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 68	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 31

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 78	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 39

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 13	89.23 25	81.51 2	88.44 30	88.09 46
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 38	87.75 7	59.07 73	87.85 5	85.03 42	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 165
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 31	86.42 15	63.28 51	83.27 15	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 96
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		80.40 6	80.84 6	79.07 25	85.30 50	59.25 64	86.84 11	85.86 23	63.31 48	83.65 12	91.48 12	64.70 10	89.91 16	77.02 34	89.43 22	88.06 49

SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 24	61.95 19	86.33 17	85.75 27	62.49 71	82.20 19	92.28 1	56.53 43	89.70 20	79.85 6	91.48 1	88.19 41
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 8	80.28 10	79.99 2	82.19 90	60.01 49	86.19 21	83.93 60	73.19 1	77.08 45	91.21 20	57.23 38	90.73 10	83.35 1	88.12 37	89.22 8

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 21	60.02 48	88.12 3	86.42 15	62.94 60	82.40 16	92.12 2	59.64 23	89.76 19	78.70 15	88.32 34	86.79 98
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		80.04 10	80.36 9	79.08 24	86.63 23	59.25 64	85.62 32	86.73 12	63.10 57	82.27 18	90.57 27	61.90 16	89.88 18	77.02 34	89.43 22	88.10 44

HPM-MVS++		79.88 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 16	83.70 79	65.37 13	78.78 29	90.64 24	58.63 30	87.24 60	79.00 14	90.37 14	85.26 177

CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 17	84.37 45	85.03 42	66.96 5	77.58 39	90.06 45	59.47 25	89.13 27	78.67 17	89.73 16	87.03 89

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 50	59.08 72	86.84 11	86.01 20	63.31 48	82.37 17	91.48 12	60.88 18	89.61 21	76.25 43	86.13 65	88.06 49

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 81	62.18 16	87.60 9	85.83 25	66.69 9	78.03 36	90.98 21	54.26 75	90.06 14	78.42 23	89.02 26	87.69 61
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 34	62.86 63	80.17 21	90.03 47	61.76 17	88.95 29	74.21 62	88.67 29	88.12 43

SF-MVS		78.82 16	79.22 15	77.60 52	82.88 83	57.83 91	84.99 37	88.13 2	61.86 89	79.16 26	90.75 23	57.96 31	87.09 69	77.08 33	90.18 15	87.87 53

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 47	75.08 61	90.47 33	53.96 82	88.68 32	76.48 39	89.63 20	87.16 86

ACMMP_NAP		78.77 18	78.78 17	78.74 34	85.44 46	61.04 31	83.84 60	85.16 37	62.88 62	78.10 34	91.26 19	52.51 107	88.39 35	79.34 9	90.52 13	86.78 99

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 51	66.73 8	74.67 75	89.38 58	55.30 64	89.18 26	74.19 63	87.34 49	86.38 115

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 71	61.62 23	84.17 53	86.85 6	63.23 53	73.84 93	90.25 40	57.68 34	89.96 15	74.62 60	89.03 25	87.89 51
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 21	78.28 22	78.98 29	80.73 115	57.91 90	84.68 41	81.64 132	68.35 2	75.77 51	90.38 34	53.98 80	90.26 13	81.30 3	87.68 45	88.77 18

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 31	84.96 56	61.41 26	84.03 56	83.82 74	59.34 154	79.37 25	89.76 54	59.84 20	87.62 57	76.69 37	86.74 58	87.68 62
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 23	78.46 20	78.03 45	84.96 56	59.52 58	82.93 70	85.39 33	62.15 81	76.41 49	91.51 11	52.47 109	86.78 76	80.66 4	89.64 19	87.80 57
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 85	63.89 39	73.60 96	90.60 25	54.85 70	86.72 77	77.20 31	88.06 39	85.74 151
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 36	63.56 43	74.29 81	90.03 47	52.56 106	88.53 34	74.79 59	88.34 32	86.63 107

APD-MVS		78.02 26	78.04 26	77.98 46	86.44 28	60.81 38	85.52 33	84.36 52	60.61 115	79.05 27	90.30 38	55.54 63	88.32 37	73.48 70	87.03 51	84.83 192
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 53	62.82 64	73.96 86	90.50 31	53.20 97	88.35 36	74.02 65	87.05 50	86.13 131

lecture		77.75 28	77.84 28	77.50 54	82.75 85	57.62 94	85.92 25	86.20 18	60.53 117	78.99 28	91.45 14	51.51 128	87.78 52	75.65 49	87.55 46	87.10 88

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 54	62.82 64	73.55 98	90.56 29	49.80 152	88.24 38	74.02 65	87.03 51	86.32 124

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 64	61.88 21	84.55 43	83.87 67	60.37 124	79.89 22	89.38 58	54.97 68	85.58 114	76.12 45	84.94 71	86.33 122
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 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 56	62.81 66	73.30 103	90.58 26	49.90 149	88.21 39	73.78 67	87.03 51	86.29 128

MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 85	83.22 66	86.93 5	56.91 206	74.91 66	88.19 76	59.15 27	87.68 56	73.67 68	87.45 48	86.57 108

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 39	62.57 69	73.09 114	89.97 50	50.90 139	87.48 58	75.30 53	86.85 56	87.33 81
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 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 46	61.98 88	73.06 115	88.88 67	53.72 88	89.06 28	68.27 105	88.04 40	87.42 73
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 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 132	90.01 49	47.95 177	88.01 45	71.55 88	86.74 58	86.37 117

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 88	62.44 73	72.68 124	90.50 31	48.18 175	87.34 59	73.59 69	85.71 67	84.76 196

CSCG		76.92 37	76.75 35	77.41 56	83.96 69	59.60 56	82.95 69	86.50 14	60.78 111	75.27 56	84.83 180	60.76 19	86.56 82	67.86 117	87.87 44	86.06 133

reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 93	60.22 131	77.85 37	91.42 16	50.67 140	87.69 54	72.46 76	84.53 75	85.46 163

our_new_method		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 93	60.22 131	77.85 37	91.42 16	50.67 140	87.69 54	72.46 76	84.53 75	85.46 163

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 255	80.97 158	65.13 15	75.77 51	90.88 22	48.63 170	86.66 79	77.23 30	88.17 36	84.81 193

PGM-MVS		76.77 41	76.06 46	78.88 32	86.14 36	62.73 9	82.55 78	83.74 76	61.71 90	72.45 130	90.34 37	48.48 173	88.13 42	72.32 78	86.85 56	85.78 145

BridgeMVS		76.58 42	76.55 41	76.68 67	81.73 96	52.90 187	80.94 99	85.70 29	61.12 104	74.90 67	87.17 111	56.46 44	88.14 41	72.87 73	88.03 41	89.00 11

mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 122	62.90 61	71.77 137	90.26 39	46.61 201	86.55 85	71.71 86	85.66 68	84.97 188

CANet		76.46 44	75.93 48	78.06 43	81.29 105	57.53 96	82.35 80	83.31 96	67.78 3	70.09 160	86.34 142	54.92 69	88.90 30	72.68 75	84.55 74	87.76 59

reproduce_model		76.43 45	76.08 45	77.49 55	83.47 75	60.09 47	84.60 42	82.90 113	59.65 144	77.31 40	91.43 15	49.62 155	87.24 60	71.99 82	83.75 87	85.14 179

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 57	56.32 223	74.05 84	88.98 63	53.34 94	87.92 48	69.23 101	88.42 31	87.59 67

train_agg		76.27 47	76.15 44	76.64 70	85.58 44	61.59 24	81.62 91	81.26 147	55.86 231	74.93 64	88.81 68	53.70 89	84.68 138	75.24 55	88.33 33	83.65 239

NormalMVS		76.26 48	75.74 51	77.83 50	82.75 85	59.89 52	84.36 46	83.21 101	64.69 22	74.21 82	87.40 96	49.48 156	86.17 97	68.04 114	87.55 46	87.42 73

CS-MVS		76.25 49	75.98 47	77.06 61	80.15 129	55.63 131	84.51 44	83.90 63	63.24 52	73.30 103	87.27 103	55.06 66	86.30 94	71.78 85	84.58 73	89.25 7

casdiffmvs_mvg		76.14 50	76.30 43	75.66 88	76.46 257	51.83 218	79.67 121	85.08 39	65.02 19	75.84 50	88.58 74	59.42 26	85.08 126	72.75 74	83.93 83	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 51	75.70 52	77.40 58	85.87 41	61.20 29	85.52 33	82.19 123	59.99 137	75.10 60	90.35 36	47.66 182	86.52 86	71.64 87	82.99 92	84.47 205

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 50	63.04 58	69.80 170	89.74 55	45.43 215	87.16 66	72.01 81	82.87 98	85.14 179
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 53	75.36 55	77.41 56	80.62 120	55.91 124	84.28 50	85.78 26	56.08 229	73.41 99	86.58 133	50.94 138	88.54 33	70.79 93	89.71 17	87.79 58

EC-MVSNet		75.84 54	75.87 50	75.74 86	78.86 159	52.65 196	83.73 61	86.08 19	63.47 45	72.77 123	87.25 108	53.13 98	87.93 47	71.97 83	85.57 69	86.66 105

3Dnovator+	66.72 4	75.84 54	74.57 67	79.66 9	82.40 87	59.92 51	85.83 27	86.32 17	66.92 7	67.80 217	89.24 60	42.03 254	89.38 24	64.07 158	86.50 62	89.69 3

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 68	80.84 113	53.06 184	78.62 139	85.13 38	59.65 144	71.53 143	87.47 94	56.92 40	88.17 40	72.18 80	86.63 61	88.80 15

SPE-MVS-test		75.62 57	75.31 57	76.56 72	80.63 119	55.13 142	83.88 59	85.22 35	62.05 85	71.49 144	86.03 153	53.83 84	86.36 92	67.74 118	86.91 55	88.19 41

DPM-MVS		75.47 58	75.00 61	76.88 62	81.38 104	59.16 67	79.94 114	85.71 28	56.59 217	72.46 128	86.76 120	56.89 41	87.86 50	66.36 138	88.91 28	83.64 240

SymmetryMVS		75.28 59	74.60 66	77.30 59	83.85 70	59.89 52	84.36 46	75.51 282	64.69 22	74.21 82	87.40 96	49.48 156	86.17 97	68.04 114	83.88 84	85.85 142

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 101	78.34 180	55.37 139	77.30 187	73.95 314	61.40 96	79.46 23	90.14 41	57.07 39	81.15 230	80.00 5	79.31 153	88.51 30

APD-MVS_3200maxsize		74.96 61	74.39 69	76.67 68	82.20 89	58.24 86	83.67 62	83.29 97	58.41 172	73.71 94	90.14 41	45.62 208	85.99 104	69.64 97	82.85 99	85.78 145

TSAR-MVS + GP.		74.90 62	74.15 73	77.17 60	82.00 92	58.77 81	81.80 88	78.57 208	58.58 169	74.32 80	84.51 196	55.94 60	87.22 63	67.11 128	84.48 78	85.52 159

hybridcas		74.86 63	75.07 60	74.24 128	76.30 258	50.58 240	79.30 127	83.88 66	63.15 56	74.69 73	88.13 78	58.91 28	82.98 176	68.30 104	82.93 95	89.15 10

casdiffmvs		74.80 64	74.89 64	74.53 118	75.59 272	50.37 249	78.17 156	85.06 41	62.80 67	74.40 78	87.86 87	57.88 32	83.61 159	69.46 100	82.79 100	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 65	74.46 68	75.65 89	77.84 199	52.25 207	75.59 237	84.17 55	63.76 40	73.15 109	82.79 232	59.58 24	86.80 75	67.24 126	86.04 66	87.89 51
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 66	74.25 72	76.19 77	80.81 114	59.01 76	82.60 77	83.64 82	63.74 41	72.52 127	87.49 93	47.18 192	85.88 107	69.47 99	80.78 120	83.66 238
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 67	74.98 62	73.71 157	78.94 157	50.56 243	80.23 107	83.87 67	60.30 128	77.15 42	86.56 134	59.65 21	82.00 210	66.01 142	82.12 104	88.58 28

canonicalmvs		74.67 67	74.98 62	73.71 157	78.94 157	50.56 243	80.23 107	83.87 67	60.30 128	77.15 42	86.56 134	59.65 21	82.00 210	66.01 142	82.12 104	88.58 28

baseline		74.61 69	74.70 65	74.34 123	75.70 267	49.99 259	77.54 177	84.63 48	62.73 68	73.98 85	87.79 90	57.67 35	83.82 155	69.49 98	82.74 101	89.20 9

SR-MVS-dyc-post		74.57 70	73.90 80	76.58 71	83.49 73	59.87 54	84.29 48	81.36 140	58.07 178	73.14 110	90.07 43	44.74 225	85.84 108	68.20 106	81.76 111	84.03 217

dcpmvs_2		74.55 71	75.23 58	72.48 196	82.34 88	53.34 176	77.87 165	81.46 136	57.80 189	75.49 53	86.81 119	62.22 14	77.75 317	71.09 91	82.02 107	86.34 119

ETV-MVS		74.46 72	73.84 82	76.33 75	79.27 147	55.24 141	79.22 128	85.00 44	64.97 21	72.65 125	79.46 315	53.65 92	87.87 49	67.45 125	82.91 96	85.89 139

HQP_MVS		74.31 73	73.73 84	76.06 78	81.41 102	56.31 113	84.22 51	84.01 58	64.52 27	69.27 179	86.10 150	45.26 219	87.21 64	68.16 110	80.58 126	84.65 197

fmvsm_s_conf0.5_n_8		74.30 74	74.39 69	74.01 142	75.33 279	52.89 189	78.24 148	77.32 241	61.65 91	78.13 33	88.90 66	52.82 103	81.54 220	78.46 22	78.67 175	87.60 66

HPM-MVS_fast		74.30 74	73.46 90	76.80 64	84.45 65	59.04 75	83.65 63	81.05 155	60.15 133	70.43 155	89.84 52	41.09 277	85.59 113	67.61 121	82.90 97	85.77 148

fmvsm_s_conf0.5_n_10		74.11 76	73.98 79	74.48 120	74.61 299	52.86 191	78.10 160	77.06 245	57.14 199	78.24 32	88.79 71	52.83 102	82.26 206	77.79 28	81.30 116	88.32 34

E5new		74.10 77	74.09 74	74.15 134	77.14 228	50.74 233	78.24 148	83.86 70	62.34 75	73.95 87	87.27 103	55.97 58	82.95 179	68.16 110	79.86 137	88.77 18

E6new		74.10 77	74.09 74	74.15 134	77.14 228	50.74 233	78.24 148	83.85 72	62.34 75	73.95 87	87.27 103	55.98 56	82.95 179	68.17 108	79.85 139	88.77 18

E6		74.10 77	74.09 74	74.15 134	77.14 228	50.74 233	78.24 148	83.85 72	62.34 75	73.95 87	87.27 103	55.98 56	82.95 179	68.17 108	79.85 139	88.77 18

E5		74.10 77	74.09 74	74.15 134	77.14 228	50.74 233	78.24 148	83.86 70	62.34 75	73.95 87	87.27 103	55.97 58	82.95 179	68.16 110	79.86 137	88.77 18

MVS_111021_HR		74.02 81	73.46 90	75.69 87	83.01 81	60.63 40	77.29 188	78.40 219	61.18 102	70.58 154	85.97 156	54.18 77	84.00 152	67.52 122	82.98 94	82.45 273

MG-MVS		73.96 82	73.89 81	74.16 132	85.65 43	49.69 268	81.59 93	81.29 146	61.45 95	71.05 148	88.11 79	51.77 123	87.73 53	61.05 198	83.09 90	85.05 184

