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++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 46	99.58 23	99.59 32

SED-MVS		98.05 2	97.99 1	98.24 11	99.42 10	95.30 18	98.25 40	98.27 55	95.13 40	99.19 13	98.89 28	95.54 5	99.85 21	97.52 42	99.66 10	99.56 40

TestfortrainingZip a		97.92 3	97.70 10	98.58 3	99.56 1	96.08 5	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51	95.46 7	99.86 9	96.63 69	99.58 23	99.80 1

MED-MVS		97.91 4	97.88 4	98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	94.23 87	98.73 30	98.53 51	95.46 7	99.86 9	97.40 50	99.58 23	99.65 20

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 47	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	97.52 42	99.67 6	99.48 56
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		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 193	98.35 42	95.16 38	98.71 35	98.80 38	95.05 12	99.89 3	96.70 68	99.73 1	99.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 29	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 8	97.44 24	98.37 8	98.90 59	95.86 7	97.27 184	98.08 93	95.81 20	97.87 58	98.31 81	94.26 15	99.68 75	97.02 57	99.49 43	99.57 36

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 98	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 98	99.50 52

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 83	98.65 24	96.70 8	99.38 5	99.07 11	89.92 91	99.81 35	99.16 14	99.43 53	99.61 30

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 68	98.25 99	92.59 100	97.81 103	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 28	98.39 88	94.25 44	98.43 27	98.27 55	95.34 32	98.11 47	98.56 47	94.53 14	99.71 67	96.57 73	99.62 17	99.65 20
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 86	98.28 94	91.49 144	97.61 138	98.71 13	97.10 5	99.70 1	98.93 22	90.95 76	99.77 52	99.35 6	99.53 33	99.65 20

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 132	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
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
lecture		97.58 15	97.63 12	97.43 58	99.37 19	92.93 86	98.86 7	98.85 5	95.27 34	98.65 36	98.90 25	91.97 52	99.80 40	97.63 38	99.21 83	99.57 36

test_fmvsm_n_1920		97.55 16	97.89 3	96.53 105	98.41 85	91.73 130	98.01 66	99.02 1	96.37 13	99.30 7	98.92 23	92.39 44	99.79 46	99.16 14	99.46 46	98.08 224

ME-MVS		97.54 17	97.39 27	98.00 23	99.21 36	94.50 35	97.75 110	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 50	99.58 23	99.65 20

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

reproduce_model		97.51 20	97.51 20	97.50 54	98.99 52	93.01 82	97.79 106	98.21 67	95.73 24	97.99 51	99.03 15	92.63 39	99.82 33	97.80 31	99.42 56	99.67 15

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 165	92.37 107	97.91 85	98.88 4	95.83 19	98.92 23	99.05 14	91.45 61	99.80 40	99.12 16	99.46 46	99.69 14

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 46	99.25 32	94.24 45	98.07 60	97.85 137	93.72 103	98.57 37	98.35 72	93.69 20	99.40 133	97.06 56	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 84	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 388	96.94 58	99.64 14	99.32 74
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
SF-MVS		97.39 24	97.13 31	98.17 16	99.02 48	95.28 20	98.23 44	98.27 55	92.37 167	98.27 44	98.65 45	93.33 25	99.72 65	96.49 75	99.52 35	99.51 49

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 44	95.42 11	97.94 81	98.18 76	90.57 252	98.85 27	98.94 21	93.33 25	99.83 31	96.72 66	99.68 4	99.63 26
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
HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 149	97.97 121	95.59 25	96.61 97	97.89 116	92.57 41	99.84 26	95.95 99	99.51 38	99.40 66

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 202	97.80 104	98.53 30	97.24 4	99.62 2	99.14 2	88.65 109	99.80 40	99.54 1	99.15 95	99.74 9

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 108	98.62 26	97.53 2	99.20 12	99.12 5	88.24 117	99.81 35	99.41 3	99.17 91	99.67 15

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 119	98.42 83	91.37 151	98.04 63	98.00 117	97.30 3	99.45 4	99.21 1	89.28 97	99.80 40	99.27 10	99.35 69	98.12 216

NCCC		97.30 29	97.03 40	98.11 18	98.77 62	95.06 26	97.34 177	98.04 108	95.96 15	97.09 79	97.88 119	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 88	98.72 12	96.77 7	99.46 3	99.06 12	87.78 127	99.84 26	99.40 4	99.27 75	99.12 92

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 60	95.76 345	97.78 1	97.52 62	98.80 38	88.09 119	99.86 9	99.44 2	99.37 67	99.80 1

ACMMP_NAP		97.20 33	96.86 49	98.23 12	99.09 40	95.16 23	97.60 139	98.19 74	92.82 154	97.93 54	98.74 42	91.60 59	99.86 9	96.26 80	99.52 35	99.67 15

XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

MCST-MVS		97.18 34	96.84 51	98.20 15	99.30 29	95.35 16	97.12 200	98.07 98	93.54 112	96.08 125	97.69 145	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 107	97.98 126	91.19 161	97.84 95	98.65 24	97.08 6	99.25 9	99.10 6	87.88 125	99.79 46	99.32 7	99.18 90	98.59 167

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 49	99.83 31	95.63 113	99.59 19	99.54 45

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 303	92.21 114	97.95 80	98.27 55	95.78 23	98.40 42	99.00 16	89.99 89	99.78 49	99.06 18	99.41 59	99.59 32

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 170	91.73 130	97.75 110	98.50 31	94.86 52	99.22 11	98.78 40	89.75 94	99.76 54	99.10 17	99.29 73	98.94 121

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 186	98.08 93	95.07 44	96.11 123	98.59 46	90.88 79	99.90 2	96.18 92	99.50 40	99.58 35

region2R		97.07 41	96.84 51	97.77 38	99.46 5	93.79 59	98.52 20	98.24 63	93.19 130	97.14 76	98.34 75	91.59 60	99.87 7	95.46 119	99.59 19	99.64 25

ACMMPR		97.07 41	96.84 51	97.79 34	99.44 9	93.88 57	98.52 20	98.31 47	93.21 127	97.15 75	98.33 78	91.35 65	99.86 9	95.63 113	99.59 19	99.62 27

CP-MVS		97.02 43	96.81 56	97.64 49	99.33 26	93.54 64	98.80 9	98.28 52	92.99 140	96.45 111	98.30 83	91.90 53	99.85 21	95.61 115	99.68 4	99.54 45

SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 71	98.07 98	93.75 102	97.45 64	98.48 61	91.43 63	99.59 95	96.22 83	99.27 75	99.54 45

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 161	92.56 101	97.68 124	98.47 35	94.02 93	98.90 25	98.89 28	88.94 103	99.78 49	99.18 12	99.03 107	98.93 125

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 161	96.39 113	98.18 91	91.61 58	99.88 4	95.59 118	99.55 30	99.57 36

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 118	98.10 91	91.50 202	98.01 50	98.32 80	92.33 45	99.58 98	94.85 133	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 48	97.06 35	96.59 102	98.72 64	91.86 128	97.67 125	98.49 32	94.66 69	97.24 72	98.41 67	92.31 47	98.94 195	96.61 71	99.46 46	98.96 114

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 167	98.04 108	94.81 59	96.59 99	98.37 70	91.24 68	99.64 87	95.16 124	99.52 35	99.42 65
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		96.89 50	97.04 39	96.45 118	98.29 93	91.66 137	99.03 4	97.85 137	95.84 18	96.90 83	97.97 109	91.24 68	98.75 227	96.92 59	99.33 70	98.94 121

SR-MVS-dyc-post		96.88 51	96.80 57	97.11 78	99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	91.40 64	99.56 106	96.05 94	99.26 78	99.43 63

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 54	98.71 237	97.10 55	99.17 91	98.90 130

mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 131	98.33 78	91.04 73	99.88 4	95.20 122	99.57 29	99.60 31

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 149	98.07 120	90.28 203	97.97 77	98.76 9	94.93 48	98.84 28	99.06 12	88.80 106	99.65 79	99.06 18	98.63 123	98.18 209

GST-MVS		96.85 54	96.52 70	97.82 31	99.36 23	94.14 49	98.29 34	98.13 84	92.72 157	96.70 91	98.06 98	91.35 65	99.86 9	94.83 136	99.28 74	99.47 58

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 153	92.22 113	98.17 53	97.82 143	94.44 79	98.23 45	97.36 175	90.97 75	99.22 151	97.74 32	99.66 10	98.61 164

patch_mono-2		96.83 57	97.44 24	95.01 221	99.05 45	85.39 362	96.98 213	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 37	99.50 40	99.72 13

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 76	99.01 51	92.31 110	97.98 71	98.06 101	93.11 136	97.44 65	98.55 49	90.93 77	99.55 108	96.06 93	99.25 80	99.51 49

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 129	98.98 2	92.22 173	97.14 76	98.44 64	91.17 71	99.85 21	94.35 159	99.46 46	99.57 36

MP-MVS		96.77 60	96.45 77	97.72 43	99.39 16	93.80 58	98.41 28	98.06 101	93.37 122	95.54 149	98.34 75	90.59 83	99.88 4	94.83 136	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 47	98.45 37	89.86 270	97.11 78	98.01 104	92.52 42	99.69 73	96.03 97	99.53 33	99.36 72

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 173	97.76 142	89.57 232	97.66 128	98.66 22	95.36 30	99.03 16	98.90 25	88.39 114	99.73 61	99.17 13	98.66 121	98.08 224

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 151	90.72 185	98.00 67	98.73 10	94.55 73	98.91 24	99.08 8	88.22 118	99.63 88	98.91 21	98.37 136	98.25 204

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 140	94.39 411	96.47 12	97.16 74	98.39 68	87.53 136	99.87 7	98.97 20	99.41 59	99.55 43

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 129	96.62 230	91.73 130	97.98 71	98.30 48	96.19 14	96.10 124	98.95 20	89.42 95	99.76 54	98.90 22	99.08 102	97.43 264

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 215	98.06 101	90.67 242	95.55 147	98.78 40	91.07 72	99.86 9	96.58 72	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 238	96.72 293	94.17 89	97.44 65	97.66 149	92.76 34	99.33 139	96.86 62	97.76 162	99.08 98

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 221	96.40 112	97.99 107	90.99 74	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 301	98.90 3	94.30 86	95.86 134	97.74 140	92.33 45	99.38 136	96.04 96	99.42 56	99.28 77

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 151	97.98 126	90.43 195	97.50 153	98.59 27	96.59 10	99.31 6	99.08 8	84.47 200	99.75 58	99.37 5	98.45 133	97.88 237

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 140	93.19 78	95.96 315	98.18 76	95.23 35	95.87 133	97.65 150	91.45 61	99.70 72	95.87 100	99.44 52	99.00 109
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
DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 212	98.09 116	86.63 328	96.00 313	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 154	96.67 228	90.25 204	97.91 85	98.38 38	94.48 77	98.84 28	99.14 2	88.06 120	99.62 89	98.82 23	98.60 125	98.15 213

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 54	97.79 145	90.43 257	97.34 70	97.52 165	91.29 67	99.19 154	98.12 28	99.64 14	98.60 165

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 223	97.73 152	94.74 65	96.49 106	98.49 58	90.88 79	99.58 98	96.44 76	98.32 138	99.13 89

HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 252	96.77 88	98.35 72	90.21 86	99.53 112	94.80 140	99.63 16	99.38 70

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 204	97.29 169	88.38 277	97.23 190	98.47 35	95.14 39	98.43 41	99.09 7	87.58 133	99.72 65	98.80 25	99.21 83	98.02 228

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 149	94.42 80	96.64 96	97.68 146	89.32 96	98.60 253	97.45 46	99.11 101	98.67 162

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 312	90.69 186	97.91 85	98.33 45	94.07 91	98.93 20	99.14 2	87.44 141	99.61 90	98.63 26	98.32 138	98.18 209

CANet		96.39 80	96.02 88	97.50 54	97.62 154	93.38 68	97.02 206	97.96 122	95.42 29	94.86 168	97.81 132	87.38 143	99.82 33	96.88 60	99.20 88	99.29 75

dcpmvs_2		96.37 81	97.05 38	94.31 268	98.96 55	84.11 383	97.56 144	97.51 189	93.92 97	97.43 67	98.52 55	92.75 35	99.32 141	97.32 54	99.50 40	99.51 49

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 68	97.63 166	95.92 16	96.57 102	97.93 111	85.34 182	99.50 120	94.99 129	99.21 83	98.97 111

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 175	96.86 226	97.72 154	94.67 68	96.16 122	98.46 62	90.43 84	99.58 98	96.23 82	97.96 155	98.90 130

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 155	97.30 168	90.37 201	97.53 150	97.92 127	96.52 11	99.14 15	99.08 8	83.21 222	99.74 59	99.22 11	98.06 150	97.88 237

train_agg		96.30 85	95.83 93	97.72 43	98.70 65	94.19 46	96.41 276	98.02 113	88.58 317	96.03 126	97.56 162	92.73 37	99.59 95	95.04 126	99.37 67	99.39 68

