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++		95.98 1	96.36 1	94.82 34	97.78 60	86.00 53	98.29 1	97.49 12	90.75 29	97.62 8	98.06 22	92.59 2	99.61 7	95.64 32	99.02 12	98.86 16

SED-MVS		95.91 2	96.28 2	94.80 37	98.77 8	85.99 55	97.13 19	97.44 21	90.31 41	97.71 2	98.07 20	92.31 6	99.58 14	95.66 30	99.13 3	98.84 19

MED-MVS		95.74 3	96.04 3	94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	89.01 94	97.21 14	97.54 44	92.42 4	99.67 1	95.27 40	98.85 20	98.95 11

TestfortrainingZip a		95.70 4	95.76 5	95.51 8	98.88 1	87.98 10	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44	92.42 4	99.67 1	93.66 60	98.85 20	98.89 15

DVP-MVS		95.67 5	96.02 4	94.64 43	98.78 6	85.93 58	97.09 21	96.73 97	90.27 45	97.04 22	98.05 25	91.47 10	99.55 20	95.62 34	99.08 7	98.45 41
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		95.57 6	95.67 6	95.25 12	98.36 31	87.28 19	95.56 118	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 50	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 7	95.64 7	94.91 22	98.26 34	86.29 47	97.46 7	97.40 26	89.03 92	96.20 35	98.10 14	89.39 18	99.34 42	95.88 29	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSP-MVS		95.42 8	95.56 8	94.98 20	98.49 20	86.52 37	96.91 30	97.47 17	91.73 14	96.10 36	96.69 87	89.90 14	99.30 48	94.70 48	98.04 80	99.13 2
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
CNVR-MVS		95.40 9	95.37 11	95.50 9	98.11 42	88.51 7	95.29 130	96.96 68	92.09 10	95.32 49	97.08 70	89.49 17	99.33 45	95.10 44	98.85 20	98.66 26

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 8	96.26 54	97.28 40	85.90 204	97.67 4	98.10 14	88.41 23	99.56 16	94.66 49	99.19 1	98.71 25
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
SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 26	96.99 81	86.33 43	97.33 8	97.30 37	91.38 19	95.39 48	97.46 50	88.98 22	99.40 34	94.12 54	98.89 18	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
ME-MVS		95.17 12	95.29 14	94.81 35	98.39 28	85.89 64	95.91 88	97.55 9	89.01 94	95.86 42	97.54 44	89.24 19	99.59 11	95.27 40	98.85 20	98.95 11

HPM-MVS++		95.14 13	94.91 26	95.83 4	98.25 35	89.65 4	95.92 87	96.96 68	91.75 13	94.02 71	96.83 82	88.12 27	99.55 20	93.41 66	98.94 16	98.28 61

lecture		95.10 14	95.46 10	94.01 65	98.40 26	84.36 106	97.70 3	97.78 4	91.19 20	96.22 34	98.08 19	86.64 43	99.37 37	94.91 46	98.26 63	98.29 60

MM		95.10 14	94.91 26	95.68 5	96.09 115	88.34 9	96.68 38	94.37 297	95.08 1	94.68 57	97.72 39	82.94 100	99.64 4	97.85 5	98.76 33	99.06 7

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 85	96.51 99	82.25 183	95.76 101	96.92 73	93.37 3	97.63 7	98.43 1	84.82 76	99.16 59	98.15 1	97.92 85	98.90 14

SF-MVS		94.97 17	94.90 28	95.20 13	97.84 56	87.76 11	96.65 39	97.48 16	87.76 149	95.71 44	97.70 40	88.28 26	99.35 41	93.89 58	98.78 30	98.48 35

SD-MVS		94.96 18	95.33 12	93.88 70	97.25 78	86.69 29	96.19 57	97.11 58	90.42 37	96.95 24	97.27 58	89.53 16	96.91 311	94.38 52	98.85 20	98.03 90
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
TSAR-MVS + MP.		94.85 19	94.94 24	94.58 46	98.25 35	86.33 43	96.11 67	96.62 107	88.14 126	96.10 36	96.96 76	89.09 21	98.94 91	94.48 51	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
reproduce-ours		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

our_new_method		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

NCCC		94.81 22	94.69 32	95.17 15	97.83 57	87.46 18	95.66 109	96.93 72	92.34 7	93.94 72	96.58 97	87.74 30	99.44 33	92.83 75	98.40 58	98.62 27

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 63	95.64 142	85.08 81	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 73	98.94 91	97.07 17	97.80 92	98.43 43

reproduce_model		94.76 24	94.92 25	94.29 60	97.92 49	85.18 80	95.95 85	97.19 44	89.67 67	95.27 51	98.16 6	86.53 47	99.36 40	95.42 37	98.15 73	98.33 50

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 47	87.70 12	95.68 106	97.34 30	88.28 120	95.30 50	97.67 41	85.90 54	99.54 24	93.91 57	98.95 15	98.60 28

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 84	95.79 132	84.62 91	96.15 62	97.64 6	89.85 56	97.19 17	97.89 33	86.28 50	98.71 121	97.11 16	98.08 79	97.17 158

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 123	95.95 128	81.83 194	95.53 119	97.12 55	91.68 16	97.89 1	98.06 22	85.71 56	98.65 125	97.32 12	98.26 63	97.83 115

fmvsm_s_conf0.5_n_11		94.60 28	95.23 16	92.69 133	96.05 119	82.00 188	96.31 46	96.71 100	92.27 8	96.68 30	98.39 2	85.32 63	98.92 94	97.20 14	98.16 71	97.17 158

test_fmvsmconf_n		94.60 28	94.81 30	93.98 66	94.62 204	84.96 84	96.15 62	97.35 29	89.37 76	96.03 39	98.11 11	86.36 48	99.01 74	97.45 10	97.83 90	97.96 95

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 211	95.76 101	97.57 8	93.48 2	97.53 10	98.32 3	81.78 126	99.13 61	97.91 2	97.81 91	98.16 74

HFP-MVS		94.52 31	94.40 38	94.86 25	98.61 13	86.81 26	96.94 25	97.34 30	88.63 108	93.65 77	97.21 62	86.10 52	99.49 30	92.35 89	98.77 32	98.30 55

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 141	95.49 150	81.10 221	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 88	98.80 110	97.89 3	97.61 99	97.78 119

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 121	93.15 87	97.04 73	86.17 51	99.62 5	92.40 86	98.81 27	98.52 31

XVS		94.45 34	94.32 41	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	97.16 68	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 153	97.12 55	87.13 170	92.51 112	96.30 104	89.24 19	99.34 42	93.46 63	98.62 50	98.73 23

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 94	95.73 135	83.19 143	95.99 79	97.31 36	91.08 21	97.67 4	98.11 11	81.87 123	99.22 52	97.86 4	97.91 87	97.20 156

region2R		94.43 36	94.27 47	94.92 21	98.65 11	86.67 31	96.92 29	97.23 43	88.60 111	93.58 79	97.27 58	85.22 64	99.54 24	92.21 94	98.74 35	98.56 30

ACMMPR		94.43 36	94.28 45	94.91 22	98.63 12	86.69 29	96.94 25	97.32 34	88.63 108	93.53 82	97.26 60	85.04 68	99.54 24	92.35 89	98.78 30	98.50 32

MTAPA		94.42 39	94.22 48	95.00 19	98.42 24	86.95 22	94.36 207	96.97 65	91.07 22	93.14 88	97.56 43	84.30 81	99.56 16	93.43 64	98.75 34	98.47 38

CP-MVS		94.34 40	94.21 50	94.74 41	98.39 28	86.64 33	97.60 5	97.24 41	88.53 113	92.73 103	97.23 61	85.20 65	99.32 46	92.15 97	98.83 26	98.25 68

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 76	95.28 157	85.43 76	95.68 106	96.43 120	86.56 188	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 165

MP-MVS		94.25 42	94.07 56	94.77 39	98.47 21	86.31 45	96.71 36	96.98 64	89.04 90	91.98 123	97.19 65	85.43 61	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 40	98.06 45	86.90 24	95.88 90	96.94 71	85.68 211	95.05 55	97.18 66	87.31 38	99.07 64	91.90 111	98.61 52	98.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 44	94.17 54	94.43 51	98.21 38	85.78 69	96.40 43	96.90 76	88.20 124	94.33 61	97.40 53	84.75 77	99.03 69	93.35 67	97.99 82	98.48 35

GST-MVS		94.21 45	93.97 60	94.90 24	98.41 25	86.82 25	96.54 41	97.19 44	88.24 121	93.26 84	96.83 82	85.48 60	99.59 11	91.43 120	98.40 58	98.30 55

MP-MVS-pluss		94.21 45	94.00 59	94.85 26	98.17 39	86.65 32	94.82 166	97.17 49	86.26 196	92.83 97	97.87 34	85.57 59	99.56 16	94.37 53	98.92 17	98.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 82	95.29 156	84.98 83	95.61 114	96.28 134	86.31 194	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 170

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 323	84.80 87	96.18 59	96.82 85	89.29 81	95.68 45	98.11 11	85.10 66	98.99 81	97.38 11	97.75 96	97.86 110

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 271	94.00 236	97.08 59	90.05 49	95.65 46	97.29 57	89.66 15	98.97 86	93.95 56	98.71 36	98.50 32

MGCNet		94.18 50	93.80 64	95.34 10	94.91 182	87.62 15	95.97 82	93.01 347	92.58 6	94.22 62	97.20 64	80.56 139	99.59 11	97.04 20	98.68 41	98.81 22

CS-MVS		94.12 51	94.44 37	93.17 98	96.55 94	83.08 152	97.63 4	96.95 70	91.71 15	93.50 83	96.21 107	85.61 57	98.24 168	93.64 61	98.17 70	98.19 71

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 125	94.98 175	81.96 192	95.79 97	97.29 39	89.31 79	97.52 11	97.61 42	83.25 94	98.88 98	97.05 19	98.22 69	97.43 141

DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 37	97.48 70	86.78 27	95.65 111	96.89 77	89.40 75	92.81 98	96.97 75	85.37 62	99.24 51	90.87 129	98.69 39	98.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		94.02 54	94.29 44	93.24 92	96.69 87	83.24 140	97.49 6	96.92 73	92.14 9	92.90 93	95.77 148	85.02 69	98.33 163	93.03 72	98.62 50	98.13 77

HPM-MVS		94.02 54	93.88 61	94.43 51	98.39 28	85.78 69	97.25 15	97.07 60	86.90 180	92.62 109	96.80 86	84.85 75	99.17 56	92.43 84	98.65 48	98.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 56	93.78 66	94.63 44	98.50 19	85.90 63	96.87 31	96.91 75	88.70 106	91.83 132	97.17 67	83.96 85	99.55 20	91.44 119	98.64 49	98.43 43

balanced_conf03		93.98 57	94.22 48	93.26 91	96.13 109	83.29 139	96.27 53	96.52 115	89.82 57	95.56 47	95.51 159	84.50 79	98.79 112	94.83 47	98.86 19	97.72 123

fmvsm_s_conf0.5_n_5		93.96 58	94.18 53	93.30 88	94.79 189	83.81 121	95.77 99	96.74 96	88.02 135	96.23 33	97.84 36	83.36 93	98.83 108	97.49 8	97.34 105	97.25 151

PGM-MVS		93.96 58	93.72 70	94.68 42	98.43 23	86.22 48	95.30 128	97.78 4	87.45 160	93.26 84	97.33 56	84.62 78	99.51 28	90.75 131	98.57 53	98.32 54

PHI-MVS		93.89 60	93.65 74	94.62 45	96.84 84	86.43 40	96.69 37	97.49 12	85.15 235	93.56 81	96.28 105	85.60 58	99.31 47	92.45 83	98.79 28	98.12 80

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 160	95.13 168	80.95 228	95.64 112	96.97 65	89.60 69	96.85 25	97.77 38	83.08 98	98.92 94	97.49 8	96.78 119	97.13 166

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 73	97.92 49	84.57 93	96.28 51	96.76 92	87.46 158	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 108

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 75	97.92 49	84.19 110	96.30 47	96.87 79	86.96 176	93.92 73	97.47 49	83.88 86	98.96 88	92.71 79	97.87 88	98.26 67

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 118	95.62 144	83.17 144	96.14 64	96.12 162	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 185

patch_mono-2		93.74 65	94.32 41	92.01 179	97.54 66	78.37 317	93.40 271	97.19 44	88.02 135	94.99 56	97.21 62	88.35 24	98.44 151	94.07 55	98.09 77	99.23 1

MSLP-MVS++		93.72 66	94.08 55	92.65 136	97.31 74	83.43 133	95.79 97	97.33 32	90.03 50	93.58 79	96.96 76	84.87 74	97.76 226	92.19 96	98.66 45	96.76 195

TSAR-MVS + GP.		93.66 67	93.41 78	94.41 53	96.59 91	86.78 27	94.40 199	93.93 315	89.77 64	94.21 63	95.59 156	87.35 37	98.61 133	92.72 78	96.15 135	97.83 115

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 164	87.27 164	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 188

CANet		93.54 69	93.20 83	94.55 47	95.65 141	85.73 71	94.94 156	96.69 103	91.89 12	90.69 163	95.88 136	81.99 121	99.54 24	93.14 70	97.95 84	98.39 45

dcpmvs_2		93.49 70	94.19 52	91.38 217	97.69 63	76.78 360	94.25 212	96.29 131	88.33 117	94.46 59	96.88 79	88.07 28	98.64 128	93.62 62	98.09 77	98.73 23

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 154	95.36 153	81.19 217	95.20 142	96.56 112	90.37 39	97.13 19	98.03 29	77.47 192	98.96 88	97.79 6	96.58 124	97.03 174

NormalMVS		93.46 72	93.16 84	94.37 56	98.40 26	86.20 49	96.30 47	96.27 135	91.65 17	92.68 105	96.13 118	77.97 183	98.84 105	90.75 131	98.26 63	98.07 82

