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.

SMA-MVS		95.20 8	95.07 10	95.59 5	98.14 38	88.48 8	96.26 43	97.28 31	85.90 144	97.67 3	98.10 2	88.41 20	99.56 10	94.66 13	99.19 1	98.71 16
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
DPE-MVS		95.57 4	95.67 4	95.25 9	98.36 27	87.28 17	95.56 82	97.51 4	89.13 61	97.14 8	97.91 11	91.64 7	99.62 1	94.61 14	99.17 2	98.86 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		95.91 2	96.28 2	94.80 36	98.77 5	85.99 58	97.13 12	97.44 14	90.31 29	97.71 1	98.07 4	92.31 4	99.58 8	95.66 4	99.13 3	98.84 12

IU-MVS					98.77 5	86.00 56		96.84 67	81.26 251	97.26 7				95.50 10	99.13 3	99.03 6

test_0728_THIRD									90.75 20	97.04 10	98.05 8	92.09 6	99.55 15	95.64 6	99.13 3	99.13 1

test_241102_TWO								97.44 14	90.31 29	97.62 5	98.07 4	91.46 10	99.58 8	95.66 4	99.12 6	98.98 8

DVP-MVS		95.67 3	96.02 3	94.64 43	98.78 3	85.93 61	97.09 14	96.73 81	90.27 31	97.04 10	98.05 8	91.47 8	99.55 15	95.62 8	99.08 7	98.45 36
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				95.01 17	98.79 2	86.43 43	97.09 14	97.49 5					99.61 3	95.62 8	99.08 7	98.99 7

MSC_two_6792asdad				96.52 1	97.78 57	90.86 1		96.85 65					99.61 3	96.03 1	99.06 9	99.07 4

No_MVS				96.52 1	97.78 57	90.86 1		96.85 65					99.61 3	96.03 1	99.06 9	99.07 4

APDe-MVS		95.46 5	95.64 5	94.91 24	98.26 30	86.29 51	97.46 4	97.40 20	89.03 65	96.20 16	98.10 2	89.39 16	99.34 36	95.88 3	99.03 11	99.10 3

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 20	97.62 5	98.06 6	92.59 2	99.61 3	95.64 6	99.02 12	98.86 9

PC_three_1452									82.47 219	97.09 9	97.07 44	92.72 1	98.04 157	92.70 42	99.02 12	98.86 9

OPU-MVS				96.21 3	98.00 46	90.85 3	97.13 12				97.08 42	92.59 2	98.94 87	92.25 51	98.99 14	98.84 12

ACMMP_NAP		94.74 15	94.56 17	95.28 8	98.02 45	87.70 12	95.68 75	97.34 22	88.28 85	95.30 24	97.67 15	85.90 52	99.54 19	93.91 20	98.95 15	98.60 21

HPM-MVS++		95.14 10	94.91 12	95.83 4	98.25 31	89.65 4	95.92 65	96.96 55	91.75 7	94.02 39	96.83 54	88.12 24	99.55 15	93.41 28	98.94 16	98.28 52

MP-MVS-pluss		94.21 34	94.00 38	94.85 29	98.17 36	86.65 35	94.82 127	97.17 41	86.26 138	92.83 68	97.87 12	85.57 55	99.56 10	94.37 17	98.92 17	98.34 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 29	96.99 81	86.33 47	97.33 5	97.30 29	91.38 11	95.39 22	97.46 19	88.98 19	99.40 31	94.12 18	98.89 18	98.82 14
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		94.96 12	95.33 8	93.88 65	97.25 78	86.69 32	96.19 47	97.11 45	90.42 28	96.95 12	97.27 29	89.53 14	96.91 246	94.38 16	98.85 19	98.03 74
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 55	92.09 3	95.32 23	97.08 42	89.49 15	99.33 39	95.10 11	98.85 19	98.66 18

CP-MVS		94.34 27	94.21 28	94.74 40	98.39 25	86.64 36	97.60 3	97.24 33	88.53 78	92.73 73	97.23 32	85.20 60	99.32 40	92.15 55	98.83 21	98.25 57

ZNCC-MVS		94.47 19	94.28 23	95.03 16	98.52 16	86.96 19	96.85 26	97.32 27	88.24 86	93.15 59	97.04 45	86.17 47	99.62 1	92.40 47	98.81 22	98.52 24

MP-MVS		94.25 30	94.07 35	94.77 38	98.47 19	86.31 49	96.71 29	96.98 51	89.04 63	91.98 90	97.19 36	85.43 57	99.56 10	92.06 60	98.79 23	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		93.89 44	93.65 49	94.62 45	96.84 84	86.43 43	96.69 30	97.49 5	85.15 166	93.56 53	96.28 82	85.60 54	99.31 41	92.45 44	98.79 23	98.12 66

xxxxxxxxxxxxxcwj		94.65 16	94.70 15	94.48 50	97.85 50	85.63 72	95.21 101	95.47 176	89.44 50	95.71 19	97.70 13	88.28 22	99.35 34	93.89 21	98.78 25	98.48 28

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 52	87.76 10	96.65 31	97.48 9	87.76 104	95.71 19	97.70 13	88.28 22	99.35 34	93.89 21	98.78 25	98.48 28

ACMMPR		94.43 23	94.28 23	94.91 24	98.63 9	86.69 32	96.94 18	97.32 27	88.63 74	93.53 54	97.26 31	85.04 62	99.54 19	92.35 49	98.78 25	98.50 26

HFP-MVS		94.52 18	94.40 20	94.86 27	98.61 10	86.81 26	96.94 18	97.34 22	88.63 74	93.65 47	97.21 34	86.10 48	99.49 26	92.35 49	98.77 28	98.30 48

#test#		94.32 29	94.14 32	94.86 27	98.61 10	86.81 26	96.43 34	97.34 22	87.51 110	93.65 47	97.21 34	86.10 48	99.49 26	91.68 73	98.77 28	98.30 48

ETH3D-3000-0.1		94.61 17	94.44 19	95.12 13	97.70 60	87.71 11	95.98 62	97.44 14	86.67 130	95.25 25	97.31 27	87.73 28	99.24 47	93.11 35	98.76 30	98.40 39

zzz-MVS		94.47 19	94.30 22	95.00 18	98.42 22	86.95 20	95.06 113	96.97 52	91.07 13	93.14 60	97.56 16	84.30 70	99.56 10	93.43 26	98.75 31	98.47 32

MTAPA		94.42 25	94.22 26	95.00 18	98.42 22	86.95 20	94.36 163	96.97 52	91.07 13	93.14 60	97.56 16	84.30 70	99.56 10	93.43 26	98.75 31	98.47 32

region2R		94.43 23	94.27 25	94.92 22	98.65 8	86.67 34	96.92 22	97.23 35	88.60 76	93.58 51	97.27 29	85.22 59	99.54 19	92.21 52	98.74 33	98.56 23

test9_res														91.91 66	98.71 34	98.07 70

DeepPCF-MVS	89.96 1	94.20 36	94.77 14	92.49 114	96.52 95	80.00 217	94.00 187	97.08 46	90.05 35	95.65 21	97.29 28	89.66 13	98.97 83	93.95 19	98.71 34	98.50 26

9.14			94.47 18		97.79 54		96.08 55	97.44 14	86.13 142	95.10 26	97.40 23	88.34 21	99.22 49	93.25 32	98.70 36

train_agg		93.44 54	93.08 59	94.52 48	97.53 63	86.49 41	94.07 180	96.78 74	81.86 237	92.77 70	96.20 86	87.63 30	99.12 58	92.14 56	98.69 37	97.94 79

DeepC-MVS_fast	89.43 2	94.04 37	93.79 43	94.80 36	97.48 67	86.78 28	95.65 79	96.89 61	89.40 53	92.81 69	96.97 48	85.37 58	99.24 47	90.87 88	98.69 37	98.38 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		94.85 13	94.94 11	94.58 46	98.25 31	86.33 47	96.11 54	96.62 94	88.14 92	96.10 17	96.96 49	89.09 18	98.94 87	94.48 15	98.68 39	98.48 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
agg_prior2														90.54 92	98.68 39	98.27 54

ETH3 D test6400		93.64 50	93.22 56	94.92 22	97.79 54	86.84 24	95.31 89	97.26 32	82.67 217	93.81 43	96.29 81	87.29 35	99.27 45	89.87 97	98.67 41	98.65 19

test_prior3		93.60 51	93.53 51	93.82 67	97.29 74	84.49 90	94.12 173	96.88 62	87.67 107	92.63 75	96.39 78	86.62 42	98.87 91	91.50 76	98.67 41	98.11 68

test_prior2							94.12 173		87.67 107	92.63 75	96.39 78	86.62 42		91.50 76	98.67 41

MSLP-MVS++		93.72 47	94.08 34	92.65 106	97.31 72	83.43 121	95.79 70	97.33 25	90.03 36	93.58 51	96.96 49	84.87 65	97.76 173	92.19 54	98.66 44	96.76 128

CDPH-MVS		92.83 66	92.30 74	94.44 51	97.79 54	86.11 54	94.06 182	96.66 91	80.09 264	92.77 70	96.63 68	86.62 42	99.04 66	87.40 125	98.66 44	98.17 61

HPM-MVS		94.02 38	93.88 40	94.43 53	98.39 25	85.78 69	97.25 8	97.07 47	86.90 125	92.62 77	96.80 58	84.85 66	99.17 53	92.43 45	98.65 46	98.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 39	93.78 44	94.63 44	98.50 17	85.90 66	96.87 24	96.91 59	88.70 72	91.83 96	97.17 38	83.96 77	99.55 15	91.44 78	98.64 47	98.43 38

MCST-MVS		94.45 21	94.20 29	95.19 11	98.46 20	87.50 15	95.00 115	97.12 43	87.13 117	92.51 80	96.30 80	89.24 17	99.34 36	93.46 25	98.62 48	98.73 15

APD-MVS		94.24 31	94.07 35	94.75 39	98.06 43	86.90 23	95.88 66	96.94 57	85.68 150	95.05 27	97.18 37	87.31 34	99.07 61	91.90 69	98.61 49	98.28 52
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		93.96 41	93.72 46	94.68 41	98.43 21	86.22 52	95.30 92	97.78 1	87.45 113	93.26 55	97.33 26	84.62 68	99.51 24	90.75 91	98.57 50	98.32 47

XVS		94.45 21	94.32 21	94.85 29	98.54 14	86.60 38	96.93 20	97.19 38	90.66 25	92.85 66	97.16 39	85.02 63	99.49 26	91.99 61	98.56 51	98.47 32

X-MVStestdata		88.31 168	86.13 211	94.85 29	98.54 14	86.60 38	96.93 20	97.19 38	90.66 25	92.85 66	23.41 371	85.02 63	99.49 26	91.99 61	98.56 51	98.47 32

agg_prior1		93.29 59	92.97 63	94.26 58	97.38 69	85.92 63	93.92 191	96.72 83	81.96 231	92.16 85	96.23 84	87.85 25	98.97 83	91.95 65	98.55 53	97.90 83

DELS-MVS		93.43 56	93.25 55	93.97 62	95.42 135	85.04 79	93.06 228	97.13 42	90.74 22	91.84 94	95.09 123	86.32 46	99.21 50	91.22 80	98.45 54	97.65 93
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
ZD-MVS					98.15 37	86.62 37		97.07 47	83.63 193	94.19 34	96.91 51	87.57 32	99.26 46	91.99 61	98.44 55

