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++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 66	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 21

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 75	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 29

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 19	86.83 8	65.51 12	83.81 10	90.51 27	63.71 12	89.23 21	81.51 2	88.44 28	88.09 35
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
DVP-MVS		80.84 4	81.64 3	78.42 35	87.75 7	59.07 70	87.85 5	85.03 38	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 147
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
DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 28	86.42 15	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 38	89.67 18	86.84 82
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 24	61.95 19	86.33 14	85.75 23	62.49 66	82.20 16	92.28 1	56.53 39	89.70 18	79.85 6	91.48 1	88.19 31
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
MM		80.20 7	80.28 9	79.99 2	82.19 86	60.01 49	86.19 18	83.93 56	73.19 1	77.08 41	91.21 18	57.23 34	90.73 10	83.35 1	88.12 35	89.22 7

APDe-MVS		80.16 8	80.59 6	78.86 30	86.64 21	60.02 48	88.12 3	86.42 15	62.94 55	82.40 14	92.12 2	59.64 20	89.76 17	78.70 15	88.32 32	86.79 84
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MED-MVS		80.04 9	80.36 8	79.08 23	86.63 23	59.25 64	85.62 29	86.73 12	63.10 52	82.27 15	90.57 25	61.90 14	89.88 16	77.02 34	89.43 22	88.10 34

HPM-MVS++		79.88 10	80.14 10	79.10 21	88.17 1	64.80 1	86.59 13	83.70 67	65.37 13	78.78 25	90.64 22	58.63 26	87.24 56	79.00 14	90.37 14	85.26 159

CNVR-MVS		79.84 11	79.97 11	79.45 11	87.90 2	62.17 17	84.37 42	85.03 38	66.96 5	77.58 35	90.06 42	59.47 22	89.13 23	78.67 17	89.73 16	87.03 76

SteuartSystems-ACMMP		79.48 12	79.31 12	79.98 3	83.01 77	62.18 16	87.60 9	85.83 21	66.69 9	78.03 32	90.98 19	54.26 63	90.06 14	78.42 23	89.02 24	87.69 48
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 13	79.00 14	79.13 19	84.92 57	60.32 46	83.03 65	85.33 30	62.86 58	80.17 18	90.03 44	61.76 15	88.95 25	74.21 58	88.67 27	88.12 33

SF-MVS		78.82 14	79.22 13	77.60 48	82.88 79	57.83 87	84.99 34	88.13 2	61.86 79	79.16 22	90.75 21	57.96 27	87.09 65	77.08 33	90.18 15	87.87 40

ZNCC-MVS		78.82 14	78.67 17	79.30 14	86.43 29	62.05 18	86.62 12	86.01 20	63.32 46	75.08 57	90.47 30	53.96 69	88.68 28	76.48 37	89.63 20	87.16 73

ACMMP_NAP		78.77 16	78.78 15	78.74 31	85.44 46	61.04 31	83.84 57	85.16 33	62.88 57	78.10 30	91.26 17	52.51 93	88.39 31	79.34 9	90.52 13	86.78 85

NCCC		78.58 17	78.31 19	79.39 12	87.51 12	62.61 13	85.20 33	84.42 47	66.73 8	74.67 70	89.38 55	55.30 52	89.18 22	74.19 59	87.34 47	86.38 100

DeepC-MVS	69.38 2	78.56 18	78.14 23	79.83 7	83.60 67	61.62 23	84.17 50	86.85 6	63.23 49	73.84 84	90.25 37	57.68 30	89.96 15	74.62 56	89.03 23	87.89 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCNet		78.45 19	78.28 20	78.98 27	80.73 111	57.91 86	84.68 38	81.64 117	68.35 2	75.77 47	90.38 31	53.98 67	90.26 13	81.30 3	87.68 43	88.77 13

TSAR-MVS + MP.		78.44 20	78.28 20	78.90 28	84.96 53	61.41 26	84.03 53	83.82 65	59.34 140	79.37 21	89.76 51	59.84 17	87.62 53	76.69 35	86.74 56	87.68 49
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		78.35 21	78.46 18	78.03 41	84.96 53	59.52 58	82.93 67	85.39 29	62.15 71	76.41 45	91.51 11	52.47 95	86.78 72	80.66 4	89.64 19	87.80 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 21	78.26 22	78.64 32	86.54 26	63.47 4	86.02 21	83.55 72	63.89 39	73.60 86	90.60 23	54.85 58	86.72 73	77.20 31	88.06 37	85.74 133
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 23	77.85 25	78.99 26	86.05 39	61.82 22	85.84 23	85.21 32	63.56 43	74.29 76	90.03 44	52.56 92	88.53 30	74.79 55	88.34 30	86.63 93

APD-MVS		78.02 24	78.04 24	77.98 42	86.44 28	60.81 38	85.52 30	84.36 48	60.61 101	79.05 23	90.30 35	55.54 51	88.32 33	73.48 66	87.03 49	84.83 174
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 25	77.65 27	79.10 21	86.71 19	62.81 8	86.29 15	84.32 49	62.82 59	73.96 81	90.50 28	53.20 83	88.35 32	74.02 61	87.05 48	86.13 115

lecture		77.75 26	77.84 26	77.50 50	82.75 81	57.62 90	85.92 22	86.20 18	60.53 103	78.99 24	91.45 12	51.51 114	87.78 48	75.65 45	87.55 44	87.10 75

ACMMPR		77.71 27	77.23 30	79.16 17	86.75 18	62.93 7	86.29 15	84.24 50	62.82 59	73.55 87	90.56 26	49.80 137	88.24 34	74.02 61	87.03 49	86.32 108

SD-MVS		77.70 28	77.62 28	77.93 43	84.47 60	61.88 21	84.55 40	83.87 62	60.37 110	79.89 19	89.38 55	54.97 56	85.58 110	76.12 41	84.94 67	86.33 106
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
region2R		77.67 29	77.18 31	79.15 18	86.76 17	62.95 6	86.29 15	84.16 52	62.81 61	73.30 89	90.58 24	49.90 134	88.21 35	73.78 63	87.03 49	86.29 112

MCST-MVS		77.48 30	77.45 29	77.54 49	86.67 20	58.36 82	83.22 63	86.93 5	56.91 191	74.91 62	88.19 72	59.15 24	87.68 52	73.67 64	87.45 46	86.57 94

HPM-MVS		77.28 31	76.85 32	78.54 33	85.00 52	60.81 38	82.91 68	85.08 35	62.57 64	73.09 100	89.97 47	50.90 125	87.48 54	75.30 49	86.85 54	87.33 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 32	76.63 35	79.12 20	86.15 35	60.86 36	84.71 37	84.85 42	61.98 78	73.06 101	88.88 63	53.72 75	89.06 24	68.27 99	88.04 38	87.42 60
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 33	76.56 38	79.00 24	86.32 30	62.62 11	85.83 24	83.92 57	64.55 25	72.17 117	90.01 46	47.95 160	88.01 41	71.55 84	86.74 56	86.37 102

CP-MVS		77.12 34	76.68 34	78.43 34	86.05 39	63.18 5	87.55 10	83.45 75	62.44 68	72.68 109	90.50 28	48.18 158	87.34 55	73.59 65	85.71 63	84.76 178

CSCG		76.92 35	76.75 33	77.41 52	83.96 65	59.60 56	82.95 66	86.50 14	60.78 97	75.27 52	84.83 164	60.76 16	86.56 78	67.86 106	87.87 42	86.06 117

reproduce-ours		76.90 36	76.58 36	77.87 44	83.99 63	60.46 43	84.75 35	83.34 80	60.22 117	77.85 33	91.42 14	50.67 126	87.69 50	72.46 72	84.53 71	85.46 145

our_new_method		76.90 36	76.58 36	77.87 44	83.99 63	60.46 43	84.75 35	83.34 80	60.22 117	77.85 33	91.42 14	50.67 126	87.69 50	72.46 72	84.53 71	85.46 145

MTAPA		76.90 36	76.42 40	78.35 36	86.08 38	63.57 2	74.92 240	80.97 143	65.13 15	75.77 47	90.88 20	48.63 153	86.66 75	77.23 30	88.17 34	84.81 175

PGM-MVS		76.77 39	76.06 44	78.88 29	86.14 36	62.73 9	82.55 75	83.74 66	61.71 80	72.45 115	90.34 34	48.48 156	88.13 38	72.32 74	86.85 54	85.78 127

balanced_conf03		76.58 40	76.55 39	76.68 63	81.73 92	52.90 184	80.94 96	85.70 25	61.12 92	74.90 63	87.17 100	56.46 40	88.14 37	72.87 69	88.03 39	89.00 9

mPP-MVS		76.54 41	75.93 46	78.34 37	86.47 27	63.50 3	85.74 27	82.28 107	62.90 56	71.77 122	90.26 36	46.61 185	86.55 81	71.71 82	85.66 64	84.97 170

CANet		76.46 42	75.93 46	78.06 40	81.29 101	57.53 92	82.35 77	83.31 83	67.78 3	70.09 143	86.34 128	54.92 57	88.90 26	72.68 71	84.55 70	87.76 46

reproduce_model		76.43 43	76.08 43	77.49 51	83.47 71	60.09 47	84.60 39	82.90 98	59.65 130	77.31 36	91.43 13	49.62 139	87.24 56	71.99 78	83.75 82	85.14 161

CDPH-MVS		76.31 44	75.67 51	78.22 38	85.35 49	59.14 68	81.31 93	84.02 53	56.32 207	74.05 79	88.98 60	53.34 81	87.92 44	69.23 97	88.42 29	87.59 54

train_agg		76.27 45	76.15 42	76.64 66	85.58 44	61.59 24	81.62 88	81.26 132	55.86 215	74.93 60	88.81 64	53.70 76	84.68 133	75.24 51	88.33 31	83.65 221

NormalMVS		76.26 46	75.74 49	77.83 46	82.75 81	59.89 52	84.36 43	83.21 87	64.69 22	74.21 77	87.40 91	49.48 140	86.17 93	68.04 104	87.55 44	87.42 60

CS-MVS		76.25 47	75.98 45	77.06 57	80.15 125	55.63 127	84.51 41	83.90 59	63.24 48	73.30 89	87.27 98	55.06 54	86.30 90	71.78 81	84.58 69	89.25 6

casdiffmvs_mvg		76.14 48	76.30 41	75.66 84	76.46 243	51.83 213	79.67 117	85.08 35	65.02 19	75.84 46	88.58 70	59.42 23	85.08 121	72.75 70	83.93 79	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS		76.13 49	75.70 50	77.40 54	85.87 41	61.20 29	85.52 30	82.19 108	59.99 123	75.10 56	90.35 33	47.66 165	86.52 82	71.64 83	82.99 87	84.47 187

ACMMP		76.02 50	75.33 54	78.07 39	85.20 50	61.91 20	85.49 32	84.44 46	63.04 53	69.80 153	89.74 52	45.43 199	87.16 62	72.01 77	82.87 92	85.14 161
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
PHI-MVS		75.87 51	75.36 53	77.41 52	80.62 116	55.91 120	84.28 47	85.78 22	56.08 213	73.41 88	86.58 119	50.94 124	88.54 29	70.79 89	89.71 17	87.79 45

EC-MVSNet		75.84 52	75.87 48	75.74 82	78.86 154	52.65 193	83.73 58	86.08 19	63.47 45	72.77 108	87.25 99	53.13 84	87.93 43	71.97 79	85.57 65	86.66 91

3Dnovator+	66.72 4	75.84 52	74.57 64	79.66 9	82.40 83	59.92 51	85.83 24	86.32 17	66.92 7	67.80 199	89.24 57	42.03 238	89.38 20	64.07 143	86.50 60	89.69 3

MVSMamba_PlusPlus		75.75 54	75.44 52	76.67 64	80.84 109	53.06 181	78.62 133	85.13 34	59.65 130	71.53 128	87.47 89	56.92 36	88.17 36	72.18 76	86.63 59	88.80 12

SPE-MVS-test		75.62 55	75.31 55	76.56 68	80.63 115	55.13 138	83.88 56	85.22 31	62.05 75	71.49 129	86.03 139	53.83 71	86.36 88	67.74 107	86.91 53	88.19 31

DPM-MVS		75.47 56	75.00 58	76.88 58	81.38 100	59.16 65	79.94 110	85.71 24	56.59 201	72.46 113	86.76 108	56.89 37	87.86 46	66.36 123	88.91 26	83.64 222

SymmetryMVS		75.28 57	74.60 63	77.30 55	83.85 66	59.89 52	84.36 43	75.51 254	64.69 22	74.21 77	87.40 91	49.48 140	86.17 93	68.04 104	83.88 80	85.85 124

fmvsm_s_conf0.5_n_9		75.16 58	75.22 57	75.01 96	78.34 175	55.37 135	77.30 176	73.95 286	61.40 84	79.46 20	90.14 38	57.07 35	81.15 215	80.00 5	79.31 141	88.51 20

APD-MVS_3200maxsize		74.96 59	74.39 66	76.67 64	82.20 85	58.24 83	83.67 59	83.29 84	58.41 157	73.71 85	90.14 38	45.62 192	85.99 100	69.64 93	82.85 93	85.78 127

TSAR-MVS + GP.		74.90 60	74.15 70	77.17 56	82.00 88	58.77 78	81.80 85	78.57 193	58.58 154	74.32 75	84.51 179	55.94 48	87.22 59	67.11 115	84.48 74	85.52 141

casdiffmvs		74.80 61	74.89 61	74.53 113	75.59 257	50.37 235	78.17 146	85.06 37	62.80 62	74.40 73	87.86 82	57.88 28	83.61 153	69.46 96	82.79 94	89.59 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
DELS-MVS		74.76 62	74.46 65	75.65 85	77.84 194	52.25 203	75.59 222	84.17 51	63.76 40	73.15 95	82.79 214	59.58 21	86.80 71	67.24 113	86.04 62	87.89 38
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
OPM-MVS		74.73 63	74.25 69	76.19 73	80.81 110	59.01 73	82.60 74	83.64 69	63.74 41	72.52 112	87.49 88	47.18 176	85.88 103	69.47 95	80.78 114	83.66 220
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 64	74.98 59	73.71 142	78.94 152	50.56 232	80.23 104	83.87 62	60.30 114	77.15 38	86.56 120	59.65 18	82.00 195	66.01 127	82.12 98	88.58 18

canonicalmvs		74.67 64	74.98 59	73.71 142	78.94 152	50.56 232	80.23 104	83.87 62	60.30 114	77.15 38	86.56 120	59.65 18	82.00 195	66.01 127	82.12 98	88.58 18

baseline		74.61 66	74.70 62	74.34 118	75.70 252	49.99 244	77.54 166	84.63 44	62.73 63	73.98 80	87.79 85	57.67 31	83.82 149	69.49 94	82.74 95	89.20 8

