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									82.47 220	97.09 9	97.07 45	92.72 1	98.04 158	92.70 45	99.02 12	98.86 10

DVP-MVS++		95.98 1	96.36 1	94.82 34	97.78 57	86.00 56	98.29 1	97.49 5	90.75 22	97.62 5	98.06 6	92.59 2	99.61 3	95.64 6	99.02 12	98.86 10

OPU-MVS				96.21 3	98.00 46	90.85 3	97.13 13				97.08 43	92.59 2	98.94 87	92.25 53	98.99 14	98.84 13

SED-MVS		95.91 2	96.28 2	94.80 36	98.77 5	85.99 58	97.13 13	97.44 14	90.31 31	97.71 1	98.07 4	92.31 4	99.58 8	95.66 4	99.13 3	98.84 13

test_241102_ONE					98.77 5	85.99 58		97.44 14	90.26 35	97.71 1	97.96 10	92.31 4	99.38 32

test_0728_THIRD									90.75 22	97.04 10	98.05 8	92.09 6	99.55 15	95.64 6	99.13 3	99.13 2

DPE-MVS		95.57 4	95.67 4	95.25 9	98.36 27	87.28 17	95.56 83	97.51 4	89.13 63	97.14 8	97.91 11	91.64 7	99.62 1	94.61 14	99.17 2	98.86 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.67 3	96.02 3	94.64 43	98.78 3	85.93 61	97.09 15	96.73 83	90.27 33	97.04 10	98.05 8	91.47 8	99.55 15	95.62 8	99.08 7	98.45 37
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					98.78 3	85.93 61	97.19 10	97.47 10	90.27 33	97.64 4	98.13 1	91.47 8

test_241102_TWO								97.44 14	90.31 31	97.62 5	98.07 4	91.46 10	99.58 8	95.66 4	99.12 6	98.98 9

test_one_0601					98.58 12	85.83 67		97.44 14	91.05 17	96.78 13	98.06 6	91.45 11

MSP-MVS		95.42 6	95.56 6	94.98 21	98.49 18	86.52 40	96.91 24	97.47 10	91.73 9	96.10 17	96.69 62	89.90 12	99.30 42	94.70 12	98.04 72	99.13 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DeepPCF-MVS	89.96 1	94.20 36	94.77 14	92.49 116	96.52 98	80.00 217	94.00 187	97.08 47	90.05 37	95.65 21	97.29 28	89.66 13	98.97 83	93.95 20	98.71 34	98.50 27

SD-MVS		94.96 12	95.33 8	93.88 65	97.25 79	86.69 32	96.19 48	97.11 46	90.42 30	96.95 12	97.27 29	89.53 14	96.91 247	94.38 16	98.85 19	98.03 77
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
CNVR-MVS		95.40 7	95.37 7	95.50 7	98.11 39	88.51 7	95.29 95	96.96 57	92.09 3	95.32 23	97.08 43	89.49 15	99.33 39	95.10 11	98.85 19	98.66 19

APDe-MVS		95.46 5	95.64 5	94.91 24	98.26 30	86.29 51	97.46 5	97.40 20	89.03 66	96.20 16	98.10 2	89.39 16	99.34 36	95.88 3	99.03 11	99.10 4

MCST-MVS		94.45 21	94.20 31	95.19 11	98.46 20	87.50 15	95.00 115	97.12 44	87.13 118	92.51 83	96.30 81	89.24 17	99.34 36	93.46 28	98.62 48	98.73 16

TSAR-MVS + MP.		94.85 13	94.94 11	94.58 46	98.25 31	86.33 47	96.11 55	96.62 95	88.14 92	96.10 17	96.96 50	89.09 18	98.94 87	94.48 15	98.68 39	98.48 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 29	96.99 82	86.33 47	97.33 6	97.30 29	91.38 13	95.39 22	97.46 19	88.98 19	99.40 31	94.12 18	98.89 18	98.82 15
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		95.20 8	95.07 10	95.59 5	98.14 38	88.48 8	96.26 44	97.28 31	85.90 145	97.67 3	98.10 2	88.41 20	99.56 10	94.66 13	99.19 1	98.71 17
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
patch_mono-2		93.74 48	94.32 22	92.01 134	97.54 63	78.37 252	93.40 210	97.19 38	88.02 94	94.99 28	97.21 34	88.35 21	98.44 122	94.07 19	98.09 70	99.23 1

9.14			94.47 18		97.79 54		96.08 56	97.44 14	86.13 143	95.10 26	97.40 23	88.34 22	99.22 49	93.25 35	98.70 36

xxxxxxxxxxxxxcwj		94.65 16	94.70 15	94.48 50	97.85 50	85.63 72	95.21 101	95.47 177	89.44 52	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 29

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 52	87.76 10	96.65 32	97.48 9	87.76 105	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 29

HPM-MVS++		95.14 10	94.91 12	95.83 4	98.25 31	89.65 4	95.92 66	96.96 57	91.75 8	94.02 40	96.83 55	88.12 25	99.55 15	93.41 31	98.94 16	98.28 53

agg_prior1		93.29 62	92.97 66	94.26 58	97.38 70	85.92 63	93.92 191	96.72 85	81.96 232	92.16 88	96.23 85	87.85 26	98.97 83	91.95 67	98.55 53	97.90 86

CSCG		93.23 65	93.05 63	93.76 72	98.04 44	84.07 103	96.22 47	97.37 21	84.15 183	90.05 124	95.66 109	87.77 27	99.15 56	89.91 97	98.27 62	98.07 73

NCCC		94.81 14	94.69 16	95.17 12	97.83 53	87.46 16	95.66 78	96.93 60	92.34 2	93.94 41	96.58 72	87.74 28	99.44 30	92.83 40	98.40 57	98.62 21

ETH3D-3000-0.1		94.61 17	94.44 20	95.12 13	97.70 60	87.71 11	95.98 63	97.44 14	86.67 131	95.25 25	97.31 27	87.73 29	99.24 47	93.11 38	98.76 30	98.40 40

TEST9					97.53 64	86.49 41	94.07 180	96.78 76	81.61 245	92.77 73	96.20 88	87.71 30	99.12 58

train_agg		93.44 57	93.08 62	94.52 48	97.53 64	86.49 41	94.07 180	96.78 76	81.86 238	92.77 73	96.20 88	87.63 31	99.12 58	92.14 58	98.69 37	97.94 82

test_8					97.49 67	86.30 50	94.02 185	96.76 79	81.86 238	92.70 77	96.20 88	87.63 31	99.02 71

ZD-MVS					98.15 37	86.62 37		97.07 48	83.63 194	94.19 35	96.91 52	87.57 33	99.26 46	91.99 63	98.44 55

TSAR-MVS + GP.		93.66 51	93.41 56	94.41 55	96.59 92	86.78 28	94.40 155	93.93 247	89.77 46	94.21 34	95.59 112	87.35 34	98.61 110	92.72 43	96.15 113	97.83 91

APD-MVS		94.24 31	94.07 37	94.75 39	98.06 43	86.90 23	95.88 67	96.94 59	85.68 151	95.05 27	97.18 38	87.31 35	99.07 61	91.90 71	98.61 49	98.28 53
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ETH3 D test6400		93.64 52	93.22 59	94.92 22	97.79 54	86.84 24	95.31 90	97.26 32	82.67 218	93.81 44	96.29 82	87.29 36	99.27 45	89.87 98	98.67 41	98.65 20

ETH3D cwj APD-0.16		93.91 43	93.53 54	95.06 15	96.76 87	87.78 9	94.92 120	97.21 37	84.33 181	93.89 43	97.09 42	87.20 37	99.29 44	91.90 71	98.44 55	98.12 69

Regformer-2		94.33 28	94.22 28	94.68 41	95.54 134	86.75 31	94.57 143	96.70 88	91.84 6	94.41 30	96.56 74	87.19 38	99.13 57	93.50 27	97.65 85	98.16 65

segment_acmp												87.16 39

Regformer-1		94.22 33	94.13 35	94.51 49	95.54 134	86.36 46	94.57 143	96.44 104	91.69 11	94.32 33	96.56 74	87.05 40	99.03 67	93.35 32	97.65 85	98.15 66

testtj		94.39 26	94.18 32	95.00 18	98.24 33	86.77 30	96.16 49	97.23 35	87.28 116	94.85 29	97.04 46	86.99 41	99.52 23	91.54 77	98.33 60	98.71 17

旧先验1					96.79 86	81.81 165		95.67 161			96.81 57	86.69 42			97.66 84	96.97 125

test_prior3		93.60 54	93.53 54	93.82 67	97.29 75	84.49 90	94.12 173	96.88 64	87.67 108	92.63 78	96.39 79	86.62 43	98.87 91	91.50 78	98.67 41	98.11 71

test_prior2							94.12 173		87.67 108	92.63 78	96.39 79	86.62 43		91.50 78	98.67 41

CDPH-MVS		92.83 69	92.30 75	94.44 51	97.79 54	86.11 54	94.06 182	96.66 92	80.09 265	92.77 73	96.63 69	86.62 43	99.04 66	87.40 126	98.66 44	98.17 64

DPM-MVS		92.58 73	91.74 81	95.08 14	96.19 106	89.31 5	92.66 239	96.56 101	83.44 200	91.68 102	95.04 126	86.60 46	98.99 80	85.60 150	97.92 77	96.93 127

DELS-MVS		93.43 59	93.25 58	93.97 62	95.42 138	85.04 79	93.06 229	97.13 43	90.74 24	91.84 96	95.09 125	86.32 47	99.21 50	91.22 82	98.45 54	97.65 96
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
ZNCC-MVS		94.47 19	94.28 25	95.03 16	98.52 16	86.96 19	96.85 27	97.32 27	88.24 87	93.15 62	97.04 46	86.17 48	99.62 1	92.40 49	98.81 22	98.52 25

HFP-MVS		94.52 18	94.40 21	94.86 27	98.61 10	86.81 26	96.94 19	97.34 22	88.63 75	93.65 48	97.21 34	86.10 49	99.49 26	92.35 51	98.77 28	98.30 49

#test#		94.32 29	94.14 34	94.86 27	98.61 10	86.81 26	96.43 35	97.34 22	87.51 111	93.65 48	97.21 34	86.10 49	99.49 26	91.68 75	98.77 28	98.30 49

MVS_111021_HR		93.45 56	93.31 57	93.84 66	96.99 82	84.84 80	93.24 222	97.24 33	88.76 72	91.60 103	95.85 102	86.07 51	98.66 105	91.91 68	98.16 66	98.03 77

Regformer-4		93.91 43	93.81 45	94.19 60	95.36 139	85.47 75	94.68 135	96.41 107	91.60 12	93.75 45	96.71 60	85.95 52	99.10 60	93.21 36	96.65 104	98.01 79

ACMMP_NAP		94.74 15	94.56 17	95.28 8	98.02 45	87.70 12	95.68 76	97.34 22	88.28 86	95.30 24	97.67 15	85.90 53	99.54 19	93.91 21	98.95 15	98.60 22

Regformer-3		93.68 50	93.64 53	93.81 70	95.36 139	84.61 84	94.68 135	95.83 150	91.27 14	93.60 51	96.71 60	85.75 54	98.86 94	92.87 39	96.65 104	97.96 81

