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 31	97.78 54	86.00 50	98.29 1	97.49 6	90.75 21	97.62 5	98.06 16	92.59 2	99.61 4	95.64 23	99.02 12	98.86 11

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 15	90.31 32	97.71 1	98.07 14	92.31 4	99.58 10	95.66 21	99.13 3	98.84 14

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 84	90.27 36	97.04 14	98.05 18	91.47 8	99.55 16	95.62 25	99.08 7	98.45 36
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 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 103	97.51 5	89.13 75	97.14 10	97.91 25	91.64 7	99.62 2	94.61 37	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 46	97.46 6	97.40 20	89.03 80	96.20 23	98.10 10	89.39 16	99.34 37	95.88 20	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 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 25	97.47 11	91.73 11	96.10 24	96.69 73	89.90 12	99.30 43	94.70 35	98.04 71	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 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 113	96.96 57	92.09 7	95.32 36	97.08 56	89.49 15	99.33 40	95.10 32	98.85 20	98.66 21

SMA-MVS		95.20 8	95.07 12	95.59 6	98.14 35	88.48 8	96.26 46	97.28 32	85.90 169	97.67 3	98.10 10	88.41 20	99.56 12	94.66 36	99.19 1	98.71 20
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 8	95.32 9	94.85 25	96.99 75	86.33 42	97.33 7	97.30 30	91.38 13	95.39 35	97.46 36	88.98 19	99.40 30	94.12 41	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 18	95.83 4	98.25 29	89.65 4	95.92 78	96.96 57	91.75 10	94.02 57	96.83 68	88.12 24	99.55 16	93.41 52	98.94 16	98.28 55

MM		95.10 11	94.91 18	95.68 5	96.09 107	88.34 9	96.68 33	94.37 248	95.08 1	94.68 44	97.72 30	82.94 90	99.64 1	97.85 2	98.76 29	99.06 7

SF-MVS		94.97 12	94.90 20	95.20 12	97.84 50	87.76 10	96.65 34	97.48 10	87.76 125	95.71 31	97.70 31	88.28 23	99.35 36	93.89 45	98.78 26	98.48 30

SD-MVS		94.96 13	95.33 8	93.88 63	97.25 72	86.69 28	96.19 49	97.11 48	90.42 29	96.95 16	97.27 44	89.53 14	96.91 266	94.38 39	98.85 20	98.03 78
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 14	94.94 16	94.58 42	98.25 29	86.33 42	96.11 59	96.62 93	88.14 110	96.10 24	96.96 62	89.09 18	98.94 83	94.48 38	98.68 37	98.48 30
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 15	94.97 14	94.38 50	97.91 47	85.46 68	95.86 81	97.15 43	89.82 48	95.23 39	98.10 10	87.09 37	99.37 33	95.30 29	98.25 60	98.30 50

our_new_method		94.82 15	94.97 14	94.38 50	97.91 47	85.46 68	95.86 81	97.15 43	89.82 48	95.23 39	98.10 10	87.09 37	99.37 33	95.30 29	98.25 60	98.30 50

NCCC		94.81 17	94.69 23	95.17 14	97.83 51	87.46 17	95.66 95	96.93 61	92.34 5	93.94 58	96.58 83	87.74 27	99.44 29	92.83 61	98.40 54	98.62 22

fmvsm_l_conf0.5_n_3		94.80 18	95.01 13	94.15 57	95.64 130	85.08 75	96.09 60	97.36 22	90.98 16	97.09 12	98.12 7	84.98 67	98.94 83	97.07 10	97.80 79	98.43 38

reproduce_model		94.76 19	94.92 17	94.29 54	97.92 43	85.18 74	95.95 76	97.19 36	89.67 58	95.27 38	98.16 3	86.53 43	99.36 35	95.42 28	98.15 64	98.33 45

ACMMP_NAP		94.74 20	94.56 24	95.28 10	98.02 41	87.70 11	95.68 92	97.34 24	88.28 104	95.30 37	97.67 32	85.90 50	99.54 20	93.91 44	98.95 15	98.60 23

test_fmvsm_n_1920		94.71 21	95.11 11	93.50 77	95.79 121	84.62 85	96.15 54	97.64 2	89.85 47	97.19 9	97.89 26	86.28 46	98.71 106	97.11 9	98.08 70	97.17 122

test_fmvsmconf_n		94.60 22	94.81 21	93.98 59	94.62 183	84.96 78	96.15 54	97.35 23	89.37 66	96.03 27	98.11 8	86.36 44	99.01 66	97.45 5	97.83 78	97.96 81

HFP-MVS		94.52 23	94.40 28	94.86 24	98.61 10	86.81 25	96.94 20	97.34 24	88.63 92	93.65 63	97.21 48	86.10 48	99.49 26	92.35 74	98.77 28	98.30 50

fmvsm_s_conf0.5_n_3		94.49 24	95.13 10	92.56 121	95.49 138	81.10 195	95.93 77	97.16 42	92.96 2	97.39 7	98.13 4	83.63 81	98.80 96	97.89 1	97.61 86	97.78 95

ZNCC-MVS		94.47 25	94.28 34	95.03 16	98.52 15	86.96 20	96.85 28	97.32 28	88.24 105	93.15 73	97.04 59	86.17 47	99.62 2	92.40 71	98.81 23	98.52 26

XVS		94.45 26	94.32 30	94.85 25	98.54 13	86.60 34	96.93 22	97.19 36	90.66 26	92.85 81	97.16 54	85.02 63	99.49 26	91.99 88	98.56 50	98.47 33

MCST-MVS		94.45 26	94.20 40	95.19 13	98.46 19	87.50 16	95.00 134	97.12 46	87.13 138	92.51 96	96.30 90	89.24 17	99.34 37	93.46 49	98.62 46	98.73 18

region2R		94.43 28	94.27 36	94.92 20	98.65 8	86.67 30	96.92 24	97.23 35	88.60 95	93.58 65	97.27 44	85.22 58	99.54 20	92.21 78	98.74 31	98.56 25

ACMMPR		94.43 28	94.28 34	94.91 21	98.63 9	86.69 28	96.94 20	97.32 28	88.63 92	93.53 68	97.26 46	85.04 62	99.54 20	92.35 74	98.78 26	98.50 27

MTAPA		94.42 30	94.22 37	95.00 18	98.42 21	86.95 21	94.36 181	96.97 55	91.07 14	93.14 74	97.56 33	84.30 74	99.56 12	93.43 50	98.75 30	98.47 33

CP-MVS		94.34 31	94.21 39	94.74 37	98.39 23	86.64 32	97.60 4	97.24 33	88.53 97	92.73 89	97.23 47	85.20 59	99.32 41	92.15 81	98.83 22	98.25 62

fmvsm_l_conf0.5_n		94.29 32	94.46 26	93.79 69	95.28 145	85.43 70	95.68 92	96.43 106	86.56 153	96.84 17	97.81 29	87.56 32	98.77 100	97.14 8	96.82 104	97.16 126

MP-MVS		94.25 33	94.07 44	94.77 35	98.47 18	86.31 44	96.71 31	96.98 54	89.04 78	91.98 106	97.19 51	85.43 56	99.56 12	92.06 87	98.79 24	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 34	94.07 44	94.75 36	98.06 39	86.90 23	95.88 80	96.94 60	85.68 175	95.05 42	97.18 52	87.31 35	99.07 56	91.90 94	98.61 48	98.28 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 35	94.17 42	94.43 47	98.21 32	85.78 63	96.40 38	96.90 64	88.20 108	94.33 48	97.40 39	84.75 70	99.03 61	93.35 53	97.99 72	98.48 30

GST-MVS		94.21 36	93.97 48	94.90 23	98.41 22	86.82 24	96.54 36	97.19 36	88.24 105	93.26 70	96.83 68	85.48 55	99.59 8	91.43 102	98.40 54	98.30 50

MP-MVS-pluss		94.21 36	94.00 47	94.85 25	98.17 33	86.65 31	94.82 146	97.17 41	86.26 161	92.83 83	97.87 27	85.57 54	99.56 12	94.37 40	98.92 17	98.34 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 38	94.40 28	93.60 75	95.29 144	84.98 77	95.61 99	96.28 119	86.31 159	96.75 19	97.86 28	87.40 33	98.74 103	97.07 10	97.02 97	97.07 128

test_fmvsmconf0.1_n		94.20 38	94.31 32	93.88 63	92.46 273	84.80 81	96.18 51	96.82 73	89.29 69	95.68 32	98.11 8	85.10 60	98.99 73	97.38 6	97.75 83	97.86 89

DeepPCF-MVS	89.96 1	94.20 38	94.77 22	92.49 125	96.52 91	80.00 229	94.00 206	97.08 49	90.05 40	95.65 33	97.29 43	89.66 13	98.97 78	93.95 43	98.71 32	98.50 27

MVS_0304		94.18 41	93.80 52	95.34 9	94.91 167	87.62 14	95.97 73	93.01 289	92.58 4	94.22 49	97.20 50	80.56 120	99.59 8	97.04 12	98.68 37	98.81 17

CS-MVS		94.12 42	94.44 27	93.17 85	96.55 88	83.08 138	97.63 3	96.95 59	91.71 12	93.50 69	96.21 93	85.61 52	98.24 150	93.64 47	98.17 62	98.19 65

DeepC-MVS_fast	89.43 2	94.04 43	93.79 53	94.80 33	97.48 64	86.78 26	95.65 97	96.89 65	89.40 65	92.81 84	96.97 61	85.37 57	99.24 46	90.87 111	98.69 35	98.38 42
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 44	94.29 33	93.24 82	96.69 81	83.24 128	97.49 5	96.92 62	92.14 6	92.90 79	95.77 117	85.02 63	98.33 145	93.03 58	98.62 46	98.13 69

HPM-MVS		94.02 44	93.88 49	94.43 47	98.39 23	85.78 63	97.25 10	97.07 50	86.90 146	92.62 93	96.80 72	84.85 69	99.17 50	92.43 69	98.65 44	98.33 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 46	93.78 54	94.63 40	98.50 16	85.90 60	96.87 26	96.91 63	88.70 90	91.83 115	97.17 53	83.96 78	99.55 16	91.44 101	98.64 45	98.43 38

balanced_conf03		93.98 47	94.22 37	93.26 81	96.13 101	83.29 127	96.27 45	96.52 101	89.82 48	95.56 34	95.51 126	84.50 72	98.79 98	94.83 34	98.86 19	97.72 98

PGM-MVS		93.96 48	93.72 57	94.68 38	98.43 20	86.22 47	95.30 111	97.78 1	87.45 132	93.26 70	97.33 42	84.62 71	99.51 24	90.75 113	98.57 49	98.32 49

PHI-MVS		93.89 49	93.65 61	94.62 41	96.84 78	86.43 39	96.69 32	97.49 6	85.15 188	93.56 67	96.28 91	85.60 53	99.31 42	92.45 68	98.79 24	98.12 72

SR-MVS-dyc-post		93.82 50	93.82 51	93.82 66	97.92 43	84.57 87	96.28 43	96.76 80	87.46 130	93.75 61	97.43 37	84.24 75	99.01 66	92.73 62	97.80 79	97.88 87

APD-MVS_3200maxsize		93.78 51	93.77 55	93.80 68	97.92 43	84.19 102	96.30 41	96.87 67	86.96 142	93.92 59	97.47 35	83.88 79	98.96 80	92.71 65	97.87 76	98.26 61

fmvsm_s_conf0.5_n		93.76 52	94.06 46	92.86 104	95.62 132	83.17 131	96.14 56	96.12 135	88.13 111	95.82 30	98.04 21	83.43 82	98.48 125	96.97 13	96.23 116	96.92 141

patch_mono-2		93.74 53	94.32 30	92.01 144	97.54 60	78.37 266	93.40 231	97.19 36	88.02 113	94.99 43	97.21 48	88.35 21	98.44 135	94.07 42	98.09 68	99.23 1

MSLP-MVS++		93.72 54	94.08 43	92.65 116	97.31 68	83.43 122	95.79 87	97.33 26	90.03 41	93.58 65	96.96 62	84.87 68	97.76 188	92.19 80	98.66 41	96.76 148

TSAR-MVS + GP.		93.66 55	93.41 65	94.41 49	96.59 85	86.78 26	94.40 173	93.93 265	89.77 55	94.21 50	95.59 124	87.35 34	98.61 117	92.72 64	96.15 119	97.83 92

fmvsm_s_conf0.5_n_a		93.57 56	93.76 56	93.00 96	95.02 157	83.67 114	96.19 49	96.10 137	87.27 135	95.98 28	98.05 18	83.07 89	98.45 133	96.68 15	95.51 128	96.88 144