SR-MVS-dyc-post		74.57 67	73.90 73	76.58 67	83.49 69	59.87 54	84.29 45	81.36 125	58.07 163	73.14 96	90.07 40	44.74 209	85.84 104	68.20 100	81.76 105	84.03 199

dcpmvs_2		74.55 68	75.23 56	72.48 180	82.34 84	53.34 173	77.87 155	81.46 121	57.80 174	75.49 49	86.81 107	62.22 13	77.75 290	71.09 87	82.02 101	86.34 104

ETV-MVS		74.46 69	73.84 75	76.33 71	79.27 142	55.24 137	79.22 123	85.00 40	64.97 21	72.65 110	79.46 297	53.65 79	87.87 45	67.45 112	82.91 90	85.89 123

HQP_MVS		74.31 70	73.73 76	76.06 74	81.41 98	56.31 109	84.22 48	84.01 54	64.52 27	69.27 162	86.10 136	45.26 203	87.21 60	68.16 102	80.58 120	84.65 179

fmvsm_s_conf0.5_n_8		74.30 71	74.39 66	74.01 128	75.33 263	52.89 186	78.24 142	77.32 225	61.65 81	78.13 29	88.90 62	52.82 89	81.54 205	78.46 22	78.67 159	87.60 53

HPM-MVS_fast		74.30 71	73.46 80	76.80 60	84.45 61	59.04 72	83.65 60	81.05 140	60.15 119	70.43 139	89.84 49	41.09 259	85.59 109	67.61 110	82.90 91	85.77 130

fmvsm_s_conf0.5_n_10		74.11 73	73.98 72	74.48 115	74.61 283	52.86 188	78.10 150	77.06 229	57.14 184	78.24 28	88.79 67	52.83 88	82.26 191	77.79 28	81.30 110	88.32 24

MVS_111021_HR		74.02 74	73.46 80	75.69 83	83.01 77	60.63 40	77.29 177	78.40 204	61.18 90	70.58 138	85.97 141	54.18 65	84.00 146	67.52 111	82.98 89	82.45 255

MG-MVS		73.96 75	73.89 74	74.16 125	85.65 43	49.69 253	81.59 90	81.29 131	61.45 83	71.05 132	88.11 74	51.77 109	87.73 49	61.05 182	83.09 85	85.05 166

alignmvs		73.86 76	73.99 71	73.45 156	78.20 179	50.50 234	78.57 135	82.43 105	59.40 138	76.57 43	86.71 112	56.42 42	81.23 214	65.84 130	81.79 104	88.62 16

MSLP-MVS++		73.77 77	73.47 79	74.66 105	83.02 76	59.29 63	82.30 82	81.88 112	59.34 140	71.59 126	86.83 106	45.94 190	83.65 152	65.09 136	85.22 66	81.06 284

viewcassd2359sk11		73.56 78	73.41 82	74.00 129	77.13 221	50.35 236	76.86 192	83.69 68	61.23 89	73.14 96	86.38 127	56.09 47	82.96 167	67.15 114	79.01 151	88.70 15

fmvsm_s_conf0.5_n_3		73.55 79	74.39 66	71.03 231	74.09 301	51.86 212	77.77 160	75.60 250	61.18 90	78.67 26	88.98 60	55.88 49	77.73 291	78.69 16	78.68 158	83.50 225

HQP-MVS		73.45 80	72.80 90	75.40 89	80.66 112	54.94 140	82.31 79	83.90 59	62.10 72	67.85 193	85.54 156	45.46 197	86.93 68	67.04 116	80.35 124	84.32 189

viewdifsd2359ckpt09		73.42 81	72.45 96	76.30 72	77.25 219	53.27 175	80.36 103	82.48 104	57.96 168	72.24 116	85.73 150	53.22 82	86.27 91	63.79 153	79.06 150	89.36 5

BP-MVS1		73.41 82	72.25 98	76.88 58	76.68 236	53.70 160	79.15 124	81.07 139	60.66 100	71.81 121	87.39 93	40.93 260	87.24 56	71.23 86	81.29 111	89.71 2

CLD-MVS		73.33 83	72.68 92	75.29 93	78.82 156	53.33 174	78.23 143	84.79 43	61.30 87	70.41 140	81.04 263	52.41 96	87.12 63	64.61 142	82.49 97	85.41 151
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 84	72.54 94	75.62 86	77.87 192	53.64 162	79.62 119	79.61 165	61.63 82	72.02 120	82.61 219	56.44 41	85.97 101	63.99 146	79.07 149	87.25 70

fmvsm_l_conf0.5_n_9		73.27 85	73.66 78	72.09 189	73.82 302	52.72 192	77.45 170	74.28 279	56.61 200	77.10 40	88.16 73	56.17 45	77.09 303	78.27 24	81.13 112	86.48 98

fmvsm_l_conf0.5_n_3		73.23 86	73.13 85	73.55 152	74.40 290	55.13 138	78.97 126	74.96 269	56.64 194	74.76 68	88.75 68	55.02 55	78.77 273	76.33 39	78.31 169	86.74 86

viewmacassd2359aftdt		73.15 87	73.16 84	73.11 165	75.15 269	49.31 260	77.53 168	83.21 87	60.42 106	73.20 93	87.34 95	53.82 72	81.05 220	67.02 118	80.79 113	88.96 10

UA-Net		73.13 88	72.93 87	73.76 137	83.58 68	51.66 215	78.75 128	77.66 216	67.75 4	72.61 111	89.42 53	49.82 136	83.29 160	53.61 250	83.14 84	86.32 108

EPNet		73.09 89	72.16 99	75.90 76	75.95 249	56.28 111	83.05 64	72.39 305	66.53 10	65.27 251	87.00 102	50.40 129	85.47 115	62.48 169	86.32 61	85.94 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 90	72.59 93	74.27 121	71.28 355	55.88 121	78.21 145	75.56 252	54.31 261	74.86 64	87.80 84	54.72 59	80.23 241	78.07 26	78.48 164	86.70 87

nrg030		72.96 91	73.01 86	72.84 171	75.41 261	50.24 237	80.02 108	82.89 100	58.36 159	74.44 72	86.73 110	58.90 25	80.83 227	65.84 130	74.46 224	87.44 59

viewmanbaseed2359cas		72.92 92	72.89 88	73.00 167	75.16 267	49.25 263	77.25 180	83.11 95	59.52 137	72.93 104	86.63 115	54.11 66	80.98 221	66.63 121	80.67 117	88.76 14

test_fmvsmconf0.1_n		72.81 93	72.33 97	74.24 122	69.89 378	55.81 122	78.22 144	75.40 257	54.17 263	75.00 59	88.03 80	53.82 72	80.23 241	78.08 25	78.34 168	86.69 88

CPTT-MVS		72.78 94	72.08 101	74.87 99	84.88 58	61.41 26	84.15 51	77.86 212	55.27 233	67.51 205	88.08 76	41.93 241	81.85 198	69.04 98	80.01 129	81.35 276

LPG-MVS_test		72.74 95	71.74 106	75.76 80	80.22 120	57.51 93	82.55 75	83.40 77	61.32 85	66.67 223	87.33 96	39.15 278	86.59 76	67.70 108	77.30 187	83.19 233

h-mvs33		72.71 96	71.49 110	76.40 69	81.99 89	59.58 57	76.92 189	76.74 235	60.40 107	74.81 65	85.95 142	45.54 195	85.76 106	70.41 91	70.61 288	83.86 209

fmvsm_s_conf0.5_n_5		72.69 97	72.80 90	72.37 185	74.11 300	53.21 177	78.12 147	73.31 293	53.98 266	76.81 42	88.05 77	53.38 80	77.37 298	76.64 36	80.78 114	86.53 96

GDP-MVS		72.64 98	71.28 117	76.70 61	77.72 198	54.22 152	79.57 120	84.45 45	55.30 232	71.38 130	86.97 103	39.94 266	87.00 67	67.02 118	79.20 145	88.89 11

PAPM_NR		72.63 99	71.80 104	75.13 94	81.72 93	53.42 172	79.91 112	83.28 85	59.14 142	66.31 230	85.90 143	51.86 106	86.06 97	57.45 215	80.62 118	85.91 122

fmvsm_s_conf0.5_n_6		72.59 100	72.87 89	71.73 200	75.14 270	51.96 210	76.28 204	77.12 228	57.63 178	73.85 83	86.91 104	51.54 113	77.87 287	77.18 32	80.18 128	85.37 153

VDD-MVS		72.50 101	72.09 100	73.75 139	81.58 94	49.69 253	77.76 161	77.63 217	63.21 50	73.21 92	89.02 59	42.14 237	83.32 159	61.72 176	82.50 96	88.25 27

3Dnovator	64.47 5	72.49 102	71.39 113	75.79 79	77.70 199	58.99 74	80.66 101	83.15 92	62.24 70	65.46 247	86.59 118	42.38 236	85.52 111	59.59 196	84.72 68	82.85 243

MGCFI-Net		72.45 103	73.34 83	69.81 256	77.77 196	43.21 337	75.84 219	81.18 136	59.59 135	75.45 50	86.64 113	57.74 29	77.94 283	63.92 147	81.90 103	88.30 25

MVS_Test		72.45 103	72.46 95	72.42 184	74.88 272	48.50 278	76.28 204	83.14 93	59.40 138	72.46 113	84.68 169	55.66 50	81.12 216	65.98 129	79.66 133	87.63 51

EI-MVSNet-Vis-set		72.42 105	71.59 107	74.91 97	78.47 168	54.02 154	77.05 185	79.33 171	65.03 18	71.68 124	79.35 301	52.75 90	84.89 128	66.46 122	74.23 228	85.83 126

viewdifsd2359ckpt13		72.40 106	71.79 105	74.22 123	75.63 254	51.77 214	78.67 131	83.13 94	57.08 185	71.59 126	85.36 160	53.10 85	82.64 182	63.07 163	78.51 163	88.24 28

ACMP	63.53 6	72.30 107	71.20 119	75.59 88	80.28 118	57.54 91	82.74 71	82.84 101	60.58 102	65.24 255	86.18 133	39.25 276	86.03 99	66.95 120	76.79 195	83.22 231
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 108	71.21 118	75.31 91	78.50 166	55.93 119	81.63 87	82.12 109	56.24 210	70.02 147	85.68 152	47.05 178	84.34 139	65.27 135	74.41 227	85.67 136

Vis-MVSNet		72.18 109	71.37 114	74.61 108	81.29 101	55.41 133	80.90 97	78.28 207	60.73 98	69.23 165	88.09 75	44.36 215	82.65 181	57.68 213	81.75 107	85.77 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 110	71.50 109	74.16 125	67.96 397	55.58 130	78.06 151	74.67 272	54.19 262	74.54 71	88.23 71	50.35 131	80.24 240	78.07 26	77.46 182	86.65 92

API-MVS		72.17 110	71.41 112	74.45 116	81.95 90	57.22 96	84.03 53	80.38 154	59.89 128	68.40 176	82.33 232	49.64 138	87.83 47	51.87 264	84.16 78	78.30 326

EPP-MVSNet		72.16 112	71.31 116	74.71 102	78.68 160	49.70 251	82.10 83	81.65 116	60.40 107	65.94 237	85.84 145	51.74 110	86.37 87	55.93 226	79.55 136	88.07 37

DP-MVS Recon		72.15 113	70.73 128	76.40 69	86.57 25	57.99 85	81.15 95	82.96 96	57.03 188	66.78 218	85.56 153	44.50 213	88.11 39	51.77 266	80.23 127	83.10 238

fmvsm_s_conf0.5_n_4		72.04 114	71.85 103	72.58 176	73.74 305	52.49 199	76.69 195	72.42 304	56.42 205	75.32 51	87.04 101	52.13 102	78.01 282	79.29 12	73.65 238	87.26 69

EI-MVSNet-UG-set		71.92 115	71.06 122	74.52 114	77.98 190	53.56 165	76.62 196	79.16 172	64.40 29	71.18 131	78.95 306	52.19 100	84.66 135	65.47 133	73.57 241	85.32 155

viewdifsd2359ckpt07		71.90 116	71.97 102	71.69 203	74.81 276	48.08 284	75.30 227	80.49 151	60.00 122	71.63 125	86.33 129	56.34 43	79.25 255	65.40 134	77.41 183	87.76 46

VDDNet		71.81 117	71.33 115	73.26 163	82.80 80	47.60 293	78.74 129	75.27 259	59.59 135	72.94 103	89.40 54	41.51 252	83.91 147	58.75 208	82.99 87	88.26 26

EIA-MVS		71.78 118	70.60 130	75.30 92	79.85 129	53.54 166	77.27 179	83.26 86	57.92 170	66.49 225	79.39 299	52.07 103	86.69 74	60.05 190	79.14 148	85.66 137

LFMVS		71.78 118	71.59 107	72.32 186	83.40 72	46.38 302	79.75 115	71.08 314	64.18 34	72.80 107	88.64 69	42.58 233	83.72 150	57.41 216	84.49 73	86.86 81

test_fmvsm_n_1920		71.73 120	71.14 120	73.50 153	72.52 327	56.53 108	75.60 221	76.16 239	48.11 352	77.22 37	85.56 153	53.10 85	77.43 295	74.86 53	77.14 189	86.55 95

PAPR		71.72 121	70.82 126	74.41 117	81.20 105	51.17 218	79.55 121	83.33 82	55.81 218	66.93 217	84.61 173	50.95 123	86.06 97	55.79 229	79.20 145	86.00 118