CS-MVS		94.05 37	94.45 19	92.84 97	96.57 95	82.09 158	97.63 3	96.97 53	91.71 10	93.51 57	96.22 86	85.65 55	98.24 135	93.60 26	98.17 64	98.20 62

PHI-MVS		93.89 45	93.65 52	94.62 45	96.84 85	86.43 43	96.69 31	97.49 5	85.15 167	93.56 54	96.28 83	85.60 56	99.31 41	92.45 46	98.79 23	98.12 69

MP-MVS-pluss		94.21 34	94.00 40	94.85 29	98.17 36	86.65 35	94.82 127	97.17 42	86.26 139	92.83 71	97.87 12	85.57 57	99.56 10	94.37 17	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
GST-MVS		94.21 34	93.97 41	94.90 26	98.41 24	86.82 25	96.54 34	97.19 38	88.24 87	93.26 58	96.83 55	85.48 58	99.59 7	91.43 81	98.40 57	98.30 49

MP-MVS		94.25 30	94.07 37	94.77 38	98.47 19	86.31 49	96.71 30	96.98 52	89.04 65	91.98 92	97.19 37	85.43 59	99.56 10	92.06 62	98.79 23	98.44 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	89.43 2	94.04 38	93.79 46	94.80 36	97.48 68	86.78 28	95.65 80	96.89 63	89.40 55	92.81 72	96.97 49	85.37 60	99.24 47	90.87 90	98.69 37	98.38 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		94.43 23	94.27 27	94.92 22	98.65 8	86.67 34	96.92 23	97.23 35	88.60 77	93.58 52	97.27 29	85.22 61	99.54 19	92.21 54	98.74 33	98.56 24

CP-MVS		94.34 27	94.21 30	94.74 40	98.39 25	86.64 36	97.60 4	97.24 33	88.53 79	92.73 76	97.23 32	85.20 62	99.32 40	92.15 57	98.83 21	98.25 58

test12				94.34 56	97.13 80	86.15 53		96.29 113		91.04 112		85.08 63	99.01 73		98.13 68	97.86 89

ACMMPR		94.43 23	94.28 25	94.91 24	98.63 9	86.69 32	96.94 19	97.32 27	88.63 75	93.53 55	97.26 31	85.04 64	99.54 19	92.35 51	98.78 25	98.50 27

XVS		94.45 21	94.32 22	94.85 29	98.54 14	86.60 38	96.93 21	97.19 38	90.66 27	92.85 69	97.16 40	85.02 65	99.49 26	91.99 63	98.56 51	98.47 33

X-MVStestdata		88.31 169	86.13 212	94.85 29	98.54 14	86.60 38	96.93 21	97.19 38	90.66 27	92.85 69	23.41 372	85.02 65	99.49 26	91.99 63	98.56 51	98.47 33

MSLP-MVS++		93.72 49	94.08 36	92.65 108	97.31 73	83.43 121	95.79 71	97.33 25	90.03 38	93.58 52	96.96 50	84.87 67	97.76 174	92.19 56	98.66 44	96.76 131

HPM-MVS		94.02 39	93.88 42	94.43 53	98.39 25	85.78 69	97.25 9	97.07 48	86.90 126	92.62 80	96.80 59	84.85 68	99.17 53	92.43 47	98.65 46	98.33 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SR-MVS		94.23 32	94.17 33	94.43 53	98.21 35	85.78 69	96.40 38	96.90 62	88.20 90	94.33 32	97.40 23	84.75 69	99.03 67	93.35 32	97.99 73	98.48 29

PGM-MVS		93.96 42	93.72 49	94.68 41	98.43 21	86.22 52	95.30 93	97.78 1	87.45 114	93.26 58	97.33 26	84.62 70	99.51 24	90.75 92	98.57 50	98.32 48

EI-MVSNet-Vis-set		93.01 68	92.92 67	93.29 77	95.01 153	83.51 119	94.48 147	95.77 154	90.87 18	92.52 82	96.67 64	84.50 71	99.00 78	91.99 63	94.44 141	97.36 107

zzz-MVS		94.47 19	94.30 24	95.00 18	98.42 22	86.95 20	95.06 113	96.97 53	91.07 15	93.14 63	97.56 16	84.30 72	99.56 10	93.43 29	98.75 31	98.47 33

MTAPA		94.42 25	94.22 28	95.00 18	98.42 22	86.95 20	94.36 163	96.97 53	91.07 15	93.14 63	97.56 16	84.30 72	99.56 10	93.43 29	98.75 31	98.47 33

test1172		93.97 41	94.07 37	93.66 74	98.11 39	83.45 120	96.26 44	96.84 69	88.33 83	94.19 35	97.43 20	84.24 74	99.01 73	93.26 34	97.98 74	98.52 25

SR-MVS-dyc-post		93.82 46	93.82 44	93.82 67	97.92 47	84.57 86	96.28 42	96.76 79	87.46 112	93.75 45	97.43 20	84.24 74	99.01 73	92.73 41	97.80 80	97.88 87

ETV-MVS		92.74 71	92.66 70	92.97 91	95.20 148	84.04 105	95.07 110	96.51 102	90.73 25	92.96 67	91.19 261	84.06 76	98.34 129	91.72 74	96.54 107	96.54 140

EI-MVSNet-UG-set		92.74 71	92.62 71	93.12 83	94.86 164	83.20 126	94.40 155	95.74 157	90.71 26	92.05 91	96.60 71	84.00 77	98.99 80	91.55 76	93.63 149	97.17 116

mPP-MVS		93.99 40	93.78 47	94.63 44	98.50 17	85.90 66	96.87 25	96.91 61	88.70 73	91.83 98	97.17 39	83.96 78	99.55 15	91.44 80	98.64 47	98.43 39

APD-MVS_3200maxsize		93.78 47	93.77 48	93.80 71	97.92 47	84.19 101	96.30 40	96.87 66	86.96 122	93.92 42	97.47 18	83.88 79	98.96 86	92.71 44	97.87 78	98.26 57

CS-MVS-test		93.62 53	93.88 42	92.86 96	96.59 92	82.12 157	96.43 35	96.57 99	91.76 7	93.52 56	94.41 149	83.85 80	98.24 135	93.62 25	98.17 64	98.21 61

EIA-MVS		91.95 80	91.94 78	91.98 138	95.16 149	80.01 216	95.36 87	96.73 83	88.44 80	89.34 131	92.16 228	83.82 81	98.45 121	89.35 103	97.06 93	97.48 104

EPP-MVSNet		91.70 86	91.56 83	92.13 132	95.88 121	80.50 202	97.33 6	95.25 193	86.15 141	89.76 126	95.60 111	83.42 82	98.32 132	87.37 128	93.25 160	97.56 102

UA-Net		92.83 69	92.54 72	93.68 73	96.10 111	84.71 83	95.66 78	96.39 109	91.92 4	93.22 60	96.49 76	83.16 83	98.87 91	84.47 163	95.47 121	97.45 106

UniMVSNet_NR-MVSNet		89.92 123	89.29 125	91.81 151	93.39 220	83.72 112	94.43 153	97.12 44	89.80 42	86.46 179	93.32 189	83.16 83	97.23 224	84.92 156	81.02 303	94.49 219

DROMVSNet		93.44 57	93.71 50	92.63 109	95.21 147	82.43 150	97.27 8	96.71 87	90.57 29	92.88 68	95.80 104	83.16 83	98.16 142	93.68 24	98.14 67	97.31 108

RE-MVS-def			93.68 51		97.92 47	84.57 86	96.28 42	96.76 79	87.46 112	93.75 45	97.43 20	82.94 86		92.73 41	97.80 80	97.88 87

1121		90.42 111	89.49 117	93.20 80	97.27 77	84.46 93	92.63 240	95.51 175	71.01 348	91.20 110	96.21 87	82.92 87	99.05 63	80.56 226	98.07 71	96.10 154

新几何1				93.10 84	97.30 74	84.35 99		95.56 169	71.09 347	91.26 109	96.24 84	82.87 88	98.86 94	79.19 245	98.10 69	96.07 156

原ACMM1				92.01 134	97.34 72	81.05 186		96.81 74	78.89 279	90.45 116	95.92 99	82.65 89	98.84 99	80.68 224	98.26 63	96.14 149

casdiffmvs		92.51 74	92.43 74	92.74 103	94.41 185	81.98 162	94.54 145	96.23 119	89.57 50	91.96 93	96.17 92	82.58 90	98.01 161	90.95 88	95.45 123	98.23 59

DeepC-MVS	88.79 3	93.31 61	92.99 65	94.26 58	96.07 113	85.83 67	94.89 122	96.99 51	89.02 67	89.56 127	97.37 25	82.51 91	99.38 32	92.20 55	98.30 61	97.57 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		93.40 60	93.22 59	93.94 64	98.36 27	84.83 81	97.15 12	96.80 75	85.77 148	92.47 84	97.13 41	82.38 92	99.07 61	90.51 94	98.40 57	97.92 85

baseline		92.39 77	92.29 76	92.69 107	94.46 182	81.77 166	94.14 172	96.27 114	89.22 59	91.88 94	96.00 96	82.35 93	97.99 163	91.05 84	95.27 128	98.30 49

canonicalmvs		93.27 63	92.75 69	94.85 29	95.70 128	87.66 13	96.33 39	96.41 107	90.00 39	94.09 38	94.60 144	82.33 94	98.62 109	92.40 49	92.86 168	98.27 55

DP-MVS Recon		91.95 80	91.28 86	93.96 63	98.33 29	85.92 63	94.66 138	96.66 92	82.69 217	90.03 125	95.82 103	82.30 95	99.03 67	84.57 162	96.48 110	96.91 128

PAPR		90.02 118	89.27 127	92.29 128	95.78 124	80.95 190	92.68 238	96.22 120	81.91 235	86.66 177	93.75 181	82.23 96	98.44 122	79.40 244	94.79 131	97.48 104

MVS_Test		91.31 92	91.11 89	91.93 142	94.37 186	80.14 208	93.46 209	95.80 152	86.46 134	91.35 108	93.77 179	82.21 97	98.09 153	87.57 124	94.95 130	97.55 103

nrg030		91.08 97	90.39 99	93.17 82	93.07 229	86.91 22	96.41 37	96.26 115	88.30 85	88.37 144	94.85 134	82.19 98	97.64 185	91.09 83	82.95 274	94.96 193

UniMVSNet (Re)		89.80 126	89.07 130	92.01 134	93.60 215	84.52 89	94.78 130	97.47 10	89.26 58	86.44 182	92.32 223	82.10 99	97.39 212	84.81 159	80.84 307	94.12 231

testdata				90.49 199	96.40 100	77.89 264		95.37 189	72.51 340	93.63 50	96.69 62	82.08 100	97.65 183	83.08 178	97.39 88	95.94 160

PAPM_NR		91.22 94	90.78 97	92.52 115	97.60 62	81.46 175	94.37 162	96.24 118	86.39 137	87.41 161	94.80 136	82.06 101	98.48 116	82.80 186	95.37 124	97.61 98

MG-MVS		91.77 83	91.70 82	92.00 137	97.08 81	80.03 215	93.60 204	95.18 197	87.85 102	90.89 113	96.47 77	82.06 101	98.36 126	85.07 154	97.04 94	97.62 97

CANet		93.54 55	93.20 61	94.55 47	95.65 129	85.73 71	94.94 118	96.69 90	91.89 5	90.69 114	95.88 101	81.99 103	99.54 19	93.14 37	97.95 76	98.39 41

