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 28

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	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 36

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 14	89.23 24	81.51 2	88.44 31	88.09 44
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 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 158
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 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 92
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 41

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 38
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 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 94
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 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 41

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 101	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 47

HPM-MVS++		79.88 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 76	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 170

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 86

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 72	90.06 14	78.42 23	89.02 27	87.69 58
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 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 40

SF-MVS		78.82 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 84	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 50

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 46	75.08 60	90.47 33	53.96 79	88.68 31	76.48 39	89.63 20	87.16 83

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 103	88.39 34	79.34 9	90.52 13	86.78 95

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 61	89.18 25	74.19 63	87.34 50	86.38 110

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 90	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 48
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	80.73 114	57.91 89	84.68 41	81.64 127	68.35 2	75.77 50	90.38 34	53.98 77	90.26 13	81.30 3	87.68 46	88.77 16

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 71	59.34 150	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 59
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 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 76	76.41 48	91.51 11	52.47 105	86.78 75	80.66 4	89.64 19	87.80 54
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 82	63.89 39	73.60 93	90.60 23	54.85 67	86.72 76	77.20 31	88.06 40	85.74 144
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 35	63.56 43	74.29 79	90.03 47	52.56 102	88.53 33	74.79 59	88.34 33	86.63 103

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 111	79.05 26	90.30 38	55.54 60	88.32 36	73.48 70	87.03 52	84.83 185
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 52	62.82 60	73.96 84	90.50 31	53.20 93	88.35 35	74.02 65	87.05 51	86.13 125

lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 113	78.99 27	91.45 12	51.51 124	87.78 51	75.65 49	87.55 47	87.10 85

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 95	90.56 29	49.80 147	88.24 37	74.02 65	87.03 52	86.32 118

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 120	79.89 22	89.38 58	54.97 65	85.58 113	76.12 45	84.94 70	86.33 116
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 55	62.81 62	73.30 99	90.58 24	49.90 144	88.21 38	73.78 67	87.03 52	86.29 122

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 202	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 104

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 110	89.97 50	50.90 135	87.48 57	75.30 53	86.85 57	87.33 78
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 45	61.98 83	73.06 111	88.88 66	53.72 85	89.06 27	68.27 103	88.04 41	87.42 70
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 60	64.55 25	72.17 128	90.01 49	47.95 170	88.01 44	71.55 88	86.74 59	86.37 112

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 85	62.44 69	72.68 120	90.50 31	48.18 168	87.34 58	73.59 69	85.71 66	84.76 189

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 107	75.27 55	84.83 175	60.76 18	86.56 81	67.86 114	87.87 45	86.06 127

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 90	60.22 127	77.85 36	91.42 14	50.67 136	87.69 53	72.46 76	84.53 74	85.46 156

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 90	60.22 127	77.85 36	91.42 14	50.67 136	87.69 53	72.46 76	84.53 74	85.46 156

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 251	80.97 153	65.13 15	75.77 50	90.88 20	48.63 163	86.66 78	77.23 30	88.17 37	84.81 186

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 73	61.71 85	72.45 126	90.34 37	48.48 166	88.13 41	72.32 78	86.85 57	85.78 138

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 99	74.90 66	87.17 108	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 117	62.90 57	71.77 133	90.26 39	46.61 195	86.55 84	71.71 86	85.66 67	84.97 181

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 93	67.78 3	70.09 154	86.34 138	54.92 66	88.90 29	72.68 75	84.55 73	87.76 56

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 108	59.65 140	77.31 39	91.43 13	49.62 149	87.24 59	71.99 82	83.75 85	85.14 172

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 218	74.05 82	88.98 63	53.34 91	87.92 47	69.23 101	88.42 32	87.59 64

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 142	55.86 226	74.93 63	88.81 67	53.70 86	84.68 136	75.24 55	88.33 34	83.65 232

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 97	64.69 22	74.21 80	87.40 94	49.48 150	86.17 96	68.04 111	87.55 47	87.42 70

CS-MVS		76.25 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 99	87.27 101	55.06 63	86.30 93	71.78 85	84.58 72	89.25 6

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 253	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	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 57	85.87 41	61.20 29	85.52 33	82.19 118	59.99 133	75.10 59	90.35 36	47.66 175	86.52 85	71.64 87	82.99 90	84.47 198

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 164	89.74 55	45.43 209	87.16 65	72.01 81	82.87 95	85.14 172
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 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 224	73.41 96	86.58 129	50.94 134	88.54 32	70.79 93	89.71 17	87.79 55

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 119	87.25 105	53.13 94	87.93 46	71.97 83	85.57 68	86.66 101

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 210	89.24 60	42.03 248	89.38 23	64.07 154	86.50 63	89.69 3

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 140	71.53 139	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 80	71.49 140	86.03 149	53.83 81	86.36 91	67.74 115	86.91 56	88.19 38

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 212	72.46 124	86.76 116	56.89 39	87.86 49	66.36 134	88.91 29	83.64 233

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 269	64.69 22	74.21 80	87.40 94	49.48 150	86.17 96	68.04 111	83.88 83	85.85 135

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 183	73.95 301	61.40 91	79.46 23	90.14 41	57.07 37	81.15 225	80.00 5	79.31 148	88.51 27

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 94	58.41 168	73.71 91	90.14 41	45.62 202	85.99 103	69.64 97	82.85 96	85.78 138

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 203	58.58 165	74.32 78	84.51 190	55.94 57	87.22 62	67.11 125	84.48 77	85.52 152

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 267	50.37 245	78.17 153	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	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 64	74.46 67	75.65 88	77.84 197	52.25 207	75.59 233	84.17 54	63.76 40	73.15 105	82.79 225	59.58 23	86.80 74	67.24 123	86.04 65	87.89 48
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 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 79	63.74 41	72.52 123	87.49 91	47.18 186	85.88 106	69.47 99	80.78 117	83.66 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 66	74.98 61	73.71 152	78.94 155	50.56 239	80.23 107	83.87 65	60.30 124	77.15 41	86.56 130	59.65 20	82.00 205	66.01 138	82.12 101	88.58 25

canonicalmvs		74.67 66	74.98 61	73.71 152	78.94 155	50.56 239	80.23 107	83.87 65	60.30 124	77.15 41	86.56 130	59.65 20	82.00 205	66.01 138	82.12 101	88.58 25

baseline		74.61 68	74.70 64	74.34 121	75.70 262	49.99 255	77.54 173	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	89.20 8

SR-MVS-dyc-post		74.57 69	73.90 78	76.58 70	83.49 72	59.87 54	84.29 48	81.36 135	58.07 174	73.14 106	90.07 43	44.74 219	85.84 107	68.20 104	81.76 108	84.03 210

dcpmvs_2		74.55 70	75.23 58	72.48 191	82.34 87	53.34 176	77.87 162	81.46 131	57.80 185	75.49 52	86.81 115	62.22 15	77.75 303	71.09 91	82.02 104	86.34 114

ETV-MVS		74.46 71	73.84 80	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 121	79.46 308	53.65 89	87.87 48	67.45 122	82.91 93	85.89 133

HQP_MVS		74.31 72	73.73 82	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 173	86.10 146	45.26 213	87.21 63	68.16 108	80.58 123	84.65 190

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 137	75.33 274	52.89 189	78.24 146	77.32 236	61.65 86	78.13 32	88.90 65	52.82 99	81.54 215	78.46 22	78.67 170	87.60 63

HPM-MVS_fast		74.30 73	73.46 88	76.80 63	84.45 64	59.04 74	83.65 63	81.05 150	60.15 129	70.43 150	89.84 52	41.09 270	85.59 112	67.61 118	82.90 94	85.77 141

fmvsm_s_conf0.5_n_10		74.11 75	73.98 77	74.48 118	74.61 294	52.86 191	78.10 157	77.06 240	57.14 195	78.24 31	88.79 70	52.83 98	82.26 201	77.79 28	81.30 113	88.32 31

E6new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 69	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 134	88.77 16

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 69	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 134	88.77 16

E5		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

MVS_111021_HR		74.02 79	73.46 88	75.69 86	83.01 80	60.63 40	77.29 184	78.40 214	61.18 97	70.58 149	85.97 152	54.18 74	84.00 149	67.52 119	82.98 92	82.45 266

MG-MVS		73.96 80	73.89 79	74.16 128	85.65 43	49.69 264	81.59 93	81.29 141	61.45 90	71.05 143	88.11 77	51.77 119	87.73 52	61.05 193	83.09 88	85.05 177

E4		73.91 81	73.83 81	74.15 130	77.13 229	50.47 242	77.15 190	83.79 72	62.21 75	73.61 92	87.19 107	56.08 52	83.03 168	67.91 113	79.35 146	88.94 11

alignmvs		73.86 82	73.99 76	73.45 166	78.20 182	50.50 241	78.57 139	82.43 115	59.40 148	76.57 46	86.71 122	56.42 44	81.23 224	65.84 141	81.79 107	88.62 22

MSLP-MVS++		73.77 83	73.47 87	74.66 108	83.02 79	59.29 63	82.30 85	81.88 122	59.34 150	71.59 137	86.83 114	45.94 200	83.65 155	65.09 147	85.22 69	81.06 297

E2		73.72 84	73.60 85	74.06 134	77.16 224	50.40 243	76.97 195	83.74 73	61.64 87	73.36 97	86.75 119	56.14 48	82.99 170	67.50 120	79.18 156	88.80 13

E3		73.72 84	73.60 85	74.06 134	77.16 224	50.40 243	76.97 195	83.74 73	61.64 87	73.36 97	86.76 116	56.13 49	82.99 170	67.50 120	79.18 156	88.80 13

viewcassd2359sk11		73.56 86	73.41 90	74.00 138	77.13 229	50.35 246	76.86 202	83.69 77	61.23 96	73.14 106	86.38 137	56.09 51	82.96 173	67.15 124	79.01 161	88.70 21

fmvsm_s_conf0.5_n_3		73.55 87	74.39 68	71.03 242	74.09 312	51.86 216	77.77 167	75.60 265	61.18 97	78.67 29	88.98 63	55.88 58	77.73 304	78.69 16	78.68 169	83.50 236

HQP-MVS		73.45 88	72.80 100	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 77	67.85 204	85.54 167	45.46 207	86.93 71	67.04 126	80.35 127	84.32 200

viewdifsd2359ckpt09		73.42 89	72.45 106	76.30 75	77.25 222	53.27 178	80.36 106	82.48 114	57.96 179	72.24 127	85.73 161	53.22 92	86.27 94	63.79 164	79.06 160	89.36 5

E3new		73.41 90	73.22 93	73.95 141	77.06 234	50.31 247	76.78 205	83.66 78	60.90 103	72.93 114	86.02 150	55.99 53	82.95 175	66.89 131	78.77 166	88.61 23

BP-MVS1		73.41 90	72.25 108	76.88 61	76.68 246	53.70 163	79.15 127	81.07 149	60.66 110	71.81 132	87.39 96	40.93 271	87.24 59	71.23 90	81.29 114	89.71 2

CLD-MVS		73.33 92	72.68 102	75.29 96	78.82 159	53.33 177	78.23 150	84.79 46	61.30 94	70.41 151	81.04 274	52.41 106	87.12 66	64.61 153	82.49 100	85.41 162
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 93	72.54 104	75.62 89	77.87 195	53.64 165	79.62 122	79.61 175	61.63 89	72.02 131	82.61 230	56.44 43	85.97 104	63.99 157	79.07 159	87.25 80

fmvsm_l_conf0.5_n_9		73.27 94	73.66 84	72.09 200	73.82 313	52.72 195	77.45 177	74.28 294	56.61 211	77.10 43	88.16 76	56.17 47	77.09 317	78.27 24	81.13 115	86.48 108

fmvsm_l_conf0.5_n_3		73.23 95	73.13 95	73.55 162	74.40 301	55.13 141	78.97 129	74.96 284	56.64 205	74.76 71	88.75 71	55.02 64	78.77 286	76.33 41	78.31 180	86.74 96

fmvsm_s_conf0.5_n_11		73.16 96	73.35 91	72.58 186	75.48 269	52.41 206	78.84 131	76.85 244	58.64 163	73.58 94	87.25 105	54.09 76	79.47 263	76.19 44	79.27 149	85.86 134

viewmacassd2359aftdt		73.15 97	73.16 94	73.11 175	75.15 280	49.31 271	77.53 175	83.21 97	60.42 116	73.20 103	87.34 98	53.82 82	81.05 230	67.02 128	80.79 116	88.96 10

UA-Net		73.13 98	72.93 97	73.76 147	83.58 71	51.66 219	78.75 132	77.66 226	67.75 4	72.61 122	89.42 56	49.82 146	83.29 163	53.61 261	83.14 87	86.32 118