ETH3D cwj APD-0.16		93.91 42	93.53 51	95.06 15	96.76 86	87.78 9	94.92 120	97.21 37	84.33 180	93.89 42	97.09 41	87.20 36	99.29 44	91.90 69	98.44 55	98.12 66

GST-MVS		94.21 34	93.97 39	94.90 26	98.41 24	86.82 25	96.54 33	97.19 38	88.24 86	93.26 55	96.83 54	85.48 56	99.59 7	91.43 79	98.40 57	98.30 48

HPM-MVS_fast		93.40 57	93.22 56	93.94 64	98.36 27	84.83 81	97.15 11	96.80 73	85.77 147	92.47 81	97.13 40	82.38 91	99.07 61	90.51 93	98.40 57	97.92 82

NCCC		94.81 14	94.69 16	95.17 12	97.83 53	87.46 16	95.66 77	96.93 58	92.34 2	93.94 40	96.58 71	87.74 27	99.44 30	92.83 37	98.40 57	98.62 20

testtj		94.39 26	94.18 30	95.00 18	98.24 33	86.77 30	96.16 48	97.23 35	87.28 115	94.85 28	97.04 45	86.99 40	99.52 23	91.54 75	98.33 60	98.71 16

DeepC-MVS	88.79 3	93.31 58	92.99 62	94.26 58	96.07 110	85.83 67	94.89 122	96.99 50	89.02 66	89.56 126	97.37 25	82.51 90	99.38 32	92.20 53	98.30 61	97.57 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		93.23 62	93.05 60	93.76 72	98.04 44	84.07 103	96.22 46	97.37 21	84.15 182	90.05 123	95.66 107	87.77 26	99.15 56	89.91 96	98.27 62	98.07 70

原ACMM1				92.01 133	97.34 71	81.05 185		96.81 72	78.89 278	90.45 115	95.92 97	82.65 88	98.84 99	80.68 223	98.26 63	96.14 148

CS-MVS		92.55 71	92.87 66	91.58 156	94.21 189	80.54 200	95.30 92	96.68 89	88.18 91	92.09 88	94.57 145	84.06 74	98.05 156	92.56 43	98.19 64	96.15 146

MVS_111021_HR		93.45 53	93.31 54	93.84 66	96.99 81	84.84 80	93.24 221	97.24 33	88.76 71	91.60 101	95.85 100	86.07 50	98.66 105	91.91 66	98.16 65	98.03 74

DROMVSNet		93.44 54	93.71 47	92.63 107	95.21 144	82.43 150	97.27 7	96.71 85	90.57 27	92.88 65	95.80 102	83.16 82	98.16 139	93.68 23	98.14 66	97.31 105

test12				94.34 56	97.13 79	86.15 53		96.29 112		91.04 111		85.08 61	99.01 73		98.13 67	97.86 86

新几何1				93.10 84	97.30 73	84.35 99		95.56 168	71.09 346	91.26 107	96.24 83	82.87 87	98.86 94	79.19 244	98.10 68	96.07 155

1121		90.42 110	89.49 116	93.20 80	97.27 76	84.46 93	92.63 239	95.51 174	71.01 347	91.20 108	96.21 85	82.92 86	99.05 63	80.56 225	98.07 69	96.10 153

MSP-MVS		95.42 6	95.56 6	94.98 21	98.49 18	86.52 40	96.91 23	97.47 10	91.73 8	96.10 17	96.69 61	89.90 12	99.30 42	94.70 12	98.04 70	99.13 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
SR-MVS		94.23 32	94.17 31	94.43 53	98.21 35	85.78 69	96.40 37	96.90 60	88.20 89	94.33 31	97.40 23	84.75 67	99.03 67	93.35 29	97.99 71	98.48 28

test1172		93.97 40	94.07 35	93.66 74	98.11 39	83.45 120	96.26 43	96.84 67	88.33 82	94.19 34	97.43 20	84.24 72	99.01 73	93.26 31	97.98 72	98.52 24

3Dnovator	86.66 5	91.73 84	90.82 95	94.44 51	94.59 174	86.37 45	97.18 10	97.02 49	89.20 58	84.31 241	96.66 64	73.74 197	99.17 53	86.74 135	97.96 73	97.79 90

CANet		93.54 52	93.20 58	94.55 47	95.65 126	85.73 71	94.94 118	96.69 88	91.89 5	90.69 113	95.88 99	81.99 102	99.54 19	93.14 34	97.95 74	98.39 40

DPM-MVS		92.58 70	91.74 80	95.08 14	96.19 103	89.31 5	92.66 238	96.56 100	83.44 199	91.68 100	95.04 124	86.60 45	98.99 80	85.60 149	97.92 75	96.93 124

APD-MVS_3200maxsize		93.78 46	93.77 45	93.80 71	97.92 47	84.19 101	96.30 39	96.87 64	86.96 121	93.92 41	97.47 18	83.88 78	98.96 86	92.71 41	97.87 76	98.26 56

CPTT-MVS		91.99 78	91.80 79	92.55 111	98.24 33	81.98 160	96.76 28	96.49 102	81.89 236	90.24 118	96.44 77	78.59 136	98.61 110	89.68 98	97.85 77	97.06 117

SR-MVS-dyc-post		93.82 45	93.82 41	93.82 67	97.92 47	84.57 86	96.28 41	96.76 77	87.46 111	93.75 44	97.43 20	84.24 72	99.01 73	92.73 38	97.80 78	97.88 84

RE-MVS-def			93.68 48		97.92 47	84.57 86	96.28 41	96.76 77	87.46 111	93.75 44	97.43 20	82.94 85		92.73 38	97.80 78	97.88 84

test222					96.55 93	81.70 165	92.22 253	95.01 203	68.36 352	90.20 119	96.14 91	80.26 115			97.80 78	96.05 157

3Dnovator+	87.14 4	92.42 75	91.37 83	95.55 6	95.63 127	88.73 6	97.07 16	96.77 76	90.84 17	84.02 246	96.62 69	75.95 162	99.34 36	87.77 120	97.68 81	98.59 22

旧先验1					96.79 85	81.81 163		95.67 160			96.81 56	86.69 41			97.66 82	96.97 122

Regformer-1		94.22 33	94.13 33	94.51 49	95.54 131	86.36 46	94.57 143	96.44 103	91.69 9	94.32 32	96.56 73	87.05 39	99.03 67	93.35 29	97.65 83	98.15 63

Regformer-2		94.33 28	94.22 26	94.68 41	95.54 131	86.75 31	94.57 143	96.70 86	91.84 6	94.41 29	96.56 73	87.19 37	99.13 57	93.50 24	97.65 83	98.16 62

EPNet		91.79 81	91.02 91	94.10 61	90.10 319	85.25 78	96.03 59	92.05 287	92.83 1	87.39 163	95.78 103	79.39 127	99.01 73	88.13 117	97.48 85	98.05 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CS-MVS-test		92.55 71	92.72 68	92.02 132	94.87 160	81.34 177	96.43 34	96.57 98	89.04 63	91.05 110	94.41 148	83.85 79	98.09 150	90.83 90	97.47 86	96.64 133

testdata				90.49 198	96.40 97	77.89 263		95.37 188	72.51 339	93.63 49	96.69 61	82.08 99	97.65 182	83.08 177	97.39 87	95.94 159

MVS_111021_LR		92.47 74	92.29 75	92.98 90	95.99 114	84.43 97	93.08 226	96.09 127	88.20 89	91.12 109	95.72 106	81.33 107	97.76 173	91.74 71	97.37 88	96.75 129

abl_6		93.18 63	93.05 60	93.57 76	97.52 65	84.27 100	95.53 83	96.67 90	87.85 101	93.20 58	97.22 33	80.35 112	99.18 52	91.91 66	97.21 89	97.26 108

MVSFormer		91.68 86	91.30 84	92.80 97	93.86 204	83.88 108	95.96 63	95.90 143	84.66 176	91.76 97	94.91 127	77.92 144	97.30 214	89.64 99	97.11 90	97.24 109

lupinMVS		90.92 97	90.21 101	93.03 88	93.86 204	83.88 108	92.81 235	93.86 250	79.84 267	91.76 97	94.29 153	77.92 144	98.04 157	90.48 94	97.11 90	97.17 113

EIA-MVS		91.95 79	91.94 77	91.98 136	95.16 146	80.01 216	95.36 86	96.73 81	88.44 79	89.34 130	92.16 227	83.82 80	98.45 121	89.35 102	97.06 92	97.48 101

MG-MVS		91.77 82	91.70 81	92.00 135	97.08 80	80.03 215	93.60 204	95.18 196	87.85 101	90.89 112	96.47 76	82.06 100	98.36 125	85.07 153	97.04 93	97.62 94

test2506		87.21 210	86.28 207	90.02 221	95.62 128	73.64 313	96.25 45	71.38 372	87.89 99	90.45 115	96.65 65	55.29 341	98.09 150	86.03 144	96.94 94	98.33 44

ECVR-MVS		89.09 146	88.53 141	90.77 188	95.62 128	75.89 294	96.16 48	84.22 359	87.89 99	90.20 119	96.65 65	63.19 302	98.10 142	85.90 145	96.94 94	98.33 44

test1111		89.10 144	88.64 138	90.48 199	95.53 133	74.97 300	96.08 55	84.89 357	88.13 93	90.16 121	96.65 65	63.29 300	98.10 142	86.14 140	96.90 96	98.39 40

jason		90.80 98	90.10 105	92.90 94	93.04 230	83.53 118	93.08 226	94.15 240	80.22 261	91.41 104	94.91 127	76.87 150	97.93 167	90.28 95	96.90 96	97.24 109
jason: jason.
Vis-MVSNet		91.75 83	91.23 86	93.29 77	95.32 139	83.78 111	96.14 51	95.98 135	89.89 38	90.45 115	96.58 71	75.09 174	98.31 132	84.75 159	96.90 96	97.78 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
114514_t		89.51 131	88.50 143	92.54 112	98.11 39	81.99 159	95.16 106	96.36 110	70.19 349	85.81 190	95.25 117	76.70 154	98.63 108	82.07 196	96.86 99	97.00 121

Vis-MVSNet (Re-imp)		89.59 129	89.44 118	90.03 219	95.74 122	75.85 295	95.61 80	90.80 322	87.66 109	87.83 152	95.40 114	76.79 152	96.46 272	78.37 249	96.73 100	97.80 89

API-MVS		90.66 104	90.07 106	92.45 116	96.36 99	84.57 86	96.06 58	95.22 195	82.39 220	89.13 132	94.27 156	80.32 113	98.46 118	80.16 232	96.71 101	94.33 223

MAR-MVS		90.30 111	89.37 121	93.07 87	96.61 90	84.48 92	95.68 75	95.67 160	82.36 222	87.85 151	92.85 205	76.63 156	98.80 101	80.01 233	96.68 102	95.91 160
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
Regformer-3		93.68 48	93.64 50	93.81 70	95.36 136	84.61 84	94.68 135	95.83 149	91.27 12	93.60 50	96.71 59	85.75 53	98.86 94	92.87 36	96.65 103	97.96 78

