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.

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 48	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 46	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 74
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
MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16
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 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 39	94.10 8	75.90 84	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 88	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 105	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
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
HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 85

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 60	93.00 42	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 86	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 86	93.39 30	77.53 43	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 43	94.70 3	74.47 111	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 37	93.19 35	77.87 34	90.32 13	94.00 46	74.83 24	93.78 139	87.63 14	94.27 59	93.65 62
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
NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 48	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 53	93.59 21	76.27 78	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 44	92.35 73	74.62 109	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 62	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 63	89.69 161	74.31 114	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 28	93.49 24	74.75 106	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 39	83.84 75	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 23	75.17 99	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 64	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 57

zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 70	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 77

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 58	94.05 13	70.80 172	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 64	84.66 61	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 36	92.83 54	73.01 142	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 95
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 60	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 37	76.73 67	84.45 64	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 140	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 96	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 57

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 156	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 177	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 77

XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 78	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 29	93.33 31	76.07 81	83.81 76	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 69	83.68 77	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 77

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 68	93.94 15	77.12 54	82.82 89	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 45	93.04 38	76.62 69	84.22 69	93.36 58	71.44 54	96.76 21	80.82 78	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 110	86.84 33	94.65 18	67.31 90	95.77 56	84.80 34	92.85 69	92.84 93

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 96	88.85 104	92.97 47	75.41 92	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 38	75.53 90	83.86 74	94.42 31	67.87 85	96.64 27	82.70 65	94.57 51	93.66 57

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 61	82.81 90	94.25 36	66.44 97	96.24 39	82.88 60	94.28 58	93.38 71

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 125	71.24 62	88.43 118	92.05 86	82.50 1	86.88 32	90.09 123	74.45 26	95.61 59	84.38 39	90.63 92	94.01 41

CANet		86.45 43	86.10 49	87.51 37	90.09 106	70.94 69	89.70 84	92.59 65	81.78 4	81.32 105	91.43 94	70.34 62	97.23 9	84.26 42	93.36 65	94.37 25

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 108	91.89 96	68.69 219	85.00 50	93.10 62	74.43 27	95.41 72	84.97 29	95.71 26	93.02 87

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 27	94.07 9	72.45 145	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 125	70.93 70	88.43 118	91.39 115	82.14 3	86.65 34	90.09 123	74.39 29	95.01 91	83.97 47	90.63 92	93.97 43

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 54	93.56 22	73.95 122	83.16 83	91.07 103	75.94 15	95.19 82	79.94 86	94.38 56	93.55 67

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 214	84.41 65	93.18 61	74.94 23	94.93 92	84.75 35	95.33 34	93.01 88

test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 91	91.79 38	92.28 75	75.07 100	86.40 35	94.58 20	65.00 114	95.56 61	84.34 41	92.60 72	92.90 91

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 98	91.93 33	92.99 44	73.54 132	85.94 37	94.51 24	65.80 106	95.61 59	83.04 57	92.51 74	93.53 69

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 112	69.44 103	90.45 66	93.00 42	76.70 68	88.01 25	91.23 97	73.28 38	93.91 134	81.50 72	88.80 113	94.77 14

ACMMP		85.89 52	85.39 58	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 64	80.73 114	93.82 50	64.33 117	96.29 37	82.67 66	90.69 91	93.23 77
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
SR-MVS-dyc-post		85.77 53	85.61 55	86.23 63	93.06 57	70.63 77	91.88 34	92.27 76	73.53 133	85.69 42	94.45 26	65.00 114	95.56 61	82.75 61	91.87 77	92.50 102

CDPH-MVS		85.76 54	85.29 62	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 224	84.61 62	93.48 54	72.32 46	96.15 45	79.00 88	95.43 30	94.28 30

TSAR-MVS + GP.		85.71 55	85.33 59	86.84 50	91.34 84	72.50 35	89.07 98	87.28 224	76.41 71	85.80 40	90.22 121	74.15 34	95.37 78	81.82 70	91.88 76	92.65 99

Regformer-4		85.68 56	85.45 57	86.35 59	88.95 142	69.67 95	88.29 128	91.29 117	81.73 5	85.36 45	90.01 126	72.62 44	95.35 79	83.28 53	87.57 125	94.03 39

alignmvs		85.48 57	85.32 60	85.96 71	89.51 119	69.47 100	89.74 82	92.47 67	76.17 79	87.73 28	91.46 93	70.32 63	93.78 139	81.51 71	88.95 110	94.63 18

3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 177	93.37 57	60.40 182	96.75 22	77.20 108	93.73 63	95.29 2

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 82	90.71 56	92.86 52	77.46 45	84.22 69	92.81 72	67.16 92	92.94 179	80.36 82	94.35 57	90.16 177

DELS-MVS		85.41 60	85.30 61	85.77 72	88.49 160	67.93 134	85.52 209	93.44 27	78.70 28	83.63 80	89.03 152	74.57 25	95.71 58	80.26 84	94.04 61	93.66 57
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
HPM-MVS_fast		85.35 61	84.95 67	86.57 58	93.69 43	70.58 80	92.15 31	91.62 106	73.89 125	82.67 92	94.09 42	62.60 139	95.54 64	80.93 76	92.93 67	93.57 66

CS-MVS		85.32 62	85.66 54	84.30 112	88.28 168	65.31 181	91.18 49	93.48 26	78.06 33	83.14 84	90.53 115	69.93 67	95.45 68	82.96 58	93.40 64	92.15 117

Regformer-3		85.23 63	85.07 64	85.70 73	88.95 142	69.01 107	88.29 128	89.91 155	80.95 8	85.01 49	90.01 126	72.45 45	94.19 120	82.50 67	87.57 125	93.90 47

abl_6		85.23 63	84.95 67	86.07 68	92.23 74	70.48 81	90.80 55	92.08 85	73.51 135	85.26 46	94.16 39	62.75 138	95.92 53	82.46 68	91.30 86	91.81 126

MVS_111021_HR		85.14 65	84.75 69	86.32 62	91.65 81	72.70 29	85.98 192	90.33 143	76.11 80	82.08 95	91.61 88	71.36 55	94.17 122	81.02 75	92.58 73	92.08 119

casdiffmvs		85.11 66	85.14 63	85.01 86	87.20 201	65.77 172	87.75 144	92.83 54	77.84 35	84.36 68	92.38 74	72.15 48	93.93 133	81.27 74	90.48 94	95.33 1

UA-Net		85.08 67	84.96 66	85.45 75	92.07 76	68.07 132	89.78 81	90.86 130	82.48 2	84.60 63	93.20 60	69.35 73	95.22 81	71.39 158	90.88 90	93.07 84

DPM-MVS		84.93 68	84.29 72	86.84 50	90.20 104	73.04 22	87.12 159	93.04 38	69.80 192	82.85 88	91.22 98	73.06 41	96.02 48	76.72 115	94.63 49	91.46 136

baseline		84.93 68	84.98 65	84.80 96	87.30 199	65.39 179	87.30 155	92.88 51	77.62 37	84.04 73	92.26 75	71.81 50	93.96 127	81.31 73	90.30 96	95.03 4

ETV-MVS		84.90 70	84.67 70	85.59 74	89.39 123	68.66 121	88.74 110	92.64 64	79.97 17	84.10 71	85.71 239	69.32 74	95.38 75	80.82 78	91.37 84	92.72 94

EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 170	67.85 135	87.66 146	89.73 160	80.05 16	82.95 85	89.59 136	70.74 60	94.82 100	80.66 81	84.72 160	93.28 76

nrg030		83.88 72	83.53 74	84.96 88	86.77 209	69.28 104	90.46 65	92.67 60	74.79 104	82.95 85	91.33 96	72.70 43	93.09 173	80.79 80	79.28 226	92.50 102

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 84	87.87 179	67.53 141	87.44 152	89.66 162	79.74 18	82.23 94	89.41 145	70.24 64	94.74 103	79.95 85	83.92 168	92.99 89

CPTT-MVS		83.73 74	83.33 77	84.92 91	93.28 50	70.86 72	92.09 32	90.38 139	68.75 218	79.57 122	92.83 70	60.60 178	93.04 177	80.92 77	91.56 82	90.86 153

EPNet		83.72 75	82.92 83	86.14 66	84.22 244	69.48 99	91.05 51	85.27 249	81.30 7	76.83 172	91.65 85	66.09 101	95.56 61	76.00 120	93.85 62	93.38 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		83.64 76	83.14 78	85.14 82	90.08 107	68.71 117	91.25 46	92.44 68	79.12 23	78.92 130	91.00 107	60.42 180	95.38 75	78.71 91	86.32 146	91.33 138

Effi-MVS+		83.62 77	83.08 79	85.24 80	88.38 165	67.45 142	88.89 102	89.15 178	75.50 91	82.27 93	88.28 171	69.61 71	94.45 110	77.81 102	87.84 123	93.84 51

OPM-MVS		83.50 78	82.95 82	85.14 82	88.79 150	70.95 68	89.13 97	91.52 109	77.55 42	80.96 112	91.75 83	60.71 174	94.50 109	79.67 87	86.51 144	89.97 193

Vis-MVSNet		83.46 79	82.80 85	85.43 76	90.25 103	68.74 115	90.30 69	90.13 149	76.33 77	80.87 113	92.89 68	61.00 171	94.20 119	72.45 152	90.97 88	93.35 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 80	83.45 75	83.28 144	92.74 66	62.28 236	88.17 133	89.50 165	75.22 97	81.49 104	92.74 73	66.75 93	95.11 85	72.85 148	91.58 81	92.45 105

EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 105	64.47 196	92.32 26	90.73 131	74.45 113	79.35 125	91.10 101	69.05 78	95.12 84	72.78 149	87.22 133	94.13 34

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 178	72.94 27	90.64 57	92.14 84	77.21 50	75.47 202	92.83 70	58.56 189	94.72 104	73.24 145	92.71 71	92.13 118

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 115	65.59 174	88.21 131	92.68 59	74.66 108	78.96 128	86.42 227	69.06 77	95.26 80	75.54 125	90.09 100	93.62 64

hse-mvs3		83.15 84	82.19 92	86.02 70	90.56 97	70.85 73	88.15 135	89.16 177	76.02 82	84.67 59	91.39 95	61.54 157	95.50 65	82.71 63	75.48 269	91.72 128

MVS_Test		83.15 84	83.06 80	83.41 141	86.86 205	63.21 222	86.11 190	92.00 90	74.31 114	82.87 87	89.44 144	70.03 65	93.21 164	77.39 107	88.50 119	93.81 53

IS-MVSNet		83.15 84	82.81 84	84.18 116	89.94 110	63.30 220	91.59 39	88.46 201	79.04 25	79.49 123	92.16 76	65.10 111	94.28 113	67.71 188	91.86 79	94.95 5

DP-MVS Recon		83.11 87	82.09 94	86.15 65	94.44 19	70.92 71	88.79 106	92.20 81	70.53 179	79.17 126	91.03 106	64.12 119	96.03 47	68.39 185	90.14 99	91.50 133

PAPM_NR		83.02 88	82.41 88	84.82 94	92.47 72	66.37 160	87.93 141	91.80 100	73.82 126	77.32 162	90.66 112	67.90 84	94.90 96	70.37 166	89.48 107	93.19 81

VDD-MVS		83.01 89	82.36 90	84.96 88	91.02 89	66.40 159	88.91 101	88.11 204	77.57 39	84.39 67	93.29 59	52.19 237	93.91 134	77.05 110	88.70 115	94.57 21

MVSFormer		82.85 90	82.05 96	85.24 80	87.35 194	70.21 83	90.50 61	90.38 139	68.55 221	81.32 105	89.47 139	61.68 154	93.46 157	78.98 89	90.26 97	92.05 120

test_part1		82.78 91	82.08 95	84.89 92	90.66 95	66.97 153	90.96 52	92.93 50	77.19 51	80.53 116	90.04 125	63.44 124	95.39 74	76.04 119	76.90 246	92.31 109

OMC-MVS		82.69 92	81.97 99	84.85 93	88.75 152	67.42 143	87.98 137	90.87 129	74.92 103	79.72 121	91.65 85	62.19 149	93.96 127	75.26 127	86.42 145	93.16 82

PVSNet_Blended_VisFu		82.62 93	81.83 101	84.96 88	90.80 94	69.76 93	88.74 110	91.70 105	69.39 199	78.96 128	88.46 166	65.47 108	94.87 99	74.42 130	88.57 116	90.24 175

MVS_111021_LR		82.61 94	82.11 93	84.11 117	88.82 147	71.58 56	85.15 212	86.16 241	74.69 107	80.47 117	91.04 104	62.29 146	90.55 245	80.33 83	90.08 101	90.20 176

HQP-MVS		82.61 94	82.02 97	84.37 107	89.33 125	66.98 151	89.17 92	92.19 82	76.41 71	77.23 165	90.23 120	60.17 183	95.11 85	77.47 105	85.99 151	91.03 147

CLD-MVS		82.31 96	81.65 102	84.29 113	88.47 161	67.73 138	85.81 200	92.35 73	75.78 85	78.33 142	86.58 222	64.01 120	94.35 111	76.05 118	87.48 130	90.79 154
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 97	82.41 88	81.62 193	90.82 93	60.93 250	84.47 228	89.78 157	76.36 76	84.07 72	91.88 82	64.71 116	90.26 247	70.68 163	88.89 111	93.66 57

diffmvs		82.10 98	81.88 100	82.76 174	83.00 270	63.78 208	83.68 244	89.76 158	72.94 143	82.02 96	89.85 129	65.96 105	90.79 241	82.38 69	87.30 132	93.71 56

LPG-MVS_test		82.08 99	81.27 105	84.50 102	89.23 133	68.76 113	90.22 71	91.94 94	75.37 94	76.64 178	91.51 90	54.29 220	94.91 94	78.44 95	83.78 169	89.83 198

FIs		82.07 100	82.42 87	81.04 211	88.80 149	58.34 274	88.26 130	93.49 24	76.93 59	78.47 139	91.04 104	69.92 68	92.34 196	69.87 172	84.97 157	92.44 106

PS-MVSNAJss		82.07 100	81.31 104	84.34 110	86.51 212	67.27 147	89.27 90	91.51 110	71.75 157	79.37 124	90.22 121	63.15 132	94.27 114	77.69 103	82.36 191	91.49 134

API-MVS		81.99 102	81.23 106	84.26 114	90.94 90	70.18 88	91.10 50	89.32 169	71.51 163	78.66 135	88.28 171	65.26 109	95.10 88	64.74 216	91.23 87	87.51 257

UniMVSNet_NR-MVSNet		81.88 103	81.54 103	82.92 163	88.46 162	63.46 216	87.13 158	92.37 72	80.19 14	78.38 140	89.14 147	71.66 53	93.05 175	70.05 169	76.46 254	92.25 112

MAR-MVS		81.84 104	80.70 113	85.27 79	91.32 85	71.53 57	89.82 78	90.92 127	69.77 193	78.50 137	86.21 231	62.36 145	94.52 108	65.36 210	92.05 75	89.77 201
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
LFMVS		81.82 105	81.23 106	83.57 136	91.89 79	63.43 218	89.84 77	81.85 295	77.04 57	83.21 81	93.10 62	52.26 236	93.43 159	71.98 153	89.95 103	93.85 49

hse-mvs2		81.72 106	80.94 111	84.07 120	88.72 153	67.68 139	85.87 196	87.26 225	76.02 82	84.67 59	88.22 174	61.54 157	93.48 155	82.71 63	73.44 296	91.06 145

xiu_mvs_v2_base		81.69 107	81.05 109	83.60 134	89.15 136	68.03 133	84.46 230	90.02 151	70.67 176	81.30 108	86.53 225	63.17 131	94.19 120	75.60 124	88.54 117	88.57 237

PS-MVSNAJ		81.69 107	81.02 110	83.70 133	89.51 119	68.21 130	84.28 236	90.09 150	70.79 173	81.26 109	85.62 243	63.15 132	94.29 112	75.62 123	88.87 112	88.59 236

PAPR		81.66 109	80.89 112	83.99 128	90.27 102	64.00 203	86.76 173	91.77 104	68.84 217	77.13 170	89.50 137	67.63 86	94.88 98	67.55 190	88.52 118	93.09 83

UniMVSNet (Re)		81.60 110	81.11 108	83.09 154	88.38 165	64.41 197	87.60 147	93.02 41	78.42 31	78.56 136	88.16 175	69.78 69	93.26 163	69.58 175	76.49 253	91.60 129

FC-MVSNet-test		81.52 111	82.02 97	80.03 228	88.42 164	55.97 310	87.95 139	93.42 29	77.10 55	77.38 160	90.98 109	69.96 66	91.79 214	68.46 184	84.50 162	92.33 107

VDDNet		81.52 111	80.67 114	84.05 122	90.44 100	64.13 202	89.73 83	85.91 244	71.11 167	83.18 82	93.48 54	50.54 260	93.49 154	73.40 142	88.25 121	94.54 22

ACMP	74.13 6	81.51 113	80.57 115	84.36 108	89.42 121	68.69 120	89.97 76	91.50 113	74.46 112	75.04 220	90.41 117	53.82 225	94.54 106	77.56 104	82.91 183	89.86 197
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 114	80.29 122	84.70 98	86.63 211	69.90 90	85.95 193	86.77 232	63.24 275	81.07 111	89.47 139	61.08 170	92.15 203	78.33 98	90.07 102	92.05 120
jason: jason.
lupinMVS		81.39 114	80.27 123	84.76 97	87.35 194	70.21 83	85.55 205	86.41 236	62.85 282	81.32 105	88.61 161	61.68 154	92.24 200	78.41 97	90.26 97	91.83 124

test_yl		81.17 116	80.47 118	83.24 147	89.13 137	63.62 209	86.21 187	89.95 153	72.43 148	81.78 101	89.61 134	57.50 198	93.58 148	70.75 161	86.90 137	92.52 100

DCV-MVSNet		81.17 116	80.47 118	83.24 147	89.13 137	63.62 209	86.21 187	89.95 153	72.43 148	81.78 101	89.61 134	57.50 198	93.58 148	70.75 161	86.90 137	92.52 100

DU-MVS		81.12 118	80.52 117	82.90 164	87.80 182	63.46 216	87.02 162	91.87 98	79.01 26	78.38 140	89.07 150	65.02 112	93.05 175	70.05 169	76.46 254	92.20 114

PVSNet_Blended		80.98 119	80.34 120	82.90 164	88.85 144	65.40 177	84.43 232	92.00 90	67.62 227	78.11 147	85.05 256	66.02 103	94.27 114	71.52 155	89.50 106	89.01 220

mvs-test1		80.88 120	79.40 138	85.29 78	85.13 232	69.75 94	89.28 89	88.10 205	74.99 101	76.44 183	86.72 211	57.27 201	94.26 118	73.53 138	83.18 180	91.87 123

QAPM		80.88 120	79.50 136	85.03 85	88.01 177	68.97 109	91.59 39	92.00 90	66.63 239	75.15 216	92.16 76	57.70 195	95.45 68	63.52 220	88.76 114	90.66 159

