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.

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 24	94.82 11	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 7	99.68 35	97.93 1	99.69 1	99.75 1

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 31	95.86 2	97.27 103	98.08 50	95.81 3	97.87 10	98.31 31	94.26 3	99.68 35	97.02 4	99.49 22	99.57 12

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 58	94.25 21	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 2	99.71 27	96.57 16	99.62 6	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 39	98.53 9	97.85 88	95.55 5	98.56 3	97.81 59	93.90 5	99.65 39	96.62 13	99.21 49	99.48 27

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 19	94.24 22	98.07 34	97.85 88	93.72 45	98.57 2	98.35 22	93.69 8	99.40 85	97.06 3	99.46 24	99.44 31

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 50	94.46 15	97.92 42	98.14 40	94.82 21	99.01 1	98.55 9	94.18 4	97.41 268	96.94 5	99.64 3	99.32 42

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 26	96.16 1	97.55 80	97.97 78	95.59 4	96.61 34	97.89 50	92.57 18	99.84 12	95.95 32	99.51 18	99.40 34

NCCC		97.30 8	97.03 9	98.11 6	98.77 34	95.06 9	97.34 97	98.04 64	95.96 2	97.09 26	97.88 52	93.18 10	99.71 27	95.84 35	99.17 52	99.56 14

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 25	95.16 7	97.60 75	98.19 33	92.82 76	97.93 9	98.74 3	91.60 37	99.86 6	96.26 20	99.52 16	99.67 2

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	98.29 34	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 4	97.12 119	98.07 55	93.54 51	96.08 52	97.69 66	93.86 6	99.71 27	96.50 17	99.39 33	99.55 16

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 64	93.84 34	96.83 142	98.10 47	95.24 10	97.49 12	98.25 37	92.57 18	99.61 45	96.80 9	99.29 42	99.56 14

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 26	98.52 10	98.32 19	93.21 58	97.18 19	98.29 34	92.08 27	99.83 13	95.63 39	99.59 8	99.54 18

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 64	93.48 45	96.83 142	97.99 76	95.20 12	97.46 13	98.25 37	92.48 21	99.58 53	96.79 11	99.29 42	99.55 16

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 17	97.24 105	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 17	97.15 117	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 36	98.52 10	98.24 28	93.19 61	97.14 22	98.34 25	91.59 38	99.87 5	95.46 44	99.59 8	99.64 4

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 32	98.52 10	98.31 21	93.21 58	97.15 21	98.33 28	91.35 40	99.86 6	95.63 39	99.59 8	99.62 6

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 26	98.15 29	98.32 19	92.57 81	97.18 19	98.29 34	92.08 27	99.83 13	95.12 49	99.59 8	99.54 18

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 43	98.80 3	98.28 23	92.99 67	96.45 43	98.30 33	91.90 32	99.85 9	95.61 41	99.68 2	99.54 18

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 61	92.66 65	96.96 129	98.01 69	95.12 13	97.14 22	98.42 16	91.82 33	99.61 45	96.90 6	99.13 55	99.50 23

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 28	94.93 10	97.72 60	98.10 47	91.50 110	98.01 7	98.32 30	92.33 22	99.58 53	94.85 58	99.51 18	99.53 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 36	91.86 87	97.67 65	98.49 12	94.66 27	97.24 17	98.41 19	92.31 25	98.94 124	96.61 14	99.46 24	98.96 70

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 45	94.30 19	97.41 89	98.04 64	94.81 22	96.59 36	98.37 21	91.24 41	99.64 44	95.16 47	99.52 16	99.42 33
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 25	96.60 27	97.64 31	99.40 5	93.44 46	98.50 13	98.09 49	93.27 57	95.95 59	98.33 28	91.04 44	99.88 3	95.20 46	99.57 12	99.60 9

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 61	92.02 83	96.96 129	97.76 91	95.01 16	97.08 27	98.42 16	91.71 34	99.54 65	96.80 9	99.13 55	99.48 27

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 29	92.31 72	97.98 40	98.06 57	93.11 64	97.44 14	98.55 9	90.93 46	99.55 63	96.06 29	99.25 45	99.51 22

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 44	97.65 68	98.98 1	92.22 86	97.14 22	98.44 14	91.17 42	99.85 9	94.35 66	99.46 24	99.57 12

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 35	98.41 17	98.06 57	93.37 53	95.54 74	98.34 25	90.59 51	99.88 3	94.83 59	99.54 14	99.49 25

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 56	94.07 28	98.21 28	98.45 15	89.86 150	97.11 25	98.01 46	92.52 20	99.69 33	96.03 31	99.53 15	99.36 40

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 22	94.71 12	96.96 129	98.06 57	90.67 132	95.55 73	98.78 2	91.07 43	99.86 6	96.58 15	99.55 13	99.38 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 66	93.39 47	96.79 149	96.72 196	94.17 36	97.44 14	97.66 69	92.76 12	99.33 90	96.86 8	97.76 94	99.08 61

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 54	98.87 1	98.06 57	91.17 121	96.40 44	97.99 48	90.99 45	99.58 53	95.61 41	99.61 7	99.49 25

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 52	92.31 72	96.20 207	98.90 2	94.30 35	95.86 61	97.74 64	92.33 22	99.38 88	96.04 30	99.42 29	99.28 47

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 97	93.19 52	95.96 219	98.18 35	95.23 11	95.87 60	97.65 70	91.45 39	99.70 32	95.87 33	99.44 28	99.00 68
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 35	97.09 7	95.15 134	98.09 79	86.63 246	96.00 218	98.15 38	95.43 7	97.95 8	98.56 7	93.40 9	99.36 89	96.77 12	99.48 23	99.45 29

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 70	91.20 106	96.89 138	97.73 94	94.74 25	96.49 40	98.49 11	90.88 48	99.58 53	96.44 18	98.32 79	99.13 56

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 62	98.74 4	98.06 57	90.57 141	96.77 29	98.35 22	90.21 55	99.53 68	94.80 61	99.63 4	99.38 38

test_prior3		96.46 39	96.20 41	97.23 48	98.67 39	92.99 56	96.35 192	98.00 71	92.80 77	96.03 53	97.59 77	92.01 29	99.41 83	95.01 53	99.38 34	99.29 44

abl_6		96.40 40	96.21 40	96.98 58	98.89 32	92.20 77	97.89 44	98.03 66	93.34 56	97.22 18	98.42 16	87.93 78	99.72 26	95.10 50	99.07 60	99.02 63

CANet		96.39 41	96.02 43	97.50 37	97.62 106	93.38 48	97.02 124	97.96 79	95.42 8	94.86 81	97.81 59	87.38 88	99.82 16	96.88 7	99.20 50	99.29 44

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 74	90.93 117	96.86 140	97.72 97	94.67 26	96.16 49	98.46 12	90.43 52	99.58 53	96.23 21	97.96 88	98.90 77

train_agg		96.30 43	95.83 46	97.72 24	98.70 37	94.19 23	96.41 184	98.02 67	88.58 193	96.03 53	97.56 81	92.73 14	99.59 50	95.04 51	99.37 38	99.39 35

ACMMP		96.27 44	95.93 44	97.28 45	99.24 20	92.62 66	98.25 25	98.81 3	92.99 67	94.56 85	98.39 20	88.96 64	99.85 9	94.57 65	97.63 95	99.36 40
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 45	96.19 42	96.39 79	98.23 73	91.35 101	96.24 205	98.79 4	93.99 39	95.80 64	97.65 70	89.92 59	99.24 95	95.87 33	99.20 50	98.58 93

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 39	93.79 36	96.28 200	98.00 71	88.76 190	95.68 67	97.55 83	92.70 16	99.57 61	95.01 53	99.32 40	99.32 42

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 39	93.88 32	96.41 184	98.00 71	87.93 212	95.81 63	97.47 85	92.33 22	99.59 50	95.04 51	99.37 38	99.39 35

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 86	93.17 53	97.30 102	98.06 57	93.92 40	93.38 104	98.66 4	86.83 93	99.73 23	95.60 43	99.22 48	98.96 70
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 49	95.45 51	97.85 13	97.75 100	94.50 14	96.87 139	97.95 81	95.46 6	95.60 71	98.01 46	80.96 190	99.83 13	97.23 2	99.25 45	99.23 48

CSCG		96.05 49	95.91 45	96.46 77	99.24 20	90.47 129	98.30 21	98.57 11	89.01 175	93.97 96	97.57 79	92.62 17	99.76 21	94.66 64	99.27 44	99.15 54

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 109	95.15 8	98.28 22	97.60 108	94.52 29	96.27 46	96.12 141	87.65 82	99.18 99	96.20 26	94.82 148	98.91 76

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 30	94.44 16	96.35 192	97.88 83	86.98 236	96.65 33	97.89 50	91.99 31	99.47 76	92.26 95	99.46 24	99.39 35

UA-Net		95.95 53	95.53 50	97.20 52	97.67 103	92.98 58	97.65 68	98.13 41	94.81 22	96.61 34	98.35 22	88.87 65	99.51 72	90.36 131	97.35 105	99.11 59

VNet		95.89 54	95.45 51	97.21 51	98.07 80	92.94 59	97.50 83	98.15 38	93.87 41	97.52 11	97.61 76	85.29 109	99.53 68	95.81 36	95.27 142	99.16 52

alignmvs		95.87 55	95.23 58	97.78 19	97.56 111	95.19 6	97.86 46	97.17 152	94.39 32	96.47 41	96.40 131	85.89 103	99.20 96	96.21 25	95.11 144	98.95 72