EPNet		73.09 99	72.16 109	75.90 79	75.95 259	56.28 114	83.05 67	72.39 320	66.53 10	65.27 262	87.00 110	50.40 139	85.47 118	62.48 180	86.32 64	85.94 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 100	72.59 103	74.27 124	71.28 367	55.88 124	78.21 152	75.56 267	54.31 273	74.86 67	87.80 87	54.72 68	80.23 251	78.07 26	78.48 175	86.70 97

nrg030		72.96 101	73.01 96	72.84 181	75.41 272	50.24 248	80.02 111	82.89 110	58.36 170	74.44 75	86.73 120	58.90 27	80.83 237	65.84 141	74.46 235	87.44 69

viewmanbaseed2359cas		72.92 102	72.89 98	73.00 177	75.16 278	49.25 274	77.25 187	83.11 105	59.52 147	72.93 114	86.63 125	54.11 75	80.98 231	66.63 132	80.67 120	88.76 20

test_fmvsmconf0.1_n		72.81 103	72.33 107	74.24 125	69.89 390	55.81 125	78.22 151	75.40 272	54.17 275	75.00 62	88.03 83	53.82 82	80.23 251	78.08 25	78.34 179	86.69 98

CPTT-MVS		72.78 104	72.08 111	74.87 102	84.88 61	61.41 26	84.15 54	77.86 222	55.27 244	67.51 216	88.08 79	41.93 251	81.85 208	69.04 102	80.01 132	81.35 288

LPG-MVS_test		72.74 105	71.74 116	75.76 83	80.22 123	57.51 96	82.55 78	83.40 87	61.32 92	66.67 234	87.33 99	39.15 289	86.59 79	67.70 116	77.30 198	83.19 244

h-mvs33		72.71 106	71.49 120	76.40 72	81.99 92	59.58 57	76.92 199	76.74 249	60.40 117	74.81 68	85.95 153	45.54 205	85.76 109	70.41 95	70.61 300	83.86 220

fmvsm_s_conf0.5_n_5		72.69 107	72.80 100	72.37 196	74.11 311	53.21 180	78.12 154	73.31 308	53.98 278	76.81 45	88.05 80	53.38 90	77.37 312	76.64 38	80.78 117	86.53 106

GDP-MVS		72.64 108	71.28 127	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 243	71.38 141	86.97 111	39.94 277	87.00 70	67.02 128	79.20 153	88.89 12

PAPM_NR		72.63 109	71.80 114	75.13 97	81.72 96	53.42 175	79.91 115	83.28 95	59.14 152	66.31 241	85.90 154	51.86 116	86.06 100	57.45 226	80.62 121	85.91 132

fmvsm_s_conf0.5_n_6		72.59 110	72.87 99	71.73 211	75.14 281	51.96 214	76.28 215	77.12 239	57.63 189	73.85 89	86.91 112	51.54 123	77.87 300	77.18 32	80.18 131	85.37 164

VDD-MVS		72.50 111	72.09 110	73.75 149	81.58 97	49.69 264	77.76 168	77.63 227	63.21 50	73.21 102	89.02 62	42.14 247	83.32 162	61.72 187	82.50 99	88.25 34

3Dnovator	64.47 5	72.49 112	71.39 123	75.79 82	77.70 202	58.99 76	80.66 104	83.15 102	62.24 74	65.46 258	86.59 128	42.38 246	85.52 114	59.59 207	84.72 71	82.85 254

MGCFI-Net		72.45 113	73.34 92	69.81 267	77.77 199	43.21 350	75.84 230	81.18 146	59.59 145	75.45 53	86.64 123	57.74 31	77.94 296	63.92 158	81.90 106	88.30 32

MVS_Test		72.45 113	72.46 105	72.42 195	74.88 283	48.50 289	76.28 215	83.14 103	59.40 148	72.46 124	84.68 180	55.66 59	81.12 226	65.98 140	79.66 139	87.63 61

EI-MVSNet-Vis-set		72.42 115	71.59 117	74.91 100	78.47 171	54.02 157	77.05 193	79.33 181	65.03 18	71.68 135	79.35 312	52.75 100	84.89 131	66.46 133	74.23 239	85.83 137

viewdifsd2359ckpt13		72.40 116	71.79 115	74.22 126	75.63 264	51.77 218	78.67 135	83.13 104	57.08 196	71.59 137	85.36 171	53.10 95	82.64 192	63.07 174	78.51 174	88.24 35

ACMP	63.53 6	72.30 117	71.20 129	75.59 91	80.28 121	57.54 94	82.74 74	82.84 111	60.58 112	65.24 266	86.18 143	39.25 287	86.03 102	66.95 130	76.79 206	83.22 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 118	71.21 128	75.31 94	78.50 169	55.93 122	81.63 90	82.12 119	56.24 221	70.02 158	85.68 163	47.05 188	84.34 142	65.27 146	74.41 238	85.67 147

Vis-MVSNet		72.18 119	71.37 124	74.61 111	81.29 104	55.41 136	80.90 100	78.28 217	60.73 108	69.23 176	88.09 78	44.36 225	82.65 191	57.68 224	81.75 110	85.77 141
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 120	71.50 119	74.16 128	67.96 412	55.58 133	78.06 158	74.67 287	54.19 274	74.54 74	88.23 74	50.35 141	80.24 250	78.07 26	77.46 193	86.65 102

API-MVS		72.17 120	71.41 122	74.45 119	81.95 93	57.22 99	84.03 56	80.38 164	59.89 138	68.40 187	82.33 243	49.64 148	87.83 50	51.87 275	84.16 81	78.30 341

EPP-MVSNet		72.16 122	71.31 126	74.71 105	78.68 163	49.70 262	82.10 86	81.65 126	60.40 117	65.94 248	85.84 156	51.74 120	86.37 90	55.93 237	79.55 142	88.07 46

DP-MVS Recon		72.15 123	70.73 138	76.40 72	86.57 25	57.99 88	81.15 98	82.96 106	57.03 199	66.78 229	85.56 164	44.50 223	88.11 42	51.77 277	80.23 130	83.10 249

fmvsm_s_conf0.5_n_4		72.04 124	71.85 113	72.58 186	73.74 316	52.49 202	76.69 206	72.42 319	56.42 216	75.32 54	87.04 109	52.13 112	78.01 295	79.29 12	73.65 249	87.26 79

EI-MVSNet-UG-set		71.92 125	71.06 132	74.52 117	77.98 193	53.56 168	76.62 207	79.16 182	64.40 29	71.18 142	78.95 317	52.19 110	84.66 138	65.47 144	73.57 252	85.32 166

viewdifsd2359ckpt07		71.90 126	71.97 112	71.69 214	74.81 287	48.08 295	75.30 238	80.49 161	60.00 132	71.63 136	86.33 139	56.34 45	79.25 268	65.40 145	77.41 194	87.76 56

VDDNet		71.81 127	71.33 125	73.26 173	82.80 83	47.60 304	78.74 133	75.27 274	59.59 145	72.94 113	89.40 57	41.51 263	83.91 150	58.75 219	82.99 90	88.26 33

EIA-MVS		71.78 128	70.60 140	75.30 95	79.85 132	53.54 169	77.27 186	83.26 96	57.92 181	66.49 236	79.39 310	52.07 113	86.69 77	60.05 201	79.14 158	85.66 148

LFMVS		71.78 128	71.59 117	72.32 197	83.40 75	46.38 313	79.75 118	71.08 329	64.18 34	72.80 118	88.64 72	42.58 243	83.72 153	57.41 227	84.49 76	86.86 91

test_fmvsm_n_1920		71.73 130	71.14 130	73.50 163	72.52 338	56.53 111	75.60 232	76.16 254	48.11 367	77.22 40	85.56 164	53.10 95	77.43 309	74.86 57	77.14 200	86.55 105

PAPR		71.72 131	70.82 136	74.41 120	81.20 108	51.17 222	79.55 124	83.33 92	55.81 229	66.93 228	84.61 184	50.95 133	86.06 100	55.79 240	79.20 153	86.00 128

IS-MVSNet		71.57 132	71.00 133	73.27 172	78.86 157	45.63 324	80.22 109	78.69 196	64.14 37	66.46 237	87.36 97	49.30 154	85.60 111	50.26 288	83.71 86	88.59 24

MAR-MVS		71.51 133	70.15 151	75.60 90	81.84 94	59.39 60	81.38 95	82.90 108	54.90 261	68.08 200	78.70 318	47.73 173	85.51 115	51.68 279	84.17 80	81.88 277
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		71.50 134	70.38 145	74.88 101	78.76 160	57.15 104	82.79 72	78.48 207	51.26 324	69.49 167	83.22 220	43.99 229	83.24 164	66.06 136	79.37 143	84.23 204

RRT-MVS		71.46 135	70.70 139	73.74 150	77.76 200	49.30 272	76.60 208	80.45 162	61.25 95	68.17 192	84.78 177	44.64 221	84.90 130	64.79 149	77.88 186	87.03 86

PVSNet_Blended_VisFu		71.45 136	70.39 144	74.65 109	82.01 90	58.82 79	79.93 114	80.35 165	55.09 249	65.82 254	82.16 251	49.17 157	82.64 192	60.34 199	78.62 172	82.50 265

OMC-MVS		71.40 137	70.60 140	73.78 145	76.60 249	53.15 181	79.74 119	79.78 171	58.37 169	68.75 181	86.45 135	45.43 209	80.60 241	62.58 178	77.73 187	87.58 65

KinetiMVS		71.26 138	70.16 150	74.57 114	74.59 295	52.77 194	75.91 227	81.20 145	60.72 109	69.10 179	85.71 162	41.67 258	83.53 158	63.91 160	78.62 172	87.42 70

UniMVSNet_NR-MVSNet		71.11 139	71.00 133	71.44 224	79.20 147	44.13 339	76.02 225	82.60 113	66.48 11	68.20 190	84.60 187	56.82 40	82.82 187	54.62 251	70.43 302	87.36 77

hse-mvs2		71.04 140	69.86 154	74.60 112	79.58 137	57.12 106	73.96 270	75.25 275	60.40 117	74.81 68	81.95 256	45.54 205	82.90 180	70.41 95	66.83 355	83.77 225

diffmvs_AUTHOR		71.02 141	70.87 135	71.45 223	69.89 390	48.97 280	73.16 292	78.33 216	57.79 186	72.11 130	85.26 172	51.84 117	77.89 299	71.00 92	78.47 177	87.49 67

GeoE		71.01 142	70.15 151	73.60 160	79.57 138	52.17 208	78.93 130	78.12 219	58.02 176	67.76 213	83.87 203	52.36 107	82.72 189	56.90 229	75.79 220	85.92 131

fmvsm_l_conf0.5_n		70.99 143	70.82 136	71.48 220	71.45 360	54.40 151	77.18 189	70.46 335	48.67 357	75.17 57	86.86 113	53.77 84	76.86 325	76.33 41	77.51 192	83.17 248

PCF-MVS	61.88 8	70.95 144	69.49 161	75.35 93	77.63 206	55.71 127	76.04 224	81.81 124	50.30 335	69.66 165	85.40 170	52.51 103	84.89 131	51.82 276	80.24 129	85.45 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 145	69.41 164	75.12 98	79.20 147	53.86 159	77.89 161	80.00 169	53.88 280	69.40 170	84.61 184	43.21 235	86.56 81	58.80 217	77.68 189	84.95 182

test_fmvsmvis_n_1920		70.84 145	70.38 145	72.22 199	71.16 368	55.39 137	75.86 228	72.21 322	49.03 352	73.28 101	86.17 144	51.83 118	77.29 314	75.80 46	78.05 183	83.98 213

114514_t		70.83 147	69.56 159	74.64 110	86.21 32	54.63 148	82.34 81	81.81 124	48.22 365	63.01 302	85.83 157	40.92 272	87.10 67	57.91 223	79.79 136	82.18 271

FIs		70.82 148	71.43 121	68.98 282	78.33 179	38.14 399	76.96 197	83.59 81	61.02 100	67.33 218	86.73 120	55.07 62	81.64 211	54.61 253	79.22 152	87.14 84

ACMM	61.98 7	70.80 149	69.73 156	74.02 136	80.59 120	58.59 82	82.68 75	82.02 121	55.46 239	67.18 223	84.39 193	38.51 296	83.17 166	60.65 197	76.10 216	80.30 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 150	70.43 143	71.46 221	69.45 397	48.95 281	72.93 295	78.46 209	57.27 193	71.69 134	83.97 202	51.48 125	77.92 298	70.70 94	77.95 185	87.53 66
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)		70.63 151	70.20 148	71.89 204	78.55 168	45.29 327	75.94 226	82.92 107	63.68 42	68.16 193	83.59 211	53.89 80	83.49 160	53.97 257	71.12 293	86.89 90

xiu_mvs_v2_base		70.52 152	69.75 155	72.84 181	81.21 107	55.63 130	75.11 244	78.92 189	54.92 260	69.96 161	79.68 303	47.00 192	82.09 204	61.60 189	79.37 143	80.81 302

PS-MVSNAJ		70.51 153	69.70 157	72.93 179	81.52 98	55.79 126	74.92 251	79.00 187	55.04 255	69.88 162	78.66 320	47.05 188	82.19 202	61.61 188	79.58 140	80.83 301

fmvsm_l_conf0.5_n_a		70.50 154	70.27 147	71.18 236	71.30 366	54.09 156	76.89 200	69.87 339	47.90 371	74.37 77	86.49 133	53.07 97	76.69 331	75.41 52	77.11 201	82.76 255

v2v482		70.50 154	69.45 163	73.66 155	72.62 335	50.03 254	77.58 170	80.51 160	59.90 134	69.52 166	82.14 252	47.53 179	84.88 133	65.07 148	70.17 310	86.09 126

v1144		70.42 156	69.31 165	73.76 147	73.22 323	50.64 236	77.83 165	81.43 132	58.58 165	69.40 170	81.16 271	47.53 179	85.29 123	64.01 156	70.64 298	85.34 165