1121		80.84 122	79.77 129	84.05 122	93.11 55	70.78 74	84.66 222	85.42 248	57.37 325	81.76 103	92.02 78	63.41 125	94.12 123	67.28 193	92.93 67	87.26 264

TranMVSNet+NR-MVSNet		80.84 122	80.31 121	82.42 179	87.85 180	62.33 234	87.74 145	91.33 116	80.55 11	77.99 150	89.86 128	65.23 110	92.62 185	67.05 198	75.24 279	92.30 110

UGNet		80.83 124	79.59 134	84.54 101	88.04 175	68.09 131	89.42 87	88.16 203	76.95 58	76.22 187	89.46 141	49.30 274	93.94 130	68.48 183	90.31 95	91.60 129
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
Fast-Effi-MVS+		80.81 125	79.92 126	83.47 137	88.85 144	64.51 193	85.53 207	89.39 167	70.79 173	78.49 138	85.06 255	67.54 87	93.58 148	67.03 199	86.58 142	92.32 108

XVG-OURS-SEG-HR		80.81 125	79.76 130	83.96 130	85.60 223	68.78 112	83.54 250	90.50 136	70.66 177	76.71 176	91.66 84	60.69 175	91.26 228	76.94 112	81.58 198	91.83 124

xiu_mvs_v1_base_debu		80.80 127	79.72 131	84.03 125	87.35 194	70.19 85	85.56 202	88.77 192	69.06 210	81.83 97	88.16 175	50.91 254	92.85 181	78.29 99	87.56 127	89.06 215

xiu_mvs_v1_base		80.80 127	79.72 131	84.03 125	87.35 194	70.19 85	85.56 202	88.77 192	69.06 210	81.83 97	88.16 175	50.91 254	92.85 181	78.29 99	87.56 127	89.06 215

xiu_mvs_v1_base_debi		80.80 127	79.72 131	84.03 125	87.35 194	70.19 85	85.56 202	88.77 192	69.06 210	81.83 97	88.16 175	50.91 254	92.85 181	78.29 99	87.56 127	89.06 215

ACMM	73.20 8	80.78 130	79.84 128	83.58 135	89.31 130	68.37 125	89.99 75	91.60 107	70.28 183	77.25 163	89.66 132	53.37 228	93.53 153	74.24 133	82.85 184	88.85 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 131	79.51 135	84.20 115	94.09 36	67.27 147	89.64 85	91.11 124	58.75 316	74.08 230	90.72 111	58.10 191	95.04 90	69.70 173	89.42 108	90.30 173

CANet_DTU		80.61 132	79.87 127	82.83 166	85.60 223	63.17 225	87.36 153	88.65 197	76.37 75	75.88 196	88.44 167	53.51 227	93.07 174	73.30 143	89.74 105	92.25 112

VPA-MVSNet		80.60 133	80.55 116	80.76 216	88.07 174	60.80 253	86.86 167	91.58 108	75.67 89	80.24 118	89.45 143	63.34 126	90.25 248	70.51 165	79.22 227	91.23 141

PVSNet_BlendedMVS		80.60 133	80.02 124	82.36 181	88.85 144	65.40 177	86.16 189	92.00 90	69.34 201	78.11 147	86.09 234	66.02 103	94.27 114	71.52 155	82.06 193	87.39 259

AdaColmap		80.58 135	79.42 137	84.06 121	93.09 56	68.91 110	89.36 88	88.97 187	69.27 202	75.70 199	89.69 131	57.20 203	95.77 56	63.06 226	88.41 120	87.50 258

EI-MVSNet		80.52 136	79.98 125	82.12 182	84.28 242	63.19 224	86.41 181	88.95 188	74.18 119	78.69 133	87.54 190	66.62 94	92.43 191	72.57 151	80.57 210	90.74 157

XVG-OURS		80.41 137	79.23 143	83.97 129	85.64 222	69.02 106	83.03 258	90.39 138	71.09 168	77.63 156	91.49 92	54.62 219	91.35 226	75.71 121	83.47 176	91.54 131

PCF-MVS	73.52 7	80.38 138	78.84 150	85.01 86	87.71 186	68.99 108	83.65 245	91.46 114	63.00 279	77.77 154	90.28 118	66.10 100	95.09 89	61.40 242	88.22 122	90.94 151
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 139	77.83 173	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 78	12.47 361	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

test_djsdf		80.30 140	79.32 141	83.27 145	83.98 249	65.37 180	90.50 61	90.38 139	68.55 221	76.19 188	88.70 157	56.44 207	93.46 157	78.98 89	80.14 216	90.97 150

v2v482		80.23 141	79.29 142	83.05 157	83.62 254	64.14 201	87.04 161	89.97 152	73.61 129	78.18 146	87.22 199	61.10 169	93.82 137	76.11 117	76.78 251	91.18 142

NR-MVSNet		80.23 141	79.38 139	82.78 172	87.80 182	63.34 219	86.31 184	91.09 125	79.01 26	72.17 248	89.07 150	67.20 91	92.81 184	66.08 205	75.65 265	92.20 114

Anonymous20240529		80.19 143	78.89 149	84.10 118	90.60 96	64.75 190	88.95 100	90.90 128	65.97 247	80.59 115	91.17 100	49.97 265	93.73 145	69.16 179	82.70 188	93.81 53

IterMVS-LS		80.06 144	79.38 139	82.11 183	85.89 218	63.20 223	86.79 170	89.34 168	74.19 118	75.45 205	86.72 211	66.62 94	92.39 193	72.58 150	76.86 248	90.75 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 145	78.57 154	84.42 105	85.13 232	68.74 115	88.77 107	88.10 205	74.99 101	74.97 221	83.49 275	57.27 201	93.36 160	73.53 138	80.88 204	91.18 142

v1144		80.03 145	79.03 146	83.01 159	83.78 252	64.51 193	87.11 160	90.57 135	71.96 155	78.08 149	86.20 232	61.41 161	93.94 130	74.93 128	77.23 241	90.60 162

v8		79.97 147	79.02 147	82.80 169	84.09 246	64.50 195	87.96 138	90.29 146	74.13 121	75.24 214	86.81 208	62.88 137	93.89 136	74.39 131	75.40 273	90.00 189

RRT_MVS		79.88 148	78.38 159	84.38 106	85.42 226	70.60 79	88.71 112	88.75 196	72.30 150	78.83 132	89.14 147	44.44 304	92.18 202	78.50 94	79.33 225	90.35 171

OpenMVS	72.83 10	79.77 149	78.33 162	84.09 119	85.17 229	69.91 89	90.57 59	90.97 126	66.70 235	72.17 248	91.91 80	54.70 217	93.96 127	61.81 239	90.95 89	88.41 241

v10		79.74 150	78.67 151	82.97 162	84.06 247	64.95 187	87.88 143	90.62 133	73.11 139	75.11 217	86.56 223	61.46 160	94.05 126	73.68 136	75.55 267	89.90 195

BH-RMVSNet		79.61 151	78.44 157	83.14 152	89.38 124	65.93 167	84.95 217	87.15 227	73.56 131	78.19 145	89.79 130	56.67 206	93.36 160	59.53 256	86.74 140	90.13 179

v1192		79.59 152	78.43 158	83.07 156	83.55 256	64.52 192	86.93 165	90.58 134	70.83 171	77.78 153	85.90 235	59.15 186	93.94 130	73.96 135	77.19 243	90.76 155

ab-mvs		79.51 153	78.97 148	81.14 208	88.46 162	60.91 251	83.84 242	89.24 174	70.36 181	79.03 127	88.87 155	63.23 130	90.21 249	65.12 212	82.57 189	92.28 111

WR-MVS		79.49 154	79.22 144	80.27 225	88.79 150	58.35 273	85.06 214	88.61 199	78.56 29	77.65 155	88.34 169	63.81 123	90.66 244	64.98 214	77.22 242	91.80 127

v144192		79.47 155	78.37 160	82.78 172	83.35 258	63.96 204	86.96 163	90.36 142	69.99 187	77.50 157	85.67 241	60.66 176	93.77 141	74.27 132	76.58 252	90.62 160

BH-untuned		79.47 155	78.60 153	82.05 185	89.19 135	65.91 168	86.07 191	88.52 200	72.18 151	75.42 206	87.69 185	61.15 168	93.54 152	60.38 249	86.83 139	86.70 278

mvs_anonymous		79.42 157	79.11 145	80.34 223	84.45 241	57.97 280	82.59 260	87.62 217	67.40 230	76.17 191	88.56 164	68.47 80	89.59 258	70.65 164	86.05 150	93.47 70

thisisatest0530		79.40 158	77.76 177	84.31 111	87.69 188	65.10 186	87.36 153	84.26 263	70.04 186	77.42 159	88.26 173	49.94 266	94.79 102	70.20 167	84.70 161	93.03 86

tttt0517		79.40 158	77.91 170	83.90 132	88.10 173	63.84 206	88.37 125	84.05 265	71.45 164	76.78 174	89.12 149	49.93 268	94.89 97	70.18 168	83.18 180	92.96 90

V42		79.38 160	78.24 164	82.83 166	81.10 305	65.50 176	85.55 205	89.82 156	71.57 162	78.21 144	86.12 233	60.66 176	93.18 168	75.64 122	75.46 271	89.81 200

jajsoiax		79.29 161	77.96 168	83.27 145	84.68 238	66.57 158	89.25 91	90.16 148	69.20 206	75.46 204	89.49 138	45.75 298	93.13 171	76.84 113	80.80 206	90.11 181

v1921920		79.22 162	78.03 167	82.80 169	83.30 260	63.94 205	86.80 169	90.33 143	69.91 190	77.48 158	85.53 244	58.44 190	93.75 143	73.60 137	76.85 249	90.71 158

