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 26

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 34

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 42
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	85.45 156
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 90
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 39

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 36
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 92
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 39

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 99	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 45

HPM-MVS++		79.88 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 74	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 168

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 84

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 70	90.06 14	78.42 23	89.02 27	87.69 56
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 38

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 82	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 48

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 77	88.68 31	76.48 39	89.63 20	87.16 81

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	52.51 101	88.39 34	79.34 9	90.52 13	86.78 93

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 59	89.18 25	74.19 63	87.34 50	86.38 108

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 88	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 46
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	81.64 125	68.35 2	75.77 50	90.38 34	53.98 75	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 69	59.34 148	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 57
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 74	76.41 48	91.51 11	52.47 103	86.78 75	80.66 4	89.64 19	87.80 52
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 80	63.89 39	73.60 91	90.60 23	54.85 65	86.72 76	77.20 31	88.06 40	85.74 142
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	52.56 100	88.53 33	74.79 59	88.34 33	86.63 101

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 109	79.05 26	90.30 38	55.54 58	88.32 36	73.48 70	87.03 52	84.83 183
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 91	88.35 35	74.02 65	87.05 51	86.13 123

lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 111	78.99 27	91.45 12	51.51 122	87.78 51	75.65 49	87.55 47	87.10 83

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 93	90.56 29	49.80 145	88.24 37	74.02 65	87.03 52	86.32 116

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 118	79.89 22	89.38 58	54.97 63	85.58 113	76.12 45	84.94 70	86.33 114
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 97	90.58 24	49.90 142	88.21 38	73.78 67	87.03 52	86.29 120

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 200	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 102

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	73.09 108	89.97 50	50.90 133	87.48 57	75.30 53	86.85 57	87.33 76
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 81	73.06 109	88.88 66	53.72 83	89.06 27	68.27 103	88.04 41	87.42 68
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	72.17 126	90.01 49	47.95 168	88.01 44	71.55 88	86.74 59	86.37 110

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 83	62.44 69	72.68 118	90.50 31	48.18 166	87.34 58	73.59 69	85.71 66	84.76 187

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 105	75.27 55	84.83 173	60.76 18	86.56 81	67.86 112	87.87 45	86.06 125

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 88	60.22 125	77.85 36	91.42 14	50.67 134	87.69 53	72.46 76	84.53 74	85.46 154

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 88	60.22 125	77.85 36	91.42 14	50.67 134	87.69 53	72.46 76	84.53 74	85.46 154

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 249	80.97 151	65.13 15	75.77 50	90.88 20	48.63 161	86.66 78	77.23 30	88.17 37	84.81 184

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 71	61.71 83	72.45 124	90.34 37	48.48 164	88.13 41	72.32 78	86.85 57	85.78 136

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 97	74.90 66	87.17 106	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	82.28 115	62.90 57	71.77 131	90.26 39	46.61 193	86.55 84	71.71 86	85.66 67	84.97 179

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 91	67.78 3	70.09 152	86.34 136	54.92 64	88.90 29	72.68 75	84.55 73	87.76 54

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 106	59.65 138	77.31 39	91.43 13	49.62 147	87.24 59	71.99 82	83.75 85	85.14 170

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 216	74.05 82	88.98 63	53.34 89	87.92 47	69.23 101	88.42 32	87.59 62

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 140	55.86 224	74.93 63	88.81 67	53.70 84	84.68 136	75.24 55	88.33 34	83.65 230

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 95	64.69 22	74.21 80	87.40 94	49.48 148	86.17 96	68.04 109	87.55 47	87.42 68

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 97	87.27 101	55.06 61	86.30 93	71.78 85	84.58 72	89.25 6

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 251	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	82.19 116	59.99 131	75.10 59	90.35 36	47.66 173	86.52 85	71.64 87	82.99 90	84.47 196

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	69.80 162	89.74 55	45.43 207	87.16 65	72.01 81	82.87 95	85.14 170
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	56.08 222	73.41 94	86.58 127	50.94 132	88.54 32	70.79 93	89.71 17	87.79 53

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	72.77 117	87.25 103	53.13 92	87.93 46	71.97 83	85.57 68	86.66 99

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	67.80 208	89.24 60	42.03 246	89.38 23	64.07 152	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	59.65 138	71.53 137	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 78	71.49 138	86.03 147	53.83 79	86.36 91	67.74 113	86.91 56	88.19 36

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 210	72.46 122	86.76 114	56.89 39	87.86 49	66.36 132	88.91 29	83.64 231

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 266	64.69 22	74.21 80	87.40 94	49.48 148	86.17 96	68.04 109	83.88 83	85.85 133

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 181	73.95 298	61.40 89	79.46 23	90.14 41	57.07 37	81.15 223	80.00 5	79.31 146	88.51 25

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 92	58.41 166	73.71 89	90.14 41	45.62 200	85.99 103	69.64 97	82.85 96	85.78 136

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 201	58.58 163	74.32 78	84.51 188	55.94 55	87.22 62	67.11 123	84.48 77	85.52 150

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 265	50.37 243	78.17 151	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	75.59 231	84.17 54	63.76 40	73.15 103	82.79 223	59.58 23	86.80 74	67.24 121	86.04 65	87.89 46
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 77	63.74 41	72.52 121	87.49 91	47.18 184	85.88 106	69.47 99	80.78 117	83.66 229
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 66	74.98 61	73.71 150	78.94 155	50.56 237	80.23 107	83.87 65	60.30 122	77.15 41	86.56 128	59.65 20	82.00 203	66.01 136	82.12 101	88.58 23

canonicalmvs		74.67 66	74.98 61	73.71 150	78.94 155	50.56 237	80.23 107	83.87 65	60.30 122	77.15 41	86.56 128	59.65 20	82.00 203	66.01 136	82.12 101	88.58 23

baseline		74.61 68	74.70 64	74.34 121	75.70 260	49.99 253	77.54 171	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 76	76.58 70	83.49 72	59.87 54	84.29 48	81.36 133	58.07 172	73.14 104	90.07 43	44.74 217	85.84 107	68.20 104	81.76 108	84.03 208

dcpmvs_2		74.55 70	75.23 58	72.48 189	82.34 87	53.34 176	77.87 160	81.46 129	57.80 183	75.49 52	86.81 113	62.22 15	77.75 300	71.09 91	82.02 104	86.34 112

ETV-MVS		74.46 71	73.84 78	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 119	79.46 306	53.65 87	87.87 48	67.45 120	82.91 93	85.89 131

HQP_MVS		74.31 72	73.73 80	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 171	86.10 144	45.26 211	87.21 63	68.16 107	80.58 123	84.65 188

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 135	75.33 272	52.89 189	78.24 146	77.32 234	61.65 84	78.13 32	88.90 65	52.82 97	81.54 213	78.46 22	78.67 168	87.60 61

HPM-MVS_fast		74.30 73	73.46 86	76.80 63	84.45 64	59.04 74	83.65 63	81.05 148	60.15 127	70.43 148	89.84 52	41.09 268	85.59 112	67.61 116	82.90 94	85.77 139

fmvsm_s_conf0.5_n_10		74.11 75	73.98 75	74.48 118	74.61 292	52.86 191	78.10 155	77.06 238	57.14 193	78.24 31	88.79 70	52.83 96	82.26 199	77.79 28	81.30 113	88.32 29

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 68	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 133	88.77 16

MVS_111021_HR		74.02 77	73.46 86	75.69 86	83.01 80	60.63 40	77.29 182	78.40 212	61.18 95	70.58 147	85.97 150	54.18 72	84.00 149	67.52 117	82.98 92	82.45 264

MG-MVS		73.96 78	73.89 77	74.16 128	85.65 43	49.69 262	81.59 93	81.29 139	61.45 88	71.05 141	88.11 77	51.77 117	87.73 52	61.05 191	83.09 88	85.05 175

E4		73.91 79	73.83 79	74.15 130	77.13 227	50.47 240	77.15 188	83.79 70	62.21 73	73.61 90	87.19 105	56.08 52	83.03 168	67.91 111	79.35 144	88.94 11

alignmvs		73.86 80	73.99 74	73.45 164	78.20 182	50.50 239	78.57 139	82.43 113	59.40 146	76.57 46	86.71 120	56.42 44	81.23 222	65.84 139	81.79 107	88.62 20

MSLP-MVS++		73.77 81	73.47 85	74.66 108	83.02 79	59.29 63	82.30 85	81.88 120	59.34 148	71.59 135	86.83 112	45.94 198	83.65 155	65.09 145	85.22 69	81.06 295

E2		73.72 82	73.60 83	74.06 132	77.16 224	50.40 241	76.97 193	83.74 71	61.64 85	73.36 95	86.75 117	56.14 48	82.99 170	67.50 118	79.18 154	88.80 13

E3		73.72 82	73.60 83	74.06 132	77.16 224	50.40 241	76.97 193	83.74 71	61.64 85	73.36 95	86.76 114	56.13 49	82.99 170	67.50 118	79.18 154	88.80 13

viewcassd2359sk11		73.56 84	73.41 88	74.00 136	77.13 227	50.35 244	76.86 200	83.69 75	61.23 94	73.14 104	86.38 135	56.09 51	82.96 173	67.15 122	79.01 159	88.70 19

fmvsm_s_conf0.5_n_3		73.55 85	74.39 68	71.03 240	74.09 310	51.86 216	77.77 165	75.60 262	61.18 95	78.67 29	88.98 63	55.88 56	77.73 301	78.69 16	78.68 167	83.50 234

HQP-MVS		73.45 86	72.80 98	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 75	67.85 202	85.54 165	45.46 205	86.93 71	67.04 124	80.35 127	84.32 198

viewdifsd2359ckpt09		73.42 87	72.45 104	76.30 75	77.25 222	53.27 178	80.36 106	82.48 112	57.96 177	72.24 125	85.73 159	53.22 90	86.27 94	63.79 162	79.06 158	89.36 5

E3new		73.41 88	73.22 91	73.95 139	77.06 232	50.31 245	76.78 203	83.66 76	60.90 101	72.93 112	86.02 148	55.99 53	82.95 175	66.89 129	78.77 164	88.61 21

BP-MVS1		73.41 88	72.25 106	76.88 61	76.68 244	53.70 163	79.15 127	81.07 147	60.66 108	71.81 130	87.39 96	40.93 269	87.24 59	71.23 90	81.29 114	89.71 2

CLD-MVS		73.33 90	72.68 100	75.29 96	78.82 159	53.33 177	78.23 148	84.79 46	61.30 92	70.41 149	81.04 272	52.41 104	87.12 66	64.61 151	82.49 100	85.41 160
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 91	72.54 102	75.62 89	77.87 195	53.64 165	79.62 122	79.61 173	61.63 87	72.02 129	82.61 228	56.44 43	85.97 104	63.99 155	79.07 157	87.25 78

fmvsm_l_conf0.5_n_9		73.27 92	73.66 82	72.09 198	73.82 311	52.72 195	77.45 175	74.28 291	56.61 209	77.10 43	88.16 76	56.17 47	77.09 314	78.27 24	81.13 115	86.48 106

fmvsm_l_conf0.5_n_3		73.23 93	73.13 93	73.55 160	74.40 299	55.13 141	78.97 129	74.96 281	56.64 203	74.76 71	88.75 71	55.02 62	78.77 283	76.33 41	78.31 178	86.74 94

fmvsm_s_conf0.5_n_11		73.16 94	73.35 89	72.58 184	75.48 267	52.41 206	78.84 131	76.85 241	58.64 161	73.58 92	87.25 103	54.09 74	79.47 260	76.19 44	79.27 147	85.86 132

viewmacassd2359aftdt		73.15 95	73.16 92	73.11 173	75.15 278	49.31 269	77.53 173	83.21 95	60.42 114	73.20 101	87.34 98	53.82 80	81.05 228	67.02 126	80.79 116	88.96 10

UA-Net		73.13 96	72.93 95	73.76 145	83.58 71	51.66 219	78.75 132	77.66 224	67.75 4	72.61 120	89.42 56	49.82 144	83.29 163	53.61 259	83.14 87	86.32 116

EPNet		73.09 97	72.16 107	75.90 79	75.95 257	56.28 114	83.05 67	72.39 317	66.53 10	65.27 260	87.00 108	50.40 137	85.47 118	62.48 178	86.32 64	85.94 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 98	72.59 101	74.27 124	71.28 365	55.88 124	78.21 150	75.56 264	54.31 271	74.86 67	87.80 87	54.72 66	80.23 249	78.07 26	78.48 173	86.70 95

nrg030		72.96 99	73.01 94	72.84 179	75.41 270	50.24 246	80.02 111	82.89 108	58.36 168	74.44 75	86.73 118	58.90 27	80.83 235	65.84 139	74.46 233	87.44 67

viewmanbaseed2359cas		72.92 100	72.89 96	73.00 175	75.16 276	49.25 272	77.25 185	83.11 103	59.52 145	72.93 112	86.63 123	54.11 73	80.98 229	66.63 130	80.67 120	88.76 18

test_fmvsmconf0.1_n		72.81 101	72.33 105	74.24 125	69.89 388	55.81 125	78.22 149	75.40 269	54.17 273	75.00 62	88.03 83	53.82 80	80.23 249	78.08 25	78.34 177	86.69 96

