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.

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 8	97.13 2	95.58 9	89.33 1	85.77 44	96.26 24	72.84 26	99.38 1	92.64 12	95.93 9	97.08 9

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 144	76.68 2	97.29 1	95.35 11	82.87 19	91.58 10	97.22 3	79.93 5	99.10 9	83.12 85	97.64 2	97.94 1

DVP-MVS++		90.53 3	91.09 4	88.87 13	97.31 4	69.91 36	93.96 65	94.37 45	72.48 167	92.07 6	96.85 12	83.82 2	99.15 2	91.53 22	97.42 4	97.55 4

MSP-MVS		90.38 4	91.87 1	85.88 79	92.83 71	64.03 180	93.06 101	94.33 47	82.19 26	93.65 3	96.15 28	85.89 1	97.19 77	91.02 26	97.75 1	96.43 25
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
CNVR-MVS		90.32 5	90.89 6	88.61 18	96.76 8	70.65 25	96.47 12	94.83 24	84.83 10	89.07 24	96.80 15	70.86 34	99.06 15	92.64 12	95.71 10	96.12 34

DELS-MVS		90.05 6	90.09 10	89.94 4	93.14 66	73.88 7	97.01 3	94.40 43	88.32 2	85.71 45	94.91 61	74.11 19	98.91 17	87.26 52	95.94 8	97.03 10
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
MVS_0304		90.01 7	90.50 8	88.53 19	90.14 135	70.94 22	96.47 12	95.72 8	87.33 3	89.60 21	96.26 24	68.44 37	98.74 23	95.82 1	94.72 29	95.90 41

SED-MVS		89.94 8	90.36 9	88.70 15	96.45 12	69.38 46	96.89 4	94.44 39	71.65 196	92.11 4	97.21 4	76.79 9	99.11 6	92.34 14	95.36 13	97.62 2

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 119	93.00 69	58.16 288	96.72 7	94.41 41	86.50 7	90.25 18	97.83 1	75.46 14	98.67 24	92.78 11	95.49 12	97.32 6

patch_mono-2		89.71 10	90.99 5	85.85 82	96.04 24	63.70 190	95.04 39	95.19 14	86.74 6	91.53 11	95.15 55	73.86 20	97.58 55	93.38 7	92.00 66	96.28 31

CANet		89.61 11	89.99 11	88.46 20	94.39 39	69.71 42	96.53 11	93.78 58	86.89 5	89.68 20	95.78 33	65.94 58	99.10 9	92.99 9	93.91 39	96.58 17

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 15	69.99 32	96.64 8	94.52 35	71.92 183	90.55 16	96.93 10	73.77 21	99.08 11	91.91 20	94.90 20	96.29 29
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
HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 30	68.23 76	95.24 32	94.49 37	82.43 23	88.90 25	96.35 21	71.89 33	98.63 25	88.76 40	96.40 6	96.06 35

NCCC		89.07 14	89.46 14	87.91 24	96.60 10	69.05 55	96.38 14	94.64 32	84.42 11	86.74 36	96.20 26	66.56 54	98.76 22	89.03 39	94.56 31	95.92 40

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 20	67.56 92	94.17 53	94.15 52	68.77 246	90.74 14	97.27 2	76.09 12	98.49 28	90.58 30	94.91 19	96.30 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		88.14 16	88.29 20	87.67 28	93.21 63	68.72 63	93.85 72	94.03 54	74.18 131	91.74 9	96.67 16	65.61 62	98.42 32	89.24 36	96.08 7	95.88 42
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
PS-MVSNAJ		88.14 16	87.61 25	89.71 6	92.06 90	76.72 1	95.75 19	93.26 82	83.86 13	89.55 22	96.06 29	53.55 199	97.89 42	91.10 24	93.31 50	94.54 93

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 62	91.73 102	68.04 80	90.36 210	93.55 71	82.89 18	91.29 12	92.89 112	72.27 30	96.03 128	87.99 43	94.77 24	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		87.96 19	88.37 19	86.70 56	93.51 56	65.32 147	95.15 35	93.84 57	78.17 78	85.93 43	94.80 64	75.80 13	98.21 33	89.38 33	88.78 100	96.59 15

DeepC-MVS_fast	79.48 2	87.95 20	88.00 21	87.79 27	95.86 27	68.32 71	95.74 20	94.11 53	83.82 14	83.49 66	96.19 27	64.53 76	98.44 30	83.42 84	94.88 23	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 21	87.38 29	89.55 11	91.41 113	76.43 3	95.74 20	93.12 90	83.53 16	89.55 22	95.95 31	53.45 203	97.68 47	91.07 25	92.62 57	94.54 93

EPNet		87.84 22	88.38 18	86.23 72	93.30 60	66.05 129	95.26 31	94.84 23	87.09 4	88.06 27	94.53 70	66.79 51	97.34 68	83.89 81	91.68 71	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 23	87.77 23	87.63 33	89.24 158	71.18 19	96.57 10	92.90 98	82.70 22	87.13 32	95.27 49	64.99 67	95.80 133	89.34 34	91.80 69	95.93 39

test_fmvsm_n_1920		87.69 24	88.50 17	85.27 100	87.05 211	63.55 196	93.69 81	91.08 175	84.18 12	90.17 19	97.04 8	67.58 46	97.99 38	95.72 2	90.03 91	94.26 101

APDe-MVS		87.54 25	87.84 22	86.65 57	96.07 23	66.30 125	94.84 44	93.78 58	69.35 237	88.39 26	96.34 22	67.74 45	97.66 50	90.62 29	93.44 48	96.01 38

SD-MVS		87.49 26	87.49 27	87.50 35	93.60 53	68.82 61	93.90 69	92.63 109	76.86 97	87.90 28	95.76 34	66.17 55	97.63 52	89.06 38	91.48 75	96.05 36
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
dcpmvs_2		87.37 27	87.55 26	86.85 49	95.04 32	68.20 77	90.36 210	90.66 187	79.37 57	81.20 82	93.67 96	74.73 15	96.55 111	90.88 27	92.00 66	95.82 43

alignmvs		87.28 28	86.97 33	88.24 23	91.30 114	71.14 21	95.61 24	93.56 70	79.30 58	87.07 34	95.25 51	68.43 38	96.93 98	87.87 44	84.33 139	96.65 13

train_agg		87.21 29	87.42 28	86.60 58	94.18 41	67.28 99	94.16 54	93.51 72	71.87 188	85.52 47	95.33 44	68.19 40	97.27 75	89.09 37	94.90 20	95.25 68

MG-MVS		87.11 30	86.27 38	89.62 7	97.79 1	76.27 4	94.96 42	94.49 37	78.74 73	83.87 65	92.94 110	64.34 77	96.94 96	75.19 140	94.09 35	95.66 46

SF-MVS		87.03 31	87.09 31	86.84 50	92.70 77	67.45 97	93.64 83	93.76 61	70.78 220	86.25 38	96.44 20	66.98 49	97.79 45	88.68 41	94.56 31	95.28 64

CSCG		86.87 32	86.26 39	88.72 14	95.05 31	70.79 24	93.83 77	95.33 12	68.48 250	77.63 123	94.35 79	73.04 24	98.45 29	84.92 72	93.71 44	96.92 11

canonicalmvs		86.85 33	86.25 40	88.66 17	91.80 101	71.92 14	93.54 88	91.71 146	80.26 45	87.55 30	95.25 51	63.59 90	96.93 98	88.18 42	84.34 138	97.11 8

PHI-MVS		86.83 34	86.85 36	86.78 54	93.47 57	65.55 143	95.39 29	95.10 17	71.77 193	85.69 46	96.52 17	62.07 105	98.77 21	86.06 63	95.60 11	96.03 37

SteuartSystems-ACMMP		86.82 35	86.90 34	86.58 60	90.42 129	66.38 122	96.09 16	93.87 56	77.73 85	84.01 64	95.66 36	63.39 92	97.94 39	87.40 50	93.55 47	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 36	86.86 35	86.31 71	93.76 49	67.53 94	96.33 15	93.61 68	82.34 25	81.00 87	93.08 106	63.19 96	97.29 71	87.08 54	91.38 77	94.13 108

test_fmvsmconf_n		86.58 37	87.17 30	84.82 113	85.28 239	62.55 216	94.26 52	89.78 219	83.81 15	87.78 29	96.33 23	65.33 64	96.98 91	94.40 5	87.55 110	94.95 77

jason		86.40 38	86.17 41	87.11 43	86.16 225	70.54 27	95.71 23	92.19 124	82.00 28	84.58 57	94.34 80	61.86 107	95.53 153	87.76 45	90.89 83	95.27 65
jason: jason.
WTY-MVS		86.32 39	85.81 46	87.85 25	92.82 73	69.37 48	95.20 33	95.25 13	82.71 21	81.91 77	94.73 65	67.93 44	97.63 52	79.55 110	82.25 151	96.54 18

MSLP-MVS++		86.27 40	85.91 45	87.35 38	92.01 93	68.97 58	95.04 39	92.70 103	79.04 67	81.50 80	96.50 19	58.98 138	96.78 103	83.49 83	93.93 38	96.29 29

VNet		86.20 41	85.65 49	87.84 26	93.92 46	69.99 32	95.73 22	95.94 6	78.43 75	86.00 42	93.07 107	58.22 143	97.00 87	85.22 67	84.33 139	96.52 19

MVS_111021_HR		86.19 42	85.80 47	87.37 37	93.17 65	69.79 39	93.99 64	93.76 61	79.08 65	78.88 111	93.99 90	62.25 104	98.15 35	85.93 64	91.15 81	94.15 107

CS-MVS-test		86.14 43	87.01 32	83.52 154	92.63 80	59.36 276	95.49 26	91.92 133	80.09 46	85.46 49	95.53 40	61.82 109	95.77 136	86.77 58	93.37 49	95.41 53

ACMMP_NAP		86.05 44	85.80 47	86.80 53	91.58 106	67.53 94	91.79 155	93.49 75	74.93 122	84.61 56	95.30 46	59.42 132	97.92 40	86.13 61	94.92 18	94.94 78

ETV-MVS		86.01 45	86.11 42	85.70 88	90.21 134	67.02 108	93.43 93	91.92 133	81.21 38	84.13 63	94.07 89	60.93 116	95.63 144	89.28 35	89.81 92	94.46 99

APD-MVS		85.93 46	85.99 44	85.76 86	95.98 26	65.21 150	93.59 86	92.58 111	66.54 264	86.17 40	95.88 32	63.83 83	97.00 87	86.39 60	92.94 54	95.06 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 47	85.46 50	87.18 41	88.20 186	72.42 13	92.41 128	92.77 101	82.11 27	80.34 92	93.07 107	68.27 39	95.02 165	78.39 123	93.59 46	94.09 110

CS-MVS		85.80 48	86.65 37	83.27 162	92.00 94	58.92 281	95.31 30	91.86 138	79.97 47	84.82 55	95.40 42	62.26 103	95.51 154	86.11 62	92.08 65	95.37 56

test_fmvsmconf0.1_n		85.71 49	86.08 43	84.62 124	80.83 291	62.33 220	93.84 75	88.81 259	83.50 17	87.00 35	96.01 30	63.36 93	96.93 98	94.04 6	87.29 113	94.61 90

