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.

SED-MVS		99.28 5	99.11 6	99.77 8	99.93 27	99.30 11	99.96 25	98.43 119	97.27 20	99.80 16	99.94 4	96.71 23	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 27	99.31 9		98.41 135	97.71 8	99.84 8				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 25				99.80 60	97.44 13	100.00 1	100.00 1	99.98 35	100.00 1

test_241102_TWO								98.43 119	97.27 20	99.80 16	99.94 4	97.18 20	100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									96.96 29	99.80 16	99.79 64	97.49 9	100.00 1	99.99 5	99.98 35	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 159	97.28 18	99.83 10	99.91 15	97.22 18	100.00 1	99.99 5	100.00 1	99.89 94
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
test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 119					100.00 1	99.99 5	100.00 1	100.00 1

DPE-MVS		99.26 6	99.10 7	99.74 10	99.89 50	99.24 18	99.87 92	98.44 111	97.48 15	99.64 39	99.94 4	96.68 25	99.99 40	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 135					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 135					100.00 1	99.96 9	100.00 1	100.00 1

ETH3 D test6400		98.81 23	98.54 27	99.59 21	99.93 27	98.93 26	99.93 66	98.46 106	94.56 104	99.84 8	99.92 13	94.32 83	99.86 94	99.96 9	99.98 35	100.00 1

DVP-MVS++		99.26 6	99.09 8	99.77 8	99.91 44	99.31 9	99.95 43	98.43 119	96.48 42	99.80 16	99.93 11	97.44 13	100.00 1	99.92 12	99.98 35	100.00 1

test_0728_THIRD									96.48 42	99.83 10	99.91 15	97.87 4	100.00 1	99.92 12	100.00 1	100.00 1

DeepPCF-MVS	95.94 2	97.71 85	98.98 11	93.92 273	99.63 93	81.76 350	99.96 25	98.56 78	99.47 1	99.19 80	99.99 1	94.16 90	100.00 1	99.92 12	99.93 67	100.00 1

TSAR-MVS + GP.		98.60 34	98.51 29	98.86 90	99.73 86	96.63 130	99.97 18	97.92 207	98.07 5	98.76 99	99.55 108	95.00 60	99.94 72	99.91 15	97.68 157	99.99 24

APDe-MVS		99.06 11	98.91 13	99.51 31	99.94 14	98.76 44	99.91 74	98.39 142	97.20 24	99.46 56	99.85 35	95.53 45	99.79 113	99.86 16	100.00 1	99.99 24

xxxxxxxxxxxxxcwj		98.98 15	98.79 16	99.54 26	99.82 70	98.79 37	99.96 25	97.52 242	97.66 10	99.81 12	99.89 21	94.70 68	99.86 94	99.84 17	99.93 67	99.96 74

SF-MVS		98.67 31	98.40 36	99.50 32	99.77 78	98.67 48	99.90 78	98.21 176	93.53 149	99.81 12	99.89 21	94.70 68	99.86 94	99.84 17	99.93 67	99.96 74

9.14			98.38 39		99.87 57		99.91 74	98.33 157	93.22 157	99.78 24	99.89 21	94.57 71	99.85 98	99.84 17	99.97 48

ETH3D-3000-0.1		98.68 30	98.42 32	99.47 37	99.83 68	98.57 55	99.90 78	98.37 149	93.81 140	99.81 12	99.90 19	94.34 79	99.86 94	99.84 17	99.98 35	99.97 67

SD-MVS		98.92 17	98.70 18	99.56 24	99.70 90	98.73 45	99.94 60	98.34 156	96.38 47	99.81 12	99.76 75	94.59 70	99.98 46	99.84 17	99.96 52	99.97 67
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.		98.93 16	98.77 17	99.41 42	99.74 82	98.67 48	99.77 131	98.38 146	96.73 36	99.88 3	99.74 84	94.89 65	99.59 144	99.80 22	99.98 35	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PHI-MVS		98.41 50	98.21 50	99.03 78	99.86 59	97.10 116	99.98 10	98.80 51	90.78 236	99.62 43	99.78 69	95.30 49	100.00 1	99.80 22	99.93 67	99.99 24

test_prior3		98.99 14	98.84 15	99.43 38	99.94 14	98.49 61	99.95 43	98.65 61	95.78 63	99.73 29	99.76 75	96.00 32	99.80 110	99.78 24	100.00 1	99.99 24

test_prior2							99.95 43		95.78 63	99.73 29	99.76 75	96.00 32		99.78 24	100.00 1

CANet		98.27 60	97.82 71	99.63 15	99.72 88	99.10 21	99.98 10	98.51 98	97.00 28	98.52 109	99.71 89	87.80 198	99.95 64	99.75 26	99.38 117	99.83 100

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 10	98.69 56	98.20 3	99.93 1	99.98 2	96.82 22	100.00 1	99.75 26	100.00 1	99.99 24

ETH3D cwj APD-0.16		98.40 52	98.07 60	99.40 44	99.59 95	98.41 64	99.86 103	98.24 172	92.18 197	99.73 29	99.87 28	93.47 106	99.85 98	99.74 28	99.95 55	99.93 85

SMA-MVS		98.76 27	98.48 30	99.62 18	99.87 57	98.87 31	99.86 103	98.38 146	93.19 158	99.77 25	99.94 4	95.54 43	100.00 1	99.74 28	99.99 22	100.00 1
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
MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 64	98.47 2	99.13 82	99.92 13	96.38 29	100.00 1	99.74 28	100.00 1	100.00 1

CHOSEN 280x420		99.01 13	99.03 9	98.95 86	99.38 109	98.87 31	98.46 289	99.42 21	97.03 27	99.02 86	99.09 143	99.35 1	98.21 224	99.73 31	99.78 92	99.77 108

agg_prior1		98.88 20	98.66 20	99.54 26	99.93 27	98.77 40	99.96 25	98.43 119	94.63 102	99.63 40	99.85 35	95.79 40	99.85 98	99.72 32	99.99 22	99.99 24

test9_res														99.71 33	99.99 22	100.00 1

ZD-MVS					99.92 36	98.57 55		98.52 91	92.34 193	99.31 70	99.83 51	95.06 55	99.80 110	99.70 34	99.97 48

train_agg		98.88 20	98.65 21	99.59 21	99.92 36	98.92 27	99.96 25	98.43 119	94.35 114	99.71 35	99.86 31	95.94 34	99.85 98	99.69 35	99.98 35	99.99 24

testtj		98.89 19	98.69 19	99.52 29	99.94 14	98.56 57	99.90 78	98.55 84	95.14 82	99.72 33	99.84 48	95.46 46	100.00 1	99.65 36	99.99 22	99.99 24

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 68	98.02 6	99.90 2	99.95 3	97.33 16	100.00 1	99.54 37	100.00 1	100.00 1

MSLP-MVS++		99.13 8	99.01 10	99.49 34	99.94 14	98.46 63	99.98 10	98.86 46	97.10 25	99.80 16	99.94 4	95.92 36	100.00 1	99.51 38	100.00 1	100.00 1

MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 131	97.50 14	99.52 53	99.88 24	97.43 15	99.71 133	99.50 39	99.98 35	100.00 1
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
agg_prior2														99.48 40	100.00 1	100.00 1

PAPM		98.60 34	98.42 32	99.14 66	96.05 258	98.96 24	99.90 78	99.35 24	96.68 38	98.35 118	99.66 100	96.45 28	98.51 192	99.45 41	99.89 78	99.96 74

SteuartSystems-ACMMP		99.02 12	98.97 12	99.18 57	98.72 143	97.71 87	99.98 10	98.44 111	96.85 30	99.80 16	99.91 15	97.57 6	99.85 98	99.44 42	99.99 22	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		98.62 33	98.35 44	99.41 42	99.90 47	98.51 60	99.87 92	98.36 151	94.08 125	99.74 28	99.73 86	94.08 91	99.74 129	99.42 43	99.99 22	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-MVSNAJ		98.44 48	98.20 51	99.16 62	98.80 140	98.92 27	99.54 180	98.17 182	97.34 16	99.85 6	99.85 35	91.20 156	99.89 83	99.41 44	99.67 99	98.69 210

xiu_mvs_v2_base		98.23 64	97.97 65	99.02 80	98.69 144	98.66 50	99.52 182	98.08 193	97.05 26	99.86 4	99.86 31	90.65 167	99.71 133	99.39 45	98.63 134	98.69 210

test1172		98.38 54	98.25 48	98.77 93	99.88 54	96.56 134	99.80 124	98.36 151	94.68 99	99.20 77	99.80 60	93.28 113	99.78 115	99.34 46	99.92 71	99.98 55

HPM-MVS++		99.07 10	98.88 14	99.63 15	99.90 47	99.02 23	99.95 43	98.56 78	97.56 13	99.44 58	99.85 35	95.38 48	100.00 1	99.31 47	99.99 22	99.87 97

SR-MVS		98.46 46	98.30 47	98.93 87	99.88 54	97.04 117	99.84 110	98.35 154	94.92 89	99.32 69	99.80 60	93.35 108	99.78 115	99.30 48	99.95 55	99.96 74

MVS_111021_HR		98.72 28	98.62 23	99.01 81	99.36 110	97.18 112	99.93 66	99.90 1	96.81 34	98.67 103	99.77 71	93.92 95	99.89 83	99.27 49	99.94 61	99.96 74

MVS_111021_LR		98.42 49	98.38 39	98.53 115	99.39 108	95.79 162	99.87 92	99.86 2	96.70 37	98.78 96	99.79 64	92.03 144	99.90 79	99.17 50	99.86 83	99.88 96

PVSNet_BlendedMVS		96.05 143	95.82 141	96.72 186	99.59 95	96.99 119	99.95 43	99.10 29	94.06 128	98.27 121	95.80 267	89.00 189	99.95 64	99.12 51	87.53 259	93.24 322

PVSNet_Blended		97.94 72	97.64 75	98.83 91	99.59 95	96.99 119	100.00 1	99.10 29	95.38 76	98.27 121	99.08 144	89.00 189	99.95 64	99.12 51	99.25 120	99.57 145

Regformer-1		98.79 25	98.60 24	99.36 48	99.85 60	98.34 66	99.87 92	98.52 91	96.05 56	99.41 61	99.79 64	94.93 63	99.76 122	99.07 53	99.90 76	99.99 24

xiu_mvs_v1_base_debu		97.43 92	97.06 97	98.55 110	97.74 196	98.14 71	99.31 212	97.86 213	96.43 44	99.62 43	99.69 94	85.56 219	99.68 137	99.05 54	98.31 141	97.83 219

xiu_mvs_v1_base		97.43 92	97.06 97	98.55 110	97.74 196	98.14 71	99.31 212	97.86 213	96.43 44	99.62 43	99.69 94	85.56 219	99.68 137	99.05 54	98.31 141	97.83 219

xiu_mvs_v1_base_debi		97.43 92	97.06 97	98.55 110	97.74 196	98.14 71	99.31 212	97.86 213	96.43 44	99.62 43	99.69 94	85.56 219	99.68 137	99.05 54	98.31 141	97.83 219

Regformer-2		98.78 26	98.59 25	99.36 48	99.85 60	98.32 67	99.87 92	98.52 91	96.04 57	99.41 61	99.79 64	94.92 64	99.76 122	99.05 54	99.90 76	99.98 55

