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.

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 22	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

PC_three_1452									91.12 22	98.33 2	98.42 28	92.51 2	99.81 21	98.96 2	99.37 1	99.70 3

patch_mono-2		95.14 12	96.08 7	92.33 116	98.44 48	77.84 235	98.43 27	97.21 20	92.58 11	97.68 8	97.65 78	86.88 24	99.83 16	98.25 3	97.60 74	99.33 17

SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 61	99.12 6	96.78 49	88.72 54	97.79 4	98.91 3	88.48 16	99.82 18	98.15 4	98.97 17	99.74 1

IU-MVS					99.03 16	85.34 49		96.86 44	92.05 16	98.74 1				98.15 4	98.97 17	99.42 13

test_241102_TWO								96.78 49	88.72 54	97.70 6	98.91 3	87.86 20	99.82 18	98.15 4	99.00 15	99.47 9

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

No_MVS				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

DVP-MVS		95.58 8	95.91 9	94.57 29	99.05 10	85.18 54	99.06 9	96.46 101	88.75 52	96.69 13	98.76 14	87.69 21	99.76 25	97.90 9	98.85 22	98.77 35
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				95.14 17	99.04 15	86.14 33	99.06 9	96.77 55					99.84 12	97.90 9	98.85 22	99.45 10

DeepPCF-MVS	89.82 1	94.61 18	96.17 5	89.91 193	97.09 104	70.21 321	98.99 14	96.69 68	95.57 1	95.08 32	99.23 1	86.40 29	99.87 8	97.84 11	98.66 34	99.65 6

DPE-MVS		95.32 10	95.55 10	94.64 28	98.79 25	84.87 66	97.77 61	96.74 60	86.11 103	96.54 17	98.89 7	88.39 18	99.74 33	97.67 12	99.05 12	99.31 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 24	94.66 3	96.79 12	98.78 11	86.42 28	99.95 3	97.59 13	99.18 7	99.00 28

DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 10	85.34 49	98.13 39	96.77 55	88.38 61	97.70 6	98.77 12	92.06 3	99.84 12	97.47 14	99.37 1	99.70 3

test_0728_THIRD									88.38 61	96.69 13	98.76 14	89.64 12	99.76 25	97.47 14	98.84 24	99.38 14

APDe-MVS		94.56 19	94.75 16	93.96 48	98.84 24	83.40 92	98.04 47	96.41 107	85.79 111	95.00 35	98.28 32	84.32 40	99.18 92	97.35 16	98.77 29	99.28 20

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 26	95.17 2	92.11 73	98.46 26	87.33 23	99.97 2	97.21 17	99.31 4	99.63 7

ETH3 D test6400		95.56 9	95.41 13	96.00 9	99.02 19	89.42 9	98.75 17	96.80 48	87.28 85	95.88 23	98.95 2	85.92 30	99.41 67	97.15 18	98.95 20	99.18 25

CANet		94.89 14	94.64 18	95.63 12	97.55 88	88.12 15	99.06 9	96.39 113	94.07 7	95.34 28	97.80 69	76.83 121	99.87 8	97.08 19	97.64 73	98.89 31

dcpmvs_2		93.10 46	93.46 43	92.02 128	97.77 78	79.73 179	94.82 237	93.86 254	86.91 94	91.33 88	96.76 118	85.20 32	98.06 145	96.90 20	97.60 74	98.27 69

PS-MVSNAJ		94.17 27	93.52 41	96.10 8	95.65 130	92.35 2	98.21 34	95.79 152	92.42 13	96.24 19	98.18 35	71.04 202	99.17 93	96.77 21	97.39 82	96.79 160

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 10	95.07 146	92.02 6	98.19 35	95.68 157	92.06 15	96.01 22	98.14 40	70.83 205	98.96 108	96.74 22	96.57 102	96.76 163

TSAR-MVS + GP.		94.35 23	94.50 20	93.89 49	97.38 98	83.04 101	98.10 41	95.29 182	91.57 17	93.81 53	97.45 87	86.64 25	99.43 66	96.28 23	94.01 130	99.20 23

SD-MVS		94.84 15	95.02 15	94.29 37	97.87 76	84.61 70	97.76 65	96.19 130	89.59 41	96.66 15	98.17 39	84.33 37	99.60 51	96.09 24	98.50 41	98.66 43
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
ETH3D-3000-0.1		94.43 21	94.42 24	94.45 31	97.78 77	85.78 40	97.98 49	96.53 93	85.29 125	95.45 26	98.81 8	83.36 47	99.38 69	96.07 25	98.53 37	98.19 73

MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 55	80.10 170	97.42 94	96.78 49	92.20 14	97.11 11	98.29 31	93.46 1	99.10 100	96.01 26	99.30 5	99.38 14
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
test9_res														96.00 27	99.03 13	98.31 64

9.14			94.26 29		98.10 66		98.14 36	96.52 94	84.74 137	94.83 39	98.80 9	82.80 56	99.37 73	95.95 28	98.42 46

train_agg		94.28 24	94.45 22	93.74 54	98.64 35	83.71 85	97.82 57	96.65 74	84.50 146	95.16 29	98.09 46	84.33 37	99.36 75	95.91 29	98.96 19	98.16 76

SMA-MVS		94.70 17	94.68 17	94.76 25	98.02 70	85.94 37	97.47 86	96.77 55	85.32 122	97.92 3	98.70 18	83.09 52	99.84 12	95.79 30	99.08 10	98.49 52
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
ETV-MVS		92.72 58	92.87 52	92.28 119	94.54 161	81.89 124	97.98 49	95.21 185	89.77 40	93.11 61	96.83 114	77.23 117	97.50 172	95.74 31	95.38 115	97.44 134

agg_prior1		94.10 30	94.31 27	93.48 70	98.59 39	83.13 98	97.77 61	96.56 88	84.38 150	94.19 48	98.13 41	84.66 35	99.16 94	95.74 31	98.74 31	98.15 78

NCCC		95.63 6	95.94 8	94.69 27	99.21 7	85.15 59	99.16 3	96.96 35	94.11 6	95.59 25	98.64 21	85.07 33	99.91 4	95.61 33	99.10 9	99.00 28

TSAR-MVS + MP.		94.79 16	95.17 14	93.64 60	97.66 82	84.10 78	95.85 202	96.42 106	91.26 21	97.49 10	96.80 117	86.50 27	98.49 132	95.54 34	99.03 13	98.33 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZD-MVS					99.09 9	83.22 97		96.60 83	82.88 189	93.61 56	98.06 51	82.93 53	99.14 96	95.51 35	98.49 42

xxxxxxxxxxxxxcwj		94.38 22	94.62 19	93.68 58	98.24 58	83.34 93	98.61 23	92.69 299	91.32 19	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 61

SF-MVS		94.17 27	94.05 32	94.55 30	97.56 87	85.95 35	97.73 67	96.43 105	84.02 160	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 61

testtj		94.09 31	94.08 31	94.09 45	99.28 6	83.32 95	97.59 76	96.61 80	83.60 175	94.77 41	98.46 26	82.72 57	99.64 47	95.29 38	98.42 46	99.32 18

ETH3D cwj APD-0.16		93.91 37	93.76 35	94.36 34	96.70 108	85.74 41	97.22 101	96.41 107	83.94 163	94.13 51	98.69 20	83.13 51	99.37 73	95.25 39	98.39 51	97.97 98

test_prior3		94.03 33	94.34 26	93.09 84	98.68 29	81.91 122	98.37 29	96.40 110	86.08 105	94.57 43	98.02 52	83.14 49	99.06 102	95.05 40	98.79 27	98.29 66

test_prior2							98.37 29		86.08 105	94.57 43	98.02 52	83.14 49		95.05 40	98.79 27

SteuartSystems-ACMMP		94.13 29	94.44 23	93.20 79	95.41 135	81.35 139	99.02 13	96.59 84	89.50 42	94.18 50	98.36 30	83.68 45	99.45 65	94.77 42	98.45 44	98.81 34
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.32 10	95.48 12	94.85 23	98.62 38	86.04 34	97.81 59	96.93 38	92.45 12	95.69 24	98.50 24	85.38 31	99.85 10	94.75 43	99.18 7	98.65 44

PHI-MVS		93.59 40	93.63 38	93.48 70	98.05 69	81.76 130	98.64 21	97.13 23	82.60 195	94.09 52	98.49 25	80.35 70	99.85 10	94.74 44	98.62 35	98.83 33

APD-MVS		93.61 39	93.59 39	93.69 57	98.76 26	83.26 96	97.21 103	96.09 135	82.41 197	94.65 42	98.21 34	81.96 62	98.81 119	94.65 45	98.36 54	99.01 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lupinMVS		93.87 38	93.58 40	94.75 26	93.00 204	88.08 16	99.15 4	95.50 167	91.03 24	94.90 36	97.66 74	78.84 89	97.56 164	94.64 46	97.46 77	98.62 46

agg_prior2														94.30 47	99.00 15	98.57 47

PVSNet_BlendedMVS		90.05 113	89.96 105	90.33 179	97.47 89	83.86 81	98.02 48	96.73 61	87.98 70	89.53 114	89.61 240	76.42 128	99.57 54	94.29 48	79.59 239	87.57 308

PVSNet_Blended		93.13 44	92.98 50	93.57 64	97.47 89	83.86 81	99.32 1	96.73 61	91.02 25	89.53 114	96.21 127	76.42 128	99.57 54	94.29 48	95.81 113	97.29 144

CS-MVS-test		92.98 49	93.67 37	90.90 161	96.52 110	76.87 253	98.68 18	94.73 209	90.36 34	94.84 38	97.89 62	77.94 102	97.15 194	94.28 50	97.80 70	98.70 42

MSLP-MVS++		94.28 24	94.39 25	93.97 47	98.30 56	84.06 79	98.64 21	96.93 38	90.71 27	93.08 62	98.70 18	79.98 76	99.21 85	94.12 51	99.07 11	98.63 45

CHOSEN 280x420		91.71 78	91.85 72	91.29 150	94.94 151	82.69 105	87.89 325	96.17 131	85.94 108	87.27 140	94.31 174	90.27 8	95.65 260	94.04 52	95.86 111	95.53 193

CS-MVS		92.73 56	93.48 42	90.48 174	96.27 113	75.93 272	98.55 25	94.93 196	89.32 45	94.54 45	97.67 73	78.91 88	97.02 198	93.80 53	97.32 84	98.49 52

DROMVSNet		91.73 75	92.11 69	90.58 170	93.54 187	77.77 237	98.07 44	94.40 230	87.44 81	92.99 64	97.11 105	74.59 166	96.87 207	93.75 54	97.08 89	97.11 149

SR-MVS		92.16 69	92.27 64	91.83 136	98.37 52	78.41 213	96.67 153	95.76 153	82.19 201	91.97 74	98.07 50	76.44 127	98.64 123	93.71 55	97.27 85	98.45 55

MVS_111021_HR		93.41 42	93.39 44	93.47 73	97.34 99	82.83 104	97.56 79	98.27 6	89.16 48	89.71 109	97.14 102	79.77 78	99.56 56	93.65 56	97.94 66	98.02 88

diffmvs		91.17 92	90.74 90	92.44 112	93.11 203	82.50 110	96.25 181	93.62 269	87.79 75	90.40 102	95.93 132	73.44 179	97.42 176	93.62 57	92.55 145	97.41 136

