High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++.		99.26 6	99.09 8	99.77 8	99.91 44	99.31 9	99.95 43	98.43 116	96.48 42	99.80 16	99.93 11	97.44 13	100.00 1	99.92 12	99.98 35	100.00 1

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 132					100.00 1	99.96 9	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

No_MVS				99.93 2	99.91 44	99.80 2		98.41 132					100.00 1	99.96 9	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 6	99.77 8	99.93 27	99.30 11	99.96 25	98.43 116	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		98.41 132	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								98.43 116	97.27 20	99.80 16	99.94 4	97.18 20	100.00 1	100.00 1	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	98.46 105	94.56 101	99.84 8	99.92 13	94.32 83	99.86 94	99.96 9	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

test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 116					100.00 1	99.99 5	100.00 1	100.00 1

SMA-MVS		98.76 27	98.48 30	99.62 18	99.87 57	98.87 31	99.86 103	98.38 143	93.19 155	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
MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 128	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
test9_res														99.71 33	99.99 22	100.00 1

agg_prior2														99.48 40	100.00 1	100.00 1

testdata				98.42 122	99.47 105	95.33 176		98.56 77	93.78 139	99.79 23	99.85 35	93.64 104	99.94 72	94.97 163	99.94 61	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 45	97.10 25	99.80 16	99.94 4	95.92 36	100.00 1	99.51 38	100.00 1	100.00 1

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 63	98.47 2	99.13 82	99.92 13	96.38 29	100.00 1	99.74 28	100.00 1	100.00 1

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 67	98.02 6	99.90 2	99.95 3	97.33 16	100.00 1	99.54 37	100.00 1	100.00 1

API-MVS		97.86 75	97.66 74	98.47 117	99.52 101	95.41 174	99.47 191	98.87 44	91.68 209	98.84 93	99.85 35	92.34 137	99.99 40	98.44 92	99.96 52	100.00 1

DeepPCF-MVS	95.94 2	97.71 85	98.98 11	93.92 270	99.63 93	81.76 347	99.96 25	98.56 77	99.47 1	99.19 80	99.99 1	94.16 90	100.00 1	99.92 12	99.93 67	100.00 1

DeepC-MVS_fast	96.59 1	98.81 23	98.54 27	99.62 18	99.90 47	98.85 33	99.24 221	98.47 103	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
MG-MVS		98.91 18	98.65 21	99.68 14	99.94 14	99.07 22	99.64 165	99.44 18	97.33 17	99.00 89	99.72 87	94.03 93	99.98 46	98.73 79	100.00 1	100.00 1

testtj		98.89 19	98.69 19	99.52 29	99.94 14	98.56 57	99.90 78	98.55 83	95.14 82	99.72 33	99.84 48	95.46 46	100.00 1	99.65 36	99.99 22	99.99 24

DPE-MVS		99.26 6	99.10 7	99.74 10	99.89 50	99.24 18	99.87 92	98.44 108	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
ACMMP_NAP		98.49 44	98.14 55	99.54 26	99.66 92	98.62 54	99.85 106	98.37 146	94.68 96	99.53 50	99.83 51	92.87 123	100.00 1	98.66 85	99.84 84	99.99 24

zzz-MVS		98.33 56	98.00 63	99.30 50	99.85 60	97.93 82	99.80 124	98.28 163	95.76 65	97.18 145	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	98.28 163	95.76 65	97.18 145	99.88 24	92.74 127	100.00 1	98.67 82	99.88 80	99.99 24

train_agg		98.88 20	98.65 21	99.59 21	99.92 36	98.92 27	99.96 25	98.43 116	94.35 111	99.71 35	99.86 31	95.94 34	99.85 98	99.69 35	99.98 35	99.99 24

agg_prior1		98.88 20	98.66 20	99.54 26	99.93 27	98.77 40	99.96 25	98.43 116	94.63 99	99.63 40	99.85 35	95.79 40	99.85 98	99.72 32	99.99 22	99.99 24

Regformer-1		98.79 25	98.60 24	99.36 48	99.85 60	98.34 66	99.87 92	98.52 90	96.05 56	99.41 61	99.79 64	94.93 63	99.76 122	99.07 53	99.90 76	99.99 24

XVS		98.70 29	98.55 26	99.15 64	99.94 14	97.50 99	99.94 60	98.42 128	96.22 52	99.41 61	99.78 69	94.34 79	99.96 57	98.92 64	99.95 55	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 60	95.78 63	99.73 29	99.76 75	96.00 32	99.80 110	99.78 24	100.00 1	99.99 24

X-MVStestdata		93.83 192	92.06 221	99.15 64	99.94 14	97.50 99	99.94 60	98.42 128	96.22 52	99.41 61	41.37 371	94.34 79	99.96 57	98.92 64	99.95 55	99.99 24

test_prior				99.43 38	99.94 14	98.49 61		98.65 60					99.80 110			99.99 24

新几何1				99.42 41	99.75 81	98.27 69		98.63 66	92.69 172	99.55 48	99.82 55	94.40 74	100.00 1	91.21 229	99.94 61	99.99 24

旧先验1					99.76 79	97.52 96		98.64 63			99.85 35	95.63 42			99.94 61	99.99 24

无先验							99.49 188	98.71 53	93.46 148				100.00 1	94.36 182		99.99 24

test222					99.55 99	97.41 107	99.34 208	98.55 83	91.86 203	99.27 75	99.83 51	93.84 99			99.95 55	99.99 24

1121		98.03 70	97.57 80	99.40 44	99.74 82	98.21 70	98.31 293	98.62 67	92.78 167	99.53 50	99.83 51	95.08 53	100.00 1	94.36 182	99.92 71	99.99 24

MVS		96.60 126	95.56 145	99.72 12	96.85 238	99.22 19	98.31 293	98.94 36	91.57 212	90.90 227	99.61 104	86.66 209	99.96 57	97.36 130	99.88 80	99.99 24

APDe-MVS		99.06 11	98.91 13	99.51 31	99.94 14	98.76 44	99.91 74	98.39 139	97.20 24	99.46 56	99.85 35	95.53 45	99.79 113	99.86 16	100.00 1	99.99 24

test12				99.43 38	99.74 82	98.56 57		98.40 136		99.65 38		94.76 66	99.75 125		99.98 35	99.99 24

TSAR-MVS + GP.		98.60 34	98.51 29	98.86 90	99.73 86	96.63 129	99.97 18	97.92 204	98.07 5	98.76 99	99.55 108	95.00 60	99.94 72	99.91 15	97.68 154	99.99 24

HPM-MVS_fast		97.80 80	97.50 81	98.68 98	99.79 75	96.42 136	99.88 89	98.16 182	91.75 208	98.94 91	99.54 110	91.82 148	99.65 142	97.62 126	99.99 22	99.99 24

HPM-MVS		97.96 71	97.72 73	98.68 98	99.84 65	96.39 139	99.90 78	98.17 179	92.61 177	98.62 106	99.57 107	91.87 146	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
APD-MVS		98.62 33	98.35 44	99.41 42	99.90 47	98.51 60	99.87 92	98.36 148	94.08 122	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
CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 10	98.69 55	98.20 3	99.93 1	99.98 2	96.82 22	100.00 1	99.75 26	100.00 1	99.99 24

CP-MVS		98.45 47	98.32 45	98.87 89	99.96 8	96.62 130	99.97 18	98.39 139	94.43 106	98.90 92	99.87 28	94.30 84	100.00 1	99.04 58	99.99 22	99.99 24

SteuartSystems-ACMMP		99.02 12	98.97 12	99.18 57	98.72 143	97.71 87	99.98 10	98.44 108	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.
CPTT-MVS		97.64 87	97.32 90	98.58 108	99.97 3	95.77 162	99.96 25	98.35 151	89.90 246	98.36 117	99.79 64	91.18 158	99.99 40	98.37 94	99.99 22	99.99 24

PAPM_NR		98.12 67	97.93 69	98.70 97	99.94 14	96.13 151	99.82 117	98.43 116	94.56 101	97.52 138	99.70 91	94.40 74	99.98 46	97.00 139	99.98 35	99.99 24

PAPR		98.52 42	98.16 54	99.58 23	99.97 3	98.77 40	99.95 43	98.43 116	95.35 77	98.03 128	99.75 80	94.03 93	99.98 46	98.11 104	99.83 85	99.99 24

PHI-MVS		98.41 50	98.21 50	99.03 78	99.86 59	97.10 116	99.98 10	98.80 50	90.78 233	99.62 43	99.78 69	95.30 49	100.00 1	99.80 22	99.93 67	99.99 24

test1172		98.38 54	98.25 48	98.77 93	99.88 54	96.56 133	99.80 124	98.36 148	94.68 96	99.20 77	99.80 60	93.28 113	99.78 115	99.34 46	99.92 71	99.98 55

DPM-MVS		98.83 22	98.46 31	99.97 1	99.33 111	99.92 1	99.96 25	98.44 108	97.96 7	99.55 48	99.94 4	97.18 20	100.00 1	93.81 195	99.94 61	99.98 55

HFP-MVS		98.56 38	98.37 41	99.14 66	99.96 8	97.43 104	99.95 43	98.61 69	94.77 91	99.31 70	99.85 35	94.22 86	100.00 1	98.70 80	99.98 35	99.98 55

region2R		98.54 40	98.37 41	99.05 76	99.96 8	97.18 112	99.96 25	98.55 83	94.87 89	99.45 57	99.85 35	94.07 92	100.00 1	98.67 82	100.00 1	99.98 55

#test#		98.59 36	98.41 34	99.14 66	99.96 8	97.43 104	99.95 43	98.61 69	95.00 84	99.31 70	99.85 35	94.22 86	100.00 1	98.78 76	99.98 35	99.98 55

Regformer-3		98.58 37	98.41 34	99.10 72	99.84 65	97.57 93	99.66 158	98.52 90	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 90	95.76 65	99.01 87	99.77 71	94.33 82	99.75 125	98.80 75	99.83 85	99.98 55

Regformer-2		98.78 26	98.59 25	99.36 48	99.85 60	98.32 67	99.87 92	98.52 90	96.04 57	99.41 61	99.79 64	94.92 64	99.76 122	99.05 54	99.90 76	99.98 55

ACMMPR		98.50 43	98.32 45	99.05 76	99.96 8	97.18 112	99.95 43	98.60 71	94.77 91	99.31 70	99.84 48	93.73 101	100.00 1	98.70 80	99.98 35	99.98 55

PGM-MVS		98.34 55	98.13 56	98.99 82	99.92 36	97.00 118	99.75 139	99.50 16	93.90 134	99.37 67	99.76 75	93.24 116	100.00 1	97.75 124	99.96 52	99.98 55

