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 84	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 56	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
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
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
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
DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 111	96.52 90	80.00 208	94.00 179	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 80	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 42	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 235	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 87	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 59	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	95.00 107	97.12 40	87.13 108	92.51 74	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 86	96.10 13	96.96 46	89.09 15	98.94 84	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 39	97.28 28	85.90 135	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	86.13 133	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 93	95.47 169	89.44 45	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 95	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	93.92 182	96.72 79	81.96 220	92.16 79	96.23 78	87.85 22	98.97 80	91.95 58	98.55 49	97.90 76

CSCG		93.23 60	93.05 58	93.76 69	98.04 42	84.07 98	96.22 41	97.37 18	84.15 173	90.05 113	95.66 100	87.77 23	99.15 53	89.91 89	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	86.67 121	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	94.07 172	96.78 70	81.61 233	92.77 64	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	94.07 172	96.78 70	81.86 226	92.77 64	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	94.02 177	96.76 73	81.86 226	92.70 68	96.20 80	87.63 27	99.02 68

ZD-MVS					98.15 35	86.62 35		97.07 44	83.63 183	94.19 30	96.91 48	87.57 29	99.26 43	91.99 54	98.44 51

TSAR-MVS + GP.		93.66 48	93.41 51	94.41 52	96.59 86	86.78 26	94.40 147	93.93 238	89.77 39	94.21 29	95.59 103	87.35 30	98.61 107	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	85.68 141	95.05 23	97.18 35	87.31 31	99.07 58	91.90 62	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 82	97.26 29	82.67 207	93.81 39	96.29 75	87.29 32	99.27 42	89.87 90	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	94.92 112	97.21 34	84.33 171	93.89 38	97.09 39	87.20 33	99.29 41	91.90 62	98.44 51	98.12 59

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 125	86.75 29	94.57 135	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	95.54 125	86.36 44	94.57 135	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 43	97.23 32	87.28 106	94.85 24	97.04 42	86.99 37	99.52 20	91.54 68	98.33 56	98.71 12

旧先验1					96.79 80	81.81 158		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	94.12 165	96.88 59	87.67 98	92.63 69	96.39 72	86.62 39	98.87 88	91.50 69	98.67 37	98.11 61

test_prior2							94.12 165		87.67 98	92.63 69	96.39 72	86.62 39		91.50 69	98.67 37

CDPH-MVS		92.83 64	92.30 71	94.44 48	97.79 52	86.11 52	94.06 174	96.66 85	80.09 253	92.77 64	96.63 62	86.62 39	99.04 63	87.40 118	98.66 40	98.17 54

DPM-MVS		92.58 69	91.74 77	95.08 12	96.19 98	89.31 3	92.66 229	96.56 93	83.44 189	91.68 93	95.04 117	86.60 42	98.99 77	85.60 139	97.92 69	96.93 116

DELS-MVS		93.43 54	93.25 53	93.97 59	95.42 128	85.04 74	93.06 219	97.13 39	90.74 20	91.84 87	95.09 116	86.32 43	99.21 47	91.22 74	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 81	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 69	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	87.51 101	93.65 43	97.21 32	86.10 45	99.49 23	91.68 66	98.77 24	98.30 41

MVS_111021_HR		93.45 52	93.31 52	93.84 63	96.99 76	84.84 75	93.24 212	97.24 30	88.76 65	91.60 94	95.85 94	86.07 47	98.66 102	91.91 59	98.16 60	98.03 67

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 129	85.47 70	94.68 127	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 80	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	95.36 129	84.61 79	94.68 127	95.83 142	91.27 12	93.60 46	96.71 56	85.75 50	98.86 91	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	85.15 157	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	94.82 119	97.17 38	86.26 129	92.83 62	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 81	93.26 51	96.83 51	85.48 53	99.59 4	91.43 72	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 58	91.98 83	97.19 34	85.43 54	99.56 7	92.06 53	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 48	92.81 63	96.97 45	85.37 55	99.24 44	90.87 82	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 71	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 73	92.73 67	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		91.04 104		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 69	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 60	97.16 37	85.02 60	99.49 23	91.99 54	98.56 47	98.47 28

X-MVStestdata		88.31 161	86.13 203	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 60	23.41 359	85.02 60	99.49 23	91.99 54	98.56 47	98.47 28

MSLP-MVS++		93.72 46	94.08 33	92.65 104	97.31 67	83.43 116	95.79 63	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 164	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	86.90 116	92.62 71	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 33	96.90 57	88.20 84	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 85	97.78 1	87.45 104	93.26 51	97.33 24	84.62 65	99.51 21	90.75 84	98.57 46	98.32 40

EI-MVSNet-Vis-set		93.01 63	92.92 62	93.29 74	95.01 141	83.51 114	94.48 139	95.77 146	90.87 15	92.52 73	96.67 60	84.50 66	99.00 75	91.99 54	94.44 131	97.36 96

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 105	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	94.36 155	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	83.45 115	96.26 39	96.84 63	88.33 77	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 37	96.76 73	87.46 102	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 99	95.55 124	82.35 149	96.14 45	96.85 62	88.71 66	91.44 97	91.51 243	84.13 71	98.48 113	91.27 73	97.47 80	97.34 97

ETV-MVS		92.74 66	92.66 65	92.97 88	95.20 137	84.04 100	95.07 102	96.51 94	90.73 21	92.96 59	91.19 250	84.06 72	98.34 126	91.72 65	96.54 97	96.54 129

EI-MVSNet-UG-set		92.74 66	92.62 66	93.12 80	94.86 152	83.20 121	94.40 147	95.74 149	90.71 22	92.05 82	96.60 64	84.00 73	98.99 77	91.55 67	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 67	91.83 89	97.17 36	83.96 74	99.55 12	91.44 71	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 35	96.87 61	86.96 112	93.92 37	97.47 16	83.88 75	98.96 83	92.71 37	97.87 70	98.26 49

EIA-MVS		91.95 76	91.94 74	91.98 132	95.16 138	80.01 207	95.36 79	96.73 77	88.44 74	89.34 120	92.16 216	83.82 76	98.45 119	89.35 95	97.06 86	97.48 93

EPP-MVSNet		91.70 82	91.56 79	92.13 127	95.88 113	80.50 194	97.33 3	95.25 185	86.15 131	89.76 115	95.60 102	83.42 77	98.32 129	87.37 120	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 88	84.47 152	95.47 111	97.45 95

UniMVSNet_NR-MVSNet		89.92 118	89.29 119	91.81 144	93.39 207	83.72 107	94.43 145	97.12 40	89.80 37	86.46 167	93.32 177	83.16 78	97.23 212	84.92 145	81.02 292	94.49 207

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 37	96.76 73	87.46 102	93.75 40	97.43 18	82.94 80		92.73 34	97.80 72	97.88 77

1121		90.42 107	89.49 113	93.20 77	97.27 71	84.46 88	92.63 230	95.51 167	71.01 336	91.20 102	96.21 79	82.92 81	99.05 60	80.56 214	98.07 63	96.10 143

新几何1				93.10 81	97.30 68	84.35 94		95.56 161	71.09 335	91.26 101	96.24 77	82.87 82	98.86 91	79.19 233	98.10 62	96.07 145

原ACMM1				92.01 129	97.34 66	81.05 178		96.81 68	78.89 267	90.45 108	95.92 91	82.65 83	98.84 96	80.68 212	98.26 59	96.14 138

casdiffmvs		92.51 70	92.43 70	92.74 98	94.41 172	81.98 155	94.54 137	96.23 111	89.57 43	91.96 84	96.17 84	82.58 84	98.01 151	90.95 80	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	94.89 114	96.99 47	89.02 60	89.56 116	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	85.77 138	92.47 75	97.13 38	82.38 86	99.07 58	90.51 86	98.40 53	97.92 75

baseline		92.39 73	92.29 72	92.69 103	94.46 169	81.77 159	94.14 164	96.27 106	89.22 52	91.88 85	96.00 88	82.35 87	97.99 153	91.05 76	95.27 118	98.30 41

canonicalmvs		93.27 58	92.75 64	94.85 27	95.70 120	87.66 11	96.33 34	96.41 99	90.00 34	94.09 33	94.60 134	82.33 88	98.62 106	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	94.66 130	96.66 85	82.69 206	90.03 114	95.82 95	82.30 89	99.03 64	84.57 151	96.48 100	96.91 117

PAPR		90.02 113	89.27 121	92.29 123	95.78 116	80.95 182	92.68 228	96.22 112	81.91 223	86.66 165	93.75 170	82.23 90	98.44 120	79.40 232	94.79 121	97.48 93

MVS_Test		91.31 88	91.11 85	91.93 136	94.37 173	80.14 199	93.46 200	95.80 144	86.46 124	91.35 100	93.77 168	82.21 91	98.09 145	87.57 116	94.95 120	97.55 92

nrg030		91.08 93	90.39 95	93.17 79	93.07 216	86.91 20	96.41 32	96.26 107	88.30 79	88.37 133	94.85 125	82.19 92	97.64 174	91.09 75	82.95 263	94.96 181

UniMVSNet (Re)		89.80 120	89.07 124	92.01 129	93.60 202	84.52 84	94.78 122	97.47 8	89.26 51	86.44 170	92.32 211	82.10 93	97.39 200	84.81 148	80.84 296	94.12 219

testdata				90.49 190	96.40 92	77.89 254		95.37 181	72.51 328	93.63 45	96.69 58	82.08 94	97.65 172	83.08 167	97.39 81	95.94 149

