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.

ESAPD		95.57 1	95.67 1	95.25 6	98.36 19	87.28 11	95.56 59	97.51 4	89.13 45	97.14 2	97.91 3	91.64 1	99.62 1	94.61 5	99.17 2	98.86 2

APDe-MVS		95.46 2	95.64 2	94.91 13	98.26 22	86.29 40	97.46 2	97.40 10	89.03 48	96.20 6	98.10 1	89.39 6	99.34 24	95.88 1	99.03 3	99.10 1

HSP-MVS		95.30 4	95.48 3	94.76 26	98.49 10	86.52 30	96.91 15	96.73 57	91.73 9	96.10 7	96.69 40	89.90 2	99.30 30	94.70 3	98.04 50	98.45 18

CNVR-MVS		95.40 3	95.37 4	95.50 4	98.11 28	88.51 3	95.29 68	96.96 40	92.09 3	95.32 11	97.08 26	89.49 5	99.33 27	95.10 2	98.85 8	98.66 6

SD-MVS		94.96 8	95.33 5	93.88 51	97.25 55	86.69 23	96.19 31	97.11 31	90.42 24	96.95 3	97.27 14	89.53 4	96.91 228	94.38 7	98.85 8	98.03 51

SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 18	96.99 58	86.33 36	97.33 3	97.30 18	91.38 12	95.39 10	97.46 10	88.98 9	99.40 22	94.12 9	98.89 7	98.82 3
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		95.20 5	95.07 7	95.59 2	98.14 27	88.48 4	96.26 29	97.28 20	85.90 119	97.67 1	98.10 1	88.41 10	99.56 3	94.66 4	99.19 1	98.71 5

TSAR-MVS + MP.		94.85 9	94.94 8	94.58 33	98.25 23	86.33 36	96.11 35	96.62 68	88.14 71	96.10 7	96.96 29	89.09 8	98.94 66	94.48 6	98.68 24	98.48 13

HPM-MVS++		95.14 7	94.91 9	95.83 1	98.25 23	89.65 1	95.92 43	96.96 40	91.75 8	94.02 21	96.83 33	88.12 11	99.55 8	93.41 17	98.94 5	98.28 29

DeepPCF-MVS	89.96 1	94.20 27	94.77 10	92.49 95	96.52 71	80.00 182	94.00 178	97.08 32	90.05 26	95.65 9	97.29 13	89.66 3	98.97 62	93.95 10	98.71 19	98.50 11

NCCC		94.81 10	94.69 11	95.17 8	97.83 35	87.46 10	95.66 54	96.93 43	92.34 2	93.94 22	96.58 47	87.74 14	99.44 21	92.83 22	98.40 39	98.62 7

ACMMP_Plus		94.74 11	94.56 12	95.28 5	98.02 33	87.70 5	95.68 52	97.34 12	88.28 66	95.30 12	97.67 5	85.90 33	99.54 11	93.91 11	98.95 4	98.60 8

HFP-MVS		94.52 12	94.40 13	94.86 16	98.61 3	86.81 18	96.94 10	97.34 12	88.63 57	93.65 25	97.21 19	86.10 29	99.49 17	92.35 29	98.77 14	98.30 26

XVS		94.45 14	94.32 14	94.85 18	98.54 7	86.60 28	96.93 12	97.19 24	90.66 22	92.85 40	97.16 24	85.02 44	99.49 17	91.99 39	98.56 35	98.47 14

zzz-MVS		94.47 13	94.30 15	95.00 10	98.42 14	86.95 13	95.06 88	96.97 37	91.07 14	93.14 38	97.56 7	84.30 50	99.56 3	93.43 15	98.75 16	98.47 14

ACMMPR		94.43 16	94.28 16	94.91 13	98.63 2	86.69 23	96.94 10	97.32 17	88.63 57	93.53 32	97.26 16	85.04 43	99.54 11	92.35 29	98.78 13	98.50 11

region2R		94.43 16	94.27 17	94.92 12	98.65 1	86.67 25	96.92 14	97.23 23	88.60 59	93.58 29	97.27 14	85.22 40	99.54 11	92.21 31	98.74 18	98.56 10

MTAPA		94.42 18	94.22 18	95.00 10	98.42 14	86.95 13	94.36 146	96.97 37	91.07 14	93.14 38	97.56 7	84.30 50	99.56 3	93.43 15	98.75 16	98.47 14

Regformer-2		94.33 20	94.22 18	94.68 29	95.54 109	86.75 22	94.57 125	96.70 61	91.84 6	94.41 14	96.56 49	87.19 20	99.13 41	93.50 13	97.65 58	98.16 40

CP-MVS		94.34 19	94.21 20	94.74 28	98.39 17	86.64 27	97.60 1	97.24 21	88.53 61	92.73 47	97.23 17	85.20 41	99.32 28	92.15 35	98.83 10	98.25 35

MCST-MVS		94.45 14	94.20 21	95.19 7	98.46 12	87.50 9	95.00 92	97.12 29	87.13 93	92.51 54	96.30 56	89.24 7	99.34 24	93.46 14	98.62 32	98.73 4

#test#		94.32 21	94.14 22	94.86 16	98.61 3	86.81 18	96.43 24	97.34 12	87.51 86	93.65 25	97.21 19	86.10 29	99.49 17	91.68 48	98.77 14	98.30 26

Regformer-1		94.22 24	94.13 23	94.51 36	95.54 109	86.36 35	94.57 125	96.44 75	91.69 10	94.32 16	96.56 49	87.05 22	99.03 51	93.35 18	97.65 58	98.15 41

MSLP-MVS++		93.72 35	94.08 24	92.65 88	97.31 49	83.43 94	95.79 47	97.33 15	90.03 27	93.58 29	96.96 29	84.87 46	97.76 149	92.19 33	98.66 28	96.76 104

MP-MVS		94.25 22	94.07 25	94.77 25	98.47 11	86.31 38	96.71 20	96.98 36	89.04 47	91.98 64	97.19 21	85.43 38	99.56 3	92.06 38	98.79 11	98.44 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 23	94.07 25	94.75 27	98.06 31	86.90 16	95.88 44	96.94 42	85.68 125	95.05 13	97.18 22	87.31 19	99.07 45	91.90 46	98.61 33	98.28 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS-pluss		94.21 25	94.00 27	94.85 18	98.17 26	86.65 26	94.82 104	97.17 27	86.26 114	92.83 42	97.87 4	85.57 36	99.56 3	94.37 8	98.92 6	98.34 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
GST-MVS		94.21 25	93.97 28	94.90 15	98.41 16	86.82 17	96.54 23	97.19 24	88.24 68	93.26 33	96.83 33	85.48 37	99.59 2	91.43 54	98.40 39	98.30 26

HPM-MVS		94.02 29	93.88 29	94.43 39	98.39 17	85.78 51	97.25 5	97.07 33	86.90 104	92.62 51	96.80 37	84.85 47	99.17 36	92.43 26	98.65 30	98.33 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-4		93.91 32	93.81 30	94.19 46	95.36 115	85.47 53	94.68 116	96.41 78	91.60 11	93.75 24	96.71 38	85.95 32	99.10 44	93.21 19	96.65 73	98.01 53

DeepC-MVS_fast	89.43 2	94.04 28	93.79 31	94.80 24	97.48 44	86.78 20	95.65 56	96.89 45	89.40 38	92.81 43	96.97 28	85.37 39	99.24 32	90.87 61	98.69 21	98.38 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS		93.99 30	93.78 32	94.63 31	98.50 9	85.90 49	96.87 16	96.91 44	88.70 55	91.83 68	97.17 23	83.96 53	99.55 8	91.44 53	98.64 31	98.43 20

APD-MVS_3200maxsize		93.78 34	93.77 33	93.80 56	97.92 34	84.19 77	96.30 27	96.87 48	86.96 100	93.92 23	97.47 9	83.88 54	98.96 65	92.71 24	97.87 53	98.26 34

PGM-MVS		93.96 31	93.72 34	94.68 29	98.43 13	86.22 41	95.30 66	97.78 1	87.45 87	93.26 33	97.33 12	84.62 48	99.51 15	90.75 63	98.57 34	98.32 25

PHI-MVS		93.89 33	93.65 35	94.62 32	96.84 61	86.43 33	96.69 21	97.49 5	85.15 138	93.56 31	96.28 57	85.60 35	99.31 29	92.45 25	98.79 11	98.12 44

Regformer-3		93.68 36	93.64 36	93.81 55	95.36 115	84.61 62	94.68 116	95.83 120	91.27 13	93.60 28	96.71 38	85.75 34	98.86 71	92.87 21	96.65 73	97.96 55

test_prior3		93.60 38	93.53 37	93.82 53	97.29 51	84.49 66	94.12 160	96.88 46	87.67 83	92.63 49	96.39 54	86.62 25	98.87 68	91.50 50	98.67 26	98.11 45

TSAR-MVS + GP.		93.66 37	93.41 38	94.41 40	96.59 67	86.78 20	94.40 136	93.93 227	89.77 32	94.21 17	95.59 83	87.35 18	98.61 87	92.72 23	96.15 81	97.83 64

MVS_111021_HR		93.45 40	93.31 39	93.84 52	96.99 58	84.84 58	93.24 218	97.24 21	88.76 54	91.60 72	95.85 75	86.07 31	98.66 82	91.91 43	98.16 46	98.03 51

DELS-MVS		93.43 42	93.25 40	93.97 48	95.42 114	85.04 57	93.06 225	97.13 28	90.74 20	91.84 66	95.09 96	86.32 28	99.21 33	91.22 55	98.45 38	97.65 69
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 43	93.22 41	93.94 50	98.36 19	84.83 59	97.15 7	96.80 52	85.77 122	92.47 55	97.13 25	82.38 63	99.07 45	90.51 65	98.40 39	97.92 60

CANet		93.54 39	93.20 42	94.55 34	95.65 106	85.73 52	94.94 95	96.69 63	91.89 5	90.69 84	95.88 74	81.99 74	99.54 11	93.14 20	97.95 52	98.39 21

train_agg		93.44 41	93.08 43	94.52 35	97.53 39	86.49 31	94.07 170	96.78 53	81.86 231	92.77 44	96.20 61	87.63 16	99.12 42	92.14 36	98.69 21	97.94 56

abl_6		93.18 51	93.05 44	93.57 60	97.52 41	84.27 76	95.53 60	96.67 64	87.85 77	93.20 36	97.22 18	80.35 86	99.18 35	91.91 43	97.21 63	97.26 82

CSCG		93.23 50	93.05 44	93.76 57	98.04 32	84.07 79	96.22 30	97.37 11	84.15 159	90.05 92	95.66 81	87.77 13	99.15 39	89.91 68	98.27 43	98.07 47

DeepC-MVS	88.79 3	93.31 44	92.99 46	94.26 44	96.07 91	85.83 50	94.89 98	96.99 35	89.02 49	89.56 95	97.37 11	82.51 62	99.38 23	92.20 32	98.30 42	97.57 73
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 45	92.97 47	94.26 44	97.38 46	85.92 46	93.92 181	96.72 59	81.96 218	92.16 60	96.23 59	87.85 12	98.97 62	91.95 42	98.55 37	97.90 61

