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 68					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 68					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 6	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 57	97.13 14	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 55	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 60	97.09 16	96.73 84	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 16	97.49 5					99.61 3	95.62 8	99.08 7	98.99 8

IU-MVS					98.77 5	86.00 55		96.84 70	81.26 256	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 98	96.96 56	92.09 4	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 25	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 46	97.28 31	85.90 150	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 86	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 57	96.62 96	88.14 95	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 50	97.11 46	90.42 30	96.95 12	97.27 29	89.53 14	96.91 250	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 42	94.85 29	98.17 36	86.65 35	94.82 132	97.17 42	86.26 143	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 7	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 49	94.32 22	92.01 133	97.54 64	78.37 257	93.40 214	97.19 38	88.02 97	94.99 28	97.21 34	88.35 21	98.44 125	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	80.00 220	94.00 191	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 79	97.34 22	88.28 88	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 71	95.21 104	95.47 177	89.44 52	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 30

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 52	87.76 10	96.65 34	97.48 9	87.76 108	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	82.43 152	97.27 9	96.71 88	90.57 29	92.88 69	95.80 105	83.16 84	98.16 145	93.68 24	98.14 67	97.31 109

CS-MVS		94.12 37	94.44 19	93.17 83	96.55 96	83.08 131	97.63 3	96.95 58	91.71 10	93.50 57	96.21 87	85.61 56	98.24 139	93.64 25	98.17 65	98.19 62

dcpmvs_2		93.49 56	94.19 33	91.38 167	97.69 61	76.78 290	94.25 170	96.29 113	88.33 84	94.46 30	96.88 53	88.07 26	98.64 107	93.62 26	98.09 70	98.73 16

Regformer-2		94.33 28	94.22 29	94.68 41	95.54 135	86.75 31	94.57 148	96.70 89	91.84 7	94.41 31	96.56 75	87.19 39	99.13 57	93.50 27	97.65 86	98.16 65

MCST-MVS		94.45 21	94.20 32	95.19 11	98.46 20	87.50 15	95.00 120	97.12 44	87.13 122	92.51 84	96.30 82	89.24 17	99.34 36	93.46 28	98.62 48	98.73 16

zzz-MVS		94.47 19	94.30 24	95.00 18	98.42 22	86.95 20	95.06 118	96.97 53	91.07 15	93.14 63	97.56 16	84.30 74	99.56 10	93.43 29	98.75 31	98.47 34

MTAPA		94.42 25	94.22 29	95.00 18	98.42 22	86.95 20	94.36 167	96.97 53	91.07 15	93.14 63	97.56 16	84.30 74	99.56 10	93.43 29	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 68	96.96 56	91.75 8	94.02 41	96.83 56	88.12 25	99.55 15	93.41 31	98.94 16	98.28 54

SR-MVS		94.23 32	94.17 35	94.43 53	98.21 35	85.78 68	96.40 40	96.90 63	88.20 93	94.33 33	97.40 23	84.75 71	99.03 67	93.35 32	97.99 74	98.48 30

Regformer-1		94.22 33	94.13 37	94.51 49	95.54 135	86.36 46	94.57 148	96.44 104	91.69 11	94.32 34	96.56 75	87.05 41	99.03 67	93.35 32	97.65 86	98.15 66

test1172		93.97 42	94.07 39	93.66 74	98.11 39	83.45 119	96.26 46	96.84 70	88.33 84	94.19 36	97.43 20	84.24 76	99.01 73	93.26 34	97.98 75	98.52 26

9.14			94.47 18		97.79 54		96.08 58	97.44 14	86.13 148	95.10 26	97.40 23	88.34 22	99.22 49	93.25 35	98.70 36

Regformer-4		93.91 44	93.81 46	94.19 60	95.36 140	85.47 74	94.68 140	96.41 107	91.60 12	93.75 46	96.71 61	85.95 53	99.10 60	93.21 36	96.65 105	98.01 80

CANet		93.54 55	93.20 62	94.55 47	95.65 130	85.73 70	94.94 123	96.69 91	91.89 6	90.69 115	95.88 102	81.99 104	99.54 19	93.14 37	97.95 77	98.39 42

ETH3D-3000-0.1		94.61 17	94.44 19	95.12 13	97.70 60	87.71 11	95.98 65	97.44 14	86.67 135	95.25 25	97.31 27	87.73 30	99.24 47	93.11 38	98.76 30	98.40 41

CS-MVS-test		94.02 39	94.29 25	93.24 79	96.69 90	83.24 124	97.49 5	96.92 61	92.14 3	92.90 68	95.77 107	85.02 66	98.33 134	93.03 39	98.62 48	98.13 68

Regformer-3		93.68 51	93.64 54	93.81 70	95.36 140	84.61 83	94.68 140	95.83 150	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 81	96.93 60	92.34 2	93.94 42	96.58 73	87.74 29	99.44 30	92.83 41	98.40 58	98.62 22

SR-MVS-dyc-post		93.82 47	93.82 45	93.82 67	97.92 47	84.57 85	96.28 44	96.76 80	87.46 115	93.75 46	97.43 20	84.24 76	99.01 73	92.73 42	97.80 81	97.88 88

RE-MVS-def			93.68 52		97.92 47	84.57 85	96.28 44	96.76 80	87.46 115	93.75 46	97.43 20	82.94 87		92.73 42	97.80 81	97.88 88

TSAR-MVS + GP.		93.66 52	93.41 57	94.41 55	96.59 94	86.78 28	94.40 160	93.93 248	89.77 46	94.21 35	95.59 114	87.35 35	98.61 111	92.72 44	96.15 114	97.83 92

APD-MVS_3200maxsize		93.78 48	93.77 49	93.80 71	97.92 47	84.19 100	96.30 42	96.87 67	86.96 126	93.92 43	97.47 18	83.88 81	98.96 86	92.71 45	97.87 79	98.26 58

PC_three_1452									82.47 224	97.09 9	97.07 45	92.72 1	98.04 161	92.70 46	99.02 12	98.86 10

PHI-MVS		93.89 46	93.65 53	94.62 45	96.84 86	86.43 43	96.69 32	97.49 5	85.15 170	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 44	94.43 53	98.39 25	85.78 68	97.25 10	97.07 48	86.90 130	92.62 81	96.80 60	84.85 70	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 63	96.07 131	89.77 46	94.12 38	94.87 133	80.56 113	98.66 105	92.42 49	93.10 166	98.15 66

ZNCC-MVS		94.47 19	94.28 26	95.03 16	98.52 16	86.96 19	96.85 28	97.32 27	88.24 89	93.15 62	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 41	96.41 107	90.00 39	94.09 39	94.60 148	82.33 95	98.62 110	92.40 50	92.86 171	98.27 56

HFP-MVS		94.52 18	94.40 21	94.86 27	98.61 10	86.81 26	96.94 20	97.34 22	88.63 76	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 26	94.91 24	98.63 9	86.69 32	96.94 20	97.32 27	88.63 76	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 14				97.08 43	92.59 2	98.94 87	92.25 54	98.99 14	98.84 13

region2R		94.43 23	94.27 28	94.92 22	98.65 8	86.67 34	96.92 24	97.23 35	88.60 78	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 66	94.89 127	96.99 51	89.02 68	89.56 129	97.37 25	82.51 92	99.38 32	92.20 56	98.30 62	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 50	94.08 38	92.65 108	97.31 74	83.43 120	95.79 73	97.33 25	90.03 38	93.58 53	96.96 50	84.87 69	97.76 177	92.19 57	98.66 44	96.76 133

CP-MVS		94.34 27	94.21 31	94.74 40	98.39 25	86.64 36	97.60 4	97.24 33	88.53 80	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	94.07 184	96.78 77	81.86 242	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	93.09 233	80.27 206	92.36 253	95.52 174	87.03 125	91.40 108	94.93 130	80.08 118	97.44 205	92.13 60	94.56 138	97.61 99

h-mvs33		90.80 100	90.15 106	92.75 102	96.01 116	82.66 148	95.43 89	95.53 173	89.80 42	93.08 65	95.64 112	75.77 165	99.00 78	92.07 61	78.05 334	96.60 138

hse-mvs2		89.88 127	89.34 125	91.51 161	94.83 168	81.12 186	93.94 194	93.91 251	89.80 42	93.08 65	93.60 189	75.77 165	97.66 183	92.07 61	77.07 341	95.74 173

MP-MVS		94.25 30	94.07 39	94.77 38	98.47 19	86.31 49	96.71 31	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	83.63 198	94.19 36	96.91 52	87.57 34	99.26 46	91.99 64	98.44 56

EI-MVSNet-Vis-set		93.01 69	92.92 68	93.29 77	95.01 155	83.51 118	94.48 152	95.77 154	90.87 18	92.52 83	96.67 65	84.50 73	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 22	97.19 38	90.66 27	92.85 70	97.16 40	85.02 66	99.49 26	91.99 64	98.56 52	98.47 34

X-MVStestdata		88.31 174	86.13 217	94.85 29	98.54 14	86.60 38	96.93 22	97.19 38	90.66 27	92.85 70	23.41 376	85.02 66	99.49 26	91.99 64	98.56 52	98.47 34

agg_prior1		93.29 63	92.97 67	94.26 58	97.38 71	85.92 62	93.92 195	96.72 86	81.96 236	92.16 89	96.23 86	87.85 27	98.97 83	91.95 68	98.55 54	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 99	95.53 87	96.67 92	87.85 105	93.20 61	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 79	93.24 226	97.24 33	88.76 73	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 44	93.53 55	95.06 15	96.76 88	87.78 9	94.92 125	97.21 37	84.33 185	93.89 44	97.09 42	87.20 38	99.29 44	91.90 72	98.44 56	98.12 70

APD-MVS		94.24 31	94.07 39	94.75 39	98.06 43	86.90 23	95.88 69	96.94 59	85.68 156	95.05 27	97.18 38	87.31 36	99.07 61	91.90 72	98.61 50	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 92	95.99 118	84.43 96	93.08 231	96.09 129	88.20 93	91.12 112	95.72 110	81.33 109	97.76 177	91.74 74	97.37 90	96.75 134

ETV-MVS		92.74 72	92.66 71	92.97 93	95.20 149	84.04 104	95.07 115	96.51 102	90.73 25	92.96 67	91.19 267	84.06 78	98.34 132	91.72 75	96.54 108	96.54 142

