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.

CNVR-MVS		98.46 1	98.38 1	98.72 10	99.80 4	96.19 15	99.80 16	97.99 52	97.05 6	99.41 4	99.59 2	92.89 23	100.00 1	98.99 25	99.90 7	99.96 10

DVP-MVS++		98.18 2	98.09 5	98.44 16	99.61 24	95.38 24	99.55 44	97.68 90	93.01 70	99.23 10	99.45 14	95.12 7	99.98 9	99.25 18	99.92 3	99.97 7

SED-MVS		98.18 2	98.10 4	98.41 18	99.63 18	95.24 27	99.77 18	97.72 81	94.17 43	99.30 8	99.54 3	93.32 17	99.98 9	99.70 5	99.81 23	99.99 1

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 10	99.70 27	97.98 53	97.18 4	95.96 97	99.33 19	92.62 24	100.00 1	98.99 25	99.93 1	99.98 6

NCCC		98.12 5	98.11 3	98.13 25	99.76 6	94.46 51	99.81 12	97.88 57	96.54 13	98.84 24	99.46 10	92.55 25	99.98 9	98.25 47	99.93 1	99.94 18

DPE-MVS		98.11 6	98.00 6	98.44 16	99.50 42	95.39 23	99.29 80	97.72 81	94.50 37	98.64 30	99.54 3	93.32 17	99.97 21	99.58 11	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.07 7	98.00 6	98.29 19	99.66 12	95.20 32	99.72 24	97.47 141	93.95 48	99.07 15	99.46 10	93.18 20	99.97 21	99.64 8	99.82 19	99.69 55
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
DPM-MVS		97.86 8	97.25 22	99.68 1	98.25 95	99.10 1	99.76 21	97.78 73	96.61 12	98.15 43	99.53 7	93.62 15	100.00 1	91.79 166	99.80 26	99.94 18

MVS_0304		97.81 9	97.51 15	98.74 9	98.97 73	96.57 11	99.91 2	98.17 36	97.45 3	98.76 26	98.97 62	86.69 109	99.96 28	99.72 3	98.92 89	99.69 55

MSP-MVS		97.77 10	98.18 2	96.53 98	99.54 36	90.14 145	99.41 68	97.70 86	95.46 28	98.60 31	99.19 30	95.71 4	99.49 112	98.15 49	99.85 13	99.95 15
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
MM		97.76 11	97.39 20	98.86 5	98.30 94	96.83 7	99.81 12	99.13 9	97.66 2	98.29 41	98.96 67	85.84 129	99.90 50	99.72 3	98.80 95	99.85 30

HPM-MVS++		97.72 12	97.59 13	98.14 24	99.53 40	94.76 45	99.19 89	97.75 76	95.66 24	98.21 42	99.29 20	91.10 31	99.99 5	97.68 59	99.87 9	99.68 57

fmvsm_l_conf0.5_n_a		97.70 13	97.80 11	97.42 50	97.59 119	92.91 88	99.86 5	98.04 48	96.70 10	99.58 2	99.26 21	90.90 36	99.94 35	99.57 12	98.66 102	99.40 91

fmvsm_l_conf0.5_n		97.65 14	97.72 12	97.41 51	97.51 124	92.78 90	99.85 8	98.05 46	96.78 8	99.60 1	99.23 26	90.42 45	99.92 41	99.55 13	98.50 107	99.55 74

APDe-MVS		97.53 15	97.47 16	97.70 39	99.58 30	93.63 69	99.56 43	97.52 131	93.59 63	98.01 52	99.12 46	90.80 39	99.55 106	99.26 17	99.79 27	99.93 20
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD-MVS		97.51 16	97.40 19	97.81 36	99.01 72	93.79 68	99.33 78	97.38 154	93.73 59	98.83 25	99.02 58	90.87 38	99.88 54	98.69 30	99.74 29	99.77 43
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
MSLP-MVS++		97.50 17	97.45 18	97.63 41	99.65 16	93.21 77	99.70 27	98.13 42	94.61 35	97.78 59	99.46 10	89.85 54	99.81 79	97.97 52	99.91 6	99.88 26

TSAR-MVS + MP.		97.44 18	97.46 17	97.39 53	99.12 65	93.49 74	98.52 172	97.50 136	94.46 38	98.99 17	98.64 99	91.58 28	99.08 148	98.49 37	99.83 15	99.60 70
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		97.25 19	97.34 21	97.01 65	97.38 128	91.46 111	99.75 22	97.66 95	94.14 47	98.13 44	99.26 21	92.16 27	99.66 94	97.91 54	99.64 43	99.90 22
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		97.24 20	96.99 24	98.00 31	99.30 54	94.20 60	99.16 95	97.65 102	89.55 159	99.22 12	99.52 8	90.34 48	99.99 5	98.32 44	99.83 15	99.82 32
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
MG-MVS		97.24 20	96.83 31	98.47 15	99.79 5	95.71 19	99.07 111	99.06 10	94.45 40	96.42 91	98.70 95	88.81 67	99.74 88	95.35 109	99.86 12	99.97 7

SF-MVS		97.22 22	96.92 25	98.12 27	99.11 66	94.88 38	99.44 62	97.45 144	89.60 155	98.70 27	99.42 17	90.42 45	99.72 89	98.47 38	99.65 41	99.77 43

train_agg		97.20 23	97.08 23	97.57 45	99.57 33	93.17 78	99.38 71	97.66 95	90.18 137	98.39 37	99.18 33	90.94 34	99.66 94	98.58 36	99.85 13	99.88 26

DeepC-MVS_fast	93.52 2	97.16 24	96.84 29	98.13 25	99.61 24	94.45 52	98.85 133	97.64 103	96.51 16	95.88 100	99.39 18	87.35 94	99.99 5	96.61 83	99.69 39	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		97.12 25	96.60 35	98.68 11	98.03 104	96.57 11	99.84 9	97.84 61	96.36 18	95.20 117	98.24 123	88.17 75	99.83 73	96.11 93	99.60 51	99.64 65
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
patch_mono-2		97.10 26	97.97 8	94.49 180	99.21 61	83.73 293	99.62 38	98.25 31	95.28 30	99.38 6	98.91 75	92.28 26	99.94 35	99.61 10	99.22 74	99.78 38

test_fmvsm_n_1920		97.08 27	97.55 14	95.67 137	97.94 106	89.61 164	99.93 1	98.48 23	97.08 5	99.08 14	99.13 44	88.17 75	99.93 39	99.11 23	99.06 79	97.47 206

CANet		97.00 28	96.49 37	98.55 12	98.86 81	96.10 16	99.83 10	97.52 131	95.90 19	97.21 70	98.90 76	82.66 182	99.93 39	98.71 29	98.80 95	99.63 67

TSAR-MVS + GP.		96.95 29	96.91 26	97.07 62	98.88 80	91.62 107	99.58 41	96.54 219	95.09 32	96.84 79	98.63 101	91.16 29	99.77 85	99.04 24	96.42 150	99.81 33

APD-MVS		96.95 29	96.72 32	97.63 41	99.51 41	93.58 70	99.16 95	97.44 147	90.08 142	98.59 32	99.07 51	89.06 63	99.42 123	97.92 53	99.66 40	99.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-MVSNAJ		96.87 31	96.40 40	98.29 19	97.35 130	97.29 5	99.03 117	97.11 179	95.83 20	98.97 19	99.14 42	82.48 185	99.60 103	98.60 33	99.08 77	98.00 192

balanced_conf03		96.83 32	96.51 36	97.81 36	97.60 118	95.15 34	98.40 190	96.77 203	93.00 72	98.69 28	96.19 210	89.75 56	98.76 161	98.45 39	99.72 33	99.51 79

EPNet		96.82 33	96.68 34	97.25 58	98.65 87	93.10 80	99.48 53	98.76 14	96.54 13	97.84 56	98.22 124	87.49 87	99.66 94	95.35 109	97.78 124	99.00 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		96.80 34	96.85 28	96.66 90	97.85 109	94.42 54	94.76 332	98.36 28	92.50 82	95.62 110	97.52 150	97.92 1	97.38 244	98.31 45	98.80 95	98.20 186

test_fmvsmconf_n		96.78 35	96.84 29	96.61 91	95.99 195	90.25 140	99.90 3	98.13 42	96.68 11	98.42 36	98.92 74	85.34 140	99.88 54	99.12 22	99.08 77	99.70 52

MVS_111021_HR		96.69 36	96.69 33	96.72 86	98.58 89	91.00 125	99.14 103	99.45 1	93.86 54	95.15 118	98.73 89	88.48 70	99.76 86	97.23 69	99.56 53	99.40 91

xiu_mvs_v2_base		96.66 37	96.17 49	98.11 28	97.11 148	96.96 6	99.01 120	97.04 186	95.51 27	98.86 23	99.11 50	82.19 193	99.36 130	98.59 35	98.14 117	98.00 192

PHI-MVS		96.65 38	96.46 39	97.21 59	99.34 50	91.77 104	99.70 27	98.05 46	86.48 247	98.05 49	99.20 29	89.33 61	99.96 28	98.38 40	99.62 47	99.90 22

ACMMP_NAP		96.59 39	96.18 46	97.81 36	98.82 82	93.55 71	98.88 132	97.59 116	90.66 121	97.98 53	99.14 42	86.59 112	100.00 1	96.47 87	99.46 58	99.89 25

CDPH-MVS		96.56 40	96.18 46	97.70 39	99.59 28	93.92 65	99.13 106	97.44 147	89.02 171	97.90 55	99.22 27	88.90 66	99.49 112	94.63 127	99.79 27	99.68 57

DeepPCF-MVS	93.56 1	96.55 41	97.84 10	92.68 230	98.71 86	78.11 351	99.70 27	97.71 85	98.18 1	97.36 66	99.76 1	90.37 47	99.94 35	99.27 16	99.54 55	99.99 1

XVS		96.47 42	96.37 41	96.77 80	99.62 22	90.66 134	99.43 65	97.58 118	92.41 86	96.86 77	98.96 67	87.37 90	99.87 58	95.65 100	99.43 62	99.78 38

HFP-MVS		96.42 43	96.26 43	96.90 74	99.69 8	90.96 126	99.47 55	97.81 68	90.54 128	96.88 76	99.05 54	87.57 85	99.96 28	95.65 100	99.72 33	99.78 38

PAPR		96.35 44	95.82 59	97.94 33	99.63 18	94.19 61	99.42 67	97.55 123	92.43 83	93.82 144	99.12 46	87.30 95	99.91 46	94.02 134	99.06 79	99.74 47

PAPM		96.35 44	95.94 55	97.58 43	94.10 264	95.25 26	98.93 127	98.17 36	94.26 42	93.94 140	98.72 91	89.68 57	97.88 208	96.36 88	99.29 71	99.62 69

lupinMVS		96.32 46	95.94 55	97.44 48	95.05 237	94.87 39	99.86 5	96.50 221	93.82 57	98.04 50	98.77 85	85.52 132	98.09 195	96.98 74	98.97 85	99.37 94

region2R		96.30 47	96.17 49	96.70 87	99.70 7	90.31 139	99.46 59	97.66 95	90.55 127	97.07 74	99.07 51	86.85 104	99.97 21	95.43 107	99.74 29	99.81 33

ACMMPR		96.28 48	96.14 53	96.73 84	99.68 9	90.47 137	99.47 55	97.80 70	90.54 128	96.83 81	99.03 56	86.51 116	99.95 32	95.65 100	99.72 33	99.75 46

CP-MVS		96.22 49	96.15 52	96.42 103	99.67 10	89.62 163	99.70 27	97.61 110	90.07 143	96.00 96	99.16 36	87.43 88	99.92 41	96.03 95	99.72 33	99.70 52

fmvsm_s_conf0.5_n		96.19 50	96.49 37	95.30 151	97.37 129	89.16 169	99.86 5	98.47 24	95.68 23	98.87 22	99.15 39	82.44 189	99.92 41	99.14 21	97.43 133	96.83 226

bld_raw_conf03		96.19 50	95.77 64	97.46 47	97.26 137	94.35 57	98.26 204	96.75 204	92.13 93	97.84 56	96.18 211	85.75 130	98.53 172	98.04 51	99.73 31	99.49 82

SR-MVS		96.13 52	96.16 51	96.07 120	99.42 47	89.04 173	98.59 167	97.33 158	90.44 131	96.84 79	99.12 46	86.75 106	99.41 126	97.47 62	99.44 61	99.76 45