PAPM_NR		91.22 90	90.78 93	92.52 110	97.60 57	81.46 168	94.37 154	96.24 110	86.39 127	87.41 149	94.80 127	82.06 95	98.48 113	82.80 175	95.37 114	97.61 87

MG-MVS		91.77 79	91.70 78	92.00 131	97.08 75	80.03 206	93.60 195	95.18 189	87.85 92	90.89 105	96.47 70	82.06 95	98.36 123	85.07 143	97.04 87	97.62 86

CANet		93.54 51	93.20 56	94.55 44	95.65 121	85.73 67	94.94 110	96.69 83	91.89 5	90.69 106	95.88 93	81.99 97	99.54 16	93.14 30	97.95 68	98.39 36

FC-MVSNet-test		90.27 109	90.18 100	90.53 186	93.71 198	79.85 212	95.77 64	97.59 2	89.31 50	86.27 173	94.67 131	81.93 98	97.01 229	84.26 154	88.09 219	94.71 192

FIs		90.51 106	90.35 96	90.99 175	93.99 188	80.98 180	95.73 65	97.54 3	89.15 55	86.72 164	94.68 130	81.83 99	97.24 211	85.18 142	88.31 215	94.76 191

ACMMP		93.24 59	92.88 63	94.30 54	98.09 40	85.33 72	96.86 22	97.45 11	88.33 77	90.15 112	97.03 44	81.44 100	99.51 21	90.85 83	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	94.35 176	83.39 118	94.60 132	95.10 193	87.10 109	90.57 107	93.10 188	81.43 101	98.07 147	89.29 96	94.48 129	97.59 89

MVS_111021_LR		92.47 71	92.29 72	92.98 87	95.99 109	84.43 92	93.08 217	96.09 120	88.20 84	91.12 103	95.72 99	81.33 102	97.76 164	91.74 64	97.37 82	96.75 121

mvs_anonymous		89.37 135	89.32 118	89.51 232	93.47 205	74.22 296	91.65 260	94.83 210	82.91 202	85.45 195	93.79 166	81.23 103	96.36 267	86.47 132	94.09 133	97.94 72

PVSNet_BlendedMVS		89.98 114	89.70 110	90.82 179	96.12 100	81.25 173	93.92 182	96.83 65	83.49 188	89.10 123	92.26 214	81.04 104	98.85 94	86.72 130	87.86 223	92.35 294

PVSNet_Blended		90.73 98	90.32 97	91.98 132	96.12 100	81.25 173	92.55 234	96.83 65	82.04 218	89.10 123	92.56 204	81.04 104	98.85 94	86.72 130	95.91 104	95.84 154

alignmvs		93.08 62	92.50 69	94.81 32	95.62 123	87.61 12	95.99 54	96.07 122	89.77 39	94.12 32	94.87 122	80.56 106	98.66 102	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 92	93.20 54	97.22 31	80.35 107	99.18 49	91.91 59	97.21 83	97.26 100

API-MVS		90.66 101	90.07 103	92.45 113	96.36 94	84.57 81	96.06 52	95.22 188	82.39 209	89.13 122	94.27 146	80.32 108	98.46 116	80.16 221	96.71 92	94.33 212

PVSNet_Blended_VisFu		91.38 86	90.91 90	92.80 94	96.39 93	83.17 122	94.87 116	96.66 85	83.29 193	89.27 121	94.46 138	80.29 109	99.17 50	87.57 116	95.37 114	96.05 147

test222					96.55 88	81.70 160	92.22 244	95.01 196	68.36 341	90.20 111	96.14 85	80.26 110			97.80 72	96.05 147

diffmvs		91.37 87	91.23 83	91.77 145	93.09 215	80.27 196	92.36 239	95.52 166	87.03 111	91.40 99	94.93 119	80.08 111	97.44 188	92.13 51	94.56 127	97.61 87

Test By Simon												80.02 112

IterMVS-LS		88.36 160	87.91 154	89.70 225	93.80 195	78.29 245	93.73 189	95.08 195	85.73 139	84.75 213	91.90 230	79.88 113	96.92 234	83.83 159	82.51 269	93.89 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 139	88.86 131	89.80 221	91.84 248	78.30 244	93.70 192	95.01 196	85.73 139	87.15 153	95.28 108	79.87 114	97.21 214	83.81 160	87.36 228	93.88 232

TAPA-MVS	84.62 6	88.16 165	87.01 173	91.62 149	96.64 84	80.65 189	94.39 149	96.21 115	76.38 292	86.19 175	95.44 104	79.75 115	98.08 146	62.75 336	95.29 116	96.13 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		89.41 132	88.64 133	91.71 147	94.74 155	80.81 186	93.54 196	95.10 193	83.11 196	86.82 163	90.67 268	79.74 116	97.75 167	80.51 216	93.55 140	96.57 127

pcd_1.5k_mvsjas		6.64 335	8.86 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	79.70 117	0.00 365	0.00 363	0.00 363	0.00 361

PS-MVSNAJss		89.97 115	89.62 111	91.02 172	91.90 246	80.85 185	95.26 90	95.98 128	86.26 129	86.21 174	94.29 143	79.70 117	97.65 172	88.87 101	88.10 217	94.57 200

PS-MVSNAJ		91.18 91	90.92 89	91.96 134	95.26 135	82.60 144	92.09 249	95.70 151	86.27 128	91.84 87	92.46 206	79.70 117	98.99 77	89.08 98	95.86 105	94.29 213

xiu_mvs_v2_base		91.13 92	90.89 91	91.86 139	94.97 144	82.42 145	92.24 243	95.64 158	86.11 134	91.74 92	93.14 186	79.67 120	98.89 87	89.06 99	95.46 112	94.28 214

WR-MVS_H		87.80 174	87.37 164	89.10 240	93.23 211	78.12 248	95.61 73	97.30 26	87.90 90	83.72 242	92.01 227	79.65 121	96.01 280	76.36 258	80.54 300	93.16 268

EPNet		91.79 78	91.02 88	94.10 58	90.10 307	85.25 73	96.03 53	92.05 278	92.83 1	87.39 152	95.78 96	79.39 122	99.01 70	88.13 110	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		87.22 202	86.62 187	89.02 243	92.13 238	77.40 268	90.91 271	94.81 212	81.28 239	84.32 228	90.08 279	79.26 123	96.62 245	83.81 160	82.94 264	93.04 273

miper_enhance_ethall		86.90 210	86.18 202	89.06 241	91.66 256	77.58 265	90.22 283	94.82 211	79.16 264	84.48 219	89.10 293	79.19 124	96.66 243	84.06 156	82.94 264	92.94 276

NR-MVSNet		88.58 156	87.47 162	91.93 136	93.04 218	84.16 97	94.77 123	96.25 109	89.05 57	80.04 294	93.29 180	79.02 125	97.05 226	81.71 197	80.05 307	94.59 198

TAMVS		89.21 137	88.29 144	91.96 134	93.71 198	82.62 143	93.30 206	94.19 230	82.22 213	87.78 144	93.94 157	78.83 126	96.95 232	77.70 246	92.98 155	96.32 132

cl_fuxian		87.14 206	86.50 192	89.04 242	92.20 235	77.26 269	91.22 267	94.70 216	82.01 219	84.34 227	90.43 272	78.81 127	96.61 248	83.70 162	81.09 289	93.25 262

1112_ss		88.42 157	87.33 165	91.72 146	94.92 148	80.98 180	92.97 222	94.54 218	78.16 281	83.82 240	93.88 162	78.78 128	97.91 159	79.45 228	89.41 195	96.26 135

CDS-MVSNet		89.45 129	88.51 135	92.29 123	93.62 201	83.61 112	93.01 220	94.68 217	81.95 221	87.82 143	93.24 182	78.69 129	96.99 230	80.34 218	93.23 150	96.28 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS		89.60 123	88.92 128	91.67 148	95.47 127	81.15 177	92.38 238	94.78 214	83.11 196	89.06 125	94.32 141	78.67 130	96.61 248	81.57 198	90.89 177	97.24 101

CPTT-MVS		91.99 75	91.80 76	92.55 108	98.24 31	81.98 155	96.76 25	96.49 95	81.89 225	90.24 110	96.44 71	78.59 131	98.61 107	89.68 91	97.85 71	97.06 109

IS-MVSNet		91.43 85	91.09 87	92.46 112	95.87 115	81.38 171	96.95 14	93.69 246	89.72 41	89.50 118	95.98 89	78.57 132	97.77 163	83.02 169	96.50 99	98.22 52

OMC-MVS		91.23 89	90.62 94	93.08 82	96.27 96	84.07 98	93.52 197	95.93 132	86.95 113	89.51 117	96.13 86	78.50 133	98.35 125	85.84 136	92.90 156	96.83 119

PCF-MVS	84.11 10	87.74 176	86.08 207	92.70 102	94.02 183	84.43 92	89.27 297	95.87 139	73.62 319	84.43 222	94.33 140	78.48 134	98.86 91	70.27 296	94.45 130	94.81 189
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LCM-MVSNet-Re		88.30 162	88.32 143	88.27 261	94.71 158	72.41 316	93.15 213	90.98 307	87.77 94	79.25 302	91.96 228	78.35 135	95.75 292	83.04 168	95.62 107	96.65 124

HY-MVS	83.01 12	89.03 142	87.94 153	92.29 123	94.86 152	82.77 133	92.08 250	94.49 219	81.52 235	86.93 158	92.79 200	78.32 136	98.23 132	79.93 223	90.55 178	95.88 152

