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		97.86 1	97.65 2	98.47 1	99.17 27	95.78 3	97.21 130	98.35 19	95.16 13	98.71 4	98.80 4	95.05 2	99.89 3	96.70 14	99.73 1	99.73 2

APDe-MVS		97.82 2	97.73 1	98.08 9	99.15 28	94.82 15	98.81 2	98.30 23	94.76 26	98.30 7	98.90 3	93.77 8	99.68 38	97.93 1	99.69 2	99.75 1

CNVR-MVS		97.68 3	97.44 6	98.37 3	98.90 37	95.86 2	97.27 122	98.08 54	95.81 3	97.87 14	98.31 36	94.26 4	99.68 38	97.02 4	99.49 23	99.57 13

SteuartSystems-ACMMP		97.62 4	97.53 3	97.87 15	98.39 64	94.25 25	98.43 16	98.27 26	95.34 9	98.11 8	98.56 10	94.53 3	99.71 30	96.57 18	99.62 7	99.65 4
Skip Steuart: Steuart Systems R&D Blog.
HSP-MVS		97.53 5	97.49 5	97.63 37	99.40 6	93.77 44	98.53 9	97.85 94	95.55 5	98.56 6	97.81 65	93.90 6	99.65 42	96.62 15	99.21 52	99.48 29

TSAR-MVS + MP.		97.42 6	97.33 7	97.69 31	99.25 22	94.24 26	98.07 36	97.85 94	93.72 49	98.57 5	98.35 27	93.69 9	99.40 92	97.06 3	99.46 25	99.44 34

SD-MVS		97.41 7	97.53 3	97.06 60	98.57 56	94.46 19	97.92 49	98.14 43	94.82 23	99.01 1	98.55 12	94.18 5	97.41 292	96.94 5	99.64 5	99.32 45

SMA-MVS		97.35 8	97.03 10	98.30 4	99.06 33	95.42 6	97.94 47	98.18 37	90.57 154	98.85 3	98.94 1	93.33 11	99.83 15	96.72 13	99.68 3	99.63 6

HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 31	96.16 1	97.55 97	97.97 83	95.59 4	96.61 41	97.89 56	92.57 20	99.84 14	95.95 36	99.51 19	99.40 37

NCCC		97.30 10	97.03 10	98.11 8	98.77 40	95.06 13	97.34 116	98.04 69	95.96 2	97.09 31	97.88 58	93.18 12	99.71 30	95.84 39	99.17 55	99.56 15

ACMMP_NAP		97.20 11	96.86 18	98.23 5	99.09 29	95.16 11	97.60 90	98.19 35	92.82 80	97.93 13	98.74 6	91.60 40	99.86 7	96.26 23	99.52 17	99.67 3

XVS		97.18 12	96.96 14	97.81 20	99.38 9	94.03 35	98.59 7	98.20 33	94.85 19	96.59 43	98.29 39	91.70 38	99.80 21	95.66 41	99.40 32	99.62 7

MCST-MVS		97.18 12	96.84 20	98.20 6	99.30 18	95.35 8	97.12 139	98.07 59	93.54 55	96.08 59	97.69 72	93.86 7	99.71 30	96.50 19	99.39 34	99.55 17

Regformer-2		97.16 14	96.99 12	97.67 32	98.32 70	93.84 39	96.83 164	98.10 51	95.24 10	97.49 16	98.25 42	92.57 20	99.61 48	96.80 9	99.29 43	99.56 15

HFP-MVS		97.14 15	96.92 16	97.83 17	99.42 4	94.12 31	98.52 10	98.32 20	93.21 62	97.18 24	98.29 39	92.08 29	99.83 15	95.63 44	99.59 9	99.54 19

Regformer-1		97.10 16	96.96 14	97.54 40	98.32 70	93.48 50	96.83 164	97.99 81	95.20 12	97.46 17	98.25 42	92.48 23	99.58 57	96.79 11	99.29 43	99.55 17

MTAPA		97.08 17	96.78 25	97.97 13	99.37 11	94.42 21	97.24 124	98.08 54	95.07 15	96.11 57	98.59 8	90.88 53	99.90 1	96.18 31	99.50 21	99.58 11

zzz-MVS		97.07 18	96.77 26	97.97 13	99.37 11	94.42 21	97.15 137	98.08 54	95.07 15	96.11 57	98.59 8	90.88 53	99.90 1	96.18 31	99.50 21	99.58 11

region2R		97.07 18	96.84 20	97.77 25	99.46 1	93.79 41	98.52 10	98.24 30	93.19 65	97.14 27	98.34 30	91.59 41	99.87 6	95.46 49	99.59 9	99.64 5

ACMMPR		97.07 18	96.84 20	97.79 22	99.44 3	93.88 37	98.52 10	98.31 22	93.21 62	97.15 26	98.33 33	91.35 44	99.86 7	95.63 44	99.59 9	99.62 7

#test#		97.02 21	96.75 27	97.83 17	99.42 4	94.12 31	98.15 31	98.32 20	92.57 87	97.18 24	98.29 39	92.08 29	99.83 15	95.12 54	99.59 9	99.54 19

CP-MVS		97.02 21	96.81 23	97.64 35	99.33 16	93.54 48	98.80 3	98.28 25	92.99 71	96.45 50	98.30 38	91.90 34	99.85 11	95.61 46	99.68 3	99.54 19

SR-MVS		97.01 23	96.86 18	97.47 42	99.09 29	93.27 57	97.98 44	98.07 59	93.75 48	97.45 18	98.48 16	91.43 43	99.59 53	96.22 25	99.27 46	99.54 19

Regformer-4		96.97 24	96.87 17	97.25 52	98.34 67	92.66 71	96.96 149	98.01 74	95.12 14	97.14 27	98.42 21	91.82 35	99.61 48	96.90 6	99.13 58	99.50 25

APD-MVS		96.95 25	96.60 30	98.01 10	99.03 34	94.93 14	97.72 68	98.10 51	91.50 118	98.01 11	98.32 35	92.33 24	99.58 57	94.85 63	99.51 19	99.53 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 26	97.06 9	96.59 72	98.72 42	91.86 93	97.67 74	98.49 12	94.66 29	97.24 22	98.41 24	92.31 27	98.94 137	96.61 16	99.46 25	98.96 75

DeepC-MVS_fast	93.89 2	96.93 27	96.64 29	97.78 23	98.64 51	94.30 23	97.41 107	98.04 69	94.81 24	96.59 43	98.37 26	91.24 46	99.64 47	95.16 52	99.52 17	99.42 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS		96.86 28	96.60 30	97.64 35	99.40 6	93.44 51	98.50 13	98.09 53	93.27 61	95.95 66	98.33 33	91.04 49	99.88 4	95.20 51	99.57 13	99.60 10

GST-MVS		96.85 29	96.52 34	97.82 19	99.36 13	94.14 30	98.29 23	98.13 44	92.72 83	96.70 35	98.06 48	91.35 44	99.86 7	94.83 64	99.28 45	99.47 31

Regformer-3		96.85 29	96.80 24	97.01 61	98.34 67	92.02 89	96.96 149	97.76 97	95.01 17	97.08 32	98.42 21	91.71 37	99.54 70	96.80 9	99.13 58	99.48 29

APD-MVS_3200maxsize		96.81 31	96.71 28	97.12 59	99.01 35	92.31 78	97.98 44	98.06 62	93.11 68	97.44 19	98.55 12	90.93 51	99.55 68	96.06 33	99.25 48	99.51 24

PGM-MVS		96.81 31	96.53 33	97.65 33	99.35 15	93.53 49	97.65 77	98.98 1	92.22 93	97.14 27	98.44 19	91.17 47	99.85 11	94.35 72	99.46 25	99.57 13

MP-MVS		96.77 33	96.45 38	97.72 28	99.39 8	93.80 40	98.41 17	98.06 62	93.37 57	95.54 84	98.34 30	90.59 56	99.88 4	94.83 64	99.54 15	99.49 27
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 33	96.46 37	97.71 30	98.40 62	94.07 33	98.21 30	98.45 15	89.86 166	97.11 30	98.01 52	92.52 22	99.69 36	96.03 35	99.53 16	99.36 43

MP-MVS-pluss		96.70 35	96.27 42	97.98 12	99.23 25	94.71 16	96.96 149	98.06 62	90.67 144	95.55 82	98.78 5	91.07 48	99.86 7	96.58 17	99.55 14	99.38 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 36	96.49 35	97.27 51	98.31 72	93.39 52	96.79 171	96.72 209	94.17 38	97.44 19	97.66 75	92.76 14	99.33 97	96.86 8	97.76 98	99.08 64

HPM-MVS		96.69 36	96.45 38	97.40 44	99.36 13	93.11 60	98.87 1	98.06 62	91.17 132	96.40 51	97.99 54	90.99 50	99.58 57	95.61 46	99.61 8	99.49 27
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 38	96.58 32	96.99 62	98.46 58	92.31 78	96.20 231	98.90 2	94.30 37	95.86 68	97.74 70	92.33 24	99.38 95	96.04 34	99.42 30	99.28 50

DELS-MVS		96.61 39	96.38 40	97.30 48	97.79 108	93.19 58	95.96 242	98.18 37	95.23 11	95.87 67	97.65 76	91.45 42	99.70 35	95.87 37	99.44 29	99.00 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
DeepPCF-MVS	93.97 1	96.61 39	97.09 8	95.15 148	98.09 88	86.63 270	96.00 241	98.15 41	95.43 7	97.95 12	98.56 10	93.40 10	99.36 96	96.77 12	99.48 24	99.45 32

EI-MVSNet-Vis-set		96.51 41	96.47 36	96.63 69	98.24 76	91.20 116	96.89 158	97.73 100	94.74 27	96.49 47	98.49 15	90.88 53	99.58 57	96.44 20	98.32 83	99.13 59

HPM-MVS_fast		96.51 41	96.27 42	97.22 55	99.32 17	92.74 68	98.74 4	98.06 62	90.57 154	96.77 34	98.35 27	90.21 60	99.53 73	94.80 67	99.63 6	99.38 41

test_prior3		96.46 43	96.20 45	97.23 53	98.67 45	92.99 62	96.35 215	98.00 76	92.80 81	96.03 60	97.59 83	92.01 31	99.41 90	95.01 58	99.38 35	99.29 47

abl_6		96.40 44	96.21 44	96.98 63	98.89 38	92.20 83	97.89 51	98.03 71	93.34 60	97.22 23	98.42 21	87.93 84	99.72 29	95.10 55	99.07 63	99.02 67

CANet		96.39 45	96.02 47	97.50 41	97.62 119	93.38 53	97.02 144	97.96 84	95.42 8	94.86 91	97.81 65	87.38 96	99.82 19	96.88 7	99.20 53	99.29 47

EI-MVSNet-UG-set		96.34 46	96.30 41	96.47 80	98.20 81	90.93 127	96.86 160	97.72 103	94.67 28	96.16 56	98.46 17	90.43 57	99.58 57	96.23 24	97.96 92	98.90 82

train_agg		96.30 47	95.83 50	97.72 28	98.70 43	94.19 27	96.41 207	98.02 72	88.58 209	96.03 60	97.56 87	92.73 16	99.59 53	95.04 56	99.37 39	99.39 38

