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.

MSC_two_6792asdad				98.86 1	98.67 64	96.94 1		97.93 106					99.86 8	97.68 2	99.67 6	99.77 1

No_MVS				98.86 1	98.67 64	96.94 1		97.93 106					99.86 8	97.68 2	99.67 6	99.77 1

OPU-MVS				98.55 3	98.82 58	96.86 3	98.25 33				98.26 56	96.04 2	99.24 124	95.36 74	99.59 17	99.56 26

HPM-MVS++		97.34 14	96.97 18	98.47 5	99.08 40	96.16 4	97.55 103	97.97 102	95.59 4	96.61 63	97.89 78	92.57 33	99.84 22	95.95 51	99.51 33	99.40 57

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 40	98.28 27					99.86 8	97.52 4	99.67 6	99.75 5

CNVR-MVS		97.68 6	97.44 9	98.37 7	98.90 53	95.86 6	97.27 130	98.08 67	95.81 3	97.87 27	98.31 49	94.26 13	99.68 50	97.02 13	99.49 38	99.57 23

DPE-MVS		97.86 4	97.65 5	98.47 5	99.17 34	95.78 7	97.21 139	98.35 19	95.16 16	98.71 12	98.80 11	95.05 10	99.89 3	96.70 24	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_part2					99.28 27	95.74 8				98.10 21

DPM-MVS		95.69 74	94.92 87	98.01 22	98.08 111	95.71 9	95.27 273	97.62 141	90.43 180	95.55 107	97.07 134	91.72 51	99.50 100	89.62 190	98.94 92	98.82 114

SMA-MVS		97.35 13	97.03 15	98.30 8	99.06 42	95.42 10	97.94 61	98.18 49	90.57 177	98.85 9	98.94 1	93.33 20	99.83 25	96.72 23	99.68 4	99.63 13
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
DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 64	95.39 11	99.29 1	98.28 27	94.78 35	98.93 6	98.87 6	96.04 2	99.86 8	97.45 8	99.58 22	99.59 19

IU-MVS					99.42 7	95.39 11		97.94 105	90.40 181	98.94 5				97.41 11	99.66 10	99.74 7

DVP-MVS		97.91 3	97.81 3	98.22 12	99.45 3	95.36 13	98.21 40	97.85 116	94.92 26	98.73 10	98.87 6	95.08 8	99.84 22	97.52 4	99.67 6	99.48 45
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 26	98.29 25	94.92 26	98.99 4	98.92 2	95.08 8

MCST-MVS		97.18 17	96.84 26	98.20 13	99.30 26	95.35 15	97.12 147	98.07 73	93.54 71	96.08 84	97.69 96	93.86 16	99.71 41	96.50 29	99.39 51	99.55 30

3Dnovator+	91.43 4	95.40 81	94.48 101	98.16 15	96.90 167	95.34 16	98.48 18	97.87 112	94.65 41	88.53 268	98.02 73	83.69 166	99.71 41	93.18 125	98.96 91	99.44 51

ETH3D-3000-0.1		97.07 23	96.71 37	98.14 16	98.90 53	95.33 17	97.68 88	98.24 37	91.57 138	97.90 25	98.37 38	92.61 32	99.66 55	95.59 70	99.51 33	99.43 53

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 18	98.25 33	98.27 30	95.13 17	99.19 1	98.89 4	95.54 5	99.85 17	97.52 4	99.66 10	99.56 26

test_241102_ONE					99.42 7	95.30 18		98.27 30	95.09 21	99.19 1	98.81 10	95.54 5	99.65 56

SF-MVS		97.39 11	97.13 12	98.17 14	99.02 45	95.28 20	98.23 37	98.27 30	92.37 116	98.27 18	98.65 15	93.33 20	99.72 38	96.49 30	99.52 29	99.51 38

test_one_0601					99.32 24	95.20 21		98.25 35	95.13 17	98.48 16	98.87 6	95.16 7

ETH3D cwj APD-0.16		96.56 51	96.06 60	98.05 20	98.26 97	95.19 22	96.99 157	98.05 83	89.85 191	97.26 39	98.22 59	91.80 50	99.69 47	94.84 88	99.28 63	99.27 71

alignmvs		95.87 72	95.23 81	97.78 36	97.56 142	95.19 22	97.86 66	97.17 193	94.39 47	96.47 71	96.40 175	85.89 138	99.20 126	96.21 42	95.11 182	98.95 101

ACMMP_NAP		97.20 16	96.86 23	98.23 11	99.09 38	95.16 24	97.60 99	98.19 47	92.82 104	97.93 24	98.74 13	91.60 56	99.86 8	96.26 35	99.52 29	99.67 10

canonicalmvs		96.02 66	95.45 74	97.75 40	97.59 139	95.15 25	98.28 29	97.60 142	94.52 43	96.27 78	96.12 187	87.65 112	99.18 129	96.20 43	94.82 186	98.91 105

ETH3 D test6400		96.16 63	95.52 71	98.07 19	98.90 53	95.06 26	97.03 149	98.21 43	88.16 243	96.64 61	97.70 95	91.18 66	99.67 52	92.44 136	99.47 40	99.48 45

NCCC		97.30 15	97.03 15	98.11 17	98.77 59	95.06 26	97.34 122	98.04 84	95.96 2	97.09 49	97.88 80	93.18 23	99.71 41	95.84 56	99.17 76	99.56 26

APD-MVS		96.95 32	96.60 41	98.01 22	99.03 44	94.93 28	97.72 84	98.10 64	91.50 140	98.01 22	98.32 48	92.33 38	99.58 74	94.85 87	99.51 33	99.53 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APDe-MVS		97.82 5	97.73 4	98.08 18	99.15 35	94.82 29	98.81 5	98.30 24	94.76 37	98.30 17	98.90 3	93.77 17	99.68 50	97.93 1	99.69 3	99.75 5

MP-MVS-pluss		96.70 45	96.27 55	97.98 24	99.23 32	94.71 30	96.96 160	98.06 76	90.67 168	95.55 107	98.78 12	91.07 68	99.86 8	96.58 27	99.55 25	99.38 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testtj		96.93 34	96.56 44	98.05 20	99.10 36	94.66 31	97.78 75	98.22 42	92.74 107	97.59 28	98.20 63	91.96 47	99.86 8	94.21 101	99.25 69	99.63 13

ZD-MVS					99.05 43	94.59 32		98.08 67	89.22 207	97.03 51	98.10 66	92.52 35	99.65 56	94.58 97	99.31 59

nrg030		94.05 120	93.31 130	96.27 110	95.22 254	94.59 32	98.34 24	97.46 159	92.93 101	91.21 200	96.64 157	87.23 122	98.22 215	94.99 85	85.80 291	95.98 231

SD-MVS		97.41 10	97.53 7	97.06 74	98.57 77	94.46 34	97.92 63	98.14 56	94.82 32	99.01 3	98.55 21	94.18 14	97.41 309	96.94 14	99.64 13	99.32 64
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
CDPH-MVS		95.97 68	95.38 77	97.77 38	98.93 49	94.44 35	96.35 216	97.88 110	86.98 273	96.65 60	97.89 78	91.99 46	99.47 103	92.26 137	99.46 42	99.39 58

zzz-MVS		97.07 23	96.77 33	97.97 25	99.37 17	94.42 36	97.15 145	98.08 67	95.07 22	96.11 82	98.59 17	90.88 73	99.90 1	96.18 44	99.50 36	99.58 21

MTAPA		97.08 22	96.78 32	97.97 25	99.37 17	94.42 36	97.24 132	98.08 67	95.07 22	96.11 82	98.59 17	90.88 73	99.90 1	96.18 44	99.50 36	99.58 21

DeepC-MVS_fast	93.89 2	96.93 34	96.64 40	97.78 36	98.64 72	94.30 38	97.41 114	98.04 84	94.81 33	96.59 65	98.37 38	91.24 64	99.64 64	95.16 77	99.52 29	99.42 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xxxxxxxxxxxxxcwj		97.36 12	97.20 11	97.83 29	98.91 51	94.28 39	97.02 152	97.22 190	95.35 8	98.27 18	98.65 15	93.33 20	99.72 38	96.49 30	99.52 29	99.51 38

save fliter					98.91 51	94.28 39	97.02 152	98.02 91	95.35 8

test12				97.65 47	98.46 79	94.26 41		97.66 135		95.52 111		90.89 72	99.46 104		99.25 69	99.22 74

SteuartSystems-ACMMP		97.62 7	97.53 7	97.87 27	98.39 85	94.25 42	98.43 20	98.27 30	95.34 10	98.11 20	98.56 19	94.53 12	99.71 41	96.57 28	99.62 15	99.65 11
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		97.42 9	97.33 10	97.69 45	99.25 29	94.24 43	98.07 50	97.85 116	93.72 63	98.57 13	98.35 40	93.69 18	99.40 112	97.06 12	99.46 42	99.44 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TEST9					98.70 62	94.19 44	96.41 208	98.02 91	88.17 241	96.03 86	97.56 112	92.74 27	99.59 71

train_agg		96.30 59	95.83 67	97.72 42	98.70 62	94.19 44	96.41 208	98.02 91	88.58 229	96.03 86	97.56 112	92.73 28	99.59 71	95.04 81	99.37 56	99.39 58

DP-MVS Recon		95.68 75	95.12 85	97.37 57	99.19 33	94.19 44	97.03 149	98.08 67	88.35 236	95.09 117	97.65 101	89.97 87	99.48 102	92.08 146	98.59 103	98.44 144

RRT_MVS		93.21 147	92.32 163	95.91 126	94.92 269	94.15 47	96.92 164	96.86 227	91.42 144	91.28 197	96.43 172	79.66 244	98.10 230	93.29 123	90.06 252	95.46 255

GST-MVS		96.85 39	96.52 46	97.82 32	99.36 20	94.14 48	98.29 28	98.13 57	92.72 108	96.70 56	98.06 70	91.35 62	99.86 8	94.83 89	99.28 63	99.47 48

ZNCC-MVS		96.96 31	96.67 39	97.85 28	99.37 17	94.12 49	98.49 17	98.18 49	92.64 111	96.39 75	98.18 64	91.61 55	99.88 4	95.59 70	99.55 25	99.57 23

HFP-MVS		97.14 20	96.92 21	97.83 29	99.42 7	94.12 49	98.52 13	98.32 21	93.21 84	97.18 42	98.29 52	92.08 42	99.83 25	95.63 65	99.59 17	99.54 33

#test#		97.02 27	96.75 34	97.83 29	99.42 7	94.12 49	98.15 45	98.32 21	92.57 112	97.18 42	98.29 52	92.08 42	99.83 25	95.12 79	99.59 17	99.54 33

PHI-MVS		96.77 43	96.46 50	97.71 44	98.40 83	94.07 52	98.21 40	98.45 15	89.86 189	97.11 48	98.01 74	92.52 35	99.69 47	96.03 50	99.53 28	99.36 62

test_8					98.67 64	94.06 53	96.37 215	98.01 94	88.58 229	95.98 91	97.55 114	92.73 28	99.58 74

XVS		97.18 17	96.96 19	97.81 33	99.38 15	94.03 54	98.59 10	98.20 45	94.85 28	96.59 65	98.29 52	91.70 53	99.80 30	95.66 60	99.40 49	99.62 15

