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.

PC_three_1452									90.77 166	98.89 8	98.28 57	96.24 1	98.35 209	95.76 61	99.58 22	99.59 20

DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 66	95.39 11	99.29 1	98.28 28	94.78 37	98.93 6	98.87 6	96.04 2	99.86 9	97.45 9	99.58 22	99.59 20

OPU-MVS				98.55 3	98.82 60	96.86 3	98.25 34				98.26 58	96.04 2	99.24 126	95.36 76	99.59 17	99.56 27

test_0728_THIRD									94.78 37	98.73 10	98.87 6	95.87 4	99.84 23	97.45 9	99.72 2	99.77 1

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 18	98.25 34	98.27 31	95.13 19	99.19 1	98.89 4	95.54 5	99.85 18	97.52 5	99.66 10	99.56 27

test_241102_ONE					99.42 7	95.30 18		98.27 31	95.09 23	99.19 1	98.81 10	95.54 5	99.65 57

test_one_0601					99.32 24	95.20 21		98.25 36	95.13 19	98.48 16	98.87 6	95.16 7

DVP-MVS		97.91 3	97.81 3	98.22 12	99.45 3	95.36 13	98.21 41	97.85 118	94.92 28	98.73 10	98.87 6	95.08 8	99.84 23	97.52 5	99.67 6	99.48 47
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
test0726					99.45 3	95.36 13	98.31 27	98.29 26	94.92 28	98.99 4	98.92 2	95.08 8

DPE-MVS		97.86 4	97.65 5	98.47 5	99.17 34	95.78 7	97.21 140	98.35 20	95.16 18	98.71 12	98.80 11	95.05 10	99.89 4	96.70 27	99.73 1	99.73 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_TWO								98.27 31	95.13 19	98.93 6	98.89 4	94.99 11	99.85 18	97.52 5	99.65 12	99.74 7

SteuartSystems-ACMMP		97.62 7	97.53 7	97.87 27	98.39 87	94.25 42	98.43 21	98.27 31	95.34 10	98.11 20	98.56 20	94.53 12	99.71 42	96.57 31	99.62 15	99.65 12
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		97.68 6	97.44 9	98.37 7	98.90 55	95.86 6	97.27 131	98.08 68	95.81 3	97.87 28	98.31 51	94.26 13	99.68 51	97.02 17	99.49 40	99.57 24

SD-MVS		97.41 10	97.53 7	97.06 74	98.57 79	94.46 34	97.92 64	98.14 57	94.82 34	99.01 3	98.55 22	94.18 14	97.41 311	96.94 18	99.64 13	99.32 66
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
MSP-MVS		97.59 8	97.54 6	97.73 41	99.40 12	93.77 62	98.53 13	98.29 26	95.55 5	98.56 14	97.81 91	93.90 15	99.65 57	96.62 28	99.21 75	99.77 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
MCST-MVS		97.18 17	96.84 29	98.20 13	99.30 26	95.35 15	97.12 148	98.07 74	93.54 75	96.08 88	97.69 99	93.86 16	99.71 42	96.50 32	99.39 53	99.55 31

APDe-MVS		97.82 5	97.73 4	98.08 18	99.15 35	94.82 29	98.81 6	98.30 25	94.76 39	98.30 17	98.90 3	93.77 17	99.68 51	97.93 1	99.69 3	99.75 5

TSAR-MVS + MP.		97.42 9	97.33 11	97.69 45	99.25 29	94.24 43	98.07 51	97.85 118	93.72 67	98.57 13	98.35 42	93.69 18	99.40 113	97.06 14	99.46 44	99.44 53
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
patch_mono-2		96.83 41	97.44 9	95.01 171	99.05 43	85.39 291	96.98 160	98.77 5	94.70 41	97.99 23	98.66 14	93.61 19	99.91 1	97.67 4	99.50 36	99.72 10

DeepPCF-MVS	93.97 1	96.61 51	97.09 14	95.15 165	98.09 114	86.63 271	96.00 243	98.15 55	95.43 6	97.95 24	98.56 20	93.40 20	99.36 117	96.77 25	99.48 41	99.45 51

xxxxxxxxxxxxxcwj		97.36 12	97.20 12	97.83 29	98.91 53	94.28 39	97.02 153	97.22 192	95.35 8	98.27 18	98.65 16	93.33 21	99.72 39	96.49 33	99.52 29	99.51 39

SF-MVS		97.39 11	97.13 13	98.17 14	99.02 46	95.28 20	98.23 38	98.27 31	92.37 118	98.27 18	98.65 16	93.33 21	99.72 39	96.49 33	99.52 29	99.51 39

SMA-MVS		97.35 13	97.03 18	98.30 8	99.06 42	95.42 10	97.94 62	98.18 50	90.57 179	98.85 9	98.94 1	93.33 21	99.83 26	96.72 26	99.68 4	99.63 14
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
NCCC		97.30 15	97.03 18	98.11 17	98.77 61	95.06 26	97.34 123	98.04 85	95.96 2	97.09 51	97.88 83	93.18 24	99.71 42	95.84 59	99.17 80	99.56 27

9.14			96.75 37		98.93 51		97.73 81	98.23 42	91.28 154	97.88 27	98.44 32	93.00 25	99.65 57	95.76 61	99.47 42

segment_acmp												92.89 26

TSAR-MVS + GP.		96.69 48	96.49 51	97.27 63	98.31 95	93.39 70	96.79 178	96.72 235	94.17 54	97.44 34	97.66 103	92.76 27	99.33 118	96.86 21	97.76 129	99.08 89

dcpmvs_2		96.37 61	97.05 16	94.31 208	98.96 50	84.11 309	97.56 102	97.51 153	93.92 59	97.43 36	98.52 25	92.75 28	99.32 120	97.32 13	99.50 36	99.51 39

TEST9					98.70 64	94.19 44	96.41 210	98.02 92	88.17 243	96.03 89	97.56 115	92.74 29	99.59 72

train_agg		96.30 63	95.83 69	97.72 42	98.70 64	94.19 44	96.41 210	98.02 92	88.58 231	96.03 89	97.56 115	92.73 30	99.59 72	95.04 83	99.37 58	99.39 60

test_8					98.67 66	94.06 53	96.37 217	98.01 95	88.58 231	95.98 94	97.55 117	92.73 30	99.58 75

agg_prior1		96.22 66	95.77 70	97.56 51	98.67 66	93.79 59	96.28 226	98.00 97	88.76 228	95.68 104	97.55 117	92.70 32	99.57 83	95.01 84	99.32 59	99.32 66

CSCG		96.05 69	95.91 67	96.46 96	99.24 30	90.47 164	98.30 28	98.57 12	89.01 214	93.97 139	97.57 113	92.62 33	99.76 34	94.66 97	99.27 67	99.15 81

ETH3D-3000-0.1		97.07 23	96.71 40	98.14 16	98.90 55	95.33 17	97.68 88	98.24 38	91.57 140	97.90 26	98.37 40	92.61 34	99.66 56	95.59 73	99.51 33	99.43 55

Regformer-2		97.16 19	96.99 20	97.67 46	98.32 93	93.84 57	96.83 174	98.10 65	95.24 13	97.49 31	98.25 59	92.57 35	99.61 66	96.80 22	99.29 63	99.56 27

HPM-MVS++		97.34 14	96.97 21	98.47 5	99.08 40	96.16 4	97.55 104	97.97 103	95.59 4	96.61 67	97.89 81	92.57 35	99.84 23	95.95 54	99.51 33	99.40 59

ZD-MVS					99.05 43	94.59 32		98.08 68	89.22 209	97.03 53	98.10 68	92.52 37	99.65 57	94.58 99	99.31 61

PHI-MVS		96.77 45	96.46 54	97.71 44	98.40 85	94.07 52	98.21 41	98.45 16	89.86 191	97.11 50	98.01 77	92.52 37	99.69 48	96.03 53	99.53 28	99.36 64

Regformer-1		97.10 21	96.96 22	97.54 52	98.32 93	93.48 68	96.83 174	97.99 101	95.20 15	97.46 32	98.25 59	92.48 39	99.58 75	96.79 24	99.29 63	99.55 31

APD-MVS		96.95 32	96.60 45	98.01 22	99.03 45	94.93 28	97.72 84	98.10 65	91.50 142	98.01 22	98.32 50	92.33 40	99.58 75	94.85 89	99.51 33	99.53 38
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		96.68 50	96.58 47	96.99 76	98.46 81	92.31 102	96.20 233	98.90 2	94.30 53	95.86 97	97.74 96	92.33 40	99.38 116	96.04 52	99.42 49	99.28 71

MSLP-MVS++		96.94 33	97.06 15	96.59 86	98.72 63	91.86 117	97.67 89	98.49 13	94.66 43	97.24 42	98.41 38	92.31 42	98.94 158	96.61 29	99.46 44	98.96 101

旧先验1					98.38 88	93.38 71		97.75 123			98.09 70	92.30 43			99.01 92	99.16 79

HFP-MVS		97.14 20	96.92 24	97.83 29	99.42 7	94.12 49	98.52 14	98.32 22	93.21 87	97.18 44	98.29 54	92.08 44	99.83 26	95.63 68	99.59 17	99.54 34

#test#		97.02 27	96.75 37	97.83 29	99.42 7	94.12 49	98.15 46	98.32 22	92.57 114	97.18 44	98.29 54	92.08 44	99.83 26	95.12 81	99.59 17	99.54 34

test_prior3		96.46 57	96.20 62	97.23 65	98.67 66	92.99 81	96.35 218	98.00 97	92.80 107	96.03 89	97.59 111	92.01 46	99.41 111	95.01 84	99.38 54	99.29 68

test_prior2							96.35 218		92.80 107	96.03 89	97.59 111	92.01 46		95.01 84	99.38 54

CDPH-MVS		95.97 72	95.38 79	97.77 38	98.93 51	94.44 35	96.35 218	97.88 112	86.98 275	96.65 64	97.89 81	91.99 48	99.47 104	92.26 139	99.46 44	99.39 60

testtj		96.93 34	96.56 48	98.05 20	99.10 36	94.66 31	97.78 76	98.22 43	92.74 109	97.59 29	98.20 65	91.96 49	99.86 9	94.21 103	99.25 71	99.63 14

CP-MVS		97.02 27	96.81 32	97.64 49	99.33 23	93.54 66	98.80 7	98.28 28	92.99 96	96.45 77	98.30 53	91.90 50	99.85 18	95.61 70	99.68 4	99.54 34

Regformer-4		96.97 30	96.87 25	97.25 64	98.34 90	92.66 90	96.96 162	98.01 95	95.12 22	97.14 47	98.42 35	91.82 51	99.61 66	96.90 19	99.13 83	99.50 43

ETH3D cwj APD-0.16		96.56 53	96.06 64	98.05 20	98.26 99	95.19 22	96.99 158	98.05 84	89.85 193	97.26 41	98.22 61	91.80 52	99.69 48	94.84 90	99.28 65	99.27 73

CS-MVS		96.79 44	97.05 16	96.00 125	98.17 111	90.38 170	99.09 3	97.89 109	95.31 12	97.02 55	98.02 75	91.74 53	98.71 180	97.06 14	99.18 78	98.90 109

DPM-MVS		95.69 76	94.92 89	98.01 22	98.08 115	95.71 9	95.27 275	97.62 142	90.43 182	95.55 110	97.07 137	91.72 54	99.50 101	89.62 192	98.94 95	98.82 118