Regformer-4		93.91 42	93.81 42	94.19 60	95.36 136	85.47 75	94.68 135	96.41 106	91.60 10	93.75 44	96.71 59	85.95 51	99.10 60	93.21 33	96.65 103	98.01 76

OpenMVS	83.78 11	88.74 158	87.29 173	93.08 85	92.70 239	85.39 76	96.57 32	96.43 105	78.74 283	80.85 291	96.07 93	69.64 247	99.01 73	78.01 255	96.65 103	94.83 199

ETV-MVS		92.74 68	92.66 69	92.97 91	95.20 145	84.04 105	95.07 110	96.51 101	90.73 23	92.96 64	91.19 260	84.06 74	98.34 128	91.72 72	96.54 106	96.54 138

QAPM		89.51 131	88.15 154	93.59 75	94.92 157	84.58 85	96.82 27	96.70 86	78.43 287	83.41 262	96.19 89	73.18 205	99.30 42	77.11 264	96.54 106	96.89 126

IS-MVSNet		91.43 88	91.09 90	92.46 115	95.87 120	81.38 176	96.95 17	93.69 255	89.72 46	89.50 128	95.98 95	78.57 137	97.77 172	83.02 179	96.50 108	98.22 59

DP-MVS Recon		91.95 79	91.28 85	93.96 63	98.33 29	85.92 63	94.66 138	96.66 91	82.69 216	90.03 124	95.82 101	82.30 94	99.03 67	84.57 161	96.48 109	96.91 125

CANet_DTU		90.26 113	89.41 120	92.81 96	93.46 218	83.01 133	93.48 207	94.47 228	89.43 52	87.76 155	94.23 157	70.54 237	99.03 67	84.97 154	96.39 110	96.38 140

UGNet		89.95 120	88.95 132	92.95 92	94.51 177	83.31 124	95.70 74	95.23 193	89.37 54	87.58 157	93.94 167	64.00 297	98.78 102	83.92 168	96.31 111	96.74 130
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
TSAR-MVS + GP.		93.66 49	93.41 53	94.41 55	96.59 91	86.78 28	94.40 155	93.93 246	89.77 44	94.21 33	95.59 110	87.35 33	98.61 110	92.72 40	96.15 112	97.83 88

PVSNet_Blended		90.73 101	90.32 100	91.98 136	96.12 105	81.25 179	92.55 243	96.83 69	82.04 229	89.10 133	92.56 215	81.04 109	98.85 97	86.72 137	95.91 113	95.84 164

PS-MVSNAJ		91.18 94	90.92 92	91.96 138	95.26 142	82.60 149	92.09 258	95.70 158	86.27 137	91.84 94	92.46 217	79.70 122	98.99 80	89.08 105	95.86 114	94.29 224

ACMMP		93.24 61	92.88 65	94.30 57	98.09 42	85.33 77	96.86 25	97.45 13	88.33 82	90.15 122	97.03 47	81.44 105	99.51 24	90.85 89	95.74 115	98.04 73
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
LCM-MVSNet-Re		88.30 169	88.32 150	88.27 271	94.71 169	72.41 329	93.15 222	90.98 316	87.77 103	79.25 313	91.96 239	78.35 140	95.75 303	83.04 178	95.62 116	96.65 132

CHOSEN 1792x2688		88.84 155	87.69 163	92.30 125	96.14 104	81.42 175	90.01 296	95.86 147	74.52 323	87.41 160	93.94 167	75.46 171	98.36 125	80.36 228	95.53 117	97.12 116

AdaColmap		89.89 123	89.07 129	92.37 121	97.41 68	83.03 131	94.42 154	95.92 140	82.81 214	86.34 183	94.65 140	73.89 193	99.02 71	80.69 222	95.51 118	95.05 187

MVS		87.44 199	86.10 214	91.44 162	92.61 241	83.62 116	92.63 239	95.66 162	67.26 353	81.47 283	92.15 228	77.95 143	98.22 136	79.71 236	95.48 119	92.47 300

UA-Net		92.83 66	92.54 71	93.68 73	96.10 108	84.71 83	95.66 77	96.39 108	91.92 4	93.22 57	96.49 75	83.16 82	98.87 91	84.47 162	95.47 120	97.45 103

xiu_mvs_v2_base		91.13 95	90.89 94	91.86 144	94.97 153	82.42 151	92.24 252	95.64 165	86.11 143	91.74 99	93.14 197	79.67 125	98.89 90	89.06 106	95.46 121	94.28 225

casdiffmvs		92.51 73	92.43 73	92.74 101	94.41 183	81.98 160	94.54 145	96.23 118	89.57 48	91.96 91	96.17 90	82.58 89	98.01 160	90.95 86	95.45 122	98.23 58

PVSNet_Blended_VisFu		91.38 89	90.91 93	92.80 97	96.39 98	83.17 127	94.87 124	96.66 91	83.29 203	89.27 131	94.46 147	80.29 114	99.17 53	87.57 123	95.37 123	96.05 157

PAPM_NR		91.22 93	90.78 96	92.52 113	97.60 62	81.46 173	94.37 162	96.24 117	86.39 136	87.41 160	94.80 134	82.06 100	98.48 116	82.80 185	95.37 123	97.61 95

CHOSEN 280x420		85.15 257	83.99 259	88.65 262	92.47 242	78.40 251	79.68 359	92.76 270	74.90 320	81.41 285	89.59 298	69.85 245	95.51 310	79.92 235	95.29 125	92.03 310

TAPA-MVS	84.62 6	88.16 172	87.01 180	91.62 154	96.64 89	80.65 196	94.39 157	96.21 122	76.38 303	86.19 186	95.44 111	79.75 120	98.08 153	62.75 347	95.29 125	96.13 149
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline		92.39 76	92.29 75	92.69 105	94.46 180	81.77 164	94.14 172	96.27 113	89.22 57	91.88 92	96.00 94	82.35 92	97.99 162	91.05 82	95.27 127	98.30 48

LS3D		87.89 178	86.32 205	92.59 109	96.07 110	82.92 136	95.23 99	94.92 211	75.66 310	82.89 269	95.98 95	72.48 213	99.21 50	68.43 321	95.23 128	95.64 172

MVS_Test		91.31 91	91.11 88	91.93 140	94.37 184	80.14 208	93.46 209	95.80 151	86.46 133	91.35 106	93.77 178	82.21 96	98.09 150	87.57 123	94.95 129	97.55 100

PAPR		90.02 117	89.27 126	92.29 126	95.78 121	80.95 189	92.68 237	96.22 119	81.91 234	86.66 176	93.75 180	82.23 95	98.44 122	79.40 243	94.79 130	97.48 101

xiu_mvs_v1_base_debu		90.64 105	90.05 107	92.40 117	93.97 201	84.46 93	93.32 211	95.46 177	85.17 163	92.25 82	94.03 159	70.59 233	98.57 112	90.97 83	94.67 131	94.18 226

xiu_mvs_v1_base		90.64 105	90.05 107	92.40 117	93.97 201	84.46 93	93.32 211	95.46 177	85.17 163	92.25 82	94.03 159	70.59 233	98.57 112	90.97 83	94.67 131	94.18 226

xiu_mvs_v1_base_debi		90.64 105	90.05 107	92.40 117	93.97 201	84.46 93	93.32 211	95.46 177	85.17 163	92.25 82	94.03 159	70.59 233	98.57 112	90.97 83	94.67 131	94.18 226

gg-mvs-nofinetune		81.77 289	79.37 302	88.99 254	90.85 301	77.73 270	86.29 336	79.63 368	74.88 321	83.19 267	69.05 362	60.34 321	96.11 287	75.46 278	94.64 134	93.11 281

BH-RMVSNet		88.37 166	87.48 168	91.02 179	95.28 140	79.45 227	92.89 233	93.07 264	85.45 157	86.91 171	94.84 133	70.35 238	97.76 173	73.97 290	94.59 135	95.85 163

diffmvs		91.37 90	91.23 86	91.77 150	93.09 227	80.27 205	92.36 248	95.52 173	87.03 120	91.40 105	94.93 126	80.08 116	97.44 199	92.13 57	94.56 136	97.61 95

BH-untuned		88.60 162	88.13 155	90.01 222	95.24 143	78.50 248	93.29 216	94.15 240	84.75 174	84.46 231	93.40 185	75.76 165	97.40 208	77.59 258	94.52 137	94.12 230

Effi-MVS+		91.59 87	91.11 88	93.01 89	94.35 187	83.39 123	94.60 140	95.10 200	87.10 118	90.57 114	93.10 199	81.43 106	98.07 154	89.29 103	94.48 138	97.59 97

PCF-MVS	84.11 10	87.74 183	86.08 215	92.70 104	94.02 195	84.43 97	89.27 306	95.87 146	73.62 330	84.43 233	94.33 150	78.48 139	98.86 94	70.27 307	94.45 139	94.81 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		93.01 65	92.92 64	93.29 77	95.01 150	83.51 119	94.48 147	95.77 153	90.87 16	92.52 79	96.67 63	84.50 69	99.00 78	91.99 61	94.44 140	97.36 104

MS-PatchMatch		85.05 259	84.16 256	87.73 283	91.42 275	78.51 247	91.25 275	93.53 256	77.50 294	80.15 301	91.58 251	61.99 308	95.51 310	75.69 276	94.35 141	89.16 346

mvs_anonymous		89.37 140	89.32 123	89.51 242	93.47 217	74.22 307	91.65 269	94.83 217	82.91 212	85.45 206	93.79 176	81.23 108	96.36 278	86.47 139	94.09 142	97.94 79

MVP-Stereo		85.97 241	84.86 247	89.32 244	90.92 297	82.19 156	92.11 257	94.19 238	78.76 282	78.77 315	91.63 249	68.38 266	96.56 264	75.01 284	93.95 143	89.20 345
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LFMVS		90.08 115	89.13 128	92.95 92	96.71 87	82.32 155	96.08 55	89.91 337	86.79 126	92.15 87	96.81 56	62.60 304	98.34 128	87.18 129	93.90 144	98.19 60

PVSNet	78.82 18	85.55 248	84.65 251	88.23 274	94.72 168	71.93 330	87.12 332	92.75 271	78.80 281	84.95 222	90.53 280	64.43 296	96.71 253	74.74 285	93.86 145	96.06 156

CNLPA		89.07 147	87.98 158	92.34 122	96.87 83	84.78 82	94.08 179	93.24 260	81.41 247	84.46 231	95.13 122	75.57 170	96.62 256	77.21 262	93.84 146	95.61 173

EPNet_dtu		86.49 235	85.94 221	88.14 276	90.24 317	72.82 321	94.11 175	92.20 283	86.66 131	79.42 312	92.36 221	73.52 198	95.81 301	71.26 301	93.66 147	95.80 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GeoE		90.05 116	89.43 119	91.90 143	95.16 146	80.37 204	95.80 69	94.65 225	83.90 187	87.55 159	94.75 135	78.18 142	97.62 186	81.28 211	93.63 148	97.71 92

EI-MVSNet-UG-set		92.74 68	92.62 70	93.12 83	94.86 162	83.20 126	94.40 155	95.74 156	90.71 24	92.05 89	96.60 70	84.00 76	98.99 80	91.55 74	93.63 148	97.17 113