CANet		93.54 57	93.20 70	94.55 43	95.65 129	85.73 65	94.94 137	96.69 89	91.89 9	90.69 131	95.88 111	81.99 111	99.54 20	93.14 56	97.95 74	98.39 40

dcpmvs_2		93.49 58	94.19 41	91.38 178	97.69 57	76.78 299	94.25 184	96.29 116	88.33 101	94.46 46	96.88 65	88.07 25	98.64 112	93.62 48	98.09 68	98.73 18

fmvsm_s_conf0.5_n_2		93.47 59	93.83 50	92.39 131	95.36 141	81.19 191	95.20 123	96.56 98	90.37 31	97.13 11	98.03 22	77.47 158	98.96 80	97.79 3	96.58 109	97.03 132

fmvsm_s_conf0.1_n		93.46 60	93.66 60	92.85 105	93.75 233	83.13 133	96.02 69	95.74 168	87.68 127	95.89 29	98.17 2	82.78 93	98.46 129	96.71 14	96.17 118	96.98 137

MVS_111021_HR		93.45 61	93.31 66	93.84 65	96.99 75	84.84 79	93.24 244	97.24 33	88.76 87	91.60 120	95.85 112	86.07 49	98.66 108	91.91 92	98.16 63	98.03 78

MVSMamba_PlusPlus		93.44 62	93.54 63	93.14 87	96.58 87	83.05 139	96.06 65	96.50 103	84.42 208	94.09 53	95.56 125	85.01 66	98.69 107	94.96 33	98.66 41	97.67 101

test_fmvsmvis_n_1920		93.44 62	93.55 62	93.10 89	93.67 237	84.26 101	95.83 85	96.14 131	89.00 82	92.43 98	97.50 34	83.37 85	98.72 104	96.61 16	97.44 88	96.32 165

train_agg		93.44 62	93.08 71	94.52 44	97.53 61	86.49 37	94.07 198	96.78 77	81.86 270	92.77 86	96.20 94	87.63 29	99.12 54	92.14 82	98.69 35	97.94 82

EC-MVSNet		93.44 62	93.71 58	92.63 117	95.21 150	82.43 159	97.27 9	96.71 87	90.57 28	92.88 80	95.80 115	83.16 86	98.16 156	93.68 46	98.14 65	97.31 114

DELS-MVS		93.43 66	93.25 68	93.97 60	95.42 140	85.04 76	93.06 251	97.13 45	90.74 23	91.84 113	95.09 145	86.32 45	99.21 48	91.22 103	98.45 52	97.65 102
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 67	93.22 69	93.94 62	98.36 25	84.83 80	97.15 13	96.80 76	85.77 172	92.47 97	97.13 55	82.38 97	99.07 56	90.51 116	98.40 54	97.92 85

DeepC-MVS	88.79 3	93.31 68	92.99 74	94.26 55	96.07 109	85.83 61	94.89 140	96.99 53	89.02 81	89.56 146	97.37 41	82.51 96	99.38 31	92.20 79	98.30 57	97.57 107
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 69	92.75 78	94.85 25	95.70 126	87.66 12	96.33 39	96.41 108	90.00 42	94.09 53	94.60 166	82.33 99	98.62 115	92.40 71	92.86 188	98.27 57

canonicalmvs		93.27 69	92.75 78	94.85 25	95.70 126	87.66 12	96.33 39	96.41 108	90.00 42	94.09 53	94.60 166	82.33 99	98.62 115	92.40 71	92.86 188	98.27 57

ACMMP		93.24 71	92.88 76	94.30 53	98.09 38	85.33 72	96.86 27	97.45 14	88.33 101	90.15 141	97.03 60	81.44 114	99.51 24	90.85 112	95.74 124	98.04 77
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 72	93.05 72	93.76 70	98.04 40	84.07 104	96.22 48	97.37 21	84.15 211	90.05 142	95.66 121	87.77 26	99.15 53	89.91 121	98.27 58	98.07 74

fmvsm_s_conf0.1_n_a		93.19 73	93.26 67	92.97 98	92.49 271	83.62 117	96.02 69	95.72 171	86.78 148	96.04 26	98.19 1	82.30 101	98.43 137	96.38 17	95.42 134	96.86 145

test_fmvsmconf0.01_n		93.19 73	93.02 73	93.71 73	89.25 367	84.42 98	96.06 65	96.29 116	89.06 76	94.68 44	98.13 4	79.22 138	98.98 77	97.22 7	97.24 92	97.74 97

fmvsm_s_conf0.1_n_2		93.16 75	93.42 64	92.37 132	94.62 183	81.13 193	95.23 118	95.89 157	90.30 34	96.74 20	98.02 23	76.14 170	98.95 82	97.64 4	96.21 117	97.03 132

alignmvs		93.08 76	92.50 84	94.81 32	95.62 132	87.61 15	95.99 71	96.07 140	89.77 55	94.12 52	94.87 152	80.56 120	98.66 108	92.42 70	93.10 184	98.15 68

MGCFI-Net		93.03 77	92.63 81	94.23 56	95.62 132	85.92 57	96.08 61	96.33 114	89.86 46	93.89 60	94.66 163	82.11 106	98.50 123	92.33 76	92.82 191	98.27 57

EI-MVSNet-Vis-set		93.01 78	92.92 75	93.29 79	95.01 158	83.51 121	94.48 165	95.77 165	90.87 17	92.52 95	96.67 75	84.50 72	99.00 71	91.99 88	94.44 158	97.36 113

casdiffmvs_mvg		92.96 79	92.83 77	93.35 78	94.59 185	83.40 124	95.00 134	96.34 113	90.30 34	92.05 104	96.05 102	83.43 82	98.15 157	92.07 84	95.67 125	98.49 29
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 80	92.54 83	93.68 74	96.10 106	84.71 83	95.66 95	96.39 110	91.92 8	93.22 72	96.49 86	83.16 86	98.87 88	84.47 188	95.47 131	97.45 112

CDPH-MVS		92.83 80	92.30 86	94.44 45	97.79 52	86.11 49	94.06 200	96.66 90	80.09 301	92.77 86	96.63 80	86.62 40	99.04 60	87.40 149	98.66 41	98.17 67

ETV-MVS		92.74 82	92.66 80	92.97 98	95.20 151	84.04 106	95.07 130	96.51 102	90.73 24	92.96 78	91.19 287	84.06 76	98.34 143	91.72 97	96.54 110	96.54 160

EI-MVSNet-UG-set		92.74 82	92.62 82	93.12 88	94.86 170	83.20 130	94.40 173	95.74 168	90.71 25	92.05 104	96.60 82	84.00 77	98.99 73	91.55 99	93.63 168	97.17 122

DPM-MVS		92.58 84	91.74 94	95.08 15	96.19 99	89.31 5	92.66 263	96.56 98	83.44 229	91.68 119	95.04 146	86.60 42	98.99 73	85.60 174	97.92 75	96.93 140

casdiffmvs		92.51 85	92.43 85	92.74 111	94.41 200	81.98 169	94.54 163	96.23 125	89.57 60	91.96 108	96.17 98	82.58 95	98.01 175	90.95 109	95.45 133	98.23 63
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 86	92.07 89	93.72 72	94.50 193	84.39 99	95.90 79	94.30 251	90.39 30	92.67 91	95.94 107	74.46 196	98.65 110	93.14 56	97.35 91	98.13 69

MVS_111021_LR		92.47 87	92.29 87	92.98 97	95.99 115	84.43 96	93.08 249	96.09 138	88.20 108	91.12 127	95.72 120	81.33 116	97.76 188	91.74 96	97.37 90	96.75 149

3Dnovator+	87.14 4	92.42 88	91.37 98	95.55 7	95.63 131	88.73 6	97.07 18	96.77 79	90.84 18	84.02 282	96.62 81	75.95 175	99.34 37	87.77 144	97.68 84	98.59 24

baseline		92.39 89	92.29 87	92.69 115	94.46 196	81.77 173	94.14 190	96.27 120	89.22 71	91.88 111	96.00 103	82.35 98	97.99 177	91.05 105	95.27 139	98.30 50

VNet		92.24 90	91.91 91	93.24 82	96.59 85	83.43 122	94.84 145	96.44 105	89.19 73	94.08 56	95.90 109	77.85 157	98.17 155	88.90 131	93.38 177	98.13 69

GDP-MVS		92.04 91	91.46 97	93.75 71	94.55 190	84.69 84	95.60 102	96.56 98	87.83 122	93.07 77	95.89 110	73.44 216	98.65 110	90.22 119	96.03 121	97.91 86

CPTT-MVS		91.99 92	91.80 92	92.55 122	98.24 31	81.98 169	96.76 30	96.49 104	81.89 269	90.24 136	96.44 88	78.59 146	98.61 117	89.68 122	97.85 77	97.06 129

EIA-MVS		91.95 93	91.94 90	91.98 148	95.16 153	80.01 228	95.36 106	96.73 84	88.44 98	89.34 151	92.16 251	83.82 80	98.45 133	89.35 125	97.06 95	97.48 110

DP-MVS Recon		91.95 93	91.28 100	93.96 61	98.33 27	85.92 57	94.66 157	96.66 90	82.69 249	90.03 143	95.82 114	82.30 101	99.03 61	84.57 186	96.48 113	96.91 142

EPNet		91.79 95	91.02 106	94.10 58	90.10 354	85.25 73	96.03 68	92.05 315	92.83 3	87.39 189	95.78 116	79.39 136	99.01 66	88.13 140	97.48 87	98.05 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 96	91.70 95	92.00 147	97.08 74	80.03 227	93.60 224	95.18 208	87.85 121	90.89 129	96.47 87	82.06 109	98.36 140	85.07 178	97.04 96	97.62 103

Vis-MVSNet		91.75 97	91.23 101	93.29 79	95.32 143	83.78 111	96.14 56	95.98 147	89.89 44	90.45 133	96.58 83	75.09 187	98.31 148	84.75 184	96.90 100	97.78 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 98	90.82 110	94.44 45	94.59 185	86.37 41	97.18 12	97.02 52	89.20 72	84.31 277	96.66 76	73.74 212	99.17 50	86.74 159	97.96 73	97.79 94

EPP-MVSNet		91.70 99	91.56 96	92.13 143	95.88 118	80.50 212	97.33 7	95.25 204	86.15 164	89.76 145	95.60 123	83.42 84	98.32 147	87.37 151	93.25 181	97.56 108

MVSFormer		91.68 100	91.30 99	92.80 107	93.86 227	83.88 109	95.96 74	95.90 155	84.66 204	91.76 116	94.91 149	77.92 154	97.30 233	89.64 123	97.11 93	97.24 118

Effi-MVS+		91.59 101	91.11 103	93.01 95	94.35 205	83.39 125	94.60 159	95.10 212	87.10 139	90.57 132	93.10 222	81.43 115	98.07 171	89.29 127	94.48 156	97.59 106

IS-MVSNet		91.43 102	91.09 105	92.46 126	95.87 120	81.38 185	96.95 19	93.69 276	89.72 57	89.50 149	95.98 105	78.57 147	97.77 187	83.02 206	96.50 112	98.22 64

PVSNet_Blended_VisFu		91.38 103	90.91 108	92.80 107	96.39 94	83.17 131	94.87 142	96.66 90	83.29 234	89.27 153	94.46 171	80.29 123	99.17 50	87.57 147	95.37 135	96.05 183

diffmvs		91.37 104	91.23 101	91.77 164	93.09 254	80.27 216	92.36 272	95.52 187	87.03 141	91.40 124	94.93 148	80.08 125	97.44 217	92.13 83	94.56 153	97.61 104
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		91.31 105	91.11 103	91.93 152	94.37 201	80.14 220	93.46 229	95.80 163	86.46 156	91.35 125	93.77 201	82.21 104	98.09 168	87.57 147	94.95 143	97.55 109

OMC-MVS		91.23 106	90.62 113	93.08 91	96.27 97	84.07 104	93.52 226	95.93 151	86.95 143	89.51 147	96.13 100	78.50 148	98.35 142	85.84 172	92.90 187	96.83 147

PAPM_NR		91.22 107	90.78 111	92.52 124	97.60 59	81.46 182	94.37 179	96.24 124	86.39 158	87.41 186	94.80 157	82.06 109	98.48 125	82.80 212	95.37 135	97.61 104

PS-MVSNAJ		91.18 108	90.92 107	91.96 150	95.26 148	82.60 158	92.09 284	95.70 172	86.27 160	91.84 113	92.46 241	79.70 131	98.99 73	89.08 129	95.86 123	94.29 255

xiu_mvs_v2_base		91.13 109	90.89 109	91.86 158	94.97 161	82.42 160	92.24 278	95.64 179	86.11 168	91.74 118	93.14 220	79.67 134	98.89 87	89.06 130	95.46 132	94.28 256

nrg030		91.08 110	90.39 114	93.17 85	93.07 255	86.91 22	96.41 37	96.26 121	88.30 103	88.37 167	94.85 155	82.19 105	97.64 198	91.09 104	82.95 313	94.96 223

mamv4		90.92 111	91.78 93	88.33 292	95.67 128	70.75 375	92.92 256	96.02 146	81.90 267	88.11 168	95.34 132	85.88 51	96.97 261	95.22 31	95.01 142	97.26 117

lupinMVS		90.92 111	90.21 117	93.03 94	93.86 227	83.88 109	92.81 260	93.86 269	79.84 304	91.76 116	94.29 176	77.92 154	98.04 173	90.48 117	97.11 93	97.17 122