Regformer-3		96.85 39	96.80 33	97.01 75	98.34 90	92.02 113	96.96 162	97.76 122	95.01 26	97.08 52	98.42 35	91.71 55	99.54 90	96.80 22	99.13 83	99.48 47

XVS		97.18 17	96.96 22	97.81 33	99.38 15	94.03 54	98.59 11	98.20 46	94.85 30	96.59 69	98.29 54	91.70 56	99.80 31	95.66 63	99.40 51	99.62 16

X-MVStestdata		91.71 204	89.67 264	97.81 33	99.38 15	94.03 54	98.59 11	98.20 46	94.85 30	96.59 69	32.69 373	91.70 56	99.80 31	95.66 63	99.40 51	99.62 16

ZNCC-MVS		96.96 31	96.67 42	97.85 28	99.37 17	94.12 49	98.49 18	98.18 50	92.64 113	96.39 79	98.18 66	91.61 58	99.88 5	95.59 73	99.55 25	99.57 24

ACMMP_NAP		97.20 16	96.86 26	98.23 11	99.09 38	95.16 24	97.60 99	98.19 48	92.82 106	97.93 25	98.74 13	91.60 59	99.86 9	96.26 38	99.52 29	99.67 11

region2R		97.07 23	96.84 29	97.77 38	99.46 2	93.79 59	98.52 14	98.24 38	93.19 90	97.14 47	98.34 45	91.59 60	99.87 8	95.46 75	99.59 17	99.64 13

DELS-MVS		96.61 51	96.38 57	97.30 60	97.79 130	93.19 77	95.96 245	98.18 50	95.23 14	95.87 96	97.65 104	91.45 61	99.70 47	95.87 55	99.44 48	99.00 99
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
SR-MVS		97.01 29	96.86 26	97.47 54	99.09 38	93.27 76	97.98 56	98.07 74	93.75 66	97.45 33	98.48 29	91.43 62	99.59 72	96.22 41	99.27 67	99.54 34

test1172		96.93 34	96.86 26	97.15 70	99.10 36	92.34 99	97.96 61	98.04 85	93.79 65	97.35 39	98.53 24	91.40 63	99.56 85	96.30 37	99.30 62	99.55 31

SR-MVS-dyc-post		96.88 37	96.80 33	97.11 73	99.02 46	92.34 99	97.98 56	98.03 88	93.52 77	97.43 36	98.51 26	91.40 63	99.56 85	96.05 50	99.26 69	99.43 55

GST-MVS		96.85 39	96.52 50	97.82 32	99.36 20	94.14 48	98.29 29	98.13 58	92.72 110	96.70 60	98.06 72	91.35 65	99.86 9	94.83 91	99.28 65	99.47 50

ACMMPR		97.07 23	96.84 29	97.79 35	99.44 6	93.88 56	98.52 14	98.31 24	93.21 87	97.15 46	98.33 48	91.35 65	99.86 9	95.63 68	99.59 17	99.62 16

DeepC-MVS_fast	93.89 2	96.93 34	96.64 43	97.78 36	98.64 74	94.30 38	97.41 115	98.04 85	94.81 35	96.59 69	98.37 40	91.24 67	99.64 65	95.16 79	99.52 29	99.42 58
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETV-MVS		96.02 70	95.89 68	96.40 99	97.16 154	92.44 97	97.47 112	97.77 121	94.55 45	96.48 74	94.51 263	91.23 68	98.92 159	95.65 66	98.19 116	97.82 179

ETH3 D test6400		96.16 67	95.52 73	98.07 19	98.90 55	95.06 26	97.03 150	98.21 44	88.16 245	96.64 65	97.70 98	91.18 69	99.67 53	92.44 138	99.47 42	99.48 47

PGM-MVS		96.81 42	96.53 49	97.65 47	99.35 22	93.53 67	97.65 92	98.98 1	92.22 121	97.14 47	98.44 32	91.17 70	99.85 18	94.35 101	99.46 44	99.57 24

MP-MVS-pluss		96.70 47	96.27 59	97.98 24	99.23 32	94.71 30	96.96 162	98.06 77	90.67 170	95.55 110	98.78 12	91.07 71	99.86 9	96.58 30	99.55 25	99.38 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mPP-MVS		96.86 38	96.60 45	97.64 49	99.40 12	93.44 69	98.50 17	98.09 67	93.27 86	95.95 95	98.33 48	91.04 72	99.88 5	95.20 78	99.57 24	99.60 19

HPM-MVS		96.69 48	96.45 55	97.40 56	99.36 20	93.11 79	98.87 5	98.06 77	91.17 158	96.40 78	97.99 78	90.99 73	99.58 75	95.61 70	99.61 16	99.49 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		96.81 42	96.71 40	97.12 72	99.01 49	92.31 102	97.98 56	98.06 77	93.11 93	97.44 34	98.55 22	90.93 74	99.55 88	96.06 49	99.25 71	99.51 39

test12				97.65 47	98.46 81	94.26 41		97.66 136		95.52 113		90.89 75	99.46 105		99.25 71	99.22 76

zzz-MVS		97.07 23	96.77 36	97.97 25	99.37 17	94.42 36	97.15 146	98.08 68	95.07 24	96.11 86	98.59 18	90.88 76	99.90 2	96.18 47	99.50 36	99.58 22

MTAPA		97.08 22	96.78 35	97.97 25	99.37 17	94.42 36	97.24 133	98.08 68	95.07 24	96.11 86	98.59 18	90.88 76	99.90 2	96.18 47	99.50 36	99.58 22

EI-MVSNet-Vis-set		96.51 54	96.47 52	96.63 83	98.24 100	91.20 140	96.89 169	97.73 126	94.74 40	96.49 73	98.49 28	90.88 76	99.58 75	96.44 35	98.32 113	99.13 83

RE-MVS-def			96.72 39		99.02 46	92.34 99	97.98 56	98.03 88	93.52 77	97.43 36	98.51 26	90.71 79		96.05 50	99.26 69	99.43 55

EIA-MVS		95.53 82	95.47 75	95.71 140	97.06 162	89.63 186	97.82 72	97.87 114	93.57 71	93.92 140	95.04 240	90.61 80	98.95 157	94.62 98	98.68 102	98.54 133

MP-MVS		96.77 45	96.45 55	97.72 42	99.39 14	93.80 58	98.41 22	98.06 77	93.37 82	95.54 112	98.34 45	90.59 81	99.88 5	94.83 91	99.54 27	99.49 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		96.34 62	96.30 58	96.47 94	98.20 105	90.93 151	96.86 170	97.72 129	94.67 42	96.16 85	98.46 30	90.43 82	99.58 75	96.23 40	97.96 123	98.90 109

原ACMM1				96.38 102	98.59 76	91.09 146		97.89 109	87.41 267	95.22 117	97.68 100	90.25 83	99.54 90	87.95 223	99.12 86	98.49 140

1121		94.71 107	93.83 112	97.34 58	98.57 79	93.64 64	96.04 239	97.73 126	81.56 338	95.68 104	97.85 87	90.23 84	99.65 57	87.68 233	99.12 86	98.73 123

HPM-MVS_fast		96.51 54	96.27 59	97.22 67	99.32 24	92.74 87	98.74 8	98.06 77	90.57 179	96.77 57	98.35 42	90.21 85	99.53 93	94.80 94	99.63 14	99.38 62

testdata				95.46 158	98.18 109	88.90 217		97.66 136	82.73 330	97.03 53	98.07 71	90.06 86	98.85 165	89.67 190	98.98 93	98.64 130

新几何1				97.32 59	98.60 75	93.59 65		97.75 123	81.58 337	95.75 101	97.85 87	90.04 87	99.67 53	86.50 255	99.13 83	98.69 127

CS-MVS-test		96.47 56	96.62 44	96.01 124	98.18 109	90.40 168	98.40 23	97.65 138	95.33 11	97.02 55	96.79 148	89.98 88	98.72 178	97.06 14	99.18 78	98.91 107

DP-MVS Recon		95.68 77	95.12 87	97.37 57	99.19 33	94.19 44	97.03 150	98.08 68	88.35 238	95.09 119	97.65 104	89.97 89	99.48 103	92.08 148	98.59 105	98.44 148

MVS_111021_LR		96.24 65	96.19 63	96.39 101	98.23 104	91.35 133	96.24 231	98.79 4	93.99 58	95.80 99	97.65 104	89.92 90	99.24 126	95.87 55	99.20 76	98.58 131

EPP-MVSNet		95.22 90	95.04 88	95.76 133	97.49 147	89.56 190	98.67 9	97.00 214	90.69 169	94.24 133	97.62 109	89.79 91	98.81 168	93.39 124	96.49 161	98.92 106

DROMVSNet		96.42 58	96.47 52	96.26 111	97.01 167	91.52 127	98.89 4	97.75 123	94.42 48	96.64 65	97.68 100	89.32 92	98.60 189	97.45 9	99.11 88	98.67 129

PAPR		94.18 114	93.42 130	96.48 93	97.64 139	91.42 132	95.55 261	97.71 133	88.99 215	92.34 174	95.82 204	89.19 93	99.11 138	86.14 261	97.38 138	98.90 109

MG-MVS		95.61 79	95.38 79	96.31 106	98.42 84	90.53 162	96.04 239	97.48 156	93.47 79	95.67 107	98.10 68	89.17 94	99.25 125	91.27 166	98.77 99	99.13 83

PAPM_NR		95.01 94	94.59 97	96.26 111	98.89 58	90.68 159	97.24 133	97.73 126	91.80 135	92.93 165	96.62 166	89.13 95	99.14 136	89.21 204	97.78 127	98.97 100

ACMMP		96.27 64	95.93 66	97.28 62	99.24 30	92.62 92	98.25 34	98.81 3	92.99 96	94.56 127	98.39 39	88.96 96	99.85 18	94.57 100	97.63 130	99.36 64
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
UA-Net		95.95 73	95.53 72	97.20 69	97.67 135	92.98 83	97.65 92	98.13 58	94.81 35	96.61 67	98.35 42	88.87 97	99.51 98	90.36 178	97.35 140	99.11 87

API-MVS		94.84 103	94.49 102	95.90 129	97.90 124	92.00 114	97.80 74	97.48 156	89.19 210	94.81 122	96.71 152	88.84 98	99.17 132	88.91 210	98.76 100	96.53 215

test222					98.24 100	92.21 105	95.33 270	97.60 143	79.22 350	95.25 115	97.84 90	88.80 99			99.15 81	98.72 124

Test By Simon												88.73 100

pcd_1.5k_mvsjas		7.39 347	9.85 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	88.65 101	0.00 380	0.00 378	0.00 378	0.00 376

PS-MVSNAJss		93.74 133	93.51 124	94.44 200	93.91 308	89.28 207	97.75 78	97.56 150	92.50 115	89.94 229	96.54 169	88.65 101	98.18 223	93.83 115	90.90 245	95.86 235

PS-MVSNAJ		95.37 84	95.33 81	95.49 154	97.35 148	90.66 160	95.31 272	97.48 156	93.85 62	96.51 72	95.70 215	88.65 101	99.65 57	94.80 94	98.27 114	96.17 224

xiu_mvs_v2_base		95.32 86	95.29 82	95.40 159	97.22 150	90.50 163	95.44 266	97.44 171	93.70 69	96.46 76	96.18 185	88.59 104	99.53 93	94.79 96	97.81 126	96.17 224