ACMMP		96.27 86	95.93 89	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 178	98.39 68	88.96 102	99.85 21	94.57 153	97.63 163	99.36 72
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
MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 298	98.79 7	93.99 95	95.80 136	97.65 150	89.92 91	99.24 149	95.87 100	99.20 88	98.58 168

test_fmvsmconf0.01_n		96.15 88	95.85 92	97.03 83	92.66 427	91.83 129	97.97 77	97.84 141	95.57 26	97.53 61	99.00 16	84.20 206	99.76 54	98.82 23	99.08 102	99.48 56

DeepC-MVS	93.07 3	96.06 89	95.66 94	97.29 64	97.96 128	93.17 79	97.30 182	98.06 101	93.92 97	93.38 218	98.66 43	86.83 150	99.73 61	95.60 117	99.22 82	98.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 90	95.91 90	96.46 117	99.24 33	90.47 192	98.30 33	98.57 29	89.01 299	93.97 199	97.57 160	92.62 40	99.76 54	94.66 147	99.27 75	99.15 87

sasdasda		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 255	87.65 130	99.18 157	96.20 88	94.82 252	98.91 127

ETV-MVS		96.02 91	95.89 91	96.40 122	97.16 176	92.44 105	97.47 162	97.77 148	94.55 73	96.48 107	94.51 337	91.23 70	98.92 198	95.65 111	98.19 144	97.82 245

canonicalmvs		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 255	87.65 130	99.18 157	96.20 88	94.82 252	98.91 127

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 285	97.88 130	86.98 364	96.65 95	97.89 116	91.99 51	99.47 125	92.26 199	99.46 46	99.39 68

UA-Net		95.95 95	95.53 97	97.20 72	97.67 147	92.98 84	97.65 129	98.13 84	94.81 59	96.61 97	98.35 72	88.87 104	99.51 117	90.36 251	97.35 174	99.11 94

SymmetryMVS		95.94 96	95.54 96	97.15 74	97.85 136	92.90 87	97.99 68	96.91 280	95.92 16	96.57 102	97.93 111	85.34 182	99.50 120	94.99 129	96.39 217	99.05 102

MGCFI-Net		95.94 96	95.40 105	97.56 53	97.59 157	94.62 32	98.21 47	97.57 178	94.41 81	96.17 121	96.16 253	87.54 135	99.17 159	96.19 90	94.73 257	98.91 127

BP-MVS1		95.89 98	95.49 98	97.08 81	96.67 228	93.20 77	98.08 58	96.32 319	94.56 72	96.32 114	97.84 126	84.07 209	99.15 163	96.75 64	98.78 116	98.90 130

VNet		95.89 98	95.45 101	97.21 71	98.07 120	92.94 85	97.50 153	98.15 81	93.87 99	97.52 62	97.61 156	85.29 184	99.53 112	95.81 105	95.27 243	99.16 85

alignmvs		95.87 100	95.23 111	97.78 36	97.56 163	95.19 22	97.86 91	97.17 244	94.39 83	96.47 108	96.40 240	85.89 169	99.20 153	96.21 87	95.11 248	98.95 118

casdiffmvs_mvg		95.81 101	95.57 95	96.51 111	96.87 203	91.49 144	97.50 153	97.56 182	93.99 95	95.13 162	97.92 114	87.89 124	98.78 215	95.97 98	97.33 175	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 356	97.62 170	90.43 257	95.55 147	97.07 195	91.72 54	99.50 120	89.62 267	98.94 111	98.82 146

DP-MVS Recon		95.68 103	95.12 116	97.37 60	99.19 37	94.19 46	97.03 204	98.08 93	88.35 326	95.09 163	97.65 150	89.97 90	99.48 124	92.08 210	98.59 126	98.44 186

casdiffmvs		95.64 104	95.49 98	96.08 145	96.76 225	90.45 193	97.29 183	97.44 209	94.00 94	95.46 152	97.98 108	87.52 138	98.73 231	95.64 112	97.33 175	99.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 88	97.83 142	93.58 107	96.80 85	97.82 130	83.06 229	99.16 161	94.40 156	97.95 156	98.87 140

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 190	96.04 310	97.48 194	93.47 117	95.67 144	98.10 94	89.17 99	99.25 148	91.27 228	98.77 117	99.13 89

baseline		95.58 107	95.42 104	96.08 145	96.78 219	90.41 196	97.16 197	97.45 205	93.69 106	95.65 145	97.85 124	87.29 144	98.68 241	95.66 108	97.25 181	99.13 89

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 345	95.17 161	98.03 101	87.09 148	99.61 90	93.51 177	99.42 56	99.02 103

EIA-MVS		95.53 109	95.47 100	95.71 184	97.06 184	89.63 228	97.82 100	97.87 132	93.57 108	93.92 200	95.04 309	90.61 82	98.95 193	94.62 149	98.68 120	98.54 171

3Dnovator+	91.43 4	95.40 110	94.48 146	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 349	98.02 103	83.69 213	99.71 67	93.18 185	98.96 110	99.44 61

PS-MVSNAJ		95.37 111	95.33 108	95.49 198	97.35 167	90.66 188	95.31 353	97.48 194	93.85 100	96.51 105	95.70 280	88.65 109	99.65 79	94.80 140	98.27 141	96.17 303

MVSFormer		95.37 111	95.16 113	95.99 156	96.34 263	91.21 158	98.22 45	97.57 178	91.42 206	96.22 119	97.32 176	86.20 164	97.92 337	94.07 162	99.05 104	98.85 142

diffmvs_AUTHOR		95.33 113	95.27 110	95.50 197	96.37 261	89.08 259	96.08 308	97.38 220	93.09 138	96.53 104	97.74 140	86.45 158	98.68 241	96.32 78	97.48 166	98.75 153

xiu_mvs_v2_base		95.32 114	95.29 109	95.40 203	97.22 172	90.50 191	95.44 346	97.44 209	93.70 105	96.46 109	96.18 250	88.59 113	99.53 112	94.79 143	97.81 159	96.17 303

E3new		95.28 115	95.11 117	95.80 170	97.03 189	89.76 222	96.78 242	97.54 186	92.06 183	95.40 153	97.75 137	87.49 139	98.76 221	94.85 133	97.10 187	98.88 138

PVSNet_Blended_VisFu		95.27 116	94.91 125	96.38 125	98.20 107	90.86 178	97.27 184	98.25 61	90.21 261	94.18 192	97.27 182	87.48 140	99.73 61	93.53 176	97.77 161	98.55 170

viewcassd2359sk11		95.26 117	95.09 118	95.80 170	96.95 198	89.72 224	96.80 237	97.56 182	92.21 175	95.37 154	97.80 134	87.17 147	98.77 219	94.82 138	97.10 187	98.90 130

KinetiMVS		95.26 117	94.75 132	96.79 90	96.99 194	92.05 120	97.82 100	97.78 146	94.77 63	96.46 109	97.70 143	80.62 284	99.34 138	92.37 198	98.28 140	98.97 111

diffmvs		95.25 119	95.13 114	95.63 187	96.43 256	89.34 246	95.99 314	97.35 225	92.83 153	96.31 115	97.37 174	86.44 159	98.67 244	96.26 80	97.19 184	98.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		95.24 120	95.02 120	95.91 159	96.87 203	89.98 213	96.82 232	97.49 192	92.26 171	95.47 151	97.82 130	86.47 157	98.69 239	94.80 140	97.20 183	99.06 101

Vis-MVSNet		95.23 121	94.81 127	96.51 111	97.18 175	91.58 141	98.26 39	98.12 86	94.38 84	94.90 167	98.15 93	82.28 250	98.92 198	91.45 225	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 122	95.04 119	95.76 177	97.49 164	89.56 233	98.67 15	97.00 270	90.69 240	94.24 188	97.62 155	89.79 93	98.81 211	93.39 182	96.49 214	98.92 126

E2		95.20 123	95.00 121	95.79 173	96.79 214	89.66 225	96.82 232	97.58 175	92.35 168	95.28 156	97.83 128	86.68 152	98.76 221	94.79 143	96.92 193	98.95 118

E3		95.20 123	95.00 121	95.79 173	96.77 221	89.66 225	96.82 232	97.58 175	92.35 168	95.28 156	97.83 128	86.69 151	98.76 221	94.79 143	96.92 193	98.95 118

EPNet		95.20 123	94.56 139	97.14 75	92.80 424	92.68 97	97.85 94	94.87 395	96.64 9	92.46 235	97.80 134	86.23 161	99.65 79	93.72 172	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 126	94.44 148	97.44 57	96.56 239	93.36 70	98.65 16	98.36 39	94.12 90	89.25 331	98.06 98	82.20 252	99.77 52	93.41 181	99.32 71	99.18 84

guyue		95.17 127	94.96 123	95.82 168	96.97 196	89.65 227	97.56 144	95.58 357	94.82 57	95.72 139	97.42 171	82.90 234	98.84 207	96.71 67	96.93 192	98.96 114

E4		95.09 128	94.86 126	95.77 176	96.58 234	89.56 233	96.85 227	97.56 182	92.50 162	95.03 164	97.86 122	86.03 167	98.78 215	94.71 146	96.65 207	98.96 114

OMC-MVS		95.09 128	94.70 133	96.25 138	98.46 79	91.28 154	96.43 272	97.57 178	92.04 184	94.77 173	97.96 110	87.01 149	99.09 174	91.31 227	96.77 198	98.36 193

viewmacassd2359aftdt		95.07 130	94.80 128	95.87 162	96.53 244	89.84 219	96.90 222	97.48 194	92.44 164	95.36 155	97.89 116	85.23 185	98.68 241	94.40 156	97.00 191	99.09 96

xiu_mvs_v1_base_debu		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

xiu_mvs_v1_base		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

xiu_mvs_v1_base_debi		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

PAPM_NR		95.01 131	94.59 137	96.26 135	98.89 60	90.68 187	97.24 186	97.73 152	91.80 189	92.93 232	96.62 230	89.13 100	99.14 166	89.21 280	97.78 160	98.97 111

lupinMVS		94.99 135	94.56 139	96.29 133	96.34 263	91.21 158	95.83 323	96.27 323	88.93 305	96.22 119	96.88 209	86.20 164	98.85 205	95.27 121	99.05 104	98.82 146

Effi-MVS+		94.93 136	94.45 147	96.36 127	96.61 231	91.47 147	96.41 276	97.41 215	91.02 229	94.50 181	95.92 264	87.53 136	98.78 215	93.89 168	96.81 197	98.84 145

IS-MVSNet		94.90 137	94.52 143	96.05 148	97.67 147	90.56 189	98.44 26	96.22 326	93.21 127	93.99 197	97.74 140	85.55 179	98.45 267	89.98 256	97.86 157	99.14 88

LuminaMVS		94.89 138	94.35 151	96.53 105	95.48 312	92.80 91	96.88 225	96.18 330	92.85 152	95.92 132	96.87 211	81.44 267	98.83 208	96.43 77	97.10 187	97.94 233

MVS_Test		94.89 138	94.62 136	95.68 185	96.83 209	89.55 235	96.70 250	97.17 244	91.17 221	95.60 146	96.11 259	87.87 126	98.76 221	93.01 193	97.17 185	98.72 157

viewdifsd2359ckpt13		94.87 140	94.52 143	95.90 160	96.88 202	90.19 206	96.92 219	97.36 223	91.26 214	94.65 175	97.46 166	85.79 173	98.64 248	93.64 174	96.76 199	98.88 138

PVSNet_Blended		94.87 140	94.56 139	95.81 169	98.27 96	89.46 241	95.47 345	98.36 39	88.84 308	94.36 184	96.09 260	88.02 121	99.58 98	93.44 179	98.18 145	98.40 189

jason		94.84 142	94.39 149	96.18 141	95.52 310	90.93 175	96.09 307	96.52 308	89.28 290	96.01 129	97.32 176	84.70 196	98.77 219	95.15 125	98.91 113	98.85 142
jason: jason.
API-MVS		94.84 142	94.49 145	95.90 160	97.90 134	92.00 123	97.80 104	97.48 194	89.19 293	94.81 171	96.71 216	88.84 105	99.17 159	88.91 287	98.76 118	96.53 292

AstraMVS		94.82 144	94.64 135	95.34 206	96.36 262	88.09 289	97.58 140	94.56 404	94.98 46	95.70 142	97.92 114	81.93 260	98.93 196	96.87 61	95.88 224	98.99 110

viewdifsd2359ckpt09		94.81 145	94.37 150	96.12 144	96.91 199	90.75 184	96.94 216	97.31 230	90.51 255	94.31 186	97.38 173	85.70 175	98.71 237	93.54 175	96.75 200	98.90 130

test_yl		94.78 146	94.23 154	96.43 119	97.74 143	91.22 156	96.85 227	97.10 251	91.23 218	95.71 140	96.93 204	84.30 203	99.31 143	93.10 186	95.12 246	98.75 153

DCV-MVSNet		94.78 146	94.23 154	96.43 119	97.74 143	91.22 156	96.85 227	97.10 251	91.23 218	95.71 140	96.93 204	84.30 203	99.31 143	93.10 186	95.12 246	98.75 153

viewdifsd2359ckpt07		94.76 148	94.68 134	95.01 221	96.76 225	87.41 304	96.38 282	97.43 212	92.65 159	94.52 179	97.75 137	85.55 179	98.81 211	94.36 158	96.69 204	98.82 146

