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.

DVP-MVS		95.67 1	96.02 1	94.64 33	98.78 2	85.93 47	97.09 9	96.73 64	90.27 26	97.04 4	98.05 4	91.47 3	99.55 9	95.62 3	99.08 4	98.45 23

DPE-MVS		95.57 2	95.67 2	95.25 6	98.36 21	87.28 11	95.56 66	97.51 4	89.13 51	97.14 3	97.91 6	91.64 2	99.62 1	94.61 8	99.17 2	98.86 5

APDe-MVS		95.46 3	95.64 3	94.91 16	98.26 24	86.29 42	97.46 2	97.40 13	89.03 54	96.20 8	98.10 2	89.39 9	99.34 27	95.88 1	99.03 6	99.10 3

MSP-MVS		95.42 4	95.56 4	94.98 14	98.49 12	86.52 31	96.91 18	97.47 7	91.73 8	96.10 9	96.69 46	89.90 5	99.30 34	94.70 6	98.04 54	99.13 1

CNVR-MVS		95.40 5	95.37 5	95.50 4	98.11 32	88.51 4	95.29 76	96.96 44	92.09 3	95.32 14	97.08 31	89.49 8	99.33 31	95.10 5	98.85 12	98.66 10

SMA-MVS		95.20 6	95.07 8	95.59 2	98.14 31	88.48 5	96.26 33	97.28 23	85.90 123	97.67 1	98.10 2	88.41 13	99.56 4	94.66 7	99.19 1	98.71 8

SteuartSystems-ACMMP		95.20 6	95.32 7	94.85 21	96.99 63	86.33 38	97.33 3	97.30 21	91.38 11	95.39 13	97.46 12	88.98 12	99.40 25	94.12 12	98.89 11	98.82 6
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 8	94.91 10	95.83 1	98.25 25	89.65 1	95.92 50	96.96 44	91.75 7	94.02 27	96.83 39	88.12 15	99.55 9	93.41 20	98.94 9	98.28 35

SD-MVS		94.96 9	95.33 6	93.88 54	97.25 60	86.69 24	96.19 35	97.11 35	90.42 25	96.95 6	97.27 19	89.53 7	96.91 219	94.38 10	98.85 12	98.03 56
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
TSAR-MVS + MP.		94.85 10	94.94 9	94.58 36	98.25 25	86.33 38	96.11 41	96.62 75	88.14 79	96.10 9	96.96 35	89.09 11	98.94 72	94.48 9	98.68 28	98.48 17
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
NCCC		94.81 11	94.69 12	95.17 8	97.83 40	87.46 10	95.66 61	96.93 47	92.34 2	93.94 28	96.58 53	87.74 19	99.44 24	92.83 27	98.40 43	98.62 11

ACMMP_NAP		94.74 12	94.56 13	95.28 5	98.02 37	87.70 6	95.68 59	97.34 15	88.28 74	95.30 15	97.67 8	85.90 39	99.54 13	93.91 14	98.95 8	98.60 12

HFP-MVS		94.52 13	94.40 15	94.86 19	98.61 6	86.81 18	96.94 13	97.34 15	88.63 64	93.65 31	97.21 24	86.10 35	99.49 20	92.35 34	98.77 18	98.30 31

zzz-MVS		94.47 14	94.30 17	95.00 11	98.42 16	86.95 13	95.06 93	96.97 41	91.07 13	93.14 43	97.56 9	84.30 57	99.56 4	93.43 18	98.75 20	98.47 19

XVS		94.45 15	94.32 16	94.85 21	98.54 9	86.60 29	96.93 15	97.19 28	90.66 23	92.85 46	97.16 29	85.02 50	99.49 20	91.99 43	98.56 39	98.47 19

MCST-MVS		94.45 15	94.20 23	95.19 7	98.46 14	87.50 9	95.00 95	97.12 33	87.13 98	92.51 60	96.30 62	89.24 10	99.34 27	93.46 17	98.62 36	98.73 7

region2R		94.43 17	94.27 19	94.92 15	98.65 4	86.67 26	96.92 17	97.23 26	88.60 66	93.58 35	97.27 19	85.22 46	99.54 13	92.21 36	98.74 22	98.56 14

ACMMPR		94.43 17	94.28 18	94.91 16	98.63 5	86.69 24	96.94 13	97.32 20	88.63 64	93.53 38	97.26 21	85.04 49	99.54 13	92.35 34	98.78 17	98.50 15

MTAPA		94.42 19	94.22 20	95.00 11	98.42 16	86.95 13	94.36 141	96.97 41	91.07 13	93.14 43	97.56 9	84.30 57	99.56 4	93.43 18	98.75 20	98.47 19

testtj		94.39 20	94.18 24	95.00 11	98.24 27	86.77 22	96.16 36	97.23 26	87.28 96	94.85 18	97.04 32	86.99 28	99.52 17	91.54 55	98.33 46	98.71 8

CP-MVS		94.34 21	94.21 22	94.74 30	98.39 19	86.64 28	97.60 1	97.24 24	88.53 68	92.73 53	97.23 22	85.20 47	99.32 32	92.15 39	98.83 14	98.25 40

Regformer-2		94.33 22	94.22 20	94.68 31	95.54 109	86.75 23	94.57 122	96.70 68	91.84 6	94.41 19	96.56 55	87.19 25	99.13 45	93.50 15	97.65 64	98.16 45

#test#		94.32 23	94.14 26	94.86 19	98.61 6	86.81 18	96.43 27	97.34 15	87.51 93	93.65 31	97.21 24	86.10 35	99.49 20	91.68 53	98.77 18	98.30 31

MP-MVS		94.25 24	94.07 29	94.77 28	98.47 13	86.31 40	96.71 23	96.98 40	89.04 53	91.98 69	97.19 26	85.43 44	99.56 4	92.06 42	98.79 15	98.44 24
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 25	94.07 29	94.75 29	98.06 35	86.90 16	95.88 51	96.94 46	85.68 129	95.05 17	97.18 27	87.31 24	99.07 49	91.90 50	98.61 37	98.28 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 26	94.17 25	94.43 42	98.21 29	85.78 54	96.40 29	96.90 49	88.20 77	94.33 21	97.40 13	84.75 54	99.03 55	93.35 21	97.99 55	98.48 17

Regformer-1		94.22 27	94.13 27	94.51 39	95.54 109	86.36 37	94.57 122	96.44 83	91.69 9	94.32 22	96.56 55	87.05 27	99.03 55	93.35 21	97.65 64	98.15 46

GST-MVS		94.21 28	93.97 32	94.90 18	98.41 18	86.82 17	96.54 26	97.19 28	88.24 75	93.26 39	96.83 39	85.48 43	99.59 3	91.43 59	98.40 43	98.30 31

MP-MVS-pluss		94.21 28	94.00 31	94.85 21	98.17 30	86.65 27	94.82 106	97.17 31	86.26 117	92.83 48	97.87 7	85.57 42	99.56 4	94.37 11	98.92 10	98.34 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	89.96 1	94.20 30	94.77 11	92.49 100	96.52 76	80.00 191	94.00 164	97.08 36	90.05 29	95.65 11	97.29 17	89.66 6	98.97 68	93.95 13	98.71 23	98.50 15

DeepC-MVS_fast	89.43 2	94.04 31	93.79 35	94.80 27	97.48 49	86.78 20	95.65 63	96.89 50	89.40 43	92.81 49	96.97 34	85.37 45	99.24 36	90.87 69	98.69 26	98.38 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		94.02 32	93.88 33	94.43 42	98.39 19	85.78 54	97.25 5	97.07 37	86.90 106	92.62 57	96.80 43	84.85 53	99.17 41	92.43 31	98.65 34	98.33 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 33	93.78 36	94.63 34	98.50 11	85.90 52	96.87 19	96.91 48	88.70 62	91.83 75	97.17 28	83.96 62	99.55 9	91.44 58	98.64 35	98.43 25

PGM-MVS		93.96 34	93.72 38	94.68 31	98.43 15	86.22 43	95.30 74	97.78 1	87.45 94	93.26 39	97.33 16	84.62 55	99.51 18	90.75 71	98.57 38	98.32 30

Regformer-4		93.91 35	93.81 34	94.19 49	95.36 113	85.47 58	94.68 114	96.41 86	91.60 10	93.75 30	96.71 44	85.95 38	99.10 48	93.21 24	96.65 81	98.01 58

PHI-MVS		93.89 36	93.65 39	94.62 35	96.84 66	86.43 34	96.69 24	97.49 5	85.15 142	93.56 37	96.28 63	85.60 41	99.31 33	92.45 30	98.79 15	98.12 49

APD-MVS_3200maxsize		93.78 37	93.77 37	93.80 59	97.92 38	84.19 82	96.30 31	96.87 53	86.96 102	93.92 29	97.47 11	83.88 63	98.96 71	92.71 29	97.87 59	98.26 39

MSLP-MVS++		93.72 38	94.08 28	92.65 93	97.31 54	83.43 100	95.79 54	97.33 18	90.03 30	93.58 35	96.96 35	84.87 52	97.76 151	92.19 38	98.66 32	96.76 109

Regformer-3		93.68 39	93.64 40	93.81 58	95.36 113	84.61 67	94.68 114	95.83 127	91.27 12	93.60 34	96.71 44	85.75 40	98.86 77	92.87 26	96.65 81	97.96 60