PLC	91.00 6	94.11 119	93.43 128	96.13 116	98.58 78	91.15 145	96.69 188	97.39 177	87.29 270	91.37 192	96.71 152	88.39 105	99.52 97	87.33 243	97.13 148	97.73 181
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet_NR-MVSNet		93.37 144	92.67 151	95.47 157	95.34 245	92.83 85	97.17 143	98.58 11	92.98 101	90.13 221	95.80 205	88.37 106	97.85 270	91.71 155	83.93 322	95.73 248

PVSNet_BlendedMVS		94.06 121	93.92 110	94.47 199	98.27 96	89.46 197	96.73 182	98.36 17	90.17 185	94.36 130	95.24 234	88.02 107	99.58 75	93.44 121	90.72 247	94.36 317

PVSNet_Blended		94.87 102	94.56 98	95.81 132	98.27 96	89.46 197	95.47 265	98.36 17	88.84 222	94.36 130	96.09 193	88.02 107	99.58 75	93.44 121	98.18 117	98.40 151

TAPA-MVS	90.10 7	92.30 185	91.22 202	95.56 147	98.33 92	89.60 188	96.79 178	97.65 138	81.83 335	91.52 189	97.23 129	87.94 109	98.91 161	71.31 357	98.37 112	98.17 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		96.40 59	96.21 61	96.98 77	98.89 58	92.20 107	97.89 65	98.03 88	93.34 85	97.22 43	98.42 35	87.93 110	99.72 39	95.10 82	99.07 89	99.02 92

MVS_Test		94.89 101	94.62 96	95.68 141	96.83 175	89.55 191	96.70 186	97.17 195	91.17 158	95.60 109	96.11 192	87.87 111	98.76 173	93.01 134	97.17 147	98.72 124

UniMVSNet (Re)		93.31 146	92.55 156	95.61 145	95.39 239	93.34 74	97.39 119	98.71 6	93.14 92	90.10 225	94.83 249	87.71 112	98.03 247	91.67 159	83.99 321	95.46 257

FC-MVSNet-test		93.94 126	93.57 119	95.04 169	95.48 236	91.45 131	98.12 47	98.71 6	93.37 82	90.23 215	96.70 154	87.66 113	97.85 270	91.49 161	90.39 252	95.83 239

canonicalmvs		96.02 70	95.45 76	97.75 40	97.59 143	95.15 25	98.28 30	97.60 143	94.52 46	96.27 82	96.12 189	87.65 114	99.18 131	96.20 46	94.82 188	98.91 107

FIs		94.09 120	93.70 115	95.27 161	95.70 228	92.03 112	98.10 48	98.68 8	93.36 84	90.39 212	96.70 154	87.63 115	97.94 261	92.25 141	90.50 251	95.84 238

CDS-MVSNet		94.14 118	93.54 121	95.93 127	96.18 208	91.46 130	96.33 221	97.04 210	88.97 217	93.56 145	96.51 170	87.55 116	97.89 268	89.80 186	95.95 167	98.44 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+		94.93 99	94.45 104	96.36 104	96.61 182	91.47 129	96.41 210	97.41 176	91.02 163	94.50 128	95.92 198	87.53 117	98.78 170	93.89 112	96.81 151	98.84 117

casdiffmvs		95.64 78	95.49 74	96.08 117	96.76 180	90.45 165	97.29 130	97.44 171	94.00 57	95.46 114	97.98 79	87.52 118	98.73 175	95.64 67	97.33 141	99.08 89

PVSNet_Blended_VisFu		95.27 87	94.91 90	96.38 102	98.20 105	90.86 153	97.27 131	98.25 36	90.21 184	94.18 134	97.27 126	87.48 119	99.73 36	93.53 118	97.77 128	98.55 132

mvs_anonymous		93.82 130	93.74 114	94.06 216	96.44 196	85.41 289	95.81 252	97.05 208	89.85 193	90.09 226	96.36 179	87.44 120	97.75 281	93.97 108	96.69 156	99.02 92

CANet		96.39 60	96.02 65	97.50 53	97.62 140	93.38 71	97.02 153	97.96 104	95.42 7	94.86 121	97.81 91	87.38 121	99.82 29	96.88 20	99.20 76	99.29 68

baseline		95.58 80	95.42 78	96.08 117	96.78 177	90.41 167	97.16 144	97.45 167	93.69 70	95.65 108	97.85 87	87.29 122	98.68 182	95.66 63	97.25 144	99.13 83

TAMVS		94.01 124	93.46 126	95.64 142	96.16 210	90.45 165	96.71 185	96.89 225	89.27 208	93.46 150	96.92 144	87.29 122	97.94 261	88.70 214	95.74 172	98.53 134

nrg030		94.05 122	93.31 132	96.27 110	95.22 256	94.59 32	98.34 25	97.46 161	92.93 103	91.21 202	96.64 159	87.23 124	98.22 217	94.99 87	85.80 293	95.98 233

CPTT-MVS		95.57 81	95.19 84	96.70 80	99.27 28	91.48 128	98.33 26	98.11 63	87.79 256	95.17 118	98.03 74	87.09 125	99.61 66	93.51 119	99.42 49	99.02 92

OMC-MVS		95.09 93	94.70 95	96.25 113	98.46 81	91.28 134	96.43 208	97.57 147	92.04 130	94.77 123	97.96 80	87.01 126	99.09 142	91.31 165	96.77 152	98.36 155

DeepC-MVS	93.07 3	96.06 68	95.66 71	97.29 61	97.96 118	93.17 78	97.30 129	98.06 77	93.92 59	93.38 152	98.66 14	86.83 127	99.73 36	95.60 72	99.22 74	98.96 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-LS		92.29 186	91.94 175	93.34 255	96.25 204	86.97 263	96.57 204	97.05 208	90.67 170	89.50 245	94.80 251	86.59 128	97.64 289	89.91 183	86.11 291	95.40 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		93.03 158	92.88 142	93.48 248	95.77 225	86.98 262	96.44 206	97.12 199	90.66 172	91.30 196	97.64 107	86.56 129	98.05 243	89.91 183	90.55 249	95.41 260

miper_enhance_ethall		91.54 215	91.01 207	93.15 262	95.35 244	87.07 261	93.97 308	96.90 223	86.79 279	89.17 255	93.43 312	86.55 130	97.64 289	89.97 182	86.93 282	94.74 307

1112_ss		93.37 144	92.42 162	96.21 114	97.05 164	90.99 147	96.31 223	96.72 235	86.87 278	89.83 233	96.69 156	86.51 131	99.14 136	88.12 220	93.67 203	98.50 138

diffmvs		95.25 88	95.13 86	95.63 143	96.43 197	89.34 202	95.99 244	97.35 183	92.83 105	96.31 80	97.37 123	86.44 132	98.67 183	96.26 38	97.19 146	98.87 114

WTY-MVS		94.71 107	94.02 109	96.79 79	97.71 134	92.05 111	96.59 201	97.35 183	90.61 176	94.64 125	96.93 141	86.41 133	99.39 114	91.20 168	94.71 192	98.94 104

EPNet		95.20 91	94.56 98	97.14 71	92.80 334	92.68 89	97.85 70	94.87 321	96.64 1	92.46 168	97.80 93	86.23 134	99.65 57	93.72 116	98.62 104	99.10 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		91.59 209	91.13 205	92.97 268	95.55 233	86.57 272	94.47 290	96.88 226	87.77 257	88.88 260	94.01 290	86.22 135	97.54 298	89.49 194	86.93 282	94.79 303

Fast-Effi-MVS+		93.46 141	92.75 147	95.59 146	96.77 178	90.03 174	96.81 177	97.13 198	88.19 241	91.30 196	94.27 279	86.21 136	98.63 186	87.66 235	96.46 163	98.12 163

MVSFormer		95.37 84	95.16 85	95.99 126	96.34 201	91.21 138	98.22 39	97.57 147	91.42 146	96.22 83	97.32 124	86.20 137	97.92 264	94.07 106	99.05 90	98.85 115

lupinMVS		94.99 98	94.56 98	96.29 109	96.34 201	91.21 138	95.83 251	96.27 261	88.93 219	96.22 83	96.88 146	86.20 137	98.85 165	95.27 77	99.05 90	98.82 118

114514_t		93.95 125	93.06 137	96.63 83	99.07 41	91.61 122	97.46 114	97.96 104	77.99 354	93.00 160	97.57 113	86.14 139	99.33 118	89.22 203	99.15 81	98.94 104

alignmvs		95.87 75	95.23 83	97.78 36	97.56 146	95.19 22	97.86 67	97.17 195	94.39 50	96.47 75	96.40 177	85.89 140	99.20 128	96.21 45	95.11 184	98.95 103

WR-MVS_H		92.00 197	91.35 193	93.95 225	95.09 263	89.47 195	98.04 53	98.68 8	91.46 144	88.34 272	94.68 257	85.86 141	97.56 296	85.77 269	84.24 318	94.82 298

Test_1112_low_res		92.84 169	91.84 178	95.85 131	97.04 165	89.97 180	95.53 263	96.64 244	85.38 298	89.65 239	95.18 235	85.86 141	99.10 139	87.70 230	93.58 208	98.49 140

HY-MVS	89.66 9	93.87 128	92.95 140	96.63 83	97.10 158	92.49 96	95.64 259	96.64 244	89.05 213	93.00 160	95.79 208	85.77 143	99.45 107	89.16 207	94.35 194	97.96 168

c3_l		91.38 222	90.89 209	92.88 271	95.58 231	86.30 275	94.68 285	96.84 231	88.17 243	88.83 263	94.23 282	85.65 144	97.47 305	89.36 197	84.63 311	94.89 293

IS-MVSNet		94.90 100	94.52 101	96.05 120	97.67 135	90.56 161	98.44 20	96.22 264	93.21 87	93.99 137	97.74 96	85.55 145	98.45 202	89.98 181	97.86 124	99.14 82

MVS		91.71 204	90.44 230	95.51 151	95.20 258	91.59 124	96.04 239	97.45 167	73.44 361	87.36 295	95.60 219	85.42 146	99.10 139	85.97 266	97.46 133	95.83 239

VNet		95.89 74	95.45 76	97.21 68	98.07 116	92.94 84	97.50 107	98.15 55	93.87 61	97.52 30	97.61 110	85.29 147	99.53 93	95.81 60	95.27 180	99.16 79

CNLPA		94.28 112	93.53 122	96.52 88	98.38 88	92.55 94	96.59 201	96.88 226	90.13 187	91.91 184	97.24 128	85.21 148	99.09 142	87.64 236	97.83 125	97.92 171

F-COLMAP		93.58 138	92.98 139	95.37 160	98.40 85	88.98 215	97.18 142	97.29 188	87.75 259	90.49 209	97.10 136	85.21 148	99.50 101	86.70 252	96.72 155	97.63 185

LCM-MVSNet-Re		92.50 175	92.52 159	92.44 281	96.82 176	81.89 330	96.92 166	93.71 340	92.41 117	84.30 326	94.60 261	85.08 150	97.03 322	91.51 160	97.36 139	98.40 151

NR-MVSNet		92.34 182	91.27 199	95.53 150	94.95 269	93.05 80	97.39 119	98.07 74	92.65 112	84.46 324	95.71 213	85.00 151	97.77 280	89.71 188	83.52 328	95.78 242