CDPH-MVS		98.65 32	98.36 43	99.49 34	99.94 14	98.73 45	99.87 92	98.33 154	93.97 129	99.76 26	99.87 28	94.99 61	99.75 125	98.55 89	100.00 1	99.98 55

mPP-MVS		98.39 53	98.20 51	98.97 84	99.97 3	96.92 122	99.95 43	98.38 143	95.04 83	98.61 107	99.80 60	93.39 107	100.00 1	98.64 86	100.00 1	99.98 55

SR-MVS-dyc-post		98.31 57	98.17 53	98.71 96	99.79 75	96.37 140	99.76 136	98.31 158	94.43 106	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	96.37 140	99.76 136	98.31 158	94.43 106	99.40 65	99.75 80	92.95 122		98.90 67	99.92 71	99.97 67

ETH3D-3000-0.1		98.68 30	98.42 32	99.47 37	99.83 68	98.57 55	99.90 78	98.37 146	93.81 137	99.81 12	99.90 19	94.34 79	99.86 94	99.84 17	99.98 35	99.97 67

TSAR-MVS + MP.		98.93 16	98.77 17	99.41 42	99.74 82	98.67 48	99.77 131	98.38 143	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
SD-MVS		98.92 17	98.70 18	99.56 24	99.70 90	98.73 45	99.94 60	98.34 153	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
APD-MVS_3200maxsize		98.25 63	98.08 59	98.78 92	99.81 73	96.60 131	99.82 117	98.30 161	93.95 131	99.37 67	99.77 71	92.84 124	99.76 122	98.95 61	99.92 71	99.97 67

DP-MVS Recon		98.41 50	98.02 62	99.56 24	99.97 3	98.70 47	99.92 70	98.44 108	92.06 199	98.40 116	99.84 48	95.68 41	100.00 1	98.19 99	99.71 97	99.97 67

xxxxxxxxxxxxxcwj		98.98 15	98.79 16	99.54 26	99.82 70	98.79 37	99.96 25	97.52 239	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	98.21 173	93.53 146	99.81 12	99.89 21	94.70 68	99.86 94	99.84 17	99.93 67	99.96 74

SR-MVS		98.46 46	98.30 47	98.93 87	99.88 54	97.04 117	99.84 110	98.35 151	94.92 86	99.32 69	99.80 60	93.35 108	99.78 115	99.30 48	99.95 55	99.96 74

1314		96.84 114	95.96 133	99.48 36	96.74 245	98.52 59	98.31 293	98.86 45	95.82 61	89.91 239	98.98 154	87.49 200	99.96 57	97.80 117	99.73 95	99.96 74

114514_t		97.41 95	96.83 104	99.14 66	99.51 103	97.83 84	99.89 86	98.27 166	88.48 270	99.06 84	99.66 100	90.30 170	99.64 143	96.32 149	99.97 48	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

PAPM		98.60 34	98.42 32	99.14 66	96.05 255	98.96 24	99.90 78	99.35 23	96.68 38	98.35 118	99.66 100	96.45 28	98.51 189	99.45 41	99.89 78	99.96 74

3Dnovator+	91.53 11	96.31 136	95.24 152	99.52 29	96.88 237	98.64 53	99.72 150	98.24 169	95.27 80	88.42 276	98.98 154	82.76 239	99.94 72	97.10 137	99.83 85	99.96 74

EI-MVSNet-Vis-set		98.27 60	98.11 58	98.75 95	99.83 68	96.59 132	99.40 198	98.51 97	95.29 79	98.51 110	99.76 75	93.60 105	99.71 133	98.53 90	99.52 110	99.95 82

CHOSEN 1792x2688		96.81 115	96.53 114	97.64 153	98.91 132	93.07 225	99.65 161	99.80 3	95.64 70	95.39 182	98.86 170	84.35 230	99.90 79	96.98 140	99.16 124	99.95 82

AdaColmap		97.23 102	96.80 106	98.51 115	99.99 1	95.60 170	99.09 230	98.84 47	93.32 151	96.74 155	99.72 87	86.04 214	100.00 1	98.01 109	99.43 116	99.94 84

ETH3D cwj APD-0.16		98.40 52	98.07 60	99.40 44	99.59 95	98.41 64	99.86 103	98.24 169	92.18 194	99.73 29	99.87 28	93.47 106	99.85 98	99.74 28	99.95 55	99.93 85

ZNCC-MVS		98.31 57	98.03 61	99.17 60	99.88 54	97.59 92	99.94 60	98.44 108	94.31 114	98.50 111	99.82 55	93.06 120	99.99 40	98.30 98	99.99 22	99.93 85

GST-MVS		98.27 60	97.97 65	99.17 60	99.92 36	97.57 93	99.93 66	98.39 139	94.04 127	98.80 95	99.74 84	92.98 121	100.00 1	98.16 101	99.76 93	99.93 85

MP-MVS		98.23 64	97.97 65	99.03 78	99.94 14	97.17 115	99.95 43	98.39 139	94.70 94	98.26 123	99.81 59	91.84 147	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.
HyFIR lowres test		96.66 125	96.43 117	97.36 166	99.05 118	93.91 210	99.70 152	99.80 3	90.54 235	96.26 168	98.08 201	92.15 141	98.23 220	96.84 145	95.46 198	99.93 85

CNLPA		97.76 82	97.38 85	98.92 88	99.53 100	96.84 123	99.87 92	98.14 185	93.78 139	96.55 160	99.69 94	92.28 138	99.98 46	97.13 135	99.44 115	99.93 85

原ACMM1				98.96 85	99.73 86	96.99 119		98.51 97	94.06 125	99.62 43	99.85 35	94.97 62	99.96 57	95.11 161	99.95 55	99.92 91

DELS-MVS		98.54 40	98.22 49	99.50 32	99.15 116	98.65 52	100.00 1	98.58 73	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
CSCG		97.10 105	97.04 99	97.27 169	99.89 50	91.92 253	99.90 78	99.07 31	88.67 266	95.26 185	99.82 55	93.17 118	99.98 46	98.15 102	99.47 113	99.90 93

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 156	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
abl_6		97.67 86	97.34 88	98.66 100	99.68 91	96.11 154	99.68 155	98.14 185	93.80 138	99.27 75	99.70 91	88.65 193	99.98 46	97.46 128	99.72 96	99.89 94

MVS_111021_LR		98.42 49	98.38 39	98.53 114	99.39 108	95.79 161	99.87 92	99.86 2	96.70 37	98.78 96	99.79 64	92.03 143	99.90 79	99.17 50	99.86 83	99.88 96

HPM-MVS++		99.07 10	98.88 14	99.63 15	99.90 47	99.02 23	99.95 43	98.56 77	97.56 13	99.44 58	99.85 35	95.38 48	100.00 1	99.31 47	99.99 22	99.87 97

ACMMP		97.74 83	97.44 83	98.66 100	99.92 36	96.13 151	99.18 225	99.45 17	94.84 90	96.41 165	99.71 89	91.40 151	99.99 40	97.99 111	98.03 150	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
3Dnovator	91.47 12	96.28 139	95.34 150	99.08 74	96.82 240	97.47 102	99.45 194	98.81 48	95.52 74	89.39 253	99.00 151	81.97 243	99.95 64	97.27 132	99.83 85	99.84 99

CANet		98.27 60	97.82 71	99.63 15	99.72 88	99.10 21	99.98 10	98.51 97	97.00 28	98.52 109	99.71 89	87.80 197	99.95 64	99.75 26	99.38 117	99.83 100

Patchmatch-test		92.65 222	91.50 232	96.10 202	96.85 238	90.49 282	91.50 354	97.19 269	82.76 331	90.23 233	95.59 273	95.02 57	98.00 231	77.41 336	96.98 171	99.82 101

EI-MVSNet-UG-set		98.14 66	97.99 64	98.60 105	99.80 74	96.27 142	99.36 207	98.50 101	95.21 81	98.30 120	99.75 80	93.29 112	99.73 132	98.37 94	99.30 119	99.81 102

HY-MVS	92.50 7	97.79 81	97.17 95	99.63 15	98.98 123	99.32 8	97.49 314	99.52 13	95.69 69	98.32 119	97.41 216	93.32 110	99.77 119	98.08 107	95.75 194	99.81 102

test_yl		97.83 77	97.37 86	99.21 54	99.18 113	97.98 79	99.64 165	99.27 25	91.43 218	97.88 132	98.99 152	95.84 38	99.84 107	98.82 72	95.32 201	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 25	91.43 218	97.88 132	98.99 152	95.84 38	99.84 107	98.82 72	95.32 201	99.79 104

Patchmatch-RL test		86.90 300	85.98 302	89.67 322	84.45 359	75.59 357	89.71 357	92.43 361	86.89 292	77.83 344	90.94 344	94.22 86	93.63 348	87.75 275	69.61 345	99.79 104

WTY-MVS		98.10 68	97.60 78	99.60 20	98.92 130	99.28 16	99.89 86	99.52 13	95.58 72	98.24 124	99.39 123	93.33 109	99.74 129	97.98 113	95.58 197	99.78 107

CHOSEN 280x420		99.01 13	99.03 9	98.95 86	99.38 109	98.87 31	98.46 286	99.42 20	97.03 27	99.02 86	99.09 143	99.35 1	98.21 221	99.73 31	99.78 92	99.77 108

MP-MVS-pluss		98.07 69	97.64 75	99.38 47	99.74 82	98.41 64	99.74 142	98.18 178	93.35 150	96.45 162	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
EPMVS		96.53 128	96.01 125	98.09 136	98.43 153	96.12 153	96.36 330	99.43 19	93.53 146	97.64 136	95.04 299	94.41 73	98.38 205	91.13 231	98.11 145	99.75 110

Vis-MVSNet (Re-imp)		96.32 135	95.98 128	97.35 167	97.93 179	94.82 191	99.47 191	98.15 184	91.83 204	95.09 186	99.11 142	91.37 152	97.47 251	93.47 204	97.43 158	99.74 111

DP-MVS		94.54 178	93.42 193	97.91 143	99.46 107	94.04 205	98.93 253	97.48 244	81.15 336	90.04 236	99.55 108	87.02 206	99.95 64	88.97 261	98.11 145	99.73 112

TAPA-MVS	92.12 8	94.42 182	93.60 186	96.90 177	99.33 111	91.78 257	99.78 128	98.00 194	89.89 247	94.52 191	99.47 114	91.97 144	99.18 159	69.90 350	99.52 110	99.73 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		97.09 107	96.32 119	99.39 46	98.93 128	98.95 25	99.72 150	97.35 257	94.45 104	97.88 132	99.42 118	86.71 208	99.52 146	98.48 91	93.97 214	99.72 114

