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.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 29	71.25 62	95.06 1	94.23 5	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 69	93.57 8	94.06 13	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 24	96.63 4	94.88 16

DVP-MVS		89.60 3	90.35 3	87.33 43	95.27 5	71.25 62	93.49 10	92.73 67	77.33 57	92.12 10	95.78 4	80.98 10	97.40 9	89.08 22	96.41 12	93.33 113
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 4	89.91 5	88.30 10	94.28 32	73.46 17	92.90 19	94.11 9	80.27 10	91.35 15	94.16 51	78.35 14	96.77 26	89.59 17	94.22 64	94.67 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 49	94.10 11	75.90 100	92.29 7	95.66 10	81.67 6	97.38 12	87.44 45	96.34 15	93.95 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		89.27 6	89.82 6	87.60 37	94.57 17	70.90 75	93.28 12	94.36 3	75.24 117	92.25 9	95.03 20	81.59 7	97.39 11	86.12 54	95.96 19	94.52 45

MM		89.16 7	89.23 9	88.97 4	90.79 100	73.65 10	92.66 26	91.17 139	86.57 1	87.39 55	94.97 23	71.70 60	97.68 1	92.19 1	95.63 30	95.57 1

APDe-MVS		89.15 8	89.63 7	87.73 29	94.49 20	71.69 54	93.83 4	93.96 16	75.70 105	91.06 17	96.03 1	76.84 16	97.03 19	89.09 21	95.65 29	94.47 47
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 33	73.73 9	92.40 27	93.63 24	74.77 136	92.29 7	95.97 2	74.28 32	97.24 14	88.58 32	96.91 1	94.87 18
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 10	89.15 11	88.63 5	95.01 9	76.03 1	92.38 30	92.85 62	80.26 11	87.78 46	94.27 45	75.89 21	96.81 25	87.45 44	96.44 9	93.05 131

MED-MVS		88.98 11	89.39 8	87.75 28	94.54 18	71.43 60	91.61 47	94.25 4	76.30 92	90.62 19	95.03 20	78.06 15	97.07 18	88.15 38	95.96 19	94.75 29

CNVR-MVS		88.93 12	89.13 12	88.33 8	94.77 12	73.82 8	90.51 68	93.00 49	80.90 7	88.06 41	94.06 56	76.43 18	96.84 23	88.48 35	95.99 18	94.34 54

SteuartSystems-ACMMP		88.72 13	88.86 13	88.32 9	92.14 76	72.96 25	93.73 5	93.67 23	80.19 12	88.10 40	94.80 25	73.76 36	97.11 16	87.51 43	95.82 23	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 14	88.74 14	87.64 36	92.78 68	71.95 51	92.40 27	94.74 2	75.71 103	89.16 27	95.10 18	75.65 23	96.19 49	87.07 46	96.01 17	94.79 23

DeepPCF-MVS	80.84 1	88.10 15	88.56 16	86.73 57	92.24 75	69.03 108	89.57 96	93.39 33	77.53 53	89.79 23	94.12 53	78.98 13	96.58 37	85.66 55	95.72 26	94.58 38

lecture		88.09 16	88.59 15	86.58 60	93.26 54	69.77 94	93.70 6	94.16 7	77.13 65	89.76 24	95.52 14	72.26 51	96.27 46	86.87 47	94.65 50	93.70 91

SD-MVS		88.06 17	88.50 17	86.71 58	92.60 73	72.71 29	91.81 44	93.19 38	77.87 42	90.32 21	94.00 60	74.83 25	93.78 156	87.63 42	94.27 63	93.65 96
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 17	88.01 21	88.24 11	94.41 24	73.62 11	91.22 60	92.83 63	81.50 5	85.79 69	93.47 77	73.02 44	97.00 20	84.90 61	94.94 42	94.10 66

ACMMP_NAP		88.05 19	88.08 20	87.94 19	93.70 43	73.05 22	90.86 63	93.59 26	76.27 93	88.14 39	95.09 19	71.06 70	96.67 31	87.67 41	96.37 14	94.09 67

TSAR-MVS + MP.		88.02 20	88.11 19	87.72 31	93.68 45	72.13 48	91.41 56	92.35 85	74.62 140	88.90 30	93.85 68	75.75 22	96.00 57	87.80 40	94.63 52	95.04 10
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 21	87.85 23	88.20 12	94.39 26	73.33 19	93.03 17	93.81 20	76.81 74	85.24 74	94.32 42	71.76 58	96.93 21	85.53 58	95.79 24	94.32 56

MP-MVS		87.71 22	87.64 25	87.93 21	94.36 28	73.88 6	92.71 25	92.65 73	77.57 49	83.84 106	94.40 39	72.24 52	96.28 45	85.65 56	95.30 37	93.62 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MGCNet		87.69 23	87.55 28	88.12 13	89.45 136	71.76 53	91.47 55	89.54 195	82.14 3	86.65 63	94.28 44	68.28 110	97.46 6	90.81 6	95.31 36	95.15 8

MP-MVS-pluss		87.67 24	87.72 24	87.54 38	93.64 46	72.04 50	89.80 87	93.50 28	75.17 124	86.34 65	95.29 17	70.86 72	96.00 57	88.78 30	96.04 16	94.58 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 25	87.47 30	87.94 19	94.58 16	73.54 15	93.04 15	93.24 36	76.78 76	84.91 79	94.44 37	70.78 73	96.61 34	84.53 69	94.89 44	93.66 92

reproduce-ours		87.47 26	87.61 26	87.07 48	93.27 52	71.60 55	91.56 52	93.19 38	74.98 127	88.96 28	95.54 12	71.20 68	96.54 38	86.28 51	93.49 69	93.06 129

our_new_method		87.47 26	87.61 26	87.07 48	93.27 52	71.60 55	91.56 52	93.19 38	74.98 127	88.96 28	95.54 12	71.20 68	96.54 38	86.28 51	93.49 69	93.06 129

ACMMPR		87.44 28	87.23 35	88.08 15	94.64 13	73.59 12	93.04 15	93.20 37	76.78 76	84.66 86	94.52 30	68.81 101	96.65 32	84.53 69	94.90 43	94.00 72

APD-MVS		87.44 28	87.52 29	87.19 45	94.24 34	72.39 41	91.86 43	92.83 63	73.01 187	88.58 32	94.52 30	73.36 37	96.49 40	84.26 72	95.01 39	92.70 145
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 30	87.26 33	87.89 24	94.12 38	72.97 24	92.39 29	93.43 31	76.89 72	84.68 83	93.99 62	70.67 75	96.82 24	84.18 76	95.01 39	93.90 78

region2R		87.42 30	87.20 36	88.09 14	94.63 14	73.55 13	93.03 17	93.12 43	76.73 79	84.45 91	94.52 30	69.09 95	96.70 29	84.37 71	94.83 47	94.03 70

fmvsm_s_conf0.5_n_9		87.39 32	87.95 22	85.70 79	89.48 135	67.88 151	88.59 143	89.05 223	80.19 12	90.70 18	95.40 15	74.56 27	93.92 149	91.54 2	92.07 89	95.31 5

MCST-MVS		87.37 33	87.25 34	87.73 29	94.53 19	72.46 40	89.82 85	93.82 19	73.07 185	84.86 82	92.89 92	76.22 19	96.33 43	84.89 63	95.13 38	94.40 50

reproduce_model		87.28 34	87.39 32	86.95 52	93.10 60	71.24 66	91.60 48	93.19 38	74.69 137	88.80 31	95.61 11	70.29 79	96.44 41	86.20 53	93.08 73	93.16 123

MTAPA		87.23 35	87.00 38	87.90 22	94.18 37	74.25 5	86.58 221	92.02 101	79.45 22	85.88 67	94.80 25	68.07 112	96.21 48	86.69 49	95.34 34	93.23 116

XVS		87.18 36	86.91 43	88.00 17	94.42 22	73.33 19	92.78 21	92.99 52	79.14 26	83.67 110	94.17 50	67.45 119	96.60 35	83.06 84	94.50 55	94.07 68

HPM-MVS		87.11 37	86.98 40	87.50 41	93.88 41	72.16 47	92.19 36	93.33 34	76.07 97	83.81 107	93.95 65	69.77 86	96.01 56	85.15 59	94.66 49	94.32 56
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 37	86.92 42	87.68 35	94.20 36	73.86 7	93.98 3	92.82 66	76.62 82	83.68 109	94.46 34	67.93 114	95.95 60	84.20 75	94.39 59	93.23 116

DeepC-MVS	79.81 2	87.08 39	86.88 44	87.69 34	91.16 89	72.32 45	90.31 77	93.94 17	77.12 66	82.82 124	94.23 48	72.13 54	97.09 17	84.83 64	95.37 33	93.65 96
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 40	86.62 48	87.76 27	93.52 48	72.37 43	91.26 57	93.04 44	76.62 82	84.22 97	93.36 81	71.44 64	96.76 27	80.82 110	95.33 35	94.16 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		86.78 41	86.99 39	86.15 68	91.24 88	67.61 160	90.51 68	92.90 59	77.26 59	87.44 54	91.63 125	71.27 67	96.06 52	85.62 57	95.01 39	94.78 24

SR-MVS		86.73 42	86.67 46	86.91 53	94.11 39	72.11 49	92.37 31	92.56 78	74.50 141	86.84 62	94.65 29	67.31 121	95.77 62	84.80 65	92.85 76	92.84 143

CS-MVS		86.69 43	86.95 41	85.90 76	90.76 101	67.57 162	92.83 20	93.30 35	79.67 19	84.57 90	92.27 104	71.47 63	95.02 98	84.24 74	93.46 71	95.13 9

PGM-MVS		86.68 44	86.27 53	87.90 22	94.22 35	73.38 18	90.22 79	93.04 44	75.53 108	83.86 105	94.42 38	67.87 116	96.64 33	82.70 95	94.57 54	93.66 92

mPP-MVS		86.67 45	86.32 51	87.72 31	94.41 24	73.55 13	92.74 23	92.22 91	76.87 73	82.81 125	94.25 47	66.44 132	96.24 47	82.88 89	94.28 62	93.38 109

fmvsm_s_conf0.5_n_8		86.56 46	87.17 37	84.73 118	87.76 218	65.62 209	89.20 111	92.21 93	79.94 17	89.74 25	94.86 24	68.63 104	94.20 134	90.83 5	91.39 101	94.38 51

CANet		86.45 47	86.10 59	87.51 40	90.09 113	70.94 73	89.70 91	92.59 77	81.78 4	81.32 147	91.43 135	70.34 77	97.23 15	84.26 72	93.36 72	94.37 52

train_agg		86.43 48	86.20 54	87.13 47	93.26 54	72.96 25	88.75 135	91.89 109	68.69 291	85.00 77	93.10 85	74.43 29	95.41 78	84.97 60	95.71 27	93.02 133

PHI-MVS		86.43 48	86.17 57	87.24 44	90.88 97	70.96 71	92.27 35	94.07 12	72.45 193	85.22 75	91.90 114	69.47 89	96.42 42	83.28 83	95.94 21	94.35 53

CSCG		86.41 50	86.19 56	87.07 48	92.91 65	72.48 37	90.81 64	93.56 27	73.95 156	83.16 117	91.07 147	75.94 20	95.19 87	79.94 121	94.38 60	93.55 104

fmvsm_s_conf0.5_n_10		86.38 51	86.76 45	85.24 93	87.33 235	67.30 172	89.50 98	90.98 144	76.25 94	90.56 20	94.75 27	68.38 107	94.24 133	90.80 7	92.32 86	94.19 61

fmvsm_s_conf0.5_n_3		86.36 52	87.46 31	83.09 196	87.08 249	65.21 218	89.09 120	90.21 172	79.67 19	89.98 22	95.02 22	73.17 41	91.71 258	91.30 3	91.60 96	92.34 162

NormalMVS		86.29 53	85.88 63	87.52 39	93.26 54	72.47 38	91.65 45	92.19 95	79.31 24	84.39 93	92.18 106	64.64 154	95.53 69	80.70 113	94.65 50	94.56 42

SPE-MVS-test		86.29 53	86.48 49	85.71 78	91.02 93	67.21 178	92.36 32	93.78 21	78.97 33	83.51 113	91.20 142	70.65 76	95.15 89	81.96 99	94.89 44	94.77 25

fmvsm_l_conf0.5_n_3		86.02 55	86.32 51	85.14 96	87.20 240	68.54 128	89.57 96	90.44 161	75.31 116	87.49 52	94.39 40	72.86 46	92.72 214	89.04 26	90.56 115	94.16 62

EC-MVSNet		86.01 56	86.38 50	84.91 110	89.31 145	66.27 192	92.32 33	93.63 24	79.37 23	84.17 99	91.88 115	69.04 99	95.43 75	83.93 78	93.77 67	93.01 134

MVSMamba_PlusPlus		85.99 57	85.96 62	86.05 71	91.09 90	67.64 159	89.63 94	92.65 73	72.89 190	84.64 87	91.71 120	71.85 56	96.03 53	84.77 66	94.45 58	94.49 46

casdiffmvs_mvg		85.99 57	86.09 60	85.70 79	87.65 223	67.22 177	88.69 139	93.04 44	79.64 21	85.33 73	92.54 101	73.30 38	94.50 122	83.49 80	91.14 105	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 59	85.88 63	86.22 65	92.69 70	69.53 97	91.93 40	92.99 52	73.54 169	85.94 66	94.51 33	65.80 144	95.61 65	83.04 86	92.51 81	93.53 106

test_fmvsmconf_n		85.92 60	86.04 61	85.57 85	85.03 305	69.51 98	89.62 95	90.58 156	73.42 173	87.75 48	94.02 58	72.85 47	93.24 184	90.37 8	90.75 112	93.96 73

sasdasda		85.91 61	85.87 65	86.04 72	89.84 123	69.44 103	90.45 74	93.00 49	76.70 80	88.01 43	91.23 139	73.28 39	93.91 150	81.50 102	88.80 147	94.77 25

canonicalmvs		85.91 61	85.87 65	86.04 72	89.84 123	69.44 103	90.45 74	93.00 49	76.70 80	88.01 43	91.23 139	73.28 39	93.91 150	81.50 102	88.80 147	94.77 25