PAPM		91.52 216	90.30 236	95.20 163	95.30 251	89.83 183	93.38 324	96.85 230	86.26 286	88.59 268	95.80 205	84.88 152	98.15 226	75.67 343	95.93 168	97.63 185

MAR-MVS		94.22 113	93.46 126	96.51 91	98.00 117	92.19 108	97.67 89	97.47 159	88.13 247	93.00 160	95.84 202	84.86 153	99.51 98	87.99 222	98.17 118	97.83 178
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
jason		94.84 103	94.39 106	96.18 115	95.52 234	90.93 151	96.09 237	96.52 251	89.28 207	96.01 93	97.32 124	84.70 154	98.77 172	95.15 80	98.91 97	98.85 115
jason: jason.
sss		94.51 109	93.80 113	96.64 81	97.07 159	91.97 115	96.32 222	98.06 77	88.94 218	94.50 128	96.78 149	84.60 155	99.27 124	91.90 149	96.02 165	98.68 128

LS3D		93.57 139	92.61 154	96.47 94	97.59 143	91.61 122	97.67 89	97.72 129	85.17 302	90.29 214	98.34 45	84.60 155	99.73 36	83.85 293	98.27 114	98.06 167

Vis-MVSNet (Re-imp)		94.15 115	93.88 111	94.95 177	97.61 141	87.92 242	98.10 48	95.80 278	92.22 121	93.02 159	97.45 119	84.53 157	97.91 267	88.24 218	97.97 122	99.02 92

GeoE		93.89 127	93.28 133	95.72 139	96.96 170	89.75 185	98.24 37	96.92 222	89.47 202	92.12 180	97.21 130	84.42 158	98.39 207	87.71 229	96.50 160	99.01 96

cdsmvs_eth3d_5k		23.24 343	30.99 345	0.00 361	0.00 384	0.00 385	0.00 372	97.63 141	0.00 379	0.00 380	96.88 146	84.38 159	0.00 380	0.00 378	0.00 378	0.00 376

test_yl		94.78 105	94.23 107	96.43 97	97.74 132	91.22 136	96.85 171	97.10 201	91.23 156	95.71 102	96.93 141	84.30 160	99.31 121	93.10 128	95.12 182	98.75 120

DCV-MVSNet		94.78 105	94.23 107	96.43 97	97.74 132	91.22 136	96.85 171	97.10 201	91.23 156	95.71 102	96.93 141	84.30 160	99.31 121	93.10 128	95.12 182	98.75 120

CHOSEN 280x420		93.12 154	92.72 150	94.34 206	96.71 181	87.27 253	90.29 351	97.72 129	86.61 282	91.34 193	95.29 231	84.29 162	98.41 203	93.25 126	98.94 95	97.35 197

baseline1		92.82 170	91.90 176	95.55 149	97.20 152	90.77 157	97.19 141	94.58 326	92.20 123	92.36 172	96.34 180	84.16 163	98.21 218	89.20 205	83.90 325	97.68 184

eth_miper_zixun_eth		91.02 241	90.59 225	92.34 285	95.33 248	84.35 305	94.10 305	96.90 223	88.56 233	88.84 262	94.33 274	84.08 164	97.60 294	88.77 213	84.37 317	95.06 282

PCF-MVS	89.48 11	91.56 212	89.95 252	96.36 104	96.60 183	92.52 95	92.51 338	97.26 189	79.41 349	88.90 258	96.56 168	84.04 165	99.55 88	77.01 339	97.30 142	97.01 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
1314		92.81 171	92.03 171	95.14 166	95.33 248	89.52 194	96.04 239	97.44 171	87.72 260	86.25 310	95.33 230	83.84 166	98.79 169	89.26 201	97.05 149	97.11 200

DP-MVS		92.76 172	91.51 191	96.52 88	98.77 61	90.99 147	97.38 121	96.08 269	82.38 331	89.29 251	97.87 84	83.77 167	99.69 48	81.37 313	96.69 156	98.89 112

3Dnovator+	91.43 4	95.40 83	94.48 103	98.16 15	96.90 171	95.34 16	98.48 19	97.87 114	94.65 44	88.53 270	98.02 75	83.69 168	99.71 42	93.18 127	98.96 94	99.44 53

h-mvs33		94.15 115	93.52 123	96.04 121	97.81 128	90.22 172	97.62 98	97.58 146	95.19 16	96.74 58	97.45 119	83.67 169	99.61 66	95.85 57	79.73 342	98.29 158

hse-mvs2		93.45 142	92.99 138	94.81 183	97.02 166	88.59 223	96.69 188	96.47 253	95.19 16	96.74 58	96.16 188	83.67 169	98.48 201	95.85 57	79.13 346	97.35 197

AdaColmap		94.34 111	93.68 117	96.31 106	98.59 76	91.68 121	96.59 201	97.81 120	89.87 190	92.15 178	97.06 138	83.62 171	99.54 90	89.34 198	98.07 120	97.70 183

DU-MVS		92.90 165	92.04 170	95.49 154	94.95 269	92.83 85	97.16 144	98.24 38	93.02 95	90.13 221	95.71 213	83.47 172	97.85 270	91.71 155	83.93 322	95.78 242

Baseline_NR-MVSNet		91.20 233	90.62 223	92.95 269	93.83 311	88.03 240	97.01 157	95.12 308	88.42 236	89.70 236	95.13 238	83.47 172	97.44 308	89.66 191	83.24 330	93.37 335

miper_lstm_enhance		90.50 261	90.06 250	91.83 295	95.33 248	83.74 313	93.86 312	96.70 240	87.56 264	87.79 286	93.81 298	83.45 174	96.92 328	87.39 241	84.62 312	94.82 298

EPNet_dtu		91.71 204	91.28 198	92.99 267	93.76 313	83.71 315	96.69 188	95.28 299	93.15 91	87.02 302	95.95 197	83.37 175	97.38 313	79.46 325	96.84 150	97.88 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-untuned		92.94 163	92.62 153	93.92 229	97.22 150	86.16 280	96.40 213	96.25 263	90.06 188	89.79 234	96.17 187	83.19 176	98.35 209	87.19 246	97.27 143	97.24 199

TranMVSNet+NR-MVSNet		92.50 175	91.63 184	95.14 166	94.76 280	92.07 110	97.53 105	98.11 63	92.90 104	89.56 242	96.12 189	83.16 177	97.60 294	89.30 199	83.20 331	95.75 246

CHOSEN 1792x2688		94.15 115	93.51 124	96.06 119	98.27 96	89.38 200	95.18 279	98.48 15	85.60 295	93.76 143	97.11 135	83.15 178	99.61 66	91.33 164	98.72 101	99.19 77

PMMVS		92.86 167	92.34 163	94.42 203	94.92 271	86.73 267	94.53 289	96.38 257	84.78 309	94.27 132	95.12 239	83.13 179	98.40 204	91.47 162	96.49 161	98.12 163

Effi-MVS+-dtu		93.08 155	93.21 135	92.68 278	96.02 217	83.25 320	97.14 147	96.72 235	93.85 62	91.20 203	93.44 310	83.08 180	98.30 213	91.69 157	95.73 173	96.50 217

mvs-test1		93.63 136	93.69 116	93.46 250	96.02 217	84.61 303	97.24 133	96.72 235	93.85 62	92.30 175	95.76 210	83.08 180	98.89 163	91.69 157	96.54 159	96.87 208

v8		91.29 230	90.53 229	93.57 245	94.15 301	88.12 239	97.34 123	97.06 207	88.99 215	88.32 273	94.26 281	83.08 180	98.01 249	87.62 237	83.92 324	94.57 312

DIV-MVS_self_test		90.97 244	90.33 233	92.88 271	95.36 243	86.19 279	94.46 292	96.63 247	87.82 253	88.18 279	94.23 282	82.99 183	97.53 300	87.72 227	85.57 295	94.93 289

cl____		90.96 245	90.32 234	92.89 270	95.37 242	86.21 278	94.46 292	96.64 244	87.82 253	88.15 280	94.18 285	82.98 184	97.54 298	87.70 230	85.59 294	94.92 291

BH-w/o		92.14 195	91.75 180	93.31 256	96.99 169	85.73 284	95.67 256	95.69 282	88.73 229	89.26 253	94.82 250	82.97 185	98.07 240	85.26 276	96.32 164	96.13 228

v148		90.99 242	90.38 232	92.81 274	93.83 311	85.80 283	96.78 180	96.68 241	89.45 203	88.75 266	93.93 294	82.96 186	97.82 274	87.83 225	83.25 329	94.80 301

HyFIR lowres test		93.66 135	92.92 141	95.87 130	98.24 100	89.88 182	94.58 287	98.49 13	85.06 304	93.78 142	95.78 209	82.86 187	98.67 183	91.77 153	95.71 174	99.07 91

test_djsdf		93.07 156	92.76 145	94.00 220	93.49 321	88.70 221	98.22 39	97.57 147	91.42 146	90.08 227	95.55 223	82.85 188	97.92 264	94.07 106	91.58 232	95.40 263

PatchmatchNet		91.91 199	91.35 193	93.59 243	95.38 240	84.11 309	93.15 328	95.39 292	89.54 199	92.10 181	93.68 303	82.82 189	98.13 227	84.81 280	95.32 179	98.52 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												82.76 190				98.45 145

xiu_mvs_v1_base_debu		95.01 94	94.76 92	95.75 135	96.58 185	91.71 118	96.25 228	97.35 183	92.99 96	96.70 60	96.63 163	82.67 191	99.44 108	96.22 41	97.46 133	96.11 229

xiu_mvs_v1_base		95.01 94	94.76 92	95.75 135	96.58 185	91.71 118	96.25 228	97.35 183	92.99 96	96.70 60	96.63 163	82.67 191	99.44 108	96.22 41	97.46 133	96.11 229

xiu_mvs_v1_base_debi		95.01 94	94.76 92	95.75 135	96.58 185	91.71 118	96.25 228	97.35 183	92.99 96	96.70 60	96.63 163	82.67 191	99.44 108	96.22 41	97.46 133	96.11 229

patchmatchnet-post											90.45 343	82.65 194	98.10 232

V42		91.58 211	90.87 210	93.73 235	94.05 305	88.50 227	97.32 126	96.97 215	88.80 227	89.71 235	94.33 274	82.54 195	98.05 243	89.01 208	85.07 305	94.64 311

WR-MVS		92.34 182	91.53 188	94.77 188	95.13 261	90.83 154	96.40 213	97.98 102	91.88 134	89.29 251	95.54 224	82.50 196	97.80 275	89.79 187	85.27 301	95.69 249

tpmrst		91.44 219	91.32 195	91.79 298	95.15 259	79.20 353	93.42 323	95.37 294	88.55 234	93.49 149	93.67 304	82.49 197	98.27 214	90.41 176	89.34 261	97.90 172

MDTV_nov1_ep13_2view						70.35 367	93.10 330		83.88 319	93.55 146		82.47 198		86.25 258		98.38 153

XVG-OURS-SEG-HR		93.86 129	93.55 120	94.81 183	97.06 162	88.53 226	95.28 273	97.45 167	91.68 138	94.08 136	97.68 100	82.41 199	98.90 162	93.84 114	92.47 217	96.98 202

QAPM		93.45 142	92.27 166	96.98 77	96.77 178	92.62 92	98.39 24	98.12 60	84.50 312	88.27 276	97.77 94	82.39 200	99.81 30	85.40 274	98.81 98	98.51 137