Regformer-1		94.00 34	94.04 33	93.87 50	98.41 49	84.29 74	97.43 92	97.04 28	89.50 42	92.75 67	98.13 41	82.60 59	99.26 80	93.55 58	96.99 91	98.06 85

Regformer-2		93.92 35	94.01 34	93.67 59	98.41 49	83.75 84	97.43 92	97.00 30	89.43 44	92.69 68	98.13 41	82.48 60	99.22 83	93.51 59	96.99 91	98.04 86

ACMMP_NAP		93.46 41	93.23 46	94.17 42	97.16 102	84.28 75	96.82 141	96.65 74	86.24 101	94.27 47	97.99 55	77.94 102	99.83 16	93.39 60	98.57 36	98.39 58

VNet		92.11 70	91.22 82	94.79 24	96.91 105	86.98 26	97.91 52	97.96 9	86.38 100	93.65 55	95.74 135	70.16 210	98.95 111	93.39 60	88.87 172	98.43 56

canonicalmvs		92.27 68	91.22 82	95.41 15	95.80 125	88.31 13	97.09 120	94.64 217	88.49 59	92.99 64	97.31 94	72.68 185	98.57 128	93.38 62	88.58 176	99.36 16

jason		92.73 56	92.23 66	94.21 41	90.50 264	87.30 25	98.65 20	95.09 188	90.61 28	92.76 66	97.13 103	75.28 155	97.30 183	93.32 63	96.75 101	98.02 88
jason: jason.
MP-MVS-pluss		92.58 64	92.35 62	93.29 75	97.30 100	82.53 108	96.44 167	96.04 139	84.68 140	89.12 119	98.37 29	77.48 111	99.74 33	93.31 64	98.38 52	97.59 126
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
alignmvs		92.97 50	92.26 65	95.12 18	95.54 132	87.77 19	98.67 19	96.38 114	88.04 69	93.01 63	97.45 87	79.20 85	98.60 126	93.25 65	88.76 173	98.99 30

test1172		91.64 79	92.00 71	90.54 172	98.20 62	74.48 285	96.45 165	95.65 158	81.97 205	91.63 80	98.02 52	75.76 140	98.61 124	93.16 66	97.17 87	98.52 51

h-mvs33		89.30 125	88.95 123	90.36 177	95.07 146	76.04 266	96.96 133	97.11 25	90.39 32	92.22 71	95.10 160	74.70 162	98.86 116	93.14 67	65.89 329	96.16 179

hse-mvs2		88.22 154	88.21 131	88.25 227	93.54 187	73.41 291	95.41 217	95.89 146	90.39 32	92.22 71	94.22 177	74.70 162	96.66 218	93.14 67	64.37 334	94.69 210

MVS_111021_LR		91.60 82	91.64 78	91.47 146	95.74 126	78.79 204	96.15 186	96.77 55	88.49 59	88.64 125	97.07 107	72.33 188	99.19 90	93.13 69	96.48 103	96.43 171

VDD-MVS		88.28 152	87.02 162	92.06 126	95.09 144	80.18 169	97.55 80	94.45 228	83.09 182	89.10 120	95.92 134	47.97 326	98.49 132	93.08 70	86.91 189	97.52 131

DELS-MVS		94.98 13	94.49 21	96.44 6	96.42 111	90.59 7	99.21 2	97.02 29	94.40 5	91.46 82	97.08 106	83.32 48	99.69 41	92.83 71	98.70 33	99.04 26
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
xiu_mvs_v1_base_debu		90.54 104	89.54 114	93.55 65	92.31 218	87.58 22	96.99 127	94.87 200	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 206

xiu_mvs_v1_base		90.54 104	89.54 114	93.55 65	92.31 218	87.58 22	96.99 127	94.87 200	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 206

xiu_mvs_v1_base_debi		90.54 104	89.54 114	93.55 65	92.31 218	87.58 22	96.99 127	94.87 200	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 206

zzz-MVS		92.74 54	92.71 54	92.86 95	97.90 72	80.85 150	96.47 162	96.33 118	87.92 71	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 99

MTAPA		92.45 66	92.31 63	92.86 95	97.90 72	80.85 150	92.88 284	96.33 118	87.92 71	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 99

Regformer-3		93.19 43	93.19 47	93.19 80	98.10 66	83.01 102	97.08 122	96.98 32	88.98 49	91.35 87	97.89 62	80.80 66	99.23 81	92.30 77	95.20 117	97.32 140

DeepC-MVS_fast	89.06 2	94.48 20	94.30 28	95.02 19	98.86 23	85.68 44	98.06 45	96.64 77	93.64 8	91.74 79	98.54 22	80.17 75	99.90 5	92.28 78	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		93.06 47	93.12 48	92.89 94	98.10 66	82.20 116	97.08 122	96.92 40	88.87 51	91.23 90	97.89 62	80.57 69	99.19 90	92.21 79	95.20 117	97.29 144

MP-MVS		92.61 63	92.67 57	92.42 113	98.13 65	79.73 179	97.33 99	96.20 128	85.63 114	90.53 99	97.66 74	78.14 100	99.70 40	92.12 80	98.30 57	97.85 106
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS_3200maxsize		91.23 91	91.35 81	90.89 162	97.89 74	76.35 262	96.30 178	95.52 166	79.82 242	91.03 94	97.88 65	74.70 162	98.54 129	92.11 81	96.89 95	97.77 112

SR-MVS-dyc-post		91.29 89	91.45 80	90.80 164	97.76 80	76.03 267	96.20 184	95.44 171	80.56 224	90.72 97	97.84 66	75.76 140	98.61 124	91.99 82	96.79 99	97.75 113

RE-MVS-def			91.18 85		97.76 80	76.03 267	96.20 184	95.44 171	80.56 224	90.72 97	97.84 66	73.36 180		91.99 82	96.79 99	97.75 113

DeepC-MVS	86.58 3	91.53 83	91.06 86	92.94 92	94.52 162	81.89 124	95.95 194	95.98 141	90.76 26	83.76 175	96.76 118	73.24 181	99.71 37	91.67 84	96.96 93	97.22 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
#test#		92.99 48	92.99 49	92.98 89	98.71 27	81.12 142	97.77 61	96.70 65	85.75 112	91.75 77	97.97 59	78.47 94	99.71 37	91.36 85	98.41 48	98.12 81

EPNet		94.06 32	94.15 30	93.76 53	97.27 101	84.35 72	98.29 31	97.64 13	94.57 4	95.36 27	96.88 112	79.96 77	99.12 99	91.30 86	96.11 106	97.82 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		90.70 100	89.90 108	93.09 84	93.61 184	83.48 90	95.20 224	92.79 297	83.22 179	91.82 76	95.70 137	71.82 193	97.48 174	91.25 87	93.67 135	98.32 61

CP-MVS		92.54 65	92.60 59	92.34 115	98.50 45	79.90 173	98.40 28	96.40 110	84.75 136	90.48 101	98.09 46	77.40 112	99.21 85	91.15 88	98.23 59	97.92 101

HFP-MVS		92.89 51	92.86 53	92.98 89	98.71 27	81.12 142	97.58 77	96.70 65	85.20 128	91.75 77	97.97 59	78.47 94	99.71 37	90.95 89	98.41 48	98.12 81

ACMMPR		92.69 60	92.67 57	92.75 99	98.66 32	80.57 157	97.58 77	96.69 68	85.20 128	91.57 81	97.92 61	77.01 118	99.67 45	90.95 89	98.41 48	98.00 93

HPM-MVS		91.62 81	91.53 79	91.89 132	97.88 75	79.22 191	96.99 127	95.73 155	82.07 202	89.50 116	97.19 101	75.59 144	98.93 114	90.91 91	97.94 66	97.54 127
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs		90.95 96	90.39 94	92.63 106	92.82 209	82.53 108	96.83 140	94.47 226	87.69 78	88.47 126	95.56 143	74.04 171	97.54 169	90.90 92	92.74 143	97.83 108

region2R		92.72 58	92.70 56	92.79 98	98.68 29	80.53 160	97.53 81	96.51 95	85.22 126	91.94 75	97.98 57	77.26 113	99.67 45	90.83 93	98.37 53	98.18 74

EIA-MVS		91.73 75	92.05 70	90.78 166	94.52 162	76.40 261	98.06 45	95.34 179	89.19 47	88.90 122	97.28 98	77.56 109	97.73 158	90.77 94	96.86 98	98.20 72

CSCG		92.02 71	91.65 77	93.12 82	98.53 41	80.59 156	97.47 86	97.18 22	77.06 285	84.64 163	97.98 57	83.98 42	99.52 58	90.72 95	97.33 83	99.23 22

ET-MVSNet_ETH3D		90.01 114	89.03 119	92.95 91	94.38 168	86.77 28	98.14 36	96.31 121	89.30 46	63.33 336	96.72 121	90.09 10	93.63 318	90.70 96	82.29 229	98.46 54

CLD-MVS		87.97 159	87.48 149	89.44 202	92.16 230	80.54 159	98.14 36	94.92 197	91.41 18	79.43 221	95.40 146	62.34 251	97.27 186	90.60 97	82.90 223	90.50 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ZNCC-MVS		92.75 53	92.60 59	93.23 78	98.24 58	81.82 128	97.63 72	96.50 97	85.00 133	91.05 93	97.74 71	78.38 96	99.80 24	90.48 98	98.34 55	98.07 84

XVS		92.69 60	92.71 54	92.63 106	98.52 42	80.29 163	97.37 97	96.44 103	87.04 92	91.38 83	97.83 68	77.24 115	99.59 52	90.46 99	98.07 62	98.02 88

X-MVStestdata		86.26 183	84.14 199	92.63 106	98.52 42	80.29 163	97.37 97	96.44 103	87.04 92	91.38 83	20.73 375	77.24 115	99.59 52	90.46 99	98.07 62	98.02 88

baseline		90.76 99	90.10 102	92.74 100	92.90 208	82.56 107	94.60 241	94.56 222	87.69 78	89.06 121	95.67 139	73.76 174	97.51 171	90.43 101	92.23 151	98.16 76

test_yl		91.46 84	90.53 92	94.24 39	97.41 93	85.18 54	98.08 42	97.72 10	80.94 215	89.85 106	96.14 128	75.61 142	98.81 119	90.42 102	88.56 177	98.74 36

DCV-MVSNet		91.46 84	90.53 92	94.24 39	97.41 93	85.18 54	98.08 42	97.72 10	80.94 215	89.85 106	96.14 128	75.61 142	98.81 119	90.42 102	88.56 177	98.74 36

EI-MVSNet-Vis-set		91.84 74	91.77 75	92.04 127	97.60 84	81.17 141	96.61 154	96.87 42	88.20 66	89.19 118	97.55 85	78.69 93	99.14 96	90.29 104	90.94 159	95.80 186

HY-MVS	84.06 6	91.63 80	90.37 96	95.39 16	96.12 118	88.25 14	90.22 308	97.58 14	88.33 64	90.50 100	91.96 206	79.26 83	99.06 102	90.29 104	89.07 169	98.88 32