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 271	83.13 146	96.02 77	95.74 198	87.68 152	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 179

MVS_111021_HR		93.45 74	93.31 79	93.84 72	96.99 81	84.84 85	93.24 284	97.24 41	88.76 103	91.60 138	95.85 140	86.07 53	98.66 123	91.91 109	98.16 71	98.03 90

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 256	94.09 67	95.56 158	85.01 72	98.69 122	94.96 45	98.66 45	97.67 126

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 279	84.26 108	95.83 95	96.14 158	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 212

train_agg		93.44 75	93.08 85	94.52 48	97.53 67	86.49 38	94.07 227	96.78 89	81.86 323	92.77 100	96.20 108	87.63 32	99.12 62	92.14 98	98.69 39	97.94 96

EC-MVSNet		93.44 75	93.71 71	92.63 137	95.21 162	82.43 176	97.27 14	96.71 100	90.57 36	92.88 94	95.80 144	83.16 95	98.16 174	93.68 59	98.14 74	97.31 143

DELS-MVS		93.43 79	93.25 81	93.97 67	95.42 152	85.04 82	93.06 293	97.13 54	90.74 31	91.84 130	95.09 184	86.32 49	99.21 54	91.22 121	98.45 56	97.65 127
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
HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 208	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 104

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 61	96.07 117	85.83 67	94.89 159	96.99 63	89.02 93	89.56 187	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 133
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
sasdasda		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 209	82.33 109	98.62 131	92.40 86	92.86 229	98.27 63

canonicalmvs		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 209	82.33 109	98.62 131	92.40 86	92.86 229	98.27 63

ACMMP		93.24 84	92.88 90	94.30 59	98.09 44	85.33 78	96.86 32	97.45 20	88.33 117	90.15 178	97.03 74	81.44 129	99.51 28	90.85 130	95.74 142	98.04 89
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
CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 259	90.05 179	95.66 153	87.77 29	99.15 60	89.91 145	98.27 62	98.07 82

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 321	83.62 128	96.02 77	95.72 202	86.78 182	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 189

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 80	89.25 422	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 167	98.98 85	97.22 13	97.24 106	97.74 121

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 155	94.62 204	81.13 219	95.23 135	95.89 186	90.30 43	96.74 29	98.02 30	76.14 204	98.95 90	97.64 7	96.21 133	97.03 174

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 220	94.42 226	79.48 285	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 252	96.33 26	98.02 81	96.95 181

alignmvs		93.08 90	92.50 98	94.81 35	95.62 144	87.61 16	95.99 79	96.07 167	89.77 64	94.12 66	94.87 193	80.56 139	98.66 123	92.42 85	93.10 225	98.15 75

MGCFI-Net		93.03 91	92.63 95	94.23 62	95.62 144	85.92 60	96.08 69	96.33 129	89.86 55	93.89 74	94.66 206	82.11 116	98.50 139	92.33 91	92.82 232	98.27 63

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 195	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 142

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 208	83.40 135	95.00 153	96.34 128	90.30 43	92.05 121	96.05 122	83.43 89	98.15 175	92.07 100	95.67 143	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		92.83 94	92.54 97	93.68 81	96.10 114	84.71 89	95.66 109	96.39 124	91.92 11	93.22 86	96.49 100	83.16 95	98.87 99	84.47 235	95.47 149	97.45 139

CDPH-MVS		92.83 94	92.30 101	94.44 49	97.79 58	86.11 52	94.06 229	96.66 104	80.09 354	92.77 100	96.63 94	86.62 44	99.04 68	87.40 188	98.66 45	98.17 73

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 293	91.65 17	92.68 105	96.13 118	77.97 183	98.84 105	90.75 131	94.72 167	97.92 104

ETV-MVS		92.74 97	92.66 94	92.97 111	95.20 163	84.04 116	95.07 149	96.51 116	90.73 32	92.96 92	91.19 337	84.06 83	98.34 161	91.72 114	96.54 125	96.54 207

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 199	95.74 198	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 204	97.17 158

DPM-MVS		92.58 99	91.74 109	95.08 16	96.19 106	89.31 5	92.66 311	96.56 112	83.44 278	91.68 137	95.04 185	86.60 46	98.99 81	85.60 215	97.92 85	96.93 184

casdiffmvs		92.51 100	92.43 99	92.74 129	94.41 227	81.98 190	94.54 184	96.23 143	89.57 70	91.96 125	96.17 112	82.58 105	98.01 203	90.95 127	95.45 151	98.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 300	90.39 38	92.67 107	95.94 131	74.46 236	98.65 125	93.14 70	97.35 104	98.13 77

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 290	96.09 165	88.20 124	91.12 152	95.72 151	81.33 131	97.76 226	91.74 113	97.37 103	96.75 196

3Dnovator+	87.14 4	92.42 103	91.37 125	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 330	96.62 95	75.95 213	99.34 42	87.77 181	97.68 97	98.59 29

baseline		92.39 104	92.29 102	92.69 133	94.46 222	81.77 198	94.14 218	96.27 135	89.22 83	91.88 128	96.00 126	82.35 108	97.99 205	91.05 123	95.27 157	98.30 55

VNet		92.24 105	91.91 106	93.24 92	96.59 91	83.43 133	94.84 165	96.44 119	89.19 85	94.08 70	95.90 134	77.85 189	98.17 173	88.90 165	93.38 214	98.13 77

GDP-MVS		92.04 106	91.46 122	93.75 78	94.55 214	84.69 90	95.60 117	96.56 112	87.83 146	93.07 91	95.89 135	73.44 257	98.65 125	90.22 139	96.03 137	97.91 106

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 322	90.24 171	96.44 102	78.59 175	98.61 133	89.68 151	97.85 89	97.06 171

EIA-MVS		91.95 108	91.94 105	91.98 183	95.16 165	80.01 270	95.36 123	96.73 97	88.44 114	89.34 192	92.16 300	83.82 87	98.45 149	89.35 155	97.06 109	97.48 137

DP-MVS Recon		91.95 108	91.28 128	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 300	90.03 180	95.82 143	82.30 111	99.03 69	84.57 233	96.48 128	96.91 186

KinetiMVS		91.82 110	91.30 126	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 125	72.32 274	98.75 115	87.94 178	96.34 130	98.07 82

E2		91.79 111	91.61 112	92.31 162	94.49 218	80.86 234	93.74 254	96.19 148	87.63 155	91.16 148	95.94 131	81.31 132	98.06 191	89.76 147	94.29 185	97.99 92

viewcassd2359sk11		91.79 111	91.62 111	92.29 167	94.62 204	80.88 232	93.70 259	96.18 154	87.38 162	91.13 151	95.85 140	81.62 128	98.06 191	89.71 149	94.40 181	97.94 96

EPNet		91.79 111	91.02 134	94.10 64	90.10 409	85.25 79	96.03 76	92.05 374	92.83 5	87.39 236	95.78 147	79.39 165	99.01 74	88.13 175	97.48 100	98.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
E3		91.78 114	91.61 112	92.30 165	94.48 219	80.86 234	93.73 255	96.19 148	87.63 155	91.16 148	95.95 130	81.30 133	98.06 191	89.76 147	94.29 185	97.99 92

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 235	81.07 222	93.76 252	95.96 178	87.26 165	91.50 140	95.88 136	80.92 137	97.97 210	89.70 150	94.92 163	98.07 82

MG-MVS		91.77 116	91.70 110	92.00 182	97.08 80	80.03 269	93.60 264	95.18 246	87.85 145	90.89 161	96.47 101	82.06 119	98.36 158	85.07 221	97.04 110	97.62 128

E3new		91.76 117	91.58 114	92.28 171	94.69 201	80.90 231	93.68 262	96.17 155	87.15 168	91.09 158	95.70 152	81.75 127	98.05 195	89.67 152	94.35 182	97.90 107

Vis-MVSNet		91.75 118	91.23 129	93.29 89	95.32 155	83.78 122	96.14 64	95.98 174	89.89 53	90.45 167	96.58 97	75.09 225	98.31 166	84.75 227	96.90 114	97.78 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
E4		91.74 119	91.55 117	92.31 162	94.27 240	80.80 238	93.81 249	96.17 155	87.97 137	91.11 153	96.05 122	80.75 138	98.08 188	89.78 146	94.02 191	98.06 87

3Dnovator	86.66 5	91.73 120	90.82 140	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 325	96.66 90	73.74 253	99.17 56	86.74 198	97.96 83	97.79 118

E5new		91.71 121	91.55 117	92.20 173	94.33 233	80.62 244	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E6new		91.71 121	91.55 117	92.20 173	94.32 235	80.62 244	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E6		91.71 121	91.55 117	92.20 173	94.32 235	80.62 244	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E5		91.71 121	91.55 117	92.20 173	94.33 233	80.62 244	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

EPP-MVSNet		91.70 125	91.56 116	92.13 178	95.88 129	80.50 251	97.33 8	95.25 242	86.15 199	89.76 185	95.60 155	83.42 91	98.32 165	87.37 190	93.25 218	97.56 134

MVSFormer		91.68 126	91.30 126	92.80 121	93.86 264	83.88 119	95.96 83	95.90 184	84.66 252	91.76 134	94.91 190	77.92 186	97.30 278	89.64 153	97.11 107	97.24 152

viewmacassd2359aftdt		91.67 127	91.43 124	92.37 155	93.95 262	81.00 225	93.90 246	95.97 177	87.75 150	91.45 143	96.04 124	79.92 148	97.97 210	89.26 158	94.67 169	98.14 76

Effi-MVS+		91.59 128	91.11 131	93.01 108	94.35 232	83.39 136	94.60 180	95.10 250	87.10 171	90.57 166	93.10 271	81.43 130	98.07 190	89.29 157	94.48 178	97.59 132

diffmvs_AUTHOR		91.51 129	91.44 123	91.73 202	93.09 298	80.27 255	92.51 316	95.58 214	87.22 166	91.80 133	95.57 157	79.96 147	97.48 253	92.23 93	94.97 161	97.45 139

IS-MVSNet		91.43 130	91.09 133	92.46 148	95.87 131	81.38 210	96.95 24	93.69 332	89.72 66	89.50 190	95.98 128	78.57 176	97.77 225	83.02 255	96.50 127	98.22 70

PVSNet_Blended_VisFu		91.38 131	90.91 137	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 283	89.27 194	94.46 218	80.29 142	99.17 56	87.57 185	95.37 153	96.05 231

diffmvs		91.37 132	91.23 129	91.77 201	93.09 298	80.27 255	92.36 321	95.52 220	87.03 173	91.40 145	94.93 189	80.08 145	97.44 261	92.13 99	94.56 175	97.61 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.31 133	91.11 131	91.93 188	94.37 228	80.14 260	93.46 269	95.80 193	86.46 191	91.35 146	93.77 249	82.21 114	98.09 186	87.57 185	94.95 162	97.55 135

OMC-MVS		91.23 134	90.62 145	93.08 104	96.27 104	84.07 112	93.52 266	95.93 180	86.95 177	89.51 188	96.13 118	78.50 177	98.35 160	85.84 213	92.90 228	96.83 194

PAPM_NR		91.22 135	90.78 141	92.52 145	97.60 65	81.46 207	94.37 205	96.24 142	86.39 193	87.41 233	94.80 198	82.06 119	98.48 141	82.80 261	95.37 153	97.61 129

viewdifsd2359ckpt13		91.20 136	90.75 142	92.54 143	94.30 238	82.13 185	94.03 231	95.89 186	85.60 214	90.20 173	95.36 166	79.69 160	97.90 220	87.85 180	93.86 195	97.61 129

viewdifsd2359ckpt09		91.18 137	90.65 144	92.75 127	94.61 207	82.36 181	94.32 208	95.74 198	84.72 249	89.66 186	95.15 182	79.69 160	98.04 196	87.70 182	94.27 187	97.85 113

PS-MVSNAJ		91.18 137	90.92 136	91.96 185	95.26 160	82.60 175	92.09 335	95.70 204	86.27 195	91.84 130	92.46 290	79.70 157	98.99 81	89.08 160	95.86 139	94.29 305

xiu_mvs_v2_base		91.13 139	90.89 138	91.86 194	94.97 176	82.42 177	92.24 328	95.64 211	86.11 203	91.74 136	93.14 269	79.67 162	98.89 97	89.06 161	95.46 150	94.28 306

guyue		91.12 140	90.84 139	91.96 185	94.59 208	80.57 249	94.87 161	93.71 331	88.96 97	91.14 150	95.22 174	73.22 261	97.76 226	92.01 104	93.81 198	97.54 136

viewdifsd2359ckpt07		91.11 141	91.02 134	91.41 215	94.21 244	78.37 317	92.91 301	95.71 203	87.50 157	90.32 170	95.88 136	80.27 143	97.99 205	88.78 168	93.55 206	97.86 110

nrg030		91.08 142	90.39 146	93.17 98	93.07 300	86.91 23	96.41 42	96.26 139	88.30 119	88.37 212	94.85 196	82.19 115	97.64 237	91.09 122	82.95 365	94.96 272

mamv4		90.92 143	91.78 108	88.33 351	95.67 140	70.75 437	92.92 300	96.02 173	81.90 319	88.11 215	95.34 169	85.88 55	96.97 306	95.22 43	95.01 160	97.26 150

lupinMVS		90.92 143	90.21 150	93.03 107	93.86 264	83.88 119	92.81 305	93.86 319	79.84 357	91.76 134	94.29 223	77.92 186	98.04 196	90.48 137	97.11 107	97.17 158