ACMMP		85.89 63	85.39 74	87.38 42	93.59 47	72.63 33	92.74 23	93.18 42	76.78 76	80.73 161	93.82 69	64.33 156	96.29 44	82.67 96	90.69 113	93.23 116
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
fmvsm_l_conf0.5_n_9		85.84 64	86.63 47	83.46 179	87.12 248	66.01 196	88.56 145	89.43 199	75.59 107	89.32 26	94.32 42	72.89 45	91.21 283	90.11 11	92.33 85	93.16 123

SR-MVS-dyc-post		85.77 65	85.61 70	86.23 64	93.06 62	70.63 80	91.88 41	92.27 87	73.53 170	85.69 70	94.45 35	65.00 152	95.56 66	82.75 91	91.87 92	92.50 155

CDPH-MVS		85.76 66	85.29 79	87.17 46	93.49 49	71.08 67	88.58 144	92.42 83	68.32 298	84.61 88	93.48 75	72.32 50	96.15 51	79.00 129	95.43 32	94.28 58

TSAR-MVS + GP.		85.71 67	85.33 76	86.84 54	91.34 86	72.50 36	89.07 121	87.28 274	76.41 85	85.80 68	90.22 175	74.15 34	95.37 83	81.82 100	91.88 91	92.65 149

dcpmvs_2		85.63 68	86.15 58	84.06 154	91.71 82	64.94 228	86.47 224	91.87 111	73.63 165	86.60 64	93.02 90	76.57 17	91.87 252	83.36 81	92.15 87	95.35 3

test_fmvsmconf0.1_n		85.61 69	85.65 69	85.50 86	82.99 357	69.39 105	89.65 92	90.29 170	73.31 177	87.77 47	94.15 52	71.72 59	93.23 185	90.31 9	90.67 114	93.89 79

fmvsm_s_conf0.5_n_6		85.55 70	86.20 54	83.60 174	87.32 237	65.13 221	88.86 127	91.63 123	75.41 112	88.23 38	93.45 78	68.56 105	92.47 225	89.52 18	92.78 77	93.20 121

alignmvs		85.48 71	85.32 77	85.96 75	89.51 132	69.47 100	89.74 89	92.47 79	76.17 95	87.73 50	91.46 134	70.32 78	93.78 156	81.51 101	88.95 144	94.63 35

3Dnovator+	77.84 4	85.48 71	84.47 90	88.51 7	91.08 91	73.49 16	93.18 14	93.78 21	80.79 8	76.66 243	93.37 80	60.40 226	96.75 28	77.20 151	93.73 68	95.29 6

MSLP-MVS++		85.43 73	85.76 67	84.45 126	91.93 79	70.24 83	90.71 65	92.86 61	77.46 55	84.22 97	92.81 96	67.16 123	92.94 205	80.36 116	94.35 61	90.16 247

DELS-MVS		85.41 74	85.30 78	85.77 77	88.49 180	67.93 150	85.52 257	93.44 30	78.70 34	83.63 112	89.03 208	74.57 26	95.71 64	80.26 118	94.04 65	93.66 92
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
fmvsm_s_conf0.5_n_4		85.39 75	85.75 68	84.30 134	86.70 260	65.83 202	88.77 133	89.78 184	75.46 111	88.35 34	93.73 71	69.19 94	93.06 200	91.30 3	88.44 156	94.02 71

SymmetryMVS		85.38 76	84.81 84	87.07 48	91.47 85	72.47 38	91.65 45	88.06 254	79.31 24	84.39 93	92.18 106	64.64 154	95.53 69	80.70 113	90.91 110	93.21 119

HPM-MVS_fast		85.35 77	84.95 83	86.57 61	93.69 44	70.58 82	92.15 38	91.62 124	73.89 159	82.67 127	94.09 54	62.60 178	95.54 68	80.93 108	92.93 75	93.57 102

test_fmvsm_n_1920		85.29 78	85.34 75	85.13 99	86.12 275	69.93 90	88.65 141	90.78 152	69.97 258	88.27 36	93.98 63	71.39 65	91.54 268	88.49 34	90.45 117	93.91 76

fmvsm_s_conf0.5_n_5		85.22 79	85.55 71	84.25 141	86.26 269	67.40 168	89.18 112	89.31 208	72.50 192	88.31 35	93.86 67	69.66 87	91.96 246	89.81 13	91.05 106	93.38 109

MVS_111021_HR		85.14 80	84.75 85	86.32 63	91.65 83	72.70 30	85.98 239	90.33 167	76.11 96	82.08 134	91.61 128	71.36 66	94.17 137	81.02 107	92.58 80	92.08 178

casdiffmvs		85.11 81	85.14 80	85.01 103	87.20 240	65.77 206	87.75 177	92.83 63	77.84 43	84.36 96	92.38 103	72.15 53	93.93 148	81.27 106	90.48 116	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		85.08 82	84.96 82	85.45 87	92.07 77	68.07 143	89.78 88	90.86 150	82.48 2	84.60 89	93.20 84	69.35 91	95.22 86	71.39 223	90.88 111	93.07 128

MGCFI-Net		85.06 83	85.51 72	83.70 172	89.42 137	63.01 280	89.43 101	92.62 76	76.43 84	87.53 51	91.34 137	72.82 48	93.42 177	81.28 105	88.74 150	94.66 34

DPM-MVS		84.93 84	84.29 91	86.84 54	90.20 111	73.04 23	87.12 197	93.04 44	69.80 262	82.85 123	91.22 141	73.06 43	96.02 55	76.72 163	94.63 52	91.46 199

baseline		84.93 84	84.98 81	84.80 115	87.30 238	65.39 215	87.30 193	92.88 60	77.62 47	84.04 102	92.26 105	71.81 57	93.96 142	81.31 104	90.30 119	95.03 11

ETV-MVS		84.90 86	84.67 86	85.59 84	89.39 140	68.66 125	88.74 137	92.64 75	79.97 16	84.10 100	85.71 304	69.32 92	95.38 80	80.82 110	91.37 102	92.72 144

test_fmvsmconf0.01_n		84.73 87	84.52 89	85.34 90	80.25 399	69.03 108	89.47 99	89.65 191	73.24 181	86.98 60	94.27 45	66.62 128	93.23 185	90.26 10	89.95 127	93.78 88

fmvsm_l_conf0.5_n		84.47 88	84.54 87	84.27 138	85.42 292	68.81 114	88.49 147	87.26 276	68.08 300	88.03 42	93.49 74	72.04 55	91.77 254	88.90 28	89.14 143	92.24 169

BP-MVS1		84.32 89	83.71 99	86.17 66	87.84 211	67.85 152	89.38 106	89.64 192	77.73 45	83.98 103	92.12 111	56.89 256	95.43 75	84.03 77	91.75 95	95.24 7

EI-MVSNet-Vis-set		84.19 90	83.81 96	85.31 91	88.18 192	67.85 152	87.66 179	89.73 189	80.05 15	82.95 120	89.59 193	70.74 74	94.82 106	80.66 115	84.72 220	93.28 115

fmvsm_l_conf0.5_n_a		84.13 91	84.16 92	84.06 154	85.38 293	68.40 131	88.34 155	86.85 286	67.48 307	87.48 53	93.40 79	70.89 71	91.61 259	88.38 36	89.22 140	92.16 176

fmvsm_s_conf0.5_n_2		84.04 92	84.11 93	83.81 170	86.17 273	65.00 226	86.96 203	87.28 274	74.35 145	88.25 37	94.23 48	61.82 194	92.60 217	89.85 12	88.09 161	93.84 82

test_fmvsmvis_n_1920		84.02 93	83.87 95	84.49 125	84.12 323	69.37 106	88.15 163	87.96 257	70.01 256	83.95 104	93.23 83	68.80 102	91.51 271	88.61 31	89.96 126	92.57 150

viewcassd2359sk11		83.89 94	83.74 98	84.34 131	87.76 218	64.91 231	86.30 231	92.22 91	75.47 110	83.04 119	91.52 130	70.15 81	93.53 169	79.26 125	87.96 162	94.57 40

nrg030		83.88 95	83.53 103	84.96 105	86.77 258	69.28 107	90.46 73	92.67 70	74.79 135	82.95 120	91.33 138	72.70 49	93.09 198	80.79 112	79.28 302	92.50 155

EI-MVSNet-UG-set		83.81 96	83.38 106	85.09 101	87.87 209	67.53 164	87.44 188	89.66 190	79.74 18	82.23 131	89.41 202	70.24 80	94.74 112	79.95 120	83.92 235	92.99 136

fmvsm_s_conf0.1_n_2		83.80 97	83.79 97	83.83 168	85.62 286	64.94 228	87.03 200	86.62 292	74.32 146	87.97 45	94.33 41	60.67 218	92.60 217	89.72 14	87.79 165	93.96 73

fmvsm_s_conf0.5_n		83.80 97	83.71 99	84.07 151	86.69 261	67.31 171	89.46 100	83.07 346	71.09 223	86.96 61	93.70 72	69.02 100	91.47 273	88.79 29	84.62 222	93.44 108

viewmacassd2359aftdt		83.76 99	83.66 101	84.07 151	86.59 264	64.56 236	86.88 208	91.82 114	75.72 102	83.34 114	92.15 110	68.24 111	92.88 208	79.05 126	89.15 142	94.77 25

CPTT-MVS		83.73 100	83.33 108	84.92 109	93.28 51	70.86 76	92.09 39	90.38 163	68.75 290	79.57 176	92.83 94	60.60 222	93.04 203	80.92 109	91.56 99	90.86 217

EPNet		83.72 101	82.92 115	86.14 70	84.22 321	69.48 99	91.05 62	85.27 310	81.30 6	76.83 238	91.65 123	66.09 139	95.56 66	76.00 170	93.85 66	93.38 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmanbaseed2359cas		83.66 102	83.55 102	84.00 162	86.81 256	64.53 237	86.65 218	91.75 119	74.89 131	83.15 118	91.68 121	68.74 103	92.83 212	79.02 127	89.24 139	94.63 35

patch_mono-2		83.65 103	84.54 87	80.99 264	90.06 118	65.83 202	84.21 291	88.74 239	71.60 211	85.01 76	92.44 102	74.51 28	83.50 391	82.15 98	92.15 87	93.64 98

HQP_MVS		83.64 104	83.14 109	85.14 96	90.08 114	68.71 121	91.25 58	92.44 80	79.12 28	78.92 188	91.00 152	60.42 224	95.38 80	78.71 133	86.32 191	91.33 200

fmvsm_s_conf0.5_n_a		83.63 105	83.41 105	84.28 136	86.14 274	68.12 141	89.43 101	82.87 351	70.27 251	87.27 57	93.80 70	69.09 95	91.58 261	88.21 37	83.65 243	93.14 126

Effi-MVS+		83.62 106	83.08 110	85.24 93	88.38 186	67.45 165	88.89 126	89.15 219	75.50 109	82.27 130	88.28 233	69.61 88	94.45 125	77.81 143	87.84 164	93.84 82

fmvsm_s_conf0.1_n		83.56 107	83.38 106	84.10 145	84.86 307	67.28 173	89.40 105	83.01 347	70.67 235	87.08 58	93.96 64	68.38 107	91.45 274	88.56 33	84.50 223	93.56 103

GDP-MVS		83.52 108	82.64 120	86.16 67	88.14 195	68.45 130	89.13 118	92.69 68	72.82 191	83.71 108	91.86 117	55.69 263	95.35 84	80.03 119	89.74 131	94.69 30

OPM-MVS		83.50 109	82.95 114	85.14 96	88.79 170	70.95 72	89.13 118	91.52 128	77.55 52	80.96 155	91.75 119	60.71 216	94.50 122	79.67 124	86.51 189	89.97 263
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 110	82.80 117	85.43 88	90.25 110	68.74 119	90.30 78	90.13 175	76.33 91	80.87 158	92.89 92	61.00 213	94.20 134	72.45 215	90.97 108	93.35 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 111	83.45 104	83.28 186	92.74 69	62.28 297	88.17 161	89.50 197	75.22 118	81.49 145	92.74 100	66.75 126	95.11 92	72.85 205	91.58 98	92.45 159

EPP-MVSNet		83.40 112	83.02 112	84.57 121	90.13 112	64.47 242	92.32 33	90.73 153	74.45 144	79.35 182	91.10 145	69.05 98	95.12 90	72.78 206	87.22 175	94.13 64

3Dnovator	76.31 5	83.38 113	82.31 127	86.59 59	87.94 206	72.94 28	90.64 66	92.14 100	77.21 62	75.47 269	92.83 94	58.56 238	94.72 113	73.24 202	92.71 79	92.13 177

viewdifsd2359ckpt09		83.34 114	82.55 122	85.70 79	87.64 224	67.72 157	88.43 148	91.68 121	71.91 205	81.65 143	90.68 159	67.10 124	94.75 111	76.17 166	87.70 167	94.62 37

fmvsm_s_conf0.5_n_7		83.34 114	84.03 94	81.28 255	85.73 283	65.13 221	85.40 258	89.90 182	74.96 129	82.13 133	93.89 66	66.65 127	87.92 345	86.56 50	91.05 106	90.80 218

fmvsm_s_conf0.1_n_a		83.32 116	82.99 113	84.28 136	83.79 331	68.07 143	89.34 108	82.85 352	69.80 262	87.36 56	94.06 56	68.34 109	91.56 264	87.95 39	83.46 249	93.21 119

KinetiMVS		83.31 117	82.61 121	85.39 89	87.08 249	67.56 163	88.06 165	91.65 122	77.80 44	82.21 132	91.79 118	57.27 251	94.07 140	77.77 144	89.89 129	94.56 42

EIA-MVS		83.31 117	82.80 117	84.82 113	89.59 128	65.59 210	88.21 159	92.68 69	74.66 139	78.96 186	86.42 291	69.06 97	95.26 85	75.54 177	90.09 123	93.62 99

h-mvs33		83.15 119	82.19 130	86.02 74	90.56 103	70.85 77	88.15 163	89.16 218	76.02 98	84.67 84	91.39 136	61.54 199	95.50 71	82.71 93	75.48 354	91.72 189

MVS_Test		83.15 119	83.06 111	83.41 183	86.86 253	63.21 276	86.11 237	92.00 103	74.31 147	82.87 122	89.44 201	70.03 82	93.21 187	77.39 150	88.50 155	93.81 84