EI-MVSNet-Vis-set		93.01 53	92.92 48	93.29 61	95.01 132	83.51 93	94.48 128	95.77 124	90.87 16	92.52 53	96.67 42	84.50 49	99.00 59	91.99 39	94.44 110	97.36 78

agg_prior3		93.27 46	92.89 49	94.40 41	97.49 42	86.12 43	94.07 170	96.73 57	81.46 239	92.46 56	96.05 69	86.90 23	99.15 39	92.14 36	98.69 21	97.94 56

ACMMP		93.24 49	92.88 50	94.30 43	98.09 30	85.33 55	96.86 17	97.45 7	88.33 64	90.15 91	97.03 27	81.44 78	99.51 15	90.85 62	95.74 85	98.04 50
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
canonicalmvs		93.27 46	92.75 51	94.85 18	95.70 105	87.66 6	96.33 26	96.41 78	90.00 28	94.09 19	94.60 112	82.33 64	98.62 86	92.40 28	92.86 139	98.27 32

MVS_0304		93.25 48	92.62 52	95.14 9	95.72 102	87.58 8	94.71 115	96.59 70	91.78 7	91.46 75	96.18 65	75.45 150	99.55 8	93.53 12	98.19 45	98.28 29

EI-MVSNet-UG-set		92.74 56	92.62 52	93.12 68	94.86 142	83.20 99	94.40 136	95.74 127	90.71 21	92.05 63	96.60 46	84.00 52	98.99 60	91.55 49	93.63 121	97.17 88

UA-Net		92.83 54	92.54 54	93.68 58	96.10 89	84.71 61	95.66 54	96.39 81	91.92 4	93.22 35	96.49 51	83.16 57	98.87 68	84.47 128	95.47 90	97.45 77

alignmvs		93.08 52	92.50 55	94.81 23	95.62 108	87.61 7	95.99 39	96.07 101	89.77 32	94.12 18	94.87 100	80.56 84	98.66 82	92.42 27	93.10 134	98.15 41

CDPH-MVS		92.83 54	92.30 56	94.44 37	97.79 36	86.11 44	94.06 173	96.66 65	80.09 251	92.77 44	96.63 44	86.62 25	99.04 50	87.40 95	98.66 28	98.17 39

MVS_111021_LR		92.47 57	92.29 57	92.98 76	95.99 94	84.43 73	93.08 223	96.09 99	88.20 70	91.12 80	95.72 80	81.33 80	97.76 149	91.74 47	97.37 62	96.75 105

casdiffmvs1		92.43 58	92.18 58	93.17 66	95.33 118	83.03 104	95.08 85	96.41 78	83.18 186	93.20 36	94.49 115	83.84 55	98.29 108	92.16 34	95.96 82	98.20 37

VNet		92.24 60	91.91 59	93.24 63	96.59 67	83.43 94	94.84 103	96.44 75	89.19 43	94.08 20	95.90 73	77.85 116	98.17 115	88.90 76	93.38 128	98.13 43

CPTT-MVS		91.99 61	91.80 60	92.55 92	98.24 25	81.98 132	96.76 19	96.49 74	81.89 223	90.24 89	96.44 53	78.59 106	98.61 87	89.68 70	97.85 54	97.06 94

MG-MVS		91.77 64	91.70 61	92.00 114	97.08 57	80.03 181	93.60 202	95.18 175	87.85 77	90.89 82	96.47 52	82.06 72	98.36 101	85.07 119	97.04 66	97.62 70

EPP-MVSNet		91.70 68	91.56 62	92.13 111	95.88 97	80.50 171	97.33 3	95.25 168	86.15 116	89.76 94	95.60 82	83.42 56	98.32 106	87.37 97	93.25 131	97.56 74

3Dnovator+	87.14 4	92.42 59	91.37 63	95.55 3	95.63 107	88.73 2	97.07 8	96.77 55	90.84 17	84.02 225	96.62 45	75.95 139	99.34 24	87.77 90	97.68 56	98.59 9

MVSFormer		91.68 69	91.30 64	92.80 82	93.86 180	83.88 82	95.96 41	95.90 114	84.66 148	91.76 69	94.91 98	77.92 113	97.30 195	89.64 71	97.11 64	97.24 83

DP-MVS Recon		91.95 62	91.28 65	93.96 49	98.33 21	85.92 46	94.66 119	96.66 65	82.69 207	90.03 93	95.82 76	82.30 65	99.03 51	84.57 127	96.48 78	96.91 101

casdiffmvs		91.72 67	91.26 66	93.10 69	94.66 150	83.75 85	94.77 108	96.00 106	83.98 162	90.74 83	93.96 134	82.08 70	98.19 114	91.47 52	93.68 119	97.36 78

Vis-MVSNet		91.75 65	91.23 67	93.29 61	95.32 119	83.78 84	96.14 33	95.98 107	89.89 29	90.45 87	96.58 47	75.09 154	98.31 107	84.75 125	96.90 67	97.78 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs1		91.33 73	91.22 68	91.68 130	93.43 194	79.77 187	93.02 226	95.50 146	87.72 80	90.47 86	93.87 142	81.76 77	97.52 162	89.84 69	95.36 94	97.74 68

Effi-MVS+		91.59 70	91.11 69	93.01 75	94.35 164	83.39 96	94.60 122	95.10 179	87.10 94	90.57 85	93.10 165	81.43 79	98.07 134	89.29 73	94.48 107	97.59 72

MVS_Test		91.31 74	91.11 69	91.93 118	94.37 161	80.14 175	93.46 207	95.80 122	86.46 110	91.35 77	93.77 146	82.21 67	98.09 132	87.57 93	94.95 98	97.55 75

IS-MVSNet		91.43 71	91.09 71	92.46 96	95.87 99	81.38 145	96.95 9	93.69 232	89.72 34	89.50 97	95.98 70	78.57 107	97.77 148	83.02 148	96.50 77	98.22 36

EPNet		91.79 63	91.02 72	94.10 47	90.10 303	85.25 56	96.03 38	92.05 261	92.83 1	87.39 136	95.78 77	79.39 100	99.01 56	88.13 86	97.48 60	98.05 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PS-MVSNAJ		91.18 77	90.92 73	91.96 116	95.26 122	82.60 122	92.09 256	95.70 129	86.27 113	91.84 66	92.46 185	79.70 95	98.99 60	89.08 74	95.86 84	94.29 200

PVSNet_Blended_VisFu		91.38 72	90.91 74	92.80 82	96.39 74	83.17 100	94.87 101	96.66 65	83.29 183	89.27 99	94.46 116	80.29 88	99.17 36	87.57 93	95.37 92	96.05 127

xiu_mvs_v2_base		91.13 78	90.89 75	91.86 121	94.97 135	82.42 123	92.24 250	95.64 135	86.11 118	91.74 71	93.14 163	79.67 98	98.89 67	89.06 75	95.46 91	94.28 201

3Dnovator	86.66 5	91.73 66	90.82 76	94.44 37	94.59 153	86.37 34	97.18 6	97.02 34	89.20 42	84.31 221	96.66 43	73.74 175	99.17 36	86.74 105	97.96 51	97.79 66

PAPM_NR		91.22 76	90.78 77	92.52 94	97.60 38	81.46 142	94.37 142	96.24 89	86.39 112	87.41 134	94.80 105	82.06 72	98.48 93	82.80 153	95.37 92	97.61 71

OMC-MVS		91.23 75	90.62 78	93.08 71	96.27 77	84.07 79	93.52 204	95.93 110	86.95 101	89.51 96	96.13 67	78.50 108	98.35 103	85.84 114	92.90 138	96.83 103

nrg030		91.08 79	90.39 79	93.17 66	93.07 203	86.91 15	96.41 25	96.26 86	88.30 65	88.37 111	94.85 103	82.19 68	97.64 156	91.09 56	82.95 257	94.96 161

FIs		90.51 91	90.35 80	90.99 156	93.99 176	80.98 156	95.73 49	97.54 3	89.15 44	86.72 146	94.68 108	81.83 76	97.24 203	85.18 118	88.31 210	94.76 175

diffmvs		90.50 92	90.33 81	91.02 153	93.04 205	78.59 228	92.85 233	95.07 182	87.32 89	88.32 112	93.34 151	80.46 85	97.40 187	88.50 80	94.06 114	97.07 93

PVSNet_Blended		90.73 83	90.32 82	91.98 115	96.12 84	81.25 147	92.55 242	96.83 49	82.04 217	89.10 101	92.56 184	81.04 82	98.85 74	86.72 108	95.91 83	95.84 134

lupinMVS		90.92 80	90.21 83	93.03 74	93.86 180	83.88 82	92.81 234	93.86 228	79.84 253	91.76 69	94.29 121	77.92 113	98.04 136	90.48 66	97.11 64	97.17 88

HQP_MVS		90.60 90	90.19 84	91.82 125	94.70 148	82.73 116	95.85 45	96.22 90	90.81 18	86.91 142	94.86 101	74.23 164	98.12 119	88.15 84	89.99 174	94.63 181

FC-MVSNet-test		90.27 95	90.18 85	90.53 166	93.71 186	79.85 186	95.77 48	97.59 2	89.31 40	86.27 155	94.67 109	81.93 75	97.01 220	84.26 133	88.09 213	94.71 176

jason		90.80 81	90.10 86	92.90 79	93.04 205	83.53 92	93.08 223	94.15 214	80.22 248	91.41 76	94.91 98	76.87 119	97.93 143	90.28 67	96.90 67	97.24 83
jason: jason.
API-MVS		90.66 86	90.07 87	92.45 97	96.36 75	84.57 64	96.06 37	95.22 174	82.39 209	89.13 100	94.27 124	80.32 87	98.46 95	80.16 198	96.71 71	94.33 199

xiu_mvs_v1_base_debu		90.64 87	90.05 88	92.40 98	93.97 177	84.46 69	93.32 209	95.46 149	85.17 135	92.25 57	94.03 127	70.59 213	98.57 89	90.97 57	94.67 100	94.18 202

xiu_mvs_v1_base		90.64 87	90.05 88	92.40 98	93.97 177	84.46 69	93.32 209	95.46 149	85.17 135	92.25 57	94.03 127	70.59 213	98.57 89	90.97 57	94.67 100	94.18 202

xiu_mvs_v1_base_debi		90.64 87	90.05 88	92.40 98	93.97 177	84.46 69	93.32 209	95.46 149	85.17 135	92.25 57	94.03 127	70.59 213	98.57 89	90.97 57	94.67 100	94.18 202

0601test		90.69 84	90.02 91	92.71 85	95.72 102	82.41 125	94.11 162	95.12 177	85.63 126	91.49 73	94.70 106	74.75 157	98.42 99	86.13 112	92.53 143	97.31 80

Anonymous20240521		90.69 84	90.02 91	92.71 85	95.72 102	82.41 125	94.11 162	95.12 177	85.63 126	91.49 73	94.70 106	74.75 157	98.42 99	86.13 112	92.53 143	97.31 80