RRT-MVS		90.85 113	90.70 112	91.30 181	94.25 207	76.83 298	94.85 144	96.13 134	89.04 78	90.23 137	94.88 151	70.15 257	98.72 104	91.86 95	94.88 144	98.34 43

h-mvs33		90.80 114	90.15 120	92.75 110	96.01 111	82.66 155	95.43 105	95.53 186	89.80 51	93.08 75	95.64 122	75.77 176	99.00 71	92.07 84	78.05 370	96.60 155

jason		90.80 114	90.10 121	92.90 102	93.04 258	83.53 120	93.08 249	94.15 258	80.22 298	91.41 123	94.91 149	76.87 162	97.93 182	90.28 118	96.90 100	97.24 118
jason: jason.
VDD-MVS		90.74 116	89.92 128	93.20 84	96.27 97	83.02 141	95.73 89	93.86 269	88.42 100	92.53 94	96.84 67	62.09 331	98.64 112	90.95 109	92.62 193	97.93 84

PVSNet_Blended		90.73 117	90.32 116	91.98 148	96.12 102	81.25 187	92.55 267	96.83 71	82.04 262	89.10 155	92.56 239	81.04 118	98.85 92	86.72 161	95.91 122	95.84 190

test_yl		90.69 118	90.02 126	92.71 112	95.72 124	82.41 162	94.11 193	95.12 210	85.63 176	91.49 121	94.70 159	74.75 191	98.42 138	86.13 167	92.53 195	97.31 114

DCV-MVSNet		90.69 118	90.02 126	92.71 112	95.72 124	82.41 162	94.11 193	95.12 210	85.63 176	91.49 121	94.70 159	74.75 191	98.42 138	86.13 167	92.53 195	97.31 114

API-MVS		90.66 120	90.07 122	92.45 127	96.36 95	84.57 87	96.06 65	95.22 207	82.39 252	89.13 154	94.27 179	80.32 122	98.46 129	80.16 263	96.71 106	94.33 254

xiu_mvs_v1_base_debu		90.64 121	90.05 123	92.40 128	93.97 224	84.46 93	93.32 235	95.46 189	85.17 185	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 257

xiu_mvs_v1_base		90.64 121	90.05 123	92.40 128	93.97 224	84.46 93	93.32 235	95.46 189	85.17 185	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 257

xiu_mvs_v1_base_debi		90.64 121	90.05 123	92.40 128	93.97 224	84.46 93	93.32 235	95.46 189	85.17 185	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 257

HQP_MVS		90.60 124	90.19 118	91.82 161	94.70 179	82.73 151	95.85 83	96.22 126	90.81 19	86.91 195	94.86 153	74.23 200	98.12 158	88.15 138	89.99 227	94.63 235

FIs		90.51 125	90.35 115	90.99 198	93.99 223	80.98 198	95.73 89	97.54 4	89.15 74	86.72 202	94.68 161	81.83 113	97.24 241	85.18 177	88.31 260	94.76 233

mvsmamba		90.33 126	89.69 131	92.25 141	95.17 152	81.64 175	95.27 116	93.36 281	84.88 195	89.51 147	94.27 179	69.29 272	97.42 219	89.34 126	96.12 120	97.68 100

MAR-MVS		90.30 127	89.37 139	93.07 93	96.61 84	84.48 92	95.68 92	95.67 174	82.36 254	87.85 176	92.85 227	76.63 168	98.80 96	80.01 264	96.68 107	95.91 186
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		90.27 128	90.18 119	90.53 210	93.71 234	79.85 234	95.77 88	97.59 3	89.31 68	86.27 213	94.67 162	81.93 112	97.01 259	84.26 190	88.09 263	94.71 234

CANet_DTU		90.26 129	89.41 138	92.81 106	93.46 244	83.01 142	93.48 227	94.47 244	89.43 64	87.76 181	94.23 181	70.54 252	99.03 61	84.97 179	96.39 114	96.38 163

SDMVSNet		90.19 130	89.61 133	91.93 152	96.00 112	83.09 137	92.89 257	95.98 147	88.73 88	86.85 199	95.20 140	72.09 232	97.08 252	88.90 131	89.85 233	95.63 200

OPM-MVS		90.12 131	89.56 134	91.82 161	93.14 251	83.90 108	94.16 189	95.74 168	88.96 83	87.86 175	95.43 130	72.48 228	97.91 183	88.10 142	90.18 226	93.65 292
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 132	89.13 145	92.95 100	96.71 80	82.32 164	96.08 61	89.91 369	86.79 147	92.15 103	96.81 70	62.60 329	98.34 143	87.18 153	93.90 164	98.19 65

GeoE		90.05 133	89.43 137	91.90 157	95.16 153	80.37 215	95.80 86	94.65 241	83.90 216	87.55 185	94.75 158	78.18 152	97.62 200	81.28 243	93.63 168	97.71 99

PAPR		90.02 134	89.27 144	92.29 138	95.78 122	80.95 200	92.68 262	96.22 126	81.91 266	86.66 203	93.75 203	82.23 103	98.44 135	79.40 275	94.79 146	97.48 110

PVSNet_BlendedMVS		89.98 135	89.70 130	90.82 203	96.12 102	81.25 187	93.92 211	96.83 71	83.49 228	89.10 155	92.26 249	81.04 118	98.85 92	86.72 161	87.86 267	92.35 340

PS-MVSNAJss		89.97 136	89.62 132	91.02 195	91.90 291	80.85 203	95.26 117	95.98 147	86.26 161	86.21 215	94.29 176	79.70 131	97.65 196	88.87 133	88.10 261	94.57 240

XVG-OURS-SEG-HR		89.95 137	89.45 135	91.47 175	94.00 222	81.21 190	91.87 288	96.06 142	85.78 171	88.55 163	95.73 119	74.67 195	97.27 237	88.71 134	89.64 238	95.91 186

UGNet		89.95 137	88.95 149	92.95 100	94.51 192	83.31 126	95.70 91	95.23 205	89.37 66	87.58 183	93.94 191	64.00 319	98.78 99	83.92 195	96.31 115	96.74 150
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		89.92 139	89.29 142	91.81 163	93.39 246	83.72 112	94.43 171	97.12 46	89.80 51	86.46 206	93.32 211	83.16 86	97.23 242	84.92 180	81.02 343	94.49 248

AdaColmap		89.89 140	89.07 146	92.37 132	97.41 65	83.03 140	94.42 172	95.92 152	82.81 246	86.34 212	94.65 164	73.89 208	99.02 64	80.69 254	95.51 128	95.05 218

hse-mvs2		89.88 141	89.34 140	91.51 172	94.83 172	81.12 194	93.94 209	93.91 268	89.80 51	93.08 75	93.60 205	75.77 176	97.66 195	92.07 84	77.07 377	95.74 195

UniMVSNet (Re)		89.80 142	89.07 146	92.01 144	93.60 240	84.52 90	94.78 149	97.47 11	89.26 70	86.44 209	92.32 246	82.10 107	97.39 230	84.81 183	80.84 347	94.12 261

HQP-MVS		89.80 142	89.28 143	91.34 180	94.17 211	81.56 176	94.39 175	96.04 143	88.81 84	85.43 241	93.97 190	73.83 210	97.96 179	87.11 156	89.77 236	94.50 246

FA-MVS(test-final)		89.66 144	88.91 151	91.93 152	94.57 188	80.27 216	91.36 300	94.74 237	84.87 196	89.82 144	92.61 238	74.72 194	98.47 128	83.97 194	93.53 171	97.04 131

VPA-MVSNet		89.62 145	88.96 148	91.60 169	93.86 227	82.89 146	95.46 104	97.33 26	87.91 116	88.43 166	93.31 212	74.17 203	97.40 227	87.32 152	82.86 318	94.52 243

WTY-MVS		89.60 146	88.92 150	91.67 167	95.47 139	81.15 192	92.38 271	94.78 235	83.11 238	89.06 157	94.32 174	78.67 145	96.61 280	81.57 239	90.89 215	97.24 118

Vis-MVSNet (Re-imp)		89.59 147	89.44 136	90.03 235	95.74 123	75.85 313	95.61 99	90.80 353	87.66 129	87.83 178	95.40 131	76.79 164	96.46 294	78.37 281	96.73 105	97.80 93

VDDNet		89.56 148	88.49 164	92.76 109	95.07 156	82.09 166	96.30 41	93.19 284	81.05 292	91.88 111	96.86 66	61.16 347	98.33 145	88.43 137	92.49 197	97.84 91

114514_t		89.51 149	88.50 162	92.54 123	98.11 36	81.99 168	95.16 126	96.36 112	70.19 398	85.81 223	95.25 136	76.70 166	98.63 114	82.07 227	96.86 103	97.00 136

QAPM		89.51 149	88.15 173	93.59 76	94.92 165	84.58 86	96.82 29	96.70 88	78.43 328	83.41 298	96.19 97	73.18 220	99.30 43	77.11 297	96.54 110	96.89 143

CLD-MVS		89.47 151	88.90 152	91.18 186	94.22 209	82.07 167	92.13 282	96.09 138	87.90 117	85.37 247	92.45 242	74.38 198	97.56 204	87.15 154	90.43 221	93.93 270
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		89.45 152	88.90 152	91.12 187	94.47 194	81.49 180	95.30 111	96.14 131	86.73 150	85.45 238	95.16 142	69.89 259	98.10 160	87.70 145	89.23 245	93.77 285

CDS-MVSNet		89.45 152	88.51 161	92.29 138	93.62 239	83.61 119	93.01 252	94.68 240	81.95 264	87.82 179	93.24 216	78.69 144	96.99 260	80.34 260	93.23 182	96.28 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 154	88.64 157	91.71 166	94.74 174	80.81 204	93.54 225	95.10 212	83.11 238	86.82 201	90.67 310	79.74 130	97.75 191	80.51 258	93.55 170	96.57 158

ab-mvs		89.41 154	88.35 166	92.60 118	95.15 155	82.65 156	92.20 280	95.60 181	83.97 215	88.55 163	93.70 204	74.16 204	98.21 154	82.46 217	89.37 241	96.94 139

XVG-OURS		89.40 156	88.70 156	91.52 171	94.06 216	81.46 182	91.27 304	96.07 140	86.14 165	88.89 159	95.77 117	68.73 281	97.26 239	87.39 150	89.96 229	95.83 191

test_vis1_n_1920		89.39 157	89.84 129	88.04 300	92.97 262	72.64 352	94.71 154	96.03 145	86.18 163	91.94 110	96.56 85	61.63 335	95.74 331	93.42 51	95.11 141	95.74 195

mvs_anonymous		89.37 158	89.32 141	89.51 261	93.47 243	74.22 331	91.65 295	94.83 231	82.91 244	85.45 238	93.79 199	81.23 117	96.36 301	86.47 163	94.09 161	97.94 82

DU-MVS		89.34 159	88.50 162	91.85 160	93.04 258	83.72 112	94.47 168	96.59 95	89.50 61	86.46 206	93.29 214	77.25 160	97.23 242	84.92 180	81.02 343	94.59 238

TAMVS		89.21 160	88.29 170	91.96 150	93.71 234	82.62 157	93.30 239	94.19 256	82.22 257	87.78 180	93.94 191	78.83 141	96.95 263	77.70 290	92.98 186	96.32 165

ACMM	84.12 9	89.14 161	88.48 165	91.12 187	94.65 182	81.22 189	95.31 109	96.12 135	85.31 184	85.92 221	94.34 172	70.19 256	98.06 172	85.65 173	88.86 250	94.08 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 162	88.64 157	90.48 215	95.53 137	74.97 322	96.08 61	84.89 401	88.13 111	90.16 140	96.65 77	63.29 324	98.10 160	86.14 165	96.90 100	98.39 40

EI-MVSNet		89.10 162	88.86 154	89.80 248	91.84 293	78.30 268	93.70 221	95.01 216	85.73 173	87.15 190	95.28 134	79.87 128	97.21 244	83.81 197	87.36 275	93.88 274

ECVR-MVS		89.09 164	88.53 160	90.77 205	95.62 132	75.89 312	96.16 52	84.22 403	87.89 119	90.20 138	96.65 77	63.19 326	98.10 160	85.90 170	96.94 98	98.33 45

CNLPA		89.07 165	87.98 176	92.34 134	96.87 77	84.78 82	94.08 197	93.24 282	81.41 283	84.46 267	95.13 144	75.57 183	96.62 277	77.21 295	93.84 166	95.61 202

