 357	51.52 360	32.50 394	80.52 318	32.46 390	27.12 393	35.02 394	9.52 397	75.50 371	22.31 393	60.21 370	38.45 393
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		68.60 307	67.80 307	71.02 338	80.23 333	50.75 365	78.30 326	80.47 319	56.79 352	66.11 335	82.63 310	46.35 308	78.95 350	43.62 367	75.70 288	83.36 340

LCM-MVSNet-Re		77.05 222	76.94 204	77.36 284	87.20 209	51.60 359	80.06 303	80.46 320	75.20 97	67.69 314	86.72 226	62.48 147	88.98 279	63.44 242	89.25 114	91.51 143

tpm2		73.26 268	71.46 271	78.63 262	83.34 279	56.71 314	80.65 295	80.40 321	56.63 353	73.55 255	82.02 318	51.80 258	91.24 240	56.35 308	78.42 255	87.95 262

CR-MVSNet		73.37 265	71.27 275	79.67 249	81.32 322	65.19 191	75.92 339	80.30 322	59.92 327	72.73 264	81.19 321	52.50 242	86.69 303	59.84 274	77.71 260	87.11 285

Patchmtry		70.74 289	69.16 292	75.49 301	80.72 326	54.07 343	74.94 350	80.30 322	58.34 340	70.01 291	81.19 321	52.50 242	86.54 304	53.37 320	71.09 337	85.87 310

tpm cat1		70.57 291	68.31 297	77.35 285	82.41 305	57.95 295	78.08 327	80.22 324	52.04 365	68.54 309	77.66 355	52.00 253	87.84 296	51.77 326	72.07 331	86.25 299

MDTV_nov1_ep13			69.97 288		83.18 284	53.48 347	77.10 335	80.18 325	60.45 321	69.33 302	80.44 330	48.89 294	86.90 302	51.60 328	78.51 253

AllTest		70.96 286	68.09 301	79.58 251	85.15 242	63.62 221	84.58 236	79.83 326	62.31 309	60.32 361	86.73 224	32.02 368	88.96 281	50.28 336	71.57 334	86.15 302

TestCases				79.58 251	85.15 242	63.62 221		79.83 326	62.31 309	60.32 361	86.73 224	32.02 368	88.96 281	50.28 336	71.57 334	86.15 302

test_fmvs1_n		70.86 288	70.24 286	72.73 325	72.51 379	55.28 332	81.27 288	79.71 328	51.49 369	78.73 143	84.87 274	27.54 377	77.02 359	76.06 130	79.97 238	85.88 309

Vis-MVSNet (Re-imp)		78.36 191	78.45 165	78.07 274	88.64 157	51.78 358	86.70 184	79.63 329	74.14 121	75.11 236	90.83 123	61.29 170	89.75 265	58.10 292	91.60 82	92.69 107

MIMVSNet		70.69 290	69.30 289	74.88 306	84.52 256	56.35 321	75.87 341	79.42 330	64.59 281	67.76 312	82.41 311	41.10 343	81.54 339	46.64 357	81.34 218	86.75 293

dmvs_re		71.14 284	70.58 280	72.80 324	81.96 309	59.68 279	75.60 343	79.34 331	68.55 235	69.27 303	80.72 329	49.42 283	76.54 362	52.56 324	77.79 259	82.19 352

SCA		74.22 256	72.33 265	79.91 242	84.05 266	62.17 249	79.96 306	79.29 332	66.30 263	72.38 269	80.13 333	51.95 254	88.60 287	59.25 279	77.67 262	88.96 242

testing222		74.04 258	72.66 261	78.19 271	87.89 181	55.36 330	81.06 289	79.20 333	71.30 172	74.65 245	83.57 296	39.11 351	88.67 286	51.43 330	85.75 162	90.53 180

tpmrst		72.39 275	72.13 266	73.18 322	80.54 329	49.91 368	79.91 307	79.08 334	63.11 297	71.69 276	79.95 335	55.32 216	82.77 333	65.66 229	73.89 315	86.87 289

test_fmvs1		70.93 287	70.52 281	72.16 328	73.71 369	55.05 334	80.82 290	78.77 335	51.21 370	78.58 149	84.41 280	31.20 372	76.94 360	75.88 133	80.12 237	84.47 328