SSM_0404		94.73 149	94.31 153	95.98 157	97.05 186	90.90 177	97.01 209	97.29 231	91.24 215	94.17 193	97.60 157	85.03 189	98.76 221	92.14 204	97.30 178	98.29 202

WTY-MVS		94.71 150	94.02 159	96.79 90	97.71 145	92.05 120	96.59 265	97.35 225	90.61 248	94.64 176	96.93 204	86.41 160	99.39 134	91.20 230	94.71 258	98.94 121

mamv4		94.66 151	96.10 87	90.37 409	98.01 123	73.41 460	96.82 232	97.78 146	89.95 268	94.52 179	97.43 170	92.91 30	99.09 174	98.28 27	99.16 94	98.60 165

mvsmamba		94.57 152	94.14 156	95.87 162	97.03 189	89.93 217	97.84 95	95.85 341	91.34 209	94.79 172	96.80 212	80.67 282	98.81 211	94.85 133	98.12 148	98.85 142

SSM_0407		94.54 153	94.12 158	95.80 170	96.79 214	90.38 198	96.79 238	97.29 231	91.24 215	93.68 204	97.60 157	85.03 189	98.67 244	92.14 204	96.51 210	98.35 195

RRT-MVS		94.51 154	94.35 151	94.98 225	96.40 257	86.55 331	97.56 144	97.41 215	93.19 130	94.93 166	97.04 197	79.12 312	99.30 145	96.19 90	97.32 177	99.09 96

sss		94.51 154	93.80 163	96.64 94	97.07 181	91.97 124	96.32 290	98.06 101	88.94 304	94.50 181	96.78 213	84.60 197	99.27 147	91.90 211	96.02 220	98.68 161

test_cas_vis1_n_1920		94.48 156	94.55 142	94.28 270	96.78 219	86.45 334	97.63 135	97.64 164	93.32 125	97.68 60	98.36 71	73.75 375	99.08 177	96.73 65	99.05 104	97.31 271

CANet_DTU		94.37 157	93.65 169	96.55 104	96.46 254	92.13 118	96.21 299	96.67 300	94.38 84	93.53 212	97.03 202	79.34 308	99.71 67	90.76 240	98.45 133	97.82 245

AdaColmap		94.34 158	93.68 168	96.31 129	98.59 75	91.68 136	96.59 265	97.81 144	89.87 269	92.15 246	97.06 196	83.62 216	99.54 110	89.34 274	98.07 149	97.70 250

viewmambaseed2359dif		94.28 159	94.14 156	94.71 243	96.21 267	86.97 318	95.93 317	97.11 250	89.00 300	95.00 165	97.70 143	86.02 168	98.59 257	93.71 173	96.59 209	98.57 169

CNLPA		94.28 159	93.53 174	96.52 107	98.38 89	92.55 102	96.59 265	96.88 284	90.13 265	91.91 254	97.24 184	85.21 186	99.09 174	87.64 313	97.83 158	97.92 234

MAR-MVS		94.22 161	93.46 179	96.51 111	98.00 125	92.19 117	97.67 125	97.47 198	88.13 334	93.00 227	95.84 268	84.86 195	99.51 117	87.99 300	98.17 146	97.83 244
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
PAPR		94.18 162	93.42 184	96.48 114	97.64 151	91.42 150	95.55 340	97.71 158	88.99 301	92.34 242	95.82 270	89.19 98	99.11 169	86.14 340	97.38 172	98.90 130

SDMVSNet		94.17 163	93.61 170	95.86 165	98.09 116	91.37 151	97.35 176	98.20 69	93.18 132	91.79 258	97.28 180	79.13 311	98.93 196	94.61 150	92.84 290	97.28 272

test_vis1_n_1920		94.17 163	94.58 138	92.91 339	97.42 166	82.02 410	97.83 98	97.85 137	94.68 67	98.10 48	98.49 58	70.15 399	99.32 141	97.91 30	98.82 114	97.40 266

h-mvs33		94.15 165	93.52 176	96.04 149	97.81 139	90.22 205	97.62 137	97.58 175	95.19 36	96.74 89	97.45 167	83.67 214	99.61 90	95.85 102	79.73 431	98.29 202

CHOSEN 1792x2688		94.15 165	93.51 177	96.06 147	98.27 96	89.38 244	95.18 363	98.48 34	85.60 387	93.76 203	97.11 193	83.15 225	99.61 90	91.33 226	98.72 119	99.19 83

Vis-MVSNet (Re-imp)		94.15 165	93.88 162	94.95 229	97.61 155	87.92 293	98.10 56	95.80 344	92.22 173	93.02 226	97.45 167	84.53 199	97.91 340	88.24 296	97.97 154	99.02 103

CDS-MVSNet		94.14 168	93.54 173	95.93 158	96.18 275	91.46 148	96.33 289	97.04 265	88.97 303	93.56 209	96.51 234	87.55 134	97.89 341	89.80 261	95.95 222	98.44 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 169	93.43 182	96.13 143	98.58 77	91.15 167	96.69 252	97.39 217	87.29 359	91.37 268	96.71 216	88.39 114	99.52 116	87.33 321	97.13 186	97.73 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 170	93.70 167	95.27 208	95.70 301	92.03 122	98.10 56	98.68 19	93.36 124	90.39 289	96.70 218	87.63 132	97.94 334	92.25 201	90.50 331	95.84 317

PVSNet_BlendedMVS		94.06 171	93.92 161	94.47 257	98.27 96	89.46 241	96.73 246	98.36 39	90.17 262	94.36 184	95.24 303	88.02 121	99.58 98	93.44 179	90.72 327	94.36 403

nrg030		94.05 172	93.31 186	96.27 134	95.22 335	94.59 33	98.34 30	97.46 200	92.93 147	91.21 278	96.64 223	87.23 146	98.22 287	94.99 129	85.80 379	95.98 313

UGNet		94.04 173	93.28 187	96.31 129	96.85 206	91.19 161	97.88 90	97.68 159	94.40 82	93.00 227	96.18 250	73.39 377	99.61 90	91.72 217	98.46 132	98.13 214
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
TAMVS		94.01 174	93.46 179	95.64 186	96.16 277	90.45 193	96.71 249	96.89 283	89.27 291	93.46 216	96.92 207	87.29 144	97.94 334	88.70 292	95.74 228	98.53 172

Elysia		94.00 175	93.12 192	96.64 94	96.08 287	92.72 95	97.50 153	97.63 166	91.15 223	94.82 169	97.12 191	74.98 362	99.06 183	90.78 238	98.02 151	98.12 216

StellarMVS		94.00 175	93.12 192	96.64 94	96.08 287	92.72 95	97.50 153	97.63 166	91.15 223	94.82 169	97.12 191	74.98 362	99.06 183	90.78 238	98.02 151	98.12 216

IMVS_0403		93.98 177	93.79 164	94.55 253	96.19 271	86.16 343	96.35 285	97.24 238	91.54 197	93.59 208	97.04 197	85.86 170	98.73 231	90.68 243	95.59 234	98.76 149

114514_t		93.95 178	93.06 195	96.63 98	99.07 43	91.61 138	97.46 164	97.96 122	77.99 451	93.00 227	97.57 160	86.14 166	99.33 139	89.22 279	99.15 95	98.94 121

IMVS_0407		93.94 179	93.75 165	94.49 256	96.19 271	86.16 343	96.35 285	97.24 238	91.54 197	93.50 213	97.04 197	85.64 177	98.54 260	90.68 243	95.59 234	98.76 149

FC-MVSNet-test		93.94 179	93.57 171	95.04 219	95.48 312	91.45 149	98.12 55	98.71 13	93.37 122	90.23 292	96.70 218	87.66 129	97.85 343	91.49 223	90.39 332	95.83 318

mvsany_test1		93.93 181	93.98 160	93.78 302	94.94 352	86.80 321	94.62 376	92.55 444	88.77 314	96.85 84	98.49 58	88.98 101	98.08 305	95.03 127	95.62 233	96.46 297

GeoE		93.89 182	93.28 187	95.72 183	96.96 197	89.75 223	98.24 43	96.92 279	89.47 284	92.12 248	97.21 186	84.42 201	98.39 275	87.71 307	96.50 213	99.01 106

HY-MVS	89.66 9	93.87 183	92.95 200	96.63 98	97.10 180	92.49 104	95.64 337	96.64 301	89.05 298	93.00 227	95.79 274	85.77 174	99.45 128	89.16 283	94.35 260	97.96 231

XVG-OURS-SEG-HR		93.86 184	93.55 172	94.81 235	97.06 184	88.53 273	95.28 354	97.45 205	91.68 194	94.08 196	97.68 146	82.41 248	98.90 201	93.84 170	92.47 296	96.98 280

VDD-MVS		93.82 185	93.08 194	96.02 151	97.88 135	89.96 216	97.72 118	95.85 341	92.43 165	95.86 134	98.44 64	68.42 416	99.39 134	96.31 79	94.85 250	98.71 159

mvs_anonymous		93.82 185	93.74 166	94.06 280	96.44 255	85.41 360	95.81 324	97.05 263	89.85 272	90.09 302	96.36 242	87.44 141	97.75 357	93.97 164	96.69 204	99.02 103

HQP_MVS		93.78 187	93.43 182	94.82 233	96.21 267	89.99 211	97.74 113	97.51 189	94.85 53	91.34 269	96.64 223	81.32 269	98.60 253	93.02 191	92.23 299	95.86 314

PS-MVSNAJss		93.74 188	93.51 177	94.44 259	93.91 390	89.28 251	97.75 110	97.56 182	92.50 162	89.94 305	96.54 233	88.65 109	98.18 292	93.83 171	90.90 325	95.86 314

XVG-OURS		93.72 189	93.35 185	94.80 238	97.07 181	88.61 268	94.79 373	97.46 200	91.97 187	93.99 197	97.86 122	81.74 263	98.88 202	92.64 197	92.67 295	96.92 284

mamba_0408		93.70 190	92.99 196	95.83 167	96.79 214	90.38 198	88.69 463	97.07 257	90.96 231	93.68 204	97.31 178	84.97 192	98.76 221	90.95 234	96.51 210	98.35 195

HyFIR lowres test		93.66 191	92.92 201	95.87 162	98.24 100	89.88 218	94.58 378	98.49 32	85.06 397	93.78 202	95.78 275	82.86 235	98.67 244	91.77 216	95.71 230	99.07 100

LFMVS		93.60 192	92.63 215	96.52 107	98.13 115	91.27 155	97.94 81	93.39 432	90.57 252	96.29 116	98.31 81	69.00 409	99.16 161	94.18 161	95.87 225	99.12 92

icg_test_0407_2		93.58 193	93.46 179	93.94 292	96.19 271	86.16 343	93.73 413	97.24 238	91.54 197	93.50 213	97.04 197	85.64 177	96.91 408	90.68 243	95.59 234	98.76 149

F-COLMAP		93.58 193	92.98 199	95.37 204	98.40 86	88.98 261	97.18 195	97.29 231	87.75 348	90.49 287	97.10 194	85.21 186	99.50 120	86.70 331	96.72 203	97.63 252

ab-mvs		93.57 195	92.55 219	96.64 94	97.28 170	91.96 126	95.40 347	97.45 205	89.81 274	93.22 224	96.28 246	79.62 305	99.46 126	90.74 241	93.11 287	98.50 176

LS3D		93.57 195	92.61 217	96.47 115	97.59 157	91.61 138	97.67 125	97.72 154	85.17 395	90.29 291	98.34 75	84.60 197	99.73 61	83.85 376	98.27 141	98.06 226

FA-MVS(test-final)		93.52 197	92.92 201	95.31 207	96.77 221	88.54 272	94.82 372	96.21 328	89.61 279	94.20 190	95.25 302	83.24 221	99.14 166	90.01 255	96.16 219	98.25 204

SSM_04072		93.51 198	92.99 196	95.05 217	96.79 214	90.38 198	88.69 463	97.07 257	90.96 231	93.68 204	97.31 178	84.97 192	96.42 419	90.95 234	96.51 210	98.35 195

viewdifsd2359ckpt11		93.46 199	93.22 190	94.17 273	96.11 284	85.42 358	96.43 272	97.07 257	92.91 148	94.20 190	98.00 105	80.82 280	98.73 231	94.42 154	89.04 346	98.34 199

viewmsd2359difaftdt		93.46 199	93.23 189	94.17 273	96.12 282	85.42 358	96.43 272	97.08 254	92.91 148	94.21 189	98.00 105	80.82 280	98.74 229	94.41 155	89.05 344	98.34 199

Fast-Effi-MVS+		93.46 199	92.75 209	95.59 190	96.77 221	90.03 208	96.81 236	97.13 246	88.19 329	91.30 272	94.27 355	86.21 163	98.63 250	87.66 312	96.46 216	98.12 216

hse-mvs2		93.45 202	92.99 196	94.81 235	97.02 191	88.59 269	96.69 252	96.47 311	95.19 36	96.74 89	96.16 253	83.67 214	98.48 266	95.85 102	79.13 435	97.35 269

QAPM		93.45 202	92.27 229	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 405	88.27 357	97.77 136	82.39 249	99.81 35	85.40 353	98.81 115	98.51 175

UniMVSNet_NR-MVSNet		93.37 204	92.67 213	95.47 201	95.34 324	92.83 89	97.17 196	98.58 28	92.98 145	90.13 297	95.80 271	88.37 116	97.85 343	91.71 218	83.93 408	95.73 328

