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				96.21 1	98.00 44	90.85 1	97.13 9				97.08 40	92.59 1	98.94 83	92.25 45	98.99 10	98.84 8

SED-MVS		95.91 1	96.28 1	94.80 33	98.77 4	85.99 55	97.13 9	97.44 12	90.31 26	97.71 1	98.07 4	92.31 2	99.58 5	95.66 2	99.13 3	98.84 8

test_241102_ONE					98.77 4	85.99 55		97.44 12	90.26 30	97.71 1	97.96 8	92.31 2	99.38 29

test_0728_THIRD									90.75 19	97.04 8	98.05 6	92.09 4	99.55 12	95.64 4	99.13 3	99.13 1

DPE-MVS		95.57 3	95.67 3	95.25 7	98.36 25	87.28 15	95.56 75	97.51 4	89.13 55	97.14 7	97.91 9	91.64 5	99.62 1	94.61 11	99.17 2	98.86 7

DVP-MVS		95.67 2	96.02 2	94.64 40	98.78 2	85.93 58	97.09 11	96.73 77	90.27 28	97.04 8	98.05 6	91.47 6	99.55 12	95.62 5	99.08 7	98.45 32

test0726					98.78 2	85.93 58	97.19 6	97.47 8	90.27 28	97.64 4	98.13 1	91.47 6

test_241102_TWO								97.44 12	90.31 26	97.62 5	98.07 4	91.46 8	99.58 5	95.66 2	99.12 6	98.98 6

MSP-MVS		95.42 5	95.56 5	94.98 19	98.49 16	86.52 38	96.91 20	97.47 8	91.73 8	96.10 13	96.69 58	89.90 9	99.30 39	94.70 9	98.04 64	99.13 1

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 110	96.52 90	80.00 206	94.00 178	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 79	93.95 16	98.71 30	98.50 22

SD-MVS		94.96 11	95.33 7	93.88 62	97.25 73	86.69 30	96.19 43	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 233	94.38 13	98.85 15	98.03 67
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CNVR-MVS		95.40 6	95.37 6	95.50 5	98.11 37	88.51 5	95.29 86	96.96 52	92.09 3	95.32 19	97.08 40	89.49 12	99.33 36	95.10 8	98.85 15	98.66 14

APDe-MVS		95.46 4	95.64 4	94.91 22	98.26 28	86.29 49	97.46 2	97.40 17	89.03 58	96.20 12	98.10 2	89.39 13	99.34 33	95.88 1	99.03 9	99.10 3

MCST-MVS		94.45 20	94.20 28	95.19 9	98.46 18	87.50 13	95.00 106	97.12 40	87.13 107	92.51 73	96.30 74	89.24 14	99.34 33	93.46 21	98.62 44	98.73 11

TSAR-MVS + MP.		94.85 12	94.94 10	94.58 43	98.25 29	86.33 45	96.11 49	96.62 88	88.14 85	96.10 13	96.96 46	89.09 15	98.94 83	94.48 12	98.68 35	98.48 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 27	96.99 76	86.33 45	97.33 3	97.30 26	91.38 11	95.39 18	97.46 17	88.98 16	99.40 28	94.12 15	98.89 14	98.82 10
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		95.20 7	95.07 9	95.59 3	98.14 36	88.48 6	96.26 40	97.28 28	85.90 133	97.67 3	98.10 2	88.41 17	99.56 7	94.66 10	99.19 1	98.71 12
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
9.14			94.47 17		97.79 52		96.08 50	97.44 12	86.13 131	95.10 22	97.40 21	88.34 18	99.22 46	93.25 28	98.70 32

xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 47	97.85 48	85.63 68	95.21 92	95.47 169	89.44 44	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

SF-MVS		94.97 10	94.90 12	95.20 8	97.84 50	87.76 8	96.65 28	97.48 7	87.76 94	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

HPM-MVS++		95.14 9	94.91 11	95.83 2	98.25 29	89.65 2	95.92 59	96.96 52	91.75 7	94.02 35	96.83 51	88.12 21	99.55 12	93.41 24	98.94 12	98.28 45

agg_prior1		93.29 57	92.97 61	94.26 55	97.38 64	85.92 60	93.92 181	96.72 79	81.96 217	92.16 78	96.23 78	87.85 22	98.97 79	91.95 57	98.55 49	97.90 76

CSCG		93.23 60	93.05 58	93.76 69	98.04 42	84.07 98	96.22 42	97.37 18	84.15 170	90.05 112	95.66 100	87.77 23	99.15 53	89.91 88	98.27 58	98.07 63

NCCC		94.81 13	94.69 15	95.17 10	97.83 51	87.46 14	95.66 70	96.93 55	92.34 2	93.94 36	96.58 65	87.74 24	99.44 27	92.83 33	98.40 53	98.62 16

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 11	97.70 55	87.71 9	95.98 56	97.44 12	86.67 120	95.25 21	97.31 25	87.73 25	99.24 44	93.11 31	98.76 26	98.40 35

TEST9					97.53 58	86.49 39	94.07 171	96.78 70	81.61 230	92.77 63	96.20 80	87.71 26	99.12 55

train_agg		93.44 53	93.08 57	94.52 45	97.53 58	86.49 39	94.07 171	96.78 70	81.86 223	92.77 63	96.20 80	87.63 27	99.12 55	92.14 50	98.69 33	97.94 72

test_8					97.49 61	86.30 48	94.02 176	96.76 73	81.86 223	92.70 67	96.20 80	87.63 27	99.02 68

ZD-MVS					98.15 35	86.62 35		97.07 44	83.63 180	94.19 30	96.91 48	87.57 29	99.26 43	91.99 53	98.44 51

TSAR-MVS + GP.		93.66 48	93.41 51	94.41 52	96.59 86	86.78 26	94.40 146	93.93 238	89.77 38	94.21 29	95.59 102	87.35 30	98.61 106	92.72 36	96.15 103	97.83 81

APD-MVS		94.24 30	94.07 34	94.75 36	98.06 41	86.90 21	95.88 60	96.94 54	85.68 139	95.05 23	97.18 35	87.31 31	99.07 58	91.90 61	98.61 45	98.28 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ETH3 D test6400		93.64 49	93.22 54	94.92 20	97.79 52	86.84 22	95.31 81	97.26 29	82.67 204	93.81 39	96.29 75	87.29 32	99.27 42	89.87 89	98.67 37	98.65 15

ETH3D cwj APD-0.16		93.91 41	93.53 49	95.06 13	96.76 81	87.78 7	94.92 111	97.21 34	84.33 168	93.89 38	97.09 39	87.20 33	99.29 41	91.90 61	98.44 51	98.12 59

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 123	86.75 29	94.57 134	96.70 81	91.84 6	94.41 25	96.56 67	87.19 34	99.13 54	93.50 20	97.65 77	98.16 55

segment_acmp												87.16 35

Regformer-1		94.22 32	94.13 32	94.51 46	95.54 123	86.36 44	94.57 134	96.44 96	91.69 9	94.32 28	96.56 67	87.05 36	99.03 64	93.35 25	97.65 77	98.15 56

testtj		94.39 25	94.18 29	95.00 16	98.24 31	86.77 28	96.16 44	97.23 32	87.28 105	94.85 24	97.04 42	86.99 37	99.52 20	91.54 67	98.33 56	98.71 12

旧先验1					96.79 80	81.81 157		95.67 153			96.81 53	86.69 38			97.66 76	96.97 114

test_prior3		93.60 50	93.53 49	93.82 64	97.29 69	84.49 85	94.12 164	96.88 59	87.67 97	92.63 68	96.39 72	86.62 39	98.87 87	91.50 68	98.67 37	98.11 61

test_prior2							94.12 164		87.67 97	92.63 68	96.39 72	86.62 39		91.50 68	98.67 37

CDPH-MVS		92.83 64	92.30 71	94.44 48	97.79 52	86.11 52	94.06 173	96.66 85	80.09 249	92.77 63	96.63 62	86.62 39	99.04 63	87.40 116	98.66 40	98.17 54

DPM-MVS		92.58 69	91.74 77	95.08 12	96.19 98	89.31 3	92.66 226	96.56 93	83.44 186	91.68 92	95.04 116	86.60 42	98.99 76	85.60 136	97.92 69	96.93 116

DELS-MVS		93.43 54	93.25 53	93.97 59	95.42 126	85.04 74	93.06 217	97.13 39	90.74 20	91.84 86	95.09 115	86.32 43	99.21 47	91.22 73	98.45 50	97.65 85
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
ZNCC-MVS		94.47 18	94.28 22	95.03 14	98.52 14	86.96 17	96.85 23	97.32 24	88.24 80	93.15 55	97.04 42	86.17 44	99.62 1	92.40 41	98.81 18	98.52 20

HFP-MVS		94.52 17	94.40 19	94.86 25	98.61 9	86.81 24	96.94 15	97.34 19	88.63 68	93.65 43	97.21 32	86.10 45	99.49 23	92.35 43	98.77 24	98.30 41

#test#		94.32 28	94.14 31	94.86 25	98.61 9	86.81 24	96.43 31	97.34 19	87.51 100	93.65 43	97.21 32	86.10 45	99.49 23	91.68 65	98.77 24	98.30 41

MVS_111021_HR		93.45 52	93.31 52	93.84 63	96.99 76	84.84 75	93.24 210	97.24 30	88.76 64	91.60 93	95.85 94	86.07 47	98.66 101	91.91 58	98.16 60	98.03 67

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 127	85.47 70	94.68 126	96.41 99	91.60 10	93.75 40	96.71 56	85.95 48	99.10 57	93.21 29	96.65 94	98.01 69

ACMMP_NAP		94.74 14	94.56 16	95.28 6	98.02 43	87.70 10	95.68 68	97.34 19	88.28 79	95.30 20	97.67 13	85.90 49	99.54 16	93.91 17	98.95 11	98.60 17

Regformer-3		93.68 47	93.64 48	93.81 67	95.36 127	84.61 79	94.68 126	95.83 142	91.27 12	93.60 46	96.71 56	85.75 50	98.86 90	92.87 32	96.65 94	97.96 71

PHI-MVS		93.89 43	93.65 47	94.62 42	96.84 79	86.43 41	96.69 27	97.49 5	85.15 154	93.56 49	96.28 76	85.60 51	99.31 38	92.45 38	98.79 19	98.12 59