PAPM		92.87 52	92.40 61	94.30 36	92.25 225	87.85 18	96.40 171	96.38 114	91.07 23	88.72 124	96.90 110	82.11 61	97.37 180	90.05 106	97.70 72	97.67 119

abl_6		89.80 116	89.71 113	90.07 185	96.53 109	75.52 276	94.48 242	95.04 191	81.12 213	89.22 117	97.00 108	68.83 214	98.96 108	89.86 107	95.27 116	95.73 188

mPP-MVS		91.88 73	91.82 73	92.07 125	98.38 51	78.63 207	97.29 100	96.09 135	85.12 130	88.45 127	97.66 74	75.53 145	99.68 43	89.83 108	98.02 65	97.88 102

VDDNet		86.44 180	84.51 191	92.22 121	91.56 245	81.83 127	97.10 119	94.64 217	69.50 331	87.84 134	95.19 153	48.01 325	97.92 153	89.82 109	86.92 188	96.89 157

GST-MVS		92.43 67	92.22 67	93.04 87	98.17 63	81.64 135	97.40 96	96.38 114	84.71 139	90.90 95	97.40 92	77.55 110	99.76 25	89.75 110	97.74 71	97.72 115

MVS		90.60 103	88.64 126	96.50 5	94.25 170	90.53 8	93.33 271	97.21 20	77.59 276	78.88 225	97.31 94	71.52 197	99.69 41	89.60 111	98.03 64	99.27 21

WTY-MVS		92.65 62	91.68 76	95.56 13	96.00 121	88.90 12	98.23 33	97.65 12	88.57 57	89.82 108	97.22 100	79.29 81	99.06 102	89.57 112	88.73 174	98.73 40

CPTT-MVS		89.72 118	89.87 109	89.29 205	98.33 54	73.30 294	97.70 69	95.35 178	75.68 291	87.40 137	97.44 90	70.43 207	98.25 140	89.56 113	96.90 94	96.33 176

LFMVS		89.27 126	87.64 142	94.16 44	97.16 102	85.52 47	97.18 107	94.66 214	79.17 256	89.63 112	96.57 123	55.35 304	98.22 141	89.52 114	89.54 165	98.74 36

EI-MVSNet-UG-set		91.35 88	91.22 82	91.73 137	97.39 95	80.68 154	96.47 162	96.83 45	87.92 71	88.30 131	97.36 93	77.84 105	99.13 98	89.43 115	89.45 166	95.37 196

DPM-MVS		96.21 2	95.53 11	98.26 1	96.26 114	95.09 1	99.15 4	96.98 32	93.39 9	96.45 18	98.79 10	90.17 9	99.99 1	89.33 116	99.25 6	99.70 3

CDPH-MVS		93.12 45	92.91 51	93.74 54	98.65 34	83.88 80	97.67 71	96.26 123	83.00 186	93.22 60	98.24 33	81.31 63	99.21 85	89.12 117	98.74 31	98.14 79

CHOSEN 1792x2688		91.07 93	90.21 99	93.64 60	95.18 142	83.53 89	96.26 180	96.13 132	88.92 50	84.90 158	93.10 196	72.86 183	99.62 50	88.86 118	95.67 114	97.79 111

PGM-MVS		91.93 72	91.80 74	92.32 118	98.27 57	79.74 178	95.28 219	97.27 18	83.83 168	90.89 96	97.78 70	76.12 134	99.56 56	88.82 119	97.93 68	97.66 120

PVSNet_Blended_VisFu		91.24 90	90.77 89	92.66 104	95.09 144	82.40 112	97.77 61	95.87 149	88.26 65	86.39 146	93.94 185	76.77 122	99.27 78	88.80 120	94.00 131	96.31 177

PMMVS		89.46 122	89.92 107	88.06 231	94.64 157	69.57 328	96.22 182	94.95 195	87.27 86	91.37 86	96.54 124	65.88 231	97.39 178	88.54 121	93.89 132	97.23 146

MG-MVS		94.25 26	93.72 36	95.85 11	99.38 3	89.35 11	97.98 49	98.09 8	89.99 37	92.34 70	96.97 109	81.30 64	98.99 106	88.54 121	98.88 21	99.20 23

GG-mvs-BLEND				93.49 69	94.94 151	86.26 31	81.62 347	97.00 30		88.32 130	94.30 175	91.23 5	96.21 232	88.49 123	97.43 80	98.00 93

nrg030		86.79 176	85.43 176	90.87 163	88.76 287	85.34 49	97.06 124	94.33 232	84.31 152	80.45 211	91.98 205	72.36 187	96.36 225	88.48 124	71.13 282	90.93 237

旧先验2							96.97 132		74.06 304	96.10 20			97.76 157	88.38 125

ACMMP		90.39 108	89.97 104	91.64 140	97.58 86	78.21 222	96.78 144	96.72 63	84.73 138	84.72 161	97.23 99	71.22 199	99.63 49	88.37 126	92.41 148	97.08 151
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
CostFormer		89.08 128	88.39 130	91.15 154	93.13 201	79.15 194	88.61 319	96.11 134	83.14 181	89.58 113	86.93 277	83.83 44	96.87 207	88.22 127	85.92 199	97.42 135

PAPR		92.74 54	92.17 68	94.45 31	98.89 22	84.87 66	97.20 105	96.20 128	87.73 77	88.40 128	98.12 44	78.71 92	99.76 25	87.99 128	96.28 104	98.74 36

sss		90.87 98	89.96 105	93.60 63	94.15 172	83.84 83	97.14 113	98.13 7	85.93 109	89.68 110	96.09 130	71.67 194	99.30 77	87.69 129	89.16 168	97.66 120

BP-MVS														87.67 130

HQP-MVS		87.91 161	87.55 147	88.98 210	92.08 232	78.48 209	97.63 72	94.80 205	90.52 29	82.30 189	94.56 170	65.40 235	97.32 181	87.67 130	83.01 220	91.13 233

EPP-MVSNet		89.76 117	89.72 112	89.87 194	93.78 180	76.02 269	97.22 101	96.51 95	79.35 250	85.11 155	95.01 163	84.82 34	97.10 196	87.46 132	88.21 181	96.50 169

baseline2		90.39 108	90.21 99	90.93 159	90.86 258	80.99 146	95.20 224	97.41 15	86.03 107	80.07 218	94.61 169	90.58 6	97.47 175	87.29 133	89.86 164	94.35 212

HQP_MVS		87.50 164	87.09 160	88.74 216	91.86 242	77.96 229	97.18 107	94.69 210	89.89 38	81.33 201	94.15 180	64.77 240	97.30 183	87.08 134	82.82 224	90.96 235

plane_prior5								94.69 210					97.30 183	87.08 134	82.82 224	90.96 235

HyFIR lowres test		89.36 123	88.60 127	91.63 142	94.91 153	80.76 153	95.60 210	95.53 164	82.56 196	84.03 168	91.24 216	78.03 101	96.81 211	87.07 136	88.41 179	97.32 140

HPM-MVS_fast		90.38 110	90.17 101	91.03 157	97.61 83	77.35 246	97.15 112	95.48 168	79.51 248	88.79 123	96.90 110	71.64 196	98.81 119	87.01 137	97.44 79	96.94 153

cascas		86.50 179	84.48 193	92.55 109	92.64 214	85.95 35	97.04 126	95.07 190	75.32 293	80.50 209	91.02 219	54.33 311	97.98 147	86.79 138	87.62 184	93.71 222

bset_n11_16_dypcd		84.35 212	82.83 218	88.91 211	82.54 343	82.07 118	94.12 256	93.47 274	85.39 121	78.55 228	88.98 247	62.23 252	95.11 287	86.75 139	73.42 271	89.55 260

PVSNet_0	77.72 15	81.70 252	78.95 266	89.94 192	90.77 261	76.72 257	95.96 193	96.95 36	85.01 132	70.24 307	88.53 255	52.32 313	98.20 142	86.68 140	44.08 365	94.89 202

gg-mvs-nofinetune		85.48 195	82.90 215	93.24 77	94.51 165	85.82 39	79.22 351	96.97 34	61.19 352	87.33 139	53.01 365	90.58 6	96.07 234	86.07 141	97.23 86	97.81 110

gm-plane-assit					92.27 222	79.64 182			84.47 148		95.15 156		97.93 148	85.81 142

DP-MVS Recon		91.72 77	90.85 87	94.34 35	99.50 1	85.00 63	98.51 26	95.96 142	80.57 223	88.08 133	97.63 80	76.84 120	99.89 7	85.67 143	94.88 121	98.13 80

XVG-OURS-SEG-HR		85.74 191	85.16 183	87.49 245	90.22 268	71.45 314	91.29 302	94.09 244	81.37 210	83.90 173	95.22 150	60.30 266	97.53 170	85.58 144	84.42 211	93.50 224

ab-mvs		87.08 168	84.94 187	93.48 70	93.34 195	83.67 87	88.82 316	95.70 156	81.18 212	84.55 164	90.14 236	62.72 249	98.94 113	85.49 145	82.54 228	97.85 106

MVSTER		89.25 127	88.92 124	90.24 181	95.98 122	84.66 69	96.79 143	95.36 176	87.19 90	80.33 213	90.61 226	90.02 11	95.97 238	85.38 146	78.64 248	90.09 251

RRT_test8_iter05		87.14 167	86.41 168	89.32 204	94.41 167	81.10 144	97.06 124	95.33 180	84.67 141	76.27 255	90.48 227	83.60 46	96.33 226	85.10 147	70.78 285	90.53 240

OMC-MVS		88.80 137	88.16 133	90.72 167	95.30 138	77.92 232	94.81 238	94.51 223	86.80 96	84.97 157	96.85 113	67.53 220	98.60 126	85.08 148	87.62 184	95.63 190

mvs_anonymous		88.68 139	87.62 144	91.86 133	94.80 155	81.69 134	93.53 267	94.92 197	82.03 203	78.87 226	90.43 230	75.77 139	95.34 274	85.04 149	93.16 141	98.55 50

VPA-MVSNet		85.32 196	83.83 201	89.77 199	90.25 267	82.63 106	96.36 173	97.07 27	83.03 185	81.21 203	89.02 246	61.58 261	96.31 228	85.02 150	70.95 284	90.36 242

LCM-MVSNet-Re		83.75 220	83.54 208	84.39 296	93.54 187	64.14 344	92.51 287	84.03 360	83.90 166	66.14 325	86.59 282	67.36 222	92.68 325	84.89 151	92.87 142	96.35 173

ECVR-MVS		88.35 150	87.25 154	91.65 139	93.54 187	79.40 186	96.56 158	90.78 326	86.78 97	85.57 152	95.25 148	57.25 291	97.56 164	84.73 152	94.80 122	97.98 95

IB-MVS	85.34 4	88.67 140	87.14 159	93.26 76	93.12 202	84.32 73	98.76 16	97.27 18	87.19 90	79.36 222	90.45 229	83.92 43	98.53 130	84.41 153	69.79 296	96.93 154
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
CANet_DTU		90.98 94	90.04 103	93.83 51	94.76 156	86.23 32	96.32 177	93.12 292	93.11 10	93.71 54	96.82 116	63.08 248	99.48 63	84.29 154	95.12 120	95.77 187