#test#		94.32 29	94.14 36	94.86 27	98.61 10	86.81 26	96.43 37	97.34 22	87.51 114	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 85	94.86 166	83.20 126	94.40 160	95.74 157	90.71 26	92.05 92	96.60 72	84.00 79	98.99 80	91.55 77	93.63 151	97.17 117

testtj		94.39 26	94.18 34	95.00 18	98.24 33	86.77 30	96.16 51	97.23 35	87.28 120	94.85 29	97.04 46	86.99 42	99.52 23	91.54 78	98.33 61	98.71 18

test_prior3		93.60 54	93.53 55	93.82 67	97.29 76	84.49 89	94.12 177	96.88 65	87.67 111	92.63 79	96.39 80	86.62 44	98.87 91	91.50 79	98.67 41	98.11 72

test_prior2							94.12 177		87.67 111	92.63 79	96.39 80	86.62 44		91.50 79	98.67 41

mPP-MVS		93.99 41	93.78 48	94.63 44	98.50 17	85.90 65	96.87 26	96.91 62	88.70 74	91.83 99	97.17 39	83.96 80	99.55 15	91.44 81	98.64 47	98.43 40

GST-MVS		94.21 34	93.97 43	94.90 26	98.41 24	86.82 25	96.54 36	97.19 38	88.24 89	93.26 58	96.83 56	85.48 59	99.59 7	91.43 82	98.40 58	98.30 50

DELS-MVS		93.43 60	93.25 59	93.97 62	95.42 139	85.04 78	93.06 233	97.13 43	90.74 24	91.84 97	95.09 127	86.32 48	99.21 50	91.22 83	98.45 55	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 83	93.07 234	86.91 22	96.41 38	96.26 116	88.30 87	88.37 147	94.85 136	82.19 99	97.64 188	91.09 84	82.95 279	94.96 195

baseline		92.39 78	92.29 77	92.69 107	94.46 186	81.77 167	94.14 176	96.27 115	89.22 59	91.88 95	96.00 97	82.35 94	97.99 166	91.05 85	95.27 129	98.30 50

mvsmamba		89.96 122	89.50 118	91.33 170	92.90 242	81.82 165	96.68 33	92.37 281	89.03 66	87.00 173	94.85 136	73.05 209	97.65 185	91.03 86	88.63 223	94.51 217

xiu_mvs_v1_base_debu		90.64 107	90.05 109	92.40 119	93.97 207	84.46 92	93.32 216	95.46 178	85.17 167	92.25 86	94.03 166	70.59 237	98.57 114	90.97 87	94.67 133	94.18 232

xiu_mvs_v1_base		90.64 107	90.05 109	92.40 119	93.97 207	84.46 92	93.32 216	95.46 178	85.17 167	92.25 86	94.03 166	70.59 237	98.57 114	90.97 87	94.67 133	94.18 232

xiu_mvs_v1_base_debi		90.64 107	90.05 109	92.40 119	93.97 207	84.46 92	93.32 216	95.46 178	85.17 167	92.25 86	94.03 166	70.59 237	98.57 114	90.97 87	94.67 133	94.18 232

VDD-MVS		90.74 102	89.92 114	93.20 81	96.27 105	83.02 133	95.73 76	93.86 252	88.42 83	92.53 82	96.84 55	62.09 313	98.64 107	90.95 90	92.62 174	97.93 85

casdiffmvs		92.51 75	92.43 75	92.74 103	94.41 189	81.98 162	94.54 150	96.23 120	89.57 50	91.96 94	96.17 93	82.58 91	98.01 164	90.95 90	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 83	96.89 64	89.40 55	92.81 73	96.97 49	85.37 61	99.24 47	90.87 92	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 76	96.86 27	97.45 13	88.33 84	90.15 124	97.03 48	81.44 107	99.51 24	90.85 93	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 43	93.72 50	94.68 41	98.43 21	86.22 52	95.30 96	97.78 1	87.45 117	93.26 58	97.33 26	84.62 72	99.51 24	90.75 94	98.57 51	98.32 49

iter_conf05		88.85 160	88.08 162	91.17 176	94.27 194	81.64 169	95.18 107	92.15 289	86.23 145	87.28 168	94.07 164	63.89 305	97.55 194	90.63 95	89.00 219	94.32 228

iter_conf_final		89.42 141	88.69 142	91.60 157	95.12 153	82.93 137	95.75 75	92.14 290	87.32 119	87.12 171	94.07 164	67.09 277	97.55 194	90.61 96	89.01 218	94.32 228

agg_prior2														90.54 97	98.68 39	98.27 56

HPM-MVS_fast		93.40 61	93.22 60	93.94 64	98.36 27	84.83 80	97.15 13	96.80 76	85.77 153	92.47 85	97.13 41	82.38 93	99.07 61	90.51 98	98.40 58	97.92 86

lupinMVS		90.92 99	90.21 103	93.03 90	93.86 210	83.88 107	92.81 240	93.86 252	79.84 272	91.76 100	94.29 158	77.92 146	98.04 161	90.48 99	97.11 92	97.17 117

jason		90.80 100	90.10 107	92.90 96	93.04 236	83.53 117	93.08 231	94.15 242	80.22 266	91.41 107	94.91 131	76.87 152	97.93 171	90.28 100	96.90 98	97.24 113
jason: jason.
CSCG		93.23 66	93.05 64	93.76 72	98.04 44	84.07 102	96.22 49	97.37 21	84.15 187	90.05 125	95.66 111	87.77 28	99.15 56	89.91 101	98.27 63	98.07 74

ETH3 D test6400		93.64 53	93.22 60	94.92 22	97.79 54	86.84 24	95.31 93	97.26 32	82.67 222	93.81 45	96.29 83	87.29 37	99.27 45	89.87 102	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 30	96.49 103	81.89 241	90.24 120	96.44 79	78.59 138	98.61 111	89.68 103	97.85 80	97.06 121

MVSFormer		91.68 88	91.30 86	92.80 99	93.86 210	83.88 107	95.96 66	95.90 144	84.66 181	91.76 100	94.91 131	77.92 146	97.30 221	89.64 104	97.11 92	97.24 113

test_djsdf		89.03 155	88.64 143	90.21 216	90.74 311	79.28 240	95.96 66	95.90 144	84.66 181	85.33 224	92.94 209	74.02 194	97.30 221	89.64 104	88.53 225	94.05 242

EIA-MVS		91.95 81	91.94 79	91.98 137	95.16 150	80.01 219	95.36 90	96.73 84	88.44 81	89.34 133	92.16 233	83.82 82	98.45 124	89.35 106	97.06 94	97.48 105

RRT_MVS		89.09 151	88.62 146	90.49 203	92.85 243	79.65 228	96.41 38	94.41 231	88.22 91	85.50 209	94.77 140	69.36 255	97.31 220	89.33 107	86.73 253	94.51 217

Effi-MVS+		91.59 89	91.11 90	93.01 91	94.35 193	83.39 122	94.60 145	95.10 201	87.10 123	90.57 116	93.10 205	81.43 108	98.07 159	89.29 108	94.48 140	97.59 101

ET-MVSNet_ETH3D		87.51 203	85.91 229	92.32 124	93.70 218	83.93 105	92.33 254	90.94 323	84.16 186	72.09 354	92.52 222	69.90 246	95.85 303	89.20 109	88.36 231	97.17 117

PS-MVSNAJ		91.18 96	90.92 94	91.96 139	95.26 146	82.60 151	92.09 263	95.70 159	86.27 142	91.84 97	92.46 223	79.70 124	98.99 80	89.08 110	95.86 116	94.29 230

xiu_mvs_v2_base		91.13 97	90.89 96	91.86 146	94.97 158	82.42 153	92.24 257	95.64 166	86.11 149	91.74 102	93.14 203	79.67 127	98.89 90	89.06 111	95.46 123	94.28 231

VNet		92.24 79	91.91 80	93.24 79	96.59 94	83.43 120	94.84 131	96.44 104	89.19 61	94.08 40	95.90 101	77.85 149	98.17 144	88.90 112	93.38 160	98.13 68

PS-MVSNAJss		89.97 121	89.62 116	91.02 185	91.90 264	80.85 194	95.26 101	95.98 137	86.26 143	86.21 191	94.29 158	79.70 124	97.65 185	88.87 113	88.10 234	94.57 213

XVG-OURS-SEG-HR		89.95 123	89.45 120	91.47 164	94.00 205	81.21 184	91.87 266	96.06 133	85.78 152	88.55 143	95.73 109	74.67 184	97.27 225	88.71 114	89.64 207	95.91 164

jajsoiax		88.24 176	87.50 173	90.48 205	90.89 305	80.14 211	95.31 93	95.65 165	84.97 174	84.24 249	94.02 169	65.31 296	97.42 207	88.56 115	88.52 226	93.89 246

mvs_tets		88.06 182	87.28 180	90.38 211	90.94 301	79.88 222	95.22 103	95.66 163	85.10 171	84.21 250	93.94 174	63.53 306	97.40 214	88.50 116	88.40 230	93.87 249

VDDNet		89.56 134	88.49 151	92.76 101	95.07 154	82.09 159	96.30 42	93.19 265	81.05 261	91.88 95	96.86 54	61.16 323	98.33 134	88.43 117	92.49 177	97.84 91

HQP_MVS		90.60 110	90.19 104	91.82 149	94.70 174	82.73 144	95.85 70	96.22 121	90.81 20	86.91 177	94.86 134	74.23 188	98.12 146	88.15 118	89.99 198	94.63 208

plane_prior5								96.22 121					98.12 146	88.15 118	89.99 198	94.63 208

EPNet		91.79 83	91.02 93	94.10 61	90.10 325	85.25 77	96.03 62	92.05 293	92.83 1	87.39 167	95.78 106	79.39 129	99.01 73	88.13 120	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	93.14 231	83.90 106	94.16 175	95.74 157	88.96 69	87.86 154	95.43 117	72.48 217	97.91 172	88.10 121	90.18 197	93.65 265
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSTER		88.84 161	88.29 157	90.51 202	92.95 240	80.44 204	93.73 202	95.01 204	84.66 181	87.15 169	93.12 204	72.79 213	97.21 232	87.86 122	87.36 246	93.87 249

3Dnovator+	87.14 4	92.42 77	91.37 85	95.55 6	95.63 131	88.73 6	97.07 18	96.77 79	90.84 19	84.02 252	96.62 71	75.95 164	99.34 36	87.77 123	97.68 84	98.59 24

bld_raw_dy_0_64		87.60 199	86.73 193	90.21 216	91.72 270	80.26 208	95.09 114	88.61 350	85.68 156	85.55 203	94.38 153	63.93 304	96.66 258	87.73 124	87.84 241	93.72 262