Fast-Effi-MVS+		89.41 137	88.64 138	91.71 152	94.74 166	80.81 193	93.54 205	95.10 200	83.11 206	86.82 174	90.67 278	79.74 121	97.75 176	80.51 227	93.55 150	96.57 136

1314		87.51 196	86.57 196	90.34 208	92.42 244	79.74 223	92.63 239	95.35 190	78.35 288	80.14 302	91.62 250	74.05 190	97.15 227	81.05 213	93.53 151	94.12 230

BH-w/o		87.57 194	87.05 179	89.12 249	94.90 159	77.90 262	92.41 245	93.51 257	82.89 213	83.70 254	91.34 254	75.75 166	97.07 235	75.49 277	93.49 152	92.39 303

PMMVS		85.71 247	84.96 244	87.95 280	88.90 333	77.09 280	88.68 317	90.06 333	72.32 340	86.47 177	90.76 276	72.15 216	94.40 325	81.78 204	93.49 152	92.36 304

PatchMatch-RL		86.77 226	85.54 231	90.47 201	95.88 118	82.71 144	90.54 285	92.31 280	79.82 268	84.32 239	91.57 253	68.77 261	96.39 275	73.16 295	93.48 154	92.32 306

PLC	84.53 7	89.06 148	88.03 156	92.15 129	97.27 76	82.69 145	94.29 165	95.44 182	79.71 269	84.01 247	94.18 158	76.68 155	98.75 103	77.28 261	93.41 155	95.02 188
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		92.24 77	91.91 78	93.24 79	96.59 91	83.43 121	94.84 126	96.44 103	89.19 59	94.08 38	95.90 98	77.85 147	98.17 138	88.90 107	93.38 156	98.13 65

test-LLR		85.87 243	85.41 234	87.25 295	90.95 293	71.67 332	89.55 300	89.88 339	83.41 200	84.54 228	87.95 321	67.25 269	95.11 319	81.82 202	93.37 157	94.97 189

test-mter		84.54 267	83.64 265	87.25 295	90.95 293	71.67 332	89.55 300	89.88 339	79.17 274	84.54 228	87.95 321	55.56 338	95.11 319	81.82 202	93.37 157	94.97 189

EPP-MVSNet		91.70 85	91.56 82	92.13 130	95.88 118	80.50 202	97.33 5	95.25 192	86.15 140	89.76 125	95.60 109	83.42 81	98.32 131	87.37 127	93.25 159	97.56 99

CDS-MVSNet		89.45 134	88.51 142	92.29 126	93.62 213	83.61 117	93.01 229	94.68 224	81.95 232	87.82 153	93.24 193	78.69 134	96.99 241	80.34 229	93.23 160	96.28 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 227	85.39 235	90.53 194	93.05 229	79.33 234	89.79 299	94.77 222	78.82 280	81.95 280	93.24 193	76.81 151	97.30 214	66.94 330	93.16 161	94.95 195

alignmvs		93.08 64	92.50 72	94.81 35	95.62 128	87.61 14	95.99 60	96.07 129	89.77 44	94.12 36	94.87 129	80.56 111	98.66 105	92.42 46	93.10 162	98.15 63

mvs-test1		89.45 134	89.14 127	90.38 205	93.33 220	77.63 272	94.95 117	94.36 231	87.70 105	87.10 167	92.81 209	73.45 200	98.03 159	85.57 150	93.04 163	95.48 175

thisisatest0515		87.33 202	85.99 217	91.37 164	93.49 216	79.55 224	90.63 284	89.56 344	80.17 262	87.56 158	90.86 271	67.07 273	98.28 133	81.50 209	93.02 164	96.29 142

TAMVS		89.21 142	88.29 151	91.96 138	93.71 210	82.62 148	93.30 215	94.19 238	82.22 224	87.78 154	93.94 167	78.83 131	96.95 243	77.70 257	92.98 165	96.32 141

OMC-MVS		91.23 92	90.62 97	93.08 85	96.27 101	84.07 103	93.52 206	95.93 139	86.95 122	89.51 127	96.13 92	78.50 138	98.35 127	85.84 146	92.90 166	96.83 127

canonicalmvs		93.27 60	92.75 67	94.85 29	95.70 125	87.66 13	96.33 38	96.41 106	90.00 37	94.09 37	94.60 142	82.33 93	98.62 109	92.40 47	92.86 167	98.27 54

TESTMET0.1,1		83.74 275	82.85 274	86.42 310	89.96 323	71.21 336	89.55 300	87.88 348	77.41 295	83.37 263	87.31 330	56.71 335	93.65 337	80.62 224	92.85 168	94.40 222

thisisatest0530		88.67 159	87.61 166	91.86 144	94.87 160	80.07 211	94.63 139	89.90 338	84.00 185	88.46 141	93.78 177	66.88 276	98.46 118	83.30 175	92.65 169	97.06 117

VDD-MVS		90.74 100	89.92 112	93.20 80	96.27 101	83.02 132	95.73 72	93.86 250	88.42 81	92.53 78	96.84 53	62.09 307	98.64 107	90.95 86	92.62 170	97.93 81

test_yl		90.69 102	90.02 110	92.71 102	95.72 123	82.41 153	94.11 175	95.12 198	85.63 151	91.49 102	94.70 136	74.75 178	98.42 123	86.13 142	92.53 171	97.31 105

DCV-MVSNet		90.69 102	90.02 110	92.71 102	95.72 123	82.41 153	94.11 175	95.12 198	85.63 151	91.49 102	94.70 136	74.75 178	98.42 123	86.13 142	92.53 171	97.31 105

VDDNet		89.56 130	88.49 145	92.76 99	95.07 149	82.09 157	96.30 39	93.19 262	81.05 256	91.88 92	96.86 52	61.16 317	98.33 130	88.43 113	92.49 173	97.84 87

DP-MVS		87.25 206	85.36 237	92.90 94	97.65 61	83.24 125	94.81 128	92.00 289	74.99 318	81.92 281	95.00 125	72.66 210	99.05 63	66.92 332	92.33 174	96.40 139

GG-mvs-BLEND				87.94 281	89.73 327	77.91 261	87.80 325	78.23 370		80.58 295	83.86 346	59.88 325	95.33 316	71.20 302	92.22 175	90.60 335

tttt0517		88.61 161	87.78 162	91.11 174	94.96 154	77.81 266	95.35 87	89.69 341	85.09 168	88.05 148	94.59 143	66.93 274	98.48 116	83.27 176	92.13 176	97.03 119

HyFIR lowres test		88.09 174	86.81 184	91.93 140	96.00 113	80.63 197	90.01 296	95.79 152	73.42 331	87.68 156	92.10 233	73.86 194	97.96 164	80.75 221	91.70 177	97.19 112

sss		88.93 153	88.26 153	90.94 185	94.05 194	80.78 194	91.71 266	95.38 186	81.55 245	88.63 138	93.91 171	75.04 175	95.47 314	82.47 189	91.61 178	96.57 136

cascas		86.43 236	84.98 243	90.80 187	92.10 252	80.92 190	90.24 290	95.91 142	73.10 334	83.57 259	88.39 314	65.15 292	97.46 196	84.90 157	91.43 179	94.03 237

Effi-MVS+-dtu		88.65 160	88.35 147	89.54 239	93.33 220	76.39 289	94.47 150	94.36 231	87.70 105	85.43 209	89.56 300	73.45 200	97.26 220	85.57 150	91.28 180	94.97 189

thres100view900		87.63 189	86.71 188	90.38 205	96.12 105	78.55 245	95.03 114	91.58 300	87.15 116	88.06 147	92.29 224	68.91 259	98.10 142	70.13 311	91.10 181	94.48 219

tfpn200view9		87.58 193	86.64 191	90.41 202	95.99 114	78.64 243	94.58 141	91.98 291	86.94 123	88.09 144	91.77 243	69.18 256	98.10 142	70.13 311	91.10 181	94.48 219

thres600view7		87.65 186	86.67 190	90.59 191	96.08 109	78.72 241	94.88 123	91.58 300	87.06 119	88.08 146	92.30 223	68.91 259	98.10 142	70.05 314	91.10 181	94.96 192

thres400		87.62 191	86.64 191	90.57 192	95.99 114	78.64 243	94.58 141	91.98 291	86.94 123	88.09 144	91.77 243	69.18 256	98.10 142	70.13 311	91.10 181	94.96 192

F-COLMAP		87.95 177	86.80 185	91.40 163	96.35 100	80.88 191	94.73 133	95.45 180	79.65 270	82.04 279	94.61 141	71.13 224	98.50 115	76.24 272	91.05 185	94.80 201

thres200		87.21 210	86.24 209	90.12 215	95.36 136	78.53 246	93.26 218	92.10 285	86.42 135	88.00 149	91.11 266	69.24 255	98.00 161	69.58 315	91.04 186	93.83 248

WTY-MVS		89.60 128	88.92 133	91.67 153	95.47 134	81.15 183	92.38 247	94.78 221	83.11 206	89.06 135	94.32 151	78.67 135	96.61 259	81.57 208	90.89 187	97.24 109

HY-MVS	83.01 12	89.03 149	87.94 160	92.29 126	94.86 162	82.77 138	92.08 259	94.49 227	81.52 246	86.93 169	92.79 211	78.32 141	98.23 134	79.93 234	90.55 188	95.88 162

CLD-MVS		89.47 133	88.90 134	91.18 170	94.22 188	82.07 158	92.13 256	96.09 127	87.90 97	85.37 215	92.45 218	74.38 183	97.56 189	87.15 130	90.43 189	93.93 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CVMVSNet		84.69 266	84.79 249	84.37 327	91.84 260	64.92 361	93.70 201	91.47 305	66.19 355	86.16 187	95.28 115	67.18 271	93.33 340	80.89 219	90.42 190	94.88 197

SCA		86.32 237	85.18 239	89.73 234	92.15 248	76.60 285	91.12 277	91.69 298	83.53 197	85.50 202	88.81 307	66.79 277	96.48 269	76.65 267	90.35 191	96.12 150

Fast-Effi-MVS+-dtu		87.44 199	86.72 187	89.63 237	92.04 253	77.68 271	94.03 184	93.94 245	85.81 145	82.42 273	91.32 257	70.33 239	97.06 236	80.33 230	90.23 192	94.14 229

OPM-MVS		90.12 114	89.56 115	91.82 147	93.14 225	83.90 107	94.16 171	95.74 156	88.96 67	87.86 150	95.43 113	72.48 213	97.91 168	88.10 118	90.18 193	93.65 259
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 108	90.19 102	91.82 147	94.70 170	82.73 142	95.85 67	96.22 119	90.81 18	86.91 171	94.86 130	74.23 185	98.12 140	88.15 115	89.99 194	94.63 205

plane_prior5								96.22 119					98.12 140	88.15 115	89.99 194	94.63 205

XVG-OURS		89.40 139	88.70 137	91.52 157	94.06 193	81.46 173	91.27 274	96.07 129	86.14 141	88.89 137	95.77 104	68.73 262	97.26 220	87.39 126	89.96 196	95.83 165

baseline2		86.50 233	85.39 235	89.84 227	91.12 287	76.70 284	91.88 260	88.58 346	82.35 223	79.95 306	90.95 270	73.42 202	97.63 185	80.27 231	89.95 197	95.19 184