CDPH-MVS		85.71 49	85.46 50	86.46 64	94.75 34	67.19 101	93.89 70	92.83 100	70.90 216	83.09 69	95.28 47	63.62 88	97.36 66	80.63 104	94.18 34	94.84 82

casdiffmvs_mvg		85.66 51	85.18 53	87.09 44	88.22 185	69.35 49	93.74 80	91.89 136	81.47 32	80.10 94	91.45 139	64.80 72	96.35 114	87.23 53	87.69 108	95.58 49
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	77.85 3	85.52 52	85.24 52	86.37 68	88.80 168	66.64 116	92.15 135	93.68 66	81.07 39	76.91 133	93.64 97	62.59 101	98.44 30	85.50 65	92.84 56	94.03 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 53	84.87 59	86.84 50	88.25 183	69.07 54	93.04 103	91.76 143	81.27 37	80.84 89	92.07 130	64.23 78	96.06 126	84.98 71	87.43 112	95.39 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 54	85.08 55	86.06 74	93.09 68	65.65 139	93.89 70	93.41 79	73.75 142	79.94 96	94.68 67	60.61 119	98.03 37	82.63 88	93.72 43	94.52 95

MP-MVS-pluss		85.24 55	85.13 54	85.56 91	91.42 111	65.59 141	91.54 165	92.51 113	74.56 125	80.62 90	95.64 37	59.15 136	97.00 87	86.94 56	93.80 40	94.07 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 56	84.47 60	87.18 41	96.02 25	68.29 72	91.85 153	93.00 95	76.59 104	79.03 107	95.00 56	61.59 110	97.61 54	78.16 124	89.00 99	95.63 47

MP-MVS		85.02 57	84.97 57	85.17 104	92.60 81	64.27 176	93.24 96	92.27 118	73.13 153	79.63 100	94.43 73	61.90 106	97.17 78	85.00 70	92.56 58	94.06 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 58	84.44 61	86.71 55	88.33 180	68.73 62	90.24 215	91.82 142	81.05 40	81.18 83	92.50 119	63.69 86	96.08 125	84.45 76	86.71 122	95.32 60

CHOSEN 1792x2688		84.98 59	83.45 73	89.57 10	89.94 139	75.14 5	92.07 141	92.32 116	81.87 29	75.68 142	88.27 187	60.18 122	98.60 26	80.46 106	90.27 90	94.96 76

EIA-MVS		84.84 60	84.88 58	84.69 120	91.30 114	62.36 219	93.85 72	92.04 128	79.45 54	79.33 104	94.28 83	62.42 102	96.35 114	80.05 107	91.25 80	95.38 55

HFP-MVS		84.73 61	84.40 62	85.72 87	93.75 51	65.01 156	93.50 90	93.19 86	72.19 177	79.22 105	94.93 59	59.04 137	97.67 48	81.55 95	92.21 61	94.49 98

MVS		84.66 62	82.86 88	90.06 2	90.93 120	74.56 6	87.91 260	95.54 10	68.55 248	72.35 183	94.71 66	59.78 128	98.90 18	81.29 101	94.69 30	96.74 12

GST-MVS		84.63 63	84.29 63	85.66 89	92.82 73	65.27 148	93.04 103	93.13 89	73.20 151	78.89 108	94.18 86	59.41 133	97.85 44	81.45 97	92.48 60	93.86 122

EC-MVSNet		84.53 64	85.04 56	83.01 166	89.34 151	61.37 240	94.42 49	91.09 173	77.91 82	83.24 67	94.20 85	58.37 141	95.40 155	85.35 66	91.41 76	92.27 165

ACMMPR		84.37 65	84.06 64	85.28 99	93.56 54	64.37 172	93.50 90	93.15 88	72.19 177	78.85 113	94.86 62	56.69 163	97.45 60	81.55 95	92.20 62	94.02 115

region2R		84.36 66	84.03 65	85.36 97	93.54 55	64.31 174	93.43 93	92.95 96	72.16 180	78.86 112	94.84 63	56.97 158	97.53 58	81.38 99	92.11 64	94.24 102

LFMVS		84.34 67	82.73 90	89.18 12	94.76 33	73.25 9	94.99 41	91.89 136	71.90 185	82.16 76	93.49 101	47.98 250	97.05 82	82.55 89	84.82 134	97.25 7

test_yl		84.28 68	83.16 81	87.64 29	94.52 37	69.24 50	95.78 17	95.09 18	69.19 240	81.09 84	92.88 113	57.00 156	97.44 61	81.11 102	81.76 155	96.23 32

DCV-MVSNet		84.28 68	83.16 81	87.64 29	94.52 37	69.24 50	95.78 17	95.09 18	69.19 240	81.09 84	92.88 113	57.00 156	97.44 61	81.11 102	81.76 155	96.23 32

diffmvs		84.28 68	83.83 66	85.61 90	87.40 204	68.02 81	90.88 194	89.24 238	80.54 43	81.64 79	92.52 118	59.83 127	94.52 190	87.32 51	85.11 132	94.29 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 68	83.36 79	87.02 47	92.22 87	67.74 87	84.65 286	94.50 36	79.15 62	82.23 75	87.93 195	66.88 50	96.94 96	80.53 105	82.20 152	96.39 27

MAR-MVS		84.18 72	83.43 74	86.44 65	96.25 21	65.93 134	94.28 51	94.27 49	74.41 126	79.16 106	95.61 38	53.99 194	98.88 20	69.62 189	93.26 51	94.50 97
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
MVS_Test		84.16 73	83.20 80	87.05 46	91.56 107	69.82 38	89.99 224	92.05 127	77.77 84	82.84 70	86.57 213	63.93 82	96.09 122	74.91 145	89.18 98	95.25 68

CANet_DTU		84.09 74	83.52 68	85.81 83	90.30 132	66.82 111	91.87 151	89.01 252	85.27 8	86.09 41	93.74 94	47.71 254	96.98 91	77.90 126	89.78 94	93.65 127

ET-MVSNet_ETH3D		84.01 75	83.15 83	86.58 60	90.78 125	70.89 23	94.74 46	94.62 33	81.44 35	58.19 312	93.64 97	73.64 23	92.35 266	82.66 87	78.66 180	96.50 23

PVSNet_Blended_VisFu		83.97 76	83.50 70	85.39 96	90.02 137	66.59 119	93.77 78	91.73 144	77.43 93	77.08 132	89.81 169	63.77 85	96.97 93	79.67 109	88.21 104	92.60 153

MTAPA		83.91 77	83.38 78	85.50 92	91.89 99	65.16 152	81.75 309	92.23 119	75.32 117	80.53 91	95.21 53	56.06 171	97.16 79	84.86 73	92.55 59	94.18 104

XVS		83.87 78	83.47 72	85.05 105	93.22 61	63.78 184	92.92 108	92.66 106	73.99 134	78.18 117	94.31 82	55.25 177	97.41 63	79.16 114	91.58 73	93.95 117

Effi-MVS+		83.82 79	82.76 89	86.99 48	89.56 147	69.40 45	91.35 177	86.12 302	72.59 164	83.22 68	92.81 116	59.60 130	96.01 130	81.76 94	87.80 107	95.56 50

test_fmvsmvis_n_1920		83.80 80	83.48 71	84.77 116	82.51 277	63.72 188	91.37 175	83.99 322	81.42 36	77.68 122	95.74 35	58.37 141	97.58 55	93.38 7	86.87 116	93.00 146

EI-MVSNet-Vis-set		83.77 81	83.67 67	84.06 141	92.79 76	63.56 195	91.76 158	94.81 25	79.65 53	77.87 120	94.09 87	63.35 94	97.90 41	79.35 112	79.36 172	90.74 190

MVSFormer		83.75 82	82.88 87	86.37 68	89.24 158	71.18 19	89.07 243	90.69 184	65.80 269	87.13 32	94.34 80	64.99 67	92.67 252	72.83 156	91.80 69	95.27 65

CP-MVS		83.71 83	83.40 77	84.65 121	93.14 66	63.84 182	94.59 47	92.28 117	71.03 214	77.41 126	94.92 60	55.21 180	96.19 118	81.32 100	90.70 85	93.91 119

test_fmvsmconf0.01_n		83.70 84	83.52 68	84.25 137	75.26 342	61.72 234	92.17 134	87.24 291	82.36 24	84.91 54	95.41 41	55.60 175	96.83 102	92.85 10	85.87 128	94.21 103

baseline2		83.68 85	83.42 76	84.48 128	87.37 205	66.00 131	90.06 219	95.93 7	79.71 52	69.08 219	90.39 157	77.92 6	96.28 116	78.91 118	81.38 159	91.16 186

thisisatest0515		83.41 86	82.49 95	86.16 73	89.46 150	68.26 74	93.54 88	94.70 29	74.31 129	75.75 140	90.92 147	72.62 28	96.52 112	69.64 187	81.50 158	93.71 125

PVSNet_BlendedMVS		83.38 87	83.43 74	83.22 163	93.76 49	67.53 94	94.06 59	93.61 68	79.13 63	81.00 87	85.14 228	63.19 96	97.29 71	87.08 54	73.91 218	84.83 289

test2506		83.29 88	82.92 86	84.37 132	88.39 178	63.18 202	92.01 144	91.35 161	77.66 87	78.49 116	91.42 140	64.58 75	95.09 164	73.19 152	89.23 96	94.85 79

iter_conf05		83.27 89	82.70 91	84.98 108	93.32 59	71.84 15	94.16 54	81.76 333	82.74 20	73.83 164	88.40 183	72.77 27	94.61 181	82.10 91	75.21 207	88.48 222

PGM-MVS		83.25 90	82.70 91	84.92 109	92.81 75	64.07 179	90.44 206	92.20 123	71.28 208	77.23 129	94.43 73	55.17 181	97.31 70	79.33 113	91.38 77	93.37 133

HPM-MVS		83.25 90	82.95 85	84.17 139	92.25 86	62.88 211	90.91 191	91.86 138	70.30 226	77.12 130	93.96 91	56.75 161	96.28 116	82.04 92	91.34 79	93.34 134
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-UG-set		83.14 92	82.96 84	83.67 152	92.28 85	63.19 201	91.38 174	94.68 30	79.22 60	76.60 135	93.75 93	62.64 100	97.76 46	78.07 125	78.01 183	90.05 199

VDD-MVS		83.06 93	81.81 104	86.81 52	90.86 123	67.70 88	95.40 28	91.50 156	75.46 114	81.78 78	92.34 126	40.09 290	97.13 80	86.85 57	82.04 153	95.60 48

h-mvs33		83.01 94	82.56 94	84.35 133	89.34 151	62.02 226	92.72 113	93.76 61	81.45 33	82.73 72	92.25 128	60.11 123	97.13 80	87.69 46	62.96 296	93.91 119

PAPM_NR		82.97 95	81.84 103	86.37 68	94.10 44	66.76 114	87.66 265	92.84 99	69.96 230	74.07 161	93.57 99	63.10 98	97.50 59	70.66 180	90.58 87	94.85 79

mPP-MVS		82.96 96	82.44 96	84.52 126	92.83 71	62.92 209	92.76 111	91.85 140	71.52 204	75.61 145	94.24 84	53.48 202	96.99 90	78.97 117	90.73 84	93.64 128

SR-MVS		82.81 97	82.58 93	83.50 157	93.35 58	61.16 243	92.23 133	91.28 165	64.48 278	81.27 81	95.28 47	53.71 198	95.86 132	82.87 86	88.77 101	93.49 131

DP-MVS Recon		82.73 98	81.65 105	85.98 76	97.31 4	67.06 105	95.15 35	91.99 130	69.08 243	76.50 137	93.89 92	54.48 189	98.20 34	70.76 178	85.66 130	92.69 150

CLD-MVS		82.73 98	82.35 98	83.86 145	87.90 193	67.65 90	95.45 27	92.18 125	85.06 9	72.58 176	92.27 127	52.46 210	95.78 134	84.18 77	79.06 175	88.16 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 100	82.38 97	83.73 149	89.25 155	59.58 271	92.24 132	94.89 22	77.96 80	79.86 97	92.38 124	56.70 162	97.05 82	77.26 129	80.86 163	94.55 91

3Dnovator	73.91 6	82.69 101	80.82 116	88.31 22	89.57 146	71.26 18	92.60 121	94.39 44	78.84 70	67.89 240	92.48 122	48.42 245	98.52 27	68.80 199	94.40 33	95.15 70

