High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

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

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

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

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

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

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

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

DVP-MVS		95.67 3	96.02 3	94.64 43	98.78 3	85.93 61	97.09 15	96.73 83	90.27 33	97.04 10	98.05 8	91.47 8	99.55 15	95.62 8	99.08 7	98.45 38
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 15	97.49 5					99.61 3	95.62 8	99.08 7	98.99 8

IU-MVS					98.77 5	86.00 56		96.84 69	81.26 253	97.26 7				95.50 10	99.13 3	99.03 7

CNVR-MVS		95.40 7	95.37 7	95.50 7	98.11 39	88.51 7	95.29 95	96.96 57	92.09 3	95.32 23	97.08 43	89.49 15	99.33 39	95.10 11	98.85 19	98.66 20

MSP-MVS		95.42 6	95.56 6	94.98 21	98.49 18	86.52 40	96.91 24	97.47 10	91.73 9	96.10 17	96.69 63	89.90 12	99.30 42	94.70 12	98.04 73	99.13 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SMA-MVS		95.20 8	95.07 10	95.59 5	98.14 38	88.48 8	96.26 44	97.28 31	85.90 146	97.67 3	98.10 2	88.41 20	99.56 10	94.66 13	99.19 1	98.71 18
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 83	97.51 4	89.13 63	97.14 8	97.91 11	91.64 7	99.62 1	94.61 14	99.17 2	98.86 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + MP.		94.85 13	94.94 11	94.58 46	98.25 31	86.33 47	96.11 55	96.62 95	88.14 93	96.10 17	96.96 50	89.09 18	98.94 87	94.48 15	98.68 39	98.48 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		94.96 12	95.33 8	93.88 65	97.25 80	86.69 32	96.19 48	97.11 46	90.42 30	96.95 12	97.27 29	89.53 14	96.91 248	94.38 16	98.85 19	98.03 78
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
MP-MVS-pluss		94.21 34	94.00 41	94.85 29	98.17 36	86.65 35	94.82 127	97.17 42	86.26 140	92.83 72	97.87 12	85.57 58	99.56 10	94.37 17	98.92 17	98.34 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 29	96.99 83	86.33 47	97.33 6	97.30 29	91.38 13	95.39 22	97.46 19	88.98 19	99.40 31	94.12 18	98.89 18	98.82 15
Skip Steuart: Steuart Systems R&D Blog.
patch_mono-2		93.74 48	94.32 22	92.01 134	97.54 64	78.37 252	93.40 211	97.19 38	88.02 95	94.99 28	97.21 34	88.35 21	98.44 123	94.07 19	98.09 70	99.23 1

DeepPCF-MVS	89.96 1	94.20 36	94.77 14	92.49 116	96.52 99	80.00 217	94.00 188	97.08 47	90.05 37	95.65 21	97.29 28	89.66 13	98.97 83	93.95 20	98.71 34	98.50 28

ACMMP_NAP		94.74 15	94.56 17	95.28 8	98.02 45	87.70 12	95.68 76	97.34 22	88.28 87	95.30 24	97.67 15	85.90 54	99.54 19	93.91 21	98.95 15	98.60 23

xxxxxxxxxxxxxcwj		94.65 16	94.70 15	94.48 50	97.85 50	85.63 72	95.21 101	95.47 178	89.44 52	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 30

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 52	87.76 10	96.65 32	97.48 9	87.76 106	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 30

DROMVSNet		93.44 58	93.71 51	92.63 109	95.21 148	82.43 150	97.27 8	96.71 87	90.57 29	92.88 69	95.80 105	83.16 84	98.16 143	93.68 24	98.14 67	97.31 109

dcpmvs_2		93.49 56	94.19 32	91.38 165	97.69 61	76.78 285	94.25 167	96.29 113	88.33 83	94.46 30	96.88 53	88.07 26	98.64 107	93.62 25	98.09 70	98.73 16

CS-MVS-test		93.62 53	93.88 43	92.86 96	96.59 93	82.12 157	96.43 35	96.57 99	91.76 7	93.52 57	94.41 150	83.85 81	98.24 136	93.62 25	98.17 64	98.21 62

CS-MVS		94.05 37	94.45 19	92.84 97	96.57 96	82.09 158	97.63 3	96.97 53	91.71 10	93.51 58	96.22 87	85.65 56	98.24 136	93.60 27	98.17 64	98.20 63

Regformer-2		94.33 28	94.22 28	94.68 41	95.54 135	86.75 31	94.57 143	96.70 88	91.84 6	94.41 31	96.56 75	87.19 39	99.13 57	93.50 28	97.65 86	98.16 66

MCST-MVS		94.45 21	94.20 31	95.19 11	98.46 20	87.50 15	95.00 115	97.12 44	87.13 119	92.51 84	96.30 82	89.24 17	99.34 36	93.46 29	98.62 48	98.73 16

zzz-MVS		94.47 19	94.30 24	95.00 18	98.42 22	86.95 20	95.06 113	96.97 53	91.07 15	93.14 64	97.56 16	84.30 73	99.56 10	93.43 30	98.75 31	98.47 34

MTAPA		94.42 25	94.22 28	95.00 18	98.42 22	86.95 20	94.36 163	96.97 53	91.07 15	93.14 64	97.56 16	84.30 73	99.56 10	93.43 30	98.75 31	98.47 34

HPM-MVS++		95.14 10	94.91 12	95.83 4	98.25 31	89.65 4	95.92 66	96.96 57	91.75 8	94.02 41	96.83 56	88.12 25	99.55 15	93.41 32	98.94 16	98.28 54

SR-MVS		94.23 32	94.17 34	94.43 53	98.21 35	85.78 69	96.40 38	96.90 62	88.20 91	94.33 33	97.40 23	84.75 70	99.03 67	93.35 33	97.99 74	98.48 30

Regformer-1		94.22 33	94.13 36	94.51 49	95.54 135	86.36 46	94.57 143	96.44 104	91.69 11	94.32 34	96.56 75	87.05 41	99.03 67	93.35 33	97.65 86	98.15 67

test1172		93.97 41	94.07 38	93.66 74	98.11 39	83.45 120	96.26 44	96.84 69	88.33 83	94.19 36	97.43 20	84.24 75	99.01 73	93.26 35	97.98 75	98.52 26

9.14			94.47 18		97.79 54		96.08 56	97.44 14	86.13 144	95.10 26	97.40 23	88.34 22	99.22 49	93.25 36	98.70 36

Regformer-4		93.91 43	93.81 46	94.19 60	95.36 140	85.47 75	94.68 135	96.41 107	91.60 12	93.75 46	96.71 61	85.95 53	99.10 60	93.21 37	96.65 105	98.01 80

CANet		93.54 55	93.20 62	94.55 47	95.65 130	85.73 71	94.94 118	96.69 90	91.89 5	90.69 115	95.88 102	81.99 104	99.54 19	93.14 38	97.95 77	98.39 42

ETH3D-3000-0.1		94.61 17	94.44 20	95.12 13	97.70 60	87.71 11	95.98 63	97.44 14	86.67 132	95.25 25	97.31 27	87.73 30	99.24 47	93.11 39	98.76 30	98.40 41

Regformer-3		93.68 50	93.64 54	93.81 70	95.36 140	84.61 84	94.68 135	95.83 151	91.27 14	93.60 52	96.71 61	85.75 55	98.86 94	92.87 40	96.65 105	97.96 82

NCCC		94.81 14	94.69 16	95.17 12	97.83 53	87.46 16	95.66 78	96.93 60	92.34 2	93.94 42	96.58 73	87.74 29	99.44 30	92.83 41	98.40 57	98.62 22

SR-MVS-dyc-post		93.82 46	93.82 45	93.82 67	97.92 47	84.57 86	96.28 42	96.76 79	87.46 113	93.75 46	97.43 20	84.24 75	99.01 73	92.73 42	97.80 81	97.88 88

RE-MVS-def			93.68 52		97.92 47	84.57 86	96.28 42	96.76 79	87.46 113	93.75 46	97.43 20	82.94 87		92.73 42	97.80 81	97.88 88

TSAR-MVS + GP.		93.66 51	93.41 57	94.41 55	96.59 93	86.78 28	94.40 155	93.93 248	89.77 46	94.21 35	95.59 113	87.35 35	98.61 111	92.72 44	96.15 114	97.83 92

APD-MVS_3200maxsize		93.78 47	93.77 49	93.80 71	97.92 47	84.19 101	96.30 40	96.87 66	86.96 123	93.92 43	97.47 18	83.88 80	98.96 86	92.71 45	97.87 79	98.26 58

PC_three_1452									82.47 221	97.09 9	97.07 45	92.72 1	98.04 159	92.70 46	99.02 12	98.86 10

PHI-MVS		93.89 45	93.65 53	94.62 45	96.84 86	86.43 43	96.69 31	97.49 5	85.15 168	93.56 55	96.28 84	85.60 57	99.31 41	92.45 47	98.79 23	98.12 70

HPM-MVS		94.02 39	93.88 43	94.43 53	98.39 25	85.78 69	97.25 9	97.07 48	86.90 127	92.62 81	96.80 60	84.85 69	99.17 53	92.43 48	98.65 46	98.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
alignmvs		93.08 68	92.50 74	94.81 35	95.62 132	87.61 14	95.99 61	96.07 131	89.77 46	94.12 38	94.87 132	80.56 113	98.66 105	92.42 49	93.10 164	98.15 67

ZNCC-MVS		94.47 19	94.28 25	95.03 16	98.52 16	86.96 19	96.85 27	97.32 27	88.24 88	93.15 63	97.04 46	86.17 49	99.62 1	92.40 50	98.81 22	98.52 26

canonicalmvs		93.27 64	92.75 70	94.85 29	95.70 129	87.66 13	96.33 39	96.41 107	90.00 39	94.09 39	94.60 145	82.33 95	98.62 110	92.40 50	92.86 169	98.27 56

HFP-MVS		94.52 18	94.40 21	94.86 27	98.61 10	86.81 26	96.94 19	97.34 22	88.63 75	93.65 49	97.21 34	86.10 50	99.49 26	92.35 52	98.77 28	98.30 50

ACMMPR		94.43 23	94.28 25	94.91 24	98.63 9	86.69 32	96.94 19	97.32 27	88.63 75	93.53 56	97.26 31	85.04 65	99.54 19	92.35 52	98.78 25	98.50 28

OPU-MVS				96.21 3	98.00 46	90.85 3	97.13 13				97.08 43	92.59 2	98.94 87	92.25 54	98.99 14	98.84 13

region2R		94.43 23	94.27 27	94.92 22	98.65 8	86.67 34	96.92 23	97.23 35	88.60 77	93.58 53	97.27 29	85.22 62	99.54 19	92.21 55	98.74 33	98.56 25

DeepC-MVS	88.79 3	93.31 62	92.99 66	94.26 58	96.07 114	85.83 67	94.89 122	96.99 51	89.02 67	89.56 128	97.37 25	82.51 92	99.38 32	92.20 56	98.30 61	97.57 102
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		93.72 49	94.08 37	92.65 108	97.31 74	83.43 121	95.79 71	97.33 25	90.03 38	93.58 53	96.96 50	84.87 68	97.76 175	92.19 57	98.66 44	96.76 132