X-MVStestdata		91.71 202	89.67 262	97.81 33	99.38 15	94.03 54	98.59 10	98.20 45	94.85 28	96.59 65	32.69 371	91.70 53	99.80 30	95.66 60	99.40 49	99.62 15

ACMMPR		97.07 23	96.84 26	97.79 35	99.44 6	93.88 56	98.52 13	98.31 23	93.21 84	97.15 44	98.33 46	91.35 62	99.86 8	95.63 65	99.59 17	99.62 15

Regformer-2		97.16 19	96.99 17	97.67 46	98.32 91	93.84 57	96.83 172	98.10 64	95.24 11	97.49 30	98.25 57	92.57 33	99.61 65	96.80 19	99.29 61	99.56 26

MP-MVS		96.77 43	96.45 51	97.72 42	99.39 14	93.80 58	98.41 21	98.06 76	93.37 79	95.54 109	98.34 43	90.59 78	99.88 4	94.83 89	99.54 27	99.49 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
agg_prior1		96.22 62	95.77 68	97.56 51	98.67 64	93.79 59	96.28 224	98.00 96	88.76 226	95.68 101	97.55 114	92.70 30	99.57 82	95.01 82	99.32 57	99.32 64

agg_prior					98.67 64	93.79 59		98.00 96		95.68 101			99.57 82

region2R		97.07 23	96.84 26	97.77 38	99.46 2	93.79 59	98.52 13	98.24 37	93.19 87	97.14 45	98.34 43	91.59 57	99.87 7	95.46 73	99.59 17	99.64 12

MSP-MVS		97.59 8	97.54 6	97.73 41	99.40 12	93.77 62	98.53 12	98.29 25	95.55 5	98.56 14	97.81 88	93.90 15	99.65 56	96.62 25	99.21 73	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
test_prior4						93.66 63	96.42 207

1121		94.71 105	93.83 110	97.34 58	98.57 77	93.64 64	96.04 237	97.73 124	81.56 336	95.68 101	97.85 84	90.23 81	99.65 56	87.68 231	99.12 82	98.73 119

新几何1				97.32 59	98.60 73	93.59 65		97.75 121	81.58 335	95.75 98	97.85 84	90.04 85	99.67 52	86.50 253	99.13 79	98.69 123

CP-MVS		97.02 27	96.81 29	97.64 49	99.33 23	93.54 66	98.80 6	98.28 27	92.99 94	96.45 73	98.30 51	91.90 48	99.85 17	95.61 67	99.68 4	99.54 33

PGM-MVS		96.81 41	96.53 45	97.65 47	99.35 22	93.53 67	97.65 92	98.98 1	92.22 119	97.14 45	98.44 30	91.17 67	99.85 17	94.35 99	99.46 42	99.57 23

Regformer-1		97.10 21	96.96 19	97.54 52	98.32 91	93.48 68	96.83 172	97.99 100	95.20 13	97.46 31	98.25 57	92.48 37	99.58 74	96.79 21	99.29 61	99.55 30

mPP-MVS		96.86 38	96.60 41	97.64 49	99.40 12	93.44 69	98.50 16	98.09 66	93.27 83	95.95 92	98.33 46	91.04 69	99.88 4	95.20 76	99.57 24	99.60 18

TSAR-MVS + GP.		96.69 46	96.49 47	97.27 63	98.31 93	93.39 70	96.79 176	96.72 233	94.17 51	97.44 33	97.66 100	92.76 26	99.33 117	96.86 17	97.76 127	99.08 87

CANet		96.39 57	96.02 61	97.50 53	97.62 136	93.38 71	97.02 152	97.96 103	95.42 7	94.86 119	97.81 88	87.38 119	99.82 28	96.88 16	99.20 74	99.29 66

旧先验1					98.38 86	93.38 71		97.75 121			98.09 68	92.30 41			99.01 89	99.16 77

3Dnovator	91.36 5	95.19 90	94.44 103	97.44 55	96.56 185	93.36 73	98.65 9	98.36 16	94.12 52	89.25 252	98.06 70	82.20 201	99.77 32	93.41 121	99.32 57	99.18 76

FOURS1					99.55 1	93.34 74	99.29 1	98.35 19	94.98 25	98.49 15

UniMVSNet (Re)		93.31 144	92.55 154	95.61 144	95.39 237	93.34 74	97.39 118	98.71 5	93.14 89	90.10 223	94.83 247	87.71 110	98.03 245	91.67 157	83.99 319	95.46 255

SR-MVS		97.01 29	96.86 23	97.47 54	99.09 38	93.27 76	97.98 55	98.07 73	93.75 62	97.45 32	98.48 27	91.43 59	99.59 71	96.22 38	99.27 65	99.54 33

DELS-MVS		96.61 49	96.38 53	97.30 60	97.79 126	93.19 77	95.96 243	98.18 49	95.23 12	95.87 93	97.65 101	91.45 58	99.70 46	95.87 52	99.44 46	99.00 97
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
DeepC-MVS	93.07 3	96.06 64	95.66 69	97.29 61	97.96 114	93.17 78	97.30 128	98.06 76	93.92 56	93.38 150	98.66 14	86.83 125	99.73 35	95.60 69	99.22 72	98.96 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		96.69 46	96.45 51	97.40 56	99.36 20	93.11 79	98.87 4	98.06 76	91.17 156	96.40 74	97.99 75	90.99 70	99.58 74	95.61 67	99.61 16	99.49 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NR-MVSNet		92.34 180	91.27 197	95.53 150	94.95 267	93.05 80	97.39 118	98.07 73	92.65 110	84.46 322	95.71 211	85.00 149	97.77 278	89.71 186	83.52 326	95.78 240

test_prior3		96.46 54	96.20 58	97.23 65	98.67 64	92.99 81	96.35 216	98.00 96	92.80 105	96.03 86	97.59 108	92.01 44	99.41 110	95.01 82	99.38 52	99.29 66

test_prior				97.23 65	98.67 64	92.99 81		98.00 96					99.41 110			99.29 66

UA-Net		95.95 69	95.53 70	97.20 69	97.67 131	92.98 83	97.65 92	98.13 57	94.81 33	96.61 63	98.35 40	88.87 95	99.51 97	90.36 176	97.35 138	99.11 85

VNet		95.89 70	95.45 74	97.21 68	98.07 112	92.94 84	97.50 106	98.15 54	93.87 57	97.52 29	97.61 107	85.29 145	99.53 92	95.81 57	95.27 178	99.16 77

UniMVSNet_NR-MVSNet		93.37 142	92.67 149	95.47 157	95.34 243	92.83 85	97.17 142	98.58 10	92.98 99	90.13 219	95.80 203	88.37 104	97.85 268	91.71 153	83.93 320	95.73 246

DU-MVS		92.90 163	92.04 168	95.49 154	94.95 267	92.83 85	97.16 143	98.24 37	93.02 92	90.13 219	95.71 211	83.47 170	97.85 268	91.71 153	83.93 320	95.78 240

HPM-MVS_fast		96.51 52	96.27 55	97.22 67	99.32 24	92.74 87	98.74 7	98.06 76	90.57 177	96.77 53	98.35 40	90.21 82	99.53 92	94.80 92	99.63 14	99.38 60

OpenMVS	89.19 12	92.86 165	91.68 181	96.40 99	95.34 243	92.73 88	98.27 30	98.12 59	84.86 305	85.78 311	97.75 92	78.89 259	99.74 34	87.50 238	98.65 101	96.73 210

EPNet		95.20 89	94.56 96	97.14 71	92.80 332	92.68 89	97.85 69	94.87 319	96.64 1	92.46 166	97.80 90	86.23 132	99.65 56	93.72 114	98.62 102	99.10 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		92.21 190	91.10 204	95.51 151	97.80 125	92.66 90	98.02 53	97.68 133	89.79 194	88.80 262	96.02 192	76.85 282	98.18 221	90.86 168	84.11 318	95.69 247

Regformer-4		96.97 30	96.87 22	97.25 64	98.34 88	92.66 90	96.96 160	98.01 94	95.12 20	97.14 45	98.42 33	91.82 49	99.61 65	96.90 15	99.13 79	99.50 41

QAPM		93.45 140	92.27 164	96.98 77	96.77 174	92.62 92	98.39 23	98.12 59	84.50 310	88.27 274	97.77 91	82.39 198	99.81 29	85.40 272	98.81 95	98.51 133

ACMMP		96.27 60	95.93 63	97.28 62	99.24 30	92.62 92	98.25 33	98.81 3	92.99 94	94.56 125	98.39 37	88.96 94	99.85 17	94.57 98	97.63 128	99.36 62
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
CNLPA		94.28 110	93.53 120	96.52 88	98.38 86	92.55 94	96.59 199	96.88 224	90.13 185	91.91 182	97.24 125	85.21 146	99.09 140	87.64 234	97.83 123	97.92 168

PCF-MVS	89.48 11	91.56 210	89.95 250	96.36 104	96.60 180	92.52 95	92.51 336	97.26 187	79.41 347	88.90 256	96.56 166	84.04 163	99.55 87	77.01 337	97.30 140	97.01 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	89.66 9	93.87 126	92.95 138	96.63 83	97.10 154	92.49 96	95.64 257	96.64 242	89.05 211	93.00 158	95.79 206	85.77 141	99.45 106	89.16 205	94.35 192	97.96 165

ETV-MVS		96.02 66	95.89 65	96.40 99	97.16 150	92.44 97	97.47 111	97.77 119	94.55 42	96.48 70	94.51 261	91.23 65	98.92 157	95.65 63	98.19 114	97.82 176

VPA-MVSNet		93.24 146	92.48 159	95.51 151	95.70 226	92.39 98	97.86 66	98.66 9	92.30 117	92.09 180	95.37 227	80.49 227	98.40 202	93.95 107	85.86 290	95.75 244

test1172		96.93 34	96.86 23	97.15 70	99.10 36	92.34 99	97.96 60	98.04 84	93.79 61	97.35 37	98.53 23	91.40 60	99.56 84	96.30 34	99.30 60	99.55 30

SR-MVS-dyc-post		96.88 37	96.80 30	97.11 73	99.02 45	92.34 99	97.98 55	98.03 87	93.52 73	97.43 35	98.51 24	91.40 60	99.56 84	96.05 47	99.26 67	99.43 53

RE-MVS-def			96.72 36		99.02 45	92.34 99	97.98 55	98.03 87	93.52 73	97.43 35	98.51 24	90.71 76		96.05 47	99.26 67	99.43 53

APD-MVS_3200maxsize		96.81 41	96.71 37	97.12 72	99.01 48	92.31 102	97.98 55	98.06 76	93.11 90	97.44 33	98.55 21	90.93 71	99.55 87	96.06 46	99.25 69	99.51 38

MVS_111021_HR		96.68 48	96.58 43	96.99 76	98.46 79	92.31 102	96.20 231	98.90 2	94.30 50	95.86 94	97.74 93	92.33 38	99.38 115	96.04 49	99.42 47	99.28 69

FMVSNet3		91.78 200	90.69 220	95.03 170	96.53 187	92.27 104	97.02 152	96.93 216	89.79 194	89.35 246	94.65 257	77.01 281	97.47 303	86.12 260	88.82 262	95.35 265

test222					98.24 98	92.21 105	95.33 268	97.60 142	79.22 348	95.25 113	97.84 87	88.80 97			99.15 77	98.72 120