E4		73.91 83	73.83 83	74.15 134	77.13 232	50.47 246	77.15 194	83.79 75	62.21 80	73.61 95	87.19 110	56.08 54	83.03 171	67.91 116	79.35 151	88.94 13

alignmvs		73.86 84	73.99 78	73.45 171	78.20 184	50.50 245	78.57 141	82.43 120	59.40 152	76.57 47	86.71 126	56.42 46	81.23 229	65.84 145	81.79 110	88.62 25

MSLP-MVS++		73.77 85	73.47 89	74.66 110	83.02 80	59.29 63	82.30 85	81.88 127	59.34 154	71.59 141	86.83 118	45.94 206	83.65 158	65.09 151	85.22 70	81.06 306

casdiffseed414692147		73.73 86	73.22 95	75.28 98	76.76 248	52.16 209	80.05 111	83.01 110	63.38 46	73.35 102	87.11 112	53.22 95	84.14 146	61.71 192	80.38 130	89.55 5

E2		73.72 87	73.60 87	74.06 139	77.16 226	50.40 247	76.97 199	83.74 76	61.64 92	73.36 100	86.75 123	56.14 50	82.99 173	67.50 123	79.18 161	88.80 15

E3		73.72 87	73.60 87	74.06 139	77.16 226	50.40 247	76.97 199	83.74 76	61.64 92	73.36 100	86.76 120	56.13 51	82.99 173	67.50 123	79.18 161	88.80 15

viewcassd2359sk11		73.56 89	73.41 92	74.00 143	77.13 232	50.35 250	76.86 206	83.69 80	61.23 101	73.14 110	86.38 141	56.09 53	82.96 177	67.15 127	79.01 166	88.70 24

fmvsm_s_conf0.5_n_3		73.55 90	74.39 69	71.03 247	74.09 317	51.86 217	77.77 171	75.60 278	61.18 102	78.67 30	88.98 63	55.88 61	77.73 318	78.69 16	78.68 174	83.50 243

HQP-MVS		73.45 91	72.80 103	75.40 93	80.66 116	54.94 144	82.31 82	83.90 63	62.10 82	67.85 211	85.54 172	45.46 213	86.93 72	67.04 130	80.35 131	84.32 207

viewdifsd2359ckpt09		73.42 92	72.45 110	76.30 76	77.25 224	53.27 178	80.36 106	82.48 119	57.96 183	72.24 131	85.73 166	53.22 95	86.27 95	63.79 168	79.06 165	89.36 6

E3new		73.41 93	73.22 95	73.95 146	77.06 237	50.31 251	76.78 209	83.66 81	60.90 107	72.93 118	86.02 154	55.99 55	82.95 179	66.89 135	78.77 171	88.61 26

BP-MVS1		73.41 93	72.25 112	76.88 62	76.68 250	53.70 163	79.15 129	81.07 154	60.66 114	71.81 136	87.39 98	40.93 278	87.24 60	71.23 90	81.29 117	89.71 2

CLD-MVS		73.33 95	72.68 105	75.29 97	78.82 161	53.33 177	78.23 153	84.79 47	61.30 99	70.41 157	81.04 281	52.41 110	87.12 67	64.61 157	82.49 103	85.41 169
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 96	72.54 108	75.62 90	77.87 197	53.64 165	79.62 123	79.61 180	61.63 94	72.02 135	82.61 237	56.44 45	85.97 105	63.99 161	79.07 164	87.25 83

fmvsm_l_conf0.5_n_9		73.27 97	73.66 86	72.09 205	73.82 318	52.72 195	77.45 181	74.28 307	56.61 216	77.10 44	88.16 77	56.17 49	77.09 333	78.27 24	81.13 118	86.48 112

fmvsm_l_conf0.5_n_3		73.23 98	73.13 98	73.55 167	74.40 306	55.13 142	78.97 131	74.96 297	56.64 210	74.76 72	88.75 72	55.02 67	78.77 298	76.33 41	78.31 185	86.74 100

fmvsm_s_conf0.5_n_11		73.16 99	73.35 93	72.58 191	75.48 274	52.41 206	78.84 133	76.85 250	58.64 167	73.58 97	87.25 108	54.09 79	79.47 269	76.19 44	79.27 154	85.86 141

viewmacassd2359aftdt		73.15 100	73.16 97	73.11 180	75.15 285	49.31 275	77.53 179	83.21 101	60.42 120	73.20 107	87.34 100	53.82 85	81.05 235	67.02 132	80.79 119	88.96 12

UA-Net		73.13 101	72.93 100	73.76 152	83.58 72	51.66 220	78.75 134	77.66 231	67.75 4	72.61 126	89.42 56	49.82 151	83.29 166	53.61 266	83.14 89	86.32 124

EPNet		73.09 102	72.16 113	75.90 80	75.95 264	56.28 115	83.05 67	72.39 333	66.53 10	65.27 269	87.00 114	50.40 143	85.47 119	62.48 184	86.32 64	85.94 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 103	72.59 106	74.27 126	71.28 372	55.88 125	78.21 155	75.56 280	54.31 279	74.86 68	87.80 89	54.72 71	80.23 257	78.07 26	78.48 180	86.70 101

balanced_ft_v1		72.98 104	72.55 107	74.27 126	79.52 141	50.64 238	77.78 170	83.29 97	56.76 207	67.88 210	85.95 157	49.42 159	85.29 124	68.64 103	83.76 86	86.87 94

nrg030		72.96 105	73.01 99	72.84 186	75.41 277	50.24 252	80.02 112	82.89 115	58.36 174	74.44 77	86.73 124	58.90 29	80.83 242	65.84 145	74.46 242	87.44 72

viewmanbaseed2359cas		72.92 106	72.89 101	73.00 182	75.16 283	49.25 278	77.25 191	83.11 109	59.52 151	72.93 118	86.63 129	54.11 78	80.98 236	66.63 136	80.67 123	88.76 23

test_fmvsmconf0.1_n		72.81 107	72.33 111	74.24 128	69.89 396	55.81 126	78.22 154	75.40 285	54.17 281	75.00 63	88.03 85	53.82 85	80.23 257	78.08 25	78.34 184	86.69 102

CPTT-MVS		72.78 108	72.08 115	74.87 104	84.88 61	61.41 26	84.15 54	77.86 227	55.27 249	67.51 223	88.08 81	41.93 257	81.85 213	69.04 102	80.01 136	81.35 296

LPG-MVS_test		72.74 109	71.74 120	75.76 84	80.22 124	57.51 97	82.55 78	83.40 90	61.32 97	66.67 241	87.33 101	39.15 298	86.59 80	67.70 119	77.30 203	83.19 251

h-mvs33		72.71 110	71.49 124	76.40 73	81.99 93	59.58 57	76.92 203	76.74 256	60.40 121	74.81 69	85.95 157	45.54 211	85.76 110	70.41 95	70.61 308	83.86 227

fmvsm_s_conf0.5_n_5		72.69 111	72.80 103	72.37 201	74.11 316	53.21 180	78.12 157	73.31 321	53.98 284	76.81 46	88.05 82	53.38 93	77.37 328	76.64 38	80.78 120	86.53 110

GDP-MVS		72.64 112	71.28 131	76.70 65	77.72 203	54.22 155	79.57 124	84.45 49	55.30 248	71.38 145	86.97 115	39.94 284	87.00 71	67.02 132	79.20 158	88.89 14

PAPM_NR		72.63 113	71.80 118	75.13 99	81.72 97	53.42 175	79.91 116	83.28 99	59.14 156	66.31 248	85.90 159	51.86 120	86.06 101	57.45 231	80.62 124	85.91 138

fmvsm_s_conf0.5_n_6		72.59 114	72.87 102	71.73 216	75.14 286	51.96 215	76.28 219	77.12 244	57.63 193	73.85 92	86.91 116	51.54 127	77.87 314	77.18 32	80.18 135	85.37 171

VDD-MVS		72.50 115	72.09 114	73.75 154	81.58 98	49.69 268	77.76 172	77.63 232	63.21 54	73.21 106	89.02 62	42.14 253	83.32 165	61.72 191	82.50 102	88.25 37

3Dnovator	64.47 5	72.49 116	71.39 127	75.79 83	77.70 204	58.99 77	80.66 104	83.15 106	62.24 79	65.46 265	86.59 132	42.38 252	85.52 115	59.59 211	84.72 72	82.85 261

MGCFI-Net		72.45 117	73.34 94	69.81 274	77.77 201	43.21 359	75.84 234	81.18 151	59.59 149	75.45 54	86.64 127	57.74 33	77.94 309	63.92 162	81.90 109	88.30 35

MVS_Test		72.45 117	72.46 109	72.42 200	74.88 288	48.50 293	76.28 219	83.14 107	59.40 152	72.46 128	84.68 185	55.66 62	81.12 231	65.98 144	79.66 144	87.63 64

EI-MVSNet-Vis-set		72.42 119	71.59 121	74.91 102	78.47 173	54.02 157	77.05 197	79.33 186	65.03 18	71.68 139	79.35 318	52.75 104	84.89 133	66.46 137	74.23 246	85.83 144

viewdifsd2359ckpt13		72.40 120	71.79 119	74.22 130	75.63 269	51.77 219	78.67 137	83.13 108	57.08 200	71.59 141	85.36 176	53.10 99	82.64 197	63.07 178	78.51 179	88.24 38

ACMP	63.53 6	72.30 121	71.20 133	75.59 92	80.28 122	57.54 95	82.74 74	82.84 116	60.58 116	65.24 273	86.18 147	39.25 296	86.03 103	66.95 134	76.79 211	83.22 249
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 122	71.21 132	75.31 95	78.50 171	55.93 123	81.63 90	82.12 124	56.24 226	70.02 164	85.68 168	47.05 194	84.34 144	65.27 150	74.41 245	85.67 154

Vis-MVSNet		72.18 123	71.37 128	74.61 113	81.29 105	55.41 137	80.90 100	78.28 222	60.73 112	69.23 182	88.09 80	44.36 231	82.65 196	57.68 229	81.75 113	85.77 148
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 124	71.50 123	74.16 132	67.96 425	55.58 134	78.06 161	74.67 300	54.19 280	74.54 76	88.23 75	50.35 145	80.24 256	78.07 26	77.46 198	86.65 106

API-MVS		72.17 124	71.41 126	74.45 121	81.95 94	57.22 100	84.03 56	80.38 169	59.89 142	68.40 193	82.33 250	49.64 154	87.83 51	51.87 280	84.16 82	78.30 357

EPP-MVSNet		72.16 126	71.31 130	74.71 107	78.68 165	49.70 266	82.10 86	81.65 131	60.40 121	65.94 255	85.84 161	51.74 124	86.37 91	55.93 242	79.55 147	88.07 48

DP-MVS Recon		72.15 127	70.73 142	76.40 73	86.57 25	57.99 89	81.15 98	82.96 111	57.03 203	66.78 236	85.56 169	44.50 229	88.11 43	51.77 282	80.23 134	83.10 256

fmvsm_s_conf0.5_n_4		72.04 128	71.85 117	72.58 191	73.74 321	52.49 202	76.69 210	72.42 332	56.42 221	75.32 55	87.04 113	52.13 116	78.01 308	79.29 12	73.65 256	87.26 82

EI-MVSNet-UG-set		71.92 129	71.06 136	74.52 119	77.98 195	53.56 168	76.62 211	79.16 187	64.40 29	71.18 146	78.95 323	52.19 114	84.66 140	65.47 148	73.57 259	85.32 173

viewdifsd2359ckpt07		71.90 130	71.97 116	71.69 219	74.81 292	48.08 301	75.30 242	80.49 166	60.00 136	71.63 140	86.33 143	56.34 47	79.25 274	65.40 149	77.41 199	87.76 59

VDDNet		71.81 131	71.33 129	73.26 178	82.80 84	47.60 310	78.74 135	75.27 287	59.59 149	72.94 117	89.40 57	41.51 270	83.91 153	58.75 223	82.99 92	88.26 36

EIA-MVS		71.78 132	70.60 144	75.30 96	79.85 133	53.54 169	77.27 190	83.26 100	57.92 185	66.49 243	79.39 316	52.07 117	86.69 78	60.05 205	79.14 163	85.66 155

LFMVS		71.78 132	71.59 121	72.32 202	83.40 76	46.38 319	79.75 119	71.08 342	64.18 34	72.80 122	88.64 73	42.58 249	83.72 156	57.41 232	84.49 77	86.86 95

test_fmvsm_n_1920		71.73 134	71.14 134	73.50 168	72.52 343	56.53 112	75.60 236	76.16 265	48.11 381	77.22 41	85.56 169	53.10 99	77.43 325	74.86 57	77.14 205	86.55 109

PAPR		71.72 135	70.82 140	74.41 122	81.20 109	51.17 223	79.55 125	83.33 95	55.81 234	66.93 235	84.61 190	50.95 137	86.06 101	55.79 245	79.20 158	86.00 134

IS-MVSNet		71.57 136	71.00 137	73.27 177	78.86 159	45.63 330	80.22 109	78.69 201	64.14 37	66.46 244	87.36 99	49.30 161	85.60 112	50.26 293	83.71 88	88.59 27

MAR-MVS		71.51 137	70.15 155	75.60 91	81.84 95	59.39 60	81.38 95	82.90 113	54.90 267	68.08 206	78.70 324	47.73 180	85.51 116	51.68 284	84.17 81	81.88 284
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		71.50 138	70.38 149	74.88 103	78.76 162	57.15 105	82.79 72	78.48 212	51.26 335	69.49 173	83.22 227	43.99 235	83.24 167	66.06 140	79.37 148	84.23 211

RRT-MVS		71.46 139	70.70 143	73.74 155	77.76 202	49.30 276	76.60 212	80.45 167	61.25 100	68.17 198	84.78 182	44.64 227	84.90 132	64.79 153	77.88 191	87.03 89

PVSNet_Blended_VisFu		71.45 140	70.39 148	74.65 111	82.01 91	58.82 80	79.93 115	80.35 170	55.09 255	65.82 261	82.16 258	49.17 164	82.64 197	60.34 203	78.62 177	82.50 272

OMC-MVS		71.40 141	70.60 144	73.78 150	76.60 253	53.15 181	79.74 120	79.78 176	58.37 173	68.75 187	86.45 139	45.43 215	80.60 246	62.58 182	77.73 192	87.58 68

KinetiMVS		71.26 142	70.16 154	74.57 116	74.59 300	52.77 194	75.91 231	81.20 150	60.72 113	69.10 185	85.71 167	41.67 265	83.53 161	63.91 164	78.62 177	87.42 73

UniMVSNet_NR-MVSNet		71.11 143	71.00 137	71.44 229	79.20 149	44.13 345	76.02 229	82.60 118	66.48 11	68.20 196	84.60 193	56.82 42	82.82 192	54.62 256	70.43 310	87.36 80

hse-mvs2		71.04 144	69.86 158	74.60 114	79.58 138	57.12 107	73.96 274	75.25 288	60.40 121	74.81 69	81.95 263	45.54 211	82.90 185	70.41 95	66.83 364	83.77 232

diffmvs_AUTHOR		71.02 145	70.87 139	71.45 228	69.89 396	48.97 284	73.16 296	78.33 221	57.79 190	72.11 134	85.26 177	51.84 121	77.89 313	71.00 92	78.47 182	87.49 70