MVS		87.44 192	86.10 206	91.44 155	92.61 229	83.62 111	92.63 230	95.66 155	67.26 342	81.47 272	92.15 217	77.95 137	98.22 134	79.71 225	95.48 110	92.47 289

MVSFormer		91.68 83	91.30 81	92.80 94	93.86 192	83.88 103	95.96 57	95.90 136	84.66 167	91.76 90	94.91 120	77.92 138	97.30 203	89.64 92	97.11 84	97.24 101

lupinMVS		90.92 94	90.21 98	93.03 85	93.86 192	83.88 103	92.81 226	93.86 241	79.84 256	91.76 90	94.29 143	77.92 138	98.04 149	90.48 87	97.11 84	97.17 105

Test_1112_low_res		87.65 179	86.51 191	91.08 168	94.94 147	79.28 226	91.77 254	94.30 226	76.04 297	83.51 249	92.37 209	77.86 140	97.73 168	78.69 237	89.13 202	96.22 136

VNet		92.24 74	91.91 75	93.24 76	96.59 86	83.43 116	94.84 118	96.44 96	89.19 54	94.08 34	95.90 92	77.85 141	98.17 136	88.90 100	93.38 146	98.13 58

DU-MVS		89.34 136	88.50 136	91.85 141	93.04 218	83.72 107	94.47 142	96.59 90	89.50 44	86.46 167	93.29 180	77.25 142	97.23 212	84.92 145	81.02 292	94.59 198

Baseline_NR-MVSNet		87.07 207	86.63 186	88.40 257	91.44 259	77.87 255	94.23 161	92.57 266	84.12 174	85.74 181	92.08 223	77.25 142	96.04 277	82.29 183	79.94 308	91.30 311

jason		90.80 95	90.10 102	92.90 91	93.04 218	83.53 113	93.08 217	94.15 232	80.22 250	91.41 98	94.91 120	76.87 144	97.93 158	90.28 88	96.90 88	97.24 101
jason: jason.
PAPM		86.68 219	85.39 227	90.53 186	93.05 217	79.33 225	89.79 290	94.77 215	78.82 269	81.95 269	93.24 182	76.81 145	97.30 203	66.94 319	93.16 151	94.95 184

Vis-MVSNet (Re-imp)		89.59 124	89.44 115	90.03 210	95.74 117	75.85 285	95.61 73	90.80 313	87.66 100	87.83 142	95.40 107	76.79 146	96.46 261	78.37 238	96.73 91	97.80 82

baseline1		88.10 166	87.28 167	90.57 184	94.96 145	80.07 202	94.27 158	91.29 300	86.74 118	87.41 149	94.00 154	76.77 147	96.20 272	80.77 209	79.31 315	95.44 166

114514_t		89.51 126	88.50 136	92.54 109	98.11 37	81.99 154	95.16 98	96.36 103	70.19 338	85.81 179	95.25 110	76.70 148	98.63 105	82.07 186	96.86 90	97.00 113

PLC	84.53 7	89.06 141	88.03 149	92.15 126	97.27 71	82.69 140	94.29 157	95.44 175	79.71 258	84.01 236	94.18 148	76.68 149	98.75 100	77.28 250	93.41 145	95.02 177
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TranMVSNet+NR-MVSNet		88.84 148	87.95 152	91.49 152	92.68 228	83.01 128	94.92 112	96.31 104	89.88 36	85.53 188	93.85 164	76.63 150	96.96 231	81.91 190	79.87 310	94.50 205

MAR-MVS		90.30 108	89.37 117	93.07 84	96.61 85	84.48 87	95.68 68	95.67 153	82.36 211	87.85 141	92.85 194	76.63 150	98.80 98	80.01 222	96.68 93	95.91 150
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		88.38 158	87.67 158	90.52 188	93.30 210	80.18 197	93.26 209	95.96 130	88.57 72	85.47 194	92.81 198	76.12 152	96.91 235	81.24 201	82.29 271	94.47 210

v8		87.50 191	86.71 181	89.89 215	91.37 265	79.40 219	94.50 138	95.38 179	84.81 164	83.60 247	91.33 245	76.05 153	97.42 190	82.84 173	80.51 304	92.84 280

v148		87.04 208	86.32 198	89.21 236	90.94 283	77.26 269	93.71 191	94.43 221	84.84 163	84.36 226	90.80 264	76.04 154	97.05 226	82.12 185	79.60 312	93.31 259

eth_miper_zixun_eth		86.50 225	85.77 219	88.68 251	91.94 245	75.81 286	90.47 277	94.89 205	82.05 216	84.05 234	90.46 271	75.96 155	96.77 239	82.76 176	79.36 314	93.46 256

3Dnovator+	87.14 4	92.42 72	91.37 80	95.55 4	95.63 122	88.73 4	97.07 13	96.77 72	90.84 16	84.02 235	96.62 63	75.95 156	99.34 33	87.77 113	97.68 75	98.59 18

hse-mvs3		90.80 95	90.15 101	92.75 97	96.01 107	82.66 141	95.43 78	95.53 165	89.80 37	93.08 58	95.64 101	75.77 157	99.00 75	92.07 52	78.05 319	96.60 125

BH-untuned		88.60 155	88.13 148	90.01 212	95.24 136	78.50 239	93.29 207	94.15 232	84.75 165	84.46 220	93.40 174	75.76 158	97.40 197	77.59 247	94.52 128	94.12 219

cl-mvsnet1		86.53 223	85.78 217	88.75 248	92.02 243	76.45 279	90.74 273	94.30 226	81.83 228	83.34 253	90.82 263	75.75 159	96.57 251	81.73 196	81.52 284	93.24 263

BH-w/o		87.57 187	87.05 172	89.12 239	94.90 150	77.90 253	92.41 236	93.51 248	82.89 203	83.70 243	91.34 244	75.75 159	97.07 224	75.49 266	93.49 142	92.39 292

cl-mvsnet_		86.52 224	85.78 217	88.75 248	92.03 242	76.46 278	90.74 273	94.30 226	81.83 228	83.34 253	90.78 265	75.74 161	96.57 251	81.74 195	81.54 283	93.22 265

cdsmvs_eth3d_5k		22.14 330	29.52 333	0.00 347	0.00 368	0.00 369	0.00 359	95.76 147	0.00 364	0.00 365	94.29 143	75.66 162	0.00 365	0.00 363	0.00 363	0.00 361

CNLPA		89.07 140	87.98 151	92.34 119	96.87 78	84.78 77	94.08 171	93.24 251	81.41 236	84.46 220	95.13 115	75.57 163	96.62 245	77.21 251	93.84 137	95.61 162

CHOSEN 1792x2688		88.84 148	87.69 156	92.30 122	96.14 99	81.42 170	90.01 287	95.86 140	74.52 312	87.41 149	93.94 157	75.46 164	98.36 123	80.36 217	95.53 108	97.12 108

CP-MVSNet		87.63 182	87.26 169	88.74 250	93.12 214	76.59 277	95.29 87	96.58 91	88.43 75	83.49 250	92.98 191	75.28 165	95.83 288	78.97 234	81.15 288	93.79 238

v10		87.25 199	86.38 193	89.85 216	91.19 271	79.50 216	94.48 139	95.45 173	83.79 180	83.62 246	91.19 250	75.13 166	97.42 190	81.94 189	80.60 298	92.63 285

Vis-MVSNet		91.75 80	91.23 83	93.29 74	95.32 132	83.78 106	96.14 45	95.98 128	89.89 35	90.45 108	96.58 65	75.09 167	98.31 130	84.75 149	96.90 88	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		88.93 146	88.26 146	90.94 178	94.05 182	80.78 187	91.71 257	95.38 179	81.55 234	88.63 128	93.91 161	75.04 168	95.47 303	82.47 179	91.61 168	96.57 127

v1144		87.61 185	86.79 179	90.06 209	91.01 278	79.34 222	93.95 181	95.42 178	83.36 192	85.66 184	91.31 248	74.98 169	97.42 190	83.37 164	82.06 274	93.42 257

miper_lstm_enhance		85.27 247	84.59 245	87.31 282	91.28 269	74.63 291	87.69 318	94.09 236	81.20 243	81.36 275	89.85 285	74.97 170	94.30 317	81.03 205	79.84 311	93.01 274

test_yl		90.69 99	90.02 107	92.71 100	95.72 118	82.41 147	94.11 167	95.12 191	85.63 142	91.49 95	94.70 128	74.75 171	98.42 121	86.13 134	92.53 161	97.31 98

DCV-MVSNet		90.69 99	90.02 107	92.71 100	95.72 118	82.41 147	94.11 167	95.12 191	85.63 142	91.49 95	94.70 128	74.75 171	98.42 121	86.13 134	92.53 161	97.31 98

V42		87.68 177	86.86 175	90.15 204	90.58 298	80.14 199	94.24 160	95.28 184	83.66 182	85.67 183	91.33 245	74.73 173	97.41 195	84.43 153	81.83 278	92.89 278

XVG-OURS-SEG-HR		89.95 116	89.45 114	91.47 154	94.00 187	81.21 176	91.87 252	96.06 124	85.78 137	88.55 129	95.73 98	74.67 174	97.27 207	88.71 103	89.64 193	95.91 150

v2v482		87.84 172	87.06 171	90.17 202	90.99 279	79.23 229	94.00 179	95.13 190	84.87 162	85.53 188	92.07 225	74.45 175	97.45 186	84.71 150	81.75 280	93.85 236