PatchmatchNet		73.12 270	71.33 274	78.49 268	83.18 284	60.85 264	79.63 308	78.57 336	64.13 287	71.73 275	79.81 338	51.20 263	85.97 309	57.40 298	76.36 283	88.66 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet-bldmvs		66.68 321	63.66 330	75.75 296	79.28 347	60.56 269	73.92 353	78.35 337	64.43 283	50.13 382	79.87 337	44.02 326	83.67 326	46.10 359	56.86 372	83.03 345

new-patchmatchnet		61.73 338	61.73 339	61.70 361	72.74 377	24.50 402	69.16 370	78.03 338	61.40 316	56.72 373	75.53 366	38.42 353	76.48 364	45.95 360	57.67 371	84.13 332

our_test_3		69.14 303	67.00 316	75.57 299	79.80 340	58.80 284	77.96 328	77.81 339	59.55 330	62.90 354	78.25 351	47.43 298	83.97 324	51.71 327	67.58 350	83.93 335

test20.03		67.45 316	66.95 317	68.94 346	75.48 363	44.84 381	77.50 331	77.67 340	66.66 255	63.01 352	83.80 291	47.02 302	78.40 352	42.53 370	68.86 347	83.58 338

WB-MVSnew		71.96 280	71.65 270	72.89 323	84.67 255	51.88 356	82.29 276	77.57 341	62.31 309	73.67 254	83.00 303	53.49 237	81.10 342	45.75 361	82.13 210	85.70 311

bld_raw_dy_0_64		77.29 220	75.98 222	81.22 213	85.04 246	65.47 184	88.14 142	77.56 342	69.20 220	73.77 253	89.40 159	42.24 338	88.85 284	76.78 124	81.64 216	89.33 228

test-LLR		72.94 273	72.43 263	74.48 310	81.35 320	58.04 292	78.38 323	77.46 343	66.66 255	69.95 294	79.00 344	48.06 296	79.24 348	66.13 222	84.83 167	86.15 302

test-mter		71.41 282	70.39 285	74.48 310	81.35 320	58.04 292	78.38 323	77.46 343	60.32 323	69.95 294	79.00 344	36.08 362	79.24 348	66.13 222	84.83 167	86.15 302

ECVR-MVS		79.61 157	79.26 148	80.67 228	90.08 102	54.69 337	87.89 150	77.44 345	74.88 104	80.27 124	92.79 79	48.96 293	92.45 192	68.55 203	92.50 72	94.86 17

tpm		72.37 277	71.71 269	74.35 312	82.19 307	52.00 354	79.22 313	77.29 346	64.56 282	72.95 262	83.68 295	51.35 261	83.26 331	58.33 290	75.80 287	87.81 266

LF4IMVS		64.02 333	62.19 337	69.50 344	70.90 380	53.29 351	76.13 336	77.18 347	52.65 364	58.59 366	80.98 325	23.55 382	76.52 363	53.06 322	66.66 352	78.68 366

test1111		79.43 164	79.18 152	80.15 238	89.99 107	53.31 350	87.33 164	77.05 348	75.04 101	80.23 126	92.77 81	48.97 292	92.33 200	68.87 200	92.40 74	94.81 20

K. test v3		71.19 283	68.51 295	79.21 257	83.04 289	57.78 299	84.35 245	76.91 349	72.90 151	62.99 353	82.86 306	39.27 349	91.09 247	61.65 261	52.66 380	88.75 250

testgi		66.67 322	66.53 319	67.08 355	75.62 362	41.69 390	75.93 338	76.50 350	66.11 264	65.20 342	86.59 234	35.72 363	74.71 376	43.71 366	73.38 322	84.84 324

test_fmvs2		68.35 312	67.48 313	70.98 339	69.50 382	51.95 355	80.05 304	76.38 351	49.33 372	74.65 245	84.38 281	23.30 383	75.40 374	74.51 144	75.17 304	85.60 312

test_vis1_n		69.85 300	69.21 291	71.77 330	72.66 378	55.27 333	81.48 285	76.21 352	52.03 366	75.30 231	83.20 301	28.97 375	76.22 367	74.60 143	78.41 256	83.81 336