VDD-MVS		90.74 82	89.92 93	93.20 64	96.27 77	83.02 106	95.73 49	93.86 228	88.42 63	92.53 52	96.84 32	62.09 297	98.64 84	90.95 60	92.62 142	97.93 59

PVSNet_BlendedMVS		89.98 100	89.70 94	90.82 160	96.12 84	81.25 147	93.92 181	96.83 49	83.49 177	89.10 101	92.26 195	81.04 82	98.85 74	86.72 108	87.86 215	92.35 286

PS-MVSNAJss		89.97 101	89.62 95	91.02 153	91.90 226	80.85 161	95.26 75	95.98 107	86.26 114	86.21 156	94.29 121	79.70 95	97.65 154	88.87 77	88.10 211	94.57 187

OPM-MVS		90.12 97	89.56 96	91.82 125	93.14 201	83.90 81	94.16 159	95.74 127	88.96 50	87.86 121	95.43 86	72.48 192	97.91 144	88.10 87	90.18 173	93.65 240

1121		90.42 93	89.49 97	93.20 64	97.27 53	84.46 69	92.63 238	95.51 145	71.01 332	91.20 79	96.21 60	82.92 59	99.05 47	80.56 189	98.07 49	96.10 123

XVG-OURS-SEG-HR		89.95 102	89.45 98	91.47 136	94.00 175	81.21 150	91.87 258	96.06 103	85.78 121	88.55 107	95.73 79	74.67 160	97.27 199	88.71 78	89.64 182	95.91 130

Vis-MVSNet (Re-imp)		89.59 110	89.44 99	90.03 204	95.74 101	75.85 284	95.61 57	90.80 301	87.66 85	87.83 127	95.40 87	76.79 121	96.46 254	78.37 222	96.73 70	97.80 65

CANet_DTU		90.26 96	89.41 100	92.81 81	93.46 193	83.01 107	93.48 205	94.47 204	89.43 37	87.76 130	94.23 125	70.54 217	99.03 51	84.97 120	96.39 79	96.38 112

MAR-MVS		90.30 94	89.37 101	93.07 73	96.61 66	84.48 68	95.68 52	95.67 130	82.36 211	87.85 122	92.85 174	76.63 124	98.80 78	80.01 199	96.68 72	95.91 130
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
mvs_anonymous		89.37 121	89.32 102	89.51 226	93.47 192	74.22 291	91.65 265	94.83 195	82.91 201	85.45 182	93.79 144	81.23 81	96.36 259	86.47 111	94.09 113	97.94 56

UniMVSNet_NR-MVSNet		89.92 104	89.29 103	91.81 127	93.39 195	83.72 86	94.43 134	97.12 29	89.80 31	86.46 149	93.32 154	83.16 57	97.23 205	84.92 121	81.02 286	94.49 194

HQP-MVS		89.80 106	89.28 104	91.34 140	94.17 166	81.56 136	94.39 138	96.04 104	88.81 51	85.43 185	93.97 133	73.83 173	97.96 140	87.11 102	89.77 180	94.50 192

PAPR		90.02 99	89.27 105	92.29 105	95.78 100	80.95 158	92.68 237	96.22 90	81.91 221	86.66 147	93.75 148	82.23 66	98.44 98	79.40 216	94.79 99	97.48 76

mvs-test1		89.45 115	89.14 106	90.38 182	93.33 196	77.63 264	94.95 94	94.36 207	87.70 81	87.10 139	92.81 178	73.45 178	98.03 137	85.57 116	93.04 135	95.48 145

LFMVS		90.08 98	89.13 107	92.95 77	96.71 63	82.32 127	96.08 36	89.91 317	86.79 105	92.15 62	96.81 35	62.60 294	98.34 104	87.18 99	93.90 116	98.19 38

UniMVSNet (Re)		89.80 106	89.07 108	92.01 112	93.60 189	84.52 65	94.78 107	97.47 6	89.26 41	86.44 152	92.32 190	82.10 69	97.39 191	84.81 124	80.84 290	94.12 206

AdaColmap		89.89 105	89.07 108	92.37 101	97.41 45	83.03 104	94.42 135	95.92 111	82.81 203	86.34 154	94.65 110	73.89 171	99.02 54	80.69 186	95.51 88	95.05 155

VPA-MVSNet		89.62 108	88.96 110	91.60 133	93.86 180	82.89 111	95.46 61	97.33 15	87.91 74	88.43 110	93.31 155	74.17 167	97.40 187	87.32 98	82.86 259	94.52 190

UGNet		89.95 102	88.95 111	92.95 77	94.51 156	83.31 97	95.70 51	95.23 172	89.37 39	87.58 132	93.94 135	64.00 289	98.78 79	83.92 138	96.31 80	96.74 106
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
WTY-MVS		89.60 109	88.92 112	91.67 131	95.47 113	81.15 152	92.38 247	94.78 197	83.11 187	89.06 103	94.32 119	78.67 105	96.61 246	81.57 174	90.89 165	97.24 83

LPG-MVS_test		89.45 115	88.90 113	91.12 145	94.47 157	81.49 140	95.30 66	96.14 95	86.73 106	85.45 182	95.16 93	69.89 222	98.10 125	87.70 91	89.23 189	93.77 230

CLD-MVS		89.47 114	88.90 113	91.18 144	94.22 165	82.07 130	92.13 254	96.09 99	87.90 75	85.37 192	92.45 186	74.38 162	97.56 159	87.15 100	90.43 167	93.93 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EI-MVSNet		89.10 125	88.86 115	89.80 214	91.84 228	78.30 243	93.70 197	95.01 183	85.73 123	87.15 137	95.28 88	79.87 92	97.21 207	83.81 140	87.36 220	93.88 220

XVG-OURS		89.40 120	88.70 116	91.52 134	94.06 169	81.46 142	91.27 271	96.07 101	86.14 117	88.89 105	95.77 78	68.73 246	97.26 201	87.39 96	89.96 176	95.83 135

Fast-Effi-MVS+		89.41 118	88.64 117	91.71 129	94.74 144	80.81 162	93.54 203	95.10 179	83.11 187	86.82 145	90.67 256	79.74 94	97.75 152	80.51 191	93.55 122	96.57 109

test_djsdf		89.03 128	88.64 117	90.21 187	90.74 287	79.28 213	95.96 41	95.90 114	84.66 148	85.33 194	92.94 173	74.02 170	97.30 195	89.64 71	88.53 203	94.05 212

CDS-MVSNet		89.45 115	88.51 119	92.29 105	93.62 188	83.61 91	93.01 227	94.68 199	81.95 219	87.82 128	93.24 159	78.69 104	96.99 221	80.34 194	93.23 132	96.28 115
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DU-MVS		89.34 122	88.50 120	91.85 123	93.04 205	83.72 86	94.47 131	96.59 70	89.50 36	86.46 149	93.29 157	77.25 117	97.23 205	84.92 121	81.02 286	94.59 185

114514_t		89.51 112	88.50 120	92.54 93	98.11 28	81.99 131	95.16 81	96.36 83	70.19 334	85.81 161	95.25 90	76.70 122	98.63 85	82.07 165	96.86 69	97.00 98

VDDNet		89.56 111	88.49 122	92.76 84	95.07 131	82.09 129	96.30 27	93.19 238	81.05 244	91.88 65	96.86 31	61.16 307	98.33 105	88.43 82	92.49 145	97.84 63

ACMM	84.12 9	89.14 124	88.48 123	91.12 145	94.65 152	81.22 149	95.31 64	96.12 98	85.31 134	85.92 160	94.34 117	70.19 221	98.06 135	85.65 115	88.86 200	94.08 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		88.65 137	88.35 124	89.54 223	93.33 196	76.39 279	94.47 131	94.36 207	87.70 81	85.43 185	89.56 278	73.45 178	97.26 201	85.57 116	91.28 152	94.97 158

ab-mvs		89.41 118	88.35 124	92.60 89	95.15 130	82.65 120	92.20 252	95.60 136	83.97 163	88.55 107	93.70 149	74.16 168	98.21 113	82.46 159	89.37 185	96.94 100

ACMP	84.23 8	89.01 130	88.35 124	90.99 156	94.73 145	81.27 146	95.07 86	95.89 116	86.48 109	83.67 233	94.30 120	69.33 229	97.99 139	87.10 104	88.55 202	93.72 234
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		88.30 146	88.32 127	88.27 267	94.71 147	72.41 312	93.15 219	90.98 296	87.77 79	79.25 288	91.96 208	78.35 110	95.75 282	83.04 147	95.62 86	96.65 107

MVSTER		88.84 132	88.29 128	90.51 173	92.95 210	80.44 172	93.73 193	95.01 183	84.66 148	87.15 137	93.12 164	72.79 186	97.21 207	87.86 89	87.36 220	93.87 221

TAMVS		89.21 123	88.29 128	91.96 116	93.71 186	82.62 121	93.30 213	94.19 212	82.22 212	87.78 129	93.94 135	78.83 102	96.95 225	77.70 230	92.98 137	96.32 113

sss		88.93 131	88.26 130	90.94 159	94.05 170	80.78 163	91.71 262	95.38 159	81.55 237	88.63 106	93.91 139	75.04 155	95.47 293	82.47 158	91.61 150	96.57 109

QAPM		89.51 112	88.15 131	93.59 59	94.92 138	84.58 63	96.82 18	96.70 61	78.43 269	83.41 240	96.19 64	73.18 182	99.30 30	77.11 237	96.54 76	96.89 102

BH-untuned		88.60 139	88.13 132	90.01 206	95.24 129	78.50 238	93.29 214	94.15 214	84.75 146	84.46 213	93.40 150	75.76 144	97.40 187	77.59 231	94.52 106	94.12 206

PLC	84.53 7	89.06 127	88.03 133	92.15 109	97.27 53	82.69 119	94.29 147	95.44 155	79.71 255	84.01 226	94.18 126	76.68 123	98.75 80	77.28 234	93.41 127	95.02 156
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		89.07 126	87.98 134	92.34 102	96.87 60	84.78 60	94.08 168	93.24 237	81.41 240	84.46 213	95.13 95	75.57 147	96.62 244	77.21 235	93.84 118	95.61 143

TranMVSNet+NR-MVSNet		88.84 132	87.95 135	91.49 135	92.68 215	83.01 107	94.92 97	96.31 84	89.88 30	85.53 176	93.85 143	76.63 124	96.96 224	81.91 169	79.87 305	94.50 192

HY-MVS	83.01 12	89.03 128	87.94 136	92.29 105	94.86 142	82.77 112	92.08 257	94.49 203	81.52 238	86.93 141	92.79 180	78.32 111	98.23 110	79.93 202	90.55 166	95.88 132

IterMVS-LS		88.36 144	87.91 137	89.70 219	93.80 183	78.29 244	93.73 193	95.08 181	85.73 123	84.75 207	91.90 211	79.88 91	96.92 227	83.83 139	82.51 261	93.89 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tttt0517		88.61 138	87.78 138	91.11 148	94.96 136	77.81 257	95.35 62	89.69 322	85.09 140	88.05 119	94.59 113	66.93 267	98.48 93	83.27 145	92.13 148	97.03 96