MVSTER		82.47 102	82.05 99	83.74 147	92.68 78	69.01 56	91.90 150	93.21 83	79.83 48	72.14 184	85.71 225	74.72 16	94.72 176	75.72 136	72.49 229	87.50 233

TESTMET0.1,1		82.41 103	81.98 102	83.72 150	88.08 187	63.74 186	92.70 115	93.77 60	79.30 58	77.61 124	87.57 201	58.19 144	94.08 206	73.91 151	86.68 123	93.33 136

CostFormer		82.33 104	81.15 109	85.86 81	89.01 163	68.46 68	82.39 306	93.01 93	75.59 112	80.25 93	81.57 271	72.03 32	94.96 168	79.06 116	77.48 191	94.16 106

API-MVS		82.28 105	80.53 123	87.54 34	96.13 22	70.59 26	93.63 84	91.04 179	65.72 271	75.45 147	92.83 115	56.11 170	98.89 19	64.10 242	89.75 95	93.15 140

IB-MVS	77.80 4	82.18 106	80.46 125	87.35 38	89.14 160	70.28 30	95.59 25	95.17 16	78.85 69	70.19 207	85.82 223	70.66 35	97.67 48	72.19 167	66.52 269	94.09 110
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
xiu_mvs_v1_base_debu		82.16 107	81.12 110	85.26 101	86.42 219	68.72 63	92.59 123	90.44 194	73.12 154	84.20 60	94.36 75	38.04 304	95.73 138	84.12 78	86.81 117	91.33 179

xiu_mvs_v1_base		82.16 107	81.12 110	85.26 101	86.42 219	68.72 63	92.59 123	90.44 194	73.12 154	84.20 60	94.36 75	38.04 304	95.73 138	84.12 78	86.81 117	91.33 179

xiu_mvs_v1_base_debi		82.16 107	81.12 110	85.26 101	86.42 219	68.72 63	92.59 123	90.44 194	73.12 154	84.20 60	94.36 75	38.04 304	95.73 138	84.12 78	86.81 117	91.33 179

3Dnovator+	73.60 7	82.10 110	80.60 122	86.60 58	90.89 122	66.80 113	95.20 33	93.44 77	74.05 133	67.42 246	92.49 121	49.46 235	97.65 51	70.80 177	91.68 71	95.33 58

MVS_111021_LR		82.02 111	81.52 106	83.51 156	88.42 176	62.88 211	89.77 228	88.93 255	76.78 100	75.55 146	93.10 104	50.31 227	95.38 157	83.82 82	87.02 115	92.26 166

PMMVS		81.98 112	82.04 100	81.78 199	89.76 143	56.17 308	91.13 187	90.69 184	77.96 80	80.09 95	93.57 99	46.33 264	94.99 167	81.41 98	87.46 111	94.17 105

baseline1		81.84 113	81.03 114	84.28 136	91.60 105	66.62 117	91.08 188	91.66 150	81.87 29	74.86 151	91.67 137	69.98 36	94.92 171	71.76 170	64.75 284	91.29 184

EPP-MVSNet		81.79 114	81.52 106	82.61 175	88.77 169	60.21 263	93.02 105	93.66 67	68.52 249	72.90 171	90.39 157	72.19 31	94.96 168	74.93 144	79.29 174	92.67 151

iter_conf_final		81.74 115	80.93 115	84.18 138	92.66 79	69.10 53	92.94 107	82.80 331	79.01 68	74.85 152	88.40 183	61.83 108	94.61 181	79.36 111	76.52 200	88.83 213

test_vis1_n_1920		81.66 116	82.01 101	80.64 226	82.24 280	55.09 316	94.76 45	86.87 293	81.67 31	84.40 59	94.63 68	38.17 301	94.67 180	91.98 19	83.34 145	92.16 169

APD-MVS_3200maxsize		81.64 117	81.32 108	82.59 176	92.36 83	58.74 283	91.39 172	91.01 180	63.35 287	79.72 99	94.62 69	51.82 213	96.14 120	79.71 108	87.93 106	92.89 149

ACMMP		81.49 118	80.67 119	83.93 144	91.71 103	62.90 210	92.13 136	92.22 122	71.79 192	71.68 191	93.49 101	50.32 226	96.96 94	78.47 122	84.22 143	91.93 171
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
CDS-MVSNet		81.43 119	80.74 117	83.52 154	86.26 223	64.45 166	92.09 139	90.65 188	75.83 111	73.95 163	89.81 169	63.97 81	92.91 242	71.27 173	82.82 148	93.20 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 120	79.99 130	85.46 93	90.39 131	68.40 69	86.88 276	90.61 189	74.41 126	70.31 206	84.67 234	63.79 84	92.32 267	73.13 153	85.70 129	95.67 45

ECVR-MVS		81.29 121	80.38 126	84.01 143	88.39 178	61.96 228	92.56 126	86.79 295	77.66 87	76.63 134	91.42 140	46.34 263	95.24 162	74.36 149	89.23 96	94.85 79

thisisatest0530		81.15 122	80.07 127	84.39 131	88.26 182	65.63 140	91.40 170	94.62 33	71.27 209	70.93 197	89.18 174	72.47 29	96.04 127	65.62 231	76.89 197	91.49 175

Fast-Effi-MVS+		81.14 123	80.01 129	84.51 127	90.24 133	65.86 135	94.12 58	89.15 244	73.81 141	75.37 148	88.26 188	57.26 151	94.53 189	66.97 216	84.92 133	93.15 140

HQP-MVS		81.14 123	80.64 120	82.64 174	87.54 200	63.66 193	94.06 59	91.70 148	79.80 49	74.18 157	90.30 159	51.63 217	95.61 146	77.63 127	78.90 176	88.63 218

hse-mvs2		81.12 125	81.11 113	81.16 213	86.52 218	57.48 298	89.40 236	91.16 168	81.45 33	82.73 72	90.49 155	60.11 123	94.58 183	87.69 46	60.41 323	91.41 178

SR-MVS-dyc-post		81.06 126	80.70 118	82.15 190	92.02 91	58.56 285	90.90 192	90.45 191	62.76 294	78.89 108	94.46 71	51.26 221	95.61 146	78.77 120	86.77 120	92.28 162

HyFIR lowres test		81.03 127	79.56 137	85.43 94	87.81 196	68.11 79	90.18 216	90.01 214	70.65 222	72.95 170	86.06 221	63.61 89	94.50 191	75.01 143	79.75 170	93.67 126

nrg030		80.93 128	79.86 132	84.13 140	83.69 265	68.83 60	93.23 97	91.20 166	75.55 113	75.06 150	88.22 191	63.04 99	94.74 175	81.88 93	66.88 266	88.82 216

Vis-MVSNet		80.92 129	79.98 131	83.74 147	88.48 173	61.80 230	93.44 92	88.26 278	73.96 137	77.73 121	91.76 134	49.94 231	94.76 173	65.84 228	90.37 89	94.65 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 130	80.02 128	83.33 160	87.87 194	60.76 251	92.62 120	86.86 294	77.86 83	75.73 141	91.39 142	46.35 262	94.70 179	72.79 158	88.68 102	94.52 95

1314		80.70 131	78.95 148	85.94 78	87.77 198	67.56 92	87.91 260	92.55 112	72.17 179	67.44 245	93.09 105	50.27 228	97.04 85	71.68 172	87.64 109	93.23 138

tpmrst		80.57 132	79.14 147	84.84 112	90.10 136	68.28 73	81.70 310	89.72 226	77.63 89	75.96 139	79.54 303	64.94 69	92.71 249	75.43 138	77.28 194	93.55 129

1112_ss		80.56 133	79.83 133	82.77 170	88.65 170	60.78 249	92.29 130	88.36 272	72.58 165	72.46 180	94.95 57	65.09 66	93.42 230	66.38 222	77.71 185	94.10 109

VDDNet		80.50 134	78.26 156	87.21 40	86.19 224	69.79 39	94.48 48	91.31 162	60.42 312	79.34 103	90.91 148	38.48 299	96.56 110	82.16 90	81.05 161	95.27 65

BH-w/o		80.49 135	79.30 144	84.05 142	90.83 124	64.36 173	93.60 85	89.42 233	74.35 128	69.09 218	90.15 164	55.23 179	95.61 146	64.61 239	86.43 126	92.17 168

test_cas_vis1_n_1920		80.45 136	80.61 121	79.97 243	78.25 326	57.01 304	94.04 63	88.33 273	79.06 66	82.81 71	93.70 95	38.65 296	91.63 281	90.82 28	79.81 168	91.27 185

TAMVS		80.37 137	79.45 140	83.13 165	85.14 242	63.37 197	91.23 182	90.76 183	74.81 124	72.65 174	88.49 180	60.63 118	92.95 237	69.41 191	81.95 154	93.08 143

HQP_MVS		80.34 138	79.75 134	82.12 192	86.94 212	62.42 217	93.13 99	91.31 162	78.81 71	72.53 177	89.14 176	50.66 224	95.55 151	76.74 130	78.53 181	88.39 225

SDMVSNet		80.26 139	78.88 149	84.40 130	89.25 155	67.63 91	85.35 282	93.02 92	76.77 101	70.84 198	87.12 207	47.95 251	96.09 122	85.04 69	74.55 209	89.48 209

HPM-MVS_fast		80.25 140	79.55 139	82.33 182	91.55 108	59.95 266	91.32 179	89.16 243	65.23 275	74.71 154	93.07 107	47.81 253	95.74 137	74.87 147	88.23 103	91.31 183

ab-mvs		80.18 141	78.31 155	85.80 84	88.44 175	65.49 146	83.00 303	92.67 105	71.82 191	77.36 127	85.01 229	54.50 186	96.59 107	76.35 134	75.63 205	95.32 60

IS-MVSNet		80.14 142	79.41 141	82.33 182	87.91 192	60.08 265	91.97 148	88.27 276	72.90 160	71.44 194	91.73 136	61.44 111	93.66 225	62.47 256	86.53 124	93.24 137

test-LLR		80.10 143	79.56 137	81.72 201	86.93 214	61.17 241	92.70 115	91.54 153	71.51 205	75.62 143	86.94 209	53.83 195	92.38 263	72.21 165	84.76 136	91.60 173

PVSNet	73.49 8	80.05 144	78.63 151	84.31 134	90.92 121	64.97 157	92.47 127	91.05 178	79.18 61	72.43 181	90.51 154	37.05 316	94.06 208	68.06 203	86.00 127	93.90 121

UA-Net		80.02 145	79.65 135	81.11 215	89.33 153	57.72 293	86.33 279	89.00 254	77.44 92	81.01 86	89.15 175	59.33 134	95.90 131	61.01 263	84.28 141	89.73 205

test-mter		79.96 146	79.38 143	81.72 201	86.93 214	61.17 241	92.70 115	91.54 153	73.85 139	75.62 143	86.94 209	49.84 233	92.38 263	72.21 165	84.76 136	91.60 173

QAPM		79.95 147	77.39 173	87.64 29	89.63 145	71.41 17	93.30 95	93.70 65	65.34 274	67.39 248	91.75 135	47.83 252	98.96 16	57.71 279	89.81 92	92.54 155

UGNet		79.87 148	78.68 150	83.45 159	89.96 138	61.51 237	92.13 136	90.79 182	76.83 99	78.85 113	86.33 217	38.16 302	96.17 119	67.93 206	87.17 114	92.67 151
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
tpm2		79.80 149	77.95 162	85.34 98	88.28 181	68.26 74	81.56 312	91.42 159	70.11 228	77.59 125	80.50 289	67.40 47	94.26 200	67.34 211	77.35 192	93.51 130

thres200		79.66 150	78.33 154	83.66 153	92.54 82	65.82 137	93.06 101	96.31 3	74.90 123	73.30 167	88.66 178	59.67 129	95.61 146	47.84 316	78.67 179	89.56 208