AUN-MVS		79.21 163	77.60 182	84.05 122	88.71 154	67.61 140	85.84 198	87.26 225	69.08 209	77.23 165	88.14 179	53.20 230	93.47 156	75.50 126	73.45 295	91.06 145

TAPA-MVS	73.13 9	79.15 164	77.94 169	82.79 171	89.59 115	62.99 229	88.16 134	91.51 110	65.77 248	77.14 169	91.09 102	60.91 172	93.21 164	50.26 311	87.05 135	92.17 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 165	77.77 176	83.22 149	84.70 237	66.37 160	89.17 92	90.19 147	69.38 200	75.40 207	89.46 141	44.17 306	93.15 169	76.78 114	80.70 208	90.14 178

UniMVSNet_ETH3D		79.10 166	78.24 164	81.70 192	86.85 206	60.24 260	87.28 156	88.79 191	74.25 117	76.84 171	90.53 115	49.48 271	91.56 220	67.98 186	82.15 192	93.29 75

CDS-MVSNet		79.07 167	77.70 179	83.17 151	87.60 189	68.23 129	84.40 234	86.20 240	67.49 229	76.36 184	86.54 224	61.54 157	90.79 241	61.86 238	87.33 131	90.49 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 168	77.88 172	82.38 180	83.07 267	64.80 189	84.08 241	88.95 188	69.01 213	78.69 133	87.17 202	54.70 217	92.43 191	74.69 129	80.57 210	89.89 196

v1240		78.99 169	77.78 175	82.64 175	83.21 262	63.54 213	86.62 176	90.30 145	69.74 196	77.33 161	85.68 240	57.04 204	93.76 142	73.13 146	76.92 245	90.62 160

Anonymous20231211		78.97 170	77.69 180	82.81 168	90.54 98	64.29 199	90.11 73	91.51 110	65.01 258	76.16 192	88.13 180	50.56 259	93.03 178	69.68 174	77.56 239	91.11 144

v7n		78.97 170	77.58 183	83.14 152	83.45 257	65.51 175	88.32 126	91.21 120	73.69 128	72.41 245	86.32 230	57.93 192	93.81 138	69.18 178	75.65 265	90.11 181

TAMVS		78.89 172	77.51 184	83.03 158	87.80 182	67.79 137	84.72 221	85.05 252	67.63 226	76.75 175	87.70 184	62.25 147	90.82 240	58.53 267	87.13 134	90.49 166

cl_fuxian		78.75 173	77.91 170	81.26 204	82.89 274	61.56 245	84.09 240	89.13 180	69.97 188	75.56 200	84.29 263	66.36 98	92.09 205	73.47 141	75.48 269	90.12 180

v148		78.72 174	77.80 174	81.47 197	82.73 277	61.96 240	86.30 185	88.08 207	73.26 138	76.18 189	85.47 246	62.46 143	92.36 195	71.92 154	73.82 292	90.09 183

VPNet		78.69 175	78.66 152	78.76 249	88.31 167	55.72 312	84.45 231	86.63 234	76.79 63	78.26 143	90.55 114	59.30 185	89.70 257	66.63 200	77.05 244	90.88 152

ET-MVSNet_ETH3D		78.63 176	76.63 205	84.64 99	86.73 210	69.47 100	85.01 215	84.61 256	69.54 197	66.51 305	86.59 220	50.16 263	91.75 215	76.26 116	84.24 166	92.69 97

anonymousdsp		78.60 177	77.15 190	82.98 161	80.51 311	67.08 149	87.24 157	89.53 164	65.66 250	75.16 215	87.19 201	52.52 231	92.25 199	77.17 109	79.34 224	89.61 205

miper_ehance_all_eth		78.59 178	77.76 177	81.08 210	82.66 279	61.56 245	83.65 245	89.15 178	68.87 216	75.55 201	83.79 271	66.49 96	92.03 206	73.25 144	76.39 256	89.64 204

WR-MVS_H		78.51 179	78.49 155	78.56 252	88.02 176	56.38 305	88.43 118	92.67 60	77.14 53	73.89 231	87.55 189	66.25 99	89.24 264	58.92 262	73.55 294	90.06 187

GBi-Net		78.40 180	77.40 185	81.40 199	87.60 189	63.01 226	88.39 122	89.28 170	71.63 159	75.34 209	87.28 195	54.80 213	91.11 231	62.72 227	79.57 219	90.09 183

test1		78.40 180	77.40 185	81.40 199	87.60 189	63.01 226	88.39 122	89.28 170	71.63 159	75.34 209	87.28 195	54.80 213	91.11 231	62.72 227	79.57 219	90.09 183

RRT_test8_iter05		78.38 182	77.40 185	81.34 202	86.00 217	58.86 269	86.55 179	91.26 118	72.13 154	75.91 194	87.42 193	44.97 301	93.73 145	77.02 111	75.30 276	91.45 137

Vis-MVSNet (Re-imp)		78.36 183	78.45 156	78.07 260	88.64 156	51.78 332	86.70 174	79.63 316	74.14 120	75.11 217	90.83 110	61.29 165	89.75 255	58.10 271	91.60 80	92.69 97

Anonymous202405211		78.25 184	77.01 192	81.99 187	91.03 88	60.67 254	84.77 220	83.90 267	70.65 178	80.00 119	91.20 99	41.08 323	91.43 224	65.21 211	85.26 155	93.85 49

CP-MVSNet		78.22 185	78.34 161	77.84 262	87.83 181	54.54 317	87.94 140	91.17 122	77.65 36	73.48 233	88.49 165	62.24 148	88.43 277	62.19 233	74.07 287	90.55 164

BH-w/o		78.21 186	77.33 188	80.84 214	88.81 148	65.13 185	84.87 218	87.85 214	69.75 194	74.52 226	84.74 259	61.34 163	93.11 172	58.24 270	85.84 153	84.27 308

FMVSNet2		78.20 187	77.21 189	81.20 206	87.60 189	62.89 230	87.47 151	89.02 183	71.63 159	75.29 213	87.28 195	54.80 213	91.10 234	62.38 231	79.38 223	89.61 205

MVS		78.19 188	76.99 194	81.78 190	85.66 221	66.99 150	84.66 222	90.47 137	55.08 335	72.02 250	85.27 249	63.83 122	94.11 125	66.10 204	89.80 104	84.24 309

Baseline_NR-MVSNet		78.15 189	78.33 162	77.61 267	85.79 219	56.21 308	86.78 171	85.76 245	73.60 130	77.93 151	87.57 188	65.02 112	88.99 268	67.14 197	75.33 275	87.63 253

CNLPA		78.08 190	76.79 199	81.97 188	90.40 101	71.07 64	87.59 148	84.55 257	66.03 246	72.38 246	89.64 133	57.56 197	86.04 296	59.61 255	83.35 177	88.79 231

cl-mvsnet2		78.07 191	77.01 192	81.23 205	82.37 286	61.83 242	83.55 249	87.98 209	68.96 214	75.06 219	83.87 267	61.40 162	91.88 213	73.53 138	76.39 256	89.98 192

PLC	70.83 11	78.05 192	76.37 209	83.08 155	91.88 80	67.80 136	88.19 132	89.46 166	64.33 266	69.87 274	88.38 168	53.66 226	93.58 148	58.86 263	82.73 186	87.86 249
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 193	76.49 206	82.62 176	83.16 266	66.96 154	86.94 164	87.45 222	72.45 145	71.49 255	84.17 264	54.79 216	91.58 219	67.61 189	80.31 213	89.30 211

PS-CasMVS		78.01 194	78.09 166	77.77 264	87.71 186	54.39 319	88.02 136	91.22 119	77.50 44	73.26 235	88.64 160	60.73 173	88.41 278	61.88 237	73.88 291	90.53 165

HY-MVS	69.67 12	77.95 195	77.15 190	80.36 222	87.57 193	60.21 261	83.37 252	87.78 215	66.11 243	75.37 208	87.06 206	63.27 128	90.48 246	61.38 243	82.43 190	90.40 170

eth_miper_zixun_eth		77.92 196	76.69 203	81.61 195	83.00 270	61.98 239	83.15 254	89.20 176	69.52 198	74.86 223	84.35 262	61.76 153	92.56 188	71.50 157	72.89 300	90.28 174

FMVSNet3		77.88 197	76.85 197	80.97 212	86.84 207	62.36 233	86.52 180	88.77 192	71.13 166	75.34 209	86.66 218	54.07 223	91.10 234	62.72 227	79.57 219	89.45 208

miper_enhance_ethall		77.87 198	76.86 196	80.92 213	81.65 293	61.38 247	82.68 259	88.98 185	65.52 252	75.47 202	82.30 289	65.76 107	92.00 208	72.95 147	76.39 256	89.39 209

PEN-MVS		77.73 199	77.69 180	77.84 262	87.07 204	53.91 322	87.91 142	91.18 121	77.56 41	73.14 237	88.82 156	61.23 166	89.17 265	59.95 252	72.37 302	90.43 168

cl-mvsnet____		77.72 200	76.76 200	80.58 218	82.49 283	60.48 257	83.09 255	87.87 212	69.22 204	74.38 228	85.22 251	62.10 150	91.53 221	71.09 159	75.41 272	89.73 203

cl-mvsnet1		77.72 200	76.76 200	80.58 218	82.48 284	60.48 257	83.09 255	87.86 213	69.22 204	74.38 228	85.24 250	62.10 150	91.53 221	71.09 159	75.40 273	89.74 202