LPG-MVS_test		89.45 138	88.90 138	91.12 177	94.47 184	81.49 174	95.30 96	96.14 126	86.73 133	85.45 213	95.16 124	69.89 247	98.10 148	87.70 125	89.23 214	93.77 258

LGP-MVS_train				91.12 177	94.47 184	81.49 174		96.14 126	86.73 133	85.45 213	95.16 124	69.89 247	98.10 148	87.70 125	89.23 214	93.77 258

MVS_Test		91.31 93	91.11 90	91.93 141	94.37 190	80.14 211	93.46 213	95.80 152	86.46 138	91.35 109	93.77 184	82.21 98	98.09 156	87.57 127	94.95 131	97.55 104

PVSNet_Blended_VisFu		91.38 91	90.91 95	92.80 99	96.39 102	83.17 127	94.87 129	96.66 93	83.29 208	89.27 134	94.46 152	80.29 116	99.17 53	87.57 127	95.37 125	96.05 161

CDPH-MVS		92.83 70	92.30 76	94.44 51	97.79 54	86.11 54	94.06 186	96.66 93	80.09 269	92.77 74	96.63 70	86.62 44	99.04 66	87.40 129	98.66 44	98.17 64

XVG-OURS		89.40 144	88.70 141	91.52 160	94.06 199	81.46 176	91.27 279	96.07 131	86.14 147	88.89 140	95.77 107	68.73 267	97.26 227	87.39 130	89.96 200	95.83 169

EPP-MVSNet		91.70 87	91.56 84	92.13 131	95.88 122	80.50 203	97.33 7	95.25 193	86.15 146	89.76 128	95.60 113	83.42 83	98.32 136	87.37 131	93.25 163	97.56 103

VPA-MVSNet		89.62 131	88.96 134	91.60 157	93.86 210	82.89 139	95.46 88	97.33 25	87.91 100	88.43 146	93.31 195	74.17 191	97.40 214	87.32 132	82.86 284	94.52 216

LFMVS		90.08 117	89.13 131	92.95 94	96.71 89	82.32 157	96.08 58	89.91 341	86.79 131	92.15 91	96.81 58	62.60 311	98.34 132	87.18 133	93.90 147	98.19 62

anonymousdsp		87.84 185	87.09 183	90.12 222	89.13 336	80.54 202	94.67 142	95.55 170	82.05 232	83.82 256	92.12 236	71.47 226	97.15 234	87.15 134	87.80 242	92.67 299

CLD-MVS		89.47 137	88.90 138	91.18 175	94.22 195	82.07 160	92.13 261	96.09 129	87.90 101	85.37 222	92.45 224	74.38 186	97.56 193	87.15 134	90.43 193	93.93 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BP-MVS														87.11 136

HQP-MVS		89.80 128	89.28 128	91.34 169	94.17 196	81.56 170	94.39 162	96.04 135	88.81 70	85.43 216	93.97 173	73.83 198	97.96 168	87.11 136	89.77 205	94.50 220

ACMP	84.23 8	89.01 157	88.35 153	90.99 188	94.73 171	81.27 180	95.07 115	95.89 146	86.48 137	83.67 260	94.30 157	69.33 256	97.99 166	87.10 138	88.55 224	93.72 262
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
旧先验2							93.36 215		71.25 350	94.37 32			97.13 237	86.74 139

3Dnovator	86.66 5	91.73 86	90.82 97	94.44 51	94.59 178	86.37 45	97.18 12	97.02 50	89.20 60	84.31 248	96.66 66	73.74 200	99.17 53	86.74 139	97.96 76	97.79 94

PVSNet_BlendedMVS		89.98 120	89.70 115	90.82 192	96.12 109	81.25 181	93.92 195	96.83 72	83.49 203	89.10 136	92.26 231	81.04 111	98.85 97	86.72 141	87.86 240	92.35 310

PVSNet_Blended		90.73 103	90.32 102	91.98 137	96.12 109	81.25 181	92.55 248	96.83 72	82.04 234	89.10 136	92.56 221	81.04 111	98.85 97	86.72 141	95.91 115	95.84 168

mvs_anonymous		89.37 145	89.32 126	89.51 249	93.47 223	74.22 314	91.65 273	94.83 217	82.91 217	85.45 213	93.79 182	81.23 110	96.36 283	86.47 143	94.09 145	97.94 83

test1111		89.10 149	88.64 143	90.48 205	95.53 137	74.97 307	96.08 58	84.89 362	88.13 96	90.16 123	96.65 67	63.29 307	98.10 148	86.14 144	96.90 98	98.39 42

AUN-MVS		87.78 188	86.54 204	91.48 163	94.82 169	81.05 187	93.91 198	93.93 248	83.00 214	86.93 175	93.53 190	69.50 253	97.67 182	86.14 144	77.12 340	95.73 174

test_yl		90.69 104	90.02 112	92.71 104	95.72 127	82.41 155	94.11 179	95.12 199	85.63 158	91.49 105	94.70 142	74.75 180	98.42 127	86.13 146	92.53 175	97.31 109

DCV-MVSNet		90.69 104	90.02 112	92.71 104	95.72 127	82.41 155	94.11 179	95.12 199	85.63 158	91.49 105	94.70 142	74.75 180	98.42 127	86.13 146	92.53 175	97.31 109

test2506		87.21 218	86.28 213	90.02 228	95.62 132	73.64 319	96.25 48	71.38 377	87.89 103	90.45 117	96.65 67	55.29 346	98.09 156	86.03 148	96.94 96	98.33 46

ECVR-MVS		89.09 151	88.53 147	90.77 194	95.62 132	75.89 301	96.16 51	84.22 364	87.89 103	90.20 121	96.65 67	63.19 309	98.10 148	85.90 149	96.94 96	98.33 46

OMC-MVS		91.23 94	90.62 99	93.08 87	96.27 105	84.07 102	93.52 210	95.93 140	86.95 127	89.51 130	96.13 95	78.50 140	98.35 131	85.84 150	92.90 170	96.83 132

test_part1		89.00 158	87.99 164	92.04 132	95.94 121	83.81 109	96.14 54	96.05 134	86.44 139	85.69 199	93.73 187	71.57 223	97.66 183	85.80 151	80.54 315	94.66 207

ACMM	84.12 9	89.14 148	88.48 152	91.12 177	94.65 177	81.22 183	95.31 93	96.12 128	85.31 166	85.92 195	94.34 154	70.19 245	98.06 160	85.65 152	88.86 221	94.08 240
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	92.66 243	96.56 101	83.44 204	91.68 103	95.04 128	86.60 47	98.99 80	85.60 153	97.92 78	96.93 129

Effi-MVS+-dtu		88.65 166	88.35 153	89.54 246	93.33 226	76.39 296	94.47 155	94.36 233	87.70 109	85.43 216	89.56 306	73.45 203	97.26 227	85.57 154	91.28 184	94.97 192

mvs-test1		89.45 138	89.14 130	90.38 211	93.33 226	77.63 278	94.95 122	94.36 233	87.70 109	87.10 172	92.81 214	73.45 203	98.03 163	85.57 154	93.04 167	95.48 179

FIs		90.51 111	90.35 101	90.99 188	93.99 206	80.98 189	95.73 76	97.54 3	89.15 62	86.72 181	94.68 144	81.83 106	97.24 229	85.18 156	88.31 232	94.76 205

MG-MVS		91.77 84	91.70 83	92.00 136	97.08 82	80.03 218	93.60 208	95.18 197	87.85 105	90.89 114	96.47 78	82.06 102	98.36 129	85.07 157	97.04 95	97.62 98

CANet_DTU		90.26 115	89.41 123	92.81 98	93.46 224	83.01 134	93.48 211	94.47 229	89.43 54	87.76 159	94.23 162	70.54 241	99.03 67	84.97 158	96.39 112	96.38 145

UniMVSNet_NR-MVSNet		89.92 125	89.29 127	91.81 151	93.39 225	83.72 111	94.43 158	97.12 44	89.80 42	86.46 184	93.32 194	83.16 84	97.23 230	84.92 159	81.02 307	94.49 222

DU-MVS		89.34 146	88.50 149	91.85 148	93.04 236	83.72 111	94.47 155	96.59 98	89.50 51	86.46 184	93.29 197	77.25 150	97.23 230	84.92 159	81.02 307	94.59 211

cascas		86.43 242	84.98 249	90.80 193	92.10 259	80.92 192	90.24 296	95.91 143	73.10 339	83.57 264	88.39 320	65.15 297	97.46 202	84.90 161	91.43 183	94.03 243

UniMVSNet (Re)		89.80 128	89.07 132	92.01 133	93.60 220	84.52 88	94.78 135	97.47 10	89.26 58	86.44 187	92.32 228	82.10 100	97.39 217	84.81 162	80.84 311	94.12 236

Vis-MVSNet		91.75 85	91.23 88	93.29 77	95.32 143	83.78 110	96.14 54	95.98 137	89.89 40	90.45 117	96.58 73	75.09 176	98.31 137	84.75 163	96.90 98	97.78 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v2v482		87.84 185	87.06 184	90.17 218	90.99 297	79.23 243	94.00 191	95.13 198	84.87 175	85.53 206	92.07 242	74.45 185	97.45 203	84.71 164	81.75 295	93.85 252

DP-MVS Recon		91.95 81	91.28 87	93.96 63	98.33 29	85.92 62	94.66 143	96.66 93	82.69 221	90.03 126	95.82 104	82.30 96	99.03 67	84.57 165	96.48 111	96.91 130

UA-Net		92.83 70	92.54 73	93.68 73	96.10 112	84.71 82	95.66 81	96.39 109	91.92 5	93.22 60	96.49 77	83.16 84	98.87 91	84.47 166	95.47 122	97.45 107

V42		87.68 190	86.86 188	90.15 220	90.58 316	80.14 211	94.24 172	95.28 192	83.66 197	85.67 200	91.33 262	74.73 182	97.41 212	84.43 167	81.83 293	92.89 294

FC-MVSNet-test		90.27 114	90.18 105	90.53 199	93.71 216	79.85 224	95.77 74	97.59 2	89.31 57	86.27 190	94.67 145	81.93 105	97.01 244	84.26 168	88.09 236	94.71 206

cl22		86.78 229	85.98 225	89.18 255	92.34 252	77.62 279	90.84 286	94.13 244	81.33 254	83.97 254	90.15 293	73.96 195	96.60 266	84.19 169	82.94 280	93.33 275

miper_enhance_ethall		86.90 226	86.18 216	89.06 258	91.66 275	77.58 280	90.22 298	94.82 218	79.16 280	84.48 237	89.10 309	79.19 131	96.66 258	84.06 170	82.94 280	92.94 292