GeoE		71.01 146	70.15 155	73.60 165	79.57 139	52.17 208	78.93 132	78.12 224	58.02 180	67.76 220	83.87 210	52.36 111	82.72 194	56.90 234	75.79 226	85.92 137

fmvsm_l_conf0.5_n		70.99 147	70.82 140	71.48 225	71.45 365	54.40 151	77.18 193	70.46 351	48.67 370	75.17 58	86.86 117	53.77 87	76.86 341	76.33 41	77.51 197	83.17 255

PCF-MVS	61.88 8	70.95 148	69.49 165	75.35 94	77.63 208	55.71 128	76.04 228	81.81 129	50.30 348	69.66 171	85.40 175	52.51 107	84.89 133	51.82 281	80.24 133	85.45 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 149	69.41 168	75.12 100	79.20 149	53.86 159	77.89 164	80.00 174	53.88 286	69.40 176	84.61 190	43.21 241	86.56 82	58.80 221	77.68 194	84.95 189

test_fmvsmvis_n_1920		70.84 149	70.38 149	72.22 204	71.16 373	55.39 138	75.86 232	72.21 335	49.03 365	73.28 105	86.17 148	51.83 122	77.29 330	75.80 46	78.05 188	83.98 220

114514_t		70.83 151	69.56 163	74.64 112	86.21 32	54.63 149	82.34 81	81.81 129	48.22 379	63.01 309	85.83 162	40.92 279	87.10 68	57.91 228	79.79 141	82.18 278

FIs		70.82 152	71.43 125	68.98 289	78.33 181	38.14 415	76.96 201	83.59 84	61.02 105	67.33 225	86.73 124	55.07 65	81.64 216	54.61 258	79.22 157	87.14 87

ACMM	61.98 7	70.80 153	69.73 160	74.02 141	80.59 121	58.59 83	82.68 75	82.02 126	55.46 244	67.18 230	84.39 199	38.51 307	83.17 169	60.65 201	76.10 222	80.30 326
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 154	70.43 147	71.46 226	69.45 403	48.95 285	72.93 299	78.46 214	57.27 197	71.69 138	83.97 209	51.48 129	77.92 312	70.70 94	77.95 190	87.53 69
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)		70.63 155	70.20 152	71.89 209	78.55 170	45.29 333	75.94 230	82.92 112	63.68 42	68.16 199	83.59 218	53.89 83	83.49 163	53.97 262	71.12 301	86.89 93

xiu_mvs_v2_base		70.52 156	69.75 159	72.84 186	81.21 108	55.63 131	75.11 248	78.92 194	54.92 266	69.96 167	79.68 310	47.00 198	82.09 209	61.60 194	79.37 148	80.81 311

PS-MVSNAJ		70.51 157	69.70 161	72.93 184	81.52 99	55.79 127	74.92 255	79.00 192	55.04 261	69.88 168	78.66 326	47.05 194	82.19 207	61.61 193	79.58 145	80.83 310

fmvsm_l_conf0.5_n_a		70.50 158	70.27 151	71.18 241	71.30 371	54.09 156	76.89 204	69.87 355	47.90 385	74.37 79	86.49 137	53.07 101	76.69 347	75.41 52	77.11 206	82.76 262

v2v482		70.50 158	69.45 167	73.66 160	72.62 340	50.03 258	77.58 174	80.51 165	59.90 138	69.52 172	82.14 259	47.53 185	84.88 135	65.07 152	70.17 318	86.09 132

v1144		70.42 160	69.31 169	73.76 152	73.22 328	50.64 238	77.83 168	81.43 137	58.58 169	69.40 176	81.16 278	47.53 185	85.29 124	64.01 160	70.64 306	85.34 172

SSM_0407		70.41 161	68.96 178	74.75 106	78.65 166	53.46 171	77.28 189	80.00 174	53.88 286	68.14 200	84.61 190	43.21 241	86.26 96	58.80 221	76.11 219	84.54 199

TranMVSNet+NR-MVSNet		70.36 162	70.10 157	71.17 242	78.64 169	42.97 366	76.53 214	81.16 153	66.95 6	68.53 191	85.42 174	51.61 126	83.07 170	52.32 274	69.70 331	87.46 71

v8		70.33 163	69.28 170	73.49 169	73.15 330	50.22 253	78.62 139	80.78 161	60.79 110	66.45 245	82.11 261	49.35 160	84.98 129	63.58 171	68.71 347	85.28 175

Fast-Effi-MVS+		70.28 164	69.12 174	73.73 156	78.50 171	51.50 221	75.01 251	79.46 184	56.16 228	68.59 188	79.55 313	53.97 81	84.05 148	53.34 268	77.53 196	85.65 156

X-MVStestdata		70.21 165	67.28 226	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 132	6.49 510	47.95 177	88.01 45	71.55 88	86.74 58	86.37 117

v10		70.21 165	69.02 175	73.81 149	73.51 324	50.92 229	78.74 135	81.39 138	60.05 135	66.39 246	81.83 266	47.58 184	85.41 122	62.80 181	68.86 346	85.09 183

Elysia		70.19 167	68.29 198	75.88 81	74.15 313	54.33 153	78.26 145	83.21 101	55.04 261	67.28 226	83.59 218	30.16 402	86.11 99	63.67 169	79.26 155	87.20 84

StellarMVS		70.19 167	68.29 198	75.88 81	74.15 313	54.33 153	78.26 145	83.21 101	55.04 261	67.28 226	83.59 218	30.16 402	86.11 99	63.67 169	79.26 155	87.20 84

QAPM		70.05 169	68.81 182	73.78 150	76.54 255	53.43 174	83.23 65	83.48 86	52.89 303	65.90 257	86.29 144	41.55 269	86.49 88	51.01 287	78.40 183	81.42 290

DU-MVS		70.01 170	69.53 164	71.44 229	78.05 192	44.13 345	75.01 251	81.51 135	64.37 30	68.20 196	84.52 194	49.12 167	82.82 192	54.62 256	70.43 310	87.37 78

AdaColmap		69.99 171	68.66 186	73.97 145	84.94 58	57.83 91	82.63 76	78.71 200	56.28 225	64.34 288	84.14 203	41.57 267	87.06 70	46.45 332	78.88 167	77.02 378

v1192		69.97 172	68.68 185	73.85 147	73.19 329	50.94 227	77.68 173	81.36 140	57.51 195	68.95 186	80.85 288	45.28 218	85.33 123	62.97 180	70.37 312	85.27 176

Anonymous20240529		69.91 173	69.02 175	72.56 193	80.19 127	47.65 308	77.56 176	80.99 157	55.45 245	69.88 168	86.76 120	39.24 297	82.18 208	54.04 261	77.10 207	87.85 54

patch_mono-2		69.85 174	71.09 135	66.16 337	79.11 154	54.80 148	71.97 319	74.31 305	53.50 295	70.90 150	84.17 202	57.63 36	63.31 432	66.17 139	82.02 107	80.38 321

fmvsm_s_conf0.5_n_2		69.82 175	69.27 171	71.46 226	72.00 355	51.08 224	73.30 289	67.79 374	55.06 260	75.24 57	87.51 92	44.02 234	77.00 337	75.67 48	72.86 274	86.31 127

FA-MVS(test-final)		69.82 175	68.48 189	73.84 148	78.44 174	50.04 257	75.58 239	78.99 193	58.16 176	67.59 221	82.14 259	42.66 247	85.63 111	56.60 235	76.19 218	85.84 143

FC-MVSNet-test		69.80 177	70.58 146	67.46 312	77.61 213	34.73 449	76.05 227	83.19 105	60.84 109	65.88 259	86.46 138	54.52 74	80.76 245	52.52 273	78.12 187	86.91 92

v144192		69.71 178	68.51 188	73.33 176	73.10 331	50.13 255	77.54 177	80.64 162	56.65 209	68.57 190	80.55 291	46.87 199	84.96 131	62.98 179	69.66 332	84.89 191

test_yl		69.69 179	69.13 172	71.36 235	78.37 178	45.74 326	74.71 259	80.20 171	57.91 186	70.01 165	83.83 211	42.44 250	82.87 188	54.97 252	79.72 142	85.48 161

DCV-MVSNet		69.69 179	69.13 172	71.36 235	78.37 178	45.74 326	74.71 259	80.20 171	57.91 186	70.01 165	83.83 211	42.44 250	82.87 188	54.97 252	79.72 142	85.48 161

VNet		69.68 181	70.19 153	68.16 302	79.73 135	41.63 381	70.53 344	77.38 238	60.37 124	70.69 151	86.63 129	51.08 135	77.09 333	53.61 266	81.69 115	85.75 150

jason		69.65 182	68.39 195	73.43 173	78.27 183	56.88 109	77.12 195	73.71 317	46.53 405	69.34 178	83.22 227	43.37 239	79.18 276	64.77 154	79.20 158	84.23 211
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 183	69.01 177	71.52 224	71.66 360	51.04 225	73.39 288	67.14 380	55.02 264	75.11 59	87.64 91	42.94 246	77.01 336	75.55 50	72.63 280	86.52 111

Effi-MVS+-dtu		69.64 183	67.53 216	75.95 79	76.10 262	62.29 15	80.20 110	76.06 269	59.83 143	65.26 272	77.09 359	41.56 268	84.02 151	60.60 202	71.09 304	81.53 289

fmvsm_s_conf0.5_n		69.58 185	68.84 181	71.79 214	72.31 351	52.90 187	77.90 163	62.43 425	49.97 353	72.85 121	85.90 159	52.21 113	76.49 350	75.75 47	70.26 317	85.97 135

lupinMVS		69.57 186	68.28 200	73.44 172	78.76 162	57.15 105	76.57 213	73.29 323	46.19 408	69.49 173	82.18 255	43.99 235	79.23 275	64.66 155	79.37 148	83.93 222

fmvsm_s_conf0.5_n_7		69.54 187	69.67 162	69.15 288	73.47 326	51.41 222	70.35 348	73.34 320	57.05 202	68.41 192	85.83 162	49.86 150	72.84 371	71.86 84	76.83 210	83.19 251

fmvsm_s_conf0.5_n_a		69.54 187	68.74 184	71.93 208	72.47 345	53.82 161	78.25 147	62.26 427	49.78 355	73.12 113	86.21 146	52.66 105	76.79 343	75.02 56	68.88 344	85.18 178

NR-MVSNet		69.54 187	68.85 180	71.59 223	78.05 192	43.81 350	74.20 270	80.86 160	65.18 14	62.76 313	84.52 194	52.35 112	83.59 160	50.96 289	70.78 305	87.37 78

MVS_111021_LR		69.50 190	68.78 183	71.65 221	78.38 176	59.33 61	74.82 257	70.11 353	58.08 177	67.83 216	84.68 185	41.96 255	76.34 354	65.62 147	77.54 195	79.30 345

v1921920		69.47 191	68.17 202	73.36 175	73.06 332	50.10 256	77.39 182	80.56 163	56.58 218	68.59 188	80.37 293	44.72 226	84.98 129	62.47 185	69.82 326	85.00 185

test_djsdf		69.45 192	67.74 209	74.58 115	74.57 302	54.92 146	82.79 72	78.48 212	51.26 335	65.41 266	83.49 223	38.37 309	83.24 167	66.06 140	69.25 339	85.56 158

fmvsm_s_conf0.1_n		69.41 193	68.60 187	71.83 211	71.07 374	52.88 190	77.85 167	62.44 424	49.58 358	72.97 116	86.22 145	51.68 125	76.48 351	75.53 51	70.10 320	86.14 130

hybrid		69.38 194	68.93 179	70.75 253	67.86 427	48.20 297	72.49 310	76.90 248	55.23 251	70.42 156	84.34 200	49.76 153	77.62 322	67.11 128	76.20 217	86.42 114

fmvsm_s_conf0.1_n_a		69.32 195	68.44 193	71.96 206	70.91 376	53.78 162	78.12 157	62.30 426	49.35 361	73.20 107	86.55 136	51.99 118	76.79 343	74.83 58	68.68 349	85.32 173

Anonymous20231211		69.28 196	68.47 191	71.73 216	80.28 122	47.18 314	79.98 113	82.37 121	54.61 272	67.24 228	84.01 207	39.43 291	82.41 204	55.45 250	72.83 275	85.62 157

EI-MVSNet		69.27 197	68.44 193	71.73 216	74.47 303	49.39 273	75.20 246	78.45 215	59.60 146	69.16 183	76.51 372	51.29 131	82.50 201	59.86 210	71.45 298	83.30 246

v1240		69.24 198	67.91 207	73.25 179	73.02 334	49.82 260	77.21 192	80.54 164	56.43 220	68.34 195	80.51 292	43.33 240	84.99 127	62.03 189	69.77 329	84.95 189

IterMVS-LS		69.22 199	68.48 189	71.43 231	74.44 305	49.40 272	76.23 221	77.55 233	59.60 146	65.85 260	81.59 273	51.28 132	81.58 219	59.87 209	69.90 325	83.30 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 200	68.38 196	71.38 233	71.57 362	48.61 290	73.22 294	73.18 324	57.65 191	70.67 152	84.73 183	50.03 147	79.80 261	63.25 174	71.10 302	85.74 151

viewmsd2359difaftdt		69.13 200	68.38 196	71.38 233	71.57 362	48.61 290	73.22 294	73.18 324	57.65 191	70.67 152	84.73 183	50.03 147	79.80 261	63.25 174	71.10 302	85.74 151

IMVS_0403		69.09 202	68.14 203	71.95 207	77.06 237	49.73 262	74.51 263	78.60 204	52.70 305	66.69 239	82.58 238	46.43 202	83.38 164	59.20 216	75.46 232	82.74 263

VPA-MVSNet		69.02 203	69.47 166	67.69 308	77.42 218	41.00 388	74.04 272	79.68 178	60.06 134	69.26 181	84.81 181	51.06 136	77.58 323	54.44 259	74.43 244	84.48 204

v7n		69.01 204	67.36 223	73.98 144	72.51 344	52.65 196	78.54 143	81.30 145	60.26 130	62.67 315	81.62 270	43.61 237	84.49 141	57.01 233	68.70 348	84.79 194

viewmambaseed2359dif		68.91 205	68.18 201	71.11 244	70.21 388	48.05 304	72.28 314	75.90 271	51.96 319	70.93 149	84.47 197	51.37 130	78.59 300	61.55 196	74.97 237	86.68 103

IMVS_0407		68.90 206	67.93 206	71.82 212	77.06 237	49.73 262	74.40 268	78.60 204	52.70 305	66.19 249	82.58 238	45.17 221	83.00 172	59.20 216	75.46 232	82.74 263

OpenMVS	61.03 9	68.85 207	67.56 213	72.70 190	74.26 311	53.99 158	81.21 97	81.34 144	52.70 305	62.75 314	85.55 171	38.86 302	84.14 146	48.41 309	83.01 91	79.97 332

XVG-OURS-SEG-HR		68.81 208	67.47 219	72.82 188	74.40 306	56.87 110	70.59 343	79.04 191	54.77 269	66.99 233	86.01 155	39.57 290	78.21 305	62.54 183	73.33 266	83.37 245

BH-RMVSNet		68.81 208	67.42 220	72.97 183	80.11 130	52.53 200	74.26 269	76.29 264	58.48 171	68.38 194	84.20 201	42.59 248	83.83 154	46.53 331	75.91 224	82.56 267

UGNet		68.81 208	67.39 221	73.06 181	78.33 181	54.47 150	79.77 118	75.40 285	60.45 119	63.22 302	84.40 198	32.71 379	80.91 241	51.71 283	80.56 128	83.81 228
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		68.76 211	67.37 222	72.90 185	74.32 309	57.22 100	70.09 352	78.81 197	55.24 250	67.79 218	85.81 165	36.54 333	78.28 304	62.04 188	75.74 227	83.19 251