FC-MVSNet-test		90.27 113	90.18 104	90.53 195	93.71 211	79.85 221	95.77 72	97.59 2	89.31 57	86.27 185	94.67 141	81.93 104	97.01 241	84.26 165	88.09 230	94.71 204

FIs		90.51 110	90.35 100	90.99 183	93.99 201	80.98 188	95.73 73	97.54 3	89.15 62	86.72 176	94.68 140	81.83 105	97.24 223	85.18 153	88.31 226	94.76 203

ACMMP		93.24 64	92.88 68	94.30 57	98.09 42	85.33 77	96.86 26	97.45 13	88.33 83	90.15 123	97.03 48	81.44 106	99.51 24	90.85 91	95.74 116	98.04 76
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
Effi-MVS+		91.59 88	91.11 89	93.01 89	94.35 189	83.39 123	94.60 140	95.10 201	87.10 119	90.57 115	93.10 200	81.43 107	98.07 156	89.29 104	94.48 139	97.59 100

MVS_111021_LR		92.47 75	92.29 76	92.98 90	95.99 117	84.43 97	93.08 227	96.09 128	88.20 90	91.12 111	95.72 108	81.33 108	97.76 174	91.74 73	97.37 89	96.75 132

mvs_anonymous		89.37 141	89.32 124	89.51 243	93.47 218	74.22 308	91.65 270	94.83 218	82.91 213	85.45 207	93.79 177	81.23 109	96.36 279	86.47 140	94.09 143	97.94 82

PVSNet_BlendedMVS		89.98 119	89.70 114	90.82 187	96.12 108	81.25 180	93.92 191	96.83 71	83.49 199	89.10 134	92.26 226	81.04 110	98.85 97	86.72 138	87.86 234	92.35 306

PVSNet_Blended		90.73 102	90.32 101	91.98 138	96.12 108	81.25 180	92.55 244	96.83 71	82.04 230	89.10 134	92.56 216	81.04 110	98.85 97	86.72 138	95.91 114	95.84 165

alignmvs		93.08 67	92.50 73	94.81 35	95.62 131	87.61 14	95.99 61	96.07 130	89.77 46	94.12 37	94.87 131	80.56 112	98.66 105	92.42 48	93.10 163	98.15 66

abl_6		93.18 66	93.05 63	93.57 76	97.52 66	84.27 100	95.53 84	96.67 91	87.85 102	93.20 61	97.22 33	80.35 113	99.18 52	91.91 68	97.21 90	97.26 111

API-MVS		90.66 105	90.07 107	92.45 118	96.36 102	84.57 86	96.06 59	95.22 196	82.39 221	89.13 133	94.27 157	80.32 114	98.46 118	80.16 233	96.71 102	94.33 224

PVSNet_Blended_VisFu		91.38 90	90.91 94	92.80 99	96.39 101	83.17 127	94.87 124	96.66 92	83.29 204	89.27 132	94.46 148	80.29 115	99.17 53	87.57 124	95.37 124	96.05 158

test222					96.55 96	81.70 167	92.22 254	95.01 204	68.36 353	90.20 120	96.14 93	80.26 116			97.80 80	96.05 158

diffmvs		91.37 91	91.23 87	91.77 152	93.09 228	80.27 205	92.36 249	95.52 174	87.03 121	91.40 107	94.93 128	80.08 117	97.44 200	92.13 59	94.56 137	97.61 98

Test By Simon												80.02 118

IterMVS-LS		88.36 168	87.91 162	89.70 236	93.80 208	78.29 255	93.73 198	95.08 203	85.73 149	84.75 225	91.90 242	79.88 119	96.92 246	83.83 170	82.51 280	93.89 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 145	88.86 137	89.80 232	91.84 261	78.30 254	93.70 201	95.01 204	85.73 149	87.15 165	95.28 117	79.87 120	97.21 226	83.81 171	87.36 239	93.88 244

TAPA-MVS	84.62 6	88.16 173	87.01 181	91.62 156	96.64 90	80.65 197	94.39 157	96.21 123	76.38 304	86.19 187	95.44 113	79.75 121	98.08 155	62.75 348	95.29 126	96.13 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		89.41 138	88.64 139	91.71 154	94.74 168	80.81 194	93.54 205	95.10 201	83.11 207	86.82 175	90.67 279	79.74 122	97.75 177	80.51 228	93.55 151	96.57 138

pcd_1.5k_mvsjas		6.64 346	8.86 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	79.70 123	0.00 379	0.00 377	0.00 377	0.00 375

PS-MVSNAJss		89.97 120	89.62 115	91.02 180	91.90 259	80.85 193	95.26 98	95.98 136	86.26 139	86.21 186	94.29 154	79.70 123	97.65 183	88.87 109	88.10 228	94.57 212

PS-MVSNAJ		91.18 95	90.92 93	91.96 140	95.26 145	82.60 149	92.09 259	95.70 159	86.27 138	91.84 96	92.46 218	79.70 123	98.99 80	89.08 106	95.86 115	94.29 225

xiu_mvs_v2_base		91.13 96	90.89 95	91.86 146	94.97 156	82.42 151	92.24 253	95.64 166	86.11 144	91.74 101	93.14 198	79.67 126	98.89 90	89.06 107	95.46 122	94.28 226

WR-MVS_H		87.80 182	87.37 172	89.10 251	93.23 224	78.12 258	95.61 81	97.30 29	87.90 98	83.72 254	92.01 239	79.65 127	96.01 292	76.36 270	80.54 311	93.16 280

EPNet		91.79 82	91.02 92	94.10 61	90.10 320	85.25 78	96.03 60	92.05 288	92.83 1	87.39 164	95.78 105	79.39 128	99.01 73	88.13 118	97.48 87	98.05 75
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		87.22 210	86.62 195	89.02 254	92.13 251	77.40 278	90.91 281	94.81 220	81.28 251	84.32 240	90.08 290	79.26 129	96.62 257	83.81 171	82.94 275	93.04 285

miper_enhance_ethall		86.90 219	86.18 211	89.06 252	91.66 269	77.58 275	90.22 293	94.82 219	79.16 276	84.48 231	89.10 304	79.19 130	96.66 255	84.06 167	82.94 275	92.94 288

NR-MVSNet		88.58 164	87.47 170	91.93 142	93.04 231	84.16 102	94.77 131	96.25 117	89.05 64	80.04 306	93.29 192	79.02 131	97.05 238	81.71 208	80.05 318	94.59 210

TAMVS		89.21 143	88.29 152	91.96 140	93.71 211	82.62 148	93.30 216	94.19 239	82.22 225	87.78 155	93.94 168	78.83 132	96.95 244	77.70 258	92.98 166	96.32 143

c3_l		87.14 215	86.50 200	89.04 253	92.20 248	77.26 279	91.22 277	94.70 224	82.01 231	84.34 239	90.43 283	78.81 133	96.61 260	83.70 173	81.09 300	93.25 274

1112_ss		88.42 165	87.33 173	91.72 153	94.92 160	80.98 188	92.97 232	94.54 227	78.16 293	83.82 252	93.88 173	78.78 134	97.91 169	79.45 240	89.41 206	96.26 146

CDS-MVSNet		89.45 135	88.51 143	92.29 128	93.62 214	83.61 117	93.01 230	94.68 225	81.95 233	87.82 154	93.24 194	78.69 135	96.99 242	80.34 230	93.23 161	96.28 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS		89.60 129	88.92 134	91.67 155	95.47 137	81.15 184	92.38 248	94.78 222	83.11 207	89.06 136	94.32 152	78.67 136	96.61 260	81.57 209	90.89 188	97.24 112

CPTT-MVS		91.99 79	91.80 80	92.55 113	98.24 33	81.98 162	96.76 29	96.49 103	81.89 237	90.24 119	96.44 78	78.59 137	98.61 110	89.68 99	97.85 79	97.06 120

IS-MVSNet		91.43 89	91.09 91	92.46 117	95.87 123	81.38 178	96.95 18	93.69 256	89.72 48	89.50 129	95.98 97	78.57 138	97.77 173	83.02 180	96.50 109	98.22 60

OMC-MVS		91.23 93	90.62 98	93.08 85	96.27 104	84.07 103	93.52 206	95.93 140	86.95 123	89.51 128	96.13 94	78.50 139	98.35 128	85.84 147	92.90 167	96.83 130

PCF-MVS	84.11 10	87.74 184	86.08 216	92.70 106	94.02 196	84.43 97	89.27 307	95.87 147	73.62 331	84.43 234	94.33 151	78.48 140	98.86 94	70.27 308	94.45 140	94.81 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LCM-MVSNet-Re		88.30 170	88.32 151	88.27 272	94.71 171	72.41 330	93.15 223	90.98 317	87.77 104	79.25 314	91.96 240	78.35 141	95.75 304	83.04 179	95.62 117	96.65 135

HY-MVS	83.01 12	89.03 150	87.94 161	92.29 128	94.86 164	82.77 138	92.08 260	94.49 228	81.52 247	86.93 170	92.79 212	78.32 142	98.23 137	79.93 235	90.55 189	95.88 163

GeoE		90.05 117	89.43 120	91.90 145	95.16 149	80.37 204	95.80 70	94.65 226	83.90 188	87.55 160	94.75 137	78.18 143	97.62 187	81.28 212	93.63 149	97.71 95

MVS		87.44 200	86.10 215	91.44 163	92.61 242	83.62 116	92.63 240	95.66 163	67.26 354	81.47 284	92.15 229	77.95 144	98.22 139	79.71 237	95.48 120	92.47 301

MVSFormer		91.68 87	91.30 85	92.80 99	93.86 205	83.88 108	95.96 64	95.90 144	84.66 177	91.76 99	94.91 129	77.92 145	97.30 215	89.64 100	97.11 91	97.24 112

lupinMVS		90.92 98	90.21 102	93.03 88	93.86 205	83.88 108	92.81 236	93.86 251	79.84 268	91.76 99	94.29 154	77.92 145	98.04 158	90.48 95	97.11 91	97.17 116

Test_1112_low_res		87.65 187	86.51 199	91.08 176	94.94 159	79.28 235	91.77 264	94.30 235	76.04 309	83.51 261	92.37 221	77.86 147	97.73 178	78.69 249	89.13 213	96.22 147

VNet		92.24 78	91.91 79	93.24 79	96.59 92	83.43 121	94.84 126	96.44 104	89.19 61	94.08 39	95.90 100	77.85 148	98.17 141	88.90 108	93.38 157	98.13 68

DU-MVS		89.34 142	88.50 144	91.85 148	93.04 231	83.72 112	94.47 150	96.59 97	89.50 51	86.46 179	93.29 192	77.25 149	97.23 224	84.92 156	81.02 303	94.59 210

Baseline_NR-MVSNet		87.07 216	86.63 194	88.40 268	91.44 272	77.87 265	94.23 169	92.57 276	84.12 184	85.74 193	92.08 235	77.25 149	96.04 289	82.29 194	79.94 319	91.30 323

jason		90.80 99	90.10 106	92.90 94	93.04 231	83.53 118	93.08 227	94.15 241	80.22 262	91.41 106	94.91 129	76.87 151	97.93 168	90.28 96	96.90 97	97.24 112
jason: jason.
PAPM		86.68 228	85.39 236	90.53 195	93.05 230	79.33 234	89.79 300	94.77 223	78.82 281	81.95 281	93.24 194	76.81 152	97.30 215	66.94 331	93.16 162	94.95 196