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 23	94.19 23	97.03 122	98.08 50	88.35 201	95.09 79	97.65 70	89.97 58	99.48 75	92.08 104	98.59 74	98.44 108

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 55	90.53 127	96.04 214	97.48 119	93.47 52	95.67 70	98.10 40	89.17 62	99.25 94	91.27 124	98.77 69	99.13 56

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 18	91.48 96	98.33 20	98.11 45	87.79 215	95.17 78	98.03 44	87.09 91	99.61 45	93.51 81	99.42 29	99.02 63

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 127	95.34 5	98.48 14	97.87 85	94.65 28	88.53 226	98.02 45	83.69 126	99.71 27	93.18 89	98.96 65	99.44 31

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 113	90.66 125	95.31 248	97.48 119	93.85 42	96.51 39	95.70 165	88.65 69	99.65 39	94.80 61	98.27 80	96.17 183

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 153	91.21 104	98.22 26	97.57 111	91.42 114	96.22 47	97.32 87	86.20 100	97.92 229	94.07 68	99.05 61	98.85 81

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 115	90.50 128	95.44 243	97.44 131	93.70 47	96.46 42	96.18 138	88.59 72	99.53 68	94.79 63	97.81 91	96.17 183

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 74	90.86 119	97.27 103	98.25 26	90.21 144	94.18 92	97.27 89	87.48 86	99.73 23	93.53 80	97.77 93	98.55 94

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 117	91.58 95	98.26 24	98.12 42	94.38 33	94.90 80	98.15 39	82.28 169	98.92 125	91.45 121	98.58 75	99.01 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 112	89.56 153	98.67 5	97.00 175	90.69 131	94.24 91	97.62 75	89.79 60	98.81 135	93.39 87	96.49 125	98.92 75

EPNet		95.20 66	94.56 71	97.14 53	92.80 297	92.68 64	97.85 48	94.87 282	96.64 1	92.46 126	97.80 61	86.23 98	99.65 39	93.72 78	98.62 73	99.10 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 141	93.36 50	98.65 6	98.36 16	94.12 37	89.25 216	98.06 43	82.20 172	99.77 20	93.41 86	99.32 40	99.18 51

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 52	91.28 102	96.43 182	97.57 111	92.04 99	94.77 83	97.96 49	87.01 92	99.09 114	91.31 123	96.77 117	98.36 115

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 138	91.71 88	96.25 202	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 138	91.71 88	96.25 202	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 138	91.71 88	96.25 202	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 32	90.68 124	97.24 105	97.73 94	91.80 104	92.93 123	96.62 122	89.13 63	99.14 104	89.21 149	97.78 92	98.97 69

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 153	91.21 104	95.83 225	96.27 215	88.93 181	96.22 47	96.88 103	86.20 100	98.85 132	95.27 45	99.05 61	98.82 84

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 136	91.47 97	96.41 184	97.41 135	91.02 126	94.50 86	95.92 148	87.53 85	98.78 137	93.89 74	96.81 116	98.84 83

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 103	90.56 126	98.44 15	96.22 219	93.21 58	93.99 94	97.74 64	85.55 107	98.45 163	89.98 132	97.86 89	99.14 55

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 131	89.55 154	96.70 162	97.17 152	91.17 121	95.60 71	96.11 143	87.87 79	98.76 140	93.01 92	97.17 109	98.72 87

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 67	89.46 160	95.47 242	98.36 16	88.84 184	94.36 88	96.09 144	88.02 75	99.58 53	93.44 84	98.18 82	98.40 111

jason		94.84 78	94.39 79	96.18 92	95.52 182	90.93 117	96.09 211	96.52 208	89.28 162	96.01 57	97.32 87	84.70 117	98.77 139	95.15 48	98.91 67	98.85 81
jason: jason.
API-MVS		94.84 78	94.49 75	95.90 100	97.90 93	92.00 84	97.80 51	97.48 119	89.19 165	94.81 82	96.71 108	88.84 66	99.17 100	88.91 157	98.76 70	96.53 173

1121		94.71 80	93.83 83	97.34 41	98.57 50	93.64 41	96.04 214	97.73 94	81.56 298	95.68 67	97.85 56	90.23 54	99.65 39	87.68 179	99.12 58	98.73 86

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 102	92.05 81	96.59 175	97.35 142	90.61 138	94.64 84	96.93 101	86.41 97	99.39 86	91.20 126	94.71 152	98.94 73

sss		94.51 82	93.80 84	96.64 62	97.07 121	91.97 85	96.32 196	98.06 57	88.94 180	94.50 86	96.78 105	84.60 118	99.27 93	91.90 107	96.02 130	98.68 91

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 149	92.13 79	96.21 206	96.67 203	94.38 33	93.53 101	97.03 100	79.34 219	99.71 27	90.76 127	98.45 77	97.82 135

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 47	91.68 91	96.59 175	97.81 90	89.87 149	92.15 135	97.06 99	83.62 127	99.54 65	89.34 144	98.07 85	97.70 139

CNLPA		94.28 85	93.53 93	96.52 69	98.38 59	92.55 68	96.59 175	96.88 190	90.13 146	91.91 139	97.24 91	85.21 110	99.09 114	87.64 182	97.83 90	97.92 128

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 81	92.19 78	97.67 65	97.47 122	88.13 210	93.00 118	95.84 152	84.86 116	99.51 72	87.99 171	98.17 83	97.83 134
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 87	93.42 100	96.48 74	97.64 105	91.42 100	95.55 237	97.71 100	88.99 176	92.34 131	95.82 154	89.19 61	99.11 106	86.14 207	97.38 103	98.90 77

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 67	89.38 166	95.18 254	98.48 14	85.60 256	93.76 98	97.11 97	83.15 133	99.61 45	91.33 122	98.72 71	99.19 50

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 146	97.61 107	87.92 218	98.10 31	95.80 240	92.22 86	93.02 117	97.45 86	84.53 120	97.91 232	88.24 166	97.97 87	99.02 63

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 160	91.46 98	96.33 195	97.04 171	88.97 179	93.56 99	96.51 126	87.55 84	97.89 233	89.80 135	95.95 132	98.44 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 49	91.15 111	96.69 164	97.39 136	87.29 227	91.37 149	96.71 108	88.39 73	99.52 71	87.33 190	97.13 110	97.73 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 92	93.70 86	95.27 127	95.70 178	92.03 82	98.10 31	98.68 7	93.36 55	90.39 169	96.70 110	87.63 83	97.94 225	92.25 97	90.50 215	95.84 197

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 164	98.27 67	89.46 160	96.73 154	98.36 16	90.17 145	94.36 88	95.24 187	88.02 75	99.58 53	93.44 84	90.72 211	94.36 275

nrg030		94.05 94	93.31 102	96.27 88	95.22 200	94.59 13	98.34 19	97.46 124	92.93 74	91.21 159	96.64 115	87.23 90	98.22 177	94.99 56	85.80 252	95.98 192

UGNet		94.04 95	93.28 103	96.31 84	96.85 128	91.19 107	97.88 45	97.68 102	94.40 31	93.00 118	96.18 138	73.39 280	99.61 45	91.72 112	98.46 76	98.13 120
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 96	93.46 96	95.64 111	96.16 162	90.45 130	96.71 159	96.89 189	89.27 163	93.46 103	96.92 102	87.29 89	97.94 225	88.70 163	95.74 136	98.53 96

114514_t		93.95 97	93.06 106	96.63 64	99.07 27	91.61 92	97.46 88	97.96 79	77.99 314	93.00 118	97.57 79	86.14 102	99.33 90	89.22 148	99.15 53	98.94 73

FC-MVSNet-test		93.94 98	93.57 90	95.04 139	95.48 184	91.45 99	98.12 30	98.71 5	93.37 53	90.23 172	96.70 110	87.66 81	97.85 235	91.49 119	90.39 216	95.83 198

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 120	92.49 70	95.64 234	96.64 204	89.05 174	93.00 118	95.79 158	85.77 106	99.45 79	89.16 151	94.35 153	97.96 126

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 151	97.06 123	88.53 187	95.28 249	97.45 128	91.68 107	94.08 93	97.68 67	82.41 167	98.90 127	93.84 76	92.47 181	96.98 156

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 94	89.96 141	97.72 60	95.85 237	92.43 83	95.86 61	98.44 14	68.42 301	99.39 86	96.31 19	94.85 146	98.71 89

mvs_anonymous		93.82 101	93.74 85	94.06 178	96.44 150	85.41 258	95.81 226	97.05 168	89.85 152	90.09 182	96.36 133	87.44 87	97.75 245	93.97 70	96.69 121	99.02 63

HQP_MVS		93.78 103	93.43 98	94.82 149	96.21 157	89.99 136	97.74 56	97.51 117	94.85 17	91.34 151	96.64 115	81.32 186	98.60 150	93.02 90	92.23 184	95.86 194

PS-MVSNAJss		93.74 104	93.51 94	94.44 165	93.91 265	89.28 174	97.75 54	97.56 114	92.50 82	89.94 185	96.54 125	88.65 69	98.18 181	93.83 77	90.90 208	95.86 194

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 121	88.61 185	94.79 258	97.46 124	91.97 102	93.99 94	97.86 55	81.74 181	98.88 131	92.64 94	92.67 180	96.92 164