TESTMET0.1,1		96.74 120	96.26 120	98.16 131	97.36 214	96.48 134	99.96 25	98.29 162	91.93 201	95.77 177	98.07 202	95.54 43	98.29 211	90.55 244	98.89 128	99.70 115

PatchmatchNet		95.94 145	95.45 146	97.39 164	97.83 186	94.41 200	96.05 335	98.40 136	92.86 161	97.09 147	95.28 294	94.21 89	98.07 228	89.26 259	98.11 145	99.70 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VNet		97.21 103	96.57 113	99.13 71	98.97 124	97.82 85	99.03 243	99.21 27	94.31 114	99.18 81	98.88 166	86.26 213	99.89 83	98.93 63	94.32 209	99.69 117

Anonymous202405211		93.10 210	91.99 222	96.40 194	99.10 117	89.65 297	98.88 258	97.93 202	83.71 325	94.00 199	98.75 175	68.79 325	99.88 89	95.08 162	91.71 224	99.68 118

mvs_anonymous		95.65 153	95.03 158	97.53 156	98.19 166	95.74 164	99.33 209	97.49 243	90.87 230	90.47 232	97.10 225	88.23 195	97.16 267	95.92 154	97.66 155	99.68 118

GG-mvs-BLEND				98.54 112	98.21 165	98.01 77	93.87 345	98.52 90		97.92 130	97.92 208	99.02 2	97.94 237	98.17 100	99.58 107	99.67 120

gg-mvs-nofinetune		93.51 202	91.86 226	98.47 117	97.72 197	97.96 81	92.62 349	98.51 97	74.70 353	97.33 142	69.59 363	98.91 3	97.79 240	97.77 122	99.56 108	99.67 120

alignmvs		97.81 79	97.33 89	99.25 52	98.77 142	98.66 50	99.99 5	98.44 108	94.40 110	98.41 114	99.47 114	93.65 103	99.42 155	98.57 88	94.26 210	99.67 120

LFMVS		94.75 171	93.56 189	98.30 127	99.03 119	95.70 167	98.74 270	97.98 197	87.81 279	98.47 112	99.39 123	67.43 333	99.53 145	98.01 109	95.20 203	99.67 120

MDTV_nov1_ep13_2view						96.26 143	96.11 334		91.89 202	98.06 127		94.40 74		94.30 185		99.67 120

MAR-MVS		97.43 91	97.19 93	98.15 134	99.47 105	94.79 193	99.05 241	98.76 51	92.65 175	98.66 104	99.82 55	88.52 194	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
CS-MVS-test		97.44 90	97.41 84	97.53 156	97.46 210	94.66 196	100.00 1	97.04 288	94.69 95	99.72 33	99.25 135	91.22 153	98.29 211	98.33 97	98.95 127	99.64 126

test-LLR		96.47 129	96.04 124	97.78 146	97.02 229	95.44 172	99.96 25	98.21 173	94.07 123	95.55 179	96.38 250	93.90 97	98.27 216	90.42 247	98.83 130	99.64 126

test-mter		96.39 133	95.93 135	97.78 146	97.02 229	95.44 172	99.96 25	98.21 173	91.81 206	95.55 179	96.38 250	95.17 50	98.27 216	90.42 247	98.83 130	99.64 126

DROMVSNet		97.38 97	97.24 91	97.80 144	97.41 211	95.64 169	99.99 5	97.06 284	94.59 100	99.63 40	99.32 127	89.20 186	98.14 223	98.76 78	99.23 122	99.62 129

sss		97.57 88	97.03 100	99.18 57	98.37 154	98.04 76	99.73 147	99.38 21	93.46 148	98.76 99	99.06 145	91.21 154	99.89 83	96.33 148	97.01 170	99.62 129

QAPM		95.40 157	94.17 173	99.10 72	96.92 232	97.71 87	99.40 198	98.68 56	89.31 251	88.94 265	98.89 164	82.48 240	99.96 57	93.12 212	99.83 85	99.62 129

MVS_Test		96.46 130	95.74 141	98.61 104	98.18 167	97.23 110	99.31 212	97.15 275	91.07 226	98.84 93	97.05 229	88.17 196	98.97 165	94.39 181	97.50 157	99.61 132

EPNet		98.49 44	98.40 36	98.77 93	99.62 94	96.80 125	99.90 78	99.51 15	97.60 12	99.20 77	99.36 126	93.71 102	99.91 78	97.99 111	98.71 133	99.61 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.85 6	94.99 165	93.94 179	98.16 131	97.72 197	95.69 168	99.99 5	98.81 48	94.28 116	92.70 215	96.90 233	95.08 53	99.17 160	96.07 151	73.88 341	99.60 134
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
ET-MVSNet_ETH3D		94.37 184	93.28 199	97.64 153	98.30 156	97.99 78	99.99 5	97.61 227	94.35 111	71.57 355	99.45 117	96.23 30	95.34 330	96.91 144	85.14 272	99.59 135

EIA-MVS		97.53 89	97.46 82	97.76 149	98.04 174	94.84 190	99.98 10	97.61 227	94.41 109	97.90 131	99.59 105	92.40 135	98.87 167	98.04 108	99.13 125	99.59 135

GSMVS																99.59 135

sam_mvs1												94.72 67				99.59 135

Fast-Effi-MVS+		95.02 164	94.19 172	97.52 158	97.88 181	94.55 197	99.97 18	97.08 282	88.85 263	94.47 193	97.96 207	84.59 227	98.41 197	89.84 255	97.10 167	99.59 135

SCA		94.69 172	93.81 183	97.33 168	97.10 224	94.44 198	98.86 262	98.32 156	93.30 152	96.17 170	95.59 273	76.48 290	97.95 235	91.06 233	97.43 158	99.59 135

PVSNet	91.05 13	97.13 104	96.69 109	98.45 119	99.52 101	95.81 160	99.95 43	99.65 10	94.73 93	99.04 85	99.21 139	84.48 228	99.95 64	94.92 164	98.74 132	99.58 141

PVSNet_Blended		97.94 72	97.64 75	98.83 91	99.59 95	96.99 119	100.00 1	99.10 28	95.38 76	98.27 121	99.08 144	89.00 188	99.95 64	99.12 51	99.25 120	99.57 142

ab-mvs		94.69 172	93.42 193	98.51 115	98.07 172	96.26 143	96.49 329	98.68 56	90.31 240	94.54 190	97.00 231	76.30 292	99.71 133	95.98 153	93.38 219	99.56 143

DWT-MVSNet_test		97.31 98	97.19 93	97.66 152	98.24 163	94.67 195	98.86 262	98.20 177	93.60 145	98.09 126	98.89 164	97.51 7	98.78 172	94.04 189	97.28 163	99.55 144

Test_1112_low_res		95.72 149	94.83 161	98.42 122	97.79 189	96.41 137	99.65 161	96.65 319	92.70 171	92.86 214	96.13 259	92.15 141	99.30 156	91.88 223	93.64 216	99.55 144

1112_ss		96.01 144	95.20 154	98.42 122	97.80 188	96.41 137	99.65 161	96.66 318	92.71 170	92.88 213	99.40 121	92.16 140	99.30 156	91.92 222	93.66 215	99.55 144

DeepC-MVS	94.51 4	96.92 112	96.40 118	98.45 119	99.16 115	95.90 158	99.66 158	98.06 191	96.37 50	94.37 194	99.49 113	83.29 237	99.90 79	97.63 125	99.61 105	99.55 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LCM-MVSNet-Re		92.31 228	92.60 208	91.43 308	97.53 204	79.27 356	99.02 244	91.83 363	92.07 197	80.31 336	94.38 320	83.50 235	95.48 327	97.22 134	97.58 156	99.54 148

casdiffmvs		96.42 132	95.97 131	97.77 148	97.30 219	94.98 186	99.84 110	97.09 281	93.75 141	96.58 158	99.26 134	85.07 224	98.78 172	97.77 122	97.04 169	99.54 148

dp		95.05 163	94.43 168	96.91 176	97.99 176	92.73 234	96.29 332	97.98 197	89.70 249	95.93 173	94.67 312	93.83 100	98.45 194	86.91 289	96.53 177	99.54 148

CS-MVS		97.74 83	97.61 77	98.15 134	97.52 208	96.69 127	100.00 1	97.11 279	94.93 85	99.73 29	99.41 120	91.68 149	98.25 219	98.84 71	99.24 121	99.52 151

Effi-MVS+		96.30 137	95.69 142	98.16 131	97.85 185	96.26 143	97.41 315	97.21 268	90.37 238	98.65 105	98.58 185	86.61 210	98.70 180	97.11 136	97.37 162	99.52 151

PatchT		90.38 267	88.75 281	95.25 221	95.99 257	90.16 288	91.22 356	97.54 235	76.80 346	97.26 143	86.01 355	91.88 145	96.07 320	66.16 357	95.91 189	99.51 153

tpm		93.70 200	93.41 195	94.58 242	95.36 278	87.41 319	97.01 323	96.90 303	90.85 231	96.72 156	94.14 322	90.40 169	96.84 289	90.75 243	88.54 245	99.51 153

CostFormer		96.10 141	95.88 138	96.78 180	97.03 228	92.55 240	97.08 322	97.83 213	90.04 245	98.72 101	94.89 306	95.01 59	98.29 211	96.54 147	95.77 192	99.50 155

tpmrst		96.27 140	95.98 128	97.13 171	97.96 177	93.15 224	96.34 331	98.17 179	92.07 197	98.71 102	95.12 297	93.91 96	98.73 177	94.91 166	96.62 175	99.50 155

IS-MVSNet		96.29 138	95.90 137	97.45 160	98.13 171	94.80 192	99.08 232	97.61 227	92.02 200	95.54 181	98.96 158	90.64 167	98.08 226	93.73 200	97.41 161	99.47 157

ETV-MVS		97.92 74	97.80 72	98.25 129	98.14 170	96.48 134	99.98 10	97.63 222	95.61 71	99.29 74	99.46 116	92.55 132	98.82 169	99.02 60	98.54 135	99.46 158

baseline		96.43 131	95.98 128	97.76 149	97.34 215	95.17 183	99.51 184	97.17 272	93.92 133	96.90 151	99.28 128	85.37 221	98.64 183	97.50 127	96.86 174	99.46 158

lupinMVS		97.85 76	97.60 78	98.62 103	97.28 221	97.70 89	99.99 5	97.55 233	95.50 75	99.43 59	99.67 98	90.92 162	98.71 179	98.40 93	99.62 102	99.45 160

PMMVS		96.76 118	96.76 107	96.76 181	98.28 159	92.10 248	99.91 74	97.98 197	94.12 120	99.53 50	99.39 123	86.93 207	98.73 177	96.95 142	97.73 152	99.45 160