SSM_0407		70.41 157	68.96 174	74.75 104	78.65 164	53.46 171	77.28 185	80.00 169	53.88 280	68.14 194	84.61 184	43.21 235	86.26 95	58.80 217	76.11 213	84.54 192

TranMVSNet+NR-MVSNet		70.36 158	70.10 153	71.17 237	78.64 167	42.97 353	76.53 210	81.16 148	66.95 6	68.53 185	85.42 169	51.61 122	83.07 167	52.32 269	69.70 322	87.46 68

v8		70.33 159	69.28 166	73.49 164	73.15 325	50.22 249	78.62 137	80.78 156	60.79 106	66.45 238	82.11 254	49.35 153	84.98 127	63.58 167	68.71 338	85.28 168

Fast-Effi-MVS+		70.28 160	69.12 170	73.73 151	78.50 169	51.50 220	75.01 247	79.46 179	56.16 223	68.59 182	79.55 306	53.97 78	84.05 145	53.34 263	77.53 191	85.65 149

X-MVStestdata		70.21 161	67.28 220	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 128	6.49 485	47.95 170	88.01 44	71.55 88	86.74 59	86.37 112

v10		70.21 161	69.02 171	73.81 144	73.51 319	50.92 228	78.74 133	81.39 133	60.05 131	66.39 239	81.83 259	47.58 177	85.41 121	62.80 177	68.86 337	85.09 176

Elysia		70.19 163	68.29 193	75.88 80	74.15 308	54.33 153	78.26 143	83.21 97	55.04 255	67.28 219	83.59 211	30.16 389	86.11 98	63.67 165	79.26 150	87.20 81

StellarMVS		70.19 163	68.29 193	75.88 80	74.15 308	54.33 153	78.26 143	83.21 97	55.04 255	67.28 219	83.59 211	30.16 389	86.11 98	63.67 165	79.26 150	87.20 81

QAPM		70.05 165	68.81 177	73.78 145	76.54 251	53.43 174	83.23 65	83.48 83	52.89 296	65.90 250	86.29 140	41.55 262	86.49 87	51.01 282	78.40 178	81.42 282

DU-MVS		70.01 166	69.53 160	71.44 224	78.05 190	44.13 339	75.01 247	81.51 130	64.37 30	68.20 190	84.52 188	49.12 160	82.82 187	54.62 251	70.43 302	87.37 75

AdaColmap		69.99 167	68.66 181	73.97 140	84.94 58	57.83 90	82.63 76	78.71 195	56.28 220	64.34 281	84.14 196	41.57 260	87.06 69	46.45 322	78.88 162	77.02 362

v1192		69.97 168	68.68 180	73.85 142	73.19 324	50.94 226	77.68 169	81.36 135	57.51 191	68.95 180	80.85 281	45.28 212	85.33 122	62.97 176	70.37 304	85.27 169

Anonymous20240529		69.91 169	69.02 171	72.56 188	80.19 126	47.65 302	77.56 172	80.99 152	55.45 240	69.88 162	86.76 116	39.24 288	82.18 203	54.04 256	77.10 202	87.85 51

patch_mono-2		69.85 170	71.09 131	66.16 321	79.11 152	54.80 147	71.97 313	74.31 292	53.50 289	70.90 145	84.17 195	57.63 34	63.31 415	66.17 135	82.02 104	80.38 310

fmvsm_s_conf0.5_n_2		69.82 171	69.27 167	71.46 221	72.00 350	51.08 223	73.30 285	67.79 358	55.06 254	75.24 56	87.51 90	44.02 228	77.00 321	75.67 48	72.86 267	86.31 121

FA-MVS(test-final)		69.82 171	68.48 184	73.84 143	78.44 172	50.04 253	75.58 235	78.99 188	58.16 172	67.59 214	82.14 252	42.66 241	85.63 110	56.60 230	76.19 212	85.84 136

FC-MVSNet-test		69.80 173	70.58 142	67.46 300	77.61 211	34.73 432	76.05 223	83.19 101	60.84 105	65.88 252	86.46 134	54.52 71	80.76 240	52.52 268	78.12 182	86.91 89

v144192		69.71 174	68.51 183	73.33 171	73.10 326	50.13 251	77.54 173	80.64 157	56.65 204	68.57 184	80.55 284	46.87 193	84.96 129	62.98 175	69.66 323	84.89 184

test_yl		69.69 175	69.13 168	71.36 230	78.37 176	45.74 320	74.71 255	80.20 166	57.91 182	70.01 159	83.83 204	42.44 244	82.87 183	54.97 247	79.72 137	85.48 154

DCV-MVSNet		69.69 175	69.13 168	71.36 230	78.37 176	45.74 320	74.71 255	80.20 166	57.91 182	70.01 159	83.83 204	42.44 244	82.87 183	54.97 247	79.72 137	85.48 154

VNet		69.68 177	70.19 149	68.16 294	79.73 134	41.63 367	70.53 335	77.38 233	60.37 120	70.69 146	86.63 125	51.08 131	77.09 317	53.61 261	81.69 112	85.75 143

jason		69.65 178	68.39 190	73.43 168	78.27 181	56.88 108	77.12 191	73.71 304	46.53 389	69.34 172	83.22 220	43.37 233	79.18 270	64.77 150	79.20 153	84.23 204
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 179	69.01 173	71.52 219	71.66 355	51.04 224	73.39 284	67.14 364	55.02 258	75.11 58	87.64 89	42.94 240	77.01 320	75.55 50	72.63 273	86.52 107

Effi-MVS+-dtu		69.64 179	67.53 210	75.95 78	76.10 257	62.29 15	80.20 110	76.06 258	59.83 139	65.26 265	77.09 351	41.56 261	84.02 148	60.60 198	71.09 296	81.53 281

fmvsm_s_conf0.5_n		69.58 181	68.84 176	71.79 209	72.31 346	52.90 187	77.90 160	62.43 408	49.97 340	72.85 117	85.90 154	52.21 109	76.49 334	75.75 47	70.26 309	85.97 129

lupinMVS		69.57 182	68.28 195	73.44 167	78.76 160	57.15 104	76.57 209	73.29 310	46.19 392	69.49 167	82.18 248	43.99 229	79.23 269	64.66 151	79.37 143	83.93 215

fmvsm_s_conf0.5_n_7		69.54 183	69.67 158	69.15 281	73.47 321	51.41 221	70.35 339	73.34 307	57.05 198	68.41 186	85.83 157	49.86 145	72.84 355	71.86 84	76.83 205	83.19 244

fmvsm_s_conf0.5_n_a		69.54 183	68.74 179	71.93 203	72.47 340	53.82 161	78.25 145	62.26 410	49.78 342	73.12 109	86.21 142	52.66 101	76.79 327	75.02 56	68.88 335	85.18 171

NR-MVSNet		69.54 183	68.85 175	71.59 218	78.05 190	43.81 344	74.20 266	80.86 155	65.18 14	62.76 306	84.52 188	52.35 108	83.59 157	50.96 284	70.78 297	87.37 75

MVS_111021_LR		69.50 186	68.78 178	71.65 216	78.38 174	59.33 61	74.82 253	70.11 337	58.08 173	67.83 209	84.68 180	41.96 249	76.34 338	65.62 143	77.54 190	79.30 332

v1921920		69.47 187	68.17 197	73.36 170	73.06 327	50.10 252	77.39 178	80.56 158	56.58 213	68.59 182	80.37 286	44.72 220	84.98 127	62.47 181	69.82 318	85.00 178

test_djsdf		69.45 188	67.74 203	74.58 113	74.57 297	54.92 145	82.79 72	78.48 207	51.26 324	65.41 259	83.49 216	38.37 298	83.24 164	66.06 136	69.25 330	85.56 151

fmvsm_s_conf0.1_n		69.41 189	68.60 182	71.83 206	71.07 369	52.88 190	77.85 164	62.44 407	49.58 345	72.97 112	86.22 141	51.68 121	76.48 335	75.53 51	70.10 312	86.14 124

fmvsm_s_conf0.1_n_a		69.32 190	68.44 188	71.96 201	70.91 371	53.78 162	78.12 154	62.30 409	49.35 348	73.20 103	86.55 132	51.99 114	76.79 327	74.83 58	68.68 340	85.32 166

Anonymous20231211		69.28 191	68.47 186	71.73 211	80.28 121	47.18 308	79.98 112	82.37 116	54.61 266	67.24 221	84.01 200	39.43 284	82.41 199	55.45 245	72.83 268	85.62 150

EI-MVSNet		69.27 192	68.44 188	71.73 211	74.47 298	49.39 269	75.20 242	78.45 210	59.60 142	69.16 177	76.51 364	51.29 127	82.50 196	59.86 206	71.45 290	83.30 239

v1240		69.24 193	67.91 202	73.25 174	73.02 329	49.82 256	77.21 188	80.54 159	56.43 215	68.34 189	80.51 285	43.33 234	84.99 125	62.03 185	69.77 321	84.95 182

IterMVS-LS		69.22 194	68.48 184	71.43 226	74.44 300	49.40 268	76.23 217	77.55 228	59.60 142	65.85 253	81.59 266	51.28 128	81.58 214	59.87 205	69.90 317	83.30 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 195	68.38 191	71.38 228	71.57 357	48.61 286	73.22 290	73.18 311	57.65 187	70.67 147	84.73 178	50.03 142	79.80 255	63.25 170	71.10 294	85.74 144

viewmsd2359difaftdt		69.13 195	68.38 191	71.38 228	71.57 357	48.61 286	73.22 290	73.18 311	57.65 187	70.67 147	84.73 178	50.03 142	79.80 255	63.25 170	71.10 294	85.74 144

IMVS_0403		69.09 197	68.14 198	71.95 202	77.06 234	49.73 258	74.51 259	78.60 199	52.70 298	66.69 232	82.58 231	46.43 196	83.38 161	59.20 212	75.46 226	82.74 256

VPA-MVSNet		69.02 198	69.47 162	67.69 298	77.42 216	41.00 374	74.04 268	79.68 173	60.06 130	69.26 175	84.81 176	51.06 132	77.58 307	54.44 254	74.43 237	84.48 197

v7n		69.01 199	67.36 217	73.98 139	72.51 339	52.65 196	78.54 141	81.30 140	60.26 126	62.67 308	81.62 263	43.61 231	84.49 139	57.01 228	68.70 339	84.79 187

viewmambaseed2359dif		68.91 200	68.18 196	71.11 239	70.21 382	48.05 298	72.28 308	75.90 260	51.96 310	70.93 144	84.47 191	51.37 126	78.59 287	61.55 191	74.97 231	86.68 99

IMVS_0407		68.90 201	67.93 201	71.82 207	77.06 234	49.73 258	74.40 264	78.60 199	52.70 298	66.19 242	82.58 231	45.17 215	83.00 169	59.20 212	75.46 226	82.74 256

OpenMVS	61.03 9	68.85 202	67.56 207	72.70 185	74.26 306	53.99 158	81.21 97	81.34 139	52.70 298	62.75 307	85.55 166	38.86 293	84.14 144	48.41 304	83.01 89	79.97 319

XVG-OURS-SEG-HR		68.81 203	67.47 213	72.82 183	74.40 301	56.87 109	70.59 334	79.04 186	54.77 263	66.99 226	86.01 151	39.57 283	78.21 292	62.54 179	73.33 259	83.37 238

BH-RMVSNet		68.81 203	67.42 214	72.97 178	80.11 129	52.53 200	74.26 265	76.29 253	58.48 167	68.38 188	84.20 194	42.59 242	83.83 151	46.53 321	75.91 218	82.56 260

UGNet		68.81 203	67.39 215	73.06 176	78.33 179	54.47 149	79.77 117	75.40 272	60.45 115	63.22 295	84.40 192	32.71 366	80.91 236	51.71 278	80.56 125	83.81 221
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		68.76 206	67.37 216	72.90 180	74.32 304	57.22 99	70.09 343	78.81 192	55.24 245	67.79 211	85.81 160	36.54 321	78.28 291	62.04 184	75.74 221	83.19 244

V42		68.65 207	67.35 218	72.56 188	68.93 404	50.18 250	72.90 297	79.47 178	56.92 201	69.45 169	80.26 290	46.29 198	82.99 170	64.07 154	67.82 346	84.53 195