TSAR-MVS + GP.		93.66 40	93.41 42	94.41 44	96.59 72	86.78 20	94.40 134	93.93 220	89.77 35	94.21 23	95.59 89	87.35 23	98.61 93	92.72 28	96.15 90	97.83 69

test_prior3		93.60 41	93.53 41	93.82 56	97.29 56	84.49 71	94.12 150	96.88 51	87.67 90	92.63 55	96.39 60	86.62 30	98.87 74	91.50 56	98.67 30	98.11 50

CANet		93.54 42	93.20 46	94.55 37	95.65 105	85.73 56	94.94 98	96.69 70	91.89 5	90.69 92	95.88 80	81.99 84	99.54 13	93.14 25	97.95 57	98.39 26

MVS_111021_HR		93.45 43	93.31 43	93.84 55	96.99 63	84.84 63	93.24 196	97.24 24	88.76 60	91.60 80	95.85 81	86.07 37	98.66 88	91.91 47	98.16 50	98.03 56

train_agg		93.44 44	93.08 47	94.52 38	97.53 45	86.49 32	94.07 157	96.78 60	81.86 211	92.77 50	96.20 67	87.63 21	99.12 46	92.14 40	98.69 26	97.94 61

DELS-MVS		93.43 45	93.25 44	93.97 51	95.42 112	85.04 62	93.06 203	97.13 32	90.74 20	91.84 73	95.09 102	86.32 34	99.21 38	91.22 61	98.45 42	97.65 73
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
HPM-MVS_fast		93.40 46	93.22 45	93.94 53	98.36 21	84.83 64	97.15 8	96.80 59	85.77 126	92.47 61	97.13 30	82.38 73	99.07 49	90.51 73	98.40 43	97.92 65

DeepC-MVS	88.79 3	93.31 47	92.99 50	94.26 47	96.07 91	85.83 53	94.89 101	96.99 39	89.02 55	89.56 102	97.37 15	82.51 72	99.38 26	92.20 37	98.30 47	97.57 78
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		93.29 48	92.97 51	94.26 47	97.38 51	85.92 49	93.92 167	96.72 66	81.96 205	92.16 65	96.23 65	87.85 16	98.97 68	91.95 46	98.55 41	97.90 66

canonicalmvs		93.27 49	92.75 54	94.85 21	95.70 104	87.66 7	96.33 30	96.41 86	90.00 31	94.09 25	94.60 120	82.33 75	98.62 92	92.40 33	92.86 145	98.27 37

ACMMP		93.24 50	92.88 53	94.30 46	98.09 34	85.33 60	96.86 20	97.45 10	88.33 72	90.15 98	97.03 33	81.44 87	99.51 18	90.85 70	95.74 93	98.04 55
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
CSCG		93.23 51	93.05 48	93.76 60	98.04 36	84.07 84	96.22 34	97.37 14	84.15 158	90.05 99	95.66 87	87.77 18	99.15 44	89.91 76	98.27 48	98.07 52

abl_6		93.18 52	93.05 48	93.57 63	97.52 47	84.27 81	95.53 67	96.67 71	87.85 85	93.20 42	97.22 23	80.35 94	99.18 40	91.91 47	97.21 70	97.26 88

alignmvs		93.08 53	92.50 59	94.81 26	95.62 107	87.61 8	95.99 46	96.07 109	89.77 35	94.12 24	94.87 108	80.56 93	98.66 88	92.42 32	93.10 140	98.15 46

EI-MVSNet-Vis-set		93.01 54	92.92 52	93.29 64	95.01 125	83.51 99	94.48 126	95.77 131	90.87 15	92.52 59	96.67 48	84.50 56	99.00 64	91.99 43	94.44 118	97.36 84

UA-Net		92.83 55	92.54 58	93.68 61	96.10 89	84.71 66	95.66 61	96.39 88	91.92 4	93.22 41	96.49 57	83.16 66	98.87 74	84.47 135	95.47 98	97.45 83

CDPH-MVS		92.83 55	92.30 61	94.44 40	97.79 41	86.11 45	94.06 159	96.66 72	80.09 234	92.77 50	96.63 50	86.62 30	99.04 54	87.40 102	98.66 32	98.17 44

EIA-MVS		92.74 57	92.66 55	92.97 78	95.20 121	84.04 86	95.07 90	96.51 81	90.73 21	92.96 45	91.19 232	84.06 60	98.34 112	91.72 52	96.54 84	96.54 117

EI-MVSNet-UG-set		92.74 57	92.62 56	93.12 70	94.86 136	83.20 105	94.40 134	95.74 134	90.71 22	92.05 68	96.60 52	84.00 61	98.99 65	91.55 54	93.63 127	97.17 93

CS-MVS		92.60 59	92.56 57	92.73 88	95.55 108	82.35 132	96.14 38	96.85 54	88.71 61	91.44 83	91.51 225	84.13 59	98.48 99	91.27 60	97.47 67	97.34 85

DPM-MVS		92.58 60	91.74 67	95.08 9	96.19 84	89.31 2	92.66 213	96.56 80	83.44 174	91.68 79	95.04 103	86.60 33	98.99 65	85.60 122	97.92 58	96.93 105

casdiffmvs		92.51 61	92.43 60	92.74 87	94.41 155	81.98 138	94.54 124	96.23 98	89.57 39	91.96 70	96.17 71	82.58 71	98.01 138	90.95 67	95.45 100	98.23 41

MVS_111021_LR		92.47 62	92.29 62	92.98 77	95.99 94	84.43 78	93.08 201	96.09 107	88.20 77	91.12 89	95.72 86	81.33 89	97.76 151	91.74 51	97.37 69	96.75 110

3Dnovator+	87.14 4	92.42 63	91.37 70	95.55 3	95.63 106	88.73 3	97.07 11	96.77 62	90.84 16	84.02 215	96.62 51	75.95 141	99.34 27	87.77 97	97.68 62	98.59 13

baseline		92.39 64	92.29 62	92.69 92	94.46 152	81.77 142	94.14 149	96.27 93	89.22 47	91.88 71	96.00 75	82.35 74	97.99 140	91.05 63	95.27 105	98.30 31

VNet		92.24 65	91.91 65	93.24 66	96.59 72	83.43 100	94.84 105	96.44 83	89.19 49	94.08 26	95.90 79	77.85 126	98.17 122	88.90 85	93.38 134	98.13 48

CPTT-MVS		91.99 66	91.80 66	92.55 97	98.24 27	81.98 138	96.76 22	96.49 82	81.89 210	90.24 96	96.44 59	78.59 116	98.61 93	89.68 77	97.85 60	97.06 98

ETV-MVS		91.95 67	91.94 64	91.98 120	95.16 122	80.01 190	95.36 69	96.73 64	88.44 69	89.34 106	92.16 198	83.82 64	98.45 105	89.35 80	97.06 73	97.48 81

DP-MVS Recon		91.95 67	91.28 72	93.96 52	98.33 23	85.92 49	94.66 117	96.66 72	82.69 192	90.03 100	95.82 82	82.30 76	99.03 55	84.57 134	96.48 87	96.91 106

EPNet		91.79 69	91.02 78	94.10 50	90.10 287	85.25 61	96.03 45	92.05 255	92.83 1	87.39 138	95.78 83	79.39 109	99.01 61	88.13 93	97.48 66	98.05 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 70	91.70 68	92.00 119	97.08 62	80.03 189	93.60 179	95.18 174	87.85 85	90.89 91	96.47 58	82.06 82	98.36 109	85.07 126	97.04 74	97.62 74

Vis-MVSNet		91.75 71	91.23 73	93.29 64	95.32 116	83.78 91	96.14 38	95.98 114	89.89 32	90.45 94	96.58 53	75.09 151	98.31 116	84.75 132	96.90 75	97.78 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 72	90.82 82	94.44 40	94.59 146	86.37 36	97.18 7	97.02 38	89.20 48	84.31 210	96.66 49	73.74 173	99.17 41	86.74 112	97.96 56	97.79 71

EPP-MVSNet		91.70 73	91.56 69	92.13 116	95.88 97	80.50 177	97.33 3	95.25 170	86.15 119	89.76 101	95.60 88	83.42 65	98.32 115	87.37 104	93.25 137	97.56 79

MVSFormer		91.68 74	91.30 71	92.80 84	93.86 175	83.88 89	95.96 48	95.90 121	84.66 153	91.76 76	94.91 106	77.92 123	97.30 187	89.64 78	97.11 71	97.24 89

Effi-MVS+		91.59 75	91.11 75	93.01 76	94.35 159	83.39 102	94.60 119	95.10 178	87.10 99	90.57 93	93.10 171	81.43 88	98.07 134	89.29 81	94.48 116	97.59 77

IS-MVSNet		91.43 76	91.09 77	92.46 101	95.87 99	81.38 153	96.95 12	93.69 226	89.72 37	89.50 104	95.98 76	78.57 117	97.77 150	83.02 149	96.50 86	98.22 42

PVSNet_Blended_VisFu		91.38 77	90.91 80	92.80 84	96.39 79	83.17 106	94.87 103	96.66 72	83.29 178	89.27 107	94.46 124	80.29 96	99.17 41	87.57 100	95.37 101	96.05 135

diffmvs		91.37 78	91.23 73	91.77 133	93.09 198	80.27 179	92.36 223	95.52 151	87.03 101	91.40 85	94.93 105	80.08 98	97.44 172	92.13 41	94.56 114	97.61 75

MVS_Test		91.31 79	91.11 75	91.93 124	94.37 156	80.14 182	93.46 184	95.80 129	86.46 113	91.35 86	93.77 153	82.21 78	98.09 132	87.57 100	94.95 107	97.55 80