Anonymous202405211		87.68 184	86.13 211	92.31 124	96.66 88	80.74 195	94.87 124	91.49 304	80.47 260	89.46 129	95.44 111	54.72 343	98.23 134	82.19 194	89.89 198	97.97 77

plane_prior						82.73 142	95.21 101		89.66 47						89.88 199

TR-MVS		86.78 223	85.76 228	89.82 228	94.37 184	78.41 250	92.47 244	92.83 268	81.11 255	86.36 182	92.40 219	68.73 262	97.48 194	73.75 293	89.85 200	93.57 261

HQP3-MVS								96.04 133							89.77 201

HQP-MVS		89.80 125	89.28 125	91.34 165	94.17 190	81.56 167	94.39 157	96.04 133	88.81 68	85.43 209	93.97 166	73.83 195	97.96 164	87.11 132	89.77 201	94.50 216

XVG-OURS-SEG-HR		89.95 120	89.45 117	91.47 161	94.00 199	81.21 182	91.87 261	96.06 131	85.78 146	88.55 139	95.73 105	74.67 181	97.27 218	88.71 110	89.64 203	95.91 160

GA-MVS		86.61 228	85.27 238	90.66 189	91.33 280	78.71 242	90.40 287	93.81 253	85.34 160	85.12 219	89.57 299	61.25 314	97.11 231	80.99 217	89.59 204	96.15 146

1112_ss		88.42 164	87.33 172	91.72 151	94.92 157	80.98 187	92.97 231	94.54 226	78.16 292	83.82 251	93.88 172	78.78 133	97.91 168	79.45 239	89.41 205	96.26 144

ab-mvs		89.41 137	88.35 147	92.60 108	95.15 148	82.65 147	92.20 254	95.60 167	83.97 186	88.55 139	93.70 182	74.16 189	98.21 137	82.46 190	89.37 206	96.94 123

CR-MVSNet		85.35 252	83.76 262	90.12 215	90.58 310	79.34 231	85.24 342	91.96 293	78.27 289	85.55 197	87.87 324	71.03 226	95.61 305	73.96 291	89.36 207	95.40 179

RPMNet		83.95 272	81.53 282	91.21 168	90.58 310	79.34 231	85.24 342	96.76 77	71.44 344	85.55 197	82.97 351	70.87 229	98.91 89	61.01 351	89.36 207	95.40 179

DSMNet-mixed		76.94 321	76.29 320	78.89 339	83.10 362	56.11 369	87.78 326	79.77 367	60.65 359	75.64 334	88.71 310	61.56 311	88.34 361	60.07 354	89.29 209	92.21 309

LPG-MVS_test		89.45 134	88.90 134	91.12 171	94.47 178	81.49 171	95.30 92	96.14 124	86.73 128	85.45 206	95.16 120	69.89 243	98.10 142	87.70 121	89.23 210	93.77 253

LGP-MVS_train				91.12 171	94.47 178	81.49 171		96.14 124	86.73 128	85.45 206	95.16 120	69.89 243	98.10 142	87.70 121	89.23 210	93.77 253

Test_1112_low_res		87.65 186	86.51 198	91.08 175	94.94 156	79.28 235	91.77 263	94.30 234	76.04 308	83.51 260	92.37 220	77.86 146	97.73 177	78.69 248	89.13 212	96.22 145

PatchmatchNet		85.85 244	84.70 250	89.29 245	91.76 263	75.54 298	88.49 319	91.30 308	81.63 243	85.05 220	88.70 311	71.71 217	96.24 282	74.61 287	89.05 213	96.08 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.56 266		91.69 267	69.93 346	87.75 327	91.54 302	78.60 285	84.86 223	88.90 306	69.54 248	96.03 289	70.25 308	88.93 214

MIMVSNet		82.59 283	80.53 288	88.76 257	91.51 270	78.32 252	86.57 335	90.13 331	79.32 271	80.70 293	88.69 312	52.98 350	93.07 344	66.03 335	88.86 215	94.90 196

ACMM	84.12 9	89.14 143	88.48 146	91.12 171	94.65 173	81.22 181	95.31 89	96.12 126	85.31 161	85.92 189	94.34 149	70.19 241	98.06 155	85.65 148	88.86 215	94.08 234
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	84.23 8	89.01 151	88.35 147	90.99 182	94.73 167	81.27 178	95.07 110	95.89 145	86.48 132	83.67 255	94.30 152	69.33 251	97.99 162	87.10 134	88.55 217	93.72 257
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		89.03 149	88.64 138	90.21 210	90.74 305	79.28 235	95.96 63	95.90 143	84.66 176	85.33 217	92.94 203	74.02 191	97.30 214	89.64 99	88.53 218	94.05 236

jajsoiax		88.24 170	87.50 167	90.48 199	90.89 299	80.14 208	95.31 89	95.65 164	84.97 170	84.24 243	94.02 162	65.31 291	97.42 201	88.56 111	88.52 219	93.89 241

PatchT		82.68 282	81.27 284	86.89 305	90.09 320	70.94 340	84.06 348	90.15 330	74.91 319	85.63 196	83.57 348	69.37 250	94.87 323	65.19 337	88.50 220	94.84 198

MSDG		84.86 263	83.09 270	90.14 214	93.80 207	80.05 213	89.18 309	93.09 263	78.89 278	78.19 316	91.91 240	65.86 290	97.27 218	68.47 320	88.45 221	93.11 281

MVS-HIRNet		73.70 324	72.20 327	78.18 342	91.81 262	56.42 368	82.94 354	82.58 362	55.24 361	68.88 354	66.48 363	55.32 340	95.13 318	58.12 356	88.42 222	83.01 357

mvs_tets		88.06 176	87.28 174	90.38 205	90.94 295	79.88 219	95.22 100	95.66 162	85.10 167	84.21 244	93.94 167	63.53 299	97.40 208	88.50 112	88.40 223	93.87 244

ET-MVSNet_ETH3D		87.51 196	85.91 222	92.32 123	93.70 212	83.93 106	92.33 249	90.94 318	84.16 181	72.09 349	92.52 216	69.90 242	95.85 298	89.20 104	88.36 224	97.17 113

FIs		90.51 109	90.35 99	90.99 182	93.99 200	80.98 187	95.73 72	97.54 3	89.15 60	86.72 175	94.68 138	81.83 104	97.24 222	85.18 152	88.31 225	94.76 202

MVS_0304		83.46 276	81.92 279	88.10 277	90.63 309	77.49 275	93.26 218	93.75 254	80.04 265	80.44 298	87.24 332	47.94 358	95.55 307	75.79 275	88.16 226	91.26 323

PS-MVSNAJss		89.97 119	89.62 114	91.02 179	91.90 258	80.85 192	95.26 98	95.98 135	86.26 138	86.21 185	94.29 153	79.70 122	97.65 182	88.87 108	88.10 227	94.57 211

CMPMVS	59.16 21	80.52 304	79.20 306	84.48 326	83.98 359	67.63 355	89.95 298	93.84 252	64.79 356	66.81 357	91.14 265	57.93 333	95.17 317	76.25 271	88.10 227	90.65 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		90.27 112	90.18 103	90.53 194	93.71 210	79.85 221	95.77 71	97.59 2	89.31 55	86.27 184	94.67 139	81.93 103	97.01 240	84.26 164	88.09 229	94.71 203

ACMMP++															88.01 230

D2MVS		85.90 242	85.09 241	88.35 269	90.79 302	77.42 276	91.83 262	95.70 158	80.77 258	80.08 304	90.02 290	66.74 279	96.37 276	81.88 201	87.97 231	91.26 323

UniMVSNet_ETH3D		87.53 195	86.37 201	91.00 181	92.44 243	78.96 240	94.74 132	95.61 166	84.07 184	85.36 216	94.52 146	59.78 326	97.34 213	82.93 180	87.88 232	96.71 131

PVSNet_BlendedMVS		89.98 118	89.70 113	90.82 186	96.12 105	81.25 179	93.92 191	96.83 69	83.49 198	89.10 133	92.26 225	81.04 109	98.85 97	86.72 137	87.86 233	92.35 305

anonymousdsp		87.84 179	87.09 177	90.12 215	89.13 330	80.54 200	94.67 137	95.55 169	82.05 227	83.82 251	92.12 230	71.47 222	97.15 227	87.15 130	87.80 234	92.67 294

Anonymous20240529		88.09 174	86.59 195	92.58 110	96.53 94	81.92 162	95.99 60	95.84 148	74.11 326	89.06 135	95.21 119	61.44 312	98.81 100	83.67 173	87.47 235	97.01 120

ACMMP++_ref															87.47 235

XVG-ACMP-BASELINE		86.00 240	84.84 248	89.45 243	91.20 282	78.00 259	91.70 267	95.55 169	85.05 169	82.97 268	92.25 226	54.49 344	97.48 194	82.93 180	87.45 237	92.89 289

EI-MVSNet		89.10 144	88.86 136	89.80 231	91.84 260	78.30 253	93.70 201	95.01 203	85.73 148	87.15 164	95.28 115	79.87 119	97.21 225	83.81 170	87.36 238	93.88 243

MVSTER		88.84 155	88.29 151	90.51 197	92.95 235	80.44 203	93.73 198	95.01 203	84.66 176	87.15 164	93.12 198	72.79 209	97.21 225	87.86 119	87.36 238	93.87 244

EG-PatchMatch MVS		82.37 285	80.34 291	88.46 266	90.27 316	79.35 230	92.80 236	94.33 233	77.14 299	73.26 346	90.18 286	47.47 360	96.72 251	70.25 308	87.32 240	89.30 343

EPMVS		83.90 274	82.70 276	87.51 287	90.23 318	72.67 323	88.62 318	81.96 364	81.37 248	85.01 221	88.34 315	66.31 284	94.45 324	75.30 280	87.12 241	95.43 178

tpm2		84.08 270	82.94 272	87.48 290	91.39 276	71.27 334	89.23 308	90.37 327	71.95 342	84.64 225	89.33 301	67.30 268	96.55 266	75.17 281	87.09 242	94.63 205

CostFormer		85.77 246	84.94 245	88.26 272	91.16 286	72.58 327	89.47 304	91.04 315	76.26 306	86.45 180	89.97 292	70.74 231	96.86 249	82.35 191	87.07 243	95.34 182

Patchmatch-test		81.37 297	79.30 303	87.58 286	90.92 297	74.16 309	80.99 357	87.68 351	70.52 348	76.63 328	88.81 307	71.21 223	92.76 346	60.01 355	86.93 244	95.83 165

RRT_MVS		88.86 154	87.68 164	92.39 120	92.02 255	86.09 55	94.38 161	94.94 206	85.45 157	87.14 166	93.84 175	65.88 289	97.11 231	88.73 109	86.77 245	93.98 239

LTVRE_ROB	82.13 13	86.26 238	84.90 246	90.34 208	94.44 182	81.50 169	92.31 251	94.89 212	83.03 208	79.63 310	92.67 212	69.69 246	97.79 171	71.20 302	86.26 246	91.72 314
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
COLMAP_ROB	80.39 16	83.96 271	82.04 278	89.74 232	95.28 140	79.75 222	94.25 167	92.28 281	75.17 316	78.02 319	93.77 178	58.60 331	97.84 170	65.06 340	85.92 247	91.63 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DWT-MVSNet_test		84.95 261	83.68 263	88.77 256	91.43 274	73.75 311	91.74 265	90.98 316	80.66 259	83.84 250	87.36 329	62.44 305	97.11 231	78.84 247	85.81 248	95.46 176