Patchmatch-test		89.42 279	87.99 286	93.70 238	95.27 252	85.11 295	88.98 358	94.37 331	81.11 339	87.10 300	93.69 301	82.28 201	97.50 303	74.37 347	94.76 189	98.48 142

Vis-MVSNet		95.23 89	94.81 91	96.51 91	97.18 153	91.58 125	98.26 33	98.12 60	94.38 51	94.90 120	98.15 67	82.28 201	98.92 159	91.45 163	98.58 106	99.01 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	91.36 5	95.19 92	94.44 105	97.44 55	96.56 188	93.36 73	98.65 10	98.36 17	94.12 55	89.25 254	98.06 72	82.20 203	99.77 33	93.41 123	99.32 59	99.18 78

v10		91.04 240	90.23 241	93.49 247	94.12 302	88.16 238	97.32 126	97.08 204	88.26 240	88.29 275	94.22 284	82.17 204	97.97 254	86.45 256	84.12 319	94.33 318

v1144		91.37 224	90.60 224	93.68 240	93.89 309	88.23 234	96.84 173	97.03 212	88.37 237	89.69 237	94.39 270	82.04 205	97.98 251	87.80 226	85.37 298	94.84 295

MVSTER		93.20 150	92.81 144	94.37 204	96.56 188	89.59 189	97.06 149	97.12 199	91.24 155	91.30 196	95.96 196	82.02 206	98.05 243	93.48 120	90.55 249	95.47 256

CP-MVSNet		91.89 200	91.24 200	93.82 232	95.05 264	88.57 224	97.82 72	98.19 48	91.70 137	88.21 278	95.76 210	81.96 207	97.52 302	87.86 224	84.65 310	95.37 266

Patchmatch-RL test		87.38 300	86.24 301	90.81 317	88.74 363	78.40 357	88.12 360	93.17 345	87.11 274	82.17 341	89.29 350	81.95 208	95.60 347	88.64 215	77.02 349	98.41 150

sam_mvs												81.94 209

pmmvs4		90.93 246	89.85 256	94.17 212	93.34 325	90.79 156	94.60 286	96.02 270	84.62 310	87.45 291	95.15 236	81.88 210	97.45 307	87.70 230	87.87 273	94.27 322

test_post											17.58 376	81.76 211	98.08 237

XVG-OURS		93.72 134	93.35 131	94.80 186	97.07 159	88.61 222	94.79 283	97.46 161	91.97 133	93.99 137	97.86 86	81.74 212	98.88 164	92.64 137	92.67 215	96.92 206

v2v482		91.59 209	90.85 213	93.80 233	93.87 310	88.17 237	96.94 165	96.88 226	89.54 199	89.53 243	94.90 245	81.70 213	98.02 248	89.25 202	85.04 307	95.20 278

baseline2		91.63 207	90.86 211	93.94 227	94.33 297	86.32 274	95.92 247	91.64 357	89.37 205	86.94 303	94.69 256	81.62 214	98.69 181	88.64 215	94.57 193	96.81 210

v144192		91.06 239	90.28 237	93.39 252	93.66 316	87.23 256	96.83 174	97.07 205	87.43 266	89.69 237	94.28 278	81.48 215	98.00 250	87.18 247	84.92 309	94.93 289

MDTV_nov1_ep13			90.76 218		95.22 256	80.33 342	93.03 331	95.28 299	88.14 246	92.84 166	93.83 295	81.34 216	98.08 237	82.86 298	94.34 195

HQP_MVS		93.78 132	93.43 128	94.82 181	96.21 205	89.99 177	97.74 79	97.51 153	94.85 30	91.34 193	96.64 159	81.32 217	98.60 189	93.02 132	92.23 220	95.86 235

plane_prior6					96.10 215	90.00 175						81.32 217

v7n		90.76 250	89.86 255	93.45 251	93.54 318	87.60 250	97.70 87	97.37 180	88.85 221	87.65 289	94.08 289	81.08 219	98.10 232	84.68 282	83.79 326	94.66 310

HQP2-MVS												80.95 220

HQP-MVS		93.19 151	92.74 148	94.54 198	95.86 220	89.33 203	96.65 192	97.39 177	93.55 72	90.14 217	95.87 200	80.95 220	98.50 197	92.13 145	92.10 225	95.78 242

CR-MVSNet		90.82 249	89.77 260	93.95 225	94.45 293	87.19 257	90.23 352	95.68 284	86.89 277	92.40 169	92.36 327	80.91 222	97.05 321	81.09 315	93.95 201	97.60 190

Patchmtry		88.64 290	87.25 293	92.78 275	94.09 303	86.64 268	89.82 355	95.68 284	80.81 343	87.63 290	92.36 327	80.91 222	97.03 322	78.86 328	85.12 304	94.67 309

v1192		91.07 238	90.23 241	93.58 244	93.70 314	87.82 246	96.73 182	97.07 205	87.77 257	89.58 240	94.32 276	80.90 224	97.97 254	86.52 254	85.48 296	94.95 285

cl22		91.21 232	90.56 228	93.14 263	96.09 216	86.80 265	94.41 294	96.58 250	87.80 255	88.58 269	93.99 292	80.85 225	97.62 292	89.87 185	86.93 282	94.99 284

anonymousdsp		92.16 193	91.55 187	93.97 223	92.58 338	89.55 191	97.51 106	97.42 175	89.42 204	88.40 271	94.84 248	80.66 226	97.88 269	91.87 151	91.28 238	94.48 313

CLD-MVS		92.98 160	92.53 158	94.32 207	96.12 214	89.20 209	95.28 273	97.47 159	92.66 111	89.90 230	95.62 218	80.58 227	98.40 204	92.73 136	92.40 218	95.38 265
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_post1							92.81 334				16.58 377	80.53 228	97.68 285	86.20 259

VPA-MVSNet		93.24 148	92.48 161	95.51 151	95.70 228	92.39 98	97.86 67	98.66 10	92.30 119	92.09 182	95.37 229	80.49 229	98.40 204	93.95 109	85.86 292	95.75 246

tpmvs		89.83 276	89.15 274	91.89 293	94.92 271	80.30 343	93.11 329	95.46 291	86.28 285	88.08 281	92.65 319	80.44 230	98.52 196	81.47 309	89.92 256	96.84 209

PatchMatch-RL		92.90 165	92.02 172	95.56 147	98.19 107	90.80 155	95.27 275	97.18 193	87.96 249	91.86 186	95.68 216	80.44 230	98.99 155	84.01 289	97.54 132	96.89 207

PEN-MVS		91.20 233	90.44 230	93.48 248	94.49 291	87.91 244	97.76 77	98.18 50	91.29 151	87.78 287	95.74 212	80.35 232	97.33 315	85.46 273	82.96 332	95.19 279

Fast-Effi-MVS+-dtu		92.29 186	91.99 173	93.21 261	95.27 252	85.52 287	97.03 150	96.63 247	92.09 128	89.11 256	95.14 237	80.33 233	98.08 237	87.54 239	94.74 191	96.03 232

MSDG		91.42 220	90.24 240	94.96 176	97.15 156	88.91 216	93.69 317	96.32 259	85.72 294	86.93 304	96.47 172	80.24 234	98.98 156	80.57 316	95.05 185	96.98 202

v1921920		90.85 248	90.03 251	93.29 257	93.55 317	86.96 264	96.74 181	97.04 210	87.36 268	89.52 244	94.34 273	80.23 235	97.97 254	86.27 257	85.21 302	94.94 287

RPMNet		88.98 282	87.05 297	94.77 188	94.45 293	87.19 257	90.23 352	98.03 88	77.87 356	92.40 169	87.55 358	80.17 236	99.51 98	68.84 361	93.95 201	97.60 190

ET-MVSNet_ETH3D		91.49 217	90.11 246	95.63 143	96.40 198	91.57 126	95.34 269	93.48 342	90.60 178	75.58 358	95.49 226	80.08 237	96.79 331	94.25 102	89.76 258	98.52 135

PatchT		88.87 286	87.42 291	93.22 260	94.08 304	85.10 296	89.51 356	94.64 325	81.92 334	92.36 172	88.15 355	80.05 238	97.01 325	72.43 353	93.65 204	97.54 193

our_test_3		88.78 288	87.98 287	91.20 312	92.45 341	82.53 324	93.61 321	95.69 282	85.77 293	84.88 321	93.71 300	79.99 239	96.78 332	79.47 324	86.24 288	94.28 321

DTE-MVSNet		90.56 258	89.75 262	93.01 266	93.95 306	87.25 254	97.64 96	97.65 138	90.74 167	87.12 298	95.68 216	79.97 240	97.00 326	83.33 294	81.66 337	94.78 305

D2MVS		91.30 229	90.95 208	92.35 284	94.71 283	85.52 287	96.18 234	98.21 44	88.89 220	86.60 307	93.82 297	79.92 241	97.95 260	89.29 200	90.95 244	93.56 331

TransMVSNet (Re)		88.94 283	87.56 290	93.08 265	94.35 296	88.45 229	97.73 81	95.23 303	87.47 265	84.26 327	95.29 231	79.86 242	97.33 315	79.44 326	74.44 355	93.45 334

ACMM	89.79 8	92.96 161	92.50 160	94.35 205	96.30 203	88.71 220	97.58 100	97.36 182	91.40 149	90.53 208	96.65 158	79.77 243	98.75 174	91.24 167	91.64 230	95.59 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		92.16 193	91.23 201	94.95 177	94.75 281	90.94 150	97.47 112	97.43 174	89.14 211	88.90 258	96.43 174	79.71 244	98.24 215	89.56 193	87.68 275	95.67 251

PS-CasMVS		91.55 213	90.84 214	93.69 239	94.96 268	88.28 231	97.84 71	98.24 38	91.46 144	88.04 282	95.80 205	79.67 245	97.48 304	87.02 249	84.54 315	95.31 269

RRT_MVS		93.21 149	92.32 165	95.91 128	94.92 271	94.15 47	96.92 166	96.86 229	91.42 146	91.28 199	96.43 174	79.66 246	98.10 232	93.29 125	90.06 254	95.46 257

ab-mvs		93.57 139	92.55 156	96.64 81	97.28 149	91.96 116	95.40 267	97.45 167	89.81 195	93.22 158	96.28 182	79.62 247	99.46 105	90.74 173	93.11 209	98.50 138

v1240		90.70 255	89.85 256	93.23 259	93.51 320	86.80 265	96.61 198	97.02 213	87.16 273	89.58 240	94.31 277	79.55 248	97.98 251	85.52 272	85.44 297	94.90 292

CostFormer		91.18 237	90.70 221	92.62 279	94.84 277	81.76 331	94.09 306	94.43 328	84.15 315	92.72 167	93.77 299	79.43 249	98.20 220	90.70 174	92.18 223	97.90 172

CANet_DTU		94.37 110	93.65 118	96.55 87	96.46 195	92.13 109	96.21 232	96.67 243	94.38 51	93.53 148	97.03 139	79.34 250	99.71 42	90.76 172	98.45 111	97.82 179

OPM-MVS		93.28 147	92.76 145	94.82 181	94.63 287	90.77 157	96.65 192	97.18 193	93.72 67	91.68 187	97.26 127	79.33 251	98.63 186	92.13 145	92.28 219	95.07 281
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
JIA-IIPM		88.26 294	87.04 298	91.91 292	93.52 319	81.42 333	89.38 357	94.38 330	80.84 342	90.93 205	80.74 363	79.22 252	97.92 264	82.76 300	91.62 231	96.38 220