OMC-MVS		91.23 80	90.62 84	93.08 72	96.27 82	84.07 84	93.52 181	95.93 117	86.95 103	89.51 103	96.13 73	78.50 118	98.35 111	85.84 120	92.90 144	96.83 108

PAPM_NR		91.22 81	90.78 83	92.52 99	97.60 44	81.46 150	94.37 140	96.24 97	86.39 115	87.41 135	94.80 113	82.06 82	98.48 99	82.80 155	95.37 101	97.61 75

PS-MVSNAJ		91.18 82	90.92 79	91.96 122	95.26 119	82.60 127	92.09 233	95.70 136	86.27 116	91.84 73	92.46 188	79.70 104	98.99 65	89.08 83	95.86 92	94.29 199

xiu_mvs_v2_base		91.13 83	90.89 81	91.86 127	94.97 128	82.42 128	92.24 227	95.64 143	86.11 122	91.74 78	93.14 169	79.67 107	98.89 73	89.06 84	95.46 99	94.28 200

nrg030		91.08 84	90.39 85	93.17 69	93.07 199	86.91 15	96.41 28	96.26 94	88.30 73	88.37 119	94.85 111	82.19 79	97.64 158	91.09 62	82.95 251	94.96 169

lupinMVS		90.92 85	90.21 88	93.03 75	93.86 175	83.88 89	92.81 210	93.86 221	79.84 237	91.76 76	94.29 129	77.92 123	98.04 136	90.48 74	97.11 71	97.17 93

jason		90.80 86	90.10 91	92.90 81	93.04 201	83.53 98	93.08 201	94.15 215	80.22 231	91.41 84	94.91 106	76.87 129	97.93 145	90.28 75	96.90 75	97.24 89
jason: jason.
VDD-MVS		90.74 87	89.92 98	93.20 67	96.27 82	83.02 111	95.73 56	93.86 221	88.42 71	92.53 58	96.84 38	62.09 278	98.64 90	90.95 67	92.62 148	97.93 64

PVSNet_Blended		90.73 88	90.32 87	91.98 120	96.12 86	81.25 155	92.55 218	96.83 55	82.04 203	89.10 109	92.56 186	81.04 91	98.85 80	86.72 115	95.91 91	95.84 142

test_yl		90.69 89	90.02 96	92.71 89	95.72 102	82.41 130	94.11 152	95.12 176	85.63 130	91.49 81	94.70 114	74.75 155	98.42 107	86.13 118	92.53 149	97.31 86

DCV-MVSNet		90.69 89	90.02 96	92.71 89	95.72 102	82.41 130	94.11 152	95.12 176	85.63 130	91.49 81	94.70 114	74.75 155	98.42 107	86.13 118	92.53 149	97.31 86

API-MVS		90.66 91	90.07 92	92.45 102	96.36 80	84.57 69	96.06 44	95.22 173	82.39 194	89.13 108	94.27 132	80.32 95	98.46 102	80.16 203	96.71 79	94.33 198

xiu_mvs_v1_base_debu		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 62	94.03 135	70.59 208	98.57 95	90.97 64	94.67 109	94.18 201

xiu_mvs_v1_base		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 62	94.03 135	70.59 208	98.57 95	90.97 64	94.67 109	94.18 201

xiu_mvs_v1_base_debi		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 62	94.03 135	70.59 208	98.57 95	90.97 64	94.67 109	94.18 201

HQP_MVS		90.60 95	90.19 89	91.82 130	94.70 142	82.73 121	95.85 52	96.22 99	90.81 17	86.91 144	94.86 109	74.23 162	98.12 124	88.15 91	89.99 172	94.63 181

FIs		90.51 96	90.35 86	90.99 161	93.99 171	80.98 162	95.73 56	97.54 3	89.15 50	86.72 148	94.68 116	81.83 86	97.24 195	85.18 125	88.31 203	94.76 179

1121		90.42 97	89.49 102	93.20 67	97.27 58	84.46 74	92.63 214	95.51 152	71.01 312	91.20 88	96.21 66	82.92 68	99.05 51	80.56 196	98.07 53	96.10 131

MAR-MVS		90.30 98	89.37 106	93.07 74	96.61 71	84.48 73	95.68 59	95.67 138	82.36 196	87.85 127	92.85 176	76.63 135	98.80 84	80.01 204	96.68 80	95.91 138
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
FC-MVSNet-test		90.27 99	90.18 90	90.53 172	93.71 181	79.85 195	95.77 55	97.59 2	89.31 45	86.27 157	94.67 117	81.93 85	97.01 212	84.26 137	88.09 207	94.71 180

CANet_DTU		90.26 100	89.41 105	92.81 83	93.46 189	83.01 112	93.48 182	94.47 203	89.43 42	87.76 131	94.23 133	70.54 212	99.03 55	84.97 127	96.39 88	96.38 119

OPM-MVS		90.12 101	89.56 101	91.82 130	93.14 196	83.90 88	94.16 148	95.74 134	88.96 56	87.86 126	95.43 92	72.48 189	97.91 146	88.10 94	90.18 171	93.65 234

LFMVS		90.08 102	89.13 112	92.95 79	96.71 68	82.32 133	96.08 42	89.91 305	86.79 107	92.15 67	96.81 41	62.60 275	98.34 112	87.18 106	93.90 123	98.19 43

PAPR		90.02 103	89.27 110	92.29 111	95.78 100	80.95 164	92.68 212	96.22 99	81.91 208	86.66 149	93.75 155	82.23 77	98.44 106	79.40 214	94.79 108	97.48 81

PVSNet_BlendedMVS		89.98 104	89.70 99	90.82 165	96.12 86	81.25 155	93.92 167	96.83 55	83.49 173	89.10 109	92.26 196	81.04 91	98.85 80	86.72 115	87.86 211	92.35 276

PS-MVSNAJss		89.97 105	89.62 100	91.02 158	91.90 225	80.85 167	95.26 79	95.98 114	86.26 117	86.21 158	94.29 129	79.70 104	97.65 156	88.87 86	88.10 205	94.57 186

XVG-OURS-SEG-HR		89.95 106	89.45 103	91.47 141	94.00 170	81.21 158	91.87 236	96.06 111	85.78 125	88.55 115	95.73 85	74.67 158	97.27 191	88.71 87	89.64 181	95.91 138

UGNet		89.95 106	88.95 116	92.95 79	94.51 149	83.31 103	95.70 58	95.23 171	89.37 44	87.58 133	93.94 143	64.00 270	98.78 85	83.92 139	96.31 89	96.74 111
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
UniMVSNet_NR-MVSNet		89.92 108	89.29 108	91.81 132	93.39 190	83.72 92	94.43 132	97.12 33	89.80 34	86.46 151	93.32 160	83.16 66	97.23 197	84.92 128	81.02 276	94.49 193

AdaColmap		89.89 109	89.07 113	92.37 106	97.41 50	83.03 110	94.42 133	95.92 118	82.81 189	86.34 156	94.65 118	73.89 169	99.02 59	80.69 193	95.51 96	95.05 163

UniMVSNet (Re)		89.80 110	89.07 113	92.01 117	93.60 185	84.52 70	94.78 109	97.47 7	89.26 46	86.44 154	92.32 193	82.10 80	97.39 184	84.81 131	80.84 280	94.12 205

HQP-MVS		89.80 110	89.28 109	91.34 145	94.17 161	81.56 144	94.39 136	96.04 112	88.81 57	85.43 180	93.97 142	73.83 171	97.96 142	87.11 109	89.77 179	94.50 191

VPA-MVSNet		89.62 112	88.96 115	91.60 138	93.86 175	82.89 116	95.46 68	97.33 18	87.91 82	88.43 118	93.31 161	74.17 165	97.40 181	87.32 105	82.86 253	94.52 189

WTY-MVS		89.60 113	88.92 117	91.67 136	95.47 111	81.15 159	92.38 222	94.78 196	83.11 181	89.06 111	94.32 127	78.67 115	96.61 231	81.57 179	90.89 165	97.24 89

Vis-MVSNet (Re-imp)		89.59 114	89.44 104	90.03 197	95.74 101	75.85 265	95.61 64	90.80 290	87.66 92	87.83 128	95.40 93	76.79 131	96.46 243	78.37 220	96.73 78	97.80 70

VDDNet		89.56 115	88.49 127	92.76 86	95.07 124	82.09 135	96.30 31	93.19 232	81.05 226	91.88 71	96.86 37	61.16 288	98.33 114	88.43 90	92.49 151	97.84 68

114514_t		89.51 116	88.50 125	92.54 98	98.11 32	81.99 137	95.16 86	96.36 90	70.19 314	85.81 163	95.25 96	76.70 133	98.63 91	82.07 167	96.86 77	97.00 102

QAPM		89.51 116	88.15 136	93.59 62	94.92 132	84.58 68	96.82 21	96.70 68	78.43 253	83.41 230	96.19 70	73.18 181	99.30 34	77.11 235	96.54 84	96.89 107

CLD-MVS		89.47 118	88.90 118	91.18 149	94.22 160	82.07 136	92.13 231	96.09 107	87.90 83	85.37 186	92.45 189	74.38 160	97.56 162	87.15 107	90.43 167	93.93 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
mvs-test1		89.45 119	89.14 111	90.38 182	93.33 191	77.63 246	94.95 97	94.36 206	87.70 88	87.10 141	92.81 180	73.45 176	98.03 137	85.57 123	93.04 141	95.48 151

LPG-MVS_test		89.45 119	88.90 118	91.12 150	94.47 150	81.49 148	95.30 74	96.14 104	86.73 109	85.45 177	95.16 99	69.89 218	98.10 126	87.70 98	89.23 188	93.77 228