RPSCF		85.07 258	84.27 255	87.48 290	92.91 236	70.62 342	91.69 268	92.46 276	76.20 307	82.67 272	95.22 118	63.94 298	97.29 217	77.51 260	85.80 249	94.53 213

USDC		82.76 280	81.26 285	87.26 294	91.17 284	74.55 303	89.27 306	93.39 259	78.26 290	75.30 336	92.08 234	54.43 345	96.63 255	71.64 300	85.79 250	90.61 333

GBi-Net		87.26 204	85.98 218	91.08 175	94.01 196	83.10 128	95.14 107	94.94 206	83.57 194	84.37 234	91.64 246	66.59 281	96.34 279	78.23 252	85.36 251	93.79 249

test1		87.26 204	85.98 218	91.08 175	94.01 196	83.10 128	95.14 107	94.94 206	83.57 194	84.37 234	91.64 246	66.59 281	96.34 279	78.23 252	85.36 251	93.79 249

FMVSNet3		87.40 201	86.11 213	91.30 166	93.79 209	83.64 115	94.20 170	94.81 219	83.89 188	84.37 234	91.87 242	68.45 265	96.56 264	78.23 252	85.36 251	93.70 258

FMVSNet2		87.19 212	85.82 224	91.30 166	94.01 196	83.67 114	94.79 129	94.94 206	83.57 194	83.88 249	92.05 237	66.59 281	96.51 267	77.56 259	85.01 254	93.73 256

ACMH	80.38 17	85.36 251	83.68 263	90.39 203	94.45 181	80.63 197	94.73 133	94.85 215	82.09 226	77.24 323	92.65 213	60.01 324	97.58 187	72.25 299	84.87 255	92.96 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				88.24 273	91.88 259	77.05 281		92.92 266	85.54 154	80.13 303	93.30 190	57.29 334	96.20 283	72.46 298	84.71 256	91.49 318

JIA-IIPM		81.04 300	78.98 310	87.25 295	88.64 334	73.48 315	81.75 356	89.61 343	73.19 333	82.05 278	73.71 359	66.07 288	95.87 297	71.18 304	84.60 257	92.41 302

bset_n11_16_dypcd		86.83 220	85.55 230	90.65 190	88.22 341	81.70 165	88.88 314	90.42 325	85.26 162	85.49 203	90.69 277	67.11 272	97.02 239	89.51 101	84.39 258	93.23 275

OpenMVS_ROB	74.94 19	79.51 312	77.03 318	86.93 302	87.00 347	76.23 292	92.33 249	90.74 323	68.93 351	74.52 340	88.23 318	49.58 355	96.62 256	57.64 357	84.29 259	87.94 354

AllTest		83.42 277	81.39 283	89.52 240	95.01 150	77.79 267	93.12 223	90.89 320	77.41 295	76.12 331	93.34 186	54.08 346	97.51 192	68.31 322	84.27 260	93.26 271

TestCases				89.52 240	95.01 150	77.79 267		90.89 320	77.41 295	76.12 331	93.34 186	54.08 346	97.51 192	68.31 322	84.27 260	93.26 271

tpm		84.73 264	84.02 258	86.87 306	90.33 315	68.90 349	89.06 311	89.94 336	80.85 257	85.75 191	89.86 294	68.54 264	95.97 292	77.76 256	84.05 262	95.75 168

FMVSNet1		85.85 244	84.11 257	91.08 175	92.81 237	83.10 128	95.14 107	94.94 206	81.64 242	82.68 271	91.64 246	59.01 330	96.34 279	75.37 279	83.78 263	93.79 249

ADS-MVSNet2		81.66 292	79.71 300	87.50 288	91.35 278	74.19 308	83.33 351	88.48 347	72.90 336	82.24 276	85.77 341	64.98 293	93.20 342	64.57 341	83.74 264	95.12 185

ADS-MVSNet		81.56 294	79.78 298	86.90 304	91.35 278	71.82 331	83.33 351	89.16 345	72.90 336	82.24 276	85.77 341	64.98 293	93.76 335	64.57 341	83.74 264	95.12 185

XXY-MVS		87.65 186	86.85 183	90.03 219	92.14 249	80.60 199	93.76 197	95.23 193	82.94 211	84.60 226	94.02 162	74.27 184	95.49 313	81.04 214	83.68 266	94.01 238

test_0402		81.30 299	79.17 307	87.67 284	93.19 224	78.17 256	92.98 230	91.71 296	75.25 315	76.02 333	90.31 284	59.23 328	96.37 276	50.22 362	83.63 267	88.47 352

tpmvs		83.35 279	82.07 277	87.20 299	91.07 289	71.00 339	88.31 322	91.70 297	78.91 277	80.49 297	87.18 333	69.30 254	97.08 234	68.12 325	83.56 268	93.51 265

pmmvs5		84.21 269	82.84 275	88.34 270	88.95 332	76.94 282	92.41 245	91.91 295	75.63 311	80.28 299	91.18 262	64.59 295	95.57 306	77.09 265	83.47 269	92.53 298

pmmvs4		85.43 250	83.86 261	90.16 212	90.02 322	82.97 135	90.27 288	92.67 273	75.93 309	80.73 292	91.74 245	71.05 225	95.73 304	78.85 246	83.46 270	91.78 313

test0.0.03 1		82.41 284	81.69 280	84.59 325	88.23 340	72.89 320	90.24 290	87.83 349	83.41 200	79.86 307	89.78 296	67.25 269	88.99 360	65.18 338	83.42 271	91.90 312

tpmrst		85.35 252	84.99 242	86.43 309	90.88 300	67.88 353	88.71 316	91.43 306	80.13 263	86.08 188	88.80 309	73.05 206	96.02 290	82.48 188	83.40 272	95.40 179

nrg030		91.08 96	90.39 98	93.17 82	93.07 228	86.91 22	96.41 36	96.26 114	88.30 84	88.37 143	94.85 132	82.19 97	97.64 184	91.09 81	82.95 273	94.96 192

cl22		86.78 223	85.98 218	89.18 248	92.34 245	77.62 273	90.84 281	94.13 242	81.33 249	83.97 248	90.15 287	73.96 192	96.60 261	84.19 165	82.94 274	93.33 269

miper_ehance_all_eth		87.22 209	86.62 194	89.02 253	92.13 250	77.40 277	90.91 280	94.81 219	81.28 250	84.32 239	90.08 289	79.26 128	96.62 256	83.81 170	82.94 274	93.04 284

miper_enhance_ethall		86.90 218	86.18 210	89.06 251	91.66 268	77.58 274	90.22 292	94.82 218	79.16 275	84.48 230	89.10 303	79.19 129	96.66 254	84.06 166	82.94 274	92.94 287

ACMH+	81.04 14	85.05 259	83.46 267	89.82 228	94.66 172	79.37 229	94.44 152	94.12 243	82.19 225	78.04 318	92.82 208	58.23 332	97.54 190	73.77 292	82.90 277	92.54 297

VPA-MVSNet		89.62 127	88.96 131	91.60 155	93.86 204	82.89 137	95.46 84	97.33 25	87.91 96	88.43 142	93.31 189	74.17 188	97.40 208	87.32 128	82.86 278	94.52 214

IterMVS-LS		88.36 167	87.91 161	89.70 235	93.80 207	78.29 254	93.73 198	95.08 202	85.73 148	84.75 224	91.90 241	79.88 118	96.92 245	83.83 169	82.51 279	93.89 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testgi		80.94 303	80.20 294	83.18 332	87.96 345	66.29 356	91.28 273	90.70 324	83.70 191	78.12 317	92.84 206	51.37 352	90.82 356	63.34 344	82.46 280	92.43 301

WR-MVS		88.38 165	87.67 165	90.52 196	93.30 222	80.18 206	93.26 218	95.96 137	88.57 77	85.47 205	92.81 209	76.12 158	96.91 246	81.24 212	82.29 281	94.47 221

RRT_test8_iter05		86.90 218	86.36 202	88.52 265	93.00 233	73.27 317	94.32 164	95.96 137	85.50 156	84.26 242	92.86 204	60.76 319	97.70 178	88.32 114	82.29 281	94.60 208

tpm cat1		81.96 286	80.27 292	87.01 301	91.09 288	71.02 338	87.38 331	91.53 303	66.25 354	80.17 300	86.35 337	68.22 267	96.15 286	69.16 316	82.29 281	93.86 246

v1192		87.25 206	86.33 204	90.00 223	90.76 304	79.04 239	93.80 195	95.48 175	82.57 218	85.48 204	91.18 262	73.38 204	97.42 201	82.30 192	82.06 284	93.53 262

v1144		87.61 192	86.79 186	90.06 218	91.01 290	79.34 231	93.95 189	95.42 185	83.36 202	85.66 195	91.31 258	74.98 176	97.42 201	83.37 174	82.06 284	93.42 268

v1240		86.78 223	85.85 223	89.56 238	90.45 314	77.79 267	93.61 203	95.37 188	81.65 241	85.43 209	91.15 264	71.50 221	97.43 200	81.47 210	82.05 286	93.47 266

Anonymous20231206		81.03 301	79.77 299	84.82 324	87.85 346	70.26 344	91.42 272	92.08 286	73.67 329	77.75 320	89.25 302	62.43 306	93.08 343	61.50 350	82.00 287	91.12 328

V42		87.68 184	86.86 182	90.15 213	90.58 310	80.14 208	94.24 168	95.28 191	83.66 192	85.67 194	91.33 255	74.73 180	97.41 206	84.43 163	81.83 288	92.89 289

v1921920		86.97 217	86.06 216	89.69 236	90.53 313	78.11 258	93.80 195	95.43 183	81.90 235	85.33 217	91.05 268	72.66 210	97.41 206	82.05 197	81.80 289	93.53 262

v2v482		87.84 179	87.06 178	90.17 211	90.99 291	79.23 238	94.00 187	95.13 197	84.87 171	85.53 199	92.07 236	74.45 182	97.45 197	84.71 160	81.75 290	93.85 247

Anonymous20231211		86.59 230	85.13 240	90.98 184	96.52 95	81.50 169	96.14 51	96.16 123	73.78 328	83.65 256	92.15 228	63.26 301	97.37 212	82.82 184	81.74 291	94.06 235

v144192		87.19 212	86.35 203	89.74 232	90.64 308	78.24 255	93.92 191	95.43 183	81.93 233	85.51 201	91.05 268	74.21 187	97.45 197	82.86 182	81.56 292	93.53 262

cl____		86.52 232	85.78 225	88.75 258	92.03 254	76.46 287	90.74 282	94.30 234	81.83 239	83.34 264	90.78 275	75.74 168	96.57 262	81.74 205	81.54 293	93.22 276

DIV-MVS_self_test		86.53 231	85.78 225	88.75 258	92.02 255	76.45 288	90.74 282	94.30 234	81.83 239	83.34 264	90.82 273	75.75 166	96.57 262	81.73 206	81.52 294	93.24 274

Anonymous20240521		80.44 305	79.21 305	84.11 330	85.75 354	67.89 352	92.86 234	93.23 261	75.61 312	75.59 335	87.47 328	50.03 353	94.33 327	71.14 305	81.21 295	90.12 338