CP-MVS		94.34 27	94.21 30	94.74 40	98.39 25	86.64 36	97.60 4	97.24 33	88.53 79	92.73 77	97.23 32	85.20 63	99.32 40	92.15 58	98.83 21	98.25 59

train_agg		93.44 58	93.08 63	94.52 48	97.53 65	86.49 41	94.07 181	96.78 76	81.86 239	92.77 74	96.20 89	87.63 32	99.12 58	92.14 59	98.69 37	97.94 83

diffmvs		91.37 92	91.23 88	91.77 152	93.09 229	80.27 205	92.36 250	95.52 175	87.03 122	91.40 108	94.93 129	80.08 118	97.44 201	92.13 60	94.56 138	97.61 99

h-mvs33		90.80 100	90.15 106	92.75 102	96.01 116	82.66 146	95.43 86	95.53 174	89.80 42	93.08 66	95.64 111	75.77 165	99.00 78	92.07 61	78.05 331	96.60 137

hse-mvs2		89.88 126	89.34 124	91.51 159	94.83 167	81.12 185	93.94 191	93.91 251	89.80 42	93.08 66	93.60 185	75.77 165	97.66 182	92.07 61	77.07 338	95.74 171

MP-MVS		94.25 30	94.07 38	94.77 38	98.47 19	86.31 49	96.71 30	96.98 52	89.04 65	91.98 93	97.19 37	85.43 60	99.56 10	92.06 63	98.79 23	98.44 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZD-MVS					98.15 37	86.62 37		97.07 48	83.63 195	94.19 36	96.91 52	87.57 34	99.26 46	91.99 64	98.44 55

EI-MVSNet-Vis-set		93.01 69	92.92 68	93.29 77	95.01 154	83.51 119	94.48 147	95.77 155	90.87 18	92.52 83	96.67 65	84.50 72	99.00 78	91.99 64	94.44 142	97.36 108

XVS		94.45 21	94.32 22	94.85 29	98.54 14	86.60 38	96.93 21	97.19 38	90.66 27	92.85 70	97.16 40	85.02 66	99.49 26	91.99 64	98.56 51	98.47 34

X-MVStestdata		88.31 170	86.13 213	94.85 29	98.54 14	86.60 38	96.93 21	97.19 38	90.66 27	92.85 70	23.41 373	85.02 66	99.49 26	91.99 64	98.56 51	98.47 34

agg_prior1		93.29 63	92.97 67	94.26 58	97.38 71	85.92 63	93.92 192	96.72 85	81.96 233	92.16 89	96.23 86	87.85 27	98.97 83	91.95 68	98.55 53	97.90 87

test9_res														91.91 69	98.71 34	98.07 74

abl_6		93.18 67	93.05 64	93.57 76	97.52 67	84.27 100	95.53 84	96.67 91	87.85 103	93.20 62	97.22 33	80.35 114	99.18 52	91.91 69	97.21 91	97.26 112

MVS_111021_HR		93.45 57	93.31 58	93.84 66	96.99 83	84.84 80	93.24 223	97.24 33	88.76 72	91.60 104	95.85 103	86.07 52	98.66 105	91.91 69	98.16 66	98.03 78

ETH3D cwj APD-0.16		93.91 43	93.53 55	95.06 15	96.76 88	87.78 9	94.92 120	97.21 37	84.33 182	93.89 44	97.09 42	87.20 38	99.29 44	91.90 72	98.44 55	98.12 70

APD-MVS		94.24 31	94.07 38	94.75 39	98.06 43	86.90 23	95.88 67	96.94 59	85.68 152	95.05 27	97.18 38	87.31 36	99.07 61	91.90 72	98.61 49	98.28 54
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		92.47 76	92.29 77	92.98 90	95.99 118	84.43 97	93.08 228	96.09 129	88.20 91	91.12 112	95.72 109	81.33 109	97.76 175	91.74 74	97.37 90	96.75 133

ETV-MVS		92.74 72	92.66 71	92.97 91	95.20 149	84.04 105	95.07 110	96.51 102	90.73 25	92.96 68	91.19 262	84.06 77	98.34 130	91.72 75	96.54 108	96.54 141

#test#		94.32 29	94.14 35	94.86 27	98.61 10	86.81 26	96.43 35	97.34 22	87.51 112	93.65 49	97.21 34	86.10 50	99.49 26	91.68 76	98.77 28	98.30 50

EI-MVSNet-UG-set		92.74 72	92.62 72	93.12 83	94.86 165	83.20 126	94.40 155	95.74 158	90.71 26	92.05 92	96.60 72	84.00 78	98.99 80	91.55 77	93.63 150	97.17 117

testtj		94.39 26	94.18 33	95.00 18	98.24 33	86.77 30	96.16 49	97.23 35	87.28 117	94.85 29	97.04 46	86.99 42	99.52 23	91.54 78	98.33 60	98.71 18

test_prior3		93.60 54	93.53 55	93.82 67	97.29 76	84.49 90	94.12 174	96.88 64	87.67 109	92.63 79	96.39 80	86.62 44	98.87 91	91.50 79	98.67 41	98.11 72

test_prior2							94.12 174		87.67 109	92.63 79	96.39 80	86.62 44		91.50 79	98.67 41

mPP-MVS		93.99 40	93.78 48	94.63 44	98.50 17	85.90 66	96.87 25	96.91 61	88.70 73	91.83 99	97.17 39	83.96 79	99.55 15	91.44 81	98.64 47	98.43 40

GST-MVS		94.21 34	93.97 42	94.90 26	98.41 24	86.82 25	96.54 34	97.19 38	88.24 88	93.26 59	96.83 56	85.48 59	99.59 7	91.43 82	98.40 57	98.30 50

DELS-MVS		93.43 60	93.25 59	93.97 62	95.42 139	85.04 79	93.06 230	97.13 43	90.74 24	91.84 97	95.09 126	86.32 48	99.21 50	91.22 83	98.45 54	97.65 97
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
nrg030		91.08 98	90.39 100	93.17 82	93.07 230	86.91 22	96.41 37	96.26 116	88.30 86	88.37 145	94.85 135	82.19 99	97.64 186	91.09 84	82.95 275	94.96 194

baseline		92.39 78	92.29 77	92.69 107	94.46 183	81.77 166	94.14 173	96.27 115	89.22 59	91.88 95	96.00 97	82.35 94	97.99 164	91.05 85	95.27 129	98.30 50

xiu_mvs_v1_base_debu		90.64 107	90.05 109	92.40 119	93.97 203	84.46 93	93.32 213	95.46 179	85.17 165	92.25 86	94.03 161	70.59 235	98.57 113	90.97 86	94.67 133	94.18 228

xiu_mvs_v1_base		90.64 107	90.05 109	92.40 119	93.97 203	84.46 93	93.32 213	95.46 179	85.17 165	92.25 86	94.03 161	70.59 235	98.57 113	90.97 86	94.67 133	94.18 228

xiu_mvs_v1_base_debi		90.64 107	90.05 109	92.40 119	93.97 203	84.46 93	93.32 213	95.46 179	85.17 165	92.25 86	94.03 161	70.59 235	98.57 113	90.97 86	94.67 133	94.18 228

VDD-MVS		90.74 102	89.92 114	93.20 80	96.27 105	83.02 132	95.73 73	93.86 252	88.42 82	92.53 82	96.84 55	62.09 309	98.64 107	90.95 89	92.62 172	97.93 85

casdiffmvs		92.51 75	92.43 75	92.74 103	94.41 186	81.98 162	94.54 145	96.23 120	89.57 50	91.96 94	96.17 93	82.58 91	98.01 162	90.95 89	95.45 124	98.23 60

DeepC-MVS_fast	89.43 2	94.04 38	93.79 47	94.80 36	97.48 69	86.78 28	95.65 80	96.89 63	89.40 55	92.81 73	96.97 49	85.37 61	99.24 47	90.87 91	98.69 37	98.38 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		93.24 65	92.88 69	94.30 57	98.09 42	85.33 77	96.86 26	97.45 13	88.33 83	90.15 124	97.03 48	81.44 107	99.51 24	90.85 92	95.74 117	98.04 77
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
PGM-MVS		93.96 42	93.72 50	94.68 41	98.43 21	86.22 52	95.30 93	97.78 1	87.45 115	93.26 59	97.33 26	84.62 71	99.51 24	90.75 93	98.57 50	98.32 49

agg_prior2														90.54 94	98.68 39	98.27 56

HPM-MVS_fast		93.40 61	93.22 60	93.94 64	98.36 27	84.83 81	97.15 12	96.80 75	85.77 149	92.47 85	97.13 41	82.38 93	99.07 61	90.51 95	98.40 57	97.92 86

lupinMVS		90.92 99	90.21 103	93.03 88	93.86 206	83.88 108	92.81 237	93.86 252	79.84 269	91.76 100	94.29 155	77.92 146	98.04 159	90.48 96	97.11 92	97.17 117

jason		90.80 100	90.10 107	92.90 94	93.04 232	83.53 118	93.08 228	94.15 242	80.22 263	91.41 107	94.91 130	76.87 152	97.93 169	90.28 97	96.90 98	97.24 113
jason: jason.
CSCG		93.23 66	93.05 64	93.76 72	98.04 44	84.07 103	96.22 47	97.37 21	84.15 184	90.05 125	95.66 110	87.77 28	99.15 56	89.91 98	98.27 62	98.07 74

ETH3 D test6400		93.64 52	93.22 60	94.92 22	97.79 54	86.84 24	95.31 90	97.26 32	82.67 219	93.81 45	96.29 83	87.29 37	99.27 45	89.87 99	98.67 41	98.65 21

CPTT-MVS		91.99 80	91.80 81	92.55 113	98.24 33	81.98 162	96.76 29	96.49 103	81.89 238	90.24 120	96.44 79	78.59 138	98.61 111	89.68 100	97.85 80	97.06 121

MVSFormer		91.68 88	91.30 86	92.80 99	93.86 206	83.88 108	95.96 64	95.90 145	84.66 178	91.76 100	94.91 130	77.92 146	97.30 216	89.64 101	97.11 92	97.24 113

test_djsdf		89.03 151	88.64 140	90.21 212	90.74 307	79.28 235	95.96 64	95.90 145	84.66 178	85.33 219	92.94 205	74.02 193	97.30 216	89.64 101	88.53 220	94.05 238

bset_n11_16_dypcd		86.83 222	85.55 232	90.65 192	88.22 343	81.70 167	88.88 316	90.42 327	85.26 164	85.49 205	90.69 279	67.11 274	97.02 241	89.51 103	84.39 260	93.23 277

EIA-MVS		91.95 81	91.94 79	91.98 138	95.16 150	80.01 216	95.36 87	96.73 83	88.44 80	89.34 132	92.16 229	83.82 82	98.45 122	89.35 104	97.06 94	97.48 105

Effi-MVS+		91.59 89	91.11 90	93.01 89	94.35 190	83.39 123	94.60 140	95.10 202	87.10 120	90.57 116	93.10 201	81.43 108	98.07 157	89.29 105	94.48 140	97.59 101