IS-MVSNet		71.57 122	71.00 123	73.27 162	78.86 154	45.63 313	80.22 106	78.69 186	64.14 37	66.46 226	87.36 94	49.30 144	85.60 108	50.26 277	83.71 83	88.59 17

MAR-MVS		71.51 123	70.15 141	75.60 87	81.84 91	59.39 60	81.38 92	82.90 98	54.90 250	68.08 189	78.70 307	47.73 163	85.51 112	51.68 268	84.17 77	81.88 266
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
MVSFormer		71.50 124	70.38 135	74.88 98	78.76 157	57.15 101	82.79 69	78.48 197	51.26 309	69.49 156	83.22 209	43.99 219	83.24 161	66.06 125	79.37 137	84.23 193

RRT-MVS		71.46 125	70.70 129	73.74 140	77.76 197	49.30 261	76.60 197	80.45 152	61.25 88	68.17 181	84.78 166	44.64 211	84.90 127	64.79 138	77.88 175	87.03 76

PVSNet_Blended_VisFu		71.45 126	70.39 134	74.65 106	82.01 87	58.82 77	79.93 111	80.35 155	55.09 238	65.82 243	82.16 240	49.17 147	82.64 182	60.34 188	78.62 161	82.50 254

OMC-MVS		71.40 127	70.60 130	73.78 135	76.60 239	53.15 178	79.74 116	79.78 161	58.37 158	68.75 170	86.45 125	45.43 199	80.60 231	62.58 167	77.73 176	87.58 55

KinetiMVS		71.26 128	70.16 140	74.57 111	74.59 284	52.77 191	75.91 216	81.20 135	60.72 99	69.10 168	85.71 151	41.67 247	83.53 155	63.91 149	78.62 161	87.42 60

UniMVSNet_NR-MVSNet		71.11 129	71.00 123	71.44 213	79.20 144	44.13 326	76.02 214	82.60 103	66.48 11	68.20 179	84.60 176	56.82 38	82.82 177	54.62 240	70.43 290	87.36 67

hse-mvs2		71.04 130	69.86 144	74.60 109	79.58 134	57.12 103	73.96 259	75.25 260	60.40 107	74.81 65	81.95 245	45.54 195	82.90 170	70.41 91	66.83 343	83.77 214

diffmvs_AUTHOR		71.02 131	70.87 125	71.45 212	69.89 378	48.97 269	73.16 280	78.33 206	57.79 175	72.11 119	85.26 161	51.84 107	77.89 286	71.00 88	78.47 166	87.49 57

GeoE		71.01 132	70.15 141	73.60 150	79.57 135	52.17 204	78.93 127	78.12 209	58.02 165	67.76 202	83.87 192	52.36 97	82.72 179	56.90 218	75.79 209	85.92 121

fmvsm_l_conf0.5_n		70.99 133	70.82 126	71.48 209	71.45 348	54.40 148	77.18 182	70.46 320	48.67 342	75.17 54	86.86 105	53.77 74	76.86 311	76.33 39	77.51 181	83.17 237

PCF-MVS	61.88 8	70.95 134	69.49 151	75.35 90	77.63 203	55.71 124	76.04 213	81.81 114	50.30 320	69.66 154	85.40 159	52.51 93	84.89 128	51.82 265	80.24 126	85.45 147
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 135	69.41 154	75.12 95	79.20 144	53.86 156	77.89 154	80.00 159	53.88 268	69.40 159	84.61 173	43.21 225	86.56 78	58.80 206	77.68 178	84.95 171

test_fmvsmvis_n_1920		70.84 135	70.38 135	72.22 188	71.16 356	55.39 134	75.86 217	72.21 307	49.03 337	73.28 91	86.17 134	51.83 108	77.29 300	75.80 42	78.05 172	83.98 202

114514_t		70.83 137	69.56 149	74.64 107	86.21 32	54.63 145	82.34 78	81.81 114	48.22 350	63.01 291	85.83 146	40.92 261	87.10 64	57.91 212	79.79 130	82.18 260

FIs		70.82 138	71.43 111	68.98 271	78.33 176	38.14 384	76.96 187	83.59 71	61.02 93	67.33 207	86.73 110	55.07 53	81.64 201	54.61 242	79.22 144	87.14 74

ACMM	61.98 7	70.80 139	69.73 146	74.02 127	80.59 117	58.59 80	82.68 72	82.02 111	55.46 228	67.18 212	84.39 182	38.51 284	83.17 163	60.65 186	76.10 205	80.30 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 140	70.43 133	71.46 210	69.45 385	48.95 270	72.93 283	78.46 199	57.27 182	71.69 123	83.97 191	51.48 115	77.92 285	70.70 90	77.95 174	87.53 56
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet (Re)		70.63 141	70.20 138	71.89 193	78.55 165	45.29 316	75.94 215	82.92 97	63.68 42	68.16 182	83.59 200	53.89 70	83.49 157	53.97 246	71.12 281	86.89 80

xiu_mvs_v2_base		70.52 142	69.75 145	72.84 171	81.21 104	55.63 127	75.11 233	78.92 179	54.92 249	69.96 150	79.68 292	47.00 182	82.09 194	61.60 178	79.37 137	80.81 289

PS-MVSNAJ		70.51 143	69.70 147	72.93 169	81.52 95	55.79 123	74.92 240	79.00 177	55.04 244	69.88 151	78.66 309	47.05 178	82.19 192	61.61 177	79.58 134	80.83 288

fmvsm_l_conf0.5_n_a		70.50 144	70.27 137	71.18 225	71.30 354	54.09 153	76.89 190	69.87 324	47.90 356	74.37 74	86.49 123	53.07 87	76.69 316	75.41 48	77.11 190	82.76 244

v2v482		70.50 144	69.45 153	73.66 145	72.62 324	50.03 243	77.58 163	80.51 150	59.90 124	69.52 155	82.14 241	47.53 169	84.88 130	65.07 137	70.17 298	86.09 116

v1144		70.42 146	69.31 155	73.76 137	73.22 312	50.64 229	77.83 158	81.43 122	58.58 154	69.40 159	81.16 260	47.53 169	85.29 120	64.01 145	70.64 286	85.34 154

SSM_0407		70.41 147	68.96 164	74.75 101	78.65 161	53.46 168	77.28 178	80.00 159	53.88 268	68.14 183	84.61 173	43.21 225	86.26 92	58.80 206	76.11 202	84.54 181

TranMVSNet+NR-MVSNet		70.36 148	70.10 143	71.17 226	78.64 164	42.97 340	76.53 199	81.16 138	66.95 6	68.53 174	85.42 158	51.61 112	83.07 164	52.32 258	69.70 310	87.46 58

v8		70.33 149	69.28 156	73.49 154	73.15 314	50.22 238	78.62 133	80.78 146	60.79 96	66.45 227	82.11 243	49.35 143	84.98 124	63.58 156	68.71 326	85.28 157

Fast-Effi-MVS+		70.28 150	69.12 160	73.73 141	78.50 166	51.50 216	75.01 236	79.46 169	56.16 212	68.59 171	79.55 295	53.97 68	84.05 142	53.34 252	77.53 180	85.65 138

X-MVStestdata		70.21 151	67.28 210	79.00 24	86.32 30	62.62 11	85.83 24	83.92 57	64.55 25	72.17 117	6.49 470	47.95 160	88.01 41	71.55 84	86.74 56	86.37 102

v10		70.21 151	69.02 161	73.81 134	73.51 308	50.92 224	78.74 129	81.39 123	60.05 121	66.39 228	81.83 248	47.58 167	85.41 118	62.80 166	68.86 325	85.09 165

Elysia		70.19 153	68.29 183	75.88 77	74.15 297	54.33 150	78.26 139	83.21 87	55.04 244	67.28 208	83.59 200	30.16 376	86.11 95	63.67 154	79.26 142	87.20 71

StellarMVS		70.19 153	68.29 183	75.88 77	74.15 297	54.33 150	78.26 139	83.21 87	55.04 244	67.28 208	83.59 200	30.16 376	86.11 95	63.67 154	79.26 142	87.20 71

QAPM		70.05 155	68.81 167	73.78 135	76.54 241	53.43 171	83.23 62	83.48 73	52.89 283	65.90 239	86.29 130	41.55 251	86.49 84	51.01 271	78.40 167	81.42 270

DU-MVS		70.01 156	69.53 150	71.44 213	78.05 187	44.13 326	75.01 236	81.51 120	64.37 30	68.20 179	84.52 177	49.12 150	82.82 177	54.62 240	70.43 290	87.37 65

AdaColmap		69.99 157	68.66 171	73.97 131	84.94 55	57.83 87	82.63 73	78.71 185	56.28 209	64.34 270	84.14 185	41.57 249	87.06 66	46.45 309	78.88 152	77.02 347

v1192		69.97 158	68.68 170	73.85 132	73.19 313	50.94 222	77.68 162	81.36 125	57.51 180	68.95 169	80.85 270	45.28 202	85.33 119	62.97 165	70.37 292	85.27 158

Anonymous20240529		69.91 159	69.02 161	72.56 177	80.19 123	47.65 291	77.56 165	80.99 142	55.45 229	69.88 151	86.76 108	39.24 277	82.18 193	54.04 245	77.10 191	87.85 41

patch_mono-2		69.85 160	71.09 121	66.16 307	79.11 149	54.80 144	71.97 300	74.31 277	53.50 277	70.90 134	84.17 184	57.63 32	63.31 400	66.17 124	82.02 101	80.38 297

fmvsm_s_conf0.5_n_2		69.82 161	69.27 157	71.46 210	72.00 338	51.08 219	73.30 273	67.79 343	55.06 243	75.24 53	87.51 87	44.02 218	77.00 307	75.67 44	72.86 256	86.31 111

FA-MVS(test-final)		69.82 161	68.48 174	73.84 133	78.44 169	50.04 242	75.58 224	78.99 178	58.16 161	67.59 203	82.14 241	42.66 231	85.63 107	56.60 219	76.19 201	85.84 125

FC-MVSNet-test		69.80 163	70.58 132	67.46 287	77.61 208	34.73 417	76.05 212	83.19 91	60.84 95	65.88 241	86.46 124	54.52 62	80.76 230	52.52 257	78.12 171	86.91 79

v144192		69.71 164	68.51 173	73.33 161	73.10 315	50.13 240	77.54 166	80.64 147	56.65 193	68.57 173	80.55 273	46.87 183	84.96 126	62.98 164	69.66 311	84.89 173

test_yl		69.69 165	69.13 158	71.36 219	78.37 173	45.74 309	74.71 244	80.20 156	57.91 171	70.01 148	83.83 193	42.44 234	82.87 173	54.97 236	79.72 131	85.48 143

DCV-MVSNet		69.69 165	69.13 158	71.36 219	78.37 173	45.74 309	74.71 244	80.20 156	57.91 171	70.01 148	83.83 193	42.44 234	82.87 173	54.97 236	79.72 131	85.48 143

VNet		69.68 167	70.19 139	68.16 281	79.73 131	41.63 354	70.53 321	77.38 222	60.37 110	70.69 135	86.63 115	51.08 121	77.09 303	53.61 250	81.69 109	85.75 132

jason		69.65 168	68.39 180	73.43 158	78.27 178	56.88 105	77.12 183	73.71 289	46.53 374	69.34 161	83.22 209	43.37 223	79.18 257	64.77 139	79.20 145	84.23 193
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 169	69.01 163	71.52 208	71.66 343	51.04 220	73.39 272	67.14 349	55.02 247	75.11 55	87.64 86	42.94 230	77.01 306	75.55 46	72.63 262	86.52 97

Effi-MVS+-dtu		69.64 169	67.53 200	75.95 75	76.10 247	62.29 15	80.20 107	76.06 243	59.83 129	65.26 254	77.09 339	41.56 250	84.02 145	60.60 187	71.09 284	81.53 269

fmvsm_s_conf0.5_n		69.58 171	68.84 166	71.79 198	72.31 334	52.90 184	77.90 153	62.43 393	49.97 325	72.85 106	85.90 143	52.21 99	76.49 319	75.75 43	70.26 297	85.97 119

lupinMVS		69.57 172	68.28 185	73.44 157	78.76 157	57.15 101	76.57 198	73.29 295	46.19 377	69.49 156	82.18 237	43.99 219	79.23 256	64.66 140	79.37 137	83.93 204

fmvsm_s_conf0.5_n_7		69.54 173	69.67 148	69.15 270	73.47 310	51.41 217	70.35 325	73.34 292	57.05 187	68.41 175	85.83 146	49.86 135	72.84 340	71.86 80	76.83 194	83.19 233

fmvsm_s_conf0.5_n_a		69.54 173	68.74 169	71.93 192	72.47 329	53.82 158	78.25 141	62.26 395	49.78 327	73.12 99	86.21 132	52.66 91	76.79 313	75.02 52	68.88 323	85.18 160

NR-MVSNet		69.54 173	68.85 165	71.59 207	78.05 187	43.81 331	74.20 255	80.86 145	65.18 14	62.76 295	84.52 177	52.35 98	83.59 154	50.96 273	70.78 285	87.37 65

MVS_111021_LR		69.50 176	68.78 168	71.65 205	78.38 171	59.33 61	74.82 242	70.11 322	58.08 162	67.83 198	84.68 169	41.96 239	76.34 323	65.62 132	77.54 179	79.30 317

v1921920		69.47 177	68.17 187	73.36 160	73.06 316	50.10 241	77.39 171	80.56 148	56.58 202	68.59 171	80.37 275	44.72 210	84.98 124	62.47 170	69.82 306	85.00 167

test_djsdf		69.45 178	67.74 193	74.58 110	74.57 286	54.92 142	82.79 69	78.48 197	51.26 309	65.41 248	83.49 205	38.37 286	83.24 161	66.06 125	69.25 318	85.56 140

fmvsm_s_conf0.1_n		69.41 179	68.60 172	71.83 195	71.07 357	52.88 187	77.85 157	62.44 392	49.58 330	72.97 102	86.22 131	51.68 111	76.48 320	75.53 47	70.10 300	86.14 114

fmvsm_s_conf0.1_n_a		69.32 180	68.44 178	71.96 190	70.91 359	53.78 159	78.12 147	62.30 394	49.35 333	73.20 93	86.55 122	51.99 104	76.79 313	74.83 54	68.68 328	85.32 155