CHOSEN 1792x2688		88.84 132	87.69 139	92.30 104	96.14 83	81.42 144	90.01 284	95.86 118	74.52 304	87.41 134	93.94 135	75.46 149	98.36 101	80.36 193	95.53 87	97.12 91

WR-MVS		88.38 142	87.67 140	90.52 172	93.30 198	80.18 173	93.26 216	95.96 109	88.57 60	85.47 181	92.81 178	76.12 128	96.91 228	81.24 177	82.29 263	94.47 197

thisisatest0530		88.67 136	87.61 141	91.86 121	94.87 141	80.07 178	94.63 120	89.90 318	84.00 161	88.46 109	93.78 145	66.88 269	98.46 95	83.30 144	92.65 141	97.06 94

jajsoiax		88.24 147	87.50 142	90.48 175	90.89 282	80.14 175	95.31 64	95.65 134	84.97 142	84.24 223	94.02 130	65.31 283	97.42 180	88.56 79	88.52 204	93.89 218

BH-RMVSNet		88.37 143	87.48 143	91.02 153	95.28 120	79.45 199	92.89 232	93.07 240	85.45 131	86.91 142	94.84 104	70.35 218	97.76 149	73.97 262	94.59 104	95.85 133

VPNet		88.20 148	87.47 144	90.39 180	93.56 190	79.46 197	94.04 174	95.54 141	88.67 56	86.96 140	94.58 114	69.33 229	97.15 209	84.05 137	80.53 295	94.56 188

NR-MVSNet		88.58 140	87.47 144	91.93 118	93.04 205	84.16 78	94.77 108	96.25 88	89.05 46	80.04 282	93.29 157	79.02 101	97.05 218	81.71 173	80.05 300	94.59 185

WR-MVS_H		87.80 165	87.37 146	89.10 243	93.23 199	78.12 248	95.61 57	97.30 18	87.90 75	83.72 231	92.01 207	79.65 99	96.01 271	76.36 241	80.54 294	93.16 261

1112_ss		88.42 141	87.33 147	91.72 128	94.92 138	80.98 156	92.97 230	94.54 202	78.16 274	83.82 229	93.88 140	78.78 103	97.91 144	79.45 212	89.41 184	96.26 116

OpenMVS	83.78 11	88.74 135	87.29 148	93.08 71	92.70 214	85.39 54	96.57 22	96.43 77	78.74 266	80.85 270	96.07 68	69.64 226	99.01 56	78.01 228	96.65 73	94.83 172

mvs_tets		88.06 154	87.28 149	90.38 182	90.94 278	79.88 184	95.22 77	95.66 132	85.10 139	84.21 224	93.94 135	63.53 291	97.40 187	88.50 80	88.40 209	93.87 221

CP-MVSNet		87.63 174	87.26 150	88.74 248	93.12 202	76.59 278	95.29 68	96.58 72	88.43 62	83.49 239	92.98 172	75.28 151	95.83 278	78.97 218	81.15 283	93.79 226

v1neww		87.98 155	87.25 151	90.16 189	91.38 247	79.41 201	94.37 142	95.28 164	84.48 151	85.77 163	91.53 225	76.12 128	97.45 169	84.45 130	81.89 270	93.61 245

v7new		87.98 155	87.25 151	90.16 189	91.38 247	79.41 201	94.37 142	95.28 164	84.48 151	85.77 163	91.53 225	76.12 128	97.45 169	84.45 130	81.89 270	93.61 245

v6		87.98 155	87.25 151	90.16 189	91.36 250	79.39 206	94.37 142	95.27 167	84.48 151	85.78 162	91.51 227	76.15 127	97.46 167	84.46 129	81.88 272	93.62 244

v1		87.85 160	87.10 154	90.11 200	91.21 264	79.24 217	94.09 166	95.24 169	84.44 155	85.70 168	91.31 238	75.96 138	97.45 169	84.18 134	81.73 278	93.64 241

anonymousdsp		87.84 161	87.09 155	90.12 195	89.13 314	80.54 169	94.67 118	95.55 139	82.05 215	83.82 229	92.12 199	71.47 202	97.15 209	87.15 100	87.80 216	92.67 275

v1141		87.84 161	87.09 155	90.11 200	91.23 262	79.25 215	94.08 168	95.24 169	84.44 155	85.69 170	91.31 238	75.91 140	97.44 176	84.17 135	81.74 276	93.63 243

divwei89l23v2f112		87.84 161	87.09 155	90.10 202	91.23 262	79.24 217	94.09 166	95.24 169	84.44 155	85.70 168	91.31 238	75.91 140	97.44 176	84.17 135	81.73 278	93.64 241

v2v482		87.84 161	87.06 158	90.17 188	90.99 274	79.23 219	94.00 178	95.13 176	84.87 143	85.53 176	92.07 205	74.45 161	97.45 169	84.71 126	81.75 275	93.85 224

BH-w/o		87.57 184	87.05 159	89.12 241	94.90 140	77.90 253	92.41 245	93.51 234	82.89 202	83.70 232	91.34 234	75.75 145	97.07 216	75.49 248	93.49 124	92.39 284

TAPA-MVS	84.62 6	88.16 149	87.01 160	91.62 132	96.64 65	80.65 165	94.39 138	96.21 93	76.38 285	86.19 157	95.44 84	79.75 93	98.08 133	62.75 330	95.29 95	96.13 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v7		87.75 166	86.96 161	90.12 195	91.20 265	79.50 192	94.28 148	95.46 149	83.45 178	85.75 165	91.56 224	75.13 152	97.43 178	83.60 142	82.18 265	93.42 254

PS-CasMVS		87.32 191	86.88 162	88.63 251	92.99 209	76.33 281	95.33 63	96.61 69	88.22 69	83.30 242	93.07 166	73.03 184	95.79 281	78.36 223	81.00 288	93.75 232

V42		87.68 168	86.86 163	90.15 193	90.58 292	80.14 175	94.24 150	95.28 164	83.66 170	85.67 171	91.33 235	74.73 159	97.41 185	84.43 132	81.83 273	92.89 269

XXY-MVS		87.65 170	86.85 164	90.03 204	92.14 222	80.60 168	93.76 190	95.23 172	82.94 199	84.60 209	94.02 130	74.27 163	95.49 292	81.04 179	83.68 250	94.01 215

DI_MVS_plusplus_test		88.15 150	86.82 165	92.14 110	90.67 290	81.07 153	93.01 227	94.59 201	83.83 167	77.78 295	90.63 257	68.51 249	98.16 116	88.02 88	94.37 111	97.17 88

HyFIR lowres test		88.09 152	86.81 166	91.93 118	96.00 93	80.63 166	90.01 284	95.79 123	73.42 312	87.68 131	92.10 202	73.86 172	97.96 140	80.75 185	91.70 149	97.19 87

F-COLMAP		87.95 158	86.80 167	91.40 138	96.35 76	80.88 160	94.73 110	95.45 153	79.65 256	82.04 257	94.61 111	71.13 204	98.50 92	76.24 244	91.05 159	94.80 174

v1144		87.61 182	86.79 168	90.06 203	91.01 273	79.34 209	93.95 180	95.42 158	83.36 182	85.66 172	91.31 238	74.98 156	97.42 180	83.37 143	82.06 266	93.42 254

test_normal		88.13 151	86.78 169	92.18 108	90.55 295	81.19 151	92.74 236	94.64 200	83.84 165	77.49 299	90.51 263	68.49 250	98.16 116	88.22 83	94.55 105	97.21 86

Fast-Effi-MVS+-dtu		87.44 187	86.72 170	89.63 221	92.04 225	77.68 263	94.03 175	93.94 226	85.81 120	82.42 249	91.32 237	70.33 219	97.06 217	80.33 195	90.23 172	94.14 205

conf200view11		87.65 170	86.71 171	90.46 178	96.12 84	78.55 229	95.03 89	91.58 274	87.15 90	88.06 116	92.29 192	68.91 237	98.10 125	70.13 283	91.10 153	94.71 176

thres100view900		87.63 174	86.71 171	90.38 182	96.12 84	78.55 229	95.03 89	91.58 274	87.15 90	88.06 116	92.29 192	68.91 237	98.10 125	70.13 283	91.10 153	94.48 195

v8		87.50 186	86.71 171	89.89 209	91.37 249	79.40 205	94.50 127	95.38 159	84.81 145	83.60 236	91.33 235	76.05 132	97.42 180	82.84 151	80.51 297	92.84 271

tfpn111		87.63 174	86.68 174	90.47 176	96.12 84	78.55 229	95.03 89	91.58 274	87.15 90	88.06 116	92.29 192	68.91 237	98.15 118	69.88 288	91.10 153	94.71 176

thres600view7		87.65 170	86.67 175	90.59 163	96.08 90	78.72 224	94.88 100	91.58 274	87.06 99	88.08 115	92.30 191	68.91 237	98.10 125	70.05 287	91.10 153	94.96 161

view600		87.62 177	86.65 176	90.53 166	96.19 79	78.52 233	95.29 68	91.09 288	87.08 95	87.84 123	93.03 168	68.86 241	98.11 121	69.44 290	91.02 161	94.96 161

view800		87.62 177	86.65 176	90.53 166	96.19 79	78.52 233	95.29 68	91.09 288	87.08 95	87.84 123	93.03 168	68.86 241	98.11 121	69.44 290	91.02 161	94.96 161

conf0.05thres1000		87.62 177	86.65 176	90.53 166	96.19 79	78.52 233	95.29 68	91.09 288	87.08 95	87.84 123	93.03 168	68.86 241	98.11 121	69.44 290	91.02 161	94.96 161

tfpn		87.62 177	86.65 176	90.53 166	96.19 79	78.52 233	95.29 68	91.09 288	87.08 95	87.84 123	93.03 168	68.86 241	98.11 121	69.44 290	91.02 161	94.96 161

tfpn200view9		87.58 183	86.64 180	90.41 179	95.99 94	78.64 226	94.58 123	91.98 265	86.94 102	88.09 113	91.77 213	69.18 234	98.10 125	70.13 283	91.10 153	94.48 195

thres400		87.62 177	86.64 180	90.57 164	95.99 94	78.64 226	94.58 123	91.98 265	86.94 102	88.09 113	91.77 213	69.18 234	98.10 125	70.13 283	91.10 153	94.96 161

Baseline_NR-MVSNet		87.07 200	86.63 182	88.40 264	91.44 240	77.87 255	94.23 151	92.57 250	84.12 160	85.74 167	92.08 203	77.25 117	96.04 268	82.29 162	79.94 303	91.30 305

Anonymous20240529		88.09 152	86.59 183	92.58 91	96.53 70	81.92 133	95.99 39	95.84 119	74.11 307	89.06 103	95.21 92	61.44 302	98.81 77	83.67 141	87.47 217	97.01 97