PVSNet_Blended		68.59 208	67.72 204	71.19 235	77.03 240	50.57 237	72.51 304	81.52 128	51.91 311	64.22 287	77.77 342	49.13 158	82.87 183	55.82 238	79.58 140	80.14 317

xiu_mvs_v1_base_debu		68.58 209	67.28 220	72.48 191	78.19 183	57.19 101	75.28 239	75.09 280	51.61 315	70.04 155	81.41 268	32.79 362	79.02 279	63.81 161	77.31 195	81.22 291

xiu_mvs_v1_base		68.58 209	67.28 220	72.48 191	78.19 183	57.19 101	75.28 239	75.09 280	51.61 315	70.04 155	81.41 268	32.79 362	79.02 279	63.81 161	77.31 195	81.22 291

xiu_mvs_v1_base_debi		68.58 209	67.28 220	72.48 191	78.19 183	57.19 101	75.28 239	75.09 280	51.61 315	70.04 155	81.41 268	32.79 362	79.02 279	63.81 161	77.31 195	81.22 291

PVSNet_BlendedMVS		68.56 212	67.72 204	71.07 241	77.03 240	50.57 237	74.50 260	81.52 128	53.66 288	64.22 287	79.72 302	49.13 158	82.87 183	55.82 238	73.92 243	79.77 327

WR-MVS		68.47 213	68.47 186	68.44 289	80.20 125	39.84 382	73.75 278	76.07 257	64.68 24	68.11 198	83.63 210	50.39 140	79.14 275	49.78 289	69.66 323	86.34 114

mvsmamba		68.47 213	66.56 235	74.21 127	79.60 136	52.95 185	74.94 250	75.48 270	52.09 309	60.10 340	83.27 219	36.54 321	84.70 135	59.32 211	77.69 188	84.99 180

AUN-MVS		68.45 215	66.41 242	74.57 114	79.53 139	57.08 107	73.93 273	75.23 276	54.44 271	66.69 232	81.85 258	37.10 316	82.89 181	62.07 183	66.84 354	83.75 226

c3_l		68.33 216	67.56 207	70.62 251	70.87 372	46.21 316	74.47 261	78.80 193	56.22 222	66.19 242	78.53 325	51.88 115	81.40 218	62.08 182	69.04 333	84.25 203

BH-untuned		68.27 217	67.29 219	71.21 234	79.74 133	53.22 179	76.06 222	77.46 231	57.19 194	66.10 245	81.61 264	45.37 211	83.50 159	45.42 338	76.68 208	76.91 366

jajsoiax		68.25 218	66.45 238	73.66 155	75.62 265	55.49 135	80.82 101	78.51 206	52.33 306	64.33 282	84.11 197	28.28 409	81.81 210	63.48 168	70.62 299	83.67 229

LuminaMVS		68.24 219	66.82 232	72.51 190	73.46 322	53.60 167	76.23 217	78.88 190	52.78 297	68.08 200	80.13 292	32.70 367	81.41 217	63.16 173	75.97 217	82.53 262

v148		68.24 219	67.19 227	71.40 227	70.43 379	47.77 301	75.76 231	77.03 241	58.91 156	67.36 217	80.10 294	48.60 165	81.89 207	60.01 202	66.52 358	84.53 195

CANet_DTU		68.18 221	67.71 206	69.59 270	74.83 286	46.24 315	78.66 136	76.85 244	59.60 142	63.45 293	82.09 255	35.25 331	77.41 310	59.88 204	78.76 167	85.14 172

mvs_tets		68.18 221	66.36 244	73.63 158	75.61 266	55.35 139	80.77 102	78.56 204	52.48 305	64.27 284	84.10 198	27.45 417	81.84 209	63.45 169	70.56 301	83.69 228

guyue		68.10 223	67.23 226	70.71 250	73.67 318	49.27 273	73.65 280	76.04 259	55.62 236	67.84 208	82.26 246	41.24 268	78.91 285	61.01 194	73.72 247	83.94 214

SDMVSNet		68.03 224	68.10 200	67.84 296	77.13 229	48.72 285	65.32 386	79.10 183	58.02 176	65.08 269	82.55 236	47.83 172	73.40 352	63.92 158	73.92 243	81.41 283

miper_ehance_all_eth		68.03 224	67.24 224	70.40 255	70.54 376	46.21 316	73.98 269	78.68 197	55.07 252	66.05 246	77.80 339	52.16 111	81.31 221	61.53 192	69.32 327	83.67 229

mvs_anonymous		68.03 224	67.51 211	69.59 270	72.08 348	44.57 336	71.99 312	75.23 276	51.67 313	67.06 225	82.57 235	54.68 69	77.94 296	56.56 233	75.71 222	86.26 123

ET-MVSNet_ETH3D		67.96 227	65.72 256	74.68 107	76.67 247	55.62 132	75.11 244	74.74 285	52.91 295	60.03 342	80.12 293	33.68 351	82.64 192	61.86 186	76.34 210	85.78 138

thisisatest0530		67.92 228	65.78 255	74.33 122	76.29 254	51.03 225	76.89 200	74.25 295	53.67 287	65.59 256	81.76 261	35.15 332	85.50 116	55.94 236	72.47 274	86.47 109

PAPM		67.92 228	66.69 234	71.63 217	78.09 188	49.02 277	77.09 192	81.24 144	51.04 327	60.91 334	83.98 201	47.71 174	84.99 125	40.81 375	79.32 147	80.90 300

AstraMVS		67.86 230	66.83 231	70.93 244	73.50 320	49.34 270	73.28 288	74.01 299	55.45 240	68.10 199	83.28 218	38.93 292	79.14 275	63.22 172	71.74 285	84.30 202

tttt0517		67.83 231	65.66 257	74.33 122	76.69 245	50.82 230	77.86 163	73.99 300	54.54 269	64.64 279	82.53 239	35.06 333	85.50 116	55.71 241	69.91 316	86.67 100

mamba_0408		67.78 232	65.42 261	74.85 103	78.65 164	53.46 171	50.83 458	79.09 184	53.75 283	68.14 194	83.83 204	41.79 256	86.56 81	56.58 231	76.11 213	84.54 192

tt0805		67.77 233	67.24 224	69.34 275	74.87 284	40.08 379	77.36 179	81.37 134	55.31 242	66.33 240	84.65 182	37.35 310	82.55 195	55.65 243	72.28 279	85.39 163

ECVR-MVS		67.72 234	67.51 211	68.35 290	79.46 140	36.29 422	74.79 254	66.93 366	58.72 159	67.19 222	88.05 80	36.10 323	81.38 219	52.07 272	84.25 78	87.39 73

eth_miper_zixun_eth		67.63 235	66.28 248	71.67 215	71.60 356	48.33 291	73.68 279	77.88 221	55.80 230	65.91 249	78.62 323	47.35 185	82.88 182	59.45 208	66.25 359	83.81 221

UniMVSNet_ETH3D		67.60 236	67.07 229	69.18 279	77.39 217	42.29 358	74.18 267	75.59 266	60.37 120	66.77 230	86.06 148	37.64 306	78.93 284	52.16 271	73.49 254	86.32 118

VPNet		67.52 237	68.11 199	65.74 331	79.18 149	36.80 414	72.17 310	72.83 316	62.04 81	67.79 211	85.83 157	48.88 162	76.60 333	51.30 280	72.97 266	83.81 221

cl22		67.47 238	66.45 238	70.54 253	69.85 392	46.49 312	73.85 276	77.35 234	55.07 252	65.51 257	77.92 334	47.64 176	81.10 227	61.58 190	69.32 327	84.01 212

Fast-Effi-MVS+-dtu		67.37 239	65.33 265	73.48 165	72.94 330	57.78 92	77.47 176	76.88 243	57.60 190	61.97 320	76.85 355	39.31 285	80.49 245	54.72 250	70.28 308	82.17 273

MVS		67.37 239	66.33 245	70.51 254	75.46 270	50.94 226	73.95 271	81.85 123	41.57 429	62.54 312	78.57 324	47.98 169	85.47 118	52.97 266	82.05 103	75.14 382

test1111		67.21 241	67.14 228	67.42 301	79.24 146	34.76 431	73.89 275	65.65 376	58.71 161	66.96 227	87.95 84	36.09 324	80.53 242	52.03 273	83.79 84	86.97 88

GBi-Net		67.21 241	66.55 236	69.19 276	77.63 206	43.33 347	77.31 180	77.83 223	56.62 208	65.04 271	82.70 226	41.85 253	80.33 247	47.18 316	72.76 269	83.92 216

test1		67.21 241	66.55 236	69.19 276	77.63 206	43.33 347	77.31 180	77.83 223	56.62 208	65.04 271	82.70 226	41.85 253	80.33 247	47.18 316	72.76 269	83.92 216

cl____		67.18 244	66.26 249	69.94 262	70.20 383	45.74 320	73.30 285	76.83 246	55.10 247	65.27 262	79.57 305	47.39 183	80.53 242	59.41 210	69.22 331	83.53 235

DIV-MVS_self_test		67.18 244	66.26 249	69.94 262	70.20 383	45.74 320	73.29 287	76.83 246	55.10 247	65.27 262	79.58 304	47.38 184	80.53 242	59.43 209	69.22 331	83.54 234

MVSTER		67.16 246	65.58 259	71.88 205	70.37 381	49.70 262	70.25 341	78.45 210	51.52 318	69.16 177	80.37 286	38.45 297	82.50 196	60.19 200	71.46 289	83.44 237

miper_enhance_ethall		67.11 247	66.09 251	70.17 259	69.21 400	45.98 318	72.85 298	78.41 213	51.38 321	65.65 255	75.98 374	51.17 130	81.25 222	60.82 196	69.32 327	83.29 241

Baseline_NR-MVSNet		67.05 248	67.56 207	65.50 335	75.65 263	37.70 405	75.42 236	74.65 288	59.90 134	68.14 194	83.15 223	49.12 160	77.20 315	52.23 270	69.78 319	81.60 279

WR-MVS_H		67.02 249	66.92 230	67.33 304	77.95 194	37.75 403	77.57 171	82.11 120	62.03 82	62.65 309	82.48 240	50.57 138	79.46 264	42.91 361	64.01 376	84.79 187

anonymousdsp		67.00 250	64.82 270	73.57 161	70.09 386	56.13 117	76.35 213	77.35 234	48.43 362	64.99 274	80.84 282	33.01 359	80.34 246	64.66 151	67.64 348	84.23 204

FMVSNet2		66.93 251	66.31 247	68.79 285	77.63 206	42.98 352	76.11 220	77.47 229	56.62 208	65.22 268	82.17 250	41.85 253	80.18 253	47.05 319	72.72 272	83.20 243

BH-w/o		66.85 252	65.83 254	69.90 265	79.29 142	52.46 203	74.66 257	76.65 250	54.51 270	64.85 276	78.12 328	45.59 204	82.95 175	43.26 357	75.54 224	74.27 396

Anonymous202405211		66.84 253	65.99 252	69.40 274	80.19 126	42.21 360	71.11 327	71.31 328	58.80 158	67.90 202	86.39 136	29.83 394	79.65 258	49.60 295	78.78 165	86.33 116

CDS-MVSNet		66.80 254	65.37 263	71.10 240	78.98 154	53.13 183	73.27 289	71.07 330	52.15 308	64.72 277	80.23 291	43.56 232	77.10 316	45.48 336	78.88 162	83.05 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 255	65.27 266	71.33 233	79.16 151	53.67 164	73.84 277	69.59 343	52.32 307	65.28 261	81.72 262	44.49 224	77.40 311	42.32 365	78.66 171	82.92 251

FMVSNet1		66.70 256	65.87 253	69.19 276	77.49 214	43.33 347	77.31 180	77.83 223	56.45 214	64.60 280	82.70 226	38.08 304	80.33 247	46.08 326	72.31 278	83.92 216

ab-mvs		66.65 257	66.42 241	67.37 302	76.17 256	41.73 364	70.41 338	76.14 256	53.99 277	65.98 247	83.51 215	49.48 150	76.24 339	48.60 302	73.46 256	84.14 208

PEN-MVS		66.60 258	66.45 238	67.04 305	77.11 233	36.56 416	77.03 194	80.42 163	62.95 55	62.51 314	84.03 199	46.69 194	79.07 277	44.22 343	63.08 386	85.51 153

TAPA-MVS	59.36 10	66.60 258	65.20 267	70.81 246	76.63 248	48.75 283	76.52 211	80.04 168	50.64 332	65.24 266	84.93 174	39.15 289	78.54 288	36.77 402	76.88 204	85.14 172
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 260	65.07 268	71.17 237	79.18 149	49.63 266	73.48 281	75.20 278	52.95 294	67.90 202	80.33 289	39.81 281	83.68 154	43.20 358	73.56 253	80.20 315

CP-MVSNet		66.49 261	66.41 242	66.72 307	77.67 204	36.33 419	76.83 204	79.52 177	62.45 68	62.54 312	83.47 217	46.32 197	78.37 289	45.47 337	63.43 383	85.45 158

PS-CasMVS		66.42 262	66.32 246	66.70 309	77.60 212	36.30 421	76.94 198	79.61 175	62.36 70	62.43 317	83.66 209	45.69 201	78.37 289	45.35 339	63.26 384	85.42 161

icg_test_0407_2		66.41 263	66.75 233	65.37 339	77.06 234	49.73 258	63.79 400	78.60 199	52.70 298	66.19 242	82.58 231	45.17 215	63.65 414	59.20 212	75.46 226	82.74 256

