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	97.27 106	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	97.41 271	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 83	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	97.34 100	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 78	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	97.12 122	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	96.83 145	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	96.83 145	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	97.24 108	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	97.15 120	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	96.96 132	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 92	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	96.96 132	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	96.96 132	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	96.79 152	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	96.20 210	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	95.96 222	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	86.63 249	96.00 221	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	96.89 141	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	96.35 195	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	97.02 127	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	96.86 143	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	96.41 187	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	96.24 208	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	96.28 203	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	96.41 187	98.00 71	87.93 215	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	97.30 105	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	96.87 142	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	96.35 195	97.88 83	86.98 239	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 86	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	97.03 125	98.08 50	88.35 204	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	96.04 217	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	87.79 218	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	96.90 130	95.34 5	98.48 14	97.87 85	94.65 28	88.53 229	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	97.35 116	90.66 125	95.31 251	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	96.34 156	91.21 104	98.22 26	97.57 111	91.42 114	96.22 47	97.32 87	86.20 100	97.92 232	94.07 68	99.05 61	98.85 81

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 118	90.50 128	95.44 246	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	97.27 106	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	97.18 120	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	97.49 115	89.56 156	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	92.80 300	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	96.56 144	93.36 50	98.65 6	98.36 16	94.12 37	89.25 219	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	96.43 185	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	96.58 141	91.71 88	96.25 205	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	96.58 141	91.71 88	96.25 205	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	96.58 141	91.71 88	96.25 205	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	97.24 108	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	96.34 156	91.21 104	95.83 228	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	96.61 139	91.47 97	96.41 187	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	96.83 134	89.55 157	96.70 165	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	89.46 163	95.47 245	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	95.52 185	90.93 117	96.09 214	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	96.04 217	97.73 94	81.56 301	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	96.59 178	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	97.07 124	91.97 85	96.32 199	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	96.46 152	92.13 79	96.21 209	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	96.59 178	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	96.59 178	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	88.13 213	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	95.55 240	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	89.38 169	95.18 257	98.48 14	85.60 259	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	87.92 221	98.10 31	95.80 240	92.22 86	93.02 117	97.45 86	84.53 120	97.91 235	88.24 166	97.97 87	99.02 63

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 163	91.46 98	96.33 198	97.04 171	88.97 179	93.56 99	96.51 126	87.55 84	97.89 236	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	96.69 167	97.39 136	87.29 230	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	95.70 181	92.03 82	98.10 31	98.68 7	93.36 55	90.39 172	96.70 110	87.63 83	97.94 228	92.25 97	90.50 218	95.84 200

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 167	98.27 67	89.46 163	96.73 157	98.36 16	90.17 145	94.36 88	95.24 187	88.02 75	99.58 53	93.44 84	90.72 214	94.36 278

nrg030		94.05 94	93.31 102	96.27 88	95.22 203	94.59 13	98.34 19	97.46 124	92.93 74	91.21 162	96.64 115	87.23 90	98.22 180	94.99 56	85.80 255	95.98 195

UGNet		94.04 95	93.28 103	96.31 84	96.85 131	91.19 107	97.88 45	97.68 102	94.40 31	93.00 118	96.18 138	73.39 283	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	96.16 165	90.45 130	96.71 162	96.89 189	89.27 163	93.46 103	96.92 102	87.29 89	97.94 228	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 91	97.96 79	77.99 317	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	95.48 187	91.45 99	98.12 30	98.71 5	93.37 53	90.23 175	96.70 110	87.66 81	97.85 238	91.49 119	90.39 219	95.83 201

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 123	92.49 70	95.64 237	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	97.06 126	88.53 190	95.28 252	97.45 128	91.68 107	94.08 93	97.68 67	82.41 167	98.90 127	93.84 76	92.47 184	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	68.42 304	99.39 86	96.31 19	94.85 146	98.71 89

mvs_anonymous		93.82 101	93.74 85	94.06 181	96.44 153	85.41 261	95.81 229	97.05 168	89.85 152	90.09 185	96.36 133	87.44 87	97.75 248	93.97 70	96.69 121	99.02 63