IS-MVSNet		83.15 119	82.81 116	84.18 143	89.94 121	63.30 274	91.59 49	88.46 247	79.04 30	79.49 177	92.16 108	65.10 149	94.28 128	67.71 261	91.86 94	94.95 12

DP-MVS Recon		83.11 122	82.09 133	86.15 68	94.44 21	70.92 74	88.79 132	92.20 94	70.53 240	79.17 184	91.03 150	64.12 158	96.03 53	68.39 258	90.14 122	91.50 195

PAPM_NR		83.02 123	82.41 124	84.82 113	92.47 74	66.37 190	87.93 171	91.80 115	73.82 160	77.32 226	90.66 160	67.90 115	94.90 102	70.37 233	89.48 136	93.19 122

VDD-MVS		83.01 124	82.36 126	84.96 105	91.02 93	66.40 189	88.91 125	88.11 250	77.57 49	84.39 93	93.29 82	52.19 297	93.91 150	77.05 154	88.70 151	94.57 40

viewdifsd2359ckpt13		82.91 125	82.29 128	84.77 116	86.96 252	66.90 185	87.47 184	91.62 124	72.19 198	81.68 142	90.71 158	66.92 125	93.28 180	75.90 171	87.15 177	94.12 65

MVSFormer		82.85 126	82.05 134	85.24 93	87.35 230	70.21 84	90.50 70	90.38 163	68.55 293	81.32 147	89.47 196	61.68 196	93.46 174	78.98 130	90.26 120	92.05 179

viewdifsd2359ckpt07		82.83 127	82.78 119	82.99 203	86.51 266	62.58 288	85.09 266	90.83 151	75.22 118	82.28 129	91.63 125	69.43 90	92.03 242	77.71 145	86.32 191	94.34 54

OMC-MVS		82.69 128	81.97 137	84.85 112	88.75 172	67.42 166	87.98 167	90.87 149	74.92 130	79.72 174	91.65 123	62.19 188	93.96 142	75.26 181	86.42 190	93.16 123

PVSNet_Blended_VisFu		82.62 129	81.83 139	84.96 105	90.80 99	69.76 95	88.74 137	91.70 120	69.39 270	78.96 186	88.46 228	65.47 146	94.87 105	74.42 188	88.57 152	90.24 245

MVS_111021_LR		82.61 130	82.11 131	84.11 144	88.82 164	71.58 57	85.15 263	86.16 300	74.69 137	80.47 166	91.04 148	62.29 185	90.55 301	80.33 117	90.08 124	90.20 246

HQP-MVS		82.61 130	82.02 135	84.37 128	89.33 142	66.98 181	89.17 113	92.19 95	76.41 85	77.23 229	90.23 174	60.17 227	95.11 92	77.47 148	85.99 200	91.03 210

RRT-MVS		82.60 132	82.10 132	84.10 145	87.98 205	62.94 285	87.45 187	91.27 135	77.42 56	79.85 172	90.28 171	56.62 259	94.70 115	79.87 122	88.15 160	94.67 31

diffmvs_AUTHOR		82.38 133	82.27 129	82.73 222	83.26 345	63.80 256	83.89 298	89.76 186	73.35 176	82.37 128	90.84 155	66.25 135	90.79 295	82.77 90	87.93 163	93.59 101

CLD-MVS		82.31 134	81.65 140	84.29 135	88.47 181	67.73 156	85.81 247	92.35 85	75.78 101	78.33 203	86.58 286	64.01 159	94.35 126	76.05 169	87.48 171	90.79 219
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 135	82.41 124	81.62 244	90.82 98	60.93 313	84.47 282	89.78 184	76.36 90	84.07 101	91.88 115	64.71 153	90.26 303	70.68 230	88.89 145	93.66 92

diffmvs		82.10 136	81.88 138	82.76 220	83.00 355	63.78 258	83.68 303	89.76 186	72.94 188	82.02 135	89.85 180	65.96 143	90.79 295	82.38 97	87.30 174	93.71 90
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		82.08 137	81.27 143	84.50 123	89.23 150	68.76 117	90.22 79	91.94 107	75.37 114	76.64 244	91.51 131	54.29 276	94.91 100	78.44 135	83.78 236	89.83 268

FIs		82.07 138	82.42 123	81.04 263	88.80 169	58.34 342	88.26 158	93.49 29	76.93 71	78.47 200	91.04 148	69.92 84	92.34 233	69.87 242	84.97 216	92.44 160

PS-MVSNAJss		82.07 138	81.31 142	84.34 131	86.51 266	67.27 174	89.27 109	91.51 129	71.75 206	79.37 181	90.22 175	63.15 170	94.27 129	77.69 146	82.36 264	91.49 196

API-MVS		81.99 140	81.23 144	84.26 140	90.94 95	70.18 89	91.10 61	89.32 207	71.51 213	78.66 193	88.28 233	65.26 147	95.10 95	64.74 288	91.23 104	87.51 335

SSM_0404		81.91 141	80.84 152	85.13 99	89.24 149	68.26 135	87.84 176	89.25 213	71.06 225	80.62 162	90.39 168	59.57 229	94.65 117	72.45 215	87.19 176	92.47 158

UniMVSNet_NR-MVSNet		81.88 142	81.54 141	82.92 207	88.46 182	63.46 270	87.13 196	92.37 84	80.19 12	78.38 201	89.14 204	71.66 62	93.05 201	70.05 238	76.46 337	92.25 167

MAR-MVS		81.84 143	80.70 153	85.27 92	91.32 87	71.53 58	89.82 85	90.92 146	69.77 264	78.50 197	86.21 295	62.36 184	94.52 121	65.36 282	92.05 90	89.77 271
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		81.82 144	81.23 144	83.57 177	91.89 80	63.43 272	89.84 84	81.85 363	77.04 69	83.21 115	93.10 85	52.26 296	93.43 176	71.98 218	89.95 127	93.85 80

hse-mvs2		81.72 145	80.94 150	84.07 151	88.72 173	67.68 158	85.87 243	87.26 276	76.02 98	84.67 84	88.22 236	61.54 199	93.48 172	82.71 93	73.44 382	91.06 208

GeoE		81.71 146	81.01 149	83.80 171	89.51 132	64.45 243	88.97 123	88.73 240	71.27 219	78.63 194	89.76 186	66.32 134	93.20 190	69.89 241	86.02 199	93.74 89

xiu_mvs_v2_base		81.69 147	81.05 147	83.60 174	89.15 153	68.03 145	84.46 284	90.02 177	70.67 235	81.30 150	86.53 289	63.17 169	94.19 136	75.60 176	88.54 153	88.57 313

PS-MVSNAJ		81.69 147	81.02 148	83.70 172	89.51 132	68.21 140	84.28 290	90.09 176	70.79 232	81.26 151	85.62 309	63.15 170	94.29 127	75.62 175	88.87 146	88.59 312

PAPR		81.66 149	80.89 151	83.99 163	90.27 109	64.00 250	86.76 215	91.77 118	68.84 289	77.13 236	89.50 194	67.63 117	94.88 104	67.55 263	88.52 154	93.09 127

UniMVSNet (Re)		81.60 150	81.11 146	83.09 196	88.38 186	64.41 244	87.60 180	93.02 48	78.42 37	78.56 196	88.16 237	69.78 85	93.26 183	69.58 245	76.49 336	91.60 190

SSM_0407		81.58 151	80.48 159	84.87 111	88.81 165	67.96 147	87.37 189	89.25 213	71.06 225	79.48 178	90.39 168	59.57 229	94.48 124	72.45 215	85.93 202	92.18 172

Elysia		81.53 152	80.16 167	85.62 82	85.51 289	68.25 137	88.84 130	92.19 95	71.31 216	80.50 164	89.83 181	46.89 357	94.82 106	76.85 156	89.57 133	93.80 86

StellarMVS		81.53 152	80.16 167	85.62 82	85.51 289	68.25 137	88.84 130	92.19 95	71.31 216	80.50 164	89.83 181	46.89 357	94.82 106	76.85 156	89.57 133	93.80 86

FC-MVSNet-test		81.52 154	82.02 135	80.03 286	88.42 185	55.97 381	87.95 169	93.42 32	77.10 67	77.38 224	90.98 154	69.96 83	91.79 253	68.46 257	84.50 223	92.33 163

VDDNet		81.52 154	80.67 154	84.05 157	90.44 106	64.13 249	89.73 90	85.91 303	71.11 222	83.18 116	93.48 75	50.54 323	93.49 171	73.40 199	88.25 158	94.54 44

ACMP	74.13 6	81.51 156	80.57 156	84.36 129	89.42 137	68.69 124	89.97 83	91.50 132	74.46 143	75.04 291	90.41 167	53.82 282	94.54 119	77.56 147	82.91 256	89.86 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 157	80.29 164	84.70 119	86.63 263	69.90 92	85.95 240	86.77 287	63.24 360	81.07 153	89.47 196	61.08 212	92.15 239	78.33 138	90.07 125	92.05 179
jason: jason.
lupinMVS		81.39 157	80.27 165	84.76 117	87.35 230	70.21 84	85.55 253	86.41 294	62.85 367	81.32 147	88.61 223	61.68 196	92.24 237	78.41 137	90.26 120	91.83 182

test_yl		81.17 159	80.47 160	83.24 189	89.13 154	63.62 259	86.21 234	89.95 180	72.43 196	81.78 140	89.61 191	57.50 248	93.58 164	70.75 228	86.90 181	92.52 153

DCV-MVSNet		81.17 159	80.47 160	83.24 189	89.13 154	63.62 259	86.21 234	89.95 180	72.43 196	81.78 140	89.61 191	57.50 248	93.58 164	70.75 228	86.90 181	92.52 153

guyue		81.13 161	80.64 155	82.60 225	86.52 265	63.92 254	86.69 217	87.73 265	73.97 155	80.83 160	89.69 187	56.70 257	91.33 279	78.26 142	85.40 213	92.54 152

DU-MVS		81.12 162	80.52 158	82.90 208	87.80 213	63.46 270	87.02 201	91.87 111	79.01 31	78.38 201	89.07 206	65.02 150	93.05 201	70.05 238	76.46 337	92.20 170

PVSNet_Blended		80.98 163	80.34 162	82.90 208	88.85 161	65.40 213	84.43 286	92.00 103	67.62 304	78.11 208	85.05 325	66.02 141	94.27 129	71.52 220	89.50 135	89.01 293

FA-MVS(test-final)		80.96 164	79.91 174	84.10 145	88.30 189	65.01 225	84.55 281	90.01 178	73.25 180	79.61 175	87.57 253	58.35 240	94.72 113	71.29 224	86.25 194	92.56 151

QAPM		80.88 165	79.50 188	85.03 102	88.01 204	68.97 112	91.59 49	92.00 103	66.63 320	75.15 287	92.16 108	57.70 245	95.45 73	63.52 294	88.76 149	90.66 226

TranMVSNet+NR-MVSNet		80.84 166	80.31 163	82.42 228	87.85 210	62.33 295	87.74 178	91.33 134	80.55 9	77.99 212	89.86 179	65.23 148	92.62 215	67.05 270	75.24 364	92.30 165

UGNet		80.83 167	79.59 186	84.54 122	88.04 201	68.09 142	89.42 103	88.16 249	76.95 70	76.22 255	89.46 198	49.30 340	93.94 145	68.48 256	90.31 118	91.60 190
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
AstraMVS		80.81 168	80.14 169	82.80 214	86.05 278	63.96 251	86.46 225	85.90 304	73.71 163	80.85 159	90.56 164	54.06 280	91.57 263	79.72 123	83.97 234	92.86 141

Fast-Effi-MVS+		80.81 168	79.92 173	83.47 178	88.85 161	64.51 239	85.53 255	89.39 201	70.79 232	78.49 198	85.06 324	67.54 118	93.58 164	67.03 271	86.58 187	92.32 164

XVG-OURS-SEG-HR		80.81 168	79.76 179	83.96 165	85.60 287	68.78 116	83.54 310	90.50 159	70.66 238	76.71 242	91.66 122	60.69 217	91.26 280	76.94 155	81.58 272	91.83 182

IMVS_0403		80.80 171	80.12 170	82.87 210	87.13 243	63.59 263	85.19 260	89.33 203	70.51 241	78.49 198	89.03 208	63.26 166	93.27 182	72.56 211	85.56 209	91.74 185

xiu_mvs_v1_base_debu		80.80 171	79.72 182	84.03 159	87.35 230	70.19 86	85.56 250	88.77 235	69.06 283	81.83 136	88.16 237	50.91 317	92.85 209	78.29 139	87.56 168	89.06 288

xiu_mvs_v1_base		80.80 171	79.72 182	84.03 159	87.35 230	70.19 86	85.56 250	88.77 235	69.06 283	81.83 136	88.16 237	50.91 317	92.85 209	78.29 139	87.56 168	89.06 288

xiu_mvs_v1_base_debi		80.80 171	79.72 182	84.03 159	87.35 230	70.19 86	85.56 250	88.77 235	69.06 283	81.83 136	88.16 237	50.91 317	92.85 209	78.29 139	87.56 168	89.06 288

ACMM	73.20 8	80.78 175	79.84 177	83.58 176	89.31 145	68.37 132	89.99 82	91.60 126	70.28 250	77.25 227	89.66 189	53.37 287	93.53 169	74.24 191	82.85 257	88.85 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 176	79.62 185	83.83 168	85.07 304	68.01 146	86.99 202	88.83 232	70.36 246	81.38 146	87.99 244	50.11 328	92.51 224	79.02 127	86.89 183	90.97 213

114514_t		80.68 176	79.51 187	84.20 142	94.09 40	67.27 174	89.64 93	91.11 142	58.75 407	74.08 306	90.72 157	58.10 241	95.04 97	69.70 243	89.42 137	90.30 243

IMVS_0407		80.61 178	79.90 175	82.75 221	87.13 243	63.59 263	85.33 259	89.33 203	70.51 241	77.82 214	89.03 208	61.84 192	92.91 206	72.56 211	85.56 209	91.74 185

CANet_DTU		80.61 178	79.87 176	82.83 211	85.60 287	63.17 279	87.36 190	88.65 243	76.37 89	75.88 262	88.44 229	53.51 285	93.07 199	73.30 200	89.74 131	92.25 167