MP-MVS-pluss		94.21 33	94.00 37	94.85 27	98.17 34	86.65 33	94.82 118	97.17 38	86.26 127	92.83 61	97.87 10	85.57 52	99.56 7	94.37 14	98.92 13	98.34 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
GST-MVS		94.21 33	93.97 38	94.90 24	98.41 22	86.82 23	96.54 30	97.19 35	88.24 80	93.26 51	96.83 51	85.48 53	99.59 4	91.43 71	98.40 53	98.30 41

MP-MVS		94.25 29	94.07 34	94.77 35	98.47 17	86.31 47	96.71 26	96.98 48	89.04 57	91.98 82	97.19 34	85.43 54	99.56 7	92.06 52	98.79 19	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	89.43 2	94.04 36	93.79 42	94.80 33	97.48 62	86.78 26	95.65 72	96.89 58	89.40 47	92.81 62	96.97 45	85.37 55	99.24 44	90.87 81	98.69 33	98.38 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		94.43 22	94.27 24	94.92 20	98.65 7	86.67 32	96.92 19	97.23 32	88.60 70	93.58 47	97.27 27	85.22 56	99.54 16	92.21 46	98.74 29	98.56 19

CP-MVS		94.34 26	94.21 27	94.74 37	98.39 23	86.64 34	97.60 1	97.24 30	88.53 72	92.73 66	97.23 30	85.20 57	99.32 37	92.15 49	98.83 17	98.25 50

test12				94.34 53	97.13 74	86.15 51		96.29 105		91.04 103		85.08 58	99.01 70		98.13 61	97.86 79

ACMMPR		94.43 22	94.28 22	94.91 22	98.63 8	86.69 30	96.94 15	97.32 24	88.63 68	93.53 50	97.26 29	85.04 59	99.54 16	92.35 43	98.78 21	98.50 22

XVS		94.45 20	94.32 20	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	97.16 37	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

X-MVStestdata		88.31 159	86.13 200	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	23.41 352	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

MSLP-MVS++		93.72 46	94.08 33	92.65 103	97.31 67	83.43 115	95.79 63	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 163	92.19 48	98.66 40	96.76 120

HPM-MVS		94.02 37	93.88 39	94.43 50	98.39 23	85.78 65	97.25 5	97.07 44	86.90 115	92.62 70	96.80 55	84.85 63	99.17 50	92.43 39	98.65 42	98.33 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SR-MVS		94.23 31	94.17 30	94.43 50	98.21 33	85.78 65	96.40 34	96.90 57	88.20 83	94.33 27	97.40 21	84.75 64	99.03 64	93.35 25	97.99 65	98.48 24

PGM-MVS		93.96 40	93.72 45	94.68 38	98.43 19	86.22 50	95.30 84	97.78 1	87.45 103	93.26 51	97.33 24	84.62 65	99.51 21	90.75 83	98.57 46	98.32 40

EI-MVSNet-Vis-set		93.01 63	92.92 62	93.29 74	95.01 139	83.51 113	94.48 138	95.77 146	90.87 15	92.52 72	96.67 60	84.50 66	99.00 75	91.99 53	94.44 131	97.36 96

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 104	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

MTAPA		94.42 24	94.22 25	95.00 16	98.42 20	86.95 18	94.36 154	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

test1172		93.97 39	94.07 34	93.66 71	98.11 37	83.45 114	96.26 40	96.84 63	88.33 76	94.19 30	97.43 18	84.24 69	99.01 70	93.26 27	97.98 66	98.52 20

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 64	97.92 45	84.57 81	96.28 38	96.76 73	87.46 101	93.75 40	97.43 18	84.24 69	99.01 70	92.73 34	97.80 72	97.88 77

CS-MVS		92.60 68	92.56 67	92.73 98	95.55 122	82.35 148	96.14 46	96.85 62	88.71 65	91.44 96	91.51 240	84.13 71	98.48 112	91.27 72	97.47 80	97.34 97

ETV-MVS		92.74 66	92.66 65	92.97 88	95.20 135	84.04 100	95.07 101	96.51 94	90.73 21	92.96 58	91.19 248	84.06 72	98.34 125	91.72 64	96.54 97	96.54 128

EI-MVSNet-UG-set		92.74 66	92.62 66	93.12 80	94.86 150	83.20 120	94.40 146	95.74 149	90.71 22	92.05 81	96.60 64	84.00 73	98.99 76	91.55 66	93.63 139	97.17 105

mPP-MVS		93.99 38	93.78 43	94.63 41	98.50 15	85.90 63	96.87 21	96.91 56	88.70 66	91.83 88	97.17 36	83.96 74	99.55 12	91.44 70	98.64 43	98.43 34

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 68	97.92 45	84.19 96	96.30 36	96.87 61	86.96 111	93.92 37	97.47 16	83.88 75	98.96 82	92.71 37	97.87 70	98.26 49

EIA-MVS		91.95 76	91.94 74	91.98 130	95.16 136	80.01 205	95.36 78	96.73 77	88.44 73	89.34 119	92.16 213	83.82 76	98.45 118	89.35 93	97.06 86	97.48 93

EPP-MVSNet		91.70 82	91.56 79	92.13 126	95.88 111	80.50 192	97.33 3	95.25 185	86.15 129	89.76 114	95.60 101	83.42 77	98.32 128	87.37 118	93.25 149	97.56 91

UA-Net		92.83 64	92.54 68	93.68 70	96.10 103	84.71 78	95.66 70	96.39 101	91.92 4	93.22 53	96.49 69	83.16 78	98.87 87	84.47 150	95.47 111	97.45 95

UniMVSNet_NR-MVSNet		89.92 117	89.29 118	91.81 142	93.39 204	83.72 106	94.43 144	97.12 40	89.80 37	86.46 165	93.32 174	83.16 78	97.23 211	84.92 142	81.02 291	94.49 204

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 38	96.76 73	87.46 101	93.75 40	97.43 18	82.94 80		92.73 34	97.80 72	97.88 77

1121		90.42 106	89.49 112	93.20 77	97.27 71	84.46 88	92.63 227	95.51 167	71.01 329	91.20 101	96.21 79	82.92 81	99.05 60	80.56 212	98.07 63	96.10 142

新几何1				93.10 81	97.30 68	84.35 94		95.56 162	71.09 328	91.26 100	96.24 77	82.87 82	98.86 90	79.19 231	98.10 62	96.07 144

原ACMM1				92.01 127	97.34 66	81.05 176		96.81 68	78.89 261	90.45 107	95.92 91	82.65 83	98.84 95	80.68 210	98.26 59	96.14 137

casdiffmvs		92.51 70	92.43 70	92.74 97	94.41 169	81.98 154	94.54 136	96.23 111	89.57 42	91.96 83	96.17 84	82.58 84	98.01 150	90.95 79	95.45 113	98.23 51

DeepC-MVS	88.79 3	93.31 56	92.99 60	94.26 55	96.07 105	85.83 64	94.89 113	96.99 47	89.02 59	89.56 115	97.37 23	82.51 85	99.38 29	92.20 47	98.30 57	97.57 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		93.40 55	93.22 54	93.94 61	98.36 25	84.83 76	97.15 8	96.80 69	85.77 136	92.47 74	97.13 38	82.38 86	99.07 58	90.51 85	98.40 53	97.92 75

baseline		92.39 73	92.29 72	92.69 102	94.46 166	81.77 158	94.14 163	96.27 106	89.22 51	91.88 84	96.00 88	82.35 87	97.99 152	91.05 75	95.27 118	98.30 41

canonicalmvs		93.27 58	92.75 64	94.85 27	95.70 118	87.66 11	96.33 35	96.41 99	90.00 34	94.09 33	94.60 133	82.33 88	98.62 105	92.40 41	92.86 157	98.27 47

DP-MVS Recon		91.95 76	91.28 82	93.96 60	98.33 27	85.92 60	94.66 129	96.66 85	82.69 203	90.03 113	95.82 95	82.30 89	99.03 64	84.57 148	96.48 100	96.91 117

PAPR		90.02 112	89.27 120	92.29 122	95.78 114	80.95 179	92.68 225	96.22 112	81.91 220	86.66 163	93.75 169	82.23 90	98.44 119	79.40 230	94.79 121	97.48 93

MVS_Test		91.31 88	91.11 85	91.93 134	94.37 170	80.14 197	93.46 198	95.80 144	86.46 123	91.35 99	93.77 167	82.21 91	98.09 144	87.57 114	94.95 120	97.55 92

nrg030		91.08 93	90.39 95	93.17 79	93.07 213	86.91 20	96.41 33	96.26 107	88.30 78	88.37 132	94.85 124	82.19 92	97.64 171	91.09 74	82.95 263	94.96 179

UniMVSNet (Re)		89.80 119	89.07 123	92.01 127	93.60 199	84.52 84	94.78 121	97.47 8	89.26 50	86.44 168	92.32 208	82.10 93	97.39 198	84.81 145	80.84 295	94.12 217

testdata				90.49 188	96.40 92	77.89 252		95.37 181	72.51 321	93.63 45	96.69 58	82.08 94	97.65 169	83.08 165	97.39 81	95.94 148

PAPM_NR		91.22 90	90.78 93	92.52 109	97.60 57	81.46 166	94.37 153	96.24 110	86.39 125	87.41 148	94.80 126	82.06 95	98.48 112	82.80 173	95.37 114	97.61 87

MG-MVS		91.77 79	91.70 78	92.00 129	97.08 75	80.03 204	93.60 193	95.18 189	87.85 91	90.89 104	96.47 70	82.06 95	98.36 122	85.07 140	97.04 87	97.62 86

CANet		93.54 51	93.20 56	94.55 44	95.65 119	85.73 67	94.94 109	96.69 83	91.89 5	90.69 105	95.88 93	81.99 97	99.54 16	93.14 30	97.95 68	98.39 36

FC-MVSNet-test		90.27 108	90.18 100	90.53 184	93.71 195	79.85 210	95.77 64	97.59 2	89.31 49	86.27 171	94.67 130	81.93 98	97.01 227	84.26 152	88.09 219	94.71 190

FIs		90.51 105	90.35 96	90.99 173	93.99 185	80.98 177	95.73 65	97.54 3	89.15 54	86.72 162	94.68 129	81.83 99	97.24 209	85.18 139	88.31 215	94.76 189

ACMMP		93.24 59	92.88 63	94.30 54	98.09 40	85.33 72	96.86 22	97.45 11	88.33 76	90.15 111	97.03 44	81.44 100	99.51 21	90.85 82	95.74 106	98.04 66
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
Effi-MVS+		91.59 84	91.11 85	93.01 86	94.35 173	83.39 117	94.60 131	95.10 193	87.10 108	90.57 106	93.10 185	81.43 101	98.07 146	89.29 94	94.48 129	97.59 89