HQP_MVS		93.78 103	93.43 98	94.82 149	96.21 160	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	92.23 187	95.86 197

PS-MVSNAJss		93.74 104	93.51 94	94.44 168	93.91 268	89.28 177	97.75 54	97.56 114	92.50 82	89.94 188	96.54 125	88.65 69	98.18 184	93.83 77	90.90 211	95.86 197

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 124	88.61 188	94.79 261	97.46 124	91.97 102	93.99 94	97.86 55	81.74 181	98.88 131	92.64 94	92.67 183	96.92 164

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 70	89.88 143	94.58 264	98.49 12	85.06 266	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	93.46 221	96.02 171	84.61 271	97.24 108	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	69.00 300	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	88.98 183	97.18 117	97.29 146	87.75 220	90.49 169	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	97.28 117	91.96 86	95.40 247	97.45 128	89.81 154	93.22 111	96.28 135	79.62 216	99.46 77	90.74 128	93.11 178	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	85.17 264	90.29 174	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	96.77 136	90.03 133	96.81 149	97.13 158	88.19 209	91.30 154	94.27 237	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	96.77 136	92.62 66	98.39 18	98.12 42	84.50 274	88.27 235	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	96.47 151	89.61 153	96.09 214	97.14 156	85.97 256	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	95.34 193	92.83 60	97.17 118	98.58 10	92.98 72	90.13 180	95.80 155	88.37 74	97.85 238	91.71 113	83.93 282	95.73 210

1112_ss		93.37 115	92.42 129	96.21 91	97.05 127	90.99 113	96.31 200	96.72 196	86.87 245	89.83 194	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	95.39 190	93.34 51	97.39 96	98.71 5	93.14 63	90.10 184	94.83 202	87.71 80	98.03 211	91.67 117	83.99 281	95.46 217

OPM-MVS		93.28 118	92.76 112	94.82 149	94.63 231	90.77 123	96.65 170	97.18 150	93.72 45	91.68 144	97.26 90	79.33 220	98.63 147	92.13 101	92.28 186	95.07 243

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 181	92.39 71	97.86 46	98.66 9	92.30 85	92.09 137	95.37 181	80.49 202	98.40 168	93.95 71	85.86 254	95.75 208

MVSTER		93.20 120	92.81 111	94.37 171	96.56 144	89.59 155	97.06 124	97.12 159	91.24 120	91.30 154	95.96 146	82.02 175	98.05 206	93.48 83	90.55 216	95.47 216

HQP-MVS		93.19 121	92.74 116	94.54 166	95.86 174	89.33 172	96.65 170	97.39 136	93.55 48	90.14 176	95.87 150	80.95 191	98.50 159	92.13 101	92.10 192	95.78 204

CHOSEN 280x420		93.12 122	92.72 117	94.34 173	96.71 138	87.27 232	90.29 319	97.72 97	86.61 249	91.34 151	95.29 184	84.29 122	98.41 167	93.25 88	98.94 66	97.35 152

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 247	96.02 171	83.25 283	97.14 121	96.72 196	93.85 42	91.20 163	93.44 265	83.08 139	98.30 177	91.69 115	95.73 137	96.50 175

test_djsdf		93.07 124	92.76 112	94.00 184	93.49 281	88.70 187	98.22 26	97.57 111	91.42 114	90.08 186	95.55 172	82.85 155	97.92 232	94.07 68	91.58 200	95.40 224

VDDNet		93.05 125	92.07 133	96.02 96	96.84 132	90.39 131	98.08 33	95.85 237	86.22 253	95.79 65	98.46 12	67.59 307	99.19 97	94.92 57	94.85 146	98.47 106

EI-MVSNet		93.03 126	92.88 110	93.48 219	95.77 179	86.98 241	96.44 183	97.12 159	90.66 134	91.30 154	97.64 73	86.56 95	98.05 206	89.91 133	90.55 216	95.41 220