Vis-MVSNet (Re-imp)		89.59 130	89.44 119	90.03 220	95.74 125	75.85 296	95.61 81	90.80 323	87.66 110	87.83 153	95.40 116	76.79 153	96.46 273	78.37 250	96.73 101	97.80 92

baseline1		88.10 174	87.28 175	90.57 193	94.96 157	80.07 211	94.27 166	91.29 310	86.74 128	87.41 161	94.00 165	76.77 154	96.20 284	80.77 221	79.31 326	95.44 178

114514_t		89.51 132	88.50 144	92.54 114	98.11 39	81.99 161	95.16 106	96.36 111	70.19 350	85.81 191	95.25 119	76.70 155	98.63 108	82.07 197	96.86 100	97.00 124

PLC	84.53 7	89.06 149	88.03 157	92.15 131	97.27 77	82.69 145	94.29 165	95.44 183	79.71 270	84.01 248	94.18 159	76.68 156	98.75 103	77.28 262	93.41 156	95.02 189
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TranMVSNet+NR-MVSNet		88.84 156	87.95 160	91.49 160	92.68 241	83.01 133	94.92 120	96.31 112	89.88 41	85.53 200	93.85 175	76.63 157	96.96 243	81.91 201	79.87 321	94.50 217

MAR-MVS		90.30 112	89.37 122	93.07 87	96.61 91	84.48 92	95.68 76	95.67 161	82.36 223	87.85 152	92.85 206	76.63 157	98.80 101	80.01 234	96.68 103	95.91 161
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
WR-MVS		88.38 166	87.67 166	90.52 197	93.30 223	80.18 206	93.26 219	95.96 138	88.57 78	85.47 206	92.81 210	76.12 159	96.91 247	81.24 213	82.29 282	94.47 222

v8		87.50 199	86.71 189	89.89 226	91.37 278	79.40 228	94.50 146	95.38 187	84.81 174	83.60 259	91.33 256	76.05 160	97.42 202	82.84 184	80.51 315	92.84 292

v148		87.04 217	86.32 206	89.21 247	90.94 296	77.26 279	93.71 200	94.43 230	84.84 173	84.36 238	90.80 275	76.04 161	97.05 238	82.12 196	79.60 323	93.31 271

eth_miper_zixun_eth		86.50 234	85.77 228	88.68 262	91.94 258	75.81 297	90.47 287	94.89 213	82.05 228	84.05 246	90.46 282	75.96 162	96.77 251	82.76 187	79.36 325	93.46 268

3Dnovator+	87.14 4	92.42 76	91.37 84	95.55 6	95.63 130	88.73 6	97.07 17	96.77 78	90.84 19	84.02 247	96.62 70	75.95 163	99.34 36	87.77 121	97.68 83	98.59 23

h-mvs33		90.80 99	90.15 105	92.75 102	96.01 115	82.66 146	95.43 86	95.53 173	89.80 42	93.08 65	95.64 110	75.77 164	99.00 78	92.07 60	78.05 330	96.60 136

hse-mvs2		89.88 125	89.34 123	91.51 159	94.83 166	81.12 185	93.94 190	93.91 250	89.80 42	93.08 65	93.60 184	75.77 164	97.66 181	92.07 60	77.07 337	95.74 170

BH-untuned		88.60 163	88.13 156	90.01 223	95.24 146	78.50 248	93.29 217	94.15 241	84.75 175	84.46 232	93.40 186	75.76 166	97.40 209	77.59 259	94.52 138	94.12 231

DIV-MVS_self_test		86.53 232	85.78 226	88.75 259	92.02 256	76.45 289	90.74 283	94.30 235	81.83 240	83.34 265	90.82 274	75.75 167	96.57 263	81.73 207	81.52 295	93.24 275

BH-w/o		87.57 195	87.05 180	89.12 250	94.90 162	77.90 263	92.41 246	93.51 258	82.89 214	83.70 255	91.34 255	75.75 167	97.07 236	75.49 278	93.49 153	92.39 304

cl____		86.52 233	85.78 226	88.75 259	92.03 255	76.46 288	90.74 283	94.30 235	81.83 240	83.34 265	90.78 276	75.74 169	96.57 263	81.74 206	81.54 294	93.22 277

cdsmvs_eth3d_5k		22.14 341	29.52 344	0.00 360	0.00 383	0.00 384	0.00 371	95.76 155	0.00 378	0.00 379	94.29 154	75.66 170	0.00 379	0.00 377	0.00 377	0.00 375

CNLPA		89.07 148	87.98 159	92.34 124	96.87 84	84.78 82	94.08 179	93.24 261	81.41 248	84.46 232	95.13 124	75.57 171	96.62 257	77.21 263	93.84 147	95.61 174

CHOSEN 1792x2688		88.84 156	87.69 164	92.30 127	96.14 107	81.42 177	90.01 297	95.86 148	74.52 324	87.41 161	93.94 168	75.46 172	98.36 126	80.36 229	95.53 118	97.12 119

CP-MVSNet		87.63 190	87.26 177	88.74 261	93.12 227	76.59 287	95.29 95	96.58 98	88.43 81	83.49 262	92.98 203	75.28 173	95.83 300	78.97 246	81.15 299	93.79 250

v10		87.25 207	86.38 201	89.85 227	91.19 284	79.50 225	94.48 147	95.45 181	83.79 191	83.62 258	91.19 261	75.13 174	97.42 202	81.94 200	80.60 309	92.63 297

Vis-MVSNet		91.75 84	91.23 87	93.29 77	95.32 142	83.78 111	96.14 52	95.98 136	89.89 40	90.45 116	96.58 72	75.09 175	98.31 133	84.75 160	96.90 97	97.78 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		88.93 154	88.26 154	90.94 186	94.05 195	80.78 195	91.71 267	95.38 187	81.55 246	88.63 139	93.91 172	75.04 176	95.47 315	82.47 190	91.61 179	96.57 138

v1144		87.61 193	86.79 187	90.06 219	91.01 291	79.34 231	93.95 189	95.42 186	83.36 203	85.66 196	91.31 259	74.98 177	97.42 202	83.37 175	82.06 285	93.42 269

miper_lstm_enhance		85.27 256	84.59 254	87.31 293	91.28 282	74.63 303	87.69 329	94.09 245	81.20 255	81.36 287	89.85 296	74.97 178	94.30 329	81.03 217	79.84 322	93.01 286

test_yl		90.69 103	90.02 111	92.71 104	95.72 126	82.41 153	94.11 175	95.12 199	85.63 152	91.49 104	94.70 138	74.75 179	98.42 124	86.13 143	92.53 172	97.31 108

DCV-MVSNet		90.69 103	90.02 111	92.71 104	95.72 126	82.41 153	94.11 175	95.12 199	85.63 152	91.49 104	94.70 138	74.75 179	98.42 124	86.13 143	92.53 172	97.31 108

V42		87.68 185	86.86 183	90.15 214	90.58 311	80.14 208	94.24 168	95.28 192	83.66 193	85.67 195	91.33 256	74.73 181	97.41 207	84.43 164	81.83 289	92.89 290

XVG-OURS-SEG-HR		89.95 121	89.45 118	91.47 162	94.00 200	81.21 183	91.87 262	96.06 132	85.78 147	88.55 140	95.73 107	74.67 182	97.27 219	88.71 111	89.64 204	95.91 161

v2v482		87.84 180	87.06 179	90.17 212	90.99 292	79.23 238	94.00 187	95.13 198	84.87 172	85.53 200	92.07 237	74.45 183	97.45 198	84.71 161	81.75 291	93.85 248

CLD-MVS		89.47 134	88.90 135	91.18 171	94.22 190	82.07 160	92.13 257	96.09 128	87.90 98	85.37 216	92.45 219	74.38 184	97.56 190	87.15 131	90.43 190	93.93 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		87.65 187	86.85 184	90.03 220	92.14 250	80.60 200	93.76 197	95.23 194	82.94 212	84.60 227	94.02 163	74.27 185	95.49 314	81.04 215	83.68 267	94.01 239

HQP_MVS		90.60 109	90.19 103	91.82 149	94.70 172	82.73 142	95.85 68	96.22 120	90.81 20	86.91 172	94.86 132	74.23 186	98.12 143	88.15 116	89.99 195	94.63 206

plane_prior6					94.52 178	82.75 139						74.23 186

v144192		87.19 213	86.35 204	89.74 233	90.64 309	78.24 256	93.92 191	95.43 184	81.93 234	85.51 202	91.05 269	74.21 188	97.45 198	82.86 183	81.56 293	93.53 263

VPA-MVSNet		89.62 128	88.96 132	91.60 157	93.86 205	82.89 137	95.46 85	97.33 25	87.91 97	88.43 143	93.31 190	74.17 189	97.40 209	87.32 129	82.86 279	94.52 215

ab-mvs		89.41 138	88.35 148	92.60 110	95.15 151	82.65 147	92.20 255	95.60 168	83.97 187	88.55 140	93.70 183	74.16 190	98.21 140	82.46 191	89.37 207	96.94 126

1314		87.51 197	86.57 197	90.34 209	92.42 245	79.74 223	92.63 240	95.35 191	78.35 289	80.14 303	91.62 251	74.05 191	97.15 228	81.05 214	93.53 152	94.12 231

test_djsdf		89.03 150	88.64 139	90.21 211	90.74 306	79.28 235	95.96 64	95.90 144	84.66 177	85.33 218	92.94 204	74.02 192	97.30 215	89.64 100	88.53 219	94.05 237

cl22		86.78 224	85.98 219	89.18 249	92.34 246	77.62 274	90.84 282	94.13 243	81.33 250	83.97 249	90.15 288	73.96 193	96.60 262	84.19 166	82.94 275	93.33 270

AdaColmap		89.89 124	89.07 130	92.37 123	97.41 69	83.03 131	94.42 154	95.92 141	82.81 215	86.34 184	94.65 142	73.89 194	99.02 71	80.69 223	95.51 119	95.05 188

HyFIR lowres test		88.09 175	86.81 185	91.93 142	96.00 116	80.63 198	90.01 297	95.79 153	73.42 332	87.68 157	92.10 234	73.86 195	97.96 165	80.75 222	91.70 178	97.19 115

HQP2-MVS												73.83 196

HQP-MVS		89.80 126	89.28 126	91.34 166	94.17 191	81.56 169	94.39 157	96.04 134	88.81 69	85.43 210	93.97 167	73.83 196	97.96 165	87.11 133	89.77 202	94.50 217

3Dnovator	86.66 5	91.73 85	90.82 96	94.44 51	94.59 176	86.37 45	97.18 11	97.02 50	89.20 60	84.31 242	96.66 65	73.74 198	99.17 53	86.74 136	97.96 75	97.79 93

EPNet_dtu		86.49 236	85.94 222	88.14 277	90.24 318	72.82 322	94.11 175	92.20 284	86.66 132	79.42 313	92.36 222	73.52 199	95.81 302	71.26 302	93.66 148	95.80 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		84.43 269	83.06 272	88.54 265	91.72 265	78.44 249	95.18 104	92.82 270	82.73 216	79.67 310	92.12 231	73.49 200	95.96 294	71.10 307	68.73 355	91.21 326