ACMMP		96.27 48	95.93 48	97.28 50	99.24 23	92.62 72	98.25 27	98.81 3	92.99 71	94.56 97	98.39 25	88.96 70	99.85 11	94.57 71	97.63 99	99.36 43
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
MVS_111021_LR		96.24 49	96.19 46	96.39 88	98.23 80	91.35 109	96.24 229	98.79 4	93.99 41	95.80 71	97.65 76	89.92 64	99.24 104	95.87 37	99.20 53	98.58 104

agg_prior1		96.22 50	95.77 51	97.56 39	98.67 45	93.79 41	96.28 223	98.00 76	88.76 206	95.68 76	97.55 89	92.70 18	99.57 65	95.01 58	99.32 41	99.32 45

agg_prior3		96.16 51	95.67 52	97.62 38	98.67 45	93.88 37	96.41 207	98.00 76	87.93 234	95.81 70	97.47 91	92.33 24	99.59 53	95.04 56	99.37 39	99.39 38

DeepC-MVS	93.07 3	96.06 52	95.66 53	97.29 49	97.96 96	93.17 59	97.30 121	98.06 62	93.92 42	93.38 122	98.66 7	86.83 101	99.73 26	95.60 48	99.22 51	98.96 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		96.05 53	95.45 56	97.85 16	97.75 111	94.50 18	96.87 159	97.95 86	95.46 6	95.60 80	98.01 52	80.96 200	99.83 15	97.23 2	99.25 48	99.23 51

CSCG		96.05 53	95.91 49	96.46 82	99.24 23	90.47 139	98.30 22	98.57 11	89.01 191	93.97 109	97.57 85	92.62 19	99.76 24	94.66 70	99.27 46	99.15 57

canonicalmvs		96.02 55	95.45 56	97.75 27	97.59 122	95.15 12	98.28 24	97.60 116	94.52 31	96.27 53	96.12 156	87.65 90	99.18 108	96.20 30	94.82 160	98.91 81

CDPH-MVS		95.97 56	95.38 59	97.77 25	98.93 36	94.44 20	96.35 215	97.88 89	86.98 259	96.65 39	97.89 56	91.99 33	99.47 83	92.26 105	99.46 25	99.39 38

UA-Net		95.95 57	95.53 55	97.20 57	97.67 116	92.98 64	97.65 77	98.13 44	94.81 24	96.61 41	98.35 27	88.87 71	99.51 78	90.36 144	97.35 111	99.11 62

VNet		95.89 58	95.45 56	97.21 56	98.07 90	92.94 65	97.50 100	98.15 41	93.87 43	97.52 15	97.61 82	85.29 118	99.53 73	95.81 40	95.27 152	99.16 55

alignmvs		95.87 59	95.23 63	97.78 23	97.56 124	95.19 10	97.86 53	97.17 163	94.39 34	96.47 48	96.40 145	85.89 112	99.20 105	96.21 29	95.11 156	98.95 77

casdiffmvs1		95.77 60	95.55 54	96.44 83	97.30 133	91.43 107	97.57 95	97.58 119	91.21 131	96.65 39	96.60 136	89.18 67	98.83 147	96.27 22	97.60 100	99.05 66

DPM-MVS		95.69 61	94.92 68	98.01 10	98.08 89	95.71 5	95.27 273	97.62 115	90.43 157	95.55 82	97.07 107	91.72 36	99.50 80	89.62 156	98.94 69	98.82 90

DP-MVS Recon		95.68 62	95.12 66	97.37 45	99.19 26	94.19 27	97.03 142	98.08 54	88.35 223	95.09 89	97.65 76	89.97 63	99.48 82	92.08 114	98.59 78	98.44 122

MG-MVS		95.61 63	95.38 59	96.31 93	98.42 61	90.53 137	96.04 237	97.48 128	93.47 56	95.67 79	98.10 45	89.17 68	99.25 103	91.27 134	98.77 73	99.13 59

CPTT-MVS		95.57 64	95.19 64	96.70 66	99.27 21	91.48 103	98.33 20	98.11 49	87.79 238	95.17 88	98.03 50	87.09 99	99.61 48	93.51 88	99.42 30	99.02 67

3Dnovator+	91.43 4	95.40 65	94.48 84	98.16 7	96.90 149	95.34 9	98.48 14	97.87 91	94.65 30	88.53 250	98.02 51	83.69 136	99.71 30	93.18 96	98.96 68	99.44 34

PS-MVSNAJ		95.37 66	95.33 61	95.49 131	97.35 132	90.66 135	95.31 270	97.48 128	93.85 44	96.51 46	95.70 182	88.65 75	99.65 42	94.80 67	98.27 84	96.17 202

MVSFormer		95.37 66	95.16 65	95.99 107	96.34 177	91.21 114	98.22 28	97.57 120	91.42 122	96.22 54	97.32 95	86.20 109	97.92 253	94.07 74	99.05 64	98.85 87

xiu_mvs_v2_base		95.32 68	95.29 62	95.40 137	97.22 135	90.50 138	95.44 265	97.44 140	93.70 51	96.46 49	96.18 152	88.59 78	99.53 73	94.79 69	97.81 95	96.17 202

PVSNet_Blended_VisFu		95.27 69	94.91 69	96.38 89	98.20 81	90.86 129	97.27 122	98.25 29	90.21 159	94.18 104	97.27 97	87.48 94	99.73 26	93.53 87	97.77 97	98.55 105

casdiffmvs		95.23 70	94.84 70	96.40 86	96.90 149	91.71 94	97.36 114	97.30 156	91.02 137	94.81 93	96.18 152	87.74 87	98.77 153	95.65 43	96.55 131	98.71 98

Vis-MVSNet		95.23 70	94.81 71	96.51 77	97.18 137	91.58 102	98.26 26	98.12 46	94.38 35	94.90 90	98.15 44	82.28 179	98.92 138	91.45 131	98.58 79	99.01 71
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 72	95.04 67	95.76 114	97.49 131	89.56 168	98.67 5	97.00 188	90.69 143	94.24 103	97.62 81	89.79 65	98.81 149	93.39 94	96.49 133	98.92 80

EPNet		95.20 73	94.56 79	97.14 58	92.80 322	92.68 70	97.85 56	94.87 298	96.64 1	92.46 144	97.80 67	86.23 107	99.65 42	93.72 85	98.62 77	99.10 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 74	94.44 86	97.44 43	96.56 165	93.36 55	98.65 6	98.36 16	94.12 39	89.25 240	98.06 48	82.20 182	99.77 23	93.41 93	99.32 41	99.18 54

OMC-MVS		95.09 75	94.70 76	96.25 99	98.46 58	91.28 110	96.43 204	97.57 120	92.04 107	94.77 95	97.96 55	87.01 100	99.09 124	91.31 133	96.77 123	98.36 129

xiu_mvs_v1_base_debu		95.01 76	94.76 73	95.75 116	96.58 162	91.71 94	96.25 226	97.35 152	92.99 71	96.70 35	96.63 131	82.67 168	99.44 87	96.22 25	97.46 103	96.11 207

xiu_mvs_v1_base		95.01 76	94.76 73	95.75 116	96.58 162	91.71 94	96.25 226	97.35 152	92.99 71	96.70 35	96.63 131	82.67 168	99.44 87	96.22 25	97.46 103	96.11 207

xiu_mvs_v1_base_debi		95.01 76	94.76 73	95.75 116	96.58 162	91.71 94	96.25 226	97.35 152	92.99 71	96.70 35	96.63 131	82.67 168	99.44 87	96.22 25	97.46 103	96.11 207

PAPM_NR		95.01 76	94.59 78	96.26 98	98.89 38	90.68 134	97.24 124	97.73 100	91.80 112	92.93 141	96.62 134	89.13 69	99.14 113	89.21 165	97.78 96	98.97 74

diffmvs1		94.99 80	94.79 72	95.60 124	96.52 168	89.20 193	96.43 204	97.36 150	92.59 86	94.85 92	96.10 159	87.85 86	98.74 157	93.99 76	97.41 108	98.86 86

lupinMVS		94.99 80	94.56 79	96.29 96	96.34 177	91.21 114	95.83 248	96.27 228	88.93 197	96.22 54	96.88 115	86.20 109	98.85 145	95.27 50	99.05 64	98.82 90

Effi-MVS+		94.93 82	94.45 85	96.36 91	96.61 159	91.47 104	96.41 207	97.41 144	91.02 137	94.50 98	95.92 165	87.53 93	98.78 151	93.89 81	96.81 122	98.84 89

IS-MVSNet		94.90 83	94.52 82	96.05 104	97.67 116	90.56 136	98.44 15	96.22 232	93.21 62	93.99 107	97.74 70	85.55 116	98.45 179	89.98 146	97.86 93	99.14 58

MVS_Test		94.89 84	94.62 77	95.68 121	96.83 154	89.55 169	96.70 184	97.17 163	91.17 132	95.60 80	96.11 158	87.87 85	98.76 155	93.01 101	97.17 115	98.72 96

PVSNet_Blended		94.87 85	94.56 79	95.81 112	98.27 73	89.46 175	95.47 264	98.36 16	88.84 200	94.36 100	96.09 160	88.02 81	99.58 57	93.44 91	98.18 86	98.40 125

jason		94.84 86	94.39 87	96.18 101	95.52 207	90.93 127	96.09 235	96.52 221	89.28 178	96.01 64	97.32 95	84.70 125	98.77 153	95.15 53	98.91 71	98.85 87
jason: jason.
API-MVS		94.84 86	94.49 83	95.90 109	97.90 104	92.00 90	97.80 59	97.48 128	89.19 181	94.81 93	96.71 120	88.84 72	99.17 109	88.91 173	98.76 74	96.53 192

test_yl		94.78 88	94.23 88	96.43 84	97.74 112	91.22 112	96.85 161	97.10 172	91.23 129	95.71 74	96.93 111	84.30 130	99.31 99	93.10 97	95.12 154	98.75 92

DCV-MVSNet		94.78 88	94.23 88	96.43 84	97.74 112	91.22 112	96.85 161	97.10 172	91.23 129	95.71 74	96.93 111	84.30 130	99.31 99	93.10 97	95.12 154	98.75 92

1121		94.71 90	93.83 94	97.34 46	98.57 56	93.64 46	96.04 237	97.73 100	81.56 323	95.68 76	97.85 62	90.23 59	99.65 42	87.68 195	99.12 61	98.73 95

WTY-MVS		94.71 90	94.02 91	96.79 65	97.71 115	92.05 87	96.59 197	97.35 152	90.61 151	94.64 96	96.93 111	86.41 106	99.39 93	91.20 136	94.71 164	98.94 78

sss		94.51 92	93.80 95	96.64 67	97.07 142	91.97 91	96.32 219	98.06 62	88.94 196	94.50 98	96.78 117	84.60 126	99.27 102	91.90 117	96.02 137	98.68 101

diffmvs		94.47 93	94.23 88	95.18 142	96.32 179	88.22 219	96.27 224	97.04 183	92.55 88	93.60 114	95.94 164	86.79 102	98.70 161	92.98 102	96.61 129	98.63 103