MVS_111021_LR		92.47 71	92.29 72	92.98 87	95.99 108	84.43 92	93.08 215	96.09 120	88.20 83	91.12 102	95.72 99	81.33 102	97.76 163	91.74 63	97.37 82	96.75 121

mvs_anonymous		89.37 134	89.32 117	89.51 230	93.47 202	74.22 292	91.65 257	94.83 210	82.91 199	85.45 191	93.79 165	81.23 103	96.36 265	86.47 131	94.09 133	97.94 72

PVSNet_BlendedMVS		89.98 113	89.70 109	90.82 177	96.12 100	81.25 171	93.92 181	96.83 65	83.49 185	89.10 122	92.26 211	81.04 104	98.85 93	86.72 129	87.86 223	92.35 292

PVSNet_Blended		90.73 97	90.32 97	91.98 130	96.12 100	81.25 171	92.55 231	96.83 65	82.04 215	89.10 122	92.56 201	81.04 104	98.85 93	86.72 129	95.91 104	95.84 153

alignmvs		93.08 62	92.50 69	94.81 32	95.62 121	87.61 12	95.99 54	96.07 122	89.77 38	94.12 32	94.87 121	80.56 106	98.66 101	92.42 40	93.10 152	98.15 56

abl_6		93.18 61	93.05 58	93.57 73	97.52 60	84.27 95	95.53 76	96.67 84	87.85 91	93.20 54	97.22 31	80.35 107	99.18 49	91.91 58	97.21 83	97.26 100

API-MVS		90.66 100	90.07 102	92.45 112	96.36 94	84.57 81	96.06 52	95.22 188	82.39 206	89.13 121	94.27 145	80.32 108	98.46 115	80.16 219	96.71 92	94.33 209

PVSNet_Blended_VisFu		91.38 86	90.91 90	92.80 94	96.39 93	83.17 121	94.87 115	96.66 85	83.29 190	89.27 120	94.46 137	80.29 109	99.17 50	87.57 114	95.37 114	96.05 146

test222					96.55 88	81.70 159	92.22 241	95.01 196	68.36 334	90.20 110	96.14 85	80.26 110			97.80 72	96.05 146

diffmvs		91.37 87	91.23 83	91.77 143	93.09 212	80.27 194	92.36 236	95.52 166	87.03 110	91.40 98	94.93 118	80.08 111	97.44 185	92.13 51	94.56 127	97.61 87

Test By Simon												80.02 112

IterMVS-LS		88.36 158	87.91 152	89.70 223	93.80 192	78.29 243	93.73 187	95.08 195	85.73 137	84.75 209	91.90 227	79.88 113	96.92 232	83.83 157	82.51 269	93.89 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 138	88.86 130	89.80 219	91.84 246	78.30 242	93.70 190	95.01 196	85.73 137	87.15 152	95.28 107	79.87 114	97.21 213	83.81 158	87.36 228	93.88 231

TAPA-MVS	84.62 6	88.16 163	87.01 171	91.62 147	96.64 84	80.65 186	94.39 148	96.21 115	76.38 285	86.19 173	95.44 103	79.75 115	98.08 145	62.75 329	95.29 116	96.13 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		89.41 131	88.64 132	91.71 145	94.74 152	80.81 183	93.54 194	95.10 193	83.11 193	86.82 161	90.67 265	79.74 116	97.75 166	80.51 214	93.55 140	96.57 126

pcd_1.5k_mvsjas		6.64 328	8.86 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	79.70 117	0.00 358	0.00 356	0.00 356	0.00 354

PS-MVSNAJss		89.97 114	89.62 110	91.02 170	91.90 244	80.85 182	95.26 89	95.98 128	86.26 127	86.21 172	94.29 142	79.70 117	97.65 169	88.87 99	88.10 217	94.57 197

PS-MVSNAJ		91.18 91	90.92 89	91.96 132	95.26 133	82.60 143	92.09 246	95.70 151	86.27 126	91.84 86	92.46 203	79.70 117	98.99 76	89.08 96	95.86 105	94.29 210

xiu_mvs_v2_base		91.13 92	90.89 91	91.86 137	94.97 142	82.42 144	92.24 240	95.64 158	86.11 132	91.74 91	93.14 183	79.67 120	98.89 86	89.06 97	95.46 112	94.28 211

WR-MVS_H		87.80 172	87.37 162	89.10 238	93.23 208	78.12 246	95.61 73	97.30 26	87.90 89	83.72 238	92.01 224	79.65 121	96.01 278	76.36 256	80.54 299	93.16 266

EPNet		91.79 78	91.02 88	94.10 58	90.10 305	85.25 73	96.03 53	92.05 272	92.83 1	87.39 151	95.78 96	79.39 122	99.01 70	88.13 108	97.48 79	98.05 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		87.22 199	86.62 185	89.02 241	92.13 236	77.40 266	90.91 268	94.81 212	81.28 236	84.32 224	90.08 276	79.26 123	96.62 243	83.81 158	82.94 264	93.04 271

miper_enhance_ethall		86.90 207	86.18 199	89.06 239	91.66 254	77.58 263	90.22 279	94.82 211	79.16 258	84.48 215	89.10 290	79.19 124	96.66 241	84.06 154	82.94 264	92.94 274

NR-MVSNet		88.58 154	87.47 160	91.93 134	93.04 215	84.16 97	94.77 122	96.25 109	89.05 56	80.04 291	93.29 177	79.02 125	97.05 225	81.71 195	80.05 305	94.59 195

TAMVS		89.21 136	88.29 143	91.96 132	93.71 195	82.62 142	93.30 204	94.19 230	82.22 210	87.78 143	93.94 156	78.83 126	96.95 230	77.70 244	92.98 155	96.32 131

cl_fuxian		87.14 203	86.50 189	89.04 240	92.20 233	77.26 267	91.22 265	94.70 216	82.01 216	84.34 223	90.43 269	78.81 127	96.61 246	83.70 160	81.09 288	93.25 261

1112_ss		88.42 155	87.33 163	91.72 144	94.92 146	80.98 177	92.97 220	94.54 218	78.16 274	83.82 236	93.88 161	78.78 128	97.91 158	79.45 226	89.41 195	96.26 134

CDS-MVSNet		89.45 128	88.51 134	92.29 122	93.62 198	83.61 111	93.01 218	94.68 217	81.95 218	87.82 142	93.24 179	78.69 129	96.99 228	80.34 216	93.23 150	96.28 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS		89.60 122	88.92 127	91.67 146	95.47 125	81.15 175	92.38 235	94.78 214	83.11 193	89.06 124	94.32 140	78.67 130	96.61 246	81.57 196	90.89 177	97.24 101

CPTT-MVS		91.99 75	91.80 76	92.55 107	98.24 31	81.98 154	96.76 25	96.49 95	81.89 222	90.24 109	96.44 71	78.59 131	98.61 106	89.68 90	97.85 71	97.06 109

IS-MVSNet		91.43 85	91.09 87	92.46 111	95.87 113	81.38 169	96.95 14	93.69 244	89.72 40	89.50 117	95.98 89	78.57 132	97.77 162	83.02 167	96.50 99	98.22 52

OMC-MVS		91.23 89	90.62 94	93.08 82	96.27 96	84.07 98	93.52 195	95.93 132	86.95 112	89.51 116	96.13 86	78.50 133	98.35 124	85.84 134	92.90 156	96.83 119

PCF-MVS	84.11 10	87.74 173	86.08 204	92.70 101	94.02 180	84.43 92	89.27 293	95.87 139	73.62 311	84.43 218	94.33 139	78.48 134	98.86 90	70.27 292	94.45 130	94.81 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LCM-MVSNet-Re		88.30 160	88.32 142	88.27 259	94.71 155	72.41 311	93.15 211	90.98 301	87.77 93	79.25 298	91.96 225	78.35 135	95.75 290	83.04 166	95.62 107	96.65 124

HY-MVS	83.01 12	89.03 141	87.94 151	92.29 122	94.86 150	82.77 133	92.08 247	94.49 219	81.52 232	86.93 157	92.79 197	78.32 136	98.23 131	79.93 221	90.55 178	95.88 151

MVS		87.44 189	86.10 203	91.44 153	92.61 227	83.62 110	92.63 227	95.66 155	67.26 335	81.47 269	92.15 214	77.95 137	98.22 133	79.71 223	95.48 110	92.47 287

MVSFormer		91.68 83	91.30 81	92.80 94	93.86 189	83.88 103	95.96 57	95.90 136	84.66 164	91.76 89	94.91 119	77.92 138	97.30 201	89.64 91	97.11 84	97.24 101

lupinMVS		90.92 94	90.21 98	93.03 85	93.86 189	83.88 103	92.81 223	93.86 239	79.84 252	91.76 89	94.29 142	77.92 138	98.04 148	90.48 86	97.11 84	97.17 105

Test_1112_low_res		87.65 176	86.51 188	91.08 166	94.94 145	79.28 224	91.77 251	94.30 226	76.04 290	83.51 245	92.37 206	77.86 140	97.73 167	78.69 235	89.13 202	96.22 135

VNet		92.24 74	91.91 75	93.24 76	96.59 86	83.43 115	94.84 117	96.44 96	89.19 53	94.08 34	95.90 92	77.85 141	98.17 135	88.90 98	93.38 146	98.13 58

DU-MVS		89.34 135	88.50 135	91.85 139	93.04 215	83.72 106	94.47 141	96.59 90	89.50 43	86.46 165	93.29 177	77.25 142	97.23 211	84.92 142	81.02 291	94.59 195

Baseline_NR-MVSNet		87.07 204	86.63 184	88.40 255	91.44 257	77.87 253	94.23 160	92.57 262	84.12 171	85.74 179	92.08 220	77.25 142	96.04 275	82.29 181	79.94 306	91.30 309

jason		90.80 95	90.10 101	92.90 91	93.04 215	83.53 112	93.08 215	94.15 232	80.22 246	91.41 97	94.91 119	76.87 144	97.93 157	90.28 87	96.90 88	97.24 101
jason: jason.
PAPM		86.68 215	85.39 223	90.53 184	93.05 214	79.33 223	89.79 286	94.77 215	78.82 263	81.95 266	93.24 179	76.81 145	97.30 201	66.94 312	93.16 151	94.95 182