Effi-MVS+-dtu		88.65 161	88.35 148	89.54 240	93.33 221	76.39 290	94.47 150	94.36 232	87.70 106	85.43 210	89.56 301	73.45 201	97.26 221	85.57 151	91.28 181	94.97 190

mvs-test1		89.45 135	89.14 128	90.38 206	93.33 221	77.63 273	94.95 117	94.36 232	87.70 106	87.10 168	92.81 210	73.45 201	98.03 160	85.57 151	93.04 164	95.48 176

baseline2		86.50 234	85.39 236	89.84 228	91.12 288	76.70 285	91.88 261	88.58 347	82.35 224	79.95 307	90.95 271	73.42 203	97.63 186	80.27 232	89.95 198	95.19 185

PEN-MVS		86.80 223	86.27 209	88.40 268	92.32 247	75.71 298	95.18 104	96.38 110	87.97 95	82.82 271	93.15 197	73.39 204	95.92 295	76.15 274	79.03 328	93.59 261

v1192		87.25 207	86.33 205	90.00 224	90.76 305	79.04 239	93.80 195	95.48 176	82.57 219	85.48 205	91.18 263	73.38 205	97.42 202	82.30 193	82.06 285	93.53 263

QAPM		89.51 132	88.15 155	93.59 75	94.92 160	84.58 85	96.82 28	96.70 88	78.43 288	83.41 263	96.19 91	73.18 206	99.30 42	77.11 265	96.54 107	96.89 129

tpmrst		85.35 253	84.99 243	86.43 310	90.88 301	67.88 354	88.71 317	91.43 307	80.13 264	86.08 189	88.80 310	73.05 207	96.02 291	82.48 189	83.40 273	95.40 180

PS-CasMVS		87.32 204	86.88 182	88.63 264	92.99 235	76.33 292	95.33 89	96.61 96	88.22 89	83.30 267	93.07 201	73.03 208	95.79 303	78.36 251	81.00 305	93.75 256

DTE-MVSNet		86.11 240	85.48 234	87.98 280	91.65 270	74.92 302	94.93 119	95.75 156	87.36 115	82.26 276	93.04 202	72.85 209	95.82 301	74.04 290	77.46 334	93.20 278

MVSTER		88.84 156	88.29 152	90.51 198	92.95 236	80.44 203	93.73 198	95.01 204	84.66 177	87.15 165	93.12 199	72.79 210	97.21 226	87.86 120	87.36 239	93.87 245

v1921920		86.97 218	86.06 217	89.69 237	90.53 314	78.11 259	93.80 195	95.43 184	81.90 236	85.33 218	91.05 269	72.66 211	97.41 207	82.05 198	81.80 290	93.53 263

DP-MVS		87.25 207	85.36 238	92.90 94	97.65 61	83.24 125	94.81 128	92.00 290	74.99 319	81.92 282	95.00 127	72.66 211	99.05 63	66.92 333	92.33 175	96.40 141

v7n		86.81 222	85.76 229	89.95 225	90.72 307	79.25 237	95.07 110	95.92 141	84.45 180	82.29 275	90.86 272	72.60 213	97.53 192	79.42 243	80.52 314	93.08 284

OPM-MVS		90.12 115	89.56 116	91.82 149	93.14 226	83.90 107	94.16 171	95.74 157	88.96 68	87.86 151	95.43 115	72.48 214	97.91 169	88.10 119	90.18 194	93.65 260
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		87.89 179	86.32 206	92.59 111	96.07 113	82.92 136	95.23 99	94.92 212	75.66 311	82.89 270	95.98 97	72.48 214	99.21 50	68.43 322	95.23 129	95.64 173

pm-mvs1		86.61 229	85.54 232	89.82 229	91.44 272	80.18 206	95.28 97	94.85 216	83.84 190	81.66 283	92.62 215	72.45 216	96.48 270	79.67 238	78.06 329	92.82 293

PMMVS		85.71 248	84.96 245	87.95 281	88.90 334	77.09 281	88.68 318	90.06 334	72.32 341	86.47 178	90.76 277	72.15 217	94.40 326	81.78 205	93.49 153	92.36 305

PatchmatchNet		85.85 245	84.70 251	89.29 246	91.76 264	75.54 299	88.49 320	91.30 309	81.63 244	85.05 221	88.70 312	71.71 218	96.24 283	74.61 288	89.05 214	96.08 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												71.70 219				96.12 151

test_part1		89.00 153	87.99 158	92.04 133	95.94 120	83.81 110	96.14 52	96.05 133	86.44 135	85.69 194	93.73 182	71.57 220	97.66 181	85.80 148	80.54 311	94.66 205

patchmatchnet-post											83.76 348	71.53 221	96.48 270

v1240		86.78 224	85.85 224	89.56 239	90.45 315	77.79 268	93.61 203	95.37 189	81.65 242	85.43 210	91.15 265	71.50 222	97.43 201	81.47 211	82.05 287	93.47 267

anonymousdsp		87.84 180	87.09 178	90.12 216	89.13 331	80.54 201	94.67 137	95.55 170	82.05 228	83.82 252	92.12 231	71.47 223	97.15 228	87.15 131	87.80 235	92.67 295

Patchmatch-test		81.37 298	79.30 304	87.58 287	90.92 298	74.16 310	80.99 358	87.68 352	70.52 349	76.63 329	88.81 308	71.21 224	92.76 347	60.01 356	86.93 245	95.83 166

F-COLMAP		87.95 178	86.80 186	91.40 164	96.35 103	80.88 192	94.73 133	95.45 181	79.65 271	82.04 280	94.61 143	71.13 225	98.50 115	76.24 273	91.05 186	94.80 202

pmmvs4		85.43 251	83.86 262	90.16 213	90.02 323	82.97 135	90.27 289	92.67 274	75.93 310	80.73 293	91.74 246	71.05 226	95.73 305	78.85 247	83.46 271	91.78 314

CR-MVSNet		85.35 253	83.76 263	90.12 216	90.58 311	79.34 231	85.24 343	91.96 294	78.27 290	85.55 198	87.87 325	71.03 227	95.61 306	73.96 292	89.36 208	95.40 180

Patchmtry		82.71 282	80.93 288	88.06 279	90.05 322	76.37 291	84.74 347	91.96 294	72.28 342	81.32 288	87.87 325	71.03 227	95.50 313	68.97 318	80.15 317	92.32 307

CL-MVSNet_self_test		81.74 291	80.53 289	85.36 320	85.96 353	72.45 329	90.25 290	93.07 265	81.24 253	79.85 309	87.29 332	70.93 229	92.52 348	66.95 330	69.23 351	91.11 330

RPMNet		83.95 273	81.53 283	91.21 169	90.58 311	79.34 231	85.24 343	96.76 79	71.44 345	85.55 198	82.97 352	70.87 230	98.91 89	61.01 352	89.36 208	95.40 180

Patchmatch-RL test		81.67 292	79.96 298	86.81 308	85.42 357	71.23 336	82.17 356	87.50 353	78.47 287	77.19 325	82.50 353	70.81 231	93.48 339	82.66 188	72.89 345	95.71 172

CostFormer		85.77 247	84.94 246	88.26 273	91.16 287	72.58 328	89.47 305	91.04 316	76.26 307	86.45 181	89.97 293	70.74 232	96.86 250	82.35 192	87.07 244	95.34 183

sam_mvs												70.60 233

xiu_mvs_v1_base_debu		90.64 106	90.05 108	92.40 119	93.97 202	84.46 93	93.32 212	95.46 178	85.17 164	92.25 85	94.03 160	70.59 234	98.57 112	90.97 85	94.67 132	94.18 227

xiu_mvs_v1_base		90.64 106	90.05 108	92.40 119	93.97 202	84.46 93	93.32 212	95.46 178	85.17 164	92.25 85	94.03 160	70.59 234	98.57 112	90.97 85	94.67 132	94.18 227

xiu_mvs_v1_base_debi		90.64 106	90.05 108	92.40 119	93.97 202	84.46 93	93.32 212	95.46 178	85.17 164	92.25 85	94.03 160	70.59 234	98.57 112	90.97 85	94.67 132	94.18 227

test_post											10.29 373	70.57 237	95.91 297

CANet_DTU		90.26 114	89.41 121	92.81 98	93.46 219	83.01 133	93.48 207	94.47 229	89.43 54	87.76 156	94.23 158	70.54 238	99.03 67	84.97 155	96.39 111	96.38 142

BH-RMVSNet		88.37 167	87.48 169	91.02 180	95.28 143	79.45 227	92.89 234	93.07 265	85.45 158	86.91 172	94.84 135	70.35 239	97.76 174	73.97 291	94.59 136	95.85 164

Fast-Effi-MVS+-dtu		87.44 200	86.72 188	89.63 238	92.04 254	77.68 272	94.03 184	93.94 246	85.81 146	82.42 274	91.32 258	70.33 240	97.06 237	80.33 231	90.23 193	94.14 230

MDTV_nov1_ep13_2view						55.91 371	87.62 331		73.32 333	84.59 228		70.33 240		74.65 287		95.50 175

ACMM	84.12 9	89.14 144	88.48 147	91.12 172	94.65 175	81.22 182	95.31 90	96.12 127	85.31 162	85.92 190	94.34 150	70.19 242	98.06 157	85.65 149	88.86 216	94.08 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		87.51 197	85.91 223	92.32 125	93.70 213	83.93 106	92.33 250	90.94 319	84.16 182	72.09 350	92.52 217	69.90 243	95.85 299	89.20 105	88.36 225	97.17 116

LPG-MVS_test		89.45 135	88.90 135	91.12 172	94.47 180	81.49 173	95.30 93	96.14 125	86.73 129	85.45 207	95.16 122	69.89 244	98.10 145	87.70 122	89.23 211	93.77 254

LGP-MVS_train				91.12 172	94.47 180	81.49 173		96.14 125	86.73 129	85.45 207	95.16 122	69.89 244	98.10 145	87.70 122	89.23 211	93.77 254

CHOSEN 280x420		85.15 258	83.99 260	88.65 263	92.47 243	78.40 251	79.68 360	92.76 271	74.90 321	81.41 286	89.59 299	69.85 246	95.51 311	79.92 236	95.29 126	92.03 311

LTVRE_ROB	82.13 13	86.26 239	84.90 247	90.34 209	94.44 184	81.50 171	92.31 252	94.89 213	83.03 209	79.63 311	92.67 213	69.69 247	97.79 172	71.20 303	86.26 247	91.72 315
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
OpenMVS	83.78 11	88.74 159	87.29 174	93.08 85	92.70 240	85.39 76	96.57 33	96.43 106	78.74 284	80.85 292	96.07 95	69.64 248	99.01 73	78.01 256	96.65 104	94.83 200

MDTV_nov1_ep13			83.56 267		91.69 268	69.93 347	87.75 328	91.54 303	78.60 286	84.86 224	88.90 307	69.54 249	96.03 290	70.25 309	88.93 215

AUN-MVS		87.78 183	86.54 198	91.48 161	94.82 167	81.05 186	93.91 194	93.93 247	83.00 210	86.93 170	93.53 185	69.50 250	97.67 180	86.14 141	77.12 336	95.73 171

PatchT		82.68 283	81.27 285	86.89 306	90.09 321	70.94 341	84.06 349	90.15 331	74.91 320	85.63 197	83.57 349	69.37 251	94.87 324	65.19 338	88.50 221	94.84 199