ET-MVSNet_ETH3D		87.51 198	85.91 224	92.32 125	93.70 214	83.93 106	92.33 251	90.94 320	84.16 183	72.09 351	92.52 218	69.90 244	95.85 300	89.20 106	88.36 226	97.17 117

PS-MVSNAJ		91.18 96	90.92 94	91.96 140	95.26 146	82.60 149	92.09 260	95.70 160	86.27 139	91.84 97	92.46 219	79.70 124	98.99 80	89.08 107	95.86 116	94.29 226

xiu_mvs_v2_base		91.13 97	90.89 96	91.86 146	94.97 157	82.42 151	92.24 254	95.64 167	86.11 145	91.74 102	93.14 199	79.67 127	98.89 90	89.06 108	95.46 123	94.28 227

VNet		92.24 79	91.91 80	93.24 79	96.59 93	83.43 121	94.84 126	96.44 104	89.19 61	94.08 40	95.90 101	77.85 149	98.17 142	88.90 109	93.38 158	98.13 69

PS-MVSNAJss		89.97 121	89.62 116	91.02 181	91.90 260	80.85 193	95.26 98	95.98 137	86.26 140	86.21 187	94.29 155	79.70 124	97.65 184	88.87 110	88.10 229	94.57 213

RRT_MVS		88.86 156	87.68 166	92.39 122	92.02 257	86.09 55	94.38 161	94.94 208	85.45 159	87.14 168	93.84 177	65.88 291	97.11 233	88.73 111	86.77 247	93.98 241

XVG-OURS-SEG-HR		89.95 122	89.45 119	91.47 162	94.00 201	81.21 183	91.87 263	96.06 133	85.78 148	88.55 141	95.73 108	74.67 183	97.27 220	88.71 112	89.64 205	95.91 162

jajsoiax		88.24 172	87.50 169	90.48 201	90.89 301	80.14 208	95.31 90	95.65 166	84.97 172	84.24 245	94.02 164	65.31 293	97.42 203	88.56 113	88.52 221	93.89 243

mvs_tets		88.06 178	87.28 176	90.38 207	90.94 297	79.88 219	95.22 100	95.66 164	85.10 169	84.21 246	93.94 169	63.53 301	97.40 210	88.50 114	88.40 225	93.87 246

VDDNet		89.56 132	88.49 147	92.76 101	95.07 153	82.09 158	96.30 40	93.19 264	81.05 258	91.88 95	96.86 54	61.16 319	98.33 132	88.43 115	92.49 175	97.84 91

RRT_test8_iter05		86.90 220	86.36 204	88.52 267	93.00 235	73.27 319	94.32 164	95.96 139	85.50 158	84.26 244	92.86 206	60.76 321	97.70 180	88.32 116	82.29 283	94.60 210

HQP_MVS		90.60 110	90.19 104	91.82 149	94.70 173	82.73 142	95.85 68	96.22 121	90.81 20	86.91 173	94.86 133	74.23 187	98.12 144	88.15 117	89.99 196	94.63 207

plane_prior5								96.22 121					98.12 144	88.15 117	89.99 196	94.63 207

EPNet		91.79 83	91.02 93	94.10 61	90.10 321	85.25 78	96.03 60	92.05 289	92.83 1	87.39 165	95.78 106	79.39 129	99.01 73	88.13 119	97.48 88	98.05 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		90.12 116	89.56 117	91.82 149	93.14 227	83.90 107	94.16 172	95.74 158	88.96 68	87.86 152	95.43 116	72.48 215	97.91 170	88.10 120	90.18 195	93.65 261
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSTER		88.84 157	88.29 153	90.51 199	92.95 237	80.44 203	93.73 199	95.01 205	84.66 178	87.15 166	93.12 200	72.79 211	97.21 227	87.86 121	87.36 240	93.87 246

3Dnovator+	87.14 4	92.42 77	91.37 85	95.55 6	95.63 131	88.73 6	97.07 17	96.77 78	90.84 19	84.02 248	96.62 71	75.95 164	99.34 36	87.77 122	97.68 84	98.59 24

LPG-MVS_test		89.45 136	88.90 136	91.12 173	94.47 181	81.49 173	95.30 93	96.14 126	86.73 130	85.45 208	95.16 123	69.89 245	98.10 146	87.70 123	89.23 212	93.77 255

LGP-MVS_train				91.12 173	94.47 181	81.49 173		96.14 126	86.73 130	85.45 208	95.16 123	69.89 245	98.10 146	87.70 123	89.23 212	93.77 255

MVS_Test		91.31 93	91.11 90	91.93 142	94.37 187	80.14 208	93.46 210	95.80 153	86.46 135	91.35 109	93.77 180	82.21 98	98.09 154	87.57 125	94.95 131	97.55 104

PVSNet_Blended_VisFu		91.38 91	90.91 95	92.80 99	96.39 102	83.17 127	94.87 124	96.66 92	83.29 205	89.27 133	94.46 149	80.29 116	99.17 53	87.57 125	95.37 125	96.05 159

CDPH-MVS		92.83 70	92.30 76	94.44 51	97.79 54	86.11 54	94.06 183	96.66 92	80.09 266	92.77 74	96.63 70	86.62 44	99.04 66	87.40 127	98.66 44	98.17 65

XVG-OURS		89.40 141	88.70 139	91.52 158	94.06 195	81.46 175	91.27 276	96.07 131	86.14 143	88.89 139	95.77 107	68.73 264	97.26 222	87.39 128	89.96 198	95.83 167

EPP-MVSNet		91.70 87	91.56 84	92.13 132	95.88 122	80.50 202	97.33 6	95.25 194	86.15 142	89.76 127	95.60 112	83.42 83	98.32 133	87.37 129	93.25 161	97.56 103

VPA-MVSNet		89.62 129	88.96 133	91.60 157	93.86 206	82.89 137	95.46 85	97.33 25	87.91 98	88.43 144	93.31 191	74.17 190	97.40 210	87.32 130	82.86 280	94.52 216

LFMVS		90.08 117	89.13 130	92.95 92	96.71 89	82.32 155	96.08 56	89.91 339	86.79 128	92.15 91	96.81 58	62.60 306	98.34 130	87.18 131	93.90 146	98.19 64

anonymousdsp		87.84 181	87.09 179	90.12 217	89.13 332	80.54 201	94.67 137	95.55 171	82.05 229	83.82 253	92.12 232	71.47 224	97.15 229	87.15 132	87.80 236	92.67 296

CLD-MVS		89.47 135	88.90 136	91.18 172	94.22 191	82.07 160	92.13 258	96.09 129	87.90 99	85.37 217	92.45 220	74.38 185	97.56 191	87.15 132	90.43 191	93.93 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BP-MVS														87.11 134

HQP-MVS		89.80 127	89.28 127	91.34 167	94.17 192	81.56 169	94.39 157	96.04 135	88.81 69	85.43 211	93.97 168	73.83 197	97.96 166	87.11 134	89.77 203	94.50 218

ACMP	84.23 8	89.01 153	88.35 149	90.99 184	94.73 170	81.27 179	95.07 110	95.89 147	86.48 134	83.67 257	94.30 154	69.33 253	97.99 164	87.10 136	88.55 219	93.72 259
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
旧先验2							93.36 212		71.25 347	94.37 32			97.13 232	86.74 137

3Dnovator	86.66 5	91.73 86	90.82 97	94.44 51	94.59 177	86.37 45	97.18 11	97.02 50	89.20 60	84.31 243	96.66 66	73.74 199	99.17 53	86.74 137	97.96 76	97.79 94

PVSNet_BlendedMVS		89.98 120	89.70 115	90.82 188	96.12 109	81.25 180	93.92 192	96.83 71	83.49 200	89.10 135	92.26 227	81.04 111	98.85 97	86.72 139	87.86 235	92.35 307

PVSNet_Blended		90.73 103	90.32 102	91.98 138	96.12 109	81.25 180	92.55 245	96.83 71	82.04 231	89.10 135	92.56 217	81.04 111	98.85 97	86.72 139	95.91 115	95.84 166

mvs_anonymous		89.37 142	89.32 125	89.51 244	93.47 219	74.22 309	91.65 271	94.83 219	82.91 214	85.45 208	93.79 178	81.23 110	96.36 280	86.47 141	94.09 144	97.94 83

test1111		89.10 146	88.64 140	90.48 201	95.53 137	74.97 302	96.08 56	84.89 359	88.13 94	90.16 123	96.65 67	63.29 302	98.10 146	86.14 142	96.90 98	98.39 42

AUN-MVS		87.78 184	86.54 199	91.48 161	94.82 168	81.05 186	93.91 195	93.93 248	83.00 211	86.93 171	93.53 186	69.50 251	97.67 181	86.14 142	77.12 337	95.73 172

test_yl		90.69 104	90.02 112	92.71 104	95.72 127	82.41 153	94.11 176	95.12 200	85.63 153	91.49 105	94.70 139	74.75 180	98.42 125	86.13 144	92.53 173	97.31 109

DCV-MVSNet		90.69 104	90.02 112	92.71 104	95.72 127	82.41 153	94.11 176	95.12 200	85.63 153	91.49 105	94.70 139	74.75 180	98.42 125	86.13 144	92.53 173	97.31 109

test2506		87.21 212	86.28 209	90.02 223	95.62 132	73.64 315	96.25 46	71.38 374	87.89 101	90.45 117	96.65 67	55.29 343	98.09 154	86.03 146	96.94 96	98.33 46

ECVR-MVS		89.09 148	88.53 143	90.77 190	95.62 132	75.89 296	96.16 49	84.22 361	87.89 101	90.20 121	96.65 67	63.19 304	98.10 146	85.90 147	96.94 96	98.33 46

OMC-MVS		91.23 94	90.62 99	93.08 85	96.27 105	84.07 103	93.52 207	95.93 141	86.95 124	89.51 129	96.13 95	78.50 140	98.35 129	85.84 148	92.90 168	96.83 131

test_part1		89.00 154	87.99 159	92.04 133	95.94 121	83.81 110	96.14 52	96.05 134	86.44 136	85.69 195	93.73 183	71.57 221	97.66 182	85.80 149	80.54 312	94.66 206

ACMM	84.12 9	89.14 145	88.48 148	91.12 173	94.65 176	81.22 182	95.31 90	96.12 128	85.31 163	85.92 191	94.34 151	70.19 243	98.06 158	85.65 150	88.86 217	94.08 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		92.58 74	91.74 82	95.08 14	96.19 107	89.31 5	92.66 240	96.56 101	83.44 201	91.68 103	95.04 127	86.60 47	98.99 80	85.60 151	97.92 78	96.93 128

Effi-MVS+-dtu		88.65 162	88.35 149	89.54 241	93.33 222	76.39 291	94.47 150	94.36 233	87.70 107	85.43 211	89.56 302	73.45 202	97.26 222	85.57 152	91.28 182	94.97 191

mvs-test1		89.45 136	89.14 129	90.38 207	93.33 222	77.63 273	94.95 117	94.36 233	87.70 107	87.10 169	92.81 211	73.45 202	98.03 161	85.57 152	93.04 165	95.48 177