CP-MVS		98.45 47	98.32 45	98.87 89	99.96 8	96.62 131	99.97 18	98.39 142	94.43 109	98.90 92	99.87 28	94.30 84	100.00 1	99.04 58	99.99 22	99.99 24

DeepC-MVS_fast	96.59 1	98.81 23	98.54 27	99.62 18	99.90 47	98.85 33	99.24 221	98.47 104	98.14 4	99.08 83	99.91 15	93.09 119	100.00 1	99.04 58	99.99 22	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETV-MVS		97.92 74	97.80 72	98.25 130	98.14 173	96.48 135	99.98 10	97.63 225	95.61 71	99.29 74	99.46 116	92.55 132	98.82 172	99.02 60	98.54 135	99.46 161

VDD-MVS		93.77 199	92.94 205	96.27 201	98.55 150	90.22 290	98.77 272	97.79 218	90.85 234	96.82 155	99.42 118	61.18 354	99.77 119	98.95 61	94.13 214	98.82 205

APD-MVS_3200maxsize		98.25 63	98.08 59	98.78 92	99.81 73	96.60 132	99.82 117	98.30 164	93.95 134	99.37 67	99.77 71	92.84 124	99.76 122	98.95 61	99.92 71	99.97 67

VNet		97.21 104	96.57 114	99.13 71	98.97 124	97.82 85	99.03 243	99.21 28	94.31 117	99.18 81	98.88 169	86.26 214	99.89 83	98.93 63	94.32 212	99.69 117

XVS		98.70 29	98.55 26	99.15 64	99.94 14	97.50 99	99.94 60	98.42 131	96.22 52	99.41 61	99.78 69	94.34 79	99.96 57	98.92 64	99.95 55	99.99 24

X-MVStestdata		93.83 195	92.06 224	99.15 64	99.94 14	97.50 99	99.94 60	98.42 131	96.22 52	99.41 61	41.37 375	94.34 79	99.96 57	98.92 64	99.95 55	99.99 24

MP-MVS-pluss		98.07 69	97.64 75	99.38 47	99.74 82	98.41 64	99.74 142	98.18 181	93.35 153	96.45 165	99.85 35	92.64 130	99.97 55	98.91 66	99.89 78	99.77 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SR-MVS-dyc-post		98.31 57	98.17 53	98.71 96	99.79 75	96.37 141	99.76 136	98.31 161	94.43 109	99.40 65	99.75 80	93.28 113	99.78 115	98.90 67	99.92 71	99.97 67

RE-MVS-def			98.13 56		99.79 75	96.37 141	99.76 136	98.31 161	94.43 109	99.40 65	99.75 80	92.95 122		98.90 67	99.92 71	99.97 67

HPM-MVS		97.96 71	97.72 73	98.68 98	99.84 65	96.39 140	99.90 78	98.17 182	92.61 180	98.62 106	99.57 107	91.87 147	99.67 140	98.87 69	99.99 22	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS		98.23 64	97.97 65	99.03 78	99.94 14	97.17 115	99.95 43	98.39 142	94.70 97	98.26 123	99.81 59	91.84 148	100.00 1	98.85 70	99.97 48	99.93 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS		97.74 83	97.61 77	98.15 135	97.52 211	96.69 128	100.00 1	97.11 282	94.93 86	99.73 29	99.41 120	91.68 150	98.25 222	98.84 71	99.24 121	99.52 154

test_yl		97.83 77	97.37 86	99.21 54	99.18 113	97.98 79	99.64 165	99.27 26	91.43 221	97.88 133	98.99 152	95.84 38	99.84 107	98.82 72	95.32 204	99.79 104

DCV-MVSNet		97.83 77	97.37 86	99.21 54	99.18 113	97.98 79	99.64 165	99.27 26	91.43 221	97.88 133	98.99 152	95.84 38	99.84 107	98.82 72	95.32 204	99.79 104

Regformer-3		98.58 37	98.41 34	99.10 72	99.84 65	97.57 93	99.66 158	98.52 91	95.79 62	99.01 87	99.77 71	94.40 74	99.75 125	98.82 72	99.83 85	99.98 55

Regformer-4		98.56 38	98.39 38	99.08 74	99.84 65	97.52 96	99.66 158	98.52 91	95.76 65	99.01 87	99.77 71	94.33 82	99.75 125	98.80 75	99.83 85	99.98 55

#test#		98.59 36	98.41 34	99.14 66	99.96 8	97.43 104	99.95 43	98.61 70	95.00 84	99.31 70	99.85 35	94.22 86	100.00 1	98.78 76	99.98 35	99.98 55

PVSNet_0	88.03 19	91.80 243	90.27 255	96.38 199	98.27 164	90.46 286	99.94 60	99.61 12	93.99 131	86.26 310	97.39 221	71.13 323	99.89 83	98.77 77	67.05 357	98.79 207

DROMVSNet		97.38 98	97.24 91	97.80 145	97.41 214	95.64 170	99.99 5	97.06 287	94.59 103	99.63 40	99.32 127	89.20 187	98.14 226	98.76 78	99.23 122	99.62 132

MG-MVS		98.91 18	98.65 21	99.68 14	99.94 14	99.07 22	99.64 165	99.44 19	97.33 17	99.00 89	99.72 87	94.03 93	99.98 46	98.73 79	100.00 1	100.00 1

HFP-MVS		98.56 38	98.37 41	99.14 66	99.96 8	97.43 104	99.95 43	98.61 70	94.77 94	99.31 70	99.85 35	94.22 86	100.00 1	98.70 80	99.98 35	99.98 55

ACMMPR		98.50 43	98.32 45	99.05 76	99.96 8	97.18 112	99.95 43	98.60 72	94.77 94	99.31 70	99.84 48	93.73 101	100.00 1	98.70 80	99.98 35	99.98 55

zzz-MVS		98.33 56	98.00 63	99.30 50	99.85 60	97.93 82	99.80 124	98.28 166	95.76 65	97.18 146	99.88 24	92.74 127	100.00 1	98.67 82	99.88 80	99.99 24

MTAPA		98.29 59	97.96 68	99.30 50	99.85 60	97.93 82	99.39 202	98.28 166	95.76 65	97.18 146	99.88 24	92.74 127	100.00 1	98.67 82	99.88 80	99.99 24

region2R		98.54 40	98.37 41	99.05 76	99.96 8	97.18 112	99.96 25	98.55 84	94.87 92	99.45 57	99.85 35	94.07 92	100.00 1	98.67 82	100.00 1	99.98 55

ACMMP_NAP		98.49 44	98.14 55	99.54 26	99.66 92	98.62 54	99.85 106	98.37 149	94.68 99	99.53 50	99.83 51	92.87 123	100.00 1	98.66 85	99.84 84	99.99 24

mPP-MVS		98.39 53	98.20 51	98.97 84	99.97 3	96.92 122	99.95 43	98.38 146	95.04 83	98.61 107	99.80 60	93.39 107	100.00 1	98.64 86	100.00 1	99.98 55

DELS-MVS		98.54 40	98.22 49	99.50 32	99.15 116	98.65 52	100.00 1	98.58 74	97.70 9	98.21 125	99.24 137	92.58 131	99.94 72	98.63 87	99.94 61	99.92 91
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
alignmvs		97.81 79	97.33 89	99.25 52	98.77 142	98.66 50	99.99 5	98.44 111	94.40 113	98.41 114	99.47 114	93.65 103	99.42 155	98.57 88	94.26 213	99.67 120

CDPH-MVS		98.65 32	98.36 43	99.49 34	99.94 14	98.73 45	99.87 92	98.33 157	93.97 132	99.76 26	99.87 28	94.99 61	99.75 125	98.55 89	100.00 1	99.98 55

EI-MVSNet-Vis-set		98.27 60	98.11 58	98.75 95	99.83 68	96.59 133	99.40 198	98.51 98	95.29 79	98.51 110	99.76 75	93.60 105	99.71 133	98.53 90	99.52 110	99.95 82

canonicalmvs		97.09 108	96.32 120	99.39 46	98.93 128	98.95 25	99.72 150	97.35 260	94.45 107	97.88 133	99.42 118	86.71 209	99.52 146	98.48 91	93.97 217	99.72 114

API-MVS		97.86 75	97.66 74	98.47 118	99.52 101	95.41 175	99.47 191	98.87 45	91.68 212	98.84 93	99.85 35	92.34 138	99.99 40	98.44 92	99.96 52	100.00 1

lupinMVS		97.85 76	97.60 78	98.62 103	97.28 224	97.70 89	99.99 5	97.55 236	95.50 75	99.43 59	99.67 98	90.92 163	98.71 182	98.40 93	99.62 102	99.45 163

EI-MVSNet-UG-set		98.14 66	97.99 64	98.60 105	99.80 74	96.27 143	99.36 207	98.50 102	95.21 81	98.30 120	99.75 80	93.29 112	99.73 132	98.37 94	99.30 119	99.81 102

diffmvs		97.00 109	96.64 111	98.09 137	97.64 203	96.17 151	99.81 119	97.19 272	94.67 101	98.95 90	99.28 128	86.43 212	98.76 178	98.37 94	97.42 163	99.33 177

CPTT-MVS		97.64 87	97.32 90	98.58 108	99.97 3	95.77 163	99.96 25	98.35 154	89.90 249	98.36 117	99.79 64	91.18 159	99.99 40	98.37 94	99.99 22	99.99 24

CS-MVS-test		97.44 91	97.41 84	97.53 157	97.46 213	94.66 197	100.00 1	97.04 291	94.69 98	99.72 33	99.25 135	91.22 154	98.29 214	98.33 97	98.95 127	99.64 126

ZNCC-MVS		98.31 57	98.03 61	99.17 60	99.88 54	97.59 92	99.94 60	98.44 111	94.31 117	98.50 111	99.82 55	93.06 120	99.99 40	98.30 98	99.99 22	99.93 85

DP-MVS Recon		98.41 50	98.02 62	99.56 24	99.97 3	98.70 47	99.92 70	98.44 111	92.06 202	98.40 116	99.84 48	95.68 41	100.00 1	98.19 99	99.71 97	99.97 67

GG-mvs-BLEND				98.54 113	98.21 168	98.01 77	93.87 349	98.52 91		97.92 131	97.92 211	99.02 2	97.94 240	98.17 100	99.58 107	99.67 120

GST-MVS		98.27 60	97.97 65	99.17 60	99.92 36	97.57 93	99.93 66	98.39 142	94.04 130	98.80 95	99.74 84	92.98 121	100.00 1	98.16 101	99.76 93	99.93 85

CSCG		97.10 106	97.04 100	97.27 172	99.89 50	91.92 256	99.90 78	99.07 32	88.67 269	95.26 188	99.82 55	93.17 118	99.98 46	98.15 102	99.47 113	99.90 93

MAR-MVS		97.43 92	97.19 93	98.15 135	99.47 105	94.79 194	99.05 241	98.76 52	92.65 178	98.66 104	99.82 55	88.52 195	99.98 46	98.12 103	99.63 101	99.67 120
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPR		98.52 42	98.16 54	99.58 23	99.97 3	98.77 40	99.95 43	98.43 119	95.35 77	98.03 128	99.75 80	94.03 93	99.98 46	98.11 104	99.83 85	99.99 24