ZNCC-MVS		96.09 53	95.81 61	96.95 73	99.42 47	91.19 115	99.55 44	97.53 127	89.72 150	95.86 102	98.94 73	86.59 112	99.97 21	95.13 114	99.56 53	99.68 57

MTAPA		96.09 53	95.80 62	96.96 72	99.29 55	91.19 115	97.23 268	97.45 144	92.58 80	94.39 132	99.24 25	86.43 118	99.99 5	96.22 89	99.40 65	99.71 51

ETV-MVS		96.00 55	96.00 54	96.00 124	96.56 165	91.05 123	99.63 37	96.61 211	93.26 68	97.39 65	98.30 121	86.62 111	98.13 192	98.07 50	97.57 127	98.82 146

MP-MVS		96.00 55	95.82 59	96.54 97	99.47 46	90.13 147	99.36 75	97.41 151	90.64 124	95.49 112	98.95 70	85.51 134	99.98 9	96.00 96	99.59 52	99.52 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS-test		95.98 57	96.34 42	94.90 165	98.06 103	87.66 208	99.69 34	96.10 248	93.66 60	98.35 40	99.05 54	86.28 120	97.66 226	96.96 75	98.90 91	99.37 94

fmvsm_s_conf0.5_n_a		95.97 58	96.19 44	95.31 150	96.51 169	89.01 175	99.81 12	98.39 26	95.46 28	99.19 13	99.16 36	81.44 204	99.91 46	98.83 28	96.97 142	97.01 222

GST-MVS		95.97 58	95.66 68	96.90 74	99.49 45	91.22 113	99.45 61	97.48 139	89.69 151	95.89 99	98.72 91	86.37 119	99.95 32	94.62 128	99.22 74	99.52 77

WTY-MVS		95.97 58	95.11 82	98.54 13	97.62 115	96.65 9	99.44 62	98.74 15	92.25 89	95.21 116	98.46 116	86.56 114	99.46 118	95.00 119	92.69 193	99.50 81

test_fmvsmconf0.1_n		95.94 61	95.79 63	96.40 105	92.42 301	89.92 156	99.79 17	96.85 198	96.53 15	97.22 69	98.67 97	82.71 181	99.84 69	98.92 27	98.98 84	99.43 90

PVSNet_Blended		95.94 61	95.66 68	96.75 82	98.77 84	91.61 108	99.88 4	98.04 48	93.64 62	94.21 134	97.76 137	83.50 160	99.87 58	97.41 63	97.75 125	98.79 149

mPP-MVS		95.90 63	95.75 65	96.38 106	99.58 30	89.41 167	99.26 84	97.41 151	90.66 121	94.82 122	98.95 70	86.15 124	99.98 9	95.24 113	99.64 43	99.74 47

PGM-MVS		95.85 64	95.65 70	96.45 101	99.50 42	89.77 160	98.22 208	98.90 13	89.19 166	96.74 84	98.95 70	85.91 128	99.92 41	93.94 135	99.46 58	99.66 61

DP-MVS Recon		95.85 64	95.15 79	97.95 32	99.87 2	94.38 55	99.60 39	97.48 139	86.58 242	94.42 130	99.13 44	87.36 93	99.98 9	93.64 142	98.33 113	99.48 84

MP-MVS-pluss		95.80 66	95.30 74	97.29 55	98.95 77	92.66 91	98.59 167	97.14 175	88.95 174	93.12 153	99.25 23	85.62 131	99.94 35	96.56 85	99.48 57	99.28 103
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVS_111021_LR		95.78 67	95.94 55	95.28 152	98.19 99	87.69 205	98.80 139	99.26 7	93.39 65	95.04 120	98.69 96	84.09 154	99.76 86	96.96 75	99.06 79	98.38 172

alignmvs		95.77 68	95.00 85	98.06 29	97.35 130	95.68 20	99.71 26	97.50 136	91.50 104	96.16 95	98.61 103	86.28 120	99.00 151	96.19 90	91.74 212	99.51 79

EI-MVSNet-Vis-set		95.76 69	95.63 72	96.17 116	99.14 64	90.33 138	98.49 178	97.82 65	91.92 95	94.75 124	98.88 80	87.06 100	99.48 116	95.40 108	97.17 140	98.70 156

SR-MVS-dyc-post		95.75 70	95.86 58	95.41 145	99.22 59	87.26 224	98.40 190	97.21 167	89.63 153	96.67 87	98.97 62	86.73 108	99.36 130	96.62 81	99.31 69	99.60 70

CS-MVS		95.75 70	96.19 44	94.40 184	97.88 108	86.22 244	99.66 35	96.12 247	92.69 79	98.07 48	98.89 78	87.09 98	97.59 232	96.71 78	98.62 103	99.39 93

MVSMamba_PlusPlus		95.73 72	95.15 79	97.44 48	97.28 135	94.35 57	98.26 204	96.75 204	83.09 301	97.84 56	95.97 219	89.59 58	98.48 176	97.86 55	99.73 31	99.49 82

dcpmvs_2		95.67 73	96.18 46	94.12 196	98.82 82	84.22 286	97.37 261	95.45 302	90.70 120	95.77 105	98.63 101	90.47 43	98.68 167	99.20 20	99.22 74	99.45 87

APD-MVS_3200maxsize		95.64 74	95.65 70	95.62 139	99.24 58	87.80 204	98.42 185	97.22 166	88.93 176	96.64 89	98.98 61	85.49 135	99.36 130	96.68 80	99.27 72	99.70 52

fmvsm_s_conf0.1_n		95.56 75	95.68 67	95.20 154	94.35 256	89.10 171	99.50 51	97.67 94	94.76 34	98.68 29	99.03 56	81.13 207	99.86 63	98.63 32	97.36 135	96.63 229

test_fmvsmvis_n_1920		95.47 76	95.40 73	95.70 135	94.33 257	90.22 143	99.70 27	96.98 193	96.80 7	92.75 157	98.89 78	82.46 188	99.92 41	98.36 41	98.33 113	96.97 223

EI-MVSNet-UG-set		95.43 77	95.29 75	95.86 130	99.07 70	89.87 157	98.43 184	97.80 70	91.78 97	94.11 136	98.77 85	86.25 122	99.48 116	94.95 121	96.45 149	98.22 184

PAPM_NR		95.43 77	95.05 84	96.57 96	99.42 47	90.14 145	98.58 169	97.51 133	90.65 123	92.44 161	98.90 76	87.77 84	99.90 50	90.88 174	99.32 68	99.68 57

HPM-MVS		95.41 79	95.22 77	95.99 125	99.29 55	89.14 170	99.17 94	97.09 183	87.28 227	95.40 113	98.48 113	84.93 144	99.38 128	95.64 104	99.65 41	99.47 86
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
jason		95.40 80	94.86 87	97.03 64	92.91 296	94.23 59	99.70 27	96.30 232	93.56 64	96.73 85	98.52 106	81.46 203	97.91 205	96.08 94	98.47 110	98.96 129
jason: jason.
testing11		95.33 81	94.98 86	96.37 107	97.20 139	92.31 97	99.29 80	97.68 90	90.59 125	94.43 129	97.20 166	90.79 40	98.60 170	95.25 112	92.38 198	98.18 187

HY-MVS	88.56 7	95.29 82	94.23 97	98.48 14	97.72 111	96.41 13	94.03 340	98.74 15	92.42 85	95.65 109	94.76 241	86.52 115	99.49 112	95.29 111	92.97 189	99.53 76

test_yl		95.27 83	94.60 90	97.28 56	98.53 90	92.98 84	99.05 115	98.70 18	86.76 239	94.65 127	97.74 139	87.78 82	99.44 119	95.57 105	92.61 194	99.44 88

DCV-MVSNet		95.27 83	94.60 90	97.28 56	98.53 90	92.98 84	99.05 115	98.70 18	86.76 239	94.65 127	97.74 139	87.78 82	99.44 119	95.57 105	92.61 194	99.44 88

fmvsm_s_conf0.1_n_a		95.16 85	95.15 79	95.18 155	92.06 307	88.94 179	99.29 80	97.53 127	94.46 38	98.98 18	98.99 60	79.99 213	99.85 67	98.24 48	96.86 144	96.73 227

EIA-MVS		95.11 86	95.27 76	94.64 177	96.34 178	86.51 233	99.59 40	96.62 210	92.51 81	94.08 137	98.64 99	86.05 125	98.24 188	95.07 116	98.50 107	99.18 111

EC-MVSNet		95.09 87	95.17 78	94.84 168	95.42 214	88.17 196	99.48 53	95.92 268	91.47 105	97.34 67	98.36 118	82.77 177	97.41 243	97.24 68	98.58 104	98.94 134

VNet		95.08 88	94.26 96	97.55 46	98.07 102	93.88 66	98.68 152	98.73 17	90.33 134	97.16 73	97.43 155	79.19 223	99.53 109	96.91 77	91.85 210	99.24 106

sasdasda		95.02 89	93.96 110	98.20 21	97.53 122	95.92 17	98.71 147	96.19 241	91.78 97	95.86 102	98.49 110	79.53 218	99.03 149	96.12 91	91.42 224	99.66 61

canonicalmvs		95.02 89	93.96 110	98.20 21	97.53 122	95.92 17	98.71 147	96.19 241	91.78 97	95.86 102	98.49 110	79.53 218	99.03 149	96.12 91	91.42 224	99.66 61

MGCFI-Net		94.89 91	93.84 118	98.06 29	97.49 125	95.55 21	98.64 158	96.10 248	91.60 102	95.75 106	98.46 116	79.31 222	98.98 153	95.95 97	91.24 228	99.65 64

HPM-MVS_fast		94.89 91	94.62 89	95.70 135	99.11 66	88.44 194	99.14 103	97.11 179	85.82 255	95.69 108	98.47 114	83.46 162	99.32 135	93.16 152	99.63 46	99.35 96

testing91		94.88 93	94.44 93	96.21 112	97.19 141	91.90 103	99.23 86	97.66 95	89.91 146	93.66 146	97.05 177	90.21 50	98.50 173	93.52 144	91.53 221	98.25 180

testing99		94.88 93	94.45 92	96.17 116	97.20 139	91.91 102	99.20 88	97.66 95	89.95 145	93.68 145	97.06 175	90.28 49	98.50 173	93.52 144	91.54 218	98.12 189

CSCG		94.87 95	94.71 88	95.36 146	99.54 36	86.49 234	99.34 77	98.15 40	82.71 311	90.15 199	99.25 23	89.48 60	99.86 63	94.97 120	98.82 94	99.72 50

sss		94.85 96	93.94 112	97.58 43	96.43 172	94.09 63	98.93 127	99.16 8	89.50 160	95.27 115	97.85 131	81.50 201	99.65 98	92.79 158	94.02 180	98.99 126

test2506		94.80 97	94.21 98	96.58 94	96.41 174	92.18 100	98.01 228	98.96 11	90.82 118	93.46 149	97.28 159	85.92 126	98.45 178	89.82 187	97.19 138	99.12 117

API-MVS		94.78 98	94.18 101	96.59 93	99.21 61	90.06 152	98.80 139	97.78 73	83.59 293	93.85 142	99.21 28	83.79 157	99.97 21	92.37 161	99.00 83	99.74 47

thisisatest0515		94.75 99	94.19 99	96.43 102	96.13 193	92.64 94	99.47 55	97.60 112	87.55 223	93.17 152	97.59 147	94.71 11	98.42 179	88.28 205	93.20 186	98.24 183

xiu_mvs_v1_base_debu		94.73 100	93.98 107	96.99 67	95.19 223	95.24 27	98.62 161	96.50 221	92.99 73	97.52 61	98.83 82	72.37 269	99.15 141	97.03 71	96.74 145	96.58 232

xiu_mvs_v1_base		94.73 100	93.98 107	96.99 67	95.19 223	95.24 27	98.62 161	96.50 221	92.99 73	97.52 61	98.83 82	72.37 269	99.15 141	97.03 71	96.74 145	96.58 232

xiu_mvs_v1_base_debi		94.73 100	93.98 107	96.99 67	95.19 223	95.24 27	98.62 161	96.50 221	92.99 73	97.52 61	98.83 82	72.37 269	99.15 141	97.03 71	96.74 145	96.58 232

MVSFormer		94.71 103	94.08 104	96.61 91	95.05 237	94.87 39	97.77 242	96.17 244	86.84 236	98.04 50	98.52 106	85.52 132	95.99 309	89.83 185	98.97 85	98.96 129