UA-Net		96.54 127	95.96 133	98.27 128	98.23 164	95.71 166	98.00 307	98.45 107	93.72 142	98.41 114	99.27 131	88.71 192	99.66 141	91.19 230	97.69 153	99.44 162

CVMVSNet		94.68 174	94.94 159	93.89 272	96.80 241	86.92 321	99.06 237	98.98 34	94.45 104	94.23 197	99.02 147	85.60 217	95.31 331	90.91 239	95.39 200	99.43 163

PVSNet_Blended_VisFu		97.27 100	96.81 105	98.66 100	98.81 139	96.67 128	99.92 70	98.64 63	94.51 103	96.38 166	98.49 189	89.05 187	99.88 89	97.10 137	98.34 139	99.43 163

PLC	95.54 3	97.93 73	97.89 70	98.05 138	99.82 70	94.77 194	99.92 70	98.46 105	93.93 132	97.20 144	99.27 131	95.44 47	99.97 55	97.41 129	99.51 112	99.41 165
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	94.20 5	95.18 160	94.10 175	98.43 121	98.55 147	95.99 156	97.91 309	97.31 262	90.35 239	89.48 252	99.22 138	85.19 223	99.89 83	90.40 249	98.47 137	99.41 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpm2		95.47 156	95.18 155	96.35 197	96.91 233	91.70 262	96.96 325	97.93 202	88.04 276	98.44 113	95.40 283	93.32 110	97.97 232	94.00 190	95.61 196	99.38 167

OMC-MVS		97.28 99	97.23 92	97.41 162	99.76 79	93.36 223	99.65 161	97.95 200	96.03 58	97.41 141	99.70 91	89.61 177	99.51 147	96.73 146	98.25 144	99.38 167

GeoE		94.36 186	93.48 191	96.99 174	97.29 220	93.54 217	99.96 25	96.72 316	88.35 273	93.43 204	98.94 161	82.05 242	98.05 229	88.12 272	96.48 179	99.37 169

ADS-MVSNet2		93.80 195	93.88 181	93.55 282	97.87 183	85.94 325	94.24 341	96.84 307	90.07 243	96.43 163	94.48 317	90.29 171	95.37 329	87.44 277	97.23 164	99.36 170

ADS-MVSNet		94.79 168	94.02 177	97.11 173	97.87 183	93.79 211	94.24 341	98.16 182	90.07 243	96.43 163	94.48 317	90.29 171	98.19 222	87.44 277	97.23 164	99.36 170

BH-RMVSNet		95.18 160	94.31 171	97.80 144	98.17 168	95.23 181	99.76 136	97.53 237	92.52 184	94.27 196	99.25 135	76.84 286	98.80 170	90.89 240	99.54 109	99.35 172

TR-MVS		94.54 178	93.56 189	97.49 159	97.96 177	94.34 201	98.71 273	97.51 241	90.30 241	94.51 192	98.69 177	75.56 297	98.77 174	92.82 214	95.99 186	99.35 172

diffmvs		97.00 108	96.64 110	98.09 136	97.64 200	96.17 150	99.81 119	97.19 269	94.67 98	98.95 90	99.28 128	86.43 211	98.76 175	98.37 94	97.42 160	99.33 174

JIA-IIPM		91.76 243	90.70 243	94.94 229	96.11 253	87.51 318	93.16 348	98.13 187	75.79 350	97.58 137	77.68 360	92.84 124	97.97 232	88.47 267	96.54 176	99.33 174

thres200		96.96 109	96.21 121	99.22 53	98.97 124	98.84 34	99.85 106	99.71 5	93.17 156	96.26 168	98.88 166	89.87 175	99.51 147	94.26 186	94.91 204	99.31 176

CDS-MVSNet		96.34 134	96.07 123	97.13 171	97.37 213	94.96 187	99.53 181	97.91 205	91.55 213	95.37 183	98.32 197	95.05 56	97.13 270	93.80 196	95.75 194	99.30 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		95.72 149	95.15 156	97.45 160	97.62 201	94.28 202	99.28 218	98.24 169	94.27 117	96.84 152	98.94 161	79.39 270	98.76 175	93.25 206	98.49 136	99.30 177
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thres100view900		96.74 120	95.92 136	99.18 57	98.90 133	98.77 40	99.74 142	99.71 5	92.59 179	95.84 174	98.86 170	89.25 183	99.50 149	93.84 192	94.57 205	99.27 179

tfpn200view9		96.79 116	95.99 126	99.19 56	98.94 126	98.82 35	99.78 128	99.71 5	92.86 161	96.02 171	98.87 168	89.33 181	99.50 149	93.84 192	94.57 205	99.27 179

MVSFormer		96.94 110	96.60 111	97.95 140	97.28 221	97.70 89	99.55 178	97.27 265	91.17 222	99.43 59	99.54 110	90.92 162	96.89 286	94.67 176	99.62 102	99.25 181

jason		97.24 101	96.86 103	98.38 125	95.73 267	97.32 108	99.97 18	97.40 254	95.34 78	98.60 108	99.54 110	87.70 198	98.56 186	97.94 114	99.47 113	99.25 181
jason: jason.
EPP-MVSNet		96.69 123	96.60 111	96.96 175	97.74 193	93.05 227	99.37 205	98.56 77	88.75 264	95.83 176	99.01 149	96.01 31	98.56 186	96.92 143	97.20 166	99.25 181

EPNet_dtu		95.71 151	95.39 148	96.66 185	98.92 130	93.41 221	99.57 174	98.90 41	96.19 54	97.52 138	98.56 187	92.65 129	97.36 254	77.89 334	98.33 140	99.20 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GA-MVS		93.83 192	92.84 203	96.80 179	95.73 267	93.57 215	99.88 89	97.24 267	92.57 182	92.92 211	96.66 243	78.73 276	97.67 244	87.75 275	94.06 213	99.17 185

thisisatest0515		97.41 95	97.02 101	98.59 107	97.71 199	97.52 96	99.97 18	98.54 87	91.83 204	97.45 140	99.04 146	97.50 8	99.10 161	94.75 172	96.37 181	99.16 186

thres600view7		96.69 123	95.87 139	99.14 66	98.90 133	98.78 39	99.74 142	99.71 5	92.59 179	95.84 174	98.86 170	89.25 183	99.50 149	93.44 205	94.50 208	99.16 186

thres400		96.78 117	95.99 126	99.16 62	98.94 126	98.82 35	99.78 128	99.71 5	92.86 161	96.02 171	98.87 168	89.33 181	99.50 149	93.84 192	94.57 205	99.16 186

TAMVS		95.85 146	95.58 144	96.65 186	97.07 225	93.50 218	99.17 226	97.82 214	91.39 221	95.02 187	98.01 203	92.20 139	97.30 259	93.75 199	95.83 191	99.14 189

CR-MVSNet		93.45 205	92.62 207	95.94 205	96.29 250	92.66 236	92.01 352	96.23 327	92.62 176	96.94 149	93.31 330	91.04 159	96.03 321	79.23 327	95.96 187	99.13 190

RPMNet		89.76 281	87.28 296	97.19 170	96.29 250	92.66 236	92.01 352	98.31 158	70.19 358	96.94 149	85.87 356	87.25 203	99.78 115	62.69 360	95.96 187	99.13 190

tpm cat1		93.51 202	92.52 213	96.47 189	97.77 190	91.47 268	96.13 333	98.06 191	80.98 337	92.91 212	93.78 325	89.66 176	98.87 167	87.03 285	96.39 180	99.09 192

BH-w/o		95.71 151	95.38 149	96.68 184	98.49 151	92.28 244	99.84 110	97.50 242	92.12 196	92.06 218	98.79 174	84.69 226	98.67 182	95.29 160	99.66 100	99.09 192

LS3D		95.84 147	95.11 157	98.02 139	99.85 60	95.10 184	98.74 270	98.50 101	87.22 286	93.66 203	99.86 31	87.45 201	99.95 64	90.94 238	99.81 91	99.02 194

MIMVSNet		90.30 270	88.67 282	95.17 223	96.45 249	91.64 264	92.39 350	97.15 275	85.99 301	90.50 231	93.19 332	66.95 334	94.86 337	82.01 317	93.43 217	99.01 195

thisisatest0530		97.10 105	96.72 108	98.22 130	97.60 202	96.70 126	99.92 70	98.54 87	91.11 225	97.07 148	98.97 156	97.47 11	99.03 162	93.73 200	96.09 184	98.92 196

BH-untuned		95.18 160	94.83 161	96.22 199	98.36 155	91.22 270	99.80 124	97.32 261	90.91 229	91.08 225	98.67 178	83.51 234	98.54 188	94.23 187	99.61 105	98.92 196

F-COLMAP		96.93 111	96.95 102	96.87 178	99.71 89	91.74 258	99.85 106	97.95 200	93.11 158	95.72 178	99.16 141	92.35 136	99.94 72	95.32 159	99.35 118	98.92 196

Anonymous20240529		92.10 233	90.65 244	96.47 189	98.82 138	90.61 279	98.72 272	98.67 59	75.54 351	93.90 201	98.58 185	66.23 336	99.90 79	94.70 175	90.67 225	98.90 199

tttt0517		96.85 113	96.49 115	97.92 142	97.48 209	95.89 159	99.85 106	98.54 87	90.72 234	96.63 157	98.93 163	97.47 11	99.02 163	93.03 213	95.76 193	98.85 200

baseline1		95.78 148	94.86 160	98.54 112	98.47 152	98.07 74	99.06 237	97.99 195	92.68 173	94.13 198	98.62 182	93.28 113	98.69 181	93.79 197	85.76 265	98.84 201

VDD-MVS		93.77 196	92.94 202	96.27 198	98.55 147	90.22 287	98.77 269	97.79 215	90.85 231	96.82 153	99.42 118	61.18 351	99.77 119	98.95 61	94.13 211	98.82 202

PatchMatch-RL		96.04 143	95.40 147	97.95 140	99.59 95	95.22 182	99.52 182	99.07 31	93.96 130	96.49 161	98.35 196	82.28 241	99.82 109	90.15 252	99.22 123	98.81 203

PVSNet_0	88.03 19	91.80 240	90.27 252	96.38 196	98.27 161	90.46 283	99.94 60	99.61 11	93.99 128	86.26 307	97.39 218	71.13 320	99.89 83	98.77 77	67.05 353	98.79 204

tpmvs		94.28 188	93.57 188	96.40 194	98.55 147	91.50 267	95.70 340	98.55 83	87.47 281	92.15 217	94.26 321	91.42 150	98.95 166	88.15 270	95.85 190	98.76 205

h-mvs33		94.92 166	94.36 169	96.59 188	98.85 137	91.29 269	98.93 253	98.94 36	95.90 59	98.77 97	98.42 195	90.89 164	99.77 119	97.80 117	70.76 343	98.72 206

xiu_mvs_v2_base		98.23 64	97.97 65	99.02 80	98.69 144	98.66 50	99.52 182	98.08 190	97.05 26	99.86 4	99.86 31	90.65 166	99.71 133	99.39 45	98.63 134	98.69 207