1314		87.51 185	86.57 184	90.34 185	92.42 218	79.74 189	92.63 238	95.35 163	78.35 270	80.14 280	91.62 220	74.05 169	97.15 209	81.05 178	93.53 123	94.12 206

Test_1112_low_res		87.65 170	86.51 185	91.08 149	94.94 137	79.28 213	91.77 259	94.30 210	76.04 290	83.51 238	92.37 188	77.86 115	97.73 153	78.69 221	89.13 197	96.22 117

v10		87.25 194	86.38 186	89.85 210	91.19 267	79.50 192	94.48 128	95.45 153	83.79 168	83.62 235	91.19 243	75.13 152	97.42 180	81.94 168	80.60 292	92.63 277

v144192		87.19 198	86.35 187	89.74 215	90.64 291	78.24 246	93.92 181	95.43 156	81.93 220	85.51 178	91.05 250	74.21 166	97.45 169	82.86 150	81.56 280	93.53 249

v1192		87.25 194	86.33 188	90.00 207	90.76 286	79.04 221	93.80 187	95.48 148	82.57 208	85.48 180	91.18 244	73.38 181	97.42 180	82.30 161	82.06 266	93.53 249

v148		87.04 201	86.32 189	89.21 239	90.94 278	77.26 272	93.71 196	94.43 205	84.84 144	84.36 219	90.80 254	76.04 134	97.05 218	82.12 164	79.60 306	93.31 256

LS3D		87.89 159	86.32 189	92.59 90	96.07 91	82.92 110	95.23 76	94.92 190	75.66 292	82.89 245	95.98 70	72.48 192	99.21 33	68.43 300	95.23 97	95.64 142

PEN-MVS		86.80 204	86.27 191	88.40 264	92.32 220	75.71 285	95.18 79	96.38 82	87.97 72	82.82 246	93.15 162	73.39 180	95.92 274	76.15 245	79.03 309	93.59 247

thres200		87.21 197	86.24 192	90.12 195	95.36 115	78.53 232	93.26 216	92.10 258	86.42 111	88.00 120	91.11 248	69.24 233	98.00 138	69.58 289	91.04 160	93.83 225

Anonymous202405211		87.68 168	86.13 193	92.31 103	96.66 64	80.74 164	94.87 101	91.49 281	80.47 247	89.46 98	95.44 84	54.72 330	98.23 110	82.19 163	89.89 177	97.97 54

X-MVStestdata		88.31 145	86.13 193	94.85 18	98.54 7	86.60 28	96.93 12	97.19 24	90.66 22	92.85 40	23.41 365	85.02 44	99.49 17	91.99 39	98.56 35	98.47 14

FMVSNet3		87.40 189	86.11 195	91.30 141	93.79 185	83.64 89	94.20 158	94.81 196	83.89 164	84.37 216	91.87 212	68.45 252	96.56 247	78.23 225	85.36 234	93.70 235

MVS		87.44 187	86.10 196	91.44 137	92.61 216	83.62 90	92.63 238	95.66 132	67.26 342	81.47 262	92.15 197	77.95 112	98.22 112	79.71 208	95.48 89	92.47 281

PCF-MVS	84.11 10	87.74 167	86.08 197	92.70 87	94.02 171	84.43 73	89.27 295	95.87 117	73.62 311	84.43 215	94.33 118	78.48 109	98.86 71	70.27 279	94.45 109	94.81 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1921920		86.97 202	86.06 198	89.69 220	90.53 296	78.11 249	93.80 187	95.43 156	81.90 222	85.33 194	91.05 250	72.66 188	97.41 185	82.05 166	81.80 274	93.53 249

thisisatest0515		87.33 190	85.99 199	91.37 139	93.49 191	79.55 190	90.63 276	89.56 326	80.17 249	87.56 133	90.86 252	67.07 266	98.28 109	81.50 175	93.02 136	96.29 114

GBi-Net		87.26 192	85.98 200	91.08 149	94.01 172	83.10 101	95.14 82	94.94 186	83.57 173	84.37 216	91.64 216	66.59 272	96.34 260	78.23 225	85.36 234	93.79 226

test1		87.26 192	85.98 200	91.08 149	94.01 172	83.10 101	95.14 82	94.94 186	83.57 173	84.37 216	91.64 216	66.59 272	96.34 260	78.23 225	85.36 234	93.79 226

EPNet_dtu		86.49 214	85.94 202	88.14 272	90.24 301	72.82 304	94.11 162	92.20 256	86.66 108	79.42 287	92.36 189	73.52 176	95.81 280	71.26 274	93.66 120	95.80 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1240		86.78 205	85.85 203	89.56 222	90.45 297	77.79 258	93.61 201	95.37 161	81.65 233	85.43 185	91.15 246	71.50 201	97.43 178	81.47 176	82.05 268	93.47 253

FMVSNet2		87.19 198	85.82 204	91.30 141	94.01 172	83.67 88	94.79 106	94.94 186	83.57 173	83.88 227	92.05 206	66.59 272	96.51 250	77.56 232	85.01 238	93.73 233

v7n		86.81 203	85.76 205	89.95 208	90.72 288	79.25 215	95.07 86	95.92 111	84.45 154	82.29 250	90.86 252	72.60 190	97.53 161	79.42 215	80.52 296	93.08 266

TR-MVS		86.78 205	85.76 205	89.82 211	94.37 161	78.41 240	92.47 244	92.83 243	81.11 243	86.36 153	92.40 187	68.73 246	97.48 165	73.75 265	89.85 179	93.57 248

pm-mvs1		86.61 209	85.54 207	89.82 211	91.44 240	80.18 173	95.28 74	94.85 193	83.84 165	81.66 261	92.62 183	72.45 194	96.48 252	79.67 209	78.06 311	92.82 273

V4		86.50 212	85.54 207	89.39 230	89.13 314	78.99 222	94.73 110	95.54 141	83.59 171	82.10 254	90.61 258	71.60 198	97.45 169	82.52 155	80.01 301	91.74 295

PatchMatch-RL		86.77 207	85.54 207	90.47 176	95.88 97	82.71 118	90.54 277	92.31 253	79.82 254	84.32 220	91.57 223	68.77 245	96.39 257	73.16 267	93.48 126	92.32 287

v52		86.50 212	85.53 210	89.39 230	89.17 313	78.99 222	94.72 113	95.54 141	83.59 171	82.10 254	90.60 259	71.59 199	97.45 169	82.52 155	79.99 302	91.73 296

DTE-MVSNet		86.11 218	85.48 211	87.98 274	91.65 236	74.92 288	94.93 96	95.75 126	87.36 88	82.26 251	93.04 167	72.85 185	95.82 279	74.04 261	77.46 315	93.20 259

test-LLR		85.87 224	85.41 212	87.25 289	90.95 276	71.67 315	89.55 289	89.88 319	83.41 179	84.54 211	87.95 300	67.25 263	95.11 307	81.82 170	93.37 129	94.97 158

PAPM		86.68 208	85.39 213	90.53 166	93.05 204	79.33 212	89.79 288	94.77 198	78.82 263	81.95 258	93.24 159	76.81 120	97.30 195	66.94 307	93.16 133	94.95 168

DP-MVS		87.25 194	85.36 214	92.90 79	97.65 37	83.24 98	94.81 105	92.00 263	74.99 299	81.92 259	95.00 97	72.66 188	99.05 47	66.92 309	92.33 146	96.40 111

v748		86.27 216	85.28 215	89.25 238	90.26 300	77.58 271	94.89 98	95.50 146	84.28 158	81.41 264	90.46 264	72.57 191	97.32 194	79.81 207	78.36 310	92.84 271

GA-MVS		86.61 209	85.27 216	90.66 162	91.33 255	78.71 225	90.40 278	93.81 231	85.34 133	85.12 196	89.57 277	61.25 304	97.11 213	80.99 182	89.59 183	96.15 118

Anonymous20231211		86.59 211	85.13 217	90.98 158	96.52 71	81.50 138	96.14 33	96.16 94	73.78 309	83.65 234	92.15 197	63.26 292	97.37 192	82.82 152	81.74 276	94.06 211

PatchFormer-LS_test		86.02 221	85.13 217	88.70 249	91.52 237	74.12 294	91.19 273	92.09 259	82.71 206	84.30 222	87.24 309	70.87 208	96.98 222	81.04 179	85.17 237	95.00 157

tpmrst		85.35 240	84.99 219	86.43 303	90.88 283	67.88 335	88.71 303	91.43 283	80.13 250	86.08 159	88.80 286	73.05 183	96.02 270	82.48 157	83.40 256	95.40 148

tfpn_ndepth		86.10 219	84.98 220	89.43 229	95.52 112	78.29 244	94.62 121	89.60 325	81.88 230	85.43 185	90.54 260	68.47 251	96.85 232	68.46 299	90.34 170	93.15 263

cascas		86.43 215	84.98 220	90.80 161	92.10 224	80.92 159	90.24 280	95.91 113	73.10 315	83.57 237	88.39 293	65.15 284	97.46 167	84.90 123	91.43 151	94.03 213

PMMVS		85.71 236	84.96 222	87.95 275	88.90 318	77.09 273	88.68 304	90.06 313	72.32 322	86.47 148	90.76 255	72.15 195	94.40 314	81.78 172	93.49 124	92.36 285

CostFormer		85.77 230	84.94 223	88.26 268	91.16 270	72.58 311	89.47 293	91.04 295	76.26 288	86.45 151	89.97 271	70.74 211	96.86 231	82.35 160	87.07 225	95.34 151

tfpn1000		86.06 220	84.92 224	89.49 227	95.54 109	77.79 258	94.72 113	89.07 334	82.05 215	85.36 193	91.94 209	68.32 260	96.65 242	67.04 306	90.24 171	94.02 214

LTVRE_ROB	82.13 13	86.26 217	84.90 225	90.34 185	94.44 160	81.50 138	92.31 249	94.89 191	83.03 194	79.63 285	92.67 181	69.69 225	97.79 147	71.20 275	86.26 228	91.72 297
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
MVP-Stereo		85.97 223	84.86 226	89.32 236	90.92 280	82.19 128	92.11 255	94.19 212	78.76 265	78.77 290	91.63 219	68.38 259	96.56 247	75.01 255	93.95 115	89.20 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		86.00 222	84.84 227	89.45 228	91.20 265	78.00 250	91.70 263	95.55 139	85.05 141	82.97 244	92.25 196	54.49 331	97.48 165	82.93 149	87.45 219	92.89 269

CVMVSNet		84.69 261	84.79 228	84.37 319	91.84 228	64.92 342	93.70 197	91.47 282	66.19 344	86.16 158	95.28 88	67.18 265	93.33 325	80.89 184	90.42 168	94.88 170

Patchmatch-test1		85.81 229	84.71 229	89.12 241	92.15 221	76.60 277	91.12 274	91.69 272	83.53 176	85.50 179	88.56 291	66.79 270	95.00 310	72.69 269	90.35 169	95.76 138