PVSNet_Blended_VisFu		94.67 104	94.11 102	96.34 109	97.14 145	91.10 120	99.32 79	97.43 149	92.10 94	91.53 176	96.38 206	83.29 166	99.68 92	93.42 149	96.37 151	98.25 180

ACMMP		94.67 104	94.30 95	95.79 132	99.25 57	88.13 198	98.41 187	98.67 21	90.38 133	91.43 177	98.72 91	82.22 192	99.95 32	93.83 139	95.76 163	99.29 102
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
iter_conf05		94.60 106	93.87 117	96.79 79	97.28 135	94.04 64	95.67 324	95.94 262	83.09 301	90.06 200	95.97 219	89.59 58	98.48 176	97.86 55	99.34 66	97.86 196

CPTT-MVS		94.60 106	94.43 94	95.09 158	99.66 12	86.85 229	99.44 62	97.47 141	83.22 298	94.34 133	98.96 67	82.50 183	99.55 106	94.81 122	99.50 56	98.88 139

diffmvs		94.59 108	94.19 99	95.81 131	95.54 210	90.69 132	98.70 150	95.68 289	91.61 100	95.96 97	97.81 133	80.11 212	98.06 197	96.52 86	95.76 163	98.67 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mvsany_test1		94.57 109	95.09 83	92.98 221	95.84 200	82.07 315	98.76 145	95.24 315	92.87 78	96.45 90	98.71 94	84.81 147	99.15 141	97.68 59	95.49 168	97.73 198

DeepC-MVS	91.02 4	94.56 110	93.92 113	96.46 100	97.16 144	90.76 130	98.39 194	97.11 179	93.92 50	88.66 214	98.33 119	78.14 232	99.85 67	95.02 117	98.57 105	98.78 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETVMVS		94.50 111	93.90 115	96.31 110	97.48 126	92.98 84	99.07 111	97.86 59	88.09 204	94.40 131	96.90 184	88.35 72	97.28 248	90.72 179	92.25 204	98.66 161

testing222		94.48 112	94.00 106	95.95 127	97.30 132	92.27 98	98.82 136	97.92 55	89.20 165	94.82 122	97.26 161	87.13 97	97.32 247	91.95 164	91.56 216	98.25 180

MAR-MVS		94.43 113	94.09 103	95.45 143	99.10 68	87.47 214	98.39 194	97.79 72	88.37 193	94.02 139	99.17 35	78.64 229	99.91 46	92.48 160	98.85 93	98.96 129
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
CHOSEN 1792x2688		94.35 114	93.82 119	95.95 127	97.40 127	88.74 187	98.41 187	98.27 30	92.18 91	91.43 177	96.40 203	78.88 224	99.81 79	93.59 143	97.81 121	99.30 101

CANet_DTU		94.31 115	93.35 130	97.20 60	97.03 153	94.71 47	98.62 161	95.54 297	95.61 25	97.21 70	98.47 114	71.88 274	99.84 69	88.38 204	97.46 132	97.04 220

mvsmamba		94.27 116	93.91 114	95.35 147	96.42 173	88.61 189	97.77 242	96.38 227	91.17 113	94.05 138	95.27 233	78.41 230	97.96 204	97.36 65	98.40 111	99.48 84

PLC	91.07 3	94.23 117	94.01 105	94.87 166	99.17 63	87.49 213	99.25 85	96.55 218	88.43 191	91.26 181	98.21 126	85.92 126	99.86 63	89.77 189	97.57 127	97.24 213
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_fmvsmconf0.01_n		94.14 118	93.51 126	96.04 121	86.79 372	89.19 168	99.28 83	95.94 262	95.70 21	95.50 111	98.49 110	73.27 262	99.79 82	98.28 46	98.32 115	99.15 113

114514_t		94.06 119	93.05 138	97.06 63	99.08 69	92.26 99	98.97 125	97.01 191	82.58 313	92.57 159	98.22 124	80.68 210	99.30 136	89.34 195	99.02 82	99.63 67

baseline2		94.04 120	93.80 120	94.74 172	93.07 295	90.25 140	98.12 218	98.16 39	89.86 147	86.53 235	96.95 181	95.56 5	98.05 199	91.44 168	94.53 175	95.93 245

thisisatest0530		94.00 121	93.52 125	95.43 144	95.76 203	90.02 154	98.99 122	97.60 112	86.58 242	91.74 168	97.36 158	94.78 10	98.34 181	86.37 226	92.48 197	97.94 194

casdiffmvs_mvg		94.00 121	93.33 131	96.03 122	95.22 221	90.90 128	99.09 109	95.99 255	90.58 126	91.55 175	97.37 157	79.91 214	98.06 197	95.01 118	95.22 170	99.13 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		93.98 123	93.43 127	95.61 140	95.07 236	89.86 158	98.80 139	95.84 281	90.98 115	92.74 158	97.66 144	79.71 215	98.10 194	94.72 125	95.37 169	98.87 141
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		93.92 124	92.28 153	98.83 7	95.69 205	96.82 8	96.22 305	98.17 36	84.89 273	84.34 252	98.61 103	79.32 221	99.83 73	93.88 137	99.43 62	99.86 29

baseline		93.91 125	93.30 132	95.72 134	95.10 234	90.07 149	97.48 257	95.91 273	91.03 114	93.54 148	97.68 142	79.58 216	98.02 201	94.27 132	95.14 171	99.08 121

OMC-MVS		93.90 126	93.62 123	94.73 173	98.63 88	87.00 227	98.04 227	96.56 217	92.19 90	92.46 160	98.73 89	79.49 220	99.14 145	92.16 163	94.34 178	98.03 191

Effi-MVS+		93.87 127	93.15 136	96.02 123	95.79 201	90.76 130	96.70 290	95.78 282	86.98 233	95.71 107	97.17 170	79.58 216	98.01 202	94.57 129	96.09 158	99.31 100

test_cas_vis1_n_1920		93.86 128	93.74 121	94.22 192	95.39 217	86.08 250	99.73 23	96.07 252	96.38 17	97.19 72	97.78 136	65.46 324	99.86 63	96.71 78	98.92 89	96.73 227

TESTMET0.1,1		93.82 129	93.26 134	95.49 142	95.21 222	90.25 140	99.15 100	97.54 126	89.18 167	91.79 167	94.87 239	89.13 62	97.63 229	86.21 228	96.29 155	98.60 162

AdaColmap		93.82 129	93.06 137	96.10 119	99.88 1	89.07 172	98.33 198	97.55 123	86.81 238	90.39 196	98.65 98	75.09 244	99.98 9	93.32 150	97.53 130	99.26 105

EPP-MVSNet		93.75 131	93.67 122	94.01 202	95.86 199	85.70 262	98.67 154	97.66 95	84.46 278	91.36 180	97.18 169	91.16 29	97.79 214	92.93 155	93.75 182	98.53 164

thres200		93.69 132	92.59 149	96.97 71	97.76 110	94.74 46	99.35 76	99.36 2	89.23 164	91.21 183	96.97 180	83.42 163	98.77 159	85.08 240	90.96 229	97.39 208

PVSNet	87.13 12	93.69 132	92.83 144	96.28 111	97.99 105	90.22 143	99.38 71	98.93 12	91.42 108	93.66 146	97.68 142	71.29 281	99.64 100	87.94 210	97.20 137	98.98 127

HyFIR lowres test		93.68 134	93.29 133	94.87 166	97.57 121	88.04 200	98.18 212	98.47 24	87.57 222	91.24 182	95.05 237	85.49 135	97.46 239	93.22 151	92.82 190	99.10 119

MVS_Test		93.67 135	92.67 147	96.69 88	96.72 162	92.66 91	97.22 269	96.03 254	87.69 220	95.12 119	94.03 249	81.55 199	98.28 185	89.17 199	96.46 148	99.14 114

CNLPA		93.64 136	92.74 145	96.36 108	98.96 76	90.01 155	99.19 89	95.89 276	86.22 250	89.40 209	98.85 81	80.66 211	99.84 69	88.57 202	96.92 143	99.24 106

PMMVS		93.62 137	93.90 115	92.79 225	96.79 160	81.40 321	98.85 133	96.81 199	91.25 111	96.82 82	98.15 128	77.02 238	98.13 192	93.15 153	96.30 154	98.83 145

CDS-MVSNet		93.47 138	93.04 139	94.76 170	94.75 248	89.45 166	98.82 136	97.03 188	87.91 211	90.97 184	96.48 201	89.06 63	96.36 288	89.50 191	92.81 192	98.49 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		93.44 139	91.98 161	97.84 34	95.24 219	94.38 55	96.22 305	97.92 55	90.18 137	82.28 279	97.71 141	77.63 235	99.80 81	91.94 165	98.67 101	99.34 98

tfpn200view9		93.43 140	92.27 154	96.90 74	97.68 113	94.84 41	99.18 91	99.36 2	88.45 188	90.79 186	96.90 184	83.31 164	98.75 162	84.11 256	90.69 231	97.12 215

3Dnovator+	87.72 8	93.43 140	91.84 164	98.17 23	95.73 204	95.08 35	98.92 129	97.04 186	91.42 108	81.48 296	97.60 146	74.60 247	99.79 82	90.84 175	98.97 85	99.64 65

thres400		93.39 142	92.27 154	96.73 84	97.68 113	94.84 41	99.18 91	99.36 2	88.45 188	90.79 186	96.90 184	83.31 164	98.75 162	84.11 256	90.69 231	96.61 230

PVSNet_BlendedMVS		93.36 143	93.20 135	93.84 207	98.77 84	91.61 108	99.47 55	98.04 48	91.44 106	94.21 134	92.63 283	83.50 160	99.87 58	97.41 63	83.37 279	90.05 342

thres100view900		93.34 144	92.15 157	96.90 74	97.62 115	94.84 41	99.06 114	99.36 2	87.96 209	90.47 194	96.78 192	83.29 166	98.75 162	84.11 256	90.69 231	97.12 215

tttt0517		93.30 145	93.01 140	94.17 194	95.57 208	86.47 235	98.51 175	97.60 112	85.99 253	90.55 191	97.19 168	94.80 9	98.31 182	85.06 241	91.86 209	97.74 197

UA-Net		93.30 145	92.62 148	95.34 148	96.27 181	88.53 193	95.88 315	96.97 194	90.90 116	95.37 114	97.07 174	82.38 190	99.10 147	83.91 260	94.86 174	98.38 172

test-mter		93.27 147	92.89 143	94.40 184	94.94 242	87.27 222	99.15 100	97.25 161	88.95 174	91.57 172	94.04 247	88.03 80	97.58 233	85.94 232	96.13 156	98.36 176

Vis-MVSNet (Re-imp)		93.26 148	93.00 141	94.06 199	96.14 190	86.71 232	98.68 152	96.70 206	88.30 197	89.71 208	97.64 145	85.43 138	96.39 286	88.06 209	96.32 152	99.08 121

UWE-MVS		93.18 149	93.40 129	92.50 233	96.56 165	83.55 295	98.09 224	97.84 61	89.50 160	91.72 169	96.23 209	91.08 32	96.70 269	86.28 227	93.33 185	97.26 212

thres600view7		93.18 149	92.00 160	96.75 82	97.62 115	94.92 36	99.07 111	99.36 2	87.96 209	90.47 194	96.78 192	83.29 166	98.71 166	82.93 270	90.47 235	96.61 230

3Dnovator	87.35 11	93.17 151	91.77 166	97.37 54	95.41 215	93.07 81	98.82 136	97.85 60	91.53 103	82.56 272	97.58 148	71.97 273	99.82 76	91.01 172	99.23 73	99.22 109

test-LLR		93.11 152	92.68 146	94.40 184	94.94 242	87.27 222	99.15 100	97.25 161	90.21 135	91.57 172	94.04 247	84.89 145	97.58 233	85.94 232	96.13 156	98.36 176

test_vis1_n_1920		93.08 153	93.42 128	92.04 243	96.31 179	79.36 338	99.83 10	96.06 253	96.72 9	98.53 34	98.10 129	58.57 349	99.91 46	97.86 55	98.79 98	96.85 225

IS-MVSNet		93.00 154	92.51 150	94.49 180	96.14 190	87.36 218	98.31 201	95.70 287	88.58 184	90.17 198	97.50 151	83.02 173	97.22 249	87.06 215	96.07 160	98.90 138