FIs		90.51 111	90.35 101	90.99 184	93.99 202	80.98 188	95.73 73	97.54 3	89.15 62	86.72 177	94.68 141	81.83 106	97.24 224	85.18 154	88.31 227	94.76 204

MG-MVS		91.77 84	91.70 83	92.00 137	97.08 82	80.03 215	93.60 205	95.18 198	87.85 103	90.89 114	96.47 78	82.06 102	98.36 127	85.07 155	97.04 95	97.62 98

CANet_DTU		90.26 115	89.41 122	92.81 98	93.46 220	83.01 133	93.48 208	94.47 230	89.43 54	87.76 157	94.23 159	70.54 239	99.03 67	84.97 156	96.39 112	96.38 143

UniMVSNet_NR-MVSNet		89.92 124	89.29 126	91.81 151	93.39 221	83.72 112	94.43 153	97.12 44	89.80 42	86.46 180	93.32 190	83.16 84	97.23 225	84.92 157	81.02 304	94.49 220

DU-MVS		89.34 143	88.50 145	91.85 148	93.04 232	83.72 112	94.47 150	96.59 97	89.50 51	86.46 180	93.29 193	77.25 150	97.23 225	84.92 157	81.02 304	94.59 211

cascas		86.43 238	84.98 245	90.80 189	92.10 254	80.92 191	90.24 292	95.91 144	73.10 336	83.57 261	88.39 316	65.15 294	97.46 198	84.90 159	91.43 181	94.03 239

UniMVSNet (Re)		89.80 127	89.07 131	92.01 134	93.60 216	84.52 89	94.78 130	97.47 10	89.26 58	86.44 183	92.32 224	82.10 100	97.39 213	84.81 160	80.84 308	94.12 232

Vis-MVSNet		91.75 85	91.23 88	93.29 77	95.32 143	83.78 111	96.14 52	95.98 137	89.89 40	90.45 117	96.58 73	75.09 176	98.31 134	84.75 161	96.90 98	97.78 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v2v482		87.84 181	87.06 180	90.17 213	90.99 293	79.23 238	94.00 188	95.13 199	84.87 173	85.53 201	92.07 238	74.45 184	97.45 199	84.71 162	81.75 292	93.85 249

DP-MVS Recon		91.95 81	91.28 87	93.96 63	98.33 29	85.92 63	94.66 138	96.66 92	82.69 218	90.03 126	95.82 104	82.30 96	99.03 67	84.57 163	96.48 111	96.91 129

UA-Net		92.83 70	92.54 73	93.68 73	96.10 112	84.71 83	95.66 78	96.39 109	91.92 4	93.22 61	96.49 77	83.16 84	98.87 91	84.47 164	95.47 122	97.45 107

V42		87.68 186	86.86 184	90.15 215	90.58 312	80.14 208	94.24 169	95.28 193	83.66 194	85.67 196	91.33 257	74.73 182	97.41 208	84.43 165	81.83 290	92.89 291

FC-MVSNet-test		90.27 114	90.18 105	90.53 196	93.71 212	79.85 221	95.77 72	97.59 2	89.31 57	86.27 186	94.67 142	81.93 105	97.01 242	84.26 166	88.09 231	94.71 205

cl22		86.78 225	85.98 220	89.18 250	92.34 247	77.62 274	90.84 283	94.13 244	81.33 251	83.97 250	90.15 289	73.96 194	96.60 263	84.19 167	82.94 276	93.33 271

miper_enhance_ethall		86.90 220	86.18 212	89.06 253	91.66 270	77.58 275	90.22 294	94.82 220	79.16 277	84.48 232	89.10 305	79.19 131	96.66 256	84.06 168	82.94 276	92.94 289

VPNet		88.20 173	87.47 171	90.39 205	93.56 217	79.46 226	94.04 184	95.54 173	88.67 74	86.96 170	94.58 147	69.33 253	97.15 229	84.05 169	80.53 314	94.56 214

UGNet		89.95 122	88.95 134	92.95 92	94.51 180	83.31 124	95.70 75	95.23 195	89.37 56	87.58 159	93.94 169	64.00 299	98.78 102	83.92 170	96.31 113	96.74 134
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
IterMVS-LS		88.36 169	87.91 163	89.70 237	93.80 209	78.29 255	93.73 199	95.08 204	85.73 150	84.75 226	91.90 243	79.88 120	96.92 247	83.83 171	82.51 281	93.89 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		87.22 211	86.62 196	89.02 255	92.13 252	77.40 278	90.91 282	94.81 221	81.28 252	84.32 241	90.08 291	79.26 130	96.62 258	83.81 172	82.94 276	93.04 286

EI-MVSNet		89.10 146	88.86 138	89.80 233	91.84 262	78.30 254	93.70 202	95.01 205	85.73 150	87.15 166	95.28 118	79.87 121	97.21 227	83.81 172	87.36 240	93.88 245

c3_l		87.14 216	86.50 201	89.04 254	92.20 249	77.26 279	91.22 278	94.70 225	82.01 232	84.34 240	90.43 284	78.81 134	96.61 261	83.70 174	81.09 301	93.25 275

Anonymous20240529		88.09 176	86.59 197	92.58 112	96.53 98	81.92 164	95.99 61	95.84 150	74.11 328	89.06 137	95.21 122	61.44 314	98.81 100	83.67 175	87.47 237	97.01 124

v1144		87.61 194	86.79 188	90.06 220	91.01 292	79.34 231	93.95 190	95.42 187	83.36 204	85.66 197	91.31 260	74.98 178	97.42 203	83.37 176	82.06 286	93.42 270

thisisatest0530		88.67 161	87.61 168	91.86 146	94.87 164	80.07 211	94.63 139	89.90 340	84.00 187	88.46 143	93.78 179	66.88 278	98.46 119	83.30 177	92.65 171	97.06 121

tttt0517		88.61 163	87.78 164	91.11 176	94.96 158	77.81 267	95.35 88	89.69 343	85.09 170	88.05 150	94.59 146	66.93 276	98.48 117	83.27 178	92.13 178	97.03 123

testdata				90.49 200	96.40 101	77.89 264		95.37 190	72.51 341	93.63 51	96.69 63	82.08 101	97.65 184	83.08 179	97.39 89	95.94 161

LCM-MVSNet-Re		88.30 171	88.32 152	88.27 273	94.71 172	72.41 331	93.15 224	90.98 318	87.77 105	79.25 315	91.96 241	78.35 142	95.75 305	83.04 180	95.62 118	96.65 136

IS-MVSNet		91.43 90	91.09 92	92.46 117	95.87 124	81.38 178	96.95 18	93.69 257	89.72 48	89.50 130	95.98 98	78.57 139	97.77 174	83.02 181	96.50 110	98.22 61

UniMVSNet_ETH3D		87.53 197	86.37 203	91.00 183	92.44 245	78.96 240	94.74 132	95.61 168	84.07 186	85.36 218	94.52 148	59.78 328	97.34 215	82.93 182	87.88 234	96.71 135

XVG-ACMP-BASELINE		86.00 242	84.84 250	89.45 245	91.20 284	78.00 260	91.70 269	95.55 171	85.05 171	82.97 270	92.25 228	54.49 346	97.48 196	82.93 182	87.45 239	92.89 291

v144192		87.19 214	86.35 205	89.74 234	90.64 310	78.24 256	93.92 192	95.43 185	81.93 235	85.51 203	91.05 270	74.21 189	97.45 199	82.86 184	81.56 294	93.53 264

v8		87.50 200	86.71 190	89.89 227	91.37 279	79.40 228	94.50 146	95.38 188	84.81 175	83.60 260	91.33 257	76.05 161	97.42 203	82.84 185	80.51 316	92.84 293

Anonymous20231211		86.59 232	85.13 242	90.98 186	96.52 99	81.50 171	96.14 52	96.16 125	73.78 330	83.65 258	92.15 230	63.26 303	97.37 214	82.82 186	81.74 293	94.06 237

PAPM_NR		91.22 95	90.78 98	92.52 115	97.60 63	81.46 175	94.37 162	96.24 119	86.39 138	87.41 162	94.80 137	82.06 102	98.48 117	82.80 187	95.37 125	97.61 99

eth_miper_zixun_eth		86.50 235	85.77 229	88.68 263	91.94 259	75.81 298	90.47 288	94.89 214	82.05 229	84.05 247	90.46 283	75.96 163	96.77 252	82.76 188	79.36 326	93.46 269

Patchmatch-RL test		81.67 293	79.96 299	86.81 309	85.42 358	71.23 337	82.17 357	87.50 354	78.47 288	77.19 326	82.50 354	70.81 232	93.48 340	82.66 189	72.89 346	95.71 173

tpmrst		85.35 254	84.99 244	86.43 311	90.88 302	67.88 355	88.71 318	91.43 308	80.13 265	86.08 190	88.80 311	73.05 208	96.02 292	82.48 190	83.40 274	95.40 181

sss		88.93 155	88.26 155	90.94 187	94.05 196	80.78 195	91.71 268	95.38 188	81.55 247	88.63 140	93.91 173	75.04 177	95.47 316	82.47 191	91.61 180	96.57 139

ab-mvs		89.41 139	88.35 149	92.60 110	95.15 152	82.65 147	92.20 256	95.60 169	83.97 188	88.55 141	93.70 184	74.16 191	98.21 141	82.46 192	89.37 208	96.94 127

CostFormer		85.77 248	84.94 247	88.26 274	91.16 288	72.58 329	89.47 306	91.04 317	76.26 308	86.45 182	89.97 294	70.74 233	96.86 251	82.35 193	87.07 245	95.34 184

v1192		87.25 208	86.33 206	90.00 225	90.76 306	79.04 239	93.80 196	95.48 177	82.57 220	85.48 206	91.18 264	73.38 206	97.42 203	82.30 194	82.06 286	93.53 264

Baseline_NR-MVSNet		87.07 217	86.63 195	88.40 269	91.44 273	77.87 265	94.23 170	92.57 277	84.12 185	85.74 194	92.08 236	77.25 150	96.04 290	82.29 195	79.94 320	91.30 324

Anonymous202405211		87.68 186	86.13 213	92.31 126	96.66 90	80.74 196	94.87 124	91.49 306	80.47 262	89.46 131	95.44 114	54.72 345	98.23 138	82.19 196	89.89 200	97.97 81

v148		87.04 218	86.32 207	89.21 248	90.94 297	77.26 279	93.71 201	94.43 231	84.84 174	84.36 239	90.80 276	76.04 162	97.05 239	82.12 197	79.60 324	93.31 272

114514_t		89.51 133	88.50 145	92.54 114	98.11 39	81.99 161	95.16 106	96.36 111	70.19 351	85.81 192	95.25 120	76.70 156	98.63 109	82.07 198	96.86 101	97.00 125

v1921920		86.97 219	86.06 218	89.69 238	90.53 315	78.11 259	93.80 196	95.43 185	81.90 237	85.33 219	91.05 270	72.66 212	97.41 208	82.05 199	81.80 291	93.53 264

