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.

patch_mono-2		98.36 48	98.87 3	96.82 206	99.53 39	90.68 311	98.64 147	99.29 8	97.88 5	99.19 29	99.52 3	96.80 15	99.97 1	99.11 1	99.86 1	99.82 10

dcpmvs_2		98.08 61	98.59 10	96.56 230	99.57 35	90.34 317	99.15 41	98.38 196	96.82 64	99.29 20	99.49 7	95.78 49	99.57 139	98.94 2	99.86 1	99.77 23

test_0728_THIRD									97.32 33	99.45 11	99.46 13	97.88 1	99.94 4	98.47 22	99.86 1	99.85 4

CP-MVS		98.57 27	98.36 27	99.19 46	99.66 28	97.86 73	99.34 16	98.87 58	95.96 97	98.60 75	99.13 73	96.05 36	99.94 4	97.77 59	99.86 1	99.77 23

CHOSEN 280x420		97.18 111	97.18 94	97.20 180	98.81 138	93.27 266	95.78 344	99.15 19	95.25 132	96.79 167	98.11 191	92.29 122	99.07 198	98.56 13	99.85 5	99.25 136

SD-MVS		98.64 15	98.68 7	98.53 94	99.33 69	98.36 47	98.90 88	98.85 68	97.28 36	99.72 3	99.39 18	96.63 19	97.60 332	98.17 36	99.85 5	99.64 76
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
APDe-MVS		99.02 4	98.84 4	99.55 9	99.57 35	98.96 16	99.39 10	98.93 39	97.38 30	99.41 13	99.54 1	96.66 17	99.84 57	98.86 4	99.85 5	99.87 1

HPM-MVS_fast		98.38 46	98.13 51	99.12 60	99.75 4	97.86 73	99.44 9	98.82 74	94.46 171	98.94 45	99.20 59	95.16 76	99.74 111	97.58 74	99.85 5	99.77 23

SteuartSystems-ACMMP		98.90 6	98.75 6	99.36 24	99.22 98	98.43 38	99.10 52	98.87 58	97.38 30	99.35 17	99.40 17	97.78 5	99.87 48	97.77 59	99.85 5	99.78 16
Skip Steuart: Steuart Systems R&D Blog.
DPE-MVS		98.92 5	98.67 8	99.65 2	99.58 34	99.20 9	98.42 181	98.91 45	97.58 15	99.54 8	99.46 13	97.10 12	99.94 4	97.64 70	99.84 10	99.83 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS		98.36 48	98.10 54	99.13 57	99.74 8	97.82 77	99.53 6	98.80 91	94.63 164	98.61 74	98.97 97	95.13 77	99.77 105	97.65 69	99.83 11	99.79 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SED-MVS		99.09 1	98.91 1	99.63 4	99.71 21	99.24 5	99.02 67	98.87 58	97.65 10	99.73 1	99.48 8	97.53 7	99.94 4	98.43 26	99.81 12	99.70 54

IU-MVS					99.71 21	99.23 7		98.64 141	95.28 130	99.63 4				98.35 32	99.81 12	99.83 7

ZNCC-MVS		98.49 37	98.20 49	99.35 25	99.73 12	98.39 39	99.19 37	98.86 64	95.77 103	98.31 92	99.10 78	95.46 57	99.93 19	97.57 77	99.81 12	99.74 37

DVP-MVS		99.03 3	98.83 5	99.63 4	99.72 13	99.25 2	98.97 77	98.58 153	97.62 12	99.45 11	99.46 13	97.42 9	99.94 4	98.47 22	99.81 12	99.69 57
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				99.71 1	99.72 13	99.35 1	98.97 77	98.88 51					99.94 4	98.47 22	99.81 12	99.84 6

SMA-MVS		98.58 24	98.25 43	99.56 8	99.51 43	99.04 15	98.95 81	98.80 91	93.67 209	99.37 16	99.52 3	96.52 21	99.89 39	98.06 41	99.81 12	99.76 30
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
mPP-MVS		98.51 36	98.26 42	99.25 42	99.75 4	98.04 65	99.28 22	98.81 80	96.24 86	98.35 89	99.23 52	95.46 57	99.94 4	97.42 84	99.81 12	99.77 23

MSC_two_6792asdad				99.62 6	99.17 103	99.08 11		98.63 143					99.94 4	98.53 14	99.80 19	99.86 2

No_MVS				99.62 6	99.17 103	99.08 11		98.63 143					99.94 4	98.53 14	99.80 19	99.86 2

test_241102_TWO								98.87 58	97.65 10	99.53 9	99.48 8	97.34 11	99.94 4	98.43 26	99.80 19	99.83 7

MP-MVS-pluss		98.31 56	97.92 63	99.49 12	99.72 13	98.88 18	98.43 179	98.78 99	94.10 179	97.69 129	99.42 16	95.25 72	99.92 25	98.09 40	99.80 19	99.67 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		98.55 31	98.25 43	99.46 15	99.76 2	98.64 27	98.55 163	98.74 108	97.27 40	98.02 104	99.39 18	94.81 84	99.96 2	97.91 48	99.79 23	99.77 23

MTAPA		98.58 24	98.29 40	99.46 15	99.76 2	98.64 27	98.90 88	98.74 108	97.27 40	98.02 104	99.39 18	94.81 84	99.96 2	97.91 48	99.79 23	99.77 23

region2R		98.61 18	98.38 25	99.29 34	99.74 8	98.16 60	99.23 27	98.93 39	96.15 89	98.94 45	99.17 63	95.91 43	99.94 4	97.55 78	99.79 23	99.78 16

ACMMPR		98.59 21	98.36 27	99.29 34	99.74 8	98.15 61	99.23 27	98.95 35	96.10 94	98.93 49	99.19 62	95.70 50	99.94 4	97.62 71	99.79 23	99.78 16

HFP-MVS		98.63 17	98.40 23	99.32 31	99.72 13	98.29 51	99.23 27	98.96 33	96.10 94	98.94 45	99.17 63	96.06 34	99.92 25	97.62 71	99.78 27	99.75 32

#test#		98.54 33	98.27 41	99.32 31	99.72 13	98.29 51	98.98 76	98.96 33	95.65 110	98.94 45	99.17 63	96.06 34	99.92 25	97.21 91	99.78 27	99.75 32

MP-MVS		98.33 54	98.01 58	99.28 38	99.75 4	98.18 58	99.22 31	98.79 96	96.13 91	97.92 117	99.23 52	94.54 90	99.94 4	96.74 122	99.78 27	99.73 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		98.49 37	98.23 47	99.27 41	99.72 13	98.08 64	98.99 73	99.49 5	95.43 120	99.03 39	99.32 37	95.56 53	99.94 4	96.80 117	99.77 30	99.78 16

APD-MVS		98.35 50	98.00 59	99.42 18	99.51 43	98.72 21	98.80 114	98.82 74	94.52 168	99.23 24	99.25 50	95.54 55	99.80 84	96.52 128	99.77 30	99.74 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		96.93 120	96.27 133	98.92 72	99.50 45	97.63 83	98.85 100	98.90 46	84.80 353	97.77 122	99.11 76	92.84 114	99.66 127	94.85 180	99.77 30	99.47 107

CPTT-MVS		97.72 77	97.32 89	98.92 72	99.64 30	97.10 105	99.12 48	98.81 80	92.34 258	98.09 97	99.08 86	93.01 113	99.92 25	96.06 143	99.77 30	99.75 32

DeepPCF-MVS	96.37 2	97.93 69	98.48 22	96.30 254	99.00 121	89.54 327	97.43 278	98.87 58	98.16 2	99.26 22	99.38 25	96.12 32	99.64 130	98.30 34	99.77 30	99.72 46

DeepC-MVS_fast	96.70 1	98.55 31	98.34 33	99.18 50	99.25 90	98.04 65	98.50 170	98.78 99	97.72 7	98.92 51	99.28 44	95.27 70	99.82 68	97.55 78	99.77 30	99.69 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-3		98.59 21	98.50 17	98.86 76	99.43 61	97.05 106	98.40 183	98.68 125	97.43 26	99.06 37	99.31 39	95.80 47	99.77 105	98.62 10	99.76 36	99.78 16

Regformer-4		98.64 15	98.53 14	98.99 66	99.43 61	97.37 92	98.40 183	98.79 96	97.46 23	99.09 36	99.31 39	95.86 46	99.80 84	98.64 8	99.76 36	99.79 13

DELS-MVS		98.40 45	98.20 49	98.99 66	99.00 121	97.66 81	97.75 260	98.89 48	97.71 9	98.33 90	98.97 97	94.97 81	99.88 47	98.42 28	99.76 36	99.42 117
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
MVS_111021_HR		98.47 40	98.34 33	98.88 75	99.22 98	97.32 93	97.91 244	99.58 3	97.20 44	98.33 90	99.00 95	95.99 39	99.64 130	98.05 43	99.76 36	99.69 57

PHI-MVS		98.34 52	98.06 55	99.18 50	99.15 109	98.12 63	99.04 60	99.09 21	93.32 222	98.83 57	99.10 78	96.54 20	99.83 60	97.70 67	99.76 36	99.59 87

DeepC-MVS	95.98 3	97.88 70	97.58 72	98.77 78	99.25 90	96.93 111	98.83 104	98.75 106	96.96 58	96.89 161	99.50 5	90.46 166	99.87 48	97.84 56	99.76 36	99.52 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		98.61 18	98.30 39	99.55 9	99.62 32	98.95 17	98.82 107	98.81 80	95.80 102	99.16 32	99.47 10	95.37 63	99.92 25	97.89 51	99.75 42	99.79 13

MVS_111021_LR		98.34 52	98.23 47	98.67 83	99.27 87	96.90 113	97.95 240	99.58 3	97.14 49	98.44 83	99.01 94	95.03 80	99.62 135	97.91 48	99.75 42	99.50 100

3Dnovator	94.51 5	97.46 92	96.93 105	99.07 63	97.78 214	97.64 82	99.35 15	99.06 23	97.02 55	93.75 260	99.16 68	89.25 187	99.92 25	97.22 90	99.75 42	99.64 76

XVS		98.70 10	98.49 20	99.34 26	99.70 24	98.35 48	99.29 20	98.88 51	97.40 27	98.46 79	99.20 59	95.90 44	99.89 39	97.85 54	99.74 45	99.78 16

X-MVStestdata		94.06 273	92.30 293	99.34 26	99.70 24	98.35 48	99.29 20	98.88 51	97.40 27	98.46 79	43.50 373	95.90 44	99.89 39	97.85 54	99.74 45	99.78 16

OPU-MVS				99.37 23	99.24 96	99.05 14	99.02 67				99.16 68	97.81 3	99.37 165	97.24 89	99.73 47	99.70 54

xxxxxxxxxxxxxcwj		98.70 10	98.50 17	99.30 33	99.46 55	98.38 40	98.21 208	98.52 164	97.95 3	99.32 18	99.39 18	96.22 24	99.84 57	97.72 62	99.73 47	99.67 67

SF-MVS		98.59 21	98.32 38	99.41 19	99.54 38	98.71 22	99.04 60	98.81 80	95.12 139	99.32 18	99.39 18	96.22 24	99.84 57	97.72 62	99.73 47	99.67 67

ETH3D-3000-0.1		98.35 50	98.00 59	99.38 20	99.47 52	98.68 25	98.67 142	98.84 69	94.66 163	99.11 34	99.25 50	95.46 57	99.81 75	96.80 117	99.73 47	99.63 79