CostFormer		92.89 155	92.48 151	94.12 196	94.99 239	85.89 257	92.89 350	97.00 192	86.98 233	95.00 121	90.78 314	90.05 53	97.51 237	92.92 156	91.73 213	98.96 129

tpmrst		92.78 156	92.16 156	94.65 175	96.27 181	87.45 215	91.83 360	97.10 182	89.10 170	94.68 126	90.69 318	88.22 74	97.73 224	89.78 188	91.80 211	98.77 152

MVSTER		92.71 157	92.32 152	93.86 206	97.29 133	92.95 87	99.01 120	96.59 213	90.09 141	85.51 242	94.00 251	94.61 14	96.56 275	90.77 178	83.03 281	92.08 282

1112_ss		92.71 157	91.55 170	96.20 113	95.56 209	91.12 118	98.48 180	94.69 333	88.29 198	86.89 232	98.50 108	87.02 101	98.66 168	84.75 245	89.77 239	98.81 147

Vis-MVSNet		92.64 159	91.85 163	95.03 162	95.12 230	88.23 195	98.48 180	96.81 199	91.61 100	92.16 165	97.22 165	71.58 279	98.00 203	85.85 235	97.81 121	98.88 139
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		92.62 160	92.09 159	94.20 193	94.10 264	87.68 206	98.41 187	96.97 194	87.53 224	89.74 206	96.04 217	84.77 149	96.49 281	88.97 201	92.31 201	98.42 168

baseline1		92.61 161	91.28 175	96.58 94	97.05 152	94.63 49	97.72 247	96.20 239	89.82 148	88.56 215	96.85 188	86.85 104	97.82 212	88.42 203	80.10 296	97.30 210

EPMVS		92.59 162	91.59 169	95.59 141	97.22 138	90.03 153	91.78 361	98.04 48	90.42 132	91.66 171	90.65 321	86.49 117	97.46 239	81.78 281	96.31 153	99.28 103

ET-MVSNet_ETH3D		92.56 163	91.45 172	95.88 129	96.39 176	94.13 62	99.46 59	96.97 194	92.18 91	66.94 381	98.29 122	94.65 13	94.28 352	94.34 131	83.82 275	99.24 106

mvs_anonymous		92.50 164	91.65 168	95.06 159	96.60 164	89.64 162	97.06 274	96.44 225	86.64 241	84.14 253	93.93 254	82.49 184	96.17 303	91.47 167	96.08 159	99.35 96

h-mvs33		92.47 165	91.95 162	94.05 200	97.13 146	85.01 276	98.36 196	98.08 44	93.85 55	96.27 93	96.73 194	83.19 169	99.43 122	95.81 98	68.09 366	97.70 199

test_fmvs1		92.35 166	92.94 142	90.57 275	97.19 141	75.43 360	99.55 44	94.97 322	95.20 31	96.82 82	97.57 149	59.59 347	99.84 69	97.30 66	98.29 116	96.46 237

BH-w/o		92.32 167	91.79 165	93.91 205	96.85 155	86.18 246	99.11 108	95.74 285	88.13 202	84.81 246	97.00 179	77.26 237	97.91 205	89.16 200	98.03 118	97.64 200

ECVR-MVS		92.29 168	91.33 174	95.15 156	96.41 174	87.84 203	98.10 221	94.84 326	90.82 118	91.42 179	97.28 159	65.61 321	98.49 175	90.33 181	97.19 138	99.12 117

EPNet_dtu		92.28 169	92.15 157	92.70 229	97.29 133	84.84 278	98.64 158	97.82 65	92.91 76	93.02 155	97.02 178	85.48 137	95.70 323	72.25 347	94.89 173	97.55 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		92.27 170	90.97 181	96.18 114	95.53 211	91.10 120	98.47 182	94.66 334	88.28 199	86.83 233	93.50 267	87.00 102	98.65 169	84.69 246	89.74 240	98.80 148

LFMVS		92.23 171	90.84 185	96.42 103	98.24 96	91.08 122	98.24 207	96.22 238	83.39 296	94.74 125	98.31 120	61.12 342	98.85 156	94.45 130	92.82 190	99.32 99

FA-MVS(test-final)		92.22 172	91.08 179	95.64 138	96.05 194	88.98 176	91.60 364	97.25 161	86.99 230	91.84 166	92.12 286	83.03 172	99.00 151	86.91 220	93.91 181	98.93 135

test1111		92.12 173	91.19 177	94.94 164	96.15 188	87.36 218	98.12 218	94.84 326	90.85 117	90.97 184	97.26 161	65.60 322	98.37 180	89.74 190	97.14 141	99.07 123

IB-MVS	89.43 6	92.12 173	90.83 187	95.98 126	95.40 216	90.78 129	99.81 12	98.06 45	91.23 112	85.63 241	93.66 262	90.63 41	98.78 158	91.22 169	71.85 355	98.36 176
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
F-COLMAP		92.07 175	91.75 167	93.02 220	98.16 100	82.89 305	98.79 143	95.97 257	86.54 244	87.92 219	97.80 134	78.69 228	99.65 98	85.97 230	95.93 162	96.53 235

PatchmatchNet		92.05 176	91.04 180	95.06 159	96.17 187	89.04 173	91.26 369	97.26 160	89.56 158	90.64 190	90.56 327	88.35 72	97.11 252	79.53 294	96.07 160	99.03 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UGNet		91.91 177	90.85 184	95.10 157	97.06 151	88.69 188	98.01 228	98.24 33	92.41 86	92.39 162	93.61 263	60.52 344	99.68 92	88.14 207	97.25 136	96.92 224
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
tpm2		91.77 178	91.09 178	93.82 208	94.83 246	85.56 265	92.51 355	97.16 174	84.00 284	93.83 143	90.66 320	87.54 86	97.17 250	87.73 212	91.55 217	98.72 154

Fast-Effi-MVS+		91.72 179	90.79 188	94.49 180	95.89 197	87.40 217	99.54 49	95.70 287	85.01 271	89.28 211	95.68 226	77.75 234	97.57 236	83.22 265	95.06 172	98.51 165

hse-mvs2		91.67 180	91.51 171	92.15 240	96.22 183	82.61 311	97.74 246	97.53 127	93.85 55	96.27 93	96.15 212	83.19 169	97.44 241	95.81 98	66.86 373	96.40 239

HQP-MVS		91.50 181	91.23 176	92.29 235	93.95 269	86.39 238	99.16 95	96.37 228	93.92 50	87.57 222	96.67 197	73.34 259	97.77 216	93.82 140	86.29 252	92.72 263

PatchMatch-RL		91.47 182	90.54 192	94.26 190	98.20 97	86.36 240	96.94 278	97.14 175	87.75 216	88.98 212	95.75 224	71.80 276	99.40 127	80.92 286	97.39 134	97.02 221

BH-untuned		91.46 183	90.84 185	93.33 215	96.51 169	84.83 279	98.84 135	95.50 299	86.44 249	83.50 257	96.70 195	75.49 243	97.77 216	86.78 223	97.81 121	97.40 207

mamv4		91.41 184	93.57 124	84.91 348	97.11 148	58.11 395	95.68 323	95.93 266	82.09 323	89.78 205	95.71 225	90.09 52	98.24 188	97.26 67	98.50 107	98.38 172

QAPM		91.41 184	89.49 205	97.17 61	95.66 207	93.42 75	98.60 165	97.51 133	80.92 337	81.39 297	97.41 156	72.89 266	99.87 58	82.33 275	98.68 100	98.21 185

FE-MVS		91.38 186	90.16 197	95.05 161	96.46 171	87.53 212	89.69 378	97.84 61	82.97 305	92.18 164	92.00 292	84.07 155	98.93 155	80.71 288	95.52 167	98.68 157

HQP_MVS		91.26 187	90.95 182	92.16 239	93.84 276	86.07 252	99.02 118	96.30 232	93.38 66	86.99 229	96.52 199	72.92 264	97.75 222	93.46 147	86.17 255	92.67 265

PCF-MVS	89.78 5	91.26 187	89.63 202	96.16 118	95.44 213	91.58 110	95.29 327	96.10 248	85.07 268	82.75 266	97.45 154	78.28 231	99.78 84	80.60 290	95.65 166	97.12 215
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 189	89.99 198	95.03 162	96.75 161	88.55 191	98.65 156	94.95 323	87.74 217	87.74 221	97.80 134	68.27 298	98.14 191	80.53 291	97.49 131	98.41 169

VDD-MVS		91.24 190	90.18 196	94.45 183	97.08 150	85.84 260	98.40 190	96.10 248	86.99 230	93.36 150	98.16 127	54.27 366	99.20 138	96.59 84	90.63 234	98.31 179

SDMVSNet		91.09 191	89.91 199	94.65 175	96.80 158	90.54 136	97.78 240	97.81 68	88.34 195	85.73 238	95.26 234	66.44 316	98.26 186	94.25 133	86.75 249	95.14 248

test_fmvs1_n		91.07 192	91.41 173	90.06 289	94.10 264	74.31 364	99.18 91	94.84 326	94.81 33	96.37 92	97.46 153	50.86 377	99.82 76	97.14 70	97.90 119	96.04 244

CLD-MVS		91.06 193	90.71 189	92.10 241	94.05 268	86.10 249	99.55 44	96.29 235	94.16 45	84.70 247	97.17 170	69.62 290	97.82 212	94.74 124	86.08 257	92.39 268
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ab-mvs		91.05 194	89.17 211	96.69 88	95.96 196	91.72 106	92.62 354	97.23 165	85.61 259	89.74 206	93.89 256	68.55 295	99.42 123	91.09 170	87.84 244	98.92 137

XVG-OURS-SEG-HR		90.95 195	90.66 191	91.83 246	95.18 226	81.14 328	95.92 312	95.92 268	88.40 192	90.33 197	97.85 131	70.66 284	99.38 128	92.83 157	88.83 241	94.98 251

cascas		90.93 196	89.33 209	95.76 133	95.69 205	93.03 83	98.99 122	96.59 213	80.49 339	86.79 234	94.45 244	65.23 325	98.60 170	93.52 144	92.18 205	95.66 247

XVG-OURS		90.83 197	90.49 193	91.86 245	95.23 220	81.25 325	95.79 320	95.92 268	88.96 173	90.02 202	98.03 130	71.60 278	99.35 133	91.06 171	87.78 245	94.98 251

TR-MVS		90.77 198	89.44 206	94.76 170	96.31 179	88.02 201	97.92 232	95.96 259	85.52 260	88.22 218	97.23 164	66.80 312	98.09 195	84.58 248	92.38 198	98.17 188

OpenMVS	85.28 14	90.75 199	88.84 218	96.48 99	93.58 283	93.51 73	98.80 139	97.41 151	82.59 312	78.62 325	97.49 152	68.00 302	99.82 76	84.52 250	98.55 106	96.11 243

FIs		90.70 200	89.87 200	93.18 217	92.29 302	91.12 118	98.17 214	98.25 31	89.11 169	83.44 258	94.82 240	82.26 191	96.17 303	87.76 211	82.76 283	92.25 273

X-MVStestdata		90.69 201	88.66 223	96.77 80	99.62 22	90.66 134	99.43 65	97.58 118	92.41 86	96.86 77	29.59 412	87.37 90	99.87 58	95.65 100	99.43 62	99.78 38

SCA		90.64 202	89.25 210	94.83 169	94.95 241	88.83 183	96.26 302	97.21 167	90.06 144	90.03 201	90.62 323	66.61 313	96.81 265	83.16 266	94.36 177	98.84 142

GeoE		90.60 203	89.56 203	93.72 211	95.10 234	85.43 266	99.41 68	94.94 324	83.96 286	87.21 228	96.83 191	74.37 251	97.05 256	80.50 292	93.73 183	98.67 158

test_vis1_n		90.40 204	90.27 195	90.79 270	91.55 317	76.48 356	99.12 107	94.44 338	94.31 41	97.34 67	96.95 181	43.60 388	99.42 123	97.57 61	97.60 126	96.47 236

TAPA-MVS	87.50 9	90.35 205	89.05 214	94.25 191	98.48 92	85.17 273	98.42 185	96.58 216	82.44 318	87.24 227	98.53 105	82.77 177	98.84 157	59.09 386	97.88 120	98.72 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
miper_enhance_ethall		90.33 206	89.70 201	92.22 236	97.12 147	88.93 181	98.35 197	95.96 259	88.60 183	83.14 264	92.33 285	87.38 89	96.18 302	86.49 225	77.89 305	91.55 297