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 70	89.88 143	94.58 261	98.49 12	85.06 263	93.78 97	95.78 159	82.86 154	98.67 145	91.77 111	95.71 138	99.07 62

mvs-test1		93.63 107	93.69 87	93.46 218	96.02 168	84.61 268	97.24 105	96.72 196	93.85 42	92.30 132	95.76 160	83.08 139	98.89 129	91.69 115	96.54 124	96.87 166

LFMVS		93.60 108	92.63 119	96.52 69	98.13 78	91.27 103	97.94 41	93.39 314	90.57 141	96.29 45	98.31 31	69.00 297	99.16 101	94.18 67	95.87 134	99.12 58

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 56	88.98 180	97.18 114	97.29 146	87.75 217	90.49 166	97.10 98	85.21 110	99.50 74	86.70 199	96.72 120	97.63 140

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 114	91.96 86	95.40 244	97.45 128	89.81 154	93.22 111	96.28 135	79.62 216	99.46 77	90.74 128	93.11 175	98.50 101

LS3D		93.57 110	92.61 121	96.47 75	97.59 109	91.61 92	97.67 65	97.72 97	85.17 261	90.29 171	98.34 25	84.60 118	99.73 23	83.85 247	98.27 80	98.06 125

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 133	90.03 133	96.81 146	97.13 158	88.19 206	91.30 154	94.27 234	86.21 99	98.63 147	87.66 181	96.46 127	98.12 121

QAPM		93.45 113	92.27 131	96.98 58	96.77 133	92.62 66	98.39 18	98.12 42	84.50 271	88.27 232	97.77 62	82.39 168	99.81 17	85.40 221	98.81 68	98.51 99

diffmvs		93.43 114	92.75 114	95.48 121	96.47 148	89.61 150	96.09 211	97.14 156	85.97 253	93.09 116	95.35 182	84.87 115	98.55 155	89.51 142	96.26 129	98.28 117

UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 190	92.83 60	97.17 115	98.58 10	92.98 72	90.13 177	95.80 155	88.37 74	97.85 235	91.71 113	83.93 279	95.73 207

1112_ss		93.37 115	92.42 129	96.21 91	97.05 124	90.99 113	96.31 197	96.72 196	86.87 242	89.83 191	96.69 112	86.51 96	99.14 104	88.12 168	93.67 169	98.50 101

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 187	93.34 51	97.39 93	98.71 5	93.14 63	90.10 181	94.83 202	87.71 80	98.03 208	91.67 117	83.99 278	95.46 214

OPM-MVS		93.28 118	92.76 112	94.82 149	94.63 228	90.77 123	96.65 167	97.18 150	93.72 45	91.68 144	97.26 90	79.33 220	98.63 147	92.13 101	92.28 183	95.07 240

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 178	92.39 71	97.86 46	98.66 9	92.30 85	92.09 137	95.37 181	80.49 202	98.40 165	93.95 71	85.86 251	95.75 205

MVSTER		93.20 120	92.81 111	94.37 168	96.56 141	89.59 152	97.06 121	97.12 159	91.24 120	91.30 154	95.96 146	82.02 175	98.05 203	93.48 83	90.55 213	95.47 213

HQP-MVS		93.19 121	92.74 116	94.54 163	95.86 171	89.33 169	96.65 167	97.39 136	93.55 48	90.14 173	95.87 150	80.95 191	98.50 159	92.13 101	92.10 189	95.78 201

CHOSEN 280x420		93.12 122	92.72 117	94.34 170	96.71 135	87.27 229	90.29 316	97.72 97	86.61 246	91.34 151	95.29 184	84.29 122	98.41 164	93.25 88	98.94 66	97.35 152

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 244	96.02 168	83.25 280	97.14 118	96.72 196	93.85 42	91.20 160	93.44 262	83.08 139	98.30 174	91.69 115	95.73 137	96.50 175

test_djsdf		93.07 124	92.76 112	94.00 181	93.49 278	88.70 184	98.22 26	97.57 111	91.42 114	90.08 183	95.55 172	82.85 155	97.92 229	94.07 68	91.58 197	95.40 221

VDDNet		93.05 125	92.07 133	96.02 96	96.84 129	90.39 131	98.08 33	95.85 237	86.22 250	95.79 65	98.46 12	67.59 304	99.19 97	94.92 57	94.85 146	98.47 106

EI-MVSNet		93.03 126	92.88 110	93.48 216	95.77 176	86.98 238	96.44 180	97.12 159	90.66 134	91.30 154	97.64 73	86.56 95	98.05 203	89.91 133	90.55 213	95.41 217

CLD-MVS		92.98 127	92.53 125	94.32 171	96.12 166	89.20 176	95.28 249	97.47 122	92.66 79	89.90 186	95.62 168	80.58 200	98.40 165	92.73 93	92.40 182	95.38 223
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMM	89.79 8	92.96 128	92.50 127	94.35 169	96.30 155	88.71 183	97.58 78	97.36 141	91.40 116	90.53 165	96.65 114	79.77 213	98.75 141	91.24 125	91.64 195	95.59 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 129	92.56 122	94.10 176	96.16 162	88.26 193	97.65 68	97.46 124	91.29 117	90.12 179	97.16 94	79.05 223	98.73 142	92.25 97	91.89 192	95.31 227

BH-untuned		92.94 129	92.62 120	93.92 190	97.22 115	86.16 250	96.40 188	96.25 217	90.06 147	89.79 193	96.17 140	83.19 131	98.35 169	87.19 193	97.27 107	97.24 153

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 214	92.83 60	97.16 116	98.24 28	93.02 66	90.13 177	95.71 163	83.47 128	97.85 235	91.71 113	83.93 279	95.78 201

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 76	90.80 121	95.27 251	97.18 150	87.96 211	91.86 141	95.68 166	80.44 203	98.99 122	84.01 243	97.54 97	96.89 165

PMMVS		92.86 133	92.34 130	94.42 167	94.92 216	86.73 242	94.53 263	96.38 211	84.78 268	94.27 90	95.12 192	83.13 135	98.40 165	91.47 120	96.49 125	98.12 121

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 190	92.73 63	98.27 23	98.12 42	84.86 266	85.78 265	97.75 63	78.89 231	99.74 22	87.50 186	98.65 72	96.73 169

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 125	89.97 139	95.53 239	96.64 204	85.38 257	89.65 201	95.18 188	85.86 104	99.10 111	87.70 177	93.58 174	98.49 103

1314		92.81 136	92.03 135	95.14 135	95.33 193	89.52 157	96.04 214	97.44 131	87.72 218	86.25 262	95.33 183	83.84 124	98.79 136	89.26 146	97.05 111	97.11 154

DP-MVS		92.76 137	91.51 159	96.52 69	98.77 34	90.99 113	97.38 95	96.08 224	82.38 289	89.29 213	97.87 53	83.77 125	99.69 33	81.37 278	96.69 121	98.89 79

BH-RMVSNet		92.72 138	91.97 138	94.97 144	97.16 118	87.99 213	96.15 208	95.60 245	90.62 136	91.87 140	97.15 96	78.41 236	98.57 153	83.16 252	97.60 96	98.36 115

ACMP	89.59 10	92.62 139	92.14 132	94.05 179	96.40 151	88.20 199	97.36 96	97.25 149	91.52 109	88.30 230	96.64 115	78.46 235	98.72 144	91.86 110	91.48 199	95.23 234
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
view600		92.55 140	91.68 146	95.18 129	97.98 82	89.44 162	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 242	99.11 106	81.61 267	94.04 161	96.98 156

view800		92.55 140	91.68 146	95.18 129	97.98 82	89.44 162	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 242	99.11 106	81.61 267	94.04 161	96.98 156

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 82	89.44 162	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 242	99.11 106	81.61 267	94.04 161	96.98 156

tfpn		92.55 140	91.68 146	95.18 129	97.98 82	89.44 162	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 242	99.11 106	81.61 267	94.04 161	96.98 156

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 247	96.82 132	81.89 288	96.92 136	93.71 309	92.41 84	84.30 275	94.60 212	85.08 112	97.03 281	91.51 118	97.36 104	98.40 111

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 223	92.07 80	97.53 81	98.11 45	92.90 75	89.56 204	96.12 141	83.16 132	97.60 256	89.30 145	83.20 289	95.75 205

thres600view7		92.49 146	91.60 152	95.18 129	97.91 92	89.47 158	97.65 68	94.66 284	92.18 92	93.33 105	94.91 195	78.06 246	99.10 111	81.61 267	94.06 160	96.98 156

conf200view11		92.45 147	91.58 153	95.05 138	97.92 90	89.37 167	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 246	99.08 116	81.40 274	94.08 156	96.70 171

thres100view900		92.43 148	91.58 153	94.98 143	97.92 90	89.37 167	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 246	99.08 116	81.40 274	94.08 156	96.48 176

jajsoiax		92.42 149	91.89 140	94.03 180	93.33 284	88.50 188	97.73 58	97.53 115	92.00 101	88.85 220	96.50 127	75.62 263	98.11 187	93.88 75	91.56 198	95.48 211

thres400		92.42 149	91.52 157	95.12 137	97.85 95	89.29 172	97.41 89	94.88 279	92.19 90	93.27 109	94.46 218	78.17 239	99.08 116	81.40 274	94.08 156	96.98 156

tfpn200view9		92.38 151	91.52 157	94.95 146	97.85 95	89.29 172	97.41 89	94.88 279	92.19 90	93.27 109	94.46 218	78.17 239	99.08 116	81.40 274	94.08 156	96.48 176