TSAR-MVS + MP.		98.78 7	98.62 9	99.24 43	99.69 26	98.28 53	99.14 43	98.66 136	96.84 62	99.56 6	99.31 39	96.34 23	99.70 119	98.32 33	99.73 47	99.73 42
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Regformer-1		98.66 13	98.51 16	99.12 60	99.35 64	97.81 79	98.37 185	98.76 103	97.49 19	99.20 26	99.21 55	96.08 33	99.79 96	98.42 28	99.73 47	99.75 32

Regformer-2		98.69 12	98.52 15	99.19 46	99.35 64	98.01 67	98.37 185	98.81 80	97.48 20	99.21 25	99.21 55	96.13 31	99.80 84	98.40 30	99.73 47	99.75 32

DVP-MVS++		99.08 2	98.89 2	99.64 3	99.17 103	99.23 7	99.69 1	98.88 51	97.32 33	99.53 9	99.47 10	97.81 3	99.94 4	98.47 22	99.72 54	99.74 37

PC_three_1452									95.08 144	99.60 5	99.16 68	97.86 2	98.47 265	97.52 81	99.72 54	99.74 37

9.14			98.06 55		99.47 52		98.71 132	98.82 74	94.36 173	99.16 32	99.29 43	96.05 36	99.81 75	97.00 96	99.71 56

MSLP-MVS++		98.56 29	98.57 11	98.55 90	99.26 89	96.80 116	98.71 132	99.05 25	97.28 36	98.84 55	99.28 44	96.47 22	99.40 163	98.52 20	99.70 57	99.47 107

ETH3 D test6400		97.59 86	97.01 101	99.34 26	99.40 63	98.56 30	98.20 211	98.81 80	91.63 281	98.44 83	98.85 115	93.98 104	99.82 68	94.11 208	99.69 58	99.64 76

CDPH-MVS		97.94 68	97.49 80	99.28 38	99.47 52	98.44 36	97.91 244	98.67 133	92.57 250	98.77 60	98.85 115	95.93 42	99.72 113	95.56 163	99.69 58	99.68 63

HPM-MVS++		98.58 24	98.25 43	99.55 9	99.50 45	99.08 11	98.72 131	98.66 136	97.51 18	98.15 93	98.83 119	95.70 50	99.92 25	97.53 80	99.67 60	99.66 71

APD-MVS_3200maxsize		98.53 35	98.33 37	99.15 56	99.50 45	97.92 72	99.15 41	98.81 80	96.24 86	99.20 26	99.37 26	95.30 68	99.80 84	97.73 61	99.67 60	99.72 46

abl_6		98.30 57	98.03 57	99.13 57	99.56 37	97.76 80	99.13 46	98.82 74	96.14 90	99.26 22	99.37 26	93.33 109	99.93 19	96.96 100	99.67 60	99.69 57

CNVR-MVS		98.78 7	98.56 12	99.45 17	99.32 72	98.87 19	98.47 173	98.81 80	97.72 7	98.76 61	99.16 68	97.05 13	99.78 100	98.06 41	99.66 63	99.69 57

SR-MVS-dyc-post		98.54 33	98.35 29	99.13 57	99.49 49	97.86 73	99.11 49	98.80 91	96.49 78	99.17 30	99.35 32	95.34 65	99.82 68	97.72 62	99.65 64	99.71 50

RE-MVS-def			98.34 33		99.49 49	97.86 73	99.11 49	98.80 91	96.49 78	99.17 30	99.35 32	95.29 69		97.72 62	99.65 64	99.71 50

CANet		98.05 62	97.76 67	98.90 74	98.73 142	97.27 96	98.35 188	98.78 99	97.37 32	97.72 127	98.96 103	91.53 145	99.92 25	98.79 6	99.65 64	99.51 98

EI-MVSNet-Vis-set		98.47 40	98.39 24	98.69 81	99.46 55	96.49 131	98.30 199	98.69 122	97.21 43	98.84 55	99.36 30	95.41 60	99.78 100	98.62 10	99.65 64	99.80 12

test1172		98.56 29	98.35 29	99.16 53	99.53 39	97.94 71	99.09 53	98.83 72	96.52 77	99.05 38	99.34 35	95.34 65	99.82 68	97.86 53	99.64 68	99.73 42

CSCG		97.85 72	97.74 68	98.20 119	99.67 27	95.16 189	99.22 31	99.32 7	93.04 233	97.02 154	98.92 109	95.36 64	99.91 34	97.43 83	99.64 68	99.52 94

SR-MVS		98.57 27	98.35 29	99.24 43	99.53 39	98.18 58	99.09 53	98.82 74	96.58 74	99.10 35	99.32 37	95.39 61	99.82 68	97.70 67	99.63 70	99.72 46

GST-MVS		98.43 42	98.12 52	99.34 26	99.72 13	98.38 40	99.09 53	98.82 74	95.71 106	98.73 64	99.06 88	95.27 70	99.93 19	97.07 95	99.63 70	99.72 46

QAPM		96.29 143	95.40 163	98.96 70	97.85 211	97.60 85	99.23 27	98.93 39	89.76 323	93.11 283	99.02 90	89.11 192	99.93 19	91.99 269	99.62 72	99.34 121

MCST-MVS		98.65 14	98.37 26	99.48 13	99.60 33	98.87 19	98.41 182	98.68 125	97.04 54	98.52 78	98.80 122	96.78 16	99.83 60	97.93 47	99.61 73	99.74 37

test_prior3		98.22 59	97.90 64	99.19 46	99.31 74	98.22 55	97.80 256	98.84 69	96.12 92	97.89 119	98.69 132	95.96 40	99.70 119	96.89 106	99.60 74	99.65 73

test_prior2							97.80 256		96.12 92	97.89 119	98.69 132	95.96 40		96.89 106	99.60 74

jason		97.32 104	97.08 98	98.06 131	97.45 243	95.59 172	97.87 250	97.91 270	94.79 155	98.55 77	98.83 119	91.12 153	99.23 175	97.58 74	99.60 74	99.34 121
jason: jason.
MSP-MVS		98.74 9	98.55 13	99.29 34	99.75 4	98.23 54	99.26 24	98.88 51	97.52 17	99.41 13	98.78 124	96.00 38	99.79 96	97.79 58	99.59 77	99.85 4
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
MVSFormer		97.57 88	97.49 80	97.84 141	98.07 197	95.76 169	99.47 7	98.40 191	94.98 147	98.79 58	98.83 119	92.34 120	98.41 278	96.91 102	99.59 77	99.34 121

lupinMVS		97.44 96	97.22 93	98.12 127	98.07 197	95.76 169	97.68 264	97.76 275	94.50 169	98.79 58	98.61 140	92.34 120	99.30 169	97.58 74	99.59 77	99.31 127

ZD-MVS					99.46 55	98.70 23		98.79 96	93.21 226	98.67 67	98.97 97	95.70 50	99.83 60	96.07 140	99.58 80

test9_res														96.39 134	99.57 81	99.69 57

train_agg		97.97 63	97.52 77	99.33 30	99.31 74	98.50 34	97.92 242	98.73 112	92.98 235	97.74 125	98.68 134	96.20 27	99.80 84	96.59 124	99.57 81	99.68 63

agg_prior2														95.87 150	99.57 81	99.68 63

3Dnovator+	94.38 6	97.43 97	96.78 112	99.38 20	97.83 212	98.52 32	99.37 12	98.71 118	97.09 53	92.99 286	99.13 73	89.36 184	99.89 39	96.97 98	99.57 81	99.71 50

LS3D		97.16 112	96.66 121	98.68 82	98.53 161	97.19 103	98.93 85	98.90 46	92.83 243	95.99 195	99.37 26	92.12 129	99.87 48	93.67 221	99.57 81	98.97 170

CS-MVS-test		98.49 37	98.50 17	98.46 101	99.20 101	97.05 106	99.64 4	98.50 172	97.45 25	98.88 53	99.14 72	95.25 72	99.15 184	98.83 5	99.56 86	99.20 139

agg_prior1		97.95 67	97.51 79	99.28 38	99.30 79	98.38 40	97.81 255	98.72 114	93.16 229	97.57 138	98.66 137	96.14 30	99.81 75	96.63 123	99.56 86	99.66 71

test12				99.18 50	99.16 107	98.19 57		98.53 162		98.07 98		95.13 77	99.72 113		99.56 86	99.63 79

CHOSEN 1792x2688		97.12 114	96.80 109	98.08 129	99.30 79	94.56 222	98.05 231	99.71 1	93.57 213	97.09 148	98.91 110	88.17 216	99.89 39	96.87 112	99.56 86	99.81 11

ETH3D cwj APD-0.16		97.96 64	97.52 77	99.29 34	99.05 114	98.52 32	98.33 191	98.68 125	93.18 227	98.68 66	99.13 73	94.62 88	99.83 60	96.45 130	99.55 90	99.52 94

EI-MVSNet-UG-set		98.41 43	98.34 33	98.61 86	99.45 59	96.32 140	98.28 202	98.68 125	97.17 46	98.74 62	99.37 26	95.25 72	99.79 96	98.57 12	99.54 91	99.73 42

testtj		98.33 54	97.95 61	99.47 14	99.49 49	98.70 23	98.83 104	98.86 64	95.48 117	98.91 52	99.17 63	95.48 56	99.93 19	95.80 153	99.53 92	99.76 30

test222					99.23 97	97.17 104	97.40 279	98.66 136	88.68 334	98.05 99	98.96 103	94.14 100			99.53 92	99.61 82

MG-MVS		97.81 73	97.60 71	98.44 103	99.12 111	95.97 155	97.75 260	98.78 99	96.89 61	98.46 79	99.22 54	93.90 105	99.68 125	94.81 183	99.52 94	99.67 67

DROMVSNet		98.21 60	98.11 53	98.49 98	98.34 175	97.26 100	99.61 5	98.43 186	96.78 65	98.87 54	98.84 117	93.72 106	99.01 208	98.91 3	99.50 95	99.19 143

UGNet		96.78 126	96.30 132	98.19 121	98.24 182	95.89 165	98.88 95	98.93 39	97.39 29	96.81 165	97.84 216	82.60 304	99.90 37	96.53 127	99.49 96	98.79 181
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
API-MVS		97.41 99	97.25 91	97.91 138	98.70 147	96.80 116	98.82 107	98.69 122	94.53 166	98.11 95	98.28 177	94.50 94	99.57 139	94.12 207	99.49 96	97.37 233

CS-MVS		98.41 43	98.49 20	98.18 122	99.08 113	96.33 139	99.67 3	98.49 177	97.17 46	98.93 49	99.10 78	95.79 48	99.12 188	98.67 7	99.48 98	99.10 157

新几何1				99.16 53	99.34 66	98.01 67		98.69 122	90.06 318	98.13 94	98.95 105	94.60 89	99.89 39	91.97 270	99.47 99	99.59 87

旧先验1					99.29 82	97.48 88		98.70 121			99.09 84	95.56 53			99.47 99	99.61 82

OpenMVS	93.04 13	95.83 164	95.00 186	98.32 111	97.18 262	97.32 93	99.21 34	98.97 31	89.96 319	91.14 320	99.05 89	86.64 248	99.92 25	93.38 227	99.47 99	97.73 223

原ACMM1				98.65 84	99.32 72	96.62 122		98.67 133	93.27 225	97.81 121	98.97 97	95.18 75	99.83 60	93.84 215	99.46 102	99.50 100