OurMVSNet-221017-0		85.35 254	84.64 254	87.49 291	90.77 305	72.59 328	94.01 187	94.40 232	84.72 177	79.62 313	93.17 197	61.91 311	96.72 253	81.99 200	81.16 298	93.16 281

v10		87.25 208	86.38 202	89.85 228	91.19 285	79.50 225	94.48 147	95.45 182	83.79 192	83.62 259	91.19 262	75.13 175	97.42 203	81.94 201	80.60 310	92.63 298

TranMVSNet+NR-MVSNet		88.84 157	87.95 161	91.49 160	92.68 242	83.01 133	94.92 120	96.31 112	89.88 41	85.53 201	93.85 176	76.63 158	96.96 244	81.91 202	79.87 322	94.50 218

D2MVS		85.90 244	85.09 243	88.35 271	90.79 304	77.42 277	91.83 264	95.70 160	80.77 260	80.08 306	90.02 292	66.74 281	96.37 278	81.88 203	87.97 233	91.26 325

test-LLR		85.87 245	85.41 236	87.25 297	90.95 295	71.67 334	89.55 302	89.88 341	83.41 202	84.54 230	87.95 323	67.25 271	95.11 321	81.82 204	93.37 159	94.97 191

test-mter		84.54 269	83.64 267	87.25 297	90.95 295	71.67 334	89.55 302	89.88 341	79.17 276	84.54 230	87.95 323	55.56 340	95.11 321	81.82 204	93.37 159	94.97 191

PMMVS		85.71 249	84.96 246	87.95 282	88.90 335	77.09 281	88.68 319	90.06 335	72.32 342	86.47 179	90.76 278	72.15 218	94.40 327	81.78 206	93.49 154	92.36 306

cl____		86.52 234	85.78 227	88.75 260	92.03 256	76.46 289	90.74 284	94.30 236	81.83 241	83.34 266	90.78 277	75.74 170	96.57 264	81.74 207	81.54 295	93.22 278

DIV-MVS_self_test		86.53 233	85.78 227	88.75 260	92.02 257	76.45 290	90.74 284	94.30 236	81.83 241	83.34 266	90.82 275	75.75 168	96.57 264	81.73 208	81.52 296	93.24 276

NR-MVSNet		88.58 165	87.47 171	91.93 142	93.04 232	84.16 102	94.77 131	96.25 118	89.05 64	80.04 307	93.29 193	79.02 132	97.05 239	81.71 209	80.05 319	94.59 211

WTY-MVS		89.60 130	88.92 135	91.67 155	95.47 138	81.15 184	92.38 249	94.78 223	83.11 208	89.06 137	94.32 153	78.67 137	96.61 261	81.57 210	90.89 189	97.24 113

thisisatest0515		87.33 204	85.99 219	91.37 166	93.49 218	79.55 224	90.63 286	89.56 346	80.17 264	87.56 160	90.86 273	67.07 275	98.28 135	81.50 211	93.02 166	96.29 145

v1240		86.78 225	85.85 225	89.56 240	90.45 316	77.79 268	93.61 204	95.37 190	81.65 243	85.43 211	91.15 266	71.50 223	97.43 202	81.47 212	82.05 288	93.47 268

GeoE		90.05 118	89.43 121	91.90 145	95.16 150	80.37 204	95.80 70	94.65 227	83.90 189	87.55 161	94.75 138	78.18 144	97.62 188	81.28 213	93.63 150	97.71 96

WR-MVS		88.38 167	87.67 167	90.52 198	93.30 224	80.18 206	93.26 220	95.96 139	88.57 78	85.47 207	92.81 211	76.12 160	96.91 248	81.24 214	82.29 283	94.47 223

1314		87.51 198	86.57 198	90.34 210	92.42 246	79.74 223	92.63 241	95.35 192	78.35 290	80.14 304	91.62 252	74.05 192	97.15 229	81.05 215	93.53 153	94.12 232

IterMVS-SCA-FT		85.45 251	84.53 256	88.18 277	91.71 267	76.87 284	90.19 295	92.65 276	85.40 161	81.44 286	90.54 281	66.79 279	95.00 324	81.04 216	81.05 302	92.66 297

XXY-MVS		87.65 188	86.85 185	90.03 221	92.14 251	80.60 200	93.76 198	95.23 195	82.94 213	84.60 228	94.02 164	74.27 186	95.49 315	81.04 216	83.68 268	94.01 240

miper_lstm_enhance		85.27 257	84.59 255	87.31 294	91.28 283	74.63 304	87.69 330	94.09 246	81.20 256	81.36 288	89.85 297	74.97 179	94.30 330	81.03 218	79.84 323	93.01 287

GA-MVS		86.61 230	85.27 240	90.66 191	91.33 282	78.71 242	90.40 289	93.81 255	85.34 162	85.12 221	89.57 301	61.25 316	97.11 233	80.99 219	89.59 206	96.15 149

IB-MVS	80.51 15	85.24 258	83.26 270	91.19 171	92.13 252	79.86 220	91.75 266	91.29 311	83.28 206	80.66 296	88.49 315	61.28 315	98.46 119	80.99 219	79.46 325	95.25 185
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
CVMVSNet		84.69 268	84.79 251	84.37 329	91.84 262	64.92 363	93.70 202	91.47 307	66.19 357	86.16 189	95.28 118	67.18 273	93.33 342	80.89 221	90.42 192	94.88 199

baseline1		88.10 175	87.28 176	90.57 194	94.96 158	80.07 211	94.27 166	91.29 311	86.74 129	87.41 162	94.00 166	76.77 155	96.20 285	80.77 222	79.31 327	95.44 179

HyFIR lowres test		88.09 176	86.81 186	91.93 142	96.00 117	80.63 198	90.01 298	95.79 154	73.42 333	87.68 158	92.10 235	73.86 196	97.96 166	80.75 223	91.70 179	97.19 116

AdaColmap		89.89 125	89.07 131	92.37 123	97.41 70	83.03 131	94.42 154	95.92 142	82.81 216	86.34 185	94.65 143	73.89 195	99.02 71	80.69 224	95.51 120	95.05 189

原ACMM1				92.01 134	97.34 73	81.05 186		96.81 74	78.89 280	90.45 117	95.92 100	82.65 90	98.84 99	80.68 225	98.26 63	96.14 150

TESTMET0.1,1		83.74 277	82.85 276	86.42 312	89.96 325	71.21 338	89.55 302	87.88 350	77.41 297	83.37 265	87.31 332	56.71 337	93.65 339	80.62 226	92.85 170	94.40 224

无先验							93.28 219	96.26 116	73.95 329				99.05 63	80.56 227		96.59 138

1121		90.42 112	89.49 118	93.20 80	97.27 78	84.46 93	92.63 241	95.51 176	71.01 349	91.20 111	96.21 88	82.92 88	99.05 63	80.56 227	98.07 72	96.10 155

Fast-Effi-MVS+		89.41 139	88.64 140	91.71 154	94.74 169	80.81 194	93.54 206	95.10 202	83.11 208	86.82 176	90.67 280	79.74 123	97.75 178	80.51 229	93.55 152	96.57 139

CHOSEN 1792x2688		88.84 157	87.69 165	92.30 127	96.14 108	81.42 177	90.01 298	95.86 149	74.52 325	87.41 162	93.94 169	75.46 173	98.36 127	80.36 230	95.53 119	97.12 120

CDS-MVSNet		89.45 136	88.51 144	92.29 128	93.62 215	83.61 117	93.01 231	94.68 226	81.95 234	87.82 155	93.24 195	78.69 136	96.99 243	80.34 231	93.23 162	96.28 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		87.44 201	86.72 189	89.63 239	92.04 255	77.68 272	94.03 185	93.94 247	85.81 147	82.42 275	91.32 259	70.33 241	97.06 238	80.33 232	90.23 194	94.14 231

baseline2		86.50 235	85.39 237	89.84 229	91.12 289	76.70 286	91.88 262	88.58 348	82.35 225	79.95 308	90.95 272	73.42 204	97.63 187	80.27 233	89.95 199	95.19 186

API-MVS		90.66 106	90.07 108	92.45 118	96.36 103	84.57 86	96.06 59	95.22 197	82.39 222	89.13 134	94.27 158	80.32 115	98.46 119	80.16 234	96.71 103	94.33 225

MAR-MVS		90.30 113	89.37 123	93.07 87	96.61 92	84.48 92	95.68 76	95.67 162	82.36 224	87.85 153	92.85 207	76.63 158	98.80 101	80.01 235	96.68 104	95.91 162
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
HY-MVS	83.01 12	89.03 151	87.94 162	92.29 128	94.86 165	82.77 138	92.08 261	94.49 229	81.52 248	86.93 171	92.79 213	78.32 143	98.23 138	79.93 236	90.55 190	95.88 164

CHOSEN 280x420		85.15 259	83.99 261	88.65 264	92.47 244	78.40 251	79.68 361	92.76 272	74.90 322	81.41 287	89.59 300	69.85 247	95.51 312	79.92 237	95.29 127	92.03 312

MVS		87.44 201	86.10 216	91.44 163	92.61 243	83.62 116	92.63 241	95.66 164	67.26 355	81.47 285	92.15 230	77.95 145	98.22 140	79.71 238	95.48 121	92.47 302

pm-mvs1		86.61 230	85.54 233	89.82 230	91.44 273	80.18 206	95.28 97	94.85 217	83.84 191	81.66 284	92.62 216	72.45 217	96.48 271	79.67 239	78.06 330	92.82 294

IterMVS		84.88 264	83.98 262	87.60 287	91.44 273	76.03 295	90.18 296	92.41 279	83.24 207	81.06 292	90.42 285	66.60 282	94.28 331	79.46 240	80.98 307	92.48 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1112_ss		88.42 166	87.33 174	91.72 153	94.92 161	80.98 188	92.97 233	94.54 228	78.16 294	83.82 253	93.88 174	78.78 135	97.91 170	79.45 241	89.41 207	96.26 147

gm-plane-assit					89.60 331	68.00 353			77.28 300		88.99 306		97.57 190	79.44 242

PM-MVS		78.11 321	76.12 323	84.09 333	83.54 363	70.08 347	88.97 315	85.27 358	79.93 268	74.73 341	86.43 337	34.70 368	93.48 340	79.43 243	72.06 348	88.72 351

v7n		86.81 223	85.76 230	89.95 226	90.72 308	79.25 237	95.07 110	95.92 142	84.45 181	82.29 276	90.86 273	72.60 214	97.53 193	79.42 244	80.52 315	93.08 285

PAPR		90.02 119	89.27 128	92.29 128	95.78 125	80.95 190	92.68 239	96.22 121	81.91 236	86.66 178	93.75 182	82.23 97	98.44 123	79.40 245	94.79 132	97.48 105

新几何1				93.10 84	97.30 75	84.35 99		95.56 170	71.09 348	91.26 110	96.24 85	82.87 89	98.86 94	79.19 246	98.10 69	96.07 157

CP-MVSNet		87.63 191	87.26 178	88.74 262	93.12 228	76.59 288	95.29 95	96.58 98	88.43 81	83.49 263	92.98 204	75.28 174	95.83 301	78.97 247	81.15 300	93.79 251

pmmvs4		85.43 252	83.86 263	90.16 214	90.02 324	82.97 135	90.27 290	92.67 275	75.93 311	80.73 294	91.74 247	71.05 227	95.73 306	78.85 248	83.46 272	91.78 315