Anonymous20231211		69.28 181	68.47 176	71.73 200	80.28 118	47.18 297	79.98 109	82.37 106	54.61 254	67.24 210	84.01 189	39.43 273	82.41 189	55.45 234	72.83 257	85.62 139

EI-MVSNet		69.27 182	68.44 178	71.73 200	74.47 287	49.39 258	75.20 231	78.45 200	59.60 132	69.16 166	76.51 351	51.29 117	82.50 186	59.86 195	71.45 278	83.30 228

v1240		69.24 183	67.91 192	73.25 164	73.02 318	49.82 245	77.21 181	80.54 149	56.43 204	68.34 178	80.51 274	43.33 224	84.99 122	62.03 174	69.77 309	84.95 171

IterMVS-LS		69.22 184	68.48 174	71.43 215	74.44 289	49.40 257	76.23 206	77.55 218	59.60 132	65.85 242	81.59 255	51.28 118	81.58 204	59.87 194	69.90 305	83.30 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 185	68.38 181	71.38 217	71.57 345	48.61 275	73.22 278	73.18 296	57.65 176	70.67 136	84.73 167	50.03 132	79.80 245	63.25 159	71.10 282	85.74 133

viewmsd2359difaftdt		69.13 185	68.38 181	71.38 217	71.57 345	48.61 275	73.22 278	73.18 296	57.65 176	70.67 136	84.73 167	50.03 132	79.80 245	63.25 159	71.10 282	85.74 133

IMVS_0403		69.09 187	68.14 188	71.95 191	77.06 225	49.73 247	74.51 248	78.60 189	52.70 285	66.69 221	82.58 220	46.43 186	83.38 158	59.20 201	75.46 215	82.74 245

VPA-MVSNet		69.02 188	69.47 152	67.69 285	77.42 213	41.00 361	74.04 257	79.68 163	60.06 120	69.26 164	84.81 165	51.06 122	77.58 293	54.44 243	74.43 226	84.48 186

v7n		69.01 189	67.36 207	73.98 130	72.51 328	52.65 193	78.54 137	81.30 130	60.26 116	62.67 297	81.62 252	43.61 221	84.49 136	57.01 217	68.70 327	84.79 176

viewmambaseed2359dif		68.91 190	68.18 186	71.11 228	70.21 370	48.05 287	72.28 295	75.90 245	51.96 297	70.93 133	84.47 180	51.37 116	78.59 274	61.55 180	74.97 220	86.68 89

IMVS_0407		68.90 191	67.93 191	71.82 196	77.06 225	49.73 247	74.40 253	78.60 189	52.70 285	66.19 231	82.58 220	45.17 205	83.00 165	59.20 201	75.46 215	82.74 245

OpenMVS	61.03 9	68.85 192	67.56 197	72.70 175	74.26 295	53.99 155	81.21 94	81.34 129	52.70 285	62.75 296	85.55 155	38.86 282	84.14 141	48.41 293	83.01 86	79.97 304

XVG-OURS-SEG-HR		68.81 193	67.47 203	72.82 173	74.40 290	56.87 106	70.59 320	79.04 176	54.77 252	66.99 215	86.01 140	39.57 272	78.21 279	62.54 168	73.33 248	83.37 227

BH-RMVSNet		68.81 193	67.42 204	72.97 168	80.11 126	52.53 197	74.26 254	76.29 238	58.48 156	68.38 177	84.20 183	42.59 232	83.83 148	46.53 308	75.91 207	82.56 249

UGNet		68.81 193	67.39 205	73.06 166	78.33 176	54.47 146	79.77 114	75.40 257	60.45 105	63.22 284	84.40 181	32.71 353	80.91 226	51.71 267	80.56 122	83.81 210
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
XVG-OURS		68.76 196	67.37 206	72.90 170	74.32 293	57.22 96	70.09 329	78.81 182	55.24 234	67.79 200	85.81 149	36.54 309	78.28 278	62.04 173	75.74 210	83.19 233

V42		68.65 197	67.35 208	72.56 177	68.93 391	50.18 239	72.90 284	79.47 168	56.92 190	69.45 158	80.26 279	46.29 188	82.99 166	64.07 143	67.82 334	84.53 184

PVSNet_Blended		68.59 198	67.72 194	71.19 224	77.03 230	50.57 230	72.51 291	81.52 118	51.91 298	64.22 276	77.77 330	49.13 148	82.87 173	55.82 227	79.58 134	80.14 302

xiu_mvs_v1_base_debu		68.58 199	67.28 210	72.48 180	78.19 180	57.19 98	75.28 228	75.09 265	51.61 300	70.04 144	81.41 257	32.79 349	79.02 266	63.81 150	77.31 184	81.22 279

xiu_mvs_v1_base		68.58 199	67.28 210	72.48 180	78.19 180	57.19 98	75.28 228	75.09 265	51.61 300	70.04 144	81.41 257	32.79 349	79.02 266	63.81 150	77.31 184	81.22 279

xiu_mvs_v1_base_debi		68.58 199	67.28 210	72.48 180	78.19 180	57.19 98	75.28 228	75.09 265	51.61 300	70.04 144	81.41 257	32.79 349	79.02 266	63.81 150	77.31 184	81.22 279

PVSNet_BlendedMVS		68.56 202	67.72 194	71.07 230	77.03 230	50.57 230	74.50 249	81.52 118	53.66 276	64.22 276	79.72 291	49.13 148	82.87 173	55.82 227	73.92 232	79.77 312

WR-MVS		68.47 203	68.47 176	68.44 278	80.20 122	39.84 368	73.75 267	76.07 242	64.68 24	68.11 187	83.63 199	50.39 130	79.14 262	49.78 278	69.66 311	86.34 104

mvsmamba		68.47 203	66.56 225	74.21 124	79.60 133	52.95 182	74.94 239	75.48 255	52.09 296	60.10 328	83.27 208	36.54 309	84.70 132	59.32 200	77.69 177	84.99 169

AUN-MVS		68.45 205	66.41 232	74.57 111	79.53 136	57.08 104	73.93 262	75.23 261	54.44 259	66.69 221	81.85 247	37.10 304	82.89 171	62.07 172	66.84 342	83.75 215

c3_l		68.33 206	67.56 197	70.62 240	70.87 360	46.21 305	74.47 250	78.80 183	56.22 211	66.19 231	78.53 314	51.88 105	81.40 208	62.08 171	69.04 321	84.25 192

BH-untuned		68.27 207	67.29 209	71.21 223	79.74 130	53.22 176	76.06 211	77.46 221	57.19 183	66.10 234	81.61 253	45.37 201	83.50 156	45.42 324	76.68 197	76.91 351

jajsoiax		68.25 208	66.45 228	73.66 145	75.62 255	55.49 132	80.82 98	78.51 196	52.33 293	64.33 271	84.11 186	28.28 395	81.81 200	63.48 157	70.62 287	83.67 218

LuminaMVS		68.24 209	66.82 222	72.51 179	73.46 311	53.60 164	76.23 206	78.88 180	52.78 284	68.08 189	80.13 281	32.70 354	81.41 207	63.16 162	75.97 206	82.53 251

v148		68.24 209	67.19 217	71.40 216	70.43 367	47.77 290	75.76 220	77.03 230	58.91 146	67.36 206	80.10 283	48.60 155	81.89 197	60.01 191	66.52 346	84.53 184

CANet_DTU		68.18 211	67.71 196	69.59 259	74.83 275	46.24 304	78.66 132	76.85 232	59.60 132	63.45 282	82.09 244	35.25 318	77.41 296	59.88 193	78.76 156	85.14 161

mvs_tets		68.18 211	66.36 234	73.63 148	75.61 256	55.35 136	80.77 99	78.56 194	52.48 292	64.27 273	84.10 187	27.45 403	81.84 199	63.45 158	70.56 289	83.69 217

guyue		68.10 213	67.23 216	70.71 239	73.67 307	49.27 262	73.65 269	76.04 244	55.62 225	67.84 197	82.26 235	41.24 257	78.91 272	61.01 183	73.72 236	83.94 203

SDMVSNet		68.03 214	68.10 190	67.84 283	77.13 221	48.72 274	65.32 371	79.10 173	58.02 165	65.08 258	82.55 225	47.83 162	73.40 337	63.92 147	73.92 232	81.41 271

miper_ehance_all_eth		68.03 214	67.24 214	70.40 244	70.54 364	46.21 305	73.98 258	78.68 187	55.07 241	66.05 235	77.80 327	52.16 101	81.31 211	61.53 181	69.32 315	83.67 218

mvs_anonymous		68.03 214	67.51 201	69.59 259	72.08 336	44.57 323	71.99 299	75.23 261	51.67 299	67.06 214	82.57 224	54.68 60	77.94 283	56.56 222	75.71 211	86.26 113

ET-MVSNet_ETH3D		67.96 217	65.72 246	74.68 104	76.67 237	55.62 129	75.11 233	74.74 270	52.91 282	60.03 330	80.12 282	33.68 338	82.64 182	61.86 175	76.34 199	85.78 127

thisisatest0530		67.92 218	65.78 245	74.33 119	76.29 244	51.03 221	76.89 190	74.25 280	53.67 275	65.59 245	81.76 250	35.15 319	85.50 113	55.94 225	72.47 263	86.47 99

PAPM		67.92 218	66.69 224	71.63 206	78.09 185	49.02 266	77.09 184	81.24 134	51.04 312	60.91 322	83.98 190	47.71 164	84.99 122	40.81 360	79.32 140	80.90 287

AstraMVS		67.86 220	66.83 221	70.93 233	73.50 309	49.34 259	73.28 276	74.01 284	55.45 229	68.10 188	83.28 207	38.93 281	79.14 262	63.22 161	71.74 273	84.30 191

tttt0517		67.83 221	65.66 247	74.33 119	76.69 235	50.82 226	77.86 156	73.99 285	54.54 257	64.64 268	82.53 228	35.06 320	85.50 113	55.71 230	69.91 304	86.67 90

mamba_0408		67.78 222	65.42 251	74.85 100	78.65 161	53.46 168	50.83 443	79.09 174	53.75 271	68.14 183	83.83 193	41.79 245	86.56 78	56.58 220	76.11 202	84.54 181

tt0805		67.77 223	67.24 214	69.34 264	74.87 273	40.08 365	77.36 172	81.37 124	55.31 231	66.33 229	84.65 171	37.35 298	82.55 185	55.65 232	72.28 268	85.39 152

ECVR-MVS		67.72 224	67.51 201	68.35 279	79.46 137	36.29 407	74.79 243	66.93 351	58.72 149	67.19 211	88.05 77	36.10 311	81.38 209	52.07 261	84.25 75	87.39 63

eth_miper_zixun_eth		67.63 225	66.28 238	71.67 204	71.60 344	48.33 280	73.68 268	77.88 211	55.80 219	65.91 238	78.62 312	47.35 175	82.88 172	59.45 197	66.25 347	83.81 210

UniMVSNet_ETH3D		67.60 226	67.07 219	69.18 268	77.39 214	42.29 345	74.18 256	75.59 251	60.37 110	66.77 219	86.06 138	37.64 294	78.93 271	52.16 260	73.49 243	86.32 108

VPNet		67.52 227	68.11 189	65.74 317	79.18 146	36.80 399	72.17 297	72.83 301	62.04 76	67.79 200	85.83 146	48.88 152	76.60 318	51.30 269	72.97 255	83.81 210

cl22		67.47 228	66.45 228	70.54 242	69.85 380	46.49 301	73.85 265	77.35 223	55.07 241	65.51 246	77.92 323	47.64 166	81.10 217	61.58 179	69.32 315	84.01 201

Fast-Effi-MVS+-dtu		67.37 229	65.33 255	73.48 155	72.94 319	57.78 89	77.47 169	76.88 231	57.60 179	61.97 309	76.85 343	39.31 274	80.49 235	54.72 239	70.28 296	82.17 262

MVS		67.37 229	66.33 235	70.51 243	75.46 259	50.94 222	73.95 260	81.85 113	41.57 414	62.54 301	78.57 313	47.98 159	85.47 115	52.97 255	82.05 100	75.14 367

test1111		67.21 231	67.14 218	67.42 288	79.24 143	34.76 416	73.89 264	65.65 361	58.71 151	66.96 216	87.95 81	36.09 312	80.53 232	52.03 262	83.79 81	86.97 78

GBi-Net		67.21 231	66.55 226	69.19 265	77.63 203	43.33 334	77.31 173	77.83 213	56.62 197	65.04 260	82.70 215	41.85 242	80.33 237	47.18 303	72.76 258	83.92 205

test1		67.21 231	66.55 226	69.19 265	77.63 203	43.33 334	77.31 173	77.83 213	56.62 197	65.04 260	82.70 215	41.85 242	80.33 237	47.18 303	72.76 258	83.92 205

cl____		67.18 234	66.26 239	69.94 251	70.20 371	45.74 309	73.30 273	76.83 233	55.10 236	65.27 251	79.57 294	47.39 173	80.53 232	59.41 199	69.22 319	83.53 224

DIV-MVS_self_test		67.18 234	66.26 239	69.94 251	70.20 371	45.74 309	73.29 275	76.83 233	55.10 236	65.27 251	79.58 293	47.38 174	80.53 232	59.43 198	69.22 319	83.54 223

MVSTER		67.16 236	65.58 249	71.88 194	70.37 369	49.70 251	70.25 327	78.45 200	51.52 303	69.16 166	80.37 275	38.45 285	82.50 186	60.19 189	71.46 277	83.44 226

miper_enhance_ethall		67.11 237	66.09 241	70.17 248	69.21 388	45.98 307	72.85 285	78.41 203	51.38 306	65.65 244	75.98 361	51.17 120	81.25 212	60.82 185	69.32 315	83.29 230

Baseline_NR-MVSNet		67.05 238	67.56 197	65.50 321	75.65 253	37.70 390	75.42 225	74.65 273	59.90 124	68.14 183	83.15 212	49.12 150	77.20 301	52.23 259	69.78 307	81.60 268