VPNet		88.20 177	87.47 175	90.39 209	93.56 221	79.46 231	94.04 187	95.54 172	88.67 75	86.96 174	94.58 150	69.33 256	97.15 234	84.05 171	80.53 317	94.56 214

FA-MVS(test-final)		89.66 130	88.91 137	91.93 141	94.57 180	80.27 206	91.36 277	94.74 223	84.87 175	89.82 127	92.61 220	74.72 183	98.47 120	83.97 172	93.53 154	97.04 123

UGNet		89.95 123	88.95 135	92.95 94	94.51 183	83.31 123	95.70 78	95.23 194	89.37 56	87.58 161	93.94 174	64.00 302	98.78 102	83.92 173	96.31 113	96.74 135
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		88.36 173	87.91 168	89.70 242	93.80 213	78.29 260	93.73 202	95.08 203	85.73 154	84.75 231	91.90 247	79.88 120	96.92 249	83.83 174	82.51 285	93.89 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		87.22 217	86.62 201	89.02 260	92.13 257	77.40 283	90.91 285	94.81 219	81.28 255	84.32 246	90.08 295	79.26 130	96.62 261	83.81 175	82.94 280	93.04 289

EI-MVSNet		89.10 149	88.86 140	89.80 238	91.84 266	78.30 259	93.70 205	95.01 204	85.73 154	87.15 169	95.28 119	79.87 121	97.21 232	83.81 175	87.36 246	93.88 248

c3_l		87.14 222	86.50 206	89.04 259	92.20 254	77.26 284	91.22 281	94.70 224	82.01 235	84.34 245	90.43 288	78.81 134	96.61 264	83.70 177	81.09 304	93.25 279

Anonymous20240529		88.09 180	86.59 202	92.58 112	96.53 98	81.92 164	95.99 63	95.84 149	74.11 331	89.06 138	95.21 123	61.44 318	98.81 100	83.67 178	87.47 243	97.01 125

v1144		87.61 198	86.79 192	90.06 225	91.01 296	79.34 236	93.95 193	95.42 186	83.36 207	85.66 201	91.31 265	74.98 178	97.42 207	83.37 179	82.06 289	93.42 274

thisisatest0530		88.67 165	87.61 172	91.86 146	94.87 165	80.07 214	94.63 144	89.90 342	84.00 190	88.46 145	93.78 183	66.88 281	98.46 121	83.30 180	92.65 173	97.06 121

tttt0517		88.61 167	87.78 169	91.11 180	94.96 159	77.81 272	95.35 91	89.69 345	85.09 172	88.05 152	94.59 149	66.93 279	98.48 118	83.27 181	92.13 180	97.03 124

testdata				90.49 203	96.40 101	77.89 269		95.37 189	72.51 344	93.63 51	96.69 63	82.08 101	97.65 185	83.08 182	97.39 89	95.94 163

LCM-MVSNet-Re		88.30 175	88.32 156	88.27 276	94.71 173	72.41 334	93.15 227	90.98 322	87.77 107	79.25 318	91.96 245	78.35 142	95.75 308	83.04 183	95.62 118	96.65 137

IS-MVSNet		91.43 90	91.09 92	92.46 117	95.87 124	81.38 179	96.95 19	93.69 258	89.72 48	89.50 131	95.98 98	78.57 139	97.77 176	83.02 184	96.50 110	98.22 61

UniMVSNet_ETH3D		87.53 202	86.37 208	91.00 187	92.44 250	78.96 245	94.74 137	95.61 167	84.07 189	85.36 223	94.52 151	59.78 331	97.34 219	82.93 185	87.88 239	96.71 136

XVG-ACMP-BASELINE		86.00 246	84.84 254	89.45 250	91.20 288	78.00 265	91.70 271	95.55 170	85.05 173	82.97 273	92.25 232	54.49 349	97.48 200	82.93 185	87.45 245	92.89 294

v144192		87.19 220	86.35 209	89.74 239	90.64 314	78.24 261	93.92 195	95.43 184	81.93 238	85.51 208	91.05 275	74.21 190	97.45 203	82.86 187	81.56 297	93.53 268

v8		87.50 205	86.71 195	89.89 232	91.37 283	79.40 233	94.50 151	95.38 187	84.81 178	83.60 263	91.33 262	76.05 161	97.42 207	82.84 188	80.51 319	92.84 296

Anonymous20231211		86.59 236	85.13 246	90.98 190	96.52 99	81.50 172	96.14 54	96.16 125	73.78 333	83.65 261	92.15 234	63.26 308	97.37 218	82.82 189	81.74 296	94.06 241

PAPM_NR		91.22 95	90.78 98	92.52 115	97.60 63	81.46 176	94.37 166	96.24 119	86.39 141	87.41 164	94.80 139	82.06 102	98.48 118	82.80 190	95.37 125	97.61 99

eth_miper_zixun_eth		86.50 239	85.77 234	88.68 267	91.94 263	75.81 303	90.47 291	94.89 212	82.05 232	84.05 251	90.46 287	75.96 163	96.77 254	82.76 191	79.36 329	93.46 273

Patchmatch-RL test		81.67 296	79.96 302	86.81 312	85.42 361	71.23 340	82.17 360	87.50 357	78.47 291	77.19 329	82.50 357	70.81 234	93.48 343	82.66 192	72.89 349	95.71 175

tpmrst		85.35 258	84.99 248	86.43 314	90.88 306	67.88 358	88.71 321	91.43 312	80.13 268	86.08 194	88.80 315	73.05 209	96.02 295	82.48 193	83.40 278	95.40 182

sss		88.93 159	88.26 159	90.94 191	94.05 200	80.78 196	91.71 270	95.38 187	81.55 250	88.63 142	93.91 178	75.04 177	95.47 319	82.47 194	91.61 182	96.57 140

ab-mvs		89.41 142	88.35 153	92.60 110	95.15 152	82.65 149	92.20 259	95.60 168	83.97 191	88.55 143	93.70 188	74.16 192	98.21 143	82.46 195	89.37 210	96.94 128

CostFormer		85.77 252	84.94 251	88.26 277	91.16 292	72.58 332	89.47 310	91.04 321	76.26 311	86.45 186	89.97 298	70.74 235	96.86 253	82.35 196	87.07 251	95.34 185

v1192		87.25 214	86.33 210	90.00 230	90.76 310	79.04 244	93.80 199	95.48 176	82.57 223	85.48 211	91.18 269	73.38 207	97.42 207	82.30 197	82.06 289	93.53 268

Baseline_NR-MVSNet		87.07 223	86.63 200	88.40 272	91.44 278	77.87 270	94.23 173	92.57 278	84.12 188	85.74 198	92.08 240	77.25 150	96.04 293	82.29 198	79.94 323	91.30 327

Anonymous202405211		87.68 190	86.13 217	92.31 125	96.66 91	80.74 197	94.87 129	91.49 310	80.47 265	89.46 132	95.44 115	54.72 348	98.23 140	82.19 199	89.89 202	97.97 81

v148		87.04 224	86.32 211	89.21 253	90.94 301	77.26 284	93.71 204	94.43 230	84.84 177	84.36 244	90.80 281	76.04 162	97.05 242	82.12 200	79.60 327	93.31 276

114514_t		89.51 135	88.50 149	92.54 114	98.11 39	81.99 161	95.16 110	96.36 111	70.19 354	85.81 196	95.25 121	76.70 156	98.63 109	82.07 201	96.86 101	97.00 126

v1921920		86.97 225	86.06 222	89.69 243	90.53 319	78.11 264	93.80 199	95.43 184	81.90 240	85.33 224	91.05 275	72.66 214	97.41 212	82.05 202	81.80 294	93.53 268

OurMVSNet-221017-0		85.35 258	84.64 258	87.49 294	90.77 309	72.59 331	94.01 190	94.40 232	84.72 180	79.62 316	93.17 201	61.91 315	96.72 255	81.99 203	81.16 301	93.16 284

v10		87.25 214	86.38 207	89.85 233	91.19 289	79.50 230	94.48 152	95.45 181	83.79 195	83.62 262	91.19 267	75.13 175	97.42 207	81.94 204	80.60 313	92.63 301

TranMVSNet+NR-MVSNet		88.84 161	87.95 166	91.49 162	92.68 247	83.01 134	94.92 125	96.31 112	89.88 41	85.53 206	93.85 181	76.63 158	96.96 246	81.91 205	79.87 325	94.50 220

D2MVS		85.90 248	85.09 247	88.35 274	90.79 308	77.42 282	91.83 267	95.70 159	80.77 263	80.08 309	90.02 296	66.74 284	96.37 281	81.88 206	87.97 238	91.26 328

test-LLR		85.87 249	85.41 240	87.25 300	90.95 299	71.67 337	89.55 306	89.88 343	83.41 205	84.54 235	87.95 327	67.25 274	95.11 324	81.82 207	93.37 161	94.97 192

test-mter		84.54 272	83.64 270	87.25 300	90.95 299	71.67 337	89.55 306	89.88 343	79.17 279	84.54 235	87.95 327	55.56 343	95.11 324	81.82 207	93.37 161	94.97 192

PMMVS		85.71 253	84.96 250	87.95 285	88.90 339	77.09 286	88.68 322	90.06 337	72.32 345	86.47 183	90.76 283	72.15 220	94.40 330	81.78 209	93.49 156	92.36 309

cl____		86.52 238	85.78 232	88.75 264	92.03 261	76.46 294	90.74 287	94.30 236	81.83 244	83.34 269	90.78 282	75.74 170	96.57 267	81.74 210	81.54 298	93.22 281

DIV-MVS_self_test		86.53 237	85.78 232	88.75 264	92.02 262	76.45 295	90.74 287	94.30 236	81.83 244	83.34 269	90.82 280	75.75 168	96.57 267	81.73 211	81.52 299	93.24 280

NR-MVSNet		88.58 169	87.47 175	91.93 141	93.04 236	84.16 101	94.77 136	96.25 118	89.05 64	80.04 310	93.29 197	79.02 132	97.05 242	81.71 212	80.05 322	94.59 211