Vis-MVSNet (Re-imp)		89.59 123	89.44 114	90.03 208	95.74 115	75.85 283	95.61 73	90.80 307	87.66 99	87.83 141	95.40 106	76.79 146	96.46 259	78.37 236	96.73 91	97.80 82

baseline1		88.10 164	87.28 165	90.57 181	94.96 143	80.07 200	94.27 157	91.29 294	86.74 117	87.41 148	94.00 153	76.77 147	96.20 270	80.77 207	79.31 313	95.44 164

114514_t		89.51 125	88.50 135	92.54 108	98.11 37	81.99 153	95.16 97	96.36 103	70.19 331	85.81 177	95.25 109	76.70 148	98.63 104	82.07 184	96.86 90	97.00 113

PLC	84.53 7	89.06 140	88.03 148	92.15 125	97.27 71	82.69 140	94.29 156	95.44 175	79.71 254	84.01 232	94.18 147	76.68 149	98.75 99	77.28 248	93.41 145	95.02 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TranMVSNet+NR-MVSNet		88.84 146	87.95 150	91.49 150	92.68 225	83.01 127	94.92 111	96.31 104	89.88 36	85.53 185	93.85 163	76.63 150	96.96 229	81.91 188	79.87 308	94.50 202

MAR-MVS		90.30 107	89.37 116	93.07 84	96.61 85	84.48 87	95.68 68	95.67 153	82.36 208	87.85 140	92.85 191	76.63 150	98.80 97	80.01 220	96.68 93	95.91 149
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
WR-MVS		88.38 156	87.67 156	90.52 186	93.30 207	80.18 195	93.26 207	95.96 130	88.57 71	85.47 190	92.81 195	76.12 152	96.91 233	81.24 199	82.29 271	94.47 207

v8		87.50 188	86.71 179	89.89 213	91.37 263	79.40 217	94.50 137	95.38 179	84.81 161	83.60 243	91.33 243	76.05 153	97.42 188	82.84 171	80.51 302	92.84 278

v148		87.04 205	86.32 195	89.21 234	90.94 281	77.26 267	93.71 189	94.43 221	84.84 160	84.36 222	90.80 262	76.04 154	97.05 225	82.12 183	79.60 310	93.31 258

eth_miper_zixun_eth		86.50 221	85.77 216	88.68 249	91.94 243	75.81 284	90.47 274	94.89 205	82.05 213	84.05 230	90.46 268	75.96 155	96.77 237	82.76 174	79.36 312	93.46 255

3Dnovator+	87.14 4	92.42 72	91.37 80	95.55 4	95.63 120	88.73 4	97.07 13	96.77 72	90.84 16	84.02 231	96.62 63	75.95 156	99.34 33	87.77 111	97.68 75	98.59 18

BH-untuned		88.60 153	88.13 147	90.01 210	95.24 134	78.50 237	93.29 205	94.15 232	84.75 162	84.46 216	93.40 171	75.76 157	97.40 195	77.59 245	94.52 128	94.12 217

cl-mvsnet1		86.53 219	85.78 214	88.75 246	92.02 241	76.45 277	90.74 270	94.30 226	81.83 225	83.34 249	90.82 261	75.75 158	96.57 249	81.73 194	81.52 284	93.24 262

BH-w/o		87.57 184	87.05 170	89.12 237	94.90 148	77.90 251	92.41 233	93.51 246	82.89 200	83.70 239	91.34 242	75.75 158	97.07 223	75.49 264	93.49 142	92.39 290

cl-mvsnet_		86.52 220	85.78 214	88.75 246	92.03 240	76.46 276	90.74 270	94.30 226	81.83 225	83.34 249	90.78 263	75.74 160	96.57 249	81.74 193	81.54 283	93.22 263

cdsmvs_eth3d_5k		22.14 323	29.52 326	0.00 340	0.00 361	0.00 362	0.00 352	95.76 147	0.00 357	0.00 358	94.29 142	75.66 161	0.00 358	0.00 356	0.00 356	0.00 354

CNLPA		89.07 139	87.98 149	92.34 118	96.87 78	84.78 77	94.08 170	93.24 249	81.41 233	84.46 216	95.13 114	75.57 162	96.62 243	77.21 249	93.84 137	95.61 160

CHOSEN 1792x2688		88.84 146	87.69 154	92.30 121	96.14 99	81.42 168	90.01 283	95.86 140	74.52 304	87.41 148	93.94 156	75.46 163	98.36 122	80.36 215	95.53 108	97.12 108

CP-MVSNet		87.63 179	87.26 167	88.74 248	93.12 211	76.59 275	95.29 86	96.58 91	88.43 74	83.49 246	92.98 188	75.28 164	95.83 286	78.97 232	81.15 287	93.79 237

v10		87.25 196	86.38 190	89.85 214	91.19 269	79.50 214	94.48 138	95.45 173	83.79 177	83.62 242	91.19 248	75.13 165	97.42 188	81.94 187	80.60 297	92.63 283

Vis-MVSNet		91.75 80	91.23 83	93.29 74	95.32 130	83.78 105	96.14 46	95.98 128	89.89 35	90.45 107	96.58 65	75.09 166	98.31 129	84.75 146	96.90 88	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		88.93 144	88.26 145	90.94 176	94.05 179	80.78 184	91.71 254	95.38 179	81.55 231	88.63 127	93.91 160	75.04 167	95.47 301	82.47 177	91.61 168	96.57 126

v1144		87.61 182	86.79 177	90.06 207	91.01 276	79.34 220	93.95 180	95.42 178	83.36 189	85.66 181	91.31 246	74.98 168	97.42 188	83.37 162	82.06 274	93.42 256

miper_lstm_enhance		85.27 244	84.59 241	87.31 280	91.28 267	74.63 289	87.69 313	94.09 236	81.20 239	81.36 272	89.85 282	74.97 169	94.30 314	81.03 203	79.84 309	93.01 272

test_yl		90.69 98	90.02 106	92.71 99	95.72 116	82.41 146	94.11 166	95.12 191	85.63 140	91.49 94	94.70 127	74.75 170	98.42 120	86.13 132	92.53 161	97.31 98

DCV-MVSNet		90.69 98	90.02 106	92.71 99	95.72 116	82.41 146	94.11 166	95.12 191	85.63 140	91.49 94	94.70 127	74.75 170	98.42 120	86.13 132	92.53 161	97.31 98

V42		87.68 174	86.86 173	90.15 202	90.58 296	80.14 197	94.24 159	95.28 184	83.66 179	85.67 180	91.33 243	74.73 172	97.41 193	84.43 151	81.83 278	92.89 276

XVG-OURS-SEG-HR		89.95 115	89.45 113	91.47 151	94.00 184	81.21 174	91.87 249	96.06 124	85.78 135	88.55 128	95.73 98	74.67 173	97.27 205	88.71 101	89.64 193	95.91 149

v2v482		87.84 170	87.06 169	90.17 200	90.99 277	79.23 227	94.00 178	95.13 190	84.87 159	85.53 185	92.07 222	74.45 174	97.45 183	84.71 147	81.75 280	93.85 235

CLD-MVS		89.47 127	88.90 128	91.18 161	94.22 174	82.07 152	92.13 244	96.09 120	87.90 89	85.37 200	92.45 204	74.38 175	97.56 175	87.15 121	90.43 179	93.93 227
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		87.65 176	86.85 174	90.03 208	92.14 235	80.60 189	93.76 186	95.23 186	82.94 197	84.60 211	94.02 151	74.27 176	95.49 300	81.04 201	83.68 256	94.01 225

HQP_MVS		90.60 104	90.19 99	91.82 140	94.70 156	82.73 137	95.85 61	96.22 112	90.81 17	86.91 158	94.86 122	74.23 177	98.12 136	88.15 106	89.99 184	94.63 191

plane_prior6					94.52 162	82.75 134						74.23 177

v144192		87.19 201	86.35 193	89.74 220	90.64 294	78.24 244	93.92 181	95.43 176	81.93 219	85.51 187	91.05 256	74.21 179	97.45 183	82.86 170	81.56 282	93.53 250

VPA-MVSNet		89.62 121	88.96 125	91.60 148	93.86 189	82.89 131	95.46 77	97.33 22	87.91 88	88.43 131	93.31 175	74.17 180	97.40 195	87.32 119	82.86 268	94.52 200

ab-mvs		89.41 131	88.35 139	92.60 104	95.15 137	82.65 141	92.20 242	95.60 160	83.97 174	88.55 128	93.70 170	74.16 181	98.21 134	82.46 178	89.37 196	96.94 115

1314		87.51 186	86.57 187	90.34 197	92.42 230	79.74 212	92.63 227	95.35 183	78.35 270	80.14 288	91.62 236	74.05 182	97.15 215	81.05 200	93.53 141	94.12 217

test_djsdf		89.03 141	88.64 132	90.21 199	90.74 291	79.28 224	95.96 57	95.90 136	84.66 164	85.33 202	92.94 189	74.02 183	97.30 201	89.64 91	88.53 208	94.05 223

cl-mvsnet2		86.78 211	85.98 207	89.18 236	92.34 231	77.62 262	90.84 269	94.13 234	81.33 235	83.97 233	90.15 274	73.96 184	96.60 248	84.19 153	82.94 264	93.33 257

AdaColmap		89.89 118	89.07 123	92.37 117	97.41 63	83.03 125	94.42 145	95.92 133	82.81 201	86.34 170	94.65 131	73.89 185	99.02 68	80.69 209	95.51 109	95.05 174

HyFIR lowres test		88.09 165	86.81 175	91.93 134	96.00 107	80.63 187	90.01 283	95.79 145	73.42 313	87.68 145	92.10 219	73.86 186	97.96 154	80.75 208	91.70 167	97.19 104

HQP2-MVS												73.83 187

HQP-MVS		89.80 119	89.28 119	91.34 156	94.17 175	81.56 160	94.39 148	96.04 126	88.81 61	85.43 194	93.97 155	73.83 187	97.96 154	87.11 123	89.77 191	94.50 202

3Dnovator	86.66 5	91.73 81	90.82 92	94.44 48	94.59 160	86.37 43	97.18 7	97.02 46	89.20 52	84.31 226	96.66 61	73.74 189	99.17 50	86.74 126	97.96 67	97.79 83

EPNet_dtu		86.49 223	85.94 210	88.14 264	90.24 303	72.82 304	94.11 166	92.20 268	86.66 121	79.42 297	92.36 207	73.52 190	95.81 288	71.26 287	93.66 138	95.80 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		84.43 257	83.06 260	88.54 252	91.72 250	78.44 238	95.18 95	92.82 256	82.73 202	79.67 294	92.12 216	73.49 191	95.96 280	71.10 291	68.73 337	91.21 312