CVMVSNet		90.30 207	90.91 183	88.46 322	94.32 258	73.58 368	97.61 254	97.59 116	90.16 140	88.43 217	97.10 172	76.83 239	92.86 362	82.64 272	93.54 184	98.93 135

nrg030		90.23 208	88.87 217	94.32 188	91.53 318	93.54 72	98.79 143	95.89 276	88.12 203	84.55 249	94.61 243	78.80 227	96.88 262	92.35 162	75.21 320	92.53 267

FC-MVSNet-test		90.22 209	89.40 207	92.67 231	91.78 314	89.86 158	97.89 233	98.22 34	88.81 179	82.96 265	94.66 242	81.90 197	95.96 311	85.89 234	82.52 286	92.20 278

LS3D		90.19 210	88.72 221	94.59 179	98.97 73	86.33 241	96.90 280	96.60 212	74.96 365	84.06 255	98.74 88	75.78 241	99.83 73	74.93 327	97.57 127	97.62 203

AUN-MVS		90.17 211	89.50 204	92.19 238	96.21 184	82.67 309	97.76 245	97.53 127	88.05 205	91.67 170	96.15 212	83.10 171	97.47 238	88.11 208	66.91 372	96.43 238

dp		90.16 212	88.83 219	94.14 195	96.38 177	86.42 236	91.57 365	97.06 185	84.76 275	88.81 213	90.19 339	84.29 152	97.43 242	75.05 326	91.35 227	98.56 163

GA-MVS		90.10 213	88.69 222	94.33 187	92.44 300	87.97 202	99.08 110	96.26 236	89.65 152	86.92 231	93.11 275	68.09 300	96.96 258	82.54 274	90.15 236	98.05 190

VDDNet		90.08 214	88.54 228	94.69 174	94.41 255	87.68 206	98.21 210	96.40 226	76.21 359	93.33 151	97.75 138	54.93 364	98.77 159	94.71 126	90.96 229	97.61 204

gg-mvs-nofinetune		90.00 215	87.71 240	96.89 78	96.15 188	94.69 48	85.15 387	97.74 77	68.32 387	92.97 156	60.16 400	96.10 3	96.84 263	93.89 136	98.87 92	99.14 114

Effi-MVS+-dtu		89.97 216	90.68 190	87.81 326	95.15 227	71.98 375	97.87 236	95.40 306	91.92 95	87.57 222	91.44 302	74.27 253	96.84 263	89.45 192	93.10 188	94.60 253

EI-MVSNet		89.87 217	89.38 208	91.36 257	94.32 258	85.87 258	97.61 254	96.59 213	85.10 266	85.51 242	97.10 172	81.30 206	96.56 275	83.85 262	83.03 281	91.64 289

OPM-MVS		89.76 218	89.15 212	91.57 254	90.53 330	85.58 264	98.11 220	95.93 266	92.88 77	86.05 236	96.47 202	67.06 311	97.87 209	89.29 198	86.08 257	91.26 310
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpm		89.67 219	88.95 216	91.82 247	92.54 299	81.43 320	92.95 349	95.92 268	87.81 213	90.50 193	89.44 346	84.99 143	95.65 324	83.67 263	82.71 284	98.38 172

UniMVSNet_NR-MVSNet		89.60 220	88.55 227	92.75 227	92.17 305	90.07 149	98.74 146	98.15 40	88.37 193	83.21 260	93.98 252	82.86 175	95.93 313	86.95 218	72.47 349	92.25 273

cl22		89.57 221	88.79 220	91.91 244	97.94 106	87.62 209	97.98 230	96.51 220	85.03 269	82.37 278	91.79 295	83.65 158	96.50 279	85.96 231	77.89 305	91.61 294

PS-MVSNAJss		89.54 222	89.05 214	91.00 263	88.77 352	84.36 284	97.39 258	95.97 257	88.47 185	81.88 289	93.80 258	82.48 185	96.50 279	89.34 195	83.34 280	92.15 279

UniMVSNet (Re)		89.50 223	88.32 231	93.03 219	92.21 304	90.96 126	98.90 131	98.39 26	89.13 168	83.22 259	92.03 288	81.69 198	96.34 294	86.79 222	72.53 348	91.81 287

sd_testset		89.23 224	88.05 237	92.74 228	96.80 158	85.33 269	95.85 318	97.03 188	88.34 195	85.73 238	95.26 234	61.12 342	97.76 221	85.61 236	86.75 249	95.14 248

tpmvs		89.16 225	87.76 238	93.35 214	97.19 141	84.75 280	90.58 376	97.36 156	81.99 324	84.56 248	89.31 349	83.98 156	98.17 190	74.85 329	90.00 238	97.12 215

VPA-MVSNet		89.10 226	87.66 241	93.45 213	92.56 298	91.02 124	97.97 231	98.32 29	86.92 235	86.03 237	92.01 290	68.84 294	97.10 254	90.92 173	75.34 319	92.23 275

ADS-MVSNet		88.99 227	87.30 246	94.07 198	96.21 184	87.56 211	87.15 382	96.78 202	83.01 303	89.91 203	87.27 362	78.87 225	97.01 257	74.20 334	92.27 202	97.64 200

test0.0.03 1		88.96 228	88.61 224	90.03 293	91.09 324	84.43 283	98.97 125	97.02 190	90.21 135	80.29 306	96.31 208	84.89 145	91.93 376	72.98 343	85.70 260	93.73 255

miper_ehance_all_eth		88.94 229	88.12 235	91.40 255	95.32 218	86.93 228	97.85 237	95.55 296	84.19 281	81.97 287	91.50 301	84.16 153	95.91 316	84.69 246	77.89 305	91.36 305

tpm cat1		88.89 230	87.27 247	93.76 209	95.79 201	85.32 270	90.76 374	97.09 183	76.14 360	85.72 240	88.59 352	82.92 174	98.04 200	76.96 313	91.43 223	97.90 195

LPG-MVS_test		88.86 231	88.47 229	90.06 289	93.35 290	80.95 330	98.22 208	95.94 262	87.73 218	83.17 262	96.11 214	66.28 317	97.77 216	90.19 183	85.19 262	91.46 300

Anonymous202405211		88.84 232	87.03 251	94.27 189	98.14 101	84.18 287	98.44 183	95.58 295	76.79 358	89.34 210	96.88 187	53.42 369	99.54 108	87.53 214	87.12 248	99.09 120

Fast-Effi-MVS+-dtu		88.84 232	88.59 226	89.58 304	93.44 288	78.18 349	98.65 156	94.62 335	88.46 187	84.12 254	95.37 232	68.91 292	96.52 278	82.06 278	91.70 214	94.06 254

DU-MVS		88.83 234	87.51 242	92.79 225	91.46 319	90.07 149	98.71 147	97.62 109	88.87 178	83.21 260	93.68 260	74.63 245	95.93 313	86.95 218	72.47 349	92.36 269

CR-MVSNet		88.83 234	87.38 245	93.16 218	93.47 285	86.24 242	84.97 389	94.20 346	88.92 177	90.76 188	86.88 366	84.43 150	94.82 344	70.64 351	92.17 206	98.41 169

FMVSNet3		88.81 236	87.08 250	93.99 203	96.52 168	94.59 50	98.08 225	96.20 239	85.85 254	82.12 282	91.60 299	74.05 255	95.40 332	79.04 298	80.24 293	91.99 285

ACMM	86.95 13	88.77 237	88.22 233	90.43 280	93.61 282	81.34 323	98.50 176	95.92 268	87.88 212	83.85 256	95.20 236	67.20 309	97.89 207	86.90 221	84.90 264	92.06 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		88.75 238	86.56 257	95.34 148	98.92 78	87.45 215	97.64 253	93.52 357	70.55 378	81.49 295	97.25 163	74.43 250	99.88 54	71.14 350	94.09 179	98.67 158

ACMP	87.39 10	88.71 239	88.24 232	90.12 288	93.91 274	81.06 329	98.50 176	95.67 290	89.43 162	80.37 305	95.55 227	65.67 319	97.83 211	90.55 180	84.51 266	91.47 299
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
WB-MVSnew		88.69 240	88.34 230	89.77 299	94.30 262	85.99 255	98.14 215	97.31 159	87.15 229	87.85 220	96.07 216	69.91 285	95.52 327	72.83 345	91.47 222	87.80 366

dmvs_re		88.69 240	88.06 236	90.59 274	93.83 278	78.68 345	95.75 321	96.18 243	87.99 208	84.48 251	96.32 207	67.52 306	96.94 260	84.98 243	85.49 261	96.14 242

myMVS_eth3d		88.68 242	89.07 213	87.50 330	95.14 228	79.74 336	97.68 250	96.66 208	86.52 245	82.63 269	96.84 189	85.22 142	89.89 383	69.43 356	91.54 218	92.87 261

LCM-MVSNet-Re		88.59 243	88.61 224	88.51 321	95.53 211	72.68 373	96.85 282	88.43 393	88.45 188	73.14 357	90.63 322	75.82 240	94.38 351	92.95 154	95.71 165	98.48 167

WR-MVS		88.54 244	87.22 249	92.52 232	91.93 312	89.50 165	98.56 170	97.84 61	86.99 230	81.87 290	93.81 257	74.25 254	95.92 315	85.29 238	74.43 329	92.12 280

IterMVS-LS		88.34 245	87.44 243	91.04 262	94.10 264	85.85 259	98.10 221	95.48 300	85.12 265	82.03 286	91.21 307	81.35 205	95.63 325	83.86 261	75.73 317	91.63 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPNet		88.30 246	86.57 256	93.49 212	91.95 310	91.35 112	98.18 212	97.20 171	88.61 182	84.52 250	94.89 238	62.21 337	96.76 268	89.34 195	72.26 352	92.36 269

MSDG		88.29 247	86.37 259	94.04 201	96.90 154	86.15 248	96.52 293	94.36 343	77.89 354	79.22 320	96.95 181	69.72 288	99.59 104	73.20 342	92.58 196	96.37 240

test_djsdf		88.26 248	87.73 239	89.84 296	88.05 361	82.21 313	97.77 242	96.17 244	86.84 236	82.41 277	91.95 294	72.07 272	95.99 309	89.83 185	84.50 267	91.32 307

c3_l		88.19 249	87.23 248	91.06 261	94.97 240	86.17 247	97.72 247	95.38 307	83.43 295	81.68 294	91.37 303	82.81 176	95.72 322	84.04 259	73.70 337	91.29 309

D2MVS		87.96 250	87.39 244	89.70 301	91.84 313	83.40 297	98.31 201	98.49 22	88.04 206	78.23 331	90.26 333	73.57 257	96.79 267	84.21 253	83.53 277	88.90 358

cl____		87.82 251	86.79 255	90.89 267	94.88 244	85.43 266	97.81 238	95.24 315	82.91 310	80.71 302	91.22 306	81.97 196	95.84 318	81.34 283	75.06 321	91.40 304

DIV-MVS_self_test		87.82 251	86.81 254	90.87 268	94.87 245	85.39 268	97.81 238	95.22 320	82.92 309	80.76 301	91.31 305	81.99 194	95.81 320	81.36 282	75.04 322	91.42 303

eth_miper_zixun_eth		87.76 253	87.00 252	90.06 289	94.67 250	82.65 310	97.02 277	95.37 308	84.19 281	81.86 292	91.58 300	81.47 202	95.90 317	83.24 264	73.61 338	91.61 294

testing3		87.75 254	88.22 233	86.36 338	94.66 251	77.41 354	99.52 50	97.95 54	86.05 252	81.12 298	96.69 196	86.18 123	89.31 387	61.65 381	90.12 237	92.35 272

TranMVSNet+NR-MVSNet		87.75 254	86.31 260	92.07 242	90.81 327	88.56 190	98.33 198	97.18 172	87.76 215	81.87 290	93.90 255	72.45 268	95.43 330	83.13 268	71.30 359	92.23 275

XXY-MVS		87.75 254	86.02 264	92.95 223	90.46 331	89.70 161	97.71 249	95.90 274	84.02 283	80.95 299	94.05 246	67.51 307	97.10 254	85.16 239	78.41 302	92.04 284

NR-MVSNet		87.74 257	86.00 265	92.96 222	91.46 319	90.68 133	96.65 291	97.42 150	88.02 207	73.42 354	93.68 260	77.31 236	95.83 319	84.26 252	71.82 356	92.36 269

Anonymous20240529		87.66 258	85.58 271	93.92 204	97.59 119	85.01 276	98.13 216	97.13 177	66.69 392	88.47 216	96.01 218	55.09 363	99.51 110	87.00 217	84.12 271	97.23 214