1121		97.37 102	96.77 116	99.16 53	99.34 66	97.99 70	98.19 215	98.68 125	90.14 317	98.01 108	98.97 97	94.80 86	99.87 48	93.36 229	99.46 102	99.61 82

testdata				98.26 115	99.20 101	95.36 182		98.68 125	91.89 273	98.60 75	99.10 78	94.44 96	99.82 68	94.27 202	99.44 104	99.58 91

DP-MVS Recon		97.86 71	97.46 82	99.06 64	99.53 39	98.35 48	98.33 191	98.89 48	92.62 247	98.05 99	98.94 106	95.34 65	99.65 128	96.04 144	99.42 105	99.19 143

NCCC		98.61 18	98.35 29	99.38 20	99.28 86	98.61 29	98.45 174	98.76 103	97.82 6	98.45 82	98.93 107	96.65 18	99.83 60	97.38 86	99.41 106	99.71 50

TAPA-MVS	93.98 7	95.35 190	94.56 205	97.74 151	99.13 110	94.83 208	98.33 191	98.64 141	86.62 342	96.29 187	98.61 140	94.00 103	99.29 170	80.00 357	99.41 106	99.09 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended		97.38 101	97.12 95	98.14 123	99.25 90	95.35 184	97.28 292	99.26 9	93.13 230	97.94 114	98.21 184	92.74 116	99.81 75	96.88 109	99.40 108	99.27 134

MS-PatchMatch		93.84 277	93.63 265	94.46 318	96.18 311	89.45 328	97.76 259	98.27 215	92.23 264	92.13 310	97.49 245	79.50 325	98.69 242	89.75 304	99.38 109	95.25 341

CANet_DTU		96.96 119	96.55 124	98.21 118	98.17 192	96.07 149	97.98 238	98.21 222	97.24 42	97.13 147	98.93 107	86.88 245	99.91 34	95.00 178	99.37 110	98.66 191

DPM-MVS		97.55 90	96.99 103	99.23 45	99.04 116	98.55 31	97.17 300	98.35 200	94.85 154	97.93 116	98.58 145	95.07 79	99.71 118	92.60 250	99.34 111	99.43 115

MVP-Stereo		94.28 258	93.92 243	95.35 289	94.95 342	92.60 277	97.97 239	97.65 280	91.61 282	90.68 325	97.09 272	86.32 255	98.42 271	89.70 306	99.34 111	95.02 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		97.45 95	97.03 100	98.73 79	99.05 114	97.44 91	98.07 229	98.53 162	95.32 128	96.80 166	98.53 149	93.32 110	99.72 113	94.31 201	99.31 113	99.02 165

AdaColmap		97.15 113	96.70 117	98.48 99	99.16 107	96.69 121	98.01 235	98.89 48	94.44 172	96.83 162	98.68 134	90.69 163	99.76 107	94.36 197	99.29 114	98.98 169

Vis-MVSNet		97.42 98	97.11 96	98.34 110	98.66 151	96.23 143	99.22 31	99.00 28	96.63 73	98.04 101	99.21 55	88.05 221	99.35 166	96.01 146	99.21 115	99.45 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS		97.75 75	97.58 72	98.27 113	98.38 168	96.44 133	99.01 69	98.60 146	95.88 99	97.26 143	97.53 243	94.97 81	99.33 168	97.38 86	99.20 116	99.05 163

EPNet		97.28 105	96.87 108	98.51 95	94.98 341	96.14 147	98.90 88	97.02 321	98.28 1	95.99 195	99.11 76	91.36 147	99.89 39	96.98 97	99.19 117	99.50 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PS-MVSNAJ		97.73 76	97.77 66	97.62 162	98.68 150	95.58 173	97.34 287	98.51 167	97.29 35	98.66 71	97.88 211	94.51 91	99.90 37	97.87 52	99.17 118	97.39 231

PVSNet_Blended_VisFu		97.70 78	97.46 82	98.44 103	99.27 87	95.91 163	98.63 149	99.16 18	94.48 170	97.67 130	98.88 112	92.80 115	99.91 34	97.11 93	99.12 119	99.50 100

BH-RMVSNet		95.92 160	95.32 172	97.69 156	98.32 179	94.64 214	98.19 215	97.45 300	94.56 165	96.03 193	98.61 140	85.02 274	99.12 188	90.68 290	99.06 120	99.30 130

test2506		94.44 248	93.91 245	96.04 263	99.02 118	88.99 337	99.06 57	79.47 380	96.96 58	98.36 87	99.26 47	77.21 343	99.52 151	96.78 119	99.04 121	99.59 87

test1111		95.94 158	95.78 148	96.41 246	98.99 124	90.12 319	99.04 60	92.45 369	96.99 57	98.03 102	99.27 46	81.40 311	99.48 157	96.87 112	99.04 121	99.63 79

ECVR-MVS		95.95 156	95.71 153	96.65 217	99.02 118	90.86 305	99.03 63	91.80 370	96.96 58	98.10 96	99.26 47	81.31 312	99.51 152	96.90 105	99.04 121	99.59 87

PVSNet	91.96 18	96.35 141	96.15 137	96.96 196	99.17 103	92.05 283	96.08 337	98.68 125	93.69 205	97.75 124	97.80 222	88.86 201	99.69 124	94.26 203	99.01 124	99.15 150

PatchMatch-RL		96.59 132	96.03 142	98.27 113	99.31 74	96.51 130	97.91 244	99.06 23	93.72 201	96.92 159	98.06 194	88.50 210	99.65 128	91.77 274	99.00 125	98.66 191

PCF-MVS	93.45 11	94.68 228	93.43 273	98.42 106	98.62 155	96.77 118	95.48 349	98.20 224	84.63 354	93.34 274	98.32 174	88.55 208	99.81 75	84.80 344	98.96 126	98.68 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		96.91 121	96.40 129	98.45 102	98.69 149	96.90 113	98.66 145	98.68 125	92.40 257	97.07 151	97.96 203	91.54 144	99.75 109	93.68 219	98.92 127	98.69 187
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
F-COLMAP		97.09 116	96.80 109	97.97 135	99.45 59	94.95 203	98.55 163	98.62 145	93.02 234	96.17 190	98.58 145	94.01 102	99.81 75	93.95 212	98.90 128	99.14 152

ETV-MVS		97.96 64	97.81 65	98.40 107	98.42 166	97.27 96	98.73 127	98.55 158	96.84 62	98.38 86	97.44 250	95.39 61	99.35 166	97.62 71	98.89 129	98.58 197

DP-MVS		96.59 132	95.93 144	98.57 88	99.34 66	96.19 146	98.70 136	98.39 193	89.45 328	94.52 220	99.35 32	91.85 135	99.85 54	92.89 246	98.88 130	99.68 63

OMC-MVS		97.55 90	97.34 88	98.20 119	99.33 69	95.92 162	98.28 202	98.59 148	95.52 116	97.97 111	99.10 78	93.28 111	99.49 153	95.09 176	98.88 130	99.19 143

PAPM_NR		97.46 92	97.11 96	98.50 96	99.50 45	96.41 135	98.63 149	98.60 146	95.18 135	97.06 152	98.06 194	94.26 99	99.57 139	93.80 217	98.87 132	99.52 94

ACMMP		98.23 58	97.95 61	99.09 62	99.74 8	97.62 84	99.03 63	99.41 6	95.98 96	97.60 137	99.36 30	94.45 95	99.93 19	97.14 92	98.85 133	99.70 54
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
UA-Net		97.96 64	97.62 70	98.98 68	98.86 133	97.47 89	98.89 92	99.08 22	96.67 71	98.72 65	99.54 1	93.15 112	99.81 75	94.87 179	98.83 134	99.65 73

MSDG		95.93 159	95.30 174	97.83 142	98.90 129	95.36 182	96.83 324	98.37 197	91.32 292	94.43 227	98.73 130	90.27 170	99.60 136	90.05 299	98.82 135	98.52 198

EPNet_dtu		95.21 198	94.95 190	95.99 265	96.17 312	90.45 315	98.16 221	97.27 310	96.77 66	93.14 282	98.33 173	90.34 168	98.42 271	85.57 337	98.81 136	99.09 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	95.07 4	97.20 110	96.78 112	98.44 103	99.29 82	96.31 142	98.14 222	98.76 103	92.41 256	96.39 185	98.31 175	94.92 83	99.78 100	94.06 210	98.77 137	99.23 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		97.60 83	97.56 74	97.72 152	98.35 170	95.98 150	97.86 251	98.51 167	97.13 50	99.01 41	98.40 162	91.56 141	99.80 84	98.53 14	98.68 138	97.37 233

xiu_mvs_v1_base		97.60 83	97.56 74	97.72 152	98.35 170	95.98 150	97.86 251	98.51 167	97.13 50	99.01 41	98.40 162	91.56 141	99.80 84	98.53 14	98.68 138	97.37 233

xiu_mvs_v1_base_debi		97.60 83	97.56 74	97.72 152	98.35 170	95.98 150	97.86 251	98.51 167	97.13 50	99.01 41	98.40 162	91.56 141	99.80 84	98.53 14	98.68 138	97.37 233

MVS-HIRNet		89.46 323	88.40 322	92.64 335	97.58 228	82.15 365	94.16 360	93.05 368	75.73 364	90.90 322	82.52 366	79.42 326	98.33 286	83.53 349	98.68 138	97.43 228

xiu_mvs_v2_base		97.66 80	97.70 69	97.56 166	98.61 156	95.46 179	97.44 276	98.46 179	97.15 48	98.65 72	98.15 188	94.33 97	99.80 84	97.84 56	98.66 142	97.41 229

Vis-MVSNet (Re-imp)		96.87 123	96.55 124	97.83 142	98.73 142	95.46 179	99.20 35	98.30 212	94.96 149	96.60 173	98.87 113	90.05 172	98.59 254	93.67 221	98.60 143	99.46 111

IS-MVSNet		97.22 107	96.88 107	98.25 116	98.85 135	96.36 137	99.19 37	97.97 264	95.39 122	97.23 144	98.99 96	91.11 154	98.93 219	94.60 189	98.59 144	99.47 107

PAPR		96.84 124	96.24 135	98.65 84	98.72 146	96.92 112	97.36 285	98.57 154	93.33 221	96.67 169	97.57 240	94.30 98	99.56 142	91.05 285	98.59 144	99.47 107

TSAR-MVS + GP.		98.38 46	98.24 46	98.81 77	99.22 98	97.25 101	98.11 227	98.29 214	97.19 45	98.99 44	99.02 90	96.22 24	99.67 126	98.52 20	98.56 146	99.51 98

diffmvs		97.58 87	97.40 86	98.13 125	98.32 179	95.81 168	98.06 230	98.37 197	96.20 88	98.74 62	98.89 111	91.31 150	99.25 172	98.16 37	98.52 147	99.34 121

BH-untuned		95.95 156	95.72 150	96.65 217	98.55 160	92.26 279	98.23 206	97.79 274	93.73 200	94.62 217	98.01 198	88.97 199	99.00 209	93.04 239	98.51 148	98.68 188

test-LLR		95.10 204	94.87 193	95.80 275	96.77 284	89.70 323	96.91 314	95.21 351	95.11 140	94.83 212	95.72 335	87.71 228	98.97 210	93.06 237	98.50 149	98.72 184