FMVSNet2		91.31 226	90.08 245	94.99 171	96.51 188	92.21 105	97.41 114	96.95 214	88.82 222	88.62 265	94.75 251	73.87 301	97.42 308	85.20 275	88.55 267	95.35 265

abl_6		96.40 56	96.21 57	96.98 77	98.89 56	92.20 107	97.89 64	98.03 87	93.34 82	97.22 41	98.42 33	87.93 108	99.72 38	95.10 80	99.07 86	99.02 90

MAR-MVS		94.22 111	93.46 124	96.51 91	98.00 113	92.19 108	97.67 89	97.47 157	88.13 245	93.00 158	95.84 200	84.86 151	99.51 97	87.99 220	98.17 116	97.83 175
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
CANet_DTU		94.37 108	93.65 116	96.55 87	96.46 192	92.13 109	96.21 230	96.67 241	94.38 48	93.53 146	97.03 136	79.34 248	99.71 41	90.76 170	98.45 109	97.82 176

TranMVSNet+NR-MVSNet		92.50 173	91.63 182	95.14 166	94.76 278	92.07 110	97.53 104	98.11 62	92.90 102	89.56 240	96.12 187	83.16 175	97.60 292	89.30 197	83.20 329	95.75 244

WTY-MVS		94.71 105	94.02 107	96.79 79	97.71 130	92.05 111	96.59 199	97.35 181	90.61 174	94.64 123	96.93 138	86.41 131	99.39 113	91.20 166	94.71 190	98.94 102

FIs		94.09 118	93.70 113	95.27 161	95.70 226	92.03 112	98.10 47	98.68 7	93.36 81	90.39 210	96.70 152	87.63 113	97.94 259	92.25 139	90.50 249	95.84 236

Regformer-3		96.85 39	96.80 30	97.01 75	98.34 88	92.02 113	96.96 160	97.76 120	95.01 24	97.08 50	98.42 33	91.71 52	99.54 89	96.80 19	99.13 79	99.48 45

API-MVS		94.84 101	94.49 100	95.90 127	97.90 120	92.00 114	97.80 73	97.48 154	89.19 208	94.81 120	96.71 150	88.84 96	99.17 130	88.91 208	98.76 97	96.53 213

sss		94.51 107	93.80 111	96.64 81	97.07 155	91.97 115	96.32 220	98.06 76	88.94 216	94.50 126	96.78 146	84.60 153	99.27 122	91.90 147	96.02 163	98.68 124

ab-mvs		93.57 137	92.55 154	96.64 81	97.28 145	91.96 116	95.40 265	97.45 165	89.81 193	93.22 156	96.28 180	79.62 245	99.46 104	90.74 171	93.11 207	98.50 134

MSLP-MVS++		96.94 33	97.06 14	96.59 86	98.72 61	91.86 117	97.67 89	98.49 12	94.66 40	97.24 40	98.41 36	92.31 40	98.94 156	96.61 26	99.46 42	98.96 99

xiu_mvs_v1_base_debu		95.01 92	94.76 90	95.75 134	96.58 182	91.71 118	96.25 226	97.35 181	92.99 94	96.70 56	96.63 161	82.67 189	99.44 107	96.22 38	97.46 131	96.11 227

xiu_mvs_v1_base		95.01 92	94.76 90	95.75 134	96.58 182	91.71 118	96.25 226	97.35 181	92.99 94	96.70 56	96.63 161	82.67 189	99.44 107	96.22 38	97.46 131	96.11 227

xiu_mvs_v1_base_debi		95.01 92	94.76 90	95.75 134	96.58 182	91.71 118	96.25 226	97.35 181	92.99 94	96.70 56	96.63 161	82.67 189	99.44 107	96.22 38	97.46 131	96.11 227

AdaColmap		94.34 109	93.68 115	96.31 106	98.59 74	91.68 121	96.59 199	97.81 118	89.87 188	92.15 176	97.06 135	83.62 169	99.54 89	89.34 196	98.07 118	97.70 180

114514_t		93.95 123	93.06 135	96.63 83	99.07 41	91.61 122	97.46 113	97.96 103	77.99 352	93.00 158	97.57 110	86.14 137	99.33 117	89.22 201	99.15 77	98.94 102

LS3D		93.57 137	92.61 152	96.47 94	97.59 139	91.61 122	97.67 89	97.72 128	85.17 300	90.29 212	98.34 43	84.60 153	99.73 35	83.85 291	98.27 112	98.06 164

MVS		91.71 202	90.44 228	95.51 151	95.20 256	91.59 124	96.04 237	97.45 165	73.44 359	87.36 293	95.60 217	85.42 144	99.10 137	85.97 264	97.46 131	95.83 237

Vis-MVSNet		95.23 87	94.81 89	96.51 91	97.18 149	91.58 125	98.26 32	98.12 59	94.38 48	94.90 118	98.15 65	82.28 199	98.92 157	91.45 161	98.58 104	99.01 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ET-MVSNet_ETH3D		91.49 215	90.11 244	95.63 142	96.40 196	91.57 126	95.34 267	93.48 340	90.60 176	75.58 356	95.49 224	80.08 235	96.79 329	94.25 100	89.76 256	98.52 131

DROMVSNet		96.42 55	96.47 48	96.26 111	97.01 163	91.52 127	98.89 3	97.75 121	94.42 45	96.64 61	97.68 97	89.32 90	98.60 185	97.45 8	99.11 84	98.67 125

CPTT-MVS		95.57 79	95.19 82	96.70 80	99.27 28	91.48 128	98.33 25	98.11 62	87.79 254	95.17 116	98.03 72	87.09 123	99.61 65	93.51 117	99.42 47	99.02 90

Effi-MVS+		94.93 97	94.45 102	96.36 104	96.61 179	91.47 129	96.41 208	97.41 174	91.02 161	94.50 126	95.92 196	87.53 115	98.78 168	93.89 110	96.81 149	98.84 113

CDS-MVSNet		94.14 116	93.54 119	95.93 125	96.18 206	91.46 130	96.33 219	97.04 208	88.97 215	93.56 143	96.51 168	87.55 114	97.89 266	89.80 184	95.95 165	98.44 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FC-MVSNet-test		93.94 124	93.57 117	95.04 169	95.48 234	91.45 131	98.12 46	98.71 5	93.37 79	90.23 213	96.70 152	87.66 111	97.85 268	91.49 159	90.39 250	95.83 237

PAPR		94.18 112	93.42 128	96.48 93	97.64 135	91.42 132	95.55 259	97.71 132	88.99 213	92.34 172	95.82 202	89.19 91	99.11 136	86.14 259	97.38 136	98.90 106

MVS_111021_LR		96.24 61	96.19 59	96.39 101	98.23 102	91.35 133	96.24 229	98.79 4	93.99 55	95.80 96	97.65 101	89.92 88	99.24 124	95.87 52	99.20 74	98.58 127

OMC-MVS		95.09 91	94.70 93	96.25 113	98.46 79	91.28 134	96.43 206	97.57 146	92.04 128	94.77 121	97.96 77	87.01 124	99.09 140	91.31 163	96.77 150	98.36 151

LFMVS		93.60 135	92.63 150	96.52 88	98.13 109	91.27 135	97.94 61	93.39 342	90.57 177	96.29 77	98.31 49	69.00 329	99.16 131	94.18 103	95.87 167	99.12 84

test_yl		94.78 103	94.23 105	96.43 97	97.74 128	91.22 136	96.85 169	97.10 199	91.23 154	95.71 99	96.93 138	84.30 158	99.31 119	93.10 126	95.12 180	98.75 116

DCV-MVSNet		94.78 103	94.23 105	96.43 97	97.74 128	91.22 136	96.85 169	97.10 199	91.23 154	95.71 99	96.93 138	84.30 158	99.31 119	93.10 126	95.12 180	98.75 116

MVSFormer		95.37 82	95.16 83	95.99 124	96.34 199	91.21 138	98.22 38	97.57 146	91.42 144	96.22 79	97.32 121	86.20 135	97.92 262	94.07 104	99.05 87	98.85 111

lupinMVS		94.99 96	94.56 96	96.29 109	96.34 199	91.21 138	95.83 249	96.27 259	88.93 217	96.22 79	96.88 143	86.20 135	98.85 163	95.27 75	99.05 87	98.82 114

EI-MVSNet-Vis-set		96.51 52	96.47 48	96.63 83	98.24 98	91.20 140	96.89 167	97.73 124	94.74 38	96.49 69	98.49 26	90.88 73	99.58 74	96.44 32	98.32 111	99.13 81

UGNet		94.04 121	93.28 131	96.31 106	96.85 168	91.19 141	97.88 65	97.68 133	94.40 46	93.00 158	96.18 183	73.39 307	99.61 65	91.72 152	98.46 108	98.13 159
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
GBi-Net		91.35 223	90.27 236	94.59 191	96.51 188	91.18 142	97.50 106	96.93 216	88.82 222	89.35 246	94.51 261	73.87 301	97.29 315	86.12 260	88.82 262	95.31 267

test1		91.35 223	90.27 236	94.59 191	96.51 188	91.18 142	97.50 106	96.93 216	88.82 222	89.35 246	94.51 261	73.87 301	97.29 315	86.12 260	88.82 262	95.31 267

FMVSNet1		89.88 272	88.31 281	94.59 191	95.41 236	91.18 142	97.50 106	96.93 216	86.62 279	87.41 291	94.51 261	65.94 348	97.29 315	83.04 295	87.43 276	95.31 267

PLC	91.00 6	94.11 117	93.43 126	96.13 116	98.58 76	91.15 145	96.69 186	97.39 175	87.29 268	91.37 190	96.71 150	88.39 103	99.52 96	87.33 241	97.13 146	97.73 178
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
原ACMM1				96.38 102	98.59 74	91.09 146		97.89 108	87.41 265	95.22 115	97.68 97	90.25 80	99.54 89	87.95 221	99.12 82	98.49 136

1112_ss		93.37 142	92.42 160	96.21 114	97.05 160	90.99 147	96.31 221	96.72 233	86.87 276	89.83 231	96.69 154	86.51 129	99.14 134	88.12 218	93.67 201	98.50 134

DP-MVS		92.76 170	91.51 189	96.52 88	98.77 59	90.99 147	97.38 120	96.08 267	82.38 329	89.29 249	97.87 81	83.77 165	99.69 47	81.37 311	96.69 154	98.89 108

VPNet		92.23 188	91.31 194	94.99 171	95.56 230	90.96 149	97.22 138	97.86 115	92.96 100	90.96 202	96.62 164	75.06 295	98.20 218	91.90 147	83.65 325	95.80 239

XXY-MVS		92.16 191	91.23 199	94.95 176	94.75 279	90.94 150	97.47 111	97.43 172	89.14 209	88.90 256	96.43 172	79.71 242	98.24 213	89.56 191	87.68 273	95.67 249

EI-MVSNet-UG-set		96.34 58	96.30 54	96.47 94	98.20 103	90.93 151	96.86 168	97.72 128	94.67 39	96.16 81	98.46 28	90.43 79	99.58 74	96.23 37	97.96 121	98.90 106

jason		94.84 101	94.39 104	96.18 115	95.52 232	90.93 151	96.09 235	96.52 249	89.28 205	96.01 90	97.32 121	84.70 152	98.77 170	95.15 78	98.91 94	98.85 111
jason: jason.
PVSNet_Blended_VisFu		95.27 85	94.91 88	96.38 102	98.20 103	90.86 153	97.27 130	98.25 35	90.21 182	94.18 132	97.27 123	87.48 117	99.73 35	93.53 116	97.77 126	98.55 128