CPTT-MVS		79.59 151	79.16 146	80.89 224	91.54 109	59.80 268	92.10 138	88.54 270	60.42 312	72.96 169	93.28 103	48.27 246	92.80 246	78.89 119	86.50 125	90.06 198

Test_1112_low_res		79.56 152	78.60 152	82.43 178	88.24 184	60.39 260	92.09 139	87.99 282	72.10 181	71.84 187	87.42 203	64.62 74	93.04 234	65.80 229	77.30 193	93.85 123

tttt0517		79.50 153	78.53 153	82.41 181	87.22 207	61.43 239	89.75 229	94.76 26	69.29 238	67.91 238	88.06 194	72.92 25	95.63 144	62.91 252	73.90 219	90.16 197

FIs		79.47 154	79.41 141	79.67 250	85.95 228	59.40 273	91.68 162	93.94 55	78.06 79	68.96 223	88.28 186	66.61 53	91.77 278	66.20 225	74.99 208	87.82 230

BH-RMVSNet		79.46 155	77.65 165	84.89 110	91.68 104	65.66 138	93.55 87	88.09 280	72.93 158	73.37 166	91.12 146	46.20 266	96.12 121	56.28 283	85.61 131	92.91 148

PCF-MVS	73.15 9	79.29 156	77.63 166	84.29 135	86.06 226	65.96 133	87.03 272	91.10 172	69.86 232	69.79 214	90.64 150	57.54 150	96.59 107	64.37 241	82.29 150	90.32 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 157	79.57 136	78.24 271	88.46 174	52.29 327	90.41 208	89.12 246	74.24 130	69.13 217	91.91 132	65.77 60	90.09 303	59.00 275	88.09 105	92.33 159

114514_t		79.17 158	77.67 164	83.68 151	95.32 29	65.53 144	92.85 110	91.60 152	63.49 285	67.92 237	90.63 152	46.65 259	95.72 142	67.01 215	83.54 144	89.79 203

FA-MVS(test-final)		79.12 159	77.23 175	84.81 115	90.54 127	63.98 181	81.35 315	91.71 146	71.09 213	74.85 152	82.94 252	52.85 206	97.05 82	67.97 204	81.73 157	93.41 132

VPA-MVSNet		79.03 160	78.00 160	82.11 195	85.95 228	64.48 165	93.22 98	94.66 31	75.05 121	74.04 162	84.95 230	52.17 212	93.52 227	74.90 146	67.04 265	88.32 227

OPM-MVS		79.00 161	78.09 158	81.73 200	83.52 268	63.83 183	91.64 164	90.30 201	76.36 107	71.97 186	89.93 168	46.30 265	95.17 163	75.10 141	77.70 186	86.19 261
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 162	78.22 157	81.25 210	85.33 237	62.73 214	89.53 233	93.21 83	72.39 172	72.14 184	90.13 165	60.99 114	94.72 176	67.73 208	72.49 229	86.29 257

AdaColmap		78.94 163	77.00 179	84.76 117	96.34 17	65.86 135	92.66 119	87.97 284	62.18 299	70.56 200	92.37 125	43.53 278	97.35 67	64.50 240	82.86 147	91.05 188

GeoE		78.90 164	77.43 169	83.29 161	88.95 164	62.02 226	92.31 129	86.23 300	70.24 227	71.34 195	89.27 173	54.43 190	94.04 211	63.31 248	80.81 165	93.81 124

miper_enhance_ethall		78.86 165	77.97 161	81.54 205	88.00 191	65.17 151	91.41 168	89.15 244	75.19 119	68.79 226	83.98 243	67.17 48	92.82 244	72.73 159	65.30 275	86.62 254

VPNet		78.82 166	77.53 168	82.70 172	84.52 252	66.44 121	93.93 67	92.23 119	80.46 44	72.60 175	88.38 185	49.18 239	93.13 233	72.47 163	63.97 293	88.55 221

EPNet_dtu		78.80 167	79.26 145	77.43 279	88.06 188	49.71 340	91.96 149	91.95 132	77.67 86	76.56 136	91.28 144	58.51 140	90.20 301	56.37 282	80.95 162	92.39 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 168	77.43 169	82.88 168	92.21 88	64.49 163	92.05 142	96.28 4	73.48 148	71.75 189	88.26 188	60.07 125	95.32 158	45.16 327	77.58 188	88.83 213

TR-MVS		78.77 169	77.37 174	82.95 167	90.49 128	60.88 247	93.67 82	90.07 210	70.08 229	74.51 155	91.37 143	45.69 269	95.70 143	60.12 269	80.32 166	92.29 161

thres400		78.68 170	77.43 169	82.43 178	92.21 88	64.49 163	92.05 142	96.28 4	73.48 148	71.75 189	88.26 188	60.07 125	95.32 158	45.16 327	77.58 188	87.48 234

BH-untuned		78.68 170	77.08 176	83.48 158	89.84 140	63.74 186	92.70 115	88.59 268	71.57 202	66.83 255	88.65 179	51.75 215	95.39 156	59.03 274	84.77 135	91.32 182

OMC-MVS		78.67 172	77.91 163	80.95 222	85.76 233	57.40 300	88.49 252	88.67 265	73.85 139	72.43 181	92.10 129	49.29 238	94.55 188	72.73 159	77.89 184	90.91 189

tpm		78.58 173	77.03 177	83.22 163	85.94 230	64.56 161	83.21 300	91.14 171	78.31 76	73.67 165	79.68 301	64.01 80	92.09 272	66.07 226	71.26 239	93.03 144

OpenMVS	70.45 11	78.54 174	75.92 192	86.41 67	85.93 231	71.68 16	92.74 112	92.51 113	66.49 265	64.56 270	91.96 131	43.88 277	98.10 36	54.61 288	90.65 86	89.44 211

EPMVS		78.49 175	75.98 191	86.02 75	91.21 116	69.68 43	80.23 324	91.20 166	75.25 118	72.48 179	78.11 311	54.65 185	93.69 224	57.66 280	83.04 146	94.69 85

AUN-MVS		78.37 176	77.43 169	81.17 212	86.60 217	57.45 299	89.46 235	91.16 168	74.11 132	74.40 156	90.49 155	55.52 176	94.57 185	74.73 148	60.43 322	91.48 176

thres100view900		78.37 176	77.01 178	82.46 177	91.89 99	63.21 200	91.19 186	96.33 1	72.28 175	70.45 203	87.89 196	60.31 120	95.32 158	45.16 327	77.58 188	88.83 213

GA-MVS		78.33 178	76.23 188	84.65 121	83.65 266	66.30 125	91.44 166	90.14 208	76.01 109	70.32 205	84.02 242	42.50 282	94.72 176	70.98 175	77.00 196	92.94 147

cascas		78.18 179	75.77 194	85.41 95	87.14 209	69.11 52	92.96 106	91.15 170	66.71 263	70.47 201	86.07 220	37.49 310	96.48 113	70.15 183	79.80 169	90.65 191

UniMVSNet_NR-MVSNet		78.15 180	77.55 167	79.98 241	84.46 254	60.26 261	92.25 131	93.20 85	77.50 91	68.88 224	86.61 212	66.10 56	92.13 270	66.38 222	62.55 300	87.54 232

thres600view7		78.00 181	76.66 183	82.03 197	91.93 96	63.69 191	91.30 180	96.33 1	72.43 170	70.46 202	87.89 196	60.31 120	94.92 171	42.64 339	76.64 198	87.48 234

FC-MVSNet-test		77.99 182	78.08 159	77.70 274	84.89 247	55.51 313	90.27 213	93.75 64	76.87 96	66.80 256	87.59 200	65.71 61	90.23 300	62.89 253	73.94 217	87.37 237

Anonymous202405211		77.96 183	75.33 201	85.87 80	93.73 52	64.52 162	94.85 43	85.36 308	62.52 297	76.11 138	90.18 162	29.43 345	97.29 71	68.51 201	77.24 195	95.81 44

cl22		77.94 184	76.78 181	81.42 207	87.57 199	64.93 159	90.67 201	88.86 258	72.45 169	67.63 244	82.68 256	64.07 79	92.91 242	71.79 168	65.30 275	86.44 255

XXY-MVS		77.94 184	76.44 185	82.43 178	82.60 276	64.44 167	92.01 144	91.83 141	73.59 147	70.00 210	85.82 223	54.43 190	94.76 173	69.63 188	68.02 259	88.10 229

MS-PatchMatch		77.90 186	76.50 184	82.12 192	85.99 227	69.95 35	91.75 160	92.70 103	73.97 136	62.58 290	84.44 238	41.11 287	95.78 134	63.76 245	92.17 63	80.62 334

FMVSNet3		77.73 187	76.04 190	82.80 169	91.20 117	68.99 57	91.87 151	91.99 130	73.35 150	67.04 251	83.19 251	56.62 164	92.14 269	59.80 271	69.34 247	87.28 241

miper_ehance_all_eth		77.60 188	76.44 185	81.09 219	85.70 234	64.41 170	90.65 202	88.64 267	72.31 173	67.37 249	82.52 257	64.77 73	92.64 256	70.67 179	65.30 275	86.24 259

UniMVSNet (Re)		77.58 189	76.78 181	79.98 241	84.11 260	60.80 248	91.76 158	93.17 87	76.56 105	69.93 213	84.78 233	63.32 95	92.36 265	64.89 238	62.51 302	86.78 249

PatchmatchNet		77.46 190	74.63 207	85.96 77	89.55 148	70.35 29	79.97 329	89.55 229	72.23 176	70.94 196	76.91 322	57.03 154	92.79 247	54.27 290	81.17 160	94.74 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 191	75.65 197	82.73 171	80.38 297	67.13 104	91.85 153	90.23 205	75.09 120	69.37 215	83.39 249	53.79 197	94.44 192	71.77 169	65.00 281	86.63 253

CHOSEN 280x420		77.35 192	76.95 180	78.55 266	87.07 210	62.68 215	69.71 357	82.95 329	68.80 245	71.48 193	87.27 206	66.03 57	84.00 344	76.47 133	82.81 149	88.95 212

PS-MVSNAJss		77.26 193	76.31 187	80.13 237	80.64 295	59.16 278	90.63 205	91.06 177	72.80 161	68.58 230	84.57 236	53.55 199	93.96 216	72.97 154	71.96 233	87.27 242

gg-mvs-nofinetune		77.18 194	74.31 214	85.80 84	91.42 111	68.36 70	71.78 351	94.72 28	49.61 352	77.12 130	45.92 375	77.41 8	93.98 215	67.62 209	93.16 52	95.05 73

MVP-Stereo		77.12 195	76.23 188	79.79 248	81.72 285	66.34 124	89.29 237	90.88 181	70.56 224	62.01 293	82.88 253	49.34 236	94.13 203	65.55 233	93.80 40	78.88 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 196	75.37 199	82.20 188	89.25 155	62.11 225	82.06 307	89.09 248	76.77 101	70.84 198	87.12 207	41.43 286	95.01 166	67.23 213	74.55 209	89.48 209

dmvs_re		76.93 197	75.36 200	81.61 203	87.78 197	60.71 254	80.00 328	87.99 282	79.42 55	69.02 221	89.47 172	46.77 257	94.32 194	63.38 247	74.45 212	89.81 202

X-MVStestdata		76.86 198	74.13 218	85.05 105	93.22 61	63.78 184	92.92 108	92.66 106	73.99 134	78.18 117	10.19 390	55.25 177	97.41 63	79.16 114	91.58 73	93.95 117