CPTT-MVS		72.78 102	72.08 109	74.87 102	84.88 61	61.41 26	84.15 54	77.86 220	55.27 242	67.51 214	88.08 79	41.93 249	81.85 206	69.04 102	80.01 132	81.35 286

LPG-MVS_test		72.74 103	71.74 114	75.76 83	80.22 123	57.51 96	82.55 78	83.40 85	61.32 90	66.67 232	87.33 99	39.15 287	86.59 79	67.70 114	77.30 196	83.19 242

h-mvs33		72.71 104	71.49 118	76.40 72	81.99 92	59.58 57	76.92 197	76.74 246	60.40 115	74.81 68	85.95 151	45.54 203	85.76 109	70.41 95	70.61 298	83.86 218

fmvsm_s_conf0.5_n_5		72.69 105	72.80 98	72.37 194	74.11 309	53.21 180	78.12 152	73.31 305	53.98 276	76.81 45	88.05 80	53.38 88	77.37 309	76.64 38	80.78 117	86.53 104

GDP-MVS		72.64 106	71.28 125	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 241	71.38 139	86.97 109	39.94 275	87.00 70	67.02 126	79.20 151	88.89 12

PAPM_NR		72.63 107	71.80 112	75.13 97	81.72 96	53.42 175	79.91 115	83.28 93	59.14 150	66.31 239	85.90 152	51.86 114	86.06 100	57.45 224	80.62 121	85.91 130

fmvsm_s_conf0.5_n_6		72.59 108	72.87 97	71.73 209	75.14 279	51.96 214	76.28 213	77.12 237	57.63 187	73.85 87	86.91 110	51.54 121	77.87 297	77.18 32	80.18 131	85.37 162

VDD-MVS		72.50 109	72.09 108	73.75 147	81.58 97	49.69 262	77.76 166	77.63 225	63.21 50	73.21 100	89.02 62	42.14 245	83.32 162	61.72 185	82.50 99	88.25 32

3Dnovator	64.47 5	72.49 110	71.39 121	75.79 82	77.70 202	58.99 76	80.66 104	83.15 100	62.24 72	65.46 256	86.59 126	42.38 244	85.52 114	59.59 205	84.72 71	82.85 252

MGCFI-Net		72.45 111	73.34 90	69.81 265	77.77 199	43.21 347	75.84 228	81.18 144	59.59 143	75.45 53	86.64 121	57.74 31	77.94 293	63.92 156	81.90 106	88.30 30

MVS_Test		72.45 111	72.46 103	72.42 193	74.88 281	48.50 287	76.28 213	83.14 101	59.40 146	72.46 122	84.68 178	55.66 57	81.12 224	65.98 138	79.66 137	87.63 59

EI-MVSNet-Vis-set		72.42 113	71.59 115	74.91 100	78.47 171	54.02 157	77.05 191	79.33 179	65.03 18	71.68 133	79.35 310	52.75 98	84.89 131	66.46 131	74.23 237	85.83 135

viewdifsd2359ckpt13		72.40 114	71.79 113	74.22 126	75.63 262	51.77 218	78.67 135	83.13 102	57.08 194	71.59 135	85.36 169	53.10 93	82.64 190	63.07 172	78.51 172	88.24 33

ACMP	63.53 6	72.30 115	71.20 127	75.59 91	80.28 121	57.54 94	82.74 74	82.84 109	60.58 110	65.24 264	86.18 141	39.25 285	86.03 102	66.95 128	76.79 204	83.22 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 116	71.21 126	75.31 94	78.50 169	55.93 122	81.63 90	82.12 117	56.24 219	70.02 156	85.68 161	47.05 186	84.34 142	65.27 144	74.41 236	85.67 145

Vis-MVSNet		72.18 117	71.37 122	74.61 111	81.29 104	55.41 136	80.90 100	78.28 215	60.73 106	69.23 174	88.09 78	44.36 223	82.65 189	57.68 222	81.75 110	85.77 139
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 118	71.50 117	74.16 128	67.96 409	55.58 133	78.06 156	74.67 284	54.19 272	74.54 74	88.23 74	50.35 139	80.24 248	78.07 26	77.46 191	86.65 100

API-MVS		72.17 118	71.41 120	74.45 119	81.95 93	57.22 99	84.03 56	80.38 162	59.89 136	68.40 185	82.33 241	49.64 146	87.83 50	51.87 273	84.16 81	78.30 338

EPP-MVSNet		72.16 120	71.31 124	74.71 105	78.68 163	49.70 260	82.10 86	81.65 124	60.40 115	65.94 246	85.84 154	51.74 118	86.37 90	55.93 235	79.55 140	88.07 44

DP-MVS Recon		72.15 121	70.73 136	76.40 72	86.57 25	57.99 88	81.15 98	82.96 104	57.03 197	66.78 227	85.56 162	44.50 221	88.11 42	51.77 275	80.23 130	83.10 247

fmvsm_s_conf0.5_n_4		72.04 122	71.85 111	72.58 184	73.74 314	52.49 202	76.69 204	72.42 316	56.42 214	75.32 54	87.04 107	52.13 110	78.01 292	79.29 12	73.65 247	87.26 77

EI-MVSNet-UG-set		71.92 123	71.06 130	74.52 117	77.98 193	53.56 168	76.62 205	79.16 180	64.40 29	71.18 140	78.95 315	52.19 108	84.66 138	65.47 142	73.57 250	85.32 164

viewdifsd2359ckpt07		71.90 124	71.97 110	71.69 212	74.81 285	48.08 293	75.30 236	80.49 159	60.00 130	71.63 134	86.33 137	56.34 45	79.25 265	65.40 143	77.41 192	87.76 54

VDDNet		71.81 125	71.33 123	73.26 171	82.80 83	47.60 302	78.74 133	75.27 271	59.59 143	72.94 111	89.40 57	41.51 261	83.91 150	58.75 217	82.99 90	88.26 31

EIA-MVS		71.78 126	70.60 138	75.30 95	79.85 132	53.54 169	77.27 184	83.26 94	57.92 179	66.49 234	79.39 308	52.07 111	86.69 77	60.05 199	79.14 156	85.66 146

LFMVS		71.78 126	71.59 115	72.32 195	83.40 75	46.38 311	79.75 118	71.08 326	64.18 34	72.80 116	88.64 72	42.58 241	83.72 153	57.41 225	84.49 76	86.86 89

test_fmvsm_n_1920		71.73 128	71.14 128	73.50 161	72.52 336	56.53 111	75.60 230	76.16 251	48.11 364	77.22 40	85.56 162	53.10 93	77.43 306	74.86 57	77.14 198	86.55 103

PAPR		71.72 129	70.82 134	74.41 120	81.20 108	51.17 222	79.55 124	83.33 90	55.81 227	66.93 226	84.61 182	50.95 131	86.06 100	55.79 238	79.20 151	86.00 126

IS-MVSNet		71.57 130	71.00 131	73.27 170	78.86 157	45.63 322	80.22 109	78.69 194	64.14 37	66.46 235	87.36 97	49.30 152	85.60 111	50.26 286	83.71 86	88.59 22

MAR-MVS		71.51 131	70.15 149	75.60 90	81.84 94	59.39 60	81.38 95	82.90 106	54.90 259	68.08 198	78.70 316	47.73 171	85.51 115	51.68 277	84.17 80	81.88 275
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		71.50 132	70.38 143	74.88 101	78.76 160	57.15 104	82.79 72	78.48 205	51.26 321	69.49 165	83.22 218	43.99 227	83.24 164	66.06 134	79.37 141	84.23 202

RRT-MVS		71.46 133	70.70 137	73.74 148	77.76 200	49.30 270	76.60 206	80.45 160	61.25 93	68.17 190	84.78 175	44.64 219	84.90 130	64.79 147	77.88 184	87.03 84

PVSNet_Blended_VisFu		71.45 134	70.39 142	74.65 109	82.01 90	58.82 79	79.93 114	80.35 163	55.09 247	65.82 252	82.16 249	49.17 155	82.64 190	60.34 197	78.62 170	82.50 263

OMC-MVS		71.40 135	70.60 138	73.78 143	76.60 247	53.15 181	79.74 119	79.78 169	58.37 167	68.75 179	86.45 133	45.43 207	80.60 239	62.58 176	77.73 185	87.58 63

KinetiMVS		71.26 136	70.16 148	74.57 114	74.59 293	52.77 194	75.91 225	81.20 143	60.72 107	69.10 177	85.71 160	41.67 256	83.53 158	63.91 158	78.62 170	87.42 68

UniMVSNet_NR-MVSNet		71.11 137	71.00 131	71.44 222	79.20 147	44.13 336	76.02 223	82.60 111	66.48 11	68.20 188	84.60 185	56.82 40	82.82 185	54.62 249	70.43 300	87.36 75

hse-mvs2		71.04 138	69.86 152	74.60 112	79.58 137	57.12 106	73.96 268	75.25 272	60.40 115	74.81 68	81.95 254	45.54 203	82.90 178	70.41 95	66.83 353	83.77 223

diffmvs_AUTHOR		71.02 139	70.87 133	71.45 221	69.89 388	48.97 278	73.16 289	78.33 214	57.79 184	72.11 128	85.26 170	51.84 115	77.89 296	71.00 92	78.47 175	87.49 65

GeoE		71.01 140	70.15 149	73.60 158	79.57 138	52.17 208	78.93 130	78.12 217	58.02 174	67.76 211	83.87 201	52.36 105	82.72 187	56.90 227	75.79 218	85.92 129

fmvsm_l_conf0.5_n		70.99 141	70.82 134	71.48 218	71.45 358	54.40 151	77.18 187	70.46 332	48.67 354	75.17 57	86.86 111	53.77 82	76.86 322	76.33 41	77.51 190	83.17 246

PCF-MVS	61.88 8	70.95 142	69.49 159	75.35 93	77.63 206	55.71 127	76.04 222	81.81 122	50.30 332	69.66 163	85.40 168	52.51 101	84.89 131	51.82 274	80.24 129	85.45 156
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 143	69.41 162	75.12 98	79.20 147	53.86 159	77.89 159	80.00 167	53.88 278	69.40 168	84.61 182	43.21 233	86.56 81	58.80 215	77.68 187	84.95 180

test_fmvsmvis_n_1920		70.84 143	70.38 143	72.22 197	71.16 366	55.39 137	75.86 226	72.21 319	49.03 349	73.28 99	86.17 142	51.83 116	77.29 311	75.80 46	78.05 181	83.98 211

114514_t		70.83 145	69.56 157	74.64 110	86.21 32	54.63 148	82.34 81	81.81 122	48.22 362	63.01 300	85.83 155	40.92 270	87.10 67	57.91 221	79.79 134	82.18 269

FIs		70.82 146	71.43 119	68.98 280	78.33 179	38.14 396	76.96 195	83.59 79	61.02 98	67.33 216	86.73 118	55.07 60	81.64 209	54.61 251	79.22 150	87.14 82

ACMM	61.98 7	70.80 147	69.73 154	74.02 134	80.59 120	58.59 82	82.68 75	82.02 119	55.46 237	67.18 221	84.39 191	38.51 293	83.17 166	60.65 195	76.10 214	80.30 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 148	70.43 141	71.46 219	69.45 395	48.95 279	72.93 292	78.46 207	57.27 191	71.69 132	83.97 200	51.48 123	77.92 295	70.70 94	77.95 183	87.53 64
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)		70.63 149	70.20 146	71.89 202	78.55 168	45.29 325	75.94 224	82.92 105	63.68 42	68.16 191	83.59 209	53.89 78	83.49 160	53.97 255	71.12 291	86.89 88

xiu_mvs_v2_base		70.52 150	69.75 153	72.84 179	81.21 107	55.63 130	75.11 242	78.92 187	54.92 258	69.96 159	79.68 301	47.00 190	82.09 202	61.60 187	79.37 141	80.81 300

PS-MVSNAJ		70.51 151	69.70 155	72.93 177	81.52 98	55.79 126	74.92 249	79.00 185	55.04 253	69.88 160	78.66 318	47.05 186	82.19 200	61.61 186	79.58 138	80.83 299

fmvsm_l_conf0.5_n_a		70.50 152	70.27 145	71.18 234	71.30 364	54.09 156	76.89 198	69.87 336	47.90 368	74.37 77	86.49 131	53.07 95	76.69 328	75.41 52	77.11 199	82.76 253

v2v482		70.50 152	69.45 161	73.66 153	72.62 333	50.03 252	77.58 168	80.51 158	59.90 132	69.52 164	82.14 250	47.53 177	84.88 133	65.07 146	70.17 308	86.09 124

v1144		70.42 154	69.31 163	73.76 145	73.22 321	50.64 234	77.83 163	81.43 130	58.58 163	69.40 168	81.16 269	47.53 177	85.29 123	64.01 154	70.64 296	85.34 163