WR-MVS		92.34 152	91.53 156	94.77 155	95.13 206	90.83 120	96.40 188	97.98 77	91.88 103	89.29 213	95.54 173	82.50 163	97.80 240	89.79 136	85.27 258	95.69 208

NR-MVSNet		92.34 152	91.27 166	95.53 116	94.95 214	93.05 55	97.39 93	98.07 55	92.65 80	84.46 273	95.71 163	85.00 113	97.77 244	89.71 137	83.52 286	95.78 201

mvs_tets		92.31 154	91.76 142	93.94 189	93.41 280	88.29 191	97.63 73	97.53 115	92.04 99	88.76 221	96.45 129	74.62 270	98.09 190	93.91 73	91.48 199	95.45 215

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 63	89.60 151	96.79 149	97.65 105	81.83 293	91.52 146	97.23 92	87.94 77	98.91 126	71.31 314	98.37 78	98.17 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		92.29 156	91.99 137	93.21 229	95.27 195	85.52 257	97.03 122	96.63 206	92.09 93	89.11 217	95.14 190	80.33 206	98.08 191	87.54 185	94.74 151	96.03 191

IterMVS-LS		92.29 156	91.94 139	93.34 223	96.25 156	86.97 239	96.57 178	97.05 168	90.67 132	89.50 207	94.80 204	86.59 94	97.64 253	89.91 133	86.11 250	95.40 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 158	91.74 145	93.73 201	97.77 99	83.69 277	92.88 295	96.72 196	87.91 213	93.00 118	94.86 199	78.51 234	99.05 120	86.53 200	97.45 102	98.47 106

VPNet		92.23 159	91.31 164	94.99 141	95.56 181	90.96 115	97.22 110	97.86 87	92.96 73	90.96 161	96.62 122	75.06 266	98.20 178	91.90 107	83.65 285	95.80 200

thres200		92.23 159	91.39 160	94.75 156	97.61 107	89.03 179	96.60 174	95.09 269	92.08 98	93.28 108	94.00 241	78.39 237	99.04 121	81.26 279	94.18 155	96.19 182

anonymousdsp		92.16 161	91.55 155	93.97 184	92.58 301	89.55 154	97.51 82	97.42 134	89.42 160	88.40 227	94.84 200	80.66 199	97.88 234	91.87 109	91.28 203	94.48 271

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 224	90.94 116	97.47 87	97.43 133	89.14 172	88.90 218	96.43 130	79.71 214	98.24 176	89.56 141	87.68 239	95.67 209

BH-w/o		92.14 163	91.75 143	93.31 224	96.99 126	85.73 253	95.67 231	95.69 242	88.73 191	89.26 215	94.82 203	82.97 149	98.07 195	85.26 223	96.32 128	96.13 187

test_normal		92.01 164	90.75 187	95.80 104	93.24 286	89.97 139	95.93 221	96.24 218	90.62 136	81.63 291	93.45 261	74.98 267	98.89 129	93.61 79	97.04 112	98.55 94

DI_MVS_plusplus_test		92.01 164	90.77 185	95.73 109	93.34 282	89.78 146	96.14 209	96.18 221	90.58 140	81.80 290	93.50 258	74.95 268	98.90 127	93.51 81	96.94 113	98.51 99

WR-MVS_H		92.00 166	91.35 161	93.95 186	95.09 208	89.47 158	98.04 35	98.68 7	91.46 112	88.34 228	94.68 208	85.86 104	97.56 257	85.77 215	84.24 276	94.82 258

tfpn1000		91.99 167	91.05 172	94.80 152	97.78 98	89.66 148	97.91 43	92.90 319	88.99 176	91.73 142	94.84 200	78.99 229	98.33 172	82.41 263	93.91 167	96.40 178

PatchmatchNet		91.91 168	91.35 161	93.59 210	95.38 188	84.11 272	93.15 291	95.39 252	89.54 156	92.10 136	93.68 251	82.82 156	98.13 184	84.81 227	95.32 141	98.52 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		91.89 169	91.24 167	93.82 192	95.05 209	88.57 186	97.82 50	98.19 33	91.70 106	88.21 233	95.76 160	81.96 176	97.52 260	87.86 173	84.65 272	95.37 224

tfpn_ndepth		91.88 170	90.96 176	94.62 157	97.73 101	89.93 142	97.75 54	92.92 318	88.93 181	91.73 142	93.80 248	78.91 230	98.49 162	83.02 255	93.86 168	95.45 215

FMVSNet3		91.78 171	90.69 190	95.03 140	96.53 143	92.27 74	97.02 124	96.93 185	89.79 155	89.35 210	94.65 210	77.01 254	97.47 263	86.12 208	88.82 228	95.35 225

X-MVStestdata		91.71 172	89.67 225	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	32.69 342	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

MVS		91.71 172	90.44 195	95.51 117	95.20 202	91.59 94	96.04 214	97.45 128	73.44 326	87.36 248	95.60 169	85.42 108	99.10 111	85.97 212	97.46 98	95.83 198

EPNet_dtu		91.71 172	91.28 165	92.99 234	93.76 270	83.71 275	96.69 164	95.28 259	93.15 62	87.02 256	95.95 147	83.37 130	97.38 271	79.46 288	96.84 114	97.88 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1neww		91.70 175	91.01 173	93.75 198	94.19 242	88.14 204	97.20 111	96.98 176	89.18 167	89.87 189	94.44 220	83.10 137	98.06 200	89.06 153	85.09 262	95.06 243

v7new		91.70 175	91.01 173	93.75 198	94.19 242	88.14 204	97.20 111	96.98 176	89.18 167	89.87 189	94.44 220	83.10 137	98.06 200	89.06 153	85.09 262	95.06 243

v6		91.69 177	91.00 175	93.75 198	94.14 247	88.12 206	97.20 111	96.98 176	89.19 165	89.90 186	94.42 222	83.04 143	98.07 195	89.07 152	85.10 261	95.07 240

PatchFormer-LS_test		91.68 178	91.18 171	93.19 230	95.24 199	83.63 278	95.53 239	95.44 251	89.82 153	91.37 149	92.58 274	80.85 198	98.52 157	89.65 140	90.16 218	97.42 151

v1141		91.61 179	90.89 177	93.78 195	94.01 260	88.24 195	96.96 129	96.96 180	89.17 169	89.75 195	94.29 230	82.99 147	98.03 208	88.85 159	85.00 267	95.07 240

divwei89l23v2f112		91.61 179	90.89 177	93.78 195	94.01 260	88.22 197	96.96 129	96.96 180	89.17 169	89.75 195	94.28 232	83.02 145	98.03 208	88.86 158	84.98 269	95.08 238

v1		91.61 179	90.89 177	93.78 195	94.01 260	88.21 198	96.96 129	96.96 180	89.17 169	89.78 194	94.29 230	82.97 149	98.05 203	88.85 159	84.99 268	95.08 238

v2v482		91.59 182	90.85 182	93.80 193	93.87 267	88.17 201	96.94 135	96.88 190	89.54 156	89.53 205	94.90 196	81.70 182	98.02 211	89.25 147	85.04 266	95.20 235

V42		91.58 183	90.87 180	93.73 201	94.05 259	88.50 188	97.32 100	96.97 179	88.80 189	89.71 197	94.33 227	82.54 162	98.05 203	89.01 155	85.07 264	94.64 268

PCF-MVS	89.48 11	91.56 184	89.95 214	96.36 82	96.60 137	92.52 69	92.51 300	97.26 147	79.41 307	88.90 218	96.56 124	84.04 123	99.55 63	77.01 299	97.30 106	97.01 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-CasMVS		91.55 185	90.84 184	93.69 205	94.96 213	88.28 192	97.84 49	98.24 28	91.46 112	88.04 235	95.80 155	79.67 215	97.48 262	87.02 196	84.54 274	95.31 227

Patchmatch-test1		91.54 186	90.85 182	93.59 210	95.59 180	84.95 264	94.72 259	95.58 247	90.82 127	92.25 133	93.58 255	75.80 260	97.41 268	83.35 249	95.98 131	98.40 111

PAPM		91.52 187	90.30 199	95.20 128	95.30 194	89.83 144	93.38 286	96.85 192	86.26 249	88.59 225	95.80 155	84.88 114	98.15 183	75.67 302	95.93 133	97.63 140

TR-MVS		91.48 188	90.59 193	94.16 175	96.40 151	87.33 227	95.67 231	95.34 258	87.68 219	91.46 147	95.52 174	76.77 255	98.35 169	82.85 257	93.61 172	96.79 168

v7		91.47 189	90.73 188	93.68 206	94.13 248	88.16 202	97.09 120	97.05 168	88.38 199	89.80 192	94.52 213	82.21 171	98.01 212	88.00 170	85.42 255	94.87 252

tpmrst		91.44 190	91.32 163	91.79 269	95.15 204	79.20 310	93.42 285	95.37 254	88.55 195	93.49 102	93.67 252	82.49 164	98.27 175	90.41 130	89.34 225	97.90 129

test-LLR		91.42 191	91.19 170	92.12 259	94.59 229	80.66 295	94.29 268	92.98 316	91.11 123	90.76 163	92.37 277	79.02 225	98.07 195	88.81 161	96.74 118	97.63 140

MSDG		91.42 191	90.24 203	94.96 145	97.15 119	88.91 181	93.69 280	96.32 213	85.72 255	86.93 257	96.47 128	80.24 207	98.98 123	80.57 281	95.05 145	96.98 156