VortexMVS		66.41 263	65.50 260	69.16 280	73.75 314	48.14 293	73.41 283	78.28 217	53.73 285	64.98 275	78.33 326	40.62 273	79.07 277	58.88 216	67.50 349	80.26 314

FMVSNet3		66.32 265	65.61 258	68.46 288	76.48 252	42.34 357	74.98 249	77.15 238	55.83 228	65.04 271	81.16 271	39.91 278	80.14 254	47.18 316	72.76 269	82.90 253

ACMH+	57.40 11	66.12 266	64.06 275	72.30 198	77.79 198	52.83 192	80.39 105	78.03 220	57.30 192	57.47 374	82.55 236	27.68 415	84.17 143	45.54 333	69.78 319	79.90 321

cascas		65.98 267	63.42 288	73.64 157	77.26 221	52.58 199	72.26 309	77.21 237	48.56 358	61.21 331	74.60 389	32.57 373	85.82 108	50.38 287	76.75 207	82.52 264

FE-MVS		65.91 268	63.33 290	73.63 158	77.36 218	51.95 215	72.62 301	75.81 261	53.70 286	65.31 260	78.96 316	28.81 404	86.39 89	43.93 348	73.48 255	82.55 261

thisisatest0515		65.83 269	63.50 286	72.82 183	73.75 314	49.50 267	71.32 321	73.12 315	49.39 347	63.82 289	76.50 366	34.95 335	84.84 134	53.20 265	75.49 225	84.13 209

DP-MVS		65.68 270	63.66 283	71.75 210	84.93 59	56.87 109	80.74 103	73.16 313	53.06 293	59.09 356	82.35 242	36.79 320	85.94 105	32.82 426	69.96 315	72.45 411

HyFIR lowres test		65.67 271	63.01 295	73.67 154	79.97 131	55.65 129	69.07 354	75.52 268	42.68 423	63.53 292	77.95 332	40.43 275	81.64 211	46.01 327	71.91 283	83.73 227

DTE-MVSNet		65.58 272	65.34 264	66.31 317	76.06 258	34.79 429	76.43 212	79.38 180	62.55 66	61.66 326	83.83 204	45.60 203	79.15 274	41.64 373	60.88 403	85.00 178

GA-MVS		65.53 273	63.70 282	71.02 243	70.87 372	48.10 294	70.48 336	74.40 290	56.69 203	64.70 278	76.77 356	33.66 352	81.10 227	55.42 246	70.32 307	83.87 219

CNLPA		65.43 274	64.02 276	69.68 268	78.73 162	58.07 87	77.82 166	70.71 333	51.49 319	61.57 328	83.58 214	38.23 302	70.82 370	43.90 349	70.10 312	80.16 316

MVP-Stereo		65.41 275	63.80 280	70.22 256	77.62 210	55.53 134	76.30 214	78.53 205	50.59 333	56.47 385	78.65 321	39.84 280	82.68 190	44.10 347	72.12 282	72.44 412
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 276	62.73 299	73.40 169	74.89 282	52.78 193	73.09 294	75.13 279	55.69 232	58.48 365	73.73 397	32.86 361	86.32 92	50.63 285	70.11 311	81.10 295
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		65.33 277	64.61 271	67.50 299	79.46 140	34.19 437	74.43 263	51.92 448	58.72 159	66.75 231	88.05 80	25.99 429	80.92 235	51.94 274	84.25 78	87.39 73

pm-mvs1		65.24 278	64.97 269	66.04 325	72.38 343	39.40 388	72.62 301	75.63 264	55.53 237	62.35 319	83.18 222	47.45 181	76.47 336	49.06 299	66.54 357	82.24 270

ACMH	55.70 15	65.20 279	63.57 284	70.07 260	78.07 189	52.01 213	79.48 125	79.69 172	55.75 231	56.59 382	80.98 276	27.12 420	80.94 233	42.90 362	71.58 288	77.25 360
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 280	63.21 293	70.72 249	81.04 110	54.87 146	78.57 139	77.47 229	48.51 360	55.71 390	81.89 257	33.71 350	79.71 257	41.66 371	70.37 304	77.58 353
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 281	62.84 297	71.82 207	81.49 100	56.26 115	66.32 374	74.20 297	40.53 435	63.16 298	78.65 321	41.30 264	77.80 302	45.80 329	74.09 240	81.40 285

SSM_04072		64.98 282	65.42 261	63.68 354	78.65 164	53.46 171	50.83 458	79.09 184	53.75 283	68.14 194	83.83 204	41.79 256	53.03 459	56.58 231	76.11 213	84.54 192

TransMVSNet (Re)		64.72 283	64.33 273	65.87 330	75.22 275	38.56 394	74.66 257	75.08 283	58.90 157	61.79 323	82.63 229	51.18 129	78.07 294	43.63 354	55.87 427	80.99 299

EG-PatchMatch MVS		64.71 284	62.87 296	70.22 256	77.68 203	53.48 170	77.99 159	78.82 191	53.37 290	56.03 389	77.41 347	24.75 437	84.04 146	46.37 323	73.42 258	73.14 402

LS3D		64.71 284	62.50 301	71.34 232	79.72 135	55.71 127	79.82 116	74.72 286	48.50 361	56.62 381	84.62 183	33.59 353	82.34 200	29.65 448	75.23 230	75.97 372

IMVS_0404		64.63 286	64.22 274	65.88 329	77.06 234	49.73 258	64.40 394	78.60 199	52.70 298	53.16 420	82.58 231	34.82 336	65.16 408	59.20 212	75.46 226	82.74 256

1314		64.61 287	63.21 293	68.80 284	71.87 353	47.46 305	73.95 271	78.39 215	42.88 422	59.97 343	76.60 363	38.11 303	79.39 266	54.84 249	72.32 277	79.55 328

HY-MVS	56.14 13	64.55 288	63.89 277	66.55 313	74.73 290	41.02 371	69.96 344	74.43 289	49.29 349	61.66 326	80.92 278	47.43 182	76.68 332	44.91 341	71.69 286	81.94 275

testing91		64.46 289	63.80 280	66.47 314	78.43 173	40.06 380	67.63 364	69.59 343	59.06 153	63.18 297	78.05 330	34.05 344	76.99 322	48.30 305	75.87 219	82.37 268

sd_testset		64.46 289	64.45 272	64.51 347	77.13 229	42.25 359	62.67 407	72.11 323	58.02 176	65.08 269	82.55 236	41.22 269	69.88 378	47.32 314	73.92 243	81.41 283

XVG-ACMP-BASELINE		64.36 291	62.23 305	70.74 248	72.35 344	52.45 204	70.80 332	78.45 210	53.84 282	59.87 345	81.10 273	16.24 456	79.32 267	55.64 244	71.76 284	80.47 306

FE-MVSNET3		64.34 292	63.57 284	66.66 311	72.44 342	40.74 377	69.60 348	76.80 248	53.21 292	61.73 325	77.92 334	41.92 252	77.68 306	46.23 324	72.25 280	81.57 280

MonoMVSNet		64.15 293	63.31 291	66.69 310	70.51 377	44.12 341	74.47 261	74.21 296	57.81 184	63.03 300	76.62 360	38.33 299	77.31 313	54.22 255	60.59 409	78.64 339

testing99		64.05 294	63.29 292	66.34 316	78.17 186	39.76 384	67.33 369	68.00 357	58.60 164	63.03 300	78.10 329	32.57 373	76.94 324	48.22 306	75.58 223	82.34 269

CostFormer		64.04 295	62.51 300	68.61 287	71.88 352	45.77 319	71.30 322	70.60 334	47.55 376	64.31 283	76.61 362	41.63 259	79.62 260	49.74 291	69.00 334	80.42 308

1112_ss		64.00 296	63.36 289	65.93 327	79.28 144	42.58 356	71.35 320	72.36 321	46.41 390	60.55 337	77.89 337	46.27 199	73.28 353	46.18 325	69.97 314	81.92 276

baseline1		63.81 297	63.87 279	63.62 355	76.29 254	36.36 417	71.78 317	67.29 362	56.05 225	64.23 286	82.95 224	47.11 187	74.41 348	47.30 315	61.85 397	80.10 318

pmmvs6		63.69 298	62.82 298	66.27 319	70.63 374	39.27 389	73.13 293	75.47 271	52.69 303	59.75 349	82.30 244	39.71 282	77.03 319	47.40 313	64.35 375	82.53 262

Vis-MVSNet (Re-imp)		63.69 298	63.88 278	63.14 360	74.75 289	31.04 455	71.16 325	63.64 396	56.32 218	59.80 347	84.99 173	44.51 222	75.46 343	39.12 387	80.62 121	82.92 251

baseline2		63.42 300	61.26 319	69.89 266	72.55 337	47.62 303	71.54 318	68.38 354	50.11 337	54.82 402	75.55 379	43.06 238	80.96 232	48.13 307	67.16 353	81.11 294

thres400		63.31 301	62.18 306	66.72 307	76.85 243	39.62 385	71.96 314	69.44 346	56.63 206	62.61 310	79.83 297	37.18 312	79.17 271	31.84 432	73.25 261	81.36 286

thres600view7		63.30 302	62.27 304	66.41 315	77.18 223	38.87 391	72.35 306	69.11 350	56.98 200	62.37 318	80.96 277	37.01 318	79.00 282	31.43 439	73.05 265	81.36 286

thres100view900		63.28 303	62.41 302	65.89 328	77.31 220	38.66 393	72.65 299	69.11 350	57.07 197	62.45 315	81.03 275	37.01 318	79.17 271	31.84 432	73.25 261	79.83 324

test_0402		63.25 304	61.01 324	69.96 261	80.00 130	54.37 152	76.86 202	72.02 324	54.58 268	58.71 359	80.79 283	35.00 334	84.36 141	26.41 460	64.71 370	71.15 430

tfpn200view9		63.18 305	62.18 306	66.21 320	76.85 243	39.62 385	71.96 314	69.44 346	56.63 206	62.61 310	79.83 297	37.18 312	79.17 271	31.84 432	73.25 261	79.83 324

LTVRE_ROB	55.42 16	63.15 306	61.23 320	68.92 283	76.57 250	47.80 299	59.92 423	76.39 252	54.35 272	58.67 361	82.46 241	29.44 398	81.49 216	42.12 366	71.14 292	77.46 354
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		63.07 307	63.49 287	61.82 368	75.16 278	31.14 454	71.89 316	73.47 305	53.34 291	58.22 367	81.81 260	45.17 215	73.86 351	37.43 396	74.87 233	80.45 307

F-COLMAP		63.05 308	60.87 328	69.58 272	76.99 242	53.63 166	78.12 154	76.16 254	47.97 370	52.41 423	81.61 264	27.87 412	78.11 293	40.07 378	66.66 356	77.00 363

testing11		62.81 309	61.90 309	65.54 333	78.38 174	40.76 376	67.59 366	66.78 368	55.48 238	60.13 339	77.11 350	31.67 380	76.79 327	45.53 334	74.45 236	79.06 334

IterMVS		62.79 310	61.27 318	67.35 303	69.37 398	52.04 212	71.17 324	68.24 356	52.63 304	59.82 346	76.91 354	37.32 311	72.36 358	52.80 267	63.19 385	77.66 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
usedtu_blend_shiyan5		62.63 311	60.77 329	68.20 292	68.53 408	44.64 333	73.47 282	77.00 242	51.91 311	57.10 378	69.95 427	38.83 294	79.61 261	47.44 310	62.67 388	80.37 311

reproduce_monomvs		62.56 312	61.20 321	66.62 312	70.62 375	44.30 338	70.13 342	73.13 314	54.78 262	61.13 332	76.37 367	25.63 432	75.63 342	58.75 219	60.29 410	79.93 320

IterMVS-SCA-FT		62.49 313	61.52 313	65.40 338	71.99 351	50.80 231	71.15 326	69.63 342	45.71 398	60.61 336	77.93 333	37.45 308	65.99 404	55.67 242	63.50 382	79.42 330

tfpnnormal		62.47 314	61.63 312	64.99 344	74.81 287	39.01 390	71.22 323	73.72 303	55.22 246	60.21 338	80.09 295	41.26 267	76.98 323	30.02 446	68.09 344	78.97 337

MS-PatchMatch		62.42 315	61.46 314	65.31 341	75.21 276	52.10 209	72.05 311	74.05 298	46.41 390	57.42 376	74.36 390	34.35 342	77.57 308	45.62 332	73.67 248	66.26 449

Test_1112_low_res		62.32 316	61.77 310	64.00 352	79.08 153	39.53 387	68.17 360	70.17 336	43.25 418	59.03 357	79.90 296	44.08 226	71.24 368	43.79 351	68.42 341	81.25 290