1112_ss		93.37 204	92.42 226	96.21 139	97.05 186	90.99 170	96.31 291	96.72 293	86.87 367	89.83 309	96.69 220	86.51 156	99.14 166	88.12 297	93.67 281	98.50 176

UniMVSNet (Re)		93.31 206	92.55 219	95.61 189	95.39 318	93.34 71	97.39 172	98.71 13	93.14 135	90.10 301	94.83 320	87.71 128	98.03 316	91.67 221	83.99 407	95.46 337

OPM-MVS		93.28 207	92.76 207	94.82 233	94.63 368	90.77 182	96.65 256	97.18 242	93.72 103	91.68 262	97.26 183	79.33 309	98.63 250	92.13 207	92.28 298	95.07 366
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 208	92.48 224	95.51 195	95.70 301	92.39 106	97.86 91	98.66 22	92.30 170	92.09 250	95.37 295	80.49 287	98.40 270	93.95 165	85.86 378	95.75 326

test_fmvs1		93.21 209	93.53 174	92.25 362	96.55 241	81.20 417	97.40 171	96.96 272	90.68 241	96.80 85	98.04 100	69.25 407	98.40 270	97.58 41	98.50 128	97.16 277

MVSTER		93.20 210	92.81 206	94.37 262	96.56 239	89.59 231	97.06 203	97.12 247	91.24 215	91.30 272	95.96 262	82.02 256	98.05 312	93.48 178	90.55 329	95.47 336

test1111		93.19 211	92.82 205	94.30 269	97.58 161	84.56 377	98.21 47	89.02 463	93.53 113	94.58 177	98.21 88	72.69 378	99.05 186	93.06 189	98.48 131	99.28 77

ECVR-MVS		93.19 211	92.73 211	94.57 252	97.66 149	85.41 360	98.21 47	88.23 465	93.43 120	94.70 174	98.21 88	72.57 379	99.07 181	93.05 190	98.49 129	99.25 80

HQP-MVS		93.19 211	92.74 210	94.54 254	95.86 293	89.33 247	96.65 256	97.39 217	93.55 109	90.14 293	95.87 266	80.95 274	98.50 263	92.13 207	92.10 304	95.78 322

CHOSEN 280x420		93.12 214	92.72 212	94.34 265	96.71 227	87.27 308	90.29 453	97.72 154	86.61 371	91.34 269	95.29 297	84.29 205	98.41 269	93.25 183	98.94 111	97.35 269

sd_testset		93.10 215	92.45 225	95.05 217	98.09 116	89.21 253	96.89 223	97.64 164	93.18 132	91.79 258	97.28 180	75.35 359	98.65 247	88.99 285	92.84 290	97.28 272

Effi-MVS+-dtu		93.08 216	93.21 191	92.68 350	96.02 290	83.25 393	97.14 199	96.72 293	93.85 100	91.20 279	93.44 394	83.08 227	98.30 282	91.69 220	95.73 229	96.50 294

test_djsdf		93.07 217	92.76 207	94.00 284	93.49 406	88.70 267	98.22 45	97.57 178	91.42 206	90.08 303	95.55 288	82.85 236	97.92 337	94.07 162	91.58 311	95.40 344

VDDNet		93.05 218	92.07 233	96.02 151	96.84 207	90.39 197	98.08 58	95.85 341	86.22 379	95.79 137	98.46 62	67.59 419	99.19 154	94.92 132	94.85 250	98.47 181

thisisatest0530		93.03 219	92.21 231	95.49 198	97.07 181	89.11 258	97.49 161	92.19 446	90.16 263	94.09 195	96.41 239	76.43 350	99.05 186	90.38 250	95.68 231	98.31 201

EI-MVSNet		93.03 219	92.88 203	93.48 318	95.77 299	86.98 317	96.44 270	97.12 247	90.66 244	91.30 272	97.64 153	86.56 154	98.05 312	89.91 258	90.55 329	95.41 341

CLD-MVS		92.98 221	92.53 221	94.32 266	96.12 282	89.20 254	95.28 354	97.47 198	92.66 158	89.90 306	95.62 284	80.58 285	98.40 270	92.73 196	92.40 297	95.38 346
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 222	92.33 228	94.87 232	97.11 179	87.16 314	97.97 77	92.09 447	90.63 246	93.88 201	97.01 203	76.50 347	99.06 183	90.29 253	95.45 240	98.38 191

ACMM	89.79 8	92.96 222	92.50 223	94.35 263	96.30 265	88.71 266	97.58 140	97.36 223	91.40 208	90.53 286	96.65 222	79.77 301	98.75 227	91.24 229	91.64 309	95.59 332
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 224	92.56 218	94.10 278	96.16 277	88.26 281	97.65 129	97.46 200	91.29 210	90.12 299	97.16 188	79.05 314	98.73 231	92.25 201	91.89 307	95.31 351

BH-untuned		92.94 224	92.62 216	93.92 296	97.22 172	86.16 343	96.40 280	96.25 325	90.06 266	89.79 310	96.17 252	83.19 223	98.35 278	87.19 324	97.27 180	97.24 274

DU-MVS		92.90 226	92.04 235	95.49 198	94.95 350	92.83 89	97.16 197	98.24 63	93.02 139	90.13 297	95.71 278	83.47 217	97.85 343	91.71 218	83.93 408	95.78 322

PatchMatch-RL		92.90 226	92.02 237	95.56 191	98.19 109	90.80 180	95.27 356	97.18 242	87.96 336	91.86 257	95.68 281	80.44 288	98.99 191	84.01 371	97.54 165	96.89 285

VortexMVS		92.88 228	92.64 214	93.58 313	96.58 234	87.53 303	96.93 218	97.28 234	92.78 156	89.75 311	94.99 310	82.73 239	97.76 355	94.60 151	88.16 355	95.46 337

PMMVS		92.86 229	92.34 227	94.42 261	94.92 353	86.73 324	94.53 380	96.38 317	84.78 402	94.27 187	95.12 308	83.13 226	98.40 270	91.47 224	96.49 214	98.12 216

OpenMVS	89.19 12	92.86 229	91.68 250	96.40 122	95.34 324	92.73 94	98.27 37	98.12 86	84.86 400	85.78 401	97.75 137	78.89 321	99.74 59	87.50 318	98.65 122	96.73 289

Test_1112_low_res		92.84 231	91.84 244	95.85 166	97.04 188	89.97 215	95.53 342	96.64 301	85.38 390	89.65 316	95.18 304	85.86 170	99.10 171	87.70 308	93.58 286	98.49 178

baseline1		92.82 232	91.90 242	95.55 193	97.20 174	90.77 182	97.19 194	94.58 403	92.20 176	92.36 239	96.34 243	84.16 207	98.21 288	89.20 281	83.90 411	97.68 251

1314		92.81 233	92.03 236	95.14 213	95.33 327	89.52 238	96.04 310	97.44 209	87.72 349	86.25 398	95.33 296	83.84 211	98.79 214	89.26 277	97.05 190	97.11 278

DP-MVS		92.76 234	91.51 258	96.52 107	98.77 62	90.99 170	97.38 174	96.08 333	82.38 427	89.29 328	97.87 120	83.77 212	99.69 73	81.37 400	96.69 204	98.89 136

test_fmvs1_n		92.73 235	92.88 203	92.29 359	96.08 287	81.05 418	97.98 71	97.08 254	90.72 239	96.79 87	98.18 91	63.07 443	98.45 267	97.62 40	98.42 135	97.36 267

BH-RMVSNet		92.72 236	91.97 239	94.97 227	97.16 176	87.99 291	96.15 305	95.60 355	90.62 247	91.87 256	97.15 190	78.41 327	98.57 258	83.16 378	97.60 164	98.36 193

ACMP	89.59 10	92.62 237	92.14 232	94.05 281	96.40 257	88.20 284	97.36 175	97.25 237	91.52 201	88.30 355	96.64 223	78.46 326	98.72 236	91.86 214	91.48 313	95.23 358
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 238	92.52 222	92.44 352	96.82 211	81.89 411	96.92 219	93.71 429	92.41 166	84.30 414	94.60 332	85.08 188	97.03 402	91.51 222	97.36 173	98.40 189

TranMVSNet+NR-MVSNet		92.50 238	91.63 251	95.14 213	94.76 361	92.07 119	97.53 150	98.11 89	92.90 151	89.56 319	96.12 255	83.16 224	97.60 371	89.30 275	83.20 417	95.75 326

thres600view7		92.49 240	91.60 252	95.18 211	97.91 133	89.47 239	97.65 129	94.66 400	92.18 180	93.33 219	94.91 315	78.06 334	99.10 171	81.61 393	94.06 275	96.98 280

IMVS_0404		92.44 241	91.92 241	94.00 284	96.19 271	86.16 343	93.84 410	97.24 238	91.54 197	88.17 361	97.04 197	76.96 344	97.09 399	90.68 243	95.59 234	98.76 149

thres100view900		92.43 242	91.58 253	94.98 225	97.92 132	89.37 245	97.71 120	94.66 400	92.20 176	93.31 220	94.90 316	78.06 334	99.08 177	81.40 397	94.08 271	96.48 295

jajsoiax		92.42 243	91.89 243	94.03 283	93.33 414	88.50 274	97.73 115	97.53 187	92.00 186	88.85 341	96.50 235	75.62 357	98.11 299	93.88 169	91.56 312	95.48 334

thres400		92.42 243	91.52 256	95.12 215	97.85 136	89.29 249	97.41 167	94.88 392	92.19 178	93.27 222	94.46 342	78.17 330	99.08 177	81.40 397	94.08 271	96.98 280

tfpn200view9		92.38 245	91.52 256	94.95 229	97.85 136	89.29 249	97.41 167	94.88 392	92.19 178	93.27 222	94.46 342	78.17 330	99.08 177	81.40 397	94.08 271	96.48 295

test_vis1_n		92.37 246	92.26 230	92.72 347	94.75 362	82.64 400	98.02 65	96.80 290	91.18 220	97.77 59	97.93 111	58.02 453	98.29 283	97.63 38	98.21 143	97.23 275

WR-MVS		92.34 247	91.53 255	94.77 240	95.13 343	90.83 179	96.40 280	97.98 120	91.88 188	89.29 328	95.54 289	82.50 245	97.80 350	89.79 262	85.27 387	95.69 329

NR-MVSNet		92.34 247	91.27 266	95.53 194	94.95 350	93.05 81	97.39 172	98.07 98	92.65 159	84.46 412	95.71 278	85.00 191	97.77 354	89.71 263	83.52 414	95.78 322

mvs_tets		92.31 249	91.76 246	93.94 292	93.41 411	88.29 279	97.63 135	97.53 187	92.04 184	88.76 344	96.45 237	74.62 367	98.09 304	93.91 167	91.48 313	95.45 339

TAPA-MVS	90.10 7	92.30 250	91.22 269	95.56 191	98.33 91	89.60 230	96.79 238	97.65 162	81.83 431	91.52 264	97.23 185	87.94 123	98.91 200	71.31 454	98.37 136	98.17 212
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 251	91.30 264	95.25 209	96.60 232	88.90 263	94.36 389	92.32 445	87.92 337	93.43 217	94.57 333	77.28 341	99.00 190	89.42 272	95.86 226	97.86 241

Fast-Effi-MVS+-dtu		92.29 251	91.99 238	93.21 329	95.27 331	85.52 356	97.03 204	96.63 304	92.09 181	89.11 335	95.14 306	80.33 291	98.08 305	87.54 316	94.74 256	96.03 312

IterMVS-LS		92.29 251	91.94 240	93.34 323	96.25 266	86.97 318	96.57 268	97.05 263	90.67 242	89.50 322	94.80 322	86.59 153	97.64 366	89.91 258	86.11 377	95.40 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 254	91.74 249	93.73 303	97.77 141	83.69 390	92.88 433	96.72 293	87.91 338	93.00 227	94.86 318	78.51 325	99.05 186	86.53 332	97.45 171	98.47 181

VPNet		92.23 255	91.31 263	94.99 223	95.56 308	90.96 172	97.22 192	97.86 136	92.96 146	90.96 280	96.62 230	75.06 360	98.20 289	91.90 211	83.65 413	95.80 320

thres200		92.23 255	91.39 259	94.75 242	97.61 155	89.03 260	96.60 264	95.09 381	92.08 182	93.28 221	94.00 370	78.39 328	99.04 189	81.26 403	94.18 267	96.19 302

anonymousdsp		92.16 257	91.55 254	93.97 288	92.58 429	89.55 235	97.51 152	97.42 214	89.42 287	88.40 351	94.84 319	80.66 283	97.88 342	91.87 213	91.28 317	94.48 398

XXY-MVS		92.16 257	91.23 268	94.95 229	94.75 362	90.94 174	97.47 162	97.43 212	89.14 294	88.90 337	96.43 238	79.71 302	98.24 285	89.56 268	87.68 360	95.67 330

BH-w/o		92.14 259	91.75 247	93.31 324	96.99 194	85.73 353	95.67 332	95.69 350	88.73 315	89.26 330	94.82 321	82.97 232	98.07 309	85.26 356	96.32 218	96.13 308

testing3-2		92.10 260	92.05 234	92.27 360	97.71 145	79.56 437	97.42 166	94.41 410	93.53 113	93.22 224	95.49 291	69.16 408	99.11 169	93.25 183	94.22 265	98.13 214