CDS-MVSNet		89.45 119	88.51 124	92.29 111	93.62 184	83.61 97	93.01 204	94.68 199	81.95 206	87.82 129	93.24 165	78.69 114	96.99 213	80.34 200	93.23 138	96.28 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 122	88.64 122	91.71 135	94.74 138	80.81 168	93.54 180	95.10 178	83.11 181	86.82 147	90.67 249	79.74 103	97.75 154	80.51 198	93.55 128	96.57 115

ab-mvs		89.41 122	88.35 129	92.60 94	95.15 123	82.65 125	92.20 229	95.60 145	83.97 162	88.55 115	93.70 156	74.16 166	98.21 121	82.46 160	89.37 184	96.94 104

XVG-OURS		89.40 124	88.70 121	91.52 139	94.06 164	81.46 150	91.27 250	96.07 109	86.14 120	88.89 113	95.77 84	68.73 236	97.26 193	87.39 103	89.96 174	95.83 143

mvs_anonymous		89.37 125	89.32 107	89.51 219	93.47 188	74.22 274	91.65 244	94.83 194	82.91 187	85.45 177	93.79 151	81.23 90	96.36 249	86.47 117	94.09 121	97.94 61

DU-MVS		89.34 126	88.50 125	91.85 129	93.04 201	83.72 92	94.47 129	96.59 77	89.50 40	86.46 151	93.29 163	77.25 127	97.23 197	84.92 128	81.02 276	94.59 184

TAMVS		89.21 127	88.29 133	91.96 122	93.71 181	82.62 126	93.30 190	94.19 213	82.22 198	87.78 130	93.94 143	78.83 111	96.95 216	77.70 228	92.98 143	96.32 120

ACMM	84.12 9	89.14 128	88.48 128	91.12 150	94.65 145	81.22 157	95.31 72	96.12 106	85.31 138	85.92 162	94.34 125	70.19 216	98.06 135	85.65 121	88.86 193	94.08 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EI-MVSNet		89.10 129	88.86 120	89.80 208	91.84 227	78.30 227	93.70 176	95.01 181	85.73 127	87.15 139	95.28 94	79.87 101	97.21 199	83.81 141	87.36 216	93.88 218

CNLPA		89.07 130	87.98 139	92.34 107	96.87 65	84.78 65	94.08 156	93.24 231	81.41 221	84.46 201	95.13 101	75.57 147	96.62 229	77.21 233	93.84 125	95.61 149

PLC	84.53 7	89.06 131	88.03 138	92.15 114	97.27 58	82.69 124	94.29 142	95.44 159	79.71 239	84.01 216	94.18 134	76.68 134	98.75 86	77.28 232	93.41 133	95.02 164
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 132	88.64 122	90.21 187	90.74 272	79.28 209	95.96 48	95.90 121	84.66 153	85.33 188	92.94 175	74.02 168	97.30 187	89.64 78	88.53 196	94.05 211

HY-MVS	83.01 12	89.03 132	87.94 141	92.29 111	94.86 136	82.77 117	92.08 234	94.49 202	81.52 220	86.93 143	92.79 182	78.32 121	98.23 118	79.93 205	90.55 166	95.88 140

ACMP	84.23 8	89.01 134	88.35 129	90.99 161	94.73 139	81.27 154	95.07 90	95.89 123	86.48 112	83.67 223	94.30 128	69.33 225	97.99 140	87.10 111	88.55 195	93.72 232
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 135	88.26 135	90.94 164	94.05 165	80.78 169	91.71 241	95.38 164	81.55 219	88.63 114	93.91 147	75.04 152	95.47 286	82.47 159	91.61 156	96.57 115

TranMVSNet+NR-MVSNet		88.84 136	87.95 140	91.49 140	92.68 210	83.01 112	94.92 100	96.31 91	89.88 33	85.53 171	93.85 150	76.63 135	96.96 215	81.91 171	79.87 293	94.50 191

CHOSEN 1792x2688		88.84 136	87.69 144	92.30 110	96.14 85	81.42 152	90.01 268	95.86 125	74.52 288	87.41 135	93.94 143	75.46 148	98.36 109	80.36 199	95.53 95	97.12 97

MVSTER		88.84 136	88.29 133	90.51 175	92.95 205	80.44 178	93.73 173	95.01 181	84.66 153	87.15 139	93.12 170	72.79 185	97.21 199	87.86 96	87.36 216	93.87 219

OpenMVS	83.78 11	88.74 139	87.29 153	93.08 72	92.70 209	85.39 59	96.57 25	96.43 85	78.74 250	80.85 259	96.07 74	69.64 222	99.01 61	78.01 226	96.65 81	94.83 176

thisisatest0530		88.67 140	87.61 146	91.86 127	94.87 135	80.07 185	94.63 118	89.90 306	84.00 161	88.46 117	93.78 152	66.88 250	98.46 102	83.30 145	92.65 147	97.06 98

Effi-MVS+-dtu		88.65 141	88.35 129	89.54 216	93.33 191	76.39 260	94.47 129	94.36 206	87.70 88	85.43 180	89.56 270	73.45 176	97.26 193	85.57 123	91.28 158	94.97 166

tttt0517		88.61 142	87.78 143	91.11 153	94.96 129	77.81 240	95.35 70	89.69 309	85.09 144	88.05 124	94.59 121	66.93 248	98.48 99	83.27 146	92.13 154	97.03 100

BH-untuned		88.60 143	88.13 137	90.01 199	95.24 120	78.50 222	93.29 191	94.15 215	84.75 151	84.46 201	93.40 157	75.76 142	97.40 181	77.59 229	94.52 115	94.12 205

NR-MVSNet		88.58 144	87.47 149	91.93 124	93.04 201	84.16 83	94.77 110	96.25 96	89.05 52	80.04 273	93.29 163	79.02 110	97.05 210	81.71 178	80.05 290	94.59 184

1112_ss		88.42 145	87.33 152	91.72 134	94.92 132	80.98 162	92.97 207	94.54 201	78.16 258	83.82 219	93.88 148	78.78 113	97.91 146	79.45 210	89.41 183	96.26 123

WR-MVS		88.38 146	87.67 145	90.52 174	93.30 193	80.18 180	93.26 193	95.96 116	88.57 67	85.47 176	92.81 180	76.12 137	96.91 219	81.24 182	82.29 256	94.47 196

BH-RMVSNet		88.37 147	87.48 148	91.02 158	95.28 117	79.45 201	92.89 209	93.07 234	85.45 135	86.91 144	94.84 112	70.35 213	97.76 151	73.97 261	94.59 113	95.85 141

IterMVS-LS		88.36 148	87.91 142	89.70 212	93.80 178	78.29 228	93.73 173	95.08 180	85.73 127	84.75 195	91.90 212	79.88 100	96.92 218	83.83 140	82.51 254	93.89 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 149	86.13 187	94.85 21	98.54 9	86.60 29	96.93 15	97.19 28	90.66 23	92.85 46	23.41 336	85.02 50	99.49 20	91.99 43	98.56 39	98.47 19

LCM-MVSNet-Re		88.30 150	88.32 132	88.27 245	94.71 141	72.41 293	93.15 197	90.98 284	87.77 87	79.25 280	91.96 210	78.35 120	95.75 274	83.04 148	95.62 94	96.65 113

jajsoiax		88.24 151	87.50 147	90.48 177	90.89 266	80.14 182	95.31 72	95.65 142	84.97 146	84.24 212	94.02 138	65.31 264	97.42 174	88.56 88	88.52 197	93.89 216

VPNet		88.20 152	87.47 149	90.39 180	93.56 186	79.46 200	94.04 160	95.54 150	88.67 63	86.96 142	94.58 122	69.33 225	97.15 201	84.05 138	80.53 285	94.56 187

TAPA-MVS	84.62 6	88.16 153	87.01 160	91.62 137	96.64 70	80.65 171	94.39 136	96.21 102	76.38 269	86.19 159	95.44 90	79.75 102	98.08 133	62.75 313	95.29 103	96.13 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DI_MVS_plusplus_test		88.15 154	86.82 164	92.14 115	90.67 275	81.07 160	93.01 204	94.59 200	83.83 165	77.78 287	90.63 250	68.51 239	98.16 123	88.02 95	94.37 119	97.17 93

baseline1		88.10 155	87.28 154	90.57 169	94.96 129	80.07 185	94.27 143	91.29 277	86.74 108	87.41 135	94.00 140	76.77 132	96.20 254	80.77 191	79.31 298	95.44 153

Anonymous20240529		88.09 156	86.59 175	92.58 96	96.53 75	81.92 140	95.99 46	95.84 126	74.11 291	89.06 111	95.21 98	61.44 283	98.81 83	83.67 143	87.47 213	97.01 101

HyFIR lowres test		88.09 156	86.81 165	91.93 124	96.00 93	80.63 172	90.01 268	95.79 130	73.42 296	87.68 132	92.10 204	73.86 170	97.96 142	80.75 192	91.70 155	97.19 92

mvs_tets		88.06 158	87.28 154	90.38 182	90.94 262	79.88 193	95.22 81	95.66 140	85.10 143	84.21 213	93.94 143	63.53 272	97.40 181	88.50 89	88.40 201	93.87 219

F-COLMAP		87.95 159	86.80 166	91.40 143	96.35 81	80.88 166	94.73 112	95.45 157	79.65 240	82.04 247	94.61 119	71.13 199	98.50 98	76.24 243	91.05 163	94.80 178