D2MVS		62.30 317	60.29 332	68.34 291	66.46 424	48.42 290	65.70 378	73.42 306	47.71 374	58.16 368	75.02 385	30.51 384	77.71 305	53.96 258	71.68 287	78.90 338

testing222		62.29 318	61.31 317	65.25 342	77.87 195	38.53 395	68.34 358	66.31 372	56.37 217	63.15 299	77.58 345	28.47 406	76.18 341	37.04 400	76.65 209	81.05 298

thres200		62.20 319	61.16 322	65.34 340	75.38 273	39.99 381	69.60 348	69.29 348	55.64 235	61.87 322	76.99 352	37.07 317	78.96 283	31.28 440	73.28 260	77.06 361

tpm2		62.07 320	60.10 333	67.99 295	72.79 332	43.86 343	71.05 329	66.85 367	43.14 420	62.77 305	75.39 383	38.32 300	80.80 238	41.69 370	68.88 335	79.32 331

testing3-2		62.06 321	62.36 303	61.17 376	79.29 142	30.31 457	64.09 399	63.49 397	63.50 44	62.84 303	82.22 247	32.35 377	69.02 382	40.01 381	73.43 257	84.17 207

miper_lstm_enhance		62.03 322	60.88 326	65.49 336	66.71 421	46.25 314	56.29 442	75.70 263	50.68 330	61.27 330	75.48 381	40.21 276	68.03 388	56.31 235	65.25 366	82.18 271

FE-MVSNET2		62.01 323	60.88 326	65.42 337	68.74 405	38.43 397	72.92 296	77.39 232	54.74 265	55.40 395	76.71 357	35.46 329	76.72 330	44.25 342	62.31 393	81.10 295

EPNet_dtu		61.90 324	61.97 308	61.68 369	72.89 331	39.78 383	75.85 229	65.62 377	55.09 249	54.56 406	79.36 311	37.59 307	67.02 397	39.80 383	76.95 203	78.25 342
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 325	61.35 316	63.46 356	74.58 296	31.48 453	61.42 414	58.14 426	58.71 161	53.02 421	79.55 306	43.07 237	76.80 326	45.69 330	77.96 184	82.11 274

MSDG		61.81 326	59.23 338	69.55 273	72.64 334	52.63 198	70.45 337	75.81 261	51.38 321	53.70 413	76.11 369	29.52 396	81.08 229	37.70 394	65.79 363	74.93 387

SixPastTwentyTwo		61.65 327	58.80 345	70.20 258	75.80 260	47.22 307	75.59 233	69.68 341	54.61 266	54.11 410	79.26 313	27.07 421	82.96 173	43.27 356	49.79 449	80.41 309

CL-MVSNet_self_test		61.53 328	60.94 325	63.30 358	68.95 402	36.93 413	67.60 365	72.80 317	55.67 233	59.95 344	76.63 359	45.01 218	72.22 362	39.74 384	62.09 396	80.74 304

RPMNet		61.53 328	58.42 348	70.86 245	69.96 388	52.07 210	65.31 387	81.36 135	43.20 419	59.36 352	70.15 425	35.37 330	85.47 118	36.42 409	64.65 371	75.06 383

pmmvs4		61.48 330	59.39 337	67.76 297	71.57 357	53.86 159	71.42 319	65.34 379	44.20 409	59.46 351	77.92 334	35.90 325	74.71 346	43.87 350	64.87 369	74.71 392

blend_shiyan4		61.38 331	59.10 341	68.20 292	68.94 403	44.64 333	70.81 331	76.52 251	51.63 314	57.56 373	69.94 428	28.30 408	79.61 261	47.44 310	60.78 405	80.36 312

OurMVSNet-221017-0		61.37 332	58.63 347	69.61 269	72.05 349	48.06 296	73.93 273	72.51 318	47.23 382	54.74 403	80.92 278	21.49 447	81.24 223	48.57 303	56.22 426	79.53 329

COLMAP_ROB	52.97 17	61.27 333	58.81 343	68.64 286	74.63 293	52.51 201	78.42 142	73.30 309	49.92 341	50.96 428	81.51 267	23.06 440	79.40 265	31.63 436	65.85 361	74.01 399
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 334	61.67 311	57.70 403	70.43 379	38.45 396	64.19 396	66.47 369	48.05 369	63.22 295	80.86 280	49.28 155	60.47 424	45.25 340	67.28 352	74.19 397

myMVS_eth3d28		60.66 335	61.04 323	59.51 383	77.32 219	31.58 452	63.11 404	63.87 393	59.00 154	60.90 335	78.26 327	32.69 368	66.15 403	36.10 411	78.13 181	80.81 302

SSC-MVS3.2		60.57 336	61.39 315	58.12 399	74.29 305	32.63 447	59.52 424	65.53 378	59.90 134	62.45 315	79.75 301	41.96 249	63.90 413	39.47 385	69.65 325	77.84 350

WBMVS		60.54 337	60.61 330	60.34 380	78.00 192	35.95 424	64.55 393	64.89 382	49.63 343	63.39 294	78.70 318	33.85 349	67.65 391	42.10 367	70.35 306	77.43 355

SCA		60.49 338	58.38 349	66.80 306	74.14 310	48.06 296	63.35 403	63.23 400	49.13 351	59.33 355	72.10 408	37.45 308	74.27 349	44.17 344	62.57 390	78.05 345

K. test v3		60.47 339	57.11 358	70.56 252	73.74 316	48.22 292	75.10 246	62.55 405	58.27 171	53.62 416	76.31 368	27.81 413	81.59 213	47.42 312	39.18 464	81.88 277

mmtdpeth		60.40 340	59.12 340	64.27 350	69.59 394	48.99 278	70.67 333	70.06 338	54.96 259	62.78 304	73.26 402	27.00 422	67.66 390	58.44 222	45.29 456	76.16 371

UWE-MVS		60.18 341	59.78 334	61.39 374	77.67 204	33.92 440	69.04 355	63.82 394	48.56 358	64.27 284	77.64 344	27.20 419	70.40 375	33.56 423	76.24 211	79.83 324

OpenMVS_ROB	52.78 18	60.03 342	58.14 352	65.69 332	70.47 378	44.82 329	75.33 237	70.86 332	45.04 401	56.06 388	76.00 371	26.89 424	79.65 258	35.36 415	67.29 351	72.60 407

CR-MVSNet		59.91 343	57.90 355	65.96 326	69.96 388	52.07 210	65.31 387	63.15 401	42.48 424	59.36 352	74.84 386	35.83 326	70.75 371	45.50 335	64.65 371	75.06 383

PatchmatchNet		59.84 344	58.24 350	64.65 346	73.05 328	46.70 311	69.42 351	62.18 411	47.55 376	58.88 358	71.96 410	34.49 340	69.16 380	42.99 360	63.60 380	78.07 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 345	57.84 356	65.54 333	74.87 284	42.95 354	69.61 347	64.16 391	48.90 354	58.68 360	77.12 349	28.19 410	72.35 359	43.75 353	55.28 429	81.31 289

WTY-MVS		59.75 346	60.39 331	57.85 401	72.32 345	37.83 402	61.05 419	64.18 389	45.95 397	61.91 321	79.11 315	47.01 191	60.88 423	42.50 364	69.49 326	74.83 388

WB-MVSnew		59.66 347	59.69 335	59.56 382	75.19 277	35.78 426	69.34 352	64.28 388	46.88 386	61.76 324	75.79 375	40.61 274	65.20 407	32.16 428	71.21 291	77.70 351

CVMVSNet		59.63 348	59.14 339	61.08 378	74.47 298	38.84 392	75.20 242	68.74 352	31.15 455	58.24 366	76.51 364	32.39 375	68.58 384	49.77 290	65.84 362	75.81 374

UBG		59.62 349	59.53 336	59.89 381	78.12 187	35.92 425	64.11 398	60.81 418	49.45 346	61.34 329	75.55 379	33.05 357	67.39 395	38.68 389	74.62 234	76.35 370

ETVMVS		59.51 350	58.81 343	61.58 371	77.46 215	34.87 428	64.94 391	59.35 421	54.06 276	61.08 333	76.67 358	29.54 395	71.87 364	32.16 428	74.07 241	78.01 349

tpm cat1		59.25 351	56.95 361	66.15 322	72.19 347	46.96 309	68.09 361	65.76 375	40.03 439	57.81 371	70.56 420	38.32 300	74.51 347	38.26 392	61.50 400	77.00 363

test_vis1_n_1920		58.86 352	59.06 342	58.25 395	63.76 437	43.14 351	67.49 367	66.36 371	40.22 437	65.89 251	71.95 411	31.04 381	59.75 429	59.94 203	64.90 368	71.85 420

pmmvs-eth3d		58.81 353	56.31 370	66.30 318	67.61 414	52.42 205	72.30 307	64.76 384	43.55 415	54.94 401	74.19 392	28.95 401	72.60 356	43.31 355	57.21 421	73.88 400

tt0320		58.59 354	56.81 364	63.92 353	75.46 270	41.32 369	68.63 357	64.06 392	47.05 384	56.19 387	74.19 392	30.34 386	71.36 366	39.92 382	55.45 428	79.09 333

tpmvs		58.47 355	56.95 361	63.03 362	70.20 383	41.21 370	67.90 363	67.23 363	49.62 344	54.73 404	70.84 418	34.14 343	76.24 339	36.64 406	61.29 401	71.64 422

PVSNet	50.76 19	58.40 356	57.39 357	61.42 372	75.53 268	44.04 342	61.43 413	63.45 398	47.04 385	56.91 379	73.61 398	27.00 422	64.76 409	39.12 387	72.40 275	75.47 379

tt0320-xc		58.33 357	56.41 369	64.08 351	75.79 261	41.34 368	68.30 359	62.72 404	47.90 371	56.29 386	74.16 394	28.53 405	71.04 369	41.50 374	52.50 441	79.88 322

tpmrst		58.24 358	58.70 346	56.84 405	66.97 418	34.32 435	69.57 350	61.14 416	47.17 383	58.58 364	71.60 413	41.28 266	60.41 425	49.20 297	62.84 387	75.78 375

Patchmatch-RL test		58.16 359	55.49 376	66.15 322	67.92 413	48.89 282	60.66 421	51.07 452	47.86 373	59.36 352	62.71 457	34.02 346	72.27 361	56.41 234	59.40 413	77.30 357

test-LLR		58.15 360	58.13 353	58.22 396	68.57 406	44.80 330	65.46 383	57.92 427	50.08 338	55.44 393	69.82 429	32.62 370	57.44 441	49.66 293	73.62 250	72.41 413

ppachtmachnet_test		58.06 361	55.38 377	66.10 324	69.51 395	48.99 278	68.01 362	66.13 374	44.50 406	54.05 411	70.74 419	32.09 378	72.34 360	36.68 405	56.71 425	76.99 365

gg-mvs-nofinetune		57.86 362	56.43 368	62.18 366	72.62 335	35.35 427	66.57 371	56.33 436	50.65 331	57.64 372	57.10 463	30.65 383	76.36 337	37.38 397	78.88 162	74.82 389

CMPMVS	42.80 21	57.81 363	55.97 372	63.32 357	60.98 453	47.38 306	64.66 392	69.50 345	32.06 453	46.83 446	77.80 339	29.50 397	71.36 366	48.68 301	73.75 246	71.21 429
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 364	57.07 359	58.22 396	74.21 307	37.18 408	62.46 408	60.88 417	48.88 355	55.29 397	75.99 373	31.68 379	62.04 420	31.87 431	72.35 276	75.43 380

tpm		57.34 365	58.16 351	54.86 415	71.80 354	34.77 430	67.47 368	56.04 439	48.20 366	60.10 340	76.92 353	37.17 314	53.41 458	40.76 376	65.01 367	76.40 369

Patchmtry		57.16 366	56.47 367	59.23 387	69.17 401	34.58 433	62.98 405	63.15 401	44.53 405	56.83 380	74.84 386	35.83 326	68.71 383	40.03 379	60.91 402	74.39 395

AllTest		57.08 367	54.65 381	64.39 348	71.44 361	49.03 275	69.92 345	67.30 360	45.97 395	47.16 444	79.77 299	17.47 450	67.56 393	33.65 420	59.16 414	76.57 367

test_cas_vis1_n_1920		56.91 368	56.71 365	57.51 404	59.13 459	45.40 326	63.58 401	61.29 415	36.24 447	67.14 224	71.85 412	29.89 393	56.69 445	57.65 225	63.58 381	70.46 434

mamv4		56.85 369	58.00 354	53.43 425	72.46 341	54.47 149	57.56 437	54.74 440	38.81 443	57.42 376	79.45 309	47.57 178	38.70 478	60.88 195	53.07 438	67.11 448

dmvs_re		56.77 370	56.83 363	56.61 406	69.23 399	41.02 371	58.37 429	64.18 389	50.59 333	57.45 375	71.42 414	35.54 328	58.94 434	37.23 398	67.45 350	69.87 439

testing3		56.54 371	55.92 373	58.41 394	77.52 213	27.93 465	69.72 346	56.36 435	54.75 264	58.63 363	77.80 339	20.88 448	71.75 365	25.31 462	62.25 394	75.53 378

our_test_3		56.49 372	54.42 384	62.68 364	69.51 395	45.48 325	66.08 375	61.49 414	44.11 412	50.73 432	69.60 432	33.05 357	68.15 385	38.38 391	56.86 422	74.40 394

pmmvs5		56.47 373	55.68 375	58.86 391	61.41 449	36.71 415	66.37 373	62.75 403	40.38 436	53.70 413	76.62 360	34.56 338	67.05 396	40.02 380	65.27 365	72.83 405

test-mter		56.42 374	55.82 374	58.22 396	68.57 406	44.80 330	65.46 383	57.92 427	39.94 440	55.44 393	69.82 429	21.92 443	57.44 441	49.66 293	73.62 250	72.41 413