CLD-MVS		92.98 127	92.53 125	94.32 174	96.12 169	89.20 179	95.28 252	97.47 122	92.66 79	89.90 189	95.62 168	80.58 200	98.40 168	92.73 93	92.40 185	95.38 226
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	94.35 172	96.30 158	88.71 186	97.58 81	97.36 141	91.40 116	90.53 168	96.65 114	79.77 213	98.75 141	91.24 125	91.64 198	95.59 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 129	92.56 122	94.10 179	96.16 165	88.26 196	97.65 68	97.46 124	91.29 117	90.12 182	97.16 94	79.05 223	98.73 142	92.25 97	91.89 195	95.31 230

BH-untuned		92.94 129	92.62 120	93.92 193	97.22 118	86.16 253	96.40 191	96.25 217	90.06 147	89.79 196	96.17 140	83.19 131	98.35 172	87.19 193	97.27 107	97.24 153

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 217	92.83 60	97.16 119	98.24 28	93.02 66	90.13 180	95.71 163	83.47 128	97.85 238	91.71 113	83.93 282	95.78 204

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 76	90.80 121	95.27 254	97.18 150	87.96 214	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	94.42 170	94.92 219	86.73 245	94.53 266	96.38 211	84.78 271	94.27 90	95.12 192	83.13 135	98.40 168	91.47 120	96.49 125	98.12 121

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 193	92.73 63	98.27 23	98.12 42	84.86 269	85.78 268	97.75 63	78.89 234	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	97.04 128	89.97 139	95.53 242	96.64 204	85.38 260	89.65 204	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	95.33 196	89.52 160	96.04 217	97.44 131	87.72 221	86.25 265	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 98	96.08 224	82.38 292	89.29 216	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	97.16 121	87.99 216	96.15 211	95.60 245	90.62 136	91.87 140	97.15 96	78.41 239	98.57 153	83.16 252	97.60 96	98.36 115

ACMP	89.59 10	92.62 139	92.14 132	94.05 182	96.40 154	88.20 202	97.36 99	97.25 149	91.52 109	88.30 233	96.64 115	78.46 238	98.72 144	91.86 110	91.48 202	95.23 237
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	89.44 165	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 245	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	89.44 165	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 245	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	89.44 165	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 245	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	89.44 165	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 245	99.11 106	81.61 267	94.04 161	96.98 156

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 250	96.82 135	81.89 291	96.92 139	93.71 309	92.41 84	84.30 278	94.60 212	85.08 112	97.03 284	91.51 118	97.36 104	98.40 111

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 226	92.07 80	97.53 84	98.11 45	92.90 75	89.56 207	96.12 141	83.16 132	97.60 259	89.30 145	83.20 292	95.75 208

thres600view7		92.49 146	91.60 152	95.18 129	97.91 92	89.47 161	97.65 68	94.66 284	92.18 92	93.33 105	94.91 195	78.06 249	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	89.37 170	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 249	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	89.37 170	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 249	99.08 116	81.40 274	94.08 156	96.48 176

jajsoiax		92.42 149	91.89 140	94.03 183	93.33 287	88.50 191	97.73 58	97.53 115	92.00 101	88.85 223	96.50 127	75.62 266	98.11 190	93.88 75	91.56 201	95.48 214

thres400		92.42 149	91.52 157	95.12 137	97.85 95	89.29 175	97.41 92	94.88 279	92.19 90	93.27 109	94.46 218	78.17 242	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	89.29 175	97.41 92	94.88 279	92.19 90	93.27 109	94.46 218	78.17 242	99.08 116	81.40 274	94.08 156	96.48 176

WR-MVS		92.34 152	91.53 156	94.77 155	95.13 209	90.83 120	96.40 191	97.98 77	91.88 103	89.29 216	95.54 173	82.50 163	97.80 243	89.79 136	85.27 261	95.69 211

NR-MVSNet		92.34 152	91.27 166	95.53 116	94.95 217	93.05 55	97.39 96	98.07 55	92.65 80	84.46 276	95.71 163	85.00 113	97.77 247	89.71 137	83.52 289	95.78 204

mvs_tets		92.31 154	91.76 142	93.94 192	93.41 283	88.29 194	97.63 76	97.53 115	92.04 99	88.76 224	96.45 129	74.62 273	98.09 193	93.91 73	91.48 202	95.45 218