AdaColmap		88.81 136	87.61 145	92.39 114	99.33 4	79.95 171	96.70 152	95.58 162	77.51 277	83.05 183	96.69 122	61.90 260	99.72 36	84.29 154	93.47 137	97.50 132

test2506		90.96 95	90.39 94	92.65 105	93.54 187	82.46 111	96.37 172	97.35 16	86.78 97	87.55 136	95.25 148	77.83 106	97.50 172	84.07 156	94.80 122	97.98 95

ACMP	81.66 11	84.00 216	83.22 212	86.33 263	91.53 248	72.95 299	95.91 198	93.79 260	83.70 172	73.79 279	92.22 202	54.31 312	96.89 205	83.98 157	79.74 238	89.16 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1111		88.11 155	87.04 161	91.35 147	93.15 199	78.79 204	96.57 156	90.78 326	86.88 95	85.04 156	95.20 152	57.23 292	97.39 178	83.88 158	94.59 124	97.87 104

OPM-MVS		85.84 188	85.10 185	88.06 231	88.34 293	77.83 236	95.72 205	94.20 236	87.89 74	80.45 211	94.05 182	58.57 278	97.26 187	83.88 158	82.76 226	89.09 272
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpmrst		88.36 149	87.38 152	91.31 148	94.36 169	79.92 172	87.32 329	95.26 184	85.32 122	88.34 129	86.13 293	80.60 68	96.70 215	83.78 160	85.34 207	97.30 143

MVSFormer		91.36 87	90.57 91	93.73 56	93.00 204	88.08 16	94.80 239	94.48 224	80.74 219	94.90 36	97.13 103	78.84 89	95.10 289	83.77 161	97.46 77	98.02 88

test_djsdf		83.00 235	82.45 224	84.64 289	84.07 338	69.78 325	94.80 239	94.48 224	80.74 219	75.41 270	87.70 265	61.32 263	95.10 289	83.77 161	79.76 236	89.04 275

LPG-MVS_test		84.20 215	83.49 209	86.33 263	90.88 256	73.06 297	95.28 219	94.13 240	82.20 199	76.31 252	93.20 193	54.83 309	96.95 201	83.72 163	80.83 232	88.98 278

LGP-MVS_train				86.33 263	90.88 256	73.06 297		94.13 240	82.20 199	76.31 252	93.20 193	54.83 309	96.95 201	83.72 163	80.83 232	88.98 278

XVG-OURS		85.18 198	84.38 195	87.59 240	90.42 266	71.73 311	91.06 305	94.07 245	82.00 204	83.29 179	95.08 161	56.42 298	97.55 166	83.70 165	83.42 216	93.49 225

MAR-MVS		90.63 102	90.22 98	91.86 133	98.47 47	78.20 223	97.18 107	96.61 80	83.87 167	88.18 132	98.18 35	68.71 215	99.75 31	83.66 166	97.15 88	97.63 123
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
Effi-MVS+-dtu		84.61 207	84.90 189	83.72 303	91.96 238	63.14 348	94.95 234	93.34 283	85.57 115	79.79 219	87.12 274	61.99 257	95.61 264	83.55 167	85.83 201	92.41 229

mvs-test1		86.83 174	87.17 156	85.81 272	91.96 238	65.24 341	97.90 54	93.34 283	85.57 115	84.51 165	95.14 157	61.99 257	97.19 190	83.55 167	90.55 161	95.00 201

AUN-MVS		86.25 184	85.57 174	88.26 226	93.57 186	73.38 292	95.45 215	95.88 147	83.94 163	85.47 153	94.21 178	73.70 177	96.67 217	83.54 169	64.41 333	94.73 209

testdata				90.13 184	95.92 123	74.17 288		96.49 100	73.49 309	94.82 40	97.99 55	78.80 91	97.93 148	83.53 170	97.52 76	98.29 66

1314		88.94 131	87.20 155	94.17 42	93.21 196	85.73 42	93.33 271	96.64 77	82.89 188	75.98 260	96.36 125	66.83 227	99.39 68	83.52 171	96.02 109	97.39 138

CDS-MVSNet		89.50 121	88.96 122	91.14 155	91.94 241	80.93 148	97.09 120	95.81 151	84.26 155	84.72 161	94.20 179	80.31 71	95.64 261	83.37 172	88.96 171	96.85 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RRT_MVS		86.89 171	85.96 171	89.68 201	95.01 150	84.13 77	96.33 176	94.98 194	84.20 157	80.10 217	92.07 204	70.52 206	95.01 293	83.30 173	77.14 257	89.91 255

PAPM_NR		91.46 84	90.82 88	93.37 74	98.50 45	81.81 129	95.03 233	96.13 132	84.65 142	86.10 150	97.65 78	79.24 84	99.75 31	83.20 174	96.88 96	98.56 48

PS-MVSNAJss		84.91 202	84.30 196	86.74 257	85.89 319	74.40 287	94.95 234	94.16 239	83.93 165	76.45 250	90.11 237	71.04 202	95.77 251	83.16 175	79.02 245	90.06 253

MVS_Test		90.29 111	89.18 118	93.62 62	95.23 139	84.93 64	94.41 245	94.66 214	84.31 152	90.37 103	91.02 219	75.13 157	97.82 155	83.11 176	94.42 126	98.12 81

VPNet		84.69 206	82.92 214	90.01 187	89.01 286	83.45 91	96.71 150	95.46 169	85.71 113	79.65 220	92.18 203	56.66 296	96.01 237	83.05 177	67.84 316	90.56 239

3Dnovator+	82.88 8	89.63 120	87.85 137	94.99 20	94.49 166	86.76 29	97.84 56	95.74 154	86.10 104	75.47 269	96.02 131	65.00 239	99.51 61	82.91 178	97.07 90	98.72 41

TAMVS		88.48 145	87.79 139	90.56 171	91.09 253	79.18 192	96.45 165	95.88 147	83.64 173	83.12 181	93.33 192	75.94 137	95.74 256	82.40 179	88.27 180	96.75 164

baseline1		88.85 135	87.49 148	92.93 93	95.21 141	86.85 27	95.47 214	94.61 219	87.29 84	83.11 182	94.99 164	80.70 67	96.89 205	82.28 180	73.72 269	95.05 200

jajsoiax		82.12 248	81.15 241	85.03 283	84.19 336	70.70 317	94.22 254	93.95 248	83.07 183	73.48 281	89.75 238	49.66 321	95.37 273	82.24 181	79.76 236	89.02 276

mvs_tets		81.74 251	80.71 246	84.84 284	84.22 335	70.29 320	93.91 259	93.78 261	82.77 191	73.37 282	89.46 242	47.36 330	95.31 277	81.99 182	79.55 241	88.92 282

3Dnovator	82.32 10	89.33 124	87.64 142	94.42 33	93.73 183	85.70 43	97.73 67	96.75 59	86.73 99	76.21 257	95.93 132	62.17 253	99.68 43	81.67 183	97.81 69	97.88 102

TESTMET0.1,1		89.83 115	89.34 117	91.31 148	92.54 216	80.19 168	97.11 116	96.57 86	86.15 102	86.85 145	91.83 210	79.32 80	96.95 201	81.30 184	92.35 149	96.77 162

API-MVS		90.18 112	88.97 121	93.80 52	98.66 32	82.95 103	97.50 85	95.63 161	75.16 295	86.31 147	97.69 72	72.49 186	99.90 5	81.26 185	96.07 107	98.56 48

test-LLR		88.48 145	87.98 135	89.98 189	92.26 223	77.23 248	97.11 116	95.96 142	83.76 170	86.30 148	91.38 213	72.30 189	96.78 213	80.82 186	91.92 153	95.94 183

test-mter		88.95 130	88.60 127	89.98 189	92.26 223	77.23 248	97.11 116	95.96 142	85.32 122	86.30 148	91.38 213	76.37 130	96.78 213	80.82 186	91.92 153	95.94 183

miper_enhance_ethall		85.95 187	85.20 180	88.19 230	94.85 154	79.76 175	96.00 191	94.06 246	82.98 187	77.74 235	88.76 250	79.42 79	95.46 270	80.58 188	72.42 277	89.36 266

thisisatest0515		90.95 96	90.26 97	93.01 88	94.03 178	84.27 76	97.91 52	96.67 70	83.18 180	86.87 144	95.51 144	88.66 15	97.85 154	80.46 189	89.01 170	96.92 156

114514_t		88.79 138	87.57 146	92.45 111	98.21 61	81.74 131	96.99 127	95.45 170	75.16 295	82.48 186	95.69 138	68.59 216	98.50 131	80.33 190	95.18 119	97.10 150

PVSNet	82.34 9	89.02 129	87.79 139	92.71 102	95.49 133	81.50 137	97.70 69	97.29 17	87.76 76	85.47 153	95.12 159	56.90 293	98.90 115	80.33 190	94.02 129	97.71 117

tpm2		87.35 166	86.26 169	90.62 169	92.93 207	78.67 206	88.06 324	95.99 140	79.33 251	87.40 137	86.43 288	80.28 72	96.40 223	80.23 192	85.73 203	96.79 160

BH-w/o		88.24 153	87.47 150	90.54 172	95.03 149	78.54 208	97.41 95	93.82 256	84.08 158	78.23 232	94.51 172	69.34 213	97.21 188	80.21 193	94.58 125	95.87 185

UGNet		87.73 162	86.55 167	91.27 151	95.16 143	79.11 195	96.35 174	96.23 125	88.14 67	87.83 135	90.48 227	50.65 316	99.09 101	80.13 194	94.03 128	95.60 191
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
无先验							96.87 138	96.78 49	77.39 278				99.52 58	79.95 195		98.43 56

1121		90.66 101	89.82 110	93.16 81	97.39 95	81.71 133	93.33 271	96.66 73	74.45 301	91.38 83	97.55 85	79.27 82	99.52 58	79.95 195	98.43 45	98.26 70

cl22		85.11 199	84.17 198	87.92 233	95.06 148	78.82 201	95.51 212	94.22 235	79.74 244	76.77 245	87.92 263	75.96 136	95.68 257	79.93 197	72.42 277	89.27 267

原ACMM1				91.22 153	97.77 78	78.10 225		96.61 80	81.05 214	91.28 89	97.42 91	77.92 104	98.98 107	79.85 198	98.51 38	96.59 167

FIs		86.73 178	86.10 170	88.61 218	90.05 272	80.21 167	96.14 187	96.95 36	85.56 118	78.37 231	92.30 201	76.73 123	95.28 278	79.51 199	79.27 242	90.35 243

Anonymous202405211		84.41 211	81.93 230	91.85 135	96.78 107	78.41 213	97.44 88	91.34 316	70.29 327	84.06 167	94.26 176	41.09 348	98.96 108	79.46 200	82.65 227	98.17 75

test_part1		84.72 204	82.85 216	90.34 178	95.73 128	84.79 68	96.75 147	94.10 243	79.05 262	75.97 261	89.51 241	67.69 217	95.94 242	79.34 201	67.50 319	90.30 246

anonymousdsp		80.98 262	79.97 258	84.01 297	81.73 345	70.44 319	92.49 288	93.58 272	77.10 284	72.98 288	86.31 290	57.58 286	94.90 294	79.32 202	78.63 250	86.69 320