RRT-MVS		90.85 145	90.70 143	91.30 221	94.25 241	76.83 359	94.85 164	96.13 161	89.04 90	90.23 172	94.88 192	70.15 304	98.72 119	91.86 112	94.88 164	98.34 48

h-mvs33		90.80 146	90.15 153	92.75 127	96.01 120	82.66 169	95.43 122	95.53 219	89.80 60	93.08 89	95.64 154	75.77 214	99.00 79	92.07 100	78.05 422	96.60 202

jason		90.80 146	90.10 154	92.90 115	93.04 303	83.53 131	93.08 290	94.15 308	80.22 351	91.41 144	94.91 190	76.87 196	97.93 216	90.28 138	96.90 114	97.24 152
jason: jason.
VDD-MVS		90.74 148	89.92 162	93.20 94	96.27 104	83.02 155	95.73 103	93.86 319	88.42 116	92.53 110	96.84 81	62.09 387	98.64 128	90.95 127	92.62 239	97.93 103

SSM_0404		90.73 149	90.08 155	92.69 133	95.00 174	83.13 146	94.32 208	95.00 258	85.41 223	89.84 181	95.35 167	76.13 205	97.98 208	85.46 218	94.18 189	96.95 181

PVSNet_Blended		90.73 149	90.32 148	91.98 183	96.12 110	81.25 213	92.55 315	96.83 83	82.04 314	89.10 196	92.56 288	81.04 135	98.85 103	86.72 200	95.91 138	95.84 239

AstraMVS		90.69 151	90.30 149	91.84 197	93.81 267	79.85 276	94.76 171	92.39 362	88.96 97	91.01 160	95.87 139	70.69 293	97.94 215	92.49 82	92.70 233	97.73 122

test_yl		90.69 151	90.02 160	92.71 130	95.72 136	82.41 179	94.11 221	95.12 248	85.63 212	91.49 141	94.70 200	74.75 229	98.42 154	86.13 208	92.53 241	97.31 143

DCV-MVSNet		90.69 151	90.02 160	92.71 130	95.72 136	82.41 179	94.11 221	95.12 248	85.63 212	91.49 141	94.70 200	74.75 229	98.42 154	86.13 208	92.53 241	97.31 143

API-MVS		90.66 154	90.07 156	92.45 150	96.36 102	84.57 93	96.06 73	95.22 245	82.39 303	89.13 195	94.27 226	80.32 141	98.46 145	80.16 312	96.71 121	94.33 304

xiu_mvs_v1_base_debu		90.64 155	90.05 157	92.40 151	93.97 259	84.46 99	93.32 275	95.46 223	85.17 230	92.25 115	94.03 231	70.59 295	98.57 136	90.97 124	94.67 169	94.18 307

xiu_mvs_v1_base		90.64 155	90.05 157	92.40 151	93.97 259	84.46 99	93.32 275	95.46 223	85.17 230	92.25 115	94.03 231	70.59 295	98.57 136	90.97 124	94.67 169	94.18 307

xiu_mvs_v1_base_debi		90.64 155	90.05 157	92.40 151	93.97 259	84.46 99	93.32 275	95.46 223	85.17 230	92.25 115	94.03 231	70.59 295	98.57 136	90.97 124	94.67 169	94.18 307

HQP_MVS		90.60 158	90.19 151	91.82 198	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 242	94.86 194	74.23 240	98.12 176	88.15 173	89.99 274	94.63 285

LuminaMVS		90.55 159	89.81 164	92.77 123	92.78 316	84.21 109	94.09 225	94.17 307	85.82 205	91.54 139	94.14 230	69.93 305	97.92 217	91.62 116	94.21 188	96.18 220

FIs		90.51 160	90.35 147	90.99 238	93.99 258	80.98 226	95.73 103	97.54 10	89.15 86	86.72 249	94.68 202	81.83 124	97.24 286	85.18 220	88.31 307	94.76 283

SSM_0407		90.47 161	89.80 165	92.46 148	94.76 190	82.66 169	93.98 238	95.00 258	85.41 223	88.96 200	95.35 167	76.13 205	97.88 221	85.46 218	93.15 222	96.85 190

mvsmamba		90.33 162	89.69 168	92.25 172	95.17 164	81.64 200	95.27 133	93.36 337	84.88 242	89.51 188	94.27 226	69.29 320	97.42 263	89.34 156	96.12 136	97.68 125

MAR-MVS		90.30 163	89.37 178	93.07 106	96.61 90	84.48 98	95.68 106	95.67 206	82.36 305	87.85 223	92.85 276	76.63 202	98.80 110	80.01 313	96.68 122	95.91 234
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
FC-MVSNet-test		90.27 164	90.18 152	90.53 255	93.71 276	79.85 276	95.77 99	97.59 7	89.31 79	86.27 260	94.67 205	81.93 122	97.01 304	84.26 237	88.09 310	94.71 284

CANet_DTU		90.26 165	89.41 177	92.81 120	93.46 286	83.01 156	93.48 267	94.47 292	89.43 74	87.76 228	94.23 228	70.54 299	99.03 69	84.97 222	96.39 129	96.38 210

SDMVSNet		90.19 166	89.61 171	91.93 188	96.00 121	83.09 151	92.89 302	95.98 174	88.73 104	86.85 246	95.20 178	72.09 278	97.08 297	88.90 165	89.85 280	95.63 249

Elysia		90.12 167	89.10 185	93.18 96	93.16 293	84.05 114	95.22 137	96.27 135	85.16 233	90.59 164	94.68 202	64.64 367	98.37 156	86.38 204	95.77 140	97.12 167

StellarMVS		90.12 167	89.10 185	93.18 96	93.16 293	84.05 114	95.22 137	96.27 135	85.16 233	90.59 164	94.68 202	64.64 367	98.37 156	86.38 204	95.77 140	97.12 167

OPM-MVS		90.12 167	89.56 172	91.82 198	93.14 295	83.90 118	94.16 217	95.74 198	88.96 97	87.86 222	95.43 164	72.48 271	97.91 218	88.10 177	90.18 272	93.65 342
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 170	89.13 184	92.95 113	96.71 86	82.32 182	96.08 69	89.91 432	86.79 181	92.15 120	96.81 84	62.60 385	98.34 161	87.18 192	93.90 194	98.19 71

GeoE		90.05 171	89.43 176	91.90 193	95.16 165	80.37 254	95.80 96	94.65 284	83.90 264	87.55 232	94.75 199	78.18 182	97.62 239	81.28 292	93.63 204	97.71 124

viewmambaseed2359dif		90.04 172	89.78 166	90.83 244	92.85 313	77.92 329	92.23 329	95.01 254	81.90 319	90.20 173	95.45 161	79.64 164	97.34 276	87.52 187	93.17 220	97.23 155

PAPR		90.02 173	89.27 183	92.29 167	95.78 133	80.95 228	92.68 310	96.22 144	81.91 318	86.66 250	93.75 251	82.23 113	98.44 151	79.40 332	94.79 166	97.48 137

PVSNet_BlendedMVS		89.98 174	89.70 167	90.82 246	96.12 110	81.25 213	93.92 242	96.83 83	83.49 277	89.10 196	92.26 298	81.04 135	98.85 103	86.72 200	87.86 314	92.35 399

IMVS_0403		89.97 175	89.64 169	90.96 241	93.72 272	77.75 340	93.00 295	95.34 237	85.53 218	88.77 205	94.49 214	78.49 178	97.84 222	84.75 227	92.65 234	97.28 146

PS-MVSNAJss		89.97 175	89.62 170	91.02 235	91.90 341	80.85 236	95.26 134	95.98 174	86.26 196	86.21 262	94.29 223	79.70 157	97.65 235	88.87 167	88.10 308	94.57 290

XVG-OURS-SEG-HR		89.95 177	89.45 174	91.47 213	94.00 257	81.21 216	91.87 340	96.06 169	85.78 207	88.55 208	95.73 150	74.67 233	97.27 282	88.71 169	89.64 285	95.91 234

UGNet		89.95 177	88.95 193	92.95 113	94.51 216	83.31 138	95.70 105	95.23 243	89.37 76	87.58 230	93.94 239	64.00 375	98.78 113	83.92 242	96.31 131	96.74 197
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
UniMVSNet_NR-MVSNet		89.92 179	89.29 181	91.81 200	93.39 288	83.72 123	94.43 193	97.12 55	89.80 60	86.46 253	93.32 260	83.16 95	97.23 287	84.92 223	81.02 395	94.49 298

AdaColmap		89.89 180	89.07 187	92.37 155	97.41 71	83.03 154	94.42 194	95.92 181	82.81 297	86.34 259	94.65 207	73.89 249	99.02 72	80.69 303	95.51 146	95.05 267

hse-mvs2		89.88 181	89.34 179	91.51 210	94.83 187	81.12 220	93.94 240	93.91 318	89.80 60	93.08 89	93.60 254	75.77 214	97.66 234	92.07 100	77.07 429	95.74 244

IMVS_0407		89.85 182	89.51 173	90.88 243	93.72 272	77.75 340	93.07 292	95.34 237	85.53 218	88.34 213	94.49 214	77.69 190	97.60 240	84.75 227	92.65 234	97.28 146

UniMVSNet (Re)		89.80 183	89.07 187	92.01 179	93.60 282	84.52 96	94.78 169	97.47 17	89.26 82	86.44 256	92.32 295	82.10 117	97.39 274	84.81 226	80.84 399	94.12 311

HQP-MVS		89.80 183	89.28 182	91.34 219	94.17 246	81.56 201	94.39 201	96.04 170	88.81 100	85.43 288	93.97 238	73.83 251	97.96 212	87.11 195	89.77 283	94.50 296

FA-MVS(test-final)		89.66 185	88.91 195	91.93 188	94.57 212	80.27 255	91.36 355	94.74 280	84.87 243	89.82 182	92.61 287	74.72 232	98.47 144	83.97 241	93.53 208	97.04 173

VPA-MVSNet		89.62 186	88.96 192	91.60 207	93.86 264	82.89 160	95.46 120	97.33 32	87.91 140	88.43 211	93.31 261	74.17 243	97.40 271	87.32 191	82.86 370	94.52 293

WTY-MVS		89.60 187	88.92 194	91.67 205	95.47 151	81.15 218	92.38 320	94.78 278	83.11 287	89.06 198	94.32 221	78.67 174	96.61 331	81.57 288	90.89 261	97.24 152

Vis-MVSNet (Re-imp)		89.59 188	89.44 175	90.03 284	95.74 134	75.85 374	95.61 114	90.80 412	87.66 154	87.83 225	95.40 165	76.79 198	96.46 349	78.37 338	96.73 120	97.80 117

VDDNet		89.56 189	88.49 208	92.76 125	95.07 169	82.09 186	96.30 47	93.19 342	81.05 345	91.88 128	96.86 80	61.16 403	98.33 163	88.43 172	92.49 243	97.84 114

114514_t		89.51 190	88.50 206	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 458	85.81 270	95.25 173	76.70 200	98.63 130	82.07 276	96.86 117	97.00 178

QAPM		89.51 190	88.15 217	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 381	83.41 348	96.19 111	73.18 262	99.30 48	77.11 354	96.54 125	96.89 187

CLD-MVS		89.47 192	88.90 196	91.18 226	94.22 243	82.07 187	92.13 333	96.09 165	87.90 141	85.37 294	92.45 291	74.38 238	97.56 244	87.15 193	90.43 267	93.93 320
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 193	88.90 196	91.12 227	94.47 220	81.49 205	95.30 128	96.14 158	86.73 184	85.45 285	95.16 180	69.89 307	98.10 178	87.70 182	89.23 292	93.77 335

CDS-MVSNet		89.45 193	88.51 205	92.29 167	93.62 281	83.61 130	93.01 294	94.68 283	81.95 316	87.82 226	93.24 265	78.69 173	96.99 305	80.34 309	93.23 219	96.28 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt11		89.43 195	89.05 189	90.56 253	92.89 311	77.00 355	92.81 305	94.52 289	87.03 173	89.77 183	95.79 145	74.67 233	97.51 248	88.97 163	84.98 342	97.17 158

viewmsd2359difaftdt		89.43 195	89.05 189	90.56 253	92.89 311	77.00 355	92.81 305	94.52 289	87.03 173	89.77 183	95.79 145	74.67 233	97.51 248	88.97 163	84.98 342	97.17 158

Fast-Effi-MVS+		89.41 197	88.64 201	91.71 204	94.74 193	80.81 237	93.54 265	95.10 250	83.11 287	86.82 248	90.67 360	79.74 156	97.75 230	80.51 307	93.55 206	96.57 205

ab-mvs		89.41 197	88.35 210	92.60 138	95.15 167	82.65 173	92.20 331	95.60 213	83.97 263	88.55 208	93.70 253	74.16 244	98.21 172	82.46 266	89.37 288	96.94 183

XVG-OURS		89.40 199	88.70 200	91.52 209	94.06 251	81.46 207	91.27 360	96.07 167	86.14 200	88.89 203	95.77 148	68.73 329	97.26 284	87.39 189	89.96 276	95.83 240

test_vis1_n_1920		89.39 200	89.84 163	88.04 360	92.97 307	72.64 414	94.71 175	96.03 172	86.18 198	91.94 127	96.56 99	61.63 391	95.74 386	93.42 65	95.11 159	95.74 244

mvs_anonymous		89.37 201	89.32 180	89.51 319	93.47 285	74.22 392	91.65 348	94.83 274	82.91 295	85.45 285	93.79 247	81.23 134	96.36 356	86.47 202	94.09 190	97.94 96

DU-MVS		89.34 202	88.50 206	91.85 196	93.04 303	83.72 123	94.47 190	96.59 109	89.50 71	86.46 253	93.29 263	77.25 194	97.23 287	84.92 223	81.02 395	94.59 288

TAMVS		89.21 203	88.29 214	91.96 185	93.71 276	82.62 174	93.30 279	94.19 305	82.22 308	87.78 227	93.94 239	78.83 170	96.95 308	77.70 347	92.98 227	96.32 212

icg_test_0407_2		89.15 204	88.97 191	89.68 309	93.72 272	77.75 340	88.26 427	95.34 237	85.53 218	88.34 213	94.49 214	77.69 190	93.99 423	84.75 227	92.65 234	97.28 146