PLC	84.53 7	89.06 166	88.03 175	92.15 142	97.27 71	82.69 154	94.29 182	95.44 194	79.71 306	84.01 283	94.18 182	76.68 167	98.75 101	77.28 294	93.41 176	95.02 219
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 167	88.64 157	90.21 226	90.74 340	79.28 249	95.96 74	95.90 155	84.66 204	85.33 249	92.94 226	74.02 206	97.30 233	89.64 123	88.53 253	94.05 267

HY-MVS	83.01 12	89.03 167	87.94 178	92.29 138	94.86 170	82.77 147	92.08 285	94.49 243	81.52 282	86.93 193	92.79 233	78.32 151	98.23 151	79.93 265	90.55 219	95.88 188

ACMP	84.23 8	89.01 169	88.35 166	90.99 198	94.73 175	81.27 186	95.07 130	95.89 157	86.48 154	83.67 291	94.30 175	69.33 268	97.99 177	87.10 158	88.55 252	93.72 290
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 170	88.26 172	90.94 201	94.05 217	80.78 205	91.71 292	95.38 198	81.55 281	88.63 162	93.91 195	75.04 188	95.47 343	82.47 216	91.61 203	96.57 158

TranMVSNet+NR-MVSNet		88.84 171	87.95 177	91.49 173	92.68 269	83.01 142	94.92 139	96.31 115	89.88 45	85.53 232	93.85 198	76.63 168	96.96 262	81.91 231	79.87 360	94.50 246

CHOSEN 1792x2688		88.84 171	87.69 182	92.30 137	96.14 100	81.42 184	90.01 336	95.86 160	74.52 367	87.41 186	93.94 191	75.46 184	98.36 140	80.36 259	95.53 127	97.12 127

MVSTER		88.84 171	88.29 170	90.51 213	92.95 263	80.44 213	93.73 218	95.01 216	84.66 204	87.15 190	93.12 221	72.79 224	97.21 244	87.86 143	87.36 275	93.87 275

test_cas_vis1_n_1920		88.83 174	88.85 155	88.78 277	91.15 321	76.72 300	93.85 214	94.93 223	83.23 237	92.81 84	96.00 103	61.17 346	94.45 354	91.67 98	94.84 145	95.17 214

OpenMVS	83.78 11	88.74 175	87.29 192	93.08 91	92.70 268	85.39 71	96.57 35	96.43 106	78.74 323	80.85 330	96.07 101	69.64 263	99.01 66	78.01 288	96.65 108	94.83 230

thisisatest0530		88.67 176	87.61 184	91.86 158	94.87 169	80.07 223	94.63 158	89.90 370	84.00 214	88.46 165	93.78 200	66.88 296	98.46 129	83.30 202	92.65 192	97.06 129

Effi-MVS+-dtu		88.65 177	88.35 166	89.54 258	93.33 247	76.39 306	94.47 168	94.36 249	87.70 126	85.43 241	89.56 339	73.45 215	97.26 239	85.57 175	91.28 207	94.97 220

tttt0517		88.61 178	87.78 181	91.11 190	94.96 162	77.81 281	95.35 107	89.69 373	85.09 190	88.05 173	94.59 168	66.93 294	98.48 125	83.27 203	92.13 200	97.03 132

BH-untuned		88.60 179	88.13 174	90.01 238	95.24 149	78.50 262	93.29 240	94.15 258	84.75 201	84.46 267	93.40 208	75.76 178	97.40 227	77.59 291	94.52 155	94.12 261

sd_testset		88.59 180	87.85 180	90.83 202	96.00 112	80.42 214	92.35 273	94.71 238	88.73 88	86.85 199	95.20 140	67.31 288	96.43 296	79.64 269	89.85 233	95.63 200

NR-MVSNet		88.58 181	87.47 188	91.93 152	93.04 258	84.16 103	94.77 150	96.25 123	89.05 77	80.04 343	93.29 214	79.02 140	97.05 257	81.71 238	80.05 357	94.59 238

1112_ss		88.42 182	87.33 191	91.72 165	94.92 165	80.98 198	92.97 254	94.54 242	78.16 334	83.82 286	93.88 196	78.78 143	97.91 183	79.45 271	89.41 240	96.26 169

WR-MVS		88.38 183	87.67 183	90.52 212	93.30 248	80.18 218	93.26 242	95.96 150	88.57 96	85.47 237	92.81 231	76.12 171	96.91 266	81.24 244	82.29 323	94.47 251

BH-RMVSNet		88.37 184	87.48 187	91.02 195	95.28 145	79.45 241	92.89 257	93.07 287	85.45 181	86.91 195	94.84 156	70.35 253	97.76 188	73.97 327	94.59 152	95.85 189

IterMVS-LS		88.36 185	87.91 179	89.70 252	93.80 230	78.29 269	93.73 218	95.08 214	85.73 173	84.75 259	91.90 266	79.88 127	96.92 265	83.83 196	82.51 319	93.89 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 186	86.13 234	94.85 25	98.54 13	86.60 34	96.93 22	97.19 36	90.66 26	92.85 81	23.41 428	85.02 63	99.49 26	91.99 88	98.56 50	98.47 33

LCM-MVSNet-Re		88.30 187	88.32 169	88.27 293	94.71 178	72.41 357	93.15 245	90.98 346	87.77 124	79.25 352	91.96 263	78.35 150	95.75 330	83.04 205	95.62 126	96.65 154

jajsoiax		88.24 188	87.50 186	90.48 215	90.89 334	80.14 220	95.31 109	95.65 178	84.97 193	84.24 278	94.02 186	65.31 312	97.42 219	88.56 135	88.52 254	93.89 271

VPNet		88.20 189	87.47 188	90.39 220	93.56 241	79.46 240	94.04 201	95.54 185	88.67 91	86.96 192	94.58 169	69.33 268	97.15 246	84.05 193	80.53 352	94.56 241

TAPA-MVS	84.62 6	88.16 190	87.01 200	91.62 168	96.64 83	80.65 207	94.39 175	96.21 129	76.38 347	86.19 216	95.44 128	79.75 129	98.08 170	62.75 391	95.29 137	96.13 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 191	87.28 193	90.57 208	94.96 162	80.07 223	94.27 183	91.29 339	86.74 149	87.41 186	94.00 188	76.77 165	96.20 307	80.77 252	79.31 366	95.44 204

Anonymous20240529		88.09 192	86.59 216	92.58 120	96.53 90	81.92 171	95.99 71	95.84 161	74.11 371	89.06 157	95.21 139	61.44 339	98.81 95	83.67 200	87.47 272	97.01 135

HyFIR lowres test		88.09 192	86.81 204	91.93 152	96.00 112	80.63 208	90.01 336	95.79 164	73.42 378	87.68 182	92.10 257	73.86 209	97.96 179	80.75 253	91.70 202	97.19 121

mvs_tets		88.06 194	87.28 193	90.38 222	90.94 330	79.88 232	95.22 120	95.66 176	85.10 189	84.21 279	93.94 191	63.53 322	97.40 227	88.50 136	88.40 258	93.87 275

F-COLMAP		87.95 195	86.80 205	91.40 177	96.35 96	80.88 202	94.73 152	95.45 192	79.65 307	82.04 317	94.61 165	71.13 239	98.50 123	76.24 307	91.05 213	94.80 232

LS3D		87.89 196	86.32 227	92.59 119	96.07 109	82.92 145	95.23 118	94.92 224	75.66 354	82.89 305	95.98 105	72.48 228	99.21 48	68.43 362	95.23 140	95.64 199

anonymousdsp		87.84 197	87.09 196	90.12 231	89.13 368	80.54 211	94.67 156	95.55 183	82.05 260	83.82 286	92.12 254	71.47 237	97.15 246	87.15 154	87.80 270	92.67 328

v2v482		87.84 197	87.06 197	90.17 227	90.99 326	79.23 252	94.00 206	95.13 209	84.87 196	85.53 232	92.07 260	74.45 197	97.45 214	84.71 185	81.75 331	93.85 278

WR-MVS_H		87.80 199	87.37 190	89.10 270	93.23 249	78.12 272	95.61 99	97.30 30	87.90 117	83.72 289	92.01 262	79.65 135	96.01 316	76.36 304	80.54 351	93.16 312

AUN-MVS		87.78 200	86.54 219	91.48 174	94.82 173	81.05 196	93.91 213	93.93 265	83.00 241	86.93 193	93.53 206	69.50 266	97.67 193	86.14 165	77.12 376	95.73 197

PCF-MVS	84.11 10	87.74 201	86.08 238	92.70 114	94.02 218	84.43 96	89.27 349	95.87 159	73.62 376	84.43 269	94.33 173	78.48 149	98.86 90	70.27 348	94.45 157	94.81 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 202	86.13 234	92.31 136	96.66 82	80.74 206	94.87 142	91.49 334	80.47 297	89.46 150	95.44 128	54.72 383	98.23 151	82.19 223	89.89 231	97.97 80

V42		87.68 202	86.86 202	90.15 229	90.58 345	80.14 220	94.24 186	95.28 203	83.66 222	85.67 227	91.33 282	74.73 193	97.41 225	84.43 189	81.83 329	92.89 322

thres600view7		87.65 204	86.67 211	90.59 207	96.08 108	78.72 255	94.88 141	91.58 330	87.06 140	88.08 171	92.30 247	68.91 278	98.10 160	70.05 355	91.10 208	94.96 223

XXY-MVS		87.65 204	86.85 203	90.03 235	92.14 281	80.60 210	93.76 217	95.23 205	82.94 243	84.60 262	94.02 186	74.27 199	95.49 342	81.04 246	83.68 305	94.01 269

Test_1112_low_res		87.65 204	86.51 220	91.08 191	94.94 164	79.28 249	91.77 290	94.30 251	76.04 352	83.51 296	92.37 244	77.86 156	97.73 192	78.69 280	89.13 247	96.22 170

thres100view900		87.63 207	86.71 208	90.38 222	96.12 102	78.55 259	95.03 133	91.58 330	87.15 137	88.06 172	92.29 248	68.91 278	98.10 160	70.13 352	91.10 208	94.48 249

CP-MVSNet		87.63 207	87.26 195	88.74 281	93.12 252	76.59 303	95.29 113	96.58 96	88.43 99	83.49 297	92.98 225	75.28 185	95.83 325	78.97 277	81.15 339	93.79 280

thres400		87.62 209	86.64 212	90.57 208	95.99 115	78.64 257	94.58 160	91.98 319	86.94 144	88.09 169	91.77 268	69.18 274	98.10 160	70.13 352	91.10 208	94.96 223

v1144		87.61 210	86.79 206	90.06 234	91.01 325	79.34 245	93.95 208	95.42 197	83.36 233	85.66 228	91.31 285	74.98 189	97.42 219	83.37 201	82.06 325	93.42 301

tfpn200view9		87.58 211	86.64 212	90.41 219	95.99 115	78.64 257	94.58 160	91.98 319	86.94 144	88.09 169	91.77 268	69.18 274	98.10 160	70.13 352	91.10 208	94.48 249

BH-w/o		87.57 212	87.05 198	89.12 269	94.90 168	77.90 277	92.41 269	93.51 278	82.89 245	83.70 290	91.34 281	75.75 179	97.07 254	75.49 311	93.49 173	92.39 338

UniMVSNet_ETH3D		87.53 213	86.37 224	91.00 197	92.44 274	78.96 254	94.74 151	95.61 180	84.07 213	85.36 248	94.52 170	59.78 355	97.34 232	82.93 207	87.88 266	96.71 151

ET-MVSNet_ETH3D		87.51 214	85.91 246	92.32 135	93.70 236	83.93 107	92.33 275	90.94 349	84.16 210	72.09 394	92.52 240	69.90 258	95.85 324	89.20 128	88.36 259	97.17 122

1314		87.51 214	86.57 217	90.34 224	92.42 275	79.74 236	92.63 264	95.35 202	78.35 329	80.14 340	91.62 276	74.05 205	97.15 246	81.05 245	93.53 171	94.12 261

v8		87.50 216	86.71 208	89.89 242	91.37 311	79.40 242	94.50 164	95.38 198	84.81 199	83.60 294	91.33 282	76.05 172	97.42 219	82.84 210	80.51 354	92.84 324

Fast-Effi-MVS+-dtu		87.44 217	86.72 207	89.63 256	92.04 285	77.68 287	94.03 202	93.94 264	85.81 170	82.42 310	91.32 284	70.33 254	97.06 255	80.33 261	90.23 225	94.14 260

MVS		87.44 217	86.10 237	91.44 176	92.61 270	83.62 117	92.63 264	95.66 176	67.26 403	81.47 322	92.15 252	77.95 153	98.22 153	79.71 267	95.48 130	92.47 334