ACMM	80.70 13	83.72 221	82.85 216	86.31 266	91.19 251	72.12 304	95.88 199	94.29 233	80.44 227	77.02 242	91.96 206	55.24 305	97.14 195	79.30 203	80.38 234	89.67 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		83.15 230	80.60 248	90.80 164	95.74 126	78.27 217	96.81 142	94.92 197	60.10 357	81.89 198	92.54 200	45.82 333	98.82 118	79.25 204	78.32 253	95.31 198

UniMVSNet_NR-MVSNet		85.49 194	84.59 190	88.21 229	89.44 283	79.36 187	96.71 150	96.41 107	85.22 126	78.11 233	90.98 221	76.97 119	95.14 285	79.14 205	68.30 310	90.12 249

DU-MVS		84.57 208	83.33 211	88.28 225	88.76 287	79.36 187	96.43 169	95.41 175	85.42 119	78.11 233	90.82 222	67.61 218	95.14 285	79.14 205	68.30 310	90.33 244

XXY-MVS		83.84 218	82.00 229	89.35 203	87.13 304	81.38 138	95.72 205	94.26 234	80.15 236	75.92 263	90.63 225	61.96 259	96.52 220	78.98 207	73.28 275	90.14 248

Vis-MVSNet (Re-imp)		88.88 134	88.87 125	88.91 211	93.89 179	74.43 286	96.93 136	94.19 237	84.39 149	83.22 180	95.67 139	78.24 98	94.70 299	78.88 208	94.40 127	97.61 125

miper_ehance_all_eth		84.57 208	83.60 207	87.50 244	92.64 214	78.25 218	95.40 218	93.47 274	79.28 254	76.41 251	87.64 266	76.53 126	95.24 280	78.58 209	72.42 277	89.01 277

UniMVSNet (Re)		85.31 197	84.23 197	88.55 219	89.75 275	80.55 158	96.72 148	96.89 41	85.42 119	78.40 230	88.93 248	75.38 151	95.52 268	78.58 209	68.02 313	89.57 259

IS-MVSNet		88.67 140	88.16 133	90.20 183	93.61 184	76.86 254	96.77 146	93.07 293	84.02 160	83.62 176	95.60 142	74.69 165	96.24 231	78.43 211	93.66 136	97.49 133

thisisatest0530		89.65 119	89.02 120	91.53 144	93.46 193	80.78 152	96.52 159	96.67 70	81.69 208	83.79 174	94.90 165	88.85 14	97.68 159	77.80 212	87.49 187	96.14 180

v2v482		83.46 224	81.86 231	88.25 227	86.19 313	79.65 181	96.34 175	94.02 247	81.56 209	77.32 238	88.23 258	65.62 232	96.03 235	77.77 213	69.72 298	89.09 272

V42		83.04 233	81.53 236	87.57 242	86.27 312	79.09 197	95.87 200	94.11 242	80.35 231	77.22 240	86.79 280	65.32 237	96.02 236	77.74 214	70.14 290	87.61 307

GA-MVS		85.79 190	84.04 200	91.02 158	89.47 282	80.27 165	96.90 137	94.84 203	85.57 115	80.88 205	89.08 244	56.56 297	96.47 222	77.72 215	85.35 206	96.34 174

PLC	83.97 7	88.00 158	87.38 152	89.83 196	98.02 70	76.46 259	97.16 111	94.43 229	79.26 255	81.98 196	96.28 126	69.36 212	99.27 78	77.71 216	92.25 150	93.77 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1112_ss		88.60 143	87.47 150	92.00 129	93.21 196	80.97 147	96.47 162	92.46 301	83.64 173	80.86 206	97.30 96	80.24 73	97.62 161	77.60 217	85.49 204	97.40 137

Vis-MVSNet		88.67 140	87.82 138	91.24 152	92.68 210	78.82 201	96.95 134	93.85 255	87.55 80	87.07 142	95.13 158	63.43 246	97.21 188	77.58 218	96.15 105	97.70 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
c3_l		83.80 219	82.65 221	87.25 251	92.10 231	77.74 239	95.25 222	93.04 294	78.58 266	76.01 259	87.21 273	75.25 156	95.11 287	77.54 219	68.89 304	88.91 283

Test_1112_low_res		88.03 157	86.73 165	91.94 131	93.15 199	80.88 149	96.44 167	92.41 302	83.59 176	80.74 208	91.16 217	80.18 74	97.59 162	77.48 220	85.40 205	97.36 139

新几何1				93.12 82	97.44 91	81.60 136		96.71 64	74.54 300	91.22 91	97.57 81	79.13 86	99.51 61	77.40 221	98.46 43	98.26 70

FC-MVSNet-test		85.96 186	85.39 177	87.66 238	89.38 284	78.02 226	95.65 209	96.87 42	85.12 130	77.34 237	91.94 208	76.28 132	94.74 298	77.09 222	78.82 246	90.21 247

Patchmatch-RL test		76.65 296	74.01 303	84.55 291	77.37 359	64.23 343	78.49 355	82.84 364	78.48 267	64.63 331	73.40 355	76.05 135	91.70 338	76.99 223	57.84 346	97.72 115

IterMVS-LS		83.93 217	82.80 219	87.31 249	91.46 249	77.39 245	95.66 208	93.43 277	80.44 227	75.51 268	87.26 271	73.72 175	95.16 284	76.99 223	70.72 287	89.39 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		85.80 189	85.20 180	87.59 240	91.55 246	77.41 244	95.13 227	95.36 176	80.43 229	80.33 213	94.71 167	73.72 175	95.97 238	76.96 225	78.64 248	89.39 261

eth_miper_zixun_eth		83.12 231	82.01 228	86.47 262	91.85 244	74.80 281	94.33 248	93.18 289	79.11 257	75.74 267	87.25 272	72.71 184	95.32 276	76.78 226	67.13 323	89.27 267

Fast-Effi-MVS+		87.93 160	86.94 164	90.92 160	94.04 176	79.16 193	98.26 32	93.72 265	81.29 211	83.94 172	92.90 197	69.83 211	96.68 216	76.70 227	91.74 155	96.93 154

CMPMVS	54.94 21	75.71 302	74.56 297	79.17 331	79.69 351	55.98 362	89.59 310	93.30 285	60.28 355	53.85 359	89.07 245	47.68 329	96.33 226	76.55 228	81.02 231	85.22 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view						81.74 131	86.80 332		80.65 221	85.65 151		74.26 168		76.52 229		96.98 152

testdata2													99.48 63	76.45 230

D2MVS		82.67 239	81.55 235	86.04 270	87.77 299	76.47 258	95.21 223	96.58 85	82.66 194	70.26 306	85.46 302	60.39 265	95.80 250	76.40 231	79.18 243	85.83 332

UniMVSNet_ETH3D		80.86 263	78.75 267	87.22 252	86.31 310	72.02 305	91.95 293	93.76 264	73.51 307	75.06 273	90.16 235	43.04 342	95.66 258	76.37 232	78.55 251	93.98 218

DWT-MVSNet_test		90.52 107	89.80 111	92.70 103	95.73 128	82.20 116	93.69 262	96.55 90	88.34 63	87.04 143	95.34 147	86.53 26	97.55 166	76.32 233	88.66 175	98.34 59

tpm		85.55 193	84.47 194	88.80 215	90.19 269	75.39 278	88.79 317	94.69 210	84.83 135	83.96 171	85.21 305	78.22 99	94.68 300	76.32 233	78.02 255	96.34 174

BH-untuned		86.95 170	85.94 172	89.99 188	94.52 162	77.46 243	96.78 144	93.37 282	81.80 206	76.62 248	93.81 189	66.64 228	97.02 198	76.06 235	93.88 133	95.48 194

tttt0517		88.57 144	88.19 132	89.71 200	93.00 204	75.99 270	95.67 207	96.67 70	80.78 218	81.82 199	94.40 173	88.97 13	97.58 163	76.05 236	86.31 193	95.57 192

XVG-ACMP-BASELINE		79.38 275	77.90 272	83.81 299	84.98 330	67.14 338	89.03 315	93.18 289	80.26 235	72.87 289	88.15 260	38.55 351	96.26 229	76.05 236	78.05 254	88.02 298

UA-Net		88.92 132	88.48 129	90.24 181	94.06 175	77.18 250	93.04 280	94.66 214	87.39 83	91.09 92	93.89 186	74.92 160	98.18 144	75.83 238	91.43 156	95.35 197

WR-MVS		84.32 213	82.96 213	88.41 221	89.38 284	80.32 162	96.59 155	96.25 124	83.97 162	76.63 247	90.36 231	67.53 220	94.86 296	75.82 239	70.09 294	90.06 253

Baseline_NR-MVSNet		81.22 259	80.07 256	84.68 287	85.32 327	75.12 280	96.48 161	88.80 341	76.24 289	77.28 239	86.40 289	67.61 218	94.39 305	75.73 240	66.73 327	84.54 339

v148		82.41 245	80.89 242	86.99 255	86.18 314	76.81 255	96.27 179	93.82 256	80.49 226	75.28 271	86.11 294	67.32 223	95.75 253	75.48 241	67.03 325	88.42 291

pmmvs4		82.54 241	80.79 243	87.79 235	86.11 315	80.49 161	93.55 266	93.18 289	77.29 280	73.35 283	89.40 243	65.26 238	95.05 292	75.32 242	73.61 270	87.83 301

v1144		82.90 236	81.27 240	87.78 236	86.29 311	79.07 198	96.14 187	93.93 249	80.05 238	77.38 236	86.80 279	65.50 233	95.93 244	75.21 243	70.13 291	88.33 293

Fast-Effi-MVS+-dtu		83.33 226	82.60 222	85.50 278	89.55 280	69.38 329	96.09 190	91.38 313	82.30 198	75.96 262	91.41 212	56.71 294	95.58 266	75.13 244	84.90 209	91.54 231

TR-MVS		86.30 182	84.93 188	90.42 175	94.63 158	77.58 241	96.57 156	93.82 256	80.30 232	82.42 188	95.16 155	58.74 277	97.55 166	74.88 245	87.82 183	96.13 181

NR-MVSNet		83.35 225	81.52 237	88.84 213	88.76 287	81.31 140	94.45 244	95.16 186	84.65 142	67.81 315	90.82 222	70.36 208	94.87 295	74.75 246	66.89 326	90.33 244

CNLPA		86.96 169	85.37 178	91.72 138	97.59 85	79.34 189	97.21 103	91.05 321	74.22 302	78.90 224	96.75 120	67.21 224	98.95 111	74.68 247	90.77 160	96.88 158

cl____		83.27 227	82.12 226	86.74 257	92.20 226	75.95 271	95.11 229	93.27 286	78.44 269	74.82 274	87.02 276	74.19 169	95.19 282	74.67 248	69.32 300	89.09 272

DIV-MVS_self_test		83.27 227	82.12 226	86.74 257	92.19 227	75.92 273	95.11 229	93.26 287	78.44 269	74.81 275	87.08 275	74.19 169	95.19 282	74.66 249	69.30 301	89.11 271

TranMVSNet+NR-MVSNet		83.24 229	81.71 233	87.83 234	87.71 300	78.81 203	96.13 189	94.82 204	84.52 145	76.18 258	90.78 224	64.07 243	94.60 301	74.60 250	66.59 328	90.09 251