CANet_DTU		94.37 94	93.65 100	96.55 73	96.46 173	92.13 85	96.21 230	96.67 216	94.38 35	93.53 118	97.03 109	79.34 230	99.71 30	90.76 138	98.45 81	97.82 151

AdaColmap		94.34 95	93.68 99	96.31 93	98.59 53	91.68 98	96.59 197	97.81 96	89.87 165	92.15 153	97.06 108	83.62 137	99.54 70	89.34 160	98.07 89	97.70 155

CNLPA		94.28 96	93.53 104	96.52 74	98.38 65	92.55 74	96.59 197	96.88 203	90.13 162	91.91 157	97.24 99	85.21 119	99.09 124	87.64 198	97.83 94	97.92 143

MAR-MVS		94.22 97	93.46 107	96.51 77	98.00 91	92.19 84	97.67 74	97.47 131	88.13 232	93.00 136	95.84 169	84.86 124	99.51 78	87.99 187	98.17 87	97.83 150
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPR		94.18 98	93.42 111	96.48 79	97.64 118	91.42 108	95.55 259	97.71 107	88.99 192	92.34 149	95.82 171	89.19 66	99.11 115	86.14 224	97.38 109	98.90 82

CHOSEN 1792x2688		94.15 99	93.51 105	96.06 103	98.27 73	89.38 182	95.18 277	98.48 14	85.60 280	93.76 112	97.11 105	83.15 143	99.61 48	91.33 132	98.72 75	99.19 53

Vis-MVSNet (Re-imp)		94.15 99	93.88 93	94.95 161	97.61 120	87.92 241	98.10 33	95.80 254	92.22 93	93.02 135	97.45 92	84.53 128	97.91 256	88.24 182	97.97 91	99.02 67

CDS-MVSNet		94.14 101	93.54 103	95.93 108	96.18 185	91.46 105	96.33 218	97.04 183	88.97 195	93.56 115	96.51 139	87.55 92	97.89 257	89.80 149	95.95 139	98.44 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 102	93.43 109	96.13 102	98.58 55	91.15 121	96.69 186	97.39 145	87.29 250	91.37 167	96.71 120	88.39 79	99.52 77	87.33 206	97.13 116	97.73 153
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 103	93.70 97	95.27 139	95.70 203	92.03 88	98.10 33	98.68 7	93.36 59	90.39 193	96.70 122	87.63 91	97.94 249	92.25 107	90.50 236	95.84 220

PVSNet_BlendedMVS		94.06 104	93.92 92	94.47 186	98.27 73	89.46 175	96.73 176	98.36 16	90.17 160	94.36 100	95.24 203	88.02 81	99.58 57	93.44 91	90.72 232	94.36 299

nrg030		94.05 105	93.31 113	96.27 97	95.22 225	94.59 17	98.34 19	97.46 133	92.93 78	91.21 183	96.64 127	87.23 98	98.22 199	94.99 61	85.80 274	95.98 215

UGNet		94.04 106	93.28 114	96.31 93	96.85 151	91.19 117	97.88 52	97.68 109	94.40 33	93.00 136	96.18 152	73.39 302	99.61 48	91.72 122	98.46 80	98.13 135
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
TAMVS		94.01 107	93.46 107	95.64 122	96.16 187	90.45 140	96.71 181	96.89 202	89.27 179	93.46 120	96.92 114	87.29 97	97.94 249	88.70 179	95.74 144	98.53 107

114514_t		93.95 108	93.06 117	96.63 69	99.07 32	91.61 99	97.46 106	97.96 84	77.99 341	93.00 136	97.57 85	86.14 111	99.33 97	89.22 164	99.15 56	98.94 78

FC-MVSNet-test		93.94 109	93.57 101	95.04 154	95.48 209	91.45 106	98.12 32	98.71 5	93.37 57	90.23 196	96.70 122	87.66 89	97.85 259	91.49 129	90.39 237	95.83 221

HY-MVS	89.66 9	93.87 110	92.95 119	96.63 69	97.10 141	92.49 76	95.64 257	96.64 217	89.05 190	93.00 136	95.79 175	85.77 115	99.45 86	89.16 167	94.35 165	97.96 141

XVG-OURS-SEG-HR		93.86 111	93.55 102	94.81 169	97.06 145	88.53 209	95.28 271	97.45 137	91.68 115	94.08 106	97.68 73	82.41 177	98.90 140	93.84 83	92.47 201	96.98 172

VDD-MVS		93.82 112	93.08 116	96.02 105	97.88 105	89.96 151	97.72 68	95.85 251	92.43 90	95.86 68	98.44 19	68.42 325	99.39 93	96.31 21	94.85 158	98.71 98

mvs_anonymous		93.82 112	93.74 96	94.06 201	96.44 174	85.41 282	95.81 249	97.05 180	89.85 168	90.09 206	96.36 147	87.44 95	97.75 269	93.97 77	96.69 127	99.02 67

HQP_MVS		93.78 114	93.43 109	94.82 167	96.21 182	89.99 146	97.74 64	97.51 126	94.85 19	91.34 169	96.64 127	81.32 196	98.60 167	93.02 99	92.23 204	95.86 217

PS-MVSNAJss		93.74 115	93.51 105	94.44 187	93.91 290	89.28 191	97.75 62	97.56 123	92.50 89	89.94 209	96.54 138	88.65 75	98.18 203	93.83 84	90.90 229	95.86 217

XVG-OURS		93.72 116	93.35 112	94.80 170	97.07 142	88.61 207	94.79 281	97.46 133	91.97 110	93.99 107	97.86 61	81.74 191	98.88 144	92.64 104	92.67 200	96.92 180

HyFIR lowres test		93.66 117	92.92 120	95.87 110	98.24 76	89.88 153	94.58 284	98.49 12	85.06 287	93.78 111	95.78 176	82.86 164	98.67 162	91.77 121	95.71 146	99.07 65

mvs-test1		93.63 118	93.69 98	93.46 241	96.02 193	84.61 292	97.24 124	96.72 209	93.85 44	92.30 150	95.76 177	83.08 149	98.89 142	91.69 125	96.54 132	96.87 182

LFMVS		93.60 119	92.63 129	96.52 74	98.13 87	91.27 111	97.94 47	93.39 330	90.57 154	96.29 52	98.31 36	69.00 321	99.16 110	94.18 73	95.87 141	99.12 61

F-COLMAP		93.58 120	92.98 118	95.37 138	98.40 62	88.98 200	97.18 134	97.29 157	87.75 240	90.49 190	97.10 106	85.21 119	99.50 80	86.70 215	96.72 126	97.63 156

ab-mvs		93.57 121	92.55 133	96.64 67	97.28 134	91.96 92	95.40 266	97.45 137	89.81 170	93.22 130	96.28 149	79.62 227	99.46 84	90.74 139	93.11 194	98.50 112

LS3D		93.57 121	92.61 131	96.47 80	97.59 122	91.61 99	97.67 74	97.72 103	85.17 285	90.29 195	98.34 30	84.60 126	99.73 26	83.85 264	98.27 84	98.06 140

Fast-Effi-MVS+		93.46 123	92.75 125	95.59 125	96.77 156	90.03 143	96.81 168	97.13 168	88.19 228	91.30 172	94.27 259	86.21 108	98.63 164	87.66 197	96.46 135	98.12 136

QAPM		93.45 124	92.27 142	96.98 63	96.77 156	92.62 72	98.39 18	98.12 46	84.50 295	88.27 256	97.77 68	82.39 178	99.81 20	85.40 238	98.81 72	98.51 110

UniMVSNet_NR-MVSNet		93.37 125	92.67 128	95.47 134	95.34 215	92.83 66	97.17 135	98.58 10	92.98 76	90.13 201	95.80 172	88.37 80	97.85 259	91.71 123	83.93 303	95.73 230

1112_ss		93.37 125	92.42 139	96.21 100	97.05 146	90.99 123	96.31 220	96.72 209	86.87 265	89.83 215	96.69 124	86.51 105	99.14 113	88.12 184	93.67 182	98.50 112

UniMVSNet (Re)		93.31 127	92.55 133	95.61 123	95.39 212	93.34 56	97.39 111	98.71 5	93.14 67	90.10 205	94.83 220	87.71 88	98.03 232	91.67 127	83.99 302	95.46 238

OPM-MVS		93.28 128	92.76 123	94.82 167	94.63 253	90.77 133	96.65 189	97.18 161	93.72 49	91.68 162	97.26 98	79.33 231	98.63 164	92.13 111	92.28 203	95.07 264

VPA-MVSNet		93.24 129	92.48 138	95.51 129	95.70 203	92.39 77	97.86 53	98.66 9	92.30 92	92.09 155	95.37 198	80.49 212	98.40 187	93.95 78	85.86 273	95.75 228

MVSTER		93.20 130	92.81 122	94.37 190	96.56 165	89.59 167	97.06 141	97.12 169	91.24 128	91.30 172	95.96 162	82.02 185	98.05 227	93.48 90	90.55 234	95.47 237

HQP-MVS		93.19 131	92.74 126	94.54 185	95.86 196	89.33 186	96.65 189	97.39 145	93.55 52	90.14 197	95.87 167	80.95 201	98.50 175	92.13 111	92.10 209	95.78 224

CHOSEN 280x420		93.12 132	92.72 127	94.34 192	96.71 158	87.27 252	90.29 343	97.72 103	86.61 269	91.34 169	95.29 200	84.29 132	98.41 186	93.25 95	98.94 69	97.35 168

Effi-MVS+-dtu		93.08 133	93.21 115	92.68 267	96.02 193	83.25 305	97.14 138	96.72 209	93.85 44	91.20 184	93.44 289	83.08 149	98.30 196	91.69 125	95.73 145	96.50 194

test_djsdf		93.07 134	92.76 123	94.00 204	93.49 303	88.70 206	98.22 28	97.57 120	91.42 122	90.08 207	95.55 189	82.85 165	97.92 253	94.07 74	91.58 217	95.40 245

VDDNet		93.05 135	92.07 145	96.02 105	96.84 152	90.39 141	98.08 35	95.85 251	86.22 273	95.79 72	98.46 17	67.59 328	99.19 106	94.92 62	94.85 158	98.47 117

thisisatest0530		93.03 136	92.21 143	95.49 131	97.07 142	89.11 198	97.49 104	92.19 347	90.16 161	94.09 105	96.41 144	76.43 279	99.05 131	90.38 143	95.68 147	98.31 131

EI-MVSNet		93.03 136	92.88 121	93.48 239	95.77 201	86.98 262	96.44 202	97.12 169	90.66 146	91.30 172	97.64 79	86.56 104	98.05 227	89.91 147	90.55 234	95.41 241

CLD-MVS		92.98 138	92.53 135	94.32 193	96.12 191	89.20 193	95.28 271	97.47 131	92.66 84	89.90 210	95.62 185	80.58 210	98.40 187	92.73 103	92.40 202	95.38 247
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 139	92.33 141	94.87 166	97.11 140	87.16 258	97.97 46	92.09 348	90.63 148	93.88 110	97.01 110	76.50 276	99.06 130	90.29 145	95.45 149	98.38 127