FE-MVS		87.40 219	86.02 240	91.57 170	94.56 189	79.69 237	90.27 323	93.72 275	80.57 295	88.80 160	91.62 276	65.32 311	98.59 119	74.97 319	94.33 160	96.44 161

FMVSNet3		87.40 219	86.11 236	91.30 181	93.79 232	83.64 116	94.20 188	94.81 233	83.89 217	84.37 270	91.87 267	68.45 284	96.56 285	78.23 285	85.36 289	93.70 291

test_fmvs1		87.34 221	87.56 185	86.68 339	90.59 344	71.80 361	94.01 204	94.04 263	78.30 330	91.97 107	95.22 137	56.28 374	93.71 369	92.89 60	94.71 147	94.52 243

thisisatest0515		87.33 222	85.99 241	91.37 179	93.49 242	79.55 238	90.63 318	89.56 378	80.17 299	87.56 184	90.86 300	67.07 293	98.28 149	81.50 240	93.02 185	96.29 167

PS-CasMVS		87.32 223	86.88 201	88.63 284	92.99 261	76.33 308	95.33 108	96.61 94	88.22 107	83.30 302	93.07 223	73.03 222	95.79 329	78.36 282	81.00 345	93.75 287

GBi-Net		87.26 224	85.98 242	91.08 191	94.01 219	83.10 134	95.14 127	94.94 219	83.57 224	84.37 270	91.64 272	66.59 301	96.34 302	78.23 285	85.36 289	93.79 280

test1		87.26 224	85.98 242	91.08 191	94.01 219	83.10 134	95.14 127	94.94 219	83.57 224	84.37 270	91.64 272	66.59 301	96.34 302	78.23 285	85.36 289	93.79 280

v1192		87.25 226	86.33 226	90.00 239	90.76 339	79.04 253	93.80 215	95.48 188	82.57 250	85.48 236	91.18 289	73.38 219	97.42 219	82.30 220	82.06 325	93.53 295

v10		87.25 226	86.38 223	89.85 243	91.19 317	79.50 239	94.48 165	95.45 192	83.79 220	83.62 293	91.19 287	75.13 186	97.42 219	81.94 230	80.60 349	92.63 330

DP-MVS		87.25 226	85.36 263	92.90 102	97.65 58	83.24 128	94.81 147	92.00 317	74.99 362	81.92 319	95.00 147	72.66 225	99.05 58	66.92 374	92.33 198	96.40 162

miper_ehance_all_eth		87.22 229	86.62 215	89.02 273	92.13 282	77.40 291	90.91 313	94.81 233	81.28 286	84.32 275	90.08 326	79.26 137	96.62 277	83.81 197	82.94 314	93.04 317

test2506		87.21 230	86.28 229	90.02 237	95.62 132	73.64 338	96.25 47	71.38 426	87.89 119	90.45 133	96.65 77	55.29 380	98.09 168	86.03 169	96.94 98	98.33 45

thres200		87.21 230	86.24 231	90.12 231	95.36 141	78.53 260	93.26 242	92.10 313	86.42 157	88.00 174	91.11 293	69.24 273	98.00 176	69.58 356	91.04 214	93.83 279

v144192		87.19 232	86.35 225	89.74 249	90.64 343	78.24 270	93.92 211	95.43 195	81.93 265	85.51 234	91.05 296	74.21 202	97.45 214	82.86 209	81.56 333	93.53 295

FMVSNet2		87.19 232	85.82 249	91.30 181	94.01 219	83.67 114	94.79 148	94.94 219	83.57 224	83.88 285	92.05 261	66.59 301	96.51 289	77.56 292	85.01 292	93.73 289

c3_l		87.14 234	86.50 221	89.04 272	92.20 279	77.26 292	91.22 307	94.70 239	82.01 263	84.34 274	90.43 315	78.81 142	96.61 280	83.70 199	81.09 340	93.25 306

testing91		87.11 235	86.18 232	89.92 241	94.43 199	75.38 321	91.53 297	92.27 309	86.48 154	86.50 204	90.24 318	61.19 345	97.53 206	82.10 225	90.88 216	96.84 146

Baseline_NR-MVSNet		87.07 236	86.63 214	88.40 287	91.44 306	77.87 279	94.23 187	92.57 301	84.12 212	85.74 226	92.08 258	77.25 160	96.04 312	82.29 221	79.94 358	91.30 361

v148		87.04 237	86.32 227	89.21 266	90.94 330	77.26 292	93.71 220	94.43 245	84.84 198	84.36 273	90.80 304	76.04 173	97.05 257	82.12 224	79.60 363	93.31 303

test_fmvs1_n		87.03 238	87.04 199	86.97 330	89.74 362	71.86 359	94.55 162	94.43 245	78.47 326	91.95 109	95.50 127	51.16 394	93.81 367	93.02 59	94.56 153	95.26 211

v1921920		86.97 239	86.06 239	89.69 253	90.53 348	78.11 273	93.80 215	95.43 195	81.90 267	85.33 249	91.05 296	72.66 225	97.41 225	82.05 228	81.80 330	93.53 295

tt0805		86.92 240	85.74 255	90.48 215	92.22 278	79.98 230	95.63 98	94.88 227	83.83 219	84.74 260	92.80 232	57.61 369	97.67 193	85.48 176	84.42 296	93.79 280

miper_enhance_ethall		86.90 241	86.18 232	89.06 271	91.66 302	77.58 289	90.22 329	94.82 232	79.16 313	84.48 266	89.10 344	79.19 139	96.66 275	84.06 192	82.94 314	92.94 320

MonoMVSNet		86.89 242	86.55 218	87.92 304	89.46 366	73.75 335	94.12 191	93.10 285	87.82 123	85.10 252	90.76 306	69.59 264	94.94 352	86.47 163	82.50 320	95.07 217

v7n		86.81 243	85.76 253	89.95 240	90.72 341	79.25 251	95.07 130	95.92 152	84.45 207	82.29 311	90.86 300	72.60 227	97.53 206	79.42 274	80.52 353	93.08 316

PEN-MVS		86.80 244	86.27 230	88.40 287	92.32 277	75.71 316	95.18 124	96.38 111	87.97 114	82.82 306	93.15 219	73.39 218	95.92 320	76.15 308	79.03 368	93.59 293

cl22		86.78 245	85.98 242	89.18 268	92.34 276	77.62 288	90.84 314	94.13 260	81.33 285	83.97 284	90.15 323	73.96 207	96.60 282	84.19 191	82.94 314	93.33 302

v1240		86.78 245	85.85 248	89.56 257	90.45 349	77.79 283	93.61 223	95.37 200	81.65 276	85.43 241	91.15 291	71.50 236	97.43 218	81.47 241	82.05 327	93.47 299

TR-MVS		86.78 245	85.76 253	89.82 245	94.37 201	78.41 264	92.47 268	92.83 293	81.11 291	86.36 210	92.40 243	68.73 281	97.48 210	73.75 331	89.85 233	93.57 294

PatchMatch-RL		86.77 248	85.54 257	90.47 218	95.88 118	82.71 153	90.54 320	92.31 307	79.82 305	84.32 275	91.57 280	68.77 280	96.39 298	73.16 333	93.48 175	92.32 341

testing3-2		86.72 249	86.71 208	86.74 338	96.11 105	65.92 396	93.39 232	89.65 376	89.46 62	87.84 177	92.79 233	59.17 361	97.60 201	81.31 242	90.72 217	96.70 152

testing99		86.72 249	85.73 256	89.69 253	94.23 208	74.91 324	91.35 301	90.97 347	86.14 165	86.36 210	90.22 319	59.41 358	97.48 210	82.24 222	90.66 218	96.69 153

PAPM		86.68 251	85.39 261	90.53 210	93.05 257	79.33 248	89.79 339	94.77 236	78.82 320	81.95 318	93.24 216	76.81 163	97.30 233	66.94 372	93.16 183	94.95 226

pm-mvs1		86.61 252	85.54 257	89.82 245	91.44 306	80.18 218	95.28 115	94.85 229	83.84 218	81.66 320	92.62 237	72.45 230	96.48 291	79.67 268	78.06 369	92.82 325

GA-MVS		86.61 252	85.27 266	90.66 206	91.33 314	78.71 256	90.40 322	93.81 272	85.34 183	85.12 251	89.57 338	61.25 342	97.11 251	80.99 249	89.59 239	96.15 173

Anonymous20231211		86.59 254	85.13 269	90.98 200	96.52 91	81.50 178	96.14 56	96.16 130	73.78 374	83.65 292	92.15 252	63.26 325	97.37 231	82.82 211	81.74 332	94.06 266

test_vis1_n		86.56 255	86.49 222	86.78 337	88.51 373	72.69 349	94.68 155	93.78 274	79.55 308	90.70 130	95.31 133	48.75 399	93.28 375	93.15 55	93.99 162	94.38 253

DIV-MVS_self_test		86.53 256	85.78 250	88.75 279	92.02 287	76.45 305	90.74 315	94.30 251	81.83 272	83.34 300	90.82 303	75.75 179	96.57 283	81.73 237	81.52 335	93.24 307

cl____		86.52 257	85.78 250	88.75 279	92.03 286	76.46 304	90.74 315	94.30 251	81.83 272	83.34 300	90.78 305	75.74 181	96.57 283	81.74 236	81.54 334	93.22 308

eth_miper_zixun_eth		86.50 258	85.77 252	88.68 282	91.94 288	75.81 314	90.47 321	94.89 225	82.05 260	84.05 281	90.46 314	75.96 174	96.77 270	82.76 213	79.36 365	93.46 300

baseline2		86.50 258	85.39 261	89.84 244	91.12 322	76.70 301	91.88 287	88.58 381	82.35 255	79.95 344	90.95 298	73.42 217	97.63 199	80.27 262	89.95 230	95.19 213

EPNet_dtu		86.49 260	85.94 245	88.14 298	90.24 352	72.82 347	94.11 193	92.20 311	86.66 152	79.42 351	92.36 245	73.52 213	95.81 327	71.26 340	93.66 167	95.80 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 261	85.35 264	89.69 253	94.29 206	75.40 320	91.30 302	90.53 356	84.76 200	85.06 253	90.13 324	58.95 364	97.45 214	82.08 226	91.09 212	96.21 172

cascas		86.43 262	84.98 272	90.80 204	92.10 284	80.92 201	90.24 327	95.91 154	73.10 381	83.57 295	88.39 357	65.15 313	97.46 213	84.90 182	91.43 205	94.03 268

reproduce_monomvs		86.37 263	85.87 247	87.87 305	93.66 238	73.71 336	93.44 230	95.02 215	88.61 94	82.64 309	91.94 264	57.88 368	96.68 274	89.96 120	79.71 362	93.22 308

SCA		86.32 264	85.18 268	89.73 251	92.15 280	76.60 302	91.12 308	91.69 326	83.53 227	85.50 235	88.81 350	66.79 297	96.48 291	76.65 300	90.35 223	96.12 176

LTVRE_ROB	82.13 13	86.26 265	84.90 275	90.34 224	94.44 198	81.50 178	92.31 277	94.89 225	83.03 240	79.63 349	92.67 235	69.69 262	97.79 186	71.20 341	86.26 284	91.72 351
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		86.11 266	85.48 259	87.98 301	91.65 303	74.92 323	94.93 138	95.75 167	87.36 134	82.26 312	93.04 224	72.85 223	95.82 326	74.04 326	77.46 374	93.20 310

XVG-ACMP-BASELINE		86.00 267	84.84 277	89.45 262	91.20 316	78.00 274	91.70 293	95.55 183	85.05 191	82.97 304	92.25 250	54.49 384	97.48 210	82.93 207	87.45 274	92.89 322

MVP-Stereo		85.97 268	84.86 276	89.32 264	90.92 332	82.19 165	92.11 283	94.19 256	78.76 322	78.77 357	91.63 275	68.38 285	96.56 285	75.01 318	93.95 163	89.20 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 269	85.09 270	88.35 289	90.79 337	77.42 290	91.83 289	95.70 172	80.77 294	80.08 342	90.02 328	66.74 299	96.37 299	81.88 232	87.97 265	91.26 362

test-LLR		85.87 270	85.41 260	87.25 322	90.95 328	71.67 364	89.55 343	89.88 371	83.41 230	84.54 264	87.95 364	67.25 290	95.11 348	81.82 233	93.37 178	94.97 220

FMVSNet1		85.85 271	84.11 290	91.08 191	92.81 265	83.10 134	95.14 127	94.94 219	81.64 277	82.68 307	91.64 272	59.01 363	96.34 302	75.37 313	83.78 302	93.79 280

PatchmatchNet		85.85 271	84.70 279	89.29 265	91.76 297	75.54 317	88.49 361	91.30 338	81.63 278	85.05 254	88.70 354	71.71 233	96.24 306	74.61 323	89.05 248	96.08 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
myMVS_eth3d28		85.80 273	85.26 267	87.42 317	94.73 175	69.92 382	90.60 319	90.95 348	87.21 136	86.06 219	90.04 327	59.47 356	96.02 314	74.89 320	93.35 180	96.33 164