GA-MVS		91.38 193	90.31 198	94.59 158	94.65 227	87.62 225	94.34 266	96.19 220	90.73 130	90.35 170	93.83 246	71.84 283	97.96 223	87.22 192	93.61 172	98.21 118

v1144		91.37 194	90.60 192	93.68 206	93.89 266	88.23 196	96.84 141	97.03 173	88.37 200	89.69 199	94.39 223	82.04 174	97.98 216	87.80 175	85.37 256	94.84 254

GBi-Net		91.35 195	90.27 201	94.59 158	96.51 144	91.18 108	97.50 83	96.93 185	88.82 186	89.35 210	94.51 214	73.87 274	97.29 275	86.12 208	88.82 228	95.31 227

test1		91.35 195	90.27 201	94.59 158	96.51 144	91.18 108	97.50 83	96.93 185	88.82 186	89.35 210	94.51 214	73.87 274	97.29 275	86.12 208	88.82 228	95.31 227

FMVSNet2		91.31 197	90.08 208	94.99 141	96.51 144	92.21 75	97.41 89	96.95 183	88.82 186	88.62 223	94.75 206	73.87 274	97.42 267	85.20 224	88.55 234	95.35 225

v8		91.29 198	90.53 194	93.57 213	94.15 246	88.12 206	97.34 97	97.06 167	88.99 176	88.32 229	94.26 236	83.08 139	98.01 212	87.62 183	83.92 281	94.57 269

CVMVSNet		91.23 199	91.75 143	89.67 298	95.77 176	74.69 318	96.44 180	94.88 279	85.81 254	92.18 134	97.64 73	79.07 222	95.58 309	88.06 169	95.86 135	98.74 85

PEN-MVS		91.20 200	90.44 195	93.48 216	94.49 232	87.91 220	97.76 53	98.18 35	91.29 117	87.78 238	95.74 162	80.35 205	97.33 273	85.46 220	82.96 290	95.19 236

Baseline_NR-MVSNet		91.20 200	90.62 191	92.95 235	93.83 268	88.03 212	97.01 126	95.12 268	88.42 198	89.70 198	95.13 191	83.47 128	97.44 265	89.66 139	83.24 288	93.37 290

cascas		91.20 200	90.08 208	94.58 162	94.97 212	89.16 178	93.65 282	97.59 110	79.90 306	89.40 208	92.92 268	75.36 264	98.36 168	92.14 100	94.75 150	96.23 180

CostFormer		91.18 203	90.70 189	92.62 245	94.84 220	81.76 289	94.09 274	94.43 293	84.15 273	92.72 125	93.77 249	79.43 218	98.20 178	90.70 129	92.18 187	97.90 129

v1192		91.07 204	90.23 204	93.58 212	93.70 271	87.82 221	96.73 154	97.07 165	87.77 216	89.58 202	94.32 228	80.90 197	97.97 219	86.52 201	85.48 253	94.95 246

v144192		91.06 205	90.28 200	93.39 220	93.66 273	87.23 232	96.83 142	97.07 165	87.43 223	89.69 199	94.28 232	81.48 183	98.00 215	87.18 194	84.92 270	94.93 250

v10		91.04 206	90.23 204	93.49 215	94.12 250	88.16 202	97.32 100	97.08 164	88.26 203	88.29 231	94.22 237	82.17 173	97.97 219	86.45 203	84.12 277	94.33 276

v148		90.99 207	90.38 197	92.81 239	93.83 268	85.80 252	96.78 151	96.68 201	89.45 159	88.75 222	93.93 244	82.96 151	97.82 239	87.83 174	83.25 287	94.80 260

LTVRE_ROB	88.41 13	90.99 207	89.92 215	94.19 173	96.18 160	89.55 154	96.31 197	97.09 162	87.88 214	85.67 266	95.91 149	78.79 232	98.57 153	81.50 272	89.98 219	94.44 273
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		90.93 209	89.85 218	94.17 174	93.34 282	90.79 122	94.60 260	96.02 225	84.62 269	87.45 244	95.15 189	81.88 179	97.45 264	87.70 177	87.87 238	94.27 279

XVG-ACMP-BASELINE		90.93 209	90.21 206	93.09 231	94.31 239	85.89 251	95.33 246	97.26 147	91.06 125	89.38 209	95.44 180	68.61 299	98.60 150	89.46 143	91.05 206	94.79 262

v1921920		90.85 211	90.03 211	93.29 225	93.55 274	86.96 240	96.74 153	97.04 171	87.36 225	89.52 206	94.34 226	80.23 208	97.97 219	86.27 204	85.21 259	94.94 248

CR-MVSNet		90.82 212	89.77 221	93.95 186	94.45 234	87.19 233	90.23 317	95.68 243	86.89 241	92.40 127	92.36 280	80.91 194	97.05 279	81.09 280	93.95 165	97.60 145

v7n		90.76 213	89.86 217	93.45 219	93.54 275	87.60 226	97.70 64	97.37 139	88.85 183	87.65 242	94.08 240	81.08 188	98.10 188	84.68 230	83.79 284	94.66 267

DWT-MVSNet_test		90.76 213	89.89 216	93.38 221	95.04 210	83.70 276	95.85 224	94.30 299	88.19 206	90.46 167	92.80 269	73.61 278	98.50 159	88.16 167	90.58 212	97.95 127

RPSCF		90.75 215	90.86 181	90.42 292	96.84 129	76.29 316	95.61 236	96.34 212	83.89 276	91.38 148	97.87 53	76.45 256	98.78 137	87.16 195	92.23 184	96.20 181

MVP-Stereo		90.74 216	90.08 208	92.71 242	93.19 291	88.20 199	95.86 223	96.27 215	86.07 252	84.86 271	94.76 205	77.84 250	97.75 245	83.88 246	98.01 86	92.17 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 217	89.65 227	93.96 185	94.29 240	89.63 149	97.79 52	96.82 193	89.07 173	86.12 264	95.48 179	78.61 233	97.78 242	86.97 197	81.67 296	94.46 272

V4		90.71 218	90.00 212	92.82 236	93.21 289	87.03 236	97.59 77	97.16 155	88.21 204	87.69 240	93.92 245	80.93 193	98.06 200	87.39 187	83.90 282	93.39 289

v1240		90.70 219	89.85 218	93.23 227	93.51 277	86.80 241	96.61 172	97.02 174	87.16 230	89.58 202	94.31 229	79.55 217	97.98 216	85.52 219	85.44 254	94.90 251

v52		90.70 219	90.00 212	92.82 236	93.24 286	87.03 236	97.60 75	97.14 156	88.21 204	87.69 240	93.94 243	80.91 194	98.07 195	87.39 187	83.87 283	93.36 291

EPMVS		90.70 219	89.81 220	93.37 222	94.73 225	84.21 270	93.67 281	88.02 335	89.50 158	92.38 129	93.49 259	77.82 251	97.78 242	86.03 211	92.68 179	98.11 124

DTE-MVSNet		90.56 222	89.75 223	93.01 233	93.95 263	87.25 230	97.64 72	97.65 105	90.74 129	87.12 252	95.68 166	79.97 211	97.00 284	83.33 251	81.66 297	94.78 263

ACMH	87.59 16	90.53 223	89.42 230	93.87 191	96.21 157	87.92 218	97.24 105	96.94 184	88.45 197	83.91 281	96.27 136	71.92 282	98.62 149	84.43 235	89.43 224	95.05 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		90.51 224	90.19 207	91.44 277	93.41 280	81.25 292	96.98 128	96.28 214	91.68 107	86.55 260	96.30 134	74.20 273	97.98 216	88.96 156	87.40 244	95.09 237

COLMAP_ROB	87.81 15	90.40 225	89.28 232	93.79 194	97.95 87	87.13 235	96.92 136	95.89 236	82.83 286	86.88 259	97.18 93	73.77 277	99.29 92	78.44 293	93.62 171	94.95 246
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v748		90.34 226	89.54 228	92.75 241	93.25 285	85.71 254	97.61 74	97.17 152	88.54 196	87.20 251	93.54 256	81.02 189	98.01 212	85.73 217	81.80 294	94.52 270

MS-PatchMatch		90.27 227	89.77 221	91.78 270	94.33 238	84.72 267	95.55 237	96.73 195	86.17 251	86.36 261	95.28 186	71.28 287	97.80 240	84.09 240	98.14 84	92.81 296

tpm		90.25 228	89.74 224	91.76 272	93.92 264	79.73 306	93.98 275	93.54 313	88.28 202	91.99 138	93.25 265	77.51 253	97.44 265	87.30 191	87.94 237	98.12 121

AllTest		90.23 229	88.98 236	93.98 182	97.94 88	86.64 243	96.51 179	95.54 248	85.38 257	85.49 268	96.77 106	70.28 293	99.15 102	80.02 284	92.87 176	96.15 185

ACMH+	87.92 14	90.20 230	89.18 234	93.25 226	96.48 147	86.45 247	96.99 127	96.68 201	88.83 185	84.79 272	96.22 137	70.16 295	98.53 156	84.42 236	88.04 236	94.77 264

test-mter		90.19 231	89.54 228	92.12 259	94.59 229	80.66 295	94.29 268	92.98 316	87.68 219	90.76 163	92.37 277	67.67 303	98.07 195	88.81 161	96.74 118	97.63 140