VPA-MVSNet		80.60 180	80.55 157	80.76 270	88.07 200	60.80 316	86.86 209	91.58 127	75.67 106	80.24 168	89.45 200	63.34 163	90.25 304	70.51 232	79.22 303	91.23 203

mvsmamba		80.60 180	79.38 190	84.27 138	89.74 126	67.24 176	87.47 184	86.95 282	70.02 255	75.38 275	88.93 213	51.24 314	92.56 220	75.47 179	89.22 140	93.00 135

PVSNet_BlendedMVS		80.60 180	80.02 171	82.36 230	88.85 161	65.40 213	86.16 236	92.00 103	69.34 272	78.11 208	86.09 299	66.02 141	94.27 129	71.52 220	82.06 267	87.39 337

AdaColmap		80.58 183	79.42 189	84.06 154	93.09 61	68.91 113	89.36 107	88.97 229	69.27 274	75.70 265	89.69 187	57.20 253	95.77 62	63.06 299	88.41 157	87.50 336

EI-MVSNet		80.52 184	79.98 172	82.12 233	84.28 319	63.19 278	86.41 226	88.95 230	74.18 152	78.69 191	87.54 256	66.62 128	92.43 227	72.57 209	80.57 286	90.74 223

viewmambaseed2359dif		80.41 185	79.84 177	82.12 233	82.95 359	62.50 291	83.39 311	88.06 254	67.11 309	80.98 154	90.31 170	66.20 137	91.01 291	74.62 185	84.90 217	92.86 141

XVG-OURS		80.41 185	79.23 196	83.97 164	85.64 285	69.02 110	83.03 323	90.39 162	71.09 223	77.63 220	91.49 133	54.62 275	91.35 277	75.71 173	83.47 248	91.54 193

SDMVSNet		80.38 187	80.18 166	80.99 264	89.03 159	64.94 228	80.45 355	89.40 200	75.19 122	76.61 246	89.98 177	60.61 221	87.69 349	76.83 159	83.55 245	90.33 241

PCF-MVS	73.52 7	80.38 187	78.84 205	85.01 103	87.71 220	68.99 111	83.65 304	91.46 133	63.00 364	77.77 218	90.28 171	66.10 138	95.09 96	61.40 318	88.22 159	90.94 215
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
viewdifsd2359ckpt11		80.37 189	79.73 180	82.30 231	83.70 335	62.39 292	84.20 292	86.67 288	73.22 182	80.90 156	90.62 161	63.00 175	91.56 264	76.81 160	78.44 309	92.95 138

viewmsd2359difaftdt		80.37 189	79.73 180	82.30 231	83.70 335	62.39 292	84.20 292	86.67 288	73.22 182	80.90 156	90.62 161	63.00 175	91.56 264	76.81 160	78.44 309	92.95 138

X-MVStestdata		80.37 189	77.83 229	88.00 17	94.42 22	73.33 19	92.78 21	92.99 52	79.14 26	83.67 110	12.47 471	67.45 119	96.60 35	83.06 84	94.50 55	94.07 68

test_djsdf		80.30 192	79.32 193	83.27 187	83.98 327	65.37 216	90.50 70	90.38 163	68.55 293	76.19 256	88.70 219	56.44 260	93.46 174	78.98 130	80.14 292	90.97 213

v2v482		80.23 193	79.29 194	83.05 200	83.62 337	64.14 248	87.04 199	89.97 179	73.61 166	78.18 207	87.22 264	61.10 211	93.82 154	76.11 167	76.78 333	91.18 204

NR-MVSNet		80.23 193	79.38 190	82.78 218	87.80 213	63.34 273	86.31 230	91.09 143	79.01 31	72.17 332	89.07 206	67.20 122	92.81 213	66.08 277	75.65 350	92.20 170

Anonymous20240529		80.19 195	78.89 204	84.10 145	90.60 102	64.75 234	88.95 124	90.90 147	65.97 328	80.59 163	91.17 144	49.97 330	93.73 162	69.16 249	82.70 261	93.81 84

IterMVS-LS		80.06 196	79.38 190	82.11 235	85.89 279	63.20 277	86.79 212	89.34 202	74.19 151	75.45 272	86.72 276	66.62 128	92.39 229	72.58 208	76.86 330	90.75 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 197	78.57 209	84.42 127	85.13 302	68.74 119	88.77 133	88.10 251	74.99 126	74.97 293	83.49 360	57.27 251	93.36 178	73.53 196	80.88 280	91.18 204

v1144		80.03 197	79.03 200	83.01 202	83.78 332	64.51 239	87.11 198	90.57 158	71.96 204	78.08 210	86.20 296	61.41 203	93.94 145	74.93 183	77.23 324	90.60 229

v8		79.97 199	79.02 201	82.80 214	84.09 324	64.50 241	87.96 168	90.29 170	74.13 154	75.24 284	86.81 273	62.88 177	93.89 153	74.39 189	75.40 359	90.00 259

OpenMVS	72.83 10	79.77 200	78.33 216	84.09 149	85.17 298	69.91 91	90.57 67	90.97 145	66.70 314	72.17 332	91.91 113	54.70 273	93.96 142	61.81 315	90.95 109	88.41 317

v10		79.74 201	78.67 206	82.97 206	84.06 325	64.95 227	87.88 174	90.62 155	73.11 184	75.11 288	86.56 287	61.46 202	94.05 141	73.68 194	75.55 352	89.90 265

ECVR-MVS		79.61 202	79.26 195	80.67 272	90.08 114	54.69 396	87.89 173	77.44 410	74.88 132	80.27 167	92.79 97	48.96 346	92.45 226	68.55 255	92.50 82	94.86 19

BH-RMVSNet		79.61 202	78.44 212	83.14 194	89.38 141	65.93 199	84.95 270	87.15 279	73.56 168	78.19 206	89.79 185	56.67 258	93.36 178	59.53 334	86.74 185	90.13 249

v1192		79.59 204	78.43 213	83.07 199	83.55 339	64.52 238	86.93 206	90.58 156	70.83 231	77.78 217	85.90 300	59.15 233	93.94 145	73.96 193	77.19 326	90.76 221

ab-mvs		79.51 205	78.97 202	81.14 260	88.46 182	60.91 314	83.84 299	89.24 215	70.36 246	79.03 185	88.87 216	63.23 168	90.21 305	65.12 284	82.57 262	92.28 166

WR-MVS		79.49 206	79.22 197	80.27 281	88.79 170	58.35 341	85.06 267	88.61 245	78.56 35	77.65 219	88.34 231	63.81 162	90.66 300	64.98 286	77.22 325	91.80 184

v144192		79.47 207	78.37 214	82.78 218	83.35 342	63.96 251	86.96 203	90.36 166	69.99 257	77.50 221	85.67 307	60.66 219	93.77 158	74.27 190	76.58 334	90.62 227

BH-untuned		79.47 207	78.60 208	82.05 236	89.19 152	65.91 200	86.07 238	88.52 246	72.18 199	75.42 273	87.69 250	61.15 210	93.54 168	60.38 326	86.83 184	86.70 358

test1111		79.43 209	79.18 198	80.15 284	89.99 119	53.31 409	87.33 192	77.05 414	75.04 125	80.23 169	92.77 99	48.97 345	92.33 234	68.87 252	92.40 84	94.81 22

mvs_anonymous		79.42 210	79.11 199	80.34 279	84.45 318	57.97 348	82.59 325	87.62 267	67.40 308	76.17 259	88.56 226	68.47 106	89.59 316	70.65 231	86.05 198	93.47 107

thisisatest0530		79.40 211	77.76 234	84.31 133	87.69 222	65.10 224	87.36 190	84.26 326	70.04 254	77.42 223	88.26 235	49.94 331	94.79 110	70.20 236	84.70 221	93.03 132

tttt0517		79.40 211	77.91 225	83.90 167	88.10 198	63.84 255	88.37 154	84.05 328	71.45 214	76.78 240	89.12 205	49.93 333	94.89 103	70.18 237	83.18 254	92.96 137

V42		79.38 213	78.24 218	82.83 211	81.10 391	65.50 212	85.55 253	89.82 183	71.57 212	78.21 205	86.12 298	60.66 219	93.18 193	75.64 174	75.46 356	89.81 270

mamba_0408		79.37 214	77.52 241	84.93 108	88.81 165	67.96 147	65.03 455	88.66 241	70.96 229	79.48 178	89.80 183	58.69 235	94.65 117	70.35 234	85.93 202	92.18 172

jajsoiax		79.29 215	77.96 223	83.27 187	84.68 312	66.57 188	89.25 110	90.16 174	69.20 279	75.46 271	89.49 195	45.75 373	93.13 196	76.84 158	80.80 282	90.11 251

v1921920		79.22 216	78.03 222	82.80 214	83.30 344	63.94 253	86.80 211	90.33 167	69.91 260	77.48 222	85.53 311	58.44 239	93.75 160	73.60 195	76.85 331	90.71 225

AUN-MVS		79.21 217	77.60 239	84.05 157	88.71 174	67.61 160	85.84 245	87.26 276	69.08 282	77.23 229	88.14 241	53.20 289	93.47 173	75.50 178	73.45 381	91.06 208

TAPA-MVS	73.13 9	79.15 218	77.94 224	82.79 217	89.59 128	62.99 284	88.16 162	91.51 129	65.77 329	77.14 235	91.09 146	60.91 214	93.21 187	50.26 402	87.05 179	92.17 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 219	77.77 233	83.22 191	84.70 311	66.37 190	89.17 113	90.19 173	69.38 271	75.40 274	89.46 198	44.17 385	93.15 194	76.78 162	80.70 284	90.14 248

UniMVSNet_ETH3D		79.10 220	78.24 218	81.70 243	86.85 254	60.24 325	87.28 194	88.79 234	74.25 150	76.84 237	90.53 166	49.48 336	91.56 264	67.98 259	82.15 265	93.29 114

CDS-MVSNet		79.07 221	77.70 236	83.17 193	87.60 225	68.23 139	84.40 288	86.20 299	67.49 306	76.36 252	86.54 288	61.54 199	90.79 295	61.86 314	87.33 173	90.49 234
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 222	77.88 228	82.38 229	83.07 352	64.80 233	84.08 297	88.95 230	69.01 286	78.69 191	87.17 267	54.70 273	92.43 227	74.69 184	80.57 286	89.89 266

v1240		78.99 223	77.78 232	82.64 223	83.21 347	63.54 267	86.62 220	90.30 169	69.74 267	77.33 225	85.68 306	57.04 254	93.76 159	73.13 203	76.92 328	90.62 227

Anonymous20231211		78.97 224	77.69 237	82.81 213	90.54 104	64.29 246	90.11 81	91.51 129	65.01 340	76.16 260	88.13 242	50.56 322	93.03 204	69.68 244	77.56 323	91.11 206

v7n		78.97 224	77.58 240	83.14 194	83.45 341	65.51 211	88.32 156	91.21 137	73.69 164	72.41 328	86.32 294	57.93 242	93.81 155	69.18 248	75.65 350	90.11 251

icg_test_0407_2		78.92 226	78.93 203	78.90 309	87.13 243	63.59 263	76.58 402	89.33 203	70.51 241	77.82 214	89.03 208	61.84 192	81.38 406	72.56 211	85.56 209	91.74 185

TAMVS		78.89 227	77.51 243	83.03 201	87.80 213	67.79 155	84.72 274	85.05 315	67.63 303	76.75 241	87.70 249	62.25 186	90.82 294	58.53 345	87.13 178	90.49 234

c3_l		78.75 228	77.91 225	81.26 256	82.89 360	61.56 306	84.09 296	89.13 221	69.97 258	75.56 267	84.29 339	66.36 133	92.09 241	73.47 198	75.48 354	90.12 250

tt0805		78.73 229	77.83 229	81.43 249	85.17 298	60.30 324	89.41 104	90.90 147	71.21 220	77.17 234	88.73 218	46.38 362	93.21 187	72.57 209	78.96 304	90.79 219

v148		78.72 230	77.80 231	81.47 248	82.73 363	61.96 301	86.30 231	88.08 252	73.26 179	76.18 257	85.47 313	62.46 182	92.36 231	71.92 219	73.82 378	90.09 253

VPNet		78.69 231	78.66 207	78.76 311	88.31 188	55.72 385	84.45 285	86.63 291	76.79 75	78.26 204	90.55 165	59.30 232	89.70 315	66.63 272	77.05 327	90.88 216

ET-MVSNet_ETH3D		78.63 232	76.63 264	84.64 120	86.73 259	69.47 100	85.01 268	84.61 319	69.54 268	66.51 398	86.59 284	50.16 327	91.75 255	76.26 165	84.24 231	92.69 147

anonymousdsp		78.60 233	77.15 249	82.98 205	80.51 397	67.08 179	87.24 195	89.53 196	65.66 331	75.16 286	87.19 266	52.52 291	92.25 236	77.17 152	79.34 301	89.61 275

miper_ehance_all_eth		78.59 234	77.76 234	81.08 262	82.66 365	61.56 306	83.65 304	89.15 219	68.87 288	75.55 268	83.79 351	66.49 131	92.03 242	73.25 201	76.39 339	89.64 274

VortexMVS		78.57 235	77.89 227	80.59 273	85.89 279	62.76 287	85.61 248	89.62 193	72.06 202	74.99 292	85.38 315	55.94 262	90.77 298	74.99 182	76.58 334	88.23 319

WR-MVS_H		78.51 236	78.49 210	78.56 316	88.02 202	56.38 375	88.43 148	92.67 70	77.14 64	73.89 308	87.55 255	66.25 135	89.24 323	58.92 340	73.55 380	90.06 257

GBi-Net		78.40 237	77.40 244	81.40 251	87.60 225	63.01 280	88.39 151	89.28 209	71.63 208	75.34 277	87.28 260	54.80 269	91.11 284	62.72 301	79.57 296	90.09 253

test1		78.40 237	77.40 244	81.40 251	87.60 225	63.01 280	88.39 151	89.28 209	71.63 208	75.34 277	87.28 260	54.80 269	91.11 284	62.72 301	79.57 296	90.09 253