DWT-MVSNet_test		84.95 263	83.68 265	88.77 258	91.43 276	73.75 313	91.74 267	90.98 318	80.66 261	83.84 252	87.36 331	62.44 307	97.11 233	78.84 249	85.81 250	95.46 178

Test_1112_low_res		87.65 188	86.51 200	91.08 177	94.94 160	79.28 235	91.77 265	94.30 236	76.04 310	83.51 262	92.37 222	77.86 148	97.73 179	78.69 250	89.13 214	96.22 148

Vis-MVSNet (Re-imp)		89.59 131	89.44 120	90.03 221	95.74 126	75.85 297	95.61 81	90.80 324	87.66 111	87.83 154	95.40 117	76.79 154	96.46 274	78.37 251	96.73 102	97.80 93

PS-CasMVS		87.32 205	86.88 183	88.63 265	92.99 236	76.33 293	95.33 89	96.61 96	88.22 90	83.30 268	93.07 202	73.03 209	95.79 304	78.36 252	81.00 306	93.75 257

testdata2													98.75 103	78.30 253

GBi-Net		87.26 206	85.98 220	91.08 177	94.01 198	83.10 128	95.14 107	94.94 208	83.57 196	84.37 236	91.64 248	66.59 283	96.34 281	78.23 254	85.36 253	93.79 251

test1		87.26 206	85.98 220	91.08 177	94.01 198	83.10 128	95.14 107	94.94 208	83.57 196	84.37 236	91.64 248	66.59 283	96.34 281	78.23 254	85.36 253	93.79 251

FMVSNet3		87.40 203	86.11 215	91.30 168	93.79 211	83.64 115	94.20 171	94.81 221	83.89 190	84.37 236	91.87 244	68.45 267	96.56 266	78.23 254	85.36 253	93.70 260

OpenMVS	83.78 11	88.74 160	87.29 175	93.08 85	92.70 241	85.39 76	96.57 33	96.43 106	78.74 285	80.85 293	96.07 96	69.64 249	99.01 73	78.01 257	96.65 105	94.83 201

tpm		84.73 266	84.02 260	86.87 308	90.33 317	68.90 351	89.06 313	89.94 338	80.85 259	85.75 193	89.86 296	68.54 266	95.97 294	77.76 258	84.05 264	95.75 170

TAMVS		89.21 144	88.29 153	91.96 140	93.71 212	82.62 148	93.30 217	94.19 240	82.22 226	87.78 156	93.94 169	78.83 133	96.95 245	77.70 259	92.98 167	96.32 144

BH-untuned		88.60 164	88.13 157	90.01 224	95.24 147	78.50 248	93.29 218	94.15 242	84.75 176	84.46 233	93.40 187	75.76 167	97.40 210	77.59 260	94.52 139	94.12 232

FMVSNet2		87.19 214	85.82 226	91.30 168	94.01 198	83.67 114	94.79 129	94.94 208	83.57 196	83.88 251	92.05 239	66.59 283	96.51 269	77.56 261	85.01 256	93.73 258

RPSCF		85.07 260	84.27 257	87.48 292	92.91 238	70.62 344	91.69 270	92.46 278	76.20 309	82.67 274	95.22 121	63.94 300	97.29 219	77.51 262	85.80 251	94.53 215

PLC	84.53 7	89.06 150	88.03 158	92.15 131	97.27 78	82.69 145	94.29 165	95.44 184	79.71 271	84.01 249	94.18 160	76.68 157	98.75 103	77.28 263	93.41 157	95.02 190
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		89.07 149	87.98 160	92.34 124	96.87 85	84.78 82	94.08 180	93.24 262	81.41 249	84.46 233	95.13 125	75.57 172	96.62 258	77.21 264	93.84 148	95.61 175

K. test v3		81.59 295	80.15 297	85.91 318	89.89 327	69.42 350	92.57 244	87.71 352	85.56 155	73.44 347	89.71 299	55.58 339	95.52 311	77.17 265	69.76 350	92.78 295

QAPM		89.51 133	88.15 156	93.59 75	94.92 161	84.58 85	96.82 28	96.70 88	78.43 289	83.41 264	96.19 92	73.18 207	99.30 42	77.11 266	96.54 108	96.89 130

pmmvs5		84.21 271	82.84 277	88.34 272	88.95 334	76.94 283	92.41 247	91.91 297	75.63 313	80.28 301	91.18 264	64.59 297	95.57 308	77.09 267	83.47 271	92.53 300

pmmvs6		83.42 279	81.60 283	88.87 257	88.01 346	77.87 265	94.96 116	94.24 239	74.67 324	78.80 316	91.09 269	60.17 325	96.49 270	77.06 268	75.40 342	92.23 310

test_post1							88.00 326				9.81 375	69.31 255	95.53 310	76.65 269

SCA		86.32 239	85.18 241	89.73 236	92.15 250	76.60 287	91.12 279	91.69 300	83.53 199	85.50 204	88.81 309	66.79 279	96.48 271	76.65 269	90.35 193	96.12 152

WR-MVS_H		87.80 183	87.37 173	89.10 252	93.23 225	78.12 258	95.61 81	97.30 29	87.90 99	83.72 255	92.01 240	79.65 128	96.01 293	76.36 271	80.54 312	93.16 281

EU-MVSNet		81.32 300	80.95 288	82.42 338	88.50 338	63.67 364	93.32 213	91.33 309	64.02 359	80.57 298	92.83 209	61.21 318	92.27 351	76.34 272	80.38 317	91.32 323

CMPMVS	59.16 21	80.52 306	79.20 308	84.48 328	83.98 361	67.63 357	89.95 300	93.84 254	64.79 358	66.81 359	91.14 267	57.93 335	95.17 319	76.25 273	88.10 229	90.65 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
F-COLMAP		87.95 179	86.80 187	91.40 164	96.35 104	80.88 192	94.73 133	95.45 182	79.65 272	82.04 281	94.61 144	71.13 226	98.50 116	76.24 274	91.05 187	94.80 203

PEN-MVS		86.80 224	86.27 210	88.40 269	92.32 248	75.71 299	95.18 104	96.38 110	87.97 96	82.82 272	93.15 198	73.39 205	95.92 296	76.15 275	79.03 329	93.59 262

SixPastTwentyTwo		83.91 275	82.90 275	86.92 305	90.99 293	70.67 343	93.48 208	91.99 292	85.54 156	77.62 324	92.11 234	60.59 322	96.87 250	76.05 276	77.75 332	93.20 279

MVS_0304		83.46 278	81.92 281	88.10 279	90.63 311	77.49 276	93.26 220	93.75 256	80.04 267	80.44 300	87.24 334	47.94 360	95.55 309	75.79 277	88.16 228	91.26 325

MS-PatchMatch		85.05 261	84.16 258	87.73 285	91.42 277	78.51 247	91.25 277	93.53 258	77.50 296	80.15 303	91.58 253	61.99 310	95.51 312	75.69 278	94.35 143	89.16 348

BH-w/o		87.57 196	87.05 181	89.12 251	94.90 163	77.90 263	92.41 247	93.51 259	82.89 215	83.70 256	91.34 256	75.75 168	97.07 237	75.49 279	93.49 154	92.39 305

gg-mvs-nofinetune		81.77 291	79.37 304	88.99 256	90.85 303	77.73 271	86.29 338	79.63 370	74.88 323	83.19 269	69.05 364	60.34 323	96.11 289	75.46 280	94.64 136	93.11 283

FMVSNet1		85.85 246	84.11 259	91.08 177	92.81 239	83.10 128	95.14 107	94.94 208	81.64 244	82.68 273	91.64 248	59.01 332	96.34 281	75.37 281	83.78 265	93.79 251

EPMVS		83.90 276	82.70 278	87.51 289	90.23 320	72.67 325	88.62 320	81.96 366	81.37 250	85.01 223	88.34 317	66.31 286	94.45 326	75.30 282	87.12 243	95.43 180

pmmvs-eth3d		80.97 304	78.72 313	87.74 284	84.99 360	79.97 218	90.11 297	91.65 301	75.36 315	73.51 346	86.03 340	59.45 329	93.96 336	75.17 283	72.21 347	89.29 346

tpm2		84.08 272	82.94 274	87.48 292	91.39 278	71.27 336	89.23 310	90.37 329	71.95 344	84.64 227	89.33 303	67.30 270	96.55 268	75.17 283	87.09 244	94.63 207

lessismore_v0				86.04 314	88.46 339	68.78 352		80.59 368		73.01 349	90.11 290	55.39 341	96.43 276	75.06 285	65.06 358	92.90 290

MVP-Stereo		85.97 243	84.86 249	89.32 246	90.92 299	82.19 156	92.11 259	94.19 240	78.76 284	78.77 317	91.63 251	68.38 268	96.56 266	75.01 286	93.95 145	89.20 347
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet	78.82 18	85.55 250	84.65 253	88.23 276	94.72 171	71.93 332	87.12 334	92.75 273	78.80 283	84.95 224	90.53 282	64.43 298	96.71 255	74.74 287	93.86 147	96.06 158

MDTV_nov1_ep13_2view						55.91 372	87.62 332		73.32 334	84.59 229		70.33 241		74.65 288		95.50 176

PatchmatchNet		85.85 246	84.70 252	89.29 247	91.76 265	75.54 300	88.49 321	91.30 310	81.63 245	85.05 222	88.70 313	71.71 219	96.24 284	74.61 289	89.05 215	96.08 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LF4IMVS		80.37 308	79.07 311	84.27 331	86.64 350	69.87 349	89.39 307	91.05 316	76.38 305	74.97 340	90.00 293	47.85 361	94.25 332	74.55 290	80.82 309	88.69 352

DTE-MVSNet		86.11 241	85.48 235	87.98 281	91.65 271	74.92 303	94.93 119	95.75 157	87.36 116	82.26 277	93.04 203	72.85 210	95.82 302	74.04 291	77.46 335	93.20 279

BH-RMVSNet		88.37 168	87.48 170	91.02 181	95.28 144	79.45 227	92.89 235	93.07 266	85.45 159	86.91 173	94.84 136	70.35 240	97.76 175	73.97 292	94.59 137	95.85 165

CR-MVSNet		85.35 254	83.76 264	90.12 217	90.58 312	79.34 231	85.24 344	91.96 295	78.27 291	85.55 199	87.87 326	71.03 228	95.61 307	73.96 293	89.36 209	95.40 181

ACMH+	81.04 14	85.05 261	83.46 269	89.82 230	94.66 175	79.37 229	94.44 152	94.12 245	82.19 227	78.04 320	92.82 210	58.23 334	97.54 192	73.77 294	82.90 279	92.54 299

TR-MVS		86.78 225	85.76 230	89.82 230	94.37 187	78.41 250	92.47 246	92.83 270	81.11 257	86.36 184	92.40 221	68.73 264	97.48 196	73.75 295	89.85 202	93.57 263

UnsupCasMVSNet_eth		80.07 310	78.27 314	85.46 320	85.24 359	72.63 327	88.45 323	94.87 216	82.99 212	71.64 354	88.07 322	56.34 338	91.75 355	73.48 296	63.36 361	92.01 313