Anonymous202405211		92.07 261	90.83 285	95.76 177	98.19 109	88.75 265	97.58 140	95.00 384	86.00 382	93.64 207	97.45 167	66.24 431	99.53 112	90.68 243	92.71 293	99.01 106

FE-MVS		92.05 262	91.05 274	95.08 216	96.83 209	87.93 292	93.91 407	95.70 348	86.30 376	94.15 194	94.97 311	76.59 346	99.21 152	84.10 369	96.86 195	98.09 223

WR-MVS_H		92.00 263	91.35 260	93.95 290	95.09 345	89.47 239	98.04 63	98.68 19	91.46 204	88.34 353	94.68 327	85.86 170	97.56 373	85.77 348	84.24 405	94.82 383

Anonymous20240529		91.98 264	90.73 291	95.73 182	98.14 113	89.40 243	97.99 68	97.72 154	79.63 445	93.54 211	97.41 172	69.94 401	99.56 106	91.04 233	91.11 320	98.22 206

MonoMVSNet		91.92 265	91.77 245	92.37 354	92.94 420	83.11 396	97.09 202	95.55 359	92.91 148	90.85 282	94.55 334	81.27 271	96.52 417	93.01 193	87.76 359	97.47 263

PatchmatchNet		91.91 266	91.35 260	93.59 312	95.38 319	84.11 383	93.15 428	95.39 364	89.54 281	92.10 249	93.68 383	82.82 237	98.13 295	84.81 360	95.32 242	98.52 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 267	91.02 275	94.53 255	96.54 242	86.55 331	95.86 321	95.64 354	91.77 191	91.89 255	93.47 393	69.94 401	98.86 203	90.23 254	93.86 278	98.18 209

CP-MVSNet		91.89 268	91.24 267	93.82 299	95.05 346	88.57 270	97.82 100	98.19 74	91.70 193	88.21 359	95.76 276	81.96 257	97.52 379	87.86 302	84.65 396	95.37 347

SCA		91.84 269	91.18 271	93.83 298	95.59 306	84.95 373	94.72 374	95.58 357	90.82 234	92.25 244	93.69 381	75.80 354	98.10 300	86.20 338	95.98 221	98.45 183

FMVSNet3		91.78 270	90.69 294	95.03 220	96.53 244	92.27 112	97.02 206	96.93 275	89.79 275	89.35 325	94.65 330	77.01 342	97.47 382	86.12 341	88.82 347	95.35 348

AUN-MVS		91.76 271	90.75 289	94.81 235	97.00 193	88.57 270	96.65 256	96.49 310	89.63 278	92.15 246	96.12 255	78.66 323	98.50 263	90.83 236	79.18 434	97.36 267

X-MVStestdata		91.71 272	89.67 339	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 480	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

MVS		91.71 272	90.44 302	95.51 195	95.20 337	91.59 140	96.04 310	97.45 205	73.44 461	87.36 377	95.60 285	85.42 181	99.10 171	85.97 345	97.46 167	95.83 318

EPNet_dtu		91.71 272	91.28 265	92.99 336	93.76 395	83.71 389	96.69 252	95.28 371	93.15 134	87.02 386	95.95 263	83.37 220	97.38 390	79.46 416	96.84 196	97.88 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 275	90.75 289	94.47 257	96.53 244	86.56 330	95.76 328	94.51 407	91.10 227	91.24 277	93.59 388	68.59 413	98.86 203	91.10 231	94.29 263	98.00 230

FE-MVSNET3		91.65 276	90.67 295	94.60 246	93.65 401	90.95 173	94.86 371	97.12 247	89.69 277	89.21 332	93.62 386	81.17 272	97.67 362	87.54 316	89.14 343	95.17 364

baseline2		91.63 277	90.86 281	93.94 292	94.33 379	86.32 336	95.92 318	91.64 451	89.37 288	86.94 389	94.69 326	81.62 265	98.69 239	88.64 293	94.57 259	96.81 287

testing99		91.62 278	90.72 292	94.32 266	96.48 251	86.11 348	95.81 324	94.76 397	91.55 196	91.75 260	93.44 394	68.55 414	98.82 209	90.43 248	93.69 280	98.04 227

test2506		91.60 279	90.78 286	94.04 282	97.66 149	83.81 386	98.27 37	75.53 481	93.43 120	95.23 159	98.21 88	67.21 422	99.07 181	93.01 193	98.49 129	99.25 80

miper_ehance_all_eth		91.59 280	91.13 272	92.97 337	95.55 309	86.57 329	94.47 383	96.88 284	87.77 346	88.88 339	94.01 369	86.22 162	97.54 375	89.49 269	86.93 368	94.79 388

v2v482		91.59 280	90.85 283	93.80 300	93.87 392	88.17 286	96.94 216	96.88 284	89.54 281	89.53 320	94.90 316	81.70 264	98.02 317	89.25 278	85.04 393	95.20 359

V42		91.58 282	90.87 280	93.73 303	94.05 387	88.50 274	97.32 180	96.97 271	88.80 313	89.71 312	94.33 350	82.54 244	98.05 312	89.01 284	85.07 391	94.64 396

PCF-MVS	89.48 11	91.56 283	89.95 327	96.36 127	96.60 232	92.52 103	92.51 438	97.26 235	79.41 446	88.90 337	96.56 232	84.04 210	99.55 108	77.01 430	97.30 178	97.01 279
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 284	90.76 287	93.94 292	96.52 247	85.06 369	95.22 359	94.54 405	90.47 256	91.98 252	92.71 405	72.02 382	98.74 229	88.10 298	95.26 244	98.01 229

PS-CasMVS		91.55 284	90.84 284	93.69 307	94.96 349	88.28 280	97.84 95	98.24 63	91.46 204	88.04 364	95.80 271	79.67 303	97.48 381	87.02 328	84.54 402	95.31 351

miper_enhance_ethall		91.54 286	91.01 276	93.15 331	95.35 323	87.07 316	93.97 402	96.90 281	86.79 368	89.17 333	93.43 397	86.55 155	97.64 366	89.97 257	86.93 368	94.74 392

myMVS_eth3d28		91.52 287	90.97 277	93.17 330	96.91 199	83.24 394	95.61 338	94.96 388	92.24 172	91.98 252	93.28 398	69.31 406	98.40 270	88.71 291	95.68 231	97.88 237

PAPM		91.52 287	90.30 308	95.20 210	95.30 330	89.83 220	93.38 424	96.85 287	86.26 378	88.59 347	95.80 271	84.88 194	98.15 294	75.67 435	95.93 223	97.63 252

ET-MVSNet_ETH3D		91.49 289	90.11 318	95.63 187	96.40 257	91.57 142	95.34 350	93.48 431	90.60 250	75.58 456	95.49 291	80.08 295	96.79 413	94.25 160	89.76 337	98.52 173

TR-MVS		91.48 290	90.59 298	94.16 276	96.40 257	87.33 305	95.67 332	95.34 370	87.68 350	91.46 266	95.52 290	76.77 345	98.35 278	82.85 383	93.61 284	96.79 288

tpmrst		91.44 291	91.32 262	91.79 377	95.15 341	79.20 443	93.42 423	95.37 366	88.55 320	93.49 215	93.67 384	82.49 246	98.27 284	90.41 249	89.34 341	97.90 235

test-LLR		91.42 292	91.19 270	92.12 365	94.59 369	80.66 421	94.29 394	92.98 437	91.11 225	90.76 284	92.37 413	79.02 316	98.07 309	88.81 288	96.74 201	97.63 252

MSDG		91.42 292	90.24 312	94.96 228	97.15 178	88.91 262	93.69 416	96.32 319	85.72 386	86.93 390	96.47 236	80.24 292	98.98 192	80.57 407	95.05 249	96.98 280

c3_l		91.38 294	90.89 279	92.88 341	95.58 307	86.30 337	94.68 375	96.84 288	88.17 330	88.83 343	94.23 358	85.65 176	97.47 382	89.36 273	84.63 397	94.89 378

GA-MVS		91.38 294	90.31 307	94.59 247	94.65 367	87.62 301	94.34 390	96.19 329	90.73 238	90.35 290	93.83 374	71.84 384	97.96 328	87.22 323	93.61 284	98.21 207

v1144		91.37 296	90.60 297	93.68 308	93.89 391	88.23 283	96.84 230	97.03 267	88.37 325	89.69 314	94.39 344	82.04 255	97.98 321	87.80 304	85.37 384	94.84 380

GBi-Net		91.35 297	90.27 310	94.59 247	96.51 248	91.18 163	97.50 153	96.93 275	88.82 310	89.35 325	94.51 337	73.87 371	97.29 394	86.12 341	88.82 347	95.31 351

test1		91.35 297	90.27 310	94.59 247	96.51 248	91.18 163	97.50 153	96.93 275	88.82 310	89.35 325	94.51 337	73.87 371	97.29 394	86.12 341	88.82 347	95.31 351

UniMVSNet_ETH3D		91.34 299	90.22 315	94.68 244	94.86 357	87.86 296	97.23 190	97.46 200	87.99 335	89.90 306	96.92 207	66.35 429	98.23 286	90.30 252	90.99 323	97.96 231

FMVSNet2		91.31 300	90.08 319	94.99 223	96.51 248	92.21 114	97.41 167	96.95 273	88.82 310	88.62 346	94.75 324	73.87 371	97.42 387	85.20 357	88.55 352	95.35 348

reproduce_monomvs		91.30 301	91.10 273	91.92 369	96.82 211	82.48 404	97.01 209	97.49 192	94.64 71	88.35 352	95.27 300	70.53 394	98.10 300	95.20 122	84.60 399	95.19 362

D2MVS		91.30 301	90.95 278	92.35 355	94.71 365	85.52 356	96.18 303	98.21 67	88.89 306	86.60 393	93.82 376	79.92 299	97.95 332	89.29 276	90.95 324	93.56 418

v8		91.29 303	90.53 301	93.57 315	94.15 383	88.12 288	97.34 177	97.06 262	88.99 301	88.32 354	94.26 357	83.08 227	98.01 318	87.62 314	83.92 410	94.57 397

CVMVSNet		91.23 304	91.75 247	89.67 418	95.77 299	74.69 455	96.44 270	94.88 392	85.81 384	92.18 245	97.64 153	79.07 313	95.58 435	88.06 299	95.86 226	98.74 156

cl22		91.21 305	90.56 300	93.14 332	96.09 286	86.80 321	94.41 387	96.58 307	87.80 344	88.58 348	93.99 371	80.85 279	97.62 369	89.87 260	86.93 368	94.99 369

PEN-MVS		91.20 306	90.44 302	93.48 318	94.49 373	87.91 295	97.76 108	98.18 76	91.29 210	87.78 368	95.74 277	80.35 290	97.33 392	85.46 352	82.96 418	95.19 362

Baseline_NR-MVSNet		91.20 306	90.62 296	92.95 338	93.83 393	88.03 290	97.01 209	95.12 380	88.42 324	89.70 313	95.13 307	83.47 217	97.44 385	89.66 266	83.24 416	93.37 422

cascas		91.20 306	90.08 319	94.58 251	94.97 348	89.16 257	93.65 418	97.59 174	79.90 444	89.40 323	92.92 403	75.36 358	98.36 277	92.14 204	94.75 255	96.23 299

CostFormer		91.18 309	90.70 293	92.62 351	94.84 358	81.76 412	94.09 400	94.43 408	84.15 408	92.72 234	93.77 378	79.43 307	98.20 289	90.70 242	92.18 302	97.90 235

tt0805		91.09 310	90.07 322	94.16 276	95.61 305	88.31 278	97.56 144	96.51 309	89.56 280	89.17 333	95.64 283	67.08 426	98.38 276	91.07 232	88.44 353	95.80 320

v1192		91.07 311	90.23 313	93.58 313	93.70 396	87.82 298	96.73 246	97.07 257	87.77 346	89.58 317	94.32 352	80.90 278	97.97 324	86.52 333	85.48 382	94.95 370

v144192		91.06 312	90.28 309	93.39 321	93.66 399	87.23 311	96.83 231	97.07 257	87.43 355	89.69 314	94.28 354	81.48 266	98.00 319	87.18 325	84.92 395	94.93 374

v10		91.04 313	90.23 313	93.49 317	94.12 384	88.16 287	97.32 180	97.08 254	88.26 328	88.29 356	94.22 360	82.17 253	97.97 324	86.45 335	84.12 406	94.33 404

eth_miper_zixun_eth		91.02 314	90.59 298	92.34 357	95.33 327	84.35 379	94.10 399	96.90 281	88.56 319	88.84 342	94.33 350	84.08 208	97.60 371	88.77 290	84.37 404	95.06 367

v148		90.99 315	90.38 304	92.81 344	93.83 393	85.80 350	96.78 242	96.68 298	89.45 286	88.75 345	93.93 373	82.96 233	97.82 347	87.83 303	83.25 415	94.80 386