TESTMET0.1,1		94.18 264	93.69 263	95.63 281	96.92 276	89.12 333	96.91 314	94.78 356	93.17 228	94.88 209	96.45 315	78.52 331	98.92 220	93.09 236	98.50 149	98.85 177

test-mter		94.08 271	93.51 270	95.80 275	96.77 284	89.70 323	96.91 314	95.21 351	92.89 240	94.83 212	95.72 335	77.69 338	98.97 210	93.06 237	98.50 149	98.72 184

1314		96.25 147	95.73 149	97.79 146	97.13 265	95.55 176	98.19 215	98.59 148	93.47 216	92.03 312	97.82 220	91.33 149	99.49 153	94.62 188	98.44 152	98.32 207

LCM-MVSNet-Re		95.22 197	95.32 172	94.91 301	98.18 190	87.85 352	98.75 120	95.66 348	95.11 140	88.96 338	96.85 299	90.26 171	97.65 330	95.65 161	98.44 152	99.22 138

EPP-MVSNet		97.46 92	97.28 90	97.99 134	98.64 153	95.38 181	99.33 19	98.31 206	93.61 212	97.19 145	99.07 87	94.05 101	99.23 175	96.89 106	98.43 154	99.37 120

casdiffmvs		97.63 82	97.41 85	98.28 112	98.33 177	96.14 147	98.82 107	98.32 204	96.38 83	97.95 112	99.21 55	91.23 152	99.23 175	98.12 38	98.37 155	99.48 105

PatchmatchNet		95.71 169	95.52 161	96.29 255	97.58 228	90.72 310	96.84 323	97.52 293	94.06 180	97.08 149	96.96 290	89.24 188	98.90 224	92.03 268	98.37 155	99.26 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS		94.67 231	93.54 269	98.08 129	96.88 280	96.56 128	98.19 215	98.50 172	78.05 362	92.69 294	98.02 196	91.07 156	99.63 133	90.09 296	98.36 157	98.04 214

gg-mvs-nofinetune		92.21 300	90.58 307	97.13 185	96.75 287	95.09 194	95.85 342	89.40 374	85.43 352	94.50 221	81.98 367	80.80 319	98.40 284	92.16 262	98.33 158	97.88 217

SCA		95.46 179	95.13 180	96.46 243	97.67 221	91.29 300	97.33 288	97.60 284	94.68 160	96.92 159	97.10 268	83.97 295	98.89 225	92.59 252	98.32 159	99.20 139

baseline		97.64 81	97.44 84	98.25 116	98.35 170	96.20 144	99.00 71	98.32 204	96.33 85	98.03 102	99.17 63	91.35 148	99.16 181	98.10 39	98.29 160	99.39 118

MVS_Test		97.28 105	97.00 102	98.13 125	98.33 177	95.97 155	98.74 123	98.07 254	94.27 175	98.44 83	98.07 193	92.48 118	99.26 171	96.43 132	98.19 161	99.16 149

sss		97.39 100	96.98 104	98.61 86	98.60 157	96.61 124	98.22 207	98.93 39	93.97 187	98.01 108	98.48 154	91.98 133	99.85 54	96.45 130	98.15 162	99.39 118

Patchmatch-test		94.42 249	93.68 264	96.63 221	97.60 226	91.76 288	94.83 355	97.49 297	89.45 328	94.14 242	97.10 268	88.99 195	98.83 233	85.37 340	98.13 163	99.29 132

COLMAP_ROB	93.27 12	95.33 192	94.87 193	96.71 212	99.29 82	93.24 268	98.58 155	98.11 243	89.92 320	93.57 264	99.10 78	86.37 254	99.79 96	90.78 288	98.10 164	97.09 238
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		96.58 134	96.07 139	98.10 128	98.35 170	95.89 165	99.34 16	98.12 240	93.12 231	96.09 191	98.87 113	89.71 178	98.97 210	92.95 242	98.08 165	99.43 115

mvs-test1		96.60 130	96.68 120	96.37 249	97.89 209	91.81 286	98.56 161	98.10 245	96.57 75	96.52 180	97.94 205	90.81 158	99.45 161	95.72 156	98.01 166	97.86 219

Effi-MVS+-dtu		96.29 143	96.56 123	95.51 283	97.89 209	90.22 318	98.80 114	98.10 245	96.57 75	96.45 184	96.66 306	90.81 158	98.91 221	95.72 156	97.99 167	97.40 230

Fast-Effi-MVS+		96.28 145	95.70 155	98.03 132	98.29 181	95.97 155	98.58 155	98.25 220	91.74 276	95.29 203	97.23 262	91.03 157	99.15 184	92.90 244	97.96 168	98.97 170

mvs_anonymous		96.70 128	96.53 126	97.18 182	98.19 188	93.78 244	98.31 197	98.19 225	94.01 184	94.47 222	98.27 180	92.08 131	98.46 266	97.39 85	97.91 169	99.31 127

PMMVS		96.60 130	96.33 131	97.41 172	97.90 208	93.93 240	97.35 286	98.41 189	92.84 242	97.76 123	97.45 249	91.10 155	99.20 178	96.26 136	97.91 169	99.11 155

AllTest		95.24 196	94.65 201	96.99 192	99.25 90	93.21 269	98.59 153	98.18 228	91.36 288	93.52 266	98.77 126	84.67 281	99.72 113	89.70 306	97.87 171	98.02 215

TestCases				96.99 192	99.25 90	93.21 269		98.18 228	91.36 288	93.52 266	98.77 126	84.67 281	99.72 113	89.70 306	97.87 171	98.02 215

TAMVS		97.02 117	96.79 111	97.70 155	98.06 199	95.31 186	98.52 165	98.31 206	93.95 188	97.05 153	98.61 140	93.49 108	98.52 260	95.33 168	97.81 173	99.29 132

Effi-MVS+		97.12 114	96.69 118	98.39 108	98.19 188	96.72 120	97.37 283	98.43 186	93.71 202	97.65 133	98.02 196	92.20 127	99.25 172	96.87 112	97.79 174	99.19 143

Fast-Effi-MVS+-dtu		95.87 161	95.85 146	95.91 270	97.74 218	91.74 290	98.69 138	98.15 236	95.56 113	94.92 208	97.68 231	88.98 198	98.79 237	93.19 234	97.78 175	97.20 237

DSMNet-mixed		92.52 298	92.58 289	92.33 337	94.15 351	82.65 364	98.30 199	94.26 362	89.08 332	92.65 295	95.73 333	85.01 275	95.76 358	86.24 332	97.76 176	98.59 195

CDS-MVSNet		96.99 118	96.69 118	97.90 139	98.05 200	95.98 150	98.20 211	98.33 203	93.67 209	96.95 155	98.49 153	93.54 107	98.42 271	95.24 174	97.74 177	99.31 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0515		95.61 177	94.89 192	97.76 149	98.15 193	95.15 191	96.77 325	94.41 359	92.95 237	97.18 146	97.43 251	84.78 279	99.45 161	94.63 186	97.73 178	98.68 188

thisisatest0530		96.01 153	95.36 168	97.97 135	98.38 168	95.52 177	98.88 95	94.19 363	94.04 181	97.64 134	98.31 175	83.82 300	99.46 160	95.29 171	97.70 179	98.93 174

BH-w/o		95.38 186	95.08 183	96.26 256	98.34 175	91.79 287	97.70 263	97.43 302	92.87 241	94.24 237	97.22 263	88.66 204	98.84 231	91.55 278	97.70 179	98.16 212

PAPM		94.95 214	94.00 238	97.78 147	97.04 270	95.65 171	96.03 340	98.25 220	91.23 297	94.19 240	97.80 222	91.27 151	98.86 230	82.61 351	97.61 181	98.84 179

tttt0517		96.07 150	95.51 162	97.78 147	98.41 167	94.84 206	99.28 22	94.33 361	94.26 176	97.64 134	98.64 139	84.05 293	99.47 159	95.34 167	97.60 182	99.03 164

HyFIR lowres test		96.90 122	96.49 127	98.14 123	99.33 69	95.56 174	97.38 281	99.65 2	92.34 258	97.61 136	98.20 185	89.29 186	99.10 195	96.97 98	97.60 182	99.77 23

CVMVSNet		95.43 182	96.04 141	93.57 326	97.93 206	83.62 361	98.12 225	98.59 148	95.68 107	96.56 174	99.02 90	87.51 232	97.51 336	93.56 225	97.44 184	99.60 85

MDTV_nov1_ep13			95.40 163		97.48 237	88.34 346	96.85 322	97.29 308	93.74 199	97.48 141	97.26 259	89.18 189	99.05 199	91.92 271	97.43 185

baseline2		95.11 203	94.52 207	96.87 203	96.65 293	93.56 253	98.27 204	94.10 365	93.45 217	92.02 313	97.43 251	87.45 236	99.19 179	93.88 214	97.41 186	97.87 218

EPMVS		94.99 210	94.48 209	96.52 236	97.22 256	91.75 289	97.23 294	91.66 371	94.11 178	97.28 142	96.81 301	85.70 264	98.84 231	93.04 239	97.28 187	98.97 170

LFMVS		95.86 162	94.98 188	98.47 100	98.87 132	96.32 140	98.84 103	96.02 342	93.40 219	98.62 73	99.20 59	74.99 352	99.63 133	97.72 62	97.20 188	99.46 111

ADS-MVSNet2		94.58 237	94.40 217	95.11 296	98.00 201	88.74 340	96.04 338	97.30 307	90.15 315	96.47 182	96.64 309	87.89 224	97.56 334	90.08 297	97.06 189	99.02 165

ADS-MVSNet		95.00 209	94.45 213	96.63 221	98.00 201	91.91 285	96.04 338	97.74 277	90.15 315	96.47 182	96.64 309	87.89 224	98.96 214	90.08 297	97.06 189	99.02 165

GG-mvs-BLEND				96.59 226	96.34 306	94.98 200	96.51 334	88.58 375		93.10 284	94.34 351	80.34 322	98.05 312	89.53 309	96.99 191	96.74 273

cascas		94.63 233	93.86 249	96.93 198	96.91 278	94.27 232	96.00 341	98.51 167	85.55 351	94.54 219	96.23 322	84.20 291	98.87 228	95.80 153	96.98 192	97.66 226

WTY-MVS		97.37 102	96.92 106	98.72 80	98.86 133	96.89 115	98.31 197	98.71 118	95.26 131	97.67 130	98.56 148	92.21 126	99.78 100	95.89 148	96.85 193	99.48 105

VDD-MVS		95.82 165	95.23 176	97.61 163	98.84 136	93.98 239	98.68 139	97.40 304	95.02 146	97.95 112	99.34 35	74.37 356	99.78 100	98.64 8	96.80 194	99.08 161

test_yl		97.22 107	96.78 112	98.54 92	98.73 142	96.60 125	98.45 174	98.31 206	94.70 157	98.02 104	98.42 160	90.80 160	99.70 119	96.81 115	96.79 195	99.34 121

DCV-MVSNet		97.22 107	96.78 112	98.54 92	98.73 142	96.60 125	98.45 174	98.31 206	94.70 157	98.02 104	98.42 160	90.80 160	99.70 119	96.81 115	96.79 195	99.34 121

PatchT		93.06 292	91.97 297	96.35 251	96.69 290	92.67 276	94.48 357	97.08 315	86.62 342	97.08 149	92.23 359	87.94 223	97.90 322	78.89 361	96.69 197	98.49 199