PatchMatch-RL		86.77 228	85.54 233	90.47 203	95.88 122	82.71 144	90.54 287	92.31 282	79.82 270	84.32 241	91.57 255	68.77 263	96.39 277	73.16 297	93.48 156	92.32 308

ambc				83.06 336	79.99 367	63.51 365	77.47 362	92.86 269		74.34 344	84.45 347	28.74 369	95.06 323	73.06 298	68.89 355	90.61 335

KD-MVS_self_test		80.20 309	79.24 306	83.07 335	85.64 357	65.29 362	91.01 281	93.93 248	78.71 286	76.32 331	86.40 338	59.20 331	92.93 347	72.59 299	69.35 351	91.00 333

ITE_SJBPF				88.24 275	91.88 261	77.05 282		92.92 268	85.54 156	80.13 305	93.30 192	57.29 336	96.20 285	72.46 300	84.71 258	91.49 320

ACMH	80.38 17	85.36 253	83.68 265	90.39 205	94.45 184	80.63 198	94.73 133	94.85 217	82.09 228	77.24 325	92.65 215	60.01 326	97.58 189	72.25 301	84.87 257	92.96 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
USDC		82.76 282	81.26 287	87.26 296	91.17 286	74.55 305	89.27 308	93.39 261	78.26 292	75.30 338	92.08 236	54.43 347	96.63 257	71.64 302	85.79 252	90.61 335

EPNet_dtu		86.49 237	85.94 223	88.14 278	90.24 319	72.82 323	94.11 176	92.20 285	86.66 133	79.42 314	92.36 223	73.52 200	95.81 303	71.26 303	93.66 149	95.80 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				87.94 283	89.73 329	77.91 262	87.80 327	78.23 372		80.58 297	83.86 348	59.88 327	95.33 318	71.20 304	92.22 177	90.60 337

LTVRE_ROB	82.13 13	86.26 240	84.90 248	90.34 210	94.44 185	81.50 171	92.31 253	94.89 214	83.03 210	79.63 312	92.67 214	69.69 248	97.79 173	71.20 304	86.26 248	91.72 316
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
JIA-IIPM		81.04 302	78.98 312	87.25 297	88.64 336	73.48 317	81.75 358	89.61 345	73.19 335	82.05 280	73.71 361	66.07 290	95.87 299	71.18 306	84.60 259	92.41 304

Anonymous20240521		80.44 307	79.21 307	84.11 332	85.75 356	67.89 354	92.86 236	93.23 263	75.61 314	75.59 337	87.47 330	50.03 355	94.33 329	71.14 307	81.21 297	90.12 340

TransMVSNet (Re)		84.43 270	83.06 273	88.54 266	91.72 266	78.44 249	95.18 104	92.82 271	82.73 217	79.67 311	92.12 232	73.49 201	95.96 295	71.10 308	68.73 356	91.21 327

PCF-MVS	84.11 10	87.74 185	86.08 217	92.70 106	94.02 197	84.43 97	89.27 308	95.87 148	73.62 332	84.43 235	94.33 152	78.48 141	98.86 94	70.27 309	94.45 141	94.81 202
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		82.37 287	80.34 293	88.46 268	90.27 318	79.35 230	92.80 238	94.33 235	77.14 301	73.26 348	90.18 288	47.47 362	96.72 253	70.25 310	87.32 242	89.30 345

MDTV_nov1_ep13			83.56 268		91.69 269	69.93 348	87.75 329	91.54 304	78.60 287	84.86 225	88.90 308	69.54 250	96.03 291	70.25 310	88.93 216

TDRefinement		79.81 312	77.34 316	87.22 300	79.24 368	75.48 301	93.12 225	92.03 290	76.45 304	75.01 339	91.58 253	49.19 358	96.44 275	70.22 312	69.18 353	89.75 342

thres100view900		87.63 191	86.71 190	90.38 207	96.12 109	78.55 245	95.03 114	91.58 302	87.15 118	88.06 149	92.29 226	68.91 261	98.10 146	70.13 313	91.10 183	94.48 221

tfpn200view9		87.58 195	86.64 193	90.41 204	95.99 118	78.64 243	94.58 141	91.98 293	86.94 125	88.09 146	91.77 245	69.18 258	98.10 146	70.13 313	91.10 183	94.48 221

thres400		87.62 193	86.64 193	90.57 194	95.99 118	78.64 243	94.58 141	91.98 293	86.94 125	88.09 146	91.77 245	69.18 258	98.10 146	70.13 313	91.10 183	94.96 194

thres600view7		87.65 188	86.67 192	90.59 193	96.08 113	78.72 241	94.88 123	91.58 302	87.06 121	88.08 148	92.30 225	68.91 261	98.10 146	70.05 316	91.10 183	94.96 194

thres200		87.21 212	86.24 211	90.12 217	95.36 140	78.53 246	93.26 220	92.10 287	86.42 137	88.00 151	91.11 268	69.24 257	98.00 163	69.58 317	91.04 188	93.83 250

tpm cat1		81.96 288	80.27 294	87.01 303	91.09 290	71.02 340	87.38 333	91.53 305	66.25 356	80.17 302	86.35 339	68.22 269	96.15 288	69.16 318	82.29 283	93.86 248

Patchmtry		82.71 283	80.93 289	88.06 280	90.05 323	76.37 292	84.74 348	91.96 295	72.28 343	81.32 289	87.87 326	71.03 228	95.50 314	68.97 319	80.15 318	92.32 308

our_test_3		81.93 289	80.46 292	86.33 313	88.46 339	73.48 317	88.46 322	91.11 313	76.46 303	76.69 329	88.25 319	66.89 277	94.36 328	68.75 320	79.08 328	91.14 329

PVSNet_0	73.20 20	77.22 322	74.83 327	84.37 329	90.70 309	71.10 339	83.09 355	89.67 344	72.81 340	73.93 345	83.13 352	60.79 320	93.70 338	68.54 321	50.84 367	88.30 355

MSDG		84.86 265	83.09 272	90.14 216	93.80 209	80.05 213	89.18 311	93.09 265	78.89 280	78.19 318	91.91 242	65.86 292	97.27 220	68.47 322	88.45 223	93.11 283

LS3D		87.89 180	86.32 207	92.59 111	96.07 114	82.92 136	95.23 99	94.92 213	75.66 312	82.89 271	95.98 98	72.48 215	99.21 50	68.43 323	95.23 130	95.64 174

AllTest		83.42 279	81.39 285	89.52 242	95.01 154	77.79 268	93.12 225	90.89 322	77.41 297	76.12 333	93.34 188	54.08 348	97.51 194	68.31 324	84.27 262	93.26 273

TestCases				89.52 242	95.01 154	77.79 268		90.89 322	77.41 297	76.12 333	93.34 188	54.08 348	97.51 194	68.31 324	84.27 262	93.26 273

dp		81.47 298	80.23 295	85.17 324	89.92 326	65.49 361	86.74 335	90.10 334	76.30 307	81.10 290	87.12 336	62.81 305	95.92 296	68.13 326	79.88 321	94.09 235

tpmvs		83.35 281	82.07 279	87.20 301	91.07 291	71.00 341	88.31 324	91.70 299	78.91 279	80.49 299	87.18 335	69.30 256	97.08 236	68.12 327	83.56 270	93.51 267

FMVSNet5		81.52 297	79.60 303	87.27 295	91.17 286	77.95 261	91.49 273	92.26 284	76.87 302	76.16 332	87.91 325	51.67 353	92.34 350	67.74 328	81.16 298	91.52 319

KD-MVS_2432*1600		78.50 319	76.02 324	85.93 316	86.22 352	74.47 306	84.80 346	92.33 280	79.29 274	76.98 327	85.92 341	53.81 350	93.97 334	67.39 329	57.42 364	89.36 343

miper_refine_blended		78.50 319	76.02 324	85.93 316	86.22 352	74.47 306	84.80 346	92.33 280	79.29 274	76.98 327	85.92 341	53.81 350	93.97 334	67.39 329	57.42 364	89.36 343

CL-MVSNet_self_test		81.74 292	80.53 290	85.36 321	85.96 354	72.45 330	90.25 291	93.07 266	81.24 254	79.85 310	87.29 333	70.93 230	92.52 349	66.95 331	69.23 352	91.11 331

YYNet1		79.22 316	77.20 318	85.28 323	88.20 345	72.66 326	85.87 340	90.05 337	74.33 327	62.70 361	87.61 328	66.09 289	92.03 352	66.94 332	72.97 345	91.15 328

PAPM		86.68 229	85.39 237	90.53 196	93.05 231	79.33 234	89.79 301	94.77 224	78.82 282	81.95 282	93.24 195	76.81 153	97.30 216	66.94 332	93.16 163	94.95 197

DP-MVS		87.25 208	85.36 239	92.90 94	97.65 62	83.24 125	94.81 128	92.00 291	74.99 320	81.92 283	95.00 128	72.66 212	99.05 63	66.92 334	92.33 176	96.40 142

MDA-MVSNet_test_wron		79.21 317	77.19 319	85.29 322	88.22 343	72.77 324	85.87 340	90.06 335	74.34 326	62.62 362	87.56 329	66.14 288	91.99 353	66.90 335	73.01 344	91.10 332

UnsupCasMVSNet_bld		76.23 325	73.27 328	85.09 325	83.79 362	72.92 321	85.65 343	93.47 260	71.52 345	68.84 357	79.08 358	49.77 356	93.21 343	66.81 336	60.52 363	89.13 350

MIMVSNet		82.59 285	80.53 290	88.76 259	91.51 272	78.32 253	86.57 337	90.13 333	79.32 273	80.70 295	88.69 314	52.98 352	93.07 346	66.03 337	88.86 217	94.90 198

LCM-MVSNet		66.00 330	62.16 334	77.51 345	64.51 375	58.29 367	83.87 352	90.90 321	48.17 366	54.69 364	73.31 362	16.83 378	86.75 364	65.47 338	61.67 362	87.48 357

PatchT		82.68 284	81.27 286	86.89 307	90.09 322	70.94 342	84.06 350	90.15 332	74.91 321	85.63 198	83.57 350	69.37 252	94.87 325	65.19 339	88.50 222	94.84 200

test0.0.03 1		82.41 286	81.69 282	84.59 327	88.23 342	72.89 322	90.24 292	87.83 351	83.41 202	79.86 309	89.78 298	67.25 271	88.99 362	65.18 340	83.42 273	91.90 314

ppachtmachnet_test		81.84 290	80.07 298	87.15 302	88.46 339	74.43 308	89.04 314	92.16 286	75.33 316	77.75 322	88.99 306	66.20 287	95.37 317	65.12 341	77.60 333	91.65 317

COLMAP_ROB	80.39 16	83.96 273	82.04 280	89.74 234	95.28 144	79.75 222	94.25 167	92.28 283	75.17 318	78.02 321	93.77 180	58.60 333	97.84 172	65.06 342	85.92 249	91.63 318
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		81.66 294	79.71 302	87.50 290	91.35 280	74.19 310	83.33 353	88.48 349	72.90 338	82.24 278	85.77 343	64.98 295	93.20 344	64.57 343	83.74 266	95.12 187