CLD-MVS		89.47 128	88.90 129	91.18 163	94.22 177	82.07 153	92.13 247	96.09 120	87.90 90	85.37 204	92.45 207	74.38 176	97.56 178	87.15 123	90.43 179	93.93 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		87.65 179	86.85 176	90.03 210	92.14 237	80.60 192	93.76 188	95.23 186	82.94 201	84.60 215	94.02 152	74.27 177	95.49 302	81.04 203	83.68 256	94.01 227

HQP_MVS		90.60 105	90.19 99	91.82 142	94.70 159	82.73 137	95.85 61	96.22 112	90.81 17	86.91 160	94.86 123	74.23 178	98.12 137	88.15 108	89.99 184	94.63 194

plane_prior6					94.52 165	82.75 134						74.23 178

v144192		87.19 204	86.35 196	89.74 222	90.64 296	78.24 246	93.92 182	95.43 176	81.93 222	85.51 190	91.05 258	74.21 180	97.45 186	82.86 172	81.56 282	93.53 251

VPA-MVSNet		89.62 122	88.96 126	91.60 150	93.86 192	82.89 132	95.46 77	97.33 22	87.91 89	88.43 132	93.31 178	74.17 181	97.40 197	87.32 121	82.86 268	94.52 203

ab-mvs		89.41 132	88.35 140	92.60 105	95.15 139	82.65 142	92.20 245	95.60 160	83.97 177	88.55 129	93.70 172	74.16 182	98.21 135	82.46 180	89.37 196	96.94 115

1314		87.51 189	86.57 189	90.34 199	92.42 232	79.74 214	92.63 230	95.35 183	78.35 277	80.14 291	91.62 239	74.05 183	97.15 216	81.05 202	93.53 141	94.12 219

test_djsdf		89.03 142	88.64 133	90.21 201	90.74 293	79.28 226	95.96 57	95.90 136	84.66 167	85.33 206	92.94 192	74.02 184	97.30 203	89.64 92	88.53 208	94.05 225

cl-mvsnet2		86.78 215	85.98 210	89.18 238	92.34 233	77.62 264	90.84 272	94.13 234	81.33 238	83.97 237	90.15 277	73.96 185	96.60 250	84.19 155	82.94 264	93.33 258

AdaColmap		89.89 119	89.07 124	92.37 118	97.41 63	83.03 126	94.42 146	95.92 133	82.81 204	86.34 172	94.65 132	73.89 186	99.02 68	80.69 211	95.51 109	95.05 176

HyFIR lowres test		88.09 167	86.81 177	91.93 136	96.00 108	80.63 190	90.01 287	95.79 145	73.42 320	87.68 146	92.10 222	73.86 187	97.96 155	80.75 210	91.70 167	97.19 104

HQP2-MVS												73.83 188

HQP-MVS		89.80 120	89.28 120	91.34 158	94.17 178	81.56 162	94.39 149	96.04 126	88.81 62	85.43 198	93.97 156	73.83 188	97.96 155	87.11 125	89.77 191	94.50 205

3Dnovator	86.66 5	91.73 81	90.82 92	94.44 48	94.59 163	86.37 43	97.18 7	97.02 46	89.20 53	84.31 230	96.66 61	73.74 190	99.17 50	86.74 128	97.96 67	97.79 83

EPNet_dtu		86.49 227	85.94 213	88.14 266	90.24 305	72.82 308	94.11 167	92.20 274	86.66 122	79.42 301	92.36 210	73.52 191	95.81 290	71.26 290	93.66 138	95.80 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		84.43 260	83.06 263	88.54 254	91.72 252	78.44 240	95.18 96	92.82 260	82.73 205	79.67 298	92.12 219	73.49 192	95.96 282	71.10 295	68.73 342	91.21 314

Effi-MVS+-dtu		88.65 153	88.35 140	89.54 229	93.33 208	76.39 280	94.47 142	94.36 223	87.70 96	85.43 198	89.56 290	73.45 193	97.26 209	85.57 140	91.28 170	94.97 178

mvs-test1		89.45 129	89.14 122	90.38 196	93.33 208	77.63 263	94.95 109	94.36 223	87.70 96	87.10 156	92.81 198	73.45 193	98.03 150	85.57 140	93.04 153	95.48 164

baseline2		86.50 225	85.39 227	89.84 217	91.12 275	76.70 275	91.88 251	88.58 337	82.35 212	79.95 295	90.95 260	73.42 195	97.63 175	80.27 220	89.95 187	95.19 173

PEN-MVS		86.80 214	86.27 200	88.40 257	92.32 234	75.71 287	95.18 96	96.38 102	87.97 87	82.82 259	93.15 185	73.39 196	95.92 283	76.15 262	79.03 317	93.59 249

v1192		87.25 199	86.33 197	90.00 213	90.76 292	79.04 230	93.80 186	95.48 168	82.57 208	85.48 193	91.18 252	73.38 197	97.42 190	82.30 182	82.06 274	93.53 251

QAPM		89.51 126	88.15 147	93.59 72	94.92 148	84.58 80	96.82 24	96.70 81	78.43 276	83.41 251	96.19 83	73.18 198	99.30 39	77.11 253	96.54 97	96.89 118

tpmrst		85.35 244	84.99 234	86.43 299	90.88 288	67.88 340	88.71 306	91.43 297	80.13 252	86.08 177	88.80 299	73.05 199	96.02 279	82.48 178	83.40 262	95.40 168

PS-CasMVS		87.32 196	86.88 174	88.63 253	92.99 222	76.33 282	95.33 81	96.61 89	88.22 83	83.30 255	93.07 189	73.03 200	95.79 291	78.36 239	81.00 294	93.75 244

DTE-MVSNet		86.11 231	85.48 225	87.98 269	91.65 257	74.92 290	94.93 111	95.75 148	87.36 105	82.26 264	93.04 190	72.85 201	95.82 289	74.04 278	77.46 323	93.20 266

MVSTER		88.84 148	88.29 144	90.51 189	92.95 223	80.44 195	93.73 189	95.01 196	84.66 167	87.15 153	93.12 187	72.79 202	97.21 214	87.86 112	87.36 228	93.87 233

v1921920		86.97 209	86.06 208	89.69 226	90.53 301	78.11 249	93.80 186	95.43 176	81.90 224	85.33 206	91.05 258	72.66 203	97.41 195	82.05 187	81.80 279	93.53 251

DP-MVS		87.25 199	85.36 229	92.90 91	97.65 56	83.24 120	94.81 120	92.00 280	74.99 307	81.92 270	95.00 118	72.66 203	99.05 60	66.92 321	92.33 164	96.40 130

v7n		86.81 213	85.76 220	89.95 214	90.72 294	79.25 228	95.07 102	95.92 133	84.45 170	82.29 263	90.86 261	72.60 205	97.53 180	79.42 231	80.52 303	93.08 272

OPM-MVS		90.12 111	89.56 112	91.82 142	93.14 213	83.90 102	94.16 163	95.74 149	88.96 61	87.86 140	95.43 106	72.48 206	97.91 159	88.10 111	90.18 183	93.65 248

LS3D		87.89 171	86.32 198	92.59 106	96.07 105	82.92 131	95.23 91	94.92 204	75.66 299	82.89 258	95.98 89	72.48 206	99.21 47	68.43 310	95.23 119	95.64 161

pm-mvs1		86.61 220	85.54 223	89.82 218	91.44 259	80.18 197	95.28 89	94.85 208	83.84 179	81.66 271	92.62 203	72.45 208	96.48 258	79.67 226	78.06 318	92.82 281

PMMVS		85.71 239	84.96 236	87.95 270	88.90 320	77.09 271	88.68 307	90.06 324	72.32 329	86.47 166	90.76 266	72.15 209	94.40 314	81.78 194	93.49 142	92.36 293

PatchmatchNet		85.85 236	84.70 242	89.29 235	91.76 251	75.54 288	88.49 309	91.30 299	81.63 232	85.05 209	88.70 301	71.71 210	96.24 271	74.61 276	89.05 203	96.08 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												71.70 211				96.12 140

test_part1		89.00 145	87.99 150	92.04 128	95.94 112	83.81 105	96.14 45	96.05 125	86.44 125	85.69 182	93.73 171	71.57 212	97.66 171	85.80 137	80.54 300	94.66 193

patchmatchnet-post											83.76 336	71.53 213	96.48 258

v1240		86.78 215	85.85 215	89.56 228	90.45 302	77.79 258	93.61 194	95.37 181	81.65 230	85.43 198	91.15 254	71.50 214	97.43 189	81.47 200	82.05 276	93.47 255

anonymousdsp		87.84 172	87.09 170	90.12 206	89.13 317	80.54 193	94.67 129	95.55 162	82.05 216	83.82 240	92.12 219	71.47 215	97.15 216	87.15 123	87.80 224	92.67 283

Patchmatch-test		81.37 289	79.30 295	87.58 276	90.92 285	74.16 298	80.99 347	87.68 342	70.52 337	76.63 317	88.81 297	71.21 216	92.76 335	60.01 344	86.93 234	95.83 155

F-COLMAP		87.95 170	86.80 178	91.40 156	96.35 95	80.88 184	94.73 125	95.45 173	79.65 259	82.04 268	94.61 133	71.13 217	98.50 112	76.24 261	91.05 175	94.80 190

pmmvs4		85.43 242	83.86 253	90.16 203	90.02 310	82.97 130	90.27 279	92.67 264	75.93 298	80.73 281	91.74 234	71.05 218	95.73 293	78.85 235	83.46 260	91.78 302