VNet		97.79 74	97.40 86	98.96 70	98.88 131	97.55 86	98.63 149	98.93 39	96.74 68	99.02 40	98.84 117	90.33 169	99.83 60	98.53 14	96.66 198	99.50 100

CR-MVSNet		94.76 225	94.15 229	96.59 226	97.00 271	93.43 259	94.96 351	97.56 286	92.46 251	96.93 157	96.24 320	88.15 217	97.88 326	87.38 326	96.65 199	98.46 200

RPMNet		92.81 294	91.34 302	97.24 178	97.00 271	93.43 259	94.96 351	98.80 91	82.27 357	96.93 157	92.12 360	86.98 243	99.82 68	76.32 365	96.65 199	98.46 200

VDDNet		95.36 189	94.53 206	97.86 140	98.10 196	95.13 193	98.85 100	97.75 276	90.46 309	98.36 87	99.39 18	73.27 358	99.64 130	97.98 44	96.58 201	98.81 180

alignmvs		97.56 89	97.07 99	99.01 65	98.66 151	98.37 46	98.83 104	98.06 259	96.74 68	98.00 110	97.65 232	90.80 160	99.48 157	98.37 31	96.56 202	99.19 143

HY-MVS	93.96 8	96.82 125	96.23 136	98.57 88	98.46 165	97.00 108	98.14 222	98.21 222	93.95 188	96.72 168	97.99 200	91.58 140	99.76 107	94.51 194	96.54 203	98.95 173

1112_ss		96.63 129	96.00 143	98.50 96	98.56 158	96.37 136	98.18 219	98.10 245	92.92 238	94.84 210	98.43 158	92.14 128	99.58 138	94.35 198	96.51 204	99.56 93

thres200		95.25 195	94.57 204	97.28 177	98.81 138	94.92 204	98.20 211	97.11 314	95.24 134	96.54 178	96.22 324	84.58 283	99.53 148	87.93 324	96.50 205	97.39 231

Test_1112_low_res		96.34 142	95.66 158	98.36 109	98.56 158	95.94 158	97.71 262	98.07 254	92.10 268	94.79 214	97.29 258	91.75 137	99.56 142	94.17 205	96.50 205	99.58 91

tpmrst		95.63 174	95.69 156	95.44 287	97.54 233	88.54 343	96.97 309	97.56 286	93.50 215	97.52 140	96.93 294	89.49 180	99.16 181	95.25 173	96.42 207	98.64 193

ab-mvs		96.42 139	95.71 153	98.55 90	98.63 154	96.75 119	97.88 249	98.74 108	93.84 193	96.54 178	98.18 187	85.34 271	99.75 109	95.93 147	96.35 208	99.15 150

thres600view7		95.49 178	94.77 195	97.67 158	98.98 125	95.02 196	98.85 100	96.90 327	95.38 123	96.63 171	96.90 295	84.29 286	99.59 137	88.65 319	96.33 209	98.40 202

RPSCF		94.87 219	95.40 163	93.26 332	98.89 130	82.06 366	98.33 191	98.06 259	90.30 314	96.56 174	99.26 47	87.09 240	99.49 153	93.82 216	96.32 210	98.24 208

thres100view900		95.38 186	94.70 199	97.41 172	98.98 125	94.92 204	98.87 97	96.90 327	95.38 123	96.61 172	96.88 296	84.29 286	99.56 142	88.11 320	96.29 211	97.76 220

tfpn200view9		95.32 193	94.62 202	97.43 171	98.94 127	94.98 200	98.68 139	96.93 325	95.33 126	96.55 176	96.53 312	84.23 289	99.56 142	88.11 320	96.29 211	97.76 220

thres400		95.38 186	94.62 202	97.65 161	98.94 127	94.98 200	98.68 139	96.93 325	95.33 126	96.55 176	96.53 312	84.23 289	99.56 142	88.11 320	96.29 211	98.40 202

canonicalmvs		97.67 79	97.23 92	98.98 68	98.70 147	98.38 40	99.34 16	98.39 193	96.76 67	97.67 130	97.40 253	92.26 123	99.49 153	98.28 35	96.28 214	99.08 161

XVG-OURS		96.55 135	96.41 128	96.99 192	98.75 141	93.76 245	97.50 275	98.52 164	95.67 108	96.83 162	99.30 42	88.95 200	99.53 148	95.88 149	96.26 215	97.69 225

GA-MVS		94.81 222	94.03 234	97.14 184	97.15 264	93.86 242	96.76 326	97.58 285	94.00 185	94.76 215	97.04 281	80.91 316	98.48 262	91.79 273	96.25 216	99.09 158

tpm2		94.19 262	93.76 258	95.46 286	97.23 255	89.04 335	97.31 290	96.85 333	87.08 341	96.21 189	96.79 302	83.75 301	98.74 240	92.43 260	96.23 217	98.59 195

MIMVSNet		93.26 287	92.21 294	96.41 246	97.73 219	93.13 271	95.65 346	97.03 319	91.27 296	94.04 247	96.06 327	75.33 350	97.19 340	86.56 330	96.23 217	98.92 175

TR-MVS		94.94 216	94.20 224	97.17 183	97.75 215	94.14 236	97.59 271	97.02 321	92.28 263	95.75 198	97.64 234	83.88 297	98.96 214	89.77 303	96.15 219	98.40 202

MVS_0304		92.81 294	92.01 296	95.23 291	97.46 239	91.33 298	98.17 220	98.81 80	91.13 301	93.80 258	95.68 338	66.08 366	98.06 311	90.79 287	96.13 220	96.32 321

CostFormer		94.95 214	94.73 198	95.60 282	97.28 252	89.06 334	97.53 274	96.89 329	89.66 325	96.82 164	96.72 304	86.05 259	98.95 218	95.53 164	96.13 220	98.79 181

tpmvs		94.60 234	94.36 218	95.33 290	97.46 239	88.60 342	96.88 320	97.68 278	91.29 294	93.80 258	96.42 317	88.58 205	99.24 174	91.06 283	96.04 222	98.17 211

tpm cat1		93.36 282	92.80 284	95.07 298	97.58 228	87.97 350	96.76 326	97.86 272	82.17 358	93.53 265	96.04 328	86.13 257	99.13 187	89.24 314	95.87 223	98.10 213

XVG-OURS-SEG-HR		96.51 136	96.34 130	97.02 191	98.77 140	93.76 245	97.79 258	98.50 172	95.45 119	96.94 156	99.09 84	87.87 226	99.55 147	96.76 121	95.83 224	97.74 222

DWT-MVSNet_test		94.82 220	94.36 218	96.20 258	97.35 249	90.79 308	98.34 189	96.57 340	92.91 239	95.33 202	96.44 316	82.00 306	99.12 188	94.52 193	95.78 225	98.70 186

JIA-IIPM		93.35 283	92.49 290	95.92 269	96.48 301	90.65 312	95.01 350	96.96 323	85.93 348	96.08 192	87.33 364	87.70 230	98.78 238	91.35 280	95.58 226	98.34 205

Anonymous202405211		95.28 194	94.49 208	97.67 158	99.00 121	93.75 247	98.70 136	97.04 318	90.66 305	96.49 181	98.80 122	78.13 335	99.83 60	96.21 138	95.36 227	99.44 114

Anonymous20240529		95.10 204	94.22 223	97.75 150	99.01 120	94.26 233	98.87 97	98.83 72	85.79 350	96.64 170	98.97 97	78.73 330	99.85 54	96.27 135	94.89 228	99.12 154

CLD-MVS		95.62 175	95.34 169	96.46 243	97.52 236	93.75 247	97.27 293	98.46 179	95.53 114	94.42 228	98.00 199	86.21 256	98.97 210	96.25 137	94.37 229	96.66 286
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
dp		94.15 265	93.90 246	94.90 302	97.31 251	86.82 357	96.97 309	97.19 313	91.22 298	96.02 194	96.61 311	85.51 267	99.02 206	90.00 301	94.30 230	98.85 177

HQP_MVS		96.14 149	95.90 145	96.85 204	97.42 244	94.60 220	98.80 114	98.56 156	97.28 36	95.34 200	98.28 177	87.09 240	99.03 203	96.07 140	94.27 231	96.92 249

plane_prior5								98.56 156					99.03 203	96.07 140	94.27 231	96.92 249

plane_prior						94.60 220	98.44 177		96.74 68						94.22 233

OPM-MVS		95.69 172	95.33 171	96.76 209	96.16 314	94.63 215	98.43 179	98.39 193	96.64 72	95.02 206	98.78 124	85.15 273	99.05 199	95.21 175	94.20 234	96.60 291
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP3-MVS								98.46 179							94.18 235

HQP-MVS		95.72 168	95.40 163	96.69 215	97.20 258	94.25 234	98.05 231	98.46 179	96.43 80	94.45 223	97.73 225	86.75 246	98.96 214	95.30 169	94.18 235	96.86 262

LPG-MVS_test		95.62 175	95.34 169	96.47 240	97.46 239	93.54 254	98.99 73	98.54 160	94.67 161	94.36 230	98.77 126	85.39 268	99.11 192	95.71 158	94.15 237	96.76 271

LGP-MVS_train				96.47 240	97.46 239	93.54 254		98.54 160	94.67 161	94.36 230	98.77 126	85.39 268	99.11 192	95.71 158	94.15 237	96.76 271

test_djsdf		96.00 154	95.69 156	96.93 198	95.72 327	95.49 178	99.47 7	98.40 191	94.98 147	94.58 218	97.86 213	89.16 190	98.41 278	96.91 102	94.12 239	96.88 258

jajsoiax		95.45 181	95.03 185	96.73 211	95.42 338	94.63 215	99.14 43	98.52 164	95.74 104	93.22 277	98.36 167	83.87 298	98.65 248	96.95 101	94.04 240	96.91 254

anonymousdsp		95.42 183	94.91 191	96.94 197	95.10 340	95.90 164	99.14 43	98.41 189	93.75 197	93.16 279	97.46 247	87.50 234	98.41 278	95.63 162	94.03 241	96.50 310

mvs_tets		95.41 185	95.00 186	96.65 217	95.58 331	94.42 226	99.00 71	98.55 158	95.73 105	93.21 278	98.38 165	83.45 302	98.63 249	97.09 94	94.00 242	96.91 254

ACMP	93.49 10	95.34 191	94.98 188	96.43 245	97.67 221	93.48 258	98.73 127	98.44 183	94.94 152	92.53 299	98.53 149	84.50 285	99.14 186	95.48 166	94.00 242	96.66 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.85 9	95.69 172	95.38 167	96.61 223	97.61 225	93.84 243	98.91 87	98.44 183	95.25 132	94.28 234	98.47 155	86.04 261	99.12 188	95.50 165	93.95 244	96.87 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		94.24 259	93.33 275	96.97 195	97.19 261	93.38 263	98.74 123	98.57 154	91.21 299	93.81 257	98.58 145	72.85 359	98.77 239	95.05 177	93.93 245	98.77 183

XVG-ACMP-BASELINE		94.54 240	94.14 230	95.75 278	96.55 296	91.65 292	98.11 227	98.44 183	94.96 149	94.22 238	97.90 208	79.18 328	99.11 192	94.05 211	93.85 246	96.48 312