PatchMatch-RL		72.38 276	70.90 278	76.80 291	88.60 158	67.38 145	79.53 309	76.17 353	62.75 305	69.36 301	82.00 319	45.51 318	84.89 319	53.62 318	80.58 229	78.12 367

JIA-IIPM		66.32 325	62.82 336	76.82 290	77.09 356	61.72 256	65.34 381	75.38 354	58.04 344	64.51 344	62.32 382	42.05 340	86.51 305	51.45 329	69.22 344	82.21 351

ADS-MVSNet2		66.20 328	63.33 331	74.82 307	79.92 336	58.75 285	67.55 374	75.19 355	53.37 362	65.25 340	75.86 363	42.32 335	80.53 345	41.57 371	68.91 345	85.18 318

PatchT		68.46 311	67.85 304	70.29 341	80.70 327	43.93 383	72.47 356	74.88 356	60.15 325	70.55 282	76.57 359	49.94 277	81.59 338	50.58 332	74.83 307	85.34 315

dp		66.80 320	65.43 322	70.90 340	79.74 342	48.82 371	75.12 348	74.77 357	59.61 329	64.08 347	77.23 356	42.89 331	80.72 344	48.86 344	66.58 353	83.16 342

MDA-MVSNet_test_wron		65.03 329	62.92 333	71.37 333	75.93 358	56.73 312	69.09 372	74.73 358	57.28 350	54.03 378	77.89 352	45.88 313	74.39 378	49.89 340	61.55 365	82.99 346

TESTMET0.1,1		69.89 299	69.00 293	72.55 326	79.27 348	56.85 310	78.38 323	74.71 359	57.64 346	68.09 311	77.19 357	37.75 357	76.70 361	63.92 239	84.09 180	84.10 333

YYNet1		65.03 329	62.91 334	71.38 332	75.85 360	56.60 316	69.12 371	74.66 360	57.28 350	54.12 377	77.87 353	45.85 314	74.48 377	49.95 339	61.52 366	83.05 344

test_fmvs3		63.36 335	61.82 338	67.98 352	62.51 389	46.96 375	77.37 333	74.03 361	45.24 375	67.50 316	78.79 347	12.16 394	72.98 382	72.77 164	66.02 355	83.99 334

PMMVS		69.34 302	68.67 294	71.35 335	75.67 361	62.03 250	75.17 345	73.46 362	50.00 371	68.68 306	79.05 342	52.07 252	78.13 353	61.16 266	82.77 202	73.90 374

PVSNet_0	57.27 20	61.67 339	59.27 342	68.85 348	79.61 343	57.44 304	68.01 373	73.44 363	55.93 356	58.54 367	70.41 377	44.58 323	77.55 357	47.01 354	35.91 389	71.55 377

Syy-MVS		68.05 313	67.85 304	68.67 350	84.68 252	40.97 391	78.62 321	73.08 364	66.65 258	66.74 326	79.46 339	52.11 250	82.30 335	32.89 383	76.38 281	82.75 348

myMVS_eth3d		67.02 319	66.29 320	69.21 345	84.68 252	42.58 386	78.62 321	73.08 364	66.65 258	66.74 326	79.46 339	31.53 371	82.30 335	39.43 376	76.38 281	82.75 348

test0.0.03 1		68.00 314	67.69 309	68.90 347	77.55 353	47.43 372	75.70 342	72.95 366	66.66 255	66.56 328	82.29 314	48.06 296	75.87 369	44.97 365	74.51 310	83.41 339

testing3		68.56 309	67.67 310	71.22 337	87.33 206	42.87 385	83.06 270	71.54 367	70.36 191	69.08 304	84.38 281	30.33 374	85.69 311	37.50 379	75.45 296	85.09 322