SSM_0407		70.41 155	68.96 172	74.75 104	78.65 164	53.46 171	77.28 183	80.00 167	53.88 278	68.14 192	84.61 182	43.21 233	86.26 95	58.80 215	76.11 211	84.54 190

TranMVSNet+NR-MVSNet		70.36 156	70.10 151	71.17 235	78.64 167	42.97 350	76.53 208	81.16 146	66.95 6	68.53 183	85.42 167	51.61 120	83.07 167	52.32 267	69.70 320	87.46 66

v8		70.33 157	69.28 164	73.49 162	73.15 323	50.22 247	78.62 137	80.78 154	60.79 104	66.45 236	82.11 252	49.35 151	84.98 127	63.58 165	68.71 336	85.28 166

Fast-Effi-MVS+		70.28 158	69.12 168	73.73 149	78.50 169	51.50 220	75.01 245	79.46 177	56.16 221	68.59 180	79.55 304	53.97 76	84.05 145	53.34 261	77.53 189	85.65 147

X-MVStestdata		70.21 159	67.28 218	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 126	6.49 482	47.95 168	88.01 44	71.55 88	86.74 59	86.37 110

v10		70.21 159	69.02 169	73.81 142	73.51 317	50.92 228	78.74 133	81.39 131	60.05 129	66.39 237	81.83 257	47.58 175	85.41 121	62.80 175	68.86 335	85.09 174

Elysia		70.19 161	68.29 191	75.88 80	74.15 306	54.33 153	78.26 143	83.21 95	55.04 253	67.28 217	83.59 209	30.16 386	86.11 98	63.67 163	79.26 148	87.20 79

StellarMVS		70.19 161	68.29 191	75.88 80	74.15 306	54.33 153	78.26 143	83.21 95	55.04 253	67.28 217	83.59 209	30.16 386	86.11 98	63.67 163	79.26 148	87.20 79

QAPM		70.05 163	68.81 175	73.78 143	76.54 249	53.43 174	83.23 65	83.48 81	52.89 294	65.90 248	86.29 138	41.55 260	86.49 87	51.01 280	78.40 176	81.42 280

DU-MVS		70.01 164	69.53 158	71.44 222	78.05 190	44.13 336	75.01 245	81.51 128	64.37 30	68.20 188	84.52 186	49.12 158	82.82 185	54.62 249	70.43 300	87.37 73

AdaColmap		69.99 165	68.66 179	73.97 138	84.94 58	57.83 90	82.63 76	78.71 193	56.28 218	64.34 279	84.14 194	41.57 258	87.06 69	46.45 319	78.88 160	77.02 359

v1192		69.97 166	68.68 178	73.85 140	73.19 322	50.94 226	77.68 167	81.36 133	57.51 189	68.95 178	80.85 279	45.28 210	85.33 122	62.97 174	70.37 302	85.27 167

Anonymous20240529		69.91 167	69.02 169	72.56 186	80.19 126	47.65 300	77.56 170	80.99 150	55.45 238	69.88 160	86.76 114	39.24 286	82.18 201	54.04 254	77.10 200	87.85 49

patch_mono-2		69.85 168	71.09 129	66.16 318	79.11 152	54.80 147	71.97 310	74.31 289	53.50 287	70.90 143	84.17 193	57.63 34	63.31 412	66.17 133	82.02 104	80.38 308

fmvsm_s_conf0.5_n_2		69.82 169	69.27 165	71.46 219	72.00 348	51.08 223	73.30 282	67.79 355	55.06 252	75.24 56	87.51 90	44.02 226	77.00 318	75.67 48	72.86 265	86.31 119

FA-MVS(test-final)		69.82 169	68.48 182	73.84 141	78.44 172	50.04 251	75.58 233	78.99 186	58.16 170	67.59 212	82.14 250	42.66 239	85.63 110	56.60 228	76.19 210	85.84 134

FC-MVSNet-test		69.80 171	70.58 140	67.46 297	77.61 211	34.73 429	76.05 221	83.19 99	60.84 103	65.88 250	86.46 132	54.52 69	80.76 238	52.52 266	78.12 180	86.91 87

v144192		69.71 172	68.51 181	73.33 169	73.10 324	50.13 249	77.54 171	80.64 155	56.65 202	68.57 182	80.55 282	46.87 191	84.96 129	62.98 173	69.66 321	84.89 182

test_yl		69.69 173	69.13 166	71.36 228	78.37 176	45.74 318	74.71 253	80.20 164	57.91 180	70.01 157	83.83 202	42.44 242	82.87 181	54.97 245	79.72 135	85.48 152

DCV-MVSNet		69.69 173	69.13 166	71.36 228	78.37 176	45.74 318	74.71 253	80.20 164	57.91 180	70.01 157	83.83 202	42.44 242	82.87 181	54.97 245	79.72 135	85.48 152

VNet		69.68 175	70.19 147	68.16 291	79.73 134	41.63 364	70.53 332	77.38 231	60.37 118	70.69 144	86.63 123	51.08 129	77.09 314	53.61 259	81.69 112	85.75 141

jason		69.65 176	68.39 188	73.43 166	78.27 181	56.88 108	77.12 189	73.71 301	46.53 386	69.34 170	83.22 218	43.37 231	79.18 267	64.77 148	79.20 151	84.23 202
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 177	69.01 171	71.52 217	71.66 353	51.04 224	73.39 281	67.14 361	55.02 256	75.11 58	87.64 89	42.94 238	77.01 317	75.55 50	72.63 271	86.52 105

Effi-MVS+-dtu		69.64 177	67.53 208	75.95 78	76.10 255	62.29 15	80.20 110	76.06 255	59.83 137	65.26 263	77.09 349	41.56 259	84.02 148	60.60 196	71.09 294	81.53 279

fmvsm_s_conf0.5_n		69.58 179	68.84 174	71.79 207	72.31 344	52.90 187	77.90 158	62.43 405	49.97 337	72.85 115	85.90 152	52.21 107	76.49 331	75.75 47	70.26 307	85.97 127

lupinMVS		69.57 180	68.28 193	73.44 165	78.76 160	57.15 104	76.57 207	73.29 307	46.19 389	69.49 165	82.18 246	43.99 227	79.23 266	64.66 149	79.37 141	83.93 213

fmvsm_s_conf0.5_n_7		69.54 181	69.67 156	69.15 279	73.47 319	51.41 221	70.35 336	73.34 304	57.05 196	68.41 184	85.83 155	49.86 143	72.84 352	71.86 84	76.83 203	83.19 242

fmvsm_s_conf0.5_n_a		69.54 181	68.74 177	71.93 201	72.47 338	53.82 161	78.25 145	62.26 407	49.78 339	73.12 107	86.21 140	52.66 99	76.79 324	75.02 56	68.88 333	85.18 169

NR-MVSNet		69.54 181	68.85 173	71.59 216	78.05 190	43.81 341	74.20 264	80.86 153	65.18 14	62.76 304	84.52 186	52.35 106	83.59 157	50.96 282	70.78 295	87.37 73

MVS_111021_LR		69.50 184	68.78 176	71.65 214	78.38 174	59.33 61	74.82 251	70.11 334	58.08 171	67.83 207	84.68 178	41.96 247	76.34 335	65.62 141	77.54 188	79.30 329

v1921920		69.47 185	68.17 195	73.36 168	73.06 325	50.10 250	77.39 176	80.56 156	56.58 211	68.59 180	80.37 284	44.72 218	84.98 127	62.47 179	69.82 316	85.00 176

test_djsdf		69.45 186	67.74 201	74.58 113	74.57 295	54.92 145	82.79 72	78.48 205	51.26 321	65.41 257	83.49 214	38.37 295	83.24 164	66.06 134	69.25 328	85.56 149

fmvsm_s_conf0.1_n		69.41 187	68.60 180	71.83 204	71.07 367	52.88 190	77.85 162	62.44 404	49.58 342	72.97 110	86.22 139	51.68 119	76.48 332	75.53 51	70.10 310	86.14 122

fmvsm_s_conf0.1_n_a		69.32 188	68.44 186	71.96 199	70.91 369	53.78 162	78.12 152	62.30 406	49.35 345	73.20 101	86.55 130	51.99 112	76.79 324	74.83 58	68.68 338	85.32 164

Anonymous20231211		69.28 189	68.47 184	71.73 209	80.28 121	47.18 306	79.98 112	82.37 114	54.61 264	67.24 219	84.01 198	39.43 282	82.41 197	55.45 243	72.83 266	85.62 148

EI-MVSNet		69.27 190	68.44 186	71.73 209	74.47 296	49.39 267	75.20 240	78.45 208	59.60 140	69.16 175	76.51 362	51.29 125	82.50 194	59.86 204	71.45 288	83.30 237

v1240		69.24 191	67.91 200	73.25 172	73.02 327	49.82 254	77.21 186	80.54 157	56.43 213	68.34 187	80.51 283	43.33 232	84.99 125	62.03 183	69.77 319	84.95 180

IterMVS-LS		69.22 192	68.48 182	71.43 224	74.44 298	49.40 266	76.23 215	77.55 226	59.60 140	65.85 251	81.59 264	51.28 126	81.58 212	59.87 203	69.90 315	83.30 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 193	68.38 189	71.38 226	71.57 355	48.61 284	73.22 287	73.18 308	57.65 185	70.67 145	84.73 176	50.03 140	79.80 253	63.25 168	71.10 292	85.74 142

viewmsd2359difaftdt		69.13 193	68.38 189	71.38 226	71.57 355	48.61 284	73.22 287	73.18 308	57.65 185	70.67 145	84.73 176	50.03 140	79.80 253	63.25 168	71.10 292	85.74 142

IMVS_0403		69.09 195	68.14 196	71.95 200	77.06 232	49.73 256	74.51 257	78.60 197	52.70 296	66.69 230	82.58 229	46.43 194	83.38 161	59.20 210	75.46 224	82.74 254

VPA-MVSNet		69.02 196	69.47 160	67.69 295	77.42 216	41.00 371	74.04 266	79.68 171	60.06 128	69.26 173	84.81 174	51.06 130	77.58 304	54.44 252	74.43 235	84.48 195

v7n		69.01 197	67.36 215	73.98 137	72.51 337	52.65 196	78.54 141	81.30 138	60.26 124	62.67 306	81.62 261	43.61 229	84.49 139	57.01 226	68.70 337	84.79 185

viewmambaseed2359dif		68.91 198	68.18 194	71.11 237	70.21 380	48.05 296	72.28 305	75.90 257	51.96 308	70.93 142	84.47 189	51.37 124	78.59 284	61.55 189	74.97 229	86.68 97

IMVS_0407		68.90 199	67.93 199	71.82 205	77.06 232	49.73 256	74.40 262	78.60 197	52.70 296	66.19 240	82.58 229	45.17 213	83.00 169	59.20 210	75.46 224	82.74 254

OpenMVS	61.03 9	68.85 200	67.56 205	72.70 183	74.26 304	53.99 158	81.21 97	81.34 137	52.70 296	62.75 305	85.55 164	38.86 291	84.14 144	48.41 302	83.01 89	79.97 316

XVG-OURS-SEG-HR		68.81 201	67.47 211	72.82 181	74.40 299	56.87 109	70.59 331	79.04 184	54.77 261	66.99 224	86.01 149	39.57 281	78.21 289	62.54 177	73.33 257	83.37 236

BH-RMVSNet		68.81 201	67.42 212	72.97 176	80.11 129	52.53 200	74.26 263	76.29 250	58.48 165	68.38 186	84.20 192	42.59 240	83.83 151	46.53 318	75.91 216	82.56 258

UGNet		68.81 201	67.39 213	73.06 174	78.33 179	54.47 149	79.77 117	75.40 269	60.45 113	63.22 293	84.40 190	32.71 363	80.91 234	51.71 276	80.56 125	83.81 219
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		68.76 204	67.37 214	72.90 178	74.32 302	57.22 99	70.09 340	78.81 190	55.24 243	67.79 209	85.81 158	36.54 318	78.28 288	62.04 182	75.74 219	83.19 242

V42		68.65 205	67.35 216	72.56 186	68.93 402	50.18 248	72.90 294	79.47 176	56.92 199	69.45 167	80.26 288	46.29 196	82.99 170	64.07 152	67.82 344	84.53 193

PVSNet_Blended		68.59 206	67.72 202	71.19 233	77.03 238	50.57 235	72.51 301	81.52 126	51.91 309	64.22 285	77.77 340	49.13 156	82.87 181	55.82 236	79.58 138	80.14 314

xiu_mvs_v1_base_debu		68.58 207	67.28 218	72.48 189	78.19 183	57.19 101	75.28 237	75.09 277	51.61 312	70.04 153	81.41 266	32.79 359	79.02 276	63.81 159	77.31 193	81.22 289

xiu_mvs_v1_base		68.58 207	67.28 218	72.48 189	78.19 183	57.19 101	75.28 237	75.09 277	51.61 312	70.04 153	81.41 266	32.79 359	79.02 276	63.81 159	77.31 193	81.22 289

xiu_mvs_v1_base_debi		68.58 207	67.28 218	72.48 189	78.19 183	57.19 101	75.28 237	75.09 277	51.61 312	70.04 153	81.41 266	32.79 359	79.02 276	63.81 159	77.31 193	81.22 289