ADS-MVSNet2		87.62 259	86.88 253	89.86 295	96.21 184	79.14 341	87.15 382	92.99 360	83.01 303	89.91 203	87.27 362	78.87 225	92.80 365	74.20 334	92.27 202	97.64 200

pmmvs4		87.58 260	86.17 263	91.80 248	89.58 342	88.92 182	97.25 266	95.28 311	82.54 314	80.49 304	93.17 274	75.62 242	96.05 308	82.75 271	78.90 300	90.42 333

jajsoiax		87.35 261	86.51 258	89.87 294	87.75 366	81.74 317	97.03 275	95.98 256	88.47 185	80.15 308	93.80 258	61.47 339	96.36 288	89.44 193	84.47 268	91.50 298

PVSNet_0	83.28 16	87.31 262	85.16 277	93.74 210	94.78 247	84.59 281	98.91 130	98.69 20	89.81 149	78.59 327	93.23 272	61.95 338	99.34 134	94.75 123	55.72 393	97.30 210

v2v482		87.27 263	85.76 268	91.78 252	89.59 341	87.58 210	98.56 170	95.54 297	84.53 277	82.51 273	91.78 296	73.11 263	96.47 282	82.07 277	74.14 335	91.30 308

mvs_tets		87.09 264	86.22 261	89.71 300	87.87 362	81.39 322	96.73 289	95.90 274	88.19 201	79.99 310	93.61 263	59.96 346	96.31 296	89.40 194	84.34 269	91.43 302

V42		87.00 265	85.68 270	90.98 264	89.91 335	86.08 250	98.32 200	95.61 293	83.67 292	82.72 267	90.67 319	74.00 256	96.53 277	81.94 280	74.28 332	90.32 335

miper_lstm_enhance		86.90 266	86.20 262	89.00 316	94.53 253	81.19 326	96.74 288	95.24 315	82.33 319	80.15 308	90.51 330	81.99 194	94.68 348	80.71 288	73.58 339	91.12 313

FMVSNet2		86.90 266	84.79 285	93.24 216	95.11 231	92.54 95	97.67 252	95.86 280	82.94 306	80.55 303	91.17 308	62.89 334	95.29 334	77.23 310	79.71 299	91.90 286

v1144		86.83 268	85.31 276	91.40 255	89.75 339	87.21 226	98.31 201	95.45 302	83.22 298	82.70 268	90.78 314	73.36 258	96.36 288	79.49 295	74.69 326	90.63 330

MS-PatchMatch		86.75 269	85.92 266	89.22 311	91.97 308	82.47 312	96.91 279	96.14 246	83.74 289	77.73 332	93.53 266	58.19 351	97.37 246	76.75 316	98.35 112	87.84 364

anonymousdsp		86.69 270	85.75 269	89.53 305	86.46 374	82.94 302	96.39 296	95.71 286	83.97 285	79.63 315	90.70 317	68.85 293	95.94 312	86.01 229	84.02 272	89.72 348

GBi-Net		86.67 271	84.96 279	91.80 248	95.11 231	88.81 184	96.77 284	95.25 312	82.94 306	82.12 282	90.25 334	62.89 334	94.97 339	79.04 298	80.24 293	91.62 291

test1		86.67 271	84.96 279	91.80 248	95.11 231	88.81 184	96.77 284	95.25 312	82.94 306	82.12 282	90.25 334	62.89 334	94.97 339	79.04 298	80.24 293	91.62 291

MVP-Stereo		86.61 273	85.83 267	88.93 318	88.70 354	83.85 292	96.07 309	94.41 342	82.15 322	75.64 343	91.96 293	67.65 305	96.45 284	77.20 312	98.72 99	86.51 376
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CP-MVSNet		86.54 274	85.45 274	89.79 298	91.02 326	82.78 308	97.38 260	97.56 122	85.37 262	79.53 317	93.03 276	71.86 275	95.25 335	79.92 293	73.43 343	91.34 306

WR-MVS_H		86.53 275	85.49 273	89.66 303	91.04 325	83.31 299	97.53 256	98.20 35	84.95 272	79.64 314	90.90 312	78.01 233	95.33 333	76.29 319	72.81 345	90.35 334

tt0805		86.50 276	84.79 285	91.63 253	91.97 308	81.49 319	96.49 294	97.38 154	82.24 320	82.44 274	95.82 223	51.22 374	98.25 187	84.55 249	80.96 292	95.13 250

v144192		86.40 277	84.89 282	90.91 265	89.48 345	85.59 263	98.21 210	95.43 305	82.45 317	82.62 271	90.58 326	72.79 267	96.36 288	78.45 305	74.04 336	90.79 322

v148		86.38 278	85.06 278	90.37 284	89.47 346	84.10 288	98.52 172	95.48 300	83.80 288	80.93 300	90.22 337	74.60 247	96.31 296	80.92 286	71.55 357	90.69 328

v1192		86.32 279	84.71 287	91.17 259	89.53 344	86.40 237	98.13 216	95.44 304	82.52 315	82.42 276	90.62 323	71.58 279	96.33 295	77.23 310	74.88 323	90.79 322

Patchmatch-test		86.25 280	84.06 297	92.82 224	94.42 254	82.88 306	82.88 396	94.23 345	71.58 374	79.39 318	90.62 323	89.00 65	96.42 285	63.03 377	91.37 226	99.16 112

v8		86.11 281	84.45 292	91.10 260	89.99 334	86.85 229	97.24 267	95.36 309	81.99 324	79.89 312	89.86 342	74.53 249	96.39 286	78.83 302	72.32 351	90.05 342

v1921920		86.02 282	84.44 293	90.77 271	89.32 347	85.20 271	98.10 221	95.35 310	82.19 321	82.25 280	90.71 316	70.73 282	96.30 299	76.85 315	74.49 328	90.80 321

JIA-IIPM		85.97 283	84.85 283	89.33 310	93.23 292	73.68 367	85.05 388	97.13 177	69.62 383	91.56 174	68.03 398	88.03 80	96.96 258	77.89 308	93.12 187	97.34 209

pmmvs5		85.87 284	84.40 295	90.30 285	88.53 356	84.23 285	98.60 165	93.71 353	81.53 329	80.29 306	92.02 289	64.51 327	95.52 327	82.04 279	78.34 303	91.15 312

XVG-ACMP-BASELINE		85.86 285	84.95 281	88.57 320	89.90 336	77.12 355	94.30 336	95.60 294	87.40 226	82.12 282	92.99 278	53.42 369	97.66 226	85.02 242	83.83 273	90.92 318

Baseline_NR-MVSNet		85.83 286	84.82 284	88.87 319	88.73 353	83.34 298	98.63 160	91.66 377	80.41 342	82.44 274	91.35 304	74.63 245	95.42 331	84.13 255	71.39 358	87.84 364

PS-CasMVS		85.81 287	84.58 290	89.49 308	90.77 328	82.11 314	97.20 270	97.36 156	84.83 274	79.12 322	92.84 279	67.42 308	95.16 337	78.39 306	73.25 344	91.21 311

IterMVS		85.81 287	84.67 288	89.22 311	93.51 284	83.67 294	96.32 299	94.80 329	85.09 267	78.69 323	90.17 340	66.57 315	93.17 361	79.48 296	77.42 311	90.81 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		85.77 289	84.11 296	90.73 272	89.26 348	85.15 274	97.88 235	95.23 319	81.89 327	82.16 281	90.55 328	69.60 291	96.31 296	75.59 324	74.87 324	90.72 327

IterMVS-SCA-FT		85.73 290	84.64 289	89.00 316	93.46 287	82.90 304	96.27 300	94.70 332	85.02 270	78.62 325	90.35 332	66.61 313	93.33 358	79.38 297	77.36 312	90.76 324

v10		85.73 290	84.01 298	90.87 268	90.03 333	86.73 231	97.20 270	95.22 320	81.25 332	79.85 313	89.75 343	73.30 261	96.28 300	76.87 314	72.64 347	89.61 350

UniMVSNet_ETH3D		85.65 292	83.79 300	91.21 258	90.41 332	80.75 332	95.36 326	95.78 282	78.76 348	81.83 293	94.33 245	49.86 379	96.66 270	84.30 251	83.52 278	96.22 241

PatchT		85.44 293	83.19 303	92.22 236	93.13 294	83.00 301	83.80 395	96.37 228	70.62 377	90.55 191	79.63 390	84.81 147	94.87 342	58.18 388	91.59 215	98.79 149

RPSCF		85.33 294	85.55 272	84.67 351	94.63 252	62.28 390	93.73 342	93.76 351	74.38 368	85.23 245	97.06 175	64.09 328	98.31 182	80.98 284	86.08 257	93.41 259

PEN-MVS		85.21 295	83.93 299	89.07 315	89.89 337	81.31 324	97.09 273	97.24 164	84.45 279	78.66 324	92.68 282	68.44 297	94.87 342	75.98 321	70.92 360	91.04 315

test_fmvs2		85.10 296	85.45 274	84.02 354	89.85 338	65.63 388	98.49 178	92.59 365	90.45 130	85.43 244	93.32 268	43.94 386	96.59 273	90.81 176	84.19 270	89.85 346

RPMNet		85.07 297	81.88 315	94.64 177	93.47 285	86.24 242	84.97 389	97.21 167	64.85 394	90.76 188	78.80 391	80.95 209	99.27 137	53.76 392	92.17 206	98.41 169

AllTest		84.97 298	83.12 304	90.52 278	96.82 156	78.84 343	95.89 313	92.17 370	77.96 352	75.94 339	95.50 228	55.48 359	99.18 139	71.15 348	87.14 246	93.55 257

USDC		84.74 299	82.93 305	90.16 287	91.73 315	83.54 296	95.00 330	93.30 359	88.77 180	73.19 356	93.30 270	53.62 368	97.65 228	75.88 322	81.54 290	89.30 353

Anonymous20231211		84.72 300	82.65 312	90.91 265	97.71 112	84.55 282	97.28 264	96.67 207	66.88 391	79.18 321	90.87 313	58.47 350	96.60 272	82.61 273	74.20 333	91.59 296

pm-mvs1		84.68 301	82.78 309	90.40 281	89.58 342	85.18 272	97.31 262	94.73 331	81.93 326	76.05 338	92.01 290	65.48 323	96.11 306	78.75 303	69.14 363	89.91 345

ACMH	83.09 17	84.60 302	82.61 313	90.57 275	93.18 293	82.94 302	96.27 300	94.92 325	81.01 335	72.61 363	93.61 263	56.54 355	97.79 214	74.31 332	81.07 291	90.99 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	81.71 19	84.59 303	82.72 311	90.18 286	92.89 297	83.18 300	93.15 347	94.74 330	78.99 345	75.14 346	92.69 281	65.64 320	97.63 229	69.46 355	81.82 289	89.74 347
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
COLMAP_ROB	82.69 18	84.54 304	82.82 306	89.70 301	96.72 162	78.85 342	95.89 313	92.83 363	71.55 375	77.54 334	95.89 222	59.40 348	99.14 145	67.26 364	88.26 242	91.11 314
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		84.48 305	81.83 316	92.42 234	91.73 315	87.36 218	85.52 385	94.42 341	81.40 330	81.91 288	87.58 356	51.92 372	92.81 364	73.84 337	88.15 243	97.08 219

our_test_3		84.47 306	82.80 307	89.50 306	89.01 349	83.90 291	97.03 275	94.56 336	81.33 331	75.36 345	90.52 329	71.69 277	94.54 350	68.81 358	76.84 313	90.07 340

v7n		84.42 307	82.75 310	89.43 309	88.15 359	81.86 316	96.75 287	95.67 290	80.53 338	78.38 329	89.43 347	69.89 286	96.35 293	73.83 338	72.13 353	90.07 340

kuosan		84.40 308	83.34 302	87.60 328	95.87 198	79.21 339	92.39 356	96.87 197	76.12 361	73.79 351	93.98 252	81.51 200	90.63 379	64.13 373	75.42 318	92.95 260

ACMH+	83.78 15	84.21 309	82.56 314	89.15 313	93.73 281	79.16 340	96.43 295	94.28 344	81.09 334	74.00 350	94.03 249	54.58 365	97.67 225	76.10 320	78.81 301	90.63 330

EU-MVSNet		84.19 310	84.42 294	83.52 358	88.64 355	67.37 386	96.04 310	95.76 284	85.29 263	78.44 328	93.18 273	70.67 283	91.48 378	75.79 323	75.98 315	91.70 288