Anonymous20231211		79.72 271	77.19 277	87.33 247	95.59 131	77.16 251	95.18 226	94.18 238	59.31 359	72.57 292	86.20 292	47.89 327	95.66 258	74.53 251	69.24 302	89.18 269

CVMVSNet		84.83 203	85.57 174	82.63 314	91.55 246	60.38 355	95.13 227	95.03 192	80.60 222	82.10 195	94.71 167	66.40 230	90.19 350	74.30 252	90.32 162	97.31 142

v144192		82.43 242	80.73 245	87.54 243	85.81 320	78.22 219	95.98 192	93.78 261	79.09 258	77.11 241	86.49 284	64.66 242	95.91 245	74.20 253	69.42 299	88.49 287

pmmvs5		81.34 257	79.54 261	86.73 260	85.02 329	76.91 252	96.22 182	91.65 311	77.65 275	73.55 280	88.61 252	55.70 302	94.43 304	74.12 254	73.35 274	88.86 284

test_post1							85.88 339				30.24 374	73.77 173	95.07 291	73.89 255

SCA		85.63 192	83.64 205	91.60 143	92.30 221	81.86 126	92.88 284	95.56 163	84.85 134	82.52 185	85.12 309	58.04 282	95.39 271	73.89 255	87.58 186	97.54 127

v8		81.88 250	80.06 257	87.32 248	86.63 307	79.04 199	94.41 245	93.65 268	78.77 264	73.19 286	85.57 299	66.87 226	95.81 249	73.84 257	67.61 318	87.11 315

miper_lstm_enhance		81.66 254	80.66 247	84.67 288	91.19 251	71.97 307	91.94 294	93.19 288	77.86 273	72.27 294	85.26 303	73.46 178	93.42 320	73.71 258	67.05 324	88.61 285

GeoE		86.36 181	85.20 180	89.83 196	93.17 198	76.13 264	97.53 81	92.11 304	79.58 247	80.99 204	94.01 183	66.60 229	96.17 233	73.48 259	89.30 167	97.20 148

v1192		82.31 246	80.55 249	87.60 239	85.94 317	78.47 212	95.85 202	93.80 259	79.33 251	76.97 243	86.51 283	63.33 247	95.87 246	73.11 260	70.13 291	88.46 289

PCF-MVS	84.09 5	86.77 177	85.00 186	92.08 124	92.06 235	83.07 100	92.14 292	94.47 226	79.63 246	76.90 244	94.78 166	71.15 200	99.20 89	72.87 261	91.05 158	93.98 218
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1921920		82.02 249	80.23 253	87.41 246	85.62 321	77.92 232	95.79 204	93.69 266	78.86 263	76.67 246	86.44 286	62.50 250	95.83 248	72.69 262	69.77 297	88.47 288

F-COLMAP		84.50 210	83.44 210	87.67 237	95.22 140	72.22 301	95.95 194	93.78 261	75.74 290	76.30 254	95.18 154	59.50 271	98.45 134	72.67 263	86.59 192	92.35 230

IterMVS		80.67 264	79.16 264	85.20 281	89.79 274	76.08 265	92.97 282	91.86 307	80.28 233	71.20 299	85.14 308	57.93 285	91.34 340	72.52 264	70.74 286	88.18 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		80.51 266	79.10 265	84.73 286	89.63 279	74.66 282	92.98 281	91.81 309	80.05 238	71.06 301	85.18 306	58.04 282	91.40 339	72.48 265	70.70 288	88.12 297

v10		81.43 256	79.53 262	87.11 253	86.38 308	78.87 200	94.31 249	93.43 277	77.88 272	73.24 285	85.26 303	65.44 234	95.75 253	72.14 266	67.71 317	86.72 319

MVP-Stereo		82.65 240	81.67 234	85.59 277	86.10 316	78.29 216	93.33 271	92.82 296	77.75 274	69.17 313	87.98 262	59.28 274	95.76 252	71.77 267	96.88 96	82.73 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		77.18 293	76.06 285	80.55 325	83.78 340	60.00 357	90.35 307	91.05 321	77.01 286	66.62 323	87.92 263	47.73 328	94.03 310	71.63 268	68.44 308	87.62 306

v1240		81.70 252	79.83 260	87.30 250	85.50 322	77.70 240	95.48 213	93.44 276	78.46 268	76.53 249	86.44 286	60.85 264	95.84 247	71.59 269	70.17 289	88.35 292

OpenMVS	79.58 14	86.09 185	83.62 206	93.50 68	90.95 255	86.71 30	97.44 88	95.83 150	75.35 292	72.64 291	95.72 136	57.42 290	99.64 47	71.41 270	95.85 112	94.13 216

pm-mvs1		80.05 268	78.02 271	86.15 268	85.42 323	75.81 274	95.11 229	92.69 299	77.13 282	70.36 305	87.43 268	58.44 280	95.27 279	71.36 271	64.25 335	87.36 313

GBi-Net		82.42 243	80.43 251	88.39 222	92.66 211	81.95 119	94.30 250	93.38 279	79.06 259	75.82 264	85.66 295	56.38 299	93.84 313	71.23 272	75.38 263	89.38 263

test1		82.42 243	80.43 251	88.39 222	92.66 211	81.95 119	94.30 250	93.38 279	79.06 259	75.82 264	85.66 295	56.38 299	93.84 313	71.23 272	75.38 263	89.38 263

FMVSNet3		84.71 205	82.71 220	90.70 168	94.55 160	87.71 20	95.92 196	94.67 213	81.73 207	75.82 264	88.08 261	66.99 225	94.47 303	71.23 272	75.38 263	89.91 255

EPMVS		87.47 165	85.90 173	92.18 122	95.41 135	82.26 115	87.00 331	96.28 122	85.88 110	84.23 166	85.57 299	75.07 159	96.26 229	71.14 275	92.50 146	98.03 87

QAPM		86.88 172	84.51 191	93.98 46	94.04 176	85.89 38	97.19 106	96.05 138	73.62 306	75.12 272	95.62 141	62.02 256	99.74 33	70.88 276	96.06 108	96.30 178

thres200		88.92 132	87.65 141	92.73 101	96.30 112	85.62 45	97.85 55	98.86 1	84.38 150	84.82 159	93.99 184	75.12 158	98.01 146	70.86 277	86.67 190	94.56 211

PM-MVS		69.32 323	66.93 325	76.49 337	73.60 367	55.84 363	85.91 338	79.32 369	74.72 299	61.09 345	78.18 346	21.76 368	91.10 343	70.86 277	56.90 348	82.51 351

MS-PatchMatch		83.05 232	81.82 232	86.72 261	89.64 278	79.10 196	94.88 236	94.59 221	79.70 245	70.67 303	89.65 239	50.43 318	96.82 210	70.82 279	95.99 110	84.25 342

PatchMatch-RL		85.00 201	83.66 204	89.02 209	95.86 124	74.55 284	92.49 288	93.60 270	79.30 253	79.29 223	91.47 211	58.53 279	98.45 134	70.22 280	92.17 152	94.07 217

BH-RMVSNet		86.84 173	85.28 179	91.49 145	95.35 137	80.26 166	96.95 134	92.21 303	82.86 190	81.77 200	95.46 145	59.34 273	97.64 160	69.79 281	93.81 134	96.57 168

FMVSNet2		82.79 237	80.44 250	89.83 196	92.66 211	85.43 48	95.42 216	94.35 231	79.06 259	74.46 276	87.28 269	56.38 299	94.31 306	69.72 282	74.68 266	89.76 257

thres100view900		88.30 151	86.95 163	92.33 116	96.10 119	84.90 65	97.14 113	98.85 2	82.69 193	83.41 177	93.66 190	75.43 149	97.93 148	69.04 283	86.24 196	94.17 213

tfpn200view9		88.48 145	87.15 157	92.47 110	96.21 115	85.30 52	97.44 88	98.85 2	83.37 177	83.99 169	93.82 187	75.36 152	97.93 148	69.04 283	86.24 196	94.17 213

thres400		88.42 148	87.15 157	92.23 120	96.21 115	85.30 52	97.44 88	98.85 2	83.37 177	83.99 169	93.82 187	75.36 152	97.93 148	69.04 283	86.24 196	93.45 226

MVS_0304		78.43 280	76.70 281	83.60 305	88.22 295	69.81 324	92.91 283	95.10 187	72.32 319	78.71 227	80.29 340	33.78 359	93.37 322	68.77 286	80.23 235	87.63 305

PatchmatchNet		86.83 174	85.12 184	91.95 130	94.12 173	82.27 114	86.55 335	95.64 160	84.59 144	82.98 184	84.99 311	77.26 113	95.96 241	68.61 287	91.34 157	97.64 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		81.01 261	80.08 255	83.79 300	87.91 298	70.51 318	94.29 253	95.65 158	80.83 217	72.54 293	88.84 249	63.71 244	92.32 329	68.58 288	68.36 309	88.55 286

v7n		79.32 276	77.34 275	85.28 280	84.05 339	72.89 300	93.38 269	93.87 253	75.02 297	70.68 302	84.37 315	59.58 270	95.62 263	67.60 289	67.50 319	87.32 314

PS-CasMVS		80.27 267	79.18 263	83.52 307	87.56 302	69.88 323	94.08 257	95.29 182	80.27 234	72.08 295	88.51 256	59.22 275	92.23 331	67.49 290	68.15 312	88.45 290

RPSCF		77.73 287	76.63 282	81.06 322	88.66 291	55.76 364	87.77 326	87.88 346	64.82 343	74.14 278	92.79 198	49.22 322	96.81 211	67.47 291	76.88 258	90.62 238

thres600view7		88.06 156	86.70 166	92.15 123	96.10 119	85.17 58	97.14 113	98.85 2	82.70 192	83.41 177	93.66 190	75.43 149	97.82 155	67.13 292	85.88 200	93.45 226

tpm cat1		83.63 222	81.38 238	90.39 176	93.53 192	78.19 224	85.56 341	95.09 188	70.78 325	78.51 229	83.28 324	74.80 161	97.03 197	66.77 293	84.05 212	95.95 182

pmmvs6		74.65 306	71.67 310	83.60 305	79.13 353	69.94 322	93.31 275	90.88 325	61.05 354	65.83 326	84.15 318	43.43 338	94.83 297	66.62 294	60.63 343	86.02 329

EPNet_dtu		87.65 163	87.89 136	86.93 256	94.57 159	71.37 315	96.72 148	96.50 97	88.56 58	87.12 141	95.02 162	75.91 138	94.01 311	66.62 294	90.00 163	95.42 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		78.65 279	76.25 284	85.85 271	87.58 301	74.60 283	89.58 311	90.58 329	84.05 159	63.13 337	88.23 258	40.69 350	96.86 209	66.57 296	75.81 261	86.09 328

JIA-IIPM		79.00 278	77.20 276	84.40 295	89.74 277	64.06 345	75.30 360	95.44 171	62.15 347	81.90 197	59.08 363	78.92 87	95.59 265	66.51 297	85.78 202	93.54 223

pmmvs-eth3d		73.59 308	70.66 314	82.38 315	76.40 363	73.38 292	89.39 314	89.43 335	72.69 316	60.34 348	77.79 347	46.43 332	91.26 342	66.42 298	57.06 347	82.51 351