WTY-MVS		89.60 132	88.92 136	91.67 155	95.47 138	81.15 185	92.38 252	94.78 221	83.11 211	89.06 138	94.32 156	78.67 137	96.61 264	81.57 213	90.89 191	97.24 113

thisisatest0515		87.33 210	85.99 224	91.37 168	93.49 222	79.55 229	90.63 289	89.56 348	80.17 267	87.56 162	90.86 278	67.07 278	98.28 138	81.50 214	93.02 168	96.29 147

v1240		86.78 229	85.85 230	89.56 245	90.45 320	77.79 273	93.61 207	95.37 189	81.65 246	85.43 216	91.15 271	71.50 225	97.43 206	81.47 215	82.05 291	93.47 272

GeoE		90.05 118	89.43 122	91.90 145	95.16 150	80.37 205	95.80 72	94.65 226	83.90 192	87.55 163	94.75 141	78.18 144	97.62 190	81.28 216	93.63 151	97.71 96

WR-MVS		88.38 171	87.67 171	90.52 201	93.30 228	80.18 209	93.26 223	95.96 139	88.57 79	85.47 212	92.81 214	76.12 160	96.91 250	81.24 217	82.29 287	94.47 225

1314		87.51 203	86.57 203	90.34 214	92.42 251	79.74 226	92.63 244	95.35 191	78.35 293	80.14 307	91.62 256	74.05 193	97.15 234	81.05 218	93.53 154	94.12 236

IterMVS-SCA-FT		85.45 255	84.53 260	88.18 280	91.71 272	76.87 289	90.19 299	92.65 277	85.40 164	81.44 289	90.54 285	66.79 282	95.00 327	81.04 219	81.05 305	92.66 300

XXY-MVS		87.65 192	86.85 189	90.03 226	92.14 256	80.60 201	93.76 201	95.23 194	82.94 216	84.60 233	94.02 169	74.27 187	95.49 318	81.04 219	83.68 272	94.01 244

miper_lstm_enhance		85.27 261	84.59 259	87.31 297	91.28 287	74.63 309	87.69 333	94.09 246	81.20 259	81.36 291	89.85 301	74.97 179	94.30 333	81.03 221	79.84 326	93.01 290

GA-MVS		86.61 234	85.27 244	90.66 195	91.33 286	78.71 247	90.40 292	93.81 255	85.34 165	85.12 226	89.57 305	61.25 320	97.11 238	80.99 222	89.59 208	96.15 151

IB-MVS	80.51 15	85.24 262	83.26 273	91.19 174	92.13 257	79.86 223	91.75 269	91.29 315	83.28 209	80.66 299	88.49 319	61.28 319	98.46 121	80.99 222	79.46 328	95.25 186
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		84.69 271	84.79 255	84.37 332	91.84 266	64.92 366	93.70 205	91.47 311	66.19 360	86.16 193	95.28 119	67.18 276	93.33 345	80.89 224	90.42 194	94.88 200

baseline1		88.10 179	87.28 180	90.57 197	94.96 159	80.07 214	94.27 169	91.29 315	86.74 132	87.41 164	94.00 171	76.77 155	96.20 288	80.77 225	79.31 330	95.44 180

HyFIR lowres test		88.09 180	86.81 190	91.93 141	96.00 117	80.63 199	90.01 302	95.79 153	73.42 336	87.68 160	92.10 239	73.86 197	97.96 168	80.75 226	91.70 181	97.19 116

AdaColmap		89.89 126	89.07 132	92.37 122	97.41 70	83.03 132	94.42 159	95.92 141	82.81 219	86.34 189	94.65 146	73.89 196	99.02 71	80.69 227	95.51 120	95.05 190

原ACMM1				92.01 133	97.34 73	81.05 187		96.81 75	78.89 283	90.45 117	95.92 100	82.65 90	98.84 99	80.68 228	98.26 64	96.14 152

TESTMET0.1,1		83.74 280	82.85 279	86.42 315	89.96 329	71.21 341	89.55 306	87.88 353	77.41 300	83.37 268	87.31 335	56.71 340	93.65 342	80.62 229	92.85 172	94.40 226

无先验							93.28 222	96.26 116	73.95 332				99.05 63	80.56 230		96.59 139

1121		90.42 112	89.49 119	93.20 81	97.27 78	84.46 92	92.63 244	95.51 175	71.01 352	91.20 111	96.21 87	82.92 88	99.05 63	80.56 230	98.07 72	96.10 157

Fast-Effi-MVS+		89.41 142	88.64 143	91.71 154	94.74 170	80.81 195	93.54 209	95.10 201	83.11 211	86.82 180	90.67 284	79.74 123	97.75 180	80.51 232	93.55 153	96.57 140

CHOSEN 1792x2688		88.84 161	87.69 170	92.30 126	96.14 108	81.42 178	90.01 302	95.86 148	74.52 328	87.41 164	93.94 174	75.46 173	98.36 129	80.36 233	95.53 119	97.12 120

CDS-MVSNet		89.45 138	88.51 148	92.29 127	93.62 219	83.61 116	93.01 234	94.68 225	81.95 237	87.82 157	93.24 199	78.69 136	96.99 245	80.34 234	93.23 164	96.28 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		87.44 206	86.72 194	89.63 244	92.04 260	77.68 277	94.03 188	93.94 247	85.81 151	82.42 278	91.32 264	70.33 243	97.06 241	80.33 235	90.23 196	94.14 235

baseline2		86.50 239	85.39 241	89.84 234	91.12 293	76.70 291	91.88 265	88.58 351	82.35 228	79.95 311	90.95 277	73.42 205	97.63 189	80.27 236	89.95 201	95.19 187

API-MVS		90.66 106	90.07 108	92.45 118	96.36 103	84.57 85	96.06 61	95.22 196	82.39 225	89.13 135	94.27 161	80.32 115	98.46 121	80.16 237	96.71 103	94.33 227

MAR-MVS		90.30 113	89.37 124	93.07 89	96.61 93	84.48 91	95.68 79	95.67 161	82.36 227	87.85 155	92.85 210	76.63 158	98.80 101	80.01 238	96.68 104	95.91 164
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	89.03 155	87.94 167	92.29 127	94.86 166	82.77 140	92.08 264	94.49 228	81.52 251	86.93 175	92.79 216	78.32 143	98.23 140	79.93 239	90.55 192	95.88 166

CHOSEN 280x420		85.15 263	83.99 265	88.65 268	92.47 249	78.40 256	79.68 364	92.76 273	74.90 325	81.41 290	89.59 304	69.85 249	95.51 315	79.92 240	95.29 127	92.03 315

MVS		87.44 206	86.10 220	91.44 165	92.61 248	83.62 115	92.63 244	95.66 163	67.26 358	81.47 288	92.15 234	77.95 145	98.22 142	79.71 241	95.48 121	92.47 305

pm-mvs1		86.61 234	85.54 237	89.82 235	91.44 278	80.18 209	95.28 100	94.85 215	83.84 194	81.66 287	92.62 219	72.45 219	96.48 274	79.67 242	78.06 333	92.82 297

IterMVS		84.88 267	83.98 266	87.60 290	91.44 278	76.03 300	90.18 300	92.41 280	83.24 210	81.06 295	90.42 289	66.60 285	94.28 334	79.46 243	80.98 310	92.48 304
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1112_ss		88.42 170	87.33 178	91.72 153	94.92 162	80.98 189	92.97 236	94.54 227	78.16 297	83.82 256	93.88 179	78.78 135	97.91 172	79.45 244	89.41 209	96.26 149

gm-plane-assit					89.60 335	68.00 356			77.28 303		88.99 310		97.57 192	79.44 245

PM-MVS		78.11 324	76.12 326	84.09 336	83.54 366	70.08 350	88.97 319	85.27 361	79.93 271	74.73 344	86.43 340	34.70 371	93.48 343	79.43 246	72.06 351	88.72 354

v7n		86.81 227	85.76 235	89.95 231	90.72 312	79.25 242	95.07 115	95.92 141	84.45 184	82.29 279	90.86 278	72.60 216	97.53 197	79.42 247	80.52 318	93.08 288

PAPR		90.02 119	89.27 129	92.29 127	95.78 125	80.95 191	92.68 242	96.22 121	81.91 239	86.66 182	93.75 186	82.23 97	98.44 125	79.40 248	94.79 132	97.48 105

新几何1				93.10 86	97.30 75	84.35 98		95.56 169	71.09 351	91.26 110	96.24 85	82.87 89	98.86 94	79.19 249	98.10 69	96.07 159

CP-MVSNet		87.63 195	87.26 182	88.74 266	93.12 232	76.59 293	95.29 98	96.58 99	88.43 82	83.49 266	92.98 208	75.28 174	95.83 304	78.97 250	81.15 303	93.79 254

pmmvs4		85.43 256	83.86 267	90.16 219	90.02 328	82.97 136	90.27 293	92.67 276	75.93 314	80.73 297	91.74 251	71.05 229	95.73 309	78.85 251	83.46 276	91.78 318

Test_1112_low_res		87.65 192	86.51 205	91.08 181	94.94 161	79.28 240	91.77 268	94.30 236	76.04 313	83.51 265	92.37 226	77.86 148	97.73 181	78.69 252	89.13 216	96.22 150

Vis-MVSNet (Re-imp)		89.59 133	89.44 121	90.03 226	95.74 126	75.85 302	95.61 84	90.80 327	87.66 113	87.83 156	95.40 118	76.79 154	96.46 277	78.37 253	96.73 102	97.80 93

PS-CasMVS		87.32 211	86.88 187	88.63 269	92.99 239	76.33 298	95.33 92	96.61 97	88.22 91	83.30 271	93.07 206	73.03 211	95.79 307	78.36 254	81.00 309	93.75 260

testdata2													98.75 103	78.30 255

GBi-Net		87.26 212	85.98 225	91.08 181	94.01 202	83.10 128	95.14 111	94.94 207	83.57 199	84.37 241	91.64 252	66.59 286	96.34 284	78.23 256	85.36 258	93.79 254

test1		87.26 212	85.98 225	91.08 181	94.01 202	83.10 128	95.14 111	94.94 207	83.57 199	84.37 241	91.64 252	66.59 286	96.34 284	78.23 256	85.36 258	93.79 254

FMVSNet3		87.40 208	86.11 219	91.30 171	93.79 215	83.64 114	94.20 174	94.81 219	83.89 193	84.37 241	91.87 248	68.45 270	96.56 269	78.23 256	85.36 258	93.70 264

OpenMVS	83.78 11	88.74 164	87.29 179	93.08 87	92.70 246	85.39 75	96.57 35	96.43 106	78.74 288	80.85 296	96.07 96	69.64 251	99.01 73	78.01 259	96.65 105	94.83 202

tpm		84.73 269	84.02 264	86.87 311	90.33 321	68.90 354	89.06 317	89.94 340	80.85 262	85.75 197	89.86 300	68.54 269	95.97 297	77.76 260	84.05 268	95.75 172