CLD-MVS		94.06 193	93.90 183	94.55 247	96.02 259	90.69 279	99.98 10	97.72 220	96.62 41	91.05 229	98.85 176	77.21 283	98.47 193	98.11 104	89.51 234	94.48 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VDDNet		93.12 212	91.91 227	96.76 184	96.67 251	92.65 241	98.69 278	98.21 176	82.81 333	97.75 136	99.28 128	61.57 352	99.48 153	98.09 106	94.09 215	98.15 215

HY-MVS	92.50 7	97.79 81	97.17 96	99.63 15	98.98 123	99.32 8	97.49 317	99.52 14	95.69 69	98.32 119	97.41 219	93.32 110	99.77 119	98.08 107	95.75 197	99.81 102

EIA-MVS		97.53 89	97.46 82	97.76 150	98.04 177	94.84 191	99.98 10	97.61 230	94.41 112	97.90 132	99.59 105	92.40 136	98.87 170	98.04 108	99.13 125	99.59 138

LFMVS		94.75 174	93.56 192	98.30 128	99.03 119	95.70 168	98.74 273	97.98 200	87.81 282	98.47 112	99.39 123	67.43 336	99.53 145	98.01 109	95.20 206	99.67 120

AdaColmap		97.23 103	96.80 107	98.51 116	99.99 1	95.60 171	99.09 230	98.84 48	93.32 154	96.74 157	99.72 87	86.04 215	100.00 1	98.01 109	99.43 116	99.94 84

EPNet		98.49 44	98.40 36	98.77 93	99.62 94	96.80 126	99.90 78	99.51 16	97.60 12	99.20 77	99.36 126	93.71 102	99.91 78	97.99 111	98.71 133	99.61 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		97.74 83	97.44 83	98.66 100	99.92 36	96.13 152	99.18 225	99.45 18	94.84 93	96.41 168	99.71 89	91.40 152	99.99 40	97.99 111	98.03 153	99.87 97
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
WTY-MVS		98.10 68	97.60 78	99.60 20	98.92 130	99.28 16	99.89 86	99.52 14	95.58 72	98.24 124	99.39 123	93.33 109	99.74 129	97.98 113	95.58 200	99.78 107

jason		97.24 102	96.86 104	98.38 126	95.73 270	97.32 108	99.97 18	97.40 257	95.34 78	98.60 108	99.54 110	87.70 199	98.56 189	97.94 114	99.47 113	99.25 184
jason: jason.
BP-MVS														97.92 115

HQP-MVS		94.61 179	94.50 170	94.92 233	95.78 264	91.85 257	99.87 92	97.89 209	96.82 31	93.37 208	98.65 182	80.65 261	98.39 204	97.92 115	89.60 229	94.53 238

h-mvs33		94.92 169	94.36 172	96.59 191	98.85 137	91.29 272	98.93 253	98.94 37	95.90 59	98.77 97	98.42 198	90.89 165	99.77 119	97.80 117	70.76 347	98.72 209

hse-mvs2		94.38 186	94.08 179	95.31 221	98.27 164	90.02 294	99.29 217	98.56 78	95.90 59	98.77 97	98.00 207	90.89 165	98.26 221	97.80 117	69.20 353	97.64 224

1314		96.84 115	95.96 134	99.48 36	96.74 248	98.52 59	98.31 296	98.86 46	95.82 61	89.91 242	98.98 154	87.49 201	99.96 57	97.80 117	99.73 95	99.96 74

HQP_MVS		94.49 184	94.36 172	94.87 234	95.71 273	91.74 261	99.84 110	97.87 211	96.38 47	93.01 212	98.59 186	80.47 265	98.37 209	97.79 120	89.55 232	94.52 240

plane_prior5								97.87 211					98.37 209	97.79 120	89.55 232	94.52 240

gg-mvs-nofinetune		93.51 205	91.86 229	98.47 118	97.72 200	97.96 81	92.62 353	98.51 98	74.70 356	97.33 143	69.59 367	98.91 3	97.79 243	97.77 122	99.56 108	99.67 120

casdiffmvs		96.42 133	95.97 132	97.77 149	97.30 222	94.98 187	99.84 110	97.09 284	93.75 144	96.58 161	99.26 134	85.07 225	98.78 175	97.77 122	97.04 172	99.54 151

PGM-MVS		98.34 55	98.13 56	98.99 82	99.92 36	97.00 118	99.75 139	99.50 17	93.90 137	99.37 67	99.76 75	93.24 116	100.00 1	97.75 124	99.96 52	99.98 55

DeepC-MVS	94.51 4	96.92 113	96.40 119	98.45 120	99.16 115	95.90 159	99.66 158	98.06 194	96.37 50	94.37 197	99.49 113	83.29 238	99.90 79	97.63 125	99.61 105	99.55 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		97.80 80	97.50 81	98.68 98	99.79 75	96.42 137	99.88 89	98.16 185	91.75 211	98.94 91	99.54 110	91.82 149	99.65 142	97.62 126	99.99 22	99.99 24

baseline		96.43 132	95.98 129	97.76 150	97.34 218	95.17 184	99.51 184	97.17 275	93.92 136	96.90 152	99.28 128	85.37 222	98.64 186	97.50 127	96.86 177	99.46 161

abl_6		97.67 86	97.34 88	98.66 100	99.68 91	96.11 155	99.68 155	98.14 188	93.80 141	99.27 75	99.70 91	88.65 194	99.98 46	97.46 128	99.72 96	99.89 94

PLC	95.54 3	97.93 73	97.89 70	98.05 139	99.82 70	94.77 195	99.92 70	98.46 106	93.93 135	97.20 145	99.27 131	95.44 47	99.97 55	97.41 129	99.51 112	99.41 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		96.60 127	95.56 146	99.72 12	96.85 241	99.22 19	98.31 296	98.94 37	91.57 215	90.90 230	99.61 104	86.66 210	99.96 57	97.36 130	99.88 80	99.99 24

XVG-OURS-SEG-HR		94.79 171	94.70 168	95.08 227	98.05 176	89.19 303	99.08 232	97.54 238	93.66 146	94.87 191	99.58 106	78.78 276	99.79 113	97.31 131	93.40 221	96.25 232

3Dnovator	91.47 12	96.28 140	95.34 151	99.08 74	96.82 243	97.47 102	99.45 194	98.81 49	95.52 74	89.39 256	99.00 151	81.97 244	99.95 64	97.27 132	99.83 85	99.84 99

cascas		94.64 178	93.61 187	97.74 152	97.82 190	96.26 144	99.96 25	97.78 219	85.76 308	94.00 202	97.54 216	76.95 286	99.21 158	97.23 133	95.43 202	97.76 223

LCM-MVSNet-Re		92.31 231	92.60 211	91.43 311	97.53 207	79.27 360	99.02 244	91.83 367	92.07 200	80.31 339	94.38 323	83.50 236	95.48 330	97.22 134	97.58 159	99.54 151

CNLPA		97.76 82	97.38 85	98.92 88	99.53 100	96.84 124	99.87 92	98.14 188	93.78 142	96.55 163	99.69 94	92.28 139	99.98 46	97.13 135	99.44 115	99.93 85

Effi-MVS+		96.30 138	95.69 143	98.16 132	97.85 188	96.26 144	97.41 318	97.21 271	90.37 241	98.65 105	98.58 188	86.61 211	98.70 183	97.11 136	97.37 165	99.52 154

PVSNet_Blended_VisFu		97.27 101	96.81 106	98.66 100	98.81 139	96.67 129	99.92 70	98.64 64	94.51 106	96.38 169	98.49 192	89.05 188	99.88 89	97.10 137	98.34 139	99.43 166

3Dnovator+	91.53 11	96.31 137	95.24 153	99.52 29	96.88 240	98.64 53	99.72 150	98.24 172	95.27 80	88.42 279	98.98 154	82.76 240	99.94 72	97.10 137	99.83 85	99.96 74

PAPM_NR		98.12 67	97.93 69	98.70 97	99.94 14	96.13 152	99.82 117	98.43 119	94.56 104	97.52 139	99.70 91	94.40 74	99.98 46	97.00 139	99.98 35	99.99 24

CHOSEN 1792x2688		96.81 116	96.53 115	97.64 154	98.91 132	93.07 228	99.65 161	99.80 3	95.64 70	95.39 185	98.86 173	84.35 231	99.90 79	96.98 140	99.16 124	99.95 82

旧先验2							99.46 193		94.21 121	99.85 6			99.95 64	96.96 141

PMMVS		96.76 119	96.76 108	96.76 184	98.28 162	92.10 251	99.91 74	97.98 200	94.12 123	99.53 50	99.39 123	86.93 208	98.73 180	96.95 142	97.73 155	99.45 163

EPP-MVSNet		96.69 124	96.60 112	96.96 178	97.74 196	93.05 230	99.37 205	98.56 78	88.75 267	95.83 179	99.01 149	96.01 31	98.56 189	96.92 143	97.20 169	99.25 184

ET-MVSNet_ETH3D		94.37 187	93.28 202	97.64 154	98.30 159	97.99 78	99.99 5	97.61 230	94.35 114	71.57 358	99.45 117	96.23 30	95.34 333	96.91 144	85.14 275	99.59 138

HyFIR lowres test		96.66 126	96.43 118	97.36 169	99.05 118	93.91 211	99.70 152	99.80 3	90.54 238	96.26 171	98.08 204	92.15 142	98.23 223	96.84 145	95.46 201	99.93 85

OMC-MVS		97.28 100	97.23 92	97.41 164	99.76 79	93.36 226	99.65 161	97.95 203	96.03 58	97.41 142	99.70 91	89.61 178	99.51 147	96.73 146	98.25 144	99.38 170

CostFormer		96.10 142	95.88 139	96.78 183	97.03 231	92.55 243	97.08 325	97.83 216	90.04 248	98.72 101	94.89 309	95.01 59	98.29 214	96.54 147	95.77 195	99.50 158

sss		97.57 88	97.03 101	99.18 57	98.37 157	98.04 76	99.73 147	99.38 22	93.46 151	98.76 99	99.06 145	91.21 155	99.89 83	96.33 148	97.01 173	99.62 132

114514_t		97.41 96	96.83 105	99.14 66	99.51 103	97.83 84	99.89 86	98.27 169	88.48 273	99.06 84	99.66 100	90.30 171	99.64 143	96.32 149	99.97 48	99.96 74