DTE-MVSNet		84.14 311	82.80 307	88.14 323	88.95 351	79.87 335	96.81 283	96.24 237	83.50 294	77.60 333	92.52 284	67.89 304	94.24 353	72.64 346	69.05 364	90.32 335

OurMVSNet-221017-0		84.13 312	83.59 301	85.77 343	87.81 363	70.24 380	94.89 331	93.65 355	86.08 251	76.53 335	93.28 271	61.41 340	96.14 305	80.95 285	77.69 310	90.93 317

Syy-MVS		84.10 313	84.53 291	82.83 360	95.14 228	65.71 387	97.68 250	96.66 208	86.52 245	82.63 269	96.84 189	68.15 299	89.89 383	45.62 398	91.54 218	92.87 261

FMVSNet1		83.94 314	81.32 322	91.80 248	91.94 311	88.81 184	96.77 284	95.25 312	77.98 350	78.25 330	90.25 334	50.37 378	94.97 339	73.27 341	77.81 309	91.62 291

tfpnnormal		83.65 315	81.35 321	90.56 277	91.37 321	88.06 199	97.29 263	97.87 58	78.51 349	76.20 336	90.91 311	64.78 326	96.47 282	61.71 380	73.50 340	87.13 373

ppachtmachnet_test		83.63 316	81.57 319	89.80 297	89.01 349	85.09 275	97.13 272	94.50 337	78.84 346	76.14 337	91.00 310	69.78 287	94.61 349	63.40 375	74.36 330	89.71 349

Patchmtry		83.61 317	81.64 317	89.50 306	93.36 289	82.84 307	84.10 392	94.20 346	69.47 384	79.57 316	86.88 366	84.43 150	94.78 345	68.48 360	74.30 331	90.88 319

KD-MVS_2432*1600		82.98 318	80.52 327	90.38 282	94.32 258	88.98 176	92.87 351	95.87 278	80.46 340	73.79 351	87.49 359	82.76 179	93.29 359	70.56 352	46.53 402	88.87 359

miper_refine_blended		82.98 318	80.52 327	90.38 282	94.32 258	88.98 176	92.87 351	95.87 278	80.46 340	73.79 351	87.49 359	82.76 179	93.29 359	70.56 352	46.53 402	88.87 359

SixPastTwentyTwo		82.63 320	81.58 318	85.79 342	88.12 360	71.01 378	95.17 328	92.54 366	84.33 280	72.93 361	92.08 287	60.41 345	95.61 326	74.47 331	74.15 334	90.75 325

testgi		82.29 321	81.00 324	86.17 340	87.24 369	74.84 363	97.39 258	91.62 378	88.63 181	75.85 342	95.42 231	46.07 385	91.55 377	66.87 367	79.94 297	92.12 280

FMVSNet5		82.29 321	80.54 326	87.52 329	93.79 280	84.01 289	93.73 342	92.47 367	76.92 357	74.27 348	86.15 370	63.69 332	89.24 388	69.07 357	74.79 325	89.29 354

TransMVSNet (Re)		81.97 323	79.61 333	89.08 314	89.70 340	84.01 289	97.26 265	91.85 376	78.84 346	73.07 360	91.62 298	67.17 310	95.21 336	67.50 363	59.46 387	88.02 363

LF4IMVS		81.94 324	81.17 323	84.25 353	87.23 370	68.87 385	93.35 346	91.93 375	83.35 297	75.40 344	93.00 277	49.25 382	96.65 271	78.88 301	78.11 304	87.22 372

Patchmatch-RL test		81.90 325	80.13 329	87.23 333	80.71 390	70.12 382	84.07 393	88.19 394	83.16 300	70.57 365	82.18 381	87.18 96	92.59 367	82.28 276	62.78 380	98.98 127

DSMNet-mixed		81.60 326	81.43 320	82.10 363	84.36 380	60.79 391	93.63 344	86.74 396	79.00 344	79.32 319	87.15 364	63.87 330	89.78 385	66.89 366	91.92 208	95.73 246

dongtai		81.36 327	80.61 325	83.62 357	94.25 263	73.32 369	95.15 329	96.81 199	73.56 371	69.79 368	92.81 280	81.00 208	86.80 394	52.08 395	70.06 362	90.75 325

test_vis1_rt		81.31 328	80.05 331	85.11 345	91.29 322	70.66 379	98.98 124	77.39 408	85.76 257	68.80 372	82.40 379	36.56 395	99.44 119	92.67 159	86.55 251	85.24 383

K. test v3		81.04 329	79.77 332	84.83 349	87.41 367	70.23 381	95.60 325	93.93 350	83.70 291	67.51 379	89.35 348	55.76 357	93.58 357	76.67 317	68.03 367	90.67 329

Anonymous20231206		80.76 330	79.42 334	84.79 350	84.78 379	72.98 370	96.53 292	92.97 361	79.56 343	74.33 347	88.83 350	61.27 341	92.15 373	60.59 383	75.92 316	89.24 355

CMPMVS	58.40 21	80.48 331	80.11 330	81.59 366	85.10 378	59.56 393	94.14 339	95.95 261	68.54 386	60.71 390	93.31 269	55.35 362	97.87 209	83.06 269	84.85 265	87.33 370
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		80.42 332	77.94 337	87.85 325	92.09 306	78.58 346	93.74 341	89.94 387	74.99 364	69.77 369	91.78 296	46.09 384	97.58 233	65.17 372	77.89 305	87.38 368

EG-PatchMatch MVS		79.92 333	77.59 338	86.90 335	87.06 371	77.90 353	96.20 307	94.06 348	74.61 366	66.53 383	88.76 351	40.40 393	96.20 301	67.02 365	83.66 276	86.61 374

pmmvs6		79.90 334	77.31 340	87.67 327	84.17 381	78.13 350	95.86 317	93.68 354	67.94 388	72.67 362	89.62 345	50.98 376	95.75 321	74.80 330	66.04 374	89.14 356

CL-MVSNet_self_test		79.89 335	78.34 336	84.54 352	81.56 388	75.01 361	96.88 281	95.62 292	81.10 333	75.86 341	85.81 371	68.49 296	90.26 381	63.21 376	56.51 391	88.35 361

MDA-MVSNet_test_wron		79.65 336	77.05 341	87.45 331	87.79 365	80.13 333	96.25 303	94.44 338	73.87 369	51.80 396	87.47 361	68.04 301	92.12 374	66.02 368	67.79 369	90.09 338

YYNet1		79.64 337	77.04 342	87.43 332	87.80 364	79.98 334	96.23 304	94.44 338	73.83 370	51.83 395	87.53 357	67.96 303	92.07 375	66.00 369	67.75 370	90.23 337

MVS-HIRNet		79.01 338	75.13 350	90.66 273	93.82 279	81.69 318	85.16 386	93.75 352	54.54 396	74.17 349	59.15 402	57.46 353	96.58 274	63.74 374	94.38 176	93.72 256

UnsupCasMVSNet_eth		78.90 339	76.67 344	85.58 344	82.81 386	74.94 362	91.98 359	96.31 231	84.64 276	65.84 385	87.71 355	51.33 373	92.23 372	72.89 344	56.50 392	89.56 351

test_0402		78.81 340	76.33 345	86.26 339	91.18 323	78.44 348	95.88 315	91.34 381	68.55 385	70.51 367	89.91 341	52.65 371	94.99 338	47.14 397	79.78 298	85.34 382

pmmvs-eth3d		78.71 341	76.16 346	86.38 337	80.25 392	81.19 326	94.17 338	92.13 372	77.97 351	66.90 382	82.31 380	55.76 357	92.56 368	73.63 340	62.31 383	85.38 380

Anonymous20240521		78.63 342	76.90 343	83.82 355	82.82 385	72.86 371	95.72 322	93.57 356	73.55 372	72.17 364	84.79 373	49.69 380	92.51 369	65.29 371	74.50 327	86.09 378

test20.03		78.51 343	77.48 339	81.62 365	83.07 384	71.03 377	96.11 308	92.83 363	81.66 328	69.31 371	89.68 344	57.53 352	87.29 393	58.65 387	68.47 365	86.53 375

TDRefinement		78.01 344	75.31 348	86.10 341	70.06 403	73.84 366	93.59 345	91.58 379	74.51 367	73.08 359	91.04 309	49.63 381	97.12 251	74.88 328	59.47 386	87.33 370

OpenMVS_ROB	73.86 20	77.99 345	75.06 351	86.77 336	83.81 383	77.94 352	96.38 297	91.53 380	67.54 389	68.38 374	87.13 365	43.94 386	96.08 307	55.03 391	81.83 288	86.29 377

MDA-MVSNet-bldmvs		77.82 346	74.75 352	87.03 334	88.33 357	78.52 347	96.34 298	92.85 362	75.57 362	48.87 398	87.89 354	57.32 354	92.49 370	60.79 382	64.80 378	90.08 339

KD-MVS_self_test		77.47 347	75.88 347	82.24 361	81.59 387	68.93 384	92.83 353	94.02 349	77.03 356	73.14 357	83.39 376	55.44 361	90.42 380	67.95 361	57.53 390	87.38 368

dmvs_testset		77.17 348	78.99 335	71.71 376	87.25 368	38.55 413	91.44 366	81.76 404	85.77 256	69.49 370	95.94 221	69.71 289	84.37 396	52.71 394	76.82 314	92.21 277

new_pmnet		76.02 349	73.71 354	82.95 359	83.88 382	72.85 372	91.26 369	92.26 369	70.44 379	62.60 388	81.37 383	47.64 383	92.32 371	61.85 379	72.10 354	83.68 388

MIMVSNet1		75.92 350	73.30 355	83.81 356	81.29 389	75.57 359	92.26 357	92.05 373	73.09 373	67.48 380	86.18 369	40.87 392	87.64 392	55.78 390	70.68 361	88.21 362

mvsany_test3		75.85 351	74.52 353	79.83 368	73.53 400	60.64 392	91.73 362	87.87 395	83.91 287	70.55 366	82.52 378	31.12 397	93.66 355	86.66 224	62.83 379	85.19 384

test_fmvs3		75.09 352	75.19 349	74.81 373	77.45 396	54.08 399	95.93 311	90.64 384	82.51 316	73.29 355	81.19 384	22.29 402	86.29 395	85.50 237	67.89 368	84.06 386

PM-MVS		74.88 353	72.85 356	80.98 367	78.98 394	64.75 389	90.81 373	85.77 397	80.95 336	68.23 376	82.81 377	29.08 399	92.84 363	76.54 318	62.46 382	85.36 381

new-patchmatchnet		74.80 354	72.40 357	81.99 364	78.36 395	72.20 374	94.44 334	92.36 368	77.06 355	63.47 387	79.98 389	51.04 375	88.85 389	60.53 384	54.35 394	84.92 385

UnsupCasMVSNet_bld		73.85 355	70.14 359	84.99 347	79.44 393	75.73 358	88.53 379	95.24 315	70.12 381	61.94 389	74.81 395	41.41 391	93.62 356	68.65 359	51.13 399	85.62 379

pmmvs3		72.86 356	69.76 361	82.17 362	73.86 399	74.19 365	94.20 337	89.01 392	64.23 395	67.72 377	80.91 387	41.48 390	88.65 390	62.40 378	54.02 395	83.68 388

test_f		71.94 357	70.82 358	75.30 372	72.77 401	53.28 400	91.62 363	89.66 390	75.44 363	64.47 386	78.31 392	20.48 403	89.56 386	78.63 304	66.02 375	83.05 391

N_pmnet		70.19 358	69.87 360	71.12 378	88.24 358	30.63 417	95.85 318	28.70 416	70.18 380	68.73 373	86.55 368	64.04 329	93.81 354	53.12 393	73.46 341	88.94 357

test_method		70.10 359	68.66 362	74.41 375	86.30 376	55.84 397	94.47 333	89.82 388	35.18 404	66.15 384	84.75 374	30.54 398	77.96 405	70.40 354	60.33 385	89.44 352

APD_test1		68.93 360	66.98 363	74.77 374	80.62 391	53.15 401	87.97 380	85.01 399	53.76 397	59.26 391	87.52 358	25.19 400	89.95 382	56.20 389	67.33 371	81.19 392

WB-MVS		66.44 361	66.29 364	66.89 381	74.84 397	44.93 408	93.00 348	84.09 402	71.15 376	55.82 393	81.63 382	63.79 331	80.31 403	21.85 407	50.47 400	75.43 394