WR-MVS_H		67.02 239	66.92 220	67.33 291	77.95 191	37.75 388	77.57 164	82.11 110	62.03 77	62.65 298	82.48 229	50.57 128	79.46 251	42.91 346	64.01 364	84.79 176

anonymousdsp		67.00 240	64.82 260	73.57 151	70.09 374	56.13 114	76.35 202	77.35 223	48.43 347	64.99 263	80.84 271	33.01 346	80.34 236	64.66 140	67.64 336	84.23 193

FMVSNet2		66.93 241	66.31 237	68.79 274	77.63 203	42.98 339	76.11 209	77.47 219	56.62 197	65.22 257	82.17 239	41.85 242	80.18 243	47.05 306	72.72 261	83.20 232

BH-w/o		66.85 242	65.83 244	69.90 254	79.29 139	52.46 200	74.66 246	76.65 236	54.51 258	64.85 265	78.12 317	45.59 194	82.95 169	43.26 342	75.54 213	74.27 381

Anonymous202405211		66.84 243	65.99 242	69.40 263	80.19 123	42.21 347	71.11 314	71.31 313	58.80 148	67.90 191	86.39 126	29.83 381	79.65 248	49.60 284	78.78 155	86.33 106

CDS-MVSNet		66.80 244	65.37 253	71.10 229	78.98 151	53.13 180	73.27 277	71.07 315	52.15 295	64.72 266	80.23 280	43.56 222	77.10 302	45.48 322	78.88 152	83.05 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 245	65.27 256	71.33 222	79.16 148	53.67 161	73.84 266	69.59 328	52.32 294	65.28 250	81.72 251	44.49 214	77.40 297	42.32 350	78.66 160	82.92 240

FMVSNet1		66.70 246	65.87 243	69.19 265	77.49 211	43.33 334	77.31 173	77.83 213	56.45 203	64.60 269	82.70 215	38.08 292	80.33 237	46.08 312	72.31 267	83.92 205

ab-mvs		66.65 247	66.42 231	67.37 289	76.17 246	41.73 351	70.41 324	76.14 241	53.99 265	65.98 236	83.51 204	49.48 140	76.24 324	48.60 291	73.46 245	84.14 197

PEN-MVS		66.60 248	66.45 228	67.04 292	77.11 224	36.56 401	77.03 186	80.42 153	62.95 54	62.51 303	84.03 188	46.69 184	79.07 264	44.22 328	63.08 374	85.51 142

TAPA-MVS	59.36 10	66.60 248	65.20 257	70.81 235	76.63 238	48.75 272	76.52 200	80.04 158	50.64 317	65.24 255	84.93 163	39.15 278	78.54 275	36.77 387	76.88 193	85.14 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 250	65.07 258	71.17 226	79.18 146	49.63 255	73.48 270	75.20 263	52.95 281	67.90 191	80.33 278	39.81 270	83.68 151	43.20 343	73.56 242	80.20 300

CP-MVSNet		66.49 251	66.41 232	66.72 294	77.67 201	36.33 404	76.83 194	79.52 167	62.45 67	62.54 301	83.47 206	46.32 187	78.37 276	45.47 323	63.43 371	85.45 147

PS-CasMVS		66.42 252	66.32 236	66.70 296	77.60 209	36.30 406	76.94 188	79.61 165	62.36 69	62.43 306	83.66 198	45.69 191	78.37 276	45.35 325	63.26 372	85.42 150

icg_test_0407_2		66.41 253	66.75 223	65.37 324	77.06 225	49.73 247	63.79 385	78.60 189	52.70 285	66.19 231	82.58 220	45.17 205	63.65 399	59.20 201	75.46 215	82.74 245

VortexMVS		66.41 253	65.50 250	69.16 269	73.75 303	48.14 282	73.41 271	78.28 207	53.73 273	64.98 264	78.33 315	40.62 262	79.07 264	58.88 205	67.50 337	80.26 299

FMVSNet3		66.32 255	65.61 248	68.46 277	76.48 242	42.34 344	74.98 238	77.15 227	55.83 217	65.04 260	81.16 260	39.91 267	80.14 244	47.18 303	72.76 258	82.90 242

ACMH+	57.40 11	66.12 256	64.06 265	72.30 187	77.79 195	52.83 189	80.39 102	78.03 210	57.30 181	57.47 361	82.55 225	27.68 401	84.17 140	45.54 319	69.78 307	79.90 306

cascas		65.98 257	63.42 277	73.64 147	77.26 218	52.58 196	72.26 296	77.21 226	48.56 343	61.21 319	74.60 376	32.57 360	85.82 105	50.38 276	76.75 196	82.52 253

FE-MVS		65.91 258	63.33 279	73.63 148	77.36 215	51.95 211	72.62 288	75.81 246	53.70 274	65.31 249	78.96 305	28.81 391	86.39 86	43.93 333	73.48 244	82.55 250

thisisatest0515		65.83 259	63.50 275	72.82 173	73.75 303	49.50 256	71.32 308	73.12 300	49.39 332	63.82 278	76.50 353	34.95 322	84.84 131	53.20 254	75.49 214	84.13 198

DP-MVS		65.68 260	63.66 273	71.75 199	84.93 56	56.87 106	80.74 100	73.16 298	53.06 280	59.09 344	82.35 231	36.79 308	85.94 102	32.82 411	69.96 303	72.45 396

HyFIR lowres test		65.67 261	63.01 284	73.67 144	79.97 128	55.65 126	69.07 339	75.52 253	42.68 408	63.53 281	77.95 321	40.43 264	81.64 201	46.01 313	71.91 271	83.73 216

DTE-MVSNet		65.58 262	65.34 254	66.31 303	76.06 248	34.79 414	76.43 201	79.38 170	62.55 65	61.66 314	83.83 193	45.60 193	79.15 261	41.64 358	60.88 389	85.00 167

GA-MVS		65.53 263	63.70 272	71.02 232	70.87 360	48.10 283	70.48 322	74.40 275	56.69 192	64.70 267	76.77 344	33.66 339	81.10 217	55.42 235	70.32 295	83.87 208

CNLPA		65.43 264	64.02 266	69.68 257	78.73 159	58.07 84	77.82 159	70.71 318	51.49 304	61.57 316	83.58 203	38.23 290	70.82 355	43.90 334	70.10 300	80.16 301

MVP-Stereo		65.41 265	63.80 270	70.22 245	77.62 207	55.53 131	76.30 203	78.53 195	50.59 318	56.47 371	78.65 310	39.84 269	82.68 180	44.10 332	72.12 270	72.44 397
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 266	62.73 288	73.40 159	74.89 271	52.78 190	73.09 282	75.13 264	55.69 221	58.48 353	73.73 384	32.86 348	86.32 89	50.63 274	70.11 299	81.10 283
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
test2506		65.33 267	64.61 261	67.50 286	79.46 137	34.19 422	74.43 252	51.92 433	58.72 149	66.75 220	88.05 77	25.99 415	80.92 225	51.94 263	84.25 75	87.39 63

pm-mvs1		65.24 268	64.97 259	66.04 311	72.38 331	39.40 374	72.62 288	75.63 249	55.53 226	62.35 308	83.18 211	47.45 171	76.47 321	49.06 288	66.54 345	82.24 259

ACMH	55.70 15	65.20 269	63.57 274	70.07 249	78.07 186	52.01 209	79.48 122	79.69 162	55.75 220	56.59 368	80.98 265	27.12 406	80.94 223	42.90 347	71.58 276	77.25 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 270	63.21 282	70.72 238	81.04 107	54.87 143	78.57 135	77.47 219	48.51 345	55.71 376	81.89 246	33.71 337	79.71 247	41.66 356	70.37 292	77.58 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 271	62.84 286	71.82 196	81.49 97	56.26 112	66.32 359	74.20 282	40.53 420	63.16 287	78.65 310	41.30 253	77.80 289	45.80 315	74.09 229	81.40 273

SSM_04072		64.98 272	65.42 251	63.68 339	78.65 161	53.46 168	50.83 443	79.09 174	53.75 271	68.14 183	83.83 193	41.79 245	53.03 444	56.58 220	76.11 202	84.54 181

TransMVSNet (Re)		64.72 273	64.33 263	65.87 316	75.22 264	38.56 380	74.66 246	75.08 268	58.90 147	61.79 312	82.63 218	51.18 119	78.07 281	43.63 339	55.87 412	80.99 286

EG-PatchMatch MVS		64.71 274	62.87 285	70.22 245	77.68 200	53.48 167	77.99 152	78.82 181	53.37 278	56.03 375	77.41 335	24.75 423	84.04 143	46.37 310	73.42 247	73.14 387

LS3D		64.71 274	62.50 290	71.34 221	79.72 132	55.71 124	79.82 113	74.72 271	48.50 346	56.62 367	84.62 172	33.59 340	82.34 190	29.65 433	75.23 219	75.97 357

IMVS_0404		64.63 276	64.22 264	65.88 315	77.06 225	49.73 247	64.40 379	78.60 189	52.70 285	53.16 405	82.58 220	34.82 323	65.16 393	59.20 201	75.46 215	82.74 245

1314		64.61 277	63.21 282	68.80 273	71.87 341	47.46 294	73.95 260	78.39 205	42.88 407	59.97 331	76.60 350	38.11 291	79.39 253	54.84 238	72.32 266	79.55 313

HY-MVS	56.14 13	64.55 278	63.89 267	66.55 299	74.73 279	41.02 358	69.96 330	74.43 274	49.29 334	61.66 314	80.92 267	47.43 172	76.68 317	44.91 327	71.69 274	81.94 264

testing91		64.46 279	63.80 270	66.47 300	78.43 170	40.06 366	67.63 349	69.59 328	59.06 143	63.18 286	78.05 319	34.05 331	76.99 308	48.30 294	75.87 208	82.37 257

sd_testset		64.46 279	64.45 262	64.51 332	77.13 221	42.25 346	62.67 392	72.11 308	58.02 165	65.08 258	82.55 225	41.22 258	69.88 363	47.32 301	73.92 232	81.41 271

XVG-ACMP-BASELINE		64.36 281	62.23 294	70.74 237	72.35 332	52.45 201	70.80 318	78.45 200	53.84 270	59.87 333	81.10 262	16.24 442	79.32 254	55.64 233	71.76 272	80.47 293

MonoMVSNet		64.15 282	63.31 280	66.69 297	70.51 365	44.12 328	74.47 250	74.21 281	57.81 173	63.03 289	76.62 347	38.33 287	77.31 299	54.22 244	60.59 394	78.64 324

testing99		64.05 283	63.29 281	66.34 302	78.17 183	39.76 370	67.33 354	68.00 342	58.60 153	63.03 289	78.10 318	32.57 360	76.94 310	48.22 295	75.58 212	82.34 258

CostFormer		64.04 284	62.51 289	68.61 276	71.88 340	45.77 308	71.30 309	70.60 319	47.55 361	64.31 272	76.61 349	41.63 248	79.62 250	49.74 280	69.00 322	80.42 295

1112_ss		64.00 285	63.36 278	65.93 313	79.28 141	42.58 343	71.35 307	72.36 306	46.41 375	60.55 325	77.89 325	46.27 189	73.28 338	46.18 311	69.97 302	81.92 265

baseline1		63.81 286	63.87 269	63.62 340	76.29 244	36.36 402	71.78 304	67.29 347	56.05 214	64.23 275	82.95 213	47.11 177	74.41 333	47.30 302	61.85 383	80.10 303

pmmvs6		63.69 287	62.82 287	66.27 305	70.63 362	39.27 375	73.13 281	75.47 256	52.69 290	59.75 337	82.30 233	39.71 271	77.03 305	47.40 300	64.35 363	82.53 251

Vis-MVSNet (Re-imp)		63.69 287	63.88 268	63.14 345	74.75 278	31.04 440	71.16 312	63.64 381	56.32 207	59.80 335	84.99 162	44.51 212	75.46 328	39.12 372	80.62 118	82.92 240

baseline2		63.42 289	61.26 308	69.89 255	72.55 326	47.62 292	71.54 305	68.38 339	50.11 322	54.82 387	75.55 366	43.06 228	80.96 222	48.13 296	67.16 341	81.11 282

thres400		63.31 290	62.18 295	66.72 294	76.85 233	39.62 371	71.96 301	69.44 331	56.63 195	62.61 299	79.83 286	37.18 300	79.17 258	31.84 417	73.25 250	81.36 274

thres600view7		63.30 291	62.27 293	66.41 301	77.18 220	38.87 377	72.35 293	69.11 335	56.98 189	62.37 307	80.96 266	37.01 306	79.00 269	31.43 424	73.05 254	81.36 274

thres100view900		63.28 292	62.41 291	65.89 314	77.31 217	38.66 379	72.65 286	69.11 335	57.07 186	62.45 304	81.03 264	37.01 306	79.17 258	31.84 417	73.25 250	79.83 309

test_0402		63.25 293	61.01 313	69.96 250	80.00 127	54.37 149	76.86 192	72.02 309	54.58 256	58.71 347	80.79 272	35.00 321	84.36 138	26.41 445	64.71 358	71.15 415

tfpn200view9		63.18 294	62.18 295	66.21 306	76.85 233	39.62 371	71.96 301	69.44 331	56.63 195	62.61 299	79.83 286	37.18 300	79.17 258	31.84 417	73.25 250	79.83 309

LTVRE_ROB	55.42 16	63.15 295	61.23 309	68.92 272	76.57 240	47.80 288	59.92 408	76.39 237	54.35 260	58.67 349	82.46 230	29.44 385	81.49 206	42.12 351	71.14 280	77.46 339
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
SD_0403		63.07 296	63.49 276	61.82 353	75.16 267	31.14 439	71.89 303	73.47 290	53.34 279	58.22 355	81.81 249	45.17 205	73.86 336	37.43 381	74.87 222	80.45 294