PS-MVSNAJ		98.44 48	98.20 51	99.16 62	98.80 140	98.92 27	99.54 180	98.17 179	97.34 16	99.85 6	99.85 35	91.20 155	99.89 83	99.41 44	99.67 99	98.69 207

MSDG		94.37 184	93.36 197	97.40 163	98.88 135	93.95 209	99.37 205	97.38 255	85.75 307	90.80 228	99.17 140	84.11 232	99.88 89	86.35 290	98.43 138	98.36 209

CANet_DTU		96.76 118	96.15 122	98.60 105	98.78 141	97.53 95	99.84 110	97.63 222	97.25 23	99.20 77	99.64 102	81.36 251	99.98 46	92.77 215	98.89 128	98.28 210

mvs-test1		95.53 154	95.97 131	94.20 258	97.77 190	85.44 329	99.95 43	97.06 284	94.92 86	96.58 158	98.72 176	85.81 215	98.98 164	94.80 169	98.11 145	98.18 211

VDDNet		93.12 209	91.91 224	96.76 181	96.67 248	92.65 238	98.69 275	98.21 173	82.81 330	97.75 135	99.28 128	61.57 349	99.48 153	98.09 106	94.09 212	98.15 212

MVS-HIRNet		86.22 302	83.19 314	95.31 218	96.71 247	90.29 286	92.12 351	97.33 260	62.85 359	86.82 295	70.37 362	69.37 324	97.49 249	75.12 343	97.99 151	98.15 212

UGNet		95.33 158	94.57 166	97.62 155	98.55 147	94.85 189	98.67 277	99.32 24	95.75 68	96.80 154	96.27 255	72.18 314	99.96 57	94.58 178	99.05 126	98.04 214
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
DSMNet-mixed		88.28 294	88.24 289	88.42 331	89.64 349	75.38 358	98.06 305	89.86 366	85.59 309	88.20 279	92.14 340	76.15 295	91.95 354	78.46 332	96.05 185	97.92 215

xiu_mvs_v1_base_debu		97.43 91	97.06 96	98.55 109	97.74 193	98.14 71	99.31 212	97.86 210	96.43 44	99.62 43	99.69 94	85.56 218	99.68 137	99.05 54	98.31 141	97.83 216

xiu_mvs_v1_base		97.43 91	97.06 96	98.55 109	97.74 193	98.14 71	99.31 212	97.86 210	96.43 44	99.62 43	99.69 94	85.56 218	99.68 137	99.05 54	98.31 141	97.83 216

xiu_mvs_v1_base_debi		97.43 91	97.06 96	98.55 109	97.74 193	98.14 71	99.31 212	97.86 210	96.43 44	99.62 43	99.69 94	85.56 218	99.68 137	99.05 54	98.31 141	97.83 216

UniMVSNet_ETH3D		90.06 277	88.58 283	94.49 248	94.67 288	88.09 315	97.81 311	97.57 232	83.91 324	88.44 272	97.41 216	57.44 355	97.62 246	91.41 227	88.59 244	97.77 219

cascas		94.64 175	93.61 184	97.74 151	97.82 187	96.26 143	99.96 25	97.78 216	85.76 305	94.00 199	97.54 213	76.95 285	99.21 158	97.23 133	95.43 199	97.76 220

hse-mvs2		94.38 183	94.08 176	95.31 218	98.27 161	90.02 291	99.29 217	98.56 77	95.90 59	98.77 97	98.00 204	90.89 164	98.26 218	97.80 117	69.20 349	97.64 221

AUN-MVS		93.28 206	92.60 208	95.34 216	98.29 157	90.09 290	99.31 212	98.56 77	91.80 207	96.35 167	98.00 204	89.38 180	98.28 214	92.46 216	69.22 348	97.64 221

OpenMVS	90.15 15	94.77 170	93.59 187	98.33 126	96.07 254	97.48 101	99.56 176	98.57 75	90.46 236	86.51 300	98.95 160	78.57 277	99.94 72	93.86 191	99.74 94	97.57 223

baseline2		96.71 122	96.49 115	97.37 165	95.63 274	95.96 157	99.74 142	98.88 43	92.94 160	91.61 220	98.97 156	97.72 5	98.62 184	94.83 168	98.08 149	97.53 224

RPSCF		91.80 240	92.79 205	88.83 327	98.15 169	69.87 360	98.11 303	96.60 320	83.93 323	94.33 195	99.27 131	79.60 269	99.46 154	91.99 220	93.16 221	97.18 225

test0.0.03 1		93.86 191	93.61 184	94.64 239	95.02 283	92.18 247	99.93 66	98.58 73	94.07 123	87.96 281	98.50 188	93.90 97	94.96 335	81.33 320	93.17 220	96.78 226

AllTest		92.48 224	91.64 227	95.00 227	99.01 120	88.43 310	98.94 252	96.82 310	86.50 295	88.71 267	98.47 193	74.73 304	99.88 89	85.39 296	96.18 182	96.71 227

TestCases				95.00 227	99.01 120	88.43 310		96.82 310	86.50 295	88.71 267	98.47 193	74.73 304	99.88 89	85.39 296	96.18 182	96.71 227

XVG-OURS-SEG-HR		94.79 168	94.70 165	95.08 224	98.05 173	89.19 300	99.08 232	97.54 235	93.66 143	94.87 188	99.58 106	78.78 275	99.79 113	97.31 131	93.40 218	96.25 229

XVG-OURS		94.82 167	94.74 164	95.06 225	98.00 175	89.19 300	99.08 232	97.55 233	94.10 121	94.71 189	99.62 103	80.51 262	99.74 129	96.04 152	93.06 222	96.25 229

Effi-MVS+-dtu		94.53 180	95.30 151	92.22 300	97.77 190	82.54 340	99.59 171	97.06 284	94.92 86	95.29 184	95.37 287	85.81 215	97.89 238	94.80 169	97.07 168	96.23 231

testgi		89.01 290	88.04 291	91.90 305	93.49 306	84.89 332	99.73 147	95.66 339	93.89 136	85.14 314	98.17 199	59.68 352	94.66 339	77.73 335	88.88 236	96.16 232

Fast-Effi-MVS+-dtu		93.72 199	93.86 182	93.29 285	97.06 226	86.16 323	99.80 124	96.83 308	92.66 174	92.58 216	97.83 209	81.39 250	97.67 244	89.75 256	96.87 173	96.05 233

COLMAP_ROB	90.47 14	92.18 231	91.49 233	94.25 257	99.00 122	88.04 316	98.42 291	96.70 317	82.30 333	88.43 274	99.01 149	76.97 284	99.85 98	86.11 293	96.50 178	94.86 234
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HQP4-MVS										93.37 205			98.39 201			94.53 235

HQP-MVS		94.61 176	94.50 167	94.92 230	95.78 261	91.85 254	99.87 92	97.89 206	96.82 31	93.37 205	98.65 179	80.65 260	98.39 201	97.92 115	89.60 226	94.53 235

HQP_MVS		94.49 181	94.36 169	94.87 231	95.71 270	91.74 258	99.84 110	97.87 208	96.38 47	93.01 209	98.59 183	80.47 264	98.37 206	97.79 120	89.55 229	94.52 237

plane_prior5								97.87 208					98.37 206	97.79 120	89.55 229	94.52 237

CLD-MVS		94.06 190	93.90 180	94.55 244	96.02 256	90.69 276	99.98 10	97.72 217	96.62 41	91.05 226	98.85 173	77.21 282	98.47 190	98.11 104	89.51 231	94.48 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
nrg030		93.51 202	92.53 212	96.45 191	94.36 291	97.20 111	99.81 119	97.16 274	91.60 211	89.86 241	97.46 214	86.37 212	97.68 243	95.88 155	80.31 310	94.46 240

VPNet		91.81 237	90.46 246	95.85 208	94.74 286	95.54 171	98.98 247	98.59 72	92.14 195	90.77 229	97.44 215	68.73 327	97.54 248	94.89 167	77.89 324	94.46 240

UniMVSNet_NR-MVSNet		92.95 214	92.11 219	95.49 211	94.61 289	95.28 178	99.83 116	99.08 30	91.49 214	89.21 259	96.86 236	87.14 204	96.73 294	93.20 207	77.52 327	94.46 240

DU-MVS		92.46 225	91.45 234	95.49 211	94.05 296	95.28 178	99.81 119	98.74 52	92.25 193	89.21 259	96.64 245	81.66 247	96.73 294	93.20 207	77.52 327	94.46 240

NR-MVSNet		91.56 245	90.22 253	95.60 210	94.05 296	95.76 163	98.25 296	98.70 54	91.16 224	80.78 335	96.64 245	83.23 238	96.57 301	91.41 227	77.73 326	94.46 240

TranMVSNet+NR-MVSNet		91.68 244	90.61 245	94.87 231	93.69 303	93.98 208	99.69 153	98.65 60	91.03 227	88.44 272	96.83 240	80.05 267	96.18 315	90.26 251	76.89 335	94.45 245

RRT_test8_iter05		94.58 177	94.11 174	95.98 204	97.88 181	96.11 154	99.89 86	97.45 245	91.66 210	88.28 277	96.71 241	96.53 27	97.40 252	94.73 174	83.85 284	94.45 245

FIs		94.10 189	93.43 192	96.11 201	94.70 287	96.82 124	99.58 172	98.93 40	92.54 183	89.34 255	97.31 219	87.62 199	97.10 273	94.22 188	86.58 261	94.40 247

ACMM	91.95 10	92.88 215	92.52 213	93.98 269	95.75 266	89.08 303	99.77 131	97.52 239	93.00 159	89.95 238	97.99 206	76.17 294	98.46 193	93.63 203	88.87 237	94.39 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_part1		92.15 232	90.72 242	96.44 193	98.87 136	97.46 103	98.99 246	98.26 167	85.89 302	86.34 305	96.34 253	81.71 245	97.48 250	91.06 233	78.99 316	94.37 249

FC-MVSNet-test		93.81 194	93.15 201	95.80 209	94.30 293	96.20 148	99.42 197	98.89 42	92.33 191	89.03 264	97.27 221	87.39 202	96.83 290	93.20 207	86.48 262	94.36 250