CR-MVSNet		85.35 244	83.76 254	90.12 206	90.58 298	79.34 222	85.24 332	91.96 284	78.27 278	85.55 186	87.87 314	71.03 219	95.61 294	73.96 280	89.36 197	95.40 168

Patchmtry		82.71 273	80.93 279	88.06 268	90.05 309	76.37 281	84.74 336	91.96 284	72.28 330	81.32 276	87.87 314	71.03 219	95.50 301	68.97 306	80.15 306	92.32 295

CL-MVSNet_2432*1600		81.74 282	80.53 280	85.36 309	85.96 339	72.45 315	90.25 280	93.07 255	81.24 241	79.85 297	87.29 321	70.93 221	92.52 336	66.95 318	69.23 338	91.11 318

RPMNet		83.95 264	81.53 274	91.21 161	90.58 298	79.34 222	85.24 332	96.76 73	71.44 333	85.55 186	82.97 340	70.87 222	98.91 86	61.01 340	89.36 197	95.40 168

Patchmatch-RL test		81.67 283	79.96 289	86.81 297	85.42 343	71.23 322	82.17 345	87.50 343	78.47 275	77.19 313	82.50 341	70.81 223	93.48 327	82.66 177	72.89 332	95.71 160

CostFormer		85.77 238	84.94 237	88.26 262	91.16 274	72.58 314	89.47 295	91.04 306	76.26 295	86.45 169	89.97 282	70.74 224	96.86 238	82.35 181	87.07 233	95.34 171

sam_mvs												70.60 225

xiu_mvs_v1_base_debu		90.64 102	90.05 104	92.40 114	93.97 189	84.46 88	93.32 202	95.46 170	85.17 154	92.25 76	94.03 149	70.59 226	98.57 109	90.97 77	94.67 122	94.18 215

xiu_mvs_v1_base		90.64 102	90.05 104	92.40 114	93.97 189	84.46 88	93.32 202	95.46 170	85.17 154	92.25 76	94.03 149	70.59 226	98.57 109	90.97 77	94.67 122	94.18 215

xiu_mvs_v1_base_debi		90.64 102	90.05 104	92.40 114	93.97 189	84.46 88	93.32 202	95.46 170	85.17 154	92.25 76	94.03 149	70.59 226	98.57 109	90.97 77	94.67 122	94.18 215

test_post											10.29 360	70.57 229	95.91 285

CANet_DTU		90.26 110	89.41 116	92.81 93	93.46 206	83.01 128	93.48 198	94.47 220	89.43 47	87.76 145	94.23 147	70.54 230	99.03 64	84.97 144	96.39 101	96.38 131

BH-RMVSNet		88.37 159	87.48 161	91.02 172	95.28 133	79.45 218	92.89 224	93.07 255	85.45 148	86.91 160	94.84 126	70.35 231	97.76 164	73.97 279	94.59 126	95.85 153

Fast-Effi-MVS+-dtu		87.44 192	86.72 180	89.63 227	92.04 241	77.68 262	94.03 176	93.94 237	85.81 136	82.42 262	91.32 247	70.33 232	97.06 225	80.33 219	90.23 182	94.14 218

MDTV_nov1_ep13_2view						55.91 357	87.62 320		73.32 321	84.59 216		70.33 232		74.65 275		95.50 163

ACMM	84.12 9	89.14 138	88.48 139	91.12 164	94.65 162	81.22 175	95.31 82	96.12 119	85.31 152	85.92 178	94.34 139	70.19 234	98.06 148	85.65 138	88.86 205	94.08 223
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		87.51 189	85.91 214	92.32 120	93.70 200	83.93 101	92.33 240	90.94 309	84.16 172	72.09 338	92.52 205	69.90 235	95.85 287	89.20 97	88.36 214	97.17 105

LPG-MVS_test		89.45 129	88.90 129	91.12 164	94.47 167	81.49 166	95.30 85	96.14 117	86.73 119	85.45 195	95.16 113	69.89 236	98.10 139	87.70 114	89.23 200	93.77 242

LGP-MVS_train				91.12 164	94.47 167	81.49 166		96.14 117	86.73 119	85.45 195	95.16 113	69.89 236	98.10 139	87.70 114	89.23 200	93.77 242

CHOSEN 280x420		85.15 249	83.99 251	88.65 252	92.47 230	78.40 242	79.68 349	92.76 261	74.90 309	81.41 274	89.59 288	69.85 238	95.51 299	79.92 224	95.29 116	92.03 299

LTVRE_ROB	82.13 13	86.26 230	84.90 238	90.34 199	94.44 171	81.50 164	92.31 242	94.89 205	83.03 198	79.63 299	92.67 201	69.69 239	97.79 162	71.20 291	86.26 236	91.72 303
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	88.74 151	87.29 166	93.08 82	92.70 227	85.39 71	96.57 29	96.43 98	78.74 272	80.85 280	96.07 87	69.64 240	99.01 70	78.01 244	96.65 94	94.83 188

MDTV_nov1_ep13			83.56 258		91.69 255	69.93 333	87.75 317	91.54 293	78.60 274	84.86 212	88.90 296	69.54 241	96.03 278	70.25 297	88.93 204

AUN-MVS		87.78 175	86.54 190	91.48 153	94.82 154	81.05 178	93.91 185	93.93 238	83.00 199	86.93 158	93.53 173	69.50 242	97.67 170	86.14 133	77.12 325	95.73 159

PatchT		82.68 274	81.27 276	86.89 295	90.09 308	70.94 327	84.06 338	90.15 321	74.91 308	85.63 185	83.57 337	69.37 243	94.87 312	65.19 326	88.50 210	94.84 187

VPNet		88.20 164	87.47 162	90.39 194	93.56 203	79.46 217	94.04 175	95.54 164	88.67 68	86.96 157	94.58 136	69.33 244	97.15 216	84.05 157	80.53 302	94.56 201

ACMP	84.23 8	89.01 144	88.35 140	90.99 175	94.73 156	81.27 172	95.07 102	95.89 138	86.48 123	83.67 244	94.30 142	69.33 244	97.99 153	87.10 127	88.55 207	93.72 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_post1							88.00 314				9.81 361	69.31 246	95.53 297	76.65 256

tpmvs		83.35 271	82.07 269	87.20 289	91.07 277	71.00 326	88.31 312	91.70 288	78.91 266	80.49 286	87.18 323	69.30 247	97.08 223	68.12 314	83.56 258	93.51 254

thres200		87.21 203	86.24 201	90.12 206	95.36 129	78.53 237	93.26 209	92.10 276	86.42 126	88.00 139	91.11 256	69.24 248	98.00 152	69.58 304	91.04 176	93.83 237

tfpn200view9		87.58 186	86.64 184	90.41 193	95.99 109	78.64 234	94.58 133	91.98 282	86.94 114	88.09 134	91.77 232	69.18 249	98.10 139	70.13 300	91.10 171	94.48 208

thres400		87.62 184	86.64 184	90.57 184	95.99 109	78.64 234	94.58 133	91.98 282	86.94 114	88.09 134	91.77 232	69.18 249	98.10 139	70.13 300	91.10 171	94.96 181

tfpnnormal		84.72 257	83.23 261	89.20 237	92.79 226	80.05 204	94.48 139	95.81 143	82.38 210	81.08 278	91.21 249	69.01 251	96.95 232	61.69 338	80.59 299	90.58 325

thres100view900		87.63 182	86.71 181	90.38 196	96.12 100	78.55 236	95.03 106	91.58 291	87.15 107	88.06 137	92.29 213	68.91 252	98.10 139	70.13 300	91.10 171	94.48 208

thres600view7		87.65 179	86.67 183	90.59 183	96.08 104	78.72 232	94.88 115	91.58 291	87.06 110	88.08 136	92.30 212	68.91 252	98.10 139	70.05 303	91.10 171	94.96 181

PatchMatch-RL		86.77 218	85.54 223	90.47 192	95.88 113	82.71 139	90.54 276	92.31 271	79.82 257	84.32 228	91.57 242	68.77 254	96.39 264	73.16 284	93.48 144	92.32 295

XVG-OURS		89.40 134	88.70 132	91.52 151	94.06 181	81.46 168	91.27 265	96.07 122	86.14 132	88.89 127	95.77 97	68.73 255	97.26 209	87.39 119	89.96 186	95.83 155

TR-MVS		86.78 215	85.76 220	89.82 218	94.37 173	78.41 241	92.47 235	92.83 259	81.11 244	86.36 171	92.40 208	68.73 255	97.48 183	73.75 282	89.85 190	93.57 250

tpm		84.73 256	84.02 250	86.87 296	90.33 303	68.90 336	89.06 301	89.94 327	80.85 246	85.75 180	89.86 284	68.54 257	95.97 281	77.76 245	84.05 252	95.75 158

FMVSNet3		87.40 194	86.11 205	91.30 159	93.79 197	83.64 110	94.20 162	94.81 212	83.89 178	84.37 223	91.87 231	68.45 258	96.56 253	78.23 241	85.36 241	93.70 247