ACMP	92.05 9	92.74 221	92.42 218	93.73 277	95.91 263	88.72 308	99.81 119	97.53 240	94.13 122	87.00 297	98.23 201	74.07 311	98.47 193	96.22 150	88.86 241	93.99 290
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	92.85 6	94.99 168	93.94 182	98.16 132	97.72 200	95.69 169	99.99 5	98.81 49	94.28 119	92.70 218	96.90 236	95.08 53	99.17 162	96.07 151	73.88 345	99.60 137
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
XVG-OURS		94.82 170	94.74 167	95.06 228	98.00 178	89.19 303	99.08 232	97.55 236	94.10 124	94.71 192	99.62 103	80.51 263	99.74 129	96.04 152	93.06 225	96.25 232

ab-mvs		94.69 175	93.42 196	98.51 116	98.07 175	96.26 144	96.49 332	98.68 57	90.31 243	94.54 193	97.00 234	76.30 293	99.71 133	95.98 153	93.38 222	99.56 146

mvs_anonymous		95.65 155	95.03 160	97.53 157	98.19 169	95.74 165	99.33 209	97.49 246	90.87 233	90.47 235	97.10 228	88.23 196	97.16 270	95.92 154	97.66 158	99.68 118

nrg030		93.51 205	92.53 215	96.45 194	94.36 294	97.20 111	99.81 119	97.16 277	91.60 214	89.86 244	97.46 217	86.37 213	97.68 246	95.88 155	80.31 314	94.46 243

LPG-MVS_test		92.96 216	92.71 209	93.71 279	95.43 279	88.67 309	99.75 139	97.62 227	92.81 167	90.05 237	98.49 192	75.24 301	98.40 202	95.84 156	89.12 236	94.07 282

LGP-MVS_train				93.71 279	95.43 279	88.67 309		97.62 227	92.81 167	90.05 237	98.49 192	75.24 301	98.40 202	95.84 156	89.12 236	94.07 282

VPA-MVSNet		92.70 222	91.55 234	96.16 203	95.09 283	96.20 149	98.88 258	99.00 34	91.02 231	91.82 222	95.29 296	76.05 297	97.96 237	95.62 158	81.19 302	94.30 258

ECVR-MVS		95.66 154	95.05 159	97.51 160	98.66 146	93.71 215	98.85 265	98.45 108	94.93 86	96.86 153	98.96 158	75.22 303	99.20 159	95.34 159	98.15 145	99.64 126

F-COLMAP		96.93 112	96.95 103	96.87 181	99.71 89	91.74 261	99.85 106	97.95 203	93.11 161	95.72 181	99.16 141	92.35 137	99.94 72	95.32 160	99.35 118	98.92 199

BH-w/o		95.71 152	95.38 150	96.68 187	98.49 154	92.28 247	99.84 110	97.50 245	92.12 199	92.06 221	98.79 177	84.69 227	98.67 185	95.29 161	99.66 100	99.09 195

原ACMM1				98.96 85	99.73 86	96.99 119		98.51 98	94.06 128	99.62 43	99.85 35	94.97 62	99.96 57	95.11 162	99.95 55	99.92 91

Anonymous202405211		93.10 213	91.99 225	96.40 197	99.10 117	89.65 300	98.88 258	97.93 205	83.71 328	94.00 202	98.75 178	68.79 328	99.88 89	95.08 163	91.71 227	99.68 118

test1111		95.57 156	94.98 161	97.37 167	98.56 148	93.37 225	98.86 262	98.45 108	94.95 85	96.63 159	98.95 162	75.21 304	99.11 163	95.02 164	98.14 147	99.64 126

testdata				98.42 123	99.47 105	95.33 177		98.56 78	93.78 142	99.79 23	99.85 35	93.64 104	99.94 72	94.97 165	99.94 61	100.00 1

test2506		97.53 89	97.19 93	98.58 108	98.66 146	96.90 123	98.81 268	99.77 5	94.93 86	97.95 130	98.96 158	92.51 133	99.20 159	94.93 166	98.15 145	99.64 126

gm-plane-assit					96.97 234	93.76 214			91.47 219		98.96 158		98.79 174	94.92 167

PVSNet	91.05 13	97.13 105	96.69 110	98.45 120	99.52 101	95.81 161	99.95 43	99.65 11	94.73 96	99.04 85	99.21 139	84.48 229	99.95 64	94.92 167	98.74 132	99.58 144

tpmrst		96.27 141	95.98 129	97.13 174	97.96 180	93.15 227	96.34 334	98.17 182	92.07 200	98.71 102	95.12 300	93.91 96	98.73 180	94.91 169	96.62 178	99.50 158

VPNet		91.81 240	90.46 249	95.85 211	94.74 289	95.54 172	98.98 247	98.59 73	92.14 198	90.77 232	97.44 218	68.73 330	97.54 251	94.89 170	77.89 328	94.46 243

baseline2		96.71 123	96.49 116	97.37 167	95.63 277	95.96 158	99.74 142	98.88 44	92.94 163	91.61 223	98.97 156	97.72 5	98.62 187	94.83 171	98.08 152	97.53 227

Effi-MVS+-dtu		94.53 183	95.30 152	92.22 303	97.77 193	82.54 343	99.59 171	97.06 287	94.92 89	95.29 187	95.37 290	85.81 216	97.89 241	94.80 172	97.07 171	96.23 234

mvs-test1		95.53 157	95.97 132	94.20 261	97.77 193	85.44 332	99.95 43	97.06 287	94.92 89	96.58 161	98.72 179	85.81 216	98.98 167	94.80 172	98.11 148	98.18 214

MVSTER		95.53 157	95.22 154	96.45 194	98.56 148	97.72 86	99.91 74	97.67 223	92.38 192	91.39 225	97.14 226	97.24 17	97.30 262	94.80 172	87.85 254	94.34 257

thisisatest0515		97.41 96	97.02 102	98.59 107	97.71 202	97.52 96	99.97 18	98.54 88	91.83 207	97.45 141	99.04 146	97.50 8	99.10 164	94.75 175	96.37 184	99.16 189

mvs_tets		91.81 240	91.08 241	94.00 270	91.63 338	90.58 283	98.67 280	97.43 251	92.43 191	87.37 294	97.05 232	71.76 318	97.32 261	94.75 175	88.68 244	94.11 280

RRT_test8_iter05		94.58 180	94.11 177	95.98 207	97.88 184	96.11 155	99.89 86	97.45 248	91.66 213	88.28 280	96.71 244	96.53 27	97.40 255	94.73 177	83.85 288	94.45 248

Anonymous20240529		92.10 236	90.65 247	96.47 192	98.82 138	90.61 282	98.72 275	98.67 60	75.54 354	93.90 204	98.58 188	66.23 339	99.90 79	94.70 178	90.67 228	98.90 202

MVSFormer		96.94 111	96.60 112	97.95 141	97.28 224	97.70 89	99.55 178	97.27 268	91.17 225	99.43 59	99.54 110	90.92 163	96.89 289	94.67 179	99.62 102	99.25 184

test_djsdf		92.83 219	92.29 220	94.47 252	91.90 334	92.46 244	99.55 178	97.27 268	91.17 225	89.96 240	96.07 264	81.10 254	96.89 289	94.67 179	88.91 238	94.05 284

UGNet		95.33 161	94.57 169	97.62 156	98.55 150	94.85 190	98.67 280	99.32 25	95.75 68	96.80 156	96.27 258	72.18 317	99.96 57	94.58 181	99.05 126	98.04 217
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
jajsoiax		91.92 238	91.18 240	94.15 262	91.35 340	90.95 276	99.00 245	97.42 253	92.61 180	87.38 293	97.08 229	72.46 316	97.36 257	94.53 182	88.77 242	94.13 279

bset_n11_16_dypcd		93.05 215	92.30 219	95.31 221	90.23 350	95.05 186	99.44 196	97.28 267	92.51 188	90.65 233	96.68 245	85.30 223	96.71 299	94.49 183	84.14 283	94.16 273

MVS_Test		96.46 131	95.74 142	98.61 104	98.18 170	97.23 110	99.31 212	97.15 278	91.07 229	98.84 93	97.05 232	88.17 197	98.97 168	94.39 184	97.50 160	99.61 135

PS-MVSNAJss		93.64 204	93.31 201	94.61 243	92.11 331	92.19 249	99.12 228	97.38 258	92.51 188	88.45 274	96.99 235	91.20 156	97.29 265	94.36 185	87.71 256	94.36 253

无先验							99.49 188	98.71 54	93.46 151				100.00 1	94.36 185		99.99 24

1121		98.03 70	97.57 80	99.40 44	99.74 82	98.21 70	98.31 296	98.62 68	92.78 170	99.53 50	99.83 51	95.08 53	100.00 1	94.36 185	99.92 71	99.99 24

MDTV_nov1_ep13_2view						96.26 144	96.11 338		91.89 205	98.06 127		94.40 74		94.30 188		99.67 120

thres200		96.96 110	96.21 122	99.22 53	98.97 124	98.84 34	99.85 106	99.71 6	93.17 159	96.26 171	98.88 169	89.87 176	99.51 147	94.26 189	94.91 207	99.31 179

BH-untuned		95.18 163	94.83 164	96.22 202	98.36 158	91.22 273	99.80 124	97.32 264	90.91 232	91.08 228	98.67 181	83.51 235	98.54 191	94.23 190	99.61 105	98.92 199

FIs		94.10 192	93.43 195	96.11 204	94.70 290	96.82 125	99.58 172	98.93 41	92.54 186	89.34 258	97.31 222	87.62 200	97.10 276	94.22 191	86.58 264	94.40 250

DWT-MVSNet_test		97.31 99	97.19 93	97.66 153	98.24 166	94.67 196	98.86 262	98.20 180	93.60 148	98.09 126	98.89 167	97.51 7	98.78 175	94.04 192	97.28 166	99.55 147

tpm2		95.47 159	95.18 156	96.35 200	96.91 236	91.70 265	96.96 328	97.93 205	88.04 279	98.44 113	95.40 286	93.32 110	97.97 235	94.00 193	95.61 199	99.38 170

OpenMVS	90.15 15	94.77 173	93.59 190	98.33 127	96.07 257	97.48 101	99.56 176	98.57 76	90.46 239	86.51 303	98.95 162	78.57 278	99.94 72	93.86 194	99.74 94	97.57 226

thres100view900		96.74 121	95.92 137	99.18 57	98.90 133	98.77 40	99.74 142	99.71 6	92.59 182	95.84 177	98.86 173	89.25 184	99.50 149	93.84 195	94.57 208	99.27 182

tfpn200view9		96.79 117	95.99 127	99.19 56	98.94 126	98.82 35	99.78 128	99.71 6	92.86 164	96.02 174	98.87 171	89.33 182	99.50 149	93.84 195	94.57 208	99.27 182

thres400		96.78 118	95.99 127	99.16 62	98.94 126	98.82 35	99.78 128	99.71 6	92.86 164	96.02 174	98.87 171	89.33 182	99.50 149	93.84 195	94.57 208	99.16 189

DPM-MVS		98.83 22	98.46 31	99.97 1	99.33 111	99.92 1	99.96 25	98.44 111	97.96 7	99.55 48	99.94 4	97.18 20	100.00 1	93.81 198	99.94 61	99.98 55

CDS-MVSNet		96.34 135	96.07 124	97.13 174	97.37 216	94.96 188	99.53 181	97.91 208	91.55 216	95.37 186	98.32 200	95.05 56	97.13 273	93.80 199	95.75 197	99.30 180
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		95.78 149	94.86 163	98.54 113	98.47 155	98.07 74	99.06 237	97.99 198	92.68 176	94.13 201	98.62 185	93.28 113	98.69 184	93.79 200	85.76 268	98.84 204