PatchmatchNet		85.85 225	84.70 230	89.29 237	91.76 231	75.54 286	88.49 306	91.30 285	81.63 235	85.05 197	88.70 288	71.71 196	96.24 263	74.61 258	89.05 198	96.08 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet	78.82 18	85.55 237	84.65 231	88.23 270	94.72 146	71.93 313	87.12 318	92.75 246	78.80 264	84.95 199	90.53 262	64.43 288	96.71 241	74.74 256	93.86 117	96.06 126

OurMVSNet-221017-0		85.35 240	84.64 232	87.49 284	90.77 285	72.59 310	94.01 177	94.40 206	84.72 147	79.62 286	93.17 161	61.91 299	96.72 239	81.99 167	81.16 281	93.16 261

conf0.01		85.83 227	84.54 233	89.71 217	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	94.71 176

conf0.002		85.83 227	84.54 233	89.71 217	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	94.71 176

thresconf0.02		85.75 231	84.54 233	89.38 232	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	93.70 235

tfpn_n400		85.75 231	84.54 233	89.38 232	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	93.70 235

tfpnconf		85.75 231	84.54 233	89.38 232	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	93.70 235

tfpnview11		85.75 231	84.54 233	89.38 232	95.26 122	77.63 264	94.21 152	89.33 327	81.89 223	84.94 200	91.51 227	68.43 253	96.80 233	66.05 312	89.23 189	93.70 235

RPSCF		85.07 245	84.27 239	87.48 285	92.91 211	70.62 325	91.69 264	92.46 251	76.20 289	82.67 248	95.22 91	63.94 290	97.29 198	77.51 233	85.80 231	94.53 189

MS-PatchMatch		85.05 246	84.16 240	87.73 278	91.42 244	78.51 237	91.25 272	93.53 233	77.50 276	80.15 279	91.58 221	61.99 298	95.51 289	75.69 247	94.35 112	89.16 332

FMVSNet1		85.85 225	84.11 241	91.08 149	92.81 212	83.10 101	95.14 82	94.94 186	81.64 234	82.68 247	91.64 216	59.01 317	96.34 260	75.37 250	83.78 247	93.79 226

tpm		84.73 258	84.02 242	86.87 300	90.33 298	68.90 332	89.06 299	89.94 316	80.85 245	85.75 165	89.86 273	68.54 248	95.97 272	77.76 229	84.05 246	95.75 139

CHOSEN 280x420		85.15 244	83.99 243	88.65 250	92.47 217	78.40 241	79.68 350	92.76 245	74.90 301	81.41 264	89.59 276	69.85 224	95.51 289	79.92 203	95.29 95	92.03 291

IterMVS		84.88 252	83.98 244	87.60 280	91.44 240	76.03 283	90.18 282	92.41 252	83.24 185	81.06 269	90.42 265	66.60 271	94.28 316	79.46 211	80.98 289	92.48 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		85.43 238	83.86 245	90.16 189	90.02 306	82.97 109	90.27 279	92.67 248	75.93 291	80.73 271	91.74 215	71.05 205	95.73 283	78.85 219	83.46 254	91.78 294

v18		84.97 248	83.76 246	88.60 254	91.36 250	79.41 201	93.82 186	94.04 217	83.00 197	76.61 304	86.60 312	76.19 126	95.43 294	80.39 192	71.79 329	90.96 311

CR-MVSNet		85.35 240	83.76 246	90.12 195	90.58 292	79.34 209	85.24 330	91.96 267	78.27 271	85.55 174	87.87 303	71.03 206	95.61 284	73.96 263	89.36 186	95.40 148

v16		84.96 249	83.74 248	88.62 252	91.40 245	79.48 195	93.83 184	94.04 217	83.03 194	76.54 305	86.59 313	76.11 131	95.42 295	80.33 195	71.80 328	90.95 313

Test4		85.75 231	83.72 249	91.83 124	88.08 328	81.03 155	92.48 243	95.54 141	83.38 181	73.40 328	88.57 290	50.99 338	97.37 192	86.61 110	94.47 108	97.09 92

v17		84.93 251	83.70 250	88.62 252	91.36 250	79.48 195	93.83 184	94.03 219	83.04 193	76.51 306	86.57 314	76.05 132	95.42 295	80.31 197	71.65 330	90.96 311

DWT-MVSNet_test		84.95 250	83.68 251	88.77 246	91.43 243	73.75 297	91.74 261	90.98 296	80.66 246	83.84 228	87.36 307	62.44 295	97.11 213	78.84 220	85.81 230	95.46 146

ACMH	80.38 17	85.36 239	83.68 251	90.39 180	94.45 159	80.63 166	94.73 110	94.85 193	82.09 214	77.24 300	92.65 182	60.01 313	97.58 157	72.25 271	84.87 239	92.96 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test-mter		84.54 263	83.64 253	87.25 289	90.95 276	71.67 315	89.55 289	89.88 319	79.17 258	84.54 211	87.95 300	55.56 326	95.11 307	81.82 170	93.37 129	94.97 158

MDTV_nov1_ep13			83.56 254		91.69 235	69.93 329	87.75 314	91.54 279	78.60 267	84.86 206	88.90 284	69.54 227	96.03 269	70.25 280	88.93 199

v15		84.79 254	83.53 255	88.57 258	91.30 261	79.41 201	93.70 197	94.01 220	83.06 190	76.27 307	86.42 318	76.03 135	95.38 297	80.01 199	71.00 333	90.92 314

V14		84.79 254	83.52 256	88.57 258	91.32 257	79.43 200	93.72 195	94.01 220	83.06 190	76.22 308	86.43 315	76.01 136	95.37 298	79.96 201	70.99 334	90.91 315

V9		84.77 256	83.50 257	88.58 255	91.33 255	79.46 197	93.75 191	94.00 223	83.07 189	76.07 313	86.43 315	75.97 137	95.37 298	79.91 204	70.93 336	90.91 315

v12		84.74 257	83.46 258	88.58 255	91.32 257	79.50 192	93.75 191	94.01 220	83.06 190	75.98 315	86.41 319	75.82 143	95.36 301	79.87 205	70.89 337	90.89 317

ACMH+	81.04 14	85.05 246	83.46 258	89.82 211	94.66 150	79.37 207	94.44 133	94.12 216	82.19 213	78.04 293	92.82 177	58.23 319	97.54 160	73.77 264	82.90 258	92.54 278

v13		84.72 259	83.44 260	88.58 255	91.31 260	79.52 191	93.77 189	94.00 223	83.03 194	75.85 316	86.38 320	75.84 142	95.35 302	79.83 206	70.95 335	90.87 318

v11		84.67 262	83.41 261	88.44 263	91.32 257	79.13 220	93.69 200	93.99 225	82.81 203	76.20 309	86.24 322	75.48 148	95.35 302	79.53 210	71.48 332	90.85 319

IB-MVS	80.51 15	85.24 243	83.26 262	91.19 143	92.13 223	79.86 185	91.75 260	91.29 286	83.28 184	80.66 273	88.49 292	61.28 303	98.46 95	80.99 182	79.46 307	95.25 152
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
tfpnnormal		84.72 259	83.23 263	89.20 240	92.79 213	80.05 179	94.48 128	95.81 121	82.38 210	81.08 268	91.21 242	69.01 236	96.95 225	61.69 332	80.59 293	90.58 324

MSDG		84.86 253	83.09 264	90.14 194	93.80 183	80.05 179	89.18 298	93.09 239	78.89 261	78.19 291	91.91 210	65.86 282	97.27 199	68.47 298	88.45 206	93.11 264

TransMVSNet (Re)		84.43 264	83.06 265	88.54 260	91.72 232	78.44 239	95.18 79	92.82 244	82.73 205	79.67 284	92.12 199	73.49 177	95.96 273	71.10 278	68.73 344	91.21 306

tpm2		84.08 266	82.94 266	87.48 285	91.39 246	71.27 317	89.23 297	90.37 306	71.95 325	84.64 208	89.33 279	67.30 262	96.55 249	75.17 252	87.09 224	94.63 181

SixPastTwentyTwo		83.91 268	82.90 267	86.92 297	90.99 274	70.67 324	93.48 205	91.99 264	85.54 129	77.62 298	92.11 201	60.59 309	96.87 230	76.05 246	77.75 312	93.20 259

TESTMET0.1,1		83.74 271	82.85 268	86.42 304	89.96 307	71.21 319	89.55 289	87.88 341	77.41 277	83.37 241	87.31 308	56.71 323	93.65 322	80.62 188	92.85 140	94.40 198

pmmvs5		84.21 265	82.84 269	88.34 266	88.95 317	76.94 275	92.41 245	91.91 269	75.63 293	80.28 277	91.18 244	64.59 287	95.57 286	77.09 238	83.47 253	92.53 279

EPMVS		83.90 269	82.70 270	87.51 282	90.23 302	72.67 307	88.62 305	81.96 357	81.37 241	85.01 198	88.34 294	66.31 275	94.45 313	75.30 251	87.12 223	95.43 147

tpmp4_e23		83.87 270	82.33 271	88.48 261	91.46 239	72.82 304	89.82 287	91.57 278	73.02 317	81.86 260	89.05 281	66.20 277	96.97 223	71.57 273	86.39 227	95.66 141

tpmvs		83.35 275	82.07 272	87.20 293	91.07 272	71.00 322	88.31 309	91.70 271	78.91 260	80.49 276	87.18 310	69.30 232	97.08 215	68.12 304	83.56 252	93.51 252

COLMAP_ROB	80.39 16	83.96 267	82.04 273	89.74 215	95.28 120	79.75 188	94.25 149	92.28 254	75.17 297	78.02 294	93.77 146	58.60 318	97.84 146	65.06 323	85.92 229	91.63 299
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test0.0.03 1		82.41 281	81.69 274	84.59 317	88.23 325	72.89 303	90.24 280	87.83 342	83.41 179	79.86 283	89.78 274	67.25 263	88.99 343	65.18 321	83.42 255	91.90 293

pmmvs6		83.42 272	81.60 275	88.87 245	88.01 329	77.87 255	94.96 93	94.24 211	74.67 303	78.80 289	91.09 249	60.17 312	96.49 251	77.06 239	75.40 320	92.23 289

AllTest		83.42 272	81.39 276	89.52 224	95.01 132	77.79 258	93.12 220	90.89 299	77.41 277	76.12 311	93.34 151	54.08 333	97.51 163	68.31 301	84.27 244	93.26 257

PatchT		82.68 279	81.27 277	86.89 299	90.09 304	70.94 323	84.06 337	90.15 310	74.91 300	85.63 173	83.57 332	69.37 228	94.87 312	65.19 320	88.50 205	94.84 171

USDC		82.76 277	81.26 278	87.26 288	91.17 268	74.55 289	89.27 295	93.39 236	78.26 272	75.30 318	92.08 203	54.43 332	96.63 243	71.64 272	85.79 232	90.61 321

testing_2		83.40 274	81.02 279	90.56 165	85.06 339	80.51 170	91.37 269	95.57 137	82.92 200	67.06 343	85.54 326	49.47 341	97.24 203	86.74 105	85.44 233	93.93 216

EU-MVSNet		81.32 294	80.95 280	82.42 326	88.50 321	63.67 343	93.32 209	91.33 284	64.02 348	80.57 275	92.83 176	61.21 306	92.27 333	76.34 242	80.38 298	91.32 304