DU-MVS		76.86 198	75.84 193	79.91 244	82.96 274	60.26 261	91.26 181	91.54 153	76.46 106	68.88 224	86.35 215	56.16 168	92.13 270	66.38 222	62.55 300	87.35 239

mvsmamba		76.85 200	75.71 196	80.25 234	83.07 273	59.16 278	91.44 166	80.64 338	76.84 98	67.95 236	86.33 217	46.17 267	94.24 201	76.06 135	72.92 225	87.36 238

Anonymous20240529		76.84 201	74.15 217	84.88 111	91.02 118	64.95 158	93.84 75	91.09 173	53.57 341	73.00 168	87.42 203	35.91 320	97.32 69	69.14 195	72.41 231	92.36 158

c3_l		76.83 202	75.47 198	80.93 223	85.02 245	64.18 178	90.39 209	88.11 279	71.66 195	66.65 257	81.64 269	63.58 91	92.56 257	69.31 193	62.86 297	86.04 266

WR-MVS		76.76 203	75.74 195	79.82 247	84.60 250	62.27 223	92.60 121	92.51 113	76.06 108	67.87 241	85.34 226	56.76 160	90.24 299	62.20 257	63.69 295	86.94 247

v1144		76.73 204	74.88 204	82.27 184	80.23 301	66.60 118	91.68 162	90.21 207	73.69 144	69.06 220	81.89 264	52.73 208	94.40 193	69.21 194	65.23 278	85.80 272

IterMVS-LS		76.49 205	75.18 203	80.43 229	84.49 253	62.74 213	90.64 203	88.80 260	72.40 171	65.16 264	81.72 267	60.98 115	92.27 268	67.74 207	64.65 286	86.29 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 206	74.55 210	82.19 189	79.14 314	67.82 85	90.26 214	89.42 233	73.75 142	68.63 229	81.89 264	51.31 220	94.09 205	71.69 171	64.84 282	84.66 290

v148		76.19 207	74.47 212	81.36 208	80.05 302	64.44 167	91.75 160	90.23 205	73.68 145	67.13 250	80.84 284	55.92 173	93.86 222	68.95 197	61.73 311	85.76 275

Effi-MVS+-dtu		76.14 208	75.28 202	78.72 265	83.22 270	55.17 315	89.87 225	87.78 285	75.42 115	67.98 235	81.43 273	45.08 273	92.52 259	75.08 142	71.63 234	88.48 222

cl____		76.07 209	74.67 205	80.28 232	85.15 241	61.76 232	90.12 217	88.73 263	71.16 210	65.43 261	81.57 271	61.15 112	92.95 237	66.54 219	62.17 304	86.13 264

DIV-MVS_self_test		76.07 209	74.67 205	80.28 232	85.14 242	61.75 233	90.12 217	88.73 263	71.16 210	65.42 262	81.60 270	61.15 112	92.94 241	66.54 219	62.16 306	86.14 262

FMVSNet2		76.07 209	74.01 220	82.26 186	88.85 165	67.66 89	91.33 178	91.61 151	70.84 217	65.98 258	82.25 260	48.03 247	92.00 274	58.46 276	68.73 254	87.10 244

v144192		76.05 212	74.03 219	82.12 192	79.50 308	66.55 120	91.39 172	89.71 227	72.30 174	68.17 233	81.33 276	51.75 215	94.03 213	67.94 205	64.19 288	85.77 273

NR-MVSNet		76.05 212	74.59 208	80.44 228	82.96 274	62.18 224	90.83 196	91.73 144	77.12 95	60.96 297	86.35 215	59.28 135	91.80 277	60.74 264	61.34 315	87.35 239

v1192		75.98 214	73.92 221	82.15 190	79.73 304	66.24 127	91.22 183	89.75 221	72.67 163	68.49 231	81.42 274	49.86 232	94.27 198	67.08 214	65.02 280	85.95 269

FE-MVS		75.97 215	73.02 231	84.82 113	89.78 141	65.56 142	77.44 340	91.07 176	64.55 277	72.66 173	79.85 299	46.05 268	96.69 105	54.97 287	80.82 164	92.21 167

eth_miper_zixun_eth		75.96 216	74.40 213	80.66 225	84.66 249	63.02 204	89.28 238	88.27 276	71.88 187	65.73 259	81.65 268	59.45 131	92.81 245	68.13 202	60.53 320	86.14 262

TranMVSNet+NR-MVSNet		75.86 217	74.52 211	79.89 245	82.44 278	60.64 257	91.37 175	91.37 160	76.63 103	67.65 243	86.21 219	52.37 211	91.55 283	61.84 259	60.81 318	87.48 234

SCA		75.82 218	72.76 235	85.01 107	86.63 216	70.08 31	81.06 317	89.19 241	71.60 201	70.01 209	77.09 320	45.53 270	90.25 296	60.43 266	73.27 221	94.68 86

LPG-MVS_test		75.82 218	74.58 209	79.56 254	84.31 257	59.37 274	90.44 206	89.73 224	69.49 235	64.86 265	88.42 181	38.65 296	94.30 196	72.56 161	72.76 226	85.01 287

GBi-Net		75.65 220	73.83 222	81.10 216	88.85 165	65.11 153	90.01 221	90.32 197	70.84 217	67.04 251	80.25 294	48.03 247	91.54 284	59.80 271	69.34 247	86.64 250

test1		75.65 220	73.83 222	81.10 216	88.85 165	65.11 153	90.01 221	90.32 197	70.84 217	67.04 251	80.25 294	48.03 247	91.54 284	59.80 271	69.34 247	86.64 250

v1921920		75.63 222	73.49 227	82.06 196	79.38 309	66.35 123	91.07 190	89.48 230	71.98 182	67.99 234	81.22 279	49.16 241	93.90 219	66.56 218	64.56 287	85.92 271

ACMP	71.68 10	75.58 223	74.23 216	79.62 252	84.97 246	59.64 269	90.80 197	89.07 250	70.39 225	62.95 286	87.30 205	38.28 300	93.87 220	72.89 155	71.45 237	85.36 282
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 224	73.26 229	81.61 203	80.67 294	66.82 111	89.54 232	89.27 237	71.65 196	63.30 283	80.30 293	54.99 183	94.06 208	67.33 212	62.33 303	83.94 295

tpm cat1		75.30 225	72.21 244	84.58 125	88.52 171	67.77 86	78.16 338	88.02 281	61.88 304	68.45 232	76.37 326	60.65 117	94.03 213	53.77 293	74.11 215	91.93 171

PLC	68.80 14	75.23 226	73.68 225	79.86 246	92.93 70	58.68 284	90.64 203	88.30 274	60.90 309	64.43 274	90.53 153	42.38 283	94.57 185	56.52 281	76.54 199	86.33 256
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 227	72.98 232	81.88 198	79.20 311	66.00 131	90.75 199	89.11 247	71.63 200	67.41 247	81.22 279	47.36 255	93.87 220	65.46 234	64.72 285	85.77 273

Fast-Effi-MVS+-dtu		75.04 228	73.37 228	80.07 238	80.86 290	59.52 272	91.20 185	85.38 307	71.90 185	65.20 263	84.84 232	41.46 285	92.97 236	66.50 221	72.96 224	87.73 231

dp		75.01 229	72.09 245	83.76 146	89.28 154	66.22 128	79.96 330	89.75 221	71.16 210	67.80 242	77.19 319	51.81 214	92.54 258	50.39 301	71.44 238	92.51 156

TAPA-MVS	70.22 12	74.94 230	73.53 226	79.17 259	90.40 130	52.07 328	89.19 241	89.61 228	62.69 296	70.07 208	92.67 117	48.89 244	94.32 194	38.26 353	79.97 167	91.12 187
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 231	72.54 241	81.46 206	80.33 299	66.71 115	89.15 242	89.08 249	70.94 215	63.08 285	79.86 298	52.52 209	94.04 211	65.70 230	62.17 304	83.64 297

XVG-OURS-SEG-HR		74.70 232	73.08 230	79.57 253	78.25 326	57.33 301	80.49 320	87.32 288	63.22 289	68.76 227	90.12 167	44.89 274	91.59 282	70.55 181	74.09 216	89.79 203

RRT_MVS		74.44 233	72.97 233	78.84 264	82.36 279	57.66 295	89.83 227	88.79 262	70.61 223	64.58 269	84.89 231	39.24 292	92.65 255	70.11 184	66.34 270	86.21 260

ACMM	69.62 13	74.34 234	72.73 237	79.17 259	84.25 259	57.87 291	90.36 210	89.93 215	63.17 291	65.64 260	86.04 222	37.79 308	94.10 204	65.89 227	71.52 236	85.55 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 235	72.30 243	80.32 230	91.49 110	61.66 235	90.85 195	80.72 337	56.67 333	63.85 278	90.64 150	46.75 258	90.84 291	53.79 292	75.99 204	88.47 224

XVG-OURS		74.25 236	72.46 242	79.63 251	78.45 324	57.59 297	80.33 322	87.39 287	63.86 282	68.76 227	89.62 171	40.50 289	91.72 279	69.00 196	74.25 214	89.58 206

test_fmvs1		74.07 237	73.69 224	75.22 298	78.91 318	47.34 351	89.06 245	74.69 352	63.68 284	79.41 102	91.59 138	24.36 354	87.77 322	85.22 67	76.26 202	90.55 194

CVMVSNet		74.04 238	74.27 215	73.33 313	85.33 237	43.94 362	89.53 233	88.39 271	54.33 340	70.37 204	90.13 165	49.17 240	84.05 342	61.83 260	79.36 172	91.99 170

Baseline_NR-MVSNet		73.99 239	72.83 234	77.48 278	80.78 292	59.29 277	91.79 155	84.55 315	68.85 244	68.99 222	80.70 285	56.16 168	92.04 273	62.67 254	60.98 317	81.11 328

pmmvs4		73.92 240	71.81 249	80.25 234	79.17 312	65.24 149	87.43 268	87.26 290	67.64 257	63.46 281	83.91 244	48.96 243	91.53 287	62.94 251	65.49 274	83.96 294

D2MVS		73.80 241	72.02 246	79.15 261	79.15 313	62.97 205	88.58 251	90.07 210	72.94 157	59.22 306	78.30 308	42.31 284	92.70 251	65.59 232	72.00 232	81.79 324

CR-MVSNet		73.79 242	70.82 257	82.70 172	83.15 271	67.96 82	70.25 354	84.00 320	73.67 146	69.97 211	72.41 340	57.82 147	89.48 307	52.99 296	73.13 222	90.64 192

test_djsdf		73.76 243	72.56 240	77.39 280	77.00 336	53.93 321	89.07 243	90.69 184	65.80 269	63.92 276	82.03 263	43.14 281	92.67 252	72.83 156	68.53 255	85.57 277

pmmvs5		73.35 244	71.52 251	78.86 263	78.64 322	60.61 258	91.08 188	86.90 292	67.69 254	63.32 282	83.64 245	44.33 276	90.53 293	62.04 258	66.02 272	85.46 280

Anonymous20231211		73.08 245	70.39 261	81.13 214	90.62 126	63.33 198	91.40 170	90.06 212	51.84 346	64.46 273	80.67 287	36.49 318	94.07 207	63.83 244	64.17 289	85.98 268

tt0805		73.07 246	70.73 258	80.07 238	78.37 325	57.05 303	87.78 262	92.18 125	61.23 308	67.04 251	86.49 214	31.35 339	94.58 183	65.06 237	67.12 264	88.57 220

miper_lstm_enhance		73.05 247	71.73 250	77.03 285	83.80 263	58.32 287	81.76 308	88.88 256	69.80 233	61.01 296	78.23 310	57.19 152	87.51 326	65.34 235	59.53 325	85.27 285

jajsoiax		73.05 247	71.51 252	77.67 275	77.46 333	54.83 317	88.81 247	90.04 213	69.13 242	62.85 288	83.51 247	31.16 340	92.75 248	70.83 176	69.80 243	85.43 281