V42		68.65 212	67.35 224	72.56 193	68.93 413	50.18 254	72.90 301	79.47 183	56.92 205	69.45 175	80.26 297	46.29 204	82.99 173	64.07 158	67.82 355	84.53 202

PVSNet_Blended		68.59 213	67.72 210	71.19 240	77.03 243	50.57 241	72.51 309	81.52 133	51.91 320	64.22 294	77.77 350	49.13 165	82.87 188	55.82 243	79.58 145	80.14 330

xiu_mvs_v1_base_debu		68.58 214	67.28 226	72.48 196	78.19 185	57.19 102	75.28 243	75.09 293	51.61 324	70.04 161	81.41 275	32.79 375	79.02 289	63.81 165	77.31 200	81.22 299

xiu_mvs_v1_base		68.58 214	67.28 226	72.48 196	78.19 185	57.19 102	75.28 243	75.09 293	51.61 324	70.04 161	81.41 275	32.79 375	79.02 289	63.81 165	77.31 200	81.22 299

xiu_mvs_v1_base_debi		68.58 214	67.28 226	72.48 196	78.19 185	57.19 102	75.28 243	75.09 293	51.61 324	70.04 161	81.41 275	32.79 375	79.02 289	63.81 165	77.31 200	81.22 299

PVSNet_BlendedMVS		68.56 217	67.72 210	71.07 246	77.03 243	50.57 241	74.50 264	81.52 133	53.66 294	64.22 294	79.72 309	49.13 165	82.87 188	55.82 243	73.92 250	79.77 340

dtuplus		68.48 218	67.76 208	70.63 257	70.33 387	48.09 300	72.62 305	75.88 273	52.33 313	71.09 147	84.66 187	50.09 146	77.93 311	58.02 227	74.82 240	85.87 140

WR-MVS		68.47 219	68.47 191	68.44 297	80.20 126	39.84 397	73.75 282	76.07 268	64.68 24	68.11 204	83.63 217	50.39 144	79.14 281	49.78 294	69.66 332	86.34 119

mvsmamba		68.47 219	66.56 241	74.21 131	79.60 137	52.95 185	74.94 254	75.48 283	52.09 318	60.10 348	83.27 226	36.54 333	84.70 137	59.32 215	77.69 193	84.99 187

AUN-MVS		68.45 221	66.41 248	74.57 116	79.53 140	57.08 108	73.93 277	75.23 289	54.44 277	66.69 239	81.85 265	37.10 327	82.89 186	62.07 187	66.84 363	83.75 233

c3_l		68.33 222	67.56 213	70.62 258	70.87 377	46.21 322	74.47 265	78.80 198	56.22 227	66.19 249	78.53 331	51.88 119	81.40 223	62.08 186	69.04 342	84.25 210

BH-untuned		68.27 223	67.29 225	71.21 239	79.74 134	53.22 179	76.06 226	77.46 236	57.19 198	66.10 252	81.61 271	45.37 217	83.50 162	45.42 350	76.68 213	76.91 382

jajsoiax		68.25 224	66.45 244	73.66 160	75.62 270	55.49 136	80.82 101	78.51 211	52.33 313	64.33 289	84.11 204	28.28 423	81.81 215	63.48 172	70.62 307	83.67 236

LuminaMVS		68.24 225	66.82 238	72.51 195	73.46 327	53.60 167	76.23 221	78.88 195	52.78 304	68.08 206	80.13 299	32.70 380	81.41 222	63.16 177	75.97 223	82.53 269

v148		68.24 225	67.19 233	71.40 232	70.43 384	47.77 307	75.76 235	77.03 246	58.91 160	67.36 224	80.10 301	48.60 172	81.89 212	60.01 206	66.52 367	84.53 202

CANet_DTU		68.18 227	67.71 212	69.59 277	74.83 291	46.24 321	78.66 138	76.85 250	59.60 146	63.45 300	82.09 262	35.25 343	77.41 326	59.88 208	78.76 172	85.14 179

mvs_tets		68.18 227	66.36 250	73.63 163	75.61 271	55.35 140	80.77 102	78.56 209	52.48 312	64.27 291	84.10 205	27.45 432	81.84 214	63.45 173	70.56 309	83.69 235

guyue		68.10 229	67.23 232	70.71 256	73.67 323	49.27 277	73.65 284	76.04 270	55.62 241	67.84 215	82.26 253	41.24 275	78.91 296	61.01 199	73.72 254	83.94 221

SDMVSNet		68.03 230	68.10 205	67.84 304	77.13 232	48.72 289	65.32 400	79.10 188	58.02 180	65.08 276	82.55 243	47.83 179	73.40 368	63.92 162	73.92 250	81.41 291

miper_ehance_all_eth		68.03 230	67.24 230	70.40 262	70.54 381	46.21 322	73.98 273	78.68 202	55.07 258	66.05 253	77.80 347	52.16 115	81.31 226	61.53 197	69.32 336	83.67 236

mvs_anonymous		68.03 230	67.51 217	69.59 277	72.08 353	44.57 342	71.99 318	75.23 289	51.67 322	67.06 232	82.57 242	54.68 72	77.94 309	56.56 238	75.71 228	86.26 129

ET-MVSNet_ETH3D		67.96 233	65.72 262	74.68 109	76.67 251	55.62 133	75.11 248	74.74 298	52.91 302	60.03 350	80.12 300	33.68 364	82.64 197	61.86 190	76.34 215	85.78 145

thisisatest0530		67.92 234	65.78 261	74.33 124	76.29 259	51.03 226	76.89 204	74.25 308	53.67 293	65.59 263	81.76 268	35.15 344	85.50 117	55.94 241	72.47 281	86.47 113

PAPM		67.92 234	66.69 240	71.63 222	78.09 190	49.02 281	77.09 196	81.24 149	51.04 340	60.91 342	83.98 208	47.71 181	84.99 127	40.81 390	79.32 152	80.90 309

AstraMVS		67.86 236	66.83 237	70.93 249	73.50 325	49.34 274	73.28 292	74.01 312	55.45 245	68.10 205	83.28 225	38.93 301	79.14 281	63.22 176	71.74 293	84.30 209

tttt0517		67.83 237	65.66 263	74.33 124	76.69 249	50.82 231	77.86 166	73.99 313	54.54 275	64.64 286	82.53 246	35.06 345	85.50 117	55.71 246	69.91 324	86.67 104

mamba_0408		67.78 238	65.42 267	74.85 105	78.65 166	53.46 171	50.83 474	79.09 189	53.75 289	68.14 200	83.83 211	41.79 263	86.56 82	56.58 236	76.11 219	84.54 199

tt0805		67.77 239	67.24 230	69.34 282	74.87 289	40.08 394	77.36 183	81.37 139	55.31 247	66.33 247	84.65 188	37.35 321	82.55 200	55.65 248	72.28 286	85.39 170

ECVR-MVS		67.72 240	67.51 217	68.35 298	79.46 142	36.29 438	74.79 258	66.93 382	58.72 163	67.19 229	88.05 82	36.10 335	81.38 224	52.07 277	84.25 79	87.39 76

eth_miper_zixun_eth		67.63 241	66.28 254	71.67 220	71.60 361	48.33 295	73.68 283	77.88 226	55.80 235	65.91 256	78.62 329	47.35 191	82.88 187	59.45 212	66.25 368	83.81 228

UniMVSNet_ETH3D		67.60 242	67.07 235	69.18 286	77.39 219	42.29 372	74.18 271	75.59 279	60.37 124	66.77 237	86.06 152	37.64 317	78.93 294	52.16 276	73.49 261	86.32 124

VPNet		67.52 243	68.11 204	65.74 347	79.18 151	36.80 430	72.17 316	72.83 329	62.04 86	67.79 218	85.83 162	48.88 169	76.60 349	51.30 285	72.97 273	83.81 228

cl22		67.47 244	66.45 244	70.54 260	69.85 398	46.49 318	73.85 280	77.35 239	55.07 258	65.51 264	77.92 340	47.64 183	81.10 232	61.58 195	69.32 336	84.01 219

Fast-Effi-MVS+-dtu		67.37 245	65.33 271	73.48 170	72.94 335	57.78 93	77.47 180	76.88 249	57.60 194	61.97 327	76.85 363	39.31 294	80.49 251	54.72 255	70.28 316	82.17 280

MVS		67.37 245	66.33 251	70.51 261	75.46 275	50.94 227	73.95 275	81.85 128	41.57 446	62.54 319	78.57 330	47.98 176	85.47 119	52.97 271	82.05 106	75.14 399

test1111		67.21 247	67.14 234	67.42 313	79.24 148	34.76 448	73.89 279	65.65 392	58.71 165	66.96 234	87.95 86	36.09 336	80.53 248	52.03 278	83.79 85	86.97 91

GBi-Net		67.21 247	66.55 242	69.19 283	77.63 208	43.33 356	77.31 184	77.83 228	56.62 213	65.04 278	82.70 233	41.85 260	80.33 253	47.18 323	72.76 276	83.92 223

test1		67.21 247	66.55 242	69.19 283	77.63 208	43.33 356	77.31 184	77.83 228	56.62 213	65.04 278	82.70 233	41.85 260	80.33 253	47.18 323	72.76 276	83.92 223

cl____		67.18 250	66.26 255	69.94 269	70.20 389	45.74 326	73.30 289	76.83 252	55.10 253	65.27 269	79.57 312	47.39 189	80.53 248	59.41 214	69.22 340	83.53 242

DIV-MVS_self_test		67.18 250	66.26 255	69.94 269	70.20 389	45.74 326	73.29 291	76.83 252	55.10 253	65.27 269	79.58 311	47.38 190	80.53 248	59.43 213	69.22 340	83.54 241

MVSTER		67.16 252	65.58 265	71.88 210	70.37 386	49.70 266	70.25 350	78.45 215	51.52 327	69.16 183	80.37 293	38.45 308	82.50 201	60.19 204	71.46 297	83.44 244

miper_enhance_ethall		67.11 253	66.09 257	70.17 266	69.21 407	45.98 324	72.85 302	78.41 218	51.38 332	65.65 262	75.98 382	51.17 134	81.25 227	60.82 200	69.32 336	83.29 248

Baseline_NR-MVSNet		67.05 254	67.56 213	65.50 351	75.65 268	37.70 421	75.42 240	74.65 301	59.90 138	68.14 200	83.15 230	49.12 167	77.20 331	52.23 275	69.78 327	81.60 286

WR-MVS_H		67.02 255	66.92 236	67.33 316	77.95 196	37.75 419	77.57 175	82.11 125	62.03 87	62.65 316	82.48 247	50.57 142	79.46 270	42.91 376	64.01 385	84.79 194

anonymousdsp		67.00 256	64.82 276	73.57 166	70.09 392	56.13 118	76.35 217	77.35 239	48.43 376	64.99 281	80.84 289	33.01 372	80.34 252	64.66 155	67.64 357	84.23 211

FMVSNet2		66.93 257	66.31 253	68.79 292	77.63 208	42.98 365	76.11 224	77.47 234	56.62 213	65.22 275	82.17 257	41.85 260	80.18 259	47.05 329	72.72 279	83.20 250

BH-w/o		66.85 258	65.83 260	69.90 272	79.29 144	52.46 203	74.66 261	76.65 257	54.51 276	64.85 283	78.12 334	45.59 210	82.95 179	43.26 372	75.54 230	74.27 414

Anonymous202405211		66.84 259	65.99 258	69.40 281	80.19 127	42.21 374	71.11 334	71.31 341	58.80 162	67.90 208	86.39 140	29.83 407	79.65 264	49.60 300	78.78 170	86.33 122

CDS-MVSNet		66.80 260	65.37 269	71.10 245	78.98 156	53.13 183	73.27 293	71.07 343	52.15 316	64.72 284	80.23 298	43.56 238	77.10 332	45.48 348	78.88 167	83.05 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 261	65.27 272	71.33 238	79.16 153	53.67 164	73.84 281	69.59 359	52.32 315	65.28 268	81.72 269	44.49 230	77.40 327	42.32 380	78.66 176	82.92 258

FMVSNet1		66.70 262	65.87 259	69.19 283	77.49 216	43.33 356	77.31 184	77.83 228	56.45 219	64.60 287	82.70 233	38.08 315	80.33 253	46.08 337	72.31 285	83.92 223

ab-mvs		66.65 263	66.42 247	67.37 314	76.17 261	41.73 378	70.41 347	76.14 267	53.99 283	65.98 254	83.51 222	49.48 156	76.24 355	48.60 307	73.46 263	84.14 215

PEN-MVS		66.60 264	66.45 244	67.04 318	77.11 236	36.56 432	77.03 198	80.42 168	62.95 59	62.51 321	84.03 206	46.69 200	79.07 284	44.22 358	63.08 398	85.51 160

TAPA-MVS	59.36 10	66.60 264	65.20 273	70.81 251	76.63 252	48.75 287	76.52 215	80.04 173	50.64 345	65.24 273	84.93 179	39.15 298	78.54 301	36.77 417	76.88 209	85.14 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 266	65.07 274	71.17 242	79.18 151	49.63 270	73.48 285	75.20 291	52.95 301	67.90 208	80.33 296	39.81 288	83.68 157	43.20 373	73.56 260	80.20 328

CP-MVSNet		66.49 267	66.41 248	66.72 321	77.67 206	36.33 435	76.83 208	79.52 182	62.45 72	62.54 319	83.47 224	46.32 203	78.37 302	45.47 349	63.43 394	85.45 165

PS-CasMVS		66.42 268	66.32 252	66.70 323	77.60 214	36.30 437	76.94 202	79.61 180	62.36 74	62.43 324	83.66 216	45.69 207	78.37 302	45.35 351	63.26 396	85.42 168

icg_test_0407_2		66.41 269	66.75 239	65.37 355	77.06 237	49.73 262	63.79 416	78.60 204	52.70 305	66.19 249	82.58 238	45.17 221	63.65 431	59.20 216	75.46 232	82.74 263

VortexMVS		66.41 269	65.50 266	69.16 287	73.75 319	48.14 298	73.41 287	78.28 222	53.73 291	64.98 282	78.33 332	40.62 280	79.07 284	58.88 220	67.50 358	80.26 327

FMVSNet3		66.32 271	65.61 264	68.46 296	76.48 256	42.34 371	74.98 253	77.15 243	55.83 233	65.04 278	81.16 278	39.91 285	80.14 260	47.18 323	72.76 276	82.90 260

ACMH+	57.40 11	66.12 272	64.06 281	72.30 203	77.79 200	52.83 192	80.39 105	78.03 225	57.30 196	57.47 385	82.55 243	27.68 430	84.17 145	45.54 344	69.78 327	79.90 334

cascas		65.98 273	63.42 295	73.64 162	77.26 223	52.58 199	72.26 315	77.21 242	48.56 372	61.21 339	74.60 397	32.57 386	85.82 109	50.38 292	76.75 212	82.52 271

FE-MVS		65.91 274	63.33 297	73.63 163	77.36 220	51.95 216	72.62 305	75.81 274	53.70 292	65.31 267	78.96 322	28.81 417	86.39 90	43.93 363	73.48 262	82.55 268

thisisatest0515		65.83 275	63.50 293	72.82 188	73.75 319	49.50 271	71.32 328	73.12 328	49.39 360	63.82 296	76.50 374	34.95 347	84.84 136	53.20 270	75.49 231	84.13 216