PVSNet_BlendedMVS		68.56 210	67.72 202	71.07 239	77.03 238	50.57 235	74.50 258	81.52 126	53.66 286	64.22 285	79.72 300	49.13 156	82.87 181	55.82 236	73.92 241	79.77 324

WR-MVS		68.47 211	68.47 184	68.44 287	80.20 125	39.84 379	73.75 276	76.07 254	64.68 24	68.11 196	83.63 208	50.39 138	79.14 272	49.78 287	69.66 321	86.34 112

mvsmamba		68.47 211	66.56 233	74.21 127	79.60 136	52.95 185	74.94 248	75.48 267	52.09 307	60.10 338	83.27 217	36.54 318	84.70 135	59.32 209	77.69 186	84.99 178

AUN-MVS		68.45 213	66.41 240	74.57 114	79.53 139	57.08 107	73.93 271	75.23 273	54.44 269	66.69 230	81.85 256	37.10 313	82.89 179	62.07 181	66.84 352	83.75 224

c3_l		68.33 214	67.56 205	70.62 249	70.87 370	46.21 314	74.47 259	78.80 191	56.22 220	66.19 240	78.53 323	51.88 113	81.40 216	62.08 180	69.04 331	84.25 201

BH-untuned		68.27 215	67.29 217	71.21 232	79.74 133	53.22 179	76.06 220	77.46 229	57.19 192	66.10 243	81.61 262	45.37 209	83.50 159	45.42 335	76.68 206	76.91 363

jajsoiax		68.25 216	66.45 236	73.66 153	75.62 263	55.49 135	80.82 101	78.51 204	52.33 304	64.33 280	84.11 195	28.28 406	81.81 208	63.48 166	70.62 297	83.67 227

LuminaMVS		68.24 217	66.82 230	72.51 188	73.46 320	53.60 167	76.23 215	78.88 188	52.78 295	68.08 198	80.13 290	32.70 364	81.41 215	63.16 171	75.97 215	82.53 260

v148		68.24 217	67.19 225	71.40 225	70.43 377	47.77 299	75.76 229	77.03 239	58.91 154	67.36 215	80.10 292	48.60 163	81.89 205	60.01 200	66.52 356	84.53 193

CANet_DTU		68.18 219	67.71 204	69.59 268	74.83 284	46.24 313	78.66 136	76.85 241	59.60 140	63.45 291	82.09 253	35.25 328	77.41 307	59.88 202	78.76 165	85.14 170

mvs_tets		68.18 219	66.36 242	73.63 156	75.61 264	55.35 139	80.77 102	78.56 202	52.48 303	64.27 282	84.10 196	27.45 414	81.84 207	63.45 167	70.56 299	83.69 226

guyue		68.10 221	67.23 224	70.71 248	73.67 316	49.27 271	73.65 278	76.04 256	55.62 234	67.84 206	82.26 244	41.24 266	78.91 282	61.01 192	73.72 245	83.94 212

SDMVSNet		68.03 222	68.10 198	67.84 293	77.13 227	48.72 283	65.32 383	79.10 181	58.02 174	65.08 267	82.55 234	47.83 170	73.40 349	63.92 156	73.92 241	81.41 281

miper_ehance_all_eth		68.03 222	67.24 222	70.40 253	70.54 374	46.21 314	73.98 267	78.68 195	55.07 250	66.05 244	77.80 337	52.16 109	81.31 219	61.53 190	69.32 325	83.67 227

mvs_anonymous		68.03 222	67.51 209	69.59 268	72.08 346	44.57 333	71.99 309	75.23 273	51.67 310	67.06 223	82.57 233	54.68 67	77.94 293	56.56 231	75.71 220	86.26 121

ET-MVSNet_ETH3D		67.96 225	65.72 254	74.68 107	76.67 245	55.62 132	75.11 242	74.74 282	52.91 293	60.03 340	80.12 291	33.68 348	82.64 190	61.86 184	76.34 208	85.78 136

thisisatest0530		67.92 226	65.78 253	74.33 122	76.29 252	51.03 225	76.89 198	74.25 292	53.67 285	65.59 254	81.76 259	35.15 329	85.50 116	55.94 234	72.47 272	86.47 107

PAPM		67.92 226	66.69 232	71.63 215	78.09 188	49.02 275	77.09 190	81.24 142	51.04 324	60.91 332	83.98 199	47.71 172	84.99 125	40.81 372	79.32 145	80.90 298

AstraMVS		67.86 228	66.83 229	70.93 242	73.50 318	49.34 268	73.28 285	74.01 296	55.45 238	68.10 197	83.28 216	38.93 290	79.14 272	63.22 170	71.74 283	84.30 200

tttt0517		67.83 229	65.66 255	74.33 122	76.69 243	50.82 230	77.86 161	73.99 297	54.54 267	64.64 277	82.53 237	35.06 330	85.50 116	55.71 239	69.91 314	86.67 98

mamba_0408		67.78 230	65.42 259	74.85 103	78.65 164	53.46 171	50.83 455	79.09 182	53.75 281	68.14 192	83.83 202	41.79 254	86.56 81	56.58 229	76.11 211	84.54 190

tt0805		67.77 231	67.24 222	69.34 273	74.87 282	40.08 376	77.36 177	81.37 132	55.31 240	66.33 238	84.65 180	37.35 307	82.55 193	55.65 241	72.28 277	85.39 161

ECVR-MVS		67.72 232	67.51 209	68.35 288	79.46 140	36.29 419	74.79 252	66.93 363	58.72 157	67.19 220	88.05 80	36.10 320	81.38 217	52.07 270	84.25 78	87.39 71

eth_miper_zixun_eth		67.63 233	66.28 246	71.67 213	71.60 354	48.33 289	73.68 277	77.88 219	55.80 228	65.91 247	78.62 321	47.35 183	82.88 180	59.45 206	66.25 357	83.81 219

UniMVSNet_ETH3D		67.60 234	67.07 227	69.18 277	77.39 217	42.29 355	74.18 265	75.59 263	60.37 118	66.77 228	86.06 146	37.64 303	78.93 281	52.16 269	73.49 252	86.32 116

VPNet		67.52 235	68.11 197	65.74 328	79.18 149	36.80 411	72.17 307	72.83 313	62.04 79	67.79 209	85.83 155	48.88 160	76.60 330	51.30 278	72.97 264	83.81 219

cl22		67.47 236	66.45 236	70.54 251	69.85 390	46.49 310	73.85 274	77.35 232	55.07 250	65.51 255	77.92 332	47.64 174	81.10 225	61.58 188	69.32 325	84.01 210

Fast-Effi-MVS+-dtu		67.37 237	65.33 263	73.48 163	72.94 328	57.78 92	77.47 174	76.88 240	57.60 188	61.97 318	76.85 353	39.31 283	80.49 243	54.72 248	70.28 306	82.17 271

MVS		67.37 237	66.33 243	70.51 252	75.46 268	50.94 226	73.95 269	81.85 121	41.57 426	62.54 310	78.57 322	47.98 167	85.47 118	52.97 264	82.05 103	75.14 379

test1111		67.21 239	67.14 226	67.42 298	79.24 146	34.76 428	73.89 273	65.65 373	58.71 159	66.96 225	87.95 84	36.09 321	80.53 240	52.03 271	83.79 84	86.97 86

GBi-Net		67.21 239	66.55 234	69.19 274	77.63 206	43.33 344	77.31 178	77.83 221	56.62 206	65.04 269	82.70 224	41.85 251	80.33 245	47.18 313	72.76 267	83.92 214

test1		67.21 239	66.55 234	69.19 274	77.63 206	43.33 344	77.31 178	77.83 221	56.62 206	65.04 269	82.70 224	41.85 251	80.33 245	47.18 313	72.76 267	83.92 214

cl____		67.18 242	66.26 247	69.94 260	70.20 381	45.74 318	73.30 282	76.83 243	55.10 245	65.27 260	79.57 303	47.39 181	80.53 240	59.41 208	69.22 329	83.53 233

DIV-MVS_self_test		67.18 242	66.26 247	69.94 260	70.20 381	45.74 318	73.29 284	76.83 243	55.10 245	65.27 260	79.58 302	47.38 182	80.53 240	59.43 207	69.22 329	83.54 232

MVSTER		67.16 244	65.58 257	71.88 203	70.37 379	49.70 260	70.25 338	78.45 208	51.52 315	69.16 175	80.37 284	38.45 294	82.50 194	60.19 198	71.46 287	83.44 235

miper_enhance_ethall		67.11 245	66.09 249	70.17 257	69.21 398	45.98 316	72.85 295	78.41 211	51.38 318	65.65 253	75.98 372	51.17 128	81.25 220	60.82 194	69.32 325	83.29 239

Baseline_NR-MVSNet		67.05 246	67.56 205	65.50 332	75.65 261	37.70 402	75.42 234	74.65 285	59.90 132	68.14 192	83.15 221	49.12 158	77.20 312	52.23 268	69.78 317	81.60 277

WR-MVS_H		67.02 247	66.92 228	67.33 301	77.95 194	37.75 400	77.57 169	82.11 118	62.03 80	62.65 307	82.48 238	50.57 136	79.46 261	42.91 358	64.01 374	84.79 185

anonymousdsp		67.00 248	64.82 268	73.57 159	70.09 384	56.13 117	76.35 211	77.35 232	48.43 359	64.99 272	80.84 280	33.01 356	80.34 244	64.66 149	67.64 346	84.23 202

FMVSNet2		66.93 249	66.31 245	68.79 283	77.63 206	42.98 349	76.11 218	77.47 227	56.62 206	65.22 266	82.17 248	41.85 251	80.18 251	47.05 316	72.72 270	83.20 241

BH-w/o		66.85 250	65.83 252	69.90 263	79.29 142	52.46 203	74.66 255	76.65 247	54.51 268	64.85 274	78.12 326	45.59 202	82.95 175	43.26 354	75.54 222	74.27 393

Anonymous202405211		66.84 251	65.99 250	69.40 272	80.19 126	42.21 357	71.11 324	71.31 325	58.80 156	67.90 200	86.39 134	29.83 391	79.65 256	49.60 293	78.78 163	86.33 114

CDS-MVSNet		66.80 252	65.37 261	71.10 238	78.98 154	53.13 183	73.27 286	71.07 327	52.15 306	64.72 275	80.23 289	43.56 230	77.10 313	45.48 333	78.88 160	83.05 248
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 253	65.27 264	71.33 231	79.16 151	53.67 164	73.84 275	69.59 340	52.32 305	65.28 259	81.72 260	44.49 222	77.40 308	42.32 362	78.66 169	82.92 249

FMVSNet1		66.70 254	65.87 251	69.19 274	77.49 214	43.33 344	77.31 178	77.83 221	56.45 212	64.60 278	82.70 224	38.08 301	80.33 245	46.08 323	72.31 276	83.92 214

ab-mvs		66.65 255	66.42 239	67.37 299	76.17 254	41.73 361	70.41 335	76.14 253	53.99 275	65.98 245	83.51 213	49.48 148	76.24 336	48.60 300	73.46 254	84.14 206

PEN-MVS		66.60 256	66.45 236	67.04 302	77.11 231	36.56 413	77.03 192	80.42 161	62.95 55	62.51 312	84.03 197	46.69 192	79.07 274	44.22 340	63.08 384	85.51 151

TAPA-MVS	59.36 10	66.60 256	65.20 265	70.81 244	76.63 246	48.75 281	76.52 209	80.04 166	50.64 329	65.24 264	84.93 172	39.15 287	78.54 285	36.77 399	76.88 202	85.14 170
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 258	65.07 266	71.17 235	79.18 149	49.63 264	73.48 279	75.20 275	52.95 292	67.90 200	80.33 287	39.81 279	83.68 154	43.20 355	73.56 251	80.20 312

CP-MVSNet		66.49 259	66.41 240	66.72 304	77.67 204	36.33 416	76.83 202	79.52 175	62.45 68	62.54 310	83.47 215	46.32 195	78.37 286	45.47 334	63.43 381	85.45 156

PS-CasMVS		66.42 260	66.32 244	66.70 306	77.60 212	36.30 418	76.94 196	79.61 173	62.36 70	62.43 315	83.66 207	45.69 199	78.37 286	45.35 336	63.26 382	85.42 159

icg_test_0407_2		66.41 261	66.75 231	65.37 336	77.06 232	49.73 256	63.79 397	78.60 197	52.70 296	66.19 240	82.58 229	45.17 213	63.65 411	59.20 210	75.46 224	82.74 254