MIMVSNet		79.18 277	75.99 286	88.72 217	87.37 303	80.66 155	79.96 348	91.82 308	77.38 279	74.33 277	81.87 330	41.78 345	90.74 346	66.36 299	83.10 219	94.76 205

K. test v3		73.62 307	71.59 311	79.69 328	82.98 342	59.85 358	90.85 306	88.83 340	77.13 282	58.90 349	82.11 328	43.62 337	91.72 337	65.83 300	54.10 351	87.50 311

FMVSNet1		79.50 273	76.54 283	88.39 222	88.47 292	81.95 119	94.30 250	93.38 279	73.14 311	72.04 296	85.66 295	43.86 336	93.84 313	65.48 301	72.53 276	89.38 263

LF4IMVS		72.36 316	70.82 313	76.95 335	79.18 352	56.33 361	86.12 337	86.11 353	69.30 332	63.06 338	86.66 281	33.03 361	92.25 330	65.33 302	68.64 306	82.28 354

UnsupCasMVSNet_eth		73.25 311	70.57 315	81.30 320	77.53 357	66.33 339	87.24 330	93.89 252	80.38 230	57.90 354	81.59 331	42.91 343	90.56 347	65.18 303	48.51 359	87.01 317

EU-MVSNet		76.92 295	76.95 279	76.83 336	84.10 337	54.73 366	91.77 297	92.71 298	72.74 315	69.57 310	88.69 251	58.03 284	87.43 358	64.91 304	70.00 295	88.33 293

PEN-MVS		79.47 274	78.26 270	83.08 310	86.36 309	68.58 331	93.85 260	94.77 208	79.76 243	71.37 297	88.55 253	59.79 267	92.46 327	64.50 305	65.40 330	88.19 295

WR-MVS_H		81.02 260	80.09 254	83.79 300	88.08 297	71.26 316	94.46 243	96.54 91	80.08 237	72.81 290	86.82 278	70.36 208	92.65 326	64.18 306	67.50 319	87.46 312

test0.0.03 1		82.79 237	82.48 223	83.74 302	86.81 306	72.22 301	96.52 159	95.03 192	83.76 170	73.00 287	93.20 193	72.30 189	88.88 353	64.15 307	77.52 256	90.12 249

MDTV_nov1_ep13			83.69 202		94.09 174	81.01 145	86.78 333	96.09 135	83.81 169	84.75 160	84.32 316	74.44 167	96.54 219	63.88 308	85.07 208

dp		84.30 214	82.31 225	90.28 180	94.24 171	77.97 228	86.57 334	95.53 164	79.94 241	80.75 207	85.16 307	71.49 198	96.39 224	63.73 309	83.36 217	96.48 170

tmp_tt		41.54 337	41.93 339	40.38 355	20.10 381	26.84 378	61.93 367	59.09 377	14.81 374	28.51 369	80.58 336	35.53 356	48.33 376	63.70 310	13.11 373	45.96 369

SixPastTwentyTwo		76.04 298	74.32 299	81.22 321	84.54 332	61.43 354	91.16 303	89.30 337	77.89 271	64.04 332	86.31 290	48.23 323	94.29 307	63.54 311	63.84 337	87.93 300

CR-MVSNet		83.53 223	81.36 239	90.06 186	90.16 270	79.75 176	79.02 353	91.12 318	84.24 156	82.27 193	80.35 338	75.45 147	93.67 317	63.37 312	86.25 194	96.75 164

lessismore_v0				79.98 327	80.59 348	58.34 360		80.87 366		58.49 351	83.46 323	43.10 341	93.89 312	63.11 313	48.68 358	87.72 302

ITE_SJBPF				82.38 315	87.00 305	65.59 340		89.55 334	79.99 240	69.37 311	91.30 215	41.60 347	95.33 275	62.86 314	74.63 267	86.24 325

ACMH+	76.62 16	77.47 290	74.94 292	85.05 282	91.07 254	71.58 313	93.26 276	90.01 331	71.80 321	64.76 330	88.55 253	41.62 346	96.48 221	62.35 315	71.00 283	87.09 316

KD-MVS_2432*1600		77.63 288	74.92 293	85.77 273	90.86 258	79.44 184	88.08 322	93.92 250	76.26 287	67.05 319	82.78 326	72.15 191	91.92 334	61.53 316	41.62 366	85.94 330

miper_refine_blended		77.63 288	74.92 293	85.77 273	90.86 258	79.44 184	88.08 322	93.92 250	76.26 287	67.05 319	82.78 326	72.15 191	91.92 334	61.53 316	41.62 366	85.94 330

ambc				76.02 339	68.11 369	51.43 367	64.97 366	89.59 333		60.49 347	74.49 352	17.17 371	92.46 327	61.50 318	52.85 355	84.17 343

DTE-MVSNet		78.37 281	77.06 278	82.32 317	85.22 328	67.17 337	93.40 268	93.66 267	78.71 265	70.53 304	88.29 257	59.06 276	92.23 331	61.38 319	63.28 339	87.56 309

KD-MVS_self_test		70.97 321	69.31 321	75.95 341	76.24 365	55.39 365	87.45 327	90.94 324	70.20 328	62.96 339	77.48 348	44.01 335	88.09 355	61.25 320	53.26 353	84.37 341

FMVSNet5		76.46 297	74.16 301	83.35 309	90.05 272	76.17 263	89.58 311	89.85 332	71.39 324	65.29 329	80.42 337	50.61 317	87.70 357	61.05 321	69.24 302	86.18 326

UnsupCasMVSNet_bld		68.60 326	64.50 329	80.92 323	74.63 366	67.80 333	83.97 343	92.94 295	65.12 342	54.63 358	68.23 361	35.97 355	92.17 333	60.13 322	44.83 363	82.78 349

tpmvs		83.04 233	80.77 244	89.84 195	95.43 134	77.96 229	85.59 340	95.32 181	75.31 294	76.27 255	83.70 321	73.89 172	97.41 177	59.53 323	81.93 230	94.14 215

ADS-MVSNet2		79.57 272	77.53 274	85.71 275	93.78 180	72.13 303	79.48 349	86.11 353	73.09 312	80.14 215	79.99 341	62.15 254	90.14 351	59.49 324	83.52 214	94.85 203

ADS-MVSNet		81.26 258	78.36 268	89.96 191	93.78 180	79.78 174	79.48 349	93.60 270	73.09 312	80.14 215	79.99 341	62.15 254	95.24 280	59.49 324	83.52 214	94.85 203

MSDG		80.62 265	77.77 273	89.14 206	93.43 194	77.24 247	91.89 295	90.18 330	69.86 330	68.02 314	91.94 208	52.21 314	98.84 117	59.32 326	83.12 218	91.35 232

TransMVSNet (Re)		76.94 294	74.38 298	84.62 290	85.92 318	75.25 279	95.28 219	89.18 338	73.88 305	67.22 316	86.46 285	59.64 268	94.10 309	59.24 327	52.57 356	84.50 340

ACMH	75.40 17	77.99 284	74.96 291	87.10 254	90.67 262	76.41 260	93.19 279	91.64 312	72.47 318	63.44 335	87.61 267	43.34 339	97.16 191	58.34 328	73.94 268	87.72 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmtry		77.36 291	74.59 296	85.67 276	89.75 275	75.75 275	77.85 356	91.12 318	60.28 355	71.23 298	80.35 338	75.45 147	93.56 319	57.94 329	67.34 322	87.68 304

our_test_3		77.90 286	75.37 290	85.48 279	85.39 324	76.74 256	93.63 263	91.67 310	73.39 310	65.72 327	84.65 314	58.20 281	93.13 324	57.82 330	67.87 314	86.57 321

Anonymous20240521		72.06 318	69.91 318	78.50 332	77.11 360	61.67 353	91.62 301	90.97 323	65.52 341	62.37 340	79.05 344	36.32 354	90.96 344	57.75 331	68.52 307	82.87 347

CL-MVSNet_self_test		75.81 300	74.14 302	80.83 324	78.33 355	67.79 334	94.22 254	93.52 273	77.28 281	69.82 308	81.54 332	61.47 262	89.22 352	57.59 332	53.51 352	85.48 334

test_method		56.77 330	54.53 333	63.49 349	76.49 361	40.70 374	75.68 359	74.24 371	19.47 372	48.73 361	71.89 359	19.31 369	65.80 372	57.46 333	47.51 362	83.97 344

AllTest		75.92 299	73.06 306	84.47 292	92.18 228	67.29 335	91.07 304	84.43 358	67.63 334	63.48 333	90.18 233	38.20 352	97.16 191	57.04 334	73.37 272	88.97 280

TestCases				84.47 292	92.18 228	67.29 335		84.43 358	67.63 334	63.48 333	90.18 233	38.20 352	97.16 191	57.04 334	73.37 272	88.97 280

EG-PatchMatch MVS		74.92 304	72.02 309	83.62 304	83.76 341	73.28 295	93.62 264	92.04 306	68.57 333	58.88 350	83.80 320	31.87 363	95.57 267	56.97 336	78.67 247	82.00 356

testgi		74.88 305	73.40 305	79.32 330	80.13 350	61.75 351	93.21 277	86.64 351	79.49 249	66.56 324	91.06 218	35.51 357	88.67 354	56.79 337	71.25 281	87.56 309

LTVRE_ROB	73.68 18	77.99 284	75.74 288	84.74 285	90.45 265	72.02 305	86.41 336	91.12 318	72.57 317	66.63 322	87.27 270	54.95 308	96.98 200	56.29 338	75.98 259	85.21 336
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
YYNet1		73.53 310	70.43 316	82.85 312	84.52 333	71.73 311	91.69 299	91.37 314	67.63 334	46.79 362	81.21 334	55.04 307	90.43 348	55.93 339	59.70 345	86.38 323

MDA-MVSNet_test_wron		73.54 309	70.43 316	82.86 311	84.55 331	71.85 308	91.74 298	91.32 317	67.63 334	46.73 363	81.09 335	55.11 306	90.42 349	55.91 340	59.76 344	86.31 324

TDRefinement		69.20 324	65.78 328	79.48 329	66.04 371	62.21 350	88.21 321	86.12 352	62.92 345	61.03 346	85.61 298	33.23 360	94.16 308	55.82 341	53.02 354	82.08 355

pmmvs3		65.75 328	62.18 331	76.45 338	67.12 370	64.54 342	88.68 318	85.05 356	54.77 363	57.54 356	73.79 353	29.40 366	86.21 361	55.49 342	47.77 361	78.62 359

DSMNet-mixed		73.13 312	72.45 308	75.19 342	77.51 358	46.82 369	85.09 342	82.01 365	67.61 338	69.27 312	81.33 333	50.89 315	86.28 360	54.54 343	83.80 213	92.46 228

LS3D		82.22 247	79.94 259	89.06 207	97.43 92	74.06 290	93.20 278	92.05 305	61.90 348	73.33 284	95.21 151	59.35 272	99.21 85	54.54 343	92.48 147	93.90 220

MVS-HIRNet		71.36 320	67.00 324	84.46 294	90.58 263	69.74 326	79.15 352	87.74 347	46.09 364	61.96 343	50.50 366	45.14 334	95.64 261	53.74 345	88.11 182	88.00 299