OurMVSNet-221017-0		85.35 252	84.64 252	87.49 289	90.77 303	72.59 326	94.01 186	94.40 230	84.72 175	79.62 311	93.17 195	61.91 309	96.72 251	81.99 198	81.16 296	93.16 279

FMVSNet5		81.52 295	79.60 301	87.27 293	91.17 284	77.95 260	91.49 271	92.26 282	76.87 300	76.16 330	87.91 323	51.67 351	92.34 348	67.74 326	81.16 296	91.52 317

CP-MVSNet		87.63 189	87.26 176	88.74 260	93.12 226	76.59 286	95.29 95	96.58 97	88.43 80	83.49 261	92.98 202	75.28 172	95.83 299	78.97 245	81.15 298	93.79 249

c3_l		87.14 214	86.50 199	89.04 252	92.20 247	77.26 278	91.22 276	94.70 223	82.01 230	84.34 238	90.43 282	78.81 132	96.61 259	83.70 172	81.09 299	93.25 273

IterMVS-SCA-FT		85.45 249	84.53 254	88.18 275	91.71 265	76.87 283	90.19 293	92.65 274	85.40 159	81.44 284	90.54 279	66.79 277	95.00 322	81.04 214	81.05 300	92.66 295

TinyColmap		79.76 311	77.69 313	85.97 313	91.71 265	73.12 318	89.55 300	90.36 328	75.03 317	72.03 350	90.19 285	46.22 361	96.19 285	63.11 345	81.03 301	88.59 351

UniMVSNet_NR-MVSNet		89.92 122	89.29 124	91.81 149	93.39 219	83.72 112	94.43 153	97.12 43	89.80 40	86.46 178	93.32 188	83.16 82	97.23 223	84.92 155	81.02 302	94.49 218

DU-MVS		89.34 141	88.50 143	91.85 146	93.04 230	83.72 112	94.47 150	96.59 96	89.50 49	86.46 178	93.29 191	77.25 148	97.23 223	84.92 155	81.02 302	94.59 209

PS-CasMVS		87.32 203	86.88 181	88.63 263	92.99 234	76.33 291	95.33 88	96.61 95	88.22 88	83.30 266	93.07 200	73.03 207	95.79 302	78.36 250	81.00 304	93.75 255

IterMVS		84.88 262	83.98 260	87.60 285	91.44 271	76.03 293	90.18 294	92.41 277	83.24 205	81.06 290	90.42 283	66.60 280	94.28 329	79.46 238	80.98 305	92.48 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		89.80 125	89.07 129	92.01 133	93.60 214	84.52 89	94.78 130	97.47 10	89.26 56	86.44 181	92.32 222	82.10 98	97.39 211	84.81 158	80.84 306	94.12 230

LF4IMVS		80.37 306	79.07 309	84.27 329	86.64 348	69.87 347	89.39 305	91.05 314	76.38 303	74.97 338	90.00 291	47.85 359	94.25 330	74.55 288	80.82 307	88.69 350

v10		87.25 206	86.38 200	89.85 226	91.19 283	79.50 225	94.48 147	95.45 180	83.79 190	83.62 257	91.19 260	75.13 173	97.42 201	81.94 199	80.60 308	92.63 296

tfpnnormal		84.72 265	83.23 269	89.20 247	92.79 238	80.05 213	94.48 147	95.81 150	82.38 221	81.08 289	91.21 259	69.01 258	96.95 243	61.69 349	80.59 309	90.58 336

test_part1		89.00 152	87.99 157	92.04 131	95.94 117	83.81 110	96.14 51	96.05 132	86.44 134	85.69 193	93.73 181	71.57 219	97.66 180	85.80 147	80.54 310	94.66 204

WR-MVS_H		87.80 181	87.37 171	89.10 250	93.23 223	78.12 257	95.61 80	97.30 29	87.90 97	83.72 253	92.01 238	79.65 126	96.01 291	76.36 269	80.54 310	93.16 279

VPNet		88.20 171	87.47 169	90.39 203	93.56 215	79.46 226	94.04 183	95.54 171	88.67 73	86.96 168	94.58 144	69.33 251	97.15 227	84.05 167	80.53 312	94.56 212

v7n		86.81 221	85.76 228	89.95 224	90.72 306	79.25 237	95.07 110	95.92 140	84.45 179	82.29 274	90.86 271	72.60 212	97.53 191	79.42 242	80.52 313	93.08 283

v8		87.50 198	86.71 188	89.89 225	91.37 277	79.40 228	94.50 146	95.38 186	84.81 173	83.60 258	91.33 255	76.05 159	97.42 201	82.84 183	80.51 314	92.84 291

EU-MVSNet		81.32 298	80.95 286	82.42 336	88.50 336	63.67 362	93.32 211	91.33 307	64.02 357	80.57 296	92.83 207	61.21 316	92.27 349	76.34 270	80.38 315	91.32 321

Patchmtry		82.71 281	80.93 287	88.06 278	90.05 321	76.37 290	84.74 346	91.96 293	72.28 341	81.32 287	87.87 324	71.03 226	95.50 312	68.97 317	80.15 316	92.32 306

NR-MVSNet		88.58 163	87.47 169	91.93 140	93.04 230	84.16 102	94.77 131	96.25 116	89.05 62	80.04 305	93.29 191	79.02 130	97.05 237	81.71 207	80.05 317	94.59 209

Baseline_NR-MVSNet		87.07 215	86.63 193	88.40 267	91.44 271	77.87 264	94.23 169	92.57 275	84.12 183	85.74 192	92.08 234	77.25 148	96.04 288	82.29 193	79.94 318	91.30 322

dp		81.47 296	80.23 293	85.17 322	89.92 324	65.49 359	86.74 333	90.10 332	76.30 305	81.10 288	87.12 334	62.81 303	95.92 294	68.13 324	79.88 319	94.09 233

TranMVSNet+NR-MVSNet		88.84 155	87.95 159	91.49 159	92.68 240	83.01 133	94.92 120	96.31 111	89.88 39	85.53 199	93.85 174	76.63 156	96.96 242	81.91 200	79.87 320	94.50 216

miper_lstm_enhance		85.27 255	84.59 253	87.31 292	91.28 281	74.63 302	87.69 328	94.09 244	81.20 254	81.36 286	89.85 295	74.97 177	94.30 328	81.03 216	79.84 321	93.01 285

v148		87.04 216	86.32 205	89.21 246	90.94 295	77.26 278	93.71 200	94.43 229	84.84 172	84.36 237	90.80 274	76.04 160	97.05 237	82.12 195	79.60 322	93.31 270

IB-MVS	80.51 15	85.24 256	83.26 268	91.19 169	92.13 250	79.86 220	91.75 264	91.29 309	83.28 204	80.66 294	88.49 313	61.28 313	98.46 118	80.99 217	79.46 323	95.25 183
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
eth_miper_zixun_eth		86.50 233	85.77 227	88.68 261	91.94 257	75.81 296	90.47 286	94.89 212	82.05 227	84.05 245	90.46 281	75.96 161	96.77 250	82.76 186	79.36 324	93.46 267

baseline1		88.10 173	87.28 174	90.57 192	94.96 154	80.07 211	94.27 166	91.29 309	86.74 127	87.41 160	94.00 164	76.77 153	96.20 283	80.77 220	79.31 325	95.44 177

our_test_3		81.93 287	80.46 290	86.33 311	88.46 337	73.48 315	88.46 320	91.11 311	76.46 301	76.69 327	88.25 317	66.89 275	94.36 326	68.75 318	79.08 326	91.14 327

PEN-MVS		86.80 222	86.27 208	88.40 267	92.32 246	75.71 297	95.18 104	96.38 109	87.97 94	82.82 270	93.15 196	73.39 203	95.92 294	76.15 273	79.03 327	93.59 260

pm-mvs1		86.61 228	85.54 231	89.82 228	91.44 271	80.18 206	95.28 97	94.85 215	83.84 189	81.66 282	92.62 214	72.45 215	96.48 269	79.67 237	78.06 328	92.82 292

h-mvs33		90.80 98	90.15 104	92.75 100	96.01 112	82.66 146	95.43 85	95.53 172	89.80 40	93.08 62	95.64 108	75.77 163	99.00 78	92.07 58	78.05 329	96.60 134

SixPastTwentyTwo		83.91 273	82.90 273	86.92 303	90.99 291	70.67 341	93.48 207	91.99 290	85.54 154	77.62 322	92.11 232	60.59 320	96.87 248	76.05 274	77.75 330	93.20 277

ppachtmachnet_test		81.84 288	80.07 296	87.15 300	88.46 337	74.43 306	89.04 312	92.16 284	75.33 314	77.75 320	88.99 304	66.20 285	95.37 315	65.12 339	77.60 331	91.65 315

MIMVSNet1		79.38 313	77.28 315	85.69 317	86.35 349	73.67 312	91.61 270	92.75 271	78.11 293	72.64 348	88.12 319	48.16 357	91.97 352	60.32 352	77.49 332	91.43 320

DTE-MVSNet		86.11 239	85.48 233	87.98 279	91.65 269	74.92 301	94.93 119	95.75 155	87.36 114	82.26 275	93.04 201	72.85 208	95.82 300	74.04 289	77.46 333	93.20 277

N_pmnet		68.89 327	68.44 330	70.23 346	89.07 331	28.79 378	88.06 323	19.50 379	69.47 350	71.86 351	84.93 343	61.24 315	91.75 353	54.70 359	77.15 334	90.15 337

AUN-MVS		87.78 182	86.54 197	91.48 160	94.82 165	81.05 185	93.91 194	93.93 246	83.00 209	86.93 169	93.53 184	69.50 249	97.67 179	86.14 140	77.12 335	95.73 170

hse-mvs2		89.88 124	89.34 122	91.51 158	94.83 164	81.12 184	93.94 190	93.91 249	89.80 40	93.08 62	93.60 183	75.77 163	97.66 180	92.07 58	77.07 336	95.74 169

test20.03		79.95 309	79.08 308	82.55 335	85.79 353	67.74 354	91.09 278	91.08 312	81.23 253	74.48 341	89.96 293	61.63 310	90.15 357	60.08 353	76.38 337	89.76 339

FPMVS		64.63 329	62.55 331	70.88 345	70.80 369	56.71 366	84.42 347	84.42 358	51.78 363	49.57 363	81.61 353	23.49 370	81.48 366	40.61 367	76.25 338	74.46 362

EGC-MVSNET		61.97 330	56.37 334	78.77 340	89.63 328	73.50 314	89.12 310	82.79 361	0.21 376	1.24 377	84.80 344	39.48 364	90.04 358	44.13 364	75.94 339	72.79 363

pmmvs6		83.42 277	81.60 281	88.87 255	88.01 344	77.87 264	94.96 116	94.24 237	74.67 322	78.80 314	91.09 267	60.17 323	96.49 268	77.06 266	75.40 340	92.23 308

new_pmnet		72.15 325	70.13 328	78.20 341	82.95 363	65.68 357	83.91 349	82.40 363	62.94 358	64.47 358	79.82 355	42.85 363	86.26 363	57.41 358	74.44 341	82.65 359

MDA-MVSNet_test_wron		79.21 315	77.19 317	85.29 320	88.22 341	72.77 322	85.87 338	90.06 333	74.34 324	62.62 360	87.56 327	66.14 286	91.99 351	66.90 333	73.01 342	91.10 330