ACMM	89.79 8	92.96 139	92.50 137	94.35 191	96.30 180	88.71 205	97.58 93	97.36 150	91.40 124	90.53 189	96.65 126	79.77 224	98.75 156	91.24 135	91.64 215	95.59 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 141	92.56 132	94.10 199	96.16 187	88.26 215	97.65 77	97.46 133	91.29 125	90.12 203	97.16 102	79.05 234	98.73 158	92.25 107	91.89 212	95.31 251

BH-untuned		92.94 141	92.62 130	93.92 213	97.22 135	86.16 274	96.40 211	96.25 230	90.06 163	89.79 217	96.17 155	83.19 141	98.35 191	87.19 209	97.27 113	97.24 169

DU-MVS		92.90 143	92.04 146	95.49 131	94.95 239	92.83 66	97.16 136	98.24 30	93.02 70	90.13 201	95.71 180	83.47 138	97.85 259	91.71 123	83.93 303	95.78 224

PatchMatch-RL		92.90 143	92.02 148	95.56 126	98.19 83	90.80 131	95.27 273	97.18 161	87.96 233	91.86 159	95.68 183	80.44 213	98.99 135	84.01 260	97.54 102	96.89 181

PMMVS		92.86 145	92.34 140	94.42 189	94.92 241	86.73 266	94.53 286	96.38 224	84.78 292	94.27 102	95.12 208	83.13 145	98.40 187	91.47 130	96.49 133	98.12 136

OpenMVS	89.19 12	92.86 145	91.68 158	96.40 86	95.34 215	92.73 69	98.27 25	98.12 46	84.86 290	85.78 290	97.75 69	78.89 249	99.74 25	87.50 202	98.65 76	96.73 185

Test_1112_low_res		92.84 147	91.84 153	95.85 111	97.04 147	89.97 149	95.53 261	96.64 217	85.38 281	89.65 225	95.18 204	85.86 113	99.10 121	87.70 193	93.58 187	98.49 114

1314		92.81 148	92.03 147	95.14 149	95.33 218	89.52 172	96.04 237	97.44 140	87.72 241	86.25 287	95.33 199	83.84 134	98.79 150	89.26 162	97.05 117	97.11 170

DP-MVS		92.76 149	91.51 172	96.52 74	98.77 40	90.99 123	97.38 113	96.08 237	82.38 314	89.29 237	97.87 59	83.77 135	99.69 36	81.37 294	96.69 127	98.89 84

BH-RMVSNet		92.72 150	91.97 150	94.97 159	97.16 138	87.99 236	96.15 232	95.60 260	90.62 149	91.87 158	97.15 104	78.41 254	98.57 170	83.16 268	97.60 100	98.36 129

ACMP	89.59 10	92.62 151	92.14 144	94.05 202	96.40 175	88.20 222	97.36 114	97.25 160	91.52 117	88.30 254	96.64 127	78.46 253	98.72 160	91.86 120	91.48 219	95.23 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
view600		92.55 152	91.68 158	95.18 142	97.98 92	89.44 177	98.00 39	94.57 305	92.09 101	93.17 131	95.52 191	78.14 260	99.11 115	81.61 283	94.04 174	96.98 172

view800		92.55 152	91.68 158	95.18 142	97.98 92	89.44 177	98.00 39	94.57 305	92.09 101	93.17 131	95.52 191	78.14 260	99.11 115	81.61 283	94.04 174	96.98 172

conf0.05thres1000		92.55 152	91.68 158	95.18 142	97.98 92	89.44 177	98.00 39	94.57 305	92.09 101	93.17 131	95.52 191	78.14 260	99.11 115	81.61 283	94.04 174	96.98 172

tfpn		92.55 152	91.68 158	95.18 142	97.98 92	89.44 177	98.00 39	94.57 305	92.09 101	93.17 131	95.52 191	78.14 260	99.11 115	81.61 283	94.04 174	96.98 172

LCM-MVSNet-Re		92.50 156	92.52 136	92.44 270	96.82 155	81.89 314	96.92 156	93.71 325	92.41 91	84.30 302	94.60 230	85.08 121	97.03 304	91.51 128	97.36 110	98.40 125

TranMVSNet+NR-MVSNet		92.50 156	91.63 163	95.14 149	94.76 248	92.07 86	97.53 98	98.11 49	92.90 79	89.56 228	96.12 156	83.16 142	97.60 280	89.30 161	83.20 313	95.75 228

thres600view7		92.49 158	91.60 164	95.18 142	97.91 103	89.47 173	97.65 77	94.66 300	92.18 100	93.33 123	94.91 212	78.06 264	99.10 121	81.61 283	94.06 172	96.98 172

tfpn111		92.45 159	91.58 165	95.06 152	97.92 100	89.37 183	97.71 70	94.66 300	92.20 95	93.31 124	94.90 213	78.06 264	99.11 115	81.37 294	94.06 172	96.70 187

conf200view11		92.45 159	91.58 165	95.05 153	97.92 100	89.37 183	97.71 70	94.66 300	92.20 95	93.31 124	94.90 213	78.06 264	99.08 126	81.40 290	94.08 168	96.70 187

thres100view900		92.43 161	91.58 165	94.98 158	97.92 100	89.37 183	97.71 70	94.66 300	92.20 95	93.31 124	94.90 213	78.06 264	99.08 126	81.40 290	94.08 168	96.48 195

jajsoiax		92.42 162	91.89 152	94.03 203	93.33 309	88.50 210	97.73 66	97.53 124	92.00 109	88.85 244	96.50 140	75.62 285	98.11 210	93.88 82	91.56 218	95.48 235

thres400		92.42 162	91.52 170	95.12 151	97.85 106	89.29 189	97.41 107	94.88 295	92.19 98	93.27 128	94.46 237	78.17 257	99.08 126	81.40 290	94.08 168	96.98 172

tfpn200view9		92.38 164	91.52 170	94.95 161	97.85 106	89.29 189	97.41 107	94.88 295	92.19 98	93.27 128	94.46 237	78.17 257	99.08 126	81.40 290	94.08 168	96.48 195

WR-MVS		92.34 165	91.53 169	94.77 173	95.13 231	90.83 130	96.40 211	97.98 82	91.88 111	89.29 237	95.54 190	82.50 173	97.80 264	89.79 150	85.27 280	95.69 231

NR-MVSNet		92.34 165	91.27 180	95.53 128	94.95 239	93.05 61	97.39 111	98.07 59	92.65 85	84.46 300	95.71 180	85.00 122	97.77 268	89.71 151	83.52 310	95.78 224

mvs_tets		92.31 167	91.76 154	93.94 212	93.41 305	88.29 213	97.63 88	97.53 124	92.04 107	88.76 245	96.45 142	74.62 292	98.09 214	93.91 80	91.48 219	95.45 239

TAPA-MVS	90.10 7	92.30 168	91.22 183	95.56 126	98.33 69	89.60 166	96.79 171	97.65 112	81.83 318	91.52 164	97.23 100	87.94 83	98.91 139	71.31 339	98.37 82	98.17 134
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 169	91.30 178	95.25 140	96.60 160	88.90 202	94.36 289	92.32 345	87.92 235	93.43 121	94.57 231	77.28 273	99.00 134	89.42 159	95.86 142	97.86 147

Fast-Effi-MVS+-dtu		92.29 169	91.99 149	93.21 252	95.27 220	85.52 281	97.03 142	96.63 219	92.09 101	89.11 241	95.14 206	80.33 216	98.08 215	87.54 201	94.74 163	96.03 214

IterMVS-LS		92.29 169	91.94 151	93.34 246	96.25 181	86.97 263	96.57 200	97.05 180	90.67 144	89.50 231	94.80 222	86.59 103	97.64 277	89.91 147	86.11 272	95.40 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 172	91.74 157	93.73 225	97.77 110	83.69 301	92.88 322	96.72 209	87.91 236	93.00 136	94.86 217	78.51 252	99.05 131	86.53 216	97.45 107	98.47 117

VPNet		92.23 173	91.31 177	94.99 156	95.56 206	90.96 125	97.22 129	97.86 93	92.96 77	90.96 185	96.62 134	75.06 288	98.20 200	91.90 117	83.65 309	95.80 223

thres200		92.23 173	91.39 173	94.75 174	97.61 120	89.03 199	96.60 196	95.09 284	92.08 106	93.28 127	94.00 267	78.39 255	99.04 133	81.26 302	94.18 167	96.19 201

anonymousdsp		92.16 175	91.55 168	93.97 207	92.58 326	89.55 169	97.51 99	97.42 143	89.42 176	88.40 251	94.84 218	80.66 209	97.88 258	91.87 119	91.28 223	94.48 295

XXY-MVS		92.16 175	91.23 182	94.95 161	94.75 249	90.94 126	97.47 105	97.43 142	89.14 188	88.90 242	96.43 143	79.71 225	98.24 198	89.56 157	87.68 260	95.67 232

BH-w/o		92.14 177	91.75 155	93.31 247	96.99 148	85.73 277	95.67 254	95.69 256	88.73 207	89.26 239	94.82 221	82.97 159	98.07 219	85.26 240	96.32 136	96.13 206

Anonymous202405211		92.07 178	90.83 199	95.76 114	98.19 83	88.75 204	97.58 93	95.00 288	86.00 276	93.64 113	97.45 92	66.24 334	99.53 73	90.68 141	92.71 198	99.01 71

test_normal		92.01 179	90.75 202	95.80 113	93.24 311	89.97 149	95.93 244	96.24 231	90.62 149	81.63 318	93.45 288	74.98 289	98.89 142	93.61 86	97.04 118	98.55 105

DI_MVS_plusplus_test		92.01 179	90.77 200	95.73 119	93.34 307	89.78 156	96.14 233	96.18 234	90.58 153	81.80 317	93.50 285	74.95 290	98.90 140	93.51 88	96.94 119	98.51 110

WR-MVS_H		92.00 181	91.35 174	93.95 209	95.09 233	89.47 173	98.04 38	98.68 7	91.46 120	88.34 252	94.68 226	85.86 113	97.56 281	85.77 232	84.24 300	94.82 282

tfpn1000		91.99 182	91.05 187	94.80 170	97.78 109	89.66 164	97.91 50	92.90 341	88.99 192	91.73 160	94.84 218	78.99 241	98.33 194	82.41 279	93.91 180	96.40 197

Anonymous20240529		91.98 183	90.73 203	95.73 119	98.14 86	89.40 181	97.99 43	97.72 103	79.63 333	93.54 117	97.41 94	69.94 319	99.56 67	91.04 137	91.11 225	98.22 132

PatchmatchNet		91.91 184	91.35 174	93.59 234	95.38 213	84.11 296	93.15 318	95.39 267	89.54 172	92.10 154	93.68 279	82.82 166	98.13 206	84.81 244	95.32 151	98.52 108
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		91.89 185	91.24 181	93.82 216	95.05 234	88.57 208	97.82 58	98.19 35	91.70 114	88.21 258	95.76 177	81.96 186	97.52 284	87.86 189	84.65 296	95.37 248

tfpn_ndepth		91.88 186	90.96 191	94.62 179	97.73 114	89.93 152	97.75 62	92.92 340	88.93 197	91.73 160	93.80 274	78.91 242	98.49 178	83.02 271	93.86 181	95.45 239