CVMVSNet		91.23 231	91.75 180	89.67 331	95.77 225	74.69 362	96.44 206	94.88 318	85.81 292	92.18 177	97.64 107	79.07 253	95.58 348	88.06 221	95.86 170	98.74 122

LPG-MVS_test		92.94 163	92.56 155	94.10 214	96.16 210	88.26 232	97.65 92	97.46 161	91.29 151	90.12 223	97.16 132	79.05 254	98.73 175	92.25 141	91.89 228	95.31 269

LGP-MVS_train				94.10 214	96.16 210	88.26 232		97.46 161	91.29 151	90.12 223	97.16 132	79.05 254	98.73 175	92.25 141	91.89 228	95.31 269

test-LLR		91.42 220	91.19 203	92.12 288	94.59 288	80.66 337	94.29 300	92.98 346	91.11 160	90.76 206	92.37 324	79.02 256	98.07 240	88.81 211	96.74 153	97.63 185

test0.0.03 1		89.37 280	88.70 278	91.41 308	92.47 340	85.63 285	95.22 278	92.70 349	91.11 160	86.91 305	93.65 305	79.02 256	93.19 363	78.00 332	89.18 262	95.41 260

ADS-MVSNet2		89.45 278	88.59 280	92.03 290	95.86 220	82.26 328	90.93 347	94.32 333	83.23 327	91.28 199	91.81 334	79.01 258	95.99 339	79.52 322	91.39 236	97.84 176

ADS-MVSNet		89.89 273	88.68 279	93.53 246	95.86 220	84.89 300	90.93 347	95.07 310	83.23 327	91.28 199	91.81 334	79.01 258	97.85 270	79.52 322	91.39 236	97.84 176

ppachtmachnet_test		88.35 293	87.29 292	91.53 304	92.45 341	83.57 318	93.75 315	95.97 271	84.28 313	85.32 319	94.18 285	79.00 260	96.93 327	75.71 342	84.99 308	94.10 324

OpenMVS	89.19 12	92.86 167	91.68 183	96.40 99	95.34 245	92.73 88	98.27 31	98.12 60	84.86 307	85.78 313	97.75 95	78.89 261	99.74 35	87.50 240	98.65 103	96.73 212

LTVRE_ROB	88.41 13	90.99 242	89.92 253	94.19 211	96.18 208	89.55 191	96.31 223	97.09 203	87.88 252	85.67 314	95.91 199	78.79 262	98.57 193	81.50 308	89.98 255	94.44 315
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
AUN-MVS		91.76 203	90.75 219	94.81 183	97.00 168	88.57 224	96.65 192	96.49 252	89.63 198	92.15 178	96.12 189	78.66 263	98.50 197	90.83 171	79.18 345	97.36 196

pm-mvs1		90.72 254	89.65 266	93.96 224	94.29 300	89.63 186	97.79 75	96.82 232	89.07 212	86.12 312	95.48 227	78.61 264	97.78 278	86.97 250	81.67 336	94.46 314

PVSNet	86.66 18	92.24 189	91.74 182	93.73 235	97.77 131	83.69 317	92.88 332	96.72 235	87.91 251	93.00 160	94.86 247	78.51 265	99.05 150	86.53 253	97.45 137	98.47 143

ACMP	89.59 10	92.62 174	92.14 168	94.05 217	96.40 198	88.20 235	97.36 122	97.25 191	91.52 141	88.30 274	96.64 159	78.46 266	98.72 178	91.86 152	91.48 234	95.23 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
bset_n11_16_dypcd		91.55 213	90.59 225	94.44 200	91.51 347	90.25 171	92.70 335	93.42 343	92.27 120	90.22 216	94.74 254	78.42 267	97.80 275	94.19 104	87.86 274	95.29 276

BH-RMVSNet		92.72 173	91.97 174	94.97 175	97.16 154	87.99 241	96.15 235	95.60 286	90.62 175	91.87 185	97.15 134	78.41 268	98.57 193	83.16 295	97.60 131	98.36 155

thres200		92.23 190	91.39 192	94.75 190	97.61 141	89.03 214	96.60 200	95.09 309	92.08 129	93.28 155	94.00 291	78.39 269	99.04 153	81.26 314	94.18 196	96.19 223

MDA-MVSNet_test_wron		85.87 314	84.23 318	90.80 319	92.38 343	82.57 323	93.17 326	95.15 306	82.15 332	67.65 362	92.33 330	78.20 270	95.51 349	77.33 334	79.74 341	94.31 320

tfpn200view9		92.38 181	91.52 189	94.95 177	97.85 126	89.29 205	97.41 115	94.88 318	92.19 125	93.27 156	94.46 268	78.17 271	99.08 144	81.40 310	94.08 197	96.48 218

thres400		92.42 179	91.52 189	95.12 168	97.85 126	89.29 205	97.41 115	94.88 318	92.19 125	93.27 156	94.46 268	78.17 271	99.08 144	81.40 310	94.08 197	96.98 202

YYNet1		85.87 314	84.23 318	90.78 320	92.38 343	82.46 326	93.17 326	95.14 307	82.12 333	67.69 361	92.36 327	78.16 273	95.50 350	77.31 335	79.73 342	94.39 316

CL-MVSNet_self_test		86.31 309	85.15 311	89.80 330	88.83 362	81.74 332	93.93 311	96.22 264	86.67 280	85.03 320	90.80 341	78.09 274	94.50 354	74.92 344	71.86 359	93.15 336

thres100view900		92.43 178	91.58 186	94.98 174	97.92 122	89.37 201	97.71 86	94.66 323	92.20 123	93.31 154	94.90 245	78.06 275	99.08 144	81.40 310	94.08 197	96.48 218

thres600view7		92.49 177	91.60 185	95.18 164	97.91 123	89.47 195	97.65 92	94.66 323	92.18 127	93.33 153	94.91 244	78.06 275	99.10 139	81.61 307	94.06 200	96.98 202

tpm cat1		88.36 292	87.21 295	91.81 297	95.13 261	80.55 340	92.58 337	95.70 281	74.97 358	87.45 291	91.96 332	78.01 277	98.17 225	80.39 318	88.74 267	96.72 213

MVP-Stereo		90.74 253	90.08 247	92.71 276	93.19 328	88.20 235	95.86 249	96.27 261	86.07 289	84.86 322	94.76 252	77.84 278	97.75 281	83.88 292	98.01 121	92.17 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EPMVS		90.70 255	89.81 258	93.37 254	94.73 282	84.21 307	93.67 318	88.02 367	89.50 201	92.38 171	93.49 308	77.82 279	97.78 278	86.03 265	92.68 214	98.11 166

tfpnnormal		89.70 277	88.40 282	93.60 242	95.15 259	90.10 173	97.56 102	98.16 54	87.28 271	86.16 311	94.63 260	77.57 280	98.05 243	74.48 345	84.59 313	92.65 343

tpm		90.25 265	89.74 263	91.76 301	93.92 307	79.73 349	93.98 307	93.54 341	88.28 239	91.99 183	93.25 313	77.51 281	97.44 308	87.30 244	87.94 272	98.12 163

thisisatest0515		92.29 186	91.30 197	95.25 162	96.60 183	88.90 217	94.36 296	92.32 351	87.92 250	93.43 151	94.57 262	77.28 282	99.00 154	89.42 196	95.86 170	97.86 175

FMVSNet3		91.78 202	90.69 222	95.03 170	96.53 190	92.27 104	97.02 153	96.93 218	89.79 196	89.35 248	94.65 259	77.01 283	97.47 305	86.12 262	88.82 264	95.35 267

test_part1		92.21 192	91.10 206	95.51 151	97.80 129	92.66 90	98.02 54	97.68 134	89.79 196	88.80 264	96.02 194	76.85 284	98.18 223	90.86 170	84.11 320	95.69 249

TR-MVS		91.48 218	90.59 225	94.16 213	96.40 198	87.33 251	95.67 256	95.34 298	87.68 261	91.46 190	95.52 225	76.77 285	98.35 209	82.85 299	93.61 206	96.79 211

tttt0517		92.96 161	92.33 164	94.87 180	97.11 157	87.16 259	97.97 60	92.09 353	90.63 174	93.88 141	97.01 140	76.50 286	99.06 149	90.29 180	95.45 177	98.38 153

RPSCF		90.75 252	90.86 211	90.42 324	96.84 173	76.29 360	95.61 260	96.34 258	83.89 318	91.38 191	97.87 84	76.45 287	98.78 170	87.16 248	92.23 220	96.20 222

tpm2		89.96 271	89.21 272	92.23 287	94.91 274	81.25 334	93.78 314	94.42 329	80.62 344	91.56 188	93.44 310	76.44 288	97.94 261	85.60 271	92.08 227	97.49 194

thisisatest0530		93.03 158	92.21 167	95.49 154	97.07 159	89.11 213	97.49 111	92.19 352	90.16 186	94.09 135	96.41 176	76.43 289	99.05 150	90.38 177	95.68 175	98.31 157

EU-MVSNet		88.72 289	88.90 276	88.20 336	93.15 329	74.21 363	96.63 197	94.22 335	85.18 301	87.32 296	95.97 195	76.16 290	94.98 352	85.27 275	86.17 289	95.41 260

dp		88.90 285	88.26 285	90.81 317	94.58 290	76.62 359	92.85 333	94.93 316	85.12 303	90.07 228	93.07 314	75.81 291	98.12 230	80.53 317	87.42 279	97.71 182

IterMVS-SCA-FT		90.31 263	89.81 258	91.82 296	95.52 234	84.20 308	94.30 299	96.15 267	90.61 176	87.39 294	94.27 279	75.80 292	96.44 334	87.34 242	86.88 286	94.82 298

SCA		91.84 201	91.18 204	93.83 231	95.59 230	84.95 299	94.72 284	95.58 288	90.82 164	92.25 176	93.69 301	75.80 292	98.10 232	86.20 259	95.98 166	98.45 145

IterMVS		90.15 269	89.67 264	91.61 303	95.48 236	83.72 314	94.33 298	96.12 268	89.99 189	87.31 297	94.15 287	75.78 294	96.27 337	86.97 250	86.89 285	94.83 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		92.42 179	91.89 177	94.03 219	93.33 326	88.50 227	97.73 81	97.53 151	92.00 132	88.85 261	96.50 171	75.62 295	98.11 231	93.88 113	91.56 233	95.48 254

cascas		91.20 233	90.08 247	94.58 196	94.97 267	89.16 212	93.65 319	97.59 145	79.90 347	89.40 246	92.92 316	75.36 296	98.36 208	92.14 144	94.75 190	96.23 221

VPNet		92.23 190	91.31 196	94.99 172	95.56 232	90.96 149	97.22 139	97.86 117	92.96 102	90.96 204	96.62 166	75.06 297	98.20 220	91.90 149	83.65 327	95.80 241

N_pmnet		78.73 329	78.71 331	78.79 347	92.80 334	46.50 378	94.14 304	43.71 381	78.61 352	80.83 344	91.66 337	74.94 298	96.36 335	67.24 362	84.45 316	93.50 332

mvs_tets		92.31 184	91.76 179	93.94 227	93.41 323	88.29 230	97.63 97	97.53 151	92.04 130	88.76 265	96.45 173	74.62 299	98.09 236	93.91 111	91.48 234	95.45 259