VPNet		88.20 172	87.47 170	90.39 204	93.56 216	79.46 226	94.04 183	95.54 172	88.67 74	86.96 169	94.58 146	69.33 252	97.15 228	84.05 168	80.53 313	94.56 213

ACMP	84.23 8	89.01 152	88.35 148	90.99 183	94.73 169	81.27 179	95.07 110	95.89 146	86.48 133	83.67 256	94.30 153	69.33 252	97.99 163	87.10 135	88.55 218	93.72 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_post1							88.00 325				9.81 374	69.31 254	95.53 309	76.65 268

tpmvs		83.35 280	82.07 278	87.20 300	91.07 290	71.00 340	88.31 323	91.70 298	78.91 278	80.49 298	87.18 334	69.30 255	97.08 235	68.12 326	83.56 269	93.51 266

thres200		87.21 211	86.24 210	90.12 216	95.36 139	78.53 246	93.26 219	92.10 286	86.42 136	88.00 150	91.11 267	69.24 256	98.00 162	69.58 316	91.04 187	93.83 249

tfpn200view9		87.58 194	86.64 192	90.41 203	95.99 117	78.64 243	94.58 141	91.98 292	86.94 124	88.09 145	91.77 244	69.18 257	98.10 145	70.13 312	91.10 182	94.48 220

thres400		87.62 192	86.64 192	90.57 193	95.99 117	78.64 243	94.58 141	91.98 292	86.94 124	88.09 145	91.77 244	69.18 257	98.10 145	70.13 312	91.10 182	94.96 193

tfpnnormal		84.72 266	83.23 270	89.20 248	92.79 239	80.05 213	94.48 147	95.81 151	82.38 222	81.08 290	91.21 260	69.01 259	96.95 244	61.69 350	80.59 310	90.58 337

thres100view900		87.63 190	86.71 189	90.38 206	96.12 108	78.55 245	95.03 114	91.58 301	87.15 117	88.06 148	92.29 225	68.91 260	98.10 145	70.13 312	91.10 182	94.48 220

thres600view7		87.65 187	86.67 191	90.59 192	96.08 112	78.72 241	94.88 123	91.58 301	87.06 120	88.08 147	92.30 224	68.91 260	98.10 145	70.05 315	91.10 182	94.96 193

PatchMatch-RL		86.77 227	85.54 232	90.47 202	95.88 121	82.71 144	90.54 286	92.31 281	79.82 269	84.32 240	91.57 254	68.77 262	96.39 276	73.16 296	93.48 155	92.32 307

XVG-OURS		89.40 140	88.70 138	91.52 158	94.06 194	81.46 175	91.27 275	96.07 130	86.14 142	88.89 138	95.77 106	68.73 263	97.26 221	87.39 127	89.96 197	95.83 166

TR-MVS		86.78 224	85.76 229	89.82 229	94.37 186	78.41 250	92.47 245	92.83 269	81.11 256	86.36 183	92.40 220	68.73 263	97.48 195	73.75 294	89.85 201	93.57 262

tpm		84.73 265	84.02 259	86.87 307	90.33 316	68.90 350	89.06 312	89.94 337	80.85 258	85.75 192	89.86 295	68.54 265	95.97 293	77.76 257	84.05 263	95.75 169

FMVSNet3		87.40 202	86.11 214	91.30 167	93.79 210	83.64 115	94.20 170	94.81 220	83.89 189	84.37 235	91.87 243	68.45 266	96.56 265	78.23 253	85.36 252	93.70 259

MVP-Stereo		85.97 242	84.86 248	89.32 245	90.92 298	82.19 156	92.11 258	94.19 239	78.76 283	78.77 316	91.63 250	68.38 267	96.56 265	75.01 285	93.95 144	89.20 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		81.96 287	80.27 293	87.01 302	91.09 289	71.02 339	87.38 332	91.53 304	66.25 355	80.17 301	86.35 338	68.22 268	96.15 287	69.16 317	82.29 282	93.86 247

tpm2		84.08 271	82.94 273	87.48 291	91.39 277	71.27 335	89.23 309	90.37 328	71.95 343	84.64 226	89.33 302	67.30 269	96.55 267	75.17 282	87.09 243	94.63 206

test-LLR		85.87 244	85.41 235	87.25 296	90.95 294	71.67 333	89.55 301	89.88 340	83.41 201	84.54 229	87.95 322	67.25 270	95.11 320	81.82 203	93.37 158	94.97 190

test0.0.03 1		82.41 285	81.69 281	84.59 326	88.23 341	72.89 321	90.24 291	87.83 350	83.41 201	79.86 308	89.78 297	67.25 270	88.99 361	65.18 339	83.42 272	91.90 313

CVMVSNet		84.69 267	84.79 250	84.37 328	91.84 261	64.92 362	93.70 201	91.47 306	66.19 356	86.16 188	95.28 117	67.18 272	93.33 341	80.89 220	90.42 191	94.88 198

bset_n11_16_dypcd		86.83 221	85.55 231	90.65 191	88.22 342	81.70 167	88.88 315	90.42 326	85.26 163	85.49 204	90.69 278	67.11 273	97.02 240	89.51 102	84.39 259	93.23 276

thisisatest0515		87.33 203	85.99 218	91.37 165	93.49 217	79.55 224	90.63 285	89.56 345	80.17 263	87.56 159	90.86 272	67.07 274	98.28 134	81.50 210	93.02 165	96.29 144

tttt0517		88.61 162	87.78 163	91.11 175	94.96 157	77.81 267	95.35 88	89.69 342	85.09 169	88.05 149	94.59 145	66.93 275	98.48 116	83.27 177	92.13 177	97.03 122

our_test_3		81.93 288	80.46 291	86.33 312	88.46 338	73.48 316	88.46 321	91.11 312	76.46 302	76.69 328	88.25 318	66.89 276	94.36 327	68.75 319	79.08 327	91.14 328

thisisatest0530		88.67 160	87.61 167	91.86 146	94.87 163	80.07 211	94.63 139	89.90 339	84.00 186	88.46 142	93.78 178	66.88 277	98.46 118	83.30 176	92.65 170	97.06 120

IterMVS-SCA-FT		85.45 250	84.53 255	88.18 276	91.71 266	76.87 284	90.19 294	92.65 275	85.40 160	81.44 285	90.54 280	66.79 278	95.00 323	81.04 215	81.05 301	92.66 296

SCA		86.32 238	85.18 240	89.73 235	92.15 249	76.60 286	91.12 278	91.69 299	83.53 198	85.50 203	88.81 308	66.79 278	96.48 270	76.65 268	90.35 192	96.12 151

D2MVS		85.90 243	85.09 242	88.35 270	90.79 303	77.42 277	91.83 263	95.70 159	80.77 259	80.08 305	90.02 291	66.74 280	96.37 277	81.88 202	87.97 232	91.26 324

IterMVS		84.88 263	83.98 261	87.60 286	91.44 272	76.03 294	90.18 295	92.41 278	83.24 206	81.06 291	90.42 284	66.60 281	94.28 330	79.46 239	80.98 306	92.48 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		87.26 205	85.98 219	91.08 176	94.01 197	83.10 128	95.14 107	94.94 207	83.57 195	84.37 235	91.64 247	66.59 282	96.34 280	78.23 253	85.36 252	93.79 250

test1		87.26 205	85.98 219	91.08 176	94.01 197	83.10 128	95.14 107	94.94 207	83.57 195	84.37 235	91.64 247	66.59 282	96.34 280	78.23 253	85.36 252	93.79 250

FMVSNet2		87.19 213	85.82 225	91.30 167	94.01 197	83.67 114	94.79 129	94.94 207	83.57 195	83.88 250	92.05 238	66.59 282	96.51 268	77.56 260	85.01 255	93.73 257

EPMVS		83.90 275	82.70 277	87.51 288	90.23 319	72.67 324	88.62 319	81.96 365	81.37 249	85.01 222	88.34 316	66.31 285	94.45 325	75.30 281	87.12 242	95.43 179

ppachtmachnet_test		81.84 289	80.07 297	87.15 301	88.46 338	74.43 307	89.04 313	92.16 285	75.33 315	77.75 321	88.99 305	66.20 286	95.37 316	65.12 340	77.60 332	91.65 316

MDA-MVSNet_test_wron		79.21 316	77.19 318	85.29 321	88.22 342	72.77 323	85.87 339	90.06 334	74.34 325	62.62 361	87.56 328	66.14 287	91.99 352	66.90 334	73.01 343	91.10 331

YYNet1		79.22 315	77.20 317	85.28 322	88.20 344	72.66 325	85.87 339	90.05 336	74.33 326	62.70 360	87.61 327	66.09 288	92.03 351	66.94 331	72.97 344	91.15 327

JIA-IIPM		81.04 301	78.98 311	87.25 296	88.64 335	73.48 316	81.75 357	89.61 344	73.19 334	82.05 279	73.71 360	66.07 289	95.87 298	71.18 305	84.60 258	92.41 303

RRT_MVS		88.86 155	87.68 165	92.39 122	92.02 256	86.09 55	94.38 161	94.94 207	85.45 158	87.14 167	93.84 176	65.88 290	97.11 232	88.73 110	86.77 246	93.98 240

MSDG		84.86 264	83.09 271	90.14 215	93.80 208	80.05 213	89.18 310	93.09 264	78.89 279	78.19 317	91.91 241	65.86 291	97.27 219	68.47 321	88.45 222	93.11 282

jajsoiax		88.24 171	87.50 168	90.48 200	90.89 300	80.14 208	95.31 90	95.65 165	84.97 171	84.24 244	94.02 163	65.31 292	97.42 202	88.56 112	88.52 220	93.89 242

cascas		86.43 237	84.98 244	90.80 188	92.10 253	80.92 191	90.24 291	95.91 143	73.10 335	83.57 260	88.39 315	65.15 293	97.46 197	84.90 158	91.43 180	94.03 238

ADS-MVSNet2		81.66 293	79.71 301	87.50 289	91.35 279	74.19 309	83.33 352	88.48 348	72.90 337	82.24 277	85.77 342	64.98 294	93.20 343	64.57 342	83.74 265	95.12 186

ADS-MVSNet		81.56 295	79.78 299	86.90 305	91.35 279	71.82 332	83.33 352	89.16 346	72.90 337	82.24 277	85.77 342	64.98 294	93.76 336	64.57 342	83.74 265	95.12 186

pmmvs5		84.21 270	82.84 276	88.34 271	88.95 333	76.94 283	92.41 246	91.91 296	75.63 312	80.28 300	91.18 263	64.59 296	95.57 307	77.09 266	83.47 270	92.53 299

PVSNet	78.82 18	85.55 249	84.65 252	88.23 275	94.72 170	71.93 331	87.12 333	92.75 272	78.80 282	84.95 223	90.53 281	64.43 297	96.71 254	74.74 286	93.86 146	96.06 157