PAPM		77.68 202	76.40 208	81.51 196	87.29 200	61.85 241	83.78 243	89.59 163	64.74 260	71.23 256	88.70 157	62.59 140	93.66 147	52.66 298	87.03 136	89.01 220

CHOSEN 1792x2688		77.63 203	75.69 212	83.44 138	89.98 109	68.58 123	78.70 299	87.50 220	56.38 330	75.80 198	86.84 207	58.67 188	91.40 225	61.58 241	85.75 154	90.34 172

HyFIR lowres test		77.53 204	75.40 219	83.94 131	89.59 115	66.62 156	80.36 281	88.64 198	56.29 331	76.45 180	85.17 252	57.64 196	93.28 162	61.34 244	83.10 182	91.91 122

FMVSNet1		77.44 205	76.12 211	81.40 199	86.81 208	63.01 226	88.39 122	89.28 170	70.49 180	74.39 227	87.28 195	49.06 277	91.11 231	60.91 246	78.52 229	90.09 183

TR-MVS		77.44 205	76.18 210	81.20 206	88.24 169	63.24 221	84.61 226	86.40 237	67.55 228	77.81 152	86.48 226	54.10 222	93.15 169	57.75 274	82.72 187	87.20 265

1112_ss		77.40 207	76.43 207	80.32 224	89.11 141	60.41 259	83.65 245	87.72 216	62.13 290	73.05 238	86.72 211	62.58 141	89.97 252	62.11 236	80.80 206	90.59 163

thisisatest0515		77.33 208	75.38 220	83.18 150	85.27 228	63.80 207	82.11 265	83.27 278	65.06 256	75.91 194	83.84 269	49.54 270	94.27 114	67.24 195	86.19 148	91.48 135

pm-mvs1		77.25 209	76.68 204	78.93 247	84.22 244	58.62 272	86.41 181	88.36 202	71.37 165	73.31 234	88.01 181	61.22 167	89.15 266	64.24 218	73.01 299	89.03 219

bset_n11_16_dypcd		77.12 210	75.47 216	82.06 184	81.12 304	65.99 165	81.37 274	83.20 281	69.94 189	76.09 193	83.38 277	47.75 282	92.26 198	78.51 93	77.91 235	87.95 245

LCM-MVSNet-Re		77.05 211	76.94 195	77.36 270	87.20 201	51.60 333	80.06 284	80.46 308	75.20 98	67.69 289	86.72 211	62.48 142	88.98 269	63.44 222	89.25 109	91.51 132

DTE-MVSNet		76.99 212	76.80 198	77.54 269	86.24 214	53.06 329	87.52 149	90.66 132	77.08 56	72.50 243	88.67 159	60.48 179	89.52 259	57.33 278	70.74 313	90.05 188

baseline1		76.98 213	76.75 202	77.66 265	88.13 171	55.66 313	85.12 213	81.89 293	73.04 141	76.79 173	88.90 153	62.43 144	87.78 285	63.30 224	71.18 311	89.55 207

LS3D		76.95 214	74.82 226	83.37 142	90.45 99	67.36 146	89.15 96	86.94 230	61.87 292	69.52 277	90.61 113	51.71 248	94.53 107	46.38 331	86.71 141	88.21 243

GA-MVS		76.87 215	75.17 224	81.97 188	82.75 276	62.58 231	81.44 273	86.35 239	72.16 153	74.74 224	82.89 281	46.20 293	92.02 207	68.85 182	81.09 202	91.30 140

DP-MVS		76.78 216	74.57 228	83.42 139	93.29 49	69.46 102	88.55 117	83.70 269	63.98 272	70.20 265	88.89 154	54.01 224	94.80 101	46.66 328	81.88 196	86.01 290

cascas		76.72 217	74.64 227	82.99 160	85.78 220	65.88 169	82.33 263	89.21 175	60.85 298	72.74 240	81.02 300	47.28 285	93.75 143	67.48 191	85.02 156	89.34 210

1314		76.53 218	75.30 223	80.21 226	83.93 250	62.32 235	84.66 222	88.81 190	60.23 302	70.16 268	84.07 266	55.30 211	90.73 243	67.37 192	83.21 179	87.59 256

thres100view900		76.50 219	75.55 215	79.33 241	89.52 118	56.99 294	85.83 199	83.23 279	73.94 123	76.32 185	87.12 203	51.89 245	91.95 209	48.33 319	83.75 171	89.07 213

thres600view7		76.50 219	75.44 217	79.68 235	89.40 122	57.16 291	85.53 207	83.23 279	73.79 127	76.26 186	87.09 204	51.89 245	91.89 212	48.05 324	83.72 174	90.00 189

thres400		76.50 219	75.37 221	79.86 231	89.13 137	57.65 286	85.17 210	83.60 270	73.41 136	76.45 180	86.39 228	52.12 238	91.95 209	48.33 319	83.75 171	90.00 189

tfpn200view9		76.42 222	75.37 221	79.55 240	89.13 137	57.65 286	85.17 210	83.60 270	73.41 136	76.45 180	86.39 228	52.12 238	91.95 209	48.33 319	83.75 171	89.07 213

Test_1112_low_res		76.40 223	75.44 217	79.27 242	89.28 131	58.09 276	81.69 269	87.07 228	59.53 309	72.48 244	86.67 217	61.30 164	89.33 262	60.81 248	80.15 215	90.41 169

F-COLMAP		76.38 224	74.33 233	82.50 178	89.28 131	66.95 155	88.41 121	89.03 182	64.05 270	66.83 299	88.61 161	46.78 288	92.89 180	57.48 275	78.55 228	87.67 252

LTVRE_ROB	69.57 13	76.25 225	74.54 230	81.41 198	88.60 157	64.38 198	79.24 292	89.12 181	70.76 175	69.79 276	87.86 182	49.09 276	93.20 166	56.21 286	80.16 214	86.65 279
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
MVP-Stereo		76.12 226	74.46 232	81.13 209	85.37 227	69.79 92	84.42 233	87.95 210	65.03 257	67.46 291	85.33 248	53.28 229	91.73 217	58.01 272	83.27 178	81.85 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 227	74.27 234	81.62 193	83.20 263	64.67 191	83.60 248	89.75 159	69.75 194	71.85 251	87.09 204	32.78 345	92.11 204	69.99 171	80.43 212	88.09 244

ACMH+	68.96 14	76.01 228	74.01 235	82.03 186	88.60 157	65.31 181	88.86 103	87.55 218	70.25 184	67.75 288	87.47 192	41.27 321	93.19 167	58.37 268	75.94 262	87.60 254

ACMH	67.68 16	75.89 229	73.93 236	81.77 191	88.71 154	66.61 157	88.62 114	89.01 184	69.81 191	66.78 300	86.70 216	41.95 320	91.51 223	55.64 287	78.14 234	87.17 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 230	73.36 242	83.31 143	84.76 236	66.03 163	83.38 251	85.06 251	70.21 185	69.40 278	81.05 299	45.76 297	94.66 105	65.10 213	75.49 268	89.25 212
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
baseline2		75.70 231	73.83 239	81.30 203	83.26 261	61.79 243	82.57 261	80.65 304	66.81 232	66.88 297	83.42 276	57.86 194	92.19 201	63.47 221	79.57 219	89.91 194

WTY-MVS		75.65 232	75.68 213	75.57 286	86.40 213	56.82 296	77.92 307	82.40 289	65.10 255	76.18 189	87.72 183	63.13 135	80.90 324	60.31 250	81.96 194	89.00 222

thres200		75.55 233	74.47 231	78.82 248	87.78 185	57.85 283	83.07 257	83.51 273	72.44 147	75.84 197	84.42 261	52.08 240	91.75 215	47.41 326	83.64 175	86.86 274

EPNet_dtu		75.46 234	74.86 225	77.23 274	82.57 281	54.60 316	86.89 166	83.09 283	71.64 158	66.25 307	85.86 237	55.99 208	88.04 282	54.92 289	86.55 143	89.05 218
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 235	73.87 238	80.11 227	82.69 278	64.85 188	81.57 271	83.47 275	69.16 207	70.49 262	84.15 265	51.95 243	88.15 280	69.23 177	72.14 305	87.34 261

XXY-MVS		75.41 236	75.56 214	74.96 292	83.59 255	57.82 284	80.59 280	83.87 268	66.54 240	74.93 222	88.31 170	63.24 129	80.09 327	62.16 234	76.85 249	86.97 272

TransMVSNet (Re)		75.39 237	74.56 229	77.86 261	85.50 225	57.10 293	86.78 171	86.09 243	72.17 152	71.53 254	87.34 194	63.01 136	89.31 263	56.84 282	61.83 335	87.17 266

CostFormer		75.24 238	73.90 237	79.27 242	82.65 280	58.27 275	80.80 275	82.73 287	61.57 293	75.33 212	83.13 279	55.52 209	91.07 237	64.98 214	78.34 233	88.45 239

D2MVS		74.82 239	73.21 243	79.64 237	79.81 318	62.56 232	80.34 282	87.35 223	64.37 265	68.86 281	82.66 285	46.37 290	90.10 251	67.91 187	81.24 201	86.25 283

pmmvs6		74.69 240	73.39 241	78.61 251	81.38 299	57.48 289	86.64 175	87.95 210	64.99 259	70.18 266	86.61 219	50.43 261	89.52 259	62.12 235	70.18 315	88.83 229

tfpnnormal		74.39 241	73.16 244	78.08 259	86.10 216	58.05 277	84.65 225	87.53 219	70.32 182	71.22 257	85.63 242	54.97 212	89.86 253	43.03 340	75.02 280	86.32 282