MVP-Stereo		85.97 233	84.86 239	89.32 234	90.92 285	82.19 151	92.11 248	94.19 230	78.76 271	78.77 304	91.63 238	68.38 259	96.56 253	75.01 273	93.95 134	89.20 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		81.96 278	80.27 284	87.01 291	91.09 276	71.02 325	87.38 321	91.53 294	66.25 343	80.17 289	86.35 327	68.22 260	96.15 275	69.16 305	82.29 271	93.86 235

tpm2		84.08 262	82.94 264	87.48 280	91.39 264	71.27 321	89.23 299	90.37 318	71.95 331	84.64 214	89.33 291	67.30 261	96.55 255	75.17 270	87.09 232	94.63 194

test-LLR		85.87 235	85.41 226	87.25 285	90.95 281	71.67 319	89.55 291	89.88 330	83.41 190	84.54 217	87.95 311	67.25 262	95.11 308	81.82 192	93.37 147	94.97 178

test0.0.03 1		82.41 276	81.69 272	84.59 315	88.23 327	72.89 307	90.24 281	87.83 340	83.41 190	79.86 296	89.78 286	67.25 262	88.99 348	65.18 327	83.42 261	91.90 301

CVMVSNet		84.69 258	84.79 241	84.37 317	91.84 248	64.92 348	93.70 192	91.47 296	66.19 344	86.16 176	95.28 108	67.18 264	93.33 329	80.89 208	90.42 180	94.88 186

bset_n11_16_dypcd		86.83 212	85.55 222	90.65 182	88.22 328	81.70 160	88.88 304	90.42 316	85.26 153	85.49 192	90.69 267	67.11 265	97.02 228	89.51 94	84.39 248	93.23 264

thisisatest0515		87.33 195	85.99 209	91.37 157	93.49 204	79.55 215	90.63 275	89.56 335	80.17 251	87.56 148	90.86 261	67.07 266	98.28 131	81.50 199	93.02 154	96.29 133

tttt0517		88.61 154	87.78 155	91.11 167	94.96 145	77.81 257	95.35 80	89.69 332	85.09 159	88.05 138	94.59 135	66.93 267	98.48 113	83.27 166	92.13 166	97.03 111

our_test_3		81.93 279	80.46 282	86.33 301	88.46 324	73.48 302	88.46 310	91.11 302	76.46 290	76.69 316	88.25 307	66.89 268	94.36 315	68.75 307	79.08 316	91.14 316

thisisatest0530		88.67 152	87.61 159	91.86 139	94.87 151	80.07 202	94.63 131	89.90 329	84.00 176	88.46 131	93.78 167	66.88 269	98.46 116	83.30 165	92.65 159	97.06 109

IterMVS-SCA-FT		85.45 241	84.53 246	88.18 265	91.71 253	76.87 274	90.19 284	92.65 265	85.40 150	81.44 273	90.54 269	66.79 270	95.00 311	81.04 203	81.05 290	92.66 284

SCA		86.32 229	85.18 231	89.73 224	92.15 236	76.60 276	91.12 268	91.69 289	83.53 187	85.50 191	88.81 297	66.79 270	96.48 258	76.65 256	90.35 181	96.12 140

D2MVS		85.90 234	85.09 233	88.35 259	90.79 290	77.42 267	91.83 253	95.70 151	80.77 247	80.08 293	90.02 280	66.74 272	96.37 265	81.88 191	87.97 221	91.26 312

IterMVS		84.88 254	83.98 252	87.60 275	91.44 259	76.03 284	90.18 285	92.41 268	83.24 195	81.06 279	90.42 273	66.60 273	94.28 318	79.46 227	80.98 295	92.48 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		87.26 197	85.98 210	91.08 168	94.01 184	83.10 123	95.14 99	94.94 199	83.57 184	84.37 223	91.64 235	66.59 274	96.34 268	78.23 241	85.36 241	93.79 238

test1		87.26 197	85.98 210	91.08 168	94.01 184	83.10 123	95.14 99	94.94 199	83.57 184	84.37 223	91.64 235	66.59 274	96.34 268	78.23 241	85.36 241	93.79 238

FMVSNet2		87.19 204	85.82 216	91.30 159	94.01 184	83.67 109	94.79 121	94.94 199	83.57 184	83.88 238	92.05 226	66.59 274	96.51 256	77.56 248	85.01 244	93.73 245

EPMVS		83.90 266	82.70 268	87.51 277	90.23 306	72.67 310	88.62 308	81.96 352	81.37 237	85.01 210	88.34 305	66.31 277	94.45 313	75.30 269	87.12 231	95.43 167

ppachtmachnet_test		81.84 280	80.07 288	87.15 290	88.46 324	74.43 295	89.04 302	92.16 275	75.33 303	77.75 309	88.99 294	66.20 278	95.37 304	65.12 328	77.60 321	91.65 304

MDA-MVSNet_test_wron		79.21 307	77.19 309	85.29 310	88.22 328	72.77 309	85.87 328	90.06 324	74.34 313	62.62 349	87.56 317	66.14 279	91.99 340	66.90 322	73.01 330	91.10 319

YYNet1		79.22 306	77.20 308	85.28 311	88.20 330	72.66 311	85.87 328	90.05 326	74.33 314	62.70 348	87.61 316	66.09 280	92.03 339	66.94 319	72.97 331	91.15 315

JIA-IIPM		81.04 292	78.98 302	87.25 285	88.64 321	73.48 302	81.75 346	89.61 334	73.19 322	82.05 267	73.71 347	66.07 281	95.87 286	71.18 293	84.60 247	92.41 291

RRT_MVS		88.86 147	87.68 157	92.39 117	92.02 243	86.09 53	94.38 153	94.94 199	85.45 148	87.14 155	93.84 165	65.88 282	97.11 220	88.73 102	86.77 235	93.98 228

MSDG		84.86 255	83.09 262	90.14 205	93.80 195	80.05 204	89.18 300	93.09 254	78.89 267	78.19 305	91.91 229	65.86 283	97.27 207	68.47 309	88.45 211	93.11 270

jajsoiax		88.24 163	87.50 160	90.48 191	90.89 287	80.14 199	95.31 82	95.65 157	84.97 161	84.24 232	94.02 152	65.31 284	97.42 190	88.56 104	88.52 209	93.89 230

cascas		86.43 228	84.98 235	90.80 180	92.10 240	80.92 183	90.24 281	95.91 135	73.10 323	83.57 248	88.39 304	65.15 285	97.46 185	84.90 147	91.43 169	94.03 226

ADS-MVSNet2		81.66 284	79.71 292	87.50 278	91.35 266	74.19 297	83.33 341	88.48 338	72.90 325	82.24 265	85.77 331	64.98 286	93.20 331	64.57 330	83.74 254	95.12 174

ADS-MVSNet		81.56 286	79.78 290	86.90 294	91.35 266	71.82 318	83.33 341	89.16 336	72.90 325	82.24 265	85.77 331	64.98 286	93.76 324	64.57 330	83.74 254	95.12 174

pmmvs5		84.21 261	82.84 267	88.34 260	88.95 319	76.94 273	92.41 236	91.91 286	75.63 300	80.28 288	91.18 252	64.59 288	95.57 295	77.09 254	83.47 259	92.53 287

PVSNet	78.82 18	85.55 240	84.65 243	88.23 264	94.72 157	71.93 317	87.12 322	92.75 262	78.80 270	84.95 211	90.53 270	64.43 289	96.71 242	74.74 274	93.86 136	96.06 146

UGNet		89.95 116	88.95 127	92.95 89	94.51 166	83.31 119	95.70 67	95.23 186	89.37 49	87.58 147	93.94 157	64.00 290	98.78 99	83.92 158	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		85.07 250	84.27 247	87.48 280	92.91 224	70.62 329	91.69 259	92.46 267	76.20 296	82.67 261	95.22 111	63.94 291	97.29 206	77.51 249	85.80 239	94.53 202

mvs_tets		88.06 169	87.28 167	90.38 196	90.94 283	79.88 210	95.22 92	95.66 155	85.10 158	84.21 233	93.94 157	63.53 292	97.40 197	88.50 105	88.40 213	93.87 233

Anonymous20231211		86.59 222	85.13 232	90.98 177	96.52 90	81.50 164	96.14 45	96.16 116	73.78 317	83.65 245	92.15 217	63.26 293	97.37 201	82.82 174	81.74 281	94.06 224

dp		81.47 288	80.23 285	85.17 312	89.92 312	65.49 346	86.74 323	90.10 323	76.30 294	81.10 277	87.12 324	62.81 294	95.92 283	68.13 313	79.88 309	94.09 222

LFMVS		90.08 112	89.13 123	92.95 89	96.71 82	82.32 150	96.08 50	89.91 328	86.79 117	92.15 81	96.81 53	62.60 295	98.34 126	87.18 122	93.90 135	98.19 53

DWT-MVSNet_test		84.95 253	83.68 255	88.77 246	91.43 262	73.75 300	91.74 256	90.98 307	80.66 248	83.84 239	87.36 319	62.44 296	97.11 220	78.84 236	85.81 238	95.46 165

Anonymous20231206		81.03 293	79.77 291	84.82 314	87.85 333	70.26 331	91.42 263	92.08 277	73.67 318	77.75 309	89.25 292	62.43 297	93.08 332	61.50 339	82.00 277	91.12 317

VDD-MVS		90.74 97	89.92 109	93.20 77	96.27 96	83.02 127	95.73 65	93.86 241	88.42 76	92.53 72	96.84 50	62.09 298	98.64 104	90.95 80	92.62 160	97.93 74

MS-PatchMatch		85.05 251	84.16 248	87.73 273	91.42 263	78.51 238	91.25 266	93.53 247	77.50 283	80.15 290	91.58 240	61.99 299	95.51 299	75.69 265	94.35 132	89.16 335