DP-MVS		65.68 276	63.66 289	71.75 215	84.93 59	56.87 110	80.74 103	73.16 326	53.06 300	59.09 364	82.35 249	36.79 332	85.94 106	32.82 441	69.96 323	72.45 429

HyFIR lowres test		65.67 277	63.01 302	73.67 159	79.97 132	55.65 130	69.07 366	75.52 281	42.68 440	63.53 299	77.95 338	40.43 282	81.64 216	46.01 338	71.91 291	83.73 234

DTE-MVSNet		65.58 278	65.34 270	66.31 333	76.06 263	34.79 446	76.43 216	79.38 185	62.55 70	61.66 334	83.83 211	45.60 209	79.15 280	41.64 388	60.88 420	85.00 185

GA-MVS		65.53 279	63.70 288	71.02 248	70.87 377	48.10 299	70.48 345	74.40 303	56.69 208	64.70 285	76.77 364	33.66 365	81.10 232	55.42 251	70.32 315	83.87 226

CNLPA		65.43 280	64.02 282	69.68 275	78.73 164	58.07 88	77.82 169	70.71 349	51.49 329	61.57 336	83.58 221	38.23 313	70.82 386	43.90 364	70.10 320	80.16 329

MVP-Stereo		65.41 281	63.80 286	70.22 263	77.62 212	55.53 135	76.30 218	78.53 210	50.59 346	56.47 397	78.65 327	39.84 287	82.68 195	44.10 362	72.12 290	72.44 430
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 282	62.73 306	73.40 174	74.89 287	52.78 193	73.09 298	75.13 292	55.69 237	58.48 373	73.73 405	32.86 374	86.32 93	50.63 290	70.11 319	81.10 304
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		65.33 283	64.61 277	67.50 309	79.46 142	34.19 454	74.43 267	51.92 465	58.72 163	66.75 238	88.05 82	25.99 445	80.92 240	51.94 279	84.25 79	87.39 76

pm-mvs1		65.24 284	64.97 275	66.04 341	72.38 348	39.40 404	72.62 305	75.63 277	55.53 242	62.35 326	83.18 229	47.45 187	76.47 352	49.06 304	66.54 366	82.24 277

ACMH	55.70 15	65.20 285	63.57 290	70.07 267	78.07 191	52.01 214	79.48 126	79.69 177	55.75 236	56.59 394	80.98 283	27.12 435	80.94 238	42.90 377	71.58 296	77.25 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 286	63.21 300	70.72 255	81.04 111	54.87 147	78.57 141	77.47 234	48.51 374	55.71 402	81.89 264	33.71 363	79.71 263	41.66 386	70.37 312	77.58 369
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 287	62.84 304	71.82 212	81.49 101	56.26 116	66.32 388	74.20 310	40.53 452	63.16 305	78.65 327	41.30 271	77.80 316	45.80 340	74.09 247	81.40 293

SSM_04072		64.98 288	65.42 267	63.68 370	78.65 166	53.46 171	50.83 474	79.09 189	53.75 289	68.14 200	83.83 211	41.79 263	53.03 476	56.58 236	76.11 219	84.54 199

TransMVSNet (Re)		64.72 289	64.33 279	65.87 346	75.22 280	38.56 410	74.66 261	75.08 296	58.90 161	61.79 330	82.63 236	51.18 133	78.07 307	43.63 369	55.87 445	80.99 308

EG-PatchMatch MVS		64.71 290	62.87 303	70.22 263	77.68 205	53.48 170	77.99 162	78.82 196	53.37 296	56.03 401	77.41 355	24.75 453	84.04 149	46.37 333	73.42 265	73.14 420

LS3D		64.71 290	62.50 308	71.34 237	79.72 136	55.71 128	79.82 117	74.72 299	48.50 375	56.62 393	84.62 189	33.59 366	82.34 205	29.65 463	75.23 236	75.97 389

IMVS_0404		64.63 292	64.22 280	65.88 345	77.06 237	49.73 262	64.40 409	78.60 204	52.70 305	53.16 435	82.58 238	34.82 348	65.16 425	59.20 216	75.46 232	82.74 263

1314		64.61 293	63.21 300	68.80 291	71.87 358	47.46 311	73.95 275	78.39 220	42.88 439	59.97 351	76.60 371	38.11 314	79.39 272	54.84 254	72.32 284	79.55 341

HY-MVS	56.14 13	64.55 294	63.89 283	66.55 329	74.73 295	41.02 385	69.96 353	74.43 302	49.29 362	61.66 334	80.92 285	47.43 188	76.68 348	44.91 355	71.69 294	81.94 282

testing91		64.46 295	63.80 286	66.47 330	78.43 175	40.06 395	67.63 377	69.59 359	59.06 157	63.18 304	78.05 336	34.05 357	76.99 338	48.30 310	75.87 225	82.37 275

sd_testset		64.46 295	64.45 278	64.51 363	77.13 232	42.25 373	62.67 423	72.11 336	58.02 180	65.08 276	82.55 243	41.22 276	69.88 394	47.32 321	73.92 250	81.41 291

XVG-ACMP-BASELINE		64.36 297	62.23 312	70.74 254	72.35 349	52.45 204	70.80 341	78.45 215	53.84 288	59.87 353	81.10 280	16.24 473	79.32 273	55.64 249	71.76 292	80.47 317

usedtu_dtu_shiyan1		64.34 298	63.57 290	66.66 325	72.44 346	40.74 391	69.60 359	76.80 254	53.21 298	61.73 332	77.92 340	41.92 258	77.68 320	46.23 334	72.25 287	81.57 287

FE-MVSNET3		64.34 298	63.57 290	66.66 325	72.44 346	40.74 391	69.60 359	76.80 254	53.21 298	61.73 332	77.92 340	41.92 258	77.68 320	46.23 334	72.25 287	81.57 287

MonoMVSNet		64.15 300	63.31 298	66.69 324	70.51 382	44.12 347	74.47 265	74.21 309	57.81 188	63.03 307	76.62 368	38.33 310	77.31 329	54.22 260	60.59 426	78.64 354

testing99		64.05 301	63.29 299	66.34 332	78.17 188	39.76 399	67.33 382	68.00 373	58.60 168	63.03 307	78.10 335	32.57 386	76.94 340	48.22 311	75.58 229	82.34 276

CostFormer		64.04 302	62.51 307	68.61 294	71.88 357	45.77 325	71.30 329	70.60 350	47.55 392	64.31 290	76.61 370	41.63 266	79.62 266	49.74 296	69.00 343	80.42 319

1112_ss		64.00 303	63.36 296	65.93 343	79.28 146	42.58 370	71.35 327	72.36 334	46.41 406	60.55 345	77.89 344	46.27 205	73.28 369	46.18 336	69.97 322	81.92 283

baseline1		63.81 304	63.87 285	63.62 371	76.29 259	36.36 433	71.78 323	67.29 378	56.05 230	64.23 293	82.95 231	47.11 193	74.41 364	47.30 322	61.85 414	80.10 331

pmmvs6		63.69 305	62.82 305	66.27 335	70.63 379	39.27 405	73.13 297	75.47 284	52.69 310	59.75 357	82.30 251	39.71 289	77.03 335	47.40 318	64.35 384	82.53 269

Vis-MVSNet (Re-imp)		63.69 305	63.88 284	63.14 376	74.75 294	31.04 472	71.16 332	63.64 413	56.32 223	59.80 355	84.99 178	44.51 228	75.46 359	39.12 402	80.62 124	82.92 258

baseline2		63.42 307	61.26 326	69.89 273	72.55 342	47.62 309	71.54 325	68.38 370	50.11 350	54.82 415	75.55 387	43.06 244	80.96 237	48.13 312	67.16 362	81.11 303

thres400		63.31 308	62.18 313	66.72 321	76.85 246	39.62 401	71.96 320	69.44 362	56.63 211	62.61 317	79.83 304	37.18 323	79.17 277	31.84 447	73.25 268	81.36 294

thres600view7		63.30 309	62.27 311	66.41 331	77.18 225	38.87 407	72.35 312	69.11 366	56.98 204	62.37 325	80.96 284	37.01 329	79.00 292	31.43 454	73.05 272	81.36 294

thres100view900		63.28 310	62.41 309	65.89 344	77.31 222	38.66 409	72.65 303	69.11 366	57.07 201	62.45 322	81.03 282	37.01 329	79.17 277	31.84 447	73.25 268	79.83 337

test_0402		63.25 311	61.01 331	69.96 268	80.00 131	54.37 152	76.86 206	72.02 337	54.58 274	58.71 367	80.79 290	35.00 346	84.36 143	26.41 476	64.71 379	71.15 448

tfpn200view9		63.18 312	62.18 313	66.21 336	76.85 246	39.62 401	71.96 320	69.44 362	56.63 211	62.61 317	79.83 304	37.18 323	79.17 277	31.84 447	73.25 268	79.83 337

LTVRE_ROB	55.42 16	63.15 313	61.23 327	68.92 290	76.57 254	47.80 305	59.92 440	76.39 261	54.35 278	58.67 369	82.46 248	29.44 411	81.49 221	42.12 381	71.14 300	77.46 370
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		63.07 314	63.49 294	61.82 384	75.16 283	31.14 471	71.89 322	73.47 318	53.34 297	58.22 376	81.81 267	45.17 221	73.86 367	37.43 411	74.87 239	80.45 318

F-COLMAP		63.05 315	60.87 335	69.58 279	76.99 245	53.63 166	78.12 157	76.16 265	47.97 384	52.41 439	81.61 271	27.87 427	78.11 306	40.07 393	66.66 365	77.00 379

testing11		62.81 316	61.90 316	65.54 349	78.38 176	40.76 390	67.59 379	66.78 384	55.48 243	60.13 347	77.11 358	31.67 393	76.79 343	45.53 345	74.45 243	79.06 348

IterMVS		62.79 317	61.27 325	67.35 315	69.37 404	52.04 213	71.17 331	68.24 372	52.63 311	59.82 354	76.91 362	37.32 322	72.36 374	52.80 272	63.19 397	77.66 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
usedtu_blend_shiyan5		62.63 318	60.77 336	68.20 300	68.53 418	44.64 339	73.47 286	77.00 247	51.91 320	57.10 388	69.95 437	38.83 303	79.61 267	47.44 315	62.67 401	80.37 322

reproduce_monomvs		62.56 319	61.20 328	66.62 328	70.62 380	44.30 344	70.13 351	73.13 327	54.78 268	61.13 340	76.37 375	25.63 448	75.63 358	58.75 223	60.29 427	79.93 333

IterMVS-SCA-FT		62.49 320	61.52 320	65.40 354	71.99 356	50.80 232	71.15 333	69.63 358	45.71 414	60.61 344	77.93 339	37.45 319	65.99 421	55.67 247	63.50 393	79.42 343

tfpnnormal		62.47 321	61.63 319	64.99 360	74.81 292	39.01 406	71.22 330	73.72 316	55.22 252	60.21 346	80.09 302	41.26 274	76.98 339	30.02 461	68.09 353	78.97 351

blended_shiyan8		62.46 322	60.71 337	67.71 306	69.15 409	43.43 354	70.83 338	76.52 258	51.49 329	57.67 381	71.36 425	39.38 292	79.07 284	47.37 319	62.67 401	80.62 315

blended_shiyan6		62.46 322	60.71 337	67.71 306	69.14 410	43.42 355	70.82 339	76.52 258	51.50 328	57.64 382	71.37 424	39.38 292	79.08 283	47.36 320	62.67 401	80.65 314

gbinet_0.2-2-1-0.02		62.43 324	60.41 340	68.49 295	68.91 414	43.71 351	71.73 324	75.89 272	52.10 317	58.33 374	69.67 444	36.86 331	80.59 247	47.18 323	63.05 399	81.16 302

MS-PatchMatch		62.42 325	61.46 321	65.31 357	75.21 281	52.10 210	72.05 317	74.05 311	46.41 406	57.42 387	74.36 398	34.35 354	77.57 324	45.62 343	73.67 255	66.26 466

Test_1112_low_res		62.32 326	61.77 317	64.00 368	79.08 155	39.53 403	68.17 373	70.17 352	43.25 434	59.03 365	79.90 303	44.08 232	71.24 384	43.79 366	68.42 350	81.25 298

D2MVS		62.30 327	60.29 342	68.34 299	66.46 439	48.42 294	65.70 392	73.42 319	47.71 389	58.16 377	75.02 393	30.51 397	77.71 319	53.96 263	71.68 295	78.90 352

testing222		62.29 328	61.31 324	65.25 358	77.87 197	38.53 411	68.34 371	66.31 388	56.37 222	63.15 306	77.58 353	28.47 419	76.18 357	37.04 415	76.65 214	81.05 307

thres200		62.20 329	61.16 329	65.34 356	75.38 278	39.99 396	69.60 359	69.29 364	55.64 240	61.87 329	76.99 360	37.07 328	78.96 293	31.28 455	73.28 267	77.06 377

tpm2		62.07 330	60.10 345	67.99 303	72.79 337	43.86 349	71.05 336	66.85 383	43.14 436	62.77 312	75.39 391	38.32 311	80.80 243	41.69 385	68.88 344	79.32 344

testing3-2		62.06 331	62.36 310	61.17 392	79.29 144	30.31 474	64.09 415	63.49 414	63.50 44	62.84 310	82.22 254	32.35 390	69.02 398	40.01 396	73.43 264	84.17 214

miper_lstm_enhance		62.03 332	60.88 333	65.49 352	66.71 436	46.25 320	56.29 458	75.70 276	50.68 343	61.27 338	75.48 389	40.21 283	68.03 404	56.31 240	65.25 375	82.18 278

FE-MVSNET2		62.01 333	60.88 333	65.42 353	68.74 415	38.43 413	72.92 300	77.39 237	54.74 271	55.40 407	76.71 365	35.46 341	76.72 346	44.25 357	62.31 410	81.10 304

wanda-best-256-512		62.00 334	60.17 343	67.49 310	68.53 418	43.07 363	69.65 356	76.38 262	51.26 335	57.10 388	69.95 437	38.83 303	79.04 287	47.14 327	62.67 401	80.37 322

FE-blended-shiyan7		62.00 334	60.17 343	67.49 310	68.53 418	43.07 363	69.65 356	76.38 262	51.26 335	57.10 388	69.95 437	38.83 303	79.04 287	47.14 327	62.67 401	80.37 322

EPNet_dtu		61.90 336	61.97 315	61.68 385	72.89 336	39.78 398	75.85 233	65.62 393	55.09 255	54.56 420	79.36 317	37.59 318	67.02 413	39.80 398	76.95 208	78.25 358
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 337	61.35 323	63.46 372	74.58 301	31.48 470	61.42 431	58.14 443	58.71 165	53.02 437	79.55 313	43.07 243	76.80 342	45.69 341	77.96 189	82.11 281

MSDG		61.81 338	59.23 350	69.55 280	72.64 339	52.63 198	70.45 346	75.81 274	51.38 332	53.70 427	76.11 377	29.52 409	81.08 234	37.70 409	65.79 372	74.93 404

SixPastTwentyTwo		61.65 339	58.80 357	70.20 265	75.80 265	47.22 313	75.59 237	69.68 357	54.61 272	54.11 424	79.26 319	27.07 436	82.96 177	43.27 371	49.79 466	80.41 320

CL-MVSNet_self_test		61.53 340	60.94 332	63.30 374	68.95 411	36.93 429	67.60 378	72.80 330	55.67 238	59.95 352	76.63 367	45.01 224	72.22 378	39.74 399	62.09 413	80.74 313