SCA		91.84 187	91.18 185	93.83 215	95.59 205	84.95 288	94.72 282	95.58 262	90.82 139	92.25 151	93.69 277	75.80 282	98.10 211	86.20 222	95.98 138	98.45 119

FMVSNet3		91.78 188	90.69 206	95.03 155	96.53 167	92.27 80	97.02 144	96.93 198	89.79 171	89.35 234	94.65 228	77.01 274	97.47 287	86.12 225	88.82 249	95.35 249

conf0.01		91.74 189	90.67 207	94.94 164	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.70 187

conf0.002		91.74 189	90.67 207	94.94 164	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.70 187

X-MVStestdata		91.71 191	89.67 248	97.81 20	99.38 9	94.03 35	98.59 7	98.20 33	94.85 19	96.59 43	32.69 368	91.70 38	99.80 21	95.66 41	99.40 32	99.62 7

MVS		91.71 191	90.44 217	95.51 129	95.20 227	91.59 101	96.04 237	97.45 137	73.44 353	87.36 273	95.60 186	85.42 117	99.10 121	85.97 229	97.46 103	95.83 221

EPNet_dtu		91.71 191	91.28 179	92.99 257	93.76 295	83.71 299	96.69 186	95.28 274	93.15 66	87.02 281	95.95 163	83.37 140	97.38 294	79.46 312	96.84 120	97.88 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1neww		91.70 194	91.01 188	93.75 222	94.19 267	88.14 227	97.20 131	96.98 189	89.18 183	89.87 213	94.44 239	83.10 147	98.06 224	89.06 169	85.09 285	95.06 267

v7new		91.70 194	91.01 188	93.75 222	94.19 267	88.14 227	97.20 131	96.98 189	89.18 183	89.87 213	94.44 239	83.10 147	98.06 224	89.06 169	85.09 285	95.06 267

thresconf0.02		91.69 196	90.67 207	94.75 174	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.11 207

tfpn_n400		91.69 196	90.67 207	94.75 174	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.11 207

tfpnconf		91.69 196	90.67 207	94.75 174	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.11 207

tfpnview11		91.69 196	90.67 207	94.75 174	97.55 125	89.68 158	97.64 81	93.14 332	88.43 214	91.24 177	94.30 249	78.91 242	98.45 179	81.28 296	93.57 188	96.11 207

v6		91.69 196	91.00 190	93.75 222	94.14 272	88.12 229	97.20 131	96.98 189	89.19 181	89.90 210	94.42 241	83.04 153	98.07 219	89.07 168	85.10 284	95.07 264

PatchFormer-LS_test		91.68 201	91.18 185	93.19 253	95.24 224	83.63 302	95.53 261	95.44 266	89.82 169	91.37 167	92.58 301	80.85 208	98.52 173	89.65 155	90.16 239	97.42 167

v1141		91.61 202	90.89 192	93.78 219	94.01 285	88.24 217	96.96 149	96.96 193	89.17 185	89.75 219	94.29 255	82.99 157	98.03 232	88.85 175	85.00 290	95.07 264

divwei89l23v2f112		91.61 202	90.89 192	93.78 219	94.01 285	88.22 219	96.96 149	96.96 193	89.17 185	89.75 219	94.28 257	83.02 155	98.03 232	88.86 174	84.98 293	95.08 262

v1		91.61 202	90.89 192	93.78 219	94.01 285	88.21 221	96.96 149	96.96 193	89.17 185	89.78 218	94.29 255	82.97 159	98.05 227	88.85 175	84.99 291	95.08 262

v2v482		91.59 205	90.85 197	93.80 217	93.87 292	88.17 224	96.94 155	96.88 203	89.54 172	89.53 229	94.90 213	81.70 192	98.02 235	89.25 163	85.04 289	95.20 259

V42		91.58 206	90.87 195	93.73 225	94.05 284	88.50 210	97.32 119	96.97 192	88.80 205	89.71 221	94.33 246	82.54 172	98.05 227	89.01 171	85.07 287	94.64 292

PCF-MVS	89.48 11	91.56 207	89.95 236	96.36 91	96.60 160	92.52 75	92.51 327	97.26 158	79.41 334	88.90 242	96.56 137	84.04 133	99.55 68	77.01 323	97.30 112	97.01 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-CasMVS		91.55 208	90.84 198	93.69 229	94.96 238	88.28 214	97.84 57	98.24 30	91.46 120	88.04 260	95.80 172	79.67 226	97.48 286	87.02 212	84.54 298	95.31 251

PAPM		91.52 209	90.30 221	95.20 141	95.30 219	89.83 154	93.38 313	96.85 205	86.26 272	88.59 249	95.80 172	84.88 123	98.15 205	75.67 327	95.93 140	97.63 156

TR-MVS		91.48 210	90.59 215	94.16 198	96.40 175	87.33 250	95.67 254	95.34 273	87.68 242	91.46 165	95.52 191	76.77 275	98.35 191	82.85 273	93.61 185	96.79 184

v7		91.47 211	90.73 203	93.68 230	94.13 273	88.16 225	97.09 140	97.05 180	88.38 221	89.80 216	94.52 232	82.21 181	98.01 236	88.00 186	85.42 277	94.87 276

tpmrst		91.44 212	91.32 176	91.79 292	95.15 229	79.20 336	93.42 312	95.37 269	88.55 211	93.49 119	93.67 280	82.49 174	98.27 197	90.41 142	89.34 246	97.90 144

test-LLR		91.42 213	91.19 184	92.12 282	94.59 254	80.66 321	94.29 293	92.98 338	91.11 134	90.76 187	92.37 304	79.02 236	98.07 219	88.81 177	96.74 124	97.63 156

MSDG		91.42 213	90.24 225	94.96 160	97.15 139	88.91 201	93.69 306	96.32 226	85.72 279	86.93 282	96.47 141	80.24 217	98.98 136	80.57 304	95.05 157	96.98 172

GA-MVS		91.38 215	90.31 220	94.59 180	94.65 252	87.62 248	94.34 290	96.19 233	90.73 142	90.35 194	93.83 272	71.84 306	97.96 247	87.22 208	93.61 185	98.21 133

v1144		91.37 216	90.60 214	93.68 230	93.89 291	88.23 218	96.84 163	97.03 186	88.37 222	89.69 223	94.39 242	82.04 184	97.98 240	87.80 191	85.37 278	94.84 278

GBi-Net		91.35 217	90.27 223	94.59 180	96.51 169	91.18 118	97.50 100	96.93 198	88.82 202	89.35 234	94.51 233	73.87 296	97.29 298	86.12 225	88.82 249	95.31 251

test1		91.35 217	90.27 223	94.59 180	96.51 169	91.18 118	97.50 100	96.93 198	88.82 202	89.35 234	94.51 233	73.87 296	97.29 298	86.12 225	88.82 249	95.31 251

FMVSNet2		91.31 219	90.08 230	94.99 156	96.51 169	92.21 81	97.41 107	96.95 196	88.82 202	88.62 247	94.75 224	73.87 296	97.42 291	85.20 241	88.55 255	95.35 249

v8		91.29 220	90.53 216	93.57 236	94.15 271	88.12 229	97.34 116	97.06 179	88.99 192	88.32 253	94.26 261	83.08 149	98.01 236	87.62 199	83.92 305	94.57 293

CVMVSNet		91.23 221	91.75 155	89.67 323	95.77 201	74.69 344	96.44 202	94.88 295	85.81 277	92.18 152	97.64 79	79.07 233	95.58 334	88.06 185	95.86 142	98.74 94

PEN-MVS		91.20 222	90.44 217	93.48 239	94.49 257	87.91 243	97.76 61	98.18 37	91.29 125	87.78 263	95.74 179	80.35 215	97.33 296	85.46 237	82.96 314	95.19 260

Baseline_NR-MVSNet		91.20 222	90.62 213	92.95 258	93.83 293	88.03 235	97.01 146	95.12 283	88.42 220	89.70 222	95.13 207	83.47 138	97.44 289	89.66 154	83.24 312	93.37 316

cascas		91.20 222	90.08 230	94.58 184	94.97 237	89.16 197	93.65 308	97.59 118	79.90 332	89.40 232	92.92 295	75.36 286	98.36 190	92.14 110	94.75 162	96.23 199

CostFormer		91.18 225	90.70 205	92.62 268	94.84 245	81.76 315	94.09 299	94.43 310	84.15 298	92.72 143	93.77 275	79.43 229	98.20 200	90.70 140	92.18 207	97.90 144

v1192		91.07 226	90.23 226	93.58 235	93.70 296	87.82 244	96.73 176	97.07 177	87.77 239	89.58 226	94.32 247	80.90 207	97.97 243	86.52 217	85.48 275	94.95 270

v144192		91.06 227	90.28 222	93.39 243	93.66 298	87.23 255	96.83 164	97.07 177	87.43 246	89.69 223	94.28 257	81.48 193	98.00 239	87.18 210	84.92 294	94.93 274

v10		91.04 228	90.23 226	93.49 238	94.12 275	88.16 225	97.32 119	97.08 176	88.26 225	88.29 255	94.22 262	82.17 183	97.97 243	86.45 219	84.12 301	94.33 300

v148		90.99 229	90.38 219	92.81 262	93.83 293	85.80 276	96.78 173	96.68 214	89.45 175	88.75 246	93.93 270	82.96 161	97.82 263	87.83 190	83.25 311	94.80 284

LTVRE_ROB	88.41 13	90.99 229	89.92 237	94.19 196	96.18 185	89.55 169	96.31 220	97.09 174	87.88 237	85.67 291	95.91 166	78.79 250	98.57 170	81.50 288	89.98 240	94.44 297
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
pmmvs4		90.93 231	89.85 240	94.17 197	93.34 307	90.79 132	94.60 283	96.02 238	84.62 293	87.45 269	95.15 205	81.88 189	97.45 288	87.70 193	87.87 259	94.27 304

XVG-ACMP-BASELINE		90.93 231	90.21 228	93.09 254	94.31 264	85.89 275	95.33 268	97.26 158	91.06 136	89.38 233	95.44 197	68.61 323	98.60 167	89.46 158	91.05 227	94.79 286

v1921920		90.85 233	90.03 233	93.29 248	93.55 299	86.96 264	96.74 175	97.04 183	87.36 248	89.52 230	94.34 245	80.23 218	97.97 243	86.27 220	85.21 281	94.94 272

CR-MVSNet		90.82 234	89.77 244	93.95 209	94.45 259	87.19 256	90.23 344	95.68 258	86.89 264	92.40 145	92.36 307	80.91 204	97.05 302	81.09 303	93.95 178	97.60 161

v7n		90.76 235	89.86 239	93.45 242	93.54 300	87.60 249	97.70 73	97.37 148	88.85 199	87.65 267	94.08 266	81.08 198	98.10 211	84.68 247	83.79 308	94.66 291