WR-MVS		92.34 180	91.53 186	94.77 187	95.13 259	90.83 154	96.40 211	97.98 101	91.88 132	89.29 249	95.54 222	82.50 194	97.80 273	89.79 185	85.27 299	95.69 247

PatchMatch-RL		92.90 163	92.02 170	95.56 147	98.19 105	90.80 155	95.27 273	97.18 191	87.96 247	91.86 184	95.68 214	80.44 228	98.99 153	84.01 287	97.54 130	96.89 205

pmmvs4		90.93 244	89.85 254	94.17 210	93.34 323	90.79 156	94.60 284	96.02 268	84.62 308	87.45 289	95.15 234	81.88 208	97.45 305	87.70 228	87.87 271	94.27 320

OPM-MVS		93.28 145	92.76 143	94.82 180	94.63 285	90.77 157	96.65 190	97.18 191	93.72 63	91.68 185	97.26 124	79.33 249	98.63 182	92.13 143	92.28 217	95.07 279
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline1		92.82 168	91.90 174	95.55 149	97.20 148	90.77 157	97.19 140	94.58 324	92.20 121	92.36 170	96.34 178	84.16 161	98.21 216	89.20 203	83.90 323	97.68 181

PAPM_NR		95.01 92	94.59 95	96.26 111	98.89 56	90.68 159	97.24 132	97.73 124	91.80 133	92.93 163	96.62 164	89.13 93	99.14 134	89.21 202	97.78 125	98.97 98

PS-MVSNAJ		95.37 82	95.33 79	95.49 154	97.35 144	90.66 160	95.31 270	97.48 154	93.85 58	96.51 68	95.70 213	88.65 99	99.65 56	94.80 92	98.27 112	96.17 222

CS-MVS-test		95.86 73	95.88 66	95.80 131	96.76 176	90.59 161	98.40 22	97.65 137	93.52 73	95.53 110	96.79 145	89.98 86	98.59 189	95.59 70	98.69 99	98.23 155

CS-MVS		95.88 71	95.98 62	95.58 146	96.44 193	90.56 162	97.78 75	97.73 124	93.01 93	96.07 85	96.77 147	90.13 83	98.57 190	96.83 18	99.10 85	97.60 187

IS-MVSNet		94.90 98	94.52 99	96.05 120	97.67 131	90.56 162	98.44 19	96.22 262	93.21 84	93.99 135	97.74 93	85.55 143	98.45 200	89.98 179	97.86 122	99.14 80

MG-MVS		95.61 77	95.38 77	96.31 106	98.42 82	90.53 164	96.04 237	97.48 154	93.47 76	95.67 104	98.10 66	89.17 92	99.25 123	91.27 164	98.77 96	99.13 81

xiu_mvs_v2_base		95.32 84	95.29 80	95.40 159	97.22 146	90.50 165	95.44 264	97.44 169	93.70 65	96.46 72	96.18 183	88.59 102	99.53 92	94.79 94	97.81 124	96.17 222

CSCG		96.05 65	95.91 64	96.46 96	99.24 30	90.47 166	98.30 27	98.57 11	89.01 212	93.97 137	97.57 110	92.62 31	99.76 33	94.66 95	99.27 65	99.15 79

casdiffmvs		95.64 76	95.49 72	96.08 117	96.76 176	90.45 167	97.29 129	97.44 169	94.00 54	95.46 112	97.98 76	87.52 116	98.73 173	95.64 64	97.33 139	99.08 87

TAMVS		94.01 122	93.46 124	95.64 141	96.16 208	90.45 167	96.71 183	96.89 223	89.27 206	93.46 148	96.92 141	87.29 120	97.94 259	88.70 212	95.74 170	98.53 130

baseline		95.58 78	95.42 76	96.08 117	96.78 173	90.41 169	97.16 143	97.45 165	93.69 66	95.65 105	97.85 84	87.29 120	98.68 178	95.66 60	97.25 142	99.13 81

VDDNet		93.05 155	92.07 167	96.02 122	96.84 169	90.39 170	98.08 49	95.85 274	86.22 285	95.79 97	98.46 28	67.59 336	99.19 127	94.92 86	94.85 184	98.47 139

bset_n11_16_dypcd		91.55 211	90.59 223	94.44 199	91.51 345	90.25 171	92.70 333	93.42 341	92.27 118	90.22 214	94.74 252	78.42 265	97.80 273	94.19 102	87.86 272	95.29 274

h-mvs33		94.15 113	93.52 121	96.04 121	97.81 124	90.22 172	97.62 98	97.58 145	95.19 14	96.74 54	97.45 116	83.67 167	99.61 65	95.85 54	79.73 340	98.29 154

tfpnnormal		89.70 275	88.40 280	93.60 240	95.15 257	90.10 173	97.56 102	98.16 53	87.28 269	86.16 309	94.63 258	77.57 278	98.05 241	74.48 343	84.59 311	92.65 341

Fast-Effi-MVS+		93.46 139	92.75 145	95.59 145	96.77 174	90.03 174	96.81 175	97.13 196	88.19 239	91.30 194	94.27 277	86.21 134	98.63 182	87.66 233	96.46 161	98.12 160

plane_prior6					96.10 213	90.00 175						81.32 215

plane_prior3						90.00 175			94.46 44	91.34 191

HQP_MVS		93.78 130	93.43 126	94.82 180	96.21 203	89.99 177	97.74 79	97.51 152	94.85 28	91.34 191	96.64 157	81.32 215	98.60 185	93.02 130	92.23 218	95.86 233

plane_prior						89.99 177	97.24 132		94.06 53						92.16 222

plane_prior7					96.21 203	89.98 179

Test_1112_low_res		92.84 167	91.84 176	95.85 129	97.04 161	89.97 180	95.53 261	96.64 242	85.38 296	89.65 237	95.18 233	85.86 139	99.10 137	87.70 228	93.58 206	98.49 136

VDD-MVS		93.82 128	93.08 134	96.02 122	97.88 121	89.96 181	97.72 84	95.85 274	92.43 114	95.86 94	98.44 30	68.42 333	99.39 113	96.31 33	94.85 184	98.71 122

HyFIR lowres test		93.66 133	92.92 139	95.87 128	98.24 98	89.88 182	94.58 285	98.49 12	85.06 302	93.78 140	95.78 207	82.86 185	98.67 179	91.77 151	95.71 172	99.07 89

PAPM		91.52 214	90.30 234	95.20 163	95.30 249	89.83 183	93.38 322	96.85 228	86.26 284	88.59 266	95.80 203	84.88 150	98.15 224	75.67 341	95.93 166	97.63 182

NP-MVS					95.99 217	89.81 184					95.87 198

GeoE		93.89 125	93.28 131	95.72 138	96.96 166	89.75 185	98.24 36	96.92 220	89.47 200	92.12 178	97.21 127	84.42 156	98.39 205	87.71 227	96.50 158	99.01 94

EIA-MVS		95.53 80	95.47 73	95.71 139	97.06 158	89.63 186	97.82 71	97.87 112	93.57 67	93.92 138	95.04 238	90.61 77	98.95 155	94.62 96	98.68 100	98.54 129

pm-mvs1		90.72 252	89.65 264	93.96 222	94.29 298	89.63 186	97.79 74	96.82 230	89.07 210	86.12 310	95.48 225	78.61 262	97.78 276	86.97 248	81.67 334	94.46 312

TAPA-MVS	90.10 7	92.30 183	91.22 200	95.56 147	98.33 90	89.60 188	96.79 176	97.65 137	81.83 333	91.52 187	97.23 126	87.94 107	98.91 159	71.31 355	98.37 110	98.17 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSTER		93.20 148	92.81 142	94.37 203	96.56 185	89.59 189	97.06 148	97.12 197	91.24 153	91.30 194	95.96 194	82.02 204	98.05 241	93.48 118	90.55 247	95.47 254

EPP-MVSNet		95.22 88	95.04 86	95.76 132	97.49 143	89.56 190	98.67 8	97.00 212	90.69 167	94.24 131	97.62 106	89.79 89	98.81 166	93.39 122	96.49 159	98.92 104

anonymousdsp		92.16 191	91.55 185	93.97 221	92.58 336	89.55 191	97.51 105	97.42 173	89.42 202	88.40 269	94.84 246	80.66 224	97.88 267	91.87 149	91.28 236	94.48 311

MVS_Test		94.89 99	94.62 94	95.68 140	96.83 171	89.55 191	96.70 184	97.17 193	91.17 156	95.60 106	96.11 190	87.87 109	98.76 171	93.01 132	97.17 145	98.72 120

LTVRE_ROB	88.41 13	90.99 240	89.92 251	94.19 209	96.18 206	89.55 191	96.31 221	97.09 201	87.88 250	85.67 312	95.91 197	78.79 260	98.57 190	81.50 306	89.98 253	94.44 313
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
1314		92.81 169	92.03 169	95.14 166	95.33 246	89.52 194	96.04 237	97.44 169	87.72 258	86.25 308	95.33 228	83.84 164	98.79 167	89.26 199	97.05 147	97.11 198

thres600view7		92.49 175	91.60 183	95.18 164	97.91 119	89.47 195	97.65 92	94.66 321	92.18 125	93.33 151	94.91 242	78.06 273	99.10 137	81.61 305	94.06 198	96.98 200

WR-MVS_H		92.00 195	91.35 191	93.95 223	95.09 261	89.47 195	98.04 52	98.68 7	91.46 142	88.34 270	94.68 255	85.86 139	97.56 294	85.77 267	84.24 316	94.82 296

PVSNet_BlendedMVS		94.06 119	93.92 108	94.47 198	98.27 94	89.46 197	96.73 180	98.36 16	90.17 183	94.36 128	95.24 232	88.02 105	99.58 74	93.44 119	90.72 245	94.36 315

PVSNet_Blended		94.87 100	94.56 96	95.81 130	98.27 94	89.46 197	95.47 263	98.36 16	88.84 220	94.36 128	96.09 191	88.02 105	99.58 74	93.44 119	98.18 115	98.40 147

Anonymous20240529		91.98 196	90.73 218	95.73 137	98.14 108	89.40 199	97.99 54	97.72 128	79.63 346	93.54 145	97.41 119	69.94 327	99.56 84	91.04 167	91.11 238	98.22 156

CHOSEN 1792x2688		94.15 113	93.51 122	96.06 119	98.27 94	89.38 200	95.18 277	98.48 14	85.60 293	93.76 141	97.11 132	83.15 176	99.61 65	91.33 162	98.72 98	99.19 75

thres100view900		92.43 176	91.58 184	94.98 173	97.92 118	89.37 201	97.71 86	94.66 321	92.20 121	93.31 152	94.90 243	78.06 273	99.08 142	81.40 308	94.08 195	96.48 216

diffmvs		95.25 86	95.13 84	95.63 142	96.43 195	89.34 202	95.99 242	97.35 181	92.83 103	96.31 76	97.37 120	86.44 130	98.67 179	96.26 35	97.19 144	98.87 110