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 63	89.60 154	96.79 152	97.65 105	81.83 296	91.52 146	97.23 92	87.94 77	98.91 126	71.31 317	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	93.21 232	95.27 198	85.52 260	97.03 125	96.63 206	92.09 93	89.11 220	95.14 190	80.33 206	98.08 194	87.54 185	94.74 151	96.03 194

IterMVS-LS		92.29 156	91.94 139	93.34 226	96.25 159	86.97 242	96.57 181	97.05 168	90.67 132	89.50 210	94.80 204	86.59 94	97.64 256	89.91 133	86.11 253	95.40 224
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	93.73 204	97.77 99	83.69 280	92.88 298	96.72 196	87.91 216	93.00 118	94.86 199	78.51 237	99.05 120	86.53 200	97.45 102	98.47 106

VPNet		92.23 159	91.31 164	94.99 141	95.56 184	90.96 115	97.22 113	97.86 87	92.96 73	90.96 164	96.62 122	75.06 269	98.20 181	91.90 107	83.65 288	95.80 203

thres200		92.23 159	91.39 160	94.75 156	97.61 107	89.03 182	96.60 177	95.09 269	92.08 98	93.28 108	94.00 244	78.39 240	99.04 121	81.26 282	94.18 155	96.19 182

anonymousdsp		92.16 161	91.55 155	93.97 187	92.58 304	89.55 157	97.51 85	97.42 134	89.42 160	88.40 230	94.84 200	80.66 199	97.88 237	91.87 109	91.28 206	94.48 274

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 227	90.94 116	97.47 90	97.43 133	89.14 172	88.90 221	96.43 130	79.71 214	98.24 179	89.56 141	87.68 242	95.67 212

BH-w/o		92.14 163	91.75 143	93.31 227	96.99 129	85.73 256	95.67 234	95.69 242	88.73 191	89.26 218	94.82 203	82.97 149	98.07 198	85.26 223	96.32 128	96.13 187

test_normal		92.01 164	90.75 187	95.80 104	93.24 289	89.97 139	95.93 224	96.24 218	90.62 136	81.63 294	93.45 264	74.98 270	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	93.34 285	89.78 146	96.14 212	96.18 221	90.58 140	81.80 293	93.50 261	74.95 271	98.90 127	93.51 81	96.94 113	98.51 99

WR-MVS_H		92.00 166	91.35 161	93.95 189	95.09 211	89.47 161	98.04 35	98.68 7	91.46 112	88.34 231	94.68 208	85.86 104	97.56 260	85.77 215	84.24 279	94.82 261

tfpn1000		91.99 167	91.05 172	94.80 152	97.78 98	89.66 151	97.91 43	92.90 322	88.99 176	91.73 142	94.84 200	78.99 229	98.33 175	82.41 263	93.91 167	96.40 178

PatchmatchNet		91.91 168	91.35 161	93.59 213	95.38 191	84.11 275	93.15 294	95.39 252	89.54 156	92.10 136	93.68 254	82.82 156	98.13 187	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	93.82 195	95.05 212	88.57 189	97.82 50	98.19 33	91.70 106	88.21 236	95.76 160	81.96 176	97.52 263	87.86 173	84.65 275	95.37 227

tfpn_ndepth		91.88 170	90.96 176	94.62 160	97.73 101	89.93 142	97.75 54	92.92 321	88.93 181	91.73 142	93.80 251	78.91 230	98.49 162	83.02 255	93.86 168	95.45 218

FMVSNet3		91.78 171	90.69 190	95.03 140	96.53 146	92.27 74	97.02 127	96.93 185	89.79 155	89.35 213	94.65 210	77.01 257	97.47 266	86.12 208	88.82 231	95.35 228

X-MVStestdata		91.71 172	89.67 228	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	32.69 345	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

MVS		91.71 172	90.44 198	95.51 117	95.20 205	91.59 94	96.04 217	97.45 128	73.44 329	87.36 251	95.60 169	85.42 108	99.10 111	85.97 212	97.46 98	95.83 201