LS3D		87.89 160	86.32 183	92.59 95	96.07 91	82.92 115	95.23 80	94.92 188	75.66 276	82.89 237	95.98 76	72.48 189	99.21 38	68.43 290	95.23 106	95.64 148

anonymousdsp		87.84 161	87.09 157	90.12 192	89.13 297	80.54 175	94.67 116	95.55 148	82.05 201	83.82 219	92.12 201	71.47 197	97.15 201	87.15 107	87.80 212	92.67 265

v2v482		87.84 161	87.06 158	90.17 188	90.99 258	79.23 212	94.00 164	95.13 175	84.87 147	85.53 171	92.07 207	74.45 159	97.45 170	84.71 133	81.75 264	93.85 222

WR-MVS_H		87.80 163	87.37 151	89.10 226	93.23 194	78.12 231	95.61 64	97.30 21	87.90 83	83.72 221	92.01 209	79.65 108	96.01 262	76.36 240	80.54 284	93.16 252

PCF-MVS	84.11 10	87.74 164	86.08 191	92.70 91	94.02 166	84.43 78	89.27 278	95.87 124	73.62 295	84.43 203	94.33 126	78.48 119	98.86 77	70.27 276	94.45 117	94.81 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 165	86.13 187	92.31 109	96.66 69	80.74 170	94.87 103	91.49 272	80.47 230	89.46 105	95.44 90	54.72 311	98.23 118	82.19 165	89.89 176	97.97 59

V42		87.68 165	86.86 162	90.15 190	90.58 278	80.14 182	94.24 145	95.28 169	83.66 168	85.67 166	91.33 227	74.73 157	97.41 179	84.43 136	81.83 262	92.89 260

thres600view7		87.65 167	86.67 171	90.59 168	96.08 90	78.72 215	94.88 102	91.58 268	87.06 100	88.08 122	92.30 194	68.91 233	98.10 126	70.05 283	91.10 159	94.96 169

XXY-MVS		87.65 167	86.85 163	90.03 197	92.14 218	80.60 174	93.76 172	95.23 171	82.94 185	84.60 197	94.02 138	74.27 161	95.49 285	81.04 184	83.68 244	94.01 213

Test_1112_low_res		87.65 167	86.51 177	91.08 154	94.94 131	79.28 209	91.77 238	94.30 209	76.04 274	83.51 228	92.37 191	77.86 125	97.73 155	78.69 219	89.13 190	96.22 124

thres100view900		87.63 170	86.71 169	90.38 182	96.12 86	78.55 219	95.03 94	91.58 268	87.15 97	88.06 123	92.29 195	68.91 233	98.10 126	70.13 280	91.10 159	94.48 194

CP-MVSNet		87.63 170	87.26 156	88.74 234	93.12 197	76.59 257	95.29 76	96.58 78	88.43 70	83.49 229	92.98 174	75.28 149	95.83 270	78.97 216	81.15 272	93.79 224

thres400		87.62 172	86.64 172	90.57 169	95.99 94	78.64 217	94.58 120	91.98 259	86.94 104	88.09 120	91.77 214	69.18 230	98.10 126	70.13 280	91.10 159	94.96 169

v1144		87.61 173	86.79 167	90.06 196	91.01 257	79.34 205	93.95 166	95.42 163	83.36 177	85.66 167	91.31 230	74.98 153	97.42 174	83.37 144	82.06 258	93.42 243

tfpn200view9		87.58 174	86.64 172	90.41 179	95.99 94	78.64 217	94.58 120	91.98 259	86.94 104	88.09 120	91.77 214	69.18 230	98.10 126	70.13 280	91.10 159	94.48 194

BH-w/o		87.57 175	87.05 159	89.12 225	94.90 134	77.90 236	92.41 220	93.51 228	82.89 188	83.70 222	91.34 226	75.75 143	97.07 208	75.49 248	93.49 130	92.39 274

UniMVSNet_ETH3D		87.53 176	86.37 180	91.00 160	92.44 213	78.96 214	94.74 111	95.61 144	84.07 160	85.36 187	94.52 123	59.78 296	97.34 186	82.93 150	87.88 210	96.71 112

ET-MVSNet_ETH3D		87.51 177	85.91 197	92.32 108	93.70 183	83.93 87	92.33 224	90.94 286	84.16 157	72.09 313	92.52 187	69.90 217	95.85 269	89.20 82	88.36 202	97.17 93

1314		87.51 177	86.57 176	90.34 185	92.42 214	79.74 197	92.63 214	95.35 168	78.35 254	80.14 270	91.62 221	74.05 167	97.15 201	81.05 183	93.53 129	94.12 205

v8		87.50 179	86.71 169	89.89 202	91.37 244	79.40 202	94.50 125	95.38 164	84.81 149	83.60 226	91.33 227	76.05 138	97.42 174	82.84 153	80.51 287	92.84 262

Fast-Effi-MVS+-dtu		87.44 180	86.72 168	89.63 214	92.04 221	77.68 245	94.03 161	93.94 219	85.81 124	82.42 241	91.32 229	70.33 214	97.06 209	80.33 201	90.23 170	94.14 204

MVS		87.44 180	86.10 190	91.44 142	92.61 211	83.62 96	92.63 214	95.66 140	67.26 318	81.47 251	92.15 199	77.95 122	98.22 120	79.71 207	95.48 97	92.47 271

FMVSNet3		87.40 182	86.11 189	91.30 146	93.79 180	83.64 95	94.20 147	94.81 195	83.89 163	84.37 204	91.87 213	68.45 240	96.56 235	78.23 223	85.36 229	93.70 233

thisisatest0515		87.33 183	85.99 193	91.37 144	93.49 187	79.55 198	90.63 258	89.56 312	80.17 232	87.56 134	90.86 243	67.07 247	98.28 117	81.50 180	93.02 142	96.29 121

PS-CasMVS		87.32 184	86.88 161	88.63 238	92.99 204	76.33 262	95.33 71	96.61 76	88.22 76	83.30 234	93.07 172	73.03 183	95.79 273	78.36 221	81.00 278	93.75 230

GBi-Net		87.26 185	85.98 194	91.08 154	94.01 167	83.10 107	95.14 87	94.94 184	83.57 169	84.37 204	91.64 217	66.59 255	96.34 250	78.23 223	85.36 229	93.79 224

test1		87.26 185	85.98 194	91.08 154	94.01 167	83.10 107	95.14 87	94.94 184	83.57 169	84.37 204	91.64 217	66.59 255	96.34 250	78.23 223	85.36 229	93.79 224

v1192		87.25 187	86.33 182	90.00 200	90.76 271	79.04 213	93.80 170	95.48 153	82.57 193	85.48 175	91.18 234	73.38 180	97.42 174	82.30 162	82.06 258	93.53 237

v10		87.25 187	86.38 179	89.85 203	91.19 250	79.50 199	94.48 126	95.45 157	83.79 166	83.62 225	91.19 232	75.13 150	97.42 174	81.94 170	80.60 282	92.63 267

DP-MVS		87.25 187	85.36 211	92.90 81	97.65 43	83.24 104	94.81 107	92.00 257	74.99 283	81.92 249	95.00 104	72.66 186	99.05 51	66.92 298	92.33 152	96.40 118

thres200		87.21 190	86.24 186	90.12 192	95.36 113	78.53 220	93.26 193	92.10 252	86.42 114	88.00 125	91.11 238	69.24 229	98.00 139	69.58 284	91.04 164	93.83 223

v144192		87.19 191	86.35 181	89.74 209	90.64 276	78.24 229	93.92 167	95.43 161	81.93 207	85.51 173	91.05 240	74.21 164	97.45 170	82.86 152	81.56 266	93.53 237

FMVSNet2		87.19 191	85.82 199	91.30 146	94.01 167	83.67 94	94.79 108	94.94 184	83.57 169	83.88 217	92.05 208	66.59 255	96.51 238	77.56 230	85.01 233	93.73 231

cl_fuxian		87.14 193	86.50 178	89.04 227	92.20 216	77.26 249	91.22 252	94.70 198	82.01 204	84.34 208	90.43 254	78.81 112	96.61 231	83.70 142	81.09 273	93.25 247

Baseline_NR-MVSNet		87.07 194	86.63 174	88.40 241	91.44 238	77.87 238	94.23 146	92.57 244	84.12 159	85.74 165	92.08 205	77.25 127	96.04 259	82.29 163	79.94 291	91.30 294

v148		87.04 195	86.32 183	89.21 223	90.94 262	77.26 249	93.71 175	94.43 204	84.84 148	84.36 207	90.80 246	76.04 139	97.05 210	82.12 166	79.60 295	93.31 244

v1921920		86.97 196	86.06 192	89.69 213	90.53 281	78.11 232	93.80 170	95.43 161	81.90 209	85.33 188	91.05 240	72.66 186	97.41 179	82.05 168	81.80 263	93.53 237

v7n		86.81 197	85.76 203	89.95 201	90.72 273	79.25 211	95.07 90	95.92 118	84.45 156	82.29 242	90.86 243	72.60 188	97.53 164	79.42 213	80.52 286	93.08 256

PEN-MVS		86.80 198	86.27 185	88.40 241	92.32 215	75.71 267	95.18 84	96.38 89	87.97 80	82.82 238	93.15 168	73.39 179	95.92 265	76.15 244	79.03 300	93.59 235

v1240		86.78 199	85.85 198	89.56 215	90.45 282	77.79 241	93.61 178	95.37 166	81.65 215	85.43 180	91.15 236	71.50 196	97.43 173	81.47 181	82.05 260	93.47 241