VortexMVS		66.41 261	65.50 258	69.16 278	73.75 312	48.14 291	73.41 280	78.28 215	53.73 283	64.98 273	78.33 324	40.62 271	79.07 274	58.88 214	67.50 347	80.26 311

FMVSNet3		66.32 263	65.61 256	68.46 286	76.48 250	42.34 354	74.98 247	77.15 236	55.83 226	65.04 269	81.16 269	39.91 276	80.14 252	47.18 313	72.76 267	82.90 251

ACMH+	57.40 11	66.12 264	64.06 273	72.30 196	77.79 198	52.83 192	80.39 105	78.03 218	57.30 190	57.47 372	82.55 234	27.68 412	84.17 143	45.54 330	69.78 317	79.90 318

cascas		65.98 265	63.42 286	73.64 155	77.26 221	52.58 199	72.26 306	77.21 235	48.56 355	61.21 329	74.60 387	32.57 370	85.82 108	50.38 285	76.75 205	82.52 262

FE-MVS		65.91 266	63.33 288	73.63 156	77.36 218	51.95 215	72.62 298	75.81 258	53.70 284	65.31 258	78.96 314	28.81 401	86.39 89	43.93 345	73.48 253	82.55 259

thisisatest0515		65.83 267	63.50 284	72.82 181	73.75 312	49.50 265	71.32 318	73.12 312	49.39 344	63.82 287	76.50 364	34.95 332	84.84 134	53.20 263	75.49 223	84.13 207

DP-MVS		65.68 268	63.66 281	71.75 208	84.93 59	56.87 109	80.74 103	73.16 310	53.06 291	59.09 354	82.35 240	36.79 317	85.94 105	32.82 423	69.96 313	72.45 408

HyFIR lowres test		65.67 269	63.01 293	73.67 152	79.97 131	55.65 129	69.07 351	75.52 265	42.68 420	63.53 290	77.95 330	40.43 273	81.64 209	46.01 324	71.91 281	83.73 225

DTE-MVSNet		65.58 270	65.34 262	66.31 314	76.06 256	34.79 426	76.43 210	79.38 178	62.55 66	61.66 324	83.83 202	45.60 201	79.15 271	41.64 370	60.88 400	85.00 176

GA-MVS		65.53 271	63.70 280	71.02 241	70.87 370	48.10 292	70.48 333	74.40 287	56.69 201	64.70 276	76.77 354	33.66 349	81.10 225	55.42 244	70.32 305	83.87 217

CNLPA		65.43 272	64.02 274	69.68 266	78.73 162	58.07 87	77.82 164	70.71 330	51.49 316	61.57 326	83.58 212	38.23 299	70.82 367	43.90 346	70.10 310	80.16 313

MVP-Stereo		65.41 273	63.80 278	70.22 254	77.62 210	55.53 134	76.30 212	78.53 203	50.59 330	56.47 382	78.65 319	39.84 278	82.68 188	44.10 344	72.12 280	72.44 409
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 274	62.73 297	73.40 167	74.89 280	52.78 193	73.09 291	75.13 276	55.69 230	58.48 363	73.73 395	32.86 358	86.32 92	50.63 283	70.11 309	81.10 293
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		65.33 275	64.61 269	67.50 296	79.46 140	34.19 434	74.43 261	51.92 445	58.72 157	66.75 229	88.05 80	25.99 426	80.92 233	51.94 272	84.25 78	87.39 71

pm-mvs1		65.24 276	64.97 267	66.04 322	72.38 341	39.40 385	72.62 298	75.63 261	55.53 235	62.35 317	83.18 220	47.45 179	76.47 333	49.06 297	66.54 355	82.24 268

ACMH	55.70 15	65.20 277	63.57 282	70.07 258	78.07 189	52.01 213	79.48 125	79.69 170	55.75 229	56.59 379	80.98 274	27.12 417	80.94 231	42.90 359	71.58 286	77.25 357
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 278	63.21 291	70.72 247	81.04 110	54.87 146	78.57 139	77.47 227	48.51 357	55.71 387	81.89 255	33.71 347	79.71 255	41.66 368	70.37 302	77.58 350
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 279	62.84 295	71.82 205	81.49 100	56.26 115	66.32 371	74.20 294	40.53 432	63.16 296	78.65 319	41.30 262	77.80 299	45.80 326	74.09 238	81.40 283

SSM_04072		64.98 280	65.42 259	63.68 351	78.65 164	53.46 171	50.83 455	79.09 182	53.75 281	68.14 192	83.83 202	41.79 254	53.03 456	56.58 229	76.11 211	84.54 190

TransMVSNet (Re)		64.72 281	64.33 271	65.87 327	75.22 273	38.56 391	74.66 255	75.08 280	58.90 155	61.79 321	82.63 227	51.18 127	78.07 291	43.63 351	55.87 424	80.99 297

EG-PatchMatch MVS		64.71 282	62.87 294	70.22 254	77.68 203	53.48 170	77.99 157	78.82 189	53.37 288	56.03 386	77.41 345	24.75 434	84.04 146	46.37 320	73.42 256	73.14 399

LS3D		64.71 282	62.50 299	71.34 230	79.72 135	55.71 127	79.82 116	74.72 283	48.50 358	56.62 378	84.62 181	33.59 350	82.34 198	29.65 445	75.23 228	75.97 369

IMVS_0404		64.63 284	64.22 272	65.88 326	77.06 232	49.73 256	64.40 391	78.60 197	52.70 296	53.16 417	82.58 229	34.82 333	65.16 405	59.20 210	75.46 224	82.74 254

1314		64.61 285	63.21 291	68.80 282	71.87 351	47.46 303	73.95 269	78.39 213	42.88 419	59.97 341	76.60 361	38.11 300	79.39 263	54.84 247	72.32 275	79.55 325

HY-MVS	56.14 13	64.55 286	63.89 275	66.55 310	74.73 288	41.02 368	69.96 341	74.43 286	49.29 346	61.66 324	80.92 276	47.43 180	76.68 329	44.91 338	71.69 284	81.94 273

testing91		64.46 287	63.80 278	66.47 311	78.43 173	40.06 377	67.63 361	69.59 340	59.06 151	63.18 295	78.05 328	34.05 341	76.99 319	48.30 303	75.87 217	82.37 266

sd_testset		64.46 287	64.45 270	64.51 344	77.13 227	42.25 356	62.67 404	72.11 320	58.02 174	65.08 267	82.55 234	41.22 267	69.88 375	47.32 311	73.92 241	81.41 281

XVG-ACMP-BASELINE		64.36 289	62.23 303	70.74 246	72.35 342	52.45 204	70.80 329	78.45 208	53.84 280	59.87 343	81.10 271	16.24 453	79.32 264	55.64 242	71.76 282	80.47 304

FE-MVSNET3		64.34 290	63.57 282	66.66 308	72.44 340	40.74 374	69.60 345	76.80 245	53.21 290	61.73 323	77.92 332	41.92 250	77.68 303	46.23 321	72.25 278	81.57 278

MonoMVSNet		64.15 291	63.31 289	66.69 307	70.51 375	44.12 338	74.47 259	74.21 293	57.81 182	63.03 298	76.62 358	38.33 296	77.31 310	54.22 253	60.59 406	78.64 336

testing99		64.05 292	63.29 290	66.34 313	78.17 186	39.76 381	67.33 366	68.00 354	58.60 162	63.03 298	78.10 327	32.57 370	76.94 321	48.22 304	75.58 221	82.34 267

CostFormer		64.04 293	62.51 298	68.61 285	71.88 350	45.77 317	71.30 319	70.60 331	47.55 373	64.31 281	76.61 360	41.63 257	79.62 258	49.74 289	69.00 332	80.42 306

1112_ss		64.00 294	63.36 287	65.93 324	79.28 144	42.58 353	71.35 317	72.36 318	46.41 387	60.55 335	77.89 335	46.27 197	73.28 350	46.18 322	69.97 312	81.92 274

baseline1		63.81 295	63.87 277	63.62 352	76.29 252	36.36 414	71.78 314	67.29 359	56.05 223	64.23 284	82.95 222	47.11 185	74.41 345	47.30 312	61.85 394	80.10 315

pmmvs6		63.69 296	62.82 296	66.27 316	70.63 372	39.27 386	73.13 290	75.47 268	52.69 301	59.75 347	82.30 242	39.71 280	77.03 316	47.40 310	64.35 373	82.53 260

Vis-MVSNet (Re-imp)		63.69 296	63.88 276	63.14 357	74.75 287	31.04 452	71.16 322	63.64 393	56.32 216	59.80 345	84.99 171	44.51 220	75.46 340	39.12 384	80.62 121	82.92 249

baseline2		63.42 298	61.26 317	69.89 264	72.55 335	47.62 301	71.54 315	68.38 351	50.11 334	54.82 399	75.55 377	43.06 236	80.96 230	48.13 305	67.16 351	81.11 292

thres400		63.31 299	62.18 304	66.72 304	76.85 241	39.62 382	71.96 311	69.44 343	56.63 204	62.61 308	79.83 295	37.18 309	79.17 268	31.84 429	73.25 259	81.36 284

thres600view7		63.30 300	62.27 302	66.41 312	77.18 223	38.87 388	72.35 303	69.11 347	56.98 198	62.37 316	80.96 275	37.01 315	79.00 279	31.43 436	73.05 263	81.36 284

thres100view900		63.28 301	62.41 300	65.89 325	77.31 220	38.66 390	72.65 296	69.11 347	57.07 195	62.45 313	81.03 273	37.01 315	79.17 268	31.84 429	73.25 259	79.83 321

test_0402		63.25 302	61.01 322	69.96 259	80.00 130	54.37 152	76.86 200	72.02 321	54.58 266	58.71 357	80.79 281	35.00 331	84.36 141	26.41 457	64.71 368	71.15 427

tfpn200view9		63.18 303	62.18 304	66.21 317	76.85 241	39.62 382	71.96 311	69.44 343	56.63 204	62.61 308	79.83 295	37.18 309	79.17 268	31.84 429	73.25 259	79.83 321

LTVRE_ROB	55.42 16	63.15 304	61.23 318	68.92 281	76.57 248	47.80 297	59.92 420	76.39 249	54.35 270	58.67 359	82.46 239	29.44 395	81.49 214	42.12 363	71.14 290	77.46 351
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
SD_0403		63.07 305	63.49 285	61.82 365	75.16 276	31.14 451	71.89 313	73.47 302	53.34 289	58.22 365	81.81 258	45.17 213	73.86 348	37.43 393	74.87 231	80.45 305

F-COLMAP		63.05 306	60.87 326	69.58 270	76.99 240	53.63 166	78.12 152	76.16 251	47.97 367	52.41 420	81.61 262	27.87 409	78.11 290	40.07 375	66.66 354	77.00 360

testing11		62.81 307	61.90 307	65.54 330	78.38 174	40.76 373	67.59 363	66.78 365	55.48 236	60.13 337	77.11 348	31.67 377	76.79 324	45.53 331	74.45 234	79.06 331

IterMVS		62.79 308	61.27 316	67.35 300	69.37 396	52.04 212	71.17 321	68.24 353	52.63 302	59.82 344	76.91 352	37.32 308	72.36 355	52.80 265	63.19 383	77.66 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
reproduce_monomvs		62.56 309	61.20 319	66.62 309	70.62 373	44.30 335	70.13 339	73.13 311	54.78 260	61.13 330	76.37 365	25.63 429	75.63 339	58.75 217	60.29 407	79.93 317

IterMVS-SCA-FT		62.49 310	61.52 311	65.40 335	71.99 349	50.80 231	71.15 323	69.63 339	45.71 395	60.61 334	77.93 331	37.45 305	65.99 401	55.67 240	63.50 380	79.42 327

tfpnnormal		62.47 311	61.63 310	64.99 341	74.81 285	39.01 387	71.22 320	73.72 300	55.22 244	60.21 336	80.09 293	41.26 265	76.98 320	30.02 443	68.09 342	78.97 334

MS-PatchMatch		62.42 312	61.46 312	65.31 338	75.21 274	52.10 209	72.05 308	74.05 295	46.41 387	57.42 374	74.36 388	34.35 339	77.57 305	45.62 329	73.67 246	66.26 446

Test_1112_low_res		62.32 313	61.77 308	64.00 349	79.08 153	39.53 384	68.17 357	70.17 333	43.25 415	59.03 355	79.90 294	44.08 224	71.24 365	43.79 348	68.42 339	81.25 288