TAMVS		89.21 147	88.29 157	91.96 139	93.71 216	82.62 150	93.30 220	94.19 240	82.22 229	87.78 158	93.94 174	78.83 133	96.95 247	77.70 261	92.98 169	96.32 146

BH-untuned		88.60 168	88.13 161	90.01 229	95.24 147	78.50 253	93.29 221	94.15 242	84.75 179	84.46 238	93.40 191	75.76 167	97.40 214	77.59 262	94.52 139	94.12 236

FMVSNet2		87.19 220	85.82 231	91.30 171	94.01 202	83.67 113	94.79 134	94.94 207	83.57 199	83.88 255	92.05 243	66.59 286	96.51 272	77.56 263	85.01 261	93.73 261

RPSCF		85.07 264	84.27 261	87.48 295	92.91 241	70.62 347	91.69 272	92.46 279	76.20 312	82.67 277	95.22 122	63.94 303	97.29 224	77.51 264	85.80 256	94.53 215

PLC	84.53 7	89.06 154	88.03 163	92.15 130	97.27 78	82.69 147	94.29 168	95.44 183	79.71 274	84.01 253	94.18 163	76.68 157	98.75 103	77.28 265	93.41 159	95.02 191
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		89.07 153	87.98 165	92.34 123	96.87 85	84.78 81	94.08 183	93.24 263	81.41 252	84.46 238	95.13 126	75.57 172	96.62 261	77.21 266	93.84 149	95.61 177

K. test v3		81.59 298	80.15 300	85.91 321	89.89 331	69.42 353	92.57 247	87.71 355	85.56 160	73.44 350	89.71 303	55.58 342	95.52 314	77.17 267	69.76 353	92.78 298

QAPM		89.51 135	88.15 160	93.59 75	94.92 162	84.58 84	96.82 29	96.70 89	78.43 292	83.41 267	96.19 92	73.18 208	99.30 42	77.11 268	96.54 108	96.89 131

pmmvs5		84.21 274	82.84 280	88.34 275	88.95 338	76.94 288	92.41 250	91.91 301	75.63 316	80.28 304	91.18 269	64.59 300	95.57 311	77.09 269	83.47 275	92.53 303

pmmvs6		83.42 282	81.60 286	88.87 262	88.01 349	77.87 270	94.96 121	94.24 239	74.67 327	78.80 319	91.09 274	60.17 328	96.49 273	77.06 270	75.40 345	92.23 313

test_post1							88.00 329				9.81 378	69.31 258	95.53 313	76.65 271

SCA		86.32 243	85.18 245	89.73 241	92.15 255	76.60 292	91.12 282	91.69 304	83.53 202	85.50 209	88.81 313	66.79 282	96.48 274	76.65 271	90.35 195	96.12 154

WR-MVS_H		87.80 187	87.37 177	89.10 257	93.23 229	78.12 263	95.61 84	97.30 29	87.90 101	83.72 258	92.01 244	79.65 128	96.01 296	76.36 273	80.54 315	93.16 284

EU-MVSNet		81.32 303	80.95 291	82.42 341	88.50 342	63.67 367	93.32 216	91.33 313	64.02 362	80.57 301	92.83 212	61.21 322	92.27 354	76.34 274	80.38 320	91.32 326

CMPMVS	59.16 21	80.52 309	79.20 311	84.48 331	83.98 364	67.63 360	89.95 304	93.84 254	64.79 361	66.81 362	91.14 272	57.93 338	95.17 322	76.25 275	88.10 234	90.65 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
F-COLMAP		87.95 183	86.80 191	91.40 166	96.35 104	80.88 193	94.73 138	95.45 181	79.65 275	82.04 284	94.61 147	71.13 228	98.50 117	76.24 276	91.05 189	94.80 204

PEN-MVS		86.80 228	86.27 214	88.40 272	92.32 253	75.71 304	95.18 107	96.38 110	87.97 98	82.82 275	93.15 202	73.39 206	95.92 299	76.15 277	79.03 332	93.59 266

SixPastTwentyTwo		83.91 278	82.90 278	86.92 308	90.99 297	70.67 346	93.48 211	91.99 296	85.54 161	77.62 327	92.11 238	60.59 325	96.87 252	76.05 278	77.75 335	93.20 282

MVS_0304		83.46 281	81.92 284	88.10 282	90.63 315	77.49 281	93.26 223	93.75 256	80.04 270	80.44 303	87.24 337	47.94 363	95.55 312	75.79 279	88.16 233	91.26 328

MS-PatchMatch		85.05 265	84.16 262	87.73 288	91.42 281	78.51 252	91.25 280	93.53 259	77.50 299	80.15 306	91.58 258	61.99 314	95.51 315	75.69 280	94.35 143	89.16 351

BH-w/o		87.57 201	87.05 185	89.12 256	94.90 164	77.90 268	92.41 250	93.51 260	82.89 218	83.70 259	91.34 261	75.75 168	97.07 240	75.49 281	93.49 156	92.39 308

gg-mvs-nofinetune		81.77 294	79.37 307	88.99 261	90.85 307	77.73 276	86.29 341	79.63 373	74.88 326	83.19 272	69.05 367	60.34 326	96.11 292	75.46 282	94.64 136	93.11 286

FMVSNet1		85.85 250	84.11 263	91.08 181	92.81 244	83.10 128	95.14 111	94.94 207	81.64 247	82.68 276	91.64 252	59.01 335	96.34 284	75.37 283	83.78 269	93.79 254

EPMVS		83.90 279	82.70 281	87.51 292	90.23 324	72.67 328	88.62 323	81.96 369	81.37 253	85.01 228	88.34 321	66.31 289	94.45 329	75.30 284	87.12 249	95.43 181

pmmvs-eth3d		80.97 307	78.72 316	87.74 287	84.99 363	79.97 221	90.11 301	91.65 305	75.36 318	73.51 349	86.03 343	59.45 332	93.96 339	75.17 285	72.21 350	89.29 349

tpm2		84.08 275	82.94 277	87.48 295	91.39 282	71.27 339	89.23 314	90.37 331	71.95 347	84.64 232	89.33 307	67.30 273	96.55 271	75.17 285	87.09 250	94.63 208

lessismore_v0				86.04 317	88.46 343	68.78 355		80.59 371		73.01 352	90.11 294	55.39 344	96.43 279	75.06 287	65.06 361	92.90 293

MVP-Stereo		85.97 247	84.86 253	89.32 251	90.92 303	82.19 158	92.11 262	94.19 240	78.76 287	78.77 320	91.63 255	68.38 271	96.56 269	75.01 288	93.95 146	89.20 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FE-MVS		87.40 208	86.02 223	91.57 159	94.56 181	79.69 227	90.27 293	93.72 257	80.57 264	88.80 141	91.62 256	65.32 295	98.59 113	74.97 289	94.33 144	96.44 143

PVSNet	78.82 18	85.55 254	84.65 257	88.23 279	94.72 172	71.93 335	87.12 337	92.75 274	78.80 286	84.95 229	90.53 286	64.43 301	96.71 257	74.74 290	93.86 148	96.06 160

MDTV_nov1_ep13_2view						55.91 375	87.62 335		73.32 337	84.59 234		70.33 243		74.65 291		95.50 178

PatchmatchNet		85.85 250	84.70 256	89.29 252	91.76 269	75.54 305	88.49 324	91.30 314	81.63 248	85.05 227	88.70 317	71.71 221	96.24 287	74.61 292	89.05 217	96.08 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LF4IMVS		80.37 311	79.07 314	84.27 334	86.64 353	69.87 352	89.39 311	91.05 320	76.38 308	74.97 343	90.00 297	47.85 364	94.25 335	74.55 293	80.82 312	88.69 355

DTE-MVSNet		86.11 245	85.48 239	87.98 284	91.65 276	74.92 308	94.93 124	95.75 156	87.36 118	82.26 280	93.04 207	72.85 212	95.82 305	74.04 294	77.46 338	93.20 282

BH-RMVSNet		88.37 172	87.48 174	91.02 185	95.28 144	79.45 232	92.89 238	93.07 267	85.45 163	86.91 177	94.84 138	70.35 242	97.76 177	73.97 295	94.59 137	95.85 167

CR-MVSNet		85.35 258	83.76 268	90.12 222	90.58 316	79.34 236	85.24 347	91.96 299	78.27 294	85.55 203	87.87 330	71.03 230	95.61 310	73.96 296	89.36 211	95.40 182

ACMH+	81.04 14	85.05 265	83.46 272	89.82 235	94.66 176	79.37 234	94.44 157	94.12 245	82.19 230	78.04 323	92.82 213	58.23 337	97.54 196	73.77 297	82.90 283	92.54 302

TR-MVS		86.78 229	85.76 235	89.82 235	94.37 190	78.41 255	92.47 249	92.83 271	81.11 260	86.36 188	92.40 225	68.73 267	97.48 200	73.75 298	89.85 204	93.57 267

UnsupCasMVSNet_eth		80.07 313	78.27 317	85.46 323	85.24 362	72.63 330	88.45 326	94.87 214	82.99 215	71.64 357	88.07 326	56.34 341	91.75 358	73.48 299	63.36 364	92.01 316

PatchMatch-RL		86.77 232	85.54 237	90.47 207	95.88 122	82.71 146	90.54 290	92.31 284	79.82 273	84.32 246	91.57 260	68.77 266	96.39 280	73.16 300	93.48 158	92.32 311

ambc				83.06 339	79.99 370	63.51 368	77.47 365	92.86 270		74.34 347	84.45 350	28.74 372	95.06 326	73.06 301	68.89 358	90.61 338

KD-MVS_self_test		80.20 312	79.24 309	83.07 338	85.64 360	65.29 365	91.01 284	93.93 248	78.71 289	76.32 334	86.40 341	59.20 334	92.93 350	72.59 302	69.35 354	91.00 336

ITE_SJBPF				88.24 278	91.88 265	77.05 287		92.92 269	85.54 161	80.13 308	93.30 196	57.29 339	96.20 288	72.46 303	84.71 263	91.49 323

ACMH	80.38 17	85.36 257	83.68 269	90.39 209	94.45 187	80.63 199	94.73 138	94.85 215	82.09 231	77.24 328	92.65 218	60.01 329	97.58 191	72.25 304	84.87 262	92.96 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
USDC		82.76 285	81.26 290	87.26 299	91.17 290	74.55 310	89.27 312	93.39 262	78.26 295	75.30 341	92.08 240	54.43 350	96.63 260	71.64 305	85.79 257	90.61 338