DWT-MVSNet_test		90.76 235	89.89 238	93.38 244	95.04 235	83.70 300	95.85 247	94.30 316	88.19 228	90.46 191	92.80 296	73.61 300	98.50 175	88.16 183	90.58 233	97.95 142

RPSCF		90.75 237	90.86 196	90.42 317	96.84 152	76.29 342	95.61 258	96.34 225	83.89 301	91.38 166	97.87 59	76.45 277	98.78 151	87.16 211	92.23 204	96.20 200

MVP-Stereo		90.74 238	90.08 230	92.71 265	93.19 316	88.20 222	95.86 246	96.27 228	86.07 275	84.86 298	94.76 223	77.84 269	97.75 269	83.88 263	98.01 90	92.17 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 239	89.65 250	93.96 208	94.29 265	89.63 165	97.79 60	96.82 206	89.07 189	86.12 289	95.48 196	78.61 251	97.78 266	86.97 213	81.67 320	94.46 296

V4		90.71 240	90.00 234	92.82 259	93.21 314	87.03 260	97.59 92	97.16 166	88.21 226	87.69 265	93.92 271	80.93 203	98.06 224	87.39 203	83.90 306	93.39 315

v1240		90.70 241	89.85 240	93.23 250	93.51 302	86.80 265	96.61 194	97.02 187	87.16 253	89.58 226	94.31 248	79.55 228	97.98 240	85.52 236	85.44 276	94.90 275

v52		90.70 241	90.00 234	92.82 259	93.24 311	87.03 260	97.60 90	97.14 167	88.21 226	87.69 265	93.94 269	80.91 204	98.07 219	87.39 203	83.87 307	93.36 317

EPMVS		90.70 241	89.81 242	93.37 245	94.73 250	84.21 294	93.67 307	88.02 360	89.50 174	92.38 147	93.49 286	77.82 270	97.78 266	86.03 228	92.68 199	98.11 139

Anonymous20231211		90.63 244	89.42 253	94.27 194	98.24 76	89.19 196	98.05 37	97.89 87	79.95 331	88.25 257	94.96 209	72.56 304	98.13 206	89.70 152	85.14 282	95.49 234

DTE-MVSNet		90.56 245	89.75 246	93.01 256	93.95 288	87.25 253	97.64 81	97.65 112	90.74 141	87.12 277	95.68 183	79.97 222	97.00 307	83.33 267	81.66 321	94.78 287

ACMH	87.59 16	90.53 246	89.42 253	93.87 214	96.21 182	87.92 241	97.24 124	96.94 197	88.45 213	83.91 308	96.27 150	71.92 305	98.62 166	84.43 252	89.43 245	95.05 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		90.51 247	90.19 229	91.44 301	93.41 305	81.25 318	96.98 148	96.28 227	91.68 115	86.55 285	96.30 148	74.20 295	97.98 240	88.96 172	87.40 265	95.09 261

COLMAP_ROB	87.81 15	90.40 248	89.28 256	93.79 218	97.95 97	87.13 259	96.92 156	95.89 250	82.83 311	86.88 284	97.18 101	73.77 299	99.29 101	78.44 317	93.62 184	94.95 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v748		90.34 249	89.54 251	92.75 264	93.25 310	85.71 278	97.61 89	97.17 163	88.54 212	87.20 276	93.54 283	81.02 199	98.01 236	85.73 234	81.80 318	94.52 294

IterMVS-SCA-FT		90.31 250	89.81 242	91.82 290	95.52 207	84.20 295	94.30 292	96.15 235	90.61 151	87.39 272	94.27 259	75.80 282	96.44 313	87.34 205	86.88 268	94.82 282

MS-PatchMatch		90.27 251	89.77 244	91.78 293	94.33 263	84.72 291	95.55 259	96.73 208	86.17 274	86.36 286	95.28 202	71.28 310	97.80 264	84.09 257	98.14 88	92.81 322

tpm		90.25 252	89.74 247	91.76 295	93.92 289	79.73 332	93.98 300	93.54 329	88.28 224	91.99 156	93.25 292	77.51 272	97.44 289	87.30 207	87.94 258	98.12 136

AllTest		90.23 253	88.98 260	93.98 205	97.94 98	86.64 267	96.51 201	95.54 263	85.38 281	85.49 293	96.77 118	70.28 316	99.15 111	80.02 307	92.87 195	96.15 204

ACMH+	87.92 14	90.20 254	89.18 258	93.25 249	96.48 172	86.45 271	96.99 147	96.68 214	88.83 201	84.79 299	96.22 151	70.16 318	98.53 172	84.42 253	88.04 257	94.77 288

test-mter		90.19 255	89.54 251	92.12 282	94.59 254	80.66 321	94.29 293	92.98 338	87.68 242	90.76 187	92.37 304	67.67 327	98.07 219	88.81 177	96.74 124	97.63 156

IterMVS		90.15 256	89.67 248	91.61 297	95.48 209	83.72 298	94.33 291	96.12 236	89.99 164	87.31 275	94.15 264	75.78 284	96.27 316	86.97 213	86.89 267	94.83 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 257	89.42 253	91.97 286	94.41 261	80.62 323	94.29 293	91.97 350	87.28 251	90.44 192	92.47 303	68.79 322	97.67 274	88.50 181	96.60 130	97.61 160

tpm2		89.96 258	89.21 257	92.23 276	94.91 243	81.25 318	93.78 303	94.42 311	80.62 329	91.56 163	93.44 289	76.44 278	97.94 249	85.60 235	92.08 211	97.49 165

IB-MVS	87.33 17	89.91 259	88.28 270	94.79 172	95.26 223	87.70 247	95.12 278	93.95 324	89.35 177	87.03 280	92.49 302	70.74 314	99.19 106	89.18 166	81.37 322	97.49 165
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
ADS-MVSNet		89.89 260	88.68 264	93.53 237	95.86 196	84.89 289	90.93 339	95.07 286	83.23 309	91.28 175	91.81 314	79.01 238	97.85 259	79.52 309	91.39 221	97.84 148

FMVSNet1		89.88 261	88.31 269	94.59 180	95.41 211	91.18 118	97.50 100	96.93 198	86.62 268	87.41 271	94.51 233	65.94 336	97.29 298	83.04 270	87.43 263	95.31 251

pmmvs5		89.86 262	88.87 262	92.82 259	92.86 320	86.23 273	96.26 225	95.39 267	84.24 297	87.12 277	94.51 233	74.27 294	97.36 295	87.61 200	87.57 261	94.86 277

tpmvs		89.83 263	89.15 259	91.89 288	94.92 241	80.30 327	93.11 319	95.46 265	86.28 271	88.08 259	92.65 298	80.44 213	98.52 173	81.47 289	89.92 242	96.84 183

tfpnnormal		89.70 264	88.40 268	93.60 233	95.15 229	90.10 142	97.56 96	98.16 40	87.28 251	86.16 288	94.63 229	77.57 271	98.05 227	74.48 328	84.59 297	92.65 323

tpmp4_e23		89.58 265	88.59 265	92.54 269	95.16 228	81.53 316	94.11 298	95.09 284	81.66 319	88.60 248	93.44 289	75.11 287	98.33 194	82.45 278	91.72 214	97.75 152

Test4		89.48 266	87.50 277	95.44 136	90.76 338	89.72 157	95.78 252	97.09 174	90.28 158	77.67 343	91.74 316	55.42 354	98.08 215	91.92 116	96.83 121	98.52 108

ADS-MVSNet2		89.45 267	88.59 265	92.03 285	95.86 196	82.26 312	90.93 339	94.32 315	83.23 309	91.28 175	91.81 314	79.01 238	95.99 326	79.52 309	91.39 221	97.84 148

Patchmatch-test		89.42 268	87.99 272	93.70 228	95.27 220	85.11 284	88.98 350	94.37 313	81.11 324	87.10 279	93.69 277	82.28 179	97.50 285	74.37 330	94.76 161	98.48 116

test0.0.03 1		89.37 269	88.70 263	91.41 302	92.47 327	85.63 279	95.22 276	92.70 343	91.11 134	86.91 283	93.65 281	79.02 236	93.19 346	78.00 318	89.18 247	95.41 241

SixPastTwentyTwo		89.15 270	88.54 267	90.98 306	93.49 303	80.28 328	96.70 184	94.70 299	90.78 140	84.15 305	95.57 187	71.78 307	97.71 272	84.63 248	85.07 287	94.94 272

TransMVSNet (Re)		88.94 271	87.56 275	93.08 255	94.35 262	88.45 212	97.73 66	95.23 278	87.47 245	84.26 303	95.29 200	79.86 223	97.33 296	79.44 313	74.44 348	93.45 314

USDC		88.94 271	87.83 274	92.27 272	94.66 251	84.96 287	93.86 302	95.90 245	87.34 249	83.40 310	95.56 188	67.43 329	98.19 202	82.64 277	89.67 244	93.66 311

dp		88.90 273	88.26 271	90.81 310	94.58 256	76.62 341	92.85 323	94.93 293	85.12 286	90.07 208	93.07 293	75.81 281	98.12 209	80.53 305	87.42 264	97.71 154

PatchT		88.87 274	87.42 280	93.22 251	94.08 281	85.10 285	89.51 348	94.64 304	81.92 317	92.36 148	88.15 343	80.05 220	97.01 306	72.43 335	93.65 183	97.54 164

our_test_3		88.78 275	87.98 273	91.20 304	92.45 328	82.53 308	93.61 310	95.69 256	85.77 278	84.88 297	93.71 276	79.99 221	96.78 311	79.47 311	86.24 269	94.28 303

EU-MVSNet		88.72 276	88.90 261	88.20 326	93.15 317	74.21 345	96.63 193	94.22 319	85.18 284	87.32 274	95.97 161	76.16 280	94.98 338	85.27 239	86.17 270	95.41 241

v18		88.71 277	87.52 276	92.27 272	94.16 270	88.11 231	96.82 167	95.96 240	87.03 255	80.76 324	89.81 324	83.15 143	96.22 317	84.69 246	75.31 339	92.49 327

v16		88.69 278	87.50 277	92.26 274	94.19 267	88.11 231	96.81 168	95.95 241	87.01 257	80.71 326	89.80 325	83.08 149	96.20 318	84.61 249	75.34 338	92.48 329

v17		88.67 279	87.47 279	92.26 274	94.13 273	88.09 233	96.81 168	95.95 241	87.02 256	80.72 325	89.75 326	83.11 146	96.20 318	84.61 249	75.15 341	92.49 327

Patchmtry		88.64 280	87.25 285	92.78 263	94.09 279	86.64 267	89.82 347	95.68 258	80.81 328	87.63 268	92.36 307	80.91 204	97.03 304	78.86 315	85.12 283	94.67 290

v15		88.53 281	87.31 281	92.20 277	94.09 279	88.05 234	96.72 179	95.90 245	87.01 257	80.53 329	89.60 330	83.02 155	96.13 320	84.29 254	74.64 342	92.41 333

V14		88.52 282	87.30 282	92.17 279	94.12 275	87.99 236	96.72 179	95.91 244	86.98 259	80.50 330	89.63 327	83.03 154	96.12 322	84.23 255	74.60 344	92.40 334