LCM-MVSNet-Re		72.93 249	71.84 248	76.18 294	88.49 172	48.02 346	80.07 327	70.17 363	73.96 137	52.25 336	80.09 297	49.98 230	88.24 316	67.35 210	84.23 142	92.28 162

pm-mvs1		72.89 250	71.09 254	78.26 270	79.10 315	57.62 296	90.80 197	89.30 236	67.66 255	62.91 287	81.78 266	49.11 242	92.95 237	60.29 268	58.89 328	84.22 293

tpmvs		72.88 251	69.76 267	82.22 187	90.98 119	67.05 106	78.22 337	88.30 274	63.10 292	64.35 275	74.98 333	55.09 182	94.27 198	43.25 333	69.57 246	85.34 283

test0.0.03 1		72.76 252	72.71 238	72.88 317	80.25 300	47.99 347	91.22 183	89.45 231	71.51 205	62.51 291	87.66 199	53.83 195	85.06 338	50.16 303	67.84 262	85.58 276

UniMVSNet_ETH3D		72.74 253	70.53 260	79.36 256	78.62 323	56.64 306	85.01 284	89.20 240	63.77 283	64.84 267	84.44 238	34.05 327	91.86 276	63.94 243	70.89 241	89.57 207

mvs_tets		72.71 254	71.11 253	77.52 276	77.41 334	54.52 319	88.45 253	89.76 220	68.76 247	62.70 289	83.26 250	29.49 344	92.71 249	70.51 182	69.62 245	85.34 283

FMVSNet1		72.71 254	69.91 265	81.10 216	83.60 267	65.11 153	90.01 221	90.32 197	63.92 281	63.56 280	80.25 294	36.35 319	91.54 284	54.46 289	66.75 267	86.64 250

test_fmvs1_n		72.69 256	71.92 247	74.99 301	71.15 355	47.08 353	87.34 270	75.67 347	63.48 286	78.08 119	91.17 145	20.16 365	87.87 319	84.65 74	75.57 206	90.01 200

IterMVS		72.65 257	70.83 255	78.09 272	82.17 281	62.96 206	87.64 266	86.28 298	71.56 203	60.44 299	78.85 306	45.42 272	86.66 330	63.30 249	61.83 308	84.65 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 258	72.74 236	72.10 325	87.87 194	49.45 342	88.07 257	89.01 252	72.91 159	63.11 284	88.10 192	63.63 87	85.54 335	32.73 367	69.23 250	81.32 327

PatchMatch-RL		72.06 259	69.98 262	78.28 269	89.51 149	55.70 312	83.49 293	83.39 327	61.24 307	63.72 279	82.76 254	34.77 324	93.03 235	53.37 295	77.59 187	86.12 265

PVSNet_0	68.08 15	71.81 260	68.32 276	82.27 184	84.68 248	62.31 222	88.68 249	90.31 200	75.84 110	57.93 317	80.65 288	37.85 307	94.19 202	69.94 185	29.05 380	90.31 196

MIMVSNet		71.64 261	68.44 274	81.23 211	81.97 284	64.44 167	73.05 350	88.80 260	69.67 234	64.59 268	74.79 334	32.79 331	87.82 320	53.99 291	76.35 201	91.42 177

test_vis1_n		71.63 262	70.73 258	74.31 308	69.63 361	47.29 352	86.91 274	72.11 358	63.21 290	75.18 149	90.17 163	20.40 363	85.76 334	84.59 75	74.42 213	89.87 201

bld_raw_dy_0_64		71.59 263	69.71 268	77.22 284	77.82 332	58.12 289	87.71 264	73.66 354	68.01 252	61.90 295	84.29 240	33.68 328	88.43 314	69.91 186	70.43 242	85.11 286

IterMVS-SCA-FT		71.55 264	69.97 263	76.32 292	81.48 286	60.67 256	87.64 266	85.99 303	66.17 267	59.50 304	78.88 305	45.53 270	83.65 346	62.58 255	61.93 307	84.63 292

v7n		71.31 265	68.65 271	79.28 257	76.40 338	60.77 250	86.71 277	89.45 231	64.17 280	58.77 311	78.24 309	44.59 275	93.54 226	57.76 278	61.75 310	83.52 300

anonymousdsp		71.14 266	69.37 269	76.45 291	72.95 350	54.71 318	84.19 288	88.88 256	61.92 303	62.15 292	79.77 300	38.14 303	91.44 289	68.90 198	67.45 263	83.21 306

F-COLMAP		70.66 267	68.44 274	77.32 281	86.37 222	55.91 310	88.00 258	86.32 297	56.94 331	57.28 320	88.07 193	33.58 329	92.49 260	51.02 299	68.37 256	83.55 298

WR-MVS_H		70.59 268	69.94 264	72.53 319	81.03 289	51.43 331	87.35 269	92.03 129	67.38 258	60.23 301	80.70 285	55.84 174	83.45 348	46.33 323	58.58 330	82.72 313

CP-MVSNet		70.50 269	69.91 265	72.26 322	80.71 293	51.00 334	87.23 271	90.30 201	67.84 253	59.64 303	82.69 255	50.23 229	82.30 356	51.28 298	59.28 326	83.46 302

RPMNet		70.42 270	65.68 288	84.63 123	83.15 271	67.96 82	70.25 354	90.45 191	46.83 360	69.97 211	65.10 359	56.48 167	95.30 161	35.79 358	73.13 222	90.64 192

testing3		70.38 271	70.83 255	69.03 335	85.82 232	43.93 363	90.72 200	90.56 190	68.06 251	60.24 300	86.82 211	64.83 71	84.12 340	26.33 373	64.10 290	79.04 347

tfpnnormal		70.10 272	67.36 279	78.32 268	83.45 269	60.97 246	88.85 246	92.77 101	64.85 276	60.83 298	78.53 307	43.52 279	93.48 228	31.73 370	61.70 312	80.52 335

TransMVSNet (Re)		70.07 273	67.66 278	77.31 282	80.62 296	59.13 280	91.78 157	84.94 312	65.97 268	60.08 302	80.44 290	50.78 223	91.87 275	48.84 309	45.46 358	80.94 330

CL-MVSNet_self_test		69.92 274	68.09 277	75.41 297	73.25 349	55.90 311	90.05 220	89.90 216	69.96 230	61.96 294	76.54 323	51.05 222	87.64 323	49.51 307	50.59 350	82.70 315

DP-MVS		69.90 275	66.48 281	80.14 236	95.36 28	62.93 207	89.56 230	76.11 345	50.27 351	57.69 318	85.23 227	39.68 291	95.73 138	33.35 363	71.05 240	81.78 325

PS-CasMVS		69.86 276	69.13 270	72.07 326	80.35 298	50.57 336	87.02 273	89.75 221	67.27 259	59.19 307	82.28 259	46.58 260	82.24 357	50.69 300	59.02 327	83.39 304

MSDG		69.54 277	65.73 287	80.96 221	85.11 244	63.71 189	84.19 288	83.28 328	56.95 330	54.50 327	84.03 241	31.50 337	96.03 128	42.87 337	69.13 251	83.14 308

PEN-MVS		69.46 278	68.56 272	72.17 324	79.27 310	49.71 340	86.90 275	89.24 238	67.24 262	59.08 308	82.51 258	47.23 256	83.54 347	48.42 311	57.12 331	83.25 305

LS3D		69.17 279	66.40 283	77.50 277	91.92 97	56.12 309	85.12 283	80.37 339	46.96 358	56.50 322	87.51 202	37.25 311	93.71 223	32.52 369	79.40 171	82.68 316

PatchT		69.11 280	65.37 292	80.32 230	82.07 283	63.68 192	67.96 363	87.62 286	50.86 349	69.37 215	65.18 358	57.09 153	88.53 313	41.59 342	66.60 268	88.74 217

KD-MVS_2432*1600		69.03 281	66.37 284	77.01 286	85.56 235	61.06 244	81.44 313	90.25 203	67.27 259	58.00 315	76.53 324	54.49 187	87.63 324	48.04 313	35.77 372	82.34 319

miper_refine_blended		69.03 281	66.37 284	77.01 286	85.56 235	61.06 244	81.44 313	90.25 203	67.27 259	58.00 315	76.53 324	54.49 187	87.63 324	48.04 313	35.77 372	82.34 319

mvsany_test1		68.77 283	68.56 272	69.39 333	73.57 348	45.88 358	80.93 318	60.88 376	59.65 318	71.56 192	90.26 161	43.22 280	75.05 366	74.26 150	62.70 299	87.25 243

ACMH	63.93 17	68.62 284	64.81 294	80.03 240	85.22 240	63.25 199	87.72 263	84.66 314	60.83 310	51.57 339	79.43 304	27.29 350	94.96 168	41.76 340	64.84 282	81.88 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 285	65.41 291	77.96 273	78.69 321	62.93 207	89.86 226	89.17 242	60.55 311	50.27 344	77.73 314	22.60 359	94.06 208	47.18 319	72.65 228	76.88 356

ADS-MVSNet		68.54 286	64.38 301	81.03 220	88.06 188	66.90 110	68.01 361	84.02 319	57.57 325	64.48 271	69.87 350	38.68 294	89.21 309	40.87 344	67.89 260	86.97 245

DTE-MVSNet		68.46 287	67.33 280	71.87 328	77.94 330	49.00 344	86.16 280	88.58 269	66.36 266	58.19 312	82.21 261	46.36 261	83.87 345	44.97 330	55.17 338	82.73 312

our_test_3		68.29 288	64.69 296	79.11 262	78.92 316	64.85 160	88.40 254	85.06 310	60.32 314	52.68 334	76.12 328	40.81 288	89.80 306	44.25 332	55.65 336	82.67 317

Patchmatch-RL test		68.17 289	64.49 299	79.19 258	71.22 354	53.93 321	70.07 356	71.54 362	69.22 239	56.79 321	62.89 362	56.58 165	88.61 310	69.53 190	52.61 345	95.03 75

XVG-ACMP-BASELINE		68.04 290	65.53 290	75.56 296	74.06 347	52.37 326	78.43 334	85.88 304	62.03 301	58.91 310	81.21 281	20.38 364	91.15 290	60.69 265	68.18 257	83.16 307

FMVSNet5		68.04 290	65.66 289	75.18 300	84.43 255	57.89 290	83.54 292	86.26 299	61.83 305	53.64 332	73.30 337	37.15 314	85.08 337	48.99 308	61.77 309	82.56 318

ppachtmachnet_test		67.72 292	63.70 303	79.77 249	78.92 316	66.04 130	88.68 249	82.90 330	60.11 316	55.45 324	75.96 329	39.19 293	90.55 292	39.53 348	52.55 346	82.71 314

ACMH+	65.35 16	67.65 293	64.55 297	76.96 288	84.59 251	57.10 302	88.08 256	80.79 336	58.59 324	53.00 333	81.09 283	26.63 352	92.95 237	46.51 321	61.69 313	80.82 331

pmmvs6		67.57 294	64.76 295	76.00 295	72.82 352	53.37 323	88.71 248	86.78 296	53.19 342	57.58 319	78.03 312	35.33 323	92.41 262	55.56 285	54.88 340	82.21 321

Anonymous20231206		67.53 295	65.78 286	72.79 318	74.95 343	47.59 349	88.23 255	87.32 288	61.75 306	58.07 314	77.29 317	37.79 308	87.29 328	42.91 335	63.71 294	83.48 301

Patchmtry		67.53 295	63.93 302	78.34 267	82.12 282	64.38 171	68.72 358	84.00 320	48.23 357	59.24 305	72.41 340	57.82 147	89.27 308	46.10 324	56.68 335	81.36 326

USDC		67.43 297	64.51 298	76.19 293	77.94 330	55.29 314	78.38 335	85.00 311	73.17 152	48.36 351	80.37 291	21.23 361	92.48 261	52.15 297	64.02 292	80.81 332