IterMVS		90.15 232	89.67 225	91.61 274	95.48 184	83.72 274	94.33 267	96.12 223	89.99 148	87.31 250	94.15 238	75.78 261	96.27 291	86.97 197	86.89 246	94.83 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 233	89.42 230	91.97 263	94.41 236	80.62 297	94.29 268	91.97 325	87.28 228	90.44 168	92.47 276	68.79 298	97.67 250	88.50 165	96.60 123	97.61 144

tpm2		89.96 234	89.21 233	92.23 253	94.91 218	81.25 292	93.78 278	94.42 294	80.62 304	91.56 145	93.44 262	76.44 257	97.94 225	85.60 218	92.08 191	97.49 149

IB-MVS	87.33 17	89.91 235	88.28 246	94.79 154	95.26 198	87.70 224	95.12 255	93.95 307	89.35 161	87.03 255	92.49 275	70.74 291	99.19 97	89.18 150	81.37 298	97.49 149
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		89.89 236	88.68 240	93.53 214	95.86 171	84.89 265	90.93 312	95.07 271	83.23 284	91.28 157	91.81 287	79.01 227	97.85 235	79.52 286	91.39 201	97.84 132

FMVSNet1		89.88 237	88.31 245	94.59 158	95.41 186	91.18 108	97.50 83	96.93 185	86.62 245	87.41 246	94.51 214	65.94 311	97.29 275	83.04 254	87.43 242	95.31 227

pmmvs5		89.86 238	88.87 238	92.82 236	92.86 295	86.23 249	96.26 201	95.39 252	84.24 272	87.12 252	94.51 214	74.27 272	97.36 272	87.61 184	87.57 240	94.86 253

tpmvs		89.83 239	89.15 235	91.89 265	94.92 216	80.30 301	93.11 292	95.46 250	86.28 248	88.08 234	92.65 271	80.44 203	98.52 157	81.47 273	89.92 221	96.84 167

tfpnnormal		89.70 240	88.40 244	93.60 209	95.15 204	90.10 132	97.56 79	98.16 37	87.28 228	86.16 263	94.63 211	77.57 252	98.05 203	74.48 303	84.59 273	92.65 297

tpmp4_e23		89.58 241	88.59 241	92.54 246	95.16 203	81.53 290	94.11 273	95.09 269	81.66 294	88.60 224	93.44 262	75.11 265	98.33 172	82.45 262	91.72 194	97.75 136

Test4		89.48 242	87.50 252	95.44 124	90.76 311	89.72 147	95.78 229	97.09 162	90.28 143	77.67 316	91.74 289	55.42 329	98.08 191	91.92 106	96.83 115	98.52 97

ADS-MVSNet2		89.45 243	88.59 241	92.03 262	95.86 171	82.26 286	90.93 312	94.32 298	83.23 284	91.28 157	91.81 287	79.01 227	95.99 301	79.52 286	91.39 201	97.84 132

Patchmatch-test		89.42 244	87.99 248	93.70 204	95.27 195	85.11 260	88.98 323	94.37 296	81.11 299	87.10 254	93.69 250	82.28 169	97.50 261	74.37 305	94.76 149	98.48 105

test0.0.03 1		89.37 245	88.70 239	91.41 278	92.47 302	85.63 255	95.22 253	92.70 321	91.11 123	86.91 258	93.65 253	79.02 225	93.19 322	78.00 294	89.18 226	95.41 217

SixPastTwentyTwo		89.15 246	88.54 243	90.98 281	93.49 278	80.28 302	96.70 162	94.70 283	90.78 128	84.15 278	95.57 170	71.78 284	97.71 248	84.63 231	85.07 264	94.94 248

TransMVSNet (Re)		88.94 247	87.56 250	93.08 232	94.35 237	88.45 190	97.73 58	95.23 263	87.47 222	84.26 276	95.29 184	79.86 212	97.33 273	79.44 289	74.44 324	93.45 288

USDC		88.94 247	87.83 249	92.27 249	94.66 226	84.96 263	93.86 277	95.90 231	87.34 226	83.40 283	95.56 171	67.43 305	98.19 180	82.64 261	89.67 223	93.66 285

dp		88.90 249	88.26 247	90.81 285	94.58 231	76.62 315	92.85 296	94.93 277	85.12 262	90.07 184	93.07 266	75.81 259	98.12 186	80.53 282	87.42 243	97.71 138

PatchT		88.87 250	87.42 255	93.22 228	94.08 256	85.10 261	89.51 321	94.64 287	81.92 292	92.36 130	88.15 316	80.05 210	97.01 283	72.43 310	93.65 170	97.54 148

EU-MVSNet		88.72 251	88.90 237	88.20 301	93.15 292	74.21 319	96.63 171	94.22 302	85.18 260	87.32 249	95.97 145	76.16 258	94.98 314	85.27 222	86.17 248	95.41 217

v18		88.71 252	87.52 251	92.27 249	94.16 245	88.11 208	96.82 145	95.96 226	87.03 232	80.76 297	89.81 297	83.15 133	96.22 292	84.69 229	75.31 315	92.49 301

v16		88.69 253	87.50 252	92.26 251	94.19 242	88.11 208	96.81 146	95.95 227	87.01 234	80.71 299	89.80 298	83.08 139	96.20 293	84.61 232	75.34 314	92.48 303

v17		88.67 254	87.47 254	92.26 251	94.13 248	88.09 210	96.81 146	95.95 227	87.02 233	80.72 298	89.75 299	83.11 136	96.20 293	84.61 232	75.15 317	92.49 301

Patchmtry		88.64 255	87.25 259	92.78 240	94.09 254	86.64 243	89.82 320	95.68 243	80.81 303	87.63 243	92.36 280	80.91 194	97.03 281	78.86 291	85.12 260	94.67 266

v15		88.53 256	87.31 256	92.20 254	94.09 254	88.05 211	96.72 157	95.90 231	87.01 234	80.53 302	89.60 303	83.02 145	96.13 295	84.29 237	74.64 318	92.41 307

V14		88.52 257	87.30 257	92.17 256	94.12 250	87.99 213	96.72 157	95.91 230	86.98 236	80.50 303	89.63 300	83.03 144	96.12 297	84.23 238	74.60 320	92.40 308

RPMNet		88.52 257	86.72 269	93.95 186	94.45 234	87.19 233	90.23 317	94.99 274	77.87 316	92.40 127	87.55 321	80.17 209	97.05 279	68.84 318	93.95 165	97.60 145

MIMVSNet		88.50 259	86.76 267	93.72 203	94.84 220	87.77 222	91.39 307	94.05 304	86.41 247	87.99 236	92.59 273	63.27 315	95.82 305	77.44 295	92.84 178	97.57 147

V9		88.49 260	87.26 258	92.18 255	94.12 250	87.97 216	96.73 154	95.90 231	86.95 238	80.40 305	89.61 301	82.98 148	96.13 295	84.14 239	74.55 321	92.44 305

v12		88.46 261	87.23 261	92.17 256	94.10 253	87.99 213	96.71 159	95.90 231	86.91 239	80.34 307	89.58 304	82.92 152	96.11 299	84.09 240	74.50 323	92.42 306

v13		88.45 262	87.22 262	92.16 258	94.08 256	87.95 217	96.71 159	95.90 231	86.86 243	80.27 309	89.55 305	82.92 152	96.12 297	84.02 242	74.63 319	92.40 308

v11		88.41 263	87.19 265	92.08 261	94.08 256	87.77 222	96.75 152	95.85 237	86.74 244	80.50 303	89.50 306	82.49 164	96.08 300	83.55 248	75.20 316	92.38 310

tpm cat1		88.36 264	87.21 263	91.81 268	95.13 206	80.55 298	92.58 299	95.70 241	74.97 322	87.45 244	91.96 285	78.01 249	98.17 182	80.39 283	88.74 231	96.72 170

JIA-IIPM		88.26 265	87.04 266	91.91 264	93.52 276	81.42 291	89.38 322	94.38 295	80.84 302	90.93 162	80.74 328	79.22 221	97.92 229	82.76 258	91.62 196	96.38 179

testgi		87.97 266	87.21 263	90.24 294	92.86 295	80.76 294	96.67 166	94.97 275	91.74 105	85.52 267	95.83 153	62.66 317	94.47 316	76.25 300	88.36 235	95.48 211

LF4IMVS		87.94 267	87.25 259	89.98 296	92.38 303	80.05 305	94.38 265	95.25 262	87.59 221	84.34 274	94.74 207	64.31 314	97.66 252	84.83 226	87.45 241	92.23 312

gg-mvs-nofinetune		87.82 268	85.61 275	94.44 165	94.46 233	89.27 175	91.21 311	84.61 341	80.88 301	89.89 188	74.98 331	71.50 285	97.53 259	85.75 216	97.21 108	96.51 174

pmmvs6		87.81 269	86.19 271	92.69 243	91.32 308	86.30 248	97.34 97	96.41 210	80.59 305	84.05 280	94.37 225	67.37 306	97.67 250	84.75 228	79.51 304	94.09 281

K. test v3		87.64 270	86.75 268	90.32 293	93.02 294	79.48 308	96.61 172	92.08 324	90.66 134	80.25 310	94.09 239	67.21 307	96.65 287	85.96 213	80.83 301	94.83 256