LTVRE_ROB	88.41 13	90.99 315	89.92 329	94.19 272	96.18 275	89.55 235	96.31 291	97.09 253	87.88 339	85.67 402	95.91 265	78.79 322	98.57 258	81.50 394	89.98 334	94.44 401
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
DIV-MVS_self_test		90.97 317	90.33 305	92.88 341	95.36 322	86.19 342	94.46 385	96.63 304	87.82 342	88.18 360	94.23 358	82.99 230	97.53 377	87.72 305	85.57 381	94.93 374

cl____		90.96 318	90.32 306	92.89 340	95.37 321	86.21 340	94.46 385	96.64 301	87.82 342	88.15 362	94.18 361	82.98 231	97.54 375	87.70 308	85.59 380	94.92 376

pmmvs4		90.93 319	89.85 331	94.17 273	93.34 413	90.79 181	94.60 377	96.02 334	84.62 403	87.45 373	95.15 305	81.88 261	97.45 384	87.70 308	87.87 358	94.27 408

XVG-ACMP-BASELINE		90.93 319	90.21 316	93.09 333	94.31 381	85.89 349	95.33 351	97.26 235	91.06 228	89.38 324	95.44 294	68.61 412	98.60 253	89.46 270	91.05 321	94.79 388

v1921920		90.85 321	90.03 324	93.29 325	93.55 402	86.96 320	96.74 245	97.04 265	87.36 357	89.52 321	94.34 349	80.23 293	97.97 324	86.27 336	85.21 388	94.94 372

CR-MVSNet		90.82 322	89.77 335	93.95 290	94.45 375	87.19 312	90.23 454	95.68 352	86.89 366	92.40 236	92.36 416	80.91 276	97.05 401	81.09 404	93.95 276	97.60 257

v7n		90.76 323	89.86 330	93.45 320	93.54 403	87.60 302	97.70 123	97.37 221	88.85 307	87.65 370	94.08 367	81.08 273	98.10 300	84.68 362	83.79 412	94.66 395

RPSCF		90.75 324	90.86 281	90.42 408	96.84 207	76.29 453	95.61 338	96.34 318	83.89 411	91.38 267	97.87 120	76.45 348	98.78 215	87.16 326	92.23 299	96.20 301

MVP-Stereo		90.74 325	90.08 319	92.71 348	93.19 416	88.20 284	95.86 321	96.27 323	86.07 381	84.86 410	94.76 323	77.84 337	97.75 357	83.88 375	98.01 153	92.17 443
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 326	89.65 341	93.96 289	94.29 382	89.63 228	97.79 106	96.82 289	89.07 296	86.12 400	95.48 293	78.61 324	97.78 352	86.97 329	81.67 423	94.46 399

v1240		90.70 327	89.85 331	93.23 327	93.51 405	86.80 321	96.61 262	97.02 269	87.16 362	89.58 317	94.31 353	79.55 306	97.98 321	85.52 351	85.44 383	94.90 377

EPMVS		90.70 327	89.81 333	93.37 322	94.73 364	84.21 381	93.67 417	88.02 466	89.50 283	92.38 238	93.49 391	77.82 338	97.78 352	86.03 344	92.68 294	98.11 222

WBMVS		90.69 329	89.99 326	92.81 344	96.48 251	85.00 370	95.21 361	96.30 321	89.46 285	89.04 336	94.05 368	72.45 381	97.82 347	89.46 270	87.41 365	95.61 331

Anonymous20231211		90.63 330	89.42 346	94.27 271	98.24 100	89.19 256	98.05 62	97.89 128	79.95 443	88.25 358	94.96 312	72.56 380	98.13 295	89.70 264	85.14 389	95.49 333

DTE-MVSNet		90.56 331	89.75 337	93.01 335	93.95 388	87.25 309	97.64 133	97.65 162	90.74 237	87.12 381	95.68 281	79.97 298	97.00 405	83.33 377	81.66 424	94.78 390

ACMH	87.59 16	90.53 332	89.42 346	93.87 297	96.21 267	87.92 293	97.24 186	96.94 274	88.45 323	83.91 422	96.27 247	71.92 383	98.62 252	84.43 365	89.43 340	95.05 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 333	89.14 354	94.67 245	96.81 213	87.85 297	95.91 319	93.97 423	89.71 276	92.34 242	92.48 411	65.41 437	97.96 328	81.37 400	94.27 264	98.21 207

OurMVSNet-221017-0		90.51 334	90.19 317	91.44 386	93.41 411	81.25 415	96.98 213	96.28 322	91.68 194	86.55 395	96.30 244	74.20 370	97.98 321	88.96 286	87.40 366	95.09 365

miper_lstm_enhance		90.50 335	90.06 323	91.83 374	95.33 327	83.74 387	93.86 408	96.70 297	87.56 353	87.79 367	93.81 377	83.45 219	96.92 407	87.39 319	84.62 398	94.82 383

COLMAP_ROB	87.81 15	90.40 336	89.28 349	93.79 301	97.95 129	87.13 315	96.92 219	95.89 340	82.83 424	86.88 392	97.18 187	73.77 374	99.29 146	78.44 421	93.62 283	94.95 370
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 337	88.96 356	94.35 263	96.54 242	87.29 306	95.50 343	93.84 427	90.97 230	91.75 260	92.96 402	62.18 448	98.00 319	82.86 381	94.08 271	97.76 247

IterMVS-SCA-FT		90.31 337	89.81 333	91.82 375	95.52 310	84.20 382	94.30 393	96.15 331	90.61 248	87.39 376	94.27 355	75.80 354	96.44 418	87.34 320	86.88 372	94.82 383

MS-PatchMatch		90.27 339	89.77 335	91.78 378	94.33 379	84.72 376	95.55 340	96.73 292	86.17 380	86.36 397	95.28 299	71.28 388	97.80 350	84.09 370	98.14 147	92.81 428

tpm		90.25 340	89.74 338	91.76 380	93.92 389	79.73 436	93.98 401	93.54 430	88.28 327	91.99 251	93.25 399	77.51 340	97.44 385	87.30 322	87.94 357	98.12 216

AllTest		90.23 341	88.98 355	93.98 286	97.94 130	86.64 325	96.51 269	95.54 360	85.38 390	85.49 404	96.77 214	70.28 396	99.15 163	80.02 411	92.87 288	96.15 306

dmvs_re		90.21 342	89.50 344	92.35 355	95.47 316	85.15 366	95.70 331	94.37 413	90.94 233	88.42 350	93.57 389	74.63 366	95.67 432	82.80 384	89.57 339	96.22 300

ACMH+	87.92 14	90.20 343	89.18 352	93.25 326	96.48 251	86.45 334	96.99 212	96.68 298	88.83 309	84.79 411	96.22 249	70.16 398	98.53 261	84.42 366	88.04 356	94.77 391

test-mter		90.19 344	89.54 343	92.12 365	94.59 369	80.66 421	94.29 394	92.98 437	87.68 350	90.76 284	92.37 413	67.67 418	98.07 309	88.81 288	96.74 201	97.63 252

IterMVS		90.15 345	89.67 339	91.61 382	95.48 312	83.72 388	94.33 391	96.12 332	89.99 267	87.31 379	94.15 363	75.78 356	96.27 422	86.97 329	86.89 371	94.83 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 346	89.42 346	91.97 368	94.41 377	80.62 423	94.29 394	91.97 449	87.28 360	90.44 288	92.47 412	68.79 410	97.67 362	88.50 295	96.60 208	97.61 256

SD_0403		90.01 347	90.02 325	89.96 415	95.65 304	76.76 450	95.76 328	96.46 312	90.58 251	86.59 394	96.29 245	82.12 254	94.78 444	73.00 449	93.76 279	98.35 195

tpm2		89.96 348	89.21 351	92.23 363	94.91 355	81.25 415	93.78 411	94.42 409	80.62 441	91.56 263	93.44 394	76.44 349	97.94 334	85.60 350	92.08 306	97.49 261

UWE-MVS		89.91 349	89.48 345	91.21 391	95.88 292	78.23 448	94.91 370	90.26 459	89.11 295	92.35 241	94.52 336	68.76 411	97.96 328	83.95 373	95.59 234	97.42 265

IB-MVS	87.33 17	89.91 349	88.28 366	94.79 239	95.26 334	87.70 300	95.12 365	93.95 424	89.35 289	87.03 385	92.49 410	70.74 393	99.19 154	89.18 282	81.37 425	97.49 261
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
ADS-MVSNet		89.89 351	88.68 361	93.53 316	95.86 293	84.89 374	90.93 449	95.07 382	83.23 422	91.28 275	91.81 426	79.01 318	97.85 343	79.52 413	91.39 315	97.84 242

WB-MVSnew		89.88 352	89.56 342	90.82 400	94.57 372	83.06 397	95.65 336	92.85 439	87.86 341	90.83 283	94.10 364	79.66 304	96.88 409	76.34 431	94.19 266	92.54 434

FMVSNet1		89.88 352	88.31 365	94.59 247	95.41 317	91.18 163	97.50 153	96.93 275	86.62 370	87.41 375	94.51 337	65.94 434	97.29 394	83.04 380	87.43 363	95.31 351

pmmvs5		89.86 354	88.87 359	92.82 343	92.86 422	86.23 339	96.26 294	95.39 364	84.24 407	87.12 381	94.51 337	74.27 369	97.36 391	87.61 315	87.57 361	94.86 379

tpmvs		89.83 355	89.15 353	91.89 372	94.92 353	80.30 428	93.11 429	95.46 363	86.28 377	88.08 363	92.65 406	80.44 288	98.52 262	81.47 396	89.92 335	96.84 286

test_fmvs2		89.77 356	89.93 328	89.31 425	93.68 398	76.37 452	97.64 133	95.90 338	89.84 273	91.49 265	96.26 248	58.77 451	97.10 398	94.65 148	91.13 319	94.46 399

SSC-MVS3.2		89.74 357	89.26 350	91.19 394	95.16 338	80.29 429	94.53 380	97.03 267	91.79 190	88.86 340	94.10 364	69.94 401	97.82 347	85.29 354	86.66 373	95.45 339

mmtdpeth		89.70 358	88.96 356	91.90 371	95.84 298	84.42 378	97.46 164	95.53 362	90.27 260	94.46 183	90.50 435	69.74 405	98.95 193	97.39 53	69.48 462	92.34 437

tfpnnormal		89.70 358	88.40 364	93.60 311	95.15 341	90.10 207	97.56 144	98.16 80	87.28 360	86.16 399	94.63 331	77.57 339	98.05 312	74.48 439	84.59 400	92.65 431

ADS-MVSNet2		89.45 360	88.59 362	92.03 367	95.86 293	82.26 408	90.93 449	94.32 416	83.23 422	91.28 275	91.81 426	79.01 318	95.99 424	79.52 413	91.39 315	97.84 242

Patchmatch-test		89.42 361	87.99 368	93.70 306	95.27 331	85.11 367	88.98 461	94.37 413	81.11 435	87.10 384	93.69 381	82.28 250	97.50 380	74.37 441	94.76 254	98.48 180

test0.0.03 1		89.37 362	88.70 360	91.41 387	92.47 431	85.63 354	95.22 359	92.70 442	91.11 225	86.91 391	93.65 385	79.02 316	93.19 461	78.00 423	89.18 342	95.41 341

SixPastTwentyTwo		89.15 363	88.54 363	90.98 396	93.49 406	80.28 430	96.70 250	94.70 399	90.78 235	84.15 417	95.57 286	71.78 385	97.71 360	84.63 363	85.07 391	94.94 372

RPMNet		88.98 364	87.05 378	94.77 240	94.45 375	87.19 312	90.23 454	98.03 110	77.87 453	92.40 236	87.55 460	80.17 294	99.51 117	68.84 460	93.95 276	97.60 257

TransMVSNet (Re)		88.94 365	87.56 371	93.08 334	94.35 378	88.45 276	97.73 115	95.23 375	87.47 354	84.26 415	95.29 297	79.86 300	97.33 392	79.44 417	74.44 453	93.45 421

USDC		88.94 365	87.83 370	92.27 360	94.66 366	84.96 372	93.86 408	95.90 338	87.34 358	83.40 424	95.56 287	67.43 420	98.19 291	82.64 388	89.67 338	93.66 417

dp		88.90 367	88.26 367	90.81 401	94.58 371	76.62 451	92.85 434	94.93 389	85.12 396	90.07 304	93.07 400	75.81 353	98.12 298	80.53 408	87.42 364	97.71 249

PatchT		88.87 368	87.42 372	93.22 328	94.08 386	85.10 368	89.51 459	94.64 402	81.92 430	92.36 239	88.15 456	80.05 296	97.01 404	72.43 450	93.65 282	97.54 260

our_test_3		88.78 369	87.98 369	91.20 393	92.45 432	82.53 402	93.61 420	95.69 350	85.77 385	84.88 409	93.71 379	79.99 297	96.78 414	79.47 415	86.24 374	94.28 407

EU-MVSNet		88.72 370	88.90 358	88.20 429	93.15 417	74.21 457	96.63 261	94.22 418	85.18 394	87.32 378	95.97 261	76.16 351	94.98 442	85.27 355	86.17 375	95.41 341

Patchmtry		88.64 371	87.25 374	92.78 346	94.09 385	86.64 325	89.82 458	95.68 352	80.81 439	87.63 371	92.36 416	80.91 276	97.03 402	78.86 419	85.12 390	94.67 394