HQP5-MVS						89.33 203

HQP-MVS		93.19 149	92.74 146	94.54 197	95.86 218	89.33 203	96.65 190	97.39 175	93.55 68	90.14 215	95.87 198	80.95 218	98.50 195	92.13 143	92.10 223	95.78 240

tfpn200view9		92.38 179	91.52 187	94.95 176	97.85 122	89.29 205	97.41 114	94.88 316	92.19 123	93.27 154	94.46 266	78.17 269	99.08 142	81.40 308	94.08 195	96.48 216

thres400		92.42 177	91.52 187	95.12 168	97.85 122	89.29 205	97.41 114	94.88 316	92.19 123	93.27 154	94.46 266	78.17 269	99.08 142	81.40 308	94.08 195	96.98 200

PS-MVSNAJss		93.74 131	93.51 122	94.44 199	93.91 306	89.28 207	97.75 78	97.56 149	92.50 113	89.94 227	96.54 167	88.65 99	98.18 221	93.83 113	90.90 243	95.86 233

gg-mvs-nofinetune		87.82 295	85.61 304	94.44 199	94.46 290	89.27 208	91.21 344	84.61 370	80.88 339	89.89 230	74.98 363	71.50 314	97.53 298	85.75 268	97.21 143	96.51 214

GG-mvs-BLEND				93.62 239	93.69 313	89.20 209	92.39 338	83.33 371		87.98 283	89.84 346	71.00 318	96.87 327	82.08 304	95.40 176	94.80 299

CLD-MVS		92.98 158	92.53 156	94.32 206	96.12 212	89.20 209	95.28 271	97.47 157	92.66 109	89.90 228	95.62 216	80.58 225	98.40 202	92.73 134	92.40 216	95.38 263
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		90.63 255	89.42 266	94.27 208	98.24 98	89.19 211	98.05 51	97.89 108	79.95 344	88.25 275	94.96 239	72.56 310	98.13 225	89.70 187	85.14 301	95.49 251

cascas		91.20 231	90.08 245	94.58 195	94.97 265	89.16 212	93.65 317	97.59 144	79.90 345	89.40 244	92.92 314	75.36 294	98.36 206	92.14 142	94.75 188	96.23 219

thisisatest0530		93.03 156	92.21 165	95.49 154	97.07 155	89.11 213	97.49 110	92.19 350	90.16 184	94.09 133	96.41 174	76.43 287	99.05 148	90.38 175	95.68 173	98.31 153

thres200		92.23 188	91.39 190	94.75 189	97.61 137	89.03 214	96.60 198	95.09 307	92.08 127	93.28 153	94.00 289	78.39 267	99.04 151	81.26 312	94.18 194	96.19 221

F-COLMAP		93.58 136	92.98 137	95.37 160	98.40 83	88.98 215	97.18 141	97.29 186	87.75 257	90.49 207	97.10 133	85.21 146	99.50 100	86.70 250	96.72 153	97.63 182

MSDG		91.42 218	90.24 238	94.96 175	97.15 152	88.91 216	93.69 315	96.32 257	85.72 292	86.93 302	96.47 170	80.24 232	98.98 154	80.57 314	95.05 183	96.98 200

thisisatest0515		92.29 184	91.30 195	95.25 162	96.60 180	88.90 217	94.36 294	92.32 349	87.92 248	93.43 149	94.57 260	77.28 280	99.00 152	89.42 194	95.86 168	97.86 172

testdata				95.46 158	98.18 107	88.90 217		97.66 135	82.73 328	97.03 51	98.07 69	90.06 84	98.85 163	89.67 188	98.98 90	98.64 126

Anonymous202405211		92.07 194	90.83 213	95.76 132	98.19 105	88.75 219	97.58 100	95.00 310	86.00 288	93.64 142	97.45 116	66.24 346	99.53 92	90.68 173	92.71 211	99.01 94

ACMM	89.79 8	92.96 159	92.50 158	94.35 204	96.30 201	88.71 220	97.58 100	97.36 180	91.40 147	90.53 206	96.65 156	79.77 241	98.75 172	91.24 165	91.64 228	95.59 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		93.07 154	92.76 143	94.00 218	93.49 319	88.70 221	98.22 38	97.57 146	91.42 144	90.08 225	95.55 221	82.85 186	97.92 262	94.07 104	91.58 230	95.40 261

XVG-OURS		93.72 132	93.35 129	94.80 185	97.07 155	88.61 222	94.79 281	97.46 159	91.97 131	93.99 135	97.86 83	81.74 210	98.88 162	92.64 135	92.67 213	96.92 204

hse-mvs2		93.45 140	92.99 136	94.81 182	97.02 162	88.59 223	96.69 186	96.47 251	95.19 14	96.74 54	96.16 186	83.67 167	98.48 199	95.85 54	79.13 344	97.35 195

AUN-MVS		91.76 201	90.75 217	94.81 182	97.00 164	88.57 224	96.65 190	96.49 250	89.63 196	92.15 176	96.12 187	78.66 261	98.50 195	90.83 169	79.18 343	97.36 194

CP-MVSNet		91.89 198	91.24 198	93.82 230	95.05 262	88.57 224	97.82 71	98.19 47	91.70 135	88.21 276	95.76 208	81.96 205	97.52 300	87.86 222	84.65 308	95.37 264

XVG-OURS-SEG-HR		93.86 127	93.55 118	94.81 182	97.06 158	88.53 226	95.28 271	97.45 165	91.68 136	94.08 134	97.68 97	82.41 197	98.90 160	93.84 112	92.47 215	96.98 200

jajsoiax		92.42 177	91.89 175	94.03 217	93.33 324	88.50 227	97.73 81	97.53 150	92.00 130	88.85 259	96.50 169	75.62 293	98.11 229	93.88 111	91.56 231	95.48 252

V42		91.58 209	90.87 208	93.73 233	94.05 303	88.50 227	97.32 125	96.97 213	88.80 225	89.71 233	94.33 272	82.54 193	98.05 241	89.01 206	85.07 303	94.64 309

TransMVSNet (Re)		88.94 281	87.56 288	93.08 263	94.35 294	88.45 229	97.73 81	95.23 301	87.47 263	84.26 325	95.29 229	79.86 240	97.33 313	79.44 324	74.44 353	93.45 332

mvs_tets		92.31 182	91.76 177	93.94 225	93.41 321	88.29 230	97.63 97	97.53 150	92.04 128	88.76 263	96.45 171	74.62 297	98.09 234	93.91 109	91.48 232	95.45 257

PS-CasMVS		91.55 211	90.84 212	93.69 237	94.96 266	88.28 231	97.84 70	98.24 37	91.46 142	88.04 280	95.80 203	79.67 243	97.48 302	87.02 247	84.54 313	95.31 267

LPG-MVS_test		92.94 161	92.56 153	94.10 212	96.16 208	88.26 232	97.65 92	97.46 159	91.29 149	90.12 221	97.16 129	79.05 252	98.73 173	92.25 139	91.89 226	95.31 267

LGP-MVS_train				94.10 212	96.16 208	88.26 232		97.46 159	91.29 149	90.12 221	97.16 129	79.05 252	98.73 173	92.25 139	91.89 226	95.31 267

v1144		91.37 222	90.60 222	93.68 238	93.89 307	88.23 234	96.84 171	97.03 210	88.37 235	89.69 235	94.39 268	82.04 203	97.98 249	87.80 224	85.37 296	94.84 293

MVP-Stereo		90.74 251	90.08 245	92.71 274	93.19 326	88.20 235	95.86 247	96.27 259	86.07 287	84.86 320	94.76 250	77.84 276	97.75 279	83.88 290	98.01 119	92.17 349
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	89.59 10	92.62 172	92.14 166	94.05 215	96.40 196	88.20 235	97.36 121	97.25 189	91.52 139	88.30 272	96.64 157	78.46 264	98.72 176	91.86 150	91.48 232	95.23 275
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		91.59 207	90.85 211	93.80 231	93.87 308	88.17 237	96.94 163	96.88 224	89.54 197	89.53 241	94.90 243	81.70 211	98.02 246	89.25 200	85.04 305	95.20 276

v10		91.04 238	90.23 239	93.49 245	94.12 300	88.16 238	97.32 125	97.08 202	88.26 238	88.29 273	94.22 282	82.17 202	97.97 252	86.45 254	84.12 317	94.33 316

v8		91.29 228	90.53 227	93.57 243	94.15 299	88.12 239	97.34 122	97.06 205	88.99 213	88.32 271	94.26 279	83.08 178	98.01 247	87.62 235	83.92 322	94.57 310

Baseline_NR-MVSNet		91.20 231	90.62 221	92.95 267	93.83 309	88.03 240	97.01 156	95.12 306	88.42 234	89.70 234	95.13 236	83.47 170	97.44 306	89.66 189	83.24 328	93.37 333

BH-RMVSNet		92.72 171	91.97 172	94.97 174	97.16 150	87.99 241	96.15 233	95.60 284	90.62 173	91.87 183	97.15 131	78.41 266	98.57 190	83.16 293	97.60 129	98.36 151

Vis-MVSNet (Re-imp)		94.15 113	93.88 109	94.95 176	97.61 137	87.92 242	98.10 47	95.80 276	92.22 119	93.02 157	97.45 116	84.53 155	97.91 265	88.24 216	97.97 120	99.02 90

ACMH	87.59 16	90.53 257	89.42 266	93.87 228	96.21 203	87.92 242	97.24 132	96.94 215	88.45 233	83.91 331	96.27 181	71.92 311	98.62 184	84.43 284	89.43 258	95.05 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		91.20 231	90.44 228	93.48 246	94.49 289	87.91 244	97.76 77	98.18 49	91.29 149	87.78 285	95.74 210	80.35 230	97.33 313	85.46 271	82.96 330	95.19 277

UniMVSNet_ETH3D		91.34 225	90.22 241	94.68 190	94.86 274	87.86 245	97.23 137	97.46 159	87.99 246	89.90 228	96.92 141	66.35 344	98.23 214	90.30 177	90.99 241	97.96 165

v1192		91.07 236	90.23 239	93.58 242	93.70 312	87.82 246	96.73 180	97.07 203	87.77 255	89.58 238	94.32 274	80.90 222	97.97 252	86.52 252	85.48 294	94.95 283

MIMVSNet		88.50 289	86.76 297	93.72 235	94.84 275	87.77 247	91.39 340	94.05 334	86.41 282	87.99 282	92.59 319	63.27 353	95.82 342	77.44 331	92.84 210	97.57 190

IB-MVS	87.33 17	89.91 270	88.28 282	94.79 186	95.26 253	87.70 248	95.12 279	93.95 337	89.35 204	87.03 299	92.49 320	70.74 320	99.19 127	89.18 204	81.37 336	97.49 192
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		91.38 220	90.31 233	94.59 191	94.65 283	87.62 249	94.34 295	96.19 264	90.73 166	90.35 211	93.83 293	71.84 312	97.96 256	87.22 243	93.61 204	98.21 157

v7n		90.76 248	89.86 253	93.45 249	93.54 316	87.60 250	97.70 87	97.37 178	88.85 219	87.65 287	94.08 287	81.08 217	98.10 230	84.68 280	83.79 324	94.66 308

TR-MVS		91.48 216	90.59 223	94.16 211	96.40 196	87.33 251	95.67 254	95.34 296	87.68 259	91.46 188	95.52 223	76.77 283	98.35 207	82.85 297	93.61 204	96.79 209