IterMVS		74.29 242	72.94 246	78.35 256	81.53 296	63.49 215	81.58 270	82.49 288	68.06 225	69.99 271	83.69 273	51.66 249	85.54 299	65.85 207	71.64 308	86.01 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 243	72.42 250	79.80 233	83.76 253	59.59 265	85.92 195	86.64 233	66.39 241	66.96 296	87.58 187	39.46 327	91.60 218	65.76 208	69.27 317	88.22 242

SCA		74.22 244	72.33 251	79.91 230	84.05 248	62.17 237	79.96 286	79.29 318	66.30 242	72.38 246	80.13 309	51.95 243	88.60 275	59.25 258	77.67 238	88.96 224

miper_lstm_enhance		74.11 245	73.11 245	77.13 275	80.11 314	59.62 264	72.23 329	86.92 231	66.76 234	70.40 263	82.92 280	56.93 205	82.92 317	69.06 180	72.63 301	88.87 227

EG-PatchMatch MVS		74.04 246	71.82 255	80.71 217	84.92 235	67.42 143	85.86 197	88.08 207	66.04 245	64.22 319	83.85 268	35.10 341	92.56 188	57.44 276	80.83 205	82.16 327

pmmvs4		74.03 247	71.91 253	80.39 221	81.96 290	68.32 126	81.45 272	82.14 291	59.32 310	69.87 274	85.13 253	52.40 234	88.13 281	60.21 251	74.74 283	84.73 305

MS-PatchMatch		73.83 248	72.67 247	77.30 272	83.87 251	66.02 164	81.82 266	84.66 255	61.37 296	68.61 284	82.82 283	47.29 284	88.21 279	59.27 257	84.32 165	77.68 342

DWT-MVSNet_test		73.70 249	71.86 254	79.21 244	82.91 273	58.94 268	82.34 262	82.17 290	65.21 253	71.05 259	78.31 323	44.21 305	90.17 250	63.29 225	77.28 240	88.53 238

sss		73.60 250	73.64 240	73.51 303	82.80 275	55.01 315	76.12 313	81.69 296	62.47 287	74.68 225	85.85 238	57.32 200	78.11 334	60.86 247	80.93 203	87.39 259

RPMNet		73.51 251	70.49 267	82.58 177	81.32 302	65.19 183	75.92 315	92.27 76	57.60 323	72.73 241	76.45 334	52.30 235	95.43 71	48.14 323	77.71 236	87.11 270

SixPastTwentyTwo		73.37 252	71.26 262	79.70 234	85.08 234	57.89 282	85.57 201	83.56 272	71.03 169	65.66 309	85.88 236	42.10 318	92.57 187	59.11 260	63.34 334	88.65 235

CR-MVSNet		73.37 252	71.27 261	79.67 236	81.32 302	65.19 183	75.92 315	80.30 310	59.92 305	72.73 241	81.19 297	52.50 232	86.69 291	59.84 253	77.71 236	87.11 270

MSDG		73.36 254	70.99 263	80.49 220	84.51 240	65.80 170	80.71 278	86.13 242	65.70 249	65.46 310	83.74 272	44.60 302	90.91 239	51.13 304	76.89 247	84.74 304

tpm2		73.26 255	71.46 257	78.63 250	83.34 259	56.71 299	80.65 279	80.40 309	56.63 329	73.55 232	82.02 294	51.80 247	91.24 229	56.35 285	78.42 232	87.95 245

RPSCF		73.23 256	71.46 257	78.54 253	82.50 282	59.85 262	82.18 264	82.84 286	58.96 313	71.15 258	89.41 145	45.48 300	84.77 306	58.82 264	71.83 307	91.02 149

PatchmatchNet		73.12 257	71.33 260	78.49 255	83.18 264	60.85 252	79.63 288	78.57 320	64.13 267	71.73 252	79.81 314	51.20 252	85.97 297	57.40 277	76.36 259	88.66 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 258	70.41 269	80.81 215	87.13 203	65.63 173	88.30 127	84.19 264	62.96 280	63.80 323	87.69 185	38.04 333	92.56 188	46.66 328	74.91 281	84.24 309
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 259	72.58 248	74.25 299	84.28 242	50.85 338	86.41 181	83.45 276	44.56 346	73.23 236	87.54 190	49.38 272	85.70 298	65.90 206	78.44 231	86.19 285

test-LLR		72.94 260	72.43 249	74.48 296	81.35 300	58.04 278	78.38 300	77.46 325	66.66 236	69.95 272	79.00 318	48.06 280	79.24 328	66.13 202	84.83 158	86.15 286

test_0402		72.79 261	70.44 268	79.84 232	88.13 171	65.99 165	85.93 194	84.29 261	65.57 251	67.40 293	85.49 245	46.92 287	92.61 186	35.88 349	74.38 286	80.94 333

MVS_0304		72.48 262	70.89 265	77.24 273	82.20 287	59.68 263	84.11 239	83.49 274	67.10 231	66.87 298	80.59 305	35.00 342	87.40 287	59.07 261	79.58 218	84.63 306

tpmrst		72.39 263	72.13 252	73.18 307	80.54 310	49.91 341	79.91 287	79.08 319	63.11 277	71.69 253	79.95 311	55.32 210	82.77 318	65.66 209	73.89 290	86.87 273

PatchMatch-RL		72.38 264	70.90 264	76.80 278	88.60 157	67.38 145	79.53 289	76.17 331	62.75 284	69.36 279	82.00 295	45.51 299	84.89 305	53.62 294	80.58 209	78.12 341

CL-MVSNet_2432*1600		72.37 265	71.46 257	75.09 291	79.49 324	53.53 324	80.76 277	85.01 253	69.12 208	70.51 261	82.05 293	57.92 193	84.13 309	52.27 299	66.00 328	87.60 254

tpm		72.37 265	71.71 256	74.35 298	82.19 288	52.00 330	79.22 293	77.29 327	64.56 262	72.95 239	83.68 274	51.35 250	83.26 316	58.33 269	75.80 263	87.81 250

PVSNet	64.34 18	72.08 267	70.87 266	75.69 284	86.21 215	56.44 303	74.37 325	80.73 303	62.06 291	70.17 267	82.23 291	42.86 312	83.31 315	54.77 290	84.45 164	87.32 262

pmmvs5		71.55 268	70.20 271	75.61 285	77.83 330	56.39 304	81.74 268	80.89 300	57.76 321	67.46 291	84.49 260	49.26 275	85.32 302	57.08 280	75.29 277	85.11 301

test-mter		71.41 269	70.39 270	74.48 296	81.35 300	58.04 278	78.38 300	77.46 325	60.32 301	69.95 272	79.00 318	36.08 339	79.24 328	66.13 202	84.83 158	86.15 286

K. test v3		71.19 270	68.51 278	79.21 244	83.04 269	57.78 285	84.35 235	76.91 329	72.90 144	62.99 326	82.86 282	39.27 328	91.09 236	61.65 240	52.66 347	88.75 232

tpmvs		71.09 271	69.29 274	76.49 279	82.04 289	56.04 309	78.92 297	81.37 299	64.05 270	67.18 295	78.28 324	49.74 269	89.77 254	49.67 314	72.37 302	83.67 314

AllTest		70.96 272	68.09 284	79.58 238	85.15 230	63.62 209	84.58 227	79.83 314	62.31 288	60.32 333	86.73 209	32.02 346	88.96 271	50.28 309	71.57 309	86.15 286

Patchmtry		70.74 273	69.16 275	75.49 288	80.72 307	54.07 321	74.94 324	80.30 310	58.34 317	70.01 269	81.19 297	52.50 232	86.54 292	53.37 295	71.09 312	85.87 293

MIMVSNet		70.69 274	69.30 273	74.88 293	84.52 239	56.35 306	75.87 317	79.42 317	64.59 261	67.76 287	82.41 287	41.10 322	81.54 322	46.64 330	81.34 199	86.75 277

tpm cat1		70.57 275	68.31 280	77.35 271	82.41 285	57.95 281	78.08 304	80.22 312	52.04 341	68.54 285	77.66 329	52.00 242	87.84 284	51.77 300	72.07 306	86.25 283

OpenMVS_ROB	64.09 19	70.56 276	68.19 281	77.65 266	80.26 312	59.41 267	85.01 215	82.96 285	58.76 315	65.43 311	82.33 288	37.63 335	91.23 230	45.34 336	76.03 261	82.32 325

pmmvs-eth3d		70.50 277	67.83 288	78.52 254	77.37 333	66.18 162	81.82 266	81.51 297	58.90 314	63.90 322	80.42 307	42.69 313	86.28 295	58.56 266	65.30 330	83.11 320

USDC		70.33 278	68.37 279	76.21 281	80.60 309	56.23 307	79.19 294	86.49 235	60.89 297	61.29 330	85.47 246	31.78 348	89.47 261	53.37 295	76.21 260	82.94 324

Patchmatch-RL test		70.24 279	67.78 290	77.61 267	77.43 332	59.57 266	71.16 331	70.33 343	62.94 281	68.65 283	72.77 341	50.62 258	85.49 300	69.58 175	66.58 326	87.77 251

CMPMVS	51.72 21	70.19 280	68.16 282	76.28 280	73.15 350	57.55 288	79.47 290	83.92 266	48.02 345	56.48 344	84.81 257	43.13 310	86.42 294	62.67 230	81.81 197	84.89 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 281	67.34 294	78.14 258	79.80 319	61.13 248	79.19 294	80.59 305	59.16 312	65.27 312	79.29 315	46.75 289	87.29 288	49.33 315	66.72 324	86.00 292

gg-mvs-nofinetune		69.95 282	67.96 285	75.94 282	83.07 267	54.51 318	77.23 310	70.29 344	63.11 277	70.32 264	62.33 347	43.62 308	88.69 274	53.88 293	87.76 124	84.62 307