OurMVSNet-221017-0		85.35 244	84.64 244	87.49 279	90.77 291	72.59 313	94.01 178	94.40 222	84.72 166	79.62 300	93.17 184	61.91 300	96.72 240	81.99 188	81.16 286	93.16 268

test20.03		79.95 301	79.08 300	82.55 325	85.79 340	67.74 341	91.09 269	91.08 303	81.23 242	74.48 330	89.96 283	61.63 301	90.15 346	60.08 342	76.38 326	89.76 328

DSMNet-mixed		76.94 313	76.29 312	78.89 329	83.10 349	56.11 356	87.78 316	79.77 354	60.65 348	75.64 323	88.71 300	61.56 302	88.34 349	60.07 343	89.29 199	92.21 298

Anonymous20240529		88.09 167	86.59 188	92.58 107	96.53 89	81.92 157	95.99 54	95.84 141	74.11 315	89.06 125	95.21 112	61.44 303	98.81 97	83.67 163	87.47 225	97.01 112

IB-MVS	80.51 15	85.24 248	83.26 260	91.19 162	92.13 238	79.86 211	91.75 255	91.29 300	83.28 194	80.66 283	88.49 303	61.28 304	98.46 116	80.99 206	79.46 313	95.25 172
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		86.61 220	85.27 230	90.66 181	91.33 268	78.71 233	90.40 278	93.81 244	85.34 151	85.12 208	89.57 289	61.25 305	97.11 220	80.99 206	89.59 194	96.15 137

N_pmnet		68.89 319	68.44 322	70.23 335	89.07 318	28.79 365	88.06 313	19.50 365	69.47 339	71.86 340	84.93 333	61.24 306	91.75 342	54.70 348	77.15 324	90.15 326

EU-MVSNet		81.32 290	80.95 278	82.42 326	88.50 323	63.67 349	93.32 202	91.33 298	64.02 346	80.57 285	92.83 196	61.21 307	92.27 338	76.34 259	80.38 305	91.32 310

VDDNet		89.56 125	88.49 138	92.76 96	95.07 140	82.09 152	96.30 35	93.19 253	81.05 245	91.88 85	96.86 49	61.16 308	98.33 128	88.43 106	92.49 163	97.84 80

PVSNet_0	73.20 20	77.22 312	74.83 317	84.37 317	90.70 295	71.10 324	83.09 343	89.67 333	72.81 327	73.93 332	83.13 339	60.79 309	93.70 325	68.54 308	50.84 353	88.30 342

RRT_test8_iter05		86.90 210	86.36 195	88.52 255	93.00 221	73.27 304	94.32 156	95.96 130	85.50 147	84.26 231	92.86 193	60.76 310	97.70 169	88.32 107	82.29 271	94.60 197

SixPastTwentyTwo		83.91 265	82.90 265	86.92 293	90.99 279	70.67 328	93.48 198	91.99 281	85.54 145	77.62 311	92.11 221	60.59 311	96.87 237	76.05 263	77.75 320	93.20 266

gg-mvs-nofinetune		81.77 281	79.37 294	88.99 244	90.85 289	77.73 261	86.29 326	79.63 355	74.88 310	83.19 256	69.05 350	60.34 312	96.11 276	75.46 267	94.64 125	93.11 270

MDA-MVSNet-bldmvs		78.85 308	76.31 311	86.46 298	89.76 314	73.88 299	88.79 305	90.42 316	79.16 264	59.18 350	88.33 306	60.20 313	94.04 320	62.00 337	68.96 340	91.48 308

pmmvs6		83.42 269	81.60 273	88.87 245	88.01 331	77.87 255	94.96 108	94.24 229	74.67 311	78.80 303	91.09 257	60.17 314	96.49 257	77.06 255	75.40 328	92.23 297

ACMH	80.38 17	85.36 243	83.68 255	90.39 194	94.45 170	80.63 190	94.73 125	94.85 208	82.09 215	77.24 312	92.65 202	60.01 315	97.58 176	72.25 288	84.87 245	92.96 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GG-mvs-BLEND				87.94 271	89.73 315	77.91 252	87.80 315	78.23 357		80.58 284	83.86 335	59.88 316	95.33 305	71.20 291	92.22 165	90.60 324

UniMVSNet_ETH3D		87.53 188	86.37 194	91.00 174	92.44 231	78.96 231	94.74 124	95.61 159	84.07 175	85.36 205	94.52 137	59.78 317	97.34 202	82.93 170	87.88 222	96.71 123

pmmvs-eth3d		80.97 294	78.72 303	87.74 272	84.99 345	79.97 209	90.11 286	91.65 290	75.36 302	73.51 333	86.03 328	59.45 318	93.96 323	75.17 270	72.21 333	89.29 333

test_0402		81.30 291	79.17 299	87.67 274	93.19 212	78.17 247	92.98 221	91.71 287	75.25 304	76.02 322	90.31 274	59.23 319	96.37 265	50.22 350	83.63 257	88.47 341

DIV-MVS_2432*1600		80.20 299	79.24 296	83.07 323	85.64 342	65.29 347	91.01 270	93.93 238	78.71 273	76.32 318	86.40 326	59.20 320	92.93 334	72.59 286	69.35 337	91.00 320

FMVSNet1		85.85 236	84.11 249	91.08 168	92.81 225	83.10 123	95.14 99	94.94 199	81.64 231	82.68 260	91.64 235	59.01 321	96.34 268	75.37 268	83.78 253	93.79 238

COLMAP_ROB	80.39 16	83.96 263	82.04 270	89.74 222	95.28 133	79.75 213	94.25 159	92.28 272	75.17 305	78.02 308	93.77 168	58.60 322	97.84 161	65.06 329	85.92 237	91.63 305
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	85.05 251	83.46 259	89.82 218	94.66 161	79.37 220	94.44 144	94.12 235	82.19 214	78.04 307	92.82 197	58.23 323	97.54 179	73.77 281	82.90 267	92.54 286

CMPMVS	59.16 21	80.52 296	79.20 298	84.48 316	83.98 346	67.63 342	89.95 289	93.84 243	64.79 345	66.81 346	91.14 255	57.93 324	95.17 306	76.25 260	88.10 217	90.65 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ITE_SJBPF				88.24 263	91.88 247	77.05 272		92.92 257	85.54 145	80.13 292	93.30 179	57.29 325	96.20 272	72.46 287	84.71 246	91.49 307

TESTMET0.1,1		83.74 267	82.85 266	86.42 300	89.96 311	71.21 323	89.55 291	87.88 339	77.41 284	83.37 252	87.31 320	56.71 326	93.65 326	80.62 213	92.85 158	94.40 211

UnsupCasMVSNet_eth		80.07 300	78.27 304	85.46 308	85.24 344	72.63 312	88.45 311	94.87 207	82.99 200	71.64 341	88.07 310	56.34 327	91.75 342	73.48 283	63.36 347	92.01 300

K. test v3		81.59 285	80.15 287	85.91 306	89.89 313	69.42 335	92.57 233	87.71 341	85.56 144	73.44 334	89.71 287	55.58 328	95.52 298	77.17 252	69.76 336	92.78 282

test-mter		84.54 259	83.64 257	87.25 285	90.95 281	71.67 319	89.55 291	89.88 330	79.17 263	84.54 217	87.95 311	55.56 329	95.11 308	81.82 192	93.37 147	94.97 178

lessismore_v0				86.04 302	88.46 324	68.78 337		80.59 353		73.01 336	90.11 278	55.39 330	96.43 263	75.06 272	65.06 344	92.90 277

MVS-HIRNet		73.70 316	72.20 319	78.18 331	91.81 250	56.42 355	82.94 344	82.58 350	55.24 350	68.88 343	66.48 351	55.32 331	95.13 307	58.12 345	88.42 212	83.01 346

new-patchmatchnet		76.41 314	75.17 316	80.13 328	82.65 351	59.61 351	87.66 319	91.08 303	78.23 280	69.85 342	83.22 338	54.76 332	91.63 344	64.14 332	64.89 345	89.16 335

Anonymous202405211		87.68 177	86.13 203	92.31 121	96.66 83	80.74 188	94.87 116	91.49 295	80.47 249	89.46 119	95.44 104	54.72 333	98.23 132	82.19 184	89.89 188	97.97 70

XVG-ACMP-BASELINE		86.00 232	84.84 240	89.45 233	91.20 270	78.00 250	91.70 258	95.55 162	85.05 160	82.97 257	92.25 215	54.49 334	97.48 183	82.93 170	87.45 227	92.89 278

USDC		82.76 272	81.26 277	87.26 284	91.17 272	74.55 292	89.27 297	93.39 250	78.26 279	75.30 325	92.08 223	54.43 335	96.63 244	71.64 289	85.79 240	90.61 322

AllTest		83.42 269	81.39 275	89.52 230	95.01 141	77.79 258	93.12 214	90.89 311	77.41 284	76.12 320	93.34 175	54.08 336	97.51 181	68.31 311	84.27 250	93.26 260

TestCases				89.52 230	95.01 141	77.79 258		90.89 311	77.41 284	76.12 320	93.34 175	54.08 336	97.51 181	68.31 311	84.27 250	93.26 260

KD-MVS_2432*1600		78.50 309	76.02 314	85.93 304	86.22 337	74.47 293	84.80 334	92.33 269	79.29 261	76.98 314	85.92 329	53.81 338	93.97 321	67.39 316	57.42 350	89.36 330

miper_refine_blended		78.50 309	76.02 314	85.93 304	86.22 337	74.47 293	84.80 334	92.33 269	79.29 261	76.98 314	85.92 329	53.81 338	93.97 321	67.39 316	57.42 350	89.36 330