Effi-MVS+-dtu		88.65 151	88.35 139	89.54 227	93.33 205	76.39 278	94.47 141	94.36 223	87.70 95	85.43 194	89.56 287	73.45 192	97.26 207	85.57 137	91.28 170	94.97 176

mvs-test1		89.45 128	89.14 121	90.38 194	93.33 205	77.63 261	94.95 108	94.36 223	87.70 95	87.10 155	92.81 195	73.45 192	98.03 149	85.57 137	93.04 153	95.48 162

baseline2		86.50 221	85.39 223	89.84 215	91.12 273	76.70 273	91.88 248	88.58 330	82.35 209	79.95 292	90.95 258	73.42 194	97.63 172	80.27 218	89.95 187	95.19 171

PEN-MVS		86.80 210	86.27 197	88.40 255	92.32 232	75.71 285	95.18 95	96.38 102	87.97 86	82.82 255	93.15 182	73.39 195	95.92 281	76.15 260	79.03 316	93.59 248

v1192		87.25 196	86.33 194	90.00 211	90.76 290	79.04 228	93.80 184	95.48 168	82.57 205	85.48 189	91.18 250	73.38 196	97.42 188	82.30 180	82.06 274	93.53 250

QAPM		89.51 125	88.15 146	93.59 72	94.92 146	84.58 80	96.82 24	96.70 81	78.43 269	83.41 247	96.19 83	73.18 197	99.30 39	77.11 251	96.54 97	96.89 118

tpmrst		85.35 241	84.99 230	86.43 297	90.88 286	67.88 334	88.71 301	91.43 291	80.13 248	86.08 175	88.80 296	73.05 198	96.02 277	82.48 176	83.40 262	95.40 166

PS-CasMVS		87.32 193	86.88 172	88.63 251	92.99 219	76.33 280	95.33 80	96.61 89	88.22 82	83.30 251	93.07 186	73.03 199	95.79 289	78.36 237	81.00 293	93.75 243

DTE-MVSNet		86.11 228	85.48 221	87.98 267	91.65 255	74.92 288	94.93 110	95.75 148	87.36 104	82.26 260	93.04 187	72.85 200	95.82 287	74.04 276	77.46 321	93.20 264

MVSTER		88.84 146	88.29 143	90.51 187	92.95 220	80.44 193	93.73 187	95.01 196	84.66 164	87.15 152	93.12 184	72.79 201	97.21 213	87.86 110	87.36 228	93.87 232

v1921920		86.97 206	86.06 205	89.69 224	90.53 299	78.11 247	93.80 184	95.43 176	81.90 221	85.33 202	91.05 256	72.66 202	97.41 193	82.05 185	81.80 279	93.53 250

DP-MVS		87.25 196	85.36 225	92.90 91	97.65 56	83.24 119	94.81 119	92.00 274	74.99 299	81.92 267	95.00 117	72.66 202	99.05 60	66.92 314	92.33 164	96.40 129

v7n		86.81 209	85.76 217	89.95 212	90.72 292	79.25 226	95.07 101	95.92 133	84.45 167	82.29 259	90.86 259	72.60 204	97.53 177	79.42 229	80.52 301	93.08 270

OPM-MVS		90.12 110	89.56 111	91.82 140	93.14 210	83.90 102	94.16 162	95.74 149	88.96 60	87.86 139	95.43 105	72.48 205	97.91 158	88.10 109	90.18 183	93.65 247

LS3D		87.89 169	86.32 195	92.59 105	96.07 105	82.92 130	95.23 90	94.92 204	75.66 292	82.89 254	95.98 89	72.48 205	99.21 47	68.43 306	95.23 119	95.64 159

pm-mvs1		86.61 216	85.54 219	89.82 216	91.44 257	80.18 195	95.28 88	94.85 208	83.84 176	81.66 268	92.62 200	72.45 207	96.48 256	79.67 224	78.06 317	92.82 279

PMMVS		85.71 236	84.96 232	87.95 268	88.90 318	77.09 269	88.68 302	90.06 317	72.32 322	86.47 164	90.76 264	72.15 208	94.40 312	81.78 192	93.49 142	92.36 291

PatchmatchNet		85.85 233	84.70 238	89.29 233	91.76 249	75.54 286	88.49 304	91.30 293	81.63 229	85.05 205	88.70 298	71.71 209	96.24 269	74.61 274	89.05 203	96.08 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												71.70 210				96.12 139

patchmatchnet-post											83.76 329	71.53 211	96.48 256

v1240		86.78 211	85.85 212	89.56 226	90.45 300	77.79 256	93.61 192	95.37 181	81.65 227	85.43 194	91.15 252	71.50 212	97.43 186	81.47 198	82.05 276	93.47 254

anonymousdsp		87.84 170	87.09 168	90.12 204	89.13 315	80.54 190	94.67 128	95.55 163	82.05 213	83.82 236	92.12 216	71.47 213	97.15 215	87.15 121	87.80 224	92.67 281

Patchmatch-test		81.37 286	79.30 292	87.58 274	90.92 283	74.16 294	80.99 340	87.68 335	70.52 330	76.63 311	88.81 294	71.21 214	92.76 329	60.01 337	86.93 234	95.83 154

F-COLMAP		87.95 168	86.80 176	91.40 154	96.35 95	80.88 181	94.73 124	95.45 173	79.65 255	82.04 265	94.61 132	71.13 215	98.50 111	76.24 259	91.05 175	94.80 188

pmmvs4		85.43 239	83.86 250	90.16 201	90.02 308	82.97 129	90.27 276	92.67 260	75.93 291	80.73 278	91.74 231	71.05 216	95.73 291	78.85 233	83.46 260	91.78 300

CR-MVSNet		85.35 241	83.76 251	90.12 204	90.58 296	79.34 220	85.24 327	91.96 278	78.27 271	85.55 183	87.87 311	71.03 217	95.61 292	73.96 278	89.36 197	95.40 166

Patchmtry		82.71 271	80.93 277	88.06 266	90.05 307	76.37 279	84.74 329	91.96 278	72.28 323	81.32 273	87.87 311	71.03 217	95.50 299	68.97 302	80.15 304	92.32 293

RPMNet		83.95 261	81.53 271	91.21 159	90.58 296	79.34 220	85.24 327	96.76 73	71.44 326	85.55 183	82.97 333	70.87 219	98.91 85	61.01 333	89.36 197	95.40 166

Patchmatch-RL test		81.67 280	79.96 286	86.81 295	85.42 335	71.23 317	82.17 338	87.50 336	78.47 268	77.19 309	82.50 334	70.81 220	93.48 322	82.66 175	72.89 329	95.71 158

CostFormer		85.77 235	84.94 233	88.26 260	91.16 272	72.58 310	89.47 291	91.04 300	76.26 288	86.45 167	89.97 279	70.74 221	96.86 236	82.35 179	87.07 233	95.34 169

sam_mvs												70.60 222

xiu_mvs_v1_base_debu		90.64 101	90.05 103	92.40 113	93.97 186	84.46 88	93.32 200	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 212

xiu_mvs_v1_base		90.64 101	90.05 103	92.40 113	93.97 186	84.46 88	93.32 200	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 212

xiu_mvs_v1_base_debi		90.64 101	90.05 103	92.40 113	93.97 186	84.46 88	93.32 200	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 212

test_post											10.29 353	70.57 226	95.91 283

CANet_DTU		90.26 109	89.41 115	92.81 93	93.46 203	83.01 127	93.48 196	94.47 220	89.43 46	87.76 144	94.23 146	70.54 227	99.03 64	84.97 141	96.39 101	96.38 130

BH-RMVSNet		88.37 157	87.48 159	91.02 170	95.28 131	79.45 216	92.89 222	93.07 252	85.45 146	86.91 158	94.84 125	70.35 228	97.76 163	73.97 277	94.59 126	95.85 152

Fast-Effi-MVS+-dtu		87.44 189	86.72 178	89.63 225	92.04 239	77.68 260	94.03 175	93.94 237	85.81 134	82.42 258	91.32 245	70.33 229	97.06 224	80.33 217	90.23 182	94.14 215

MDTV_nov1_ep13_2view						55.91 350	87.62 315		73.32 314	84.59 212		70.33 229		74.65 273		95.50 161

ACMM	84.12 9	89.14 137	88.48 138	91.12 162	94.65 159	81.22 173	95.31 81	96.12 119	85.31 150	85.92 176	94.34 138	70.19 231	98.06 147	85.65 135	88.86 205	94.08 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		87.51 186	85.91 211	92.32 119	93.70 197	83.93 101	92.33 237	90.94 303	84.16 169	72.09 330	92.52 202	69.90 232	95.85 285	89.20 95	88.36 214	97.17 105

LPG-MVS_test		89.45 128	88.90 128	91.12 162	94.47 164	81.49 164	95.30 84	96.14 117	86.73 118	85.45 191	95.16 112	69.89 233	98.10 138	87.70 112	89.23 200	93.77 241

LGP-MVS_train				91.12 162	94.47 164	81.49 164		96.14 117	86.73 118	85.45 191	95.16 112	69.89 233	98.10 138	87.70 112	89.23 200	93.77 241

CHOSEN 280x420		85.15 246	83.99 248	88.65 250	92.47 228	78.40 240	79.68 342	92.76 257	74.90 301	81.41 271	89.59 285	69.85 235	95.51 297	79.92 222	95.29 116	92.03 297

LTVRE_ROB	82.13 13	86.26 226	84.90 234	90.34 197	94.44 168	81.50 162	92.31 239	94.89 205	83.03 195	79.63 295	92.67 198	69.69 236	97.79 161	71.20 288	86.26 236	91.72 301
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
OpenMVS	83.78 11	88.74 149	87.29 164	93.08 82	92.70 224	85.39 71	96.57 29	96.43 98	78.74 266	80.85 277	96.07 87	69.64 237	99.01 70	78.01 242	96.65 94	94.83 186

MDTV_nov1_ep13			83.56 255		91.69 253	69.93 328	87.75 312	91.54 287	78.60 267	84.86 208	88.90 293	69.54 238	96.03 276	70.25 293	88.93 204

PatchT		82.68 272	81.27 273	86.89 293	90.09 306	70.94 322	84.06 331	90.15 314	74.91 300	85.63 182	83.57 330	69.37 239	94.87 310	65.19 319	88.50 210	94.84 185