FMVSNet5		87.29 299	85.79 303	91.78 297	94.80 277	87.28 252	95.49 262	95.28 297	84.09 314	83.85 332	91.82 331	62.95 354	94.17 355	78.48 327	85.34 298	93.91 326

CHOSEN 280x420		93.12 152	92.72 148	94.34 205	96.71 178	87.27 253	90.29 349	97.72 128	86.61 280	91.34 191	95.29 229	84.29 160	98.41 201	93.25 124	98.94 92	97.35 195

pmmvs-eth3d		86.22 308	84.45 314	91.53 302	88.34 362	87.25 254	94.47 288	95.01 309	83.47 323	79.51 351	89.61 347	69.75 328	95.71 343	83.13 294	76.73 349	91.64 350

DTE-MVSNet		90.56 256	89.75 260	93.01 264	93.95 304	87.25 254	97.64 96	97.65 137	90.74 165	87.12 296	95.68 214	79.97 238	97.00 324	83.33 292	81.66 335	94.78 303

v144192		91.06 237	90.28 235	93.39 250	93.66 314	87.23 256	96.83 172	97.07 203	87.43 264	89.69 235	94.28 276	81.48 213	98.00 248	87.18 245	84.92 307	94.93 287

CR-MVSNet		90.82 247	89.77 258	93.95 223	94.45 291	87.19 257	90.23 350	95.68 282	86.89 275	92.40 167	92.36 325	80.91 220	97.05 319	81.09 313	93.95 199	97.60 187

RPMNet		88.98 280	87.05 295	94.77 187	94.45 291	87.19 257	90.23 350	98.03 87	77.87 354	92.40 167	87.55 356	80.17 234	99.51 97	68.84 359	93.95 199	97.60 187

tttt0517		92.96 159	92.33 162	94.87 179	97.11 153	87.16 259	97.97 59	92.09 351	90.63 172	93.88 139	97.01 137	76.50 284	99.06 147	90.29 178	95.45 175	98.38 149

COLMAP_ROB	87.81 15	90.40 260	89.28 269	93.79 232	97.95 115	87.13 260	96.92 164	95.89 273	82.83 327	86.88 304	97.18 128	73.77 304	99.29 121	78.44 328	93.62 203	94.95 283
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
miper_enhance_ethall		91.54 213	91.01 205	93.15 260	95.35 242	87.07 261	93.97 306	96.90 221	86.79 277	89.17 253	93.43 310	86.55 128	97.64 287	89.97 180	86.93 280	94.74 305

EI-MVSNet		93.03 156	92.88 140	93.48 246	95.77 223	86.98 262	96.44 204	97.12 197	90.66 170	91.30 194	97.64 104	86.56 127	98.05 241	89.91 181	90.55 247	95.41 258

IterMVS-LS		92.29 184	91.94 173	93.34 253	96.25 202	86.97 263	96.57 202	97.05 206	90.67 168	89.50 243	94.80 249	86.59 126	97.64 287	89.91 181	86.11 289	95.40 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 246	90.03 249	93.29 255	93.55 315	86.96 264	96.74 179	97.04 208	87.36 266	89.52 242	94.34 271	80.23 233	97.97 252	86.27 255	85.21 300	94.94 285

cl22		91.21 230	90.56 226	93.14 261	96.09 214	86.80 265	94.41 292	96.58 248	87.80 253	88.58 267	93.99 290	80.85 223	97.62 290	89.87 183	86.93 280	94.99 282

v1240		90.70 253	89.85 254	93.23 257	93.51 318	86.80 265	96.61 196	97.02 211	87.16 271	89.58 238	94.31 275	79.55 246	97.98 249	85.52 270	85.44 295	94.90 290

PMMVS		92.86 165	92.34 161	94.42 202	94.92 269	86.73 267	94.53 287	96.38 255	84.78 307	94.27 130	95.12 237	83.13 177	98.40 202	91.47 160	96.49 159	98.12 160

AllTest		90.23 264	88.98 273	93.98 219	97.94 116	86.64 268	96.51 203	95.54 287	85.38 296	85.49 314	96.77 147	70.28 323	99.15 132	80.02 318	92.87 208	96.15 224

TestCases				93.98 219	97.94 116	86.64 268		95.54 287	85.38 296	85.49 314	96.77 147	70.28 323	99.15 132	80.02 318	92.87 208	96.15 224

Patchmtry		88.64 288	87.25 291	92.78 273	94.09 301	86.64 268	89.82 353	95.68 282	80.81 341	87.63 288	92.36 325	80.91 220	97.03 320	78.86 326	85.12 302	94.67 307

DeepPCF-MVS	93.97 1	96.61 49	97.09 13	95.15 165	98.09 110	86.63 271	96.00 241	98.15 54	95.43 6	97.95 23	98.56 19	93.40 19	99.36 116	96.77 22	99.48 39	99.45 49

miper_ehance_all_eth		91.59 207	91.13 203	92.97 266	95.55 231	86.57 272	94.47 288	96.88 224	87.77 255	88.88 258	94.01 288	86.22 133	97.54 296	89.49 192	86.93 280	94.79 301

ACMH+	87.92 14	90.20 265	89.18 271	93.25 256	96.48 191	86.45 273	96.99 157	96.68 239	88.83 221	84.79 321	96.22 182	70.16 325	98.53 193	84.42 285	88.04 269	94.77 304

baseline2		91.63 205	90.86 209	93.94 225	94.33 295	86.32 274	95.92 245	91.64 355	89.37 203	86.94 301	94.69 254	81.62 212	98.69 177	88.64 213	94.57 191	96.81 208

c3_l		91.38 220	90.89 207	92.88 269	95.58 229	86.30 275	94.68 283	96.84 229	88.17 241	88.83 261	94.23 280	85.65 142	97.47 303	89.36 195	84.63 309	94.89 291

pmmvs6		87.81 296	86.19 300	92.69 275	91.32 346	86.30 275	97.34 122	96.41 254	80.59 343	84.05 330	94.37 270	67.37 338	97.67 284	84.75 279	79.51 342	94.09 324

pmmvs5		89.86 273	88.87 275	92.82 271	92.86 330	86.23 277	96.26 225	95.39 290	84.24 312	87.12 296	94.51 261	74.27 299	97.36 312	87.61 236	87.57 274	94.86 292

cl____		90.96 243	90.32 232	92.89 268	95.37 240	86.21 278	94.46 290	96.64 242	87.82 251	88.15 278	94.18 283	82.98 182	97.54 296	87.70 228	85.59 292	94.92 289

DIV-MVS_self_test		90.97 242	90.33 231	92.88 269	95.36 241	86.19 279	94.46 290	96.63 245	87.82 251	88.18 277	94.23 280	82.99 181	97.53 298	87.72 225	85.57 293	94.93 287

BH-untuned		92.94 161	92.62 151	93.92 227	97.22 146	86.16 280	96.40 211	96.25 261	90.06 186	89.79 232	96.17 185	83.19 174	98.35 207	87.19 244	97.27 141	97.24 197

MVS_0304		88.79 285	87.57 287	92.46 278	94.65 283	86.15 281	96.40 211	97.17 193	86.44 281	88.02 281	91.71 334	56.68 361	97.03 320	84.47 283	92.58 214	94.19 321

XVG-ACMP-BASELINE		90.93 244	90.21 242	93.09 262	94.31 297	85.89 282	95.33 268	97.26 187	91.06 160	89.38 245	95.44 226	68.61 331	98.60 185	89.46 193	91.05 239	94.79 301

v148		90.99 240	90.38 230	92.81 272	93.83 309	85.80 283	96.78 178	96.68 239	89.45 201	88.75 264	93.93 292	82.96 184	97.82 272	87.83 223	83.25 327	94.80 299

BH-w/o		92.14 193	91.75 178	93.31 254	96.99 165	85.73 284	95.67 254	95.69 280	88.73 227	89.26 251	94.82 248	82.97 183	98.07 238	85.26 274	96.32 162	96.13 226

test0.0.03 1		89.37 278	88.70 276	91.41 306	92.47 338	85.63 285	95.22 276	92.70 347	91.11 158	86.91 303	93.65 303	79.02 254	93.19 361	78.00 330	89.18 260	95.41 258

test_0402		86.46 304	84.79 312	91.45 304	95.02 264	85.55 286	96.29 223	94.89 315	80.90 338	82.21 338	93.97 291	68.21 334	97.29 315	62.98 363	88.68 266	91.51 352

D2MVS		91.30 227	90.95 206	92.35 282	94.71 281	85.52 287	96.18 232	98.21 43	88.89 218	86.60 305	93.82 295	79.92 239	97.95 258	89.29 198	90.95 242	93.56 329

Fast-Effi-MVS+-dtu		92.29 184	91.99 171	93.21 259	95.27 250	85.52 287	97.03 149	96.63 245	92.09 126	89.11 254	95.14 235	80.33 231	98.08 235	87.54 237	94.74 189	96.03 230

ECVR-MVS		93.19 149	92.73 147	94.57 196	97.66 133	85.41 289	98.21 40	88.23 364	93.43 77	94.70 122	98.21 60	72.57 309	99.07 145	93.05 129	98.49 105	99.25 72

mvs_anonymous		93.82 128	93.74 112	94.06 214	96.44 193	85.41 289	95.81 250	97.05 206	89.85 191	90.09 224	96.36 177	87.44 118	97.75 279	93.97 106	96.69 154	99.02 90

ITE_SJBPF				92.43 280	95.34 243	85.37 291		95.92 270	91.47 141	87.75 286	96.39 176	71.00 318	97.96 256	82.36 302	89.86 255	93.97 325

KD-MVS_2432*1600		84.81 318	82.64 321	91.31 307	91.07 348	85.34 292	91.22 342	95.75 277	85.56 294	83.09 335	90.21 342	67.21 339	95.89 338	77.18 335	62.48 364	92.69 339

miper_refine_blended		84.81 318	82.64 321	91.31 307	91.07 348	85.34 292	91.22 342	95.75 277	85.56 294	83.09 335	90.21 342	67.21 339	95.89 338	77.18 335	62.48 364	92.69 339

Patchmatch-test		89.42 277	87.99 284	93.70 236	95.27 250	85.11 294	88.98 356	94.37 329	81.11 337	87.10 298	93.69 299	82.28 199	97.50 301	74.37 345	94.76 187	98.48 138

PatchT		88.87 284	87.42 289	93.22 258	94.08 302	85.10 295	89.51 354	94.64 323	81.92 332	92.36 170	88.15 353	80.05 236	97.01 323	72.43 351	93.65 202	97.54 191

EG-PatchMatch MVS		87.02 301	85.44 305	91.76 299	92.67 334	85.00 296	96.08 236	96.45 252	83.41 324	79.52 350	93.49 306	57.10 360	97.72 281	79.34 325	90.87 244	92.56 342

USDC		88.94 281	87.83 286	92.27 284	94.66 282	84.96 297	93.86 310	95.90 272	87.34 267	83.40 333	95.56 220	67.43 337	98.19 220	82.64 301	89.67 257	93.66 328

SCA		91.84 199	91.18 202	93.83 229	95.59 228	84.95 298	94.72 282	95.58 286	90.82 162	92.25 174	93.69 299	75.80 290	98.10 230	86.20 257	95.98 164	98.45 141