Vis-MVSNet (Re-imp)		78.36 239	78.45 211	78.07 328	88.64 176	51.78 420	86.70 216	79.63 392	74.14 153	75.11 288	90.83 156	61.29 207	89.75 313	58.10 350	91.60 96	92.69 147

Anonymous202405211		78.25 240	77.01 251	81.99 238	91.03 92	60.67 318	84.77 273	83.90 330	70.65 239	80.00 171	91.20 142	41.08 406	91.43 275	65.21 283	85.26 214	93.85 80

CP-MVSNet		78.22 241	78.34 215	77.84 332	87.83 212	54.54 398	87.94 170	91.17 139	77.65 46	73.48 314	88.49 227	62.24 187	88.43 339	62.19 309	74.07 373	90.55 231

BH-w/o		78.21 242	77.33 247	80.84 268	88.81 165	65.13 221	84.87 271	87.85 262	69.75 265	74.52 301	84.74 331	61.34 205	93.11 197	58.24 349	85.84 205	84.27 396

FMVSNet2		78.20 243	77.21 248	81.20 258	87.60 225	62.89 286	87.47 184	89.02 225	71.63 208	75.29 283	87.28 260	54.80 269	91.10 287	62.38 306	79.38 300	89.61 275

MVS		78.19 244	76.99 253	81.78 241	85.66 284	66.99 180	84.66 276	90.47 160	55.08 428	72.02 334	85.27 317	63.83 161	94.11 139	66.10 276	89.80 130	84.24 397

Baseline_NR-MVSNet		78.15 245	78.33 216	77.61 337	85.79 281	56.21 379	86.78 213	85.76 306	73.60 167	77.93 213	87.57 253	65.02 150	88.99 328	67.14 269	75.33 361	87.63 331

CNLPA		78.08 246	76.79 258	81.97 239	90.40 107	71.07 68	87.59 181	84.55 320	66.03 327	72.38 329	89.64 190	57.56 247	86.04 366	59.61 333	83.35 250	88.79 304

cl22		78.07 247	77.01 251	81.23 257	82.37 372	61.83 303	83.55 308	87.98 256	68.96 287	75.06 290	83.87 347	61.40 204	91.88 251	73.53 196	76.39 339	89.98 262

PLC	70.83 11	78.05 248	76.37 270	83.08 198	91.88 81	67.80 154	88.19 160	89.46 198	64.33 348	69.87 359	88.38 230	53.66 283	93.58 164	58.86 341	82.73 259	87.86 327
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 249	76.49 265	82.62 224	83.16 351	66.96 183	86.94 205	87.45 272	72.45 193	71.49 340	84.17 344	54.79 272	91.58 261	67.61 262	80.31 289	89.30 284

PS-CasMVS		78.01 250	78.09 221	77.77 334	87.71 220	54.39 400	88.02 166	91.22 136	77.50 54	73.26 316	88.64 222	60.73 215	88.41 340	61.88 313	73.88 377	90.53 232

HY-MVS	69.67 12	77.95 251	77.15 249	80.36 278	87.57 229	60.21 326	83.37 313	87.78 264	66.11 324	75.37 276	87.06 271	63.27 165	90.48 302	61.38 319	82.43 263	90.40 238

eth_miper_zixun_eth		77.92 252	76.69 262	81.61 246	83.00 355	61.98 300	83.15 317	89.20 217	69.52 269	74.86 295	84.35 338	61.76 195	92.56 220	71.50 222	72.89 386	90.28 244

FMVSNet3		77.88 253	76.85 256	80.97 266	86.84 255	62.36 294	86.52 223	88.77 235	71.13 221	75.34 277	86.66 282	54.07 279	91.10 287	62.72 301	79.57 296	89.45 279

miper_enhance_ethall		77.87 254	76.86 255	80.92 267	81.65 379	61.38 308	82.68 324	88.98 227	65.52 333	75.47 269	82.30 380	65.76 145	92.00 245	72.95 204	76.39 339	89.39 281

FE-MVS		77.78 255	75.68 276	84.08 150	88.09 199	66.00 197	83.13 318	87.79 263	68.42 297	78.01 211	85.23 319	45.50 376	95.12 90	59.11 338	85.83 206	91.11 206

PEN-MVS		77.73 256	77.69 237	77.84 332	87.07 251	53.91 403	87.91 172	91.18 138	77.56 51	73.14 318	88.82 217	61.23 208	89.17 325	59.95 329	72.37 388	90.43 236

cl____		77.72 257	76.76 259	80.58 274	82.49 369	60.48 321	83.09 319	87.87 260	69.22 277	74.38 304	85.22 320	62.10 189	91.53 269	71.09 225	75.41 358	89.73 273

DIV-MVS_self_test		77.72 257	76.76 259	80.58 274	82.48 370	60.48 321	83.09 319	87.86 261	69.22 277	74.38 304	85.24 318	62.10 189	91.53 269	71.09 225	75.40 359	89.74 272

sd_testset		77.70 259	77.40 244	78.60 314	89.03 159	60.02 327	79.00 376	85.83 305	75.19 122	76.61 246	89.98 177	54.81 268	85.46 374	62.63 305	83.55 245	90.33 241

PAPM		77.68 260	76.40 269	81.51 247	87.29 239	61.85 302	83.78 300	89.59 194	64.74 342	71.23 342	88.70 219	62.59 179	93.66 163	52.66 386	87.03 180	89.01 293

SSM_04072		77.67 261	77.52 241	78.12 326	88.81 165	67.96 147	65.03 455	88.66 241	70.96 229	79.48 178	89.80 183	58.69 235	74.23 447	70.35 234	85.93 202	92.18 172

CHOSEN 1792x2688		77.63 262	75.69 275	83.44 180	89.98 120	68.58 127	78.70 381	87.50 270	56.38 423	75.80 264	86.84 272	58.67 237	91.40 276	61.58 317	85.75 207	90.34 240

HyFIR lowres test		77.53 263	75.40 283	83.94 166	89.59 128	66.62 186	80.36 356	88.64 244	56.29 424	76.45 249	85.17 321	57.64 246	93.28 180	61.34 320	83.10 255	91.91 181

FMVSNet1		77.44 264	76.12 272	81.40 251	86.81 256	63.01 280	88.39 151	89.28 209	70.49 245	74.39 303	87.28 260	49.06 344	91.11 284	60.91 322	78.52 307	90.09 253

TR-MVS		77.44 264	76.18 271	81.20 258	88.24 190	63.24 275	84.61 279	86.40 295	67.55 305	77.81 216	86.48 290	54.10 278	93.15 194	57.75 353	82.72 260	87.20 343

1112_ss		77.40 266	76.43 267	80.32 280	89.11 158	60.41 323	83.65 304	87.72 266	62.13 377	73.05 319	86.72 276	62.58 180	89.97 309	62.11 312	80.80 282	90.59 230

thisisatest0515		77.33 267	75.38 284	83.18 192	85.27 297	63.80 256	82.11 330	83.27 340	65.06 338	75.91 261	83.84 349	49.54 335	94.27 129	67.24 267	86.19 195	91.48 197

test2506		77.30 268	76.49 265	79.74 292	90.08 114	52.02 414	87.86 175	63.10 457	74.88 132	80.16 170	92.79 97	38.29 421	92.35 232	68.74 254	92.50 82	94.86 19

pm-mvs1		77.25 269	76.68 263	78.93 308	84.22 321	58.62 339	86.41 226	88.36 248	71.37 215	73.31 315	88.01 243	61.22 209	89.15 326	64.24 292	73.01 385	89.03 292

IMVS_0404		77.16 270	76.42 268	79.37 300	87.13 243	63.59 263	77.12 400	89.33 203	70.51 241	66.22 401	89.03 208	50.36 325	82.78 396	72.56 211	85.56 209	91.74 185

LCM-MVSNet-Re		77.05 271	76.94 254	77.36 341	87.20 240	51.60 421	80.06 361	80.46 380	75.20 121	67.69 378	86.72 276	62.48 181	88.98 329	63.44 296	89.25 138	91.51 194

DTE-MVSNet		76.99 272	76.80 257	77.54 340	86.24 270	53.06 412	87.52 182	90.66 154	77.08 68	72.50 326	88.67 221	60.48 223	89.52 317	57.33 357	70.74 400	90.05 258

baseline1		76.98 273	76.75 261	77.66 335	88.13 196	55.66 386	85.12 264	81.89 361	73.04 186	76.79 239	88.90 214	62.43 183	87.78 348	63.30 298	71.18 398	89.55 277

LS3D		76.95 274	74.82 292	83.37 184	90.45 105	67.36 170	89.15 117	86.94 283	61.87 380	69.52 362	90.61 163	51.71 310	94.53 120	46.38 424	86.71 186	88.21 321

GA-MVS		76.87 275	75.17 289	81.97 239	82.75 362	62.58 288	81.44 339	86.35 297	72.16 201	74.74 296	82.89 371	46.20 367	92.02 244	68.85 253	81.09 277	91.30 202

mamv4		76.81 276	78.23 220	72.54 394	86.12 275	65.75 207	78.76 380	82.07 360	64.12 350	72.97 320	91.02 151	67.97 113	68.08 459	83.04 86	78.02 316	83.80 404

DP-MVS		76.78 277	74.57 295	83.42 181	93.29 50	69.46 102	88.55 146	83.70 332	63.98 355	70.20 350	88.89 215	54.01 281	94.80 109	46.66 421	81.88 270	86.01 370

cascas		76.72 278	74.64 294	82.99 203	85.78 282	65.88 201	82.33 327	89.21 216	60.85 386	72.74 322	81.02 391	47.28 353	93.75 160	67.48 264	85.02 215	89.34 283

testing91		76.54 279	75.66 278	79.18 305	88.43 184	55.89 382	81.08 342	83.00 348	73.76 162	75.34 277	84.29 339	46.20 367	90.07 307	64.33 290	84.50 223	91.58 192

1314		76.53 280	75.30 287	80.21 283	83.93 328	62.32 296	84.66 276	88.81 233	60.23 391	70.16 353	84.07 346	55.30 266	90.73 299	67.37 265	83.21 253	87.59 334

thres100view900		76.50 281	75.55 280	79.33 301	89.52 131	56.99 364	85.83 246	83.23 341	73.94 157	76.32 253	87.12 268	51.89 306	91.95 247	48.33 412	83.75 239	89.07 286

thres600view7		76.50 281	75.44 281	79.68 294	89.40 139	57.16 361	85.53 255	83.23 341	73.79 161	76.26 254	87.09 269	51.89 306	91.89 250	48.05 417	83.72 242	90.00 259

thres400		76.50 281	75.37 285	79.86 289	89.13 154	57.65 355	85.17 261	83.60 333	73.41 174	76.45 249	86.39 292	52.12 298	91.95 247	48.33 412	83.75 239	90.00 259

MonoMVSNet		76.49 284	75.80 273	78.58 315	81.55 382	58.45 340	86.36 229	86.22 298	74.87 134	74.73 297	83.73 353	51.79 309	88.73 334	70.78 227	72.15 391	88.55 314

tfpn200view9		76.42 285	75.37 285	79.55 299	89.13 154	57.65 355	85.17 261	83.60 333	73.41 174	76.45 249	86.39 292	52.12 298	91.95 247	48.33 412	83.75 239	89.07 286

Test_1112_low_res		76.40 286	75.44 281	79.27 302	89.28 147	58.09 344	81.69 334	87.07 280	59.53 398	72.48 327	86.67 281	61.30 206	89.33 320	60.81 324	80.15 291	90.41 237

F-COLMAP		76.38 287	74.33 301	82.50 227	89.28 147	66.95 184	88.41 150	89.03 224	64.05 353	66.83 390	88.61 223	46.78 359	92.89 207	57.48 354	78.55 306	87.67 330

LTVRE_ROB	69.57 13	76.25 288	74.54 297	81.41 250	88.60 177	64.38 245	79.24 371	89.12 222	70.76 234	69.79 361	87.86 246	49.09 343	93.20 190	56.21 369	80.16 290	86.65 359
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		76.12 289	74.46 299	81.13 261	85.37 294	69.79 93	84.42 287	87.95 258	65.03 339	67.46 381	85.33 316	53.28 288	91.73 257	58.01 351	83.27 252	81.85 424
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 290	74.27 302	81.62 244	83.20 348	64.67 235	83.60 307	89.75 188	69.75 265	71.85 335	87.09 269	32.78 436	92.11 240	69.99 240	80.43 288	88.09 323

testing99		76.09 291	75.12 290	79.00 306	88.16 193	55.50 388	80.79 346	81.40 368	73.30 178	75.17 285	84.27 342	44.48 382	90.02 308	64.28 291	84.22 232	91.48 197

ACMH+	68.96 14	76.01 292	74.01 303	82.03 237	88.60 177	65.31 217	88.86 127	87.55 268	70.25 252	67.75 377	87.47 258	41.27 404	93.19 192	58.37 347	75.94 347	87.60 332

ACMH	67.68 16	75.89 293	73.93 305	81.77 242	88.71 174	66.61 187	88.62 142	89.01 226	69.81 261	66.78 391	86.70 280	41.95 401	91.51 271	55.64 370	78.14 315	87.17 344
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 294	73.36 314	83.31 185	84.76 310	66.03 194	83.38 312	85.06 314	70.21 253	69.40 363	81.05 390	45.76 372	94.66 116	65.10 285	75.49 353	89.25 285
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		75.70 295	73.83 308	81.30 254	83.26 345	61.79 304	82.57 326	80.65 375	66.81 311	66.88 389	83.42 361	57.86 244	92.19 238	63.47 295	79.57 296	89.91 264

WTY-MVS		75.65 296	75.68 276	75.57 357	86.40 268	56.82 366	77.92 394	82.40 356	65.10 337	76.18 257	87.72 248	63.13 173	80.90 409	60.31 327	81.96 268	89.00 295

thres200		75.55 297	74.47 298	78.82 310	87.78 216	57.85 351	83.07 321	83.51 336	72.44 195	75.84 263	84.42 334	52.08 301	91.75 255	47.41 419	83.64 244	86.86 354

test_vis1_n_1920		75.52 298	75.78 274	74.75 371	79.84 405	57.44 359	83.26 315	85.52 308	62.83 368	79.34 183	86.17 297	45.10 378	79.71 413	78.75 132	81.21 276	87.10 350