VPNet		88.20 162	87.47 160	90.39 192	93.56 200	79.46 215	94.04 174	95.54 165	88.67 67	86.96 156	94.58 135	69.33 240	97.15 215	84.05 155	80.53 300	94.56 198

ACMP	84.23 8	89.01 143	88.35 139	90.99 173	94.73 153	81.27 170	95.07 101	95.89 138	86.48 122	83.67 240	94.30 141	69.33 240	97.99 152	87.10 125	88.55 207	93.72 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_post1							88.00 309				9.81 354	69.31 242	95.53 295	76.65 254

tpmvs		83.35 269	82.07 266	87.20 287	91.07 275	71.00 321	88.31 307	91.70 282	78.91 260	80.49 283	87.18 318	69.30 243	97.08 222	68.12 310	83.56 258	93.51 253

thres200		87.21 200	86.24 198	90.12 204	95.36 127	78.53 235	93.26 207	92.10 270	86.42 124	88.00 138	91.11 254	69.24 244	98.00 151	69.58 300	91.04 176	93.83 236

tfpn200view9		87.58 183	86.64 182	90.41 191	95.99 108	78.64 232	94.58 132	91.98 276	86.94 113	88.09 133	91.77 229	69.18 245	98.10 138	70.13 296	91.10 171	94.48 205

thres400		87.62 181	86.64 182	90.57 181	95.99 108	78.64 232	94.58 132	91.98 276	86.94 113	88.09 133	91.77 229	69.18 245	98.10 138	70.13 296	91.10 171	94.96 179

tfpnnormal		84.72 254	83.23 258	89.20 235	92.79 223	80.05 202	94.48 138	95.81 143	82.38 207	81.08 275	91.21 247	69.01 247	96.95 230	61.69 331	80.59 298	90.58 321

thres100view900		87.63 179	86.71 179	90.38 194	96.12 100	78.55 234	95.03 105	91.58 285	87.15 106	88.06 136	92.29 210	68.91 248	98.10 138	70.13 296	91.10 171	94.48 205

thres600view7		87.65 176	86.67 181	90.59 180	96.08 104	78.72 230	94.88 114	91.58 285	87.06 109	88.08 135	92.30 209	68.91 248	98.10 138	70.05 299	91.10 171	94.96 179

PatchMatch-RL		86.77 214	85.54 219	90.47 190	95.88 111	82.71 139	90.54 273	92.31 265	79.82 253	84.32 224	91.57 239	68.77 250	96.39 262	73.16 282	93.48 144	92.32 293

XVG-OURS		89.40 133	88.70 131	91.52 149	94.06 178	81.46 166	91.27 263	96.07 122	86.14 130	88.89 126	95.77 97	68.73 251	97.26 207	87.39 117	89.96 186	95.83 154

TR-MVS		86.78 211	85.76 217	89.82 216	94.37 170	78.41 239	92.47 232	92.83 255	81.11 240	86.36 169	92.40 205	68.73 251	97.48 180	73.75 280	89.85 190	93.57 249

tpm		84.73 253	84.02 247	86.87 294	90.33 301	68.90 331	89.06 297	89.94 320	80.85 242	85.75 178	89.86 281	68.54 253	95.97 279	77.76 243	84.05 252	95.75 157

FMVSNet3		87.40 191	86.11 202	91.30 157	93.79 194	83.64 109	94.20 161	94.81 212	83.89 175	84.37 219	91.87 228	68.45 254	96.56 251	78.23 239	85.36 242	93.70 246

MVP-Stereo		85.97 230	84.86 235	89.32 232	90.92 283	82.19 150	92.11 245	94.19 230	78.76 265	78.77 300	91.63 235	68.38 255	96.56 251	75.01 271	93.95 134	89.20 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		81.96 276	80.27 281	87.01 289	91.09 274	71.02 320	87.38 316	91.53 288	66.25 336	80.17 286	86.35 321	68.22 256	96.15 273	69.16 301	82.29 271	93.86 234

tpm2		84.08 259	82.94 261	87.48 278	91.39 262	71.27 316	89.23 295	90.37 311	71.95 324	84.64 210	89.33 288	67.30 257	96.55 253	75.17 268	87.09 232	94.63 191

test-LLR		85.87 232	85.41 222	87.25 283	90.95 279	71.67 314	89.55 287	89.88 323	83.41 187	84.54 213	87.95 308	67.25 258	95.11 306	81.82 190	93.37 147	94.97 176

test0.0.03 1		82.41 274	81.69 269	84.59 310	88.23 325	72.89 303	90.24 277	87.83 333	83.41 187	79.86 293	89.78 283	67.25 258	88.99 341	65.18 320	83.42 261	91.90 299

CVMVSNet		84.69 255	84.79 237	84.37 312	91.84 246	64.92 341	93.70 190	91.47 290	66.19 337	86.16 174	95.28 107	67.18 260	93.33 324	80.89 206	90.42 180	94.88 184

thisisatest0515		87.33 192	85.99 206	91.37 155	93.49 201	79.55 213	90.63 272	89.56 328	80.17 247	87.56 147	90.86 259	67.07 261	98.28 130	81.50 197	93.02 154	96.29 132

tttt0517		88.61 152	87.78 153	91.11 165	94.96 143	77.81 255	95.35 79	89.69 325	85.09 156	88.05 137	94.59 134	66.93 262	98.48 112	83.27 164	92.13 166	97.03 111

our_test_3		81.93 277	80.46 279	86.33 299	88.46 322	73.48 298	88.46 305	91.11 296	76.46 283	76.69 310	88.25 304	66.89 263	94.36 313	68.75 303	79.08 315	91.14 314

thisisatest0530		88.67 150	87.61 157	91.86 137	94.87 149	80.07 200	94.63 130	89.90 322	84.00 173	88.46 130	93.78 166	66.88 264	98.46 115	83.30 163	92.65 159	97.06 109

IterMVS-SCA-FT		85.45 238	84.53 242	88.18 263	91.71 251	76.87 272	90.19 280	92.65 261	85.40 148	81.44 270	90.54 266	66.79 265	95.00 309	81.04 201	81.05 289	92.66 282

SCA		86.32 225	85.18 227	89.73 222	92.15 234	76.60 274	91.12 266	91.69 283	83.53 184	85.50 188	88.81 294	66.79 265	96.48 256	76.65 254	90.35 181	96.12 139

D2MVS		85.90 231	85.09 229	88.35 257	90.79 288	77.42 265	91.83 250	95.70 151	80.77 243	80.08 290	90.02 277	66.74 267	96.37 263	81.88 189	87.97 221	91.26 310

IterMVS		84.88 251	83.98 249	87.60 273	91.44 257	76.03 282	90.18 281	92.41 264	83.24 192	81.06 276	90.42 270	66.60 268	94.28 315	79.46 225	80.98 294	92.48 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		87.26 194	85.98 207	91.08 166	94.01 181	83.10 122	95.14 98	94.94 199	83.57 181	84.37 219	91.64 232	66.59 269	96.34 266	78.23 239	85.36 242	93.79 237

test1		87.26 194	85.98 207	91.08 166	94.01 181	83.10 122	95.14 98	94.94 199	83.57 181	84.37 219	91.64 232	66.59 269	96.34 266	78.23 239	85.36 242	93.79 237

FMVSNet2		87.19 201	85.82 213	91.30 157	94.01 181	83.67 108	94.79 120	94.94 199	83.57 181	83.88 234	92.05 223	66.59 269	96.51 254	77.56 246	85.01 245	93.73 244

EPMVS		83.90 263	82.70 265	87.51 275	90.23 304	72.67 306	88.62 303	81.96 345	81.37 234	85.01 206	88.34 302	66.31 272	94.45 311	75.30 267	87.12 231	95.43 165

ppachtmachnet_test		81.84 278	80.07 285	87.15 288	88.46 322	74.43 291	89.04 298	92.16 269	75.33 295	77.75 305	88.99 291	66.20 273	95.37 302	65.12 321	77.60 319	91.65 302

MDA-MVSNet_test_wron		79.21 302	77.19 304	85.29 305	88.22 326	72.77 305	85.87 323	90.06 317	74.34 305	62.62 342	87.56 314	66.14 274	91.99 333	66.90 315	73.01 327	91.10 316

YYNet1		79.22 301	77.20 303	85.28 306	88.20 327	72.66 307	85.87 323	90.05 319	74.33 306	62.70 341	87.61 313	66.09 275	92.03 332	66.94 312	72.97 328	91.15 313

JIA-IIPM		81.04 289	78.98 297	87.25 283	88.64 319	73.48 298	81.75 339	89.61 327	73.19 315	82.05 264	73.71 340	66.07 276	95.87 284	71.18 290	84.60 248	92.41 289

RRT_MVS		88.86 145	87.68 155	92.39 116	92.02 241	86.09 53	94.38 152	94.94 199	85.45 146	87.14 154	93.84 164	65.88 277	97.11 219	88.73 100	86.77 235	93.98 226

MSDG		84.86 252	83.09 259	90.14 203	93.80 192	80.05 202	89.18 296	93.09 251	78.89 261	78.19 301	91.91 226	65.86 278	97.27 205	68.47 305	88.45 211	93.11 268

jajsoiax		88.24 161	87.50 158	90.48 189	90.89 285	80.14 197	95.31 81	95.65 157	84.97 158	84.24 228	94.02 151	65.31 279	97.42 188	88.56 102	88.52 209	93.89 229

cascas		86.43 224	84.98 231	90.80 178	92.10 238	80.92 180	90.24 277	95.91 135	73.10 316	83.57 244	88.39 301	65.15 280	97.46 182	84.90 144	91.43 169	94.03 224

ADS-MVSNet2		81.66 281	79.71 289	87.50 276	91.35 264	74.19 293	83.33 334	88.48 331	72.90 318	82.24 261	85.77 323	64.98 281	93.20 326	64.57 323	83.74 254	95.12 172

ADS-MVSNet		81.56 283	79.78 287	86.90 292	91.35 264	71.82 313	83.33 334	89.16 329	72.90 318	82.24 261	85.77 323	64.98 281	93.76 319	64.57 323	83.74 254	95.12 172

pmmvs5		84.21 258	82.84 264	88.34 258	88.95 317	76.94 271	92.41 233	91.91 280	75.63 293	80.28 285	91.18 250	64.59 283	95.57 293	77.09 252	83.47 259	92.53 285

PVSNet	78.82 18	85.55 237	84.65 239	88.23 262	94.72 154	71.93 312	87.12 317	92.75 258	78.80 264	84.95 207	90.53 267	64.43 284	96.71 240	74.74 272	93.86 136	96.06 145