ADS-MVSNet		81.56 296	79.78 300	86.90 306	91.35 280	71.82 333	83.33 353	89.16 347	72.90 338	82.24 278	85.77 343	64.98 295	93.76 337	64.57 343	83.74 266	95.12 187

new-patchmatchnet		76.41 324	75.17 326	80.13 340	82.65 366	59.61 366	87.66 331	91.08 314	78.23 293	69.85 355	83.22 351	54.76 344	91.63 357	64.14 345	64.89 359	89.16 348

testgi		80.94 305	80.20 296	83.18 334	87.96 347	66.29 358	91.28 275	90.70 326	83.70 193	78.12 319	92.84 208	51.37 354	90.82 358	63.34 346	82.46 282	92.43 303

TinyColmap		79.76 313	77.69 315	85.97 315	91.71 267	73.12 320	89.55 302	90.36 330	75.03 319	72.03 352	90.19 287	46.22 363	96.19 287	63.11 347	81.03 303	88.59 353

pmmvs3		71.81 328	68.71 331	81.11 339	75.86 369	70.42 345	86.74 335	83.66 362	58.95 362	68.64 358	80.89 356	36.93 367	89.52 361	63.10 348	63.59 360	83.39 358

TAPA-MVS	84.62 6	88.16 174	87.01 182	91.62 156	96.64 91	80.65 197	94.39 157	96.21 124	76.38 305	86.19 188	95.44 114	79.75 122	98.08 156	62.75 349	95.29 127	96.13 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet-bldmvs		78.85 318	76.31 321	86.46 310	89.76 328	73.88 312	88.79 317	90.42 327	79.16 277	59.18 363	88.33 318	60.20 324	94.04 333	62.00 350	68.96 354	91.48 321

tfpnnormal		84.72 267	83.23 271	89.20 249	92.79 240	80.05 213	94.48 147	95.81 152	82.38 223	81.08 291	91.21 261	69.01 260	96.95 245	61.69 351	80.59 311	90.58 338

Anonymous20231206		81.03 303	79.77 301	84.82 326	87.85 348	70.26 346	91.42 274	92.08 288	73.67 331	77.75 322	89.25 304	62.43 308	93.08 345	61.50 352	82.00 289	91.12 330

RPMNet		83.95 274	81.53 284	91.21 170	90.58 312	79.34 231	85.24 344	96.76 79	71.44 346	85.55 199	82.97 353	70.87 231	98.91 89	61.01 353	89.36 209	95.40 181

MIMVSNet1		79.38 315	77.28 317	85.69 319	86.35 351	73.67 314	91.61 272	92.75 273	78.11 295	72.64 350	88.12 321	48.16 359	91.97 354	60.32 354	77.49 334	91.43 322

test20.03		79.95 311	79.08 310	82.55 337	85.79 355	67.74 356	91.09 280	91.08 314	81.23 255	74.48 343	89.96 295	61.63 312	90.15 359	60.08 355	76.38 339	89.76 341

DSMNet-mixed		76.94 323	76.29 322	78.89 341	83.10 364	56.11 371	87.78 328	79.77 369	60.65 361	75.64 336	88.71 312	61.56 313	88.34 363	60.07 356	89.29 211	92.21 311

Patchmatch-test		81.37 299	79.30 305	87.58 288	90.92 299	74.16 311	80.99 359	87.68 353	70.52 350	76.63 330	88.81 309	71.21 225	92.76 348	60.01 357	86.93 246	95.83 167

MVS-HIRNet		73.70 326	72.20 329	78.18 344	91.81 264	56.42 370	82.94 356	82.58 364	55.24 363	68.88 356	66.48 365	55.32 342	95.13 320	58.12 358	88.42 224	83.01 359

OpenMVS_ROB	74.94 19	79.51 314	77.03 320	86.93 304	87.00 349	76.23 294	92.33 251	90.74 325	68.93 353	74.52 342	88.23 320	49.58 357	96.62 258	57.64 359	84.29 261	87.94 356

new_pmnet		72.15 327	70.13 330	78.20 343	82.95 365	65.68 359	83.91 351	82.40 365	62.94 360	64.47 360	79.82 357	42.85 365	86.26 365	57.41 360	74.44 343	82.65 361

N_pmnet		68.89 329	68.44 332	70.23 348	89.07 333	28.79 380	88.06 325	19.50 381	69.47 352	71.86 353	84.93 345	61.24 317	91.75 355	54.70 361	77.15 336	90.15 339

test_method		50.52 337	48.47 339	56.66 353	52.26 379	18.98 382	41.51 371	81.40 367	10.10 373	44.59 368	75.01 360	28.51 370	68.16 371	53.54 362	49.31 368	82.83 360

tmp_tt		35.64 341	39.24 343	24.84 357	14.87 381	23.90 381	62.71 367	51.51 380	6.58 375	36.66 371	62.08 368	44.37 364	30.34 377	52.40 363	22.00 374	20.27 372

test_0402		81.30 301	79.17 309	87.67 286	93.19 226	78.17 257	92.98 232	91.71 298	75.25 317	76.02 335	90.31 286	59.23 330	96.37 278	50.22 364	83.63 269	88.47 354

PMMVS2		59.60 333	56.40 335	69.21 349	68.83 372	46.58 375	73.02 366	77.48 373	55.07 364	49.21 366	72.95 363	17.43 377	80.04 369	49.32 365	44.33 369	80.99 363

EGC-MVSNET		61.97 332	56.37 336	78.77 342	89.63 330	73.50 316	89.12 312	82.79 363	0.21 378	1.24 379	84.80 346	39.48 366	90.04 360	44.13 366	75.94 341	72.79 365

ANet_high		58.88 334	54.22 338	72.86 346	56.50 378	56.67 369	80.75 360	86.00 355	73.09 337	37.39 370	64.63 367	22.17 373	79.49 370	43.51 367	23.96 372	82.43 362

DeepMVS_CX				56.31 354	74.23 370	51.81 373		56.67 379	44.85 367	48.54 367	75.16 359	27.87 371	58.74 375	40.92 368	52.22 366	58.39 368

FPMVS		64.63 331	62.55 333	70.88 347	70.80 371	56.71 368	84.42 349	84.42 360	51.78 365	49.57 365	81.61 355	23.49 372	81.48 368	40.61 369	76.25 340	74.46 364

Gipuma		57.99 335	54.91 337	67.24 350	88.51 337	65.59 360	52.21 369	90.33 331	43.58 368	42.84 369	51.18 370	20.29 375	85.07 366	34.77 370	70.45 349	51.05 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 336	48.46 340	63.48 351	45.72 380	46.20 376	73.41 365	78.31 371	41.03 369	30.06 372	65.68 366	6.05 379	83.43 367	30.04 371	65.86 357	60.80 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 338	38.59 344	57.77 352	56.52 377	48.77 374	55.38 368	58.64 378	29.33 372	28.96 373	52.65 369	4.68 380	64.62 374	28.11 372	33.07 370	59.93 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 339	42.29 341	46.03 355	65.58 374	37.41 377	73.51 364	64.62 375	33.99 370	28.47 374	47.87 371	19.90 376	67.91 372	22.23 373	24.45 371	32.77 370

EMVS		42.07 340	41.12 342	44.92 356	63.45 376	35.56 379	73.65 363	63.48 376	33.05 371	26.88 375	45.45 372	21.27 374	67.14 373	19.80 374	23.02 373	32.06 371

wuyk23d		21.27 343	20.48 346	23.63 358	68.59 373	36.41 378	49.57 370	6.85 382	9.37 374	7.89 376	4.46 378	4.03 381	31.37 376	17.47 375	16.07 375	3.12 373

testmvs		8.92 344	11.52 347	1.12 360	1.06 382	0.46 384	86.02 339	0.65 383	0.62 376	2.74 377	9.52 376	0.31 383	0.45 379	2.38 376	0.39 376	2.46 375

test123		8.76 345	11.22 348	1.39 359	0.85 383	0.97 383	85.76 342	0.35 384	0.54 377	2.45 378	8.14 377	0.60 382	0.48 378	2.16 377	0.17 377	2.71 374

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

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

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

cdsmvs_eth3d_5k		22.14 342	29.52 345	0.00 361	0.00 384	0.00 385	0.00 372	95.76 156	0.00 379	0.00 380	94.29 155	75.66 171	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.64 347	8.86 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	79.70 124	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		7.82 346	10.43 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	93.88 174	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					98.86 1	85.54 74	98.29 1	97.49 5	89.79 45	96.29 15

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

eth-test2					0.00 384

eth-test					0.00 384

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

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

test0726					98.78 3	85.93 61	97.19 10	97.47 10	90.27 33	97.64 4	98.13 1	91.47 8

GSMVS																96.12 152

test_part2					98.55 13	87.22 18				96.40 14

sam_mvs1												71.70 220				96.12 152

sam_mvs												70.60 234

MTGPA								96.97 53

test_post											10.29 374	70.57 238	95.91 298

patchmatchnet-post											83.76 349	71.53 222	96.48 271

MTMP							96.16 49	60.64 377

TEST9					97.53 65	86.49 41	94.07 181	96.78 76	81.61 246	92.77 74	96.20 89	87.71 31	99.12 58

test_8					97.49 68	86.30 50	94.02 186	96.76 79	81.86 239	92.70 78	96.20 89	87.63 32	99.02 71

agg_prior					97.38 71	85.92 63		96.72 85		92.16 89			98.97 83

test_prior4						85.96 60	94.11 176

test_prior				93.82 67	97.29 76	84.49 90		96.88 64					98.87 91			98.11 72

新几何2							93.11 227

旧先验1					96.79 87	81.81 165		95.67 162			96.81 58	86.69 43			97.66 85	96.97 126

原ACMM2							92.94 234

test222					96.55 97	81.70 167	92.22 255	95.01 205	68.36 354	90.20 121	96.14 94	80.26 117			97.80 81	96.05 159

segment_acmp												87.16 40

testdata1							92.15 257		87.94 97

test12				94.34 56	97.13 81	86.15 53		96.29 113		91.04 113		85.08 64	99.01 73		98.13 68	97.86 90

plane_prior7					94.70 173	82.74 141

plane_prior6					94.52 179	82.75 139						74.23 187

plane_prior4											94.86 133

plane_prior3						82.75 139			90.26 35	86.91 173

plane_prior2							95.85 68		90.81 20

plane_prior1					94.59 177

plane_prior						82.73 142	95.21 101		89.66 49						89.88 201

n2								0.00 385

nn								0.00 385

door-mid								85.49 356

test11								96.57 99

door								85.33 357

HQP5-MVS						81.56 169

HQP-NCC					94.17 192		94.39 157		88.81 69	85.43 211

ACMP_Plane					94.17 192		94.39 157		88.81 69	85.43 211

HQP4-MVS										85.43 211			97.96 166			94.51 217

HQP3-MVS								96.04 135							89.77 203

HQP2-MVS												73.83 197

NP-MVS					94.37 187	82.42 151					93.98 167

ACMMP++_ref															87.47 237

ACMMP++															88.01 232

Test By Simon												80.02 119

High-resolution multi-view benchmark