ADS-MVSNet		89.89 271	88.68 277	93.53 244	95.86 218	84.89 299	90.93 345	95.07 308	83.23 325	91.28 197	91.81 332	79.01 256	97.85 268	79.52 320	91.39 234	97.84 173

MIMVSNet1		84.93 317	83.05 319	90.56 320	89.56 357	84.84 300	95.40 265	95.35 293	83.91 315	80.38 346	92.21 329	57.23 359	93.34 360	70.69 358	82.75 333	93.50 330

MS-PatchMatch		90.27 262	89.77 258	91.78 297	94.33 295	84.72 301	95.55 259	96.73 232	86.17 286	86.36 307	95.28 231	71.28 316	97.80 273	84.09 286	98.14 117	92.81 338

mvs-test1		93.63 134	93.69 114	93.46 248	96.02 215	84.61 302	97.24 132	96.72 233	93.85 58	92.30 173	95.76 208	83.08 178	98.89 161	91.69 155	96.54 157	96.87 206

test1111		93.19 149	92.82 141	94.30 207	97.58 141	84.56 303	98.21 40	89.02 363	93.53 72	94.58 124	98.21 60	72.69 308	99.05 148	93.06 128	98.48 107	99.28 69

eth_miper_zixun_eth		91.02 239	90.59 223	92.34 283	95.33 246	84.35 304	94.10 303	96.90 221	88.56 231	88.84 260	94.33 272	84.08 162	97.60 292	88.77 211	84.37 315	95.06 280

TDRefinement		86.53 303	84.76 313	91.85 292	82.23 368	84.25 305	96.38 214	95.35 293	84.97 304	84.09 328	94.94 240	65.76 349	98.34 210	84.60 282	74.52 352	92.97 335

EPMVS		90.70 253	89.81 256	93.37 252	94.73 280	84.21 306	93.67 316	88.02 365	89.50 199	92.38 169	93.49 306	77.82 277	97.78 276	86.03 263	92.68 212	98.11 163

IterMVS-SCA-FT		90.31 261	89.81 256	91.82 294	95.52 232	84.20 307	94.30 297	96.15 265	90.61 174	87.39 292	94.27 277	75.80 290	96.44 332	87.34 240	86.88 284	94.82 296

PatchmatchNet		91.91 197	91.35 191	93.59 241	95.38 238	84.11 308	93.15 326	95.39 290	89.54 197	92.10 179	93.68 301	82.82 187	98.13 225	84.81 278	95.32 177	98.52 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OpenMVS_ROB	81.14 20	84.42 320	82.28 323	90.83 314	90.06 353	84.05 309	95.73 253	94.04 335	73.89 358	80.17 349	91.53 336	59.15 358	97.64 287	66.92 361	89.05 261	90.80 356

test2506		91.60 206	90.78 214	94.04 216	97.66 133	83.81 310	98.27 30	75.53 375	93.43 77	95.23 114	98.21 60	67.21 339	99.07 145	93.01 132	98.49 105	99.25 72

miper_lstm_enhance		90.50 259	90.06 248	91.83 293	95.33 246	83.74 311	93.86 310	96.70 238	87.56 262	87.79 284	93.81 296	83.45 172	96.92 326	87.39 239	84.62 310	94.82 296

IterMVS		90.15 267	89.67 262	91.61 301	95.48 234	83.72 312	94.33 296	96.12 266	89.99 187	87.31 295	94.15 285	75.78 292	96.27 335	86.97 248	86.89 283	94.83 294
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPNet_dtu		91.71 202	91.28 196	92.99 265	93.76 311	83.71 313	96.69 186	95.28 297	93.15 88	87.02 300	95.95 195	83.37 173	97.38 311	79.46 323	96.84 148	97.88 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		90.76 248	89.89 252	93.38 251	95.04 263	83.70 314	95.85 248	94.30 332	88.19 239	90.46 208	92.80 315	73.61 305	98.50 195	88.16 217	90.58 246	97.95 167

PVSNet	86.66 18	92.24 187	91.74 180	93.73 233	97.77 127	83.69 315	92.88 330	96.72 233	87.91 249	93.00 158	94.86 245	78.51 263	99.05 148	86.53 251	97.45 135	98.47 139

ppachtmachnet_test		88.35 291	87.29 290	91.53 302	92.45 339	83.57 316	93.75 313	95.97 269	84.28 311	85.32 317	94.18 283	79.00 258	96.93 325	75.71 340	84.99 306	94.10 322

MDA-MVSNet-bldmvs		85.00 316	82.95 320	91.17 311	93.13 328	83.33 317	94.56 286	95.00 310	84.57 309	65.13 364	92.65 317	70.45 321	95.85 340	73.57 348	77.49 346	94.33 316

Effi-MVS+-dtu		93.08 153	93.21 133	92.68 276	96.02 215	83.25 318	97.14 146	96.72 233	93.85 58	91.20 201	93.44 308	83.08 178	98.30 211	91.69 155	95.73 171	96.50 215

RRT_test8_iter05		91.19 234	90.78 214	92.41 281	95.76 225	83.14 319	97.32 125	97.46 159	91.37 148	89.07 255	95.57 218	70.33 322	98.21 216	93.56 115	86.62 285	95.89 232

TinyColmap		86.82 302	85.35 308	91.21 309	94.91 272	82.99 320	93.94 308	94.02 336	83.58 321	81.56 340	94.68 255	62.34 356	98.13 225	75.78 339	87.35 279	92.52 343

MDA-MVSNet_test_wron		85.87 312	84.23 316	90.80 317	92.38 341	82.57 321	93.17 324	95.15 304	82.15 330	67.65 360	92.33 328	78.20 268	95.51 347	77.33 332	79.74 339	94.31 318

our_test_3		88.78 286	87.98 285	91.20 310	92.45 339	82.53 322	93.61 319	95.69 280	85.77 291	84.88 319	93.71 298	79.99 237	96.78 330	79.47 322	86.24 286	94.28 319

UnsupCasMVSNet_bld		82.13 325	79.46 328	90.14 325	88.00 363	82.47 323	90.89 347	96.62 247	78.94 349	75.61 355	84.40 359	56.63 362	96.31 334	77.30 334	66.77 362	91.63 351

YYNet1		85.87 312	84.23 316	90.78 318	92.38 341	82.46 324	93.17 324	95.14 305	82.12 331	67.69 359	92.36 325	78.16 271	95.50 348	77.31 333	79.73 340	94.39 314

UnsupCasMVSNet_eth		85.99 310	84.45 314	90.62 319	89.97 354	82.40 325	93.62 318	97.37 178	89.86 189	78.59 353	92.37 322	65.25 350	95.35 349	82.27 303	70.75 358	94.10 322

ADS-MVSNet2		89.45 276	88.59 278	92.03 288	95.86 218	82.26 326	90.93 345	94.32 331	83.23 325	91.28 197	91.81 332	79.01 256	95.99 337	79.52 320	91.39 234	97.84 173

EGC-MVSNET		68.77 331	63.01 336	86.07 342	92.49 337	82.24 327	93.96 307	90.96 359	0.71 376	2.62 377	90.89 338	53.66 363	93.46 358	57.25 365	84.55 312	82.51 362

LCM-MVSNet-Re		92.50 173	92.52 157	92.44 279	96.82 172	81.89 328	96.92 164	93.71 338	92.41 115	84.30 324	94.60 259	85.08 148	97.03 320	91.51 158	97.36 137	98.40 147

CostFormer		91.18 235	90.70 219	92.62 277	94.84 275	81.76 329	94.09 304	94.43 326	84.15 313	92.72 165	93.77 297	79.43 247	98.20 218	90.70 172	92.18 221	97.90 169

CL-MVSNet_self_test		86.31 307	85.15 309	89.80 328	88.83 360	81.74 330	93.93 309	96.22 262	86.67 278	85.03 318	90.80 339	78.09 272	94.50 352	74.92 342	71.86 357	93.15 334

JIA-IIPM		88.26 292	87.04 296	91.91 290	93.52 317	81.42 331	89.38 355	94.38 328	80.84 340	90.93 203	80.74 361	79.22 250	97.92 262	82.76 298	91.62 229	96.38 218

OurMVSNet-221017-0		90.51 258	90.19 243	91.44 305	93.41 321	81.25 332	96.98 159	96.28 258	91.68 136	86.55 306	96.30 179	74.20 300	97.98 249	88.96 207	87.40 278	95.09 278

tpm2		89.96 269	89.21 270	92.23 285	94.91 272	81.25 332	93.78 312	94.42 327	80.62 342	91.56 186	93.44 308	76.44 286	97.94 259	85.60 269	92.08 225	97.49 192

testgi		87.97 293	87.21 293	90.24 324	92.86 330	80.76 334	96.67 189	94.97 312	91.74 134	85.52 313	95.83 201	62.66 355	94.47 354	76.25 338	88.36 268	95.48 252

test-LLR		91.42 218	91.19 201	92.12 286	94.59 286	80.66 335	94.29 298	92.98 344	91.11 158	90.76 204	92.37 322	79.02 254	98.07 238	88.81 209	96.74 151	97.63 182

test-mter		90.19 266	89.54 265	92.12 286	94.59 286	80.66 335	94.29 298	92.98 344	87.68 259	90.76 204	92.37 322	67.67 335	98.07 238	88.81 209	96.74 151	97.63 182

TESTMET0.1,1		90.06 268	89.42 266	91.97 289	94.41 293	80.62 337	94.29 298	91.97 353	87.28 269	90.44 209	92.47 321	68.79 330	97.67 284	88.50 215	96.60 156	97.61 186

tpm cat1		88.36 290	87.21 293	91.81 295	95.13 259	80.55 338	92.58 335	95.70 279	74.97 356	87.45 289	91.96 330	78.01 275	98.17 223	80.39 316	88.74 265	96.72 211

Anonymous20231206		87.09 300	86.14 301	89.93 327	91.22 347	80.35 339	96.11 234	95.35 293	83.57 322	84.16 326	93.02 313	73.54 306	95.61 344	72.16 352	86.14 288	93.84 327

MDTV_nov1_ep13			90.76 216		95.22 254	80.33 340	93.03 329	95.28 297	88.14 244	92.84 164	93.83 293	81.34 214	98.08 235	82.86 296	94.34 193

tpmvs		89.83 274	89.15 272	91.89 291	94.92 269	80.30 341	93.11 327	95.46 289	86.28 283	88.08 279	92.65 317	80.44 228	98.52 194	81.47 307	89.92 254	96.84 207

SixPastTwentyTwo		89.15 279	88.54 279	90.98 312	93.49 319	80.28 342	96.70 184	94.70 320	90.78 163	84.15 327	95.57 218	71.78 313	97.71 282	84.63 281	85.07 303	94.94 285

new_pmnet		82.89 323	81.12 327	88.18 335	89.63 356	80.18 343	91.77 339	92.57 348	76.79 355	75.56 357	88.23 352	61.22 357	94.48 353	71.43 354	82.92 331	89.87 358

test20.03		86.14 309	85.40 307	88.35 332	90.12 352	80.06 344	95.90 246	95.20 302	88.59 228	81.29 341	93.62 304	71.43 315	92.65 362	71.26 356	81.17 337	92.34 345

LF4IMVS		87.94 294	87.25 291	89.98 326	92.38 341	80.05 345	94.38 293	95.25 300	87.59 261	84.34 323	94.74 252	64.31 351	97.66 286	84.83 277	87.45 275	92.23 346