UGNet		89.95 115	88.95 126	92.95 89	94.51 163	83.31 118	95.70 67	95.23 186	89.37 48	87.58 146	93.94 156	64.00 285	98.78 98	83.92 156	96.31 102	96.74 122
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
RPSCF		85.07 247	84.27 244	87.48 278	92.91 221	70.62 324	91.69 256	92.46 263	76.20 289	82.67 257	95.22 110	63.94 286	97.29 204	77.51 247	85.80 239	94.53 199

test_part1		86.16 227	84.40 243	91.46 152	92.63 226	82.80 132	96.42 32	96.05 125	73.47 312	82.06 263	91.43 241	63.89 287	97.43 186	84.51 149	79.11 314	94.14 215

mvs_tets		88.06 167	87.28 165	90.38 194	90.94 281	79.88 208	95.22 91	95.66 155	85.10 155	84.21 229	93.94 156	63.53 288	97.40 195	88.50 103	88.40 213	93.87 232

Anonymous20231211		86.59 218	85.13 228	90.98 175	96.52 90	81.50 162	96.14 46	96.16 116	73.78 309	83.65 241	92.15 214	63.26 289	97.37 199	82.82 172	81.74 281	94.06 222

dp		81.47 285	80.23 282	85.17 307	89.92 310	65.49 340	86.74 318	90.10 316	76.30 287	81.10 274	87.12 319	62.81 290	95.92 281	68.13 309	79.88 307	94.09 220

LFMVS		90.08 111	89.13 122	92.95 89	96.71 82	82.32 149	96.08 50	89.91 321	86.79 116	92.15 80	96.81 53	62.60 291	98.34 125	87.18 120	93.90 135	98.19 53

DWT-MVSNet_test		84.95 250	83.68 252	88.77 244	91.43 260	73.75 296	91.74 253	90.98 301	80.66 244	83.84 235	87.36 315	62.44 292	97.11 219	78.84 234	85.81 238	95.46 163

Anonymous20231206		81.03 290	79.77 288	84.82 309	87.85 330	70.26 326	91.42 260	92.08 271	73.67 310	77.75 305	89.25 289	62.43 293	93.08 327	61.50 332	82.00 277	91.12 315

VDD-MVS		90.74 96	89.92 108	93.20 77	96.27 96	83.02 126	95.73 65	93.86 239	88.42 75	92.53 71	96.84 50	62.09 294	98.64 103	90.95 79	92.62 160	97.93 74

MS-PatchMatch		85.05 248	84.16 245	87.73 271	91.42 261	78.51 236	91.25 264	93.53 245	77.50 276	80.15 287	91.58 237	61.99 295	95.51 297	75.69 263	94.35 132	89.16 328

OurMVSNet-221017-0		85.35 241	84.64 240	87.49 277	90.77 289	72.59 309	94.01 177	94.40 222	84.72 163	79.62 296	93.17 181	61.91 296	96.72 238	81.99 186	81.16 285	93.16 266

test20.03		79.95 296	79.08 295	82.55 318	85.79 334	67.74 335	91.09 267	91.08 297	81.23 238	74.48 322	89.96 280	61.63 297	90.15 339	60.08 335	76.38 323	89.76 323

DSMNet-mixed		76.94 306	76.29 307	78.89 322	83.10 342	56.11 349	87.78 311	79.77 347	60.65 341	75.64 316	88.71 297	61.56 298	88.34 342	60.07 336	89.29 199	92.21 296

Anonymous20240529		88.09 165	86.59 186	92.58 106	96.53 89	81.92 156	95.99 54	95.84 141	74.11 307	89.06 124	95.21 111	61.44 299	98.81 96	83.67 161	87.47 225	97.01 112

IB-MVS	80.51 15	85.24 245	83.26 257	91.19 160	92.13 236	79.86 209	91.75 252	91.29 294	83.28 191	80.66 280	88.49 300	61.28 300	98.46 115	80.99 204	79.46 311	95.25 170
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
GA-MVS		86.61 216	85.27 226	90.66 179	91.33 266	78.71 231	90.40 275	93.81 242	85.34 149	85.12 204	89.57 286	61.25 301	97.11 219	80.99 204	89.59 194	96.15 136

N_pmnet		68.89 312	68.44 315	70.23 328	89.07 316	28.79 358	88.06 308	19.50 358	69.47 332	71.86 332	84.93 326	61.24 302	91.75 335	54.70 341	77.15 322	90.15 322

EU-MVSNet		81.32 287	80.95 276	82.42 319	88.50 321	63.67 342	93.32 200	91.33 292	64.02 339	80.57 282	92.83 193	61.21 303	92.27 331	76.34 257	80.38 303	91.32 308

VDDNet		89.56 124	88.49 137	92.76 96	95.07 138	82.09 151	96.30 36	93.19 250	81.05 241	91.88 84	96.86 49	61.16 304	98.33 127	88.43 104	92.49 163	97.84 80

PVSNet_0	73.20 20	77.22 305	74.83 310	84.37 312	90.70 293	71.10 319	83.09 336	89.67 326	72.81 320	73.93 324	83.13 332	60.79 305	93.70 320	68.54 304	50.84 346	88.30 335

RRT_test8_iter05		86.90 207	86.36 192	88.52 253	93.00 218	73.27 300	94.32 155	95.96 130	85.50 145	84.26 227	92.86 190	60.76 306	97.70 168	88.32 105	82.29 271	94.60 194

SixPastTwentyTwo		83.91 262	82.90 262	86.92 291	90.99 277	70.67 323	93.48 196	91.99 275	85.54 143	77.62 307	92.11 218	60.59 307	96.87 235	76.05 261	77.75 318	93.20 264

gg-mvs-nofinetune		81.77 279	79.37 291	88.99 242	90.85 287	77.73 259	86.29 321	79.63 348	74.88 302	83.19 252	69.05 343	60.34 308	96.11 274	75.46 265	94.64 125	93.11 268

MDA-MVSNet-bldmvs		78.85 303	76.31 306	86.46 296	89.76 312	73.88 295	88.79 300	90.42 310	79.16 258	59.18 343	88.33 303	60.20 309	94.04 317	62.00 330	68.96 335	91.48 306

pmmvs6		83.42 266	81.60 270	88.87 243	88.01 328	77.87 253	94.96 107	94.24 229	74.67 303	78.80 299	91.09 255	60.17 310	96.49 255	77.06 253	75.40 325	92.23 295

ACMH	80.38 17	85.36 240	83.68 252	90.39 192	94.45 167	80.63 187	94.73 124	94.85 208	82.09 212	77.24 308	92.65 199	60.01 311	97.58 173	72.25 285	84.87 246	92.96 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GG-mvs-BLEND				87.94 269	89.73 313	77.91 250	87.80 310	78.23 350		80.58 281	83.86 328	59.88 312	95.33 303	71.20 288	92.22 165	90.60 320

UniMVSNet_ETH3D		87.53 185	86.37 191	91.00 172	92.44 229	78.96 229	94.74 123	95.61 159	84.07 172	85.36 201	94.52 136	59.78 313	97.34 200	82.93 168	87.88 222	96.71 123

pmmvs-eth3d		80.97 291	78.72 298	87.74 270	84.99 338	79.97 207	90.11 282	91.65 284	75.36 294	73.51 325	86.03 322	59.45 314	93.96 318	75.17 268	72.21 330	89.29 326

test_0402		81.30 288	79.17 294	87.67 272	93.19 209	78.17 245	92.98 219	91.71 281	75.25 296	76.02 315	90.31 271	59.23 315	96.37 263	50.22 343	83.63 257	88.47 334

FMVSNet1		85.85 233	84.11 246	91.08 166	92.81 222	83.10 122	95.14 98	94.94 199	81.64 228	82.68 256	91.64 232	59.01 316	96.34 266	75.37 266	83.78 253	93.79 237

COLMAP_ROB	80.39 16	83.96 260	82.04 267	89.74 220	95.28 131	79.75 211	94.25 158	92.28 266	75.17 297	78.02 304	93.77 167	58.60 317	97.84 160	65.06 322	85.92 237	91.63 303
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	81.04 14	85.05 248	83.46 256	89.82 216	94.66 158	79.37 218	94.44 143	94.12 235	82.19 211	78.04 303	92.82 194	58.23 318	97.54 176	73.77 279	82.90 267	92.54 284

CMPMVS	59.16 21	80.52 293	79.20 293	84.48 311	83.98 339	67.63 336	89.95 285	93.84 241	64.79 338	66.81 339	91.14 253	57.93 319	95.17 304	76.25 258	88.10 217	90.65 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ITE_SJBPF				88.24 261	91.88 245	77.05 270		92.92 253	85.54 143	80.13 289	93.30 176	57.29 320	96.20 270	72.46 284	84.71 247	91.49 305

TESTMET0.1,1		83.74 264	82.85 263	86.42 298	89.96 309	71.21 318	89.55 287	87.88 332	77.41 277	83.37 248	87.31 316	56.71 321	93.65 321	80.62 211	92.85 158	94.40 208

UnsupCasMVSNet_eth		80.07 295	78.27 299	85.46 304	85.24 336	72.63 308	88.45 306	94.87 207	82.99 196	71.64 333	88.07 307	56.34 322	91.75 335	73.48 281	63.36 342	92.01 298

K. test v3		81.59 282	80.15 284	85.91 302	89.89 311	69.42 330	92.57 230	87.71 334	85.56 142	73.44 326	89.71 284	55.58 323	95.52 296	77.17 250	69.76 333	92.78 280

test-mter		84.54 256	83.64 254	87.25 283	90.95 279	71.67 314	89.55 287	89.88 323	79.17 257	84.54 213	87.95 308	55.56 324	95.11 306	81.82 190	93.37 147	94.97 176

lessismore_v0				86.04 300	88.46 322	68.78 332		80.59 346		73.01 328	90.11 275	55.39 325	96.43 261	75.06 270	65.06 339	92.90 275

MVS-HIRNet		73.70 309	72.20 312	78.18 324	91.81 248	56.42 348	82.94 337	82.58 343	55.24 343	68.88 335	66.48 344	55.32 326	95.13 305	58.12 338	88.42 212	83.01 339

new-patchmatchnet		76.41 307	75.17 309	80.13 321	82.65 344	59.61 344	87.66 314	91.08 297	78.23 273	69.85 334	83.22 331	54.76 327	91.63 337	64.14 325	64.89 340	89.16 328