YYNet1		79.22 314	77.20 316	85.28 321	88.20 343	72.66 324	85.87 338	90.05 335	74.33 325	62.70 359	87.61 326	66.09 287	92.03 350	66.94 330	72.97 343	91.15 326

Patchmatch-RL test		81.67 291	79.96 297	86.81 307	85.42 356	71.23 335	82.17 355	87.50 352	78.47 286	77.19 324	82.50 352	70.81 230	93.48 338	82.66 187	72.89 344	95.71 171

pmmvs-eth3d		80.97 302	78.72 311	87.74 282	84.99 358	79.97 218	90.11 295	91.65 299	75.36 313	73.51 344	86.03 338	59.45 327	93.96 334	75.17 281	72.21 345	89.29 344

PM-MVS		78.11 319	76.12 321	84.09 331	83.54 361	70.08 345	88.97 313	85.27 356	79.93 266	74.73 339	86.43 335	34.70 366	93.48 338	79.43 241	72.06 346	88.72 349

Gipuma		57.99 333	54.91 335	67.24 348	88.51 335	65.59 358	52.21 367	90.33 329	43.58 366	42.84 367	51.18 368	20.29 373	85.07 364	34.77 368	70.45 347	51.05 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
K. test v3		81.59 293	80.15 295	85.91 316	89.89 325	69.42 348	92.57 242	87.71 350	85.56 153	73.44 345	89.71 297	55.58 337	95.52 309	77.17 263	69.76 348	92.78 293

KD-MVS_self_test		80.20 307	79.24 304	83.07 333	85.64 355	65.29 360	91.01 279	93.93 246	78.71 284	76.32 329	86.40 336	59.20 329	92.93 345	72.59 297	69.35 349	91.00 331

CL-MVSNet_self_test		81.74 290	80.53 288	85.36 319	85.96 352	72.45 328	90.25 289	93.07 264	81.24 252	79.85 308	87.29 331	70.93 228	92.52 347	66.95 329	69.23 350	91.11 329

TDRefinement		79.81 310	77.34 314	87.22 298	79.24 366	75.48 299	93.12 223	92.03 288	76.45 302	75.01 337	91.58 251	49.19 356	96.44 273	70.22 310	69.18 351	89.75 340

MDA-MVSNet-bldmvs		78.85 316	76.31 319	86.46 308	89.76 326	73.88 310	88.79 315	90.42 325	79.16 275	59.18 361	88.33 316	60.20 322	94.04 331	62.00 348	68.96 352	91.48 319

ambc				83.06 334	79.99 365	63.51 363	77.47 360	92.86 267		74.34 342	84.45 345	28.74 367	95.06 321	73.06 296	68.89 353	90.61 333

TransMVSNet (Re)		84.43 268	83.06 271	88.54 264	91.72 264	78.44 249	95.18 104	92.82 269	82.73 215	79.67 309	92.12 230	73.49 199	95.96 293	71.10 306	68.73 354	91.21 325

PMVS	47.18 22	52.22 334	48.46 338	63.48 349	45.72 378	46.20 374	73.41 363	78.31 369	41.03 367	30.06 370	65.68 364	6.05 377	83.43 365	30.04 369	65.86 355	60.80 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lessismore_v0				86.04 312	88.46 337	68.78 350		80.59 366		73.01 347	90.11 288	55.39 339	96.43 274	75.06 283	65.06 356	92.90 288

new-patchmatchnet		76.41 322	75.17 324	80.13 338	82.65 364	59.61 364	87.66 329	91.08 312	78.23 291	69.85 353	83.22 349	54.76 342	91.63 355	64.14 343	64.89 357	89.16 346

pmmvs3		71.81 326	68.71 329	81.11 337	75.86 367	70.42 343	86.74 333	83.66 360	58.95 360	68.64 356	80.89 354	36.93 365	89.52 359	63.10 346	63.59 358	83.39 356

UnsupCasMVSNet_eth		80.07 308	78.27 312	85.46 318	85.24 357	72.63 325	88.45 321	94.87 214	82.99 210	71.64 352	88.07 320	56.34 336	91.75 353	73.48 294	63.36 359	92.01 311

LCM-MVSNet		66.00 328	62.16 332	77.51 343	64.51 373	58.29 365	83.87 350	90.90 319	48.17 364	54.69 362	73.31 360	16.83 376	86.75 362	65.47 336	61.67 360	87.48 355

UnsupCasMVSNet_bld		76.23 323	73.27 326	85.09 323	83.79 360	72.92 319	85.65 341	93.47 258	71.52 343	68.84 355	79.08 356	49.77 354	93.21 341	66.81 334	60.52 361	89.13 348

KD-MVS_2432*1600		78.50 317	76.02 322	85.93 314	86.22 350	74.47 304	84.80 344	92.33 278	79.29 272	76.98 325	85.92 339	53.81 348	93.97 332	67.39 327	57.42 362	89.36 341

miper_refine_blended		78.50 317	76.02 322	85.93 314	86.22 350	74.47 304	84.80 344	92.33 278	79.29 272	76.98 325	85.92 339	53.81 348	93.97 332	67.39 327	57.42 362	89.36 341

DeepMVS_CX				56.31 352	74.23 368	51.81 371		56.67 377	44.85 365	48.54 365	75.16 357	27.87 369	58.74 373	40.92 366	52.22 364	58.39 366

PVSNet_0	73.20 20	77.22 320	74.83 325	84.37 327	90.70 307	71.10 337	83.09 353	89.67 342	72.81 338	73.93 343	83.13 350	60.79 318	93.70 336	68.54 319	50.84 365	88.30 353

test_method		50.52 335	48.47 337	56.66 351	52.26 377	18.98 380	41.51 369	81.40 365	10.10 371	44.59 366	75.01 358	28.51 368	68.16 369	53.54 360	49.31 366	82.83 358

PMMVS2		59.60 331	56.40 333	69.21 347	68.83 370	46.58 373	73.02 364	77.48 371	55.07 362	49.21 364	72.95 361	17.43 375	80.04 367	49.32 363	44.33 367	80.99 361

MVE	39.65 23	43.39 336	38.59 342	57.77 350	56.52 375	48.77 372	55.38 366	58.64 376	29.33 370	28.96 371	52.65 367	4.68 378	64.62 372	28.11 370	33.07 368	59.93 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 337	42.29 339	46.03 353	65.58 372	37.41 375	73.51 362	64.62 373	33.99 368	28.47 372	47.87 369	19.90 374	67.91 370	22.23 371	24.45 369	32.77 368

ANet_high		58.88 332	54.22 336	72.86 344	56.50 376	56.67 367	80.75 358	86.00 353	73.09 335	37.39 368	64.63 365	22.17 371	79.49 368	43.51 365	23.96 370	82.43 360

EMVS		42.07 338	41.12 340	44.92 354	63.45 374	35.56 377	73.65 361	63.48 374	33.05 369	26.88 373	45.45 370	21.27 372	67.14 371	19.80 372	23.02 371	32.06 369

tmp_tt		35.64 339	39.24 341	24.84 355	14.87 379	23.90 379	62.71 365	51.51 378	6.58 373	36.66 369	62.08 366	44.37 362	30.34 375	52.40 361	22.00 372	20.27 370

wuyk23d		21.27 341	20.48 344	23.63 356	68.59 371	36.41 376	49.57 368	6.85 380	9.37 372	7.89 374	4.46 376	4.03 379	31.37 374	17.47 373	16.07 373	3.12 371

testmvs		8.92 342	11.52 345	1.12 358	1.06 380	0.46 382	86.02 337	0.65 381	0.62 374	2.74 375	9.52 374	0.31 381	0.45 377	2.38 374	0.39 374	2.46 373

test123		8.76 343	11.22 346	1.39 357	0.85 381	0.97 381	85.76 340	0.35 382	0.54 375	2.45 376	8.14 375	0.60 380	0.48 376	2.16 375	0.17 375	2.71 372

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		22.14 340	29.52 343	0.00 359	0.00 382	0.00 383	0.00 370	95.76 154	0.00 377	0.00 378	94.29 153	75.66 169	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		6.64 345	8.86 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	79.70 122	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		7.82 344	10.43 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	93.88 172	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

FOURS1					98.86 1	85.54 74	98.29 1	97.49 5	89.79 43	96.29 15

test_one_0601					98.58 12	85.83 67		97.44 14	91.05 15	96.78 13	98.06 6	91.45 11

eth-test2					0.00 382

eth-test					0.00 382

test_241102_ONE					98.77 5	85.99 58		97.44 14	90.26 33	97.71 1	97.96 10	92.31 4	99.38 32

save fliter					97.85 50	85.63 72	95.21 101	96.82 71	89.44 50

test0726					98.78 3	85.93 61	97.19 9	97.47 10	90.27 31	97.64 4	98.13 1	91.47 8

GSMVS																96.12 150

test_part2					98.55 13	87.22 18				96.40 14

sam_mvs1												71.70 218				96.12 150

sam_mvs												70.60 232

MTGPA								96.97 52

test_post1							88.00 324				9.81 373	69.31 253	95.53 308	76.65 267

test_post											10.29 372	70.57 236	95.91 296

patchmatchnet-post											83.76 347	71.53 220	96.48 269

MTMP							96.16 48	60.64 375

gm-plane-assit					89.60 329	68.00 351			77.28 298		88.99 304		97.57 188	79.44 240

TEST9					97.53 63	86.49 41	94.07 180	96.78 74	81.61 244	92.77 70	96.20 86	87.71 29	99.12 58

test_8					97.49 66	86.30 50	94.02 185	96.76 77	81.86 237	92.70 74	96.20 86	87.63 30	99.02 71

agg_prior					97.38 69	85.92 63		96.72 83		92.16 85			98.97 83

test_prior4						85.96 60	94.11 175

test_prior				93.82 67	97.29 74	84.49 90		96.88 62					98.87 91			98.11 68

旧先验2							93.36 210		71.25 345	94.37 30			97.13 230	86.74 135

新几何2							93.11 225

无先验							93.28 217	96.26 114	73.95 327				99.05 63	80.56 225		96.59 135

原ACMM2							92.94 232

testdata2													98.75 103	78.30 251

segment_acmp												87.16 38

testdata1							92.15 255		87.94 95

plane_prior7					94.70 170	82.74 141

plane_prior6					94.52 176	82.75 139						74.23 185

plane_prior4											94.86 130

plane_prior3						82.75 139			90.26 33	86.91 171

plane_prior2							95.85 67		90.81 18

plane_prior1					94.59 174

n2								0.00 383

nn								0.00 383

door-mid								85.49 354

test11								96.57 98

door								85.33 355

HQP5-MVS						81.56 167

HQP-NCC					94.17 190		94.39 157		88.81 68	85.43 209

ACMP_Plane					94.17 190		94.39 157		88.81 68	85.43 209

BP-MVS														87.11 132

HQP4-MVS										85.43 209			97.96 164			94.51 215

HQP2-MVS												73.83 195

NP-MVS					94.37 184	82.42 151					93.98 165

MDTV_nov1_ep13_2view						55.91 370	87.62 330		73.32 332	84.59 227		70.33 239		74.65 286		95.50 174

Test By Simon												80.02 117

High-resolution multi-view benchmark