ADS-MVSNet2		66.90 298	63.44 305	77.26 283	88.06 188	60.70 255	68.01 361	75.56 349	57.57 325	64.48 271	69.87 350	38.68 294	84.10 341	40.87 344	67.89 260	86.97 245

CMPMVS	48.56 21	66.77 299	64.41 300	73.84 310	70.65 358	50.31 337	77.79 339	85.73 306	45.54 362	44.76 361	82.14 262	35.40 322	90.14 302	63.18 250	74.54 211	81.07 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 300	62.92 308	76.80 290	76.51 337	57.77 292	89.22 239	83.41 326	55.48 337	53.86 331	77.84 313	26.28 353	93.95 217	34.90 360	68.76 253	78.68 350

LTVRE_ROB	59.60 19	66.27 301	63.54 304	74.45 305	84.00 262	51.55 330	67.08 364	83.53 324	58.78 322	54.94 326	80.31 292	34.54 325	93.23 232	40.64 346	68.03 258	78.58 351
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
JIA-IIPM		66.06 302	62.45 311	76.88 289	81.42 288	54.45 320	57.49 376	88.67 265	49.36 353	63.86 277	46.86 374	56.06 171	90.25 296	49.53 306	68.83 252	85.95 269

Patchmatch-test		65.86 303	60.94 317	80.62 227	83.75 264	58.83 282	58.91 375	75.26 351	44.50 365	50.95 343	77.09 320	58.81 139	87.90 318	35.13 359	64.03 291	95.12 71

UnsupCasMVSNet_eth		65.79 304	63.10 306	73.88 309	70.71 357	50.29 338	81.09 316	89.88 217	72.58 165	49.25 349	74.77 335	32.57 333	87.43 327	55.96 284	41.04 365	83.90 296

test_fmvs2		65.78 305	64.84 293	68.60 337	66.54 366	41.71 365	83.27 297	69.81 364	54.38 339	67.91 238	84.54 237	15.35 370	81.22 361	75.65 137	66.16 271	82.88 309

dmvs_testset		65.55 306	66.45 282	62.86 347	79.87 303	22.35 389	76.55 342	71.74 360	77.42 94	55.85 323	87.77 198	51.39 219	80.69 362	31.51 372	65.92 273	85.55 278

pmmvs-eth3d		65.53 307	62.32 312	75.19 299	69.39 362	59.59 270	82.80 304	83.43 325	62.52 297	51.30 341	72.49 338	32.86 330	87.16 329	55.32 286	50.73 349	78.83 349

SixPastTwentyTwo		64.92 308	61.78 315	74.34 307	78.74 320	49.76 339	83.42 296	79.51 342	62.86 293	50.27 344	77.35 315	30.92 342	90.49 294	45.89 325	47.06 355	82.78 310

OurMVSNet-221017-0		64.68 309	62.17 313	72.21 323	76.08 341	47.35 350	80.67 319	81.02 335	56.19 334	51.60 338	79.66 302	27.05 351	88.56 312	53.60 294	53.63 343	80.71 333

test_0402		64.54 310	61.09 316	74.92 302	84.10 261	60.75 252	87.95 259	79.71 341	52.03 344	52.41 335	77.20 318	32.21 335	91.64 280	23.14 375	61.03 316	72.36 364

testgi		64.48 311	62.87 309	69.31 334	71.24 353	40.62 368	85.49 281	79.92 340	65.36 273	54.18 329	83.49 248	23.74 357	84.55 339	41.60 341	60.79 319	82.77 311

RPSCF		64.24 312	61.98 314	71.01 330	76.10 340	45.00 359	75.83 346	75.94 346	46.94 359	58.96 309	84.59 235	31.40 338	82.00 358	47.76 317	60.33 324	86.04 266

EU-MVSNet		64.01 313	63.01 307	67.02 343	74.40 346	38.86 373	83.27 297	86.19 301	45.11 363	54.27 328	81.15 282	36.91 317	80.01 364	48.79 310	57.02 332	82.19 322

test20.03		63.83 314	62.65 310	67.38 342	70.58 359	39.94 369	86.57 278	84.17 317	63.29 288	51.86 337	77.30 316	37.09 315	82.47 354	38.87 352	54.13 342	79.73 341

MDA-MVSNet_test_wron		63.78 315	60.16 318	74.64 303	78.15 328	60.41 259	83.49 293	84.03 318	56.17 336	39.17 370	71.59 346	37.22 312	83.24 351	42.87 337	48.73 352	80.26 338

YYNet1		63.76 316	60.14 319	74.62 304	78.06 329	60.19 264	83.46 295	83.99 322	56.18 335	39.25 369	71.56 347	37.18 313	83.34 349	42.90 336	48.70 353	80.32 337

K. test v3		63.09 317	59.61 321	73.53 312	76.26 339	49.38 343	83.27 297	77.15 344	64.35 279	47.77 353	72.32 342	28.73 346	87.79 321	49.93 305	36.69 371	83.41 303

COLMAP_ROB	57.96 20	62.98 318	59.65 320	72.98 316	81.44 287	53.00 325	83.75 291	75.53 350	48.34 356	48.81 350	81.40 275	24.14 355	90.30 295	32.95 365	60.52 321	75.65 359
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 319	59.08 322	71.10 329	67.19 365	48.72 345	83.91 290	85.23 309	50.38 350	47.84 352	71.22 349	20.74 362	85.51 336	46.47 322	58.75 329	79.06 346

AllTest		61.66 320	58.06 324	72.46 320	79.57 305	51.42 332	80.17 325	68.61 366	51.25 347	45.88 355	81.23 277	19.86 366	86.58 331	38.98 350	57.01 333	79.39 343

UnsupCasMVSNet_bld		61.60 321	57.71 325	73.29 314	68.73 363	51.64 329	78.61 333	89.05 251	57.20 329	46.11 354	61.96 365	28.70 347	88.60 311	50.08 304	38.90 369	79.63 342

MDA-MVSNet-bldmvs		61.54 322	57.70 326	73.05 315	79.53 307	57.00 305	83.08 301	81.23 334	57.57 325	34.91 373	72.45 339	32.79 331	86.26 333	35.81 357	41.95 363	75.89 358

KD-MVS_self_test		60.87 323	58.60 323	67.68 340	66.13 367	39.93 370	75.63 347	84.70 313	57.32 328	49.57 347	68.45 353	29.55 343	82.87 352	48.09 312	47.94 354	80.25 339

TinyColmap		60.32 324	56.42 331	72.00 327	78.78 319	53.18 324	78.36 336	75.64 348	52.30 343	41.59 368	75.82 331	14.76 373	88.35 315	35.84 356	54.71 341	74.46 360

MVS-HIRNet		60.25 325	55.55 332	74.35 306	84.37 256	56.57 307	71.64 352	74.11 353	34.44 373	45.54 359	42.24 380	31.11 341	89.81 304	40.36 347	76.10 203	76.67 357

MIMVSNet1		60.16 326	57.33 327	68.67 336	69.71 360	44.13 361	78.92 332	84.21 316	55.05 338	44.63 362	71.85 344	23.91 356	81.54 360	32.63 368	55.03 339	80.35 336

PM-MVS		59.40 327	56.59 329	67.84 338	63.63 369	41.86 364	76.76 341	63.22 373	59.01 321	51.07 342	72.27 343	11.72 376	83.25 350	61.34 261	50.28 351	78.39 352

new-patchmatchnet		59.30 328	56.48 330	67.79 339	65.86 368	44.19 360	82.47 305	81.77 332	59.94 317	43.65 365	66.20 357	27.67 349	81.68 359	39.34 349	41.40 364	77.50 355

test_vis1_rt		59.09 329	57.31 328	64.43 345	68.44 364	46.02 357	83.05 302	48.63 385	51.96 345	49.57 347	63.86 361	16.30 368	80.20 363	71.21 174	62.79 298	67.07 370

test_fmvs3		56.82 330	54.86 333	62.69 348	53.59 379	35.47 375	75.87 345	65.64 371	43.91 366	55.10 325	71.43 348	6.91 384	74.40 369	68.64 200	52.63 344	78.20 353

DSMNet-mixed		56.78 331	54.44 334	63.79 346	63.21 370	29.44 384	64.43 367	64.10 372	42.12 370	51.32 340	71.60 345	31.76 336	75.04 367	36.23 355	65.20 279	86.87 248

pmmvs3		55.51 332	51.50 337	67.53 341	57.90 377	50.93 335	80.37 321	73.66 354	40.63 371	44.15 364	64.75 360	16.30 368	78.97 365	44.77 331	40.98 367	72.69 362

TDRefinement		55.28 333	51.58 336	66.39 344	59.53 376	46.15 356	76.23 344	72.80 356	44.60 364	42.49 366	76.28 327	15.29 371	82.39 355	33.20 364	43.75 360	70.62 366

LF4IMVS		54.01 334	52.12 335	59.69 349	62.41 372	39.91 371	68.59 359	68.28 368	42.96 369	44.55 363	75.18 332	14.09 375	68.39 375	41.36 343	51.68 347	70.78 365

N_pmnet		50.55 335	49.11 338	54.88 355	77.17 335	4.02 397	84.36 287	2.00 396	48.59 354	45.86 357	68.82 352	32.22 334	82.80 353	31.58 371	51.38 348	77.81 354

new_pmnet		49.31 336	46.44 339	57.93 350	62.84 371	40.74 367	68.47 360	62.96 374	36.48 372	35.09 372	57.81 369	14.97 372	72.18 371	32.86 366	46.44 356	60.88 372

mvsany_test3		48.86 337	46.35 340	56.41 351	46.00 385	31.67 380	62.26 369	47.25 386	43.71 367	45.54 359	68.15 354	10.84 377	64.44 383	57.95 277	35.44 374	73.13 361

test_f		46.58 338	43.45 342	55.96 352	45.18 386	32.05 379	61.18 370	49.49 384	33.39 374	42.05 367	62.48 364	7.00 383	65.56 379	47.08 320	43.21 362	70.27 367

WB-MVS		46.23 339	44.94 341	50.11 359	62.13 373	21.23 391	76.48 343	55.49 378	45.89 361	35.78 371	61.44 367	35.54 321	72.83 370	9.96 385	21.75 381	56.27 374

FPMVS		45.64 340	43.10 344	53.23 357	51.42 382	36.46 374	64.97 366	71.91 359	29.13 377	27.53 377	61.55 366	9.83 379	65.01 381	16.00 382	55.58 337	58.22 373

SSC-MVS		44.51 341	43.35 343	47.99 363	61.01 375	18.90 393	74.12 349	54.36 379	43.42 368	34.10 374	60.02 368	34.42 326	70.39 373	9.14 387	19.57 382	54.68 375

EGC-MVSNET		42.35 342	38.09 345	55.11 354	74.57 344	46.62 355	71.63 353	55.77 377	0.04 391	0.24 392	62.70 363	14.24 374	74.91 368	17.59 379	46.06 357	43.80 377

LCM-MVSNet		40.54 343	35.79 348	54.76 356	36.92 392	30.81 381	51.41 379	69.02 365	22.07 379	24.63 379	45.37 376	4.56 388	65.81 378	33.67 362	34.50 375	67.67 368

APD_test1		40.50 344	37.31 347	50.09 360	51.88 380	35.27 376	59.45 374	52.59 381	21.64 380	26.12 378	57.80 370	4.56 388	66.56 377	22.64 376	39.09 368	48.43 376

test_vis3_rt		40.46 345	37.79 346	48.47 362	44.49 387	33.35 378	66.56 365	32.84 393	32.39 375	29.65 375	39.13 383	3.91 391	68.65 374	50.17 302	40.99 366	43.40 378