RPMNet		61.53 340	58.42 360	70.86 250	69.96 394	52.07 211	65.31 401	81.36 140	43.20 435	59.36 360	70.15 435	35.37 342	85.47 119	36.42 424	64.65 380	75.06 400

pmmvs4		61.48 342	59.39 349	67.76 305	71.57 362	53.86 159	71.42 326	65.34 395	44.20 425	59.46 359	77.92 340	35.90 337	74.71 362	43.87 365	64.87 378	74.71 409

blend_shiyan4		61.38 343	59.10 353	68.20 300	68.94 412	44.64 339	70.81 340	76.52 258	51.63 323	57.56 384	69.94 440	28.30 422	79.61 267	47.44 315	60.78 422	80.36 325

OurMVSNet-221017-0		61.37 344	58.63 359	69.61 276	72.05 354	48.06 302	73.93 277	72.51 331	47.23 398	54.74 416	80.92 285	21.49 463	81.24 228	48.57 308	56.22 444	79.53 342

COLMAP_ROB	52.97 17	61.27 345	58.81 355	68.64 293	74.63 298	52.51 201	78.42 144	73.30 322	49.92 354	50.96 444	81.51 274	23.06 456	79.40 271	31.63 451	65.85 370	74.01 417
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 346	61.67 318	57.70 420	70.43 384	38.45 412	64.19 412	66.47 385	48.05 383	63.22 302	80.86 287	49.28 162	60.47 441	45.25 352	67.28 361	74.19 415

myMVS_eth3d28		60.66 347	61.04 330	59.51 400	77.32 221	31.58 469	63.11 420	63.87 410	59.00 158	60.90 343	78.26 333	32.69 381	66.15 420	36.10 426	78.13 186	80.81 311

SSC-MVS3.2		60.57 348	61.39 322	58.12 416	74.29 310	32.63 464	59.52 441	65.53 394	59.90 138	62.45 322	79.75 308	41.96 255	63.90 430	39.47 400	69.65 334	77.84 366

WBMVS		60.54 349	60.61 339	60.34 397	78.00 194	35.95 441	64.55 408	64.89 398	49.63 356	63.39 301	78.70 324	33.85 362	67.65 407	42.10 382	70.35 314	77.43 371

SCA		60.49 350	58.38 361	66.80 320	74.14 315	48.06 302	63.35 419	63.23 417	49.13 364	59.33 363	72.10 416	37.45 319	74.27 365	44.17 359	62.57 407	78.05 361

K. test v3		60.47 351	57.11 369	70.56 259	73.74 321	48.22 296	75.10 250	62.55 422	58.27 175	53.62 430	76.31 376	27.81 428	81.59 218	47.42 317	39.18 481	81.88 284

mmtdpeth		60.40 352	59.12 352	64.27 366	69.59 400	48.99 282	70.67 342	70.06 354	54.96 265	62.78 311	73.26 410	27.00 437	67.66 406	58.44 226	45.29 473	76.16 388

UWE-MVS		60.18 353	59.78 346	61.39 390	77.67 206	33.92 457	69.04 367	63.82 411	48.56 372	64.27 291	77.64 352	27.20 434	70.40 391	33.56 438	76.24 216	79.83 337

OpenMVS_ROB	52.78 18	60.03 354	58.14 364	65.69 348	70.47 383	44.82 335	75.33 241	70.86 348	45.04 417	56.06 400	76.00 379	26.89 439	79.65 264	35.36 430	67.29 360	72.60 425

CR-MVSNet		59.91 355	57.90 366	65.96 342	69.96 394	52.07 211	65.31 401	63.15 418	42.48 441	59.36 360	74.84 394	35.83 338	70.75 387	45.50 346	64.65 380	75.06 400

PatchmatchNet		59.84 356	58.24 362	64.65 362	73.05 333	46.70 317	69.42 363	62.18 428	47.55 392	58.88 366	71.96 418	34.49 352	69.16 396	42.99 375	63.60 391	78.07 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 357	57.84 367	65.54 349	74.87 289	42.95 367	69.61 358	64.16 408	48.90 367	58.68 368	77.12 357	28.19 425	72.35 375	43.75 368	55.28 447	81.31 297

WTY-MVS		59.75 358	60.39 341	57.85 418	72.32 350	37.83 418	61.05 436	64.18 406	45.95 413	61.91 328	79.11 321	47.01 197	60.88 440	42.50 379	69.49 335	74.83 405

WB-MVSnew		59.66 359	59.69 347	59.56 399	75.19 282	35.78 443	69.34 364	64.28 405	46.88 402	61.76 331	75.79 383	40.61 281	65.20 424	32.16 443	71.21 299	77.70 367

CVMVSNet		59.63 360	59.14 351	61.08 394	74.47 303	38.84 408	75.20 246	68.74 368	31.15 472	58.24 375	76.51 372	32.39 388	68.58 400	49.77 295	65.84 371	75.81 391

UBG		59.62 361	59.53 348	59.89 398	78.12 189	35.92 442	64.11 414	60.81 435	49.45 359	61.34 337	75.55 387	33.05 370	67.39 411	38.68 404	74.62 241	76.35 387

ETVMVS		59.51 362	58.81 355	61.58 387	77.46 217	34.87 445	64.94 406	59.35 438	54.06 282	61.08 341	76.67 366	29.54 408	71.87 380	32.16 443	74.07 248	78.01 365

0.4-1-1-0.1		59.29 363	56.70 377	67.07 317	69.35 405	43.16 360	66.59 384	70.87 347	48.59 371	55.11 411	62.25 472	28.22 424	78.92 295	45.49 347	63.79 388	79.14 346

tpm cat1		59.25 364	56.95 372	66.15 338	72.19 352	46.96 315	68.09 374	65.76 391	40.03 456	57.81 380	70.56 430	38.32 311	74.51 363	38.26 407	61.50 417	77.00 379

test_vis1_n_1920		58.86 365	59.06 354	58.25 412	63.76 452	43.14 361	67.49 380	66.36 387	40.22 454	65.89 258	71.95 419	31.04 394	59.75 446	59.94 207	64.90 377	71.85 438

pmmvs-eth3d		58.81 366	56.31 382	66.30 334	67.61 428	52.42 205	72.30 313	64.76 400	43.55 431	54.94 414	74.19 400	28.95 414	72.60 372	43.31 370	57.21 439	73.88 418

tt0320		58.59 367	56.81 375	63.92 369	75.46 275	41.32 383	68.63 369	64.06 409	47.05 400	56.19 399	74.19 400	30.34 399	71.36 382	39.92 397	55.45 446	79.09 347

tpmvs		58.47 368	56.95 372	63.03 378	70.20 389	41.21 384	67.90 376	67.23 379	49.62 357	54.73 417	70.84 428	34.14 356	76.24 355	36.64 421	61.29 418	71.64 440

0.3-1-1-0.015		58.40 369	55.56 388	66.91 319	68.08 424	43.09 362	65.25 403	70.96 346	47.89 387	53.10 436	59.82 475	26.48 440	78.79 297	45.07 354	63.43 394	78.84 353

PVSNet	50.76 19	58.40 369	57.39 368	61.42 388	75.53 273	44.04 348	61.43 430	63.45 415	47.04 401	56.91 391	73.61 406	27.00 437	64.76 426	39.12 402	72.40 282	75.47 396

tt0320-xc		58.33 371	56.41 381	64.08 367	75.79 266	41.34 382	68.30 372	62.72 421	47.90 385	56.29 398	74.16 402	28.53 418	71.04 385	41.50 389	52.50 458	79.88 335

0.4-1-1-0.2		58.31 372	55.53 389	66.64 327	67.46 430	42.78 369	64.38 410	70.97 345	47.65 390	53.38 434	59.02 476	28.39 421	78.72 299	44.86 356	63.63 390	78.42 356

tpmrst		58.24 373	58.70 358	56.84 422	66.97 433	34.32 452	69.57 362	61.14 433	47.17 399	58.58 372	71.60 421	41.28 273	60.41 442	49.20 302	62.84 400	75.78 392

Patchmatch-RL test		58.16 374	55.49 390	66.15 338	67.92 426	48.89 286	60.66 438	51.07 469	47.86 388	59.36 360	62.71 471	34.02 359	72.27 377	56.41 239	59.40 430	77.30 373

test-LLR		58.15 375	58.13 365	58.22 413	68.57 416	44.80 336	65.46 397	57.92 444	50.08 351	55.44 405	69.82 441	32.62 383	57.44 458	49.66 298	73.62 257	72.41 431

ppachtmachnet_test		58.06 376	55.38 391	66.10 340	69.51 401	48.99 282	68.01 375	66.13 390	44.50 422	54.05 425	70.74 429	32.09 391	72.34 376	36.68 420	56.71 443	76.99 381

gg-mvs-nofinetune		57.86 377	56.43 380	62.18 382	72.62 340	35.35 444	66.57 385	56.33 453	50.65 344	57.64 382	57.10 480	30.65 396	76.36 353	37.38 412	78.88 167	74.82 406

CMPMVS	42.80 21	57.81 378	55.97 384	63.32 373	60.98 469	47.38 312	64.66 407	69.50 361	32.06 470	46.83 462	77.80 347	29.50 410	71.36 382	48.68 306	73.75 253	71.21 447
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 379	57.07 370	58.22 413	74.21 312	37.18 424	62.46 424	60.88 434	48.88 368	55.29 409	75.99 381	31.68 392	62.04 437	31.87 446	72.35 283	75.43 397

tpm		57.34 380	58.16 363	54.86 432	71.80 359	34.77 447	67.47 381	56.04 457	48.20 380	60.10 348	76.92 361	37.17 325	53.41 475	40.76 391	65.01 376	76.40 386

Patchmtry		57.16 381	56.47 379	59.23 404	69.17 408	34.58 450	62.98 421	63.15 418	44.53 421	56.83 392	74.84 394	35.83 338	68.71 399	40.03 394	60.91 419	74.39 413

AllTest		57.08 382	54.65 396	64.39 364	71.44 366	49.03 279	69.92 354	67.30 376	45.97 411	47.16 460	79.77 306	17.47 467	67.56 409	33.65 435	59.16 431	76.57 384

test_cas_vis1_n_1920		56.91 383	56.71 376	57.51 421	59.13 475	45.40 332	63.58 417	61.29 432	36.24 464	67.14 231	71.85 420	29.89 406	56.69 462	57.65 230	63.58 392	70.46 452

dmvs_re		56.77 384	56.83 374	56.61 423	69.23 406	41.02 385	58.37 446	64.18 406	50.59 346	57.45 386	71.42 422	35.54 340	58.94 451	37.23 413	67.45 359	69.87 457

testing3		56.54 385	55.92 385	58.41 411	77.52 215	27.93 482	69.72 355	56.36 452	54.75 270	58.63 371	77.80 347	20.88 464	71.75 381	25.31 478	62.25 411	75.53 395

our_test_3		56.49 386	54.42 399	62.68 380	69.51 401	45.48 331	66.08 389	61.49 431	44.11 428	50.73 448	69.60 445	33.05 370	68.15 401	38.38 406	56.86 440	74.40 412

pmmvs5		56.47 387	55.68 387	58.86 408	61.41 465	36.71 431	66.37 387	62.75 420	40.38 453	53.70 427	76.62 368	34.56 350	67.05 412	40.02 395	65.27 374	72.83 423

test-mter		56.42 388	55.82 386	58.22 413	68.57 416	44.80 336	65.46 397	57.92 444	39.94 457	55.44 405	69.82 441	21.92 459	57.44 458	49.66 298	73.62 257	72.41 431

USDC		56.35 389	54.24 403	62.69 379	64.74 448	40.31 393	65.05 404	73.83 315	43.93 429	47.58 458	77.71 351	15.36 476	75.05 361	38.19 408	61.81 415	72.70 424

PatchMatch-RL		56.25 390	54.55 398	61.32 391	77.06 237	56.07 120	65.57 394	54.10 462	44.13 427	53.49 433	71.27 427	25.20 450	66.78 414	36.52 423	63.66 389	61.12 470

sss		56.17 391	56.57 378	54.96 431	66.93 434	36.32 436	57.94 449	61.69 430	41.67 444	58.64 370	75.32 392	38.72 306	56.25 465	42.04 383	66.19 369	72.31 434

Syy-MVS		56.00 392	56.23 383	55.32 429	74.69 296	26.44 488	65.52 395	57.49 447	50.97 341	56.52 395	72.18 414	39.89 286	68.09 402	24.20 479	64.59 382	71.44 444

FMVSNet5		55.86 393	54.93 394	58.66 410	71.05 375	36.35 434	64.18 413	62.48 423	46.76 404	50.66 449	74.73 396	25.80 446	64.04 428	33.11 439	65.57 373	75.59 394

RPSCF		55.80 394	54.22 404	60.53 396	65.13 447	42.91 368	64.30 411	57.62 446	36.84 463	58.05 379	82.28 252	28.01 426	56.24 466	37.14 414	58.61 434	82.44 274

mvs5depth		55.64 395	53.81 407	61.11 393	59.39 474	40.98 389	65.89 390	68.28 371	50.21 349	58.11 378	75.42 390	17.03 469	67.63 408	43.79 366	46.21 470	74.73 408

EU-MVSNet		55.61 396	54.41 400	59.19 406	65.41 445	33.42 459	72.44 311	71.91 338	28.81 474	51.27 442	73.87 404	24.76 452	69.08 397	43.04 374	58.20 435	75.06 400

Anonymous20240521		55.30 397	54.41 400	57.96 417	60.92 471	41.73 378	71.09 335	71.06 344	41.18 447	48.65 456	73.31 408	16.93 470	59.25 448	42.54 378	64.01 385	72.90 422

TESTMET0.1,1		55.28 398	54.90 395	56.42 424	66.56 437	43.67 352	65.46 397	56.27 455	39.18 459	53.83 426	67.44 455	24.21 454	55.46 469	48.04 313	73.11 271	70.13 455

KD-MVS_self_test		55.22 399	53.89 406	59.21 405	57.80 479	27.47 484	57.75 452	74.32 304	47.38 394	50.90 445	70.00 436	28.45 420	70.30 392	40.44 392	57.92 436	79.87 336

MIMVSNet1		55.17 400	54.31 402	57.77 419	70.03 393	32.01 467	65.68 393	64.81 399	49.19 363	46.75 463	76.00 379	25.53 449	64.04 428	28.65 466	62.13 412	77.26 375

FE-MVSNET		55.16 401	53.75 408	59.41 401	65.29 446	33.20 461	67.21 383	66.21 389	48.39 378	49.56 454	73.53 407	29.03 413	72.51 373	30.38 459	54.10 453	72.52 427

Anonymous20231206		55.10 402	55.30 392	54.48 434	69.81 399	33.94 456	62.91 422	62.13 429	41.08 448	55.18 410	75.65 385	32.75 378	56.59 464	30.32 460	67.86 354	72.91 421

dtuonly		54.95 403	55.26 393	54.01 437	59.03 476	35.99 439	61.92 428	56.33 453	38.48 460	54.61 419	77.85 346	34.27 355	51.60 482	45.10 353	69.74 330	74.43 411

myMVS_eth3d		54.86 404	54.61 397	55.61 428	74.69 296	27.31 485	65.52 395	57.49 447	50.97 341	56.52 395	72.18 414	21.87 462	68.09 402	27.70 470	64.59 382	71.44 444

TinyColmap		54.14 405	51.72 417	61.40 389	66.84 435	41.97 375	66.52 386	68.51 369	44.81 418	42.69 475	75.77 384	11.66 483	72.94 370	31.96 445	56.77 442	69.27 461

EPMVS		53.96 406	53.69 409	54.79 433	66.12 442	31.96 468	62.34 426	49.05 473	44.42 424	55.54 403	71.33 426	30.22 401	56.70 461	41.65 387	62.54 408	75.71 393