CostFormer		85.77 274	84.94 274	88.26 294	91.16 320	72.58 355	89.47 347	91.04 345	76.26 350	86.45 208	89.97 330	70.74 246	96.86 269	82.35 219	87.07 280	95.34 210

PMMVS		85.71 275	84.96 273	87.95 302	88.90 371	77.09 294	88.68 359	90.06 365	72.32 388	86.47 205	90.76 306	72.15 231	94.40 356	81.78 235	93.49 173	92.36 339

PVSNet	78.82 18	85.55 276	84.65 280	88.23 296	94.72 177	71.93 358	87.12 381	92.75 297	78.80 321	84.95 256	90.53 312	64.43 317	96.71 273	74.74 321	93.86 165	96.06 182

UBG		85.51 277	84.57 283	88.35 289	94.21 210	71.78 362	90.07 334	89.66 375	82.28 256	85.91 222	89.01 346	61.30 340	97.06 255	76.58 303	92.06 201	96.22 170

IterMVS-SCA-FT		85.45 278	84.53 284	88.18 297	91.71 299	76.87 297	90.19 331	92.65 300	85.40 182	81.44 323	90.54 311	66.79 297	95.00 351	81.04 246	81.05 341	92.66 329

pmmvs4		85.43 279	83.86 295	90.16 228	90.02 357	82.97 144	90.27 323	92.67 299	75.93 353	80.73 331	91.74 270	71.05 240	95.73 332	78.85 279	83.46 309	91.78 350

mvsany_test1		85.42 280	85.30 265	85.77 351	87.95 385	75.41 319	87.61 378	80.97 411	76.82 344	88.68 161	95.83 113	77.44 159	90.82 397	85.90 170	86.51 282	91.08 369

ACMH	80.38 17	85.36 281	83.68 297	90.39 220	94.45 197	80.63 208	94.73 152	94.85 229	82.09 259	77.24 366	92.65 236	60.01 353	97.58 202	72.25 337	84.87 293	92.96 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 282	84.64 281	87.49 314	90.77 338	72.59 354	94.01 204	94.40 247	84.72 202	79.62 350	93.17 218	61.91 333	96.72 271	81.99 229	81.16 337	93.16 312

CR-MVSNet		85.35 282	83.76 296	90.12 231	90.58 345	79.34 245	85.24 394	91.96 321	78.27 331	85.55 230	87.87 367	71.03 241	95.61 335	73.96 328	89.36 242	95.40 206

tpmrst		85.35 282	84.99 271	86.43 342	90.88 335	67.88 390	88.71 358	91.43 336	80.13 300	86.08 218	88.80 352	73.05 221	96.02 314	82.48 215	83.40 311	95.40 206

miper_lstm_enhance		85.27 285	84.59 282	87.31 319	91.28 315	74.63 326	87.69 375	94.09 262	81.20 290	81.36 325	89.85 333	74.97 190	94.30 359	81.03 248	79.84 361	93.01 318

IB-MVS	80.51 15	85.24 286	83.26 303	91.19 185	92.13 282	79.86 233	91.75 291	91.29 339	83.28 235	80.66 333	88.49 356	61.28 341	98.46 129	80.99 249	79.46 364	95.25 212
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		85.15 287	83.99 293	88.65 283	92.47 272	78.40 265	79.68 416	92.76 296	74.90 364	81.41 324	89.59 337	69.85 261	95.51 339	79.92 266	95.29 137	92.03 346

RPSCF		85.07 288	84.27 285	87.48 315	92.91 264	70.62 377	91.69 294	92.46 302	76.20 351	82.67 308	95.22 137	63.94 320	97.29 236	77.51 293	85.80 286	94.53 242

MS-PatchMatch		85.05 289	84.16 288	87.73 307	91.42 309	78.51 261	91.25 305	93.53 277	77.50 337	80.15 339	91.58 278	61.99 332	95.51 339	75.69 310	94.35 159	89.16 390

ACMH+	81.04 14	85.05 289	83.46 300	89.82 245	94.66 181	79.37 243	94.44 170	94.12 261	82.19 258	78.04 360	92.82 230	58.23 366	97.54 205	73.77 330	82.90 317	92.54 331

mmtdpeth		85.04 291	84.15 289	87.72 308	93.11 253	75.74 315	94.37 179	92.83 293	84.98 192	89.31 152	86.41 381	61.61 337	97.14 249	92.63 67	62.11 409	90.29 377

WBMVS		84.97 292	84.18 287	87.34 318	94.14 215	71.62 366	90.20 330	92.35 304	81.61 279	84.06 280	90.76 306	61.82 334	96.52 288	78.93 278	83.81 301	93.89 271

IterMVS		84.88 293	83.98 294	87.60 310	91.44 306	76.03 310	90.18 332	92.41 303	83.24 236	81.06 329	90.42 316	66.60 300	94.28 360	79.46 270	80.98 346	92.48 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 294	83.09 306	90.14 230	93.80 230	80.05 225	89.18 352	93.09 286	78.89 317	78.19 358	91.91 265	65.86 310	97.27 237	68.47 361	88.45 256	93.11 314

testing222		84.84 295	83.32 301	89.43 263	94.15 214	75.94 311	91.09 309	89.41 379	84.90 194	85.78 224	89.44 340	52.70 391	96.28 305	70.80 347	91.57 204	96.07 180

tpm		84.73 296	84.02 292	86.87 335	90.33 350	68.90 385	89.06 354	89.94 368	80.85 293	85.75 225	89.86 332	68.54 283	95.97 317	77.76 289	84.05 300	95.75 194

tfpnnormal		84.72 297	83.23 304	89.20 267	92.79 266	80.05 225	94.48 165	95.81 162	82.38 253	81.08 328	91.21 286	69.01 277	96.95 263	61.69 393	80.59 350	90.58 376

CVMVSNet		84.69 298	84.79 278	84.37 364	91.84 293	64.92 402	93.70 221	91.47 335	66.19 405	86.16 217	95.28 134	67.18 292	93.33 374	80.89 251	90.42 222	94.88 228

SSC-MVS3.2		84.60 299	84.19 286	85.85 350	92.74 267	68.07 387	88.15 366	93.81 272	87.42 133	83.76 288	91.07 295	62.91 327	95.73 332	74.56 324	83.24 312	93.75 287

test-mter		84.54 300	83.64 298	87.25 322	90.95 328	71.67 364	89.55 343	89.88 371	79.17 312	84.54 264	87.95 364	55.56 376	95.11 348	81.82 233	93.37 178	94.97 220

ETVMVS		84.43 301	82.92 310	88.97 275	94.37 201	74.67 325	91.23 306	88.35 383	83.37 232	86.06 219	89.04 345	55.38 378	95.67 334	67.12 370	91.34 206	96.58 157

TransMVSNet (Re)		84.43 301	83.06 308	88.54 285	91.72 298	78.44 263	95.18 124	92.82 295	82.73 248	79.67 348	92.12 254	73.49 214	95.96 318	71.10 345	68.73 399	91.21 363

pmmvs5		84.21 303	82.84 313	88.34 291	88.95 370	76.94 296	92.41 269	91.91 323	75.63 355	80.28 337	91.18 289	64.59 316	95.57 336	77.09 298	83.47 308	92.53 332

dmvs_re		84.20 304	83.22 305	87.14 328	91.83 295	77.81 281	90.04 335	90.19 361	84.70 203	81.49 321	89.17 343	64.37 318	91.13 395	71.58 339	85.65 288	92.46 335

tpm2		84.08 305	82.94 309	87.48 315	91.39 310	71.27 367	89.23 351	90.37 358	71.95 390	84.64 261	89.33 341	67.30 289	96.55 287	75.17 315	87.09 279	94.63 235

test_fmvs2		83.98 306	84.03 291	83.83 369	87.16 388	67.53 394	93.93 210	92.89 291	77.62 336	86.89 198	93.53 206	47.18 403	92.02 387	90.54 114	86.51 282	91.93 348

COLMAP_ROB	80.39 16	83.96 307	82.04 316	89.74 249	95.28 145	79.75 235	94.25 184	92.28 308	75.17 360	78.02 361	93.77 201	58.60 365	97.84 185	65.06 383	85.92 285	91.63 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 308	81.53 319	91.21 184	90.58 345	79.34 245	85.24 394	96.76 80	71.44 392	85.55 230	82.97 401	70.87 244	98.91 86	61.01 395	89.36 242	95.40 206

SixPastTwentyTwo		83.91 309	82.90 311	86.92 332	90.99 326	70.67 376	93.48 227	91.99 318	85.54 179	77.62 365	92.11 256	60.59 349	96.87 268	76.05 309	77.75 371	93.20 310

EPMVS		83.90 310	82.70 314	87.51 312	90.23 353	72.67 350	88.62 360	81.96 409	81.37 284	85.01 255	88.34 358	66.31 304	94.45 354	75.30 314	87.12 278	95.43 205

WB-MVSnew		83.77 311	83.28 302	85.26 358	91.48 305	71.03 371	91.89 286	87.98 384	78.91 315	84.78 258	90.22 319	69.11 276	94.02 363	64.70 384	90.44 220	90.71 371

TESTMET0.1,1		83.74 312	82.85 312	86.42 343	89.96 358	71.21 369	89.55 343	87.88 385	77.41 338	83.37 299	87.31 372	56.71 372	93.65 371	80.62 256	92.85 190	94.40 252

UWE-MVS		83.69 313	83.09 306	85.48 353	93.06 256	65.27 401	90.92 312	86.14 393	79.90 303	86.26 214	90.72 309	57.17 371	95.81 327	71.03 346	92.62 193	95.35 209

pmmvs6		83.42 314	81.60 318	88.87 276	88.01 383	77.87 279	94.96 136	94.24 255	74.67 366	78.80 356	91.09 294	60.17 352	96.49 290	77.06 299	75.40 383	92.23 343

AllTest		83.42 314	81.39 320	89.52 259	95.01 158	77.79 283	93.12 246	90.89 351	77.41 338	76.12 374	93.34 209	54.08 386	97.51 208	68.31 363	84.27 298	93.26 304

tpmvs		83.35 316	82.07 315	87.20 326	91.07 324	71.00 373	88.31 364	91.70 325	78.91 315	80.49 336	87.18 376	69.30 271	97.08 252	68.12 366	83.56 307	93.51 298

USDC		82.76 317	81.26 322	87.26 321	91.17 318	74.55 327	89.27 349	93.39 280	78.26 332	75.30 380	92.08 258	54.43 385	96.63 276	71.64 338	85.79 287	90.61 373

Patchmtry		82.71 318	80.93 324	88.06 299	90.05 356	76.37 307	84.74 399	91.96 321	72.28 389	81.32 326	87.87 367	71.03 241	95.50 341	68.97 358	80.15 356	92.32 341

PatchT		82.68 319	81.27 321	86.89 334	90.09 355	70.94 374	84.06 401	90.15 362	74.91 363	85.63 229	83.57 396	69.37 267	94.87 353	65.19 380	88.50 255	94.84 229

MIMVSNet		82.59 320	80.53 325	88.76 278	91.51 304	78.32 267	86.57 385	90.13 363	79.32 309	80.70 332	88.69 355	52.98 390	93.07 379	66.03 378	88.86 250	94.90 227

test0.0.03 1		82.41 321	81.69 317	84.59 362	88.23 379	72.89 346	90.24 327	87.83 386	83.41 230	79.86 346	89.78 334	67.25 290	88.99 407	65.18 381	83.42 310	91.90 349

EG-PatchMatch MVS		82.37 322	80.34 328	88.46 286	90.27 351	79.35 244	92.80 261	94.33 250	77.14 342	73.26 391	90.18 322	47.47 402	96.72 271	70.25 349	87.32 277	89.30 386

tpm cat1		81.96 323	80.27 329	87.01 329	91.09 323	71.02 372	87.38 379	91.53 333	66.25 404	80.17 338	86.35 383	68.22 286	96.15 310	69.16 357	82.29 323	93.86 277

our_test_3		81.93 324	80.46 327	86.33 344	88.46 376	73.48 340	88.46 362	91.11 341	76.46 345	76.69 370	88.25 360	66.89 295	94.36 357	68.75 359	79.08 367	91.14 365

ppachtmachnet_test		81.84 325	80.07 333	87.15 327	88.46 376	74.43 330	89.04 355	92.16 312	75.33 358	77.75 363	88.99 347	66.20 306	95.37 344	65.12 382	77.60 372	91.65 352

gg-mvs-nofinetune		81.77 326	79.37 341	88.99 274	90.85 336	77.73 286	86.29 386	79.63 414	74.88 365	83.19 303	69.05 417	60.34 350	96.11 311	75.46 312	94.64 151	93.11 314