EG-PatchMatch MVS		91.13 308	90.12 311	94.17 323	94.73 347	89.00 336	98.13 224	97.81 273	89.22 331	85.32 355	96.46 314	67.71 363	98.42 271	87.89 325	93.82 247	95.08 346

testgi		93.06 292	92.45 291	94.88 303	96.43 303	89.90 320	98.75 120	97.54 292	95.60 111	91.63 317	97.91 207	74.46 355	97.02 342	86.10 333	93.67 248	97.72 224

test0.0.03 1		94.08 271	93.51 270	95.80 275	95.53 333	92.89 275	97.38 281	95.97 344	95.11 140	92.51 301	96.66 306	87.71 228	96.94 344	87.03 328	93.67 248	97.57 227

CMPMVS	66.06 21	89.70 319	89.67 315	89.78 343	93.19 358	76.56 368	97.00 308	98.35 200	80.97 359	81.57 360	97.75 224	74.75 353	98.61 250	89.85 302	93.63 250	94.17 354
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ACMMP++															93.61 251

D2MVS		95.18 200	95.08 183	95.48 284	97.10 267	92.07 282	98.30 199	99.13 20	94.02 183	92.90 287	96.73 303	89.48 181	98.73 241	94.48 195	93.60 252	95.65 337

EI-MVSNet		95.96 155	95.83 147	96.36 250	97.93 206	93.70 251	98.12 225	98.27 215	93.70 204	95.07 204	99.02 90	92.23 125	98.54 258	94.68 185	93.46 253	96.84 263

MVSTER		96.06 151	95.72 150	97.08 189	98.23 183	95.93 161	98.73 127	98.27 215	94.86 153	95.07 204	98.09 192	88.21 214	98.54 258	96.59 124	93.46 253	96.79 267

PS-MVSNAJss		96.43 138	96.26 134	96.92 201	95.84 325	95.08 195	99.16 40	98.50 172	95.87 100	93.84 256	98.34 172	94.51 91	98.61 250	96.88 109	93.45 255	97.06 240

LTVRE_ROB	92.95 15	94.60 234	93.90 246	96.68 216	97.41 247	94.42 226	98.52 165	98.59 148	91.69 279	91.21 319	98.35 168	84.87 277	99.04 202	91.06 283	93.44 256	96.60 291
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
ITE_SJBPF				95.44 287	97.42 244	91.32 299		97.50 295	95.09 143	93.59 262	98.35 168	81.70 309	98.88 227	89.71 305	93.39 257	96.12 326

PVSNet_BlendedMVS		96.73 127	96.60 122	97.12 186	99.25 90	95.35 184	98.26 205	99.26 9	94.28 174	97.94 114	97.46 247	92.74 116	99.81 75	96.88 109	93.32 258	96.20 324

ACMH	92.88 16	94.55 239	93.95 242	96.34 252	97.63 224	93.26 267	98.81 113	98.49 177	93.43 218	89.74 332	98.53 149	81.91 308	99.08 197	93.69 218	93.30 259	96.70 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS_ROB	86.42 20	89.00 324	87.43 329	93.69 325	93.08 359	89.42 329	97.91 244	96.89 329	78.58 361	85.86 352	94.69 346	69.48 361	98.29 294	77.13 364	93.29 260	93.36 361

USDC		93.33 285	92.71 286	95.21 292	96.83 283	90.83 307	96.91 314	97.50 295	93.84 193	90.72 324	98.14 189	77.69 338	98.82 234	89.51 310	93.21 261	95.97 330

RRT_MVS		96.04 152	95.53 160	97.56 166	97.07 269	97.32 93	98.57 160	98.09 250	95.15 137	95.02 206	98.44 157	88.20 215	98.58 256	96.17 139	93.09 262	96.79 267

ACMMP++_ref															92.97 263

test_0402		91.32 305	90.27 310	94.48 316	96.60 294	91.12 302	98.50 170	97.22 312	86.10 347	88.30 343	96.98 287	77.65 340	97.99 317	78.13 363	92.94 264	94.34 352

FIs		96.51 136	96.12 138	97.67 158	97.13 265	97.54 87	99.36 13	99.22 15	95.89 98	94.03 248	98.35 168	91.98 133	98.44 269	96.40 133	92.76 265	97.01 242

FC-MVSNet-test		96.42 139	96.05 140	97.53 168	96.95 274	97.27 96	99.36 13	99.23 13	95.83 101	93.93 250	98.37 166	92.00 132	98.32 287	96.02 145	92.72 266	97.00 243

TinyColmap		92.31 299	91.53 300	94.65 311	96.92 276	89.75 322	96.92 312	96.68 337	90.45 310	89.62 333	97.85 215	76.06 348	98.81 235	86.74 329	92.51 267	95.41 339

ACMH+	92.99 14	94.30 255	93.77 256	95.88 273	97.81 213	92.04 284	98.71 132	98.37 197	93.99 186	90.60 326	98.47 155	80.86 318	99.05 199	92.75 248	92.40 268	96.55 299

GBi-Net		94.49 244	93.80 253	96.56 230	98.21 185	95.00 197	98.82 107	98.18 228	92.46 251	94.09 244	97.07 275	81.16 313	97.95 318	92.08 264	92.14 269	96.72 276

test1		94.49 244	93.80 253	96.56 230	98.21 185	95.00 197	98.82 107	98.18 228	92.46 251	94.09 244	97.07 275	81.16 313	97.95 318	92.08 264	92.14 269	96.72 276

FMVSNet3		94.97 213	94.26 222	97.11 187	98.18 190	96.62 122	98.56 161	98.26 219	93.67 209	94.09 244	97.10 268	84.25 288	98.01 314	92.08 264	92.14 269	96.70 280

FMVSNet2		94.47 246	93.61 266	97.04 190	98.21 185	96.43 134	98.79 118	98.27 215	92.46 251	93.50 269	97.09 272	81.16 313	98.00 316	91.09 281	91.93 272	96.70 280

LF4IMVS		93.14 291	92.79 285	94.20 321	95.88 323	88.67 341	97.66 266	97.07 316	93.81 196	91.71 315	97.65 232	77.96 337	98.81 235	91.47 279	91.92 273	95.12 344

OurMVSNet-221017-0		94.21 260	94.00 238	94.85 304	95.60 330	89.22 332	98.89 92	97.43 302	95.29 129	92.18 309	98.52 152	82.86 303	98.59 254	93.46 226	91.76 274	96.74 273

EGC-MVSNET		75.22 335	69.54 338	92.28 338	94.81 345	89.58 326	97.64 267	96.50 341	1.82 378	5.57 379	95.74 331	68.21 362	96.26 356	73.80 367	91.71 275	90.99 363

pmmvs4		94.69 226	93.99 240	96.81 207	95.74 326	95.94 158	97.40 279	97.67 279	90.42 311	93.37 273	97.59 238	89.08 193	98.20 299	92.97 241	91.67 276	96.30 322

tpm		94.13 266	93.80 253	95.12 295	96.50 299	87.91 351	97.44 276	95.89 347	92.62 247	96.37 186	96.30 319	84.13 292	98.30 291	93.24 232	91.66 277	99.14 152

our_test_3		93.65 280	93.30 276	94.69 309	95.45 336	89.68 325	96.91 314	97.65 280	91.97 271	91.66 316	96.88 296	89.67 179	97.93 321	88.02 323	91.49 278	96.48 312

bset_n11_16_dypcd		94.89 218	94.27 221	96.76 209	94.41 349	95.15 191	95.67 345	95.64 349	95.53 114	94.65 216	97.52 244	87.10 239	98.29 294	96.58 126	91.35 279	96.83 265

IterMVS		94.09 270	93.85 250	94.80 307	97.99 203	90.35 316	97.18 298	98.12 240	93.68 207	92.46 304	97.34 254	84.05 293	97.41 337	92.51 257	91.33 280	96.62 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		94.11 268	93.87 248	94.85 304	97.98 205	90.56 314	97.18 298	98.11 243	93.75 197	92.58 297	97.48 246	83.97 295	97.41 337	92.48 259	91.30 281	96.58 293

FMVSNet1		93.19 290	92.07 295	96.56 230	97.54 233	95.00 197	98.82 107	98.18 228	90.38 312	92.27 307	97.07 275	73.68 357	97.95 318	89.36 313	91.30 281	96.72 276

XXY-MVS		95.20 199	94.45 213	97.46 169	96.75 287	96.56 128	98.86 99	98.65 140	93.30 224	93.27 276	98.27 180	84.85 278	98.87 228	94.82 182	91.26 283	96.96 246

cl22		94.68 228	94.19 225	96.13 261	98.11 195	93.60 252	96.94 311	98.31 206	92.43 255	93.32 275	96.87 298	86.51 249	98.28 296	94.10 209	91.16 284	96.51 308

miper_ehance_all_eth		95.01 208	94.69 200	95.97 267	97.70 220	93.31 265	97.02 307	98.07 254	92.23 264	93.51 268	96.96 290	91.85 135	98.15 302	93.68 219	91.16 284	96.44 315

miper_enhance_ethall		95.10 204	94.75 197	96.12 262	97.53 235	93.73 249	96.61 331	98.08 252	92.20 267	93.89 252	96.65 308	92.44 119	98.30 291	94.21 204	91.16 284	96.34 318

RRT_test8_iter05		94.56 238	94.19 225	95.67 280	97.60 226	91.34 296	98.93 85	98.42 188	94.75 156	93.39 272	97.87 212	79.00 329	98.61 250	96.78 119	90.99 287	97.07 239

pmmvs5		93.65 280	92.97 282	95.68 279	95.49 334	92.37 278	98.20 211	97.28 309	89.66 325	92.58 297	97.26 259	82.14 305	98.09 308	93.18 235	90.95 288	96.58 293

ET-MVSNet_ETH3D		94.13 266	92.98 281	97.58 164	98.22 184	96.20 144	97.31 290	95.37 350	94.53 166	79.56 362	97.63 236	86.51 249	97.53 335	96.91 102	90.74 289	99.02 165

SixPastTwentyTwo		93.34 284	92.86 283	94.75 308	95.67 328	89.41 330	98.75 120	96.67 338	93.89 190	90.15 330	98.25 182	80.87 317	98.27 297	90.90 286	90.64 290	96.57 295

N_pmnet		87.12 328	87.77 327	85.17 348	95.46 335	61.92 375	97.37 283	70.66 381	85.83 349	88.73 342	96.04 328	85.33 272	97.76 329	80.02 356	90.48 291	95.84 332

ppachtmachnet_test		93.22 288	92.63 288	94.97 300	95.45 336	90.84 306	96.88 320	97.88 271	90.60 306	92.08 311	97.26 259	88.08 220	97.86 327	85.12 341	90.33 292	96.22 323

DIV-MVS_self_test		94.52 242	94.03 234	95.99 265	97.57 232	93.38 263	97.05 305	97.94 267	91.74 276	92.81 289	97.10 268	89.12 191	98.07 310	92.60 250	90.30 293	96.53 302

cl____		94.51 243	94.01 237	96.02 264	97.58 228	93.40 262	97.05 305	97.96 266	91.73 278	92.76 291	97.08 274	89.06 194	98.13 304	92.61 249	90.29 294	96.52 305

IterMVS-LS		95.46 179	95.21 177	96.22 257	98.12 194	93.72 250	98.32 196	98.13 239	93.71 202	94.26 235	97.31 257	92.24 124	98.10 306	94.63 186	90.12 295	96.84 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		93.22 288	92.35 292	95.84 274	96.77 284	93.09 273	94.66 356	97.56 286	87.37 340	92.90 287	96.24 320	88.15 217	97.90 322	87.37 327	90.10 296	96.53 302