ADS-MVSNet		64.36 332	62.88 335	68.78 349	79.92 336	47.17 373	67.55 374	71.18 368	53.37 362	65.25 340	75.86 363	42.32 335	73.99 379	41.57 371	68.91 345	85.18 318

Patchmatch-RL test		70.24 295	67.78 308	77.61 281	77.43 354	59.57 282	71.16 360	70.33 369	62.94 301	68.65 307	72.77 372	50.62 269	85.49 313	69.58 193	66.58 353	87.77 267

gg-mvs-nofinetune		69.95 298	67.96 302	75.94 295	83.07 287	54.51 340	77.23 334	70.29 370	63.11 297	70.32 286	62.33 381	43.62 328	88.69 285	53.88 317	87.76 131	84.62 327

door-mid								69.98 371

GG-mvs-BLEND				75.38 302	81.59 315	55.80 326	79.32 311	69.63 372		67.19 320	73.67 370	43.24 329	88.90 283	50.41 333	84.50 172	81.45 356

FPMVS		53.68 348	51.64 350	59.81 364	65.08 387	51.03 363	69.48 368	69.58 373	41.46 380	40.67 386	72.32 373	16.46 390	70.00 386	24.24 392	65.42 357	58.40 388

door								69.44 374

Patchmatch-test		64.82 331	63.24 332	69.57 343	79.42 346	49.82 369	63.49 385	69.05 375	51.98 367	59.95 363	80.13 333	50.91 265	70.98 383	40.66 373	73.57 318	87.90 264

CHOSEN 280x420		66.51 323	64.71 324	71.90 329	81.45 317	63.52 226	57.98 388	68.95 376	53.57 361	62.59 355	76.70 358	46.22 310	75.29 375	55.25 311	79.68 239	76.88 370

EGC-MVSNET		52.07 352	47.05 356	67.14 354	83.51 276	60.71 266	80.50 298	67.75 377	0.07 401	0.43 402	75.85 365	24.26 381	81.54 339	28.82 386	62.25 363	59.16 386

EPMVS		69.02 304	68.16 299	71.59 331	79.61 343	49.80 370	77.40 332	66.93 378	62.82 304	70.01 291	79.05 342	45.79 315	77.86 356	56.58 306	75.26 302	87.13 284

APD_test1		53.31 349	49.93 354	63.42 360	65.68 386	50.13 367	71.59 359	66.90 379	34.43 388	40.58 387	71.56 375	8.65 399	76.27 366	34.64 382	55.36 377	63.86 384

lessismore_v0				78.97 258	81.01 325	57.15 307		65.99 380		61.16 358	82.82 307	39.12 350	91.34 238	59.67 275	46.92 386	88.43 257

dmvs_testset		62.63 336	64.11 327	58.19 365	78.55 350	24.76 401	75.28 344	65.94 381	67.91 244	60.34 360	76.01 362	53.56 235	73.94 380	31.79 384	67.65 349	75.88 372

pmmvs3		57.79 342	54.26 347	68.37 351	64.02 388	56.72 313	75.12 348	65.17 382	40.20 381	52.93 379	69.86 378	20.36 385	75.48 372	45.45 363	55.25 378	72.90 376

MVS-HIRNet		59.14 341	57.67 344	63.57 359	81.65 313	43.50 384	71.73 358	65.06 383	39.59 383	51.43 380	57.73 387	38.34 354	82.58 334	39.53 374	73.95 314	64.62 383

PM-MVS		66.41 324	64.14 326	73.20 321	73.92 368	56.45 317	78.97 317	64.96 384	63.88 294	64.72 343	80.24 332	19.84 386	83.44 329	66.24 221	64.52 360	79.71 364