ACMM	84.12 9	89.14 205	88.48 209	91.12 227	94.65 203	81.22 215	95.31 126	96.12 162	85.31 227	85.92 268	94.34 219	70.19 303	98.06 191	85.65 214	88.86 297	94.08 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 206	88.64 201	90.48 263	95.53 149	74.97 383	96.08 69	84.89 465	88.13 127	90.16 177	96.65 91	63.29 380	98.10 178	86.14 206	96.90 114	98.39 45

EI-MVSNet		89.10 206	88.86 198	89.80 298	91.84 343	78.30 320	93.70 259	95.01 254	85.73 209	87.15 237	95.28 171	79.87 154	97.21 289	83.81 244	87.36 322	93.88 324

ECVR-MVS		89.09 208	88.53 204	90.77 248	95.62 144	75.89 373	96.16 60	84.22 467	87.89 143	90.20 173	96.65 91	63.19 382	98.10 178	85.90 211	96.94 112	98.33 50

CNLPA		89.07 209	87.98 221	92.34 159	96.87 83	84.78 88	94.08 226	93.24 339	81.41 336	84.46 315	95.13 183	75.57 221	96.62 328	77.21 352	93.84 197	95.61 251

mamba_0408		89.06 210	87.92 224	92.50 146	94.76 190	82.66 169	79.84 479	94.64 285	85.18 228	88.96 200	95.00 186	76.00 210	97.98 208	83.74 246	93.15 222	96.85 190

PLC	84.53 7	89.06 210	88.03 219	92.15 177	97.27 77	82.69 168	94.29 210	95.44 228	79.71 359	84.01 331	94.18 229	76.68 201	98.75 115	77.28 351	93.41 213	95.02 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 212	88.64 201	90.21 274	90.74 392	79.28 297	95.96 83	95.90 184	84.66 252	85.33 296	92.94 275	74.02 246	97.30 278	89.64 153	88.53 300	94.05 317

HY-MVS	83.01 12	89.03 212	87.94 223	92.29 167	94.86 185	82.77 161	92.08 336	94.49 291	81.52 335	86.93 240	92.79 282	78.32 181	98.23 169	79.93 314	90.55 265	95.88 237

ACMP	84.23 8	89.01 214	88.35 210	90.99 238	94.73 194	81.27 212	95.07 149	95.89 186	86.48 189	83.67 339	94.30 222	69.33 316	97.99 205	87.10 197	88.55 299	93.72 340
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 215	88.26 216	90.94 242	94.05 252	80.78 239	91.71 345	95.38 232	81.55 334	88.63 207	93.91 243	75.04 226	95.47 398	82.47 265	91.61 249	96.57 205

TranMVSNet+NR-MVSNet		88.84 216	87.95 222	91.49 211	92.68 319	83.01 156	94.92 158	96.31 130	89.88 54	85.53 279	93.85 246	76.63 202	96.96 307	81.91 280	79.87 412	94.50 296

CHOSEN 1792x2688		88.84 216	87.69 229	92.30 165	96.14 108	81.42 209	90.01 397	95.86 190	74.52 426	87.41 233	93.94 239	75.46 222	98.36 158	80.36 308	95.53 145	97.12 167

MVSTER		88.84 216	88.29 214	90.51 260	92.95 308	80.44 252	93.73 255	95.01 254	84.66 252	87.15 237	93.12 270	72.79 266	97.21 289	87.86 179	87.36 322	93.87 325

test_cas_vis1_n_1920		88.83 219	88.85 199	88.78 335	91.15 371	76.72 361	93.85 247	94.93 266	83.23 286	92.81 98	96.00 126	61.17 402	94.45 412	91.67 115	94.84 165	95.17 263

OpenMVS	83.78 11	88.74 220	87.29 239	93.08 104	92.70 318	85.39 77	96.57 40	96.43 120	78.74 376	80.85 380	96.07 121	69.64 311	99.01 74	78.01 345	96.65 123	94.83 280

thisisatest0530		88.67 221	87.61 231	91.86 194	94.87 184	80.07 265	94.63 179	89.90 433	84.00 262	88.46 210	93.78 248	66.88 344	98.46 145	83.30 251	92.65 234	97.06 171

Effi-MVS+-dtu		88.65 222	88.35 210	89.54 314	93.33 289	76.39 367	94.47 190	94.36 298	87.70 151	85.43 288	89.56 392	73.45 256	97.26 284	85.57 216	91.28 253	94.97 269

tttt0517		88.61 223	87.78 228	91.11 230	94.96 177	77.81 335	95.35 124	89.69 436	85.09 237	88.05 220	94.59 211	66.93 342	98.48 141	83.27 252	92.13 246	97.03 174

BH-untuned		88.60 224	88.13 218	90.01 287	95.24 161	78.50 313	93.29 280	94.15 308	84.75 248	84.46 315	93.40 257	75.76 216	97.40 271	77.59 348	94.52 177	94.12 311

sd_testset		88.59 225	87.85 227	90.83 244	96.00 121	80.42 253	92.35 323	94.71 281	88.73 104	86.85 246	95.20 178	67.31 336	96.43 351	79.64 320	89.85 280	95.63 249

NR-MVSNet		88.58 226	87.47 235	91.93 188	93.04 303	84.16 111	94.77 170	96.25 141	89.05 89	80.04 394	93.29 263	79.02 169	97.05 302	81.71 287	80.05 409	94.59 288

SSM_04072		88.57 227	87.92 224	90.51 260	94.76 190	82.66 169	79.84 479	94.64 285	85.18 228	88.96 200	95.00 186	76.00 210	92.03 448	83.74 246	93.15 222	96.85 190

VortexMVS		88.42 228	88.01 220	89.63 311	93.89 263	78.82 303	93.82 248	95.47 222	86.67 186	84.53 313	91.99 312	72.62 269	96.65 322	89.02 162	84.09 351	93.41 352

1112_ss		88.42 228	87.33 238	91.72 203	94.92 180	80.98 226	92.97 298	94.54 288	78.16 387	83.82 334	93.88 244	78.78 172	97.91 218	79.45 328	89.41 287	96.26 216

WR-MVS		88.38 230	87.67 230	90.52 259	93.30 290	80.18 258	93.26 282	95.96 178	88.57 112	85.47 284	92.81 280	76.12 207	96.91 311	81.24 293	82.29 375	94.47 301

BH-RMVSNet		88.37 231	87.48 234	91.02 235	95.28 157	79.45 287	92.89 302	93.07 345	85.45 222	86.91 242	94.84 197	70.35 300	97.76 226	73.97 386	94.59 174	95.85 238

IterMVS-LS		88.36 232	87.91 226	89.70 303	93.80 268	78.29 321	93.73 255	95.08 252	85.73 209	84.75 306	91.90 316	79.88 153	96.92 310	83.83 243	82.51 371	93.89 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 233	86.13 282	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 492	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

LCM-MVSNet-Re		88.30 234	88.32 213	88.27 353	94.71 198	72.41 419	93.15 285	90.98 405	87.77 148	79.25 407	91.96 313	78.35 180	95.75 385	83.04 254	95.62 144	96.65 201

jajsoiax		88.24 235	87.50 233	90.48 263	90.89 385	80.14 260	95.31 126	95.65 210	84.97 240	84.24 326	94.02 234	65.31 361	97.42 263	88.56 170	88.52 301	93.89 321

VPNet		88.20 236	87.47 235	90.39 268	93.56 283	79.46 286	94.04 230	95.54 218	88.67 107	86.96 239	94.58 212	69.33 316	97.15 291	84.05 240	80.53 404	94.56 291

TAPA-MVS	84.62 6	88.16 237	87.01 247	91.62 206	96.64 89	80.65 241	94.39 201	96.21 147	76.38 406	86.19 263	95.44 162	79.75 155	98.08 188	62.75 453	95.29 155	96.13 223
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 238	87.28 240	90.57 251	94.96 177	80.07 265	94.27 211	91.29 398	86.74 183	87.41 233	94.00 236	76.77 199	96.20 362	80.77 301	79.31 418	95.44 253

Anonymous20240529		88.09 239	86.59 264	92.58 140	96.53 96	81.92 193	95.99 79	95.84 191	74.11 431	89.06 198	95.21 177	61.44 395	98.81 109	83.67 249	87.47 319	97.01 177

HyFIR lowres test		88.09 239	86.81 252	91.93 188	96.00 121	80.63 242	90.01 397	95.79 194	73.42 438	87.68 229	92.10 306	73.86 250	97.96 212	80.75 302	91.70 248	97.19 157

mvs_tets		88.06 241	87.28 240	90.38 270	90.94 381	79.88 274	95.22 137	95.66 208	85.10 236	84.21 327	93.94 239	63.53 378	97.40 271	88.50 171	88.40 305	93.87 325

F-COLMAP		87.95 242	86.80 253	91.40 216	96.35 103	80.88 232	94.73 173	95.45 226	79.65 360	82.04 367	94.61 208	71.13 285	98.50 139	76.24 364	91.05 259	94.80 282

LS3D		87.89 243	86.32 275	92.59 139	96.07 117	82.92 159	95.23 135	94.92 267	75.66 413	82.89 355	95.98 128	72.48 271	99.21 54	68.43 423	95.23 158	95.64 248

anonymousdsp		87.84 244	87.09 243	90.12 279	89.13 423	80.54 250	94.67 177	95.55 216	82.05 312	83.82 334	92.12 303	71.47 283	97.15 291	87.15 193	87.80 317	92.67 381

v2v482		87.84 244	87.06 244	90.17 275	90.99 377	79.23 300	94.00 236	95.13 247	84.87 243	85.53 279	92.07 309	74.45 237	97.45 258	84.71 232	81.75 383	93.85 328

WR-MVS_H		87.80 246	87.37 237	89.10 328	93.23 291	78.12 324	95.61 114	97.30 37	87.90 141	83.72 337	92.01 311	79.65 163	96.01 371	76.36 361	80.54 403	93.16 363

AUN-MVS		87.78 247	86.54 267	91.48 212	94.82 188	81.05 223	93.91 244	93.93 315	83.00 292	86.93 240	93.53 255	69.50 314	97.67 232	86.14 206	77.12 428	95.73 246

PCF-MVS	84.11 10	87.74 248	86.08 286	92.70 132	94.02 253	84.43 102	89.27 410	95.87 189	73.62 436	84.43 317	94.33 220	78.48 179	98.86 101	70.27 409	94.45 179	94.81 281
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 249	86.13 282	92.31 162	96.66 88	80.74 240	94.87 161	91.49 393	80.47 350	89.46 191	95.44 162	54.72 442	98.23 169	82.19 272	89.89 278	97.97 94

V42		87.68 249	86.86 249	90.15 277	90.58 397	80.14 260	94.24 214	95.28 241	83.66 271	85.67 274	91.33 332	74.73 231	97.41 269	84.43 236	81.83 381	92.89 375

thres600view7		87.65 251	86.67 259	90.59 250	96.08 116	78.72 304	94.88 160	91.58 389	87.06 172	88.08 218	92.30 296	68.91 326	98.10 178	70.05 416	91.10 254	94.96 272

XXY-MVS		87.65 251	86.85 250	90.03 284	92.14 331	80.60 248	93.76 252	95.23 243	82.94 294	84.60 309	94.02 234	74.27 239	95.49 397	81.04 295	83.68 357	94.01 319

Test_1112_low_res		87.65 251	86.51 268	91.08 231	94.94 179	79.28 297	91.77 343	94.30 300	76.04 411	83.51 344	92.37 293	77.86 188	97.73 231	78.69 337	89.13 294	96.22 217

thres100view900		87.63 254	86.71 256	90.38 270	96.12 110	78.55 310	95.03 152	91.58 389	87.15 168	88.06 219	92.29 297	68.91 326	98.10 178	70.13 413	91.10 254	94.48 299

CP-MVSNet		87.63 254	87.26 242	88.74 339	93.12 296	76.59 364	95.29 130	96.58 110	88.43 115	83.49 347	92.98 274	75.28 223	95.83 380	78.97 334	81.15 391	93.79 330

thres400		87.62 256	86.64 260	90.57 251	95.99 124	78.64 307	94.58 181	91.98 378	86.94 178	88.09 216	91.77 318	69.18 322	98.10 178	70.13 413	91.10 254	94.96 272

v1144		87.61 257	86.79 254	90.06 283	91.01 376	79.34 293	93.95 239	95.42 231	83.36 282	85.66 275	91.31 335	74.98 227	97.42 263	83.37 250	82.06 377	93.42 351

IMVS_0404		87.60 258	86.84 251	89.89 291	93.72 272	77.75 340	88.56 422	95.34 237	85.53 218	79.98 395	94.49 214	66.54 352	94.64 411	84.75 227	92.65 234	97.28 146

tfpn200view9		87.58 259	86.64 260	90.41 267	95.99 124	78.64 307	94.58 181	91.98 378	86.94 178	88.09 216	91.77 318	69.18 322	98.10 178	70.13 413	91.10 254	94.48 299

BH-w/o		87.57 260	87.05 245	89.12 327	94.90 183	77.90 331	92.41 318	93.51 334	82.89 296	83.70 338	91.34 331	75.75 217	97.07 299	75.49 369	93.49 210	92.39 397

UniMVSNet_ETH3D		87.53 261	86.37 272	91.00 237	92.44 324	78.96 302	94.74 172	95.61 212	84.07 261	85.36 295	94.52 213	59.78 411	97.34 276	82.93 256	87.88 313	96.71 198

ET-MVSNet_ETH3D		87.51 262	85.91 294	92.32 161	93.70 278	83.93 117	92.33 325	90.94 408	84.16 258	72.09 456	92.52 289	69.90 306	95.85 379	89.20 159	88.36 306	97.17 158