EPNet_dtu		91.71 172	91.28 165	92.99 237	93.76 273	83.71 278	96.69 167	95.28 259	93.15 62	87.02 259	95.95 147	83.37 130	97.38 274	79.46 291	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	93.75 201	94.19 245	88.14 207	97.20 114	96.98 176	89.18 167	89.87 192	94.44 220	83.10 137	98.06 203	89.06 153	85.09 265	95.06 246

v7new		91.70 175	91.01 173	93.75 201	94.19 245	88.14 207	97.20 114	96.98 176	89.18 167	89.87 192	94.44 220	83.10 137	98.06 203	89.06 153	85.09 265	95.06 246

tfpn_n400		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

tfpnconf		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

tfpnview11		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

v6		91.69 177	91.00 175	93.75 201	94.14 250	88.12 209	97.20 114	96.98 176	89.19 165	89.90 189	94.42 222	83.04 143	98.07 198	89.07 152	85.10 264	95.07 243

PatchFormer-LS_test		91.68 181	91.18 171	93.19 233	95.24 202	83.63 281	95.53 242	95.44 251	89.82 153	91.37 149	92.58 277	80.85 198	98.52 157	89.65 140	90.16 221	97.42 151

v1141		91.61 182	90.89 177	93.78 198	94.01 263	88.24 198	96.96 132	96.96 180	89.17 169	89.75 198	94.29 233	82.99 147	98.03 211	88.85 159	85.00 270	95.07 243

divwei89l23v2f112		91.61 182	90.89 177	93.78 198	94.01 263	88.22 200	96.96 132	96.96 180	89.17 169	89.75 198	94.28 235	83.02 145	98.03 211	88.86 158	84.98 272	95.08 241

v1		91.61 182	90.89 177	93.78 198	94.01 263	88.21 201	96.96 132	96.96 180	89.17 169	89.78 197	94.29 233	82.97 149	98.05 206	88.85 159	84.99 271	95.08 241

v2v482		91.59 185	90.85 182	93.80 196	93.87 270	88.17 204	96.94 138	96.88 190	89.54 156	89.53 208	94.90 196	81.70 182	98.02 214	89.25 147	85.04 269	95.20 238

V42		91.58 186	90.87 180	93.73 204	94.05 262	88.50 191	97.32 103	96.97 179	88.80 189	89.71 200	94.33 227	82.54 162	98.05 206	89.01 155	85.07 267	94.64 271

PCF-MVS	89.48 11	91.56 187	89.95 217	96.36 82	96.60 140	92.52 69	92.51 303	97.26 147	79.41 310	88.90 221	96.56 124	84.04 123	99.55 63	77.01 302	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		91.55 188	90.84 184	93.69 208	94.96 216	88.28 195	97.84 49	98.24 28	91.46 112	88.04 238	95.80 155	79.67 215	97.48 265	87.02 196	84.54 277	95.31 230

Patchmatch-test1		91.54 189	90.85 182	93.59 213	95.59 183	84.95 267	94.72 262	95.58 247	90.82 127	92.25 133	93.58 258	75.80 263	97.41 271	83.35 249	95.98 131	98.40 111

PAPM		91.52 190	90.30 202	95.20 128	95.30 197	89.83 144	93.38 289	96.85 192	86.26 252	88.59 228	95.80 155	84.88 114	98.15 186	75.67 305	95.93 133	97.63 140

TR-MVS		91.48 191	90.59 196	94.16 178	96.40 154	87.33 230	95.67 234	95.34 258	87.68 222	91.46 147	95.52 174	76.77 258	98.35 172	82.85 257	93.61 172	96.79 168

v7		91.47 192	90.73 188	93.68 209	94.13 251	88.16 205	97.09 123	97.05 168	88.38 202	89.80 195	94.52 213	82.21 171	98.01 215	88.00 170	85.42 258	94.87 255

tpmrst		91.44 193	91.32 163	91.79 272	95.15 207	79.20 313	93.42 288	95.37 254	88.55 195	93.49 102	93.67 255	82.49 164	98.27 178	90.41 130	89.34 228	97.90 129

test-LLR		91.42 194	91.19 170	92.12 262	94.59 232	80.66 298	94.29 271	92.98 319	91.11 123	90.76 166	92.37 280	79.02 225	98.07 198	88.81 161	96.74 118	97.63 140