F-COLMAP		63.05 297	60.87 316	69.58 261	76.99 232	53.63 163	78.12 147	76.16 239	47.97 355	52.41 408	81.61 253	27.87 398	78.11 280	40.07 363	66.66 344	77.00 348

testing11		62.81 298	61.90 298	65.54 319	78.38 171	40.76 363	67.59 351	66.78 353	55.48 227	60.13 327	77.11 338	31.67 367	76.79 313	45.53 320	74.45 225	79.06 319

IterMVS		62.79 299	61.27 307	67.35 290	69.37 386	52.04 208	71.17 311	68.24 341	52.63 291	59.82 334	76.91 342	37.32 299	72.36 343	52.80 256	63.19 373	77.66 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
reproduce_monomvs		62.56 300	61.20 310	66.62 298	70.62 363	44.30 325	70.13 328	73.13 299	54.78 251	61.13 320	76.37 354	25.63 418	75.63 327	58.75 208	60.29 395	79.93 305

IterMVS-SCA-FT		62.49 301	61.52 302	65.40 323	71.99 339	50.80 227	71.15 313	69.63 327	45.71 383	60.61 324	77.93 322	37.45 296	65.99 389	55.67 231	63.50 370	79.42 315

tfpnnormal		62.47 302	61.63 301	64.99 329	74.81 276	39.01 376	71.22 310	73.72 288	55.22 235	60.21 326	80.09 284	41.26 256	76.98 309	30.02 431	68.09 332	78.97 322

MS-PatchMatch		62.42 303	61.46 303	65.31 326	75.21 265	52.10 205	72.05 298	74.05 283	46.41 375	57.42 363	74.36 377	34.35 329	77.57 294	45.62 318	73.67 237	66.26 434

Test_1112_low_res		62.32 304	61.77 299	64.00 337	79.08 150	39.53 373	68.17 345	70.17 321	43.25 403	59.03 345	79.90 285	44.08 216	71.24 353	43.79 336	68.42 329	81.25 278

D2MVS		62.30 305	60.29 319	68.34 280	66.46 409	48.42 279	65.70 363	73.42 291	47.71 359	58.16 356	75.02 372	30.51 371	77.71 292	53.96 247	71.68 275	78.90 323

testing222		62.29 306	61.31 306	65.25 327	77.87 192	38.53 381	68.34 343	66.31 357	56.37 206	63.15 288	77.58 333	28.47 393	76.18 326	37.04 385	76.65 198	81.05 285

thres200		62.20 307	61.16 311	65.34 325	75.38 262	39.99 367	69.60 334	69.29 333	55.64 224	61.87 311	76.99 340	37.07 305	78.96 270	31.28 425	73.28 249	77.06 346

tpm2		62.07 308	60.10 320	67.99 282	72.79 321	43.86 330	71.05 316	66.85 352	43.14 405	62.77 294	75.39 370	38.32 288	80.80 228	41.69 355	68.88 323	79.32 316

testing3-2		62.06 309	62.36 292	61.17 361	79.29 139	30.31 442	64.09 384	63.49 382	63.50 44	62.84 292	82.22 236	32.35 364	69.02 367	40.01 366	73.43 246	84.17 196

miper_lstm_enhance		62.03 310	60.88 315	65.49 322	66.71 406	46.25 303	56.29 427	75.70 248	50.68 315	61.27 318	75.48 368	40.21 265	68.03 373	56.31 224	65.25 354	82.18 260

EPNet_dtu		61.90 311	61.97 297	61.68 354	72.89 320	39.78 369	75.85 218	65.62 362	55.09 238	54.56 391	79.36 300	37.59 295	67.02 382	39.80 368	76.95 192	78.25 327
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 312	61.35 305	63.46 341	74.58 285	31.48 438	61.42 399	58.14 411	58.71 151	53.02 406	79.55 295	43.07 227	76.80 312	45.69 316	77.96 173	82.11 263

MSDG		61.81 313	59.23 325	69.55 262	72.64 323	52.63 195	70.45 323	75.81 246	51.38 306	53.70 398	76.11 356	29.52 383	81.08 219	37.70 379	65.79 351	74.93 372

SixPastTwentyTwo		61.65 314	58.80 331	70.20 247	75.80 250	47.22 296	75.59 222	69.68 326	54.61 254	54.11 395	79.26 302	27.07 407	82.96 167	43.27 341	49.79 434	80.41 296

CL-MVSNet_self_test		61.53 315	60.94 314	63.30 343	68.95 390	36.93 398	67.60 350	72.80 302	55.67 222	59.95 332	76.63 346	45.01 208	72.22 347	39.74 369	62.09 382	80.74 291

RPMNet		61.53 315	58.42 334	70.86 234	69.96 376	52.07 206	65.31 372	81.36 125	43.20 404	59.36 340	70.15 412	35.37 317	85.47 115	36.42 394	64.65 359	75.06 368

pmmvs4		61.48 317	59.39 324	67.76 284	71.57 345	53.86 156	71.42 306	65.34 364	44.20 394	59.46 339	77.92 323	35.90 313	74.71 331	43.87 335	64.87 357	74.71 377

OurMVSNet-221017-0		61.37 318	58.63 333	69.61 258	72.05 337	48.06 285	73.93 262	72.51 303	47.23 367	54.74 388	80.92 267	21.49 433	81.24 213	48.57 292	56.22 411	79.53 314

COLMAP_ROB	52.97 17	61.27 319	58.81 329	68.64 275	74.63 282	52.51 198	78.42 138	73.30 294	49.92 326	50.96 413	81.51 256	23.06 426	79.40 252	31.63 421	65.85 349	74.01 384
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 320	61.67 300	57.70 388	70.43 367	38.45 382	64.19 381	66.47 354	48.05 354	63.22 284	80.86 269	49.28 145	60.47 409	45.25 326	67.28 340	74.19 382

myMVS_eth3d28		60.66 321	61.04 312	59.51 368	77.32 216	31.58 437	63.11 389	63.87 378	59.00 144	60.90 323	78.26 316	32.69 355	66.15 388	36.10 396	78.13 170	80.81 289

SSC-MVS3.2		60.57 322	61.39 304	58.12 384	74.29 294	32.63 432	59.52 409	65.53 363	59.90 124	62.45 304	79.75 290	41.96 239	63.90 398	39.47 370	69.65 313	77.84 335

WBMVS		60.54 323	60.61 317	60.34 365	78.00 189	35.95 409	64.55 378	64.89 367	49.63 328	63.39 283	78.70 307	33.85 336	67.65 376	42.10 352	70.35 294	77.43 340

SCA		60.49 324	58.38 335	66.80 293	74.14 299	48.06 285	63.35 388	63.23 385	49.13 336	59.33 343	72.10 395	37.45 296	74.27 334	44.17 329	62.57 377	78.05 330

K. test v3		60.47 325	57.11 344	70.56 241	73.74 305	48.22 281	75.10 235	62.55 390	58.27 160	53.62 401	76.31 355	27.81 399	81.59 203	47.42 299	39.18 449	81.88 266

mmtdpeth		60.40 326	59.12 327	64.27 335	69.59 382	48.99 267	70.67 319	70.06 323	54.96 248	62.78 293	73.26 389	27.00 408	67.66 375	58.44 211	45.29 441	76.16 356

UWE-MVS		60.18 327	59.78 321	61.39 359	77.67 201	33.92 425	69.04 340	63.82 379	48.56 343	64.27 273	77.64 332	27.20 405	70.40 360	33.56 408	76.24 200	79.83 309

OpenMVS_ROB	52.78 18	60.03 328	58.14 338	65.69 318	70.47 366	44.82 318	75.33 226	70.86 317	45.04 386	56.06 374	76.00 358	26.89 410	79.65 248	35.36 400	67.29 339	72.60 392

CR-MVSNet		59.91 329	57.90 341	65.96 312	69.96 376	52.07 206	65.31 372	63.15 386	42.48 409	59.36 340	74.84 373	35.83 314	70.75 356	45.50 321	64.65 359	75.06 368

PatchmatchNet		59.84 330	58.24 336	64.65 331	73.05 317	46.70 300	69.42 336	62.18 396	47.55 361	58.88 346	71.96 397	34.49 327	69.16 365	42.99 345	63.60 368	78.07 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 331	57.84 342	65.54 319	74.87 273	42.95 341	69.61 333	64.16 376	48.90 339	58.68 348	77.12 337	28.19 396	72.35 344	43.75 338	55.28 414	81.31 277

WTY-MVS		59.75 332	60.39 318	57.85 386	72.32 333	37.83 387	61.05 404	64.18 374	45.95 382	61.91 310	79.11 304	47.01 181	60.88 408	42.50 349	69.49 314	74.83 373

WB-MVSnew		59.66 333	59.69 322	59.56 367	75.19 266	35.78 411	69.34 337	64.28 373	46.88 371	61.76 313	75.79 362	40.61 263	65.20 392	32.16 413	71.21 279	77.70 336

CVMVSNet		59.63 334	59.14 326	61.08 363	74.47 287	38.84 378	75.20 231	68.74 337	31.15 440	58.24 354	76.51 351	32.39 362	68.58 369	49.77 279	65.84 350	75.81 359

UBG		59.62 335	59.53 323	59.89 366	78.12 184	35.92 410	64.11 383	60.81 403	49.45 331	61.34 317	75.55 366	33.05 344	67.39 380	38.68 374	74.62 223	76.35 355

ETVMVS		59.51 336	58.81 329	61.58 356	77.46 212	34.87 413	64.94 376	59.35 406	54.06 264	61.08 321	76.67 345	29.54 382	71.87 349	32.16 413	74.07 230	78.01 334

tpm cat1		59.25 337	56.95 347	66.15 308	72.19 335	46.96 298	68.09 346	65.76 360	40.03 424	57.81 359	70.56 407	38.32 288	74.51 332	38.26 377	61.50 386	77.00 348

test_vis1_n_1920		58.86 338	59.06 328	58.25 380	63.76 422	43.14 338	67.49 352	66.36 356	40.22 422	65.89 240	71.95 398	31.04 368	59.75 414	59.94 192	64.90 356	71.85 405

pmmvs-eth3d		58.81 339	56.31 356	66.30 304	67.61 399	52.42 202	72.30 294	64.76 369	43.55 400	54.94 386	74.19 379	28.95 388	72.60 341	43.31 340	57.21 406	73.88 385

tt0320		58.59 340	56.81 350	63.92 338	75.46 259	41.32 356	68.63 342	64.06 377	47.05 369	56.19 373	74.19 379	30.34 373	71.36 351	39.92 367	55.45 413	79.09 318

tpmvs		58.47 341	56.95 347	63.03 347	70.20 371	41.21 357	67.90 348	67.23 348	49.62 329	54.73 389	70.84 405	34.14 330	76.24 324	36.64 391	61.29 387	71.64 407

PVSNet	50.76 19	58.40 342	57.39 343	61.42 357	75.53 258	44.04 329	61.43 398	63.45 383	47.04 370	56.91 365	73.61 385	27.00 408	64.76 394	39.12 372	72.40 264	75.47 364

tt0320-xc		58.33 343	56.41 355	64.08 336	75.79 251	41.34 355	68.30 344	62.72 389	47.90 356	56.29 372	74.16 381	28.53 392	71.04 354	41.50 359	52.50 426	79.88 307

tpmrst		58.24 344	58.70 332	56.84 390	66.97 403	34.32 420	69.57 335	61.14 401	47.17 368	58.58 352	71.60 400	41.28 255	60.41 410	49.20 286	62.84 375	75.78 360

Patchmatch-RL test		58.16 345	55.49 362	66.15 308	67.92 398	48.89 271	60.66 406	51.07 437	47.86 358	59.36 340	62.71 442	34.02 333	72.27 346	56.41 223	59.40 398	77.30 342

test-LLR		58.15 346	58.13 339	58.22 381	68.57 392	44.80 319	65.46 368	57.92 412	50.08 323	55.44 379	69.82 414	32.62 357	57.44 426	49.66 282	73.62 239	72.41 398

ppachtmachnet_test		58.06 347	55.38 363	66.10 310	69.51 383	48.99 267	68.01 347	66.13 359	44.50 391	54.05 396	70.74 406	32.09 365	72.34 345	36.68 390	56.71 410	76.99 350

gg-mvs-nofinetune		57.86 348	56.43 354	62.18 351	72.62 324	35.35 412	66.57 356	56.33 421	50.65 316	57.64 360	57.10 448	30.65 370	76.36 322	37.38 382	78.88 152	74.82 374

CMPMVS	42.80 21	57.81 349	55.97 358	63.32 342	60.98 438	47.38 295	64.66 377	69.50 330	32.06 438	46.83 431	77.80 327	29.50 384	71.36 351	48.68 290	73.75 235	71.21 414
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 350	57.07 345	58.22 381	74.21 296	37.18 393	62.46 393	60.88 402	48.88 340	55.29 382	75.99 360	31.68 366	62.04 405	31.87 416	72.35 265	75.43 365

tpm		57.34 351	58.16 337	54.86 400	71.80 342	34.77 415	67.47 353	56.04 424	48.20 351	60.10 328	76.92 341	37.17 302	53.41 443	40.76 361	65.01 355	76.40 354

Patchmtry		57.16 352	56.47 353	59.23 372	69.17 389	34.58 418	62.98 390	63.15 386	44.53 390	56.83 366	74.84 373	35.83 314	68.71 368	40.03 364	60.91 388	74.39 380