CL-MVSNet_self_test		81.74 327	80.53 325	85.36 355	85.96 394	72.45 356	90.25 325	93.07 287	81.24 288	79.85 347	87.29 373	70.93 243	92.52 382	66.95 371	69.23 395	91.11 367

Patchmatch-RL test		81.67 328	79.96 334	86.81 336	85.42 399	71.23 368	82.17 409	87.50 389	78.47 326	77.19 367	82.50 403	70.81 245	93.48 372	82.66 214	72.89 387	95.71 198

ADS-MVSNet2		81.66 329	79.71 338	87.50 313	91.35 312	74.19 332	83.33 404	88.48 382	72.90 383	82.24 313	85.77 387	64.98 314	93.20 377	64.57 385	83.74 303	95.12 215

K. test v3		81.59 330	80.15 332	85.91 349	89.89 360	69.42 384	92.57 266	87.71 387	85.56 178	73.44 390	89.71 336	55.58 375	95.52 338	77.17 296	69.76 393	92.78 326

ADS-MVSNet		81.56 331	79.78 335	86.90 333	91.35 312	71.82 360	83.33 404	89.16 380	72.90 383	82.24 313	85.77 387	64.98 314	93.76 368	64.57 385	83.74 303	95.12 215

FMVSNet5		81.52 332	79.60 339	87.27 320	91.17 318	77.95 275	91.49 298	92.26 310	76.87 343	76.16 373	87.91 366	51.67 392	92.34 384	67.74 367	81.16 337	91.52 356

dp		81.47 333	80.23 330	85.17 359	89.92 359	65.49 399	86.74 383	90.10 364	76.30 349	81.10 327	87.12 377	62.81 328	95.92 320	68.13 365	79.88 359	94.09 264

Patchmatch-test		81.37 334	79.30 342	87.58 311	90.92 332	74.16 333	80.99 411	87.68 388	70.52 396	76.63 371	88.81 350	71.21 238	92.76 381	60.01 399	86.93 281	95.83 191

EU-MVSNet		81.32 335	80.95 323	82.42 377	88.50 375	63.67 406	93.32 235	91.33 337	64.02 408	80.57 335	92.83 229	61.21 344	92.27 385	76.34 305	80.38 355	91.32 360

test_0402		81.30 336	79.17 346	87.67 309	93.19 250	78.17 271	92.98 253	91.71 324	75.25 359	76.02 376	90.31 317	59.23 359	96.37 299	50.22 412	83.63 306	88.47 397

JIA-IIPM		81.04 337	78.98 350	87.25 322	88.64 372	73.48 340	81.75 410	89.61 377	73.19 380	82.05 316	73.71 413	66.07 309	95.87 323	71.18 343	84.60 295	92.41 337

Anonymous20231206		81.03 338	79.77 337	84.82 361	87.85 386	70.26 379	91.42 299	92.08 314	73.67 375	77.75 363	89.25 342	62.43 330	93.08 378	61.50 394	82.00 328	91.12 366

mvs5depth		80.98 339	79.15 347	86.45 341	84.57 402	73.29 342	87.79 371	91.67 327	80.52 296	82.20 315	89.72 335	55.14 381	95.93 319	73.93 329	66.83 401	90.12 379

pmmvs-eth3d		80.97 340	78.72 352	87.74 306	84.99 401	79.97 231	90.11 333	91.65 328	75.36 357	73.51 389	86.03 384	59.45 357	93.96 366	75.17 315	72.21 388	89.29 388

testgi		80.94 341	80.20 331	83.18 370	87.96 384	66.29 395	91.28 303	90.70 355	83.70 221	78.12 359	92.84 228	51.37 393	90.82 397	63.34 388	82.46 321	92.43 336

CMPMVS	59.16 21	80.52 342	79.20 345	84.48 363	83.98 403	67.63 393	89.95 338	93.84 271	64.79 407	66.81 405	91.14 292	57.93 367	95.17 346	76.25 306	88.10 261	90.65 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 343	79.59 340	83.06 372	93.44 245	64.64 403	93.33 234	85.47 398	84.34 209	79.93 345	90.84 302	44.35 409	92.39 383	57.06 406	87.56 271	92.16 345

Anonymous20240521		80.44 344	79.21 344	84.11 367	85.75 397	67.89 389	92.86 259	93.23 283	75.61 356	75.59 379	87.47 371	50.03 395	94.33 358	71.14 344	81.21 336	90.12 379

LF4IMVS		80.37 345	79.07 349	84.27 366	86.64 390	69.87 383	89.39 348	91.05 344	76.38 347	74.97 382	90.00 329	47.85 401	94.25 361	74.55 325	80.82 348	88.69 395

KD-MVS_self_test		80.20 346	79.24 343	83.07 371	85.64 398	65.29 400	91.01 311	93.93 265	78.71 324	76.32 372	86.40 382	59.20 360	92.93 380	72.59 335	69.35 394	91.00 370

Syy-MVS		80.07 347	79.78 335	80.94 381	91.92 289	59.93 413	89.75 341	87.40 390	81.72 274	78.82 354	87.20 374	66.29 305	91.29 393	47.06 414	87.84 268	91.60 354

UnsupCasMVSNet_eth		80.07 347	78.27 354	85.46 354	85.24 400	72.63 353	88.45 363	94.87 228	82.99 242	71.64 397	88.07 363	56.34 373	91.75 390	73.48 332	63.36 407	92.01 347

test20.03		79.95 349	79.08 348	82.55 374	85.79 396	67.74 392	91.09 309	91.08 342	81.23 289	74.48 386	89.96 331	61.63 335	90.15 399	60.08 397	76.38 379	89.76 381

TDRefinement		79.81 350	77.34 356	87.22 325	79.24 416	75.48 318	93.12 246	92.03 316	76.45 346	75.01 381	91.58 278	49.19 398	96.44 295	70.22 351	69.18 396	89.75 382

TinyColmap		79.76 351	77.69 355	85.97 346	91.71 299	73.12 343	89.55 343	90.36 359	75.03 361	72.03 395	90.19 321	46.22 406	96.19 309	63.11 389	81.03 342	88.59 396

myMVS_eth3d		79.67 352	78.79 351	82.32 378	91.92 289	64.08 404	89.75 341	87.40 390	81.72 274	78.82 354	87.20 374	45.33 407	91.29 393	59.09 401	87.84 268	91.60 354

OpenMVS_ROB	74.94 19	79.51 353	77.03 361	86.93 331	87.00 389	76.23 309	92.33 275	90.74 354	68.93 400	74.52 385	88.23 361	49.58 397	96.62 277	57.64 404	84.29 297	87.94 400

MIMVSNet1		79.38 354	77.28 357	85.69 352	86.35 391	73.67 337	91.61 296	92.75 297	78.11 335	72.64 393	88.12 362	48.16 400	91.97 389	60.32 396	77.49 373	91.43 359

YYNet1		79.22 355	77.20 358	85.28 357	88.20 381	72.66 351	85.87 388	90.05 367	74.33 369	62.70 408	87.61 369	66.09 308	92.03 386	66.94 372	72.97 386	91.15 364

MDA-MVSNet_test_wron		79.21 356	77.19 359	85.29 356	88.22 380	72.77 348	85.87 388	90.06 365	74.34 368	62.62 410	87.56 370	66.14 307	91.99 388	66.90 375	73.01 385	91.10 368

UWE-MVS-28		78.98 357	78.38 353	80.80 382	88.18 382	60.66 412	90.65 317	78.51 416	78.84 319	77.93 362	90.93 299	59.08 362	89.02 406	50.96 411	90.33 224	92.72 327

MDA-MVSNet-bldmvs		78.85 358	76.31 363	86.46 340	89.76 361	73.88 334	88.79 357	90.42 357	79.16 313	59.18 413	88.33 359	60.20 351	94.04 362	62.00 392	68.96 397	91.48 358

KD-MVS_2432*1600		78.50 359	76.02 366	85.93 347	86.22 392	74.47 328	84.80 397	92.33 305	79.29 310	76.98 368	85.92 385	53.81 388	93.97 364	67.39 368	57.42 414	89.36 384

miper_refine_blended		78.50 359	76.02 366	85.93 347	86.22 392	74.47 328	84.80 397	92.33 305	79.29 310	76.98 368	85.92 385	53.81 388	93.97 364	67.39 368	57.42 414	89.36 384

PM-MVS		78.11 361	76.12 365	84.09 368	83.54 405	70.08 380	88.97 356	85.27 400	79.93 302	74.73 384	86.43 380	34.70 417	93.48 372	79.43 273	72.06 389	88.72 394

test_vis1_rt		77.96 362	76.46 362	82.48 376	85.89 395	71.74 363	90.25 325	78.89 415	71.03 395	71.30 398	81.35 405	42.49 411	91.05 396	84.55 187	82.37 322	84.65 403

test_fmvs3		77.67 363	77.16 360	79.22 385	79.52 415	61.14 410	92.34 274	91.64 329	73.98 372	78.86 353	86.59 378	27.38 421	87.03 409	88.12 141	75.97 381	89.50 383

PVSNet_0	73.20 20	77.22 364	74.83 370	84.37 364	90.70 342	71.10 370	83.09 406	89.67 374	72.81 385	73.93 388	83.13 398	60.79 348	93.70 370	68.54 360	50.84 419	88.30 398

DSMNet-mixed		76.94 365	76.29 364	78.89 386	83.10 407	56.11 422	87.78 372	79.77 413	60.65 412	75.64 378	88.71 353	61.56 338	88.34 408	60.07 398	89.29 244	92.21 344

ttmdpeth		76.55 366	74.64 371	82.29 379	82.25 410	67.81 391	89.76 340	85.69 396	70.35 397	75.76 377	91.69 271	46.88 404	89.77 401	66.16 377	63.23 408	89.30 386

new-patchmatchnet		76.41 367	75.17 369	80.13 383	82.65 409	59.61 414	87.66 376	91.08 342	78.23 333	69.85 401	83.22 397	54.76 382	91.63 392	64.14 387	64.89 405	89.16 390

UnsupCasMVSNet_bld		76.23 368	73.27 372	85.09 360	83.79 404	72.92 345	85.65 391	93.47 279	71.52 391	68.84 403	79.08 408	49.77 396	93.21 376	66.81 376	60.52 411	89.13 392

mvsany_test3		74.95 369	73.26 373	80.02 384	74.61 420	63.16 408	85.53 392	78.42 417	74.16 370	74.89 383	86.46 379	36.02 416	89.09 405	82.39 218	66.91 400	87.82 401

dmvs_testset		74.57 370	75.81 368	70.86 396	87.72 387	40.47 431	87.05 382	77.90 421	82.75 247	71.15 399	85.47 389	67.98 287	84.12 418	45.26 415	76.98 378	88.00 399

MVS-HIRNet		73.70 371	72.20 374	78.18 389	91.81 296	56.42 421	82.94 407	82.58 407	55.24 415	68.88 402	66.48 418	55.32 379	95.13 347	58.12 403	88.42 257	83.01 406

MVStest1		72.91 372	69.70 377	82.54 375	78.14 417	73.05 344	88.21 365	86.21 392	60.69 411	64.70 406	90.53 312	46.44 405	85.70 414	58.78 402	53.62 416	88.87 393

new_pmnet		72.15 373	70.13 376	78.20 388	82.95 408	65.68 397	83.91 402	82.40 408	62.94 410	64.47 407	79.82 407	42.85 410	86.26 413	57.41 405	74.44 384	82.65 408

test_f		71.95 374	70.87 375	75.21 392	74.21 422	59.37 415	85.07 396	85.82 395	65.25 406	70.42 400	83.13 398	23.62 422	82.93 420	78.32 283	71.94 390	83.33 405

pmmvs3		71.81 375	68.71 378	81.11 380	75.86 419	70.42 378	86.74 383	83.66 404	58.95 414	68.64 404	80.89 406	36.93 415	89.52 403	63.10 390	63.59 406	83.39 404

APD_test1		69.04 376	66.26 382	77.36 391	80.51 413	62.79 409	85.46 393	83.51 405	54.11 417	59.14 414	84.79 392	23.40 424	89.61 402	55.22 407	70.24 392	79.68 412

N_pmnet		68.89 377	68.44 379	70.23 397	89.07 369	28.79 436	88.06 367	19.50 436	69.47 399	71.86 396	84.93 390	61.24 343	91.75 390	54.70 408	77.15 375	90.15 378

WB-MVS		67.92 378	67.49 380	69.21 400	81.09 411	41.17 430	88.03 368	78.00 420	73.50 377	62.63 409	83.11 400	63.94 320	86.52 411	25.66 426	51.45 418	79.94 411