ANet_high		40.27 346	35.20 349	55.47 353	34.74 393	34.47 377	63.84 368	71.56 361	48.42 355	18.80 382	41.08 381	9.52 380	64.45 382	20.18 377	8.66 389	67.49 369

test_method		38.59 347	35.16 350	48.89 361	54.33 378	21.35 390	45.32 382	53.71 380	7.41 388	28.74 376	51.62 372	8.70 381	52.87 386	33.73 361	32.89 376	72.47 363

PMMVS2		37.93 348	33.61 351	50.92 358	46.31 384	24.76 387	60.55 373	50.05 382	28.94 378	20.93 380	47.59 373	4.41 390	65.13 380	25.14 374	18.55 384	62.87 371

Gipuma		34.91 349	31.44 352	45.30 364	70.99 356	39.64 372	19.85 386	72.56 357	20.10 382	16.16 386	21.47 387	5.08 387	71.16 372	13.07 383	43.70 361	25.08 384
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 350	29.47 353	42.67 366	41.89 389	30.81 381	52.07 377	43.45 387	15.45 383	18.52 383	44.82 377	2.12 392	58.38 384	16.05 380	30.87 378	38.83 379

APD_test2		32.77 350	29.47 353	42.67 366	41.89 389	30.81 381	52.07 377	43.45 387	15.45 383	18.52 383	44.82 377	2.12 392	58.38 384	16.05 380	30.87 378	38.83 379

PMVS	26.43 22	31.84 352	28.16 355	42.89 365	25.87 395	27.58 385	50.92 380	49.78 383	21.37 381	14.17 387	40.81 382	2.01 394	66.62 376	9.61 386	38.88 370	34.49 383
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 353	24.00 357	26.45 370	43.74 388	18.44 394	60.86 371	39.66 389	15.11 385	9.53 389	22.10 386	6.52 385	46.94 388	8.31 388	10.14 386	13.98 386

MVE	24.84 23	24.35 354	19.77 360	38.09 368	34.56 394	26.92 386	26.57 384	38.87 391	11.73 387	11.37 388	27.44 384	1.37 395	50.42 387	11.41 384	14.60 385	36.93 381
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 355	23.20 359	25.46 371	41.52 391	16.90 395	60.56 372	38.79 392	14.62 386	8.99 390	20.24 389	7.35 382	45.82 389	7.25 389	9.46 387	13.64 387

tmp_tt		22.26 356	23.75 358	17.80 372	5.23 396	12.06 396	35.26 383	39.48 390	2.82 390	18.94 381	44.20 379	22.23 360	24.64 391	36.30 354	9.31 388	16.69 385

cdsmvs_eth3d_5k		19.86 357	26.47 356	0.00 376	0.00 399	0.00 400	0.00 387	93.45 76	0.00 394	0.00 395	95.27 49	49.56 234	0.00 395	0.00 393	0.00 392	0.00 391

wuyk23d		11.30 358	10.95 361	12.33 373	48.05 383	19.89 392	25.89 385	1.92 397	3.58 389	3.12 391	1.37 391	0.64 396	15.77 392	6.23 390	7.77 390	1.35 388

ab-mvs-re		7.91 359	10.55 362	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	94.95 57	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

testmvs		7.23 360	9.62 363	0.06 375	0.04 397	0.02 399	84.98 285	0.02 398	0.03 392	0.18 393	1.21 392	0.01 398	0.02 393	0.14 391	0.01 391	0.13 390

test123		6.92 361	9.21 364	0.08 374	0.03 398	0.05 398	81.65 311	0.01 399	0.02 393	0.14 394	0.85 393	0.03 397	0.02 393	0.12 392	0.00 392	0.16 389

pcd_1.5k_mvsjas		4.46 362	5.95 365	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	53.55 199	0.00 395	0.00 393	0.00 392	0.00 391

test_blank		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

uanet_test		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

DCPMVS		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

sosnet-low-res		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

sosnet		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

uncertanet		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

Regformer		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

uanet		0.00 363	0.00 366	0.00 376	0.00 399	0.00 400	0.00 387	0.00 400	0.00 394	0.00 395	0.00 394	0.00 399	0.00 395	0.00 393	0.00 392	0.00 391

FOURS1					93.95 45	61.77 231	93.96 65	91.92 133	62.14 300	86.57 37

MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 20					99.07 13	92.01 17	94.77 24	96.51 20

PC_three_1452									80.91 41	94.07 2	96.83 14	83.57 4	99.12 5	95.70 4	97.42 4	97.55 4

No_MVS				89.60 8	97.31 4	73.22 10		95.05 20					99.07 13	92.01 17	94.77 24	96.51 20

test_one_0601					96.32 18	69.74 41		94.18 50	71.42 207	90.67 15	96.85 12	74.45 18

eth-test2					0.00 399

eth-test					0.00 399

ZD-MVS					96.63 9	65.50 145		93.50 74	70.74 221	85.26 52	95.19 54	64.92 70	97.29 71	87.51 48	93.01 53

RE-MVS-def			80.48 124		92.02 91	58.56 285	90.90 192	90.45 191	62.76 294	78.89 108	94.46 71	49.30 237		78.77 120	86.77 120	92.28 162

IU-MVS					96.46 11	69.91 36		95.18 15	80.75 42	95.28 1				92.34 14	95.36 13	96.47 24

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 4				97.00 9	83.82 2	99.15 2	95.72 2	97.63 3	97.62 2

test_241102_TWO								94.41 41	71.65 196	92.07 6	97.21 4	74.58 17	99.11 6	92.34 14	95.36 13	96.59 15

test_241102_ONE					96.45 12	69.38 46		94.44 39	71.65 196	92.11 4	97.05 7	76.79 9	99.11 6

9.14			87.63 24		93.86 47		94.41 50	94.18 50	72.76 162	86.21 39	96.51 18	66.64 52	97.88 43	90.08 31	94.04 36

save fliter					93.84 48	67.89 84	95.05 38	92.66 106	78.19 77

test_0728_THIRD									72.48 167	90.55 16	96.93 10	76.24 11	99.08 11	91.53 22	94.99 16	96.43 25

test_0728_SECOND				88.70 15	96.45 12	70.43 28	96.64 8	94.37 45					99.15 2	91.91 20	94.90 20	96.51 20

test0726					96.40 15	69.99 32	96.76 6	94.33 47	71.92 183	91.89 8	97.11 6	73.77 21

GSMVS																94.68 86

test_part2					96.29 19	68.16 78				90.78 13

sam_mvs1												57.85 146				94.68 86

sam_mvs												54.91 184

ambc				69.61 332	61.38 374	41.35 366	49.07 381	85.86 305		50.18 346	66.40 356	10.16 378	88.14 317	45.73 326	44.20 359	79.32 345

MTGPA								92.23 119

test_post1							78.95 331				20.70 388	53.05 204	91.50 288	60.43 266

test_post											23.01 385	56.49 166	92.67 252

patchmatchnet-post											67.62 355	57.62 149	90.25 296

GG-mvs-BLEND				86.53 63	91.91 98	69.67 44	75.02 348	94.75 27		78.67 115	90.85 149	77.91 7	94.56 187	72.25 164	93.74 42	95.36 57

MTMP							93.77 78	32.52 394

gm-plane-assit					88.42 176	67.04 107			78.62 74		91.83 133		97.37 65	76.57 132

test9_res														89.41 32	94.96 17	95.29 62

TEST9					94.18 41	67.28 99	94.16 54	93.51 72	71.75 194	85.52 47	95.33 44	68.01 42	97.27 75

test_8					94.19 40	67.19 101	94.15 57	93.42 78	71.87 188	85.38 50	95.35 43	68.19 40	96.95 95

agg_prior2														86.41 59	94.75 28	95.33 58

agg_prior					94.16 43	66.97 109		93.31 81		84.49 58			96.75 104

TestCases				72.46 320	79.57 305	51.42 332		68.61 366	51.25 347	45.88 355	81.23 277	19.86 366	86.58 331	38.98 350	57.01 333	79.39 343

test_prior4						67.18 103	93.92 68

test_prior2							95.10 37		75.40 116	85.25 53	95.61 38	67.94 43		87.47 49	94.77 24

test_prior				86.42 66	94.71 35	67.35 98		93.10 91					96.84 101			95.05 73

旧先验2							92.00 147		59.37 320	87.54 31			93.47 229	75.39 139

新几何2							91.41 168

新几何1				84.73 118	92.32 84	64.28 175		91.46 158	59.56 319	79.77 98	92.90 111	56.95 159	96.57 109	63.40 246	92.91 55	93.34 134

旧先验1					91.94 95	60.74 253		91.50 156			94.36 75	65.23 65			91.84 68	94.55 91

无先验							92.71 114	92.61 110	62.03 301				97.01 86	66.63 217		93.97 116

原ACMM2							92.01 144

原ACMM1				84.42 129	93.21 63	64.27 176		93.40 80	65.39 272	79.51 101	92.50 119	58.11 145	96.69 105	65.27 236	93.96 37	92.32 160

test222					89.77 142	61.60 236	89.55 231	89.42 233	56.83 332	77.28 128	92.43 123	52.76 207			91.14 82	93.09 142

testdata2													96.09 122	61.26 262

segment_acmp												65.94 58

testdata				81.34 209	89.02 162	57.72 293		89.84 218	58.65 323	85.32 51	94.09 87	57.03 154	93.28 231	69.34 192	90.56 88	93.03 144

testdata1							89.21 240		77.55 90

test12				87.09 44	94.60 36	68.86 59		92.91 97		82.67 74		65.44 63	97.55 57		93.69 45	94.84 82

plane_prior7					86.94 212	61.51 237

plane_prior6					87.23 206	62.32 221						50.66 224

plane_prior5								91.31 162					95.55 151	76.74 130	78.53 181	88.39 225

plane_prior4											89.14 176

plane_prior3						61.95 229			79.09 64	72.53 177

plane_prior2							93.13 99		78.81 71

plane_prior1					87.15 208

plane_prior						62.42 217	93.85 72		79.38 56						78.80 178

n2								0.00 400

nn								0.00 400

door-mid								66.01 370

lessismore_v0				73.72 311	72.93 351	47.83 348		61.72 375		45.86 357	73.76 336	28.63 348	89.81 304	47.75 318	31.37 377	83.53 299

LGP-MVS_train				79.56 254	84.31 257	59.37 274		89.73 224	69.49 235	64.86 265	88.42 181	38.65 296	94.30 196	72.56 161	72.76 226	85.01 287

test11								93.01 93

door								66.57 369

HQP5-MVS						63.66 193

HQP-NCC					87.54 200		94.06 59		79.80 49	74.18 157

ACMP_Plane					87.54 200		94.06 59		79.80 49	74.18 157

BP-MVS														77.63 127

HQP4-MVS										74.18 157			95.61 146			88.63 218

HQP3-MVS								91.70 148							78.90 176

HQP2-MVS												51.63 217

NP-MVS					87.41 203	63.04 203					90.30 159

MDTV_nov1_ep13_2view						59.90 267	80.13 326		67.65 256	72.79 172		54.33 192		59.83 270		92.58 154

MDTV_nov1_ep13			72.61 239		89.06 161	68.48 67	80.33 322	90.11 209	71.84 190	71.81 188	75.92 330	53.01 205	93.92 218	48.04 313	73.38 220

ACMMP++_ref															71.63 234

ACMMP++															69.72 244

Test By Simon												54.21 193

ITE_SJBPF				70.43 331	74.44 345	47.06 354		77.32 343	60.16 315	54.04 330	83.53 246	23.30 358	84.01 343	43.07 334	61.58 314	80.21 340

DeepMVS_CX				34.71 369	51.45 381	24.73 388		28.48 395	31.46 376	17.49 385	52.75 371	5.80 386	42.60 390	18.18 378	19.42 383	36.81 382

High-resolution multi-view benchmark