UGNet		89.95 121	88.95 133	92.95 92	94.51 179	83.31 124	95.70 75	95.23 194	89.37 56	87.58 158	93.94 168	64.00 298	98.78 102	83.92 169	96.31 112	96.74 133
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
RPSCF		85.07 259	84.27 256	87.48 291	92.91 237	70.62 343	91.69 269	92.46 277	76.20 308	82.67 273	95.22 120	63.94 299	97.29 218	77.51 261	85.80 250	94.53 214

mvs_tets		88.06 177	87.28 175	90.38 206	90.94 296	79.88 219	95.22 100	95.66 163	85.10 168	84.21 245	93.94 168	63.53 300	97.40 209	88.50 113	88.40 224	93.87 245

test1111		89.10 145	88.64 139	90.48 200	95.53 136	74.97 301	96.08 56	84.89 358	88.13 93	90.16 122	96.65 66	63.29 301	98.10 145	86.14 141	96.90 97	98.39 41

Anonymous20231211		86.59 231	85.13 241	90.98 185	96.52 98	81.50 171	96.14 52	96.16 124	73.78 329	83.65 257	92.15 229	63.26 302	97.37 213	82.82 185	81.74 292	94.06 236

ECVR-MVS		89.09 147	88.53 142	90.77 189	95.62 131	75.89 295	96.16 49	84.22 360	87.89 100	90.20 120	96.65 66	63.19 303	98.10 145	85.90 146	96.94 95	98.33 45

dp		81.47 297	80.23 294	85.17 323	89.92 325	65.49 360	86.74 334	90.10 333	76.30 306	81.10 289	87.12 335	62.81 304	95.92 295	68.13 325	79.88 320	94.09 234

LFMVS		90.08 116	89.13 129	92.95 92	96.71 88	82.32 155	96.08 56	89.91 338	86.79 127	92.15 90	96.81 57	62.60 305	98.34 129	87.18 130	93.90 145	98.19 63

DWT-MVSNet_test		84.95 262	83.68 264	88.77 257	91.43 275	73.75 312	91.74 266	90.98 317	80.66 260	83.84 251	87.36 330	62.44 306	97.11 232	78.84 248	85.81 249	95.46 177

Anonymous20231206		81.03 302	79.77 300	84.82 325	87.85 347	70.26 345	91.42 273	92.08 287	73.67 330	77.75 321	89.25 303	62.43 307	93.08 344	61.50 351	82.00 288	91.12 329

VDD-MVS		90.74 101	89.92 113	93.20 80	96.27 104	83.02 132	95.73 73	93.86 251	88.42 82	92.53 81	96.84 54	62.09 308	98.64 107	90.95 88	92.62 171	97.93 84

MS-PatchMatch		85.05 260	84.16 257	87.73 284	91.42 276	78.51 247	91.25 276	93.53 257	77.50 295	80.15 302	91.58 252	61.99 309	95.51 311	75.69 277	94.35 142	89.16 347

OurMVSNet-221017-0		85.35 253	84.64 253	87.49 290	90.77 304	72.59 327	94.01 186	94.40 231	84.72 176	79.62 312	93.17 196	61.91 310	96.72 252	81.99 199	81.16 297	93.16 280

test20.03		79.95 310	79.08 309	82.55 336	85.79 354	67.74 355	91.09 279	91.08 313	81.23 254	74.48 342	89.96 294	61.63 311	90.15 358	60.08 354	76.38 338	89.76 340

DSMNet-mixed		76.94 322	76.29 321	78.89 340	83.10 363	56.11 370	87.78 327	79.77 368	60.65 360	75.64 335	88.71 311	61.56 312	88.34 362	60.07 355	89.29 210	92.21 310

Anonymous20240529		88.09 175	86.59 196	92.58 112	96.53 97	81.92 164	95.99 61	95.84 149	74.11 327	89.06 136	95.21 121	61.44 313	98.81 100	83.67 174	87.47 236	97.01 123

IB-MVS	80.51 15	85.24 257	83.26 269	91.19 170	92.13 251	79.86 220	91.75 265	91.29 310	83.28 205	80.66 295	88.49 314	61.28 314	98.46 118	80.99 218	79.46 324	95.25 184
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
GA-MVS		86.61 229	85.27 239	90.66 190	91.33 281	78.71 242	90.40 288	93.81 254	85.34 161	85.12 220	89.57 300	61.25 315	97.11 232	80.99 218	89.59 205	96.15 148

N_pmnet		68.89 328	68.44 331	70.23 347	89.07 332	28.79 379	88.06 324	19.50 380	69.47 351	71.86 352	84.93 344	61.24 316	91.75 354	54.70 360	77.15 335	90.15 338

EU-MVSNet		81.32 299	80.95 287	82.42 337	88.50 337	63.67 363	93.32 212	91.33 308	64.02 358	80.57 297	92.83 208	61.21 317	92.27 350	76.34 271	80.38 316	91.32 322

VDDNet		89.56 131	88.49 146	92.76 101	95.07 152	82.09 158	96.30 40	93.19 263	81.05 257	91.88 94	96.86 53	61.16 318	98.33 131	88.43 114	92.49 174	97.84 90

PVSNet_0	73.20 20	77.22 321	74.83 326	84.37 328	90.70 308	71.10 338	83.09 354	89.67 343	72.81 339	73.93 344	83.13 351	60.79 319	93.70 337	68.54 320	50.84 366	88.30 354

RRT_test8_iter05		86.90 219	86.36 203	88.52 266	93.00 234	73.27 318	94.32 164	95.96 138	85.50 157	84.26 243	92.86 205	60.76 320	97.70 179	88.32 115	82.29 282	94.60 209

SixPastTwentyTwo		83.91 274	82.90 274	86.92 304	90.99 292	70.67 342	93.48 207	91.99 291	85.54 155	77.62 323	92.11 233	60.59 321	96.87 249	76.05 275	77.75 331	93.20 278

gg-mvs-nofinetune		81.77 290	79.37 303	88.99 255	90.85 302	77.73 271	86.29 337	79.63 369	74.88 322	83.19 268	69.05 363	60.34 322	96.11 288	75.46 279	94.64 135	93.11 282

MDA-MVSNet-bldmvs		78.85 317	76.31 320	86.46 309	89.76 327	73.88 311	88.79 316	90.42 326	79.16 276	59.18 362	88.33 317	60.20 323	94.04 332	62.00 349	68.96 353	91.48 320

pmmvs6		83.42 278	81.60 282	88.87 256	88.01 345	77.87 265	94.96 116	94.24 238	74.67 323	78.80 315	91.09 268	60.17 324	96.49 269	77.06 267	75.40 341	92.23 309

ACMH	80.38 17	85.36 252	83.68 264	90.39 204	94.45 183	80.63 198	94.73 133	94.85 216	82.09 227	77.24 324	92.65 214	60.01 325	97.58 188	72.25 300	84.87 256	92.96 287
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GG-mvs-BLEND				87.94 282	89.73 328	77.91 262	87.80 326	78.23 371		80.58 296	83.86 347	59.88 326	95.33 317	71.20 303	92.22 176	90.60 336

UniMVSNet_ETH3D		87.53 196	86.37 202	91.00 182	92.44 244	78.96 240	94.74 132	95.61 167	84.07 185	85.36 217	94.52 147	59.78 327	97.34 214	82.93 181	87.88 233	96.71 134

pmmvs-eth3d		80.97 303	78.72 312	87.74 283	84.99 359	79.97 218	90.11 296	91.65 300	75.36 314	73.51 345	86.03 339	59.45 328	93.96 335	75.17 282	72.21 346	89.29 345

test_0402		81.30 300	79.17 308	87.67 285	93.19 225	78.17 257	92.98 231	91.71 297	75.25 316	76.02 334	90.31 285	59.23 329	96.37 277	50.22 363	83.63 268	88.47 353

KD-MVS_self_test		80.20 308	79.24 305	83.07 334	85.64 356	65.29 361	91.01 280	93.93 247	78.71 285	76.32 330	86.40 337	59.20 330	92.93 346	72.59 298	69.35 350	91.00 332

FMVSNet1		85.85 245	84.11 258	91.08 176	92.81 238	83.10 128	95.14 107	94.94 207	81.64 243	82.68 272	91.64 247	59.01 331	96.34 280	75.37 280	83.78 264	93.79 250

COLMAP_ROB	80.39 16	83.96 272	82.04 279	89.74 233	95.28 143	79.75 222	94.25 167	92.28 282	75.17 317	78.02 320	93.77 179	58.60 332	97.84 171	65.06 341	85.92 248	91.63 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	81.04 14	85.05 260	83.46 268	89.82 229	94.66 174	79.37 229	94.44 152	94.12 244	82.19 226	78.04 319	92.82 209	58.23 333	97.54 191	73.77 293	82.90 278	92.54 298

CMPMVS	59.16 21	80.52 305	79.20 307	84.48 327	83.98 360	67.63 356	89.95 299	93.84 253	64.79 357	66.81 358	91.14 266	57.93 334	95.17 318	76.25 272	88.10 228	90.65 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ITE_SJBPF				88.24 274	91.88 260	77.05 282		92.92 267	85.54 155	80.13 304	93.30 191	57.29 335	96.20 284	72.46 299	84.71 257	91.49 319

TESTMET0.1,1		83.74 276	82.85 275	86.42 311	89.96 324	71.21 337	89.55 301	87.88 349	77.41 296	83.37 264	87.31 331	56.71 336	93.65 338	80.62 225	92.85 169	94.40 223

UnsupCasMVSNet_eth		80.07 309	78.27 313	85.46 319	85.24 358	72.63 326	88.45 322	94.87 215	82.99 211	71.64 353	88.07 321	56.34 337	91.75 354	73.48 295	63.36 360	92.01 312

K. test v3		81.59 294	80.15 296	85.91 317	89.89 326	69.42 349	92.57 243	87.71 351	85.56 154	73.44 346	89.71 298	55.58 338	95.52 310	77.17 264	69.76 349	92.78 294

test-mter		84.54 268	83.64 266	87.25 296	90.95 294	71.67 333	89.55 301	89.88 340	79.17 275	84.54 229	87.95 322	55.56 339	95.11 320	81.82 203	93.37 158	94.97 190

lessismore_v0				86.04 313	88.46 338	68.78 351		80.59 367		73.01 348	90.11 289	55.39 340	96.43 275	75.06 284	65.06 357	92.90 289

MVS-HIRNet		73.70 325	72.20 328	78.18 343	91.81 263	56.42 369	82.94 355	82.58 363	55.24 362	68.88 355	66.48 364	55.32 341	95.13 319	58.12 357	88.42 223	83.01 358

test2506		87.21 211	86.28 208	90.02 222	95.62 131	73.64 314	96.25 46	71.38 373	87.89 100	90.45 116	96.65 66	55.29 342	98.09 153	86.03 145	96.94 95	98.33 45

new-patchmatchnet		76.41 323	75.17 325	80.13 339	82.65 365	59.61 365	87.66 330	91.08 313	78.23 292	69.85 354	83.22 350	54.76 343	91.63 356	64.14 344	64.89 358	89.16 347

Anonymous202405211		87.68 185	86.13 212	92.31 126	96.66 89	80.74 196	94.87 124	91.49 305	80.47 261	89.46 130	95.44 113	54.72 344	98.23 137	82.19 195	89.89 199	97.97 80

XVG-ACMP-BASELINE		86.00 241	84.84 249	89.45 244	91.20 283	78.00 260	91.70 268	95.55 170	85.05 170	82.97 269	92.25 227	54.49 345	97.48 195	82.93 181	87.45 238	92.89 290

USDC		82.76 281	81.26 286	87.26 295	91.17 285	74.55 304	89.27 307	93.39 260	78.26 291	75.30 337	92.08 235	54.43 346	96.63 256	71.64 301	85.79 251	90.61 334