PMMVS		53.96 406	53.26 412	56.04 425	62.60 459	50.92 229	61.17 434	56.09 456	32.81 469	53.51 432	66.84 460	34.04 358	59.93 445	44.14 361	68.18 352	57.27 478

test20.03		53.87 408	54.02 405	53.41 443	61.47 464	28.11 481	61.30 432	59.21 439	51.34 334	52.09 440	77.43 354	33.29 369	58.55 453	29.76 462	60.27 428	73.58 419

MDA-MVSNet-bldmvs		53.87 408	50.81 421	63.05 377	66.25 440	48.58 292	56.93 456	63.82 411	48.09 382	41.22 476	70.48 433	30.34 399	68.00 405	34.24 433	45.92 472	72.57 426

KD-MVS_2432*1600		53.45 410	51.50 419	59.30 402	62.82 456	37.14 425	55.33 459	71.79 339	47.34 396	55.09 412	70.52 431	21.91 460	70.45 389	35.72 428	42.97 476	70.31 453

miper_refine_blended		53.45 410	51.50 419	59.30 402	62.82 456	37.14 425	55.33 459	71.79 339	47.34 396	55.09 412	70.52 431	21.91 460	70.45 389	35.72 428	42.97 476	70.31 453

TDRefinement		53.44 412	50.72 423	61.60 386	64.31 451	46.96 315	70.89 337	65.27 397	41.78 442	44.61 470	77.98 337	11.52 485	66.36 418	28.57 467	51.59 460	71.49 443

usedtu_dtu_shiyan2		53.34 413	50.78 422	61.00 395	61.86 463	39.63 400	68.47 370	64.58 402	42.94 437	45.22 467	67.61 454	19.25 466	66.71 415	28.08 468	59.05 433	76.66 383

test0.0.03 1		53.32 414	53.59 410	52.50 449	62.81 458	29.45 476	59.51 442	54.11 461	50.08 351	54.40 422	74.31 399	32.62 383	55.92 467	30.50 458	63.95 387	72.15 436

PatchT		53.17 415	53.44 411	52.33 450	68.29 423	25.34 492	58.21 447	54.41 460	44.46 423	54.56 420	69.05 448	33.32 368	60.94 439	36.93 416	61.76 416	70.73 451

UnsupCasMVSNet_eth		53.16 416	52.47 413	55.23 430	59.45 473	33.39 460	59.43 443	69.13 365	45.98 410	50.35 451	72.32 413	29.30 412	58.26 455	42.02 384	44.30 474	74.05 416

PM-MVS		52.33 417	50.19 426	58.75 409	62.10 461	45.14 334	65.75 391	40.38 491	43.60 430	53.52 431	72.65 411	9.16 491	65.87 422	50.41 291	54.18 452	65.24 468

UWE-MVS-28		52.25 418	52.35 415	51.93 453	66.99 432	22.79 496	63.48 418	48.31 477	46.78 403	52.73 438	76.11 377	27.78 429	57.82 457	20.58 485	68.41 351	75.17 398

testgi		51.90 419	52.37 414	50.51 456	60.39 472	23.55 495	58.42 445	58.15 442	49.03 365	51.83 441	79.21 320	22.39 457	55.59 468	29.24 465	62.64 406	72.40 433

dp		51.89 420	51.60 418	52.77 447	68.44 422	32.45 466	62.36 425	54.57 459	44.16 426	49.31 455	67.91 450	28.87 416	56.61 463	33.89 434	54.89 449	69.24 462

JIA-IIPM		51.56 421	47.68 435	63.21 375	64.61 449	50.73 237	47.71 480	58.77 441	42.90 438	48.46 457	51.72 484	24.97 451	70.24 393	36.06 427	53.89 454	68.64 463

test_fmvs1_n		51.37 422	50.35 425	54.42 436	52.85 483	37.71 420	61.16 435	51.93 464	28.15 476	63.81 297	69.73 443	13.72 477	53.95 473	51.16 286	60.65 424	71.59 441

ADS-MVSNet2		51.33 423	48.76 430	59.07 407	66.02 443	44.60 341	50.90 472	59.76 437	36.90 461	50.74 446	66.18 463	26.38 441	63.11 433	27.17 472	54.76 450	69.50 459

test_fmvs1		51.32 424	50.48 424	53.81 439	53.57 481	37.51 422	60.63 439	51.16 467	28.02 478	63.62 298	69.23 447	16.41 472	53.93 474	51.01 287	60.70 423	69.99 456

YYNet1		50.73 425	48.96 427	56.03 426	61.10 467	41.78 377	51.94 469	56.44 451	40.94 450	44.84 468	67.80 452	30.08 404	55.08 471	36.77 417	50.71 462	71.22 446

MDA-MVSNet_test_wron		50.71 426	48.95 428	56.00 427	61.17 466	41.84 376	51.90 470	56.45 450	40.96 449	44.79 469	67.84 451	30.04 405	55.07 472	36.71 419	50.69 463	71.11 449

dmvs_testset		50.16 427	51.90 416	44.94 464	66.49 438	11.78 504	61.01 437	51.50 466	51.17 339	50.30 452	67.44 455	39.28 295	60.29 443	22.38 482	57.49 438	62.76 469

UnsupCasMVSNet_bld		50.07 428	48.87 429	53.66 440	60.97 470	33.67 458	57.62 453	64.56 403	39.47 458	47.38 459	64.02 469	27.47 431	59.32 447	34.69 432	43.68 475	67.98 465

test_vis1_n		49.89 429	48.69 431	53.50 442	53.97 480	37.38 423	61.53 429	47.33 481	28.54 475	59.62 358	67.10 459	13.52 478	52.27 479	49.07 303	57.52 437	70.84 450

Patchmatch-test		49.08 430	48.28 432	51.50 454	64.40 450	30.85 473	45.68 484	48.46 476	35.60 465	46.10 466	72.10 416	34.47 353	46.37 488	27.08 474	60.65 424	77.27 374

test_fmvs2		48.69 431	47.49 436	52.29 451	48.63 490	33.06 463	57.76 451	48.05 479	25.71 482	59.76 356	69.60 445	11.57 484	52.23 480	49.45 301	56.86 440	71.58 442

ADS-MVSNet		48.48 432	47.77 433	50.63 455	66.02 443	29.92 475	50.90 472	50.87 471	36.90 461	50.74 446	66.18 463	26.38 441	52.47 478	27.17 472	54.76 450	69.50 459

CHOSEN 280x420		47.83 433	46.36 437	52.24 452	67.37 431	49.78 261	38.91 492	43.11 489	35.00 466	43.27 474	63.30 470	28.95 414	49.19 484	36.53 422	60.80 421	57.76 477

new-patchmatchnet		47.56 434	47.73 434	47.06 459	58.81 477	9.37 507	48.78 478	59.21 439	43.28 433	44.22 471	68.66 449	25.67 447	57.20 460	31.57 453	49.35 467	74.62 410

PVSNet_0	43.31 20	47.46 435	45.64 438	52.92 446	67.60 429	44.65 338	54.06 464	54.64 458	41.59 445	46.15 465	58.75 477	30.99 395	58.66 452	32.18 442	24.81 492	55.46 480

ttmdpeth		45.56 436	42.95 441	53.39 444	52.33 486	29.15 477	57.77 450	48.20 478	31.81 471	49.86 453	77.21 356	8.69 492	59.16 449	27.31 471	33.40 488	71.84 439

MVS-HIRNet		45.52 437	44.48 439	48.65 458	68.49 421	34.05 455	59.41 444	44.50 486	27.03 479	37.96 486	50.47 488	26.16 444	64.10 427	26.74 475	59.52 429	47.82 487

pmmvs3		44.92 438	41.95 445	53.86 438	52.58 485	43.55 353	62.11 427	46.90 483	26.05 481	40.63 477	60.19 474	11.08 488	57.91 456	31.83 450	46.15 471	60.11 471

test_fmvs3		44.30 439	42.55 442	49.55 457	42.83 495	27.15 487	53.03 466	44.93 485	22.03 490	53.69 429	64.94 466	4.21 499	49.63 483	47.47 314	49.82 465	71.88 437

WB-MVS		43.26 440	43.41 440	42.83 468	63.32 455	10.32 506	58.17 448	45.20 484	45.42 415	40.44 479	67.26 458	34.01 360	58.98 450	11.96 496	24.88 491	59.20 472

LF4IMVS		42.95 441	42.26 443	45.04 462	48.30 491	32.50 465	54.80 461	48.49 475	28.03 477	40.51 478	70.16 434	9.24 490	43.89 491	31.63 451	49.18 468	58.72 474

MVStest1		42.65 442	39.29 449	52.71 448	47.26 493	34.58 450	54.41 463	50.84 472	23.35 484	39.31 484	74.08 403	12.57 480	55.09 470	23.32 480	28.47 490	68.47 464

EGC-MVSNET		42.47 443	38.48 451	54.46 435	74.33 308	48.73 288	70.33 349	51.10 468	0.03 536	0.18 534	67.78 453	13.28 479	66.49 417	18.91 487	50.36 464	48.15 485

FPMVS		42.18 444	41.11 446	45.39 461	58.03 478	41.01 387	49.50 476	53.81 463	30.07 473	33.71 488	64.03 467	11.69 482	52.08 481	14.01 491	55.11 448	43.09 489

SSC-MVS		41.96 445	41.99 444	41.90 469	62.46 460	9.28 508	57.41 454	44.32 487	43.38 432	38.30 485	66.45 461	32.67 382	58.42 454	10.98 497	21.91 494	57.99 476

ANet_high		41.38 446	37.47 453	53.11 445	39.73 501	24.45 493	56.94 455	69.69 356	47.65 390	26.04 493	52.32 483	12.44 481	62.38 436	21.80 483	10.61 502	72.49 428

test_vis1_rt		41.35 447	39.45 448	47.03 460	46.65 494	37.86 417	47.76 479	38.65 492	23.10 486	44.21 472	51.22 486	11.20 487	44.08 490	39.27 401	53.02 456	59.14 473

LCM-MVSNet		40.30 448	35.88 454	53.57 441	42.24 496	29.15 477	45.21 486	60.53 436	22.23 489	28.02 491	50.98 487	3.72 501	61.78 438	31.22 456	38.76 482	69.78 458

mvsany_test1		39.38 449	38.16 452	43.02 467	49.05 488	34.28 453	44.16 488	25.94 502	22.74 488	46.57 464	62.21 473	23.85 455	41.16 495	33.01 440	35.91 484	53.63 481

N_pmnet		39.35 450	40.28 447	36.54 475	63.76 452	1.62 521	49.37 477	0.76 521	34.62 467	43.61 473	66.38 462	26.25 443	42.57 492	26.02 477	51.77 459	65.44 467

DSMNet-mixed		39.30 451	38.72 450	41.03 470	51.22 487	19.66 499	45.53 485	31.35 498	15.83 497	39.80 481	67.42 457	22.19 458	45.13 489	22.43 481	52.69 457	58.31 475

APD_test1		37.39 452	34.94 455	44.72 465	48.88 489	33.19 462	52.95 467	44.00 488	19.49 491	27.28 492	58.59 478	3.18 503	52.84 477	18.92 486	41.17 479	48.14 486

PMVS	28.69 22	36.22 453	33.29 458	45.02 463	36.82 503	35.98 440	54.68 462	48.74 474	26.31 480	21.02 496	51.61 485	2.88 504	60.10 444	9.99 501	47.58 469	38.99 494
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 454	31.91 459	43.33 466	62.05 462	37.87 416	20.39 497	67.03 381	23.23 485	18.41 498	25.84 502	4.24 498	62.73 434	14.71 490	51.32 461	29.38 496
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 455	34.94 455	33.26 478	61.06 468	16.00 503	52.79 468	23.78 504	40.71 451	39.33 483	48.65 492	16.91 471	48.34 485	12.18 495	19.05 496	35.44 495

new_pmnet		34.13 456	34.29 457	33.64 477	52.63 484	18.23 501	44.43 487	33.90 497	22.81 487	30.89 490	53.18 482	10.48 489	35.72 499	20.77 484	39.51 480	46.98 488

mvsany_test3		32.62 457	30.57 462	38.77 473	36.16 504	24.20 494	38.10 493	20.63 506	19.14 492	40.36 480	57.43 479	5.06 496	36.63 498	29.59 464	28.66 489	55.49 479

test_vis3_rt		32.09 458	30.20 463	37.76 474	35.36 505	27.48 483	40.60 491	28.29 501	16.69 495	32.52 489	40.53 495	1.96 505	37.40 497	33.64 437	42.21 478	48.39 484

test_f		31.86 459	31.05 460	34.28 476	32.33 507	21.86 497	32.34 494	30.46 499	16.02 496	39.78 482	55.45 481	4.80 497	32.36 501	30.61 457	37.66 483	48.64 483

testf1		31.46 460	28.89 464	39.16 471	41.99 498	28.78 479	46.45 482	37.56 493	14.28 498	21.10 494	48.96 489	1.48 507	47.11 486	13.63 492	34.56 485	41.60 490

APD_test2		31.46 460	28.89 464	39.16 471	41.99 498	28.78 479	46.45 482	37.56 493	14.28 498	21.10 494	48.96 489	1.48 507	47.11 486	13.63 492	34.56 485	41.60 490

kuosan		29.62 462	30.82 461	26.02 483	52.99 482	16.22 502	51.09 471	22.71 505	33.91 468	33.99 487	40.85 494	15.89 474	33.11 500	7.59 506	18.37 497	28.72 497

PMMVS2		27.40 463	25.91 466	31.87 480	39.46 502	6.57 510	31.17 495	28.52 500	23.96 483	20.45 497	48.94 491	4.20 500	37.94 496	16.51 488	19.97 495	51.09 482

E-PMN		23.77 464	22.73 468	26.90 481	42.02 497	20.67 498	42.66 489	35.70 495	17.43 493	10.28 506	25.05 503	6.42 494	42.39 493	10.28 500	14.71 499	17.63 501

EMVS		22.97 465	21.84 469	26.36 482	40.20 500	19.53 500	41.95 490	34.64 496	17.09 494	9.73 507	22.83 505	7.29 493	42.22 494	9.18 503	13.66 500	17.32 502

MVE	17.77 23	21.41 466	17.77 471	32.34 479	34.34 506	25.44 491	16.11 498	24.11 503	11.19 500	13.22 500	31.92 498	1.58 506	30.95 502	10.47 499	17.03 498	40.62 493
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 467	18.10 470	24.41 484	13.68 510	3.11 516	12.06 503	42.37 490	2.00 507	11.97 502	36.38 496	5.77 495	29.35 503	15.06 489	23.65 493	40.76 492

cdsmvs_eth3d_5k		17.50 468	23.34 467	0.00 521	0.00 544	0.00 545	0.00 532	78.63 203	0.00 539	0.00 540	82.18 255	49.25 163	0.00 538	0.00 538	0.00 536	0.00 536

wuyk23d		13.32 469	12.52 472	15.71 485	47.54 492	26.27 489	31.06 496	1.98 512	4.93 504	5.18 511	1.94 522	0.45 509	18.54 504	6.81 507	12.83 501	2.33 509

RoMa-SfM		11.96 470	11.39 473	13.68 486	10.24 512	6.80 509	15.83 499	1.33 515	6.34 502	13.06 501	41.41 493	0.16 511	12.72 505	10.58 498	3.56 508	21.52 498