EPNet_dtu		75.46 299	74.86 291	77.23 344	82.57 367	54.60 397	86.89 207	83.09 345	71.64 207	66.25 400	85.86 302	55.99 261	88.04 344	54.92 374	86.55 188	89.05 291
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 300	73.87 307	80.11 285	82.69 364	64.85 232	81.57 336	83.47 337	69.16 280	70.49 347	84.15 345	51.95 304	88.15 342	69.23 247	72.14 392	87.34 339

XXY-MVS		75.41 301	75.56 279	74.96 366	83.59 338	57.82 352	80.59 352	83.87 331	66.54 321	74.93 294	88.31 232	63.24 167	80.09 412	62.16 310	76.85 331	86.97 352

reproduce_monomvs		75.40 302	74.38 300	78.46 321	83.92 329	57.80 353	83.78 300	86.94 283	73.47 172	72.25 331	84.47 333	38.74 417	89.27 322	75.32 180	70.53 401	88.31 318

TransMVSNet (Re)		75.39 303	74.56 296	77.86 331	85.50 291	57.10 363	86.78 213	86.09 302	72.17 200	71.53 339	87.34 259	63.01 174	89.31 321	56.84 363	61.83 430	87.17 344

CostFormer		75.24 304	73.90 306	79.27 302	82.65 366	58.27 343	80.80 345	82.73 354	61.57 381	75.33 281	83.13 366	55.52 264	91.07 290	64.98 286	78.34 314	88.45 315

testing11		75.14 305	74.01 303	78.53 318	88.16 193	56.38 375	80.74 349	80.42 382	70.67 235	72.69 325	83.72 354	43.61 389	89.86 310	62.29 308	83.76 238	89.36 282

testing3-2		75.12 306	75.19 288	74.91 367	90.40 107	45.09 450	80.29 358	78.42 402	78.37 40	76.54 248	87.75 247	44.36 383	87.28 354	57.04 360	83.49 247	92.37 161

D2MVS		74.82 307	73.21 315	79.64 296	79.81 406	62.56 290	80.34 357	87.35 273	64.37 347	68.86 368	82.66 375	46.37 363	90.10 306	67.91 260	81.24 275	86.25 363

pmmvs6		74.69 308	73.39 312	78.61 313	81.38 386	57.48 358	86.64 219	87.95 258	64.99 341	70.18 351	86.61 283	50.43 324	89.52 317	62.12 311	70.18 403	88.83 302

SD_0403		74.65 309	74.77 293	74.29 375	86.20 272	47.42 439	83.71 302	85.12 312	69.30 273	68.50 373	87.95 245	59.40 231	86.05 365	49.38 406	83.35 250	89.40 280

tfpnnormal		74.39 310	73.16 316	78.08 327	86.10 277	58.05 345	84.65 278	87.53 269	70.32 249	71.22 343	85.63 308	54.97 267	89.86 310	43.03 436	75.02 366	86.32 362

IterMVS		74.29 311	72.94 319	78.35 322	81.53 383	63.49 269	81.58 335	82.49 355	68.06 301	69.99 356	83.69 355	51.66 311	85.54 372	65.85 279	71.64 395	86.01 370
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 312	72.42 325	79.80 291	83.76 333	59.59 332	85.92 242	86.64 290	66.39 322	66.96 388	87.58 252	39.46 412	91.60 260	65.76 280	69.27 406	88.22 320

SCA		74.22 313	72.33 326	79.91 288	84.05 326	62.17 298	79.96 364	79.29 396	66.30 323	72.38 329	80.13 403	51.95 304	88.60 337	59.25 336	77.67 322	88.96 297

mmtdpeth		74.16 314	73.01 318	77.60 339	83.72 334	61.13 309	85.10 265	85.10 313	72.06 202	77.21 233	80.33 400	43.84 387	85.75 368	77.14 153	52.61 449	85.91 373

miper_lstm_enhance		74.11 315	73.11 317	77.13 345	80.11 401	59.62 331	72.23 426	86.92 285	66.76 313	70.40 348	82.92 370	56.93 255	82.92 395	69.06 250	72.63 387	88.87 300

testing222		74.04 316	72.66 322	78.19 324	87.89 208	55.36 389	81.06 343	79.20 397	71.30 218	74.65 299	83.57 359	39.11 416	88.67 336	51.43 394	85.75 207	90.53 232

EG-PatchMatch MVS		74.04 316	71.82 330	80.71 271	84.92 306	67.42 166	85.86 244	88.08 252	66.04 326	64.22 413	83.85 348	35.10 432	92.56 220	57.44 355	80.83 281	82.16 422

pmmvs4		74.03 318	71.91 329	80.39 277	81.96 375	68.32 133	81.45 338	82.14 358	59.32 399	69.87 359	85.13 322	52.40 294	88.13 343	60.21 328	74.74 369	84.73 393

MS-PatchMatch		73.83 319	72.67 321	77.30 343	83.87 330	66.02 195	81.82 331	84.66 318	61.37 384	68.61 371	82.82 373	47.29 352	88.21 341	59.27 335	84.32 230	77.68 439

test_cas_vis1_n_1920		73.76 320	73.74 309	73.81 381	75.90 427	59.77 329	80.51 353	82.40 356	58.30 409	81.62 144	85.69 305	44.35 384	76.41 431	76.29 164	78.61 305	85.23 383

myMVS_eth3d28		73.62 321	73.53 311	73.90 380	88.20 191	47.41 440	78.06 391	79.37 394	74.29 149	73.98 307	84.29 339	44.67 379	83.54 390	51.47 392	87.39 172	90.74 223

sss		73.60 322	73.64 310	73.51 383	82.80 361	55.01 394	76.12 404	81.69 364	62.47 373	74.68 298	85.85 303	57.32 250	78.11 420	60.86 323	80.93 278	87.39 337

RPMNet		73.51 323	70.49 346	82.58 226	81.32 389	65.19 219	75.92 406	92.27 87	57.60 416	72.73 323	76.45 431	52.30 295	95.43 75	48.14 416	77.71 319	87.11 348

WBMVS		73.43 324	72.81 320	75.28 363	87.91 207	50.99 427	78.59 384	81.31 370	65.51 335	74.47 302	84.83 328	46.39 361	86.68 358	58.41 346	77.86 317	88.17 322

SixPastTwentyTwo		73.37 325	71.26 339	79.70 293	85.08 303	57.89 350	85.57 249	83.56 335	71.03 227	65.66 403	85.88 301	42.10 399	92.57 219	59.11 338	63.34 425	88.65 310

CR-MVSNet		73.37 325	71.27 338	79.67 295	81.32 389	65.19 219	75.92 406	80.30 384	59.92 394	72.73 323	81.19 388	52.50 292	86.69 357	59.84 330	77.71 319	87.11 348

MSDG		73.36 327	70.99 341	80.49 276	84.51 317	65.80 204	80.71 350	86.13 301	65.70 330	65.46 404	83.74 352	44.60 380	90.91 293	51.13 395	76.89 329	84.74 392

SSC-MVS3.2		73.35 328	73.39 312	73.23 384	85.30 296	49.01 435	74.58 419	81.57 365	75.21 120	73.68 311	85.58 310	52.53 290	82.05 401	54.33 378	77.69 321	88.63 311

tpm2		73.26 329	71.46 334	78.63 312	83.34 343	56.71 369	80.65 351	80.40 383	56.63 422	73.55 313	82.02 385	51.80 308	91.24 281	56.35 368	78.42 312	87.95 324

RPSCF		73.23 330	71.46 334	78.54 317	82.50 368	59.85 328	82.18 329	82.84 353	58.96 403	71.15 344	89.41 202	45.48 377	84.77 381	58.82 342	71.83 394	91.02 212

PatchmatchNet		73.12 331	71.33 337	78.49 320	83.18 349	60.85 315	79.63 366	78.57 401	64.13 349	71.73 336	79.81 408	51.20 315	85.97 367	57.40 356	76.36 344	88.66 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UBG		73.08 332	72.27 327	75.51 359	88.02 202	51.29 425	78.35 388	77.38 411	65.52 333	73.87 309	82.36 378	45.55 374	86.48 361	55.02 373	84.39 229	88.75 306

COLMAP_ROB	66.92 17	73.01 333	70.41 348	80.81 269	87.13 243	65.63 208	88.30 157	84.19 327	62.96 365	63.80 418	87.69 250	38.04 422	92.56 220	46.66 421	74.91 367	84.24 397
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 334	72.58 323	74.25 376	84.28 319	50.85 428	86.41 226	83.45 338	44.56 448	73.23 317	87.54 256	49.38 338	85.70 369	65.90 278	78.44 309	86.19 365

test-LLR		72.94 335	72.43 324	74.48 372	81.35 387	58.04 346	78.38 385	77.46 408	66.66 315	69.95 357	79.00 415	48.06 349	79.24 414	66.13 274	84.83 218	86.15 366

test_0402		72.79 336	70.44 347	79.84 290	88.13 196	65.99 198	85.93 241	84.29 324	65.57 332	67.40 384	85.49 312	46.92 356	92.61 216	35.88 450	74.38 372	80.94 429

tpmrst		72.39 337	72.13 328	73.18 388	80.54 396	49.91 432	79.91 365	79.08 398	63.11 362	71.69 337	79.95 405	55.32 265	82.77 397	65.66 281	73.89 376	86.87 353

PatchMatch-RL		72.38 338	70.90 342	76.80 348	88.60 177	67.38 169	79.53 367	76.17 420	62.75 370	69.36 364	82.00 386	45.51 375	84.89 380	53.62 381	80.58 285	78.12 438

CL-MVSNet_self_test		72.37 339	71.46 334	75.09 365	79.49 412	53.53 405	80.76 348	85.01 316	69.12 281	70.51 346	82.05 384	57.92 243	84.13 385	52.27 388	66.00 419	87.60 332

tpm		72.37 339	71.71 331	74.35 374	82.19 373	52.00 415	79.22 372	77.29 412	64.56 344	72.95 321	83.68 356	51.35 312	83.26 394	58.33 348	75.80 348	87.81 328

ETVMVS		72.25 341	71.05 340	75.84 353	87.77 217	51.91 417	79.39 369	74.98 423	69.26 275	73.71 310	82.95 369	40.82 408	86.14 364	46.17 425	84.43 228	89.47 278

sc_t1		72.19 342	69.51 353	80.23 282	84.81 308	61.09 311	84.68 275	80.22 386	60.70 387	71.27 341	83.58 358	36.59 427	89.24 323	60.41 325	63.31 426	90.37 239

UWE-MVS		72.13 343	71.49 333	74.03 378	86.66 262	47.70 437	81.40 340	76.89 416	63.60 359	75.59 266	84.22 343	39.94 411	85.62 371	48.98 409	86.13 197	88.77 305

PVSNet	64.34 18	72.08 344	70.87 343	75.69 355	86.21 271	56.44 373	74.37 420	80.73 374	62.06 378	70.17 352	82.23 382	42.86 393	83.31 393	54.77 375	84.45 227	87.32 340

WB-MVSnew		71.96 345	71.65 332	72.89 390	84.67 315	51.88 418	82.29 328	77.57 407	62.31 374	73.67 312	83.00 368	53.49 286	81.10 408	45.75 428	82.13 266	85.70 376

pmmvs5		71.55 346	70.20 351	75.61 356	77.83 420	56.39 374	81.74 333	80.89 371	57.76 414	67.46 381	84.49 332	49.26 341	85.32 376	57.08 359	75.29 362	85.11 387

test-mter		71.41 347	70.39 349	74.48 372	81.35 387	58.04 346	78.38 385	77.46 408	60.32 390	69.95 357	79.00 415	36.08 430	79.24 414	66.13 274	84.83 218	86.15 366

K. test v3		71.19 348	68.51 360	79.21 304	83.04 354	57.78 354	84.35 289	76.91 415	72.90 189	62.99 421	82.86 372	39.27 413	91.09 289	61.65 316	52.66 448	88.75 306

dmvs_re		71.14 349	70.58 344	72.80 391	81.96 375	59.68 330	75.60 410	79.34 395	68.55 293	69.27 366	80.72 396	49.42 337	76.54 428	52.56 387	77.79 318	82.19 421

tpmvs		71.09 350	69.29 355	76.49 349	82.04 374	56.04 380	78.92 378	81.37 369	64.05 353	67.18 386	78.28 421	49.74 334	89.77 312	49.67 405	72.37 388	83.67 405

AllTest		70.96 351	68.09 366	79.58 297	85.15 300	63.62 259	84.58 280	79.83 389	62.31 374	60.32 431	86.73 274	32.02 437	88.96 331	50.28 400	71.57 396	86.15 366

test_fmvs1		70.93 352	70.52 345	72.16 396	73.71 439	55.05 393	80.82 344	78.77 400	51.21 440	78.58 195	84.41 335	31.20 441	76.94 426	75.88 172	80.12 293	84.47 395

test_fmvs1_n		70.86 353	70.24 350	72.73 392	72.51 450	55.28 391	81.27 341	79.71 391	51.49 439	78.73 190	84.87 327	27.54 446	77.02 425	76.06 168	79.97 294	85.88 374

Patchmtry		70.74 354	69.16 357	75.49 360	80.72 393	54.07 402	74.94 417	80.30 384	58.34 408	70.01 354	81.19 388	52.50 292	86.54 359	53.37 383	71.09 399	85.87 375

MIMVSNet		70.69 355	69.30 354	74.88 368	84.52 316	56.35 377	75.87 408	79.42 393	64.59 343	67.76 376	82.41 377	41.10 405	81.54 404	46.64 423	81.34 273	86.75 357

tpm cat1		70.57 356	68.31 362	77.35 342	82.41 371	57.95 349	78.08 390	80.22 386	52.04 435	68.54 372	77.66 426	52.00 303	87.84 347	51.77 389	72.07 393	86.25 363

OpenMVS_ROB	64.09 19	70.56 357	68.19 363	77.65 336	80.26 398	59.41 335	85.01 268	82.96 350	58.76 406	65.43 405	82.33 379	37.63 424	91.23 282	45.34 431	76.03 346	82.32 419

pmmvs-eth3d		70.50 358	67.83 372	78.52 319	77.37 423	66.18 193	81.82 331	81.51 366	58.90 404	63.90 417	80.42 398	42.69 394	86.28 363	58.56 344	65.30 421	83.11 411

tt0320		70.49 359	68.03 367	77.89 330	84.78 309	59.12 336	83.55 308	80.44 381	58.13 411	67.43 383	80.41 399	39.26 414	87.54 351	55.12 372	63.18 427	86.99 351