DSMNet-mixed		86.34 308	86.12 304	87.00 341	89.88 357	70.43 366	94.93 282	90.08 363	77.97 355	85.42 318	92.78 318	74.44 300	93.96 358	74.43 346	95.14 181	96.62 214

pmmvs5		89.86 275	88.87 277	92.82 273	92.86 332	86.23 277	96.26 227	95.39 292	84.24 314	87.12 298	94.51 263	74.27 301	97.36 314	87.61 238	87.57 276	94.86 294

OurMVSNet-221017-0		90.51 260	90.19 245	91.44 307	93.41 323	81.25 334	96.98 160	96.28 260	91.68 138	86.55 308	96.30 181	74.20 302	97.98 251	88.96 209	87.40 280	95.09 280

GBi-Net		91.35 225	90.27 238	94.59 192	96.51 191	91.18 142	97.50 107	96.93 218	88.82 224	89.35 248	94.51 263	73.87 303	97.29 317	86.12 262	88.82 264	95.31 269

test1		91.35 225	90.27 238	94.59 192	96.51 191	91.18 142	97.50 107	96.93 218	88.82 224	89.35 248	94.51 263	73.87 303	97.29 317	86.12 262	88.82 264	95.31 269

FMVSNet2		91.31 228	90.08 247	94.99 172	96.51 191	92.21 105	97.41 115	96.95 216	88.82 224	88.62 267	94.75 253	73.87 303	97.42 310	85.20 277	88.55 269	95.35 267

COLMAP_ROB	87.81 15	90.40 262	89.28 271	93.79 234	97.95 119	87.13 260	96.92 166	95.89 275	82.83 329	86.88 306	97.18 131	73.77 306	99.29 123	78.44 330	93.62 205	94.95 285
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DWT-MVSNet_test		90.76 250	89.89 254	93.38 253	95.04 265	83.70 316	95.85 250	94.30 334	88.19 241	90.46 210	92.80 317	73.61 307	98.50 197	88.16 219	90.58 248	97.95 170

Anonymous20231206		87.09 302	86.14 303	89.93 329	91.22 349	80.35 341	96.11 236	95.35 295	83.57 324	84.16 328	93.02 315	73.54 308	95.61 346	72.16 354	86.14 290	93.84 329

UGNet		94.04 123	93.28 133	96.31 106	96.85 172	91.19 141	97.88 66	97.68 134	94.40 49	93.00 160	96.18 185	73.39 309	99.61 66	91.72 154	98.46 110	98.13 162
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
test1111		93.19 151	92.82 143	94.30 209	97.58 145	84.56 304	98.21 41	89.02 365	93.53 76	94.58 126	98.21 62	72.69 310	99.05 150	93.06 130	98.48 109	99.28 71

ECVR-MVS		93.19 151	92.73 149	94.57 197	97.66 137	85.41 289	98.21 41	88.23 366	93.43 80	94.70 124	98.21 62	72.57 311	99.07 147	93.05 131	98.49 107	99.25 74

Anonymous20231211		90.63 257	89.42 268	94.27 210	98.24 100	89.19 211	98.05 52	97.89 109	79.95 346	88.25 277	94.96 241	72.56 312	98.13 227	89.70 189	85.14 303	95.49 253

ACMH	87.59 16	90.53 259	89.42 268	93.87 230	96.21 205	87.92 242	97.24 133	96.94 217	88.45 235	83.91 333	96.27 183	71.92 313	98.62 188	84.43 286	89.43 260	95.05 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		91.38 222	90.31 235	94.59 192	94.65 285	87.62 249	94.34 297	96.19 266	90.73 168	90.35 213	93.83 295	71.84 314	97.96 258	87.22 245	93.61 206	98.21 160

SixPastTwentyTwo		89.15 281	88.54 281	90.98 314	93.49 321	80.28 344	96.70 186	94.70 322	90.78 165	84.15 329	95.57 220	71.78 315	97.71 284	84.63 283	85.07 305	94.94 287

gg-mvs-nofinetune		87.82 297	85.61 306	94.44 200	94.46 292	89.27 208	91.21 346	84.61 372	80.88 341	89.89 232	74.98 365	71.50 316	97.53 300	85.75 270	97.21 145	96.51 216

test20.03		86.14 311	85.40 309	88.35 334	90.12 354	80.06 346	95.90 248	95.20 304	88.59 230	81.29 343	93.62 306	71.43 317	92.65 364	71.26 358	81.17 339	92.34 347

MS-PatchMatch		90.27 264	89.77 260	91.78 299	94.33 297	84.72 302	95.55 261	96.73 234	86.17 288	86.36 309	95.28 233	71.28 318	97.80 275	84.09 288	98.14 119	92.81 340

PVSNet_0	82.17 19	85.46 317	83.64 320	90.92 315	95.27 252	79.49 350	90.55 350	95.60 286	83.76 321	83.00 339	89.95 346	71.09 319	97.97 254	82.75 301	60.79 368	95.31 269

GG-mvs-BLEND				93.62 241	93.69 315	89.20 209	92.39 340	83.33 373		87.98 285	89.84 348	71.00 320	96.87 329	82.08 306	95.40 178	94.80 301

ITE_SJBPF				92.43 282	95.34 245	85.37 292		95.92 272	91.47 143	87.75 288	96.39 178	71.00 320	97.96 258	82.36 304	89.86 257	93.97 327

IB-MVS	87.33 17	89.91 272	88.28 284	94.79 187	95.26 255	87.70 248	95.12 281	93.95 339	89.35 206	87.03 301	92.49 322	70.74 322	99.19 129	89.18 206	81.37 338	97.49 194
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
MDA-MVSNet-bldmvs		85.00 318	82.95 322	91.17 313	93.13 330	83.33 319	94.56 288	95.00 312	84.57 311	65.13 366	92.65 319	70.45 323	95.85 342	73.57 350	77.49 348	94.33 318

RRT_test8_iter05		91.19 236	90.78 216	92.41 283	95.76 227	83.14 321	97.32 126	97.46 161	91.37 150	89.07 257	95.57 220	70.33 324	98.21 218	93.56 117	86.62 287	95.89 234

AllTest		90.23 266	88.98 275	93.98 221	97.94 120	86.64 268	96.51 205	95.54 289	85.38 298	85.49 316	96.77 150	70.28 325	99.15 134	80.02 320	92.87 210	96.15 226

TestCases				93.98 221	97.94 120	86.64 268		95.54 289	85.38 298	85.49 316	96.77 150	70.28 325	99.15 134	80.02 320	92.87 210	96.15 226

ACMH+	87.92 14	90.20 267	89.18 273	93.25 258	96.48 194	86.45 273	96.99 158	96.68 241	88.83 223	84.79 323	96.22 184	70.16 327	98.53 195	84.42 287	88.04 271	94.77 306

KD-MVS_self_test		85.95 313	84.95 312	88.96 333	89.55 360	79.11 354	95.13 280	96.42 255	85.91 291	84.07 331	90.48 342	70.03 328	94.82 353	80.04 319	72.94 358	92.94 338

Anonymous20240529		91.98 198	90.73 220	95.73 138	98.14 112	89.40 199	97.99 55	97.72 129	79.63 348	93.54 147	97.41 122	69.94 329	99.56 85	91.04 169	91.11 240	98.22 159

pmmvs-eth3d		86.22 310	84.45 316	91.53 304	88.34 364	87.25 254	94.47 290	95.01 311	83.47 325	79.51 353	89.61 349	69.75 330	95.71 345	83.13 296	76.73 351	91.64 352

LFMVS		93.60 137	92.63 152	96.52 88	98.13 113	91.27 135	97.94 62	93.39 344	90.57 179	96.29 81	98.31 51	69.00 331	99.16 133	94.18 105	95.87 169	99.12 86

TESTMET0.1,1		90.06 270	89.42 268	91.97 291	94.41 295	80.62 339	94.29 300	91.97 355	87.28 271	90.44 211	92.47 323	68.79 332	97.67 286	88.50 217	96.60 158	97.61 189

XVG-ACMP-BASELINE		90.93 246	90.21 244	93.09 264	94.31 299	85.89 282	95.33 270	97.26 189	91.06 162	89.38 247	95.44 228	68.61 333	98.60 189	89.46 195	91.05 241	94.79 303

MVS-HIRNet		82.47 326	81.21 328	86.26 343	95.38 240	69.21 369	88.96 359	89.49 364	66.28 363	80.79 345	74.08 367	68.48 334	97.39 312	71.93 355	95.47 176	92.18 350

VDD-MVS		93.82 130	93.08 136	96.02 122	97.88 125	89.96 181	97.72 84	95.85 276	92.43 116	95.86 97	98.44 32	68.42 335	99.39 114	96.31 36	94.85 186	98.71 126

test_0402		86.46 306	84.79 314	91.45 306	95.02 266	85.55 286	96.29 225	94.89 317	80.90 340	82.21 340	93.97 293	68.21 336	97.29 317	62.98 365	88.68 268	91.51 354

test-mter		90.19 268	89.54 267	92.12 288	94.59 288	80.66 337	94.29 300	92.98 346	87.68 261	90.76 206	92.37 324	67.67 337	98.07 240	88.81 211	96.74 153	97.63 185

VDDNet		93.05 157	92.07 169	96.02 122	96.84 173	90.39 169	98.08 50	95.85 276	86.22 287	95.79 100	98.46 30	67.59 338	99.19 129	94.92 88	94.85 186	98.47 143

USDC		88.94 283	87.83 288	92.27 286	94.66 284	84.96 298	93.86 312	95.90 274	87.34 269	83.40 335	95.56 222	67.43 339	98.19 222	82.64 303	89.67 259	93.66 330

pmmvs6		87.81 298	86.19 302	92.69 277	91.32 348	86.30 275	97.34 123	96.41 256	80.59 345	84.05 332	94.37 272	67.37 340	97.67 286	84.75 281	79.51 344	94.09 326

test2506		91.60 208	90.78 216	94.04 218	97.66 137	83.81 312	98.27 31	75.53 377	93.43 80	95.23 116	98.21 62	67.21 341	99.07 147	93.01 134	98.49 107	99.25 74

KD-MVS_2432*1600		84.81 320	82.64 323	91.31 309	91.07 350	85.34 293	91.22 344	95.75 279	85.56 296	83.09 337	90.21 344	67.21 341	95.89 340	77.18 337	62.48 366	92.69 341

miper_refine_blended		84.81 320	82.64 323	91.31 309	91.07 350	85.34 293	91.22 344	95.75 279	85.56 296	83.09 337	90.21 344	67.21 341	95.89 340	77.18 337	62.48 366	92.69 341

K. test v3		87.64 299	86.75 300	90.32 325	93.02 331	79.48 351	96.61 198	92.08 354	90.66 172	80.25 350	94.09 288	67.21 341	96.65 333	85.96 267	80.83 340	94.83 296

CMPMVS	62.92 21	85.62 316	84.92 313	87.74 338	89.14 361	73.12 365	94.17 303	96.80 233	73.98 359	73.65 360	94.93 243	66.36 345	97.61 293	83.95 291	91.28 238	92.48 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UniMVSNet_ETH3D		91.34 227	90.22 243	94.68 191	94.86 276	87.86 245	97.23 138	97.46 161	87.99 248	89.90 230	96.92 144	66.35 346	98.23 216	90.30 179	90.99 243	97.96 168

lessismore_v0				90.45 323	91.96 346	79.09 355		87.19 370		80.32 349	94.39 270	66.31 347	97.55 297	84.00 290	76.84 350	94.70 308