EU-MVSNet		93.66 278	94.14 230	92.25 339	95.96 321	83.38 362	98.52 165	98.12 240	94.69 159	92.61 296	98.13 190	87.36 237	96.39 355	91.82 272	90.00 297	96.98 244

Anonymous20231206		91.66 303	91.10 303	93.33 330	94.02 355	87.35 354	98.58 155	97.26 311	90.48 308	90.16 329	96.31 318	83.83 299	96.53 353	79.36 359	89.90 298	96.12 326

eth_miper_zixun_eth		94.68 228	94.41 216	95.47 285	97.64 223	91.71 291	96.73 328	98.07 254	92.71 245	93.64 261	97.21 264	90.54 165	98.17 301	93.38 227	89.76 299	96.54 300

FMVSNet5		91.81 301	90.92 304	94.49 315	97.21 257	92.09 281	98.00 237	97.55 291	89.31 330	90.86 323	95.61 339	74.48 354	95.32 361	85.57 337	89.70 300	96.07 328

miper_lstm_enhance		94.33 253	94.07 233	95.11 296	97.75 215	90.97 304	97.22 295	98.03 261	91.67 280	92.76 291	96.97 288	90.03 173	97.78 328	92.51 257	89.64 301	96.56 297

v1192		94.32 254	93.58 267	96.53 235	96.10 315	94.45 224	98.50 170	98.17 233	91.54 283	94.19 240	97.06 278	86.95 244	98.43 270	90.14 295	89.57 302	96.70 280

v1144		94.59 236	93.92 243	96.60 225	96.21 309	94.78 212	98.59 153	98.14 238	91.86 275	94.21 239	97.02 283	87.97 222	98.41 278	91.72 275	89.57 302	96.61 290

Anonymous20240521		91.18 307	90.44 308	93.42 327	93.70 356	88.47 344	98.94 83	97.56 286	88.46 335	89.56 335	95.08 344	77.15 345	96.97 343	83.92 347	89.55 304	94.82 350

VPA-MVSNet		95.75 167	95.11 182	97.69 156	97.24 254	97.27 96	98.94 83	99.23 13	95.13 138	95.51 199	97.32 256	85.73 263	98.91 221	97.33 88	89.55 304	96.89 257

v1240		94.06 273	93.29 277	96.34 252	96.03 319	93.90 241	98.44 177	98.17 233	91.18 300	94.13 243	97.01 285	86.05 259	98.42 271	89.13 316	89.50 306	96.70 280

K. test v3		92.55 297	91.91 299	94.48 316	95.64 329	89.24 331	99.07 56	94.88 355	94.04 181	86.78 348	97.59 238	77.64 341	97.64 331	92.08 264	89.43 307	96.57 295

v1921920		94.20 261	93.47 272	96.40 248	95.98 320	94.08 237	98.52 165	98.15 236	91.33 291	94.25 236	97.20 265	86.41 253	98.42 271	90.04 300	89.39 308	96.69 285

new_pmnet		90.06 317	89.00 321	93.22 333	94.18 350	88.32 347	96.42 336	96.89 329	86.19 345	85.67 354	93.62 353	77.18 344	97.10 341	81.61 353	89.29 309	94.23 353

c3_l		94.79 223	94.43 215	95.89 272	97.75 215	93.12 272	97.16 301	98.03 261	92.23 264	93.46 271	97.05 280	91.39 146	98.01 314	93.58 224	89.21 310	96.53 302

v144192		94.39 251	93.70 262	96.48 239	96.06 317	94.35 230	98.58 155	98.16 235	91.45 285	94.33 232	97.02 283	87.50 234	98.45 267	91.08 282	89.11 311	96.63 288

nrg030		96.28 145	95.72 150	97.96 137	96.90 279	98.15 61	99.39 10	98.31 206	95.47 118	94.42 228	98.35 168	92.09 130	98.69 242	97.50 82	89.05 312	97.04 241

DeepMVS_CX				86.78 345	97.09 268	72.30 371		95.17 354	75.92 363	84.34 357	95.19 341	70.58 360	95.35 359	79.98 358	89.04 313	92.68 362

tfpnnormal		93.66 278	92.70 287	96.55 234	96.94 275	95.94 158	98.97 77	99.19 16	91.04 302	91.38 318	97.34 254	84.94 276	98.61 250	85.45 339	89.02 314	95.11 345

Anonymous20231211		94.10 269	93.26 278	96.61 223	99.11 112	94.28 231	99.01 69	98.88 51	86.43 344	92.81 289	97.57 240	81.66 310	98.68 245	94.83 181	89.02 314	96.88 258

v2v482		94.69 226	94.03 234	96.65 217	96.17 312	94.79 211	98.67 142	98.08 252	92.72 244	94.00 249	97.16 266	87.69 231	98.45 267	92.91 243	88.87 316	96.72 276

V42		94.78 224	94.14 230	96.70 214	96.33 307	95.22 188	98.97 77	98.09 250	92.32 260	94.31 233	97.06 278	88.39 211	98.55 257	92.90 244	88.87 316	96.34 318

WR-MVS		95.15 201	94.46 211	97.22 179	96.67 292	96.45 132	98.21 208	98.81 80	94.15 177	93.16 279	97.69 228	87.51 232	98.30 291	95.29 171	88.62 318	96.90 256

FPMVS		77.62 334	77.14 334	79.05 352	79.25 375	60.97 376	95.79 343	95.94 345	65.96 366	67.93 369	94.40 348	37.73 375	88.88 371	68.83 368	88.46 319	87.29 365

v10		94.29 256	93.55 268	96.51 237	96.39 304	94.80 210	98.99 73	98.19 225	91.35 290	93.02 285	96.99 286	88.09 219	98.41 278	90.50 292	88.41 320	96.33 320

test_part1		94.82 220	93.82 251	97.82 144	98.84 136	97.82 77	99.03 63	98.81 80	92.31 262	92.51 301	97.89 210	81.96 307	98.67 246	94.80 184	88.24 321	96.98 244

CP-MVSNet		94.94 216	94.30 220	96.83 205	96.72 289	95.56 174	99.11 49	98.95 35	93.89 190	92.42 305	97.90 208	87.19 238	98.12 305	94.32 200	88.21 322	96.82 266

MIMVSNet1		89.67 320	88.28 324	93.82 324	92.81 361	91.08 303	98.01 235	97.45 300	87.95 337	87.90 345	95.87 330	67.63 364	94.56 365	78.73 362	88.18 323	95.83 333

PS-CasMVS		94.67 231	93.99 240	96.71 212	96.68 291	95.26 187	99.13 46	99.03 26	93.68 207	92.33 306	97.95 204	85.35 270	98.10 306	93.59 223	88.16 324	96.79 267

WR-MVS_H		95.05 207	94.46 211	96.81 207	96.86 281	95.82 167	99.24 26	99.24 11	93.87 192	92.53 299	96.84 300	90.37 167	98.24 298	93.24 232	87.93 325	96.38 317

v8		94.47 246	93.77 256	96.57 229	96.36 305	94.83 208	99.05 59	98.19 225	91.92 272	93.16 279	96.97 288	88.82 203	98.48 262	91.69 276	87.79 326	96.39 316

v7n		94.19 262	93.43 273	96.47 240	95.90 322	94.38 229	99.26 24	98.34 202	91.99 270	92.76 291	97.13 267	88.31 212	98.52 260	89.48 311	87.70 327	96.52 305

UniMVSNet (Re)		95.78 166	95.19 178	97.58 164	96.99 273	97.47 89	98.79 118	99.18 17	95.60 111	93.92 251	97.04 281	91.68 138	98.48 262	95.80 153	87.66 328	96.79 267

baseline1		95.84 163	95.12 181	98.01 133	98.49 164	95.98 150	98.73 127	97.03 319	95.37 125	96.22 188	98.19 186	89.96 174	99.16 181	94.60 189	87.48 329	98.90 176

Gipuma		78.40 332	76.75 335	83.38 349	95.54 332	80.43 367	79.42 370	97.40 304	64.67 367	73.46 365	80.82 368	45.65 372	93.14 367	66.32 369	87.43 330	76.56 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet		94.98 212	94.16 228	97.44 170	96.53 297	97.22 102	98.74 123	98.95 35	94.96 149	89.25 337	97.69 228	89.32 185	98.18 300	94.59 191	87.40 331	96.92 249

VPNet		94.99 210	94.19 225	97.40 174	97.16 263	96.57 127	98.71 132	98.97 31	95.67 108	94.84 210	98.24 183	80.36 321	98.67 246	96.46 129	87.32 332	96.96 246

UniMVSNet_NR-MVSNet		95.71 169	95.15 179	97.40 174	96.84 282	96.97 109	98.74 123	99.24 11	95.16 136	93.88 253	97.72 227	91.68 138	98.31 289	95.81 151	87.25 333	96.92 249

DU-MVS		95.42 183	94.76 196	97.40 174	96.53 297	96.97 109	98.66 145	98.99 30	95.43 120	93.88 253	97.69 228	88.57 206	98.31 289	95.81 151	87.25 333	96.92 249

v148		94.29 256	93.76 258	95.91 270	96.10 315	92.93 274	98.58 155	97.97 264	92.59 249	93.47 270	96.95 292	88.53 209	98.32 287	92.56 254	87.06 335	96.49 311

Baseline_NR-MVSNet		94.35 252	93.81 252	95.96 268	96.20 310	94.05 238	98.61 152	96.67 338	91.44 286	93.85 255	97.60 237	88.57 206	98.14 303	94.39 196	86.93 336	95.68 336

PEN-MVS		94.42 249	93.73 260	96.49 238	96.28 308	94.84 206	99.17 39	99.00 28	93.51 214	92.23 308	97.83 219	86.10 258	97.90 322	92.55 255	86.92 337	96.74 273

TranMVSNet+NR-MVSNet		95.14 202	94.48 209	97.11 187	96.45 302	96.36 137	99.03 63	99.03 26	95.04 145	93.58 263	97.93 206	88.27 213	98.03 313	94.13 206	86.90 338	96.95 248

MDA-MVSNet_test_wron		90.71 312	89.38 317	94.68 310	94.83 344	90.78 309	97.19 297	97.46 298	87.60 338	72.41 367	95.72 335	86.51 249	96.71 350	85.92 335	86.80 339	96.56 297

YYNet1		90.70 313	89.39 316	94.62 312	94.79 346	90.65 312	97.20 296	97.46 298	87.54 339	72.54 366	95.74 331	86.51 249	96.66 351	86.00 334	86.76 340	96.54 300

MDA-MVSNet-bldmvs		89.97 318	88.35 323	94.83 306	95.21 339	91.34 296	97.64 267	97.51 294	88.36 336	71.17 368	96.13 326	79.22 327	96.63 352	83.65 348	86.27 341	96.52 305

test20.03		90.89 311	90.38 309	92.43 336	93.48 357	88.14 349	98.33 191	97.56 286	93.40 219	87.96 344	96.71 305	80.69 320	94.13 366	79.15 360	86.17 342	95.01 349

DTE-MVSNet		93.98 275	93.26 278	96.14 260	96.06 317	94.39 228	99.20 35	98.86 64	93.06 232	91.78 314	97.81 221	85.87 262	97.58 333	90.53 291	86.17 342	96.46 314

pm-mvs1		93.94 276	93.06 280	96.59 226	96.49 300	95.16 189	98.95 81	98.03 261	92.32 260	91.08 321	97.84 216	84.54 284	98.41 278	92.16 262	86.13 344	96.19 325

lessismore_v0				94.45 319	94.93 343	88.44 345		91.03 372		86.77 349	97.64 234	76.23 347	98.42 271	90.31 294	85.64 345	96.51 308

pmmvs6		91.77 302	90.63 306	95.17 294	94.69 348	91.24 301	98.67 142	97.92 269	86.14 346	89.62 333	97.56 242	75.79 349	98.34 285	90.75 289	84.56 346	95.94 331