OPM-MVS		93.21 210	92.80 207	94.44 254	93.12 316	90.85 278	99.77 131	97.61 230	96.19 54	91.56 224	98.65 182	75.16 305	98.47 193	93.78 201	89.39 235	93.99 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TAMVS		95.85 147	95.58 145	96.65 189	97.07 228	93.50 220	99.17 226	97.82 217	91.39 224	95.02 190	98.01 206	92.20 140	97.30 262	93.75 202	95.83 194	99.14 192

thisisatest0530		97.10 106	96.72 109	98.22 131	97.60 205	96.70 127	99.92 70	98.54 88	91.11 228	97.07 149	98.97 156	97.47 11	99.03 165	93.73 203	96.09 187	98.92 199

IS-MVSNet		96.29 139	95.90 138	97.45 162	98.13 174	94.80 193	99.08 232	97.61 230	92.02 203	95.54 184	98.96 158	90.64 168	98.08 229	93.73 203	97.41 164	99.47 160

RRT_MVS		95.23 162	94.77 166	96.61 190	98.28 162	98.32 67	99.81 119	97.41 255	92.59 182	91.28 227	97.76 213	95.02 57	97.23 268	93.65 205	87.14 261	94.28 260

ACMM	91.95 10	92.88 218	92.52 216	93.98 272	95.75 269	89.08 306	99.77 131	97.52 242	93.00 162	89.95 241	97.99 209	76.17 295	98.46 196	93.63 206	88.87 240	94.39 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		96.32 136	95.98 129	97.35 170	97.93 182	94.82 192	99.47 191	98.15 187	91.83 207	95.09 189	99.11 142	91.37 153	97.47 254	93.47 207	97.43 161	99.74 111

thres600view7		96.69 124	95.87 140	99.14 66	98.90 133	98.78 39	99.74 142	99.71 6	92.59 182	95.84 177	98.86 173	89.25 184	99.50 149	93.44 208	94.50 211	99.16 189

Vis-MVSNet		95.72 150	95.15 157	97.45 162	97.62 204	94.28 203	99.28 218	98.24 172	94.27 120	96.84 154	98.94 164	79.39 271	98.76 178	93.25 209	98.49 136	99.30 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-test		93.81 197	93.15 204	95.80 212	94.30 296	96.20 149	99.42 197	98.89 43	92.33 194	89.03 267	97.27 224	87.39 203	96.83 293	93.20 210	86.48 265	94.36 253

UniMVSNet_NR-MVSNet		92.95 217	92.11 222	95.49 214	94.61 292	95.28 179	99.83 116	99.08 31	91.49 217	89.21 262	96.86 239	87.14 205	96.73 297	93.20 210	77.52 331	94.46 243

DU-MVS		92.46 228	91.45 237	95.49 214	94.05 299	95.28 179	99.81 119	98.74 53	92.25 196	89.21 262	96.64 248	81.66 248	96.73 297	93.20 210	77.52 331	94.46 243

WR-MVS		92.31 231	91.25 239	95.48 217	94.45 293	95.29 178	99.60 170	98.68 57	90.10 245	88.07 283	96.89 237	80.68 260	96.80 295	93.14 213	79.67 318	94.36 253

UniMVSNet (Re)		93.07 214	92.13 221	95.88 209	94.84 287	96.24 148	99.88 89	98.98 35	92.49 190	89.25 260	95.40 286	87.09 206	97.14 272	93.13 214	78.16 326	94.26 261

QAPM		95.40 160	94.17 176	99.10 72	96.92 235	97.71 87	99.40 198	98.68 57	89.31 254	88.94 268	98.89 167	82.48 241	99.96 57	93.12 215	99.83 85	99.62 132

tttt0517		96.85 114	96.49 116	97.92 143	97.48 212	95.89 160	99.85 106	98.54 88	90.72 237	96.63 159	98.93 166	97.47 11	99.02 166	93.03 216	95.76 196	98.85 203

TR-MVS		94.54 181	93.56 192	97.49 161	97.96 180	94.34 202	98.71 276	97.51 244	90.30 244	94.51 195	98.69 180	75.56 298	98.77 177	92.82 217	95.99 189	99.35 175

CANet_DTU		96.76 119	96.15 123	98.60 105	98.78 141	97.53 95	99.84 110	97.63 225	97.25 23	99.20 77	99.64 102	81.36 252	99.98 46	92.77 218	98.89 128	98.28 213

AUN-MVS		93.28 209	92.60 211	95.34 219	98.29 160	90.09 293	99.31 212	98.56 78	91.80 210	96.35 170	98.00 207	89.38 181	98.28 217	92.46 219	69.22 352	97.64 224

anonymousdsp		91.79 245	90.92 243	94.41 257	90.76 345	92.93 232	98.93 253	97.17 275	89.08 256	87.46 292	95.30 293	78.43 281	96.92 288	92.38 220	88.73 243	93.39 318

XVG-ACMP-BASELINE		91.22 253	90.75 244	92.63 300	93.73 305	85.61 329	98.52 288	97.44 250	92.77 171	89.90 243	96.85 240	66.64 338	98.39 204	92.29 221	88.61 245	93.89 298

miper_enhance_ethall		94.36 189	93.98 181	95.49 214	98.68 145	95.24 181	99.73 147	97.29 266	93.28 156	89.86 244	95.97 265	94.37 78	97.05 279	92.20 222	84.45 280	94.19 267

RPSCF		91.80 243	92.79 208	88.83 330	98.15 172	69.87 364	98.11 306	96.60 323	83.93 326	94.33 198	99.27 131	79.60 270	99.46 154	91.99 223	93.16 224	97.18 228

cl22		93.77 199	93.25 203	95.33 220	99.49 104	94.43 200	99.61 169	98.09 191	90.38 240	89.16 265	95.61 274	90.56 169	97.34 259	91.93 224	84.45 280	94.21 266

1112_ss		96.01 145	95.20 155	98.42 123	97.80 191	96.41 138	99.65 161	96.66 321	92.71 173	92.88 216	99.40 121	92.16 141	99.30 156	91.92 225	93.66 218	99.55 147

Test_1112_low_res		95.72 150	94.83 164	98.42 123	97.79 192	96.41 138	99.65 161	96.65 322	92.70 174	92.86 217	96.13 262	92.15 142	99.30 156	91.88 226	93.64 219	99.55 147

tmp_tt		65.23 334	62.94 337	72.13 349	44.90 378	50.03 374	81.05 365	89.42 373	38.45 368	48.51 370	99.90 19	54.09 361	78.70 370	91.84 227	18.26 372	87.64 361

XXY-MVS		91.82 239	90.46 249	95.88 209	93.91 302	95.40 176	98.87 261	97.69 222	88.63 271	87.87 285	97.08 229	74.38 310	97.89 241	91.66 228	84.07 285	94.35 256

D2MVS		92.76 220	92.59 214	93.27 289	95.13 282	89.54 302	99.69 153	99.38 22	92.26 195	87.59 288	94.61 317	85.05 226	97.79 243	91.59 229	88.01 253	92.47 334

UniMVSNet_ETH3D		90.06 280	88.58 286	94.49 251	94.67 291	88.09 318	97.81 314	97.57 235	83.91 327	88.44 275	97.41 219	57.44 358	97.62 249	91.41 230	88.59 247	97.77 222

NR-MVSNet		91.56 248	90.22 256	95.60 213	94.05 299	95.76 164	98.25 299	98.70 55	91.16 227	80.78 338	96.64 248	83.23 239	96.57 304	91.41 230	77.73 330	94.46 243

新几何1				99.42 41	99.75 81	98.27 69		98.63 67	92.69 175	99.55 48	99.82 55	94.40 74	100.00 1	91.21 232	99.94 61	99.99 24

UA-Net		96.54 128	95.96 134	98.27 129	98.23 167	95.71 167	98.00 310	98.45 108	93.72 145	98.41 114	99.27 131	88.71 193	99.66 141	91.19 233	97.69 156	99.44 165

EPMVS		96.53 129	96.01 126	98.09 137	98.43 156	96.12 154	96.36 333	99.43 20	93.53 149	97.64 137	95.04 302	94.41 73	98.38 208	91.13 234	98.11 148	99.75 110

EI-MVSNet		93.73 201	93.40 199	94.74 238	96.80 244	92.69 238	99.06 237	97.67 223	88.96 262	91.39 225	99.02 147	88.75 192	97.30 262	91.07 235	87.85 254	94.22 264

test_part1		92.15 235	90.72 245	96.44 196	98.87 136	97.46 103	98.99 246	98.26 170	85.89 305	86.34 308	96.34 256	81.71 246	97.48 253	91.06 236	78.99 320	94.37 252

test_post1							95.78 343				59.23 374	93.20 117	97.74 245	91.06 236

SCA		94.69 175	93.81 186	97.33 171	97.10 227	94.44 199	98.86 262	98.32 159	93.30 155	96.17 173	95.59 276	76.48 291	97.95 238	91.06 236	97.43 161	99.59 138

Baseline_NR-MVSNet		90.33 272	89.51 270	92.81 298	92.84 322	89.95 296	99.77 131	93.94 362	84.69 323	89.04 266	95.66 273	81.66 248	96.52 305	90.99 239	76.98 337	91.97 340

IterMVS-LS		92.69 223	92.11 222	94.43 256	96.80 244	92.74 235	99.45 194	96.89 307	88.98 260	89.65 251	95.38 289	88.77 191	96.34 312	90.98 240	82.04 296	94.22 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LS3D		95.84 148	95.11 158	98.02 140	99.85 60	95.10 185	98.74 273	98.50 102	87.22 289	93.66 206	99.86 31	87.45 202	99.95 64	90.94 241	99.81 91	99.02 197

CVMVSNet		94.68 177	94.94 162	93.89 275	96.80 244	86.92 324	99.06 237	98.98 35	94.45 107	94.23 200	99.02 147	85.60 218	95.31 334	90.91 242	95.39 203	99.43 166

BH-RMVSNet		95.18 163	94.31 174	97.80 145	98.17 171	95.23 182	99.76 136	97.53 240	92.52 187	94.27 199	99.25 135	76.84 287	98.80 173	90.89 243	99.54 109	99.35 175

Anonymous20231211		89.86 282	88.44 288	94.13 264	98.93 128	90.68 280	98.54 286	98.26 170	76.28 350	86.73 299	95.54 278	70.60 324	97.56 250	90.82 244	80.27 315	94.15 275

miper_ehance_all_eth		93.16 211	92.60 211	94.82 237	97.57 206	93.56 218	99.50 186	97.07 286	88.75 267	88.85 269	95.52 280	90.97 162	96.74 296	90.77 245	84.45 280	94.17 268

tpm		93.70 203	93.41 198	94.58 245	95.36 281	87.41 322	97.01 326	96.90 306	90.85 234	96.72 158	94.14 325	90.40 170	96.84 292	90.75 246	88.54 248	99.51 156