D2MVS		62.30 314	60.29 329	68.34 289	66.46 421	48.42 288	65.70 375	73.42 303	47.71 371	58.16 366	75.02 383	30.51 381	77.71 302	53.96 256	71.68 285	78.90 335

testing222		62.29 315	61.31 315	65.25 339	77.87 195	38.53 392	68.34 355	66.31 369	56.37 215	63.15 297	77.58 343	28.47 403	76.18 338	37.04 397	76.65 207	81.05 296

thres200		62.20 316	61.16 320	65.34 337	75.38 271	39.99 378	69.60 345	69.29 345	55.64 233	61.87 320	76.99 350	37.07 314	78.96 280	31.28 437	73.28 258	77.06 358

tpm2		62.07 317	60.10 330	67.99 292	72.79 330	43.86 340	71.05 326	66.85 364	43.14 417	62.77 303	75.39 381	38.32 297	80.80 236	41.69 367	68.88 333	79.32 328

testing3-2		62.06 318	62.36 301	61.17 373	79.29 142	30.31 454	64.09 396	63.49 394	63.50 44	62.84 301	82.22 245	32.35 374	69.02 379	40.01 378	73.43 255	84.17 205

miper_lstm_enhance		62.03 319	60.88 324	65.49 333	66.71 418	46.25 312	56.29 439	75.70 260	50.68 327	61.27 328	75.48 379	40.21 274	68.03 385	56.31 233	65.25 364	82.18 269

FE-MVSNET2		62.01 320	60.88 324	65.42 334	68.74 403	38.43 394	72.92 293	77.39 230	54.74 263	55.40 392	76.71 355	35.46 326	76.72 327	44.25 339	62.31 390	81.10 293

EPNet_dtu		61.90 321	61.97 306	61.68 366	72.89 329	39.78 380	75.85 227	65.62 374	55.09 247	54.56 403	79.36 309	37.59 304	67.02 394	39.80 380	76.95 201	78.25 339
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 322	61.35 314	63.46 353	74.58 294	31.48 450	61.42 411	58.14 423	58.71 159	53.02 418	79.55 304	43.07 235	76.80 323	45.69 327	77.96 182	82.11 272

MSDG		61.81 323	59.23 335	69.55 271	72.64 332	52.63 198	70.45 334	75.81 258	51.38 318	53.70 410	76.11 367	29.52 393	81.08 227	37.70 391	65.79 361	74.93 384

SixPastTwentyTwo		61.65 324	58.80 342	70.20 256	75.80 258	47.22 305	75.59 231	69.68 338	54.61 264	54.11 407	79.26 311	27.07 418	82.96 173	43.27 353	49.79 446	80.41 307

CL-MVSNet_self_test		61.53 325	60.94 323	63.30 355	68.95 400	36.93 410	67.60 362	72.80 314	55.67 231	59.95 342	76.63 357	45.01 216	72.22 359	39.74 381	62.09 393	80.74 302

RPMNet		61.53 325	58.42 345	70.86 243	69.96 386	52.07 210	65.31 384	81.36 133	43.20 416	59.36 350	70.15 423	35.37 327	85.47 118	36.42 406	64.65 369	75.06 380

pmmvs4		61.48 327	59.39 334	67.76 294	71.57 355	53.86 159	71.42 316	65.34 376	44.20 406	59.46 349	77.92 332	35.90 322	74.71 343	43.87 347	64.87 367	74.71 389

blend_shiyan4		61.38 328	59.10 338	68.20 290	68.94 401	44.64 331	70.81 328	76.52 248	51.63 311	57.56 371	69.94 425	28.30 405	79.61 259	47.44 308	60.78 402	80.36 309

OurMVSNet-221017-0		61.37 329	58.63 344	69.61 267	72.05 347	48.06 294	73.93 271	72.51 315	47.23 379	54.74 400	80.92 276	21.49 444	81.24 221	48.57 301	56.22 423	79.53 326

COLMAP_ROB	52.97 17	61.27 330	58.81 340	68.64 284	74.63 291	52.51 201	78.42 142	73.30 306	49.92 338	50.96 425	81.51 265	23.06 437	79.40 262	31.63 433	65.85 359	74.01 396
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 331	61.67 309	57.70 400	70.43 377	38.45 393	64.19 393	66.47 366	48.05 366	63.22 293	80.86 278	49.28 153	60.47 421	45.25 337	67.28 350	74.19 394

myMVS_eth3d28		60.66 332	61.04 321	59.51 380	77.32 219	31.58 449	63.11 401	63.87 390	59.00 152	60.90 333	78.26 325	32.69 365	66.15 400	36.10 408	78.13 179	80.81 300

SSC-MVS3.2		60.57 333	61.39 313	58.12 396	74.29 303	32.63 444	59.52 421	65.53 375	59.90 132	62.45 313	79.75 299	41.96 247	63.90 410	39.47 382	69.65 323	77.84 347

WBMVS		60.54 334	60.61 327	60.34 377	78.00 192	35.95 421	64.55 390	64.89 379	49.63 340	63.39 292	78.70 316	33.85 346	67.65 388	42.10 364	70.35 304	77.43 352

SCA		60.49 335	58.38 346	66.80 303	74.14 308	48.06 294	63.35 400	63.23 397	49.13 348	59.33 353	72.10 406	37.45 305	74.27 346	44.17 341	62.57 387	78.05 342

K. test v3		60.47 336	57.11 355	70.56 250	73.74 314	48.22 290	75.10 244	62.55 402	58.27 169	53.62 413	76.31 366	27.81 410	81.59 211	47.42 309	39.18 461	81.88 275

mmtdpeth		60.40 337	59.12 337	64.27 347	69.59 392	48.99 276	70.67 330	70.06 335	54.96 257	62.78 302	73.26 400	27.00 419	67.66 387	58.44 220	45.29 453	76.16 368

UWE-MVS		60.18 338	59.78 331	61.39 371	77.67 204	33.92 437	69.04 352	63.82 391	48.56 355	64.27 282	77.64 342	27.20 416	70.40 372	33.56 420	76.24 209	79.83 321

OpenMVS_ROB	52.78 18	60.03 339	58.14 349	65.69 329	70.47 376	44.82 327	75.33 235	70.86 329	45.04 398	56.06 385	76.00 369	26.89 421	79.65 256	35.36 412	67.29 349	72.60 404

CR-MVSNet		59.91 340	57.90 352	65.96 323	69.96 386	52.07 210	65.31 384	63.15 398	42.48 421	59.36 350	74.84 384	35.83 323	70.75 368	45.50 332	64.65 369	75.06 380

PatchmatchNet		59.84 341	58.24 347	64.65 343	73.05 326	46.70 309	69.42 348	62.18 408	47.55 373	58.88 356	71.96 408	34.49 337	69.16 377	42.99 357	63.60 378	78.07 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 342	57.84 353	65.54 330	74.87 282	42.95 351	69.61 344	64.16 388	48.90 351	58.68 358	77.12 347	28.19 407	72.35 356	43.75 350	55.28 426	81.31 287

WTY-MVS		59.75 343	60.39 328	57.85 398	72.32 343	37.83 399	61.05 416	64.18 386	45.95 394	61.91 319	79.11 313	47.01 189	60.88 420	42.50 361	69.49 324	74.83 385

WB-MVSnew		59.66 344	59.69 332	59.56 379	75.19 275	35.78 423	69.34 349	64.28 385	46.88 383	61.76 322	75.79 373	40.61 272	65.20 404	32.16 425	71.21 289	77.70 348

CVMVSNet		59.63 345	59.14 336	61.08 375	74.47 296	38.84 389	75.20 240	68.74 349	31.15 452	58.24 364	76.51 362	32.39 372	68.58 381	49.77 288	65.84 360	75.81 371

UBG		59.62 346	59.53 333	59.89 378	78.12 187	35.92 422	64.11 395	60.81 415	49.45 343	61.34 327	75.55 377	33.05 354	67.39 392	38.68 386	74.62 232	76.35 367

ETVMVS		59.51 347	58.81 340	61.58 368	77.46 215	34.87 425	64.94 388	59.35 418	54.06 274	61.08 331	76.67 356	29.54 392	71.87 361	32.16 425	74.07 239	78.01 346

tpm cat1		59.25 348	56.95 358	66.15 319	72.19 345	46.96 307	68.09 358	65.76 372	40.03 436	57.81 369	70.56 418	38.32 297	74.51 344	38.26 389	61.50 397	77.00 360

test_vis1_n_1920		58.86 349	59.06 339	58.25 392	63.76 434	43.14 348	67.49 364	66.36 368	40.22 434	65.89 249	71.95 409	31.04 378	59.75 426	59.94 201	64.90 366	71.85 417

pmmvs-eth3d		58.81 350	56.31 367	66.30 315	67.61 411	52.42 205	72.30 304	64.76 381	43.55 412	54.94 398	74.19 390	28.95 398	72.60 353	43.31 352	57.21 418	73.88 397

tt0320		58.59 351	56.81 361	63.92 350	75.46 268	41.32 366	68.63 354	64.06 389	47.05 381	56.19 384	74.19 390	30.34 383	71.36 363	39.92 379	55.45 425	79.09 330

tpmvs		58.47 352	56.95 358	63.03 359	70.20 381	41.21 367	67.90 360	67.23 360	49.62 341	54.73 401	70.84 416	34.14 340	76.24 336	36.64 403	61.29 398	71.64 419

PVSNet	50.76 19	58.40 353	57.39 354	61.42 369	75.53 266	44.04 339	61.43 410	63.45 395	47.04 382	56.91 376	73.61 396	27.00 419	64.76 406	39.12 384	72.40 273	75.47 376

tt0320-xc		58.33 354	56.41 366	64.08 348	75.79 259	41.34 365	68.30 356	62.72 401	47.90 368	56.29 383	74.16 392	28.53 402	71.04 366	41.50 371	52.50 438	79.88 319

tpmrst		58.24 355	58.70 343	56.84 402	66.97 415	34.32 432	69.57 347	61.14 413	47.17 380	58.58 362	71.60 411	41.28 264	60.41 422	49.20 295	62.84 385	75.78 372

Patchmatch-RL test		58.16 356	55.49 373	66.15 319	67.92 410	48.89 280	60.66 418	51.07 449	47.86 370	59.36 350	62.71 454	34.02 343	72.27 358	56.41 232	59.40 410	77.30 354

test-LLR		58.15 357	58.13 350	58.22 393	68.57 404	44.80 328	65.46 380	57.92 424	50.08 335	55.44 390	69.82 426	32.62 367	57.44 438	49.66 291	73.62 248	72.41 410

ppachtmachnet_test		58.06 358	55.38 374	66.10 321	69.51 393	48.99 276	68.01 359	66.13 371	44.50 403	54.05 408	70.74 417	32.09 375	72.34 357	36.68 402	56.71 422	76.99 362

gg-mvs-nofinetune		57.86 359	56.43 365	62.18 363	72.62 333	35.35 424	66.57 368	56.33 433	50.65 328	57.64 370	57.10 460	30.65 380	76.36 334	37.38 394	78.88 160	74.82 386

CMPMVS	42.80 21	57.81 360	55.97 369	63.32 354	60.98 450	47.38 304	64.66 389	69.50 342	32.06 450	46.83 443	77.80 337	29.50 394	71.36 363	48.68 299	73.75 244	71.21 426
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 361	57.07 356	58.22 393	74.21 305	37.18 405	62.46 405	60.88 414	48.88 352	55.29 394	75.99 371	31.68 376	62.04 417	31.87 428	72.35 274	75.43 377

tpm		57.34 362	58.16 348	54.86 412	71.80 352	34.77 427	67.47 365	56.04 436	48.20 363	60.10 338	76.92 351	37.17 311	53.41 455	40.76 373	65.01 365	76.40 366

Patchmtry		57.16 363	56.47 364	59.23 384	69.17 399	34.58 430	62.98 402	63.15 398	44.53 402	56.83 377	74.84 384	35.83 323	68.71 380	40.03 376	60.91 399	74.39 392

AllTest		57.08 364	54.65 378	64.39 345	71.44 359	49.03 273	69.92 342	67.30 357	45.97 392	47.16 441	79.77 297	17.47 447	67.56 390	33.65 417	59.16 411	76.57 364

test_cas_vis1_n_1920		56.91 365	56.71 362	57.51 401	59.13 456	45.40 324	63.58 398	61.29 412	36.24 444	67.14 222	71.85 410	29.89 390	56.69 442	57.65 223	63.58 379	70.46 431

mamv4		56.85 366	58.00 351	53.43 422	72.46 339	54.47 149	57.56 434	54.74 437	38.81 440	57.42 374	79.45 307	47.57 176	38.70 475	60.88 193	53.07 435	67.11 445

dmvs_re		56.77 367	56.83 360	56.61 403	69.23 397	41.02 368	58.37 426	64.18 386	50.59 330	57.45 373	71.42 412	35.54 325	58.94 431	37.23 395	67.45 348	69.87 436

testing3		56.54 368	55.92 370	58.41 391	77.52 213	27.93 462	69.72 343	56.36 432	54.75 262	58.63 361	77.80 337	20.88 445	71.75 362	25.31 459	62.25 391	75.53 375

our_test_3		56.49 369	54.42 381	62.68 361	69.51 393	45.48 323	66.08 372	61.49 411	44.11 409	50.73 429	69.60 429	33.05 354	68.15 382	38.38 388	56.86 419	74.40 391

pmmvs5		56.47 370	55.68 372	58.86 388	61.41 446	36.71 412	66.37 370	62.75 400	40.38 433	53.70 410	76.62 358	34.56 335	67.05 393	40.02 377	65.27 363	72.83 402

test-mter		56.42 371	55.82 371	58.22 393	68.57 404	44.80 328	65.46 380	57.92 424	39.94 437	55.44 390	69.82 426	21.92 440	57.44 438	49.66 291	73.62 248	72.41 410