RPMNet		88.52 282	86.72 295	93.95 209	94.45 259	87.19 256	90.23 344	94.99 290	77.87 343	92.40 145	87.55 347	80.17 219	97.05 302	68.84 343	93.95 178	97.60 161

MIMVSNet		88.50 284	86.76 293	93.72 227	94.84 245	87.77 245	91.39 334	94.05 321	86.41 270	87.99 261	92.59 300	63.27 340	95.82 330	77.44 319	92.84 197	97.57 163

V9		88.49 285	87.26 284	92.18 278	94.12 275	87.97 239	96.73 176	95.90 245	86.95 261	80.40 332	89.61 328	82.98 158	96.13 320	84.14 256	74.55 345	92.44 331

v12		88.46 286	87.23 287	92.17 279	94.10 278	87.99 236	96.71 181	95.90 245	86.91 262	80.34 334	89.58 331	82.92 162	96.11 324	84.09 257	74.50 347	92.42 332

v13		88.45 287	87.22 288	92.16 281	94.08 281	87.95 240	96.71 181	95.90 245	86.86 266	80.27 336	89.55 332	82.92 162	96.12 322	84.02 259	74.63 343	92.40 334

v11		88.41 288	87.19 291	92.08 284	94.08 281	87.77 245	96.75 174	95.85 251	86.74 267	80.50 330	89.50 333	82.49 174	96.08 325	83.55 265	75.20 340	92.38 336

tpm cat1		88.36 289	87.21 289	91.81 291	95.13 231	80.55 324	92.58 326	95.70 255	74.97 349	87.45 269	91.96 312	78.01 268	98.17 204	80.39 306	88.74 252	96.72 186

ppachtmachnet_test		88.35 290	87.29 283	91.53 298	92.45 328	83.57 303	93.75 304	95.97 239	84.28 296	85.32 296	94.18 263	79.00 240	96.93 308	75.71 326	84.99 291	94.10 305

JIA-IIPM		88.26 291	87.04 292	91.91 287	93.52 301	81.42 317	89.38 349	94.38 312	80.84 327	90.93 186	80.74 354	79.22 232	97.92 253	82.76 274	91.62 216	96.38 198

testgi		87.97 292	87.21 289	90.24 319	92.86 320	80.76 320	96.67 188	94.97 291	91.74 113	85.52 292	95.83 170	62.66 342	94.47 340	76.25 324	88.36 256	95.48 235

LF4IMVS		87.94 293	87.25 285	89.98 321	92.38 330	80.05 331	94.38 288	95.25 277	87.59 244	84.34 301	94.74 225	64.31 339	97.66 276	84.83 243	87.45 262	92.23 338

gg-mvs-nofinetune		87.82 294	85.61 301	94.44 187	94.46 258	89.27 192	91.21 338	84.61 366	80.88 326	89.89 212	74.98 357	71.50 308	97.53 283	85.75 233	97.21 114	96.51 193

pmmvs6		87.81 295	86.19 297	92.69 266	91.32 335	86.30 272	97.34 116	96.41 223	80.59 330	84.05 307	94.37 244	67.37 330	97.67 274	84.75 245	79.51 328	94.09 307

K. test v3		87.64 296	86.75 294	90.32 318	93.02 319	79.48 334	96.61 194	92.08 349	90.66 146	80.25 337	94.09 265	67.21 331	96.65 312	85.96 230	80.83 325	94.83 280

Patchmatch-RL test		87.38 297	86.24 296	90.81 310	88.74 346	78.40 339	88.12 353	93.17 331	87.11 254	82.17 313	89.29 334	81.95 187	95.60 333	88.64 180	77.02 332	98.41 124

testing_2		87.33 298	85.03 305	94.22 195	87.77 350	89.32 188	94.97 279	97.11 171	89.22 180	71.64 351	88.73 337	55.16 355	97.94 249	91.95 115	88.73 253	95.41 241

FMVSNet5		87.29 299	85.79 300	91.78 293	94.80 247	87.28 251	95.49 263	95.28 274	84.09 299	83.85 309	91.82 313	62.95 341	94.17 341	78.48 316	85.34 279	93.91 309

Anonymous20231206		87.09 300	86.14 298	89.93 322	91.22 336	80.35 325	96.11 234	95.35 270	83.57 306	84.16 304	93.02 294	73.54 301	95.61 332	72.16 336	86.14 271	93.84 310

EG-PatchMatch MVS		87.02 301	85.44 302	91.76 295	92.67 324	85.00 286	96.08 236	96.45 222	83.41 308	79.52 339	93.49 286	57.10 350	97.72 271	79.34 314	90.87 230	92.56 325

TinyColmap		86.82 302	85.35 304	91.21 303	94.91 243	82.99 306	93.94 301	94.02 323	83.58 305	81.56 319	94.68 226	62.34 343	98.13 206	75.78 325	87.35 266	92.52 326

TDRefinement		86.53 303	84.76 308	91.85 289	82.23 359	84.25 293	96.38 213	95.35 270	84.97 289	84.09 306	94.94 210	65.76 337	98.34 193	84.60 251	74.52 346	92.97 318

test_0402		86.46 304	84.79 307	91.45 300	95.02 236	85.55 280	96.29 222	94.89 294	80.90 325	82.21 312	93.97 268	68.21 326	97.29 298	62.98 349	88.68 254	91.51 343

DSMNet-mixed		86.34 305	86.12 299	87.00 331	89.88 342	70.43 349	94.93 280	90.08 357	77.97 342	85.42 295	92.78 297	74.44 293	93.96 342	74.43 329	95.14 153	96.62 191

pmmvs-eth3d		86.22 306	84.45 309	91.53 298	88.34 347	87.25 253	94.47 287	95.01 287	83.47 307	79.51 340	89.61 328	69.75 320	95.71 331	83.13 269	76.73 334	91.64 341

test20.03		86.14 307	85.40 303	88.35 324	90.12 339	80.06 330	95.90 245	95.20 279	88.59 208	81.29 320	93.62 282	71.43 309	92.65 347	71.26 340	81.17 323	92.34 337

UnsupCasMVSNet_eth		85.99 308	84.45 309	90.62 314	89.97 341	82.40 311	93.62 309	97.37 148	89.86 166	78.59 342	92.37 304	65.25 338	95.35 337	82.27 281	70.75 351	94.10 305

YYNet1		85.87 309	84.23 311	90.78 313	92.38 330	82.46 310	93.17 316	95.14 282	82.12 316	67.69 352	92.36 307	78.16 259	95.50 336	77.31 321	79.73 327	94.39 298

MDA-MVSNet_test_wron		85.87 309	84.23 311	90.80 312	92.38 330	82.57 307	93.17 316	95.15 281	82.15 315	67.65 353	92.33 310	78.20 256	95.51 335	77.33 320	79.74 326	94.31 302

CMPMVS	62.92 21	85.62 311	84.92 306	87.74 328	89.14 345	73.12 347	94.17 296	96.80 207	73.98 351	73.65 347	94.93 211	66.36 332	97.61 279	83.95 262	91.28 223	92.48 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 312	83.64 313	90.92 308	95.27 220	79.49 333	90.55 342	95.60 260	83.76 304	83.00 311	89.95 321	71.09 311	97.97 243	82.75 275	60.79 357	95.31 251

MDA-MVSNet-bldmvs		85.00 313	82.95 315	91.17 305	93.13 318	83.33 304	94.56 285	95.00 288	84.57 294	65.13 357	92.65 298	70.45 315	95.85 328	73.57 333	77.49 331	94.33 300

MIMVSNet1		84.93 314	83.05 314	90.56 315	89.56 344	84.84 290	95.40 266	95.35 270	83.91 300	80.38 333	92.21 311	57.23 349	93.34 345	70.69 342	82.75 317	93.50 312

OpenMVS_ROB	81.14 20	84.42 315	82.28 316	90.83 309	90.06 340	84.05 297	95.73 253	94.04 322	73.89 352	80.17 338	91.53 318	59.15 347	97.64 277	66.92 345	89.05 248	90.80 346

LP		84.13 316	81.85 321	90.97 307	93.20 315	82.12 313	87.68 354	94.27 318	76.80 344	81.93 315	88.52 338	72.97 303	95.95 327	59.53 353	81.73 319	94.84 278

PM-MVS		83.48 317	81.86 320	88.31 325	87.83 349	77.59 340	93.43 311	91.75 351	86.91 262	80.63 327	89.91 322	44.42 361	95.84 329	85.17 242	76.73 334	91.50 344

new-patchmatchnet		83.18 318	81.87 319	87.11 330	86.88 352	75.99 343	93.70 305	95.18 280	85.02 288	77.30 344	88.40 340	65.99 335	93.88 343	74.19 332	70.18 352	91.47 345

new_pmnet		82.89 319	81.12 324	88.18 327	89.63 343	80.18 329	91.77 333	92.57 344	76.79 345	75.56 346	88.23 342	61.22 345	94.48 339	71.43 338	82.92 315	89.87 348

test2356		82.77 320	82.14 318	84.65 334	85.77 353	70.36 350	91.22 337	93.69 328	81.58 321	81.82 316	89.00 336	60.63 346	90.77 353	64.74 347	90.80 231	92.82 320

testus		82.63 321	82.15 317	84.07 335	87.31 351	67.67 355	93.18 314	94.29 317	82.47 313	82.14 314	90.69 319	53.01 356	91.94 350	66.30 346	89.96 241	92.62 324

MVS-HIRNet		82.47 322	81.21 323	86.26 333	95.38 213	69.21 354	88.96 351	89.49 359	66.28 356	80.79 323	74.08 359	68.48 324	97.39 293	71.93 337	95.47 148	92.18 339

UnsupCasMVSNet_bld		82.13 323	79.46 325	90.14 320	88.00 348	82.47 309	90.89 341	96.62 220	78.94 337	75.61 345	84.40 352	56.63 351	96.31 315	77.30 322	66.77 356	91.63 342

testpf		80.97 324	81.40 322	79.65 341	91.53 334	72.43 348	73.47 365	89.55 358	78.63 338	80.81 322	89.06 335	61.36 344	91.36 352	83.34 266	84.89 295	75.15 359

pmmvs3		79.97 325	77.50 329	87.39 329	82.80 357	79.38 335	92.70 325	90.75 355	70.69 355	78.66 341	87.47 348	51.34 358	93.40 344	73.39 334	69.65 353	89.38 349

test1235678		79.82 326	78.53 327	83.69 336	82.55 358	67.55 356	92.50 328	94.13 320	79.28 335	72.10 350	86.45 350	57.27 348	90.68 354	61.60 351	80.90 324	92.82 320

N_pmnet		78.73 327	78.71 326	78.79 343	92.80 322	46.50 371	94.14 297	43.71 375	78.61 339	80.83 321	91.66 317	74.94 291	96.36 314	67.24 344	84.45 299	93.50 312

1111		78.29 328	77.55 328	80.50 339	83.89 354	59.98 363	91.89 331	93.71 325	75.06 347	73.60 348	87.67 345	55.66 352	92.60 348	58.54 355	77.92 330	88.93 350

test12356		74.97 329	74.13 330	77.49 344	78.81 360	56.23 367	88.53 352	92.75 342	75.14 346	67.50 354	85.07 351	44.88 360	89.96 355	58.71 354	75.75 336	86.26 351