AllTest		57.08 353	54.65 367	64.39 333	71.44 349	49.03 264	69.92 331	67.30 345	45.97 380	47.16 429	79.77 288	17.47 436	67.56 378	33.65 405	59.16 399	76.57 352

test_cas_vis1_n_1920		56.91 354	56.71 351	57.51 389	59.13 444	45.40 315	63.58 386	61.29 400	36.24 432	67.14 213	71.85 399	29.89 380	56.69 430	57.65 214	63.58 369	70.46 419

mamv4		56.85 355	58.00 340	53.43 410	72.46 330	54.47 146	57.56 422	54.74 425	38.81 428	57.42 363	79.45 298	47.57 168	38.70 463	60.88 184	53.07 423	67.11 433

dmvs_re		56.77 356	56.83 349	56.61 391	69.23 387	41.02 358	58.37 414	64.18 374	50.59 318	57.45 362	71.42 401	35.54 316	58.94 419	37.23 383	67.45 338	69.87 424

testing3		56.54 357	55.92 359	58.41 379	77.52 210	27.93 450	69.72 332	56.36 420	54.75 253	58.63 351	77.80 327	20.88 434	71.75 350	25.31 447	62.25 380	75.53 363

our_test_3		56.49 358	54.42 370	62.68 349	69.51 383	45.48 314	66.08 360	61.49 399	44.11 397	50.73 417	69.60 417	33.05 344	68.15 370	38.38 376	56.86 407	74.40 379

pmmvs5		56.47 359	55.68 361	58.86 376	61.41 434	36.71 400	66.37 358	62.75 388	40.38 421	53.70 398	76.62 347	34.56 325	67.05 381	40.02 365	65.27 353	72.83 390

test-mter		56.42 360	55.82 360	58.22 381	68.57 392	44.80 319	65.46 368	57.92 412	39.94 425	55.44 379	69.82 414	21.92 429	57.44 426	49.66 282	73.62 239	72.41 398

USDC		56.35 361	54.24 374	62.69 348	64.74 418	40.31 364	65.05 374	73.83 287	43.93 398	47.58 427	77.71 331	15.36 445	75.05 330	38.19 378	61.81 384	72.70 391

PatchMatch-RL		56.25 362	54.55 369	61.32 360	77.06 225	56.07 116	65.57 365	54.10 430	44.13 396	53.49 404	71.27 404	25.20 420	66.78 383	36.52 393	63.66 367	61.12 438

sss		56.17 363	56.57 352	54.96 399	66.93 404	36.32 405	57.94 417	61.69 398	41.67 412	58.64 350	75.32 371	38.72 283	56.25 433	42.04 353	66.19 348	72.31 401

Syy-MVS		56.00 364	56.23 357	55.32 397	74.69 280	26.44 456	65.52 366	57.49 415	50.97 313	56.52 369	72.18 393	39.89 268	68.09 371	24.20 448	64.59 361	71.44 411

FMVSNet5		55.86 365	54.93 365	58.66 378	71.05 358	36.35 403	64.18 382	62.48 391	46.76 373	50.66 418	74.73 375	25.80 416	64.04 396	33.11 409	65.57 352	75.59 362

RPSCF		55.80 366	54.22 375	60.53 364	65.13 417	42.91 342	64.30 380	57.62 414	36.84 431	58.05 358	82.28 234	28.01 397	56.24 434	37.14 384	58.61 401	82.44 256

mvs5depth		55.64 367	53.81 378	61.11 362	59.39 443	40.98 362	65.89 361	68.28 340	50.21 321	58.11 357	75.42 369	17.03 438	67.63 377	43.79 336	46.21 438	74.73 376

EU-MVSNet		55.61 368	54.41 371	59.19 374	65.41 415	33.42 427	72.44 292	71.91 310	28.81 442	51.27 411	73.87 383	24.76 422	69.08 366	43.04 344	58.20 402	75.06 368

Anonymous20240521		55.30 369	54.41 371	57.96 385	60.92 440	41.73 351	71.09 315	71.06 316	41.18 415	48.65 425	73.31 387	16.93 439	59.25 416	42.54 348	64.01 364	72.90 389

TESTMET0.1,1		55.28 370	54.90 366	56.42 392	66.56 407	43.67 332	65.46 368	56.27 422	39.18 427	53.83 397	67.44 426	24.21 424	55.46 437	48.04 297	73.11 253	70.13 422

KD-MVS_self_test		55.22 371	53.89 377	59.21 373	57.80 447	27.47 452	57.75 420	74.32 276	47.38 363	50.90 414	70.00 413	28.45 394	70.30 361	40.44 362	57.92 403	79.87 308

MIMVSNet1		55.17 372	54.31 373	57.77 387	70.03 375	32.01 435	65.68 364	64.81 368	49.19 335	46.75 432	76.00 358	25.53 419	64.04 396	28.65 436	62.13 381	77.26 344

FE-MVSNET		55.16 373	53.75 379	59.41 369	65.29 416	33.20 429	67.21 355	66.21 358	48.39 349	49.56 423	73.53 386	29.03 387	72.51 342	30.38 429	54.10 420	72.52 394

Anonymous20231206		55.10 374	55.30 364	54.48 402	69.81 381	33.94 424	62.91 391	62.13 397	41.08 416	55.18 383	75.65 364	32.75 352	56.59 432	30.32 430	67.86 333	72.91 388

myMVS_eth3d		54.86 375	54.61 368	55.61 396	74.69 280	27.31 453	65.52 366	57.49 415	50.97 313	56.52 369	72.18 393	21.87 432	68.09 371	27.70 439	64.59 361	71.44 411

TinyColmap		54.14 376	51.72 388	61.40 358	66.84 405	41.97 348	66.52 357	68.51 338	44.81 387	42.69 443	75.77 363	11.66 452	72.94 339	31.96 415	56.77 409	69.27 428

EPMVS		53.96 377	53.69 380	54.79 401	66.12 412	31.96 436	62.34 395	49.05 441	44.42 393	55.54 377	71.33 403	30.22 375	56.70 429	41.65 357	62.54 378	75.71 361

PMMVS		53.96 377	53.26 383	56.04 393	62.60 429	50.92 224	61.17 402	56.09 423	32.81 437	53.51 403	66.84 431	34.04 332	59.93 413	44.14 331	68.18 331	57.27 446

test20.03		53.87 379	54.02 376	53.41 411	61.47 433	28.11 449	61.30 400	59.21 407	51.34 308	52.09 409	77.43 334	33.29 343	58.55 421	29.76 432	60.27 396	73.58 386

MDA-MVSNet-bldmvs		53.87 379	50.81 392	63.05 346	66.25 410	48.58 277	56.93 425	63.82 379	48.09 353	41.22 444	70.48 410	30.34 373	68.00 374	34.24 403	45.92 440	72.57 393

KD-MVS_2432*1600		53.45 381	51.50 390	59.30 370	62.82 426	37.14 394	55.33 428	71.79 311	47.34 365	55.09 384	70.52 408	21.91 430	70.45 358	35.72 398	42.97 444	70.31 420

miper_refine_blended		53.45 381	51.50 390	59.30 370	62.82 426	37.14 394	55.33 428	71.79 311	47.34 365	55.09 384	70.52 408	21.91 430	70.45 358	35.72 398	42.97 444	70.31 420

TDRefinement		53.44 383	50.72 393	61.60 355	64.31 421	46.96 298	70.89 317	65.27 366	41.78 410	44.61 438	77.98 320	11.52 454	66.36 386	28.57 437	51.59 428	71.49 410

test0.0.03 1		53.32 384	53.59 381	52.50 417	62.81 428	29.45 444	59.51 410	54.11 429	50.08 323	54.40 393	74.31 378	32.62 357	55.92 435	30.50 428	63.95 366	72.15 403

PatchT		53.17 385	53.44 382	52.33 418	68.29 396	25.34 460	58.21 415	54.41 428	44.46 392	54.56 391	69.05 420	33.32 342	60.94 407	36.93 386	61.76 385	70.73 418

UnsupCasMVSNet_eth		53.16 386	52.47 384	55.23 398	59.45 442	33.39 428	59.43 411	69.13 334	45.98 379	50.35 420	72.32 392	29.30 386	58.26 423	42.02 354	44.30 442	74.05 383

PM-MVS		52.33 387	50.19 396	58.75 377	62.10 431	45.14 317	65.75 362	40.38 459	43.60 399	53.52 402	72.65 390	9.16 460	65.87 390	50.41 275	54.18 419	65.24 436

UWE-MVS-28		52.25 388	52.35 386	51.93 421	66.99 402	22.79 464	63.48 387	48.31 445	46.78 372	52.73 407	76.11 356	27.78 400	57.82 425	20.58 454	68.41 330	75.17 366

testgi		51.90 389	52.37 385	50.51 424	60.39 441	23.55 463	58.42 413	58.15 410	49.03 337	51.83 410	79.21 303	22.39 427	55.59 436	29.24 435	62.64 376	72.40 400

dp		51.89 390	51.60 389	52.77 415	68.44 395	32.45 434	62.36 394	54.57 427	44.16 395	49.31 424	67.91 422	28.87 390	56.61 431	33.89 404	54.89 416	69.24 429

JIA-IIPM		51.56 391	47.68 405	63.21 344	64.61 419	50.73 228	47.71 449	58.77 409	42.90 406	48.46 426	51.72 452	24.97 421	70.24 362	36.06 397	53.89 421	68.64 430

test_fmvs1_n		51.37 392	50.35 395	54.42 404	52.85 451	37.71 389	61.16 403	51.93 432	28.15 444	63.81 279	69.73 416	13.72 446	53.95 441	51.16 270	60.65 392	71.59 408

ADS-MVSNet2		51.33 393	48.76 400	59.07 375	66.02 413	44.60 322	50.90 441	59.76 405	36.90 429	50.74 415	66.18 434	26.38 411	63.11 401	27.17 441	54.76 417	69.50 426

test_fmvs1		51.32 394	50.48 394	53.81 406	53.57 449	37.51 391	60.63 407	51.16 435	28.02 446	63.62 280	69.23 419	16.41 441	53.93 442	51.01 271	60.70 391	69.99 423

YYNet1		50.73 395	48.96 397	56.03 394	61.10 436	41.78 350	51.94 438	56.44 419	40.94 418	44.84 436	67.80 424	30.08 378	55.08 439	36.77 387	50.71 430	71.22 413

MDA-MVSNet_test_wron		50.71 396	48.95 398	56.00 395	61.17 435	41.84 349	51.90 439	56.45 418	40.96 417	44.79 437	67.84 423	30.04 379	55.07 440	36.71 389	50.69 431	71.11 416

dmvs_testset		50.16 397	51.90 387	44.94 432	66.49 408	11.78 472	61.01 405	51.50 434	51.17 311	50.30 421	67.44 426	39.28 275	60.29 411	22.38 451	57.49 405	62.76 437

UnsupCasMVSNet_bld		50.07 398	48.87 399	53.66 407	60.97 439	33.67 426	57.62 421	64.56 371	39.47 426	47.38 428	64.02 440	27.47 402	59.32 415	34.69 402	43.68 443	67.98 432

test_vis1_n		49.89 399	48.69 401	53.50 409	53.97 448	37.38 392	61.53 397	47.33 449	28.54 443	59.62 338	67.10 430	13.52 447	52.27 447	49.07 287	57.52 404	70.84 417

Patchmatch-test		49.08 400	48.28 402	51.50 422	64.40 420	30.85 441	45.68 453	48.46 444	35.60 433	46.10 435	72.10 395	34.47 328	46.37 455	27.08 443	60.65 392	77.27 343

test_fmvs2		48.69 401	47.49 406	52.29 419	48.63 458	33.06 431	57.76 419	48.05 447	25.71 450	59.76 336	69.60 417	11.57 453	52.23 448	49.45 285	56.86 407	71.58 409

ADS-MVSNet		48.48 402	47.77 403	50.63 423	66.02 413	29.92 443	50.90 441	50.87 439	36.90 429	50.74 415	66.18 434	26.38 411	52.47 446	27.17 441	54.76 417	69.50 426

CHOSEN 280x420		47.83 403	46.36 407	52.24 420	67.37 401	49.78 246	38.91 461	43.11 457	35.00 434	43.27 442	63.30 441	28.95 388	49.19 451	36.53 392	60.80 390	57.76 445

new-patchmatchnet		47.56 404	47.73 404	47.06 427	58.81 445	9.37 475	48.78 447	59.21 407	43.28 402	44.22 439	68.66 421	25.67 417	57.20 428	31.57 423	49.35 435	74.62 378

PVSNet_0	43.31 20	47.46 405	45.64 408	52.92 414	67.60 400	44.65 321	54.06 433	54.64 426	41.59 413	46.15 434	58.75 445	30.99 369	58.66 420	32.18 412	24.81 460	55.46 448

ttmdpeth		45.56 406	42.95 411	53.39 412	52.33 454	29.15 445	57.77 418	48.20 446	31.81 439	49.86 422	77.21 336	8.69 461	59.16 417	27.31 440	33.40 456	71.84 406

MVS-HIRNet		45.52 407	44.48 409	48.65 426	68.49 394	34.05 423	59.41 412	44.50 454	27.03 447	37.96 454	50.47 456	26.16 414	64.10 395	26.74 444	59.52 397	47.82 455

pmmvs3		44.92 408	41.95 415	53.86 405	52.58 453	43.55 333	62.11 396	46.90 451	26.05 449	40.63 445	60.19 444	11.08 457	57.91 424	31.83 420	46.15 439	60.11 439

test_fmvs3		44.30 409	42.55 412	49.55 425	42.83 463	27.15 455	53.03 435	44.93 453	22.03 458	53.69 400	64.94 437	4.21 468	49.63 450	47.47 298	49.82 433	71.88 404

WB-MVS		43.26 410	43.41 410	42.83 436	63.32 425	10.32 474	58.17 416	45.20 452	45.42 384	40.44 447	67.26 429	34.01 334	58.98 418	11.96 465	24.88 459	59.20 440

LF4IMVS		42.95 411	42.26 413	45.04 430	48.30 459	32.50 433	54.80 430	48.49 443	28.03 445	40.51 446	70.16 411	9.24 459	43.89 458	31.63 421	49.18 436	58.72 442

MVStest1		42.65 412	39.29 419	52.71 416	47.26 461	34.58 418	54.41 432	50.84 440	23.35 452	39.31 452	74.08 382	12.57 449	55.09 438	23.32 449	28.47 458	68.47 431

EGC-MVSNET		42.47 413	38.48 421	54.46 403	74.33 292	48.73 273	70.33 326	51.10 436	0.03 473	0.18 474	67.78 425	13.28 448	66.49 385	18.91 456	50.36 432	48.15 453

FPMVS		42.18 414	41.11 416	45.39 429	58.03 446	41.01 360	49.50 445	53.81 431	30.07 441	33.71 456	64.03 438	11.69 451	52.08 449	14.01 460	55.11 415	43.09 457