USDC		56.35 372	54.24 385	62.69 360	64.74 430	40.31 375	65.05 386	73.83 299	43.93 410	47.58 439	77.71 341	15.36 456	75.05 342	38.19 390	61.81 395	72.70 403

PatchMatch-RL		56.25 373	54.55 380	61.32 372	77.06 232	56.07 119	65.57 377	54.10 442	44.13 408	53.49 416	71.27 415	25.20 431	66.78 395	36.52 405	63.66 377	61.12 450

sss		56.17 374	56.57 363	54.96 411	66.93 416	36.32 417	57.94 429	61.69 410	41.67 424	58.64 360	75.32 382	38.72 292	56.25 445	42.04 365	66.19 358	72.31 413

Syy-MVS		56.00 375	56.23 368	55.32 409	74.69 289	26.44 468	65.52 378	57.49 427	50.97 325	56.52 380	72.18 404	39.89 277	68.09 383	24.20 460	64.59 371	71.44 423

FMVSNet5		55.86 376	54.93 376	58.66 390	71.05 368	36.35 415	64.18 394	62.48 403	46.76 385	50.66 430	74.73 386	25.80 427	64.04 408	33.11 421	65.57 362	75.59 374

RPSCF		55.80 377	54.22 386	60.53 376	65.13 429	42.91 352	64.30 392	57.62 426	36.84 443	58.05 368	82.28 243	28.01 408	56.24 446	37.14 396	58.61 413	82.44 265

mvs5depth		55.64 378	53.81 389	61.11 374	59.39 455	40.98 372	65.89 373	68.28 352	50.21 333	58.11 367	75.42 380	17.03 449	67.63 389	43.79 348	46.21 450	74.73 388

EU-MVSNet		55.61 379	54.41 382	59.19 386	65.41 427	33.42 439	72.44 302	71.91 322	28.81 454	51.27 423	73.87 394	24.76 433	69.08 378	43.04 356	58.20 414	75.06 380

Anonymous20240521		55.30 380	54.41 382	57.96 397	60.92 452	41.73 361	71.09 325	71.06 328	41.18 427	48.65 437	73.31 398	16.93 450	59.25 428	42.54 360	64.01 374	72.90 401

TESTMET0.1,1		55.28 381	54.90 377	56.42 404	66.56 419	43.67 342	65.46 380	56.27 434	39.18 439	53.83 409	67.44 438	24.21 435	55.46 449	48.04 306	73.11 262	70.13 434

KD-MVS_self_test		55.22 382	53.89 388	59.21 385	57.80 459	27.47 464	57.75 432	74.32 288	47.38 375	50.90 426	70.00 424	28.45 404	70.30 373	40.44 374	57.92 415	79.87 320

MIMVSNet1		55.17 383	54.31 384	57.77 399	70.03 385	32.01 447	65.68 376	64.81 380	49.19 347	46.75 444	76.00 369	25.53 430	64.04 408	28.65 448	62.13 392	77.26 356

FE-MVSNET		55.16 384	53.75 390	59.41 381	65.29 428	33.20 441	67.21 367	66.21 370	48.39 361	49.56 435	73.53 397	29.03 397	72.51 354	30.38 441	54.10 432	72.52 406

Anonymous20231206		55.10 385	55.30 375	54.48 414	69.81 391	33.94 436	62.91 403	62.13 409	41.08 428	55.18 395	75.65 375	32.75 362	56.59 444	30.32 442	67.86 343	72.91 400

myMVS_eth3d		54.86 386	54.61 379	55.61 408	74.69 289	27.31 465	65.52 378	57.49 427	50.97 325	56.52 380	72.18 404	21.87 443	68.09 383	27.70 451	64.59 371	71.44 423

TinyColmap		54.14 387	51.72 399	61.40 370	66.84 417	41.97 358	66.52 369	68.51 350	44.81 399	42.69 455	75.77 374	11.66 463	72.94 351	31.96 427	56.77 421	69.27 440

EPMVS		53.96 388	53.69 391	54.79 413	66.12 424	31.96 448	62.34 407	49.05 453	44.42 405	55.54 388	71.33 414	30.22 385	56.70 441	41.65 369	62.54 388	75.71 373

PMMVS		53.96 388	53.26 394	56.04 405	62.60 441	50.92 228	61.17 414	56.09 435	32.81 449	53.51 415	66.84 443	34.04 342	59.93 425	44.14 343	68.18 341	57.27 458

test20.03		53.87 390	54.02 387	53.41 423	61.47 445	28.11 461	61.30 412	59.21 419	51.34 320	52.09 421	77.43 344	33.29 353	58.55 433	29.76 444	60.27 408	73.58 398

MDA-MVSNet-bldmvs		53.87 390	50.81 403	63.05 358	66.25 422	48.58 286	56.93 437	63.82 391	48.09 365	41.22 456	70.48 421	30.34 383	68.00 386	34.24 415	45.92 452	72.57 405

KD-MVS_2432*1600		53.45 392	51.50 401	59.30 382	62.82 438	37.14 406	55.33 440	71.79 323	47.34 377	55.09 396	70.52 419	21.91 441	70.45 370	35.72 410	42.97 456	70.31 432

miper_refine_blended		53.45 392	51.50 401	59.30 382	62.82 438	37.14 406	55.33 440	71.79 323	47.34 377	55.09 396	70.52 419	21.91 441	70.45 370	35.72 410	42.97 456	70.31 432

TDRefinement		53.44 394	50.72 404	61.60 367	64.31 433	46.96 307	70.89 327	65.27 378	41.78 422	44.61 450	77.98 329	11.52 465	66.36 398	28.57 449	51.59 440	71.49 422

test0.0.03 1		53.32 395	53.59 392	52.50 429	62.81 440	29.45 456	59.51 422	54.11 441	50.08 335	54.40 405	74.31 389	32.62 367	55.92 447	30.50 440	63.95 376	72.15 415

PatchT		53.17 396	53.44 393	52.33 430	68.29 408	25.34 472	58.21 427	54.41 440	44.46 404	54.56 403	69.05 432	33.32 352	60.94 419	36.93 398	61.76 396	70.73 430

UnsupCasMVSNet_eth		53.16 397	52.47 395	55.23 410	59.45 454	33.39 440	59.43 423	69.13 346	45.98 391	50.35 432	72.32 403	29.30 396	58.26 435	42.02 366	44.30 454	74.05 395

PM-MVS		52.33 398	50.19 407	58.75 389	62.10 443	45.14 326	65.75 374	40.38 471	43.60 411	53.52 414	72.65 401	9.16 471	65.87 402	50.41 284	54.18 431	65.24 448

UWE-MVS-28		52.25 399	52.35 397	51.93 433	66.99 414	22.79 476	63.48 399	48.31 457	46.78 384	52.73 419	76.11 367	27.78 411	57.82 437	20.58 466	68.41 340	75.17 378

testgi		51.90 400	52.37 396	50.51 436	60.39 453	23.55 475	58.42 425	58.15 422	49.03 349	51.83 422	79.21 312	22.39 438	55.59 448	29.24 447	62.64 386	72.40 412

dp		51.89 401	51.60 400	52.77 427	68.44 407	32.45 446	62.36 406	54.57 439	44.16 407	49.31 436	67.91 434	28.87 400	56.61 443	33.89 416	54.89 428	69.24 441

JIA-IIPM		51.56 402	47.68 416	63.21 356	64.61 431	50.73 233	47.71 461	58.77 421	42.90 418	48.46 438	51.72 464	24.97 432	70.24 374	36.06 409	53.89 433	68.64 442

test_fmvs1_n		51.37 403	50.35 406	54.42 416	52.85 463	37.71 401	61.16 415	51.93 444	28.15 456	63.81 288	69.73 428	13.72 457	53.95 453	51.16 279	60.65 404	71.59 420

ADS-MVSNet2		51.33 404	48.76 411	59.07 387	66.02 425	44.60 332	50.90 453	59.76 417	36.90 441	50.74 427	66.18 446	26.38 422	63.11 413	27.17 453	54.76 429	69.50 438

test_fmvs1		51.32 405	50.48 405	53.81 418	53.57 461	37.51 403	60.63 419	51.16 447	28.02 458	63.62 289	69.23 431	16.41 452	53.93 454	51.01 280	60.70 403	69.99 435

YYNet1		50.73 406	48.96 408	56.03 406	61.10 448	41.78 360	51.94 450	56.44 431	40.94 430	44.84 448	67.80 436	30.08 388	55.08 451	36.77 399	50.71 442	71.22 425

MDA-MVSNet_test_wron		50.71 407	48.95 409	56.00 407	61.17 447	41.84 359	51.90 451	56.45 430	40.96 429	44.79 449	67.84 435	30.04 389	55.07 452	36.71 401	50.69 443	71.11 428

dmvs_testset		50.16 408	51.90 398	44.94 444	66.49 420	11.78 484	61.01 417	51.50 446	51.17 323	50.30 433	67.44 438	39.28 284	60.29 423	22.38 463	57.49 417	62.76 449

UnsupCasMVSNet_bld		50.07 409	48.87 410	53.66 419	60.97 451	33.67 438	57.62 433	64.56 383	39.47 438	47.38 440	64.02 452	27.47 413	59.32 427	34.69 414	43.68 455	67.98 444

test_vis1_n		49.89 410	48.69 412	53.50 421	53.97 460	37.38 404	61.53 409	47.33 461	28.54 455	59.62 348	67.10 442	13.52 458	52.27 459	49.07 296	57.52 416	70.84 429

Patchmatch-test		49.08 411	48.28 413	51.50 434	64.40 432	30.85 453	45.68 465	48.46 456	35.60 445	46.10 447	72.10 406	34.47 338	46.37 467	27.08 455	60.65 404	77.27 355

test_fmvs2		48.69 412	47.49 417	52.29 431	48.63 470	33.06 443	57.76 431	48.05 459	25.71 462	59.76 346	69.60 429	11.57 464	52.23 460	49.45 294	56.86 419	71.58 421

ADS-MVSNet		48.48 413	47.77 414	50.63 435	66.02 425	29.92 455	50.90 453	50.87 451	36.90 441	50.74 427	66.18 446	26.38 422	52.47 458	27.17 453	54.76 429	69.50 438

CHOSEN 280x420		47.83 414	46.36 418	52.24 432	67.37 413	49.78 255	38.91 473	43.11 469	35.00 446	43.27 454	63.30 453	28.95 398	49.19 463	36.53 404	60.80 401	57.76 457

new-patchmatchnet		47.56 415	47.73 415	47.06 439	58.81 457	9.37 487	48.78 459	59.21 419	43.28 414	44.22 451	68.66 433	25.67 428	57.20 440	31.57 435	49.35 447	74.62 390

PVSNet_0	43.31 20	47.46 416	45.64 419	52.92 426	67.60 412	44.65 330	54.06 445	54.64 438	41.59 425	46.15 446	58.75 457	30.99 379	58.66 432	32.18 424	24.81 472	55.46 460

ttmdpeth		45.56 417	42.95 422	53.39 424	52.33 466	29.15 457	57.77 430	48.20 458	31.81 451	49.86 434	77.21 346	8.69 472	59.16 429	27.31 452	33.40 468	71.84 418

MVS-HIRNet		45.52 418	44.48 420	48.65 438	68.49 406	34.05 435	59.41 424	44.50 466	27.03 459	37.96 466	50.47 468	26.16 425	64.10 407	26.74 456	59.52 409	47.82 467

pmmvs3		44.92 419	41.95 426	53.86 417	52.58 465	43.55 343	62.11 408	46.90 463	26.05 461	40.63 457	60.19 456	11.08 468	57.91 436	31.83 432	46.15 451	60.11 451

test_fmvs3		44.30 420	42.55 423	49.55 437	42.83 475	27.15 467	53.03 447	44.93 465	22.03 470	53.69 412	64.94 449	4.21 479	49.63 462	47.47 307	49.82 445	71.88 416

WB-MVS		43.26 421	43.41 421	42.83 448	63.32 437	10.32 486	58.17 428	45.20 464	45.42 396	40.44 459	67.26 441	34.01 344	58.98 430	11.96 477	24.88 471	59.20 452

LF4IMVS		42.95 422	42.26 424	45.04 442	48.30 471	32.50 445	54.80 442	48.49 455	28.03 457	40.51 458	70.16 422	9.24 470	43.89 470	31.63 433	49.18 448	58.72 454

MVStest1		42.65 423	39.29 430	52.71 428	47.26 473	34.58 430	54.41 444	50.84 452	23.35 464	39.31 464	74.08 393	12.57 460	55.09 450	23.32 461	28.47 470	68.47 443

EGC-MVSNET		42.47 424	38.48 432	54.46 415	74.33 301	48.73 282	70.33 337	51.10 448	0.03 485	0.18 486	67.78 437	13.28 459	66.49 397	18.91 468	50.36 444	48.15 465