Patchmatch-RL test		87.38 271	86.24 270	90.81 285	88.74 319	78.40 313	88.12 326	93.17 315	87.11 231	82.17 286	89.29 307	81.95 177	95.60 308	88.64 164	77.02 308	98.41 110

testing_2		87.33 272	85.03 279	94.22 172	87.77 323	89.32 171	94.97 256	97.11 161	89.22 164	71.64 325	88.73 310	55.16 330	97.94 225	91.95 105	88.73 232	95.41 217

FMVSNet5		87.29 273	85.79 274	91.78 270	94.80 222	87.28 228	95.49 241	95.28 259	84.09 274	83.85 282	91.82 286	62.95 316	94.17 317	78.48 292	85.34 257	93.91 283

Anonymous20231206		87.09 274	86.14 272	89.93 297	91.22 309	80.35 299	96.11 210	95.35 255	83.57 281	84.16 277	93.02 267	73.54 279	95.61 307	72.16 311	86.14 249	93.84 284

EG-PatchMatch MVS		87.02 275	85.44 276	91.76 272	92.67 299	85.00 262	96.08 213	96.45 209	83.41 283	79.52 312	93.49 259	57.10 325	97.72 247	79.34 290	90.87 209	92.56 299

TinyColmap		86.82 276	85.35 278	91.21 279	94.91 218	82.99 281	93.94 276	94.02 306	83.58 280	81.56 292	94.68 208	62.34 318	98.13 184	75.78 301	87.35 245	92.52 300

TDRefinement		86.53 277	84.76 282	91.85 266	82.23 333	84.25 269	96.38 190	95.35 255	84.97 265	84.09 279	94.94 193	65.76 312	98.34 171	84.60 234	74.52 322	92.97 292

test_0402		86.46 278	84.79 281	91.45 276	95.02 211	85.55 256	96.29 199	94.89 278	80.90 300	82.21 285	93.97 242	68.21 302	97.29 275	62.98 324	88.68 233	91.51 318

DSMNet-mixed		86.34 279	86.12 273	87.00 306	89.88 315	70.43 324	94.93 257	90.08 332	77.97 315	85.42 270	92.78 270	74.44 271	93.96 318	74.43 304	95.14 143	96.62 172

pmmvs-eth3d		86.22 280	84.45 283	91.53 275	88.34 320	87.25 230	94.47 264	95.01 272	83.47 282	79.51 313	89.61 301	69.75 296	95.71 306	83.13 253	76.73 310	91.64 316

test20.03		86.14 281	85.40 277	88.35 299	90.12 312	80.06 304	95.90 222	95.20 264	88.59 192	81.29 293	93.62 254	71.43 286	92.65 323	71.26 315	81.17 299	92.34 311

UnsupCasMVSNet_eth		85.99 282	84.45 283	90.62 289	89.97 314	82.40 285	93.62 283	97.37 139	89.86 150	78.59 315	92.37 277	65.25 313	95.35 312	82.27 265	70.75 327	94.10 280

YYNet1		85.87 283	84.23 285	90.78 288	92.38 303	82.46 284	93.17 289	95.14 267	82.12 291	67.69 326	92.36 280	78.16 241	95.50 311	77.31 297	79.73 303	94.39 274

MDA-MVSNet_test_wron		85.87 283	84.23 285	90.80 287	92.38 303	82.57 282	93.17 289	95.15 266	82.15 290	67.65 327	92.33 283	78.20 238	95.51 310	77.33 296	79.74 302	94.31 278

CMPMVS	62.92 21	85.62 285	84.92 280	87.74 303	89.14 318	73.12 322	94.17 271	96.80 194	73.98 324	73.65 321	94.93 194	66.36 308	97.61 255	83.95 245	91.28 203	92.48 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 286	83.64 287	90.92 283	95.27 195	79.49 307	90.55 315	95.60 245	83.76 279	83.00 284	89.95 294	71.09 288	97.97 219	82.75 259	60.79 334	95.31 227

MDA-MVSNet-bldmvs		85.00 287	82.95 289	91.17 280	93.13 293	83.33 279	94.56 262	95.00 273	84.57 270	65.13 331	92.65 271	70.45 292	95.85 303	73.57 308	77.49 307	94.33 276

MIMVSNet1		84.93 288	83.05 288	90.56 290	89.56 317	84.84 266	95.40 244	95.35 255	83.91 275	80.38 306	92.21 284	57.23 324	93.34 321	70.69 317	82.75 293	93.50 286

OpenMVS_ROB	81.14 20	84.42 289	82.28 290	90.83 284	90.06 313	84.05 273	95.73 230	94.04 305	73.89 325	80.17 311	91.53 291	59.15 322	97.64 253	66.92 320	89.05 227	90.80 321

LP		84.13 290	81.85 295	90.97 282	93.20 290	82.12 287	87.68 327	94.27 301	76.80 317	81.93 288	88.52 311	72.97 281	95.95 302	59.53 329	81.73 295	94.84 254

PM-MVS		83.48 291	81.86 294	88.31 300	87.83 322	77.59 314	93.43 284	91.75 326	86.91 239	80.63 300	89.91 295	44.42 336	95.84 304	85.17 225	76.73 310	91.50 319

new-patchmatchnet		83.18 292	81.87 293	87.11 305	86.88 325	75.99 317	93.70 279	95.18 265	85.02 264	77.30 317	88.40 313	65.99 310	93.88 319	74.19 307	70.18 328	91.47 320

new_pmnet		82.89 293	81.12 298	88.18 302	89.63 316	80.18 303	91.77 306	92.57 322	76.79 318	75.56 319	88.23 315	61.22 320	94.48 315	71.43 313	82.92 291	89.87 323

test2356		82.77 294	82.14 292	84.65 310	85.77 327	70.36 325	91.22 310	93.69 312	81.58 296	81.82 289	89.00 309	60.63 321	90.77 329	64.74 322	90.80 210	92.82 294

testus		82.63 295	82.15 291	84.07 311	87.31 324	67.67 330	93.18 287	94.29 300	82.47 288	82.14 287	90.69 292	53.01 331	91.94 326	66.30 321	89.96 220	92.62 298

MVS-HIRNet		82.47 296	81.21 297	86.26 309	95.38 188	69.21 329	88.96 324	89.49 334	66.28 330	80.79 296	74.08 333	68.48 300	97.39 270	71.93 312	95.47 139	92.18 313

UnsupCasMVSNet_bld		82.13 297	79.46 299	90.14 295	88.00 321	82.47 283	90.89 314	96.62 207	78.94 310	75.61 318	84.40 326	56.63 326	96.31 290	77.30 298	66.77 333	91.63 317

testpf		80.97 298	81.40 296	79.65 317	91.53 307	72.43 323	73.47 338	89.55 333	78.63 311	80.81 295	89.06 308	61.36 319	91.36 328	83.34 250	84.89 271	75.15 334

pmmvs3		79.97 299	77.50 303	87.39 304	82.80 331	79.38 309	92.70 298	90.75 330	70.69 328	78.66 314	87.47 322	51.34 333	93.40 320	73.39 309	69.65 329	89.38 324

test1235678		79.82 300	78.53 301	83.69 312	82.55 332	67.55 331	92.50 301	94.13 303	79.28 308	72.10 324	86.45 324	57.27 323	90.68 330	61.60 327	80.90 300	92.82 294

N_pmnet		78.73 301	78.71 300	78.79 319	92.80 297	46.50 346	94.14 272	43.71 350	78.61 312	80.83 294	91.66 290	74.94 269	96.36 289	67.24 319	84.45 275	93.50 286

1111		78.29 302	77.55 302	80.50 315	83.89 328	59.98 338	91.89 304	93.71 309	75.06 320	73.60 322	87.67 319	55.66 327	92.60 324	58.54 331	77.92 306	88.93 325

Anonymous20231211		78.22 303	75.30 304	86.99 307	86.14 326	74.16 320	95.62 235	93.88 308	66.43 329	74.44 320	87.86 318	41.39 337	95.11 313	62.49 325	69.46 330	91.71 315

test12356		74.97 304	74.13 305	77.49 320	78.81 334	56.23 342	88.53 325	92.75 320	75.14 319	67.50 328	85.07 325	44.88 335	89.96 331	58.71 330	75.75 312	86.26 326

LCM-MVSNet		72.55 305	69.39 308	82.03 313	70.81 343	65.42 334	90.12 319	94.36 297	55.02 334	65.88 330	81.72 327	24.16 347	89.96 331	74.32 306	68.10 331	90.71 322

testmv		72.22 306	70.02 306	78.82 318	73.06 341	61.75 336	91.24 309	92.31 323	74.45 323	61.06 333	80.51 329	34.21 339	88.63 334	55.31 334	68.07 332	86.06 327

FPMVS		71.27 307	69.85 307	75.50 322	74.64 336	59.03 340	91.30 308	91.50 327	58.80 333	57.92 334	88.28 314	29.98 343	85.53 337	53.43 335	82.84 292	81.95 330

PMMVS2		70.19 308	66.92 310	80.01 316	76.35 335	65.67 333	86.22 329	87.58 337	64.83 332	62.38 332	80.29 330	26.78 345	88.49 335	63.79 323	54.07 335	85.88 328

no-one		68.12 309	63.78 312	81.13 314	74.01 338	70.22 327	87.61 328	90.71 331	72.63 327	53.13 336	71.89 334	30.29 341	91.45 327	61.53 328	32.21 339	81.72 331