TESTMET0.1,1		69.89 283	69.00 276	72.55 308	79.27 327	56.85 295	78.38 300	74.71 337	57.64 322	68.09 286	77.19 331	37.75 334	76.70 339	63.92 219	84.09 167	84.10 312

FMVSNet5		69.50 284	67.96 285	74.15 300	82.97 272	55.35 314	80.01 285	82.12 292	62.56 286	63.02 324	81.53 296	36.92 336	81.92 320	48.42 318	74.06 288	85.17 300

PMMVS		69.34 285	68.67 277	71.35 315	75.67 338	62.03 238	75.17 319	73.46 339	50.00 344	68.68 282	79.05 316	52.07 241	78.13 333	61.16 245	82.77 185	73.90 345

our_test_3		69.14 286	67.00 296	75.57 286	79.80 319	58.80 270	77.96 305	77.81 323	59.55 308	62.90 327	78.25 325	47.43 283	83.97 310	51.71 301	67.58 323	83.93 313

EPMVS		69.02 287	68.16 282	71.59 311	79.61 322	49.80 343	77.40 309	66.93 351	62.82 283	70.01 269	79.05 316	45.79 296	77.86 336	56.58 283	75.26 278	87.13 269

DIV-MVS_2432*1600		68.81 288	67.59 293	72.46 309	74.29 344	45.45 348	77.93 306	87.00 229	63.12 276	63.99 321	78.99 320	42.32 315	84.77 306	56.55 284	64.09 333	87.16 268

Anonymous20240521		68.80 289	67.22 295	73.55 302	74.33 343	54.11 320	83.18 253	85.61 246	58.15 318	61.68 329	80.94 302	30.71 349	81.27 323	57.00 281	73.34 298	85.28 297

Anonymous20231206		68.60 290	67.80 289	71.02 317	80.23 313	50.75 339	78.30 303	80.47 307	56.79 328	66.11 308	82.63 286	46.35 291	78.95 330	43.62 339	75.70 264	83.36 317

MIMVSNet1		68.58 291	66.78 298	73.98 301	80.07 315	51.82 331	80.77 276	84.37 258	64.40 264	59.75 336	82.16 292	36.47 337	83.63 313	42.73 341	70.33 314	86.48 281

EU-MVSNet		68.53 292	67.61 292	71.31 316	78.51 329	47.01 347	84.47 228	84.27 262	42.27 347	66.44 306	84.79 258	40.44 325	83.76 311	58.76 265	68.54 322	83.17 318

PatchT		68.46 293	67.85 287	70.29 319	80.70 308	43.93 351	72.47 328	74.88 334	60.15 303	70.55 260	76.57 333	49.94 266	81.59 321	50.58 305	74.83 282	85.34 296

test0.0.03 1		68.00 294	67.69 291	68.90 324	77.55 331	47.43 345	75.70 318	72.95 341	66.66 236	66.56 301	82.29 290	48.06 280	75.87 343	44.97 337	74.51 285	83.41 316

TDRefinement		67.49 295	64.34 304	76.92 276	73.47 348	61.07 249	84.86 219	82.98 284	59.77 306	58.30 339	85.13 253	26.06 351	87.89 283	47.92 325	60.59 339	81.81 329

test20.03		67.45 296	66.95 297	68.94 323	75.48 340	44.84 350	77.50 308	77.67 324	66.66 236	63.01 325	83.80 270	47.02 286	78.40 332	42.53 342	68.86 321	83.58 315

UnsupCasMVSNet_eth		67.33 297	65.99 300	71.37 313	73.48 347	51.47 335	75.16 320	85.19 250	65.20 254	60.78 332	80.93 304	42.35 314	77.20 338	57.12 279	53.69 346	85.44 295

TinyColmap		67.30 298	64.81 302	74.76 295	81.92 291	56.68 300	80.29 283	81.49 298	60.33 300	56.27 345	83.22 278	24.77 352	87.66 286	45.52 334	69.47 316	79.95 337

dp		66.80 299	65.43 301	70.90 318	79.74 321	48.82 344	75.12 322	74.77 335	59.61 307	64.08 320	77.23 330	42.89 311	80.72 325	48.86 317	66.58 326	83.16 319

MDA-MVSNet-bldmvs		66.68 300	63.66 308	75.75 283	79.28 326	60.56 256	73.92 326	78.35 321	64.43 263	50.13 350	79.87 313	44.02 307	83.67 312	46.10 332	56.86 342	83.03 322

testgi		66.67 301	66.53 299	67.08 329	75.62 339	41.69 354	75.93 314	76.50 330	66.11 243	65.20 315	86.59 220	35.72 340	74.71 347	43.71 338	73.38 297	84.84 303

CHOSEN 280x420		66.51 302	64.71 303	71.90 310	81.45 297	63.52 214	57.98 352	68.95 350	53.57 337	62.59 328	76.70 332	46.22 292	75.29 346	55.25 288	79.68 217	76.88 344

PM-MVS		66.41 303	64.14 305	73.20 306	73.92 345	56.45 302	78.97 296	64.96 355	63.88 274	64.72 316	80.24 308	19.84 356	83.44 314	66.24 201	64.52 332	79.71 338

JIA-IIPM		66.32 304	62.82 314	76.82 277	77.09 334	61.72 244	65.34 347	75.38 332	58.04 320	64.51 317	62.32 348	42.05 319	86.51 293	51.45 303	69.22 318	82.21 326

KD-MVS_2432*1600		66.22 305	63.89 306	73.21 304	75.47 341	53.42 326	70.76 334	84.35 259	64.10 268	66.52 303	78.52 321	34.55 343	84.98 303	50.40 307	50.33 350	81.23 331

miper_refine_blended		66.22 305	63.89 306	73.21 304	75.47 341	53.42 326	70.76 334	84.35 259	64.10 268	66.52 303	78.52 321	34.55 343	84.98 303	50.40 307	50.33 350	81.23 331

ADS-MVSNet2		66.20 307	63.33 309	74.82 294	79.92 316	58.75 271	67.55 344	75.19 333	53.37 338	65.25 313	75.86 335	42.32 315	80.53 326	41.57 343	68.91 319	85.18 298

YYNet1		65.03 308	62.91 312	71.38 312	75.85 337	56.60 301	69.12 341	74.66 338	57.28 326	54.12 346	77.87 327	45.85 295	74.48 348	49.95 312	61.52 337	83.05 321

MDA-MVSNet_test_wron		65.03 308	62.92 311	71.37 313	75.93 336	56.73 297	69.09 342	74.73 336	57.28 326	54.03 347	77.89 326	45.88 294	74.39 349	49.89 313	61.55 336	82.99 323

Patchmatch-test		64.82 310	63.24 310	69.57 321	79.42 325	49.82 342	63.49 350	69.05 349	51.98 342	59.95 335	80.13 309	50.91 254	70.98 352	40.66 345	73.57 293	87.90 248

ADS-MVSNet		64.36 311	62.88 313	68.78 326	79.92 316	47.17 346	67.55 344	71.18 342	53.37 338	65.25 313	75.86 335	42.32 315	73.99 350	41.57 343	68.91 319	85.18 298

LF4IMVS		64.02 312	62.19 315	69.50 322	70.90 352	53.29 328	76.13 312	77.18 328	52.65 340	58.59 337	80.98 301	23.55 353	76.52 340	53.06 297	66.66 325	78.68 340

UnsupCasMVSNet_bld		63.70 313	61.53 317	70.21 320	73.69 346	51.39 336	72.82 327	81.89 293	55.63 333	57.81 340	71.80 343	38.67 330	78.61 331	49.26 316	52.21 348	80.63 334

new-patchmatchnet		61.73 314	61.73 316	61.70 332	72.74 351	24.50 364	69.16 340	78.03 322	61.40 294	56.72 343	75.53 337	38.42 331	76.48 341	45.95 333	57.67 341	84.13 311

PVSNet_0	57.27 20	61.67 315	59.27 318	68.85 325	79.61 322	57.44 290	68.01 343	73.44 340	55.93 332	58.54 338	70.41 344	44.58 303	77.55 337	47.01 327	35.91 353	71.55 347

MVS-HIRNet		59.14 316	57.67 319	63.57 331	81.65 293	43.50 352	71.73 330	65.06 354	39.59 351	51.43 349	57.73 351	38.34 332	82.58 319	39.53 346	73.95 289	64.62 350

pmmvs3		57.79 317	54.26 321	68.37 327	64.02 356	56.72 298	75.12 322	65.17 353	40.20 349	52.93 348	69.86 345	20.36 355	75.48 345	45.45 335	55.25 345	72.90 346

DSMNet-mixed		57.77 318	56.90 320	60.38 333	67.70 354	35.61 357	69.18 339	53.97 359	32.30 356	57.49 341	79.88 312	40.39 326	68.57 354	38.78 347	72.37 302	76.97 343

LCM-MVSNet		54.25 319	49.68 325	67.97 328	53.73 359	45.28 349	66.85 346	80.78 302	35.96 353	39.45 353	62.23 349	8.70 365	78.06 335	48.24 322	51.20 349	80.57 335

FPMVS		53.68 320	51.64 323	59.81 334	65.08 355	51.03 337	69.48 338	69.58 347	41.46 348	40.67 352	72.32 342	16.46 359	70.00 353	24.24 355	65.42 329	58.40 352

N_pmnet		52.79 321	53.26 322	51.40 338	78.99 328	7.68 367	69.52 337	3.89 367	51.63 343	57.01 342	74.98 338	40.83 324	65.96 355	37.78 348	64.67 331	80.56 336

new_pmnet		50.91 322	50.29 324	52.78 337	68.58 353	34.94 359	63.71 349	56.63 358	39.73 350	44.95 351	65.47 346	21.93 354	58.48 356	34.98 350	56.62 343	64.92 349