Anonymous202405211		92.07 196	90.83 215	95.76 133	98.19 107	88.75 219	97.58 100	95.00 312	86.00 290	93.64 144	97.45 119	66.24 348	99.53 93	90.68 175	92.71 213	99.01 96

new-patchmatchnet		83.18 324	81.87 326	87.11 340	86.88 367	75.99 361	93.70 316	95.18 305	85.02 305	77.30 356	88.40 352	65.99 349	93.88 359	74.19 349	70.18 361	91.47 356

FMVSNet1		89.88 274	88.31 283	94.59 192	95.41 238	91.18 142	97.50 107	96.93 218	86.62 281	87.41 293	94.51 263	65.94 350	97.29 317	83.04 297	87.43 278	95.31 269

TDRefinement		86.53 305	84.76 315	91.85 294	82.23 370	84.25 306	96.38 216	95.35 295	84.97 306	84.09 330	94.94 242	65.76 351	98.34 212	84.60 284	74.52 354	92.97 337

UnsupCasMVSNet_eth		85.99 312	84.45 316	90.62 321	89.97 356	82.40 327	93.62 320	97.37 180	89.86 191	78.59 355	92.37 324	65.25 352	95.35 351	82.27 305	70.75 360	94.10 324

LF4IMVS		87.94 296	87.25 293	89.98 328	92.38 343	80.05 347	94.38 295	95.25 302	87.59 263	84.34 325	94.74 254	64.31 353	97.66 288	84.83 279	87.45 277	92.23 348

Anonymous20240521		86.42 307	85.44 307	89.34 332	90.33 353	79.79 348	96.73 182	95.92 272	83.71 322	83.25 336	91.36 339	63.92 354	96.01 338	78.39 331	85.36 299	92.22 349

MIMVSNet		88.50 291	86.76 299	93.72 237	94.84 277	87.77 247	91.39 342	94.05 336	86.41 284	87.99 284	92.59 321	63.27 355	95.82 344	77.44 333	92.84 212	97.57 192

FMVSNet5		87.29 301	85.79 305	91.78 299	94.80 279	87.28 252	95.49 264	95.28 299	84.09 316	83.85 334	91.82 333	62.95 356	94.17 357	78.48 329	85.34 300	93.91 328

testgi		87.97 295	87.21 295	90.24 326	92.86 332	80.76 336	96.67 191	94.97 314	91.74 136	85.52 315	95.83 203	62.66 357	94.47 356	76.25 340	88.36 270	95.48 254

TinyColmap		86.82 304	85.35 310	91.21 311	94.91 274	82.99 322	93.94 310	94.02 338	83.58 323	81.56 342	94.68 257	62.34 358	98.13 227	75.78 341	87.35 281	92.52 345

new_pmnet		82.89 325	81.12 329	88.18 337	89.63 358	80.18 345	91.77 341	92.57 350	76.79 357	75.56 359	88.23 354	61.22 359	94.48 355	71.43 356	82.92 333	89.87 360

OpenMVS_ROB	81.14 20	84.42 322	82.28 325	90.83 316	90.06 355	84.05 311	95.73 255	94.04 337	73.89 360	80.17 351	91.53 338	59.15 360	97.64 289	66.92 363	89.05 263	90.80 358

MIMVSNet1		84.93 319	83.05 321	90.56 322	89.56 359	84.84 301	95.40 267	95.35 295	83.91 317	80.38 348	92.21 331	57.23 361	93.34 362	70.69 360	82.75 335	93.50 332

EG-PatchMatch MVS		87.02 303	85.44 307	91.76 301	92.67 336	85.00 297	96.08 238	96.45 254	83.41 326	79.52 352	93.49 308	57.10 362	97.72 283	79.34 327	90.87 246	92.56 344

MVS_0304		88.79 287	87.57 289	92.46 280	94.65 285	86.15 281	96.40 213	97.17 195	86.44 283	88.02 283	91.71 336	56.68 363	97.03 322	84.47 285	92.58 216	94.19 323

UnsupCasMVSNet_bld		82.13 327	79.46 330	90.14 327	88.00 365	82.47 325	90.89 349	96.62 249	78.94 351	75.61 357	84.40 361	56.63 364	96.31 336	77.30 336	66.77 364	91.63 353

EGC-MVSNET		68.77 333	63.01 338	86.07 344	92.49 339	82.24 329	93.96 309	90.96 361	0.71 378	2.62 379	90.89 340	53.66 365	93.46 360	57.25 367	84.55 314	82.51 364

tmp_tt		51.94 341	53.82 341	46.29 357	33.73 381	45.30 380	78.32 367	67.24 380	18.02 374	50.93 370	87.05 360	52.99 366	53.11 376	70.76 359	25.29 374	40.46 372

pmmvs3		79.97 328	77.50 332	87.39 339	82.80 369	79.38 352	92.70 335	90.75 362	70.69 362	78.66 354	87.47 359	51.34 367	93.40 361	73.39 351	69.65 362	89.38 361

DeepMVS_CX				74.68 351	90.84 352	64.34 373		81.61 375	65.34 364	67.47 363	88.01 357	48.60 368	80.13 372	62.33 366	73.68 357	79.58 366

PM-MVS		83.48 323	81.86 327	88.31 335	87.83 366	77.59 358	93.43 322	91.75 356	86.91 276	80.63 346	89.91 347	44.42 369	95.84 343	85.17 278	76.73 351	91.50 355

test_method		66.11 335	64.89 337	69.79 352	72.62 375	35.23 382	65.19 370	92.83 348	20.35 373	65.20 365	88.08 356	43.14 370	82.70 370	73.12 352	63.46 365	91.45 357

ambc				86.56 342	83.60 368	70.00 368	85.69 362	94.97 314		80.60 347	88.45 351	37.42 371	96.84 330	82.69 302	75.44 353	92.86 339

Gipuma		67.86 334	65.41 336	75.18 350	92.66 337	73.45 364	66.50 369	94.52 327	53.33 368	57.80 369	66.07 369	30.81 372	89.20 366	48.15 370	78.88 347	62.90 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		52.08 340	51.31 343	54.39 356	72.62 375	45.39 379	83.84 364	75.51 378	41.13 371	40.77 373	59.65 372	30.08 373	73.60 374	28.31 374	29.90 373	44.18 371

FPMVS		71.27 331	69.85 333	75.50 349	74.64 372	59.03 374	91.30 343	91.50 358	58.80 366	57.92 368	88.28 353	29.98 374	85.53 369	53.43 368	82.84 334	81.95 365

E-PMN		53.28 338	52.56 342	55.43 355	74.43 373	47.13 377	83.63 365	76.30 376	42.23 370	42.59 372	62.22 371	28.57 375	74.40 373	31.53 373	31.51 371	44.78 370

PMMVS2		70.19 332	66.92 335	80.01 346	76.35 371	65.67 371	86.22 361	87.58 369	64.83 365	62.38 367	80.29 364	26.78 376	88.49 367	63.79 364	54.07 369	85.88 362

ANet_high		63.94 336	59.58 339	77.02 348	61.24 379	66.06 370	85.66 363	87.93 368	78.53 353	42.94 371	71.04 368	25.42 377	80.71 371	52.60 369	30.83 372	84.28 363

LCM-MVSNet		72.55 330	69.39 334	82.03 345	70.81 377	65.42 372	90.12 354	94.36 332	55.02 367	65.88 364	81.72 362	24.16 378	89.96 365	74.32 348	68.10 363	90.71 359

PMVS	53.92 22	58.58 337	55.40 340	68.12 353	51.00 380	48.64 376	78.86 366	87.10 371	46.77 369	35.84 375	74.28 366	8.76 379	86.34 368	42.07 371	73.91 356	69.38 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		25.11 342	24.57 346	26.74 358	73.98 374	39.89 381	57.88 371	9.80 382	12.27 375	10.39 376	6.97 378	7.03 380	36.44 377	25.43 375	17.39 375	3.89 375

MVE	50.73 23	53.25 339	48.81 344	66.58 354	65.34 378	57.50 375	72.49 368	70.94 379	40.15 372	39.28 374	63.51 370	6.89 381	73.48 375	38.29 372	42.38 370	68.76 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		13.04 345	15.66 348	5.18 359	4.51 383	3.45 383	92.50 339	1.81 384	2.50 377	7.58 378	20.15 375	3.67 382	2.18 379	7.13 377	1.07 377	9.90 373

testmvs		13.36 344	16.33 347	4.48 360	5.04 382	2.26 384	93.18 325	3.28 383	2.70 376	8.24 377	21.66 374	2.29 383	2.19 378	7.58 376	2.96 376	9.00 374

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		8.06 346	10.74 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	96.69 156	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.55 1	93.34 74	99.29 1	98.35 20	94.98 27	98.49 15

MSC_two_6792asdad				98.86 1	98.67 66	96.94 1		97.93 107					99.86 9	97.68 2	99.67 6	99.77 1

No_MVS				98.86 1	98.67 66	96.94 1		97.93 107					99.86 9	97.68 2	99.67 6	99.77 1

eth-test2					0.00 384

eth-test					0.00 384

IU-MVS					99.42 7	95.39 11		97.94 106	90.40 183	98.94 5				97.41 12	99.66 10	99.74 7

save fliter					98.91 53	94.28 39	97.02 153	98.02 92	95.35 8

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 41	98.28 28					99.86 9	97.52 5	99.67 6	99.75 5

GSMVS																98.45 145

test_part2					99.28 27	95.74 8				98.10 21

MTGPA								98.08 68

MTMP							97.86 67	82.03 374

gm-plane-assit					93.22 327	78.89 356			84.82 308		93.52 307		98.64 185	87.72 227

test9_res														94.81 93	99.38 54	99.45 51

agg_prior2														93.94 110	99.38 54	99.50 43

agg_prior					98.67 66	93.79 59		98.00 97		95.68 104			99.57 83

test_prior4						93.66 63	96.42 209

test_prior				97.23 65	98.67 66	92.99 81		98.00 97					99.41 111			99.29 68

旧先验2							95.94 246		81.66 336	97.34 40			98.82 167	92.26 139

新几何2							95.79 253

无先验							95.79 253	97.87 114	83.87 320				99.65 57	87.68 233		98.89 112

原ACMM2							95.67 256

testdata2													99.67 53	85.96 267

testdata1							95.26 277		93.10 94

plane_prior7					96.21 205	89.98 179

plane_prior5								97.51 153					98.60 189	93.02 132	92.23 220	95.86 235

plane_prior4											96.64 159

plane_prior3						90.00 175			94.46 47	91.34 193

plane_prior2							97.74 79		94.85 30

plane_prior1					96.14 213

plane_prior						89.99 177	97.24 133		94.06 56						92.16 224

n2								0.00 385

nn								0.00 385

door-mid								91.06 360

test11								97.88 112

door								91.13 359

HQP5-MVS						89.33 203

HQP-NCC					95.86 220		96.65 192		93.55 72	90.14 217

ACMP_Plane					95.86 220		96.65 192		93.55 72	90.14 217

BP-MVS														92.13 145

HQP4-MVS										90.14 217			98.50 197			95.78 242

HQP3-MVS								97.39 177							92.10 225

NP-MVS					95.99 219	89.81 184					95.87 200

ACMMP++_ref															90.30 253

ACMMP++															91.02 242

High-resolution multi-view benchmark