SSC-MVS		67.06 379	66.56 381	68.56 402	80.54 412	40.06 432	87.77 373	77.37 423	72.38 387	61.75 411	82.66 402	63.37 323	86.45 412	24.48 427	48.69 421	79.16 413

LCM-MVSNet		66.00 380	62.16 385	77.51 390	64.51 430	58.29 416	83.87 403	90.90 350	48.17 419	54.69 416	73.31 414	16.83 430	86.75 410	65.47 379	61.67 410	87.48 402

test_vis3_rt		65.12 381	62.60 383	72.69 394	71.44 423	60.71 411	87.17 380	65.55 427	63.80 409	53.22 417	65.65 420	14.54 431	89.44 404	76.65 300	65.38 403	67.91 418

FPMVS		64.63 382	62.55 384	70.88 395	70.80 424	56.71 417	84.42 400	84.42 402	51.78 418	49.57 418	81.61 404	23.49 423	81.48 421	40.61 421	76.25 380	74.46 414

EGC-MVSNET		61.97 383	56.37 388	78.77 387	89.63 364	73.50 339	89.12 353	82.79 406	0.21 433	1.24 434	84.80 391	39.48 412	90.04 400	44.13 416	75.94 382	72.79 415

PMMVS2		59.60 384	56.40 387	69.21 400	68.83 427	46.58 426	73.02 421	77.48 422	55.07 416	49.21 419	72.95 415	17.43 429	80.04 422	49.32 413	44.33 422	80.99 410

testf1		59.54 385	56.11 389	69.85 398	69.28 425	56.61 419	80.37 413	76.55 424	42.58 422	45.68 421	75.61 409	11.26 432	84.18 416	43.20 418	60.44 412	68.75 416

APD_test2		59.54 385	56.11 389	69.85 398	69.28 425	56.61 419	80.37 413	76.55 424	42.58 422	45.68 421	75.61 409	11.26 432	84.18 416	43.20 418	60.44 412	68.75 416

ANet_high		58.88 387	54.22 392	72.86 393	56.50 433	56.67 418	80.75 412	86.00 394	73.09 382	37.39 425	64.63 421	22.17 425	79.49 423	43.51 417	23.96 427	82.43 409

dongtai		58.82 388	58.24 386	60.56 405	83.13 406	45.09 429	82.32 408	48.22 435	67.61 402	61.70 412	69.15 416	38.75 413	76.05 424	32.01 423	41.31 423	60.55 420

Gipuma		57.99 389	54.91 391	67.24 403	88.51 373	65.59 398	52.21 424	90.33 360	43.58 421	42.84 424	51.18 425	20.29 427	85.07 415	34.77 422	70.45 391	51.05 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		53.51 390	53.30 393	54.13 409	76.06 418	45.36 428	80.11 415	48.36 434	59.63 413	54.84 415	63.43 422	37.41 414	62.07 429	20.73 429	39.10 424	54.96 423

PMVS	47.18 22	52.22 391	48.46 395	63.48 404	45.72 435	46.20 427	73.41 420	78.31 418	41.03 424	30.06 427	65.68 419	6.05 434	83.43 419	30.04 424	65.86 402	60.80 419
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 392	48.47 394	56.66 407	52.26 434	18.98 438	41.51 426	81.40 410	10.10 428	44.59 423	75.01 412	28.51 419	68.16 425	53.54 409	49.31 420	82.83 407

MVE	39.65 23	43.39 393	38.59 399	57.77 406	56.52 432	48.77 425	55.38 423	58.64 431	29.33 427	28.96 428	52.65 424	4.68 435	64.62 428	28.11 425	33.07 425	59.93 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 394	42.29 396	46.03 410	65.58 429	37.41 433	73.51 419	64.62 428	33.99 425	28.47 429	47.87 426	19.90 428	67.91 426	22.23 428	24.45 426	32.77 425

EMVS		42.07 395	41.12 397	44.92 411	63.45 431	35.56 435	73.65 418	63.48 429	33.05 426	26.88 430	45.45 427	21.27 426	67.14 427	19.80 430	23.02 428	32.06 426

tmp_tt		35.64 396	39.24 398	24.84 412	14.87 436	23.90 437	62.71 422	51.51 433	6.58 430	36.66 426	62.08 423	44.37 408	30.34 432	52.40 410	22.00 429	20.27 427

cdsmvs_eth3d_5k		22.14 397	29.52 400	0.00 416	0.00 439	0.00 441	0.00 427	95.76 166	0.00 434	0.00 435	94.29 176	75.66 182	0.00 435	0.00 434	0.00 433	0.00 431

wuyk23d		21.27 398	20.48 401	23.63 413	68.59 428	36.41 434	49.57 425	6.85 437	9.37 429	7.89 431	4.46 433	4.03 436	31.37 431	17.47 431	16.07 430	3.12 428

testmvs		8.92 399	11.52 402	1.12 415	1.06 437	0.46 440	86.02 387	0.65 438	0.62 431	2.74 432	9.52 431	0.31 438	0.45 434	2.38 432	0.39 431	2.46 430

test123		8.76 400	11.22 403	1.39 414	0.85 438	0.97 439	85.76 390	0.35 439	0.54 432	2.45 433	8.14 432	0.60 437	0.48 433	2.16 433	0.17 432	2.71 429

ab-mvs-re		7.82 401	10.43 404	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	93.88 196	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

pcd_1.5k_mvsjas		6.64 402	8.86 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	79.70 131	0.00 435	0.00 434	0.00 433	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						64.08 404								59.14 400

FOURS1					98.86 1	85.54 67	98.29 1	97.49 6	89.79 54	96.29 22

MSC_two_6792asdad				96.52 1	97.78 54	90.86 1		96.85 68					99.61 4	96.03 18	99.06 9	99.07 5

PC_three_1452									82.47 251	97.09 12	97.07 58	92.72 1	98.04 173	92.70 66	99.02 12	98.86 11

No_MVS				96.52 1	97.78 54	90.86 1		96.85 68					99.61 4	96.03 18	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 61		97.44 15	91.05 15	96.78 18	98.06 16	91.45 11

eth-test2					0.00 439

eth-test					0.00 439

ZD-MVS					98.15 34	86.62 33		97.07 50	83.63 223	94.19 51	96.91 64	87.57 31	99.26 45	91.99 88	98.44 53

RE-MVS-def			93.68 59		97.92 43	84.57 87	96.28 43	96.76 80	87.46 130	93.75 61	97.43 37	82.94 90		92.73 62	97.80 79	97.88 87

IU-MVS					98.77 5	86.00 50		96.84 70	81.26 287	97.26 8				95.50 27	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 56	92.59 2	98.94 83	92.25 77	98.99 14	98.84 14

test_241102_TWO								97.44 15	90.31 32	97.62 5	98.07 14	91.46 10	99.58 10	95.66 21	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 52		97.44 15	90.26 38	97.71 1	97.96 24	92.31 4	99.38 31

9.14			94.47 25		97.79 52		96.08 61	97.44 15	86.13 167	95.10 41	97.40 39	88.34 22	99.22 47	93.25 54	98.70 34

save fliter					97.85 49	85.63 66	95.21 121	96.82 73	89.44 63

test_0728_THIRD									90.75 21	97.04 14	98.05 18	92.09 6	99.55 16	95.64 23	99.13 3	99.13 2

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 6					99.61 4	95.62 25	99.08 7	98.99 9

test0726					98.78 3	85.93 55	97.19 11	97.47 11	90.27 36	97.64 4	98.13 4	91.47 8

GSMVS																96.12 176

test_part2					98.55 12	87.22 19				96.40 21

sam_mvs1												71.70 234				96.12 176

sam_mvs												70.60 247

ambc				83.06 372	79.99 414	63.51 407	77.47 417	92.86 292		74.34 387	84.45 393	28.74 418	95.06 350	73.06 334	68.89 398	90.61 373

MTGPA								96.97 55

test_post1							88.00 369				9.81 430	69.31 270	95.53 337	76.65 300

test_post											10.29 429	70.57 251	95.91 322

patchmatchnet-post											83.76 395	71.53 235	96.48 291

GG-mvs-BLEND				87.94 303	89.73 363	77.91 276	87.80 370	78.23 419		80.58 334	83.86 394	59.88 354	95.33 345	71.20 341	92.22 199	90.60 375

MTMP							96.16 52	60.64 430

gm-plane-assit					89.60 365	68.00 388			77.28 341		88.99 347		97.57 203	79.44 272

test9_res														91.91 92	98.71 32	98.07 74

TEST9					97.53 61	86.49 37	94.07 198	96.78 77	81.61 279	92.77 86	96.20 94	87.71 28	99.12 54

test_8					97.49 63	86.30 45	94.02 203	96.76 80	81.86 270	92.70 90	96.20 94	87.63 29	99.02 64

agg_prior2														90.54 114	98.68 37	98.27 57

agg_prior					97.38 66	85.92 57		96.72 86		92.16 102			98.97 78

TestCases				89.52 259	95.01 158	77.79 283		90.89 351	77.41 338	76.12 374	93.34 209	54.08 386	97.51 208	68.31 363	84.27 298	93.26 304

test_prior4						85.96 54	94.11 193

test_prior2							94.12 191		87.67 128	92.63 92	96.39 89	86.62 40		91.50 100	98.67 40

test_prior				93.82 66	97.29 70	84.49 91		96.88 66					98.87 88			98.11 73

旧先验2							93.36 233		71.25 393	94.37 47			97.13 250	86.74 159

新几何2							93.11 248

新几何1				93.10 89	97.30 69	84.35 100		95.56 182	71.09 394	91.26 126	96.24 92	82.87 92	98.86 90	79.19 276	98.10 67	96.07 180

旧先验1					96.79 79	81.81 172		95.67 174			96.81 70	86.69 39			97.66 85	96.97 138

无先验							93.28 241	96.26 121	73.95 373				99.05 58	80.56 257		96.59 156

原ACMM2							92.94 255

原ACMM1				92.01 144	97.34 67	81.05 196		96.81 75	78.89 317	90.45 133	95.92 108	82.65 94	98.84 94	80.68 255	98.26 59	96.14 174

test222					96.55 88	81.70 174	92.22 279	95.01 216	68.36 401	90.20 138	96.14 99	80.26 124			97.80 79	96.05 183

testdata2													98.75 101	78.30 284

segment_acmp												87.16 36

testdata				90.49 214	96.40 93	77.89 278		95.37 200	72.51 386	93.63 64	96.69 73	82.08 108	97.65 196	83.08 204	97.39 89	95.94 185

testdata1							92.15 281		87.94 115

test12				94.34 52	97.13 73	86.15 48		96.29 116		91.04 128		85.08 61	99.01 66		98.13 66	97.86 89

plane_prior7					94.70 179	82.74 150

plane_prior6					94.52 191	82.75 148						74.23 200

plane_prior5								96.22 126					98.12 158	88.15 138	89.99 227	94.63 235

plane_prior4											94.86 153

plane_prior3						82.75 148			90.26 38	86.91 195

plane_prior2							95.85 83		90.81 19

plane_prior1					94.59 185

plane_prior						82.73 151	95.21 121		89.66 59						89.88 232

n2								0.00 440

nn								0.00 440

door-mid								85.49 397

lessismore_v0				86.04 345	88.46 376	68.78 386		80.59 412		73.01 392	90.11 325	55.39 377	96.43 296	75.06 317	65.06 404	92.90 321

LGP-MVS_train				91.12 187	94.47 194	81.49 180		96.14 131	86.73 150	85.45 238	95.16 142	69.89 259	98.10 160	87.70 145	89.23 245	93.77 285

test11								96.57 97

door								85.33 399

HQP5-MVS						81.56 176

HQP-NCC					94.17 211		94.39 175		88.81 84	85.43 241

ACMP_Plane					94.17 211		94.39 175		88.81 84	85.43 241

BP-MVS														87.11 156

HQP4-MVS										85.43 241			97.96 179			94.51 245

HQP3-MVS								96.04 143							89.77 236

HQP2-MVS												73.83 210

NP-MVS					94.37 201	82.42 160					93.98 189

MDTV_nov1_ep13_2view						55.91 423	87.62 377		73.32 379	84.59 263		70.33 254		74.65 322		95.50 203

MDTV_nov1_ep13			83.56 299		91.69 301	69.93 381	87.75 374	91.54 332	78.60 325	84.86 257	88.90 349	69.54 265	96.03 313	70.25 349	88.93 249

ACMMP++_ref															87.47 272

ACMMP++															88.01 264

Test By Simon												80.02 126

ITE_SJBPF				88.24 295	91.88 292	77.05 295		92.92 290	85.54 179	80.13 341	93.30 213	57.29 370	96.20 307	72.46 336	84.71 294	91.49 357

DeepMVS_CX				56.31 408	74.23 421	51.81 424		56.67 432	44.85 420	48.54 420	75.16 411	27.87 420	58.74 430	40.92 420	52.22 417	58.39 422

High-resolution multi-view benchmark