USDC		56.35 375	54.24 388	62.69 363	64.74 433	40.31 378	65.05 389	73.83 302	43.93 413	47.58 442	77.71 343	15.36 459	75.05 345	38.19 393	61.81 398	72.70 406

PatchMatch-RL		56.25 376	54.55 383	61.32 375	77.06 234	56.07 119	65.57 380	54.10 445	44.13 411	53.49 419	71.27 417	25.20 434	66.78 398	36.52 408	63.66 379	61.12 453

sss		56.17 377	56.57 366	54.96 414	66.93 419	36.32 420	57.94 432	61.69 413	41.67 427	58.64 362	75.32 384	38.72 295	56.25 448	42.04 368	66.19 360	72.31 416

Syy-MVS		56.00 378	56.23 371	55.32 412	74.69 291	26.44 471	65.52 381	57.49 430	50.97 328	56.52 383	72.18 406	39.89 279	68.09 386	24.20 463	64.59 373	71.44 426

FMVSNet5		55.86 379	54.93 379	58.66 393	71.05 370	36.35 418	64.18 397	62.48 406	46.76 388	50.66 433	74.73 388	25.80 430	64.04 411	33.11 424	65.57 364	75.59 377

RPSCF		55.80 380	54.22 389	60.53 379	65.13 432	42.91 355	64.30 395	57.62 429	36.84 446	58.05 370	82.28 245	28.01 411	56.24 449	37.14 399	58.61 416	82.44 267

mvs5depth		55.64 381	53.81 392	61.11 377	59.39 458	40.98 375	65.89 376	68.28 355	50.21 336	58.11 369	75.42 382	17.03 452	67.63 392	43.79 351	46.21 453	74.73 391

EU-MVSNet		55.61 382	54.41 385	59.19 389	65.41 430	33.42 442	72.44 305	71.91 325	28.81 457	51.27 426	73.87 396	24.76 436	69.08 381	43.04 359	58.20 417	75.06 383

Anonymous20240521		55.30 383	54.41 385	57.96 400	60.92 455	41.73 364	71.09 328	71.06 331	41.18 430	48.65 440	73.31 400	16.93 453	59.25 431	42.54 363	64.01 376	72.90 404

TESTMET0.1,1		55.28 384	54.90 380	56.42 407	66.56 422	43.67 345	65.46 383	56.27 437	39.18 442	53.83 412	67.44 441	24.21 438	55.46 452	48.04 308	73.11 264	70.13 437

KD-MVS_self_test		55.22 385	53.89 391	59.21 388	57.80 462	27.47 467	57.75 435	74.32 291	47.38 378	50.90 429	70.00 426	28.45 407	70.30 376	40.44 377	57.92 418	79.87 323

MIMVSNet1		55.17 386	54.31 387	57.77 402	70.03 387	32.01 450	65.68 379	64.81 383	49.19 350	46.75 447	76.00 371	25.53 433	64.04 411	28.65 451	62.13 395	77.26 359

FE-MVSNET		55.16 387	53.75 393	59.41 384	65.29 431	33.20 444	67.21 370	66.21 373	48.39 364	49.56 438	73.53 399	29.03 400	72.51 357	30.38 444	54.10 435	72.52 409

Anonymous20231206		55.10 388	55.30 378	54.48 417	69.81 393	33.94 439	62.91 406	62.13 412	41.08 431	55.18 398	75.65 377	32.75 365	56.59 447	30.32 445	67.86 345	72.91 403

myMVS_eth3d		54.86 389	54.61 382	55.61 411	74.69 291	27.31 468	65.52 381	57.49 430	50.97 328	56.52 383	72.18 406	21.87 446	68.09 386	27.70 454	64.59 373	71.44 426

TinyColmap		54.14 390	51.72 402	61.40 373	66.84 420	41.97 361	66.52 372	68.51 353	44.81 402	42.69 458	75.77 376	11.66 466	72.94 354	31.96 430	56.77 424	69.27 443

EPMVS		53.96 391	53.69 394	54.79 416	66.12 427	31.96 451	62.34 410	49.05 456	44.42 408	55.54 391	71.33 416	30.22 388	56.70 444	41.65 372	62.54 391	75.71 376

PMMVS		53.96 391	53.26 397	56.04 408	62.60 444	50.92 228	61.17 417	56.09 438	32.81 452	53.51 418	66.84 446	34.04 345	59.93 428	44.14 346	68.18 343	57.27 461

test20.03		53.87 393	54.02 390	53.41 426	61.47 448	28.11 464	61.30 415	59.21 422	51.34 323	52.09 424	77.43 346	33.29 356	58.55 436	29.76 447	60.27 411	73.58 401

MDA-MVSNet-bldmvs		53.87 393	50.81 406	63.05 361	66.25 425	48.58 288	56.93 440	63.82 394	48.09 368	41.22 459	70.48 423	30.34 386	68.00 389	34.24 418	45.92 455	72.57 408

KD-MVS_2432*1600		53.45 395	51.50 404	59.30 385	62.82 441	37.14 409	55.33 443	71.79 326	47.34 380	55.09 399	70.52 421	21.91 444	70.45 373	35.72 413	42.97 459	70.31 435

miper_refine_blended		53.45 395	51.50 404	59.30 385	62.82 441	37.14 409	55.33 443	71.79 326	47.34 380	55.09 399	70.52 421	21.91 444	70.45 373	35.72 413	42.97 459	70.31 435

TDRefinement		53.44 397	50.72 407	61.60 370	64.31 436	46.96 309	70.89 330	65.27 381	41.78 425	44.61 453	77.98 331	11.52 468	66.36 401	28.57 452	51.59 443	71.49 425

test0.0.03 1		53.32 398	53.59 395	52.50 432	62.81 443	29.45 459	59.51 425	54.11 444	50.08 338	54.40 408	74.31 391	32.62 370	55.92 450	30.50 443	63.95 378	72.15 418

PatchT		53.17 399	53.44 396	52.33 433	68.29 411	25.34 475	58.21 430	54.41 443	44.46 407	54.56 406	69.05 435	33.32 355	60.94 422	36.93 401	61.76 399	70.73 433

UnsupCasMVSNet_eth		53.16 400	52.47 398	55.23 413	59.45 457	33.39 443	59.43 426	69.13 349	45.98 394	50.35 435	72.32 405	29.30 399	58.26 438	42.02 369	44.30 457	74.05 398

PM-MVS		52.33 401	50.19 410	58.75 392	62.10 446	45.14 328	65.75 377	40.38 474	43.60 414	53.52 417	72.65 403	9.16 474	65.87 405	50.41 286	54.18 434	65.24 451

UWE-MVS-28		52.25 402	52.35 400	51.93 436	66.99 417	22.79 479	63.48 402	48.31 460	46.78 387	52.73 422	76.11 369	27.78 414	57.82 440	20.58 469	68.41 342	75.17 381

testgi		51.90 403	52.37 399	50.51 439	60.39 456	23.55 478	58.42 428	58.15 425	49.03 352	51.83 425	79.21 314	22.39 441	55.59 451	29.24 450	62.64 389	72.40 415

dp		51.89 404	51.60 403	52.77 430	68.44 410	32.45 449	62.36 409	54.57 442	44.16 410	49.31 439	67.91 437	28.87 403	56.61 446	33.89 419	54.89 431	69.24 444

JIA-IIPM		51.56 405	47.68 419	63.21 359	64.61 434	50.73 235	47.71 464	58.77 424	42.90 421	48.46 441	51.72 467	24.97 435	70.24 377	36.06 412	53.89 436	68.64 445

test_fmvs1_n		51.37 406	50.35 409	54.42 419	52.85 466	37.71 404	61.16 418	51.93 447	28.15 459	63.81 290	69.73 431	13.72 460	53.95 456	51.16 281	60.65 407	71.59 423

ADS-MVSNet2		51.33 407	48.76 414	59.07 390	66.02 428	44.60 335	50.90 456	59.76 420	36.90 444	50.74 430	66.18 449	26.38 425	63.11 416	27.17 456	54.76 432	69.50 441

test_fmvs1		51.32 408	50.48 408	53.81 421	53.57 464	37.51 406	60.63 422	51.16 450	28.02 461	63.62 291	69.23 434	16.41 455	53.93 457	51.01 282	60.70 406	69.99 438

YYNet1		50.73 409	48.96 411	56.03 409	61.10 451	41.78 363	51.94 453	56.44 434	40.94 433	44.84 451	67.80 439	30.08 391	55.08 454	36.77 402	50.71 445	71.22 428

MDA-MVSNet_test_wron		50.71 410	48.95 412	56.00 410	61.17 450	41.84 362	51.90 454	56.45 433	40.96 432	44.79 452	67.84 438	30.04 392	55.07 455	36.71 404	50.69 446	71.11 431

dmvs_testset		50.16 411	51.90 401	44.94 447	66.49 423	11.78 487	61.01 420	51.50 449	51.17 326	50.30 436	67.44 441	39.28 286	60.29 426	22.38 466	57.49 420	62.76 452

UnsupCasMVSNet_bld		50.07 412	48.87 413	53.66 422	60.97 454	33.67 441	57.62 436	64.56 386	39.47 441	47.38 443	64.02 455	27.47 416	59.32 430	34.69 417	43.68 458	67.98 447

test_vis1_n		49.89 413	48.69 415	53.50 424	53.97 463	37.38 407	61.53 412	47.33 464	28.54 458	59.62 350	67.10 445	13.52 461	52.27 462	49.07 298	57.52 419	70.84 432

Patchmatch-test		49.08 414	48.28 416	51.50 437	64.40 435	30.85 456	45.68 468	48.46 459	35.60 448	46.10 450	72.10 408	34.47 341	46.37 470	27.08 458	60.65 407	77.27 358

test_fmvs2		48.69 415	47.49 420	52.29 434	48.63 473	33.06 446	57.76 434	48.05 462	25.71 465	59.76 348	69.60 432	11.57 467	52.23 463	49.45 296	56.86 422	71.58 424

ADS-MVSNet		48.48 416	47.77 417	50.63 438	66.02 428	29.92 458	50.90 456	50.87 454	36.90 444	50.74 430	66.18 449	26.38 425	52.47 461	27.17 456	54.76 432	69.50 441

CHOSEN 280x420		47.83 417	46.36 421	52.24 435	67.37 416	49.78 257	38.91 476	43.11 472	35.00 449	43.27 457	63.30 456	28.95 401	49.19 466	36.53 407	60.80 404	57.76 460

new-patchmatchnet		47.56 418	47.73 418	47.06 442	58.81 460	9.37 490	48.78 462	59.21 422	43.28 417	44.22 454	68.66 436	25.67 431	57.20 443	31.57 438	49.35 450	74.62 393

PVSNet_0	43.31 20	47.46 419	45.64 422	52.92 429	67.60 415	44.65 332	54.06 448	54.64 441	41.59 428	46.15 449	58.75 460	30.99 382	58.66 435	32.18 427	24.81 475	55.46 463

ttmdpeth		45.56 420	42.95 425	53.39 427	52.33 469	29.15 460	57.77 433	48.20 461	31.81 454	49.86 437	77.21 348	8.69 475	59.16 432	27.31 455	33.40 471	71.84 421

MVS-HIRNet		45.52 421	44.48 423	48.65 441	68.49 409	34.05 438	59.41 427	44.50 469	27.03 462	37.96 469	50.47 471	26.16 428	64.10 410	26.74 459	59.52 412	47.82 470

pmmvs3		44.92 422	41.95 429	53.86 420	52.58 468	43.55 346	62.11 411	46.90 466	26.05 464	40.63 460	60.19 459	11.08 471	57.91 439	31.83 435	46.15 454	60.11 454

test_fmvs3		44.30 423	42.55 426	49.55 440	42.83 478	27.15 470	53.03 450	44.93 468	22.03 473	53.69 415	64.94 452	4.21 482	49.63 465	47.47 309	49.82 448	71.88 419

WB-MVS		43.26 424	43.41 424	42.83 451	63.32 440	10.32 489	58.17 431	45.20 467	45.42 399	40.44 462	67.26 444	34.01 347	58.98 433	11.96 480	24.88 474	59.20 455

LF4IMVS		42.95 425	42.26 427	45.04 445	48.30 474	32.50 448	54.80 445	48.49 458	28.03 460	40.51 461	70.16 424	9.24 473	43.89 473	31.63 436	49.18 451	58.72 457

MVStest1		42.65 426	39.29 433	52.71 431	47.26 476	34.58 433	54.41 447	50.84 455	23.35 467	39.31 467	74.08 395	12.57 463	55.09 453	23.32 464	28.47 473	68.47 446

EGC-MVSNET		42.47 427	38.48 435	54.46 418	74.33 303	48.73 284	70.33 340	51.10 451	0.03 488	0.18 489	67.78 440	13.28 462	66.49 400	18.91 471	50.36 447	48.15 468