EPNet_dtu		86.49 241	85.94 228	88.14 281	90.24 323	72.82 326	94.11 179	92.20 287	86.66 136	79.42 317	92.36 227	73.52 201	95.81 306	71.26 306	93.66 150	95.80 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				87.94 286	89.73 333	77.91 267	87.80 330	78.23 375		80.58 300	83.86 351	59.88 330	95.33 321	71.20 307	92.22 179	90.60 340

LTVRE_ROB	82.13 13	86.26 244	84.90 252	90.34 214	94.44 188	81.50 172	92.31 256	94.89 212	83.03 213	79.63 315	92.67 217	69.69 250	97.79 175	71.20 307	86.26 254	91.72 319
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		81.04 305	78.98 315	87.25 300	88.64 340	73.48 321	81.75 361	89.61 347	73.19 338	82.05 283	73.71 364	66.07 293	95.87 302	71.18 309	84.60 264	92.41 307

Anonymous20240521		80.44 310	79.21 310	84.11 335	85.75 359	67.89 357	92.86 239	93.23 264	75.61 317	75.59 340	87.47 334	50.03 358	94.33 332	71.14 310	81.21 300	90.12 343

TransMVSNet (Re)		84.43 273	83.06 276	88.54 270	91.72 270	78.44 254	95.18 107	92.82 272	82.73 220	79.67 314	92.12 236	73.49 202	95.96 298	71.10 311	68.73 359	91.21 330

PCF-MVS	84.11 10	87.74 189	86.08 221	92.70 106	94.02 201	84.43 96	89.27 312	95.87 147	73.62 335	84.43 240	94.33 155	78.48 141	98.86 94	70.27 312	94.45 141	94.81 203
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		82.37 290	80.34 296	88.46 271	90.27 322	79.35 235	92.80 241	94.33 235	77.14 304	73.26 351	90.18 292	47.47 365	96.72 255	70.25 313	87.32 248	89.30 348

MDTV_nov1_ep13			83.56 271		91.69 274	69.93 351	87.75 332	91.54 308	78.60 290	84.86 230	88.90 312	69.54 252	96.03 294	70.25 313	88.93 220

TDRefinement		79.81 315	77.34 319	87.22 303	79.24 371	75.48 306	93.12 228	92.03 294	76.45 307	75.01 342	91.58 258	49.19 361	96.44 278	70.22 315	69.18 356	89.75 345

thres100view900		87.63 195	86.71 195	90.38 211	96.12 109	78.55 250	95.03 119	91.58 306	87.15 121	88.06 151	92.29 230	68.91 264	98.10 148	70.13 316	91.10 185	94.48 223

tfpn200view9		87.58 200	86.64 198	90.41 208	95.99 118	78.64 248	94.58 146	91.98 297	86.94 128	88.09 148	91.77 249	69.18 261	98.10 148	70.13 316	91.10 185	94.48 223

thres400		87.62 197	86.64 198	90.57 197	95.99 118	78.64 248	94.58 146	91.98 297	86.94 128	88.09 148	91.77 249	69.18 261	98.10 148	70.13 316	91.10 185	94.96 195

thres600view7		87.65 192	86.67 197	90.59 196	96.08 113	78.72 246	94.88 128	91.58 306	87.06 124	88.08 150	92.30 229	68.91 264	98.10 148	70.05 319	91.10 185	94.96 195

thres200		87.21 218	86.24 215	90.12 222	95.36 140	78.53 251	93.26 223	92.10 291	86.42 140	88.00 153	91.11 273	69.24 260	98.00 165	69.58 320	91.04 190	93.83 253

tpm cat1		81.96 291	80.27 297	87.01 306	91.09 294	71.02 343	87.38 336	91.53 309	66.25 359	80.17 305	86.35 342	68.22 272	96.15 291	69.16 321	82.29 287	93.86 251

Patchmtry		82.71 286	80.93 292	88.06 283	90.05 327	76.37 297	84.74 351	91.96 299	72.28 346	81.32 292	87.87 330	71.03 230	95.50 317	68.97 322	80.15 321	92.32 311

our_test_3		81.93 292	80.46 295	86.33 316	88.46 343	73.48 321	88.46 325	91.11 317	76.46 306	76.69 332	88.25 323	66.89 280	94.36 331	68.75 323	79.08 331	91.14 332

PVSNet_0	73.20 20	77.22 325	74.83 330	84.37 332	90.70 313	71.10 342	83.09 358	89.67 346	72.81 343	73.93 348	83.13 355	60.79 324	93.70 341	68.54 324	50.84 370	88.30 358

MSDG		84.86 268	83.09 275	90.14 221	93.80 213	80.05 216	89.18 315	93.09 266	78.89 283	78.19 321	91.91 246	65.86 294	97.27 225	68.47 325	88.45 228	93.11 286

LS3D		87.89 184	86.32 211	92.59 111	96.07 114	82.92 138	95.23 102	94.92 211	75.66 315	82.89 274	95.98 98	72.48 217	99.21 50	68.43 326	95.23 130	95.64 176

AllTest		83.42 282	81.39 288	89.52 247	95.01 155	77.79 273	93.12 228	90.89 325	77.41 300	76.12 336	93.34 192	54.08 351	97.51 198	68.31 327	84.27 266	93.26 277

TestCases				89.52 247	95.01 155	77.79 273		90.89 325	77.41 300	76.12 336	93.34 192	54.08 351	97.51 198	68.31 327	84.27 266	93.26 277

dp		81.47 301	80.23 298	85.17 327	89.92 330	65.49 364	86.74 338	90.10 336	76.30 310	81.10 293	87.12 339	62.81 310	95.92 299	68.13 329	79.88 324	94.09 239

tpmvs		83.35 284	82.07 282	87.20 304	91.07 295	71.00 344	88.31 327	91.70 303	78.91 282	80.49 302	87.18 338	69.30 259	97.08 239	68.12 330	83.56 274	93.51 271

FMVSNet5		81.52 300	79.60 306	87.27 298	91.17 290	77.95 266	91.49 275	92.26 286	76.87 305	76.16 335	87.91 329	51.67 356	92.34 353	67.74 331	81.16 301	91.52 322

KD-MVS_2432*1600		78.50 322	76.02 327	85.93 319	86.22 355	74.47 311	84.80 349	92.33 282	79.29 277	76.98 330	85.92 344	53.81 353	93.97 337	67.39 332	57.42 367	89.36 346

miper_refine_blended		78.50 322	76.02 327	85.93 319	86.22 355	74.47 311	84.80 349	92.33 282	79.29 277	76.98 330	85.92 344	53.81 353	93.97 337	67.39 332	57.42 367	89.36 346

CL-MVSNet_self_test		81.74 295	80.53 293	85.36 324	85.96 357	72.45 333	90.25 295	93.07 267	81.24 257	79.85 313	87.29 336	70.93 232	92.52 352	66.95 334	69.23 355	91.11 334

YYNet1		79.22 319	77.20 321	85.28 326	88.20 348	72.66 329	85.87 343	90.05 339	74.33 330	62.70 364	87.61 332	66.09 292	92.03 355	66.94 335	72.97 348	91.15 331

PAPM		86.68 233	85.39 241	90.53 199	93.05 235	79.33 239	89.79 305	94.77 222	78.82 285	81.95 285	93.24 199	76.81 153	97.30 221	66.94 335	93.16 165	94.95 198

DP-MVS		87.25 214	85.36 243	92.90 96	97.65 62	83.24 124	94.81 133	92.00 295	74.99 323	81.92 286	95.00 129	72.66 214	99.05 63	66.92 337	92.33 178	96.40 144

MDA-MVSNet_test_wron		79.21 320	77.19 322	85.29 325	88.22 347	72.77 327	85.87 343	90.06 337	74.34 329	62.62 365	87.56 333	66.14 291	91.99 356	66.90 338	73.01 347	91.10 335

UnsupCasMVSNet_bld		76.23 328	73.27 331	85.09 328	83.79 365	72.92 324	85.65 346	93.47 261	71.52 348	68.84 360	79.08 361	49.77 359	93.21 346	66.81 339	60.52 366	89.13 353

MIMVSNet		82.59 288	80.53 293	88.76 263	91.51 277	78.32 258	86.57 340	90.13 335	79.32 276	80.70 298	88.69 318	52.98 355	93.07 349	66.03 340	88.86 221	94.90 199

LCM-MVSNet		66.00 333	62.16 337	77.51 348	64.51 378	58.29 370	83.87 355	90.90 324	48.17 369	54.69 367	73.31 365	16.83 381	86.75 367	65.47 341	61.67 365	87.48 360

PatchT		82.68 287	81.27 289	86.89 310	90.09 326	70.94 345	84.06 353	90.15 334	74.91 324	85.63 202	83.57 353	69.37 254	94.87 328	65.19 342	88.50 227	94.84 201

test0.0.03 1		82.41 289	81.69 285	84.59 330	88.23 346	72.89 325	90.24 296	87.83 354	83.41 205	79.86 312	89.78 302	67.25 274	88.99 365	65.18 343	83.42 277	91.90 317

ppachtmachnet_test		81.84 293	80.07 301	87.15 305	88.46 343	74.43 313	89.04 318	92.16 288	75.33 319	77.75 325	88.99 310	66.20 290	95.37 320	65.12 344	77.60 336	91.65 320

COLMAP_ROB	80.39 16	83.96 276	82.04 283	89.74 239	95.28 144	79.75 225	94.25 170	92.28 285	75.17 321	78.02 324	93.77 184	58.60 336	97.84 174	65.06 345	85.92 255	91.63 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		81.66 297	79.71 305	87.50 293	91.35 284	74.19 315	83.33 356	88.48 352	72.90 341	82.24 281	85.77 346	64.98 298	93.20 347	64.57 346	83.74 270	95.12 188

ADS-MVSNet		81.56 299	79.78 303	86.90 309	91.35 284	71.82 336	83.33 356	89.16 349	72.90 341	82.24 281	85.77 346	64.98 298	93.76 340	64.57 346	83.74 270	95.12 188

new-patchmatchnet		76.41 327	75.17 329	80.13 343	82.65 369	59.61 369	87.66 334	91.08 318	78.23 296	69.85 358	83.22 354	54.76 347	91.63 360	64.14 348	64.89 362	89.16 351

testgi		80.94 308	80.20 299	83.18 337	87.96 350	66.29 361	91.28 278	90.70 329	83.70 196	78.12 322	92.84 211	51.37 357	90.82 361	63.34 349	82.46 286	92.43 306