MIMVSNet		88.50 372	86.76 382	93.72 305	94.84 358	87.77 299	91.39 444	94.05 420	86.41 374	87.99 365	92.59 409	63.27 442	95.82 429	77.44 424	92.84 290	97.57 259

tpm cat1		88.36 373	87.21 376	91.81 376	95.13 343	80.55 424	92.58 437	95.70 348	74.97 457	87.45 373	91.96 424	78.01 336	98.17 293	80.39 409	88.74 350	96.72 290

ppachtmachnet_test		88.35 374	87.29 373	91.53 383	92.45 432	83.57 391	93.75 412	95.97 335	84.28 406	85.32 407	94.18 361	79.00 320	96.93 406	75.71 434	84.99 394	94.10 409

JIA-IIPM		88.26 375	87.04 379	91.91 370	93.52 404	81.42 414	89.38 460	94.38 412	80.84 438	90.93 281	80.74 468	79.22 310	97.92 337	82.76 385	91.62 310	96.38 298

testgi		87.97 376	87.21 376	90.24 411	92.86 422	80.76 419	96.67 255	94.97 386	91.74 192	85.52 403	95.83 269	62.66 446	94.47 447	76.25 432	88.36 354	95.48 334

LF4IMVS		87.94 377	87.25 374	89.98 414	92.38 434	80.05 434	94.38 388	95.25 374	87.59 352	84.34 413	94.74 325	64.31 440	97.66 365	84.83 359	87.45 362	92.23 440

gg-mvs-nofinetune		87.82 378	85.61 391	94.44 259	94.46 374	89.27 252	91.21 448	84.61 475	80.88 437	89.89 308	74.98 471	71.50 386	97.53 377	85.75 349	97.21 182	96.51 293

pmmvs6		87.81 379	86.19 387	92.69 349	91.32 439	86.30 337	97.34 177	96.41 315	80.59 442	84.05 421	94.37 346	67.37 421	97.67 362	84.75 361	79.51 433	94.09 411

testing3		87.67 380	86.88 381	90.05 413	96.14 280	80.71 420	97.10 201	92.85 439	90.15 264	87.54 372	94.55 334	55.70 458	94.10 450	73.77 445	94.10 270	95.35 348

K. test v3		87.64 381	86.75 383	90.32 410	93.02 419	79.48 441	96.61 262	92.08 448	90.66 244	80.25 443	94.09 366	67.21 422	96.65 416	85.96 346	80.83 427	94.83 381

Patchmatch-RL test		87.38 382	86.24 386	90.81 401	88.74 457	78.40 447	88.12 468	93.17 434	87.11 363	82.17 433	89.29 447	81.95 258	95.60 434	88.64 293	77.02 442	98.41 188

FMVSNet5		87.29 383	85.79 390	91.78 378	94.80 360	87.28 307	95.49 344	95.28 371	84.09 409	83.85 423	91.82 425	62.95 444	94.17 449	78.48 420	85.34 386	93.91 415

myMVS_eth3d		87.18 384	86.38 385	89.58 419	95.16 338	79.53 438	95.00 367	93.93 425	88.55 320	86.96 387	91.99 422	56.23 457	94.00 451	75.47 437	94.11 268	95.20 359

Syy-MVS		87.13 385	87.02 380	87.47 433	95.16 338	73.21 461	95.00 367	93.93 425	88.55 320	86.96 387	91.99 422	75.90 352	94.00 451	61.59 467	94.11 268	95.20 359

Anonymous20231206		87.09 386	86.14 388	89.93 416	91.22 440	80.35 426	96.11 306	95.35 367	83.57 418	84.16 416	93.02 401	73.54 376	95.61 433	72.16 451	86.14 376	93.84 416

EG-PatchMatch MVS		87.02 387	85.44 392	91.76 380	92.67 426	85.00 370	96.08 308	96.45 313	83.41 421	79.52 445	93.49 391	57.10 455	97.72 359	79.34 418	90.87 326	92.56 433

TinyColmap		86.82 388	85.35 395	91.21 391	94.91 355	82.99 398	93.94 404	94.02 422	83.58 417	81.56 435	94.68 327	62.34 447	98.13 295	75.78 433	87.35 367	92.52 435

UWE-MVS-28		86.81 389	86.41 384	88.02 431	92.87 421	74.60 456	95.38 349	86.70 471	88.17 330	87.28 380	94.67 329	70.83 392	93.30 459	67.45 461	94.31 262	96.17 303

mvs5depth		86.53 390	85.08 397	90.87 398	88.74 457	82.52 403	91.91 442	94.23 417	86.35 375	87.11 383	93.70 380	66.52 427	97.76 355	81.37 400	75.80 447	92.31 439

TDRefinement		86.53 390	84.76 402	91.85 373	82.23 474	84.25 380	96.38 282	95.35 367	84.97 399	84.09 419	94.94 313	65.76 435	98.34 281	84.60 364	74.52 452	92.97 425

sc_t1		86.48 392	84.10 409	93.63 309	93.45 409	85.76 352	96.79 238	94.71 398	73.06 462	86.45 396	94.35 347	55.13 459	97.95 332	84.38 367	78.55 438	97.18 276

test_0402		86.46 393	84.79 401	91.45 385	95.02 347	85.55 355	96.29 293	94.89 391	80.90 436	82.21 432	93.97 372	68.21 417	97.29 394	62.98 465	88.68 351	91.51 449

Anonymous20240521		86.42 394	85.44 392	89.34 424	90.33 444	79.79 435	96.73 246	95.92 336	83.71 416	83.25 426	91.36 431	63.92 441	96.01 423	78.39 422	85.36 385	92.22 441

FE-MVSNET2		86.36 395	84.68 404	91.39 388	87.67 462	86.47 333	96.21 299	96.41 315	87.87 340	79.31 447	89.64 444	65.29 438	95.58 435	82.42 389	77.28 441	92.14 444

DSMNet-mixed		86.34 396	86.12 389	87.00 437	89.88 448	70.43 463	94.93 369	90.08 460	77.97 452	85.42 406	92.78 404	74.44 368	93.96 453	74.43 440	95.14 245	96.62 291

CL-MVSNet_self_test		86.31 397	85.15 396	89.80 417	88.83 455	81.74 413	93.93 405	96.22 326	86.67 369	85.03 408	90.80 434	78.09 333	94.50 445	74.92 438	71.86 458	93.15 424

pmmvs-eth3d		86.22 398	84.45 405	91.53 383	88.34 459	87.25 309	94.47 383	95.01 383	83.47 419	79.51 446	89.61 445	69.75 404	95.71 430	83.13 379	76.73 445	91.64 446

test_vis1_rt		86.16 399	85.06 398	89.46 421	93.47 408	80.46 425	96.41 276	86.61 472	85.22 393	79.15 448	88.64 451	52.41 463	97.06 400	93.08 188	90.57 328	90.87 455

test20.03		86.14 400	85.40 394	88.35 427	90.12 445	80.06 433	95.90 320	95.20 376	88.59 316	81.29 436	93.62 386	71.43 387	92.65 462	71.26 455	81.17 426	92.34 437

UnsupCasMVSNet_eth		85.99 401	84.45 405	90.62 405	89.97 447	82.40 407	93.62 419	97.37 221	89.86 270	78.59 451	92.37 413	65.25 439	95.35 440	82.27 391	70.75 459	94.10 409

KD-MVS_self_test		85.95 402	84.95 399	88.96 426	89.55 451	79.11 444	95.13 364	96.42 314	85.91 383	84.07 420	90.48 436	70.03 400	94.82 443	80.04 410	72.94 456	92.94 426

ttmdpeth		85.91 403	84.76 402	89.36 423	89.14 452	80.25 431	95.66 335	93.16 436	83.77 414	83.39 425	95.26 301	66.24 431	95.26 441	80.65 406	75.57 448	92.57 432

YYNet1		85.87 404	84.23 407	90.78 404	92.38 434	82.46 406	93.17 426	95.14 379	82.12 429	67.69 464	92.36 416	78.16 332	95.50 438	77.31 426	79.73 431	94.39 402

MDA-MVSNet_test_wron		85.87 404	84.23 407	90.80 403	92.38 434	82.57 401	93.17 426	95.15 378	82.15 428	67.65 466	92.33 419	78.20 329	95.51 437	77.33 425	79.74 430	94.31 406

CMPMVS	62.92 21	85.62 406	84.92 400	87.74 432	89.14 452	73.12 462	94.17 397	96.80 290	73.98 458	73.65 460	94.93 314	66.36 428	97.61 370	83.95 373	91.28 317	92.48 436
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 407	83.64 410	90.92 397	95.27 331	79.49 440	90.55 452	95.60 355	83.76 415	83.00 429	89.95 441	71.09 389	97.97 324	82.75 386	60.79 473	95.31 351

tt0320		85.39 408	83.12 411	92.19 364	93.44 410	85.79 351	96.19 302	94.87 395	71.19 464	82.92 430	91.76 428	58.43 452	96.81 412	81.03 405	78.26 439	93.98 413

MDA-MVSNet-bldmvs		85.00 409	82.95 414	91.17 395	93.13 418	83.33 392	94.56 379	95.00 384	84.57 404	65.13 470	92.65 406	70.45 395	95.85 427	73.57 446	77.49 440	94.33 404

MIMVSNet1		84.93 410	83.05 412	90.56 406	89.56 450	84.84 375	95.40 347	95.35 367	83.91 410	80.38 441	92.21 421	57.23 454	93.34 458	70.69 457	82.75 421	93.50 419

tt0320-xc		84.83 411	82.33 419	92.31 358	93.66 399	86.20 341	96.17 304	94.06 419	71.26 463	82.04 434	92.22 420	55.07 460	96.72 415	81.49 395	75.04 451	94.02 412

KD-MVS_2432*1600		84.81 412	82.64 415	91.31 389	91.07 441	85.34 364	91.22 446	95.75 346	85.56 388	83.09 427	90.21 439	67.21 422	95.89 425	77.18 428	62.48 471	92.69 429

miper_refine_blended		84.81 412	82.64 415	91.31 389	91.07 441	85.34 364	91.22 446	95.75 346	85.56 388	83.09 427	90.21 439	67.21 422	95.89 425	77.18 428	62.48 471	92.69 429

OpenMVS_ROB	81.14 20	84.42 414	82.28 420	90.83 399	90.06 446	84.05 385	95.73 330	94.04 421	73.89 460	80.17 444	91.53 430	59.15 450	97.64 366	66.92 463	89.05 344	90.80 456

FE-MVSNET		83.85 415	81.97 421	89.51 420	87.19 464	83.19 395	95.21 361	93.17 434	83.45 420	78.90 449	89.05 449	65.46 436	93.84 455	69.71 459	75.56 449	91.51 449

mvsany_test3		83.59 416	82.44 418	87.03 436	83.80 469	73.82 458	93.70 414	90.92 457	86.42 373	82.51 431	90.26 438	46.76 468	95.71 430	90.82 237	76.76 444	91.57 448

PM-MVS		83.48 417	81.86 423	88.31 428	87.83 461	77.59 449	93.43 422	91.75 450	86.91 365	80.63 439	89.91 442	44.42 469	95.84 428	85.17 358	76.73 445	91.50 451

test_fmvs3		83.21 418	83.02 413	83.78 442	86.77 466	68.34 468	96.76 244	94.91 390	86.49 372	84.14 418	89.48 446	36.04 473	91.73 464	91.86 214	80.77 428	91.26 454

new-patchmatchnet		83.18 419	81.87 422	87.11 435	86.88 465	75.99 454	93.70 414	95.18 377	85.02 398	77.30 454	88.40 453	65.99 433	93.88 454	74.19 443	70.18 460	91.47 452

new_pmnet		82.89 420	81.12 425	88.18 430	89.63 449	80.18 432	91.77 443	92.57 443	76.79 455	75.56 457	88.23 455	61.22 449	94.48 446	71.43 453	82.92 419	89.87 459

MVS-HIRNet		82.47 421	81.21 424	86.26 439	95.38 319	69.21 466	88.96 462	89.49 461	66.28 468	80.79 438	74.08 473	68.48 415	97.39 389	71.93 452	95.47 239	92.18 442

MVStest1		82.38 422	80.04 426	89.37 422	87.63 463	82.83 399	95.03 366	93.37 433	73.90 459	73.50 461	94.35 347	62.89 445	93.25 460	73.80 444	65.92 468	92.04 445

UnsupCasMVSNet_bld		82.13 423	79.46 428	90.14 412	88.00 460	82.47 405	90.89 451	96.62 306	78.94 448	75.61 455	84.40 466	56.63 456	96.31 421	77.30 427	66.77 467	91.63 447

dmvs_testset		81.38 424	82.60 417	77.73 448	91.74 438	51.49 483	93.03 431	84.21 476	89.07 296	78.28 452	91.25 432	76.97 343	88.53 471	56.57 471	82.24 422	93.16 423

test_f		80.57 425	79.62 427	83.41 443	83.38 472	67.80 470	93.57 421	93.72 428	80.80 440	77.91 453	87.63 459	33.40 474	92.08 463	87.14 327	79.04 436	90.34 458

pmmvs3		79.97 426	77.50 431	87.39 434	82.80 473	79.38 442	92.70 436	90.75 458	70.69 465	78.66 450	87.47 461	51.34 464	93.40 457	73.39 447	69.65 461	89.38 460