PS-MVSNAJss		93.64 201	93.31 198	94.61 240	92.11 328	92.19 246	99.12 228	97.38 255	92.51 185	88.45 271	96.99 232	91.20 155	97.29 262	94.36 182	87.71 253	94.36 250

WR-MVS		92.31 228	91.25 236	95.48 214	94.45 290	95.29 177	99.60 170	98.68 56	90.10 242	88.07 280	96.89 234	80.68 259	96.80 292	93.14 210	79.67 314	94.36 250

XXY-MVS		91.82 236	90.46 246	95.88 206	93.91 299	95.40 175	98.87 261	97.69 219	88.63 268	87.87 282	97.08 226	74.38 307	97.89 238	91.66 225	84.07 281	94.35 253

MVSTER		95.53 154	95.22 153	96.45 191	98.56 146	97.72 86	99.91 74	97.67 220	92.38 189	91.39 222	97.14 223	97.24 17	97.30 259	94.80 169	87.85 251	94.34 254

VPA-MVSNet		92.70 219	91.55 231	96.16 200	95.09 280	96.20 148	98.88 258	99.00 33	91.02 228	91.82 219	95.29 293	76.05 296	97.96 234	95.62 158	81.19 298	94.30 255

FMVSNet3		92.69 220	91.58 229	95.99 203	98.29 157	97.42 106	99.26 220	97.62 224	89.80 248	89.68 245	95.32 289	81.62 249	96.27 312	87.01 286	85.65 266	94.29 256

RRT_MVS		95.23 159	94.77 163	96.61 187	98.28 159	98.32 67	99.81 119	97.41 252	92.59 179	91.28 224	97.76 210	95.02 57	97.23 265	93.65 202	87.14 258	94.28 257

EU-MVSNet		90.14 276	90.34 250	89.54 323	92.55 324	81.06 350	98.69 275	98.04 193	91.41 220	86.59 299	96.84 239	80.83 257	93.31 351	86.20 291	81.91 293	94.26 258

UniMVSNet (Re)		93.07 211	92.13 218	95.88 206	94.84 284	96.24 147	99.88 89	98.98 34	92.49 187	89.25 257	95.40 283	87.09 205	97.14 269	93.13 211	78.16 322	94.26 258

FMVSNet2		91.02 252	89.56 264	95.41 215	97.53 204	95.74 164	98.98 247	97.41 252	87.05 287	88.43 274	95.00 302	71.34 317	96.24 314	85.12 298	85.21 271	94.25 260

EI-MVSNet		93.73 198	93.40 196	94.74 235	96.80 241	92.69 235	99.06 237	97.67 220	88.96 259	91.39 222	99.02 147	88.75 191	97.30 259	91.07 232	87.85 251	94.22 261

IterMVS-LS		92.69 220	92.11 219	94.43 253	96.80 241	92.74 232	99.45 194	96.89 304	88.98 257	89.65 248	95.38 286	88.77 190	96.34 309	90.98 237	82.04 292	94.22 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl22		93.77 196	93.25 200	95.33 217	99.49 104	94.43 199	99.61 169	98.09 188	90.38 237	89.16 262	95.61 271	90.56 168	97.34 256	91.93 221	84.45 276	94.21 263

miper_enhance_ethall		94.36 186	93.98 178	95.49 211	98.68 145	95.24 180	99.73 147	97.29 263	93.28 153	89.86 241	95.97 262	94.37 78	97.05 276	92.20 219	84.45 276	94.19 264

miper_ehance_all_eth		93.16 208	92.60 208	94.82 234	97.57 203	93.56 216	99.50 186	97.07 283	88.75 264	88.85 266	95.52 277	90.97 161	96.74 293	90.77 242	84.45 276	94.17 265

DIV-MVS_self_test		92.32 227	91.60 228	94.47 249	97.31 218	92.74 232	99.58 172	96.75 314	86.99 290	87.64 284	95.54 275	89.55 178	96.50 303	88.58 264	82.44 289	94.17 265

GBi-Net		90.88 255	89.82 260	94.08 262	97.53 204	91.97 249	98.43 288	96.95 297	87.05 287	89.68 245	94.72 308	71.34 317	96.11 316	87.01 286	85.65 266	94.17 265

test1		90.88 255	89.82 260	94.08 262	97.53 204	91.97 249	98.43 288	96.95 297	87.05 287	89.68 245	94.72 308	71.34 317	96.11 316	87.01 286	85.65 266	94.17 265

FMVSNet1		88.50 292	86.64 298	94.08 262	95.62 275	91.97 249	98.43 288	96.95 297	83.00 328	86.08 309	94.72 308	59.09 353	96.11 316	81.82 319	84.07 281	94.17 265

cl____		92.31 228	91.58 229	94.52 245	97.33 217	92.77 230	99.57 174	96.78 313	86.97 291	87.56 286	95.51 278	89.43 179	96.62 299	88.60 263	82.44 289	94.16 270

bset_n11_16_dypcd		93.05 212	92.30 216	95.31 218	90.23 346	95.05 185	99.44 196	97.28 264	92.51 185	90.65 230	96.68 242	85.30 222	96.71 296	94.49 180	84.14 279	94.16 270

eth_miper_zixun_eth		92.41 226	91.93 223	93.84 273	97.28 221	90.68 277	98.83 264	96.97 296	88.57 269	89.19 261	95.73 268	89.24 185	96.69 297	89.97 254	81.55 295	94.15 272

miper_lstm_enhance		91.81 237	91.39 235	93.06 292	97.34 215	89.18 302	99.38 203	96.79 312	86.70 294	87.47 288	95.22 295	90.00 173	95.86 325	88.26 268	81.37 297	94.15 272

Anonymous20231211		89.86 279	88.44 285	94.13 261	98.93 128	90.68 277	98.54 283	98.26 167	76.28 347	86.73 296	95.54 275	70.60 321	97.56 247	90.82 241	80.27 311	94.15 272

c3_l		92.53 223	91.87 225	94.52 245	97.40 212	92.99 228	99.40 198	96.93 301	87.86 277	88.69 269	95.44 281	89.95 174	96.44 305	90.45 246	80.69 307	94.14 275

jajsoiax		91.92 235	91.18 237	94.15 259	91.35 337	90.95 273	99.00 245	97.42 250	92.61 177	87.38 290	97.08 226	72.46 313	97.36 254	94.53 179	88.77 239	94.13 276

mvs_tets		91.81 237	91.08 238	94.00 267	91.63 335	90.58 280	98.67 277	97.43 248	92.43 188	87.37 291	97.05 229	71.76 315	97.32 258	94.75 172	88.68 241	94.11 277

v2v482		91.30 246	90.07 258	95.01 226	93.13 311	93.79 211	99.77 131	97.02 289	88.05 275	89.25 257	95.37 287	80.73 258	97.15 268	87.28 281	80.04 313	94.09 278

LPG-MVS_test		92.96 213	92.71 206	93.71 276	95.43 276	88.67 306	99.75 139	97.62 224	92.81 164	90.05 234	98.49 189	75.24 300	98.40 199	95.84 156	89.12 233	94.07 279

LGP-MVS_train				93.71 276	95.43 276	88.67 306		97.62 224	92.81 164	90.05 234	98.49 189	75.24 300	98.40 199	95.84 156	89.12 233	94.07 279

test_djsdf		92.83 216	92.29 217	94.47 249	91.90 331	92.46 241	99.55 178	97.27 265	91.17 222	89.96 237	96.07 261	81.10 253	96.89 286	94.67 176	88.91 235	94.05 281

CP-MVSNet		91.23 249	90.22 253	94.26 256	93.96 298	92.39 243	99.09 230	98.57 75	88.95 260	86.42 303	96.57 247	79.19 272	96.37 307	90.29 250	78.95 317	94.02 282

Patchmtry		89.70 282	88.49 284	93.33 284	96.24 252	89.94 295	91.37 355	96.23 327	78.22 344	87.69 283	93.31 330	91.04 159	96.03 321	80.18 326	82.10 291	94.02 282

v1921920		90.46 265	89.12 273	94.50 247	92.96 318	92.46 241	99.49 188	96.98 294	86.10 300	89.61 250	95.30 290	78.55 278	97.03 280	82.17 316	80.89 306	94.01 284

v1192		90.62 263	89.25 271	94.72 237	93.13 311	93.07 225	99.50 186	97.02 289	86.33 298	89.56 251	95.01 300	79.22 271	97.09 275	82.34 315	81.16 299	94.01 284

v1240		90.20 273	88.79 280	94.44 251	93.05 316	92.27 245	99.38 203	96.92 302	85.89 302	89.36 254	94.87 307	77.89 281	97.03 280	80.66 323	81.08 302	94.01 284

OPM-MVS		93.21 207	92.80 204	94.44 251	93.12 313	90.85 275	99.77 131	97.61 227	96.19 54	91.56 221	98.65 179	75.16 302	98.47 190	93.78 198	89.39 232	93.99 287
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	92.05 9	92.74 218	92.42 215	93.73 274	95.91 260	88.72 305	99.81 119	97.53 237	94.13 119	87.00 294	98.23 198	74.07 308	98.47 190	96.22 150	88.86 238	93.99 287
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OurMVSNet-221017-0		89.81 280	89.48 269	90.83 313	91.64 334	81.21 348	98.17 301	95.38 345	91.48 215	85.65 312	97.31 219	72.66 312	97.29 262	88.15 270	84.83 273	93.97 289

pmmvs5		90.17 275	89.09 274	93.40 283	92.10 329	89.77 296	99.74 142	95.58 341	85.88 304	87.24 293	95.74 266	73.41 311	96.48 304	88.54 265	83.56 285	93.95 290

PS-CasMVS		90.63 262	89.51 267	93.99 268	93.83 300	91.70 262	98.98 247	98.52 90	88.48 270	86.15 308	96.53 249	75.46 298	96.31 310	88.83 262	78.86 319	93.95 290

IterMVS		90.91 254	90.17 255	93.12 289	96.78 244	90.42 285	98.89 256	97.05 287	89.03 255	86.49 301	95.42 282	76.59 289	95.02 333	87.22 282	84.09 280	93.93 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	89.72 17	90.64 261	89.63 262	93.66 280	95.64 273	88.64 308	98.55 281	97.45 245	89.03 255	81.62 330	97.61 212	69.75 323	98.41 197	89.37 257	87.62 255	93.92 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		90.79 258	89.52 266	94.59 241	93.11 314	92.77 230	99.56 176	96.99 292	86.38 297	89.82 244	94.95 305	80.50 263	97.10 273	83.98 305	80.41 308	93.90 294

PEN-MVS		90.19 274	89.06 275	93.57 281	93.06 315	90.90 274	99.06 237	98.47 103	88.11 274	85.91 310	96.30 254	76.67 287	95.94 324	87.07 283	76.91 334	93.89 295