MIMVSNet		82.59 275	80.53 280	88.76 247	91.51 258	78.32 243	86.57 325	90.13 322	79.32 260	80.70 282	88.69 302	52.98 340	93.07 333	66.03 324	88.86 205	94.90 185

FMVSNet5		81.52 287	79.60 293	87.27 283	91.17 272	77.95 251	91.49 262	92.26 273	76.87 289	76.16 319	87.91 313	51.67 341	92.34 337	67.74 315	81.16 286	91.52 306

testgi		80.94 295	80.20 286	83.18 322	87.96 332	66.29 343	91.28 264	90.70 315	83.70 181	78.12 306	92.84 195	51.37 342	90.82 345	63.34 333	82.46 270	92.43 290

Anonymous20240521		80.44 297	79.21 297	84.11 320	85.75 341	67.89 339	92.86 225	93.23 252	75.61 301	75.59 324	87.47 318	50.03 343	94.33 316	71.14 294	81.21 285	90.12 327

UnsupCasMVSNet_bld		76.23 315	73.27 318	85.09 313	83.79 347	72.92 306	85.65 331	93.47 249	71.52 332	68.84 344	79.08 345	49.77 344	93.21 330	66.81 323	60.52 349	89.13 337

OpenMVS_ROB	74.94 19	79.51 304	77.03 310	86.93 292	87.00 334	76.23 283	92.33 240	90.74 314	68.93 340	74.52 329	88.23 308	49.58 345	96.62 245	57.64 346	84.29 249	87.94 343

TDRefinement		79.81 302	77.34 306	87.22 288	79.24 353	75.48 289	93.12 214	92.03 279	76.45 291	75.01 326	91.58 240	49.19 346	96.44 262	70.22 299	69.18 339	89.75 329

MIMVSNet1		79.38 305	77.28 307	85.69 307	86.35 336	73.67 301	91.61 261	92.75 262	78.11 282	72.64 337	88.12 309	48.16 347	91.97 341	60.32 341	77.49 322	91.43 309

MVS_0304		83.46 268	81.92 271	88.10 267	90.63 297	77.49 266	93.26 209	93.75 245	80.04 254	80.44 287	87.24 322	47.94 348	95.55 296	75.79 264	88.16 216	91.26 312

LF4IMVS		80.37 298	79.07 301	84.27 319	86.64 335	69.87 334	89.39 296	91.05 305	76.38 292	74.97 327	90.00 281	47.85 349	94.25 319	74.55 277	80.82 297	88.69 339

EG-PatchMatch MVS		82.37 277	80.34 283	88.46 256	90.27 304	79.35 221	92.80 227	94.33 225	77.14 288	73.26 335	90.18 276	47.47 350	96.72 240	70.25 297	87.32 230	89.30 332

TinyColmap		79.76 303	77.69 305	85.97 303	91.71 253	73.12 305	89.55 291	90.36 319	75.03 306	72.03 339	90.19 275	46.22 351	96.19 274	63.11 334	81.03 291	88.59 340

tmp_tt		35.64 329	39.24 331	24.84 343	14.87 365	23.90 366	62.71 355	51.51 364	6.58 361	36.66 357	62.08 354	44.37 352	30.34 362	52.40 349	22.00 359	20.27 357

new_pmnet		72.15 317	70.13 320	78.20 330	82.95 350	65.68 344	83.91 339	82.40 351	62.94 347	64.47 347	79.82 344	42.85 353	86.26 351	57.41 347	74.44 329	82.65 347

pmmvs3		71.81 318	68.71 321	81.11 327	75.86 354	70.42 330	86.74 323	83.66 349	58.95 349	68.64 345	80.89 343	36.93 354	89.52 347	63.10 335	63.59 346	83.39 345

PM-MVS		78.11 311	76.12 313	84.09 321	83.54 348	70.08 332	88.97 303	85.27 347	79.93 255	74.73 328	86.43 325	34.70 355	93.48 327	79.43 230	72.06 334	88.72 338

ambc				83.06 324	79.99 352	63.51 350	77.47 350	92.86 258		74.34 331	84.45 334	28.74 356	95.06 310	73.06 285	68.89 341	90.61 322

DeepMVS_CX				56.31 340	74.23 355	51.81 358		56.67 363	44.85 354	48.54 354	75.16 346	27.87 357	58.74 360	40.92 353	52.22 352	58.39 353

FPMVS		64.63 321	62.55 323	70.88 334	70.80 356	56.71 353	84.42 337	84.42 348	51.78 352	49.57 352	81.61 342	23.49 358	81.48 354	40.61 354	76.25 327	74.46 350

ANet_high		58.88 323	54.22 327	72.86 333	56.50 363	56.67 354	80.75 348	86.00 344	73.09 324	37.39 356	64.63 353	22.17 359	79.49 356	43.51 352	23.96 357	82.43 348

EMVS		42.07 328	41.12 330	44.92 342	63.45 361	35.56 364	73.65 351	63.48 360	33.05 358	26.88 361	45.45 358	21.27 360	67.14 358	19.80 359	23.02 358	32.06 356

Gipuma		57.99 324	54.91 326	67.24 337	88.51 322	65.59 345	52.21 357	90.33 320	43.58 355	42.84 355	51.18 356	20.29 361	85.07 352	34.77 355	70.45 335	51.05 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 327	42.29 329	46.03 341	65.58 359	37.41 362	73.51 352	64.62 359	33.99 357	28.47 360	47.87 357	19.90 362	67.91 357	22.23 358	24.45 356	32.77 355

PMMVS2		59.60 322	56.40 325	69.21 336	68.83 357	46.58 360	73.02 354	77.48 358	55.07 351	49.21 353	72.95 349	17.43 363	80.04 355	49.32 351	44.33 354	80.99 349

LCM-MVSNet		66.00 320	62.16 324	77.51 332	64.51 360	58.29 352	83.87 340	90.90 310	48.17 353	54.69 351	73.31 348	16.83 364	86.75 350	65.47 325	61.67 348	87.48 344

PMVS	47.18 22	52.22 325	48.46 328	63.48 338	45.72 364	46.20 361	73.41 353	78.31 356	41.03 356	30.06 358	65.68 352	6.05 365	83.43 353	30.04 356	65.86 343	60.80 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 326	38.59 332	57.77 339	56.52 362	48.77 359	55.38 356	58.64 362	29.33 359	28.96 359	52.65 355	4.68 366	64.62 359	28.11 357	33.07 355	59.93 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		21.27 331	20.48 334	23.63 344	68.59 358	36.41 363	49.57 358	6.85 366	9.37 360	7.89 362	4.46 364	4.03 367	31.37 361	17.47 360	16.07 360	3.12 358

test123		8.76 333	11.22 336	1.39 345	0.85 367	0.97 367	85.76 330	0.35 368	0.54 363	2.45 364	8.14 363	0.60 368	0.48 363	2.16 362	0.17 362	2.71 359

testmvs		8.92 332	11.52 335	1.12 346	1.06 366	0.46 368	86.02 327	0.65 367	0.62 362	2.74 363	9.52 362	0.31 369	0.45 364	2.38 361	0.39 361	2.46 360

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		7.82 334	10.43 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	93.88 162	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

IU-MVS					98.77 4	86.00 54		96.84 63	81.26 240	97.26 6				95.50 7	99.13 3	99.03 4

save fliter					97.85 48	85.63 68	95.21 93	96.82 67	89.44 45

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																96.12 140

test_part2					98.55 11	87.22 16				96.40 11

MTGPA								96.97 49

MTMP							96.16 43	60.64 361

gm-plane-assit					89.60 316	68.00 338			77.28 287		88.99 294		97.57 177	79.44 229

test9_res														91.91 59	98.71 30	98.07 63

agg_prior2														90.54 85	98.68 35	98.27 47

agg_prior					97.38 64	85.92 60		96.72 79		92.16 79			98.97 80

test_prior4						85.96 57	94.11 167

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 88			98.11 61

旧先验2							93.36 201		71.25 334	94.37 26			97.13 219	86.74 128

新几何2							93.11 216

无先验							93.28 208	96.26 107	73.95 316				99.05 60	80.56 214		96.59 126

原ACMM2							92.94 223

testdata2													98.75 100	78.30 240

testdata1							92.15 246		87.94 88

plane_prior7					94.70 159	82.74 136

plane_prior5								96.22 112					98.12 137	88.15 108	89.99 184	94.63 194

plane_prior4											94.86 123

plane_prior3						82.75 134			90.26 30	86.91 160

plane_prior2							95.85 61		90.81 17

plane_prior1					94.59 163

plane_prior						82.73 137	95.21 93		89.66 42						89.88 189

n2								0.00 369

nn								0.00 369

door-mid								85.49 345

test11								96.57 92

door								85.33 346

HQP5-MVS						81.56 162

HQP-NCC					94.17 178		94.39 149		88.81 62	85.43 198

ACMP_Plane					94.17 178		94.39 149		88.81 62	85.43 198

BP-MVS														87.11 125

HQP4-MVS										85.43 198			97.96 155			94.51 204

HQP3-MVS								96.04 126							89.77 191

NP-MVS					94.37 173	82.42 145					93.98 155

ACMMP++_ref															87.47 225

ACMMP++															88.01 220

High-resolution multi-view benchmark