1314		87.51 262	86.57 265	90.34 272	92.42 325	79.74 281	92.63 312	95.35 236	78.35 382	80.14 391	91.62 326	74.05 245	97.15 291	81.05 294	93.53 208	94.12 311

v8		87.50 264	86.71 256	89.89 291	91.37 361	79.40 290	94.50 186	95.38 232	84.81 246	83.60 342	91.33 332	76.05 208	97.42 263	82.84 259	80.51 406	92.84 377

Fast-Effi-MVS+-dtu		87.44 265	86.72 255	89.63 311	92.04 335	77.68 345	94.03 231	93.94 314	85.81 206	82.42 360	91.32 334	70.33 301	97.06 300	80.33 310	90.23 271	94.14 310

MVS		87.44 265	86.10 285	91.44 214	92.61 320	83.62 128	92.63 312	95.66 208	67.26 464	81.47 372	92.15 301	77.95 185	98.22 171	79.71 317	95.48 148	92.47 392

FE-MVS		87.40 267	86.02 288	91.57 208	94.56 213	79.69 282	90.27 384	93.72 330	80.57 348	88.80 204	91.62 326	65.32 360	98.59 135	74.97 377	94.33 184	96.44 208

FMVSNet3		87.40 267	86.11 284	91.30 221	93.79 270	83.64 127	94.20 216	94.81 276	83.89 265	84.37 318	91.87 317	68.45 332	96.56 340	78.23 342	85.36 338	93.70 341

test_fmvs1		87.34 269	87.56 232	86.68 400	90.59 396	71.80 423	94.01 234	94.04 313	78.30 383	91.97 124	95.22 174	56.28 431	93.71 429	92.89 74	94.71 168	94.52 293

thisisatest0515		87.33 270	85.99 289	91.37 218	93.49 284	79.55 283	90.63 376	89.56 441	80.17 352	87.56 231	90.86 350	67.07 341	98.28 167	81.50 289	93.02 226	96.29 214

PS-CasMVS		87.32 271	86.88 248	88.63 342	92.99 306	76.33 369	95.33 125	96.61 108	88.22 123	83.30 352	93.07 272	73.03 264	95.79 384	78.36 339	81.00 397	93.75 337

GBi-Net		87.26 272	85.98 290	91.08 231	94.01 254	83.10 148	95.14 146	94.94 262	83.57 273	84.37 318	91.64 322	66.59 349	96.34 357	78.23 342	85.36 338	93.79 330

test1		87.26 272	85.98 290	91.08 231	94.01 254	83.10 148	95.14 146	94.94 262	83.57 273	84.37 318	91.64 322	66.59 349	96.34 357	78.23 342	85.36 338	93.79 330

v1192		87.25 274	86.33 274	90.00 288	90.76 391	79.04 301	93.80 250	95.48 221	82.57 301	85.48 283	91.18 339	73.38 260	97.42 263	82.30 269	82.06 377	93.53 345

v10		87.25 274	86.38 271	89.85 293	91.19 367	79.50 284	94.48 187	95.45 226	83.79 269	83.62 341	91.19 337	75.13 224	97.42 263	81.94 279	80.60 401	92.63 383

DP-MVS		87.25 274	85.36 313	92.90 115	97.65 64	83.24 140	94.81 167	92.00 376	74.99 421	81.92 369	95.00 186	72.66 267	99.05 66	66.92 435	92.33 244	96.40 209

miper_ehance_all_eth		87.22 277	86.62 263	89.02 331	92.13 332	77.40 349	90.91 371	94.81 276	81.28 339	84.32 323	90.08 378	79.26 166	96.62 328	83.81 244	82.94 366	93.04 370

test2506		87.21 278	86.28 277	90.02 286	95.62 144	73.64 399	96.25 55	71.38 490	87.89 143	90.45 167	96.65 91	55.29 438	98.09 186	86.03 210	96.94 112	98.33 50

thres200		87.21 278	86.24 279	90.12 279	95.36 153	78.53 311	93.26 282	92.10 372	86.42 192	88.00 221	91.11 343	69.24 321	98.00 204	69.58 417	91.04 260	93.83 329

v144192		87.19 280	86.35 273	89.74 300	90.64 395	78.24 322	93.92 242	95.43 229	81.93 317	85.51 281	91.05 346	74.21 242	97.45 258	82.86 258	81.56 385	93.53 345

FMVSNet2		87.19 280	85.82 297	91.30 221	94.01 254	83.67 125	94.79 168	94.94 262	83.57 273	83.88 333	92.05 310	66.59 349	96.51 344	77.56 349	85.01 341	93.73 339

c3_l		87.14 282	86.50 269	89.04 330	92.20 329	77.26 351	91.22 363	94.70 282	82.01 315	84.34 322	90.43 365	78.81 171	96.61 331	83.70 248	81.09 392	93.25 357

testing91		87.11 283	86.18 280	89.92 290	94.43 225	75.38 382	91.53 350	92.27 368	86.48 189	86.50 251	90.24 370	61.19 401	97.53 246	82.10 274	90.88 262	96.84 193

Baseline_NR-MVSNet		87.07 284	86.63 262	88.40 346	91.44 356	77.87 333	94.23 215	92.57 359	84.12 260	85.74 273	92.08 307	77.25 194	96.04 367	82.29 270	79.94 410	91.30 423

v148		87.04 285	86.32 275	89.21 324	90.94 381	77.26 351	93.71 258	94.43 293	84.84 245	84.36 321	90.80 354	76.04 209	97.05 302	82.12 273	79.60 415	93.31 354

test_fmvs1_n		87.03 286	87.04 246	86.97 391	89.74 417	71.86 421	94.55 183	94.43 293	78.47 379	91.95 126	95.50 160	51.16 453	93.81 427	93.02 73	94.56 175	95.26 260

v1921920		86.97 287	86.06 287	89.69 305	90.53 400	78.11 325	93.80 250	95.43 229	81.90 319	85.33 296	91.05 346	72.66 267	97.41 269	82.05 277	81.80 382	93.53 345

tt0805		86.92 288	85.74 303	90.48 263	92.22 328	79.98 272	95.63 113	94.88 270	83.83 267	84.74 307	92.80 281	57.61 426	97.67 232	85.48 217	84.42 347	93.79 330

miper_enhance_ethall		86.90 289	86.18 280	89.06 329	91.66 352	77.58 347	90.22 390	94.82 275	79.16 366	84.48 314	89.10 397	79.19 168	96.66 321	84.06 239	82.94 366	92.94 373

MonoMVSNet		86.89 290	86.55 266	87.92 364	89.46 421	73.75 396	94.12 219	93.10 343	87.82 147	85.10 299	90.76 356	69.59 312	94.94 409	86.47 202	82.50 372	95.07 266

usedtu_dtu_shiyan1		86.84 291	85.61 305	90.53 255	90.50 401	81.80 196	90.97 368	94.96 260	83.05 289	83.50 345	90.32 367	72.15 275	96.65 322	79.49 325	85.55 336	93.15 365

FE-MVSNET3		86.84 291	85.61 305	90.53 255	90.50 401	81.80 196	90.97 368	94.96 260	83.05 289	83.50 345	90.32 367	72.15 275	96.65 322	79.49 325	85.55 336	93.15 365

v7n		86.81 293	85.76 301	89.95 289	90.72 393	79.25 299	95.07 149	95.92 181	84.45 255	82.29 361	90.86 350	72.60 270	97.53 246	79.42 331	80.52 405	93.08 369

PEN-MVS		86.80 294	86.27 278	88.40 346	92.32 327	75.71 377	95.18 143	96.38 125	87.97 137	82.82 356	93.15 268	73.39 259	95.92 375	76.15 365	79.03 420	93.59 343

cl22		86.78 295	85.98 290	89.18 326	92.34 326	77.62 346	90.84 372	94.13 310	81.33 338	83.97 332	90.15 375	73.96 247	96.60 335	84.19 238	82.94 366	93.33 353

v1240		86.78 295	85.85 296	89.56 313	90.45 404	77.79 337	93.61 263	95.37 234	81.65 329	85.43 288	91.15 341	71.50 282	97.43 262	81.47 290	82.05 379	93.47 349

TR-MVS		86.78 295	85.76 301	89.82 295	94.37 228	78.41 315	92.47 317	92.83 351	81.11 344	86.36 257	92.40 292	68.73 329	97.48 253	73.75 390	89.85 280	93.57 344

PatchMatch-RL		86.77 298	85.54 307	90.47 266	95.88 129	82.71 167	90.54 379	92.31 366	79.82 358	84.32 323	91.57 330	68.77 328	96.39 353	73.16 392	93.48 212	92.32 400

testing3-2		86.72 299	86.71 256	86.74 399	96.11 113	65.92 460	93.39 272	89.65 439	89.46 72	87.84 224	92.79 282	59.17 417	97.60 240	81.31 291	90.72 263	96.70 199

testing99		86.72 299	85.73 304	89.69 305	94.23 242	74.91 385	91.35 356	90.97 406	86.14 200	86.36 257	90.22 371	59.41 414	97.48 253	82.24 271	90.66 264	96.69 200

PAPM		86.68 301	85.39 311	90.53 255	93.05 302	79.33 296	89.79 400	94.77 279	78.82 373	81.95 368	93.24 265	76.81 197	97.30 278	66.94 433	93.16 221	94.95 276

pm-mvs1		86.61 302	85.54 307	89.82 295	91.44 356	80.18 258	95.28 132	94.85 272	83.84 266	81.66 370	92.62 286	72.45 273	96.48 346	79.67 319	78.06 421	92.82 378

GA-MVS		86.61 302	85.27 316	90.66 249	91.33 364	78.71 306	90.40 383	93.81 325	85.34 226	85.12 298	89.57 391	61.25 398	97.11 296	80.99 298	89.59 286	96.15 221

Anonymous20231211		86.59 304	85.13 319	90.98 240	96.52 97	81.50 203	96.14 64	96.16 157	73.78 434	83.65 340	92.15 301	63.26 381	97.37 275	82.82 260	81.74 384	94.06 316

test_vis1_n		86.56 305	86.49 270	86.78 398	88.51 428	72.69 411	94.68 176	93.78 327	79.55 361	90.70 162	95.31 170	48.75 459	93.28 435	93.15 69	93.99 192	94.38 303

DIV-MVS_self_test		86.53 306	85.78 298	88.75 337	92.02 337	76.45 366	90.74 373	94.30 300	81.83 325	83.34 350	90.82 353	75.75 217	96.57 338	81.73 286	81.52 387	93.24 358

cl____		86.52 307	85.78 298	88.75 337	92.03 336	76.46 365	90.74 373	94.30 300	81.83 325	83.34 350	90.78 355	75.74 219	96.57 338	81.74 285	81.54 386	93.22 359

eth_miper_zixun_eth		86.50 308	85.77 300	88.68 340	91.94 338	75.81 375	90.47 382	94.89 268	82.05 312	84.05 329	90.46 364	75.96 212	96.77 315	82.76 262	79.36 417	93.46 350

baseline2		86.50 308	85.39 311	89.84 294	91.12 372	76.70 362	91.88 339	88.58 445	82.35 306	79.95 396	90.95 348	73.42 258	97.63 238	80.27 311	89.95 277	95.19 262

EPNet_dtu		86.49 310	85.94 293	88.14 358	90.24 407	72.82 409	94.11 221	92.20 370	86.66 187	79.42 403	92.36 294	73.52 254	95.81 382	71.26 401	93.66 203	95.80 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 311	85.35 314	89.69 305	94.29 239	75.40 381	91.30 357	90.53 417	84.76 247	85.06 300	90.13 376	58.95 420	97.45 258	82.08 275	91.09 258	96.21 219

cascas		86.43 312	84.98 322	90.80 247	92.10 334	80.92 230	90.24 388	95.91 183	73.10 441	83.57 343	88.39 410	65.15 362	97.46 257	84.90 225	91.43 251	94.03 318

reproduce_monomvs		86.37 313	85.87 295	87.87 365	93.66 280	73.71 397	93.44 270	95.02 253	88.61 110	82.64 359	91.94 314	57.88 424	96.68 320	89.96 144	79.71 414	93.22 359

SCA		86.32 314	85.18 318	89.73 302	92.15 330	76.60 363	91.12 364	91.69 385	83.53 276	85.50 282	88.81 403	66.79 345	96.48 346	76.65 357	90.35 269	96.12 224

LTVRE_ROB	82.13 13	86.26 315	84.90 325	90.34 272	94.44 224	81.50 203	92.31 327	94.89 268	83.03 291	79.63 401	92.67 284	69.69 310	97.79 224	71.20 402	86.26 331	91.72 410
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
DTE-MVSNet		86.11 316	85.48 309	87.98 361	91.65 353	74.92 384	94.93 157	95.75 197	87.36 163	82.26 362	93.04 273	72.85 265	95.82 381	74.04 385	77.46 426	93.20 361

XVG-ACMP-BASELINE		86.00 317	84.84 327	89.45 320	91.20 366	78.00 327	91.70 346	95.55 216	85.05 238	82.97 354	92.25 299	54.49 443	97.48 253	82.93 256	87.45 321	92.89 375

MVP-Stereo		85.97 318	84.86 326	89.32 322	90.92 383	82.19 184	92.11 334	94.19 305	78.76 375	78.77 416	91.63 325	68.38 333	96.56 340	75.01 376	93.95 193	89.20 452
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 319	85.09 320	88.35 348	90.79 388	77.42 348	91.83 342	95.70 204	80.77 347	80.08 393	90.02 380	66.74 347	96.37 354	81.88 281	87.97 312	91.26 424

test-LLR		85.87 320	85.41 310	87.25 383	90.95 379	71.67 426	89.55 404	89.88 434	83.41 279	84.54 311	87.95 417	67.25 338	95.11 405	81.82 282	93.37 215	94.97 269