PMVS	37.38 22	44.16 359	40.28 362	55.82 370	40.82 402	42.54 388	65.12 382	63.99 385	34.43 388	24.48 394	57.12 389	3.92 404	76.17 368	17.10 396	55.52 376	48.75 391
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2506		77.30 219	76.49 215	79.74 246	90.08 102	52.02 353	87.86 152	63.10 386	74.88 104	80.16 127	92.79 79	38.29 355	92.35 198	68.74 202	92.50 72	94.86 17

test_method		31.52 362	29.28 366	38.23 378	27.03 404	6.50 408	20.94 396	62.21 387	4.05 399	22.35 397	52.50 391	13.33 391	47.58 398	27.04 389	34.04 391	60.62 385

WB-MVS		54.94 344	54.72 346	55.60 371	73.50 371	20.90 403	74.27 352	61.19 388	59.16 334	50.61 381	74.15 368	47.19 301	75.78 370	17.31 395	35.07 390	70.12 378

test_vis1_rt		60.28 340	58.42 343	65.84 356	67.25 385	55.60 329	70.44 365	60.94 389	44.33 377	59.00 365	66.64 379	24.91 379	68.67 387	62.80 246	69.48 341	73.25 375

SSC-MVS		53.88 347	53.59 348	54.75 373	72.87 376	19.59 404	73.84 354	60.53 390	57.58 348	49.18 383	73.45 371	46.34 309	75.47 373	16.20 398	32.28 392	69.20 379

testf1		45.72 356	41.96 359	57.00 366	56.90 391	45.32 377	66.14 379	59.26 391	26.19 392	30.89 391	60.96 385	4.14 402	70.64 384	26.39 390	46.73 387	55.04 389

APD_test2		45.72 356	41.96 359	57.00 366	56.90 391	45.32 377	66.14 379	59.26 391	26.19 392	30.89 391	60.96 385	4.14 402	70.64 384	26.39 390	46.73 387	55.04 389

test_f		52.09 351	50.82 352	55.90 369	53.82 396	42.31 389	59.42 387	58.31 393	36.45 386	56.12 376	70.96 376	12.18 393	57.79 394	53.51 319	56.57 374	67.60 380

new_pmnet		50.91 353	50.29 353	52.78 374	68.58 383	34.94 396	63.71 383	56.63 394	39.73 382	44.95 384	65.47 380	21.93 384	58.48 393	34.98 381	56.62 373	64.92 382

DSMNet-mixed		57.77 343	56.90 345	60.38 363	67.70 384	35.61 394	69.18 369	53.97 395	32.30 391	57.49 371	79.88 336	40.39 347	68.57 388	38.78 377	72.37 327	76.97 369

PMMVS2		40.82 360	38.86 363	46.69 376	53.84 395	16.45 405	48.61 391	49.92 396	37.49 384	31.67 389	60.97 384	8.14 400	56.42 395	28.42 387	30.72 393	67.19 381

mvsany_test1		62.30 337	61.26 341	65.41 357	69.52 381	54.86 336	66.86 376	49.78 397	46.65 374	68.50 310	83.21 300	49.15 288	66.28 389	56.93 303	60.77 367	75.11 373

test_vis3_rt		49.26 355	47.02 357	56.00 368	54.30 394	45.27 380	66.76 378	48.08 398	36.83 385	44.38 385	53.20 390	7.17 401	64.07 391	56.77 305	55.66 375	58.65 387

E-PMN		31.77 361	30.64 364	35.15 379	52.87 398	27.67 398	57.09 389	47.86 399	24.64 394	16.40 399	33.05 395	11.23 395	54.90 396	14.46 399	18.15 396	22.87 395

EMVS		30.81 363	29.65 365	34.27 380	50.96 399	25.95 400	56.58 390	46.80 400	24.01 395	15.53 400	30.68 396	12.47 392	54.43 397	12.81 400	17.05 397	22.43 396

mvsany_test3		53.99 346	51.45 351	61.61 362	55.51 393	44.74 382	63.52 384	45.41 401	43.69 378	58.11 369	76.45 360	17.99 387	63.76 392	54.77 313	47.59 385	76.34 371