TR-MVS		86.78 199	85.76 203	89.82 205	94.37 156	78.41 224	92.47 219	92.83 237	81.11 225	86.36 155	92.40 190	68.73 236	97.48 167	73.75 264	89.85 178	93.57 236

PatchMatch-RL		86.77 201	85.54 205	90.47 178	95.88 97	82.71 123	90.54 259	92.31 247	79.82 238	84.32 209	91.57 224	68.77 235	96.39 246	73.16 266	93.48 132	92.32 277

PAPM		86.68 202	85.39 209	90.53 172	93.05 200	79.33 208	89.79 271	94.77 197	78.82 247	81.95 248	93.24 165	76.81 130	97.30 187	66.94 296	93.16 139	94.95 172

pm-mvs1		86.61 203	85.54 205	89.82 205	91.44 238	80.18 180	95.28 78	94.85 192	83.84 164	81.66 250	92.62 185	72.45 191	96.48 240	79.67 208	78.06 301	92.82 263

GA-MVS		86.61 203	85.27 212	90.66 167	91.33 247	78.71 216	90.40 261	93.81 224	85.34 137	85.12 190	89.57 269	61.25 285	97.11 205	80.99 188	89.59 182	96.15 125

Anonymous20231211		86.59 205	85.13 214	90.98 163	96.52 76	81.50 146	96.14 38	96.16 103	73.78 293	83.65 224	92.15 199	63.26 273	97.37 185	82.82 154	81.74 265	94.06 210

cl-mvsnet1		86.53 206	85.78 200	88.75 232	92.02 223	76.45 259	90.74 256	94.30 209	81.83 213	83.34 232	90.82 245	75.75 143	96.57 233	81.73 177	81.52 268	93.24 248

cl-mvsnet_		86.52 207	85.78 200	88.75 232	92.03 222	76.46 258	90.74 256	94.30 209	81.83 213	83.34 232	90.78 247	75.74 145	96.57 233	81.74 176	81.54 267	93.22 249

eth_miper_zixun_eth		86.50 208	85.77 202	88.68 236	91.94 224	75.81 266	90.47 260	94.89 189	82.05 201	84.05 214	90.46 253	75.96 140	96.77 223	82.76 156	79.36 297	93.46 242

baseline2		86.50 208	85.39 209	89.84 204	91.12 254	76.70 255	91.88 235	88.58 314	82.35 197	79.95 274	90.95 242	73.42 178	97.63 159	80.27 202	89.95 175	95.19 160

EPNet_dtu		86.49 210	85.94 196	88.14 250	90.24 285	72.82 286	94.11 152	92.20 250	86.66 111	79.42 279	92.36 192	73.52 174	95.81 272	71.26 271	93.66 126	95.80 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 211	84.98 218	90.80 166	92.10 220	80.92 165	90.24 263	95.91 120	73.10 299	83.57 227	88.39 283	65.15 265	97.46 169	84.90 130	91.43 157	94.03 212

SCA		86.32 212	85.18 213	89.73 211	92.15 217	76.60 256	91.12 254	91.69 266	83.53 172	85.50 174	88.81 276	66.79 251	96.48 240	76.65 238	90.35 169	96.12 128

LTVRE_ROB	82.13 13	86.26 213	84.90 221	90.34 185	94.44 154	81.50 146	92.31 226	94.89 189	83.03 183	79.63 277	92.67 183	69.69 221	97.79 149	71.20 272	86.26 223	91.72 286
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
DTE-MVSNet		86.11 214	85.48 207	87.98 253	91.65 235	74.92 270	94.93 99	95.75 133	87.36 95	82.26 243	93.04 173	72.85 184	95.82 271	74.04 260	77.46 305	93.20 250

PatchFormer-LS_test		86.02 215	85.13 214	88.70 235	91.52 236	74.12 277	91.19 253	92.09 253	82.71 191	84.30 211	87.24 299	70.87 203	96.98 214	81.04 184	85.17 232	95.00 165

XVG-ACMP-BASELINE		86.00 216	84.84 223	89.45 220	91.20 249	78.00 233	91.70 242	95.55 148	85.05 145	82.97 236	92.25 197	54.49 312	97.48 167	82.93 150	87.45 215	92.89 260

MVP-Stereo		85.97 217	84.86 222	89.32 221	90.92 264	82.19 134	92.11 232	94.19 213	78.76 249	78.77 282	91.63 220	68.38 241	96.56 235	75.01 255	93.95 122	89.20 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 218	85.09 216	88.35 243	90.79 269	77.42 248	91.83 237	95.70 136	80.77 228	80.08 272	90.02 260	66.74 253	96.37 247	81.88 172	87.97 209	91.26 295

test-LLR		85.87 219	85.41 208	87.25 269	90.95 260	71.67 296	89.55 272	89.88 307	83.41 175	84.54 199	87.95 290	67.25 244	95.11 291	81.82 173	93.37 135	94.97 166

FMVSNet1		85.85 220	84.11 232	91.08 154	92.81 207	83.10 107	95.14 87	94.94 184	81.64 216	82.68 239	91.64 217	59.01 299	96.34 250	75.37 250	83.78 241	93.79 224

PatchmatchNet		85.85 220	84.70 225	89.29 222	91.76 230	75.54 268	88.49 289	91.30 276	81.63 217	85.05 191	88.70 280	71.71 193	96.24 253	74.61 258	89.05 191	96.08 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 222	84.94 220	88.26 246	91.16 253	72.58 292	89.47 276	91.04 283	76.26 272	86.45 153	89.97 262	70.74 206	96.86 222	82.35 161	87.07 221	95.34 158

PMMVS		85.71 223	84.96 219	87.95 254	88.90 300	77.09 251	88.68 287	90.06 301	72.32 305	86.47 150	90.76 248	72.15 192	94.40 297	81.78 175	93.49 130	92.36 275

PVSNet	78.82 18	85.55 224	84.65 226	88.23 248	94.72 140	71.93 294	87.12 302	92.75 240	78.80 248	84.95 193	90.53 252	64.43 269	96.71 226	74.74 256	93.86 124	96.06 134

IterMVS-SCA-FT		85.45 225	84.53 229	88.18 249	91.71 232	76.87 254	90.19 265	92.65 243	85.40 136	81.44 252	90.54 251	66.79 251	95.00 294	81.04 184	81.05 274	92.66 266

pmmvs4		85.43 226	83.86 236	90.16 189	90.02 290	82.97 114	90.27 262	92.67 242	75.93 275	80.73 260	91.74 216	71.05 200	95.73 275	78.85 217	83.46 248	91.78 285

ACMH	80.38 17	85.36 227	83.68 238	90.39 180	94.45 153	80.63 172	94.73 112	94.85 192	82.09 200	77.24 291	92.65 184	60.01 294	97.58 160	72.25 269	84.87 234	92.96 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 228	84.64 227	87.49 263	90.77 270	72.59 291	94.01 163	94.40 205	84.72 152	79.62 278	93.17 167	61.91 280	96.72 224	81.99 169	81.16 270	93.16 252

CR-MVSNet		85.35 228	83.76 237	90.12 192	90.58 278	79.34 205	85.24 312	91.96 261	78.27 255	85.55 169	87.87 293	71.03 201	95.61 276	73.96 262	89.36 185	95.40 155

tpmrst		85.35 228	84.99 217	86.43 283	90.88 267	67.88 317	88.71 286	91.43 274	80.13 233	86.08 161	88.80 278	73.05 182	96.02 261	82.48 158	83.40 250	95.40 155

miper_lstm_enhance		85.27 231	84.59 228	87.31 266	91.28 248	74.63 271	87.69 298	94.09 218	81.20 224	81.36 254	89.85 265	74.97 154	94.30 299	81.03 187	79.84 294	93.01 257

IB-MVS	80.51 15	85.24 232	83.26 243	91.19 148	92.13 219	79.86 194	91.75 239	91.29 277	83.28 179	80.66 262	88.49 282	61.28 284	98.46 102	80.99 188	79.46 296	95.25 159
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
CHOSEN 280x420		85.15 233	83.99 234	88.65 237	92.47 212	78.40 225	79.68 327	92.76 239	74.90 285	81.41 253	89.59 268	69.85 220	95.51 282	79.92 206	95.29 103	92.03 282

RPSCF		85.07 234	84.27 230	87.48 264	92.91 206	70.62 306	91.69 243	92.46 245	76.20 273	82.67 240	95.22 97	63.94 271	97.29 190	77.51 231	85.80 226	94.53 188

MS-PatchMatch		85.05 235	84.16 231	87.73 257	91.42 242	78.51 221	91.25 251	93.53 227	77.50 260	80.15 269	91.58 222	61.99 279	95.51 282	75.69 247	94.35 120	89.16 313

ACMH+	81.04 14	85.05 235	83.46 242	89.82 205	94.66 144	79.37 203	94.44 131	94.12 217	82.19 199	78.04 285	92.82 179	58.23 301	97.54 163	73.77 263	82.90 252	92.54 268

DWT-MVSNet_test		84.95 237	83.68 238	88.77 230	91.43 241	73.75 279	91.74 240	90.98 284	80.66 229	83.84 218	87.36 297	62.44 276	97.11 205	78.84 218	85.81 225	95.46 152