IB-MVS	91.98 17	93.27 286	91.97 297	97.19 181	97.47 238	93.41 261	97.09 304	95.99 343	93.32 222	92.47 303	95.73 333	78.06 336	99.53 148	94.59 191	82.98 347	98.62 194
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
ambc				89.49 344	86.66 369	75.78 369	92.66 362	96.72 335		86.55 350	92.50 358	46.01 371	97.90 322	90.32 293	82.09 348	94.80 351

Patchmatch-RL test		91.49 304	90.85 305	93.41 328	91.37 364	84.40 359	92.81 361	95.93 346	91.87 274	87.25 346	94.87 345	88.99 195	96.53 353	92.54 256	82.00 349	99.30 130

PM-MVS		87.77 326	86.55 330	91.40 342	91.03 366	83.36 363	96.92 312	95.18 353	91.28 295	86.48 351	93.42 354	53.27 370	96.74 347	89.43 312	81.97 350	94.11 355

pmmvs-eth3d		90.36 315	89.05 320	94.32 320	91.10 365	92.12 280	97.63 270	96.95 324	88.86 333	84.91 356	93.13 355	78.32 332	96.74 347	88.70 318	81.81 351	94.09 356

h-mvs33		96.17 148	95.62 159	97.81 145	99.03 117	94.45 224	98.64 147	98.75 106	97.48 20	98.67 67	98.72 131	89.76 176	99.86 53	97.95 45	81.59 352	99.11 155

TransMVSNet (Re)		92.67 296	91.51 301	96.15 259	96.58 295	94.65 213	98.90 88	96.73 334	90.86 304	89.46 336	97.86 213	85.62 265	98.09 308	86.45 331	81.12 353	95.71 335

PMVS	61.03 23	65.95 338	63.57 342	73.09 355	57.90 380	51.22 381	85.05 368	93.93 366	54.45 369	44.32 375	83.57 365	13.22 379	89.15 370	58.68 371	81.00 354	78.91 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
AUN-MVS		94.53 241	93.73 260	96.92 201	98.50 162	93.52 257	98.34 189	98.10 245	93.83 195	95.94 197	97.98 202	85.59 266	99.03 203	94.35 198	80.94 355	98.22 209

hse-mvs2		95.71 169	95.30 174	96.93 198	98.50 162	93.53 256	98.36 187	98.10 245	97.48 20	98.67 67	97.99 200	89.76 176	99.02 206	97.95 45	80.91 356	98.22 209

UnsupCasMVSNet_eth		90.99 310	89.92 313	94.19 322	94.08 352	89.83 321	97.13 303	98.67 133	93.69 205	85.83 353	96.19 325	75.15 351	96.74 347	89.14 315	79.41 357	96.00 329

test_method		79.03 330	78.17 333	81.63 350	86.06 370	54.40 380	82.75 369	96.89 329	39.54 373	80.98 361	95.57 340	58.37 369	94.73 364	84.74 345	78.61 358	95.75 334

TDRefinement		91.06 309	89.68 314	95.21 292	85.35 371	91.49 295	98.51 169	97.07 316	91.47 284	88.83 341	97.84 216	77.31 342	99.09 196	92.79 247	77.98 359	95.04 347

new-patchmatchnet		88.50 325	87.45 328	91.67 341	90.31 367	85.89 358	97.16 301	97.33 306	89.47 327	83.63 358	92.77 356	76.38 346	95.06 363	82.70 350	77.29 360	94.06 357

KD-MVS_self_test		90.38 314	89.38 317	93.40 329	92.85 360	88.94 338	97.95 240	97.94 267	90.35 313	90.25 328	93.96 352	79.82 323	95.94 357	84.62 346	76.69 361	95.33 340

pmmvs3		86.67 329	84.86 332	92.11 340	88.16 368	87.19 356	96.63 330	94.75 357	79.88 360	87.22 347	92.75 357	66.56 365	95.20 362	81.24 354	76.56 362	93.96 358

CL-MVSNet_self_test		90.11 316	89.14 319	93.02 334	91.86 363	88.23 348	96.51 334	98.07 254	90.49 307	90.49 327	94.41 347	84.75 280	95.34 360	80.79 355	74.95 363	95.50 338

LCM-MVSNet		78.70 331	76.24 336	86.08 346	77.26 377	71.99 372	94.34 358	96.72 335	61.62 368	76.53 363	89.33 362	33.91 377	92.78 368	81.85 352	74.60 364	93.46 360

UnsupCasMVSNet_bld		87.17 327	85.12 331	93.31 331	91.94 362	88.77 339	94.92 353	98.30 212	84.30 355	82.30 359	90.04 361	63.96 368	97.25 339	85.85 336	74.47 365	93.93 359

PVSNet_0	88.72 19	91.28 306	90.03 312	95.00 299	97.99 203	87.29 355	94.84 354	98.50 172	92.06 269	89.86 331	95.19 341	79.81 324	99.39 164	92.27 261	69.79 366	98.33 206

KD-MVS_2432*1600		89.61 321	87.96 325	94.54 313	94.06 353	91.59 293	95.59 347	97.63 282	89.87 321	88.95 339	94.38 349	78.28 333	96.82 345	84.83 342	68.05 367	95.21 342

miper_refine_blended		89.61 321	87.96 325	94.54 313	94.06 353	91.59 293	95.59 347	97.63 282	89.87 321	88.95 339	94.38 349	78.28 333	96.82 345	84.83 342	68.05 367	95.21 342

PMMVS2		77.95 333	75.44 337	85.46 347	82.54 372	74.95 370	94.23 359	93.08 367	72.80 365	74.68 364	87.38 363	36.36 376	91.56 369	73.95 366	63.94 369	89.87 364

MVE	62.14 22	63.28 341	59.38 344	74.99 353	74.33 378	65.47 374	85.55 367	80.50 379	52.02 371	51.10 373	75.00 372	10.91 382	80.50 373	51.60 372	53.40 370	78.99 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 339	64.25 341	67.02 356	82.28 373	59.36 378	91.83 364	85.63 376	52.69 370	60.22 371	77.28 370	41.06 374	80.12 374	46.15 373	41.14 371	61.57 372

EMVS		64.07 340	63.26 343	66.53 357	81.73 374	58.81 379	91.85 363	84.75 377	51.93 372	59.09 372	75.13 371	43.32 373	79.09 375	42.03 374	39.47 372	61.69 371

ANet_high		69.08 336	65.37 340	80.22 351	65.99 379	71.96 373	90.91 365	90.09 373	82.62 356	49.93 374	78.39 369	29.36 378	81.75 372	62.49 370	38.52 373	86.95 367

tmp_tt		68.90 337	66.97 339	74.68 354	50.78 381	59.95 377	87.13 366	83.47 378	38.80 374	62.21 370	96.23 322	64.70 367	76.91 376	88.91 317	30.49 374	87.19 366

wuyk23d		30.17 342	30.18 346	30.16 358	78.61 376	43.29 382	66.79 371	14.21 382	17.31 375	14.82 378	11.93 378	11.55 381	41.43 377	37.08 375	19.30 375	5.76 375

testmvs		21.48 344	24.95 347	11.09 360	14.89 382	6.47 384	96.56 332	9.87 383	7.55 376	17.93 376	39.02 374	9.43 383	5.90 379	16.56 377	12.72 376	20.91 374

test123		20.95 345	23.72 348	12.64 359	13.54 383	8.19 383	96.55 333	6.13 384	7.48 377	16.74 377	37.98 375	12.97 380	6.05 378	16.69 376	5.43 377	23.68 373

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

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

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

cdsmvs_eth3d_5k		23.98 343	31.98 345	0.00 361	0.00 384	0.00 385	0.00 372	98.59 148	0.00 379	0.00 380	98.61 140	90.60 164	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		7.88 347	10.50 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	94.51 91	0.00 380	0.00 378	0.00 378	0.00 376

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

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

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

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

ab-mvs-re		8.20 346	10.94 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	98.43 158	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

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

FOURS1					99.82 1	98.66 26	99.69 1	98.95 35	97.46 23	99.39 15

test_one_0601					99.66 28	99.25 2		98.86 64	97.55 16	99.20 26	99.47 10	97.57 6

eth-test2					0.00 384

eth-test					0.00 384

test_241102_ONE					99.71 21	99.24 5		98.87 58	97.62 12	99.73 1	99.39 18	97.53 7	99.74 111

save fliter					99.46 55	98.38 40	98.21 208	98.71 118	97.95 3

test0726					99.72 13	99.25 2	99.06 57	98.88 51	97.62 12	99.56 6	99.50 5	97.42 9

GSMVS																99.20 139

test_part2					99.63 31	99.18 10				99.27 21

sam_mvs1												89.45 182				99.20 139

sam_mvs												88.99 195

MTGPA								98.74 108

test_post1							96.68 329				30.43 377	87.85 227	98.69 242	92.59 252

test_post											31.83 376	88.83 202	98.91 221

patchmatchnet-post											95.10 343	89.42 183	98.89 225

MTMP							98.89 92	94.14 364

gm-plane-assit					95.88 323	87.47 353			89.74 324		96.94 293		99.19 179	93.32 231

TEST9					99.31 74	98.50 34	97.92 242	98.73 112	92.63 246	97.74 125	98.68 134	96.20 27	99.80 84

test_8					99.29 82	98.44 36	97.89 248	98.72 114	92.98 235	97.70 128	98.66 137	96.20 27	99.80 84

agg_prior					99.30 79	98.38 40		98.72 114		97.57 138			99.81 75

test_prior4						98.01 67	97.86 251

test_prior				99.19 46	99.31 74	98.22 55		98.84 69					99.70 119			99.65 73

旧先验2							97.57 273		91.30 293	98.67 67			99.80 84	95.70 160

新几何2							97.64 267

无先验							97.58 272	98.72 114	91.38 287				99.87 48	93.36 229		99.60 85

原ACMM2							97.67 265

testdata2													99.89 39	91.65 277

segment_acmp												96.85 14

testdata1							97.32 289		96.34 84

plane_prior7					97.42 244	94.63 215

plane_prior6					97.35 249	94.61 218						87.09 240

plane_prior4											98.28 177

plane_prior3						94.61 218			97.02 55	95.34 200

plane_prior2							98.80 114		97.28 36

plane_prior1					97.37 248

n2								0.00 385

nn								0.00 385

door-mid								94.37 360

test11								98.66 136

door								94.64 358

HQP5-MVS						94.25 234

HQP-NCC					97.20 258		98.05 231		96.43 80	94.45 223

ACMP_Plane					97.20 258		98.05 231		96.43 80	94.45 223

BP-MVS														95.30 169

HQP4-MVS										94.45 223			98.96 214			96.87 260

HQP2-MVS												86.75 246

NP-MVS					97.28 252	94.51 223					97.73 225

MDTV_nov1_ep13_2view						84.26 360	96.89 319		90.97 303	97.90 118		89.89 175		93.91 213		99.18 148

Test By Simon												94.64 87

High-resolution multi-view benchmark