APD_test1		79.31 427	77.70 430	84.14 441	89.11 454	69.07 467	92.36 441	91.50 452	69.07 466	73.87 459	92.63 408	39.93 471	94.32 448	70.54 458	80.25 429	89.02 461

N_pmnet		78.73 428	78.71 429	78.79 447	92.80 424	46.50 486	94.14 398	43.71 488	78.61 449	80.83 437	91.66 429	74.94 364	96.36 420	67.24 462	84.45 403	93.50 419

WB-MVS		76.77 429	76.63 432	77.18 449	85.32 467	56.82 481	94.53 380	89.39 462	82.66 426	71.35 462	89.18 448	75.03 361	88.88 469	35.42 478	66.79 466	85.84 463

SSC-MVS		76.05 430	75.83 433	76.72 453	84.77 468	56.22 482	94.32 392	88.96 464	81.82 432	70.52 463	88.91 450	74.79 365	88.71 470	33.69 479	64.71 469	85.23 464

test_vis3_rt		72.73 431	70.55 434	79.27 446	80.02 475	68.13 469	93.92 406	74.30 483	76.90 454	58.99 474	73.58 474	20.29 482	95.37 439	84.16 368	72.80 457	74.31 471

LCM-MVSNet		72.55 432	69.39 436	82.03 444	70.81 484	65.42 473	90.12 456	94.36 415	55.02 474	65.88 468	81.72 467	24.16 481	89.96 465	74.32 442	68.10 465	90.71 457

FPMVS		71.27 433	69.85 435	75.50 454	74.64 479	59.03 479	91.30 445	91.50 452	58.80 471	57.92 475	88.28 454	29.98 477	85.53 474	53.43 472	82.84 420	81.95 467

PMMVS2		70.19 434	66.92 438	80.01 445	76.35 478	65.67 472	86.22 469	87.58 468	64.83 470	62.38 471	80.29 470	26.78 479	88.49 472	63.79 464	54.07 475	85.88 462

dongtai		69.99 435	69.33 437	71.98 457	88.78 456	61.64 477	89.86 457	59.93 487	75.67 456	74.96 458	85.45 463	50.19 465	81.66 476	43.86 475	55.27 474	72.63 472

testf1		69.31 436	66.76 439	76.94 451	78.61 476	61.93 475	88.27 466	86.11 473	55.62 472	59.69 472	85.31 464	20.19 483	89.32 466	57.62 468	69.44 463	79.58 468

APD_test2		69.31 436	66.76 439	76.94 451	78.61 476	61.93 475	88.27 466	86.11 473	55.62 472	59.69 472	85.31 464	20.19 483	89.32 466	57.62 468	69.44 463	79.58 468

EGC-MVSNET		68.77 438	63.01 444	86.07 440	92.49 430	82.24 409	93.96 403	90.96 456	0.71 485	2.62 486	90.89 433	53.66 461	93.46 456	57.25 470	84.55 401	82.51 466

Gipuma		67.86 439	65.41 441	75.18 455	92.66 427	73.45 459	66.50 477	94.52 406	53.33 475	57.80 476	66.07 476	30.81 475	89.20 468	48.15 474	78.88 437	62.90 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 440	64.89 442	69.79 458	72.62 482	35.23 490	65.19 478	92.83 441	20.35 480	65.20 469	88.08 457	43.14 470	82.70 475	73.12 448	63.46 470	91.45 453

kuosan		65.27 441	64.66 443	67.11 460	83.80 469	61.32 478	88.53 465	60.77 486	68.22 467	67.67 465	80.52 469	49.12 466	70.76 482	29.67 481	53.64 476	69.26 474

ANet_high		63.94 442	59.58 445	77.02 450	61.24 486	66.06 471	85.66 471	87.93 467	78.53 450	42.94 478	71.04 475	25.42 480	80.71 477	52.60 473	30.83 479	84.28 465

PMVS	53.92 22	58.58 443	55.40 446	68.12 459	51.00 487	48.64 484	78.86 474	87.10 470	46.77 476	35.84 482	74.28 472	8.76 485	86.34 473	42.07 476	73.91 454	69.38 473
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 444	52.56 448	55.43 462	74.43 480	47.13 485	83.63 473	76.30 480	42.23 477	42.59 479	62.22 478	28.57 478	74.40 479	31.53 480	31.51 478	44.78 477

MVE	50.73 23	53.25 445	48.81 450	66.58 461	65.34 485	57.50 480	72.49 476	70.94 484	40.15 479	39.28 481	63.51 477	6.89 487	73.48 481	38.29 477	42.38 477	68.76 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 446	51.31 449	54.39 463	72.62 482	45.39 487	83.84 472	75.51 482	41.13 478	40.77 480	59.65 479	30.08 476	73.60 480	28.31 482	29.90 480	44.18 478

tmp_tt		51.94 447	53.82 447	46.29 464	33.73 488	45.30 488	78.32 475	67.24 485	18.02 481	50.93 477	87.05 462	52.99 462	53.11 483	70.76 456	25.29 481	40.46 479

wuyk23d		25.11 448	24.57 452	26.74 465	73.98 481	39.89 489	57.88 479	9.80 489	12.27 482	10.39 483	6.97 485	7.03 486	36.44 484	25.43 483	17.39 482	3.89 482

cdsmvs_eth3d_5k		23.24 449	30.99 451	0.00 468	0.00 491	0.00 493	0.00 480	97.63 166	0.00 486	0.00 487	96.88 209	84.38 202	0.00 487	0.00 486	0.00 485	0.00 483

testmvs		13.36 450	16.33 453	4.48 467	5.04 489	2.26 492	93.18 425	3.28 490	2.70 483	8.24 484	21.66 481	2.29 489	2.19 485	7.58 484	2.96 483	9.00 481

test123		13.04 451	15.66 454	5.18 466	4.51 490	3.45 491	92.50 439	1.81 491	2.50 484	7.58 485	20.15 482	3.67 488	2.18 486	7.13 485	1.07 484	9.90 480

ab-mvs-re		8.06 452	10.74 455	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	96.69 220	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		7.39 453	9.85 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	88.65 109	0.00 487	0.00 486	0.00 485	0.00 483

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

MED-MVS test				98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51		99.86 9	97.40 50	99.58 23	99.65 20

TestfortrainingZip							98.69 11

WAC-MVS						79.53 438								75.56 436

FOURS1					99.55 4	93.34 71	99.29 1	98.35 42	94.98 46	98.49 39

MSC_two_6792asdad				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

PC_three_1452									90.77 236	98.89 26	98.28 86	96.24 1	98.35 278	95.76 106	99.58 23	99.59 32

No_MVS				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

test_one_0601					99.32 27	95.20 21		98.25 61	95.13 40	98.48 40	98.87 31	95.16 9

eth-test2					0.00 491

eth-test					0.00 491

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 292	97.03 81	98.10 94	92.52 42	99.65 79	94.58 152	99.31 72

RE-MVS-def			96.72 62		99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	90.71 81		96.05 94	99.26 78	99.43 63

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 259	98.94 19				97.41 49	99.66 10	99.74 9

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 149	95.36 120	99.59 19	99.56 40

test_241102_TWO								98.27 55	95.13 40	98.93 20	98.89 28	94.99 13	99.85 21	97.52 42	99.65 13	99.74 9

test_241102_ONE					99.42 10	95.30 18		98.27 55	95.09 43	99.19 13	98.81 37	95.54 5	99.65 79

9.14			96.75 61		98.93 56		97.73 115	98.23 66	91.28 213	97.88 55	98.44 64	93.00 29	99.65 79	95.76 106	99.47 45

save fliter					98.91 58	94.28 42	97.02 206	98.02 113	95.35 31

test_0728_THIRD									94.78 61	98.73 30	98.87 31	95.87 4	99.84 26	97.45 46	99.72 2	99.77 3

test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 47	98.28 52					99.86 9	97.52 42	99.67 6	99.75 7

test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

GSMVS																98.45 183

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 238				98.45 183

sam_mvs												81.94 259

ambc				86.56 438	83.60 471	70.00 465	85.69 470	94.97 386		80.60 440	88.45 452	37.42 472	96.84 411	82.69 387	75.44 450	92.86 427

MTGPA								98.08 93

test_post1							92.81 435				16.58 484	80.53 286	97.68 361	86.20 338

test_post											17.58 483	81.76 262	98.08 305

patchmatchnet-post											90.45 437	82.65 243	98.10 300

GG-mvs-BLEND				93.62 310	93.69 397	89.20 254	92.39 440	83.33 477		87.98 366	89.84 443	71.00 390	96.87 410	82.08 392	95.40 241	94.80 386

MTMP							97.86 91	82.03 478

gm-plane-assit					93.22 415	78.89 446			84.82 401		93.52 390		98.64 248	87.72 305

test9_res														94.81 139	99.38 64	99.45 59

TEST9					98.70 65	94.19 46	96.41 276	98.02 113	88.17 330	96.03 126	97.56 162	92.74 36	99.59 95

test_8					98.67 67	94.06 53	96.37 284	98.01 116	88.58 317	95.98 130	97.55 164	92.73 37	99.58 98

agg_prior2														93.94 166	99.38 64	99.50 52

agg_prior					98.67 67	93.79 59		98.00 117		95.68 143			99.57 105

TestCases				93.98 286	97.94 130	86.64 325		95.54 360	85.38 390	85.49 404	96.77 214	70.28 396	99.15 163	80.02 411	92.87 288	96.15 306

test_prior4						93.66 62	96.42 275

test_prior2							96.35 285		92.80 155	96.03 126	97.59 159	92.01 50		95.01 128	99.38 64

test_prior				97.23 69	98.67 67	92.99 83		98.00 117					99.41 132			99.29 75

旧先验2							95.94 316		81.66 433	97.34 70			98.82 209	92.26 199

新几何2							95.79 326

新几何1				97.32 62	98.60 74	93.59 63		97.75 149	81.58 434	95.75 138	97.85 124	90.04 88	99.67 77	86.50 334	99.13 98	98.69 160

旧先验1					98.38 89	93.38 68		97.75 149			98.09 96	92.30 48			99.01 108	99.16 85

无先验							95.79 326	97.87 132	83.87 413				99.65 79	87.68 311		98.89 136

原ACMM2							95.67 332

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 356	95.22 160	97.68 146	90.25 85	99.54 110	87.95 301	99.12 100	98.49 178

test222					98.24 100	92.21 114	95.33 351	97.60 171	79.22 447	95.25 158	97.84 126	88.80 106			99.15 95	98.72 157

testdata2													99.67 77	85.96 346

segment_acmp												92.89 33

testdata				95.46 202	98.18 111	88.90 263		97.66 160	82.73 425	97.03 81	98.07 97	90.06 87	98.85 205	89.67 265	98.98 109	98.64 163

testdata1							95.26 358		93.10 137

test12				97.65 47	98.46 79	94.26 43		97.66 160		95.52 150		90.89 78	99.46 126		99.25 80	99.22 82

plane_prior7					96.21 267	89.98 213

plane_prior6					96.10 285	90.00 209						81.32 269

plane_prior5								97.51 189					98.60 253	93.02 191	92.23 299	95.86 314

plane_prior4											96.64 223

plane_prior3						90.00 209			94.46 78	91.34 269

plane_prior2							97.74 113		94.85 53

plane_prior1					96.14 280

plane_prior						89.99 211	97.24 186		94.06 92						92.16 303

n2								0.00 492

nn								0.00 492

door-mid								91.06 455

lessismore_v0				90.45 407	91.96 437	79.09 445		87.19 469		80.32 442	94.39 344	66.31 430	97.55 374	84.00 372	76.84 443	94.70 393

LGP-MVS_train				94.10 278	96.16 277	88.26 281		97.46 200	91.29 210	90.12 299	97.16 188	79.05 314	98.73 231	92.25 201	91.89 307	95.31 351

test11								97.88 130

door								91.13 454

HQP5-MVS						89.33 247

HQP-NCC					95.86 293		96.65 256		93.55 109	90.14 293

ACMP_Plane					95.86 293		96.65 256		93.55 109	90.14 293

BP-MVS														92.13 207

HQP4-MVS										90.14 293			98.50 263			95.78 322

HQP3-MVS								97.39 217							92.10 304

HQP2-MVS												80.95 274

NP-MVS					95.99 291	89.81 221					95.87 266

MDTV_nov1_ep13_2view						70.35 464	93.10 430		83.88 412	93.55 210		82.47 247		86.25 337		98.38 191

MDTV_nov1_ep13			90.76 287		95.22 335	80.33 427	93.03 431	95.28 371	88.14 333	92.84 233	93.83 374	81.34 268	98.08 305	82.86 381	94.34 261

ACMMP++_ref															90.30 333

ACMMP++															91.02 322

Test By Simon												88.73 108

ITE_SJBPF				92.43 353	95.34 324	85.37 363		95.92 336	91.47 203	87.75 369	96.39 241	71.00 390	97.96 328	82.36 390	89.86 336	93.97 414

DeepMVS_CX				74.68 456	90.84 443	64.34 474		81.61 479	65.34 469	67.47 467	88.01 458	48.60 467	80.13 478	62.33 466	73.68 455	79.58 468

High-resolution multi-view benchmark