FMVSNet1		85.85 321	84.11 341	91.08 231	92.81 314	83.10 148	95.14 146	94.94 262	81.64 330	82.68 357	91.64 322	59.01 419	96.34 357	75.37 371	83.78 354	93.79 330

PatchmatchNet		85.85 321	84.70 329	89.29 323	91.76 347	75.54 378	88.49 423	91.30 397	81.63 331	85.05 301	88.70 407	71.71 279	96.24 361	74.61 382	89.05 295	96.08 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
myMVS_eth3d28		85.80 323	85.26 317	87.42 377	94.73 194	69.92 445	90.60 377	90.95 407	87.21 167	86.06 266	90.04 379	59.47 412	96.02 369	74.89 378	93.35 217	96.33 211

CostFormer		85.77 324	84.94 324	88.26 354	91.16 370	72.58 417	89.47 408	91.04 404	76.26 409	86.45 255	89.97 382	70.74 292	96.86 314	82.35 268	87.07 327	95.34 259

PMMVS		85.71 325	84.96 323	87.95 362	88.90 426	77.09 353	88.68 420	90.06 427	72.32 448	86.47 252	90.76 356	72.15 275	94.40 415	81.78 284	93.49 210	92.36 398

PVSNet	78.82 18	85.55 326	84.65 330	88.23 356	94.72 196	71.93 420	87.12 444	92.75 355	78.80 374	84.95 303	90.53 362	64.43 370	96.71 319	74.74 379	93.86 195	96.06 230

UBG		85.51 327	84.57 334	88.35 348	94.21 244	71.78 424	90.07 395	89.66 438	82.28 307	85.91 269	89.01 399	61.30 396	97.06 300	76.58 360	92.06 247	96.22 217

IterMVS-SCA-FT		85.45 328	84.53 335	88.18 357	91.71 349	76.87 358	90.19 392	92.65 358	85.40 225	81.44 373	90.54 361	66.79 345	95.00 408	81.04 295	81.05 393	92.66 382

pmmvs4		85.43 329	83.86 346	90.16 276	90.02 412	82.97 158	90.27 384	92.67 357	75.93 412	80.73 382	91.74 320	71.05 286	95.73 387	78.85 336	83.46 361	91.78 409

mvsany_test1		85.42 330	85.30 315	85.77 412	87.95 440	75.41 380	87.61 441	80.97 475	76.82 403	88.68 206	95.83 142	77.44 193	90.82 461	85.90 211	86.51 329	91.08 431

ACMH	80.38 17	85.36 331	83.68 348	90.39 268	94.45 223	80.63 242	94.73 173	94.85 272	82.09 310	77.24 425	92.65 285	60.01 409	97.58 242	72.25 397	84.87 344	92.96 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 332	84.64 332	87.49 374	90.77 390	72.59 416	94.01 234	94.40 296	84.72 249	79.62 402	93.17 267	61.91 389	96.72 317	81.99 278	81.16 389	93.16 363

CR-MVSNet		85.35 332	83.76 347	90.12 279	90.58 397	79.34 293	85.24 457	91.96 380	78.27 384	85.55 277	87.87 420	71.03 287	95.61 390	73.96 387	89.36 289	95.40 255

tpmrst		85.35 332	84.99 321	86.43 403	90.88 386	67.88 453	88.71 419	91.43 395	80.13 353	86.08 265	88.80 405	73.05 263	96.02 369	82.48 264	83.40 363	95.40 255

miper_lstm_enhance		85.27 335	84.59 333	87.31 380	91.28 365	74.63 387	87.69 438	94.09 312	81.20 343	81.36 375	89.85 386	74.97 228	94.30 418	81.03 297	79.84 413	93.01 371

IB-MVS	80.51 15	85.24 336	83.26 354	91.19 225	92.13 332	79.86 275	91.75 344	91.29 398	83.28 284	80.66 384	88.49 409	61.28 397	98.46 145	80.99 298	79.46 416	95.25 261
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
CHOSEN 280x420		85.15 337	83.99 344	88.65 341	92.47 322	78.40 316	79.68 481	92.76 354	74.90 423	81.41 374	89.59 390	69.85 309	95.51 394	79.92 315	95.29 155	92.03 405

RPSCF		85.07 338	84.27 336	87.48 375	92.91 310	70.62 439	91.69 347	92.46 360	76.20 410	82.67 358	95.22 174	63.94 376	97.29 281	77.51 350	85.80 333	94.53 292

MS-PatchMatch		85.05 339	84.16 339	87.73 367	91.42 359	78.51 312	91.25 361	93.53 333	77.50 390	80.15 390	91.58 328	61.99 388	95.51 394	75.69 368	94.35 182	89.16 453

ACMH+	81.04 14	85.05 339	83.46 351	89.82 295	94.66 202	79.37 291	94.44 192	94.12 311	82.19 309	78.04 419	92.82 279	58.23 422	97.54 245	73.77 389	82.90 369	92.54 389

mmtdpeth		85.04 341	84.15 340	87.72 368	93.11 297	75.74 376	94.37 205	92.83 351	84.98 239	89.31 193	86.41 437	61.61 393	97.14 294	92.63 81	62.11 473	90.29 439

WBMVS		84.97 342	84.18 338	87.34 378	94.14 250	71.62 428	90.20 391	92.35 363	81.61 332	84.06 328	90.76 356	61.82 390	96.52 343	78.93 335	83.81 353	93.89 321

IterMVS		84.88 343	83.98 345	87.60 370	91.44 356	76.03 371	90.18 393	92.41 361	83.24 285	81.06 379	90.42 366	66.60 348	94.28 419	79.46 327	80.98 398	92.48 391
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 344	83.09 357	90.14 278	93.80 268	80.05 267	89.18 413	93.09 344	78.89 370	78.19 417	91.91 315	65.86 359	97.27 282	68.47 422	88.45 303	93.11 367

testing222		84.84 345	83.32 352	89.43 321	94.15 249	75.94 372	91.09 365	89.41 443	84.90 241	85.78 271	89.44 393	52.70 450	96.28 360	70.80 408	91.57 250	96.07 228

tpm		84.73 346	84.02 343	86.87 396	90.33 405	68.90 448	89.06 415	89.94 431	80.85 346	85.75 272	89.86 385	68.54 331	95.97 372	77.76 346	84.05 352	95.75 243

tfpnnormal		84.72 347	83.23 355	89.20 325	92.79 315	80.05 267	94.48 187	95.81 192	82.38 304	81.08 378	91.21 336	69.01 325	96.95 308	61.69 455	80.59 402	90.58 438

SD_0403		84.71 348	84.65 330	84.92 423	92.95 308	65.95 459	92.07 337	93.23 340	83.82 268	79.03 408	93.73 252	73.90 248	92.91 441	63.02 452	90.05 273	95.89 236

CVMVSNet		84.69 349	84.79 328	84.37 428	91.84 343	64.92 466	93.70 259	91.47 394	66.19 468	86.16 264	95.28 171	67.18 340	93.33 434	80.89 300	90.42 268	94.88 278

SSC-MVS3.2		84.60 350	84.19 337	85.85 411	92.74 317	68.07 450	88.15 429	93.81 325	87.42 161	83.76 336	91.07 345	62.91 383	95.73 387	74.56 383	83.24 364	93.75 337

test-mter		84.54 351	83.64 349	87.25 383	90.95 379	71.67 426	89.55 404	89.88 434	79.17 365	84.54 311	87.95 417	55.56 433	95.11 405	81.82 282	93.37 215	94.97 269

ETVMVS		84.43 352	82.92 361	88.97 333	94.37 228	74.67 386	91.23 362	88.35 447	83.37 281	86.06 266	89.04 398	55.38 436	95.67 389	67.12 431	91.34 252	96.58 204

TransMVSNet (Re)		84.43 352	83.06 359	88.54 343	91.72 348	78.44 314	95.18 143	92.82 353	82.73 299	79.67 400	92.12 303	73.49 255	95.96 373	71.10 406	68.73 461	91.21 425

pmmvs5		84.21 354	82.84 364	88.34 350	88.95 425	76.94 357	92.41 318	91.91 382	75.63 414	80.28 388	91.18 339	64.59 369	95.57 391	77.09 355	83.47 360	92.53 390

dmvs_re		84.20 355	83.22 356	87.14 389	91.83 345	77.81 335	90.04 396	90.19 423	84.70 251	81.49 371	89.17 396	64.37 371	91.13 459	71.58 400	85.65 335	92.46 393

tpm2		84.08 356	82.94 360	87.48 375	91.39 360	71.27 429	89.23 412	90.37 419	71.95 450	84.64 308	89.33 394	67.30 337	96.55 342	75.17 373	87.09 326	94.63 285

test_fmvs2		83.98 357	84.03 342	83.83 433	87.16 443	67.53 457	93.93 241	92.89 349	77.62 389	86.89 245	93.53 255	47.18 463	92.02 450	90.54 134	86.51 329	91.93 407

COLMAP_ROB	80.39 16	83.96 358	82.04 367	89.74 300	95.28 157	79.75 280	94.25 212	92.28 367	75.17 419	78.02 420	93.77 249	58.60 421	97.84 222	65.06 444	85.92 332	91.63 412
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 359	81.53 370	91.21 224	90.58 397	79.34 293	85.24 457	96.76 92	71.44 452	85.55 277	82.97 460	70.87 290	98.91 96	61.01 457	89.36 289	95.40 255

SixPastTwentyTwo		83.91 360	82.90 362	86.92 393	90.99 377	70.67 438	93.48 267	91.99 377	85.54 216	77.62 424	92.11 305	60.59 405	96.87 313	76.05 366	77.75 423	93.20 361

EPMVS		83.90 361	82.70 365	87.51 372	90.23 408	72.67 412	88.62 421	81.96 473	81.37 337	85.01 302	88.34 411	66.31 353	94.45 412	75.30 372	87.12 325	95.43 254

WB-MVSnew		83.77 362	83.28 353	85.26 419	91.48 355	71.03 433	91.89 338	87.98 448	78.91 368	84.78 305	90.22 371	69.11 324	94.02 422	64.70 445	90.44 266	90.71 433

TESTMET0.1,1		83.74 363	82.85 363	86.42 404	89.96 413	71.21 431	89.55 404	87.88 449	77.41 391	83.37 349	87.31 425	56.71 429	93.65 431	80.62 305	92.85 231	94.40 302

UWE-MVS		83.69 364	83.09 357	85.48 414	93.06 301	65.27 465	90.92 370	86.14 457	79.90 356	86.26 261	90.72 359	57.17 428	95.81 382	71.03 407	92.62 239	95.35 258

pmmvs6		83.42 365	81.60 369	88.87 334	88.01 438	77.87 333	94.96 155	94.24 304	74.67 425	78.80 415	91.09 344	60.17 408	96.49 345	77.06 356	75.40 435	92.23 402

AllTest		83.42 365	81.39 371	89.52 317	95.01 171	77.79 337	93.12 286	90.89 410	77.41 391	76.12 434	93.34 258	54.08 445	97.51 248	68.31 424	84.27 349	93.26 355

tpmvs		83.35 367	82.07 366	87.20 387	91.07 374	71.00 435	88.31 426	91.70 384	78.91 368	80.49 387	87.18 429	69.30 319	97.08 297	68.12 427	83.56 359	93.51 348

blended_shiyan8		82.79 368	80.49 378	89.69 305	85.50 456	79.83 278	91.38 353	93.82 322	77.14 395	79.39 404	83.73 453	64.95 366	96.63 325	79.75 316	68.77 456	92.62 385

blended_shiyan6		82.78 369	80.48 379	89.67 310	85.53 454	79.76 279	91.37 354	93.82 322	77.14 395	79.30 406	83.73 453	64.96 365	96.63 325	79.68 318	68.75 457	92.63 383

USDC		82.76 370	81.26 373	87.26 382	91.17 368	74.55 388	89.27 410	93.39 336	78.26 385	75.30 441	92.08 307	54.43 444	96.63 325	71.64 399	85.79 334	90.61 435

Patchmtry		82.71 371	80.93 375	88.06 359	90.05 411	76.37 368	84.74 462	91.96 380	72.28 449	81.32 376	87.87 420	71.03 287	95.50 396	68.97 419	80.15 408	92.32 400

PatchT		82.68 372	81.27 372	86.89 395	90.09 410	70.94 436	84.06 464	90.15 424	74.91 422	85.63 276	83.57 455	69.37 315	94.87 410	65.19 441	88.50 302	94.84 279

MIMVSNet		82.59 373	80.53 376	88.76 336	91.51 354	78.32 319	86.57 448	90.13 425	79.32 362	80.70 383	88.69 408	52.98 449	93.07 439	66.03 439	88.86 297	94.90 277

wanda-best-256-512		82.44 374	80.07 386	89.53 315	85.12 459	79.44 288	90.49 380	93.75 328	76.97 400	79.00 409	82.72 462	64.29 372	96.61 331	79.56 323	68.75 457	92.55 386

FE-blended-shiyan7		82.44 374	80.07 386	89.53 315	85.12 459	79.44 288	90.49 380	93.75 328	76.97 400	79.00 409	82.72 462	64.29 372	96.61 331	79.56 323	68.75 457	92.55 386

test0.0.03 1		82.41 376	81.69 368	84.59 426	88.23 434	72.89 408	90.24 388	87.83 450	83.41 279	79.86 398	89.78 387	67.25 338	88.99 471	65.18 442	83.42 362	91.90 408

usedtu_blend_shiyan5		82.39 377	79.93 391	89.75 299	85.12 459	80.08 263	92.36 321	93.26 338	74.29 429	79.00 409	82.72 462	64.29 372	96.60 335	79.60 321	68.75 457	92.55 386