Anonymous202405211		87.68 174	86.13 200	92.31 120	96.66 83	80.74 185	94.87 115	91.49 289	80.47 245	89.46 118	95.44 103	54.72 328	98.23 131	82.19 182	89.89 188	97.97 70

XVG-ACMP-BASELINE		86.00 229	84.84 236	89.45 231	91.20 268	78.00 248	91.70 255	95.55 163	85.05 157	82.97 253	92.25 212	54.49 329	97.48 180	82.93 168	87.45 227	92.89 276

USDC		82.76 270	81.26 274	87.26 282	91.17 270	74.55 290	89.27 293	93.39 248	78.26 272	75.30 317	92.08 220	54.43 330	96.63 242	71.64 286	85.79 240	90.61 318

AllTest		83.42 266	81.39 272	89.52 228	95.01 139	77.79 256	93.12 212	90.89 305	77.41 277	76.12 313	93.34 172	54.08 331	97.51 178	68.31 307	84.27 250	93.26 259

TestCases				89.52 228	95.01 139	77.79 256		90.89 305	77.41 277	76.12 313	93.34 172	54.08 331	97.51 178	68.31 307	84.27 250	93.26 259

MIMVSNet		82.59 273	80.53 278	88.76 245	91.51 256	78.32 241	86.57 320	90.13 315	79.32 256	80.70 279	88.69 299	52.98 333	93.07 328	66.03 317	88.86 205	94.90 183

FMVSNet5		81.52 284	79.60 290	87.27 281	91.17 270	77.95 249	91.49 259	92.26 267	76.87 282	76.16 312	87.91 310	51.67 334	92.34 330	67.74 311	81.16 285	91.52 304

testgi		80.94 292	80.20 283	83.18 316	87.96 329	66.29 337	91.28 262	90.70 309	83.70 178	78.12 302	92.84 192	51.37 335	90.82 338	63.34 326	82.46 270	92.43 288

UnsupCasMVSNet_bld		76.23 308	73.27 311	85.09 308	83.79 340	72.92 302	85.65 326	93.47 247	71.52 325	68.84 336	79.08 338	49.77 336	93.21 325	66.81 316	60.52 344	89.13 330

OpenMVS_ROB	74.94 19	79.51 299	77.03 305	86.93 290	87.00 331	76.23 281	92.33 237	90.74 308	68.93 333	74.52 321	88.23 305	49.58 337	96.62 243	57.64 339	84.29 249	87.94 336

testing_2		83.40 268	81.02 275	90.56 183	85.06 337	80.51 191	91.37 261	95.57 161	82.92 198	67.06 338	85.54 325	49.47 338	97.24 209	86.74 126	85.44 241	93.93 227

TDRefinement		79.81 297	77.34 301	87.22 286	79.24 346	75.48 287	93.12 212	92.03 273	76.45 284	75.01 318	91.58 237	49.19 339	96.44 260	70.22 295	69.18 334	89.75 324

MIMVSNet1		79.38 300	77.28 302	85.69 303	86.35 333	73.67 297	91.61 258	92.75 258	78.11 275	72.64 329	88.12 306	48.16 340	91.97 334	60.32 334	77.49 320	91.43 307

MVS_0304		83.46 265	81.92 268	88.10 265	90.63 295	77.49 264	93.26 207	93.75 243	80.04 250	80.44 284	87.24 317	47.94 341	95.55 294	75.79 262	88.16 216	91.26 310

LF4IMVS		80.37 294	79.07 296	84.27 314	86.64 332	69.87 329	89.39 292	91.05 299	76.38 285	74.97 319	90.00 278	47.85 342	94.25 316	74.55 275	80.82 296	88.69 332

EG-PatchMatch MVS		82.37 275	80.34 280	88.46 254	90.27 302	79.35 219	92.80 224	94.33 225	77.14 281	73.26 327	90.18 273	47.47 343	96.72 238	70.25 293	87.32 230	89.30 325

TinyColmap		79.76 298	77.69 300	85.97 301	91.71 251	73.12 301	89.55 287	90.36 312	75.03 298	72.03 331	90.19 272	46.22 344	96.19 272	63.11 327	81.03 290	88.59 333

tmp_tt		35.64 322	39.24 324	24.84 336	14.87 358	23.90 359	62.71 348	51.51 357	6.58 354	36.66 350	62.08 347	44.37 345	30.34 355	52.40 342	22.00 352	20.27 350

new_pmnet		72.15 310	70.13 313	78.20 323	82.95 343	65.68 338	83.91 332	82.40 344	62.94 340	64.47 340	79.82 337	42.85 346	86.26 344	57.41 340	74.44 326	82.65 340

pmmvs3		71.81 311	68.71 314	81.11 320	75.86 347	70.42 325	86.74 318	83.66 342	58.95 342	68.64 337	80.89 336	36.93 347	89.52 340	63.10 328	63.59 341	83.39 338

PM-MVS		78.11 304	76.12 308	84.09 315	83.54 341	70.08 327	88.97 299	85.27 340	79.93 251	74.73 320	86.43 320	34.70 348	93.48 322	79.43 228	72.06 331	88.72 331

ambc				83.06 317	79.99 345	63.51 343	77.47 343	92.86 254		74.34 323	84.45 327	28.74 349	95.06 308	73.06 283	68.89 336	90.61 318

DeepMVS_CX				56.31 333	74.23 348	51.81 351		56.67 356	44.85 347	48.54 347	75.16 339	27.87 350	58.74 353	40.92 346	52.22 345	58.39 346

FPMVS		64.63 314	62.55 316	70.88 327	70.80 349	56.71 346	84.42 330	84.42 341	51.78 345	49.57 345	81.61 335	23.49 351	81.48 347	40.61 347	76.25 324	74.46 343

ANet_high		58.88 316	54.22 320	72.86 326	56.50 356	56.67 347	80.75 341	86.00 337	73.09 317	37.39 349	64.63 346	22.17 352	79.49 349	43.51 345	23.96 350	82.43 341

EMVS		42.07 321	41.12 323	44.92 335	63.45 354	35.56 357	73.65 344	63.48 353	33.05 351	26.88 354	45.45 351	21.27 353	67.14 351	19.80 352	23.02 351	32.06 349

Gipuma		57.99 317	54.91 319	67.24 330	88.51 320	65.59 339	52.21 350	90.33 313	43.58 348	42.84 348	51.18 349	20.29 354	85.07 345	34.77 348	70.45 332	51.05 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 320	42.29 322	46.03 334	65.58 352	37.41 355	73.51 345	64.62 352	33.99 350	28.47 353	47.87 350	19.90 355	67.91 350	22.23 351	24.45 349	32.77 348

PMMVS2		59.60 315	56.40 318	69.21 329	68.83 350	46.58 353	73.02 347	77.48 351	55.07 344	49.21 346	72.95 342	17.43 356	80.04 348	49.32 344	44.33 347	80.99 342

LCM-MVSNet		66.00 313	62.16 317	77.51 325	64.51 353	58.29 345	83.87 333	90.90 304	48.17 346	54.69 344	73.31 341	16.83 357	86.75 343	65.47 318	61.67 343	87.48 337

PMVS	47.18 22	52.22 318	48.46 321	63.48 331	45.72 357	46.20 354	73.41 346	78.31 349	41.03 349	30.06 351	65.68 345	6.05 358	83.43 346	30.04 349	65.86 338	60.80 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 319	38.59 325	57.77 332	56.52 355	48.77 352	55.38 349	58.64 355	29.33 352	28.96 352	52.65 348	4.68 359	64.62 352	28.11 350	33.07 348	59.93 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		21.27 324	20.48 327	23.63 337	68.59 351	36.41 356	49.57 351	6.85 359	9.37 353	7.89 355	4.46 357	4.03 360	31.37 354	17.47 353	16.07 353	3.12 351

test123		8.76 326	11.22 329	1.39 338	0.85 360	0.97 360	85.76 325	0.35 361	0.54 356	2.45 357	8.14 356	0.60 361	0.48 356	2.16 355	0.17 355	2.71 352

testmvs		8.92 325	11.52 328	1.12 339	1.06 359	0.46 361	86.02 322	0.65 360	0.62 355	2.74 356	9.52 355	0.31 362	0.45 357	2.38 354	0.39 354	2.46 353

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		7.82 327	10.43 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	93.88 161	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

IU-MVS					98.77 4	86.00 54		96.84 63	81.26 237	97.26 6				95.50 7	99.13 3	99.03 4

save fliter					97.85 48	85.63 68	95.21 92	96.82 67	89.44 44

test_0728_SECOND				95.01 15	98.79 1	86.43 41	97.09 11	97.49 5					99.61 3	95.62 5	99.08 7	98.99 5

GSMVS																96.12 139

test_part2					98.55 11	87.22 16				96.40 11

MTGPA								96.97 49

MTMP							96.16 44	60.64 354

gm-plane-assit					89.60 314	68.00 333			77.28 280		88.99 291		97.57 174	79.44 227

test9_res														91.91 58	98.71 30	98.07 63

agg_prior2														90.54 84	98.68 35	98.27 47

agg_prior					97.38 64	85.92 60		96.72 79		92.16 78			98.97 79

test_prior4						85.96 57	94.11 166

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 87			98.11 61

旧先验2							93.36 199		71.25 327	94.37 26			97.13 218	86.74 126

新几何2							93.11 214

无先验							93.28 206	96.26 107	73.95 308				99.05 60	80.56 212		96.59 125

原ACMM2							92.94 221

testdata2													98.75 99	78.30 238

testdata1							92.15 243		87.94 87

plane_prior7					94.70 156	82.74 136

plane_prior5								96.22 112					98.12 136	88.15 106	89.99 184	94.63 191

plane_prior4											94.86 122

plane_prior3						82.75 134			90.26 30	86.91 158

plane_prior2							95.85 61		90.81 17

plane_prior1					94.59 160

plane_prior						82.73 137	95.21 92		89.66 41						89.88 189

n2								0.00 362

nn								0.00 362

door-mid								85.49 338

test11								96.57 92

door								85.33 339

HQP5-MVS						81.56 160

HQP-NCC					94.17 175		94.39 148		88.81 61	85.43 194

ACMP_Plane					94.17 175		94.39 148		88.81 61	85.43 194

BP-MVS														87.11 123

HQP4-MVS										85.43 194			97.96 154			94.51 201

HQP3-MVS								96.04 126							89.77 191

NP-MVS					94.37 170	82.42 144					93.98 154

ACMMP++_ref															87.47 225

ACMMP++															88.01 220

High-resolution multi-view benchmark