LCM-MVSNet		72.55 330	69.39 333	82.03 337	70.81 369	65.42 359	90.12 346	94.36 314	55.02 360	65.88 356	81.72 353	24.16 371	89.96 355	74.32 331	68.10 354	90.71 347

testmv		72.22 331	70.02 331	78.82 342	73.06 367	61.75 361	91.24 336	92.31 346	74.45 350	61.06 359	80.51 355	34.21 363	88.63 358	55.31 358	68.07 355	86.06 352

FPMVS		71.27 332	69.85 332	75.50 346	74.64 362	59.03 365	91.30 335	91.50 352	58.80 359	57.92 360	88.28 341	29.98 367	85.53 361	53.43 359	82.84 316	81.95 355

PMMVS2		70.19 333	66.92 335	80.01 340	76.35 361	65.67 358	86.22 356	87.58 362	64.83 358	62.38 358	80.29 356	26.78 369	88.49 359	63.79 348	54.07 358	85.88 353

no-one		68.12 334	63.78 337	81.13 338	74.01 364	70.22 352	87.61 355	90.71 356	72.63 354	53.13 362	71.89 360	30.29 365	91.45 351	61.53 352	32.21 362	81.72 356

Gipuma		67.86 335	65.41 336	75.18 347	92.66 325	73.45 346	66.50 367	94.52 309	53.33 361	57.80 361	66.07 363	30.81 364	89.20 357	48.15 362	78.88 329	62.90 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		65.38 336	69.22 334	53.86 355	83.89 354	59.98 363	91.89 331	93.71 325	75.06 347	73.60 348	87.67 345	55.66 352	92.60 348	58.54 355	2.96 368	9.00 368

ANet_high		63.94 337	59.58 338	77.02 345	61.24 372	66.06 357	85.66 358	87.93 361	78.53 340	42.94 364	71.04 361	25.42 370	80.71 363	52.60 360	30.83 364	84.28 354

PNet_i23d		59.01 338	55.87 339	68.44 350	73.98 365	51.37 368	81.36 361	82.41 368	52.37 362	42.49 366	70.39 362	11.39 372	79.99 365	49.77 361	38.71 360	73.97 360

PMVS	53.92 22	58.58 339	55.40 340	68.12 351	51.00 373	48.64 369	78.86 363	87.10 364	46.77 364	35.84 369	74.28 358	8.76 373	86.34 360	42.07 363	73.91 349	69.38 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 340	51.11 345	74.38 349	62.30 371	61.47 362	80.09 362	84.87 365	49.62 363	30.80 370	57.20 367	7.03 374	82.94 362	55.69 357	32.36 361	78.72 358

E-PMN		53.28 341	52.56 343	55.43 353	74.43 363	47.13 370	83.63 360	76.30 371	42.23 365	42.59 365	62.22 365	28.57 368	74.40 366	31.53 365	31.51 363	44.78 364

MVE	50.73 23	53.25 342	48.81 346	66.58 352	65.34 370	57.50 366	72.49 366	70.94 373	40.15 367	39.28 368	63.51 364	6.89 376	73.48 368	38.29 364	42.38 359	68.76 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 343	51.31 344	54.39 354	72.62 368	45.39 372	83.84 359	75.51 372	41.13 366	40.77 367	59.65 366	30.08 366	73.60 367	28.31 366	29.90 365	44.18 365

tmp_tt		51.94 344	53.82 342	46.29 356	33.73 374	45.30 373	78.32 364	67.24 374	18.02 368	50.93 363	87.05 349	52.99 357	53.11 369	70.76 341	25.29 366	40.46 366

v1.0		40.67 345	54.22 341	0.00 361	99.28 19	0.00 377	0.00 369	98.26 27	93.81 47	98.10 9	98.53 14	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

pcd1.5k->3k		38.37 346	40.51 347	31.96 357	94.29 265	0.00 377	0.00 369	97.69 108	0.00 372	0.00 374	0.00 374	81.45 194	0.00 373	0.00 370	91.11 225	95.89 216

wuyk23d		25.11 347	24.57 349	26.74 358	73.98 365	39.89 374	57.88 368	9.80 376	12.27 369	10.39 371	6.97 373	7.03 374	36.44 370	25.43 367	17.39 367	3.89 370

cdsmvs_eth3d_5k		23.24 348	30.99 348	0.00 361	0.00 377	0.00 377	0.00 369	97.63 114	0.00 372	0.00 374	96.88 115	84.38 129	0.00 373	0.00 370	0.00 371	0.00 371

testmvs		13.36 349	16.33 350	4.48 360	5.04 375	2.26 376	93.18 314	3.28 377	2.70 370	8.24 372	21.66 369	2.29 378	2.19 371	7.58 368	2.96 368	9.00 368

test123		13.04 350	15.66 351	5.18 359	4.51 376	3.45 375	92.50 328	1.81 378	2.50 371	7.58 373	20.15 370	3.67 377	2.18 372	7.13 369	1.07 370	9.90 367

ab-mvs-re		8.06 351	10.74 352	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	96.69 124	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

pcd_1.5k_mvsjas		7.39 352	9.85 353	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	88.65 75	0.00 373	0.00 370	0.00 371	0.00 371

sosnet-low-res		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

sosnet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

uncertanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

Regformer		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

uanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

test0726					99.45 2	95.36 7	98.31 21	98.29 24	94.92 18	98.99 2	98.92 2	95.08 1

GSMVS																98.45 119

test_part2					99.28 19	95.74 4				98.10 9

test_part1				0.00 361		0.00 377	0.00 369	98.26 27				0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

sam_mvs1												82.76 167				98.45 119

sam_mvs												81.94 188

ambc				86.56 332	83.60 356	70.00 353	85.69 357	94.97 291		80.60 328	88.45 339	37.42 362	96.84 310	82.69 276	75.44 337	92.86 319

MTGPA								98.08 54

test_post1							92.81 324				16.58 372	80.53 211	97.68 273	86.20 222

test_post											17.58 371	81.76 190	98.08 215

patchmatchnet-post											90.45 320	82.65 171	98.10 211

GG-mvs-BLEND				93.62 232	93.69 297	89.20 193	92.39 330	83.33 367		87.98 262	89.84 323	71.00 312	96.87 309	82.08 282	95.40 150	94.80 284

MTMP							97.86 53	82.03 369

gm-plane-assit					93.22 313	78.89 338			84.82 291		93.52 284		98.64 163	87.72 192

test9_res														94.81 66	99.38 35	99.45 32

TEST9					98.70 43	94.19 27	96.41 207	98.02 72	88.17 230	96.03 60	97.56 87	92.74 15	99.59 53

test_8					98.67 45	94.06 34	96.37 214	98.01 74	88.58 209	95.98 65	97.55 89	92.73 16	99.58 57

agg_prior2														93.94 79	99.38 35	99.50 25

agg_prior					98.67 45	93.79 41		98.00 76		95.68 76			99.57 65

TestCases				93.98 205	97.94 98	86.64 267		95.54 263	85.38 281	85.49 293	96.77 118	70.28 316	99.15 111	80.02 307	92.87 195	96.15 204

test_prior4						93.66 45	96.42 206

test_prior2							96.35 215		92.80 81	96.03 60	97.59 83	92.01 31		95.01 58	99.38 35

test_prior				97.23 53	98.67 45	92.99 62		98.00 76					99.41 90			99.29 47

旧先验2							95.94 243		81.66 319	97.34 21			98.82 148	92.26 105

新几何2							95.79 250

新几何1				97.32 47	98.60 52	93.59 47		97.75 98	81.58 321	95.75 73	97.85 62	90.04 62	99.67 40	86.50 218	99.13 58	98.69 100

旧先验1					98.38 65	93.38 53		97.75 98			98.09 46	92.30 28			99.01 66	99.16 55

无先验							95.79 250	97.87 91	83.87 303				99.65 42	87.68 195		98.89 84

原ACMM2							95.67 254

原ACMM1				96.38 89	98.59 53	91.09 122		97.89 87	87.41 247	95.22 87	97.68 73	90.25 58	99.54 70	87.95 188	99.12 61	98.49 114

test222					98.24 76	92.21 81	95.33 268	97.60 116	79.22 336	95.25 86	97.84 64	88.80 73			99.15 56	98.72 96

testdata2													99.67 40	85.96 230

segment_acmp												92.89 13

testdata				95.46 135	98.18 85	88.90 202		97.66 110	82.73 312	97.03 33	98.07 47	90.06 61	98.85 145	89.67 153	98.98 67	98.64 102

testdata1							95.26 275		93.10 69

test12				97.65 33	98.46 58	94.26 24		97.66 110		95.52 85		90.89 52	99.46 84		99.25 48	99.22 52

plane_prior7					96.21 182	89.98 148

plane_prior6					96.10 192	90.00 144						81.32 196

plane_prior5								97.51 126					98.60 167	93.02 99	92.23 204	95.86 217

plane_prior4											96.64 127

plane_prior3						90.00 144			94.46 32	91.34 169

plane_prior2							97.74 64		94.85 19

plane_prior1					96.14 190

plane_prior						89.99 146	97.24 124		94.06 40						92.16 208

n2								0.00 379

nn								0.00 379

door-mid								91.06 354

lessismore_v0				90.45 316	91.96 333	79.09 337		87.19 363		80.32 335	94.39 242	66.31 333	97.55 282	84.00 261	76.84 333	94.70 289

LGP-MVS_train				94.10 199	96.16 187	88.26 215		97.46 133	91.29 125	90.12 203	97.16 102	79.05 234	98.73 158	92.25 107	91.89 212	95.31 251

test11								97.88 89

door								91.13 353

HQP5-MVS						89.33 186

HQP-NCC					95.86 196		96.65 189		93.55 52	90.14 197

ACMP_Plane					95.86 196		96.65 189		93.55 52	90.14 197

BP-MVS														92.13 111

HQP4-MVS										90.14 197			98.50 175			95.78 224

HQP3-MVS								97.39 145							92.10 209

HQP2-MVS												80.95 201

NP-MVS					95.99 195	89.81 155					95.87 167

MDTV_nov1_ep13_2view						70.35 351	93.10 320		83.88 302	93.55 116		82.47 176		86.25 221		98.38 127

MDTV_nov1_ep13			90.76 201		95.22 225	80.33 326	93.03 321	95.28 274	88.14 231	92.84 142	93.83 272	81.34 195	98.08 215	82.86 272	94.34 166

ACMMP++_ref															90.30 238

ACMMP++															91.02 228

Test By Simon												88.73 74

ITE_SJBPF				92.43 271	95.34 215	85.37 283		95.92 243	91.47 119	87.75 264	96.39 146	71.00 312	97.96 247	82.36 280	89.86 243	93.97 308

DeepMVS_CX				74.68 348	90.84 337	64.34 360		81.61 370	65.34 357	67.47 355	88.01 344	48.60 359	80.13 364	62.33 350	73.68 350	79.58 357

High-resolution multi-view benchmark