ANet_high		50.57 323	46.10 326	63.99 330	48.67 362	39.13 355	70.99 333	80.85 301	61.39 295	31.18 355	57.70 352	17.02 358	73.65 351	31.22 351	15.89 360	79.18 339

Gipuma		45.18 324	41.86 327	55.16 336	77.03 335	51.52 334	32.50 358	80.52 306	32.46 355	27.12 356	35.02 357	9.52 364	75.50 344	22.31 356	60.21 340	38.45 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 325	40.28 328	55.82 335	40.82 364	42.54 353	65.12 348	63.99 356	34.43 354	24.48 357	57.12 353	3.92 367	76.17 342	17.10 358	55.52 344	48.75 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 326	38.86 329	46.69 339	53.84 358	16.45 365	48.61 355	49.92 360	37.49 352	31.67 354	60.97 350	8.14 366	56.42 357	28.42 352	30.72 355	67.19 348

E-PMN		31.77 327	30.64 330	35.15 342	52.87 360	27.67 361	57.09 353	47.86 361	24.64 357	16.40 362	33.05 358	11.23 362	54.90 358	14.46 360	18.15 358	22.87 357

test_method		31.52 328	29.28 332	38.23 341	27.03 366	6.50 368	20.94 360	62.21 357	4.05 362	22.35 360	52.50 354	13.33 360	47.58 360	27.04 354	34.04 354	60.62 351

EMVS		30.81 329	29.65 331	34.27 343	50.96 361	25.95 363	56.58 354	46.80 362	24.01 358	15.53 363	30.68 359	12.47 361	54.43 359	12.81 361	17.05 359	22.43 358

MVE	26.22 23	30.37 330	25.89 334	43.81 340	44.55 363	35.46 358	28.87 359	39.07 363	18.20 359	18.58 361	40.18 356	2.68 368	47.37 361	17.07 359	23.78 357	48.60 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 331	26.61 333	0.00 349	0.00 370	0.00 371	0.00 361	89.26 173	0.00 366	0.00 367	88.61 161	61.62 156	0.00 367	0.00 365	0.00 365	0.00 363

tmp_tt		18.61 332	21.40 335	10.23 346	4.82 367	10.11 366	34.70 357	30.74 365	1.48 363	23.91 359	26.07 360	28.42 350	13.41 364	27.12 353	15.35 361	7.17 359

wuyk23d		16.82 333	15.94 336	19.46 345	58.74 357	31.45 360	39.22 356	3.74 368	6.84 361	6.04 364	2.70 364	1.27 369	24.29 363	10.54 362	14.40 362	2.63 360

ab-mvs-re		7.23 334	9.64 337	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	86.72 211	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

test123		6.12 335	8.11 338	0.14 347	0.06 369	0.09 369	71.05 332	0.03 370	0.04 365	0.25 366	1.30 366	0.05 370	0.03 366	0.21 364	0.01 364	0.29 361

testmvs		6.04 336	8.02 339	0.10 348	0.08 368	0.03 370	69.74 336	0.04 369	0.05 364	0.31 365	1.68 365	0.02 371	0.04 365	0.24 363	0.02 363	0.25 362

pcd_1.5k_mvsjas		5.26 337	7.02 340	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	63.15 132	0.00 367	0.00 365	0.00 365	0.00 363

uanet_test		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

sosnet-low-res		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

sosnet		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

uncertanet		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

Regformer		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

uanet		0.00 338	0.00 341	0.00 349	0.00 370	0.00 371	0.00 361	0.00 371	0.00 366	0.00 367	0.00 367	0.00 372	0.00 367	0.00 365	0.00 365	0.00 363

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 170	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

RE-MVS-def			85.48 56		93.06 57	70.63 77	91.88 34	92.27 76	73.53 133	85.69 42	94.45 26	63.87 121		82.75 61	91.87 77	92.50 102

IU-MVS					95.30 2	71.25 59		92.95 49	66.81 232	92.39 5				88.94 8	96.63 2	94.85 10

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

test_241102_TWO								94.06 10	77.24 48	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 52	93.10 1	95.39 9	82.99 1	97.27 7

9.14			88.26 14		92.84 63		91.52 42	94.75 1	73.93 124	88.57 20	94.67 17	75.57 20	95.79 55	86.77 20	95.76 24

save fliter					93.80 39	72.35 42	90.47 63	91.17 122	74.31 114

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 46	92.81 3	95.79 3	80.98 7

GSMVS																88.96 224

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 251				88.96 224

sam_mvs												50.01 264

ambc				75.24 290	73.16 349	50.51 340	63.05 351	87.47 221		64.28 318	77.81 328	17.80 357	89.73 256	57.88 273	60.64 338	85.49 294

MTGPA								92.02 87

test_post1							78.90 298				5.43 363	48.81 279	85.44 301	59.25 258

test_post											5.46 362	50.36 262	84.24 308

patchmatchnet-post											74.00 339	51.12 253	88.60 275

GG-mvs-BLEND				75.38 289	81.59 295	55.80 311	79.32 291	69.63 346		67.19 294	73.67 340	43.24 309	88.90 273	50.41 306	84.50 162	81.45 330

MTMP							92.18 30	32.83 364

gm-plane-assit					81.40 298	53.83 323			62.72 285		80.94 302		92.39 193	63.40 223

test9_res														84.90 30	95.70 27	92.87 92

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 223	85.00 50	93.10 62	74.36 30	95.41 72

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 219	84.87 56	93.10 62	74.43 27	95.16 83

agg_prior2														82.91 59	95.45 29	92.70 95

agg_prior					92.85 61	71.94 51		91.78 102		84.41 65			94.93 92

TestCases				79.58 238	85.15 230	63.62 209		79.83 314	62.31 288	60.32 333	86.73 209	32.02 346	88.96 271	50.28 309	71.57 309	86.15 286

test_prior4						72.60 33	89.01 99

test_prior2							88.85 104		75.41 92	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

test_prior				86.33 60	92.61 69	69.59 96		92.97 47					95.48 66			93.91 45

旧先验2							86.56 178		58.10 319	87.04 31			88.98 269	74.07 134

新几何2							86.29 186

新几何1				83.42 139	93.13 53	70.71 75		85.48 247	57.43 324	81.80 100	91.98 79	63.28 127	92.27 197	64.60 217	92.99 66	87.27 263

旧先验1					91.96 77	65.79 171		86.37 238			93.08 66	69.31 75			92.74 70	88.74 233

无先验							87.48 150	88.98 185	60.00 304				94.12 123	67.28 193		88.97 223

原ACMM2							86.86 167

原ACMM1				84.35 109	93.01 59	68.79 111		92.44 68	63.96 273	81.09 110	91.57 89	66.06 102	95.45 68	67.19 196	94.82 47	88.81 230

test222					91.50 83	68.26 128	84.16 237	83.20 281	54.63 336	79.74 120	91.63 87	58.97 187			91.42 83	86.77 276

testdata2													91.01 238	62.37 232

segment_acmp												73.08 40

testdata				79.97 229	90.90 91	64.21 200		84.71 254	59.27 311	85.40 44	92.91 67	62.02 152	89.08 267	68.95 181	91.37 84	86.63 280

testdata1							84.14 238		75.71 86

test12				86.80 52	92.63 68	70.70 76		91.79 101		82.71 91		71.67 52	96.16 44		94.50 52	93.54 68

plane_prior7					90.08 107	68.51 124

plane_prior6					89.84 112	68.70 119						60.42 180

plane_prior5								92.44 68					95.38 75	78.71 91	86.32 146	91.33 138

plane_prior4											91.00 107

plane_prior3						68.60 122			78.44 30	78.92 130

plane_prior2							91.25 46		79.12 23

plane_prior1					89.90 111

plane_prior						68.71 117	90.38 67		77.62 37						86.16 149

n2								0.00 371

nn								0.00 371

door-mid								69.98 345

lessismore_v0				78.97 246	81.01 306	57.15 292		65.99 352		61.16 331	82.82 283	39.12 329	91.34 227	59.67 254	46.92 352	88.43 240

LGP-MVS_train				84.50 102	89.23 133	68.76 113		91.94 94	75.37 94	76.64 178	91.51 90	54.29 220	94.91 94	78.44 95	83.78 169	89.83 198

test11								92.23 79

door								69.44 348

HQP5-MVS						66.98 151

HQP-NCC					89.33 125		89.17 92		76.41 71	77.23 165

ACMP_Plane					89.33 125		89.17 92		76.41 71	77.23 165

BP-MVS														77.47 105

HQP4-MVS										77.24 164			95.11 85			91.03 147

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 183

NP-MVS					89.62 114	68.32 126					90.24 119

MDTV_nov1_ep13_2view						37.79 356	75.16 320		55.10 334	66.53 302		49.34 273		53.98 292		87.94 247

MDTV_nov1_ep13			69.97 272		83.18 264	53.48 325	77.10 311	80.18 313	60.45 299	69.33 280	80.44 306	48.89 278	86.90 290	51.60 302	78.51 230

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

ITE_SJBPF				78.22 257	81.77 292	60.57 255		83.30 277	69.25 203	67.54 290	87.20 200	36.33 338	87.28 289	54.34 291	74.62 284	86.80 275

DeepMVS_CX				27.40 344	40.17 365	26.90 362		24.59 366	17.44 360	23.95 358	48.61 355	9.77 363	26.48 362	18.06 357	24.47 356	28.83 356

High-resolution multi-view benchmark