Patchmtry		82.71 278	80.93 281	88.06 273	90.05 305	76.37 280	84.74 332	91.96 267	72.28 323	81.32 266	87.87 303	71.03 206	95.50 291	68.97 295	80.15 299	92.32 287

RPMNet		83.18 276	80.87 282	90.12 195	90.58 292	79.34 209	85.24 330	90.78 302	71.44 327	85.55 174	82.97 335	70.87 208	95.61 284	61.01 334	89.36 186	95.40 148

MIMVSNet		82.59 280	80.53 283	88.76 247	91.51 238	78.32 242	86.57 321	90.13 311	79.32 257	80.70 272	88.69 289	52.98 335	93.07 330	66.03 318	88.86 200	94.90 169

our_test_3		81.93 284	80.46 284	86.33 305	88.46 322	73.48 299	88.46 307	91.11 287	76.46 283	76.69 302	88.25 296	66.89 268	94.36 315	68.75 296	79.08 308	91.14 308

EG-PatchMatch MVS		82.37 282	80.34 285	88.46 262	90.27 299	79.35 208	92.80 235	94.33 209	77.14 281	73.26 329	90.18 268	47.47 345	96.72 239	70.25 280	87.32 222	89.30 329

tpm cat1		81.96 283	80.27 286	87.01 295	91.09 271	71.02 321	87.38 317	91.53 280	66.25 343	80.17 278	86.35 321	68.22 261	96.15 266	69.16 294	82.29 263	93.86 223

dp		81.47 292	80.23 287	85.17 314	89.92 308	65.49 341	86.74 319	90.10 312	76.30 287	81.10 267	87.12 311	62.81 293	95.92 274	68.13 303	79.88 304	94.09 209

testgi		80.94 299	80.20 288	83.18 323	87.96 330	66.29 338	91.28 270	90.70 304	83.70 169	78.12 292	92.84 175	51.37 337	90.82 340	63.34 327	82.46 262	92.43 282

K. test v3		81.59 289	80.15 289	85.91 308	89.89 309	69.42 331	92.57 241	87.71 343	85.56 128	73.44 327	89.71 275	55.58 325	95.52 288	77.17 236	69.76 340	92.78 274

ppachtmachnet_test		81.84 285	80.07 290	87.15 294	88.46 322	74.43 290	89.04 300	92.16 257	75.33 295	77.75 296	88.99 282	66.20 277	95.37 298	65.12 322	77.60 313	91.65 298

Patchmatch-RL test		81.67 287	79.96 291	86.81 301	85.42 337	71.23 318	82.17 345	87.50 346	78.47 268	77.19 301	82.50 336	70.81 210	93.48 323	82.66 154	72.89 325	95.71 140

ADS-MVSNet		81.56 290	79.78 292	86.90 298	91.35 253	71.82 314	83.33 341	89.16 333	72.90 318	82.24 252	85.77 324	64.98 285	93.76 320	64.57 324	83.74 248	95.12 153

Anonymous20231206		81.03 297	79.77 293	84.82 316	87.85 332	70.26 327	91.42 268	92.08 260	73.67 310	77.75 296	89.25 280	62.43 296	93.08 329	61.50 333	82.00 269	91.12 309

ADS-MVSNet2		81.66 288	79.71 294	87.50 283	91.35 253	74.19 292	83.33 341	88.48 338	72.90 318	82.24 252	85.77 324	64.98 285	93.20 327	64.57 324	83.74 248	95.12 153

FMVSNet5		81.52 291	79.60 295	87.27 287	91.17 268	77.95 251	91.49 267	92.26 255	76.87 282	76.16 310	87.91 302	51.67 336	92.34 332	67.74 305	81.16 281	91.52 300

gg-mvs-nofinetune		81.77 286	79.37 296	88.99 244	90.85 284	77.73 262	86.29 322	79.63 361	74.88 302	83.19 243	69.05 353	60.34 310	96.11 267	75.46 249	94.64 103	93.11 264

Patchmatch-test		81.37 293	79.30 297	87.58 281	90.92 280	74.16 293	80.99 347	87.68 344	70.52 333	76.63 303	88.81 285	71.21 203	92.76 331	60.01 338	86.93 226	95.83 135

CMPMVS	59.16 21	80.52 300	79.20 298	84.48 318	83.98 342	67.63 337	89.95 286	93.84 230	64.79 347	66.81 344	91.14 247	57.93 321	95.17 305	76.25 243	88.10 211	90.65 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_0402		81.30 295	79.17 299	87.67 279	93.19 200	78.17 247	92.98 229	91.71 270	75.25 296	76.02 314	90.31 266	59.23 316	96.37 258	50.22 347	83.63 251	88.47 341

test20.03		79.95 303	79.08 300	82.55 325	85.79 336	67.74 336	91.09 275	91.08 292	81.23 242	74.48 323	89.96 272	61.63 300	90.15 341	60.08 336	76.38 317	89.76 326

LF4IMVS		80.37 301	79.07 301	84.27 321	86.64 334	69.87 330	89.39 294	91.05 294	76.38 285	74.97 320	90.00 270	47.85 344	94.25 317	74.55 259	80.82 291	88.69 337

JIA-IIPM		81.04 296	78.98 302	87.25 289	88.64 319	73.48 299	81.75 346	89.61 324	73.19 314	82.05 256	73.71 350	66.07 281	95.87 277	71.18 277	84.60 241	92.41 283

pmmvs-eth3d		80.97 298	78.72 303	87.74 277	84.99 340	79.97 183	90.11 283	91.65 273	75.36 294	73.51 326	86.03 323	59.45 315	93.96 319	75.17 252	72.21 326	89.29 330

UnsupCasMVSNet_eth		80.07 302	78.27 304	85.46 311	85.24 338	72.63 309	88.45 308	94.87 192	82.99 198	71.64 336	88.07 299	56.34 324	91.75 337	73.48 266	63.36 350	92.01 292

TinyColmap		79.76 305	77.69 305	85.97 307	91.71 233	73.12 301	89.55 289	90.36 307	75.03 298	72.03 334	90.19 267	46.22 347	96.19 265	63.11 328	81.03 285	88.59 338

TDRefinement		79.81 304	77.34 306	87.22 292	79.24 353	75.48 287	93.12 220	92.03 262	76.45 284	75.01 319	91.58 221	49.19 342	96.44 255	70.22 282	69.18 341	89.75 327

MIMVSNet1		79.38 307	77.28 307	85.69 309	86.35 335	73.67 298	91.61 266	92.75 246	78.11 275	72.64 332	88.12 298	48.16 343	91.97 336	60.32 335	77.49 314	91.43 303

YYNet1		79.22 308	77.20 308	85.28 313	88.20 327	72.66 308	85.87 325	90.05 315	74.33 306	62.70 349	87.61 305	66.09 280	92.03 334	66.94 307	72.97 324	91.15 307

MDA-MVSNet_test_wron		79.21 309	77.19 309	85.29 312	88.22 326	72.77 306	85.87 325	90.06 313	74.34 305	62.62 350	87.56 306	66.14 279	91.99 335	66.90 310	73.01 323	91.10 310

OpenMVS_ROB	74.94 19	79.51 306	77.03 310	86.93 296	87.00 333	76.23 282	92.33 248	90.74 303	68.93 337	74.52 322	88.23 297	49.58 340	96.62 244	57.64 340	84.29 243	87.94 343

MDA-MVSNet-bldmvs		78.85 310	76.31 311	86.46 302	89.76 310	73.88 296	88.79 302	90.42 305	79.16 259	59.18 351	88.33 295	60.20 311	94.04 318	62.00 331	68.96 342	91.48 302

DSMNet-mixed		76.94 313	76.29 312	78.89 329	83.10 345	56.11 355	87.78 313	79.77 360	60.65 351	75.64 317	88.71 287	61.56 301	88.34 345	60.07 337	89.29 188	92.21 290

PM-MVS		78.11 311	76.12 313	84.09 322	83.54 344	70.08 328	88.97 301	85.27 351	79.93 252	74.73 321	86.43 315	34.70 356	93.48 323	79.43 214	72.06 327	88.72 336

new-patchmatchnet		76.41 314	75.17 314	80.13 328	82.65 347	59.61 348	87.66 315	91.08 292	78.23 273	69.85 338	83.22 333	54.76 329	91.63 339	64.14 326	64.89 347	89.16 332

PVSNet_0	73.20 20	77.22 312	74.83 315	84.37 319	90.70 289	71.10 320	83.09 343	89.67 323	72.81 320	73.93 325	83.13 334	60.79 308	93.70 321	68.54 297	50.84 355	88.30 342

testus		74.41 318	73.35 316	77.59 334	82.49 348	57.08 351	86.02 323	90.21 309	72.28 323	72.89 331	84.32 329	37.08 354	86.96 349	52.24 344	82.65 260	88.73 335

test2356		74.50 317	73.27 317	78.20 330	80.81 349	59.84 346	83.76 340	88.33 340	71.43 328	72.37 333	81.84 339	45.60 348	86.26 351	50.97 345	84.32 242	88.50 339

UnsupCasMVSNet_bld		76.23 315	73.27 317	85.09 315	83.79 343	72.92 302	85.65 329	93.47 235	71.52 326	68.84 340	79.08 346	49.77 339	93.21 326	66.81 311	60.52 352	89.13 334

MVS-HIRNet		73.70 319	72.20 319	78.18 332	91.81 230	56.42 354	82.94 344	82.58 355	55.24 353	68.88 339	66.48 354	55.32 328	95.13 306	58.12 339	88.42 208	83.01 348

LP		75.51 316	72.15 320	85.61 310	87.86 331	73.93 295	80.20 349	88.43 339	67.39 339	70.05 337	80.56 343	58.18 320	93.18 328	46.28 353	70.36 339	89.71 328

testpf		71.41 323	72.11 321	69.30 343	84.53 341	59.79 347	62.74 360	83.14 354	71.11 330	68.83 341	81.57 341	46.70 346	84.83 356	74.51 260	75.86 319	63.30 356

test1235678		72.22 320	70.31 322	77.93 333	78.04 354	58.04 350	85.76 327	89.80 321	70.15 335	63.43 348	80.20 344	42.24 351	87.24 348	48.68 349	74.50 321	88.50 339

new_pmnet		72.15 321	70.13 323	78.20 330	82.95 346	65.68 339	83.91 338	82.40 356	62.94 350	64.47 347	79.82 345	42.85 350	86.26 351	57.41 341	74.44 322	82.65 349

1111		70.54 324	69.71 324	73.04 338	79.30 351	44.83 363	84.23 335	88.96 335	67.33 340	65.42 345	82.28 337	41.11 352	88.11 346	47.12 351	71.60 331	86.19 345

pmmvs3		71.81 322	68.71 325	81.11 327	75.86 355	70.42 326	86.74 319	83.66 353	58.95 352	68.64 342	80.89 342	36.93 355	89.52 342	63.10 329	63.59 349	83.39 347