USDC		70.33 360	68.37 361	76.21 351	80.60 395	56.23 378	79.19 373	86.49 293	60.89 385	61.29 426	85.47 313	31.78 439	89.47 319	53.37 383	76.21 345	82.94 415

Patchmatch-RL test		70.24 361	67.78 374	77.61 337	77.43 422	59.57 333	71.16 430	70.33 437	62.94 366	68.65 370	72.77 443	50.62 321	85.49 373	69.58 245	66.58 416	87.77 329

CMPMVS	51.72 21	70.19 362	68.16 364	76.28 350	73.15 446	57.55 357	79.47 368	83.92 329	48.02 444	56.48 444	84.81 329	43.13 391	86.42 362	62.67 304	81.81 271	84.89 390
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 363	67.45 380	78.07 328	85.33 295	59.51 334	83.28 314	78.96 399	58.77 405	67.10 387	80.28 401	36.73 426	87.42 352	56.83 364	59.77 437	87.29 341

ppachtmachnet_test		70.04 364	67.34 382	78.14 325	79.80 407	61.13 309	79.19 373	80.59 376	59.16 401	65.27 406	79.29 412	46.75 360	87.29 353	49.33 407	66.72 414	86.00 372

gg-mvs-nofinetune		69.95 365	67.96 368	75.94 352	83.07 352	54.51 399	77.23 399	70.29 438	63.11 362	70.32 349	62.33 452	43.62 388	88.69 335	53.88 380	87.76 166	84.62 394

TESTMET0.1,1		69.89 366	69.00 358	72.55 393	79.27 415	56.85 365	78.38 385	74.71 427	57.64 415	68.09 375	77.19 428	37.75 423	76.70 427	63.92 293	84.09 233	84.10 400

test_vis1_n		69.85 367	69.21 356	71.77 398	72.66 449	55.27 392	81.48 337	76.21 419	52.03 436	75.30 282	83.20 365	28.97 444	76.22 433	74.60 186	78.41 313	83.81 403

FMVSNet5		69.50 368	67.96 368	74.15 377	82.97 358	55.35 390	80.01 363	82.12 359	62.56 372	63.02 419	81.53 387	36.92 425	81.92 402	48.42 411	74.06 374	85.17 386

mvs5depth		69.45 369	67.45 380	75.46 361	73.93 437	55.83 383	79.19 373	83.23 341	66.89 310	71.63 338	83.32 362	33.69 435	85.09 377	59.81 331	55.34 445	85.46 379

PMMVS		69.34 370	68.67 359	71.35 403	75.67 430	62.03 299	75.17 412	73.46 430	50.00 441	68.68 369	79.05 413	52.07 302	78.13 419	61.16 321	82.77 258	73.90 445

our_test_3		69.14 371	67.00 384	75.57 357	79.80 407	58.80 337	77.96 392	77.81 405	59.55 397	62.90 422	78.25 422	47.43 351	83.97 386	51.71 390	67.58 413	83.93 402

EPMVS		69.02 372	68.16 364	71.59 399	79.61 410	49.80 434	77.40 397	66.93 448	62.82 369	70.01 354	79.05 413	45.79 371	77.86 422	56.58 366	75.26 363	87.13 347

KD-MVS_self_test		68.81 373	67.59 378	72.46 395	74.29 436	45.45 445	77.93 393	87.00 281	63.12 361	63.99 416	78.99 417	42.32 396	84.77 381	56.55 367	64.09 424	87.16 346

Anonymous20240521		68.80 374	67.22 383	73.55 382	74.33 435	54.11 401	83.18 316	85.61 307	58.15 410	61.68 425	80.94 393	30.71 442	81.27 407	57.00 361	73.34 384	85.28 382

Anonymous20231206		68.60 375	67.80 373	71.02 406	80.23 400	50.75 429	78.30 389	80.47 379	56.79 421	66.11 402	82.63 376	46.35 364	78.95 416	43.62 434	75.70 349	83.36 408

MIMVSNet1		68.58 376	66.78 386	73.98 379	80.07 402	51.82 419	80.77 347	84.37 321	64.40 346	59.75 434	82.16 383	36.47 428	83.63 389	42.73 437	70.33 402	86.48 361

testing3		68.56 377	67.67 376	71.22 405	87.33 235	42.87 455	83.06 322	71.54 435	70.36 246	69.08 367	84.38 336	30.33 443	85.69 370	37.50 448	75.45 357	85.09 388

EU-MVSNet		68.53 378	67.61 377	71.31 404	78.51 419	47.01 442	84.47 282	84.27 325	42.27 451	66.44 399	84.79 330	40.44 409	83.76 387	58.76 343	68.54 411	83.17 409

PatchT		68.46 379	67.85 370	70.29 409	80.70 394	43.93 453	72.47 425	74.88 424	60.15 392	70.55 345	76.57 430	49.94 331	81.59 403	50.58 396	74.83 368	85.34 381

test_fmvs2		68.35 380	67.48 379	70.98 407	69.50 453	51.95 416	80.05 362	76.38 418	49.33 442	74.65 299	84.38 336	23.30 455	75.40 442	74.51 187	75.17 365	85.60 377

Syy-MVS		68.05 381	67.85 370	68.67 418	84.68 312	40.97 461	78.62 382	73.08 432	66.65 318	66.74 392	79.46 410	52.11 300	82.30 399	32.89 453	76.38 342	82.75 416

test0.0.03 1		68.00 382	67.69 375	68.90 415	77.55 421	47.43 438	75.70 409	72.95 434	66.66 315	66.56 394	82.29 381	48.06 349	75.87 437	44.97 432	74.51 371	83.41 407

TDRefinement		67.49 383	64.34 395	76.92 346	73.47 443	61.07 312	84.86 272	82.98 349	59.77 395	58.30 438	85.13 322	26.06 447	87.89 346	47.92 418	60.59 435	81.81 425

test20.03		67.45 384	66.95 385	68.94 414	75.48 432	44.84 451	77.50 396	77.67 406	66.66 315	63.01 420	83.80 350	47.02 355	78.40 418	42.53 439	68.86 410	83.58 406

UnsupCasMVSNet_eth		67.33 385	65.99 389	71.37 401	73.48 442	51.47 423	75.16 413	85.19 311	65.20 336	60.78 428	80.93 395	42.35 395	77.20 424	57.12 358	53.69 447	85.44 380

TinyColmap		67.30 386	64.81 393	74.76 370	81.92 377	56.68 370	80.29 358	81.49 367	60.33 389	56.27 445	83.22 363	24.77 451	87.66 350	45.52 429	69.47 405	79.95 434

FE-MVSNET		67.25 387	65.33 391	73.02 389	75.86 428	52.54 413	80.26 360	80.56 377	63.80 358	60.39 429	79.70 409	41.41 403	84.66 383	43.34 435	62.62 428	81.86 423

myMVS_eth3d		67.02 388	66.29 388	69.21 413	84.68 312	42.58 456	78.62 382	73.08 432	66.65 318	66.74 392	79.46 410	31.53 440	82.30 399	39.43 445	76.38 342	82.75 416

dp		66.80 389	65.43 390	70.90 408	79.74 409	48.82 436	75.12 415	74.77 425	59.61 396	64.08 415	77.23 427	42.89 392	80.72 410	48.86 410	66.58 416	83.16 410

MDA-MVSNet-bldmvs		66.68 390	63.66 400	75.75 354	79.28 414	60.56 320	73.92 422	78.35 403	64.43 345	50.13 453	79.87 407	44.02 386	83.67 388	46.10 426	56.86 439	83.03 413

testgi		66.67 391	66.53 387	67.08 425	75.62 431	41.69 460	75.93 405	76.50 417	66.11 324	65.20 409	86.59 284	35.72 431	74.71 444	43.71 433	73.38 383	84.84 391

CHOSEN 280x420		66.51 392	64.71 394	71.90 397	81.45 384	63.52 268	57.98 462	68.95 444	53.57 431	62.59 423	76.70 429	46.22 366	75.29 443	55.25 371	79.68 295	76.88 441

PM-MVS		66.41 393	64.14 396	73.20 387	73.92 438	56.45 372	78.97 377	64.96 454	63.88 357	64.72 410	80.24 402	19.84 459	83.44 392	66.24 273	64.52 423	79.71 435

JIA-IIPM		66.32 394	62.82 406	76.82 347	77.09 424	61.72 305	65.34 453	75.38 421	58.04 413	64.51 411	62.32 453	42.05 400	86.51 360	51.45 393	69.22 407	82.21 420

KD-MVS_2432*1600		66.22 395	63.89 398	73.21 385	75.47 433	53.42 407	70.76 433	84.35 322	64.10 351	66.52 396	78.52 419	34.55 433	84.98 378	50.40 398	50.33 452	81.23 427

miper_refine_blended		66.22 395	63.89 398	73.21 385	75.47 433	53.42 407	70.76 433	84.35 322	64.10 351	66.52 396	78.52 419	34.55 433	84.98 378	50.40 398	50.33 452	81.23 427

ADS-MVSNet2		66.20 397	63.33 401	74.82 369	79.92 403	58.75 338	67.55 445	75.19 422	53.37 432	65.25 407	75.86 434	42.32 396	80.53 411	41.57 440	68.91 408	85.18 384

UWE-MVS-28		65.32 398	64.93 392	66.49 426	78.70 417	38.55 463	77.86 395	64.39 455	62.00 379	64.13 414	83.60 357	41.44 402	76.00 435	31.39 455	80.89 279	84.92 389

YYNet1		65.03 399	62.91 404	71.38 400	75.85 429	56.60 371	69.12 441	74.66 428	57.28 419	54.12 447	77.87 424	45.85 370	74.48 445	49.95 403	61.52 432	83.05 412

MDA-MVSNet_test_wron		65.03 399	62.92 403	71.37 401	75.93 426	56.73 367	69.09 442	74.73 426	57.28 419	54.03 448	77.89 423	45.88 369	74.39 446	49.89 404	61.55 431	82.99 414

Patchmatch-test		64.82 401	63.24 402	69.57 411	79.42 413	49.82 433	63.49 459	69.05 443	51.98 437	59.95 433	80.13 403	50.91 317	70.98 452	40.66 442	73.57 379	87.90 326

ADS-MVSNet		64.36 402	62.88 405	68.78 417	79.92 403	47.17 441	67.55 445	71.18 436	53.37 432	65.25 407	75.86 434	42.32 396	73.99 448	41.57 440	68.91 408	85.18 384

LF4IMVS		64.02 403	62.19 407	69.50 412	70.90 451	53.29 410	76.13 403	77.18 413	52.65 434	58.59 436	80.98 392	23.55 454	76.52 429	53.06 385	66.66 415	78.68 437

UnsupCasMVSNet_bld		63.70 404	61.53 410	70.21 410	73.69 440	51.39 424	72.82 424	81.89 361	55.63 426	57.81 440	71.80 445	38.67 418	78.61 417	49.26 408	52.21 450	80.63 431

test_fmvs3		63.36 405	61.82 408	67.98 422	62.51 462	46.96 443	77.37 398	74.03 429	45.24 447	67.50 380	78.79 418	12.16 467	72.98 451	72.77 207	66.02 418	83.99 401

dmvs_testset		62.63 406	64.11 397	58.19 436	78.55 418	24.76 474	75.28 411	65.94 451	67.91 302	60.34 430	76.01 433	53.56 284	73.94 449	31.79 454	67.65 412	75.88 443

mvsany_test1		62.30 407	61.26 411	65.41 428	69.52 452	54.86 395	66.86 447	49.78 468	46.65 445	68.50 373	83.21 364	49.15 342	66.28 460	56.93 362	60.77 433	75.11 444

new-patchmatchnet		61.73 408	61.73 409	61.70 432	72.74 448	24.50 475	69.16 440	78.03 404	61.40 382	56.72 443	75.53 437	38.42 419	76.48 430	45.95 427	57.67 438	84.13 399

PVSNet_0	57.27 20	61.67 409	59.27 412	68.85 416	79.61 410	57.44 359	68.01 443	73.44 431	55.93 425	58.54 437	70.41 448	44.58 381	77.55 423	47.01 420	35.91 460	71.55 448

test_vis1_rt		60.28 410	58.42 413	65.84 427	67.25 456	55.60 387	70.44 435	60.94 460	44.33 449	59.00 435	66.64 450	24.91 450	68.67 457	62.80 300	69.48 404	73.25 446

ttmdpeth		59.91 411	57.10 415	68.34 420	67.13 457	46.65 444	74.64 418	67.41 447	48.30 443	62.52 424	85.04 326	20.40 457	75.93 436	42.55 438	45.90 458	82.44 418

MVS-HIRNet		59.14 412	57.67 414	63.57 430	81.65 379	43.50 454	71.73 427	65.06 453	39.59 455	51.43 450	57.73 458	38.34 420	82.58 398	39.53 443	73.95 375	64.62 454

pmmvs3		57.79 413	54.26 418	68.37 419	64.02 461	56.72 368	75.12 415	65.17 452	40.20 453	52.93 449	69.86 449	20.36 458	75.48 440	45.45 430	55.25 446	72.90 447

DSMNet-mixed		57.77 414	56.90 416	60.38 434	67.70 455	35.61 465	69.18 439	53.97 466	32.30 464	57.49 441	79.88 406	40.39 410	68.57 458	38.78 446	72.37 388	76.97 440

MVStest1		56.63 415	52.76 421	68.25 421	61.67 463	53.25 411	71.67 428	68.90 445	38.59 456	50.59 452	83.05 367	25.08 449	70.66 453	36.76 449	38.56 459	80.83 430

WB-MVS		54.94 416	54.72 417	55.60 442	73.50 441	20.90 476	74.27 421	61.19 459	59.16 401	50.61 451	74.15 439	47.19 354	75.78 438	17.31 467	35.07 461	70.12 449