SSC-MVS		41.96 415	41.99 414	41.90 437	62.46 430	9.28 476	57.41 423	44.32 455	43.38 401	38.30 453	66.45 432	32.67 356	58.42 422	10.98 466	21.91 462	57.99 444

ANet_high		41.38 416	37.47 423	53.11 413	39.73 469	24.45 461	56.94 424	69.69 325	47.65 360	26.04 461	52.32 451	12.44 450	62.38 404	21.80 452	10.61 470	72.49 395

test_vis1_rt		41.35 417	39.45 418	47.03 428	46.65 462	37.86 386	47.76 448	38.65 460	23.10 454	44.21 440	51.22 454	11.20 456	44.08 457	39.27 371	53.02 424	59.14 441

LCM-MVSNet		40.30 418	35.88 424	53.57 408	42.24 464	29.15 445	45.21 455	60.53 404	22.23 457	28.02 459	50.98 455	3.72 470	61.78 406	31.22 426	38.76 450	69.78 425

mvsany_test1		39.38 419	38.16 422	43.02 435	49.05 456	34.28 421	44.16 457	25.94 470	22.74 456	46.57 433	62.21 443	23.85 425	41.16 462	33.01 410	35.91 452	53.63 449

N_pmnet		39.35 420	40.28 417	36.54 443	63.76 422	1.62 480	49.37 446	0.76 479	34.62 435	43.61 441	66.38 433	26.25 413	42.57 459	26.02 446	51.77 427	65.44 435

DSMNet-mixed		39.30 421	38.72 420	41.03 438	51.22 455	19.66 467	45.53 454	31.35 466	15.83 465	39.80 449	67.42 428	22.19 428	45.13 456	22.43 450	52.69 425	58.31 443

APD_test1		37.39 422	34.94 425	44.72 433	48.88 457	33.19 430	52.95 436	44.00 456	19.49 459	27.28 460	58.59 446	3.18 472	52.84 445	18.92 455	41.17 447	48.14 454

PMVS	28.69 22	36.22 423	33.29 428	45.02 431	36.82 471	35.98 408	54.68 431	48.74 442	26.31 448	21.02 464	51.61 453	2.88 473	60.10 412	9.99 469	47.58 437	38.99 462
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 424	31.91 429	43.33 434	62.05 432	37.87 385	20.39 466	67.03 350	23.23 453	18.41 466	25.84 466	4.24 467	62.73 402	14.71 459	51.32 429	29.38 464
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 425	34.94 425	33.26 446	61.06 437	16.00 471	52.79 437	23.78 472	40.71 419	39.33 451	48.65 460	16.91 440	48.34 452	12.18 464	19.05 464	35.44 463

new_pmnet		34.13 426	34.29 427	33.64 445	52.63 452	18.23 469	44.43 456	33.90 465	22.81 455	30.89 458	53.18 450	10.48 458	35.72 467	20.77 453	39.51 448	46.98 456

mvsany_test3		32.62 427	30.57 432	38.77 441	36.16 472	24.20 462	38.10 462	20.63 474	19.14 460	40.36 448	57.43 447	5.06 465	36.63 466	29.59 434	28.66 457	55.49 447

test_vis3_rt		32.09 428	30.20 433	37.76 442	35.36 473	27.48 451	40.60 460	28.29 469	16.69 463	32.52 457	40.53 462	1.96 474	37.40 465	33.64 407	42.21 446	48.39 452

test_f		31.86 429	31.05 430	34.28 444	32.33 475	21.86 465	32.34 463	30.46 467	16.02 464	39.78 450	55.45 449	4.80 466	32.36 469	30.61 427	37.66 451	48.64 451

testf1		31.46 430	28.89 434	39.16 439	41.99 466	28.78 447	46.45 451	37.56 461	14.28 466	21.10 462	48.96 457	1.48 476	47.11 453	13.63 461	34.56 453	41.60 458

APD_test2		31.46 430	28.89 434	39.16 439	41.99 466	28.78 447	46.45 451	37.56 461	14.28 466	21.10 462	48.96 457	1.48 476	47.11 453	13.63 461	34.56 453	41.60 458

kuosan		29.62 432	30.82 431	26.02 451	52.99 450	16.22 470	51.09 440	22.71 473	33.91 436	33.99 455	40.85 461	15.89 443	33.11 468	7.59 472	18.37 465	28.72 465

PMMVS2		27.40 433	25.91 436	31.87 448	39.46 470	6.57 477	31.17 464	28.52 468	23.96 451	20.45 465	48.94 459	4.20 469	37.94 464	16.51 457	19.97 463	51.09 450

E-PMN		23.77 434	22.73 438	26.90 449	42.02 465	20.67 466	42.66 458	35.70 463	17.43 461	10.28 471	25.05 467	6.42 463	42.39 460	10.28 468	14.71 467	17.63 466

EMVS		22.97 435	21.84 439	26.36 450	40.20 468	19.53 468	41.95 459	34.64 464	17.09 462	9.73 472	22.83 468	7.29 462	42.22 461	9.18 470	13.66 468	17.32 467

MVE	17.77 23	21.41 436	17.77 441	32.34 447	34.34 474	25.44 459	16.11 467	24.11 471	11.19 468	13.22 468	31.92 464	1.58 475	30.95 470	10.47 467	17.03 466	40.62 461
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 437	18.10 440	24.41 452	13.68 477	3.11 479	12.06 469	42.37 458	2.00 471	11.97 469	36.38 463	5.77 464	29.35 471	15.06 458	23.65 461	40.76 460

cdsmvs_eth3d_5k		17.50 438	23.34 437	0.00 458	0.00 481	0.00 482	0.00 470	78.63 188	0.00 476	0.00 477	82.18 237	49.25 146	0.00 475	0.00 476	0.00 473	0.00 473

wuyk23d		13.32 439	12.52 442	15.71 453	47.54 460	26.27 457	31.06 465	1.98 478	4.93 470	5.18 473	1.94 473	0.45 478	18.54 472	6.81 473	12.83 469	2.33 470

tmp_tt		9.43 440	11.14 443	4.30 455	2.38 478	4.40 478	13.62 468	16.08 476	0.39 472	15.89 467	13.06 469	15.80 444	5.54 474	12.63 463	10.46 471	2.95 469

ab-mvs-re		6.49 441	8.65 444	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	77.89 325	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

test123		4.73 442	6.30 445	0.02 456	0.01 479	0.01 481	56.36 426	0.00 480	0.01 474	0.04 475	0.21 475	0.01 479	0.00 475	0.03 475	0.00 473	0.04 471

testmvs		4.52 443	6.03 446	0.01 457	0.01 479	0.00 482	53.86 434	0.00 480	0.01 474	0.04 475	0.27 474	0.00 480	0.00 475	0.04 474	0.00 473	0.03 472

pcd_1.5k_mvsjas		3.92 444	5.23 447	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	47.05 178	0.00 475	0.00 476	0.00 473	0.00 473

mmdepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

monomultidepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

test_blank		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

uanet_test		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

DCPMVS		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

sosnet-low-res		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

sosnet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

uncertanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

Regformer		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

uanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 482	0.00 470	0.00 480	0.00 476	0.00 477	0.00 476	0.00 480	0.00 475	0.00 476	0.00 473	0.00 473

TestfortrainingZip							86.84 11

WAC-MVS						27.31 453								27.77 438

FOURS1					86.12 37	60.82 37	88.18 1	83.61 70	60.87 94	81.50 17

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 26					90.96 1	79.31 10	90.65 8	87.85 41

PC_three_1452									55.09 238	84.46 4	89.84 49	66.68 5	89.41 19	74.24 57	91.38 2	88.42 21

No_MVS				79.95 4	87.24 14	61.04 31		85.62 26					90.96 1	79.31 10	90.65 8	87.85 41

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 481

eth-test					0.00 481

ZD-MVS					86.64 21	60.38 45		82.70 102	57.95 169	78.10 30	90.06 42	56.12 46	88.84 27	74.05 60	87.00 52

RE-MVS-def			73.71 77		83.49 69	59.87 54	84.29 45	81.36 125	58.07 163	73.14 96	90.07 40	43.06 228		68.20 100	81.76 105	84.03 199

IU-MVS					87.77 4	59.15 66		85.53 28	53.93 267	84.64 3				79.07 13	90.87 5	88.37 23

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 48	67.01 1	90.33 12	73.16 67	91.15 4	88.23 29

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 50

test_241102_ONE					87.77 4	58.90 75		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

9.14			78.75 16		83.10 74		84.15 51	88.26 1	59.90 124	78.57 27	90.36 32	57.51 33	86.86 70	77.39 29	89.52 21

save fliter					86.17 34	61.30 28	83.98 55	79.66 164	59.00 144

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 35

test_0728_SECOND				79.19 16	87.82 3	59.11 69	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 52

test0726					87.75 7	59.07 70	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 330

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 324				78.05 330

sam_mvs												33.43 341

ambc				65.13 328	63.72 424	37.07 396	47.66 450	78.78 184		54.37 394	71.42 401	11.24 455	80.94 223	45.64 317	53.85 422	77.38 341

MTGPA								80.97 143

test_post1							68.67 341				3.64 471	32.39 362	69.49 364	44.17 329

test_post											3.55 472	33.90 335	66.52 384

patchmatchnet-post											64.03 438	34.50 326	74.27 334

GG-mvs-BLEND				62.34 350	71.36 353	37.04 397	69.20 338	57.33 417		54.73 389	65.48 436	30.37 372	77.82 288	34.82 401	74.93 221	72.17 402

MTMP							86.03 20	17.08 475

gm-plane-assit					71.40 352	41.72 353			48.85 341		73.31 387		82.48 188	48.90 289

test9_res														75.28 50	88.31 33	83.81 210

TEST9					85.58 44	61.59 24	81.62 88	81.26 132	55.65 223	74.93 60	88.81 64	53.70 76	84.68 133

test_8					85.40 47	60.96 34	81.54 91	81.18 136	55.86 215	74.81 65	88.80 66	53.70 76	84.45 137

agg_prior2														73.09 68	87.93 41	84.33 188

agg_prior					85.04 51	59.96 50		81.04 141		74.68 69			84.04 143

TestCases				64.39 333	71.44 349	49.03 264		67.30 345	45.97 380	47.16 429	79.77 288	17.47 436	67.56 378	33.65 405	59.16 399	76.57 352

test_prior4						62.51 14	82.08 84

test_prior2							81.75 86		60.37 110	75.01 58	89.06 58	56.22 44		72.19 75	88.96 25

test_prior				76.69 62	84.20 62	57.27 95		84.88 41					86.43 85			86.38 100

旧先验2							76.08 210		45.32 385	76.55 44			65.56 391	58.75 208

新几何2							76.12 208

新几何1				70.76 236	85.66 42	61.13 30		66.43 355	44.68 389	70.29 141	86.64 113	41.29 254	75.23 329	49.72 281	81.75 107	75.93 358

旧先验1					83.04 75	53.15 178		67.52 344			87.85 83	44.08 216			80.76 116	78.03 333

无先验							79.66 118	74.30 278	48.40 348				80.78 229	53.62 249		79.03 321

原ACMM2							79.02 125

原ACMM1				74.69 103	85.39 48	59.40 59		83.42 76	51.47 305	70.27 142	86.61 117	48.61 154	86.51 83	53.85 248	87.96 40	78.16 328

test222					83.14 73	58.68 79	72.57 290	63.45 383	41.78 410	67.56 204	86.12 135	37.13 303			78.73 157	74.98 371

testdata2													72.18 348	46.95 307

segment_acmp												54.23 64

testdata				64.66 330	81.52 95	52.93 183		65.29 365	46.09 378	73.88 82	87.46 90	38.08 292	66.26 387	53.31 253	78.48 164	74.78 375

testdata1							72.65 286		60.50 104

test12				77.76 47	84.52 59	58.41 81		83.36 79		72.93 104		54.61 61	88.05 40		88.12 35	86.81 83

plane_prior7					81.41 98	55.96 118

plane_prior6					81.20 105	56.24 113						45.26 203

plane_prior5								84.01 54					87.21 60	68.16 102	80.58 120	84.65 179

plane_prior4											86.10 136

plane_prior3						56.09 115			63.92 38	69.27 162

plane_prior2							84.22 48		64.52 27

plane_prior1					81.27 103

plane_prior						56.31 109	83.58 61		63.19 51						80.48 123

n2								0.00 480

nn								0.00 480

door-mid								47.19 450

lessismore_v0				69.91 253	71.42 351	47.80 288		50.90 438		50.39 419	75.56 365	27.43 404	81.33 210	45.91 314	34.10 455	80.59 292

LGP-MVS_train				75.76 80	80.22 120	57.51 93		83.40 77	61.32 85	66.67 223	87.33 96	39.15 278	86.59 76	67.70 108	77.30 187	83.19 233

test11								83.47 74

door								47.60 448

HQP5-MVS						54.94 140

HQP-NCC					80.66 112		82.31 79		62.10 72	67.85 193

ACMP_Plane					80.66 112		82.31 79		62.10 72	67.85 193

BP-MVS														67.04 116

HQP4-MVS										67.85 193			86.93 68			84.32 189

HQP3-MVS								83.90 59							80.35 124

HQP2-MVS												45.46 197

NP-MVS					80.98 108	56.05 117					85.54 156

MDTV_nov1_ep13_2view						25.89 458	61.22 401		40.10 423	51.10 412		32.97 347		38.49 375		78.61 325

MDTV_nov1_ep13			57.00 346		72.73 322	38.26 383	65.02 375	64.73 370	44.74 388	55.46 378	72.48 391	32.61 359	70.47 357	37.47 380	67.75 335

ACMMP++_ref															74.07 230

ACMMP++															72.16 269

Test By Simon												48.33 157

ITE_SJBPF				62.09 352	66.16 411	44.55 324		64.32 372	47.36 364	55.31 381	80.34 277	19.27 435	62.68 403	36.29 395	62.39 379	79.04 320

DeepMVS_CX				12.03 454	17.97 476	10.91 473		10.60 477	7.46 469	11.07 470	28.36 465	3.28 471	11.29 473	8.01 471	9.74 472	13.89 468

High-resolution multi-view benchmark