XVG-ACMP-BASELINE		91.22 250	90.75 241	92.63 297	93.73 302	85.61 326	98.52 285	97.44 247	92.77 168	89.90 240	96.85 237	66.64 335	98.39 201	92.29 218	88.61 242	93.89 295

v1144		91.09 251	89.83 259	94.87 231	93.25 310	93.69 214	99.62 168	96.98 294	86.83 293	89.64 249	94.99 303	80.94 255	97.05 276	85.08 299	81.16 299	93.87 297

MDA-MVSNet_test_wron		85.51 306	83.32 313	92.10 302	90.96 340	88.58 309	99.20 223	96.52 322	79.70 341	57.12 363	92.69 335	79.11 273	93.86 346	77.10 338	77.46 329	93.86 298

IterMVS-SCA-FT		90.85 257	90.16 256	92.93 293	96.72 246	89.96 292	98.89 256	96.99 292	88.95 260	86.63 298	95.67 269	76.48 290	95.00 334	87.04 284	84.04 283	93.84 299

YYNet1		85.50 307	83.33 312	92.00 303	90.89 341	88.38 313	99.22 222	96.55 321	79.60 342	57.26 362	92.72 333	79.09 274	93.78 347	77.25 337	77.37 330	93.84 299

MDA-MVSNet-bldmvs		84.09 315	81.52 321	91.81 306	91.32 338	88.00 317	98.67 277	95.92 334	80.22 339	55.60 364	93.32 329	68.29 330	93.60 349	73.76 344	76.61 336	93.82 301

MVS_0304		89.28 288	88.31 287	92.21 301	97.05 227	86.53 322	97.76 312	99.57 12	85.58 310	93.86 202	92.71 334	51.04 361	96.30 311	84.49 302	92.72 223	93.79 302

ACMH+	89.98 16	90.35 268	89.54 265	92.78 296	95.99 257	86.12 324	98.81 266	97.18 271	89.38 250	83.14 323	97.76 210	68.42 329	98.43 195	89.11 260	86.05 264	93.78 303

v148		90.70 259	89.63 262	93.92 270	92.97 317	90.97 272	99.75 139	96.89 304	87.51 280	88.27 278	95.01 300	81.67 246	97.04 278	87.40 279	77.17 332	93.75 304

pmmvs4		92.10 233	91.07 239	95.18 222	92.82 321	94.96 187	99.48 190	96.83 308	87.45 282	88.66 270	96.56 248	83.78 233	96.83 290	89.29 258	84.77 274	93.75 304

K. test v3		88.05 295	87.24 297	90.47 316	91.82 333	82.23 343	98.96 250	97.42 250	89.05 254	76.93 346	95.60 272	68.49 328	95.42 328	85.87 295	81.01 304	93.75 304

lessismore_v0				90.53 314	90.58 343	80.90 351		95.80 335		77.01 345	95.84 263	66.15 337	96.95 283	83.03 311	75.05 340	93.74 307

SixPastTwentyTwo		88.73 291	88.01 292	90.88 311	91.85 332	82.24 342	98.22 299	95.18 350	88.97 258	82.26 326	96.89 234	71.75 316	96.67 298	84.00 304	82.98 286	93.72 308

our_test_3		90.39 266	89.48 269	93.12 289	92.40 325	89.57 298	99.33 209	96.35 326	87.84 278	85.30 313	94.99 303	84.14 231	96.09 319	80.38 324	84.56 275	93.71 309

LTVRE_ROB	88.28 18	90.29 271	89.05 276	94.02 265	95.08 281	90.15 289	97.19 319	97.43 248	84.91 318	83.99 319	97.06 228	74.00 309	98.28 214	84.08 303	87.71 253	93.62 310
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
ITE_SJBPF				92.38 298	95.69 272	85.14 330		95.71 337	92.81 164	89.33 256	98.11 200	70.23 322	98.42 196	85.91 294	88.16 249	93.59 311

v7n		89.65 283	88.29 288	93.72 275	92.22 327	90.56 281	99.07 236	97.10 280	85.42 313	86.73 296	94.72 308	80.06 266	97.13 270	81.14 321	78.12 323	93.49 312

DTE-MVSNet		89.40 285	88.24 289	92.88 294	92.66 323	89.95 293	99.10 229	98.22 172	87.29 284	85.12 315	96.22 256	76.27 293	95.30 332	83.56 309	75.74 338	93.41 313

V42		91.28 248	90.12 257	94.74 235	93.42 308	93.46 219	99.68 155	97.02 289	87.36 283	89.85 243	95.05 298	81.31 252	97.34 256	87.34 280	80.07 312	93.40 314

anonymousdsp		91.79 242	90.92 240	94.41 254	90.76 342	92.93 229	98.93 253	97.17 272	89.08 253	87.46 289	95.30 290	78.43 280	96.92 285	92.38 217	88.73 240	93.39 315

v8		90.54 264	89.17 272	94.66 238	93.43 307	93.40 222	99.20 223	96.94 300	85.76 305	87.56 286	94.51 315	81.96 244	97.19 266	84.94 300	78.25 321	93.38 316

ppachtmachnet_test		89.58 284	88.35 286	93.25 287	92.40 325	90.44 284	99.33 209	96.73 315	85.49 311	85.90 311	95.77 265	81.09 254	96.00 323	76.00 342	82.49 288	93.30 317

v10		90.25 272	88.82 279	94.57 243	93.53 305	93.43 220	99.08 232	96.87 306	85.00 315	87.34 292	94.51 315	80.93 256	97.02 282	82.85 312	79.23 315	93.26 318

PVSNet_BlendedMVS		96.05 142	95.82 140	96.72 183	99.59 95	96.99 119	99.95 43	99.10 28	94.06 125	98.27 121	95.80 264	89.00 188	99.95 64	99.12 51	87.53 256	93.24 319

WR-MVS_H		91.30 246	90.35 249	94.15 259	94.17 295	92.62 239	99.17 226	98.94 36	88.87 262	86.48 302	94.46 319	84.36 229	96.61 300	88.19 269	78.51 320	93.21 320

FMVSNet5		88.32 293	87.47 295	90.88 311	96.90 236	88.39 312	97.28 317	95.68 338	82.60 332	84.67 316	92.40 339	79.83 268	91.16 356	76.39 341	81.51 296	93.09 321

Anonymous20231206		86.32 301	85.42 303	89.02 326	89.11 351	80.53 354	99.05 241	95.28 346	85.43 312	82.82 324	93.92 323	74.40 306	93.44 350	66.99 355	81.83 294	93.08 322

pm-mvs1		89.36 286	87.81 293	94.01 266	93.40 309	91.93 252	98.62 280	96.48 324	86.25 299	83.86 320	96.14 258	73.68 310	97.04 278	86.16 292	75.73 339	93.04 323

test_method		80.79 321	79.70 324	84.08 337	92.83 320	67.06 362	99.51 184	95.42 343	54.34 361	81.07 334	93.53 327	44.48 363	92.22 353	78.90 331	77.23 331	92.94 324

UnsupCasMVSNet_eth		85.52 305	83.99 306	90.10 319	89.36 350	83.51 336	96.65 327	97.99 195	89.14 252	75.89 350	93.83 324	63.25 346	93.92 344	81.92 318	67.90 352	92.88 325

USDC		90.00 278	88.96 277	93.10 291	94.81 285	88.16 314	98.71 273	95.54 342	93.66 143	83.75 321	97.20 222	65.58 338	98.31 210	83.96 306	87.49 257	92.85 326

N_pmnet		80.06 324	80.78 322	77.89 341	91.94 330	45.28 372	98.80 267	56.82 375	78.10 345	80.08 338	93.33 328	77.03 283	95.76 326	68.14 354	82.81 287	92.64 327

KD-MVS_2432*1600		88.00 296	86.10 300	93.70 278	96.91 233	94.04 205	97.17 320	97.12 277	84.93 316	81.96 327	92.41 337	92.48 133	94.51 340	79.23 327	52.68 361	92.56 328

miper_refine_blended		88.00 296	86.10 300	93.70 278	96.91 233	94.04 205	97.17 320	97.12 277	84.93 316	81.96 327	92.41 337	92.48 133	94.51 340	79.23 327	52.68 361	92.56 328

pmmvs6		85.69 303	83.84 309	91.26 310	90.00 348	84.41 334	97.82 310	96.15 330	75.86 349	81.29 332	95.39 285	61.21 350	96.87 288	83.52 310	73.29 342	92.50 330

D2MVS		92.76 217	92.59 211	93.27 286	95.13 279	89.54 299	99.69 153	99.38 21	92.26 192	87.59 285	94.61 314	85.05 225	97.79 240	91.59 226	88.01 250	92.47 331

CL-MVSNet_self_test		84.50 313	83.15 315	88.53 330	86.00 357	81.79 346	98.82 265	97.35 257	85.12 314	83.62 322	90.91 345	76.66 288	91.40 355	69.53 351	60.36 358	92.40 332

MIMVSNet1		82.58 319	80.51 323	88.78 328	86.68 356	84.20 335	96.65 327	95.41 344	78.75 343	78.59 342	92.44 336	51.88 359	89.76 359	65.26 359	78.95 317	92.38 333

LF4IMVS		89.25 289	88.85 278	90.45 317	92.81 322	81.19 349	98.12 302	94.79 352	91.44 217	86.29 306	97.11 224	65.30 341	98.11 225	88.53 266	85.25 270	92.07 334

TransMVSNet (Re)		87.25 299	85.28 304	93.16 288	93.56 304	91.03 271	98.54 283	94.05 358	83.69 326	81.09 333	96.16 257	75.32 299	96.40 306	76.69 340	68.41 350	92.06 335

DeepMVS_CX				82.92 340	95.98 259	58.66 366		96.01 332	92.72 169	78.34 343	95.51 278	58.29 354	98.08 226	82.57 313	85.29 269	92.03 336

Baseline_NR-MVSNet		90.33 269	89.51 267	92.81 295	92.84 319	89.95 293	99.77 131	93.94 359	84.69 320	89.04 263	95.66 270	81.66 247	96.52 302	90.99 236	76.98 333	91.97 337

TinyColmap		87.87 298	86.51 299	91.94 304	95.05 282	85.57 327	97.65 313	94.08 357	84.40 321	81.82 329	96.85 237	62.14 348	98.33 208	80.25 325	86.37 263	91.91 338

MS-PatchMatch		90.65 260	90.30 251	91.71 307	94.22 294	85.50 328	98.24 297	97.70 218	88.67 266	86.42 303	96.37 252	67.82 331	98.03 230	83.62 308	99.62 102	91.60 339