TESTMET0.1,1		96.74 121	96.26 121	98.16 132	97.36 217	96.48 135	99.96 25	98.29 165	91.93 204	95.77 180	98.07 205	95.54 43	98.29 214	90.55 247	98.89 128	99.70 115

testdata2													99.99 40	90.54 248

c3_l		92.53 226	91.87 228	94.52 248	97.40 215	92.99 231	99.40 198	96.93 304	87.86 280	88.69 272	95.44 284	89.95 175	96.44 308	90.45 249	80.69 311	94.14 278

test-LLR		96.47 130	96.04 125	97.78 147	97.02 232	95.44 173	99.96 25	98.21 176	94.07 126	95.55 182	96.38 253	93.90 97	98.27 219	90.42 250	98.83 130	99.64 126

test-mter		96.39 134	95.93 136	97.78 147	97.02 232	95.44 173	99.96 25	98.21 176	91.81 209	95.55 182	96.38 253	95.17 50	98.27 219	90.42 250	98.83 130	99.64 126

PCF-MVS	94.20 5	95.18 163	94.10 178	98.43 122	98.55 150	95.99 157	97.91 312	97.31 265	90.35 242	89.48 255	99.22 138	85.19 224	99.89 83	90.40 252	98.47 137	99.41 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CP-MVSNet		91.23 252	90.22 256	94.26 259	93.96 301	92.39 246	99.09 230	98.57 76	88.95 263	86.42 306	96.57 250	79.19 273	96.37 310	90.29 253	78.95 321	94.02 285

TranMVSNet+NR-MVSNet		91.68 247	90.61 248	94.87 234	93.69 306	93.98 209	99.69 153	98.65 61	91.03 230	88.44 275	96.83 243	80.05 268	96.18 318	90.26 254	76.89 339	94.45 248

PatchMatch-RL		96.04 144	95.40 148	97.95 141	99.59 95	95.22 183	99.52 182	99.07 32	93.96 133	96.49 164	98.35 199	82.28 242	99.82 109	90.15 255	99.22 123	98.81 206

MDTV_nov1_ep13			95.69 143		97.90 183	94.15 204	95.98 340	98.44 111	93.12 160	97.98 129	95.74 269	95.10 52	98.58 188	90.02 256	96.92 175

eth_miper_zixun_eth		92.41 229	91.93 226	93.84 276	97.28 224	90.68 280	98.83 266	96.97 299	88.57 272	89.19 264	95.73 271	89.24 186	96.69 300	89.97 257	81.55 299	94.15 275

Fast-Effi-MVS+		95.02 167	94.19 175	97.52 159	97.88 184	94.55 198	99.97 18	97.08 285	88.85 266	94.47 196	97.96 210	84.59 228	98.41 200	89.84 258	97.10 170	99.59 138

Fast-Effi-MVS+-dtu		93.72 202	93.86 185	93.29 288	97.06 229	86.16 326	99.80 124	96.83 311	92.66 177	92.58 219	97.83 212	81.39 251	97.67 247	89.75 259	96.87 176	96.05 236

ACMH	89.72 17	90.64 264	89.63 265	93.66 283	95.64 276	88.64 311	98.55 284	97.45 248	89.03 258	81.62 333	97.61 215	69.75 326	98.41 200	89.37 260	87.62 258	93.92 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		92.10 236	91.07 242	95.18 225	92.82 324	94.96 188	99.48 190	96.83 311	87.45 285	88.66 273	96.56 251	83.78 234	96.83 293	89.29 261	84.77 278	93.75 307

PatchmatchNet		95.94 146	95.45 147	97.39 166	97.83 189	94.41 201	96.05 339	98.40 139	92.86 164	97.09 148	95.28 297	94.21 89	98.07 231	89.26 262	98.11 148	99.70 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH+	89.98 16	90.35 271	89.54 268	92.78 299	95.99 260	86.12 327	98.81 268	97.18 274	89.38 253	83.14 326	97.76 213	68.42 332	98.43 198	89.11 263	86.05 267	93.78 306

DP-MVS		94.54 181	93.42 196	97.91 144	99.46 107	94.04 206	98.93 253	97.48 247	81.15 339	90.04 239	99.55 108	87.02 207	99.95 64	88.97 264	98.11 148	99.73 112

PS-CasMVS		90.63 265	89.51 270	93.99 271	93.83 303	91.70 265	98.98 247	98.52 91	88.48 273	86.15 311	96.53 252	75.46 299	96.31 313	88.83 265	78.86 323	93.95 293

cl____		92.31 231	91.58 232	94.52 248	97.33 220	92.77 233	99.57 174	96.78 316	86.97 294	87.56 289	95.51 281	89.43 180	96.62 302	88.60 266	82.44 293	94.16 273

DIV-MVS_self_test		92.32 230	91.60 231	94.47 252	97.31 221	92.74 235	99.58 172	96.75 317	86.99 293	87.64 287	95.54 278	89.55 179	96.50 306	88.58 267	82.44 293	94.17 268

pmmvs5		90.17 278	89.09 277	93.40 286	92.10 332	89.77 299	99.74 142	95.58 344	85.88 307	87.24 296	95.74 269	73.41 314	96.48 307	88.54 268	83.56 289	93.95 293

LF4IMVS		89.25 292	88.85 281	90.45 320	92.81 325	81.19 353	98.12 305	94.79 355	91.44 220	86.29 309	97.11 227	65.30 344	98.11 228	88.53 269	85.25 273	92.07 337

JIA-IIPM		91.76 246	90.70 246	94.94 232	96.11 256	87.51 321	93.16 352	98.13 190	75.79 353	97.58 138	77.68 364	92.84 124	97.97 235	88.47 270	96.54 179	99.33 177

miper_lstm_enhance		91.81 240	91.39 238	93.06 295	97.34 218	89.18 305	99.38 203	96.79 315	86.70 297	87.47 291	95.22 298	90.00 174	95.86 328	88.26 271	81.37 301	94.15 275

WR-MVS_H		91.30 249	90.35 252	94.15 262	94.17 298	92.62 242	99.17 226	98.94 37	88.87 265	86.48 305	94.46 322	84.36 230	96.61 303	88.19 272	78.51 324	93.21 323

tpmvs		94.28 191	93.57 191	96.40 197	98.55 150	91.50 270	95.70 344	98.55 84	87.47 284	92.15 220	94.26 324	91.42 151	98.95 169	88.15 273	95.85 193	98.76 208

OurMVSNet-221017-0		89.81 283	89.48 272	90.83 316	91.64 337	81.21 352	98.17 304	95.38 348	91.48 218	85.65 315	97.31 222	72.66 315	97.29 265	88.15 273	84.83 277	93.97 292

GeoE		94.36 189	93.48 194	96.99 177	97.29 223	93.54 219	99.96 25	96.72 319	88.35 276	93.43 207	98.94 164	82.05 243	98.05 232	88.12 275	96.48 182	99.37 172

TDRefinement		84.76 313	82.56 320	91.38 312	74.58 369	84.80 336	97.36 319	94.56 358	84.73 322	80.21 340	96.12 263	63.56 348	98.39 204	87.92 276	63.97 358	90.95 348

CMPMVS	61.59 21	84.75 314	85.14 308	83.57 342	90.32 348	62.54 368	96.98 327	97.59 234	74.33 357	69.95 360	96.66 246	64.17 346	98.32 212	87.88 277	88.41 250	89.84 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		86.90 303	85.98 305	89.67 325	84.45 363	75.59 361	89.71 361	92.43 365	86.89 295	77.83 347	90.94 348	94.22 86	93.63 351	87.75 278	69.61 349	99.79 104

GA-MVS		93.83 195	92.84 206	96.80 182	95.73 270	93.57 217	99.88 89	97.24 270	92.57 185	92.92 214	96.66 246	78.73 277	97.67 247	87.75 278	94.06 216	99.17 188

ADS-MVSNet2		93.80 198	93.88 184	93.55 285	97.87 186	85.94 328	94.24 345	96.84 310	90.07 246	96.43 166	94.48 320	90.29 172	95.37 332	87.44 280	97.23 167	99.36 173

ADS-MVSNet		94.79 171	94.02 180	97.11 176	97.87 186	93.79 212	94.24 345	98.16 185	90.07 246	96.43 166	94.48 320	90.29 172	98.19 225	87.44 280	97.23 167	99.36 173

v148		90.70 262	89.63 265	93.92 273	92.97 320	90.97 275	99.75 139	96.89 307	87.51 283	88.27 281	95.01 303	81.67 247	97.04 281	87.40 282	77.17 336	93.75 307

V42		91.28 251	90.12 260	94.74 238	93.42 311	93.46 221	99.68 155	97.02 292	87.36 286	89.85 246	95.05 301	81.31 253	97.34 259	87.34 283	80.07 316	93.40 317

v2v482		91.30 249	90.07 261	95.01 229	93.13 314	93.79 212	99.77 131	97.02 292	88.05 278	89.25 260	95.37 290	80.73 259	97.15 271	87.28 284	80.04 317	94.09 281

IterMVS		90.91 257	90.17 258	93.12 292	96.78 247	90.42 288	98.89 256	97.05 290	89.03 258	86.49 304	95.42 285	76.59 290	95.02 336	87.22 285	84.09 284	93.93 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		90.19 277	89.06 278	93.57 284	93.06 318	90.90 277	99.06 237	98.47 104	88.11 277	85.91 313	96.30 257	76.67 288	95.94 327	87.07 286	76.91 338	93.89 298

IterMVS-SCA-FT		90.85 260	90.16 259	92.93 296	96.72 249	89.96 295	98.89 256	96.99 295	88.95 263	86.63 301	95.67 272	76.48 291	95.00 337	87.04 287	84.04 287	93.84 302

tpm cat1		93.51 205	92.52 216	96.47 192	97.77 193	91.47 271	96.13 337	98.06 194	80.98 340	92.91 215	93.78 328	89.66 177	98.87 170	87.03 288	96.39 183	99.09 195

GBi-Net		90.88 258	89.82 263	94.08 265	97.53 207	91.97 252	98.43 291	96.95 300	87.05 290	89.68 248	94.72 311	71.34 320	96.11 319	87.01 289	85.65 269	94.17 268

test1		90.88 258	89.82 263	94.08 265	97.53 207	91.97 252	98.43 291	96.95 300	87.05 290	89.68 248	94.72 311	71.34 320	96.11 319	87.01 289	85.65 269	94.17 268

FMVSNet3		92.69 223	91.58 232	95.99 206	98.29 160	97.42 106	99.26 220	97.62 227	89.80 251	89.68 248	95.32 292	81.62 250	96.27 315	87.01 289	85.65 269	94.29 259

dp		95.05 166	94.43 171	96.91 179	97.99 179	92.73 237	96.29 335	97.98 200	89.70 252	95.93 176	94.67 315	93.83 100	98.45 197	86.91 292	96.53 180	99.54 151

MSDG		94.37 187	93.36 200	97.40 165	98.88 135	93.95 210	99.37 205	97.38 258	85.75 310	90.80 231	99.17 140	84.11 233	99.88 89	86.35 293	98.43 138	98.36 212

EU-MVSNet		90.14 279	90.34 253	89.54 326	92.55 327	81.06 354	98.69 278	98.04 196	91.41 223	86.59 302	96.84 242	80.83 258	93.31 354	86.20 294	81.91 297	94.26 261

pm-mvs1		89.36 289	87.81 296	94.01 269	93.40 312	91.93 255	98.62 283	96.48 327	86.25 302	83.86 323	96.14 261	73.68 313	97.04 281	86.16 295	75.73 343	93.04 326