DKM		10.33 471	10.10 475	11.02 488	10.54 511	5.43 511	14.18 500	1.03 517	4.97 503	11.74 503	36.09 497	0.11 514	9.09 508	9.38 502	2.85 509	18.53 500

LoFTR		9.45 472	9.00 476	10.79 489	10.22 513	4.31 513	11.11 504	4.11 510	2.40 506	10.53 505	30.89 499	0.13 512	10.75 507	3.12 508	8.52 505	17.31 503

tmp_tt		9.43 473	11.14 474	4.30 494	2.38 521	4.40 512	13.62 501	16.08 508	0.39 511	15.89 499	13.06 508	15.80 475	5.54 511	12.63 494	10.46 503	2.95 508

PDCNetPlus		9.23 474	8.89 477	10.23 490	13.70 509	3.70 514	12.27 502	1.51 514	3.98 505	6.73 509	29.50 500	0.24 510	8.07 510	7.83 505	4.30 507	18.93 499

MatchFormer		7.03 475	6.96 479	7.26 491	7.64 514	3.36 515	10.21 505	3.04 511	1.31 508	9.02 508	22.94 504	0.08 520	8.15 509	1.46 510	6.91 506	10.26 506

ab-mvs-re		6.49 476	8.65 478	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	77.89 344	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

test123		4.73 477	6.30 480	0.02 519	0.01 542	0.01 544	56.36 457	0.00 543	0.01 537	0.04 538	0.21 538	0.01 538	0.00 538	0.03 530	0.00 536	0.04 534

testmvs		4.52 478	6.03 481	0.01 520	0.01 542	0.00 545	53.86 465	0.00 543	0.01 537	0.04 538	0.27 537	0.00 542	0.00 538	0.04 522	0.00 536	0.03 535

GLUNet-SfM		4.33 479	3.64 483	6.41 492	3.38 518	1.65 519	3.23 509	1.54 513	0.66 510	6.36 510	15.13 507	0.08 520	5.54 511	0.94 511	1.44 515	12.05 505

ELoFTR		4.04 480	3.55 484	5.50 493	2.33 522	1.25 522	3.58 507	1.18 516	0.90 509	4.23 512	16.28 506	0.03 526	5.46 513	1.95 509	1.42 516	9.81 507

pcd_1.5k_mvsjas		3.92 481	5.23 482	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	47.05 194	0.00 538	0.00 538	0.00 536	0.00 536

ALIKED-LG		2.35 482	2.54 485	1.78 495	5.54 515	1.79 518	3.81 506	0.96 518	0.33 512	1.86 513	7.18 509	0.13 512	1.60 514	0.20 519	2.81 510	1.94 510

ALIKED-MNN		2.09 483	2.23 486	1.67 496	5.15 516	1.82 517	3.53 508	0.77 519	0.25 513	1.45 515	6.03 511	0.09 518	1.52 515	0.17 520	2.64 511	1.66 511

ALIKED-NN		1.96 484	2.12 487	1.48 497	4.72 517	1.65 519	3.19 510	0.77 519	0.23 514	1.43 516	5.87 512	0.10 516	1.37 516	0.16 521	2.61 512	1.42 517

XFeat-MNN		1.07 485	1.17 488	0.77 499	0.52 540	0.31 537	1.15 516	0.41 522	0.15 518	1.62 514	4.35 513	0.07 524	0.77 517	0.38 513	1.88 513	1.22 518

SP-DiffGlue		0.98 486	1.05 489	0.75 502	0.81 539	0.40 529	1.24 515	0.37 523	0.19 515	1.26 518	3.80 514	0.11 514	0.34 523	0.51 512	1.18 517	1.52 515

SP-LightGlue		0.94 487	0.99 490	0.78 498	2.60 519	0.38 530	1.71 511	0.34 524	0.17 516	0.50 520	2.14 518	0.09 518	0.38 520	0.26 515	1.13 518	1.59 512

SP-SuperGlue		0.93 488	0.98 491	0.77 499	2.54 520	0.38 530	1.70 512	0.34 524	0.17 516	0.52 519	2.13 519	0.10 516	0.36 522	0.26 515	1.10 519	1.57 514

SP-MNN		0.89 489	0.93 493	0.77 499	2.32 523	0.34 534	1.68 513	0.33 526	0.13 520	0.49 521	2.07 520	0.08 520	0.39 519	0.25 517	1.07 520	1.58 513

XFeat-NN		0.87 490	0.97 492	0.59 504	0.48 541	0.24 540	0.94 517	0.29 528	0.12 521	1.41 517	3.45 517	0.06 525	0.56 518	0.29 514	1.65 514	0.95 519

SP-NN		0.85 491	0.90 494	0.73 503	2.22 524	0.33 536	1.63 514	0.31 527	0.14 519	0.47 522	1.97 521	0.08 520	0.38 520	0.25 517	1.01 521	1.47 516

SIFT-NN		0.60 492	0.65 495	0.45 505	1.90 525	0.55 523	0.90 518	0.16 529	0.10 522	0.34 523	1.43 523	0.02 527	0.28 524	0.04 522	0.95 522	0.50 520

SIFT-MNN		0.56 493	0.61 496	0.43 506	1.75 526	0.50 524	0.82 519	0.16 529	0.10 522	0.30 524	1.38 524	0.02 527	0.28 524	0.04 522	0.92 524	0.50 520

SIFT-NN-NCMNet		0.53 494	0.58 497	0.40 507	1.60 528	0.49 525	0.80 520	0.15 531	0.09 525	0.28 526	1.29 525	0.02 527	0.27 526	0.04 522	0.94 523	0.44 524

SIFT-NCM-Cal		0.51 495	0.55 498	0.38 508	1.66 527	0.45 526	0.75 521	0.12 532	0.09 525	0.21 531	1.18 530	0.02 527	0.27 526	0.03 530	0.89 525	0.43 526

SIFT-NN-CMatch		0.49 496	0.53 499	0.38 508	1.35 532	0.41 528	0.70 523	0.12 532	0.09 525	0.30 524	1.28 527	0.02 527	0.26 528	0.04 522	0.83 527	0.47 522

SIFT-NN-UMatch		0.48 497	0.52 500	0.36 510	1.27 534	0.36 532	0.75 521	0.12 532	0.10 522	0.25 528	1.29 525	0.02 527	0.26 528	0.04 522	0.85 526	0.44 524

SIFT-ConvMatch		0.48 497	0.52 500	0.35 511	1.51 529	0.42 527	0.64 525	0.11 535	0.09 525	0.26 527	1.24 528	0.02 527	0.25 530	0.04 522	0.76 529	0.38 527

SIFT-UMatch		0.45 499	0.50 502	0.32 513	1.46 530	0.34 534	0.66 524	0.10 537	0.09 525	0.22 530	1.19 529	0.02 527	0.25 530	0.04 522	0.73 530	0.36 529

SIFT-NN-PointCN		0.44 500	0.47 503	0.33 512	1.17 535	0.29 538	0.64 525	0.11 535	0.09 525	0.25 528	1.14 531	0.02 527	0.25 530	0.03 530	0.78 528	0.46 523

SIFT-CM-Cal		0.42 501	0.46 504	0.31 514	1.40 531	0.35 533	0.56 528	0.09 538	0.09 525	0.20 532	1.09 533	0.02 527	0.23 533	0.03 530	0.66 532	0.34 530

SIFT-UM-Cal		0.41 502	0.46 504	0.28 515	1.35 532	0.29 538	0.57 527	0.08 539	0.09 525	0.20 532	1.10 532	0.02 527	0.23 533	0.03 530	0.68 531	0.30 532

SIFT-PCN-Cal		0.36 503	0.39 506	0.26 516	1.16 536	0.21 541	0.46 530	0.07 541	0.08 533	0.17 535	0.92 534	0.01 538	0.20 536	0.03 530	0.59 534	0.37 528

SIFT-PointCN		0.36 503	0.39 506	0.25 517	1.14 537	0.21 541	0.50 529	0.08 539	0.08 533	0.17 535	0.89 535	0.01 538	0.21 535	0.03 530	0.60 533	0.34 530

SIFT-NCMNet		0.30 505	0.33 508	0.19 518	1.04 538	0.18 543	0.39 531	0.05 542	0.08 533	0.14 537	0.77 536	0.01 538	0.16 537	0.02 537	0.49 535	0.22 533

mmdepth		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

monomultidepth		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

test_blank		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

uanet_test		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

DCPMVS		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

sosnet-low-res		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

sosnet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

uncertanet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

Regformer		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

uanet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 545	0.00 532	0.00 543	0.00 539	0.00 540	0.00 539	0.00 542	0.00 538	0.00 538	0.00 536	0.00 536

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 23	60.95 106	83.65 12	90.57 27		89.91 16	77.02 34	89.43 22	88.10 44

TestfortrainingZip				78.05 44	84.66 62	58.22 87	86.84 11	85.98 22	63.31 48	79.39 24	88.94 65	62.01 15	89.61 21		86.45 63	86.34 119

WAC-MVS						27.31 485								27.77 469

FOURS1					86.12 37	60.82 37	88.18 1	83.61 83	60.87 108	81.50 20

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 54

PC_three_1452									55.09 255	84.46 4	89.84 52	66.68 5	89.41 23	74.24 61	91.38 2	88.42 31

No_MVS				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 54

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 12

eth-test2					0.00 544

eth-test					0.00 544

ZD-MVS					86.64 21	60.38 45		82.70 117	57.95 184	78.10 34	90.06 45	56.12 52	88.84 31	74.05 64	87.00 54

RE-MVS-def			73.71 85		83.49 73	59.87 54	84.29 48	81.36 140	58.07 178	73.14 110	90.07 43	43.06 244		68.20 106	81.76 111	84.03 217

IU-MVS					87.77 4	59.15 68		85.53 32	53.93 285	84.64 3				79.07 13	90.87 5	88.37 33

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 39

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 63

test_241102_ONE					87.77 4	58.90 78		86.78 10	64.20 33	85.97 1	91.34 18	66.87 3	90.78 7

9.14			78.75 18		83.10 78		84.15 54	88.26 1	59.90 138	78.57 31	90.36 35	57.51 37	86.86 74	77.39 29	89.52 21

save fliter					86.17 34	61.30 28	83.98 58	79.66 179	59.00 158

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 46

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 65

test0726					87.75 7	59.07 73	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 361

test_part2					87.58 9	60.47 42				83.42 14

sam_mvs1												34.74 349				78.05 361

sam_mvs												33.43 367

ambc				65.13 359	63.72 454	37.07 427	47.66 481	78.78 199		54.37 423	71.42 422	11.24 486	80.94 238	45.64 342	53.85 455	77.38 372

MTGPA								80.97 158

test_post1							68.67 368				3.64 515	32.39 388	69.49 395	44.17 359

test_post											3.55 516	33.90 361	66.52 416

patchmatchnet-post											64.03 467	34.50 351	74.27 365

GG-mvs-BLEND				62.34 381	71.36 370	37.04 428	69.20 365	57.33 449		54.73 417	65.48 465	30.37 398	77.82 315	34.82 431	74.93 238	72.17 435

MTMP							86.03 23	17.08 507

gm-plane-assit					71.40 369	41.72 380			48.85 369		73.31 408		82.48 203	48.90 305

test9_res														75.28 54	88.31 35	83.81 228

TEST9					85.58 44	61.59 24	81.62 91	81.26 147	55.65 239	74.93 64	88.81 68	53.70 89	84.68 138

test_8					85.40 47	60.96 34	81.54 94	81.18 151	55.86 231	74.81 69	88.80 70	53.70 89	84.45 142

agg_prior2														73.09 72	87.93 43	84.33 206

agg_prior					85.04 54	59.96 50		81.04 156		74.68 74			84.04 149

TestCases				64.39 364	71.44 366	49.03 279		67.30 376	45.97 411	47.16 460	79.77 306	17.47 467	67.56 409	33.65 435	59.16 431	76.57 384

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 124	75.01 62	89.06 61	56.22 48		72.19 79	88.96 27

test_prior				76.69 66	84.20 66	57.27 99		84.88 45					86.43 89			86.38 115

旧先验2							76.08 225		45.32 416	76.55 48			65.56 423	58.75 223

新几何2							76.12 223

新几何1				70.76 252	85.66 42	61.13 30		66.43 386	44.68 420	70.29 158	86.64 127	41.29 272	75.23 360	49.72 297	81.75 113	75.93 390

旧先验1					83.04 79	53.15 181		67.52 375			87.85 88	44.08 232			80.76 122	78.03 364

无先验							79.66 122	74.30 306	48.40 377				80.78 244	53.62 265		79.03 350

原ACMM2							79.02 130

原ACMM1				74.69 108	85.39 48	59.40 59		83.42 89	51.47 331	70.27 159	86.61 131	48.61 171	86.51 87	53.85 264	87.96 42	78.16 359

test222					83.14 77	58.68 82	72.57 308	63.45 415	41.78 442	67.56 222	86.12 149	37.13 326			78.73 173	74.98 403

testdata2													72.18 379	46.95 330

segment_acmp												54.23 76

testdata				64.66 361	81.52 99	52.93 186		65.29 396	46.09 409	73.88 91	87.46 95	38.08 315	66.26 419	53.31 269	78.48 180	74.78 407

testdata1							72.65 303		60.50 118

test12				77.76 51	84.52 63	58.41 84		83.36 92		72.93 118		54.61 73	88.05 44		88.12 37	86.81 97

plane_prior7					81.41 102	55.96 122

plane_prior6					81.20 109	56.24 117						45.26 219

plane_prior5								84.01 58					87.21 64	68.16 110	80.58 126	84.65 197

plane_prior4											86.10 150

plane_prior3						56.09 119			63.92 38	69.27 179

plane_prior2							84.22 51		64.52 27

plane_prior1					81.27 107

plane_prior						56.31 113	83.58 64		63.19 55						80.48 129

n2								0.00 543

nn								0.00 543

door-mid								47.19 482

lessismore_v0				69.91 271	71.42 368	47.80 305		50.90 470		50.39 450	75.56 386	27.43 433	81.33 225	45.91 339	34.10 487	80.59 316

LGP-MVS_train				75.76 84	80.22 124	57.51 97		83.40 90	61.32 97	66.67 241	87.33 101	39.15 298	86.59 80	67.70 119	77.30 203	83.19 251

test11								83.47 87

door								47.60 480

HQP5-MVS						54.94 144

HQP-NCC					80.66 116		82.31 82		62.10 82	67.85 211

ACMP_Plane					80.66 116		82.31 82		62.10 82	67.85 211

BP-MVS														67.04 130

HQP4-MVS										67.85 211			86.93 72			84.32 207

HQP3-MVS								83.90 63							80.35 131

HQP2-MVS												45.46 213

NP-MVS					80.98 112	56.05 121					85.54 172

MDTV_nov1_ep13_2view						25.89 490	61.22 433		40.10 455	51.10 443		32.97 373		38.49 405		78.61 355

MDTV_nov1_ep13			57.00 371		72.73 338	38.26 414	65.02 405	64.73 401	44.74 419	55.46 404	72.48 412	32.61 385	70.47 388	37.47 410	67.75 356

ACMMP++_ref															74.07 248

ACMMP++															72.16 289

Test By Simon												48.33 174

ITE_SJBPF				62.09 383	66.16 441	44.55 343		64.32 404	47.36 395	55.31 408	80.34 295	19.27 465	62.68 435	36.29 425	62.39 409	79.04 349

DeepMVS_CX				12.03 487	17.97 508	10.91 505		10.60 509	7.46 501	11.07 504	28.36 501	3.28 502	11.29 506	8.01 504	9.74 504	13.89 504

High-resolution multi-view benchmark