TinyColmap		79.76 316	77.69 318	85.97 318	91.71 272	73.12 323	89.55 306	90.36 332	75.03 322	72.03 355	90.19 291	46.22 366	96.19 290	63.11 350	81.03 306	88.59 356

pmmvs3		71.81 331	68.71 334	81.11 342	75.86 372	70.42 348	86.74 338	83.66 365	58.95 365	68.64 361	80.89 359	36.93 370	89.52 364	63.10 351	63.59 363	83.39 361

TAPA-MVS	84.62 6	88.16 178	87.01 186	91.62 156	96.64 92	80.65 198	94.39 162	96.21 124	76.38 308	86.19 192	95.44 115	79.75 122	98.08 158	62.75 352	95.29 127	96.13 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet-bldmvs		78.85 321	76.31 324	86.46 313	89.76 332	73.88 317	88.79 320	90.42 330	79.16 280	59.18 366	88.33 322	60.20 327	94.04 336	62.00 353	68.96 357	91.48 324

tfpnnormal		84.72 270	83.23 274	89.20 254	92.79 245	80.05 216	94.48 152	95.81 151	82.38 226	81.08 294	91.21 266	69.01 263	96.95 247	61.69 354	80.59 314	90.58 341

Anonymous20231206		81.03 306	79.77 304	84.82 329	87.85 351	70.26 349	91.42 276	92.08 292	73.67 334	77.75 325	89.25 308	62.43 312	93.08 348	61.50 355	82.00 292	91.12 333

RPMNet		83.95 277	81.53 287	91.21 173	90.58 316	79.34 236	85.24 347	96.76 80	71.44 349	85.55 203	82.97 356	70.87 233	98.91 89	61.01 356	89.36 211	95.40 182

MIMVSNet1		79.38 318	77.28 320	85.69 322	86.35 354	73.67 318	91.61 274	92.75 274	78.11 298	72.64 353	88.12 325	48.16 362	91.97 357	60.32 357	77.49 337	91.43 325

test20.03		79.95 314	79.08 313	82.55 340	85.79 358	67.74 359	91.09 283	91.08 318	81.23 258	74.48 346	89.96 299	61.63 316	90.15 362	60.08 358	76.38 342	89.76 344

DSMNet-mixed		76.94 326	76.29 325	78.89 344	83.10 367	56.11 374	87.78 331	79.77 372	60.65 364	75.64 339	88.71 316	61.56 317	88.34 366	60.07 359	89.29 213	92.21 314

Patchmatch-test		81.37 302	79.30 308	87.58 291	90.92 303	74.16 316	80.99 362	87.68 356	70.52 353	76.63 333	88.81 313	71.21 227	92.76 351	60.01 360	86.93 252	95.83 169

MVS-HIRNet		73.70 329	72.20 332	78.18 347	91.81 268	56.42 373	82.94 359	82.58 367	55.24 366	68.88 359	66.48 368	55.32 345	95.13 323	58.12 361	88.42 229	83.01 362

OpenMVS_ROB	74.94 19	79.51 317	77.03 323	86.93 307	87.00 352	76.23 299	92.33 254	90.74 328	68.93 356	74.52 345	88.23 324	49.58 360	96.62 261	57.64 362	84.29 265	87.94 359

new_pmnet		72.15 330	70.13 333	78.20 346	82.95 368	65.68 362	83.91 354	82.40 368	62.94 363	64.47 363	79.82 360	42.85 368	86.26 368	57.41 363	74.44 346	82.65 364

N_pmnet		68.89 332	68.44 335	70.23 351	89.07 337	28.79 383	88.06 328	19.50 384	69.47 355	71.86 356	84.93 348	61.24 321	91.75 358	54.70 364	77.15 339	90.15 342

test_method		50.52 340	48.47 342	56.66 356	52.26 382	18.98 385	41.51 374	81.40 370	10.10 376	44.59 371	75.01 363	28.51 373	68.16 374	53.54 365	49.31 371	82.83 363

tmp_tt		35.64 344	39.24 346	24.84 360	14.87 384	23.90 384	62.71 370	51.51 383	6.58 378	36.66 374	62.08 371	44.37 367	30.34 380	52.40 366	22.00 377	20.27 375

test_0402		81.30 304	79.17 312	87.67 289	93.19 230	78.17 262	92.98 235	91.71 302	75.25 320	76.02 338	90.31 290	59.23 333	96.37 281	50.22 367	83.63 273	88.47 357

PMMVS2		59.60 336	56.40 338	69.21 352	68.83 375	46.58 378	73.02 369	77.48 376	55.07 367	49.21 369	72.95 366	17.43 380	80.04 372	49.32 368	44.33 372	80.99 366

EGC-MVSNET		61.97 335	56.37 339	78.77 345	89.63 334	73.50 320	89.12 316	82.79 366	0.21 381	1.24 382	84.80 349	39.48 369	90.04 363	44.13 369	75.94 344	72.79 368

ANet_high		58.88 337	54.22 341	72.86 349	56.50 381	56.67 372	80.75 363	86.00 358	73.09 340	37.39 373	64.63 370	22.17 376	79.49 373	43.51 370	23.96 375	82.43 365

DeepMVS_CX				56.31 357	74.23 373	51.81 376		56.67 382	44.85 370	48.54 370	75.16 362	27.87 374	58.74 378	40.92 371	52.22 369	58.39 371

FPMVS		64.63 334	62.55 336	70.88 350	70.80 374	56.71 371	84.42 352	84.42 363	51.78 368	49.57 368	81.61 358	23.49 375	81.48 371	40.61 372	76.25 343	74.46 367

Gipuma		57.99 338	54.91 340	67.24 353	88.51 341	65.59 363	52.21 372	90.33 333	43.58 371	42.84 372	51.18 373	20.29 378	85.07 369	34.77 373	70.45 352	51.05 372
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 339	48.46 343	63.48 354	45.72 383	46.20 379	73.41 368	78.31 374	41.03 372	30.06 375	65.68 369	6.05 382	83.43 370	30.04 374	65.86 360	60.80 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 341	38.59 347	57.77 355	56.52 380	48.77 377	55.38 371	58.64 381	29.33 375	28.96 376	52.65 372	4.68 383	64.62 377	28.11 375	33.07 373	59.93 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 342	42.29 344	46.03 358	65.58 377	37.41 380	73.51 367	64.62 378	33.99 373	28.47 377	47.87 374	19.90 379	67.91 375	22.23 376	24.45 374	32.77 373

EMVS		42.07 343	41.12 345	44.92 359	63.45 379	35.56 382	73.65 366	63.48 379	33.05 374	26.88 378	45.45 375	21.27 377	67.14 376	19.80 377	23.02 376	32.06 374

wuyk23d		21.27 346	20.48 349	23.63 361	68.59 376	36.41 381	49.57 373	6.85 385	9.37 377	7.89 379	4.46 381	4.03 384	31.37 379	17.47 378	16.07 378	3.12 376

testmvs		8.92 347	11.52 350	1.12 363	1.06 385	0.46 387	86.02 342	0.65 386	0.62 379	2.74 380	9.52 379	0.31 386	0.45 382	2.38 379	0.39 379	2.46 378

test123		8.76 348	11.22 351	1.39 362	0.85 386	0.97 386	85.76 345	0.35 387	0.54 380	2.45 381	8.14 380	0.60 385	0.48 381	2.16 380	0.17 380	2.71 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		22.14 345	29.52 348	0.00 364	0.00 387	0.00 388	0.00 375	95.76 155	0.00 382	0.00 383	94.29 158	75.66 171	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		6.64 350	8.86 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	79.70 124	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		7.82 349	10.43 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	93.88 179	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					98.86 1	85.54 73	98.29 1	97.49 5	89.79 45	96.29 15

test_one_0601					98.58 12	85.83 66		97.44 14	91.05 17	96.78 13	98.06 6	91.45 11

eth-test2					0.00 387

eth-test					0.00 387

test_241102_ONE					98.77 5	85.99 57		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 71	95.21 104	96.82 74	89.44 52

test0726					98.78 3	85.93 60	97.19 11	97.47 10	90.27 33	97.64 4	98.13 1	91.47 8

GSMVS																96.12 154

test_part2					98.55 13	87.22 18				96.40 14

sam_mvs1												71.70 222				96.12 154

sam_mvs												70.60 236

MTGPA								96.97 53

test_post											10.29 377	70.57 240	95.91 301

patchmatchnet-post											83.76 352	71.53 224	96.48 274

MTMP							96.16 51	60.64 380

TEST9					97.53 65	86.49 41	94.07 184	96.78 77	81.61 249	92.77 74	96.20 89	87.71 31	99.12 58

test_8					97.49 68	86.30 50	94.02 189	96.76 80	81.86 242	92.70 78	96.20 89	87.63 32	99.02 71

agg_prior					97.38 71	85.92 62		96.72 86		92.16 89			98.97 83

test_prior4						85.96 59	94.11 179

test_prior				93.82 67	97.29 76	84.49 89		96.88 65					98.87 91			98.11 72

新几何2							93.11 230

旧先验1					96.79 87	81.81 166		95.67 161			96.81 58	86.69 43			97.66 85	96.97 127

原ACMM2							92.94 237

test222					96.55 96	81.70 168	92.22 258	95.01 204	68.36 357	90.20 121	96.14 94	80.26 117			97.80 81	96.05 161

segment_acmp												87.16 40

testdata1							92.15 260		87.94 99

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					94.70 174	82.74 143

plane_prior6					94.52 182	82.75 141						74.23 188

plane_prior4											94.86 134

plane_prior3						82.75 141			90.26 35	86.91 177

plane_prior2							95.85 70		90.81 20

plane_prior1					94.59 178

plane_prior						82.73 144	95.21 104		89.66 49						89.88 203

n2								0.00 388

nn								0.00 388

door-mid								85.49 359

test11								96.57 100

door								85.33 360

HQP5-MVS						81.56 170

HQP-NCC					94.17 196		94.39 162		88.81 70	85.43 216

ACMP_Plane					94.17 196		94.39 162		88.81 70	85.43 216

HQP4-MVS										85.43 216			97.96 168			94.51 217

HQP3-MVS								96.04 135							89.77 205

HQP2-MVS												73.83 198

NP-MVS					94.37 190	82.42 153					93.98 172

ACMMP++_ref															87.47 243

ACMMP++															88.01 237

Test By Simon												80.02 119

High-resolution multi-view benchmark