COLMAP_ROB	90.47 14	92.18 234	91.49 236	94.25 260	99.00 122	88.04 319	98.42 294	96.70 320	82.30 336	88.43 277	99.01 149	76.97 285	99.85 98	86.11 296	96.50 181	94.86 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ITE_SJBPF				92.38 301	95.69 275	85.14 333		95.71 340	92.81 167	89.33 259	98.11 203	70.23 325	98.42 199	85.91 297	88.16 252	93.59 314

K. test v3		88.05 298	87.24 300	90.47 319	91.82 336	82.23 346	98.96 250	97.42 253	89.05 257	76.93 349	95.60 275	68.49 331	95.42 331	85.87 298	81.01 308	93.75 307

AllTest		92.48 227	91.64 230	95.00 230	99.01 120	88.43 313	98.94 252	96.82 313	86.50 298	88.71 270	98.47 196	74.73 307	99.88 89	85.39 299	96.18 185	96.71 230

TestCases				95.00 230	99.01 120	88.43 313		96.82 313	86.50 298	88.71 270	98.47 196	74.73 307	99.88 89	85.39 299	96.18 185	96.71 230

FMVSNet2		91.02 255	89.56 267	95.41 218	97.53 207	95.74 165	98.98 247	97.41 255	87.05 290	88.43 277	95.00 305	71.34 320	96.24 317	85.12 301	85.21 274	94.25 263

v1144		91.09 254	89.83 262	94.87 234	93.25 313	93.69 216	99.62 168	96.98 297	86.83 296	89.64 252	94.99 306	80.94 256	97.05 279	85.08 302	81.16 303	93.87 300

v8		90.54 267	89.17 275	94.66 241	93.43 310	93.40 224	99.20 223	96.94 303	85.76 308	87.56 289	94.51 318	81.96 245	97.19 269	84.94 303	78.25 325	93.38 319

ambc				83.23 343	77.17 368	62.61 367	87.38 363	94.55 359		76.72 350	86.65 358	30.16 369	96.36 311	84.85 304	69.86 348	90.73 349

MVS_0304		89.28 291	88.31 290	92.21 304	97.05 230	86.53 325	97.76 315	99.57 13	85.58 313	93.86 205	92.71 337	51.04 365	96.30 314	84.49 305	92.72 226	93.79 305

LTVRE_ROB	88.28 18	90.29 274	89.05 279	94.02 268	95.08 284	90.15 292	97.19 322	97.43 251	84.91 321	83.99 322	97.06 231	74.00 312	98.28 217	84.08 306	87.71 256	93.62 313
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
SixPastTwentyTwo		88.73 294	88.01 295	90.88 314	91.85 335	82.24 345	98.22 302	95.18 353	88.97 261	82.26 329	96.89 237	71.75 319	96.67 301	84.00 307	82.98 290	93.72 311

v144192		90.79 261	89.52 269	94.59 244	93.11 317	92.77 233	99.56 176	96.99 295	86.38 300	89.82 247	94.95 308	80.50 264	97.10 276	83.98 308	80.41 312	93.90 297

USDC		90.00 281	88.96 280	93.10 294	94.81 288	88.16 317	98.71 276	95.54 345	93.66 146	83.75 324	97.20 225	65.58 341	98.31 213	83.96 309	87.49 260	92.85 329

MVP-Stereo		90.93 256	90.45 251	92.37 302	91.25 342	88.76 307	98.05 309	96.17 332	87.27 288	84.04 321	95.30 293	78.46 280	97.27 267	83.78 310	99.70 98	91.09 345
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		90.65 263	90.30 254	91.71 310	94.22 297	85.50 331	98.24 300	97.70 221	88.67 269	86.42 306	96.37 255	67.82 334	98.03 233	83.62 311	99.62 102	91.60 342

DTE-MVSNet		89.40 288	88.24 292	92.88 297	92.66 326	89.95 296	99.10 229	98.22 175	87.29 287	85.12 318	96.22 259	76.27 294	95.30 335	83.56 312	75.74 342	93.41 316

pmmvs6		85.69 306	83.84 312	91.26 313	90.00 352	84.41 337	97.82 313	96.15 333	75.86 352	81.29 335	95.39 288	61.21 353	96.87 291	83.52 313	73.29 346	92.50 333

lessismore_v0				90.53 317	90.58 346	80.90 355		95.80 338		77.01 348	95.84 266	66.15 340	96.95 286	83.03 314	75.05 344	93.74 310

v10		90.25 275	88.82 282	94.57 246	93.53 308	93.43 222	99.08 232	96.87 309	85.00 318	87.34 295	94.51 318	80.93 257	97.02 285	82.85 315	79.23 319	93.26 321

DeepMVS_CX				82.92 344	95.98 262	58.66 370		96.01 335	92.72 172	78.34 346	95.51 281	58.29 357	98.08 229	82.57 316	85.29 272	92.03 339

PM-MVS		80.47 325	78.88 329	85.26 340	83.79 365	72.22 363	95.89 342	91.08 368	85.71 311	76.56 351	88.30 353	36.64 368	93.90 348	82.39 317	69.57 350	89.66 356

v1192		90.62 266	89.25 274	94.72 240	93.13 314	93.07 228	99.50 186	97.02 292	86.33 301	89.56 254	95.01 303	79.22 272	97.09 278	82.34 318	81.16 303	94.01 287

v1921920		90.46 268	89.12 276	94.50 250	92.96 321	92.46 244	99.49 188	96.98 297	86.10 303	89.61 253	95.30 293	78.55 279	97.03 283	82.17 319	80.89 310	94.01 287

MIMVSNet		90.30 273	88.67 285	95.17 226	96.45 252	91.64 267	92.39 354	97.15 278	85.99 304	90.50 234	93.19 335	66.95 337	94.86 340	82.01 320	93.43 220	99.01 198

UnsupCasMVSNet_eth		85.52 308	83.99 309	90.10 322	89.36 354	83.51 339	96.65 330	97.99 198	89.14 255	75.89 353	93.83 327	63.25 349	93.92 347	81.92 321	67.90 356	92.88 328

FMVSNet1		88.50 295	86.64 301	94.08 265	95.62 278	91.97 252	98.43 291	96.95 300	83.00 331	86.08 312	94.72 311	59.09 356	96.11 319	81.82 322	84.07 285	94.17 268

test0.0.03 1		93.86 194	93.61 187	94.64 242	95.02 286	92.18 250	99.93 66	98.58 74	94.07 126	87.96 284	98.50 191	93.90 97	94.96 338	81.33 323	93.17 223	96.78 229

v7n		89.65 286	88.29 291	93.72 278	92.22 330	90.56 284	99.07 236	97.10 283	85.42 316	86.73 299	94.72 311	80.06 267	97.13 273	81.14 324	78.12 327	93.49 315

pmmvs-eth3d		84.03 319	81.97 322	90.20 321	84.15 364	87.09 323	98.10 307	94.73 357	83.05 330	74.10 356	87.77 355	65.56 342	94.01 346	81.08 325	69.24 351	89.49 357

v1240		90.20 276	88.79 283	94.44 254	93.05 319	92.27 248	99.38 203	96.92 305	85.89 305	89.36 257	94.87 310	77.89 282	97.03 283	80.66 326	81.08 306	94.01 287

our_test_3		90.39 269	89.48 272	93.12 292	92.40 328	89.57 301	99.33 209	96.35 329	87.84 281	85.30 316	94.99 306	84.14 232	96.09 322	80.38 327	84.56 279	93.71 312

TinyColmap		87.87 301	86.51 302	91.94 307	95.05 285	85.57 330	97.65 316	94.08 360	84.40 324	81.82 332	96.85 240	62.14 351	98.33 211	80.25 328	86.37 266	91.91 341

Patchmtry		89.70 285	88.49 287	93.33 287	96.24 255	89.94 298	91.37 359	96.23 330	78.22 347	87.69 286	93.31 333	91.04 160	96.03 324	80.18 329	82.10 295	94.02 285

KD-MVS_2432*1600		88.00 299	86.10 303	93.70 281	96.91 236	94.04 206	97.17 323	97.12 280	84.93 319	81.96 330	92.41 340	92.48 134	94.51 343	79.23 330	52.68 365	92.56 331

miper_refine_blended		88.00 299	86.10 303	93.70 281	96.91 236	94.04 206	97.17 323	97.12 280	84.93 319	81.96 330	92.41 340	92.48 134	94.51 343	79.23 330	52.68 365	92.56 331

CR-MVSNet		93.45 208	92.62 210	95.94 208	96.29 253	92.66 239	92.01 356	96.23 330	92.62 179	96.94 150	93.31 333	91.04 160	96.03 324	79.23 330	95.96 190	99.13 193

EG-PatchMatch MVS		85.35 311	83.81 313	89.99 324	90.39 347	81.89 348	98.21 303	96.09 334	81.78 338	74.73 355	93.72 329	51.56 364	97.12 275	79.16 333	88.61 245	90.96 347

test_method		80.79 324	79.70 327	84.08 341	92.83 323	67.06 366	99.51 184	95.42 346	54.34 364	81.07 337	93.53 330	44.48 367	92.22 357	78.90 334	77.23 335	92.94 327

DSMNet-mixed		88.28 297	88.24 292	88.42 334	89.64 353	75.38 362	98.06 308	89.86 370	85.59 312	88.20 282	92.14 344	76.15 296	91.95 358	78.46 335	96.05 188	97.92 218

UnsupCasMVSNet_bld		79.97 328	77.03 331	88.78 331	85.62 362	81.98 347	93.66 350	97.35 260	75.51 355	70.79 359	83.05 361	48.70 366	94.91 339	78.31 336	60.29 363	89.46 358

EPNet_dtu		95.71 152	95.39 149	96.66 188	98.92 130	93.41 223	99.57 174	98.90 42	96.19 54	97.52 139	98.56 190	92.65 129	97.36 257	77.89 337	98.33 140	99.20 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testgi		89.01 293	88.04 294	91.90 308	93.49 309	84.89 335	99.73 147	95.66 342	93.89 139	85.14 317	98.17 202	59.68 355	94.66 342	77.73 338	88.88 239	96.16 235

Patchmatch-test		92.65 225	91.50 235	96.10 205	96.85 241	90.49 285	91.50 358	97.19 272	82.76 334	90.23 236	95.59 276	95.02 57	98.00 234	77.41 339	96.98 174	99.82 101

YYNet1		85.50 310	83.33 315	92.00 306	90.89 344	88.38 316	99.22 222	96.55 324	79.60 345	57.26 365	92.72 336	79.09 275	93.78 350	77.25 340	77.37 334	93.84 302

MDA-MVSNet_test_wron		85.51 309	83.32 316	92.10 305	90.96 343	88.58 312	99.20 223	96.52 325	79.70 344	57.12 366	92.69 338	79.11 274	93.86 349	77.10 341	77.46 333	93.86 301

tfpnnormal		89.29 290	87.61 297	94.34 258	94.35 295	94.13 205	98.95 251	98.94 37	83.94 325	84.47 320	95.51 281	74.84 306	97.39 256	77.05 342	80.41 312	91.48 344

TransMVSNet (Re)		87.25 302	85.28 307	93.16 291	93.56 307	91.03 274	98.54 286	94.05 361	83.69 329	81.09 336	96.16 260	75.32 300	96.40 309	76.69 343	68.41 354	92.06 338