EG-PatchMatch MVS		82.37 378	80.34 381	88.46 345	90.27 406	79.35 292	92.80 308	94.33 299	77.14 395	73.26 453	90.18 374	47.47 462	96.72 317	70.25 410	87.32 324	89.30 449

tpm cat1		81.96 379	80.27 382	87.01 390	91.09 373	71.02 434	87.38 442	91.53 392	66.25 467	80.17 389	86.35 439	68.22 334	96.15 365	69.16 418	82.29 375	93.86 327

blend_shiyan4		81.94 380	79.35 399	89.70 303	85.52 455	80.08 263	91.29 358	93.82 322	77.12 398	79.31 405	82.94 461	54.81 440	96.60 335	79.60 321	69.78 449	92.41 395

our_test_3		81.93 381	80.46 380	86.33 405	88.46 431	73.48 401	88.46 424	91.11 400	76.46 404	76.69 430	88.25 413	66.89 343	94.36 416	68.75 420	79.08 419	91.14 427

ppachtmachnet_test		81.84 382	80.07 386	87.15 388	88.46 431	74.43 391	89.04 416	92.16 371	75.33 417	77.75 422	88.99 400	66.20 355	95.37 400	65.12 443	77.60 424	91.65 411

FE-MVSNET2		81.82 383	79.99 389	87.34 378	84.74 463	77.36 350	92.72 309	94.55 287	82.09 310	73.79 450	86.46 434	57.80 425	94.45 412	74.65 380	73.10 437	90.20 440

gg-mvs-nofinetune		81.77 384	79.37 398	88.99 332	90.85 387	77.73 344	86.29 449	79.63 478	74.88 424	83.19 353	69.05 481	60.34 406	96.11 366	75.46 370	94.64 173	93.11 367

CL-MVSNet_self_test		81.74 385	80.53 376	85.36 416	85.96 449	72.45 418	90.25 386	93.07 345	81.24 341	79.85 399	87.29 426	70.93 289	92.52 444	66.95 432	69.23 452	91.11 429

Patchmatch-RL test		81.67 386	79.96 390	86.81 397	85.42 457	71.23 430	82.17 472	87.50 453	78.47 379	77.19 426	82.50 466	70.81 291	93.48 432	82.66 263	72.89 440	95.71 247

ADS-MVSNet2		81.66 387	79.71 395	87.50 373	91.35 362	74.19 393	83.33 467	88.48 446	72.90 443	82.24 363	85.77 443	64.98 363	93.20 437	64.57 446	83.74 355	95.12 264

K. test v3		81.59 388	80.15 385	85.91 410	89.89 415	69.42 447	92.57 314	87.71 451	85.56 215	73.44 452	89.71 389	55.58 432	95.52 393	77.17 353	69.76 450	92.78 379

ADS-MVSNet		81.56 389	79.78 392	86.90 394	91.35 362	71.82 422	83.33 467	89.16 444	72.90 443	82.24 363	85.77 443	64.98 363	93.76 428	64.57 446	83.74 355	95.12 264

sc_t1		81.53 390	78.67 411	90.12 279	90.78 389	78.64 307	93.91 244	90.20 422	68.42 461	80.82 381	89.88 384	46.48 465	96.76 316	76.03 367	71.47 444	94.96 272

FMVSNet5		81.52 391	79.60 396	87.27 381	91.17 368	77.95 328	91.49 351	92.26 369	76.87 402	76.16 433	87.91 419	51.67 451	92.34 446	67.74 428	81.16 389	91.52 416

dp		81.47 392	80.23 383	85.17 420	89.92 414	65.49 463	86.74 446	90.10 426	76.30 408	81.10 377	87.12 430	62.81 384	95.92 375	68.13 426	79.88 411	94.09 314

Patchmatch-test		81.37 393	79.30 400	87.58 371	90.92 383	74.16 394	80.99 474	87.68 452	70.52 456	76.63 431	88.81 403	71.21 284	92.76 443	60.01 462	86.93 328	95.83 240

EU-MVSNet		81.32 394	80.95 374	82.42 441	88.50 430	63.67 470	93.32 275	91.33 396	64.02 472	80.57 386	92.83 278	61.21 400	92.27 447	76.34 362	80.38 407	91.32 422

test_0402		81.30 395	79.17 404	87.67 369	93.19 292	78.17 323	92.98 297	91.71 383	75.25 418	76.02 437	90.31 369	59.23 415	96.37 354	50.22 476	83.63 358	88.47 461

JIA-IIPM		81.04 396	78.98 408	87.25 383	88.64 427	73.48 401	81.75 473	89.61 440	73.19 440	82.05 366	73.71 477	66.07 358	95.87 378	71.18 404	84.60 346	92.41 395

Anonymous20231206		81.03 397	79.77 394	84.82 424	87.85 441	70.26 442	91.42 352	92.08 373	73.67 435	77.75 422	89.25 395	62.43 386	93.08 438	61.50 456	82.00 380	91.12 428

mvs5depth		80.98 398	79.15 405	86.45 402	84.57 464	73.29 404	87.79 434	91.67 386	80.52 349	82.20 365	89.72 388	55.14 439	95.93 374	73.93 388	66.83 464	90.12 442

pmmvs-eth3d		80.97 399	78.72 410	87.74 366	84.99 462	79.97 273	90.11 394	91.65 387	75.36 416	73.51 451	86.03 440	59.45 413	93.96 426	75.17 373	72.21 441	89.29 451

testgi		80.94 400	80.20 384	83.18 434	87.96 439	66.29 458	91.28 359	90.70 416	83.70 270	78.12 418	92.84 277	51.37 452	90.82 461	63.34 449	82.46 373	92.43 394

CMPMVS	59.16 21	80.52 401	79.20 403	84.48 427	83.98 465	67.63 456	89.95 399	93.84 321	64.79 471	66.81 469	91.14 342	57.93 423	95.17 403	76.25 363	88.10 308	90.65 434
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 402	79.59 397	83.06 436	93.44 287	64.64 467	93.33 274	85.47 462	84.34 257	79.93 397	90.84 352	44.35 471	92.39 445	57.06 470	87.56 318	92.16 404

Anonymous20240521		80.44 403	79.21 402	84.11 431	85.75 452	67.89 452	92.86 304	93.23 340	75.61 415	75.59 440	87.47 424	50.03 454	94.33 417	71.14 405	81.21 388	90.12 442

LF4IMVS		80.37 404	79.07 407	84.27 430	86.64 445	69.87 446	89.39 409	91.05 403	76.38 406	74.97 443	90.00 381	47.85 461	94.25 420	74.55 384	80.82 400	88.69 459

KD-MVS_self_test		80.20 405	79.24 401	83.07 435	85.64 453	65.29 464	91.01 367	93.93 315	78.71 377	76.32 432	86.40 438	59.20 416	92.93 440	72.59 395	69.35 451	91.00 432

tt0320		80.13 406	77.41 415	88.29 352	90.50 401	78.02 326	93.10 289	90.71 415	66.06 469	76.75 429	86.97 432	49.56 457	95.40 399	71.65 398	71.41 445	91.46 420

Syy-MVS		80.07 407	79.78 392	80.94 445	91.92 339	59.93 477	89.75 402	87.40 454	81.72 327	78.82 413	87.20 427	66.29 354	91.29 457	47.06 478	87.84 315	91.60 413

UnsupCasMVSNet_eth		80.07 407	78.27 413	85.46 415	85.24 458	72.63 415	88.45 425	94.87 271	82.99 293	71.64 460	88.07 416	56.34 430	91.75 454	73.48 391	63.36 471	92.01 406

test20.03		79.95 409	79.08 406	82.55 438	85.79 451	67.74 455	91.09 365	91.08 401	81.23 342	74.48 447	89.96 383	61.63 391	90.15 463	60.08 460	76.38 431	89.76 444

TDRefinement		79.81 410	77.34 416	87.22 386	79.24 479	75.48 379	93.12 286	92.03 375	76.45 405	75.01 442	91.58 328	49.19 458	96.44 350	70.22 412	69.18 453	89.75 445

TinyColmap		79.76 411	77.69 414	85.97 407	91.71 349	73.12 405	89.55 404	90.36 420	75.03 420	72.03 457	90.19 373	46.22 468	96.19 364	63.11 450	81.03 394	88.59 460

myMVS_eth3d		79.67 412	78.79 409	82.32 442	91.92 339	64.08 468	89.75 402	87.40 454	81.72 327	78.82 413	87.20 427	45.33 469	91.29 457	59.09 465	87.84 315	91.60 413

tt0320-xc		79.63 413	76.66 422	88.52 344	91.03 375	78.72 304	93.00 295	89.53 442	66.37 466	76.11 436	87.11 431	46.36 467	95.32 402	72.78 394	67.67 462	91.51 417

OpenMVS_ROB	74.94 19	79.51 414	77.03 421	86.93 392	87.00 444	76.23 370	92.33 325	90.74 414	68.93 460	74.52 446	88.23 414	49.58 456	96.62 328	57.64 468	84.29 348	87.94 464

MIMVSNet1		79.38 415	77.28 417	85.69 413	86.35 446	73.67 398	91.61 349	92.75 355	78.11 388	72.64 455	88.12 415	48.16 460	91.97 452	60.32 459	77.49 425	91.43 421

YYNet1		79.22 416	77.20 418	85.28 418	88.20 436	72.66 413	85.87 451	90.05 429	74.33 428	62.70 472	87.61 422	66.09 357	92.03 448	66.94 433	72.97 439	91.15 426

MDA-MVSNet_test_wron		79.21 417	77.19 419	85.29 417	88.22 435	72.77 410	85.87 451	90.06 427	74.34 427	62.62 474	87.56 423	66.14 356	91.99 451	66.90 436	73.01 438	91.10 430

UWE-MVS-28		78.98 418	78.38 412	80.80 446	88.18 437	60.66 476	90.65 375	78.51 480	78.84 372	77.93 421	90.93 349	59.08 418	89.02 470	50.96 475	90.33 270	92.72 380

MDA-MVSNet-bldmvs		78.85 419	76.31 424	86.46 401	89.76 416	73.88 395	88.79 418	90.42 418	79.16 366	59.18 477	88.33 412	60.20 407	94.04 421	62.00 454	68.96 454	91.48 419

KD-MVS_2432*1600		78.50 420	76.02 428	85.93 408	86.22 447	74.47 389	84.80 460	92.33 364	79.29 363	76.98 427	85.92 441	53.81 447	93.97 424	67.39 429	57.42 478	89.36 447

miper_refine_blended		78.50 420	76.02 428	85.93 408	86.22 447	74.47 389	84.80 460	92.33 364	79.29 363	76.98 427	85.92 441	53.81 447	93.97 424	67.39 429	57.42 478	89.36 447

FE-MVSNET		78.19 422	76.03 427	84.69 425	83.70 467	73.31 403	90.58 378	90.00 430	77.11 399	71.91 458	85.47 445	55.53 434	91.94 453	59.69 463	70.24 447	88.83 457

PM-MVS		78.11 423	76.12 426	84.09 432	83.54 468	70.08 443	88.97 417	85.27 464	79.93 355	74.73 445	86.43 436	34.70 480	93.48 432	79.43 330	72.06 442	88.72 458

test_vis1_rt		77.96 424	76.46 423	82.48 440	85.89 450	71.74 425	90.25 386	78.89 479	71.03 455	71.30 461	81.35 469	42.49 473	91.05 460	84.55 234	82.37 374	84.65 467

test_fmvs3		77.67 425	77.16 420	79.22 449	79.52 478	61.14 474	92.34 324	91.64 388	73.98 432	78.86 412	86.59 433	27.38 484	87.03 473	88.12 176	75.97 433	89.50 446

PVSNet_0	73.20 20	77.22 426	74.83 432	84.37 428	90.70 394	71.10 432	83.09 469	89.67 437	72.81 445	73.93 449	83.13 457	60.79 404	93.70 430	68.54 421	50.84 483	88.30 462

DSMNet-mixed		76.94 427	76.29 425	78.89 450	83.10 470	56.11 486	87.78 435	79.77 477	60.65 476	75.64 439	88.71 406	61.56 394	88.34 472	60.07 461	89.29 291	92.21 403

ttmdpeth		76.55 428	74.64 433	82.29 443	82.25 473	67.81 454	89.76 401	85.69 460	70.35 457	75.76 438	91.69 321	46.88 464	89.77 465	66.16 438	63.23 472	89.30 449

new-patchmatchnet		76.41 429	75.17 431	80.13 447	82.65 472	59.61 478	87.66 439	91.08 401	78.23 386	69.85 464	83.22 456	54.76 441	91.63 456	64.14 448	64.89 469	89.16 453

UnsupCasMVSNet_bld		76.23 430	73.27 434	85.09 421	83.79 466	72.92 407	85.65 454	93.47 335	71.52 451	68.84 466	79.08 472	49.77 455	93.21 436	66.81 437	60.52 475	89.13 455

mvsany_test3		74.95 431	73.26 435	80.02 448	74.61 484	63.16 472	85.53 455	78.42 481	74.16 430	74.89 444	86.46 434	36.02 479	89.09 469	82.39 267	66.91 463	87.82 465

usedtu_dtu_shiyan2		74.72 432	71.30 437	84.98 422	77.78 481	70.58 440	91.85 341	90.76 413	67.24 465	68.06 468	82.17 467	37.13 477	92.78 442	60.69 458	66.03 465	91.59 415

dmvs_testset		74.57 433	75.81 430	70.86 460	87.72 442	40.47 495	87.05 445	77.90 485	82.75 298	71.15 462	85.47 445	67.98 335	84.12 482	45.26 479	76.98 430	88.00 463

MVS-HIRNet		73.70 434	72.20 436	78.18 453	91.81 346	56.42 485	82.94 470	82.58 471	55.24 479	68.88 465	66.48 482	55.32 437	95.13 404	58.12 467	88.42 304	83.01 470