AllTest		83.42 278	81.39 284	89.52 241	95.01 153	77.79 268	93.12 224	90.89 321	77.41 296	76.12 332	93.34 187	54.08 347	97.51 193	68.31 323	84.27 261	93.26 272

TestCases				89.52 241	95.01 153	77.79 268		90.89 321	77.41 296	76.12 332	93.34 187	54.08 347	97.51 193	68.31 323	84.27 261	93.26 272

KD-MVS_2432*1600		78.50 318	76.02 323	85.93 315	86.22 351	74.47 305	84.80 345	92.33 279	79.29 273	76.98 326	85.92 340	53.81 349	93.97 333	67.39 328	57.42 363	89.36 342

miper_refine_blended		78.50 318	76.02 323	85.93 315	86.22 351	74.47 305	84.80 345	92.33 279	79.29 273	76.98 326	85.92 340	53.81 349	93.97 333	67.39 328	57.42 363	89.36 342

MIMVSNet		82.59 284	80.53 289	88.76 258	91.51 271	78.32 253	86.57 336	90.13 332	79.32 272	80.70 294	88.69 313	52.98 351	93.07 345	66.03 336	88.86 216	94.90 197

FMVSNet5		81.52 296	79.60 302	87.27 294	91.17 285	77.95 261	91.49 272	92.26 283	76.87 301	76.16 331	87.91 324	51.67 352	92.34 349	67.74 327	81.16 297	91.52 318

testgi		80.94 304	80.20 295	83.18 333	87.96 346	66.29 357	91.28 274	90.70 325	83.70 192	78.12 318	92.84 207	51.37 353	90.82 357	63.34 345	82.46 281	92.43 302

Anonymous20240521		80.44 306	79.21 306	84.11 331	85.75 355	67.89 353	92.86 235	93.23 262	75.61 313	75.59 336	87.47 329	50.03 354	94.33 328	71.14 306	81.21 296	90.12 339

UnsupCasMVSNet_bld		76.23 324	73.27 327	85.09 324	83.79 361	72.92 320	85.65 342	93.47 259	71.52 344	68.84 356	79.08 357	49.77 355	93.21 342	66.81 335	60.52 362	89.13 349

OpenMVS_ROB	74.94 19	79.51 313	77.03 319	86.93 303	87.00 348	76.23 293	92.33 250	90.74 324	68.93 352	74.52 341	88.23 319	49.58 356	96.62 257	57.64 358	84.29 260	87.94 355

TDRefinement		79.81 311	77.34 315	87.22 299	79.24 367	75.48 300	93.12 224	92.03 289	76.45 303	75.01 338	91.58 252	49.19 357	96.44 274	70.22 311	69.18 352	89.75 341

MIMVSNet1		79.38 314	77.28 316	85.69 318	86.35 350	73.67 313	91.61 271	92.75 272	78.11 294	72.64 349	88.12 320	48.16 358	91.97 353	60.32 353	77.49 333	91.43 321

MVS_0304		83.46 277	81.92 280	88.10 278	90.63 310	77.49 276	93.26 219	93.75 255	80.04 266	80.44 299	87.24 333	47.94 359	95.55 308	75.79 276	88.16 227	91.26 324

LF4IMVS		80.37 307	79.07 310	84.27 330	86.64 349	69.87 348	89.39 306	91.05 315	76.38 304	74.97 339	90.00 292	47.85 360	94.25 331	74.55 289	80.82 308	88.69 351

EG-PatchMatch MVS		82.37 286	80.34 292	88.46 267	90.27 317	79.35 230	92.80 237	94.33 234	77.14 300	73.26 347	90.18 287	47.47 361	96.72 252	70.25 309	87.32 241	89.30 344

TinyColmap		79.76 312	77.69 314	85.97 314	91.71 266	73.12 319	89.55 301	90.36 329	75.03 318	72.03 351	90.19 286	46.22 362	96.19 286	63.11 346	81.03 302	88.59 352

tmp_tt		35.64 340	39.24 342	24.84 356	14.87 380	23.90 380	62.71 366	51.51 379	6.58 374	36.66 370	62.08 367	44.37 363	30.34 376	52.40 362	22.00 373	20.27 371

new_pmnet		72.15 326	70.13 329	78.20 342	82.95 364	65.68 358	83.91 350	82.40 364	62.94 359	64.47 359	79.82 356	42.85 364	86.26 364	57.41 359	74.44 342	82.65 360

EGC-MVSNET		61.97 331	56.37 335	78.77 341	89.63 329	73.50 315	89.12 311	82.79 362	0.21 377	1.24 378	84.80 345	39.48 365	90.04 359	44.13 365	75.94 340	72.79 364

pmmvs3		71.81 327	68.71 330	81.11 338	75.86 368	70.42 344	86.74 334	83.66 361	58.95 361	68.64 357	80.89 355	36.93 366	89.52 360	63.10 347	63.59 359	83.39 357

PM-MVS		78.11 320	76.12 322	84.09 332	83.54 362	70.08 346	88.97 314	85.27 357	79.93 267	74.73 340	86.43 336	34.70 367	93.48 339	79.43 242	72.06 347	88.72 350

ambc				83.06 335	79.99 366	63.51 364	77.47 361	92.86 268		74.34 343	84.45 346	28.74 368	95.06 322	73.06 297	68.89 354	90.61 334

test_method		50.52 336	48.47 338	56.66 352	52.26 378	18.98 381	41.51 370	81.40 366	10.10 372	44.59 367	75.01 359	28.51 369	68.16 370	53.54 361	49.31 367	82.83 359

DeepMVS_CX				56.31 353	74.23 369	51.81 372		56.67 378	44.85 366	48.54 366	75.16 358	27.87 370	58.74 374	40.92 367	52.22 365	58.39 367

FPMVS		64.63 330	62.55 332	70.88 346	70.80 370	56.71 367	84.42 348	84.42 359	51.78 364	49.57 364	81.61 354	23.49 371	81.48 367	40.61 368	76.25 339	74.46 363

ANet_high		58.88 333	54.22 337	72.86 345	56.50 377	56.67 368	80.75 359	86.00 354	73.09 336	37.39 369	64.63 366	22.17 372	79.49 369	43.51 366	23.96 371	82.43 361

EMVS		42.07 339	41.12 341	44.92 355	63.45 375	35.56 378	73.65 362	63.48 375	33.05 370	26.88 374	45.45 371	21.27 373	67.14 372	19.80 373	23.02 372	32.06 370

Gipuma		57.99 334	54.91 336	67.24 349	88.51 336	65.59 359	52.21 368	90.33 330	43.58 367	42.84 368	51.18 369	20.29 374	85.07 365	34.77 369	70.45 348	51.05 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 338	42.29 340	46.03 354	65.58 373	37.41 376	73.51 363	64.62 374	33.99 369	28.47 373	47.87 370	19.90 375	67.91 371	22.23 372	24.45 370	32.77 369

PMMVS2		59.60 332	56.40 334	69.21 348	68.83 371	46.58 374	73.02 365	77.48 372	55.07 363	49.21 365	72.95 362	17.43 376	80.04 368	49.32 364	44.33 368	80.99 362

LCM-MVSNet		66.00 329	62.16 333	77.51 344	64.51 374	58.29 366	83.87 351	90.90 320	48.17 365	54.69 363	73.31 361	16.83 377	86.75 363	65.47 337	61.67 361	87.48 356

PMVS	47.18 22	52.22 335	48.46 339	63.48 350	45.72 379	46.20 375	73.41 364	78.31 370	41.03 368	30.06 371	65.68 365	6.05 378	83.43 366	30.04 370	65.86 356	60.80 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 337	38.59 343	57.77 351	56.52 376	48.77 373	55.38 367	58.64 377	29.33 371	28.96 372	52.65 368	4.68 379	64.62 373	28.11 371	33.07 369	59.93 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		21.27 342	20.48 345	23.63 357	68.59 372	36.41 377	49.57 369	6.85 381	9.37 373	7.89 375	4.46 377	4.03 380	31.37 375	17.47 374	16.07 374	3.12 372

test123		8.76 344	11.22 347	1.39 358	0.85 382	0.97 382	85.76 341	0.35 383	0.54 376	2.45 377	8.14 376	0.60 381	0.48 377	2.16 376	0.17 376	2.71 373

testmvs		8.92 343	11.52 346	1.12 359	1.06 381	0.46 383	86.02 338	0.65 382	0.62 375	2.74 376	9.52 375	0.31 382	0.45 378	2.38 375	0.39 375	2.46 374

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		7.82 345	10.43 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	93.88 173	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					98.86 1	85.54 74	98.29 1	97.49 5	89.79 45	96.29 15

MSC_two_6792asdad				96.52 1	97.78 57	90.86 1		96.85 67					99.61 3	96.03 1	99.06 9	99.07 5

No_MVS				96.52 1	97.78 57	90.86 1		96.85 67					99.61 3	96.03 1	99.06 9	99.07 5

eth-test2					0.00 383

eth-test					0.00 383

IU-MVS					98.77 5	86.00 56		96.84 69	81.26 252	97.26 7				95.50 10	99.13 3	99.03 7

save fliter					97.85 50	85.63 72	95.21 101	96.82 73	89.44 52

test_0728_SECOND				95.01 17	98.79 2	86.43 43	97.09 15	97.49 5					99.61 3	95.62 8	99.08 7	98.99 8

GSMVS																96.12 151

test_part2					98.55 13	87.22 18				96.40 14

MTGPA								96.97 53

MTMP							96.16 49	60.64 376

gm-plane-assit					89.60 330	68.00 352			77.28 299		88.99 305		97.57 189	79.44 241

test9_res														91.91 68	98.71 34	98.07 73

agg_prior2														90.54 93	98.68 39	98.27 55

agg_prior					97.38 70	85.92 63		96.72 85		92.16 88			98.97 83

test_prior4						85.96 60	94.11 175

test_prior				93.82 67	97.29 75	84.49 90		96.88 64					98.87 91			98.11 71

旧先验2							93.36 211		71.25 346	94.37 31			97.13 231	86.74 136

新几何2							93.11 226

无先验							93.28 218	96.26 115	73.95 328				99.05 63	80.56 226		96.59 137

原ACMM2							92.94 233

testdata2													98.75 103	78.30 252

testdata1							92.15 256		87.94 96

plane_prior7					94.70 172	82.74 141

plane_prior5								96.22 120					98.12 143	88.15 116	89.99 195	94.63 206

plane_prior4											94.86 132

plane_prior3						82.75 139			90.26 35	86.91 172

plane_prior2							95.85 68		90.81 20

plane_prior1					94.59 176

plane_prior						82.73 142	95.21 101		89.66 49						89.88 200

n2								0.00 384

nn								0.00 384

door-mid								85.49 355

test11								96.57 99

door								85.33 356

HQP5-MVS						81.56 169

HQP-NCC					94.17 191		94.39 157		88.81 69	85.43 210

ACMP_Plane					94.17 191		94.39 157		88.81 69	85.43 210

BP-MVS														87.11 133

HQP4-MVS										85.43 210			97.96 165			94.51 216

HQP3-MVS								96.04 134							89.77 202

NP-MVS					94.37 186	82.42 151					93.98 166

ACMMP++_ref															87.47 236

ACMMP++															88.01 231

High-resolution multi-view benchmark