MVE	26.22 23	30.37 364	25.89 368	43.81 377	44.55 401	35.46 395	28.87 395	39.07 402	18.20 396	18.58 398	40.18 393	2.68 405	47.37 399	17.07 397	23.78 395	48.60 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							92.18 35	32.83 403

tmp_tt		18.61 366	21.40 369	10.23 383	4.82 405	10.11 406	34.70 393	30.74 404	1.48 400	23.91 396	26.07 397	28.42 376	13.41 402	27.12 388	15.35 399	7.17 397

DeepMVS_CX				27.40 381	40.17 403	26.90 399		24.59 405	17.44 397	23.95 395	48.61 392	9.77 396	26.48 400	18.06 394	24.47 394	28.83 394

N_pmnet		52.79 350	53.26 349	51.40 375	78.99 349	7.68 407	69.52 367	3.89 406	51.63 368	57.01 372	74.98 367	40.83 345	65.96 390	37.78 378	64.67 359	80.56 362

wuyk23d		16.82 367	15.94 370	19.46 382	58.74 390	31.45 397	39.22 392	3.74 407	6.84 398	6.04 401	2.70 401	1.27 406	24.29 401	10.54 401	14.40 400	2.63 398

testmvs		6.04 370	8.02 373	0.10 385	0.08 406	0.03 410	69.74 366	0.04 408	0.05 402	0.31 403	1.68 402	0.02 408	0.04 403	0.24 402	0.02 401	0.25 400

test123		6.12 369	8.11 372	0.14 384	0.06 407	0.09 409	71.05 361	0.03 409	0.04 403	0.25 404	1.30 403	0.05 407	0.03 404	0.21 403	0.01 402	0.29 399

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		5.26 371	7.02 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	63.15 138	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

n2								0.00 410

nn								0.00 410

ab-mvs-re		7.23 368	9.64 371	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	86.72 226	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

WAC-MVS						42.58 386								39.46 375

PC_three_1452									68.21 241	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

eth-test2					0.00 408

eth-test					0.00 408

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 58	96.48 8	94.88 14

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 24

GSMVS																88.96 242

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 262				88.96 242

sam_mvs												50.01 275

test_post1							78.90 319				5.43 400	48.81 295	85.44 315	59.25 279

test_post											5.46 399	50.36 273	84.24 322

patchmatchnet-post											74.00 369	51.12 264	88.60 287

gm-plane-assit					81.40 318	53.83 345			62.72 306		80.94 326		92.39 195	63.40 243

test9_res														84.90 42	95.70 26	92.87 102

agg_prior2														82.91 66	95.45 30	92.70 105

test_prior4						72.60 34	89.01 105

test_prior2							88.85 111		75.41 93	84.91 59	93.54 56	74.28 29		83.31 61	95.86 20

旧先验2							86.56 188		58.10 343	87.04 39			88.98 279	74.07 149

新几何2							86.29 196

原ACMM2							86.86 177

testdata2													91.01 249	62.37 253

segment_acmp												73.08 37

testdata1							84.14 249		75.71 87

plane_prior7					90.08 102	68.51 119

plane_prior6					89.84 111	68.70 114						60.42 186

plane_prior4											91.00 120

plane_prior3						68.60 117			78.44 31	78.92 141

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 110

plane_prior						68.71 112	90.38 67		77.62 39						86.16 155

HQP5-MVS						66.98 155

HQP-NCC					89.33 127		89.17 98		76.41 72	77.23 182

ACMP_Plane					89.33 127		89.17 98		76.41 72	77.23 182

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 161

HQP2-MVS												60.17 189

NP-MVS					89.62 114	68.32 122					90.24 132

MDTV_nov1_ep13_2view						37.79 393	75.16 346		55.10 358	66.53 329		49.34 285		53.98 316		87.94 263

ACMMP++_ref															81.95 213

ACMMP++															81.25 219

Test By Simon												64.33 125

High-resolution multi-view benchmark