SSC-MVS		65.42 362	65.20 365	66.06 382	73.96 398	43.83 409	92.08 358	83.54 403	69.77 382	54.73 394	80.92 386	63.30 333	79.92 404	20.48 408	48.02 401	74.44 395

FPMVS		61.57 363	60.32 366	65.34 383	60.14 410	42.44 411	91.02 372	89.72 389	44.15 399	42.63 402	80.93 385	19.02 404	80.59 402	42.50 399	72.76 346	73.00 396

test_vis3_rt		61.29 364	58.75 367	68.92 380	67.41 404	52.84 402	91.18 371	59.23 415	66.96 390	41.96 403	58.44 403	11.37 411	94.72 347	74.25 333	57.97 389	59.20 402

EGC-MVSNET		60.70 365	55.37 369	76.72 370	86.35 375	71.08 376	89.96 377	84.44 401	0.38 413	1.50 414	84.09 375	37.30 394	88.10 391	40.85 402	73.44 342	70.97 398

LCM-MVSNet		60.07 366	56.37 368	71.18 377	54.81 412	48.67 405	82.17 397	89.48 391	37.95 402	49.13 397	69.12 396	13.75 410	81.76 397	59.28 385	51.63 398	83.10 390

PMMVS2		58.97 367	55.07 370	70.69 379	62.72 407	55.37 398	85.97 384	80.52 405	49.48 398	45.94 399	68.31 397	15.73 408	80.78 401	49.79 396	37.12 404	75.91 393

testf1		56.38 368	53.73 371	64.31 385	64.84 405	45.11 406	80.50 398	75.94 410	38.87 400	42.74 400	75.07 393	11.26 412	81.19 399	41.11 400	53.27 396	66.63 399

APD_test2		56.38 368	53.73 371	64.31 385	64.84 405	45.11 406	80.50 398	75.94 410	38.87 400	42.74 400	75.07 393	11.26 412	81.19 399	41.11 400	53.27 396	66.63 399

Gipuma		54.77 370	52.22 374	62.40 387	86.50 373	59.37 394	50.20 405	90.35 386	36.52 403	41.20 404	49.49 405	18.33 406	81.29 398	32.10 404	65.34 376	46.54 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 371	52.86 373	56.05 388	32.75 416	41.97 412	73.42 402	76.12 409	21.91 409	39.68 405	96.39 205	42.59 389	65.10 408	78.00 307	14.92 409	61.08 401

ANet_high		50.71 372	46.17 375	64.33 384	44.27 414	52.30 403	76.13 401	78.73 406	64.95 393	27.37 407	55.23 404	14.61 409	67.74 407	36.01 403	18.23 407	72.95 397

PMVS	41.42 23	45.67 373	42.50 376	55.17 389	34.28 415	32.37 415	66.24 403	78.71 407	30.72 405	22.04 410	59.59 401	4.59 414	77.85 406	27.49 405	58.84 388	55.29 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 374	37.64 379	53.90 390	49.46 413	43.37 410	65.09 404	66.66 412	26.19 408	25.77 409	48.53 406	3.58 416	63.35 409	26.15 406	27.28 405	54.97 404
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 375	40.93 377	41.29 391	61.97 408	33.83 414	84.00 394	65.17 413	27.17 406	27.56 406	46.72 407	17.63 407	60.41 410	19.32 409	18.82 406	29.61 406

EMVS		39.96 376	39.88 378	40.18 392	59.57 411	32.12 416	84.79 391	64.57 414	26.27 407	26.14 408	44.18 410	18.73 405	59.29 411	17.03 410	17.67 408	29.12 407

cdsmvs_eth3d_5k		22.52 377	30.03 380	0.00 396	0.00 419	0.00 421	0.00 407	97.17 173	0.00 414	0.00 415	98.77 85	74.35 252	0.00 415	0.00 414	0.00 413	0.00 411

testmvs		18.81 378	23.05 381	6.10 395	4.48 417	2.29 420	97.78 240	3.00 418	3.27 411	18.60 411	62.71 399	1.53 418	2.49 414	14.26 412	1.80 411	13.50 409

wuyk23d		16.71 379	16.73 383	16.65 393	60.15 409	25.22 418	41.24 406	5.17 417	6.56 410	5.48 413	3.61 413	3.64 415	22.72 412	15.20 411	9.52 410	1.99 410

test123		16.58 380	19.47 382	7.91 394	3.59 418	5.37 419	94.32 335	1.39 419	2.49 412	13.98 412	44.60 409	2.91 417	2.65 413	11.35 413	0.57 412	15.70 408

ab-mvs-re		8.21 381	10.94 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	98.50 108	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		6.87 382	9.16 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	82.48 185	0.00 415	0.00 414	0.00 413	0.00 411

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						79.74 336								67.75 362

FOURS1					99.50 42	88.94 179	99.55 44	97.47 141	91.32 110	98.12 46

MSC_two_6792asdad				99.51 2	99.61 24	98.60 2		97.69 88					99.98 9	99.55 13	99.83 15	99.96 10

PC_three_1452									94.60 36	99.41 4	99.12 46	95.50 6	99.96 28	99.84 2	99.92 3	99.97 7

No_MVS				99.51 2	99.61 24	98.60 2		97.69 88					99.98 9	99.55 13	99.83 15	99.96 10

test_one_0601					99.59 28	94.89 37		97.64 103	93.14 69	98.93 21	99.45 14	93.45 16

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					99.67 10	93.28 76		97.61 110	87.78 214	97.41 64	99.16 36	90.15 51	99.56 105	98.35 42	99.70 38

RE-MVS-def			95.70 66		99.22 59	87.26 224	98.40 190	97.21 167	89.63 153	96.67 87	98.97 62	85.24 141		96.62 81	99.31 69	99.60 70

IU-MVS					99.63 18	95.38 24		97.73 80	95.54 26	99.54 3				99.69 7	99.81 23	99.99 1

OPU-MVS				99.49 4	99.64 17	98.51 4	99.77 18				99.19 30	95.12 7	99.97 21	99.90 1	99.92 3	99.99 1

test_241102_TWO								97.72 81	94.17 43	99.23 10	99.54 3	93.14 22	99.98 9	99.70 5	99.82 19	99.99 1

test_241102_ONE					99.63 18	95.24 27		97.72 81	94.16 45	99.30 8	99.49 9	93.32 17	99.98 9

9.14			96.87 27		99.34 50		99.50 51	97.49 138	89.41 163	98.59 32	99.43 16	89.78 55	99.69 91	98.69 30	99.62 47

save fliter					99.34 50	93.85 67	99.65 36	97.63 107	95.69 22

test_0728_THIRD									93.01 70	99.07 15	99.46 10	94.66 12	99.97 21	99.25 18	99.82 19	99.95 15

test_0728_SECOND				98.77 8	99.66 12	96.37 14	99.72 24	97.68 90					99.98 9	99.64 8	99.82 19	99.96 10

test0726					99.66 12	95.20 32	99.77 18	97.70 86	93.95 48	99.35 7	99.54 3	93.18 20

GSMVS																98.84 142

test_part2					99.54 36	95.42 22				98.13 44

sam_mvs1												88.39 71				98.84 142

sam_mvs												87.08 99

ambc				79.60 369	72.76 402	56.61 396	76.20 400	92.01 374		68.25 375	80.23 388	23.34 401	94.73 346	73.78 339	60.81 384	87.48 367

MTGPA								97.45 144

test_post1							90.74 375				41.37 411	85.38 139	96.36 288	83.16 266

test_post											46.00 408	87.37 90	97.11 252

patchmatchnet-post											84.86 372	88.73 68	96.81 265

GG-mvs-BLEND				96.98 70	96.53 167	94.81 44	87.20 381	97.74 77		93.91 141	96.40 203	96.56 2	96.94 260	95.08 115	98.95 88	99.20 110

MTMP							99.21 87	91.09 382

gm-plane-assit					94.69 249	88.14 197			88.22 200		97.20 166		98.29 184	90.79 177

test9_res														98.60 33	99.87 9	99.90 22

TEST9					99.57 33	93.17 78	99.38 71	97.66 95	89.57 157	98.39 37	99.18 33	90.88 37	99.66 94

test_8					99.55 35	93.07 81	99.37 74	97.64 103	90.18 137	98.36 39	99.19 30	90.94 34	99.64 100

agg_prior2														97.84 58	99.87 9	99.91 21

agg_prior					99.54 36	92.66 91		97.64 103		97.98 53			99.61 102

TestCases				90.52 278	96.82 156	78.84 343		92.17 370	77.96 352	75.94 339	95.50 228	55.48 359	99.18 139	71.15 348	87.14 246	93.55 257

test_prior4						92.00 101	99.41 68

test_prior2							99.57 42		91.43 107	98.12 46	98.97 62	90.43 44		98.33 43	99.81 23

test_prior				97.01 65	99.58 30	91.77 104		97.57 121					99.49 112			99.79 36

旧先验2							98.67 154		85.75 258	98.96 20			98.97 154	93.84 138

新几何2							98.26 204

新几何1				97.40 52	98.92 78	92.51 96		97.77 75	85.52 260	96.69 86	99.06 53	88.08 79	99.89 53	84.88 244	99.62 47	99.79 36

旧先验1					98.97 73	92.90 89		97.74 77			99.15 39	91.05 33			99.33 67	99.60 70

无先验							98.52 172	97.82 65	87.20 228				99.90 50	87.64 213		99.85 30

原ACMM2							98.69 151

原ACMM1				96.18 114	99.03 71	90.08 148		97.63 107	88.98 172	97.00 75	98.97 62	88.14 78	99.71 90	88.23 206	99.62 47	98.76 153

test222					98.32 93	91.21 114	98.08 225	97.58 118	83.74 289	95.87 101	99.02 58	86.74 107			99.64 43	99.81 33

testdata2													99.88 54	84.16 254

segment_acmp												90.56 42

testdata				95.26 153	98.20 97	87.28 221		97.60 112	85.21 264	98.48 35	99.15 39	88.15 77	98.72 165	90.29 182	99.45 60	99.78 38

testdata1							97.89 233		92.43 83

test12				97.83 35	99.33 53	94.45 52		97.55 123		97.56 60		88.60 69	99.50 111		99.71 37	99.55 74

plane_prior7					93.84 276	85.73 261

plane_prior6					93.92 273	86.02 254						72.92 264

plane_prior5								96.30 232					97.75 222	93.46 147	86.17 255	92.67 265

plane_prior4											96.52 199

plane_prior3						85.91 256			93.65 61	86.99 229

plane_prior2							99.02 118		93.38 66

plane_prior1					93.90 275

plane_prior						86.07 252	99.14 103		93.81 58						86.26 254

n2								0.00 420

nn								0.00 420

door-mid								84.90 400

lessismore_v0				85.08 346	85.59 377	69.28 383		90.56 385		67.68 378	90.21 338	54.21 367	95.46 329	73.88 336	62.64 381	90.50 332

LGP-MVS_train				90.06 289	93.35 290	80.95 330		95.94 262	87.73 218	83.17 262	96.11 214	66.28 317	97.77 216	90.19 183	85.19 262	91.46 300

test11								97.68 90

door								85.30 398

HQP5-MVS						86.39 238

HQP-NCC					93.95 269		99.16 95		93.92 50	87.57 222

ACMP_Plane					93.95 269		99.16 95		93.92 50	87.57 222

BP-MVS														93.82 140

HQP4-MVS										87.57 222			97.77 216			92.72 263

HQP3-MVS								96.37 228							86.29 252

HQP2-MVS												73.34 259

NP-MVS					93.94 272	86.22 244					96.67 197

MDTV_nov1_ep13_2view						91.17 117	91.38 367		87.45 225	93.08 154		86.67 110		87.02 216		98.95 133

MDTV_nov1_ep13			90.47 194		96.14 190	88.55 191	91.34 368	97.51 133	89.58 156	92.24 163	90.50 331	86.99 103	97.61 231	77.64 309	92.34 200

ACMMP++_ref															82.64 285

ACMMP++															83.83 273

Test By Simon												83.62 159

ITE_SJBPF				87.93 324	92.26 303	76.44 357		93.47 358	87.67 221	79.95 311	95.49 230	56.50 356	97.38 244	75.24 325	82.33 287	89.98 344

DeepMVS_CX				76.08 371	90.74 329	51.65 404		90.84 383	86.47 248	57.89 392	87.98 353	35.88 396	92.60 366	65.77 370	65.06 377	83.97 387

High-resolution multi-view benchmark