MVStest1		72.91 435	69.70 440	82.54 439	78.14 480	73.05 406	88.21 428	86.21 456	60.69 475	64.70 470	90.53 362	46.44 466	85.70 478	58.78 466	53.62 480	88.87 456

new_pmnet		72.15 436	70.13 439	78.20 452	82.95 471	65.68 461	83.91 465	82.40 472	62.94 474	64.47 471	79.82 471	42.85 472	86.26 477	57.41 469	74.44 436	82.65 472

test_f		71.95 437	70.87 438	75.21 456	74.21 486	59.37 479	85.07 459	85.82 459	65.25 470	70.42 463	83.13 457	23.62 485	82.93 484	78.32 340	71.94 443	83.33 469

pmmvs3		71.81 438	68.71 441	81.11 444	75.86 483	70.42 441	86.74 446	83.66 468	58.95 478	68.64 467	80.89 470	36.93 478	89.52 467	63.10 451	63.59 470	83.39 468

APD_test1		69.04 439	66.26 445	77.36 455	80.51 476	62.79 473	85.46 456	83.51 469	54.11 481	59.14 478	84.79 449	23.40 487	89.61 466	55.22 471	70.24 447	79.68 476

N_pmnet		68.89 440	68.44 442	70.23 461	89.07 424	28.79 500	88.06 430	19.50 500	69.47 459	71.86 459	84.93 447	61.24 399	91.75 454	54.70 472	77.15 427	90.15 441

WB-MVS		67.92 441	67.49 443	69.21 464	81.09 474	41.17 494	88.03 431	78.00 484	73.50 437	62.63 473	83.11 459	63.94 376	86.52 475	25.66 490	51.45 482	79.94 475

SSC-MVS		67.06 442	66.56 444	68.56 466	80.54 475	40.06 496	87.77 436	77.37 487	72.38 447	61.75 475	82.66 465	63.37 379	86.45 476	24.48 491	48.69 485	79.16 477

LCM-MVSNet		66.00 443	62.16 448	77.51 454	64.51 494	58.29 480	83.87 466	90.90 409	48.17 483	54.69 480	73.31 478	16.83 493	86.75 474	65.47 440	61.67 474	87.48 466

test_vis3_rt		65.12 444	62.60 446	72.69 458	71.44 487	60.71 475	87.17 443	65.55 491	63.80 473	53.22 481	65.65 484	14.54 494	89.44 468	76.65 357	65.38 467	67.91 482

FPMVS		64.63 445	62.55 447	70.88 459	70.80 488	56.71 481	84.42 463	84.42 466	51.78 482	49.57 482	81.61 468	23.49 486	81.48 485	40.61 485	76.25 432	74.46 478

EGC-MVSNET		61.97 446	56.37 451	78.77 451	89.63 419	73.50 400	89.12 414	82.79 470	0.21 497	1.24 498	84.80 448	39.48 474	90.04 464	44.13 480	75.94 434	72.79 479

PMMVS2		59.60 447	56.40 450	69.21 464	68.83 491	46.58 490	73.02 486	77.48 486	55.07 480	49.21 483	72.95 479	17.43 492	80.04 486	49.32 477	44.33 486	80.99 474

testf1		59.54 448	56.11 452	69.85 462	69.28 489	56.61 483	80.37 476	76.55 488	42.58 486	45.68 485	75.61 473	11.26 495	84.18 480	43.20 482	60.44 476	68.75 480

APD_test2		59.54 448	56.11 452	69.85 462	69.28 489	56.61 483	80.37 476	76.55 488	42.58 486	45.68 485	75.61 473	11.26 495	84.18 480	43.20 482	60.44 476	68.75 480

ANet_high		58.88 450	54.22 455	72.86 457	56.50 497	56.67 482	80.75 475	86.00 458	73.09 442	37.39 489	64.63 485	22.17 488	79.49 487	43.51 481	23.96 491	82.43 473

dongtai		58.82 451	58.24 449	60.56 469	83.13 469	45.09 493	82.32 471	48.22 499	67.61 463	61.70 476	69.15 480	38.75 475	76.05 488	32.01 487	41.31 487	60.55 484

Gipuma		57.99 452	54.91 454	67.24 467	88.51 428	65.59 462	52.21 489	90.33 421	43.58 485	42.84 488	51.18 489	20.29 490	85.07 479	34.77 486	70.45 446	51.05 488
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		53.51 453	53.30 456	54.13 473	76.06 482	45.36 492	80.11 478	48.36 498	59.63 477	54.84 479	63.43 486	37.41 476	62.07 493	20.73 493	39.10 488	54.96 487

PMVS	47.18 22	52.22 454	48.46 458	63.48 468	45.72 499	46.20 491	73.41 485	78.31 482	41.03 488	30.06 491	65.68 483	6.05 497	83.43 483	30.04 488	65.86 466	60.80 483
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 455	48.47 457	56.66 471	52.26 498	18.98 502	41.51 491	81.40 474	10.10 492	44.59 487	75.01 476	28.51 482	68.16 489	53.54 473	49.31 484	82.83 471

MVE	39.65 23	43.39 456	38.59 462	57.77 470	56.52 496	48.77 489	55.38 488	58.64 495	29.33 491	28.96 492	52.65 488	4.68 498	64.62 492	28.11 489	33.07 489	59.93 485
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 457	42.29 459	46.03 474	65.58 493	37.41 497	73.51 484	64.62 492	33.99 489	28.47 493	47.87 490	19.90 491	67.91 490	22.23 492	24.45 490	32.77 489

EMVS		42.07 458	41.12 460	44.92 475	63.45 495	35.56 499	73.65 483	63.48 493	33.05 490	26.88 494	45.45 491	21.27 489	67.14 491	19.80 494	23.02 492	32.06 490

tmp_tt		35.64 459	39.24 461	24.84 476	14.87 500	23.90 501	62.71 487	51.51 497	6.58 494	36.66 490	62.08 487	44.37 470	30.34 496	52.40 474	22.00 493	20.27 491

cdsmvs_eth3d_5k		22.14 460	29.52 463	0.00 480	0.00 503	0.00 505	0.00 492	95.76 196	0.00 498	0.00 499	94.29 223	75.66 220	0.00 499	0.00 498	0.00 497	0.00 495

wuyk23d		21.27 461	20.48 464	23.63 477	68.59 492	36.41 498	49.57 490	6.85 501	9.37 493	7.89 495	4.46 497	4.03 499	31.37 495	17.47 495	16.07 494	3.12 492

testmvs		8.92 462	11.52 465	1.12 479	1.06 501	0.46 504	86.02 450	0.65 502	0.62 495	2.74 496	9.52 495	0.31 501	0.45 498	2.38 496	0.39 495	2.46 494

test123		8.76 463	11.22 466	1.39 478	0.85 502	0.97 503	85.76 453	0.35 503	0.54 496	2.45 497	8.14 496	0.60 500	0.48 497	2.16 497	0.17 496	2.71 493

ab-mvs-re		7.82 464	10.43 467	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	93.88 244	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

pcd_1.5k_mvsjas		6.64 465	8.86 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	79.70 157	0.00 499	0.00 498	0.00 497	0.00 495

mmdepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

monomultidepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

test_blank		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet_test		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

DCPMVS		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet-low-res		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uncertanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

Regformer		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

MED-MVS test				94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44		99.67 1	95.27 40	98.85 20	98.95 11

TestfortrainingZip							97.32 10

WAC-MVS						64.08 468								59.14 464

FOURS1					98.86 4	85.54 73	98.29 1	97.49 12	89.79 63	96.29 32

MSC_two_6792asdad				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

PC_three_1452									82.47 302	97.09 20	97.07 72	92.72 1	98.04 196	92.70 80	99.02 12	98.86 16

No_MVS				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

test_one_0601					98.58 14	85.83 67		97.44 21	91.05 23	96.78 27	98.06 22	91.45 13

eth-test2					0.00 503

eth-test					0.00 503

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 272	94.19 64	96.91 78	87.57 34	99.26 50	91.99 105	98.44 57

RE-MVS-def			93.68 72		97.92 49	84.57 93	96.28 51	96.76 92	87.46 158	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 108

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 340	97.26 13				95.50 36	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 48	90.85 3	97.13 19				97.08 70	92.59 2	98.94 91	92.25 92	98.99 14	98.84 19

test_241102_TWO								97.44 21	90.31 41	97.62 8	98.07 20	91.46 12	99.58 14	95.66 30	99.12 6	98.98 10

test_241102_ONE					98.77 8	85.99 55		97.44 21	90.26 47	97.71 2	97.96 31	92.31 6	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 21	86.13 202	95.10 54	97.40 53	88.34 25	99.22 52	93.25 68	98.70 38

save fliter					97.85 55	85.63 72	95.21 140	96.82 85	89.44 73

test_0728_THIRD									90.75 29	97.04 22	98.05 25	92.09 8	99.55 20	95.64 32	99.13 3	99.13 2

test_0728_SECOND				95.01 18	98.79 5	86.43 40	97.09 21	97.49 12					99.61 7	95.62 34	99.08 7	98.99 9

test0726					98.78 6	85.93 58	97.19 16	97.47 17	90.27 45	97.64 6	98.13 7	91.47 10

GSMVS																96.12 224

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 280				96.12 224

sam_mvs												70.60 294

ambc				83.06 436	79.99 477	63.51 471	77.47 482	92.86 350		74.34 448	84.45 450	28.74 481	95.06 407	73.06 393	68.89 455	90.61 435

MTGPA								96.97 65

test_post1							88.00 432				9.81 494	69.31 318	95.53 392	76.65 357

test_post											10.29 493	70.57 298	95.91 377

patchmatchnet-post											83.76 452	71.53 281	96.48 346

GG-mvs-BLEND				87.94 363	89.73 418	77.91 330	87.80 433	78.23 483		80.58 385	83.86 451	59.88 410	95.33 401	71.20 402	92.22 245	90.60 437

MTMP							96.16 60	60.64 494

gm-plane-assit					89.60 420	68.00 451			77.28 394		88.99 400		97.57 243	79.44 329

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 227	96.78 89	81.61 332	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 233	96.76 92	81.86 323	92.70 104	96.20 108	87.63 32	99.02 72

agg_prior2														90.54 134	98.68 41	98.27 63

agg_prior					97.38 72	85.92 60		96.72 99		92.16 119			98.97 86

TestCases				89.52 317	95.01 171	77.79 337		90.89 410	77.41 391	76.12 434	93.34 258	54.08 445	97.51 248	68.31 424	84.27 349	93.26 355

test_prior4						85.96 57	94.11 221

test_prior2							94.12 219		87.67 153	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

test_prior				93.82 73	97.29 76	84.49 97		96.88 78					98.87 99			98.11 81

旧先验2							93.36 273		71.25 453	94.37 60			97.13 295	86.74 198

新几何2							93.11 288

新几何1				93.10 102	97.30 75	84.35 107		95.56 215	71.09 454	91.26 147	96.24 106	82.87 102	98.86 101	79.19 333	98.10 76	96.07 228

旧先验1					96.79 85	81.81 195		95.67 206			96.81 84	86.69 42			97.66 98	96.97 180

无先验							93.28 281	96.26 139	73.95 433				99.05 66	80.56 306		96.59 203

原ACMM2							92.94 299

原ACMM1				92.01 179	97.34 73	81.05 223		96.81 87	78.89 370	90.45 167	95.92 133	82.65 104	98.84 105	80.68 304	98.26 63	96.14 222

test222					96.55 94	81.70 199	92.22 330	95.01 254	68.36 462	90.20 173	96.14 117	80.26 144			97.80 92	96.05 231

testdata2													98.75 115	78.30 341

segment_acmp												87.16 39

testdata				90.49 262	96.40 100	77.89 332		95.37 234	72.51 446	93.63 78	96.69 87	82.08 118	97.65 235	83.08 253	97.39 102	95.94 233

testdata1							92.15 332		87.94 139

test12				94.34 57	97.13 79	86.15 51		96.29 131		91.04 159		85.08 67	99.01 74		98.13 75	97.86 110

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 240

plane_prior5								96.22 144					98.12 176	88.15 173	89.99 274	94.63 285

plane_prior4											94.86 194

plane_prior3						82.75 162			90.26 47	86.91 242

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 140		89.66 68						89.88 279

n2								0.00 504

nn								0.00 504

door-mid								85.49 461

lessismore_v0				86.04 406	88.46 431	68.78 449		80.59 476		73.01 454	90.11 377	55.39 435	96.43 351	75.06 375	65.06 468	92.90 374

LGP-MVS_train				91.12 227	94.47 220	81.49 205		96.14 158	86.73 184	85.45 285	95.16 180	69.89 307	98.10 178	87.70 182	89.23 292	93.77 335

test11								96.57 111

door								85.33 463

HQP5-MVS						81.56 201

HQP-NCC					94.17 246		94.39 201		88.81 100	85.43 288

ACMP_Plane					94.17 246		94.39 201		88.81 100	85.43 288

BP-MVS														87.11 195

HQP4-MVS										85.43 288			97.96 212			94.51 295

HQP3-MVS								96.04 170							89.77 283

HQP2-MVS												73.83 251

NP-MVS					94.37 228	82.42 177					93.98 237

MDTV_nov1_ep13_2view						55.91 487	87.62 440		73.32 439	84.59 310		70.33 301		74.65 380		95.50 252

MDTV_nov1_ep13			83.56 350		91.69 351	69.93 444	87.75 437	91.54 391	78.60 378	84.86 304	88.90 402	69.54 313	96.03 368	70.25 410	88.93 296

ACMMP++_ref															87.47 319

ACMMP++															88.01 311

Test By Simon												80.02 146

ITE_SJBPF				88.24 355	91.88 342	77.05 354		92.92 348	85.54 216	80.13 392	93.30 262	57.29 427	96.20 362	72.46 396	84.71 345	91.49 418

DeepMVS_CX				56.31 472	74.23 485	51.81 488		56.67 496	44.85 484	48.54 484	75.16 475	27.87 483	58.74 494	40.92 484	52.22 481	58.39 486

High-resolution multi-view benchmark