LCM-MVSNet		54.25 417	49.68 427	67.97 423	53.73 471	45.28 448	66.85 448	80.78 373	35.96 460	39.45 461	62.23 454	8.70 471	78.06 421	48.24 415	51.20 451	80.57 432

mvsany_test3		53.99 418	51.45 423	61.61 433	55.51 467	44.74 452	63.52 458	45.41 472	43.69 450	58.11 439	76.45 431	17.99 460	63.76 463	54.77 375	47.59 454	76.34 442

SSC-MVS		53.88 419	53.59 419	54.75 444	72.87 447	19.59 477	73.84 423	60.53 461	57.58 417	49.18 455	73.45 442	46.34 365	75.47 441	16.20 470	32.28 463	69.20 450

FPMVS		53.68 420	51.64 422	59.81 435	65.08 459	51.03 426	69.48 438	69.58 441	41.46 452	40.67 459	72.32 444	16.46 463	70.00 456	24.24 463	65.42 420	58.40 459

APD_test1		53.31 421	49.93 426	63.42 431	65.68 458	50.13 431	71.59 429	66.90 449	34.43 461	40.58 460	71.56 446	8.65 472	76.27 432	34.64 452	55.36 444	63.86 455

N_pmnet		52.79 422	53.26 420	51.40 446	78.99 416	7.68 480	69.52 437	3.89 479	51.63 438	57.01 442	74.98 438	40.83 407	65.96 461	37.78 447	64.67 422	80.56 433

test_f		52.09 423	50.82 424	55.90 440	53.82 470	42.31 459	59.42 461	58.31 464	36.45 459	56.12 446	70.96 447	12.18 466	57.79 466	53.51 382	56.57 441	67.60 451

EGC-MVSNET		52.07 424	47.05 428	67.14 424	83.51 340	60.71 317	80.50 354	67.75 446	0.07 474	0.43 475	75.85 436	24.26 452	81.54 404	28.82 457	62.25 429	59.16 457

new_pmnet		50.91 425	50.29 425	52.78 445	68.58 454	34.94 467	63.71 457	56.63 465	39.73 454	44.95 456	65.47 451	21.93 456	58.48 465	34.98 451	56.62 440	64.92 453

ANet_high		50.57 426	46.10 430	63.99 429	48.67 474	39.13 462	70.99 432	80.85 372	61.39 383	31.18 463	57.70 459	17.02 462	73.65 450	31.22 456	15.89 471	79.18 436

test_vis3_rt		49.26 427	47.02 429	56.00 439	54.30 468	45.27 449	66.76 449	48.08 469	36.83 458	44.38 457	53.20 462	7.17 474	64.07 462	56.77 365	55.66 442	58.65 458

testf1		45.72 428	41.96 432	57.00 437	56.90 465	45.32 446	66.14 450	59.26 462	26.19 465	30.89 464	60.96 456	4.14 475	70.64 454	26.39 461	46.73 456	55.04 460

APD_test2		45.72 428	41.96 432	57.00 437	56.90 465	45.32 446	66.14 450	59.26 462	26.19 465	30.89 464	60.96 456	4.14 475	70.64 454	26.39 461	46.73 456	55.04 460

dongtai		45.42 430	45.38 431	45.55 448	73.36 444	26.85 472	67.72 444	34.19 474	54.15 430	49.65 454	56.41 461	25.43 448	62.94 464	19.45 465	28.09 465	46.86 464

Gipuma		45.18 431	41.86 434	55.16 443	77.03 425	51.52 422	32.50 468	80.52 378	32.46 463	27.12 466	35.02 467	9.52 470	75.50 439	22.31 464	60.21 436	38.45 466
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 432	40.28 436	55.82 441	40.82 476	42.54 458	65.12 454	63.99 456	34.43 461	24.48 467	57.12 460	3.92 477	76.17 434	17.10 468	55.52 443	48.75 462
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 433	38.86 437	46.69 447	53.84 469	16.45 478	48.61 465	49.92 467	37.49 457	31.67 462	60.97 455	8.14 473	56.42 467	28.42 458	30.72 464	67.19 452

kuosan		39.70 434	40.40 435	37.58 451	64.52 460	26.98 470	65.62 452	33.02 475	46.12 446	42.79 458	48.99 464	24.10 453	46.56 472	12.16 473	26.30 466	39.20 465

E-PMN		31.77 435	30.64 438	35.15 452	52.87 472	27.67 469	57.09 463	47.86 470	24.64 467	16.40 472	33.05 468	11.23 468	54.90 468	14.46 471	18.15 469	22.87 468

test_method		31.52 436	29.28 440	38.23 450	27.03 478	6.50 481	20.94 470	62.21 458	4.05 472	22.35 470	52.50 463	13.33 464	47.58 470	27.04 460	34.04 462	60.62 456

EMVS		30.81 437	29.65 439	34.27 453	50.96 473	25.95 473	56.58 464	46.80 471	24.01 468	15.53 473	30.68 469	12.47 465	54.43 469	12.81 472	17.05 470	22.43 469

MVE	26.22 23	30.37 438	25.89 442	43.81 449	44.55 475	35.46 466	28.87 469	39.07 473	18.20 469	18.58 471	40.18 466	2.68 478	47.37 471	17.07 469	23.78 468	48.60 463
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 439	26.61 441	0.00 459	0.00 482	0.00 484	0.00 471	89.26 212	0.00 477	0.00 478	88.61 223	61.62 198	0.00 478	0.00 477	0.00 476	0.00 474

tmp_tt		18.61 440	21.40 443	10.23 456	4.82 479	10.11 479	34.70 467	30.74 477	1.48 473	23.91 469	26.07 470	28.42 445	13.41 475	27.12 459	15.35 472	7.17 470

wuyk23d		16.82 441	15.94 444	19.46 455	58.74 464	31.45 468	39.22 466	3.74 480	6.84 471	6.04 474	2.70 474	1.27 479	24.29 474	10.54 474	14.40 473	2.63 471

ab-mvs-re		7.23 442	9.64 445	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	86.72 276	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

test123		6.12 443	8.11 446	0.14 457	0.06 481	0.09 482	71.05 431	0.03 482	0.04 476	0.25 477	1.30 476	0.05 480	0.03 477	0.21 476	0.01 475	0.29 472

testmvs		6.04 444	8.02 447	0.10 458	0.08 480	0.03 483	69.74 436	0.04 481	0.05 475	0.31 476	1.68 475	0.02 481	0.04 476	0.24 475	0.02 474	0.25 473

pcd_1.5k_mvsjas		5.26 445	7.02 448	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	63.15 170	0.00 478	0.00 477	0.00 476	0.00 474

mmdepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

monomultidepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

test_blank		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet_test		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

DCPMVS		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet-low-res		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uncertanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

Regformer		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

TestfortrainingZip							93.28 12

WAC-MVS						42.58 456								39.46 444

FOURS1					95.00 10	72.39 41	95.06 1	93.84 18	74.49 142	91.30 16

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 52					97.53 2	89.67 15	96.44 9	94.41 48

PC_three_1452									68.21 299	92.02 13	94.00 60	82.09 5	95.98 59	84.58 68	96.68 2	94.95 12

No_MVS				89.16 1	94.34 29	75.53 2		92.99 52					97.53 2	89.67 15	96.44 9	94.41 48

test_one_0601					95.07 7	71.46 59		94.14 8	78.27 41	92.05 12	95.74 6	80.83 12

eth-test2					0.00 482

eth-test					0.00 482

ZD-MVS					94.38 27	72.22 46		92.67 70	70.98 228	87.75 48	94.07 55	74.01 35	96.70 29	84.66 67	94.84 46

RE-MVS-def			85.48 73		93.06 62	70.63 80	91.88 41	92.27 87	73.53 170	85.69 70	94.45 35	63.87 160		82.75 91	91.87 92	92.50 155

IU-MVS					95.30 2	71.25 62		92.95 58	66.81 311	92.39 6				88.94 27	96.63 4	94.85 21

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 58	82.45 3	96.87 22	83.77 79	96.48 8	94.88 16

test_241102_TWO								94.06 13	77.24 60	92.78 4	95.72 8	81.26 9	97.44 7	89.07 24	96.58 6	94.26 59

test_241102_ONE					95.30 2	70.98 69		94.06 13	77.17 63	93.10 1	95.39 16	82.99 1	97.27 13

9.14			88.26 18		92.84 67		91.52 54	94.75 1	73.93 158	88.57 33	94.67 28	75.57 24	95.79 61	86.77 48	95.76 25

save fliter					93.80 42	72.35 44	90.47 72	91.17 139	74.31 147

test_0728_THIRD									78.38 38	92.12 10	95.78 4	81.46 8	97.40 9	89.42 19	96.57 7	94.67 31

test_0728_SECOND				87.71 33	95.34 1	71.43 60	93.49 10	94.23 5					97.49 4	89.08 22	96.41 12	94.21 60

test0726					95.27 5	71.25 62	93.60 7	94.11 9	77.33 57	92.81 3	95.79 3	80.98 10

GSMVS																88.96 297

test_part2					95.06 8	72.65 32				91.80 14

sam_mvs1												51.32 313				88.96 297

sam_mvs												50.01 329

ambc				75.24 364	73.16 445	50.51 430	63.05 460	87.47 271		64.28 412	77.81 425	17.80 461	89.73 314	57.88 352	60.64 434	85.49 378

MTGPA								92.02 101

test_post1							78.90 379				5.43 473	48.81 348	85.44 375	59.25 336

test_post											5.46 472	50.36 325	84.24 384

patchmatchnet-post											74.00 440	51.12 316	88.60 337

GG-mvs-BLEND				75.38 362	81.59 381	55.80 384	79.32 370	69.63 440		67.19 385	73.67 441	43.24 390	88.90 333	50.41 397	84.50 223	81.45 426

MTMP							92.18 37	32.83 476

gm-plane-assit					81.40 385	53.83 404			62.72 371		80.94 393		92.39 229	63.40 297

test9_res														84.90 61	95.70 28	92.87 140

TEST9					93.26 54	72.96 25	88.75 135	91.89 109	68.44 296	85.00 77	93.10 85	74.36 31	95.41 78

test_8					93.13 58	72.57 35	88.68 140	91.84 113	68.69 291	84.87 81	93.10 85	74.43 29	95.16 88

agg_prior2														82.91 88	95.45 31	92.70 145

agg_prior					92.85 66	71.94 52		91.78 117		84.41 92			94.93 99

TestCases				79.58 297	85.15 300	63.62 259		79.83 389	62.31 374	60.32 431	86.73 274	32.02 437	88.96 331	50.28 400	71.57 396	86.15 366

test_prior4						72.60 34	89.01 122

test_prior2							88.85 129		75.41 112	84.91 79	93.54 73	74.28 32		83.31 82	95.86 22

test_prior				86.33 62	92.61 72	69.59 96		92.97 57					95.48 72			93.91 76

旧先验2							86.56 222		58.10 412	87.04 59			88.98 329	74.07 192

新几何2							86.29 233

新几何1				83.42 181	93.13 58	70.71 78		85.48 309	57.43 418	81.80 139	91.98 112	63.28 164	92.27 235	64.60 289	92.99 74	87.27 342

旧先验1					91.96 78	65.79 205		86.37 296			93.08 89	69.31 93			92.74 78	88.74 308

无先验							87.48 183	88.98 227	60.00 393				94.12 138	67.28 266		88.97 296

原ACMM2							86.86 209

原ACMM1				84.35 130	93.01 64	68.79 115		92.44 80	63.96 356	81.09 152	91.57 129	66.06 140	95.45 73	67.19 268	94.82 48	88.81 303

test222					91.50 84	68.26 135	84.16 294	83.20 344	54.63 429	79.74 173	91.63 125	58.97 234			91.42 100	86.77 356

testdata2													91.01 291	62.37 307

segment_acmp												73.08 42

testdata				79.97 287	90.90 96	64.21 247		84.71 317	59.27 400	85.40 72	92.91 91	62.02 191	89.08 327	68.95 251	91.37 102	86.63 360

testdata1							84.14 295		75.71 103

test12				86.80 56	92.63 71	70.70 79		91.79 116		82.71 126		71.67 61	96.16 50		94.50 55	93.54 105

plane_prior7					90.08 114	68.51 129

plane_prior6					89.84 123	68.70 123						60.42 224

plane_prior5								92.44 80					95.38 80	78.71 133	86.32 191	91.33 200

plane_prior4											91.00 152

plane_prior3						68.60 126			78.44 36	78.92 188

plane_prior2							91.25 58		79.12 28

plane_prior1					89.90 122

plane_prior						68.71 121	90.38 76		77.62 47						86.16 196

n2								0.00 483

nn								0.00 483

door-mid								69.98 439

lessismore_v0				78.97 307	81.01 392	57.15 362		65.99 450		61.16 427	82.82 373	39.12 415	91.34 278	59.67 332	46.92 455	88.43 316

LGP-MVS_train				84.50 123	89.23 150	68.76 117		91.94 107	75.37 114	76.64 244	91.51 131	54.29 276	94.91 100	78.44 135	83.78 236	89.83 268

test11								92.23 90

door								69.44 442

HQP5-MVS						66.98 181

HQP-NCC					89.33 142		89.17 113		76.41 85	77.23 229

ACMP_Plane					89.33 142		89.17 113		76.41 85	77.23 229

BP-MVS														77.47 148

HQP4-MVS										77.24 228			95.11 92			91.03 210

HQP3-MVS								92.19 95							85.99 200

HQP2-MVS												60.17 227

NP-MVS					89.62 127	68.32 133					90.24 173

MDTV_nov1_ep13_2view						37.79 464	75.16 413		55.10 427	66.53 395		49.34 339		53.98 379		87.94 325

MDTV_nov1_ep13			69.97 352		83.18 349	53.48 406	77.10 401	80.18 388	60.45 388	69.33 365	80.44 397	48.89 347	86.90 356	51.60 391	78.51 308

ACMMP++_ref															81.95 269

ACMMP++															81.25 274

Test By Simon												64.33 156

ITE_SJBPF				78.22 323	81.77 378	60.57 319		83.30 339	69.25 276	67.54 379	87.20 265	36.33 429	87.28 354	54.34 377	74.62 370	86.80 355

DeepMVS_CX				27.40 454	40.17 477	26.90 471		24.59 478	17.44 470	23.95 468	48.61 465	9.77 469	26.48 473	18.06 466	24.47 467	28.83 467

High-resolution multi-view benchmark