Gipuma		67.86 310	65.41 311	75.18 323	92.66 300	73.45 321	66.50 340	94.52 292	53.33 335	57.80 335	66.07 337	30.81 340	89.20 333	48.15 338	78.88 305	62.90 338
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		65.38 311	69.22 309	53.86 331	83.89 328	59.98 338	91.89 304	93.71 309	75.06 320	73.60 322	87.67 319	55.66 327	92.60 324	58.54 331	2.96 345	9.00 343

ANet_high		63.94 312	59.58 313	77.02 321	61.24 346	66.06 332	85.66 331	87.93 336	78.53 313	42.94 338	71.04 335	25.42 346	80.71 339	52.60 336	30.83 341	84.28 329

PNet_i23d		59.01 313	55.87 314	68.44 326	73.98 339	51.37 343	81.36 334	82.41 343	52.37 336	42.49 340	70.39 336	11.39 348	79.99 341	49.77 337	38.71 337	73.97 335

PMVS	53.92 22	58.58 314	55.40 315	68.12 327	51.00 347	48.64 344	78.86 336	87.10 339	46.77 338	35.84 343	74.28 332	8.76 349	86.34 336	42.07 339	73.91 325	69.38 336
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 315	51.11 319	74.38 325	62.30 345	61.47 337	80.09 335	84.87 340	49.62 337	30.80 344	57.20 341	7.03 350	82.94 338	55.69 333	32.36 338	78.72 333

E-PMN		53.28 316	52.56 317	55.43 329	74.43 337	47.13 345	83.63 333	76.30 346	42.23 339	42.59 339	62.22 339	28.57 344	74.40 342	31.53 341	31.51 340	44.78 339

MVE	50.73 23	53.25 317	48.81 320	66.58 328	65.34 344	57.50 341	72.49 339	70.94 348	40.15 341	39.28 342	63.51 338	6.89 352	73.48 344	38.29 340	42.38 336	68.76 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 318	51.31 318	54.39 330	72.62 342	45.39 347	83.84 332	75.51 347	41.13 340	40.77 341	59.65 340	30.08 342	73.60 343	28.31 342	29.90 342	44.18 340

tmp_tt		51.94 319	53.82 316	46.29 332	33.73 348	45.30 348	78.32 337	67.24 349	18.02 342	50.93 337	87.05 323	52.99 332	53.11 345	70.76 316	25.29 343	40.46 341

pcd1.5k->3k		38.37 320	40.51 321	31.96 333	94.29 240	0.00 352	0.00 342	97.69 101	0.00 346	0.00 348	0.00 348	81.45 184	0.00 349	0.00 346	91.11 205	95.89 193

wuyk23d		25.11 321	24.57 323	26.74 334	73.98 339	39.89 349	57.88 341	9.80 351	12.27 343	10.39 345	6.97 347	7.03 350	36.44 346	25.43 343	17.39 344	3.89 345

cdsmvs_eth3d_5k		23.24 322	30.99 322	0.00 337	0.00 351	0.00 352	0.00 342	97.63 107	0.00 346	0.00 348	96.88 103	84.38 121	0.00 349	0.00 346	0.00 348	0.00 346

testmvs		13.36 323	16.33 324	4.48 336	5.04 349	2.26 351	93.18 287	3.28 352	2.70 344	8.24 346	21.66 343	2.29 354	2.19 347	7.58 344	2.96 345	9.00 343

test123		13.04 324	15.66 325	5.18 335	4.51 350	3.45 350	92.50 301	1.81 353	2.50 345	7.58 347	20.15 344	3.67 353	2.18 348	7.13 345	1.07 347	9.90 342

ab-mvs-re		8.06 325	10.74 326	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	96.69 112	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

pcd_1.5k_mvsjas		7.39 326	9.85 327	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	88.65 69	0.00 349	0.00 346	0.00 348	0.00 346

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

test_part2					99.28 17	95.74 3				98.10 6

test_part1								98.26 25				95.31 1			99.63 4	99.63 5

test11111								98.25 26

sam_mvs1												82.76 157

sam_mvs												81.94 178

semantic-postprocess				91.82 267	95.52 182	84.20 271		96.15 222	90.61 138	87.39 247	94.27 234	75.63 262	96.44 288	87.34 189	86.88 247	94.82 258

ambc				86.56 308	83.60 330	70.00 328	85.69 330	94.97 275		80.60 301	88.45 312	37.42 338	96.84 286	82.69 260	75.44 313	92.86 293

MTGPA								98.08 50

test_post1							92.81 297				16.58 346	80.53 201	97.68 249	86.20 206

test_post											17.58 345	81.76 180	98.08 191

patchmatchnet-post											90.45 293	82.65 161	98.10 188

GG-mvs-BLEND				93.62 208	93.69 272	89.20 176	92.39 303	83.33 342		87.98 237	89.84 296	71.00 289	96.87 285	82.08 266	95.40 140	94.80 260

MTMP								82.03 344

gm-plane-assit					93.22 288	78.89 312			84.82 267		93.52 257		98.64 146	87.72 176

test9_res														94.81 60	99.38 34	99.45 29

TEST9					98.70 37	94.19 23	96.41 184	98.02 67	88.17 208	96.03 53	97.56 81	92.74 13	99.59 50

test_8					98.67 39	94.06 29	96.37 191	98.01 69	88.58 193	95.98 58	97.55 83	92.73 14	99.58 53

agg_prior2														93.94 72	99.38 34	99.50 23

agg_prior					98.67 39	93.79 36		98.00 71		95.68 67			99.57 61

TestCases				93.98 182	97.94 88	86.64 243		95.54 248	85.38 257	85.49 268	96.77 106	70.28 293	99.15 102	80.02 284	92.87 176	96.15 185

test_prior4						93.66 40	96.42 183

test_prior2							96.35 192		92.80 77	96.03 53	97.59 77	92.01 29		95.01 53	99.38 34

test_prior				97.23 48	98.67 39	92.99 56		98.00 71					99.41 83			99.29 44

旧先验2							95.94 220		81.66 294	97.34 16			98.82 134	92.26 95

新几何2							95.79 227

新几何1				97.32 42	98.60 46	93.59 42		97.75 92	81.58 296	95.75 66	97.85 56	90.04 57	99.67 37	86.50 202	99.13 55	98.69 90

旧先验1					98.38 59	93.38 48		97.75 92			98.09 41	92.30 26			99.01 63	99.16 52

无先验							95.79 227	97.87 85	83.87 278				99.65 39	87.68 179		98.89 79

原ACMM2							95.67 231

原ACMM1				96.38 80	98.59 47	91.09 112		97.89 82	87.41 224	95.22 77	97.68 67	90.25 53	99.54 65	87.95 172	99.12 58	98.49 103

test222					98.24 70	92.21 75	95.33 246	97.60 108	79.22 309	95.25 76	97.84 58	88.80 67			99.15 53	98.72 87

testdata2													99.67 37	85.96 213

segment_acmp												92.89 11

testdata				95.46 123	98.18 77	88.90 182		97.66 103	82.73 287	97.03 28	98.07 42	90.06 56	98.85 132	89.67 138	98.98 64	98.64 92

testdata1							95.26 252		93.10 65

test12				97.65 29	98.46 52	94.26 20		97.66 103		95.52 75		90.89 47	99.46 77		99.25 45	99.22 49

plane_prior7					96.21 157	89.98 138

plane_prior6					96.10 167	90.00 134						81.32 186

plane_prior5								97.51 117					98.60 150	93.02 90	92.23 184	95.86 194

plane_prior4											96.64 115

plane_prior3						90.00 134			94.46 30	91.34 151

plane_prior2							97.74 56		94.85 17

plane_prior1					96.14 165

plane_prior						89.99 136	97.24 105		94.06 38						92.16 188

n2								0.00 354

nn								0.00 354

door-mid								91.06 329

lessismore_v0				90.45 291	91.96 306	79.09 311		87.19 338		80.32 308	94.39 223	66.31 309	97.55 258	84.00 244	76.84 309	94.70 265

LGP-MVS_train				94.10 176	96.16 162	88.26 193		97.46 124	91.29 117	90.12 179	97.16 94	79.05 223	98.73 142	92.25 97	91.89 192	95.31 227

test11								97.88 83

door								91.13 328

HQP5-MVS						89.33 169

HQP-NCC					95.86 171		96.65 167		93.55 48	90.14 173

ACMP_Plane					95.86 171		96.65 167		93.55 48	90.14 173

BP-MVS														92.13 101

HQP4-MVS										90.14 173			98.50 159			95.78 201

HQP3-MVS								97.39 136							92.10 189

HQP2-MVS												80.95 191

NP-MVS					95.99 170	89.81 145					95.87 150

MDTV_nov1_ep13_2view						70.35 326	93.10 293		83.88 277	93.55 100		82.47 166		86.25 205		98.38 114

MDTV_nov1_ep13			90.76 186		95.22 200	80.33 300	93.03 294	95.28 259	88.14 209	92.84 124	93.83 246	81.34 185	98.08 191	82.86 256	94.34 154

ACMMP++_ref															90.30 217

ACMMP++															91.02 207

Test By Simon												88.73 68

ITE_SJBPF				92.43 248	95.34 190	85.37 259		95.92 229	91.47 111	87.75 239	96.39 132	71.00 289	97.96 223	82.36 264	89.86 222	93.97 282

DeepMVS_CX				74.68 324	90.84 310	64.34 335		81.61 345	65.34 331	67.47 329	88.01 317	48.60 334	80.13 340	62.33 326	73.68 326	79.58 332

High-resolution multi-view benchmark