Anonymous20240521		86.42 305	85.44 305	89.34 330	90.33 351	79.79 346	96.73 180	95.92 270	83.71 320	83.25 334	91.36 337	63.92 352	96.01 336	78.39 329	85.36 297	92.22 347

tpm		90.25 263	89.74 261	91.76 299	93.92 305	79.73 347	93.98 305	93.54 339	88.28 237	91.99 181	93.25 311	77.51 279	97.44 306	87.30 242	87.94 270	98.12 160

PVSNet_0	82.17 19	85.46 315	83.64 318	90.92 313	95.27 250	79.49 348	90.55 348	95.60 284	83.76 319	83.00 337	89.95 344	71.09 317	97.97 252	82.75 299	60.79 366	95.31 267

K. test v3		87.64 297	86.75 298	90.32 323	93.02 329	79.48 349	96.61 196	92.08 352	90.66 170	80.25 348	94.09 286	67.21 339	96.65 331	85.96 265	80.83 338	94.83 294

pmmvs3		79.97 326	77.50 330	87.39 337	82.80 367	79.38 350	92.70 333	90.75 360	70.69 360	78.66 352	87.47 357	51.34 365	93.40 359	73.39 349	69.65 360	89.38 359

tpmrst		91.44 217	91.32 193	91.79 296	95.15 257	79.20 351	93.42 321	95.37 292	88.55 232	93.49 147	93.67 302	82.49 195	98.27 212	90.41 174	89.34 259	97.90 169

KD-MVS_self_test		85.95 311	84.95 310	88.96 331	89.55 358	79.11 352	95.13 278	96.42 253	85.91 289	84.07 329	90.48 340	70.03 326	94.82 351	80.04 317	72.94 356	92.94 336

lessismore_v0				90.45 321	91.96 344	79.09 353		87.19 368		80.32 347	94.39 268	66.31 345	97.55 295	84.00 288	76.84 348	94.70 306

gm-plane-assit					93.22 325	78.89 354			84.82 306		93.52 305		98.64 181	87.72 225

Patchmatch-RL test		87.38 298	86.24 299	90.81 315	88.74 361	78.40 355	88.12 358	93.17 343	87.11 272	82.17 339	89.29 348	81.95 206	95.60 345	88.64 213	77.02 347	98.41 146

PM-MVS		83.48 321	81.86 325	88.31 333	87.83 364	77.59 356	93.43 320	91.75 354	86.91 274	80.63 344	89.91 345	44.42 367	95.84 341	85.17 276	76.73 349	91.50 353

dp		88.90 283	88.26 283	90.81 315	94.58 288	76.62 357	92.85 331	94.93 314	85.12 301	90.07 226	93.07 312	75.81 289	98.12 228	80.53 315	87.42 277	97.71 179

RPSCF		90.75 250	90.86 209	90.42 322	96.84 169	76.29 358	95.61 258	96.34 256	83.89 316	91.38 189	97.87 81	76.45 285	98.78 168	87.16 246	92.23 218	96.20 220

new-patchmatchnet		83.18 322	81.87 324	87.11 338	86.88 365	75.99 359	93.70 314	95.18 303	85.02 303	77.30 354	88.40 350	65.99 347	93.88 357	74.19 347	70.18 359	91.47 354

CVMVSNet		91.23 229	91.75 178	89.67 329	95.77 223	74.69 360	96.44 204	94.88 316	85.81 290	92.18 175	97.64 104	79.07 251	95.58 346	88.06 219	95.86 168	98.74 118

EU-MVSNet		88.72 287	88.90 274	88.20 334	93.15 327	74.21 361	96.63 195	94.22 333	85.18 299	87.32 294	95.97 193	76.16 288	94.98 350	85.27 273	86.17 287	95.41 258

Gipuma		67.86 332	65.41 334	75.18 348	92.66 335	73.45 362	66.50 367	94.52 325	53.33 366	57.80 367	66.07 367	30.81 370	89.20 364	48.15 368	78.88 345	62.90 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	62.92 21	85.62 314	84.92 311	87.74 336	89.14 359	73.12 363	94.17 301	96.80 231	73.98 357	73.65 358	94.93 241	66.36 343	97.61 291	83.95 289	91.28 236	92.48 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		86.34 306	86.12 302	87.00 339	89.88 355	70.43 364	94.93 280	90.08 361	77.97 353	85.42 316	92.78 316	74.44 298	93.96 356	74.43 344	95.14 179	96.62 212

MDTV_nov1_ep13_2view						70.35 365	93.10 328		83.88 317	93.55 144		82.47 196		86.25 256		98.38 149

ambc				86.56 340	83.60 366	70.00 366	85.69 360	94.97 312		80.60 345	88.45 349	37.42 369	96.84 328	82.69 300	75.44 351	92.86 337

MVS-HIRNet		82.47 324	81.21 326	86.26 341	95.38 238	69.21 367	88.96 357	89.49 362	66.28 361	80.79 343	74.08 365	68.48 332	97.39 310	71.93 353	95.47 174	92.18 348

ANet_high		63.94 334	59.58 337	77.02 346	61.24 377	66.06 368	85.66 361	87.93 366	78.53 351	42.94 369	71.04 366	25.42 375	80.71 369	52.60 367	30.83 370	84.28 361

PMMVS2		70.19 330	66.92 333	80.01 344	76.35 369	65.67 369	86.22 359	87.58 367	64.83 363	62.38 365	80.29 362	26.78 374	88.49 365	63.79 362	54.07 367	85.88 360

LCM-MVSNet		72.55 328	69.39 332	82.03 343	70.81 375	65.42 370	90.12 352	94.36 330	55.02 365	65.88 362	81.72 360	24.16 376	89.96 363	74.32 346	68.10 361	90.71 357

DeepMVS_CX				74.68 349	90.84 350	64.34 371		81.61 373	65.34 362	67.47 361	88.01 355	48.60 366	80.13 370	62.33 364	73.68 355	79.58 364

FPMVS		71.27 329	69.85 331	75.50 347	74.64 370	59.03 372	91.30 341	91.50 356	58.80 364	57.92 366	88.28 351	29.98 372	85.53 367	53.43 366	82.84 332	81.95 363

MVE	50.73 23	53.25 337	48.81 342	66.58 352	65.34 376	57.50 373	72.49 366	70.94 377	40.15 370	39.28 372	63.51 368	6.89 379	73.48 373	38.29 370	42.38 368	68.76 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	53.92 22	58.58 335	55.40 338	68.12 351	51.00 378	48.64 374	78.86 364	87.10 369	46.77 367	35.84 373	74.28 364	8.76 377	86.34 366	42.07 369	73.91 354	69.38 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 336	52.56 340	55.43 353	74.43 371	47.13 375	83.63 363	76.30 374	42.23 368	42.59 370	62.22 369	28.57 373	74.40 371	31.53 371	31.51 369	44.78 368

N_pmnet		78.73 327	78.71 329	78.79 345	92.80 332	46.50 376	94.14 302	43.71 379	78.61 350	80.83 342	91.66 335	74.94 296	96.36 333	67.24 360	84.45 314	93.50 330

EMVS		52.08 338	51.31 341	54.39 354	72.62 373	45.39 377	83.84 362	75.51 376	41.13 369	40.77 371	59.65 370	30.08 371	73.60 372	28.31 372	29.90 371	44.18 369

tmp_tt		51.94 339	53.82 339	46.29 355	33.73 379	45.30 378	78.32 365	67.24 378	18.02 372	50.93 368	87.05 358	52.99 364	53.11 374	70.76 357	25.29 372	40.46 370

wuyk23d		25.11 340	24.57 344	26.74 356	73.98 372	39.89 379	57.88 369	9.80 380	12.27 373	10.39 374	6.97 376	7.03 378	36.44 375	25.43 373	17.39 373	3.89 373

test_method		66.11 333	64.89 335	69.79 350	72.62 373	35.23 380	65.19 368	92.83 346	20.35 371	65.20 363	88.08 354	43.14 368	82.70 368	73.12 350	63.46 363	91.45 355

test123		13.04 343	15.66 346	5.18 357	4.51 381	3.45 381	92.50 337	1.81 382	2.50 375	7.58 376	20.15 373	3.67 380	2.18 377	7.13 375	1.07 375	9.90 371

testmvs		13.36 342	16.33 345	4.48 358	5.04 380	2.26 382	93.18 323	3.28 381	2.70 374	8.24 375	21.66 372	2.29 381	2.19 376	7.58 374	2.96 374	9.00 372

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

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

cdsmvs_eth3d_5k		23.24 341	30.99 343	0.00 359	0.00 382	0.00 383	0.00 370	97.63 140	0.00 377	0.00 378	96.88 143	84.38 157	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		7.39 345	9.85 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	88.65 99	0.00 378	0.00 376	0.00 376	0.00 374

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

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

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

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

ab-mvs-re		8.06 344	10.74 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	96.69 154	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

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

PC_three_1452									90.77 164	98.89 8	98.28 55	96.24 1	98.35 207	95.76 58	99.58 22	99.59 19

eth-test2					0.00 382

eth-test					0.00 382

test_241102_TWO								98.27 30	95.13 17	98.93 6	98.89 4	94.99 11	99.85 17	97.52 4	99.65 12	99.74 7

9.14			96.75 34		98.93 49		97.73 81	98.23 41	91.28 152	97.88 26	98.44 30	93.00 24	99.65 56	95.76 58	99.47 40

test_0728_THIRD									94.78 35	98.73 10	98.87 6	95.87 4	99.84 22	97.45 8	99.72 2	99.77 1

GSMVS																98.45 141

sam_mvs1												82.76 188				98.45 141

sam_mvs												81.94 207

MTGPA								98.08 67

test_post1							92.81 332				16.58 375	80.53 226	97.68 283	86.20 257

test_post											17.58 374	81.76 209	98.08 235

patchmatchnet-post											90.45 341	82.65 192	98.10 230

MTMP							97.86 66	82.03 372

test9_res														94.81 91	99.38 52	99.45 49

agg_prior2														93.94 108	99.38 52	99.50 41

test_prior2							96.35 216		92.80 105	96.03 86	97.59 108	92.01 44		95.01 82	99.38 52

旧先验2							95.94 244		81.66 334	97.34 38			98.82 165	92.26 137

新几何2							95.79 251

无先验							95.79 251	97.87 112	83.87 318				99.65 56	87.68 231		98.89 108

原ACMM2							95.67 254

testdata2													99.67 52	85.96 265

segment_acmp												92.89 25

testdata1							95.26 275		93.10 91

plane_prior5								97.51 152					98.60 185	93.02 130	92.23 218	95.86 233

plane_prior4											96.64 157

plane_prior2							97.74 79		94.85 28

plane_prior1					96.14 211

n2								0.00 383

nn								0.00 383

door-mid								91.06 358

test11								97.88 110

door								91.13 357

HQP-NCC					95.86 218		96.65 190		93.55 68	90.14 215

ACMP_Plane					95.86 218		96.65 190		93.55 68	90.14 215

BP-MVS														92.13 143

HQP4-MVS										90.14 215			98.50 195			95.78 240

HQP3-MVS								97.39 175							92.10 223

HQP2-MVS												80.95 218

ACMMP++_ref															90.30 251

ACMMP++															91.02 240

Test By Simon												88.73 98

High-resolution multi-view benchmark