KD-MVS_self_test		83.59 318	82.06 318	88.20 332	86.93 355	80.70 352	97.21 318	96.38 325	82.87 329	82.49 325	88.97 348	67.63 332	92.32 352	73.75 345	62.30 357	91.58 340

tfpnnormal		89.29 287	87.61 294	94.34 255	94.35 292	94.13 204	98.95 251	98.94 36	83.94 322	84.47 317	95.51 278	74.84 303	97.39 253	77.05 339	80.41 308	91.48 341

MVP-Stereo		90.93 253	90.45 248	92.37 299	91.25 339	88.76 304	98.05 306	96.17 329	87.27 285	84.04 318	95.30 290	78.46 279	97.27 264	83.78 307	99.70 98	91.09 342
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		84.72 312	83.99 306	86.91 334	88.19 354	80.62 353	98.88 258	95.94 333	88.36 272	78.87 340	94.62 313	68.75 326	89.11 360	66.52 356	75.82 337	91.00 343

EG-PatchMatch MVS		85.35 308	83.81 310	89.99 321	90.39 344	81.89 345	98.21 300	96.09 331	81.78 335	74.73 352	93.72 326	51.56 360	97.12 272	79.16 330	88.61 242	90.96 344

TDRefinement		84.76 310	82.56 317	91.38 309	74.58 365	84.80 333	97.36 316	94.56 355	84.73 319	80.21 337	96.12 260	63.56 345	98.39 201	87.92 273	63.97 354	90.95 345

ambc				83.23 339	77.17 364	62.61 363	87.38 359	94.55 356		76.72 347	86.65 354	30.16 365	96.36 308	84.85 301	69.86 344	90.73 346

Anonymous20240521		85.15 309	83.81 310	89.16 325	88.32 352	82.69 338	98.80 267	95.74 336	79.72 340	81.53 331	90.99 343	65.38 340	94.16 342	72.69 346	81.11 301	90.63 347

OpenMVS_ROB	79.82 20	83.77 317	81.68 320	90.03 320	88.30 353	82.82 337	98.46 286	95.22 348	73.92 355	76.00 349	91.29 342	55.00 357	96.94 284	68.40 353	88.51 246	90.34 348

new_pmnet		84.49 314	82.92 316	89.21 324	90.03 347	82.60 339	96.89 326	95.62 340	80.59 338	75.77 351	89.17 347	65.04 342	94.79 338	72.12 347	81.02 303	90.23 349

test_0402		85.58 304	83.94 308	90.50 315	93.81 301	85.04 331	98.55 281	95.20 349	76.01 348	79.72 339	95.13 296	64.15 344	96.26 313	66.04 358	86.88 260	90.21 350

pmmvs3		80.27 323	77.77 327	87.76 333	80.32 363	82.43 341	98.23 298	91.97 362	72.74 356	78.75 341	87.97 350	57.30 356	90.99 357	70.31 349	62.37 356	89.87 351

CMPMVS	61.59 21	84.75 311	85.14 305	83.57 338	90.32 345	62.54 364	96.98 324	97.59 231	74.33 354	69.95 357	96.66 243	64.17 343	98.32 209	87.88 274	88.41 247	89.84 352
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		80.47 322	78.88 326	85.26 336	83.79 361	72.22 359	95.89 338	91.08 364	85.71 308	76.56 348	88.30 349	36.64 364	93.90 345	82.39 314	69.57 346	89.66 353

pmmvs-eth3d		84.03 316	81.97 319	90.20 318	84.15 360	87.09 320	98.10 304	94.73 354	83.05 327	74.10 353	87.77 351	65.56 339	94.01 343	81.08 322	69.24 347	89.49 354

UnsupCasMVSNet_bld		79.97 325	77.03 328	88.78 328	85.62 358	81.98 344	93.66 346	97.35 257	75.51 352	70.79 356	83.05 357	48.70 362	94.91 336	78.31 333	60.29 359	89.46 355

new-patchmatchnet		81.19 320	79.34 325	86.76 335	82.86 362	80.36 355	97.92 308	95.27 347	82.09 334	72.02 354	86.87 353	62.81 347	90.74 358	71.10 348	63.08 355	89.19 356

LCM-MVSNet		67.77 327	64.73 331	76.87 342	62.95 371	56.25 368	89.37 358	93.74 360	44.53 364	61.99 359	80.74 358	20.42 371	86.53 362	69.37 352	59.50 360	87.84 357

tmp_tt		65.23 330	62.94 333	72.13 345	44.90 374	50.03 370	81.05 361	89.42 369	38.45 365	48.51 367	99.90 19	54.09 358	78.70 366	91.84 224	18.26 368	87.64 358

PMMVS2		67.15 328	64.15 332	76.14 343	70.56 368	62.07 365	93.89 344	87.52 370	58.09 360	60.02 360	78.32 359	22.38 370	84.54 363	59.56 362	47.03 363	81.80 359

FPMVS		68.72 326	68.72 329	68.71 346	65.95 369	44.27 374	95.97 337	94.74 353	51.13 362	53.26 365	90.50 346	25.11 369	83.00 364	60.80 361	80.97 305	78.87 360

ANet_high		56.10 331	52.24 334	67.66 347	49.27 373	56.82 367	83.94 360	82.02 371	70.47 357	33.28 371	64.54 365	17.23 373	69.16 368	45.59 366	23.85 367	77.02 361

MVE	53.74 22	51.54 334	47.86 338	62.60 348	59.56 372	50.93 369	79.41 362	77.69 372	35.69 368	36.27 370	61.76 368	5.79 377	69.63 367	37.97 368	36.61 364	67.24 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 332	51.34 336	60.97 349	40.80 375	34.68 375	74.82 363	89.62 368	37.55 366	28.67 372	72.12 361	7.09 375	81.63 365	43.17 367	68.21 351	66.59 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		66.95 329	65.00 330	72.79 344	91.52 336	67.96 361	66.16 364	95.15 351	47.89 363	58.54 361	67.99 364	29.74 366	87.54 361	50.20 364	77.83 325	62.87 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test123		37.68 337	39.14 340	33.31 352	19.94 376	24.83 377	98.36 292	9.75 377	15.53 371	51.31 366	87.14 352	19.62 372	17.74 372	47.10 365	3.47 371	57.36 365

testmvs		40.60 336	44.45 339	29.05 353	19.49 377	14.11 378	99.68 155	18.47 376	20.74 370	64.59 358	98.48 192	10.95 374	17.09 373	56.66 363	11.01 369	55.94 366

EMVS		51.44 335	51.22 337	52.11 351	70.71 367	44.97 373	94.04 343	75.66 374	35.34 369	42.40 369	61.56 369	28.93 367	65.87 370	27.64 370	24.73 366	45.49 367

E-PMN		52.30 333	52.18 335	52.67 350	71.51 366	45.40 371	93.62 347	76.60 373	36.01 367	43.50 368	64.13 366	27.11 368	67.31 369	31.06 369	26.06 365	45.30 368

wuyk23d		20.37 339	20.84 342	18.99 354	65.34 370	27.73 376	50.43 365	7.67 378	9.50 372	8.01 373	6.34 372	6.13 376	26.24 371	23.40 371	10.69 370	2.99 369

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.02 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		23.43 338	31.24 341	0.00 355	0.00 378	0.00 379	0.00 366	98.09 188	0.00 373	0.00 374	99.67 98	83.37 236	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		7.60 341	10.13 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	91.20 155	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		8.28 340	11.04 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	99.40 121	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					99.92 36	97.66 91	99.95 43	98.36 148	95.58 72	99.52 53

test_one_0601					99.94 14	99.30 11		98.41 132	96.63 39	99.75 27	99.93 11	97.49 9

eth-test2					0.00 378

eth-test					0.00 378

ZD-MVS					99.92 36	98.57 55		98.52 90	92.34 190	99.31 70	99.83 51	95.06 55	99.80 110	99.70 34	99.97 48

test_241102_ONE					99.93 27	99.30 11		98.43 116	97.26 22	99.80 16	99.88 24	96.71 23	100.00 1

9.14			98.38 39		99.87 57		99.91 74	98.33 154	93.22 154	99.78 24	99.89 21	94.57 71	99.85 98	99.84 17	99.97 48

save fliter					99.82 70	98.79 37	99.96 25	98.40 136	97.66 10

test0726					99.93 27	99.29 14	99.96 25	98.42 128	97.28 18	99.86 4	99.94 4	97.22 18

test_part2					99.89 50	99.25 17				99.49 55

sam_mvs												94.25 85

MTGPA								98.28 163

test_post1							95.78 339				59.23 370	93.20 117	97.74 242	91.06 233

test_post											63.35 367	94.43 72	98.13 224

patchmatchnet-post											91.70 341	95.12 51	97.95 235

MTMP							99.87 92	96.49 323

gm-plane-assit					96.97 231	93.76 213			91.47 216		98.96 158		98.79 171	94.92 164

TEST9					99.92 36	98.92 27	99.96 25	98.43 116	93.90 134	99.71 35	99.86 31	95.88 37	99.85 98

test_8					99.92 36	98.88 30	99.96 25	98.43 116	94.35 111	99.69 37	99.85 35	95.94 34	99.85 98

agg_prior					99.93 27	98.77 40		98.43 116		99.63 40			99.85 98

test_prior4						98.05 75	99.94 60

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

旧先验2							99.46 193		94.21 118	99.85 6			99.95 64	96.96 141

新几何2							99.40 198

原ACMM2							99.90 78

testdata2													99.99 40	90.54 245

segment_acmp												96.68 25

testdata1							99.28 218		96.35 51

plane_prior7					95.71 270	91.59 266

plane_prior6					95.76 265	91.72 261						80.47 264

plane_prior4											98.59 183

plane_prior3						91.64 264			96.63 39	93.01 209

plane_prior2							99.84 110		96.38 47

plane_prior1					95.73 267

plane_prior						91.74 258	99.86 103		96.76 35						89.59 228

n2								0.00 379

nn								0.00 379

door-mid								89.69 367

test11								98.44 108

door								90.31 365

HQP5-MVS						91.85 254

HQP-NCC					95.78 261		99.87 92		96.82 31	93.37 205

ACMP_Plane					95.78 261		99.87 92		96.82 31	93.37 205

BP-MVS														97.92 115

HQP3-MVS								97.89 206							89.60 226

HQP2-MVS												80.65 260

NP-MVS					95.77 264	91.79 256					98.65 179

MDTV_nov1_ep13			95.69 142		97.90 180	94.15 203	95.98 336	98.44 108	93.12 157	97.98 129	95.74 266	95.10 52	98.58 185	90.02 253	96.92 172

ACMMP++_ref															87.04 259

ACMMP++															88.23 248

Test By Simon												92.82 126

High-resolution multi-view benchmark