IterMVS		84.88 238	83.98 235	87.60 259	91.44 238	76.03 264	90.18 266	92.41 246	83.24 180	81.06 258	90.42 255	66.60 254	94.28 300	79.46 209	80.98 279	92.48 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 239	83.09 245	90.14 191	93.80 178	80.05 187	89.18 281	93.09 233	78.89 245	78.19 283	91.91 211	65.86 263	97.27 191	68.47 289	88.45 199	93.11 254

tpm		84.73 240	84.02 233	86.87 280	90.33 283	68.90 314	89.06 282	89.94 304	80.85 227	85.75 164	89.86 264	68.54 238	95.97 263	77.76 227	84.05 240	95.75 146

tfpnnormal		84.72 241	83.23 244	89.20 224	92.79 208	80.05 187	94.48 126	95.81 128	82.38 195	81.08 257	91.21 231	69.01 232	96.95 216	61.69 315	80.59 283	90.58 306

CVMVSNet		84.69 242	84.79 224	84.37 299	91.84 227	64.92 324	93.70 176	91.47 273	66.19 320	86.16 160	95.28 94	67.18 246	93.33 309	80.89 190	90.42 168	94.88 174

test-mter		84.54 243	83.64 240	87.25 269	90.95 260	71.67 296	89.55 272	89.88 307	79.17 242	84.54 199	87.95 290	55.56 307	95.11 291	81.82 173	93.37 135	94.97 166

TransMVSNet (Re)		84.43 244	83.06 246	88.54 239	91.72 231	78.44 223	95.18 84	92.82 238	82.73 190	79.67 276	92.12 201	73.49 175	95.96 264	71.10 275	68.73 321	91.21 297

pmmvs5		84.21 245	82.84 250	88.34 244	88.95 299	76.94 253	92.41 220	91.91 263	75.63 277	80.28 267	91.18 234	64.59 268	95.57 278	77.09 236	83.47 247	92.53 269

tpm2		84.08 246	82.94 247	87.48 264	91.39 243	71.27 298	89.23 280	90.37 295	71.95 307	84.64 196	89.33 271	67.30 243	96.55 237	75.17 252	87.09 220	94.63 181

COLMAP_ROB	80.39 16	83.96 247	82.04 253	89.74 209	95.28 117	79.75 196	94.25 144	92.28 248	75.17 281	78.02 286	93.77 153	58.60 300	97.84 148	65.06 306	85.92 224	91.63 288
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		83.91 248	82.90 248	86.92 277	90.99 258	70.67 305	93.48 182	91.99 258	85.54 133	77.62 290	92.11 203	60.59 290	96.87 221	76.05 245	77.75 302	93.20 250

EPMVS		83.90 249	82.70 251	87.51 261	90.23 286	72.67 288	88.62 288	81.96 329	81.37 222	85.01 192	88.34 284	66.31 258	94.45 296	75.30 251	87.12 219	95.43 154

TESTMET0.1,1		83.74 250	82.85 249	86.42 284	89.96 291	71.21 300	89.55 272	87.88 316	77.41 261	83.37 231	87.31 298	56.71 304	93.65 306	80.62 195	92.85 146	94.40 197

MVS_0304		83.46 251	81.92 254	88.10 251	90.63 277	77.49 247	93.26 193	93.75 225	80.04 235	80.44 266	87.24 299	47.94 324	95.55 279	75.79 246	88.16 204	91.26 295

pmmvs6		83.42 252	81.60 256	88.87 229	88.01 310	77.87 238	94.96 96	94.24 212	74.67 287	78.80 281	91.09 239	60.17 293	96.49 239	77.06 237	75.40 309	92.23 280

AllTest		83.42 252	81.39 257	89.52 217	95.01 125	77.79 241	93.12 198	90.89 288	77.41 261	76.12 296	93.34 158	54.08 314	97.51 165	68.31 291	84.27 238	93.26 245

testing_2		83.40 254	81.02 260	90.56 171	85.06 319	80.51 176	91.37 248	95.57 146	82.92 186	67.06 321	85.54 308	49.47 321	97.24 195	86.74 112	85.44 228	93.93 214

tpmvs		83.35 255	82.07 252	87.20 273	91.07 256	71.00 303	88.31 292	91.70 265	78.91 244	80.49 265	87.18 301	69.30 228	97.08 207	68.12 294	83.56 246	93.51 240

RPMNet		83.18 256	80.87 263	90.12 192	90.58 278	79.34 205	85.24 312	90.78 291	71.44 309	85.55 169	82.97 316	70.87 203	95.61 276	61.01 317	89.36 185	95.40 155

USDC		82.76 257	81.26 259	87.26 268	91.17 251	74.55 272	89.27 278	93.39 230	78.26 256	75.30 300	92.08 205	54.43 313	96.63 228	71.64 270	85.79 227	90.61 303

Patchmtry		82.71 258	80.93 262	88.06 252	90.05 289	76.37 261	84.74 314	91.96 261	72.28 306	81.32 255	87.87 293	71.03 201	95.50 284	68.97 286	80.15 289	92.32 277

PatchT		82.68 259	81.27 258	86.89 279	90.09 288	70.94 304	84.06 316	90.15 298	74.91 284	85.63 168	83.57 313	69.37 224	94.87 295	65.19 303	88.50 198	94.84 175

MIMVSNet		82.59 260	80.53 264	88.76 231	91.51 237	78.32 226	86.57 305	90.13 299	79.32 241	80.70 261	88.69 281	52.98 316	93.07 313	66.03 301	88.86 193	94.90 173

test0.0.03 1		82.41 261	81.69 255	84.59 297	88.23 307	72.89 285	90.24 263	87.83 317	83.41 175	79.86 275	89.78 266	67.25 244	88.99 326	65.18 304	83.42 249	91.90 284

EG-PatchMatch MVS		82.37 262	80.34 266	88.46 240	90.27 284	79.35 204	92.80 211	94.33 208	77.14 265	73.26 310	90.18 258	47.47 326	96.72 224	70.25 277	87.32 218	89.30 310

tpm cat1		81.96 263	80.27 267	87.01 275	91.09 255	71.02 302	87.38 301	91.53 271	66.25 319	80.17 268	86.35 304	68.22 242	96.15 257	69.16 285	82.29 256	93.86 221

our_test_3		81.93 264	80.46 265	86.33 285	88.46 304	73.48 281	88.46 290	91.11 279	76.46 267	76.69 293	88.25 286	66.89 249	94.36 298	68.75 287	79.08 299	91.14 299

ppachtmachnet_test		81.84 265	80.07 271	87.15 274	88.46 304	74.43 273	89.04 283	92.16 251	75.33 279	77.75 288	88.99 273	66.20 259	95.37 287	65.12 305	77.60 303	91.65 287

gg-mvs-nofinetune		81.77 266	79.37 277	88.99 228	90.85 268	77.73 244	86.29 306	79.63 332	74.88 286	83.19 235	69.05 327	60.34 291	96.11 258	75.46 249	94.64 112	93.11 254

Patchmatch-RL test		81.67 267	79.96 272	86.81 281	85.42 317	71.23 299	82.17 323	87.50 320	78.47 252	77.19 292	82.50 317	70.81 205	93.48 307	82.66 157	72.89 313	95.71 147

ADS-MVSNet2		81.66 268	79.71 275	87.50 262	91.35 245	74.19 275	83.33 319	88.48 315	72.90 301	82.24 244	85.77 306	64.98 266	93.20 311	64.57 307	83.74 242	95.12 161

K. test v3		81.59 269	80.15 270	85.91 289	89.89 293	69.42 313	92.57 217	87.71 318	85.56 132	73.44 309	89.71 267	55.58 306	95.52 281	77.17 234	69.76 317	92.78 264

ADS-MVSNet		81.56 270	79.78 273	86.90 278	91.35 245	71.82 295	83.33 319	89.16 313	72.90 301	82.24 244	85.77 306	64.98 266	93.76 304	64.57 307	83.74 242	95.12 161

FMVSNet5		81.52 271	79.60 276	87.27 267	91.17 251	77.95 234	91.49 246	92.26 249	76.87 266	76.16 295	87.91 292	51.67 317	92.34 315	67.74 295	81.16 270	91.52 289

dp		81.47 272	80.23 268	85.17 294	89.92 292	65.49 323	86.74 303	90.10 300	76.30 271	81.10 256	87.12 302	62.81 274	95.92 265	68.13 293	79.88 292	94.09 208

Patchmatch-test		81.37 273	79.30 278	87.58 260	90.92 264	74.16 276	80.99 325	87.68 319	70.52 313	76.63 294	88.81 276	71.21 198	92.76 314	60.01 321	86.93 222	95.83 143

EU-MVSNet		81.32 274	80.95 261	82.42 306	88.50 303	63.67 325	93.32 186	91.33 275	64.02 322	80.57 264	92.83 178	61.21 287	92.27 316	76.34 241	80.38 288	91.32 293

test_0402		81.30 275	79.17 280	87.67 258	93.19 195	78.17 230	92.98 206	91.71 264	75.25 280	76.02 298	90.31 256	59.23 298	96.37 247	50.22 327	83.63 245	88.47 319

JIA-IIPM		81.04 276	78.98 283	87.25 269	88.64 301	73.48 281	81.75 324	89.61 311	73.19 298	82.05 246	73.71 323	66.07 262	95.87 268	71.18 274	84.60 236	92.41 273

Anonymous20231206		81.03 277	79.77 274	84.82 296	87.85 312	70.26 308	91.42 247	92.08 254	73.67 294	77.75 288	89.25 272	62.43 277	93.08 312	61.50 316	82.00 261	91.12 300

pmmvs-eth3d		80.97 278	78.72 284	87.74 256	84.99 320	79.97 192	90.11 267	91.65 267	75.36 278	73.51 308	86.03 305	59.45 297	93.96 303	75.17 252	72.21 314	89.29 311

testgi		80.94 279	80.20 269	83.18 303	87.96 311	66.29 320	91.28 249	90.70 293	83.70 167	78.12 284	92.84 177	51.37 318	90.82 323	63.34 310	82.46 255	92.43 272