FMVSNet5		88.32 296	87.47 298	90.88 314	96.90 239	88.39 315	97.28 320	95.68 341	82.60 335	84.67 319	92.40 342	79.83 269	91.16 360	76.39 344	81.51 300	93.09 324

ppachtmachnet_test		89.58 287	88.35 289	93.25 290	92.40 328	90.44 287	99.33 209	96.73 318	85.49 314	85.90 314	95.77 268	81.09 255	96.00 326	76.00 345	82.49 292	93.30 320

MVS-HIRNet		86.22 305	83.19 317	95.31 221	96.71 250	90.29 289	92.12 355	97.33 263	62.85 362	86.82 298	70.37 366	69.37 327	97.49 252	75.12 346	97.99 154	98.15 215

MDA-MVSNet-bldmvs		84.09 318	81.52 324	91.81 309	91.32 341	88.00 320	98.67 280	95.92 337	80.22 342	55.60 367	93.32 332	68.29 333	93.60 352	73.76 347	76.61 340	93.82 304

KD-MVS_self_test		83.59 321	82.06 321	88.20 335	86.93 359	80.70 356	97.21 321	96.38 328	82.87 332	82.49 328	88.97 352	67.63 335	92.32 356	73.75 348	62.30 361	91.58 343

Anonymous20240521		85.15 312	83.81 313	89.16 328	88.32 356	82.69 341	98.80 270	95.74 339	79.72 343	81.53 334	90.99 347	65.38 343	94.16 345	72.69 349	81.11 305	90.63 350

new_pmnet		84.49 317	82.92 319	89.21 327	90.03 351	82.60 342	96.89 329	95.62 343	80.59 341	75.77 354	89.17 351	65.04 345	94.79 341	72.12 350	81.02 307	90.23 352

new-patchmatchnet		81.19 323	79.34 328	86.76 338	82.86 366	80.36 359	97.92 311	95.27 350	82.09 337	72.02 357	86.87 357	62.81 350	90.74 362	71.10 351	63.08 359	89.19 359

pmmvs3		80.27 326	77.77 330	87.76 336	80.32 367	82.43 344	98.23 301	91.97 366	72.74 359	78.75 344	87.97 354	57.30 359	90.99 361	70.31 352	62.37 360	89.87 354

TAPA-MVS	92.12 8	94.42 185	93.60 189	96.90 180	99.33 111	91.78 260	99.78 128	98.00 197	89.89 250	94.52 194	99.47 114	91.97 145	99.18 161	69.90 353	99.52 110	99.73 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CL-MVSNet_self_test		84.50 316	83.15 318	88.53 333	86.00 361	81.79 349	98.82 267	97.35 260	85.12 317	83.62 325	90.91 349	76.66 289	91.40 359	69.53 354	60.36 362	92.40 335

LCM-MVSNet		67.77 331	64.73 334	76.87 346	62.95 375	56.25 372	89.37 362	93.74 364	44.53 367	61.99 362	80.74 362	20.42 375	86.53 366	69.37 355	59.50 364	87.84 360

OpenMVS_ROB	79.82 20	83.77 320	81.68 323	90.03 323	88.30 357	82.82 340	98.46 289	95.22 351	73.92 358	76.00 352	91.29 346	55.00 360	96.94 287	68.40 356	88.51 249	90.34 351

N_pmnet		80.06 327	80.78 325	77.89 345	91.94 333	45.28 376	98.80 270	56.82 379	78.10 348	80.08 341	93.33 331	77.03 284	95.76 329	68.14 357	82.81 291	92.64 330

Anonymous20231206		86.32 304	85.42 306	89.02 329	89.11 355	80.53 358	99.05 241	95.28 349	85.43 315	82.82 327	93.92 326	74.40 309	93.44 353	66.99 358	81.83 298	93.08 325

test20.03		84.72 315	83.99 309	86.91 337	88.19 358	80.62 357	98.88 258	95.94 336	88.36 275	78.87 343	94.62 316	68.75 329	89.11 364	66.52 359	75.82 341	91.00 346

PatchT		90.38 270	88.75 284	95.25 224	95.99 260	90.16 291	91.22 360	97.54 238	76.80 349	97.26 144	86.01 359	91.88 146	96.07 323	66.16 360	95.91 192	99.51 156

test_0402		85.58 307	83.94 311	90.50 318	93.81 304	85.04 334	98.55 284	95.20 352	76.01 351	79.72 342	95.13 299	64.15 347	96.26 316	66.04 361	86.88 263	90.21 353

MIMVSNet1		82.58 322	80.51 326	88.78 331	86.68 360	84.20 338	96.65 330	95.41 347	78.75 346	78.59 345	92.44 339	51.88 363	89.76 363	65.26 362	78.95 321	92.38 336

RPMNet		89.76 284	87.28 299	97.19 173	96.29 253	92.66 239	92.01 356	98.31 161	70.19 361	96.94 150	85.87 360	87.25 204	99.78 115	62.69 363	95.96 190	99.13 193

FPMVS		68.72 330	68.72 332	68.71 350	65.95 373	44.27 378	95.97 341	94.74 356	51.13 365	53.26 368	90.50 350	25.11 373	83.00 368	60.80 364	80.97 309	78.87 364

PMMVS2		67.15 332	64.15 335	76.14 347	70.56 372	62.07 369	93.89 348	87.52 374	58.09 363	60.02 363	78.32 363	22.38 374	84.54 367	59.56 365	47.03 367	81.80 363

EGC-MVSNET		69.38 329	63.76 336	86.26 339	90.32 348	81.66 351	96.24 336	93.85 363	0.99 376	3.22 377	92.33 343	52.44 362	92.92 355	59.53 366	84.90 276	84.21 362

testmvs		40.60 340	44.45 343	29.05 357	19.49 381	14.11 382	99.68 155	18.47 380	20.74 373	64.59 361	98.48 195	10.95 378	17.09 377	56.66 367	11.01 373	55.94 370

Gipuma		66.95 333	65.00 333	72.79 348	91.52 339	67.96 365	66.16 368	95.15 354	47.89 366	58.54 364	67.99 368	29.74 370	87.54 365	50.20 368	77.83 329	62.87 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test123		37.68 341	39.14 344	33.31 356	19.94 380	24.83 381	98.36 295	9.75 381	15.53 374	51.31 369	87.14 356	19.62 376	17.74 376	47.10 369	3.47 375	57.36 369

ANet_high		56.10 335	52.24 338	67.66 351	49.27 377	56.82 371	83.94 364	82.02 375	70.47 360	33.28 374	64.54 369	17.23 377	69.16 372	45.59 370	23.85 371	77.02 365

PMVS	49.05 23	53.75 336	51.34 340	60.97 353	40.80 379	34.68 379	74.82 367	89.62 372	37.55 369	28.67 375	72.12 365	7.09 379	81.63 369	43.17 371	68.21 355	66.59 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	53.74 22	51.54 338	47.86 342	62.60 352	59.56 376	50.93 373	79.41 366	77.69 376	35.69 371	36.27 373	61.76 372	5.79 381	69.63 371	37.97 372	36.61 368	67.24 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 337	52.18 339	52.67 354	71.51 370	45.40 375	93.62 351	76.60 377	36.01 370	43.50 371	64.13 370	27.11 372	67.31 373	31.06 373	26.06 369	45.30 372

EMVS		51.44 339	51.22 341	52.11 355	70.71 371	44.97 377	94.04 347	75.66 378	35.34 372	42.40 372	61.56 373	28.93 371	65.87 374	27.64 374	24.73 370	45.49 371

wuyk23d		20.37 343	20.84 346	18.99 358	65.34 374	27.73 380	50.43 369	7.67 382	9.50 375	8.01 376	6.34 376	6.13 380	26.24 375	23.40 375	10.69 374	2.99 373

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.02 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

cdsmvs_eth3d_5k		23.43 342	31.24 345	0.00 359	0.00 382	0.00 383	0.00 370	98.09 191	0.00 377	0.00 378	99.67 98	83.37 237	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		7.60 345	10.13 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	91.20 156	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

ab-mvs-re		8.28 344	11.04 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	99.40 121	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

FOURS1					99.92 36	97.66 91	99.95 43	98.36 151	95.58 72	99.52 53

test_one_0601					99.94 14	99.30 11		98.41 135	96.63 39	99.75 27	99.93 11	97.49 9

eth-test2					0.00 382

eth-test					0.00 382

test_241102_ONE					99.93 27	99.30 11		98.43 119	97.26 22	99.80 16	99.88 24	96.71 23	100.00 1

save fliter					99.82 70	98.79 37	99.96 25	98.40 139	97.66 10

test0726					99.93 27	99.29 14	99.96 25	98.42 131	97.28 18	99.86 4	99.94 4	97.22 18

GSMVS																99.59 138

test_part2					99.89 50	99.25 17				99.49 55

sam_mvs1												94.72 67				99.59 138

sam_mvs												94.25 85

MTGPA								98.28 166

test_post											63.35 371	94.43 72	98.13 227

patchmatchnet-post											91.70 345	95.12 51	97.95 238

MTMP							99.87 92	96.49 326

TEST9					99.92 36	98.92 27	99.96 25	98.43 119	93.90 137	99.71 35	99.86 31	95.88 37	99.85 98

test_8					99.92 36	98.88 30	99.96 25	98.43 119	94.35 114	99.69 37	99.85 35	95.94 34	99.85 98

agg_prior					99.93 27	98.77 40		98.43 119		99.63 40			99.85 98

test_prior4						98.05 75	99.94 60

test_prior				99.43 38	99.94 14	98.49 61		98.65 61					99.80 110			99.99 24

新几何2							99.40 198

旧先验1					99.76 79	97.52 96		98.64 64			99.85 35	95.63 42			99.94 61	99.99 24

原ACMM2							99.90 78

test222					99.55 99	97.41 107	99.34 208	98.55 84	91.86 206	99.27 75	99.83 51	93.84 99			99.95 55	99.99 24

segment_acmp												96.68 25

testdata1							99.28 218		96.35 51

test12				99.43 38	99.74 82	98.56 57		98.40 139		99.65 38		94.76 66	99.75 125		99.98 35	99.99 24

plane_prior7					95.71 273	91.59 269

plane_prior6					95.76 268	91.72 264						80.47 265

plane_prior4											98.59 186

plane_prior3						91.64 267			96.63 39	93.01 212

plane_prior2							99.84 110		96.38 47

plane_prior1					95.73 270

plane_prior						91.74 261	99.86 103		96.76 35						89.59 231

n2								0.00 383

nn								0.00 383

door-mid								89.69 371

test11								98.44 111

door								90.31 369

HQP5-MVS						91.85 257

HQP-NCC					95.78 264		99.87 92		96.82 31	93.37 208

ACMP_Plane					95.78 264		99.87 92		96.82 31	93.37 208

HQP4-MVS										93.37 208			98.39 204			94.53 238

HQP3-MVS								97.89 209							89.60 229

HQP2-MVS												80.65 261

NP-MVS					95.77 267	91.79 259					98.65 182

ACMMP++_ref															87.04 262

ACMMP++															88.23 251

Test By Simon												92.82 126

High-resolution multi-view benchmark