N_pmnet		68.89 325	68.44 326	70.23 341	89.07 316	28.79 370	88.06 310	19.50 372	69.47 336	71.86 335	84.93 327	61.24 305	91.75 337	54.70 342	77.15 316	90.15 325

test12356		64.99 328	63.78 327	68.61 345	72.69 357	39.14 366	78.46 351	87.61 345	64.91 346	55.77 352	77.48 347	28.10 358	85.59 353	44.69 354	64.35 348	81.12 351

testmv		65.49 327	62.66 328	73.96 337	68.78 360	53.14 358	84.70 333	88.56 337	65.94 345	52.35 354	74.65 349	25.02 361	85.14 354	43.54 355	60.40 353	83.60 346

FPMVS		64.63 329	62.55 329	70.88 340	70.80 358	56.71 352	84.42 334	84.42 352	51.78 355	49.57 355	81.61 340	23.49 362	81.48 358	40.61 358	76.25 318	74.46 355

LCM-MVSNet		66.00 326	62.16 330	77.51 335	64.51 365	58.29 349	83.87 339	90.90 298	48.17 356	54.69 353	73.31 351	16.83 368	86.75 350	65.47 319	61.67 351	87.48 344

.test1245		57.63 334	61.79 331	45.14 352	79.30 351	44.83 363	84.23 335	88.96 335	67.33 340	65.42 345	82.28 337	41.11 352	88.11 346	47.12 351	0.39 366	2.46 367

no-one		61.56 330	56.58 332	76.49 336	67.80 363	62.76 345	78.13 352	86.11 347	63.16 349	43.24 358	64.70 356	26.12 360	88.95 344	50.84 346	29.15 358	77.77 353

PMMVS2		59.60 331	56.40 333	69.21 344	68.83 359	46.58 361	73.02 358	77.48 364	55.07 354	49.21 356	72.95 352	17.43 367	80.04 359	49.32 348	44.33 356	80.99 352

Gipuma		57.99 333	54.91 334	67.24 346	88.51 320	65.59 340	52.21 363	90.33 308	43.58 359	42.84 359	51.18 361	20.29 365	85.07 355	34.77 360	70.45 338	51.05 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		58.88 332	54.22 335	72.86 339	56.50 369	56.67 353	80.75 348	86.00 348	73.09 316	37.39 360	64.63 357	22.17 363	79.49 361	43.51 356	23.96 362	82.43 350

v1.0		39.85 341	53.14 336	0.00 359	98.55 5	0.00 374	0.00 365	97.45 7	88.25 67	96.40 4	97.60 6	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

PMVS	47.18 22	52.22 335	48.46 337	63.48 347	45.72 370	46.20 362	73.41 356	78.31 362	41.03 360	30.06 363	65.68 355	6.05 370	83.43 357	30.04 361	65.86 345	60.80 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PNet_i23d		50.48 337	47.18 338	60.36 348	68.59 361	44.56 365	72.75 359	72.61 365	43.92 358	33.91 362	60.19 359	6.16 369	73.52 362	38.50 359	28.04 359	63.01 357

wuykxyi23d		50.55 336	44.13 339	69.81 342	56.77 367	54.58 357	73.22 357	80.78 358	39.79 361	22.08 367	46.69 363	4.03 372	79.71 360	47.65 350	26.13 360	75.14 354

E-PMN		43.23 339	42.29 340	46.03 351	65.58 364	37.41 367	73.51 355	64.62 366	33.99 362	28.47 365	47.87 362	19.90 366	67.91 363	22.23 363	24.45 361	32.77 362

EMVS		42.07 340	41.12 341	44.92 353	63.45 366	35.56 369	73.65 354	63.48 367	33.05 363	26.88 366	45.45 364	21.27 364	67.14 364	19.80 364	23.02 363	32.06 363

tmp_tt		35.64 343	39.24 342	24.84 355	14.87 371	23.90 371	62.71 361	51.51 371	6.58 366	36.66 361	62.08 358	44.37 349	30.34 368	52.40 343	22.00 364	20.27 364

pcd1.5k->3k		37.02 342	38.84 343	31.53 354	92.33 219	0.00 374	0.00 365	96.13 97	0.00 369	0.00 371	0.00 371	72.70 187	0.00 371	0.00 368	88.43 207	94.60 184

MVE	39.65 23	43.39 338	38.59 344	57.77 349	56.52 368	48.77 360	55.38 362	58.64 369	29.33 364	28.96 364	52.65 360	4.68 371	64.62 365	28.11 362	33.07 357	59.93 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		22.14 344	29.52 345	0.00 359	0.00 374	0.00 374	0.00 365	95.76 125	0.00 369	0.00 371	94.29 121	75.66 146	0.00 371	0.00 368	0.00 369	0.00 369

wuyk23d		21.27 345	20.48 346	23.63 356	68.59 361	36.41 368	49.57 364	6.85 373	9.37 365	7.89 368	4.46 370	4.03 372	31.37 367	17.47 365	16.07 365	3.12 365

testmvs		8.92 346	11.52 347	1.12 358	1.06 372	0.46 373	86.02 323	0.65 374	0.62 367	2.74 369	9.52 368	0.31 375	0.45 370	2.38 366	0.39 366	2.46 367

test123		8.76 347	11.22 348	1.39 357	0.85 373	0.97 372	85.76 327	0.35 375	0.54 368	2.45 370	8.14 369	0.60 374	0.48 369	2.16 367	0.17 368	2.71 366

ab-mvs-re		7.82 348	10.43 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	93.88 140	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		6.64 349	8.86 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	79.70 95	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																96.12 121

test_part2					98.55 5	87.22 12				96.40 4

test_part1				0.00 359		0.00 374	0.00 365	97.45 7				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												71.70 197				96.12 121

sam_mvs												70.60 212

semantic-postprocess				88.18 271	91.71 233	76.87 276		92.65 249	85.40 132	81.44 263	90.54 260	66.21 276	95.00 310	81.04 179	81.05 284	92.66 276

ambc				83.06 324	79.99 350	63.51 344	77.47 353	92.86 242		74.34 324	84.45 328	28.74 357	95.06 309	73.06 268	68.89 343	90.61 321

MTGPA								96.97 37

test_post1							88.00 311				9.81 367	69.31 231	95.53 287	76.65 240

test_post											10.29 366	70.57 216	95.91 276

patchmatchnet-post											83.76 331	71.53 200	96.48 252

GG-mvs-BLEND				87.94 276	89.73 311	77.91 252	87.80 312	78.23 363		80.58 274	83.86 330	59.88 314	95.33 304	71.20 275	92.22 147	90.60 323

MTMP							96.16 32	60.64 368

gm-plane-assit					89.60 312	68.00 334			77.28 280		88.99 282		97.57 158	79.44 213

test9_res														91.91 43	98.71 19	98.07 47

TEST9					97.53 39	86.49 31	94.07 170	96.78 53	81.61 236	92.77 44	96.20 61	87.71 15	99.12 42

test_8					97.49 42	86.30 39	94.02 176	96.76 56	81.86 231	92.70 48	96.20 61	87.63 16	99.02 54

agg_prior2														90.54 64	98.68 24	98.27 32

agg_prior					97.38 46	85.92 46		96.72 59		92.16 60			98.97 62

TestCases				89.52 224	95.01 132	77.79 258		90.89 299	77.41 277	76.12 311	93.34 151	54.08 333	97.51 163	68.31 301	84.27 244	93.26 257

test_prior4						85.96 45	94.11 162

test_prior2							94.12 160		87.67 83	92.63 49	96.39 54	86.62 25		91.50 50	98.67 26

test_prior				93.82 53	97.29 51	84.49 66		96.88 46					98.87 68			98.11 45

旧先验2							93.36 208		71.25 329	94.37 15			97.13 212	86.74 105

新几何2							93.11 222

新几何1				93.10 69	97.30 50	84.35 75		95.56 138	71.09 331	91.26 78	96.24 58	82.87 60	98.86 71	79.19 217	98.10 48	96.07 125

旧先验1					96.79 62	81.81 134		95.67 130			96.81 35	86.69 24			97.66 57	96.97 99

无先验							93.28 215	96.26 86	73.95 308				99.05 47	80.56 189		96.59 108

原ACMM2							92.94 231

原ACMM1				92.01 112	97.34 48	81.05 154		96.81 51	78.89 261	90.45 87	95.92 72	82.65 61	98.84 76	80.68 187	98.26 44	96.14 119

test222					96.55 69	81.70 135	92.22 251	95.01 183	68.36 338	90.20 90	96.14 66	80.26 89			97.80 55	96.05 127

testdata2													98.75 80	78.30 224

segment_acmp												87.16 21

testdata				90.49 174	96.40 73	77.89 254		95.37 161	72.51 321	93.63 27	96.69 40	82.08 70	97.65 154	83.08 146	97.39 61	95.94 129

testdata1							92.15 253		87.94 73

test12				94.34 42	97.13 56	86.15 42		96.29 85		91.04 81		85.08 42	99.01 56		98.13 47	97.86 62

plane_prior7					94.70 148	82.74 115

plane_prior6					94.52 155	82.75 113						74.23 164

plane_prior5								96.22 90					98.12 119	88.15 84	89.99 174	94.63 181

plane_prior4											94.86 101

plane_prior3						82.75 113			90.26 25	86.91 142

plane_prior2							95.85 45		90.81 18

plane_prior1					94.59 153

plane_prior						82.73 116	95.21 78		89.66 35						89.88 178

n2								0.00 376

nn								0.00 376

door-mid								85.49 349

lessismore_v0				86.04 306	88.46 322	68.78 333		80.59 359		73.01 330	90.11 269	55.39 327	96.43 256	75.06 254	65.06 346	92.90 268

LGP-MVS_train				91.12 145	94.47 157	81.49 140		96.14 95	86.73 106	85.45 182	95.16 93	69.89 222	98.10 125	87.70 91	89.23 189	93.77 230

test11								96.57 73

door								85.33 350

HQP5-MVS						81.56 136

HQP-NCC					94.17 166		94.39 138		88.81 51	85.43 185

ACMP_Plane					94.17 166		94.39 138		88.81 51	85.43 185

BP-MVS														87.11 102

HQP4-MVS										85.43 185			97.96 140			94.51 191

HQP3-MVS								96.04 104							89.77 180

HQP2-MVS												73.83 173

NP-MVS					94.37 161	82.42 123					93.98 132

MDTV_nov1_ep13_2view						55.91 356	87.62 316		73.32 313	84.59 210		70.33 219		74.65 257		95.50 144

ACMMP++_ref															87.47 217

ACMMP++															88.01 214

Test By Simon												80.02 90

ITE_SJBPF				88.24 269	91.88 227	77.05 274		92.92 241	85.54 129	80.13 281	93.30 156	57.29 322	96.20 264	72.46 270	84.71 240	91.49 301

DeepMVS_CX				56.31 350	74.23 356	51.81 359		56.67 370	44.85 357	48.54 357	75.16 348	27.87 359	58.74 366	40.92 357	52.22 354	58.39 360

High-resolution multi-view benchmark