CMPMVS	59.16 21	80.52 280	79.20 279	84.48 298	83.98 321	67.63 319	89.95 270	93.84 223	64.79 321	66.81 322	91.14 237	57.93 302	95.17 289	76.25 242	88.10 205	90.65 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		80.37 281	79.07 282	84.27 301	86.64 314	69.87 311	89.39 277	91.05 282	76.38 269	74.97 302	90.00 261	47.85 325	94.25 301	74.55 259	80.82 281	88.69 317

UnsupCasMVSNet_eth		80.07 282	78.27 285	85.46 291	85.24 318	72.63 290	88.45 291	94.87 191	82.99 184	71.64 316	88.07 289	56.34 305	91.75 320	73.48 265	63.36 326	92.01 283

test20.03		79.95 283	79.08 281	82.55 305	85.79 316	67.74 318	91.09 255	91.08 280	81.23 223	74.48 305	89.96 263	61.63 281	90.15 324	60.08 319	76.38 307	89.76 308

TDRefinement		79.81 284	77.34 287	87.22 272	79.24 329	75.48 269	93.12 198	92.03 256	76.45 268	75.01 301	91.58 222	49.19 322	96.44 244	70.22 279	69.18 318	89.75 309

TinyColmap		79.76 285	77.69 286	85.97 288	91.71 232	73.12 283	89.55 272	90.36 296	75.03 282	72.03 314	90.19 257	46.22 327	96.19 256	63.11 311	81.03 275	88.59 318

OpenMVS_ROB	74.94 19	79.51 286	77.03 291	86.93 276	87.00 313	76.23 263	92.33 224	90.74 292	68.93 316	74.52 304	88.23 287	49.58 320	96.62 229	57.64 323	84.29 237	87.94 321

MIMVSNet1		79.38 287	77.28 288	85.69 290	86.35 315	73.67 280	91.61 245	92.75 240	78.11 259	72.64 312	88.12 288	48.16 323	91.97 319	60.32 318	77.49 304	91.43 292

YYNet1		79.22 288	77.20 289	85.28 293	88.20 309	72.66 289	85.87 308	90.05 303	74.33 290	62.70 324	87.61 295	66.09 261	92.03 317	66.94 296	72.97 312	91.15 298

MDA-MVSNet_test_wron		79.21 289	77.19 290	85.29 292	88.22 308	72.77 287	85.87 308	90.06 301	74.34 289	62.62 325	87.56 296	66.14 260	91.99 318	66.90 299	73.01 311	91.10 301

MDA-MVSNet-bldmvs		78.85 290	76.31 292	86.46 282	89.76 294	73.88 278	88.79 285	90.42 294	79.16 243	59.18 326	88.33 285	60.20 292	94.04 302	62.00 314	68.96 319	91.48 291

PM-MVS		78.11 291	76.12 294	84.09 302	83.54 323	70.08 309	88.97 284	85.27 324	79.93 236	74.73 303	86.43 303	34.70 332	93.48 307	79.43 212	72.06 315	88.72 316

PVSNet_0	73.20 20	77.22 292	74.83 296	84.37 299	90.70 274	71.10 301	83.09 321	89.67 310	72.81 303	73.93 307	83.13 315	60.79 289	93.70 305	68.54 288	50.84 330	88.30 320

DSMNet-mixed		76.94 293	76.29 293	78.89 309	83.10 324	56.11 332	87.78 296	79.77 331	60.65 324	75.64 299	88.71 279	61.56 282	88.34 327	60.07 320	89.29 187	92.21 281

new-patchmatchnet		76.41 294	75.17 295	80.13 308	82.65 326	59.61 327	87.66 299	91.08 280	78.23 257	69.85 317	83.22 314	54.76 310	91.63 322	64.14 309	64.89 324	89.16 313

UnsupCasMVSNet_bld		76.23 295	73.27 297	85.09 295	83.79 322	72.92 284	85.65 311	93.47 229	71.52 308	68.84 319	79.08 321	49.77 319	93.21 310	66.81 300	60.52 328	89.13 315

test_normal		75.12 296	71.44 299	86.17 286	81.33 327	69.54 312	50.52 336	95.44 159	84.80 150	55.21 327	70.88 326	41.07 330	96.66 227	82.25 164	81.48 269	92.30 279

MVS-HIRNet		73.70 297	72.20 298	78.18 311	91.81 229	56.42 331	82.94 322	82.58 327	55.24 326	68.88 318	66.48 328	55.32 309	95.13 290	58.12 322	88.42 200	83.01 324

new_pmnet		72.15 298	70.13 300	78.20 310	82.95 325	65.68 321	83.91 317	82.40 328	62.94 323	64.47 323	79.82 320	42.85 329	86.26 329	57.41 324	74.44 310	82.65 325

pmmvs3		71.81 299	68.71 301	81.11 307	75.86 330	70.42 307	86.74 303	83.66 326	58.95 325	68.64 320	80.89 319	36.93 331	89.52 325	63.10 312	63.59 325	83.39 323

N_pmnet		68.89 300	68.44 302	70.23 315	89.07 298	28.79 341	88.06 293	19.50 342	69.47 315	71.86 315	84.93 309	61.24 286	91.75 320	54.70 325	77.15 306	90.15 307

LCM-MVSNet		66.00 301	62.16 304	77.51 312	64.51 336	58.29 328	83.87 318	90.90 287	48.17 329	54.69 328	73.31 324	16.83 341	86.75 328	65.47 302	61.67 327	87.48 322

FPMVS		64.63 302	62.55 303	70.88 314	70.80 332	56.71 329	84.42 315	84.42 325	51.78 328	49.57 329	81.61 318	23.49 335	81.48 332	40.61 331	76.25 308	74.46 328

PMMVS2		59.60 303	56.40 305	69.21 316	68.83 333	46.58 336	73.02 332	77.48 335	55.07 327	49.21 330	72.95 325	17.43 340	80.04 333	49.32 328	44.33 331	80.99 327

ANet_high		58.88 304	54.22 307	72.86 313	56.50 339	56.67 330	80.75 326	86.00 321	73.09 300	37.39 333	64.63 330	22.17 336	79.49 334	43.51 329	23.96 334	82.43 326

Gipuma		57.99 305	54.91 306	67.24 317	88.51 302	65.59 322	52.21 335	90.33 297	43.58 331	42.84 332	51.18 333	20.29 338	85.07 330	34.77 332	70.45 316	51.05 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 306	48.46 308	63.48 318	45.72 340	46.20 337	73.41 331	78.31 333	41.03 332	30.06 335	65.68 329	6.05 342	83.43 331	30.04 333	65.86 322	60.80 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 307	38.59 312	57.77 319	56.52 338	48.77 335	55.38 334	58.64 339	29.33 335	28.96 336	52.65 332	4.68 343	64.62 337	28.11 334	33.07 332	59.93 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 308	42.29 309	46.03 321	65.58 335	37.41 338	73.51 330	64.62 336	33.99 333	28.47 337	47.87 334	19.90 339	67.91 335	22.23 335	24.45 333	32.77 333

EMVS		42.07 309	41.12 310	44.92 322	63.45 337	35.56 340	73.65 329	63.48 337	33.05 334	26.88 338	45.45 335	21.27 337	67.14 336	19.80 336	23.02 335	32.06 334

tmp_tt		35.64 310	39.24 311	24.84 323	14.87 341	23.90 342	62.71 333	51.51 341	6.58 337	36.66 334	62.08 331	44.37 328	30.34 340	52.40 326	22.00 336	20.27 335

cdsmvs_eth3d_5k		22.14 311	29.52 313	0.00 327	0.00 344	0.00 345	0.00 338	95.76 132	0.00 340	0.00 342	94.29 129	75.66 146	0.00 343	0.00 340	0.00 340	0.00 339

wuyk23d		21.27 312	20.48 314	23.63 324	68.59 334	36.41 339	49.57 337	6.85 343	9.37 336	7.89 339	4.46 341	4.03 344	31.37 339	17.47 337	16.07 337	3.12 336

testmvs		8.92 313	11.52 315	1.12 326	1.06 342	0.46 344	86.02 307	0.65 344	0.62 338	2.74 340	9.52 339	0.31 346	0.45 342	2.38 338	0.39 338	2.46 338

test123		8.76 314	11.22 316	1.39 325	0.85 343	0.97 343	85.76 310	0.35 345	0.54 339	2.45 341	8.14 340	0.60 345	0.48 341	2.16 339	0.17 339	2.71 337

ab-mvs-re		7.82 315	10.43 317	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	93.88 148	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

pcd_1.5k_mvsjas		6.64 316	8.86 318	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	79.70 104	0.00 343	0.00 340	0.00 340	0.00 339

uanet_test		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet-low-res		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uncertanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

Regformer		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

9.14			94.47 14		97.79 41		96.08 42	97.44 12	86.13 121	95.10 16	97.40 13	88.34 14	99.22 37	93.25 23	98.70 25

save filter2										95.61 12	97.28 18	87.84 17	99.34 27	93.50 15	99.00 7	97.94 61

save fliter					97.85 39	85.63 57	95.21 82	96.82 57	89.44 41

test_0728_THIRD									90.75 19	97.04 4	98.05 4	92.09 1	99.55 9	95.64 2	99.13 3	99.13

High-resolution multi-view benchmark