Anonymous20231206		75.29 303	73.64 304	80.22 326	80.75 346	63.38 347	93.36 270	90.71 328	73.09 312	67.12 317	83.70 321	50.33 319	90.85 345	53.63 346	70.10 293	86.44 322

DP-MVS		81.47 255	78.28 269	91.04 156	98.14 64	78.48 209	95.09 232	86.97 348	61.14 353	71.12 300	92.78 199	59.59 269	99.38 69	53.11 347	86.61 191	95.27 199

ppachtmachnet_test		77.19 292	74.22 300	86.13 269	85.39 324	78.22 219	93.98 258	91.36 315	71.74 322	67.11 318	84.87 312	56.67 295	93.37 322	52.21 348	64.59 332	86.80 318

TinyColmap		72.41 315	68.99 322	82.68 313	88.11 296	69.59 327	88.41 320	85.20 355	65.55 340	57.91 353	84.82 313	30.80 365	95.94 242	51.38 349	68.70 305	82.49 353

PatchT		79.75 270	76.85 280	88.42 220	89.55 280	75.49 277	77.37 357	94.61 219	63.07 344	82.46 187	73.32 356	75.52 146	93.41 321	51.36 350	84.43 210	96.36 172

new-patchmatchnet		68.85 325	65.93 327	77.61 334	73.57 368	63.94 346	90.11 309	88.73 343	71.62 323	55.08 357	73.60 354	40.84 349	87.22 359	51.35 351	48.49 360	81.67 357

COLMAP_ROB	73.24 19	75.74 301	73.00 307	83.94 298	92.38 217	69.08 330	91.85 296	86.93 349	61.48 351	65.32 328	90.27 232	42.27 344	96.93 204	50.91 352	75.63 262	85.80 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		72.36 316	71.15 312	75.98 340	77.79 356	59.16 359	92.40 290	89.35 336	74.09 303	61.50 344	84.32 316	48.09 324	85.54 363	50.63 353	62.15 341	83.24 346

new_pmnet		66.18 327	63.18 330	75.18 343	76.27 364	61.74 352	83.79 344	84.66 357	56.64 362	51.57 360	71.85 360	31.29 364	87.93 356	49.98 354	62.55 340	75.86 361

TAPA-MVS	81.61 12	85.02 200	83.67 203	89.06 207	96.79 106	73.27 296	95.92 196	94.79 207	74.81 298	80.47 210	96.83 114	71.07 201	98.19 143	49.82 355	92.57 144	95.71 189
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet		52.52 332	48.24 335	65.35 345	47.63 377	41.45 373	72.55 364	83.62 362	31.75 367	37.66 366	57.92 364	9.19 378	76.76 367	49.26 356	44.60 364	77.84 360

Patchmatch-test		78.25 282	74.72 295	88.83 214	91.20 250	74.10 289	73.91 363	88.70 344	59.89 358	66.82 321	85.12 309	78.38 96	94.54 302	48.84 357	79.58 240	97.86 105

MDA-MVSNet-bldmvs		71.45 319	67.94 323	81.98 319	85.33 326	68.50 332	92.35 291	88.76 342	70.40 326	42.99 364	81.96 329	46.57 331	91.31 341	48.75 358	54.39 350	86.11 327

tfpnnormal		78.14 283	75.42 289	86.31 266	88.33 294	79.24 190	94.41 245	96.22 126	73.51 307	69.81 309	85.52 301	55.43 303	95.75 253	47.65 359	67.86 315	83.95 345

MIMVSNet1		69.44 322	66.65 326	77.84 333	76.48 362	62.84 349	87.42 328	88.97 339	66.96 339	57.75 355	79.72 343	32.77 362	85.83 362	46.32 360	63.42 338	84.85 338

RPMNet		79.85 269	75.92 287	91.64 140	90.16 270	79.75 176	79.02 353	95.44 171	58.43 361	82.27 193	72.55 357	73.03 182	98.41 136	46.10 361	86.25 194	96.75 164

OpenMVS_ROB	68.52 20	73.02 313	69.57 319	83.37 308	80.54 349	71.82 309	93.60 265	88.22 345	62.37 346	61.98 342	83.15 325	35.31 358	95.47 269	45.08 362	75.88 260	82.82 348

DeepMVS_CX				64.06 348	78.53 354	43.26 372		68.11 375	69.94 329	38.55 365	76.14 350	18.53 370	79.34 365	43.72 363	41.62 366	69.57 364

N_pmnet		61.30 329	60.20 332	64.60 347	84.32 334	17.00 382	91.67 300	10.98 381	61.77 349	58.45 352	78.55 345	49.89 320	91.83 336	42.27 364	63.94 336	84.97 337

PMMVS2		50.90 334	46.31 337	64.67 346	55.53 373	46.67 370	77.30 358	71.02 373	40.89 365	34.16 368	59.32 362	9.83 377	76.14 369	40.09 365	28.63 369	71.21 362

test_0402		72.68 314	69.54 320	82.09 318	88.67 290	71.81 310	92.72 286	86.77 350	61.52 350	62.21 341	83.91 319	43.22 340	93.76 316	34.60 366	72.23 280	80.72 358

FPMVS		55.09 331	52.93 334	61.57 350	55.98 372	40.51 375	83.11 345	83.41 363	37.61 366	34.95 367	71.95 358	14.40 372	76.95 366	29.81 367	65.16 331	67.25 365

EGC-MVSNET		52.46 333	47.56 336	67.15 344	81.98 344	60.11 356	82.54 346	72.44 372	0.11 378	0.70 379	74.59 351	25.11 367	83.26 364	29.04 368	61.51 342	58.09 366

ANet_high		46.22 335	41.28 340	61.04 351	39.91 379	46.25 371	70.59 365	76.18 370	58.87 360	23.09 371	48.00 368	12.58 374	66.54 371	28.65 369	13.62 372	70.35 363

Gipuma		45.11 336	42.05 338	54.30 352	80.69 347	51.30 368	35.80 370	83.81 361	28.13 368	27.94 370	34.53 370	11.41 376	76.70 368	21.45 370	54.65 349	34.90 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	35.65 22	33.85 339	29.49 344	46.92 354	41.86 378	36.28 376	50.45 369	56.52 378	18.75 373	18.28 372	37.84 369	2.41 380	58.41 373	18.71 371	20.62 370	46.06 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 338	35.53 341	50.18 353	29.72 380	30.30 377	59.60 368	66.20 376	26.06 369	17.91 373	49.53 367	3.12 379	74.09 370	18.19 372	49.40 357	46.14 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 340	32.39 342	33.65 356	53.35 375	25.70 379	74.07 362	53.33 379	21.08 370	17.17 374	33.63 372	11.85 375	54.84 374	12.98 373	14.04 371	20.42 371

EMVS		31.70 341	31.45 343	32.48 357	50.72 376	23.95 380	74.78 361	52.30 380	20.36 371	16.08 375	31.48 373	12.80 373	53.60 375	11.39 374	13.10 374	19.88 372

wuyk23d		14.10 343	13.89 346	14.72 358	55.23 374	22.91 381	33.83 371	3.56 382	4.94 375	4.11 376	2.28 378	2.06 381	19.66 377	10.23 375	8.74 375	1.59 375

testmvs		9.92 344	12.94 347	0.84 360	0.65 382	0.29 384	93.78 261	0.39 383	0.42 376	2.85 377	15.84 376	0.17 383	0.30 379	2.18 376	0.21 376	1.91 374

test123		9.07 345	11.73 348	1.11 359	0.50 383	0.77 383	89.44 313	0.20 384	0.34 377	2.15 378	10.72 377	0.34 382	0.32 378	1.79 377	0.08 377	2.23 373

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

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

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

cdsmvs_eth3d_5k		21.43 342	28.57 345	0.00 361	0.00 384	0.00 385	0.00 372	95.93 145	0.00 379	0.00 380	97.66 74	63.57 245	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		5.92 347	7.89 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	71.04 202	0.00 380	0.00 378	0.00 378	0.00 376

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

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

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

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

ab-mvs-re		8.11 346	10.81 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	97.30 96	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					98.51 44	78.01 227	98.13 39	96.21 127	83.04 184	94.39 46

test_one_0601					98.91 20	84.56 71		96.70 65	88.06 68	96.57 16	98.77 12	88.04 19

eth-test2					0.00 384

eth-test					0.00 384

test_241102_ONE					99.03 16	85.03 61		96.78 49	88.72 54	97.79 4	98.90 6	88.48 16	99.82 18

save fliter					98.24 58	83.34 93	98.61 23	96.57 86	91.32 19

test0726					99.05 10	85.18 54	99.11 8	96.78 49	88.75 52	97.65 9	98.91 3	87.69 21

GSMVS																97.54 127

test_part2					98.90 21	85.14 60				96.07 21

sam_mvs1												77.59 108				97.54 127

sam_mvs												75.35 154

MTGPA								96.33 118

test_post											33.80 371	76.17 133	95.97 238

patchmatchnet-post											77.09 349	77.78 107	95.39 271

MTMP							97.53 81	68.16 374

TEST9					98.64 35	83.71 85	97.82 57	96.65 74	84.29 154	95.16 29	98.09 46	84.39 36	99.36 75

test_8					98.63 37	83.64 88	97.81 59	96.63 79	84.50 146	95.10 31	98.11 45	84.33 37	99.23 81

agg_prior					98.59 39	83.13 98		96.56 88		94.19 48			99.16 94

test_prior4						82.34 113	97.75 66

test_prior				93.09 84	98.68 29	81.91 122		96.40 110					99.06 102			98.29 66

新几何2							96.42 170

旧先验1					97.39 95	79.58 183		96.54 91			98.08 49	84.00 41			97.42 81	97.62 124

原ACMM2							96.84 139

test222					96.15 117	78.41 213	95.87 200	96.46 101	71.97 320	89.66 111	97.45 87	76.33 131			98.24 58	98.30 65

segment_acmp												82.69 58

testdata1							95.57 211		87.44 81

test12				94.25 38	98.34 53	85.55 46		96.35 117		92.36 69		80.84 65	99.22 83		98.31 56	97.98 95

plane_prior7					91.86 242	77.55 242

plane_prior6					91.98 237	77.92 232						64.77 240

plane_prior4											94.15 180

plane_prior3						77.75 238			90.17 36	81.33 201

plane_prior2							97.18 107		89.89 38

plane_prior1					91.95 240

plane_prior						77.96 229	97.52 84		90.36 34						82.96 222

n2								0.00 385

nn								0.00 385

door-mid								79.75 368

test11								96.50 97

door								80.13 367

HQP5-MVS						78.48 209

HQP-NCC					92.08 232		97.63 72		90.52 29	82.30 189

ACMP_Plane					92.08 232		97.63 72		90.52 29	82.30 189

HQP4-MVS										82.30 189			97.32 181			91.13 233

HQP3-MVS								94.80 205							83.01 220

HQP2-MVS												65.40 235

NP-MVS					92.04 236	78.22 219					94.56 170

ACMMP++_ref															78.45 252

ACMMP++															79.05 244

Test By Simon												71.65 195

High-resolution multi-view benchmark