FPMVS		42.18 425	41.11 427	45.39 441	58.03 458	41.01 370	49.50 457	53.81 443	30.07 453	33.71 468	64.03 450	11.69 462	52.08 461	14.01 472	55.11 427	43.09 469

SSC-MVS		41.96 426	41.99 425	41.90 449	62.46 442	9.28 488	57.41 435	44.32 467	43.38 413	38.30 465	66.45 444	32.67 366	58.42 434	10.98 478	21.91 474	57.99 456

ANet_high		41.38 427	37.47 434	53.11 425	39.73 481	24.45 473	56.94 436	69.69 337	47.65 372	26.04 473	52.32 463	12.44 461	62.38 416	21.80 464	10.61 482	72.49 407

test_vis1_rt		41.35 428	39.45 429	47.03 440	46.65 474	37.86 398	47.76 460	38.65 472	23.10 466	44.21 452	51.22 466	11.20 467	44.08 469	39.27 383	53.02 436	59.14 453

LCM-MVSNet		40.30 429	35.88 435	53.57 420	42.24 476	29.15 457	45.21 467	60.53 416	22.23 469	28.02 471	50.98 467	3.72 481	61.78 418	31.22 438	38.76 462	69.78 437

mvsany_test1		39.38 430	38.16 433	43.02 447	49.05 468	34.28 433	44.16 469	25.94 482	22.74 468	46.57 445	62.21 455	23.85 436	41.16 474	33.01 422	35.91 464	53.63 461

N_pmnet		39.35 431	40.28 428	36.54 455	63.76 434	1.62 492	49.37 458	0.76 491	34.62 447	43.61 453	66.38 445	26.25 424	42.57 471	26.02 458	51.77 439	65.44 447

DSMNet-mixed		39.30 432	38.72 431	41.03 450	51.22 467	19.66 479	45.53 466	31.35 478	15.83 477	39.80 461	67.42 440	22.19 439	45.13 468	22.43 462	52.69 437	58.31 455

APD_test1		37.39 433	34.94 436	44.72 445	48.88 469	33.19 442	52.95 448	44.00 468	19.49 471	27.28 472	58.59 458	3.18 483	52.84 457	18.92 467	41.17 459	48.14 466

PMVS	28.69 22	36.22 434	33.29 439	45.02 443	36.82 483	35.98 420	54.68 443	48.74 454	26.31 460	21.02 476	51.61 465	2.88 484	60.10 424	9.99 481	47.58 449	38.99 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 435	31.91 440	43.33 446	62.05 444	37.87 397	20.39 478	67.03 362	23.23 465	18.41 478	25.84 478	4.24 478	62.73 414	14.71 471	51.32 441	29.38 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 436	34.94 436	33.26 458	61.06 449	16.00 483	52.79 449	23.78 484	40.71 431	39.33 463	48.65 472	16.91 451	48.34 464	12.18 476	19.05 476	35.44 475

new_pmnet		34.13 437	34.29 438	33.64 457	52.63 464	18.23 481	44.43 468	33.90 477	22.81 467	30.89 470	53.18 462	10.48 469	35.72 479	20.77 465	39.51 460	46.98 468

mvsany_test3		32.62 438	30.57 443	38.77 453	36.16 484	24.20 474	38.10 474	20.63 486	19.14 472	40.36 460	57.43 459	5.06 476	36.63 478	29.59 446	28.66 469	55.49 459

test_vis3_rt		32.09 439	30.20 444	37.76 454	35.36 485	27.48 463	40.60 472	28.29 481	16.69 475	32.52 469	40.53 474	1.96 485	37.40 477	33.64 419	42.21 458	48.39 464

test_f		31.86 440	31.05 441	34.28 456	32.33 487	21.86 477	32.34 475	30.46 479	16.02 476	39.78 462	55.45 461	4.80 477	32.36 481	30.61 439	37.66 463	48.64 463

testf1		31.46 441	28.89 445	39.16 451	41.99 478	28.78 459	46.45 463	37.56 473	14.28 478	21.10 474	48.96 469	1.48 487	47.11 465	13.63 473	34.56 465	41.60 470

APD_test2		31.46 441	28.89 445	39.16 451	41.99 478	28.78 459	46.45 463	37.56 473	14.28 478	21.10 474	48.96 469	1.48 487	47.11 465	13.63 473	34.56 465	41.60 470

kuosan		29.62 443	30.82 442	26.02 463	52.99 462	16.22 482	51.09 452	22.71 485	33.91 448	33.99 467	40.85 473	15.89 454	33.11 480	7.59 484	18.37 477	28.72 477

PMMVS2		27.40 444	25.91 447	31.87 460	39.46 482	6.57 489	31.17 476	28.52 480	23.96 463	20.45 477	48.94 471	4.20 480	37.94 476	16.51 469	19.97 475	51.09 462

E-PMN		23.77 445	22.73 449	26.90 461	42.02 477	20.67 478	42.66 470	35.70 475	17.43 473	10.28 483	25.05 479	6.42 474	42.39 472	10.28 480	14.71 479	17.63 478

EMVS		22.97 446	21.84 450	26.36 462	40.20 480	19.53 480	41.95 471	34.64 476	17.09 474	9.73 484	22.83 480	7.29 473	42.22 473	9.18 482	13.66 480	17.32 479

MVE	17.77 23	21.41 447	17.77 452	32.34 459	34.34 486	25.44 471	16.11 479	24.11 483	11.19 480	13.22 480	31.92 476	1.58 486	30.95 482	10.47 479	17.03 478	40.62 473
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 448	18.10 451	24.41 464	13.68 489	3.11 491	12.06 481	42.37 470	2.00 483	11.97 481	36.38 475	5.77 475	29.35 483	15.06 470	23.65 473	40.76 472

cdsmvs_eth3d_5k		17.50 449	23.34 448	0.00 470	0.00 493	0.00 494	0.00 482	78.63 196	0.00 488	0.00 489	82.18 246	49.25 154	0.00 487	0.00 488	0.00 485	0.00 485

wuyk23d		13.32 450	12.52 453	15.71 465	47.54 472	26.27 469	31.06 477	1.98 490	4.93 482	5.18 485	1.94 485	0.45 489	18.54 484	6.81 485	12.83 481	2.33 482

tmp_tt		9.43 451	11.14 454	4.30 467	2.38 490	4.40 490	13.62 480	16.08 488	0.39 484	15.89 479	13.06 481	15.80 455	5.54 486	12.63 475	10.46 483	2.95 481

ab-mvs-re		6.49 452	8.65 455	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	77.89 335	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

test123		4.73 453	6.30 456	0.02 468	0.01 491	0.01 493	56.36 438	0.00 492	0.01 486	0.04 487	0.21 487	0.01 490	0.00 487	0.03 487	0.00 485	0.04 483

testmvs		4.52 454	6.03 457	0.01 469	0.01 491	0.00 494	53.86 446	0.00 492	0.01 486	0.04 487	0.27 486	0.00 491	0.00 487	0.04 486	0.00 485	0.03 484

pcd_1.5k_mvsjas		3.92 455	5.23 458	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	47.05 186	0.00 487	0.00 488	0.00 485	0.00 485

mmdepth		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

monomultidepth		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

test_blank		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

uanet_test		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

DCPMVS		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

sosnet-low-res		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

sosnet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

uncertanet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

Regformer		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

uanet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 494	0.00 482	0.00 492	0.00 488	0.00 489	0.00 488	0.00 491	0.00 487	0.00 488	0.00 485	0.00 485

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 99	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 39

TestfortrainingZip							86.84 11

WAC-MVS						27.31 465								27.77 450

FOURS1					86.12 37	60.82 37	88.18 1	83.61 78	60.87 102	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 49

PC_three_1452									55.09 247	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 26

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 49

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					0.00 493

eth-test					0.00 493

ZD-MVS					86.64 21	60.38 45		82.70 110	57.95 178	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

RE-MVS-def			73.71 81		83.49 72	59.87 54	84.29 48	81.36 133	58.07 172	73.14 104	90.07 43	43.06 236		68.20 104	81.76 108	84.03 208

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 277	84.64 3				79.07 13	90.87 5	88.37 28

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 34

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 58

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	59.90 132	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	79.66 172	59.00 152

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 42

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 60

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																78.05 342

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 334				78.05 342

sam_mvs												33.43 351

ambc				65.13 340	63.72 436	37.07 408	47.66 462	78.78 192		54.37 406	71.42 412	11.24 466	80.94 231	45.64 328	53.85 434	77.38 353

MTGPA								80.97 151

test_post1							68.67 353				3.64 483	32.39 372	69.49 376	44.17 341

test_post											3.55 484	33.90 345	66.52 396

patchmatchnet-post											64.03 450	34.50 336	74.27 346

GG-mvs-BLEND				62.34 362	71.36 363	37.04 409	69.20 350	57.33 429		54.73 401	65.48 448	30.37 382	77.82 298	34.82 413	74.93 230	72.17 414

MTMP							86.03 23	17.08 487

gm-plane-assit					71.40 362	41.72 363			48.85 353		73.31 398		82.48 196	48.90 298

test9_res														75.28 54	88.31 36	83.81 219

TEST9					85.58 44	61.59 24	81.62 91	81.26 140	55.65 232	74.93 63	88.81 67	53.70 84	84.68 136

test_8					85.40 47	60.96 34	81.54 94	81.18 144	55.86 224	74.81 68	88.80 69	53.70 84	84.45 140

agg_prior2														73.09 72	87.93 44	84.33 197

agg_prior					85.04 54	59.96 50		81.04 149		74.68 72			84.04 146

TestCases				64.39 345	71.44 359	49.03 273		67.30 357	45.97 392	47.16 441	79.77 297	17.47 447	67.56 390	33.65 417	59.16 411	76.57 364

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 118	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			86.38 108

旧先验2							76.08 219		45.32 397	76.55 47			65.56 403	58.75 217

新几何2							76.12 217

新几何1				70.76 245	85.66 42	61.13 30		66.43 367	44.68 401	70.29 150	86.64 121	41.29 263	75.23 341	49.72 290	81.75 110	75.93 370

旧先验1					83.04 78	53.15 181		67.52 356			87.85 86	44.08 224			80.76 119	78.03 345

无先验							79.66 121	74.30 290	48.40 360				80.78 237	53.62 258		79.03 333

原ACMM2							79.02 128

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 84	51.47 317	70.27 151	86.61 125	48.61 162	86.51 86	53.85 257	87.96 43	78.16 340

test222					83.14 76	58.68 81	72.57 300	63.45 395	41.78 422	67.56 213	86.12 143	37.13 312			78.73 166	74.98 383

testdata2													72.18 360	46.95 317

segment_acmp												54.23 71

testdata				64.66 342	81.52 98	52.93 186		65.29 377	46.09 390	73.88 86	87.46 93	38.08 301	66.26 399	53.31 262	78.48 173	74.78 387

testdata1							72.65 296		60.50 112

test12				77.76 50	84.52 62	58.41 83		83.36 87		72.93 112		54.61 68	88.05 43		88.12 38	86.81 91

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 211

plane_prior5								84.01 57					87.21 63	68.16 107	80.58 123	84.65 188

plane_prior4											86.10 144

plane_prior3						56.09 118			63.92 38	69.27 171

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								0.00 492

nn								0.00 492

door-mid								47.19 462

lessismore_v0				69.91 262	71.42 361	47.80 297		50.90 450		50.39 431	75.56 376	27.43 415	81.33 218	45.91 325	34.10 467	80.59 303

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 85	61.32 90	66.67 232	87.33 99	39.15 287	86.59 79	67.70 114	77.30 196	83.19 242

test11								83.47 82

door								47.60 460

HQP5-MVS						54.94 143

HQP-NCC					80.66 115		82.31 82		62.10 75	67.85 202

ACMP_Plane					80.66 115		82.31 82		62.10 75	67.85 202

BP-MVS														67.04 124

HQP4-MVS										67.85 202			86.93 71			84.32 198

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 205

NP-MVS					80.98 111	56.05 120					85.54 165

MDTV_nov1_ep13_2view						25.89 470	61.22 413		40.10 435	51.10 424		32.97 357		38.49 387		78.61 337

MDTV_nov1_ep13			57.00 357		72.73 331	38.26 395	65.02 387	64.73 382	44.74 400	55.46 389	72.48 402	32.61 369	70.47 369	37.47 392	67.75 345

ACMMP++_ref															74.07 239

ACMMP++															72.16 279

Test By Simon												48.33 165

ITE_SJBPF				62.09 364	66.16 423	44.55 334		64.32 384	47.36 376	55.31 393	80.34 286	19.27 446	62.68 415	36.29 407	62.39 389	79.04 332

DeepMVS_CX				12.03 466	17.97 488	10.91 485		10.60 489	7.46 481	11.07 482	28.36 477	3.28 482	11.29 485	8.01 483	9.74 484	13.89 480

High-resolution multi-view benchmark