FPMVS		42.18 428	41.11 430	45.39 444	58.03 461	41.01 373	49.50 460	53.81 446	30.07 456	33.71 471	64.03 453	11.69 465	52.08 464	14.01 475	55.11 430	43.09 472

SSC-MVS		41.96 429	41.99 428	41.90 452	62.46 445	9.28 491	57.41 438	44.32 470	43.38 416	38.30 468	66.45 447	32.67 369	58.42 437	10.98 481	21.91 477	57.99 459

ANet_high		41.38 430	37.47 437	53.11 428	39.73 484	24.45 476	56.94 439	69.69 340	47.65 375	26.04 476	52.32 466	12.44 464	62.38 419	21.80 467	10.61 485	72.49 410

test_vis1_rt		41.35 431	39.45 432	47.03 443	46.65 477	37.86 401	47.76 463	38.65 475	23.10 469	44.21 455	51.22 469	11.20 470	44.08 472	39.27 386	53.02 439	59.14 456

LCM-MVSNet		40.30 432	35.88 438	53.57 423	42.24 479	29.15 460	45.21 470	60.53 419	22.23 472	28.02 474	50.98 470	3.72 484	61.78 421	31.22 441	38.76 465	69.78 440

mvsany_test1		39.38 433	38.16 436	43.02 450	49.05 471	34.28 436	44.16 472	25.94 485	22.74 471	46.57 448	62.21 458	23.85 439	41.16 477	33.01 425	35.91 467	53.63 464

N_pmnet		39.35 434	40.28 431	36.54 458	63.76 437	1.62 495	49.37 461	0.76 494	34.62 450	43.61 456	66.38 448	26.25 427	42.57 474	26.02 461	51.77 442	65.44 450

DSMNet-mixed		39.30 435	38.72 434	41.03 453	51.22 470	19.66 482	45.53 469	31.35 481	15.83 480	39.80 464	67.42 443	22.19 442	45.13 471	22.43 465	52.69 440	58.31 458

APD_test1		37.39 436	34.94 439	44.72 448	48.88 472	33.19 445	52.95 451	44.00 471	19.49 474	27.28 475	58.59 461	3.18 486	52.84 460	18.92 470	41.17 462	48.14 469

PMVS	28.69 22	36.22 437	33.29 442	45.02 446	36.82 486	35.98 423	54.68 446	48.74 457	26.31 463	21.02 479	51.61 468	2.88 487	60.10 427	9.99 484	47.58 452	38.99 477
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 438	31.91 443	43.33 449	62.05 447	37.87 400	20.39 481	67.03 365	23.23 468	18.41 481	25.84 481	4.24 481	62.73 417	14.71 474	51.32 444	29.38 479
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 439	34.94 439	33.26 461	61.06 452	16.00 486	52.79 452	23.78 487	40.71 434	39.33 466	48.65 475	16.91 454	48.34 467	12.18 479	19.05 479	35.44 478

new_pmnet		34.13 440	34.29 441	33.64 460	52.63 467	18.23 484	44.43 471	33.90 480	22.81 470	30.89 473	53.18 465	10.48 472	35.72 482	20.77 468	39.51 463	46.98 471

mvsany_test3		32.62 441	30.57 446	38.77 456	36.16 487	24.20 477	38.10 477	20.63 489	19.14 475	40.36 463	57.43 462	5.06 479	36.63 481	29.59 449	28.66 472	55.49 462

test_vis3_rt		32.09 442	30.20 447	37.76 457	35.36 488	27.48 466	40.60 475	28.29 484	16.69 478	32.52 472	40.53 477	1.96 488	37.40 480	33.64 422	42.21 461	48.39 467

test_f		31.86 443	31.05 444	34.28 459	32.33 490	21.86 480	32.34 478	30.46 482	16.02 479	39.78 465	55.45 464	4.80 480	32.36 484	30.61 442	37.66 466	48.64 466

testf1		31.46 444	28.89 448	39.16 454	41.99 481	28.78 462	46.45 466	37.56 476	14.28 481	21.10 477	48.96 472	1.48 490	47.11 468	13.63 476	34.56 468	41.60 473

APD_test2		31.46 444	28.89 448	39.16 454	41.99 481	28.78 462	46.45 466	37.56 476	14.28 481	21.10 477	48.96 472	1.48 490	47.11 468	13.63 476	34.56 468	41.60 473

kuosan		29.62 446	30.82 445	26.02 466	52.99 465	16.22 485	51.09 455	22.71 488	33.91 451	33.99 470	40.85 476	15.89 457	33.11 483	7.59 487	18.37 480	28.72 480

PMMVS2		27.40 447	25.91 450	31.87 463	39.46 485	6.57 492	31.17 479	28.52 483	23.96 466	20.45 480	48.94 474	4.20 483	37.94 479	16.51 472	19.97 478	51.09 465

E-PMN		23.77 448	22.73 452	26.90 464	42.02 480	20.67 481	42.66 473	35.70 478	17.43 476	10.28 486	25.05 482	6.42 477	42.39 475	10.28 483	14.71 482	17.63 481

EMVS		22.97 449	21.84 453	26.36 465	40.20 483	19.53 483	41.95 474	34.64 479	17.09 477	9.73 487	22.83 483	7.29 476	42.22 476	9.18 485	13.66 483	17.32 482

MVE	17.77 23	21.41 450	17.77 455	32.34 462	34.34 489	25.44 474	16.11 482	24.11 486	11.19 483	13.22 483	31.92 479	1.58 489	30.95 485	10.47 482	17.03 481	40.62 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 451	18.10 454	24.41 467	13.68 492	3.11 494	12.06 484	42.37 473	2.00 486	11.97 484	36.38 478	5.77 478	29.35 486	15.06 473	23.65 476	40.76 475

cdsmvs_eth3d_5k		17.50 452	23.34 451	0.00 473	0.00 496	0.00 497	0.00 485	78.63 198	0.00 491	0.00 492	82.18 248	49.25 156	0.00 490	0.00 491	0.00 488	0.00 488

wuyk23d		13.32 453	12.52 456	15.71 468	47.54 475	26.27 472	31.06 480	1.98 493	4.93 485	5.18 488	1.94 488	0.45 492	18.54 487	6.81 488	12.83 484	2.33 485

tmp_tt		9.43 454	11.14 457	4.30 470	2.38 493	4.40 493	13.62 483	16.08 491	0.39 487	15.89 482	13.06 484	15.80 458	5.54 489	12.63 478	10.46 486	2.95 484

ab-mvs-re		6.49 455	8.65 458	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	77.89 337	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

test123		4.73 456	6.30 459	0.02 471	0.01 494	0.01 496	56.36 441	0.00 495	0.01 489	0.04 490	0.21 490	0.01 493	0.00 490	0.03 490	0.00 488	0.04 486

testmvs		4.52 457	6.03 460	0.01 472	0.01 494	0.00 497	53.86 449	0.00 495	0.01 489	0.04 490	0.27 489	0.00 494	0.00 490	0.04 489	0.00 488	0.03 487

pcd_1.5k_mvsjas		3.92 458	5.23 461	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	47.05 188	0.00 490	0.00 491	0.00 488	0.00 488

mmdepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

monomultidepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

test_blank		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

uanet_test		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

DCPMVS		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

sosnet-low-res		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

sosnet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

uncertanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

Regformer		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

uanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 497	0.00 485	0.00 495	0.00 491	0.00 492	0.00 491	0.00 494	0.00 490	0.00 491	0.00 488	0.00 488

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 101	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 41

TestfortrainingZip							86.84 11

WAC-MVS						27.31 468								27.77 453

FOURS1					86.12 37	60.82 37	88.18 1	83.61 80	60.87 104	81.50 20

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 51

PC_three_1452									55.09 249	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 28

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 51

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

eth-test2					0.00 496

eth-test					0.00 496

ZD-MVS					86.64 21	60.38 45		82.70 112	57.95 180	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

RE-MVS-def			73.71 83		83.49 72	59.87 54	84.29 48	81.36 135	58.07 174	73.14 106	90.07 43	43.06 238		68.20 104	81.76 108	84.03 210

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 279	84.64 3				79.07 13	90.87 5	88.37 30

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 36

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 60

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 134	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

save fliter					86.17 34	61.30 28	83.98 58	79.66 174	59.00 154

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 44

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 62

test0726					87.75 7	59.07 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 345

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 337				78.05 345

sam_mvs												33.43 354

ambc				65.13 343	63.72 439	37.07 411	47.66 465	78.78 194		54.37 409	71.42 414	11.24 469	80.94 233	45.64 331	53.85 437	77.38 356

MTGPA								80.97 153

test_post1							68.67 356				3.64 486	32.39 375	69.49 379	44.17 344

test_post											3.55 487	33.90 348	66.52 399

patchmatchnet-post											64.03 453	34.50 339	74.27 349

GG-mvs-BLEND				62.34 365	71.36 365	37.04 412	69.20 353	57.33 432		54.73 404	65.48 451	30.37 385	77.82 301	34.82 416	74.93 232	72.17 417

MTMP							86.03 23	17.08 490

gm-plane-assit					71.40 364	41.72 366			48.85 356		73.31 400		82.48 198	48.90 300

test9_res														75.28 54	88.31 36	83.81 221

TEST9					85.58 44	61.59 24	81.62 91	81.26 142	55.65 234	74.93 63	88.81 67	53.70 86	84.68 136

test_8					85.40 47	60.96 34	81.54 94	81.18 146	55.86 226	74.81 68	88.80 69	53.70 86	84.45 140

agg_prior2														73.09 72	87.93 44	84.33 199

agg_prior					85.04 54	59.96 50		81.04 151		74.68 72			84.04 146

TestCases				64.39 348	71.44 361	49.03 275		67.30 360	45.97 395	47.16 444	79.77 299	17.47 450	67.56 393	33.65 420	59.16 414	76.57 367

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 120	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 110

旧先验2							76.08 221		45.32 400	76.55 47			65.56 406	58.75 219

新几何2							76.12 219

新几何1				70.76 247	85.66 42	61.13 30		66.43 370	44.68 404	70.29 152	86.64 123	41.29 265	75.23 344	49.72 292	81.75 110	75.93 373

旧先验1					83.04 78	53.15 181		67.52 359			87.85 86	44.08 226			80.76 119	78.03 348

无先验							79.66 121	74.30 293	48.40 363				80.78 239	53.62 260		79.03 336

原ACMM2							79.02 128

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 86	51.47 320	70.27 153	86.61 127	48.61 164	86.51 86	53.85 259	87.96 43	78.16 343

test222					83.14 76	58.68 81	72.57 303	63.45 398	41.78 425	67.56 215	86.12 145	37.13 315			78.73 168	74.98 386

testdata2													72.18 363	46.95 320

segment_acmp												54.23 73

testdata				64.66 345	81.52 98	52.93 186		65.29 380	46.09 393	73.88 88	87.46 93	38.08 304	66.26 402	53.31 264	78.48 175	74.78 390

testdata1							72.65 299		60.50 114

test12				77.76 50	84.52 62	58.41 83		83.36 89		72.93 114		54.61 70	88.05 43		88.12 38	86.81 93

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 213

plane_prior5								84.01 57					87.21 63	68.16 108	80.58 123	84.65 190

plane_prior4											86.10 146

plane_prior3						56.09 118			63.92 38	69.27 173

plane_prior2							84.22 51		64.52 27

plane_prior1					81.27 106

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

n2								0.00 495

nn								0.00 495

door-mid								47.19 465

lessismore_v0				69.91 264	71.42 363	47.80 299		50.90 453		50.39 434	75.56 378	27.43 418	81.33 220	45.91 328	34.10 470	80.59 305

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 87	61.32 92	66.67 234	87.33 99	39.15 289	86.59 79	67.70 116	77.30 198	83.19 244

test11								83.47 84

door								47.60 463

HQP5-MVS						54.94 143

HQP-NCC					80.66 115		82.31 82		62.10 77	67.85 204

ACMP_Plane					80.66 115		82.31 82		62.10 77	67.85 204

BP-MVS														67.04 126

HQP4-MVS										67.85 204			86.93 71			84.32 200

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 207

NP-MVS					80.98 111	56.05 120					85.54 167

MDTV_nov1_ep13_2view						25.89 473	61.22 416		40.10 438	51.10 427		32.97 360		38.49 390		78.61 340

MDTV_nov1_ep13			57.00 360		72.73 333	38.26 398	65.02 390	64.73 385	44.74 403	55.46 392	72.48 404	32.61 372	70.47 372	37.47 395	67.75 347

ACMMP++_ref															74.07 241

ACMMP++															72.16 281

Test By Simon												48.33 167

ITE_SJBPF				62.09 367	66.16 426	44.55 337		64.32 387	47.36 379	55.31 396	80.34 288	19.27 449	62.68 418	36.29 410	62.39 392	79.04 335

DeepMVS_CX				12.03 469	17.97 491	10.91 488		10.60 492	7.46 484	11.07 485	28.36 480	3.28 485	11.29 488	8.01 486	9.74 487	13.89 483

High-resolution multi-view benchmark