MSDG		91.42 194	90.24 206	94.96 145	97.15 122	88.91 184	93.69 283	96.32 213	85.72 258	86.93 260	96.47 128	80.24 207	98.98 123	80.57 284	95.05 145	96.98 156

GA-MVS		91.38 196	90.31 201	94.59 161	94.65 230	87.62 228	94.34 269	96.19 220	90.73 130	90.35 173	93.83 249	71.84 286	97.96 226	87.22 192	93.61 172	98.21 118

v1144		91.37 197	90.60 195	93.68 209	93.89 269	88.23 199	96.84 144	97.03 173	88.37 203	89.69 202	94.39 223	82.04 174	97.98 219	87.80 175	85.37 259	94.84 257

GBi-Net		91.35 198	90.27 204	94.59 161	96.51 147	91.18 108	97.50 86	96.93 185	88.82 186	89.35 213	94.51 214	73.87 277	97.29 278	86.12 208	88.82 231	95.31 230

test1		91.35 198	90.27 204	94.59 161	96.51 147	91.18 108	97.50 86	96.93 185	88.82 186	89.35 213	94.51 214	73.87 277	97.29 278	86.12 208	88.82 231	95.31 230

FMVSNet2		91.31 200	90.08 211	94.99 141	96.51 147	92.21 75	97.41 92	96.95 183	88.82 186	88.62 226	94.75 206	73.87 277	97.42 270	85.20 224	88.55 237	95.35 228

v8		91.29 201	90.53 197	93.57 216	94.15 249	88.12 209	97.34 100	97.06 167	88.99 176	88.32 232	94.26 239	83.08 139	98.01 215	87.62 183	83.92 284	94.57 272

CVMVSNet		91.23 202	91.75 143	89.67 301	95.77 179	74.69 321	96.44 183	94.88 279	85.81 257	92.18 134	97.64 73	79.07 222	95.58 312	88.06 169	95.86 135	98.74 85

PEN-MVS		91.20 203	90.44 198	93.48 219	94.49 235	87.91 223	97.76 53	98.18 35	91.29 117	87.78 241	95.74 162	80.35 205	97.33 276	85.46 220	82.96 293	95.19 239

Baseline_NR-MVSNet		91.20 203	90.62 194	92.95 238	93.83 271	88.03 215	97.01 129	95.12 268	88.42 201	89.70 201	95.13 191	83.47 128	97.44 268	89.66 139	83.24 291	93.37 293

cascas		91.20 203	90.08 211	94.58 165	94.97 215	89.16 181	93.65 285	97.59 110	79.90 309	89.40 211	92.92 271	75.36 267	98.36 171	92.14 100	94.75 150	96.23 180

CostFormer		91.18 206	90.70 189	92.62 248	94.84 223	81.76 292	94.09 277	94.43 293	84.15 276	92.72 125	93.77 252	79.43 218	98.20 181	90.70 129	92.18 190	97.90 129

v1192		91.07 207	90.23 207	93.58 215	93.70 274	87.82 224	96.73 157	97.07 165	87.77 219	89.58 205	94.32 228	80.90 197	97.97 222	86.52 201	85.48 256	94.95 249

v144192		91.06 208	90.28 203	93.39 223	93.66 276	87.23 235	96.83 145	97.07 165	87.43 226	89.69 202	94.28 235	81.48 183	98.00 218	87.18 194	84.92 273	94.93 253

v10		91.04 209	90.23 207	93.49 218	94.12 253	88.16 205	97.32 103	97.08 164	88.26 206	88.29 234	94.22 240	82.17 173	97.97 222	86.45 203	84.12 280	94.33 279

v148		90.99 210	90.38 200	92.81 242	93.83 271	85.80 255	96.78 154	96.68 201	89.45 159	88.75 225	93.93 247	82.96 151	97.82 242	87.83 174	83.25 290	94.80 263

LTVRE_ROB	88.41 13	90.99 210	89.92 218	94.19 176	96.18 163	89.55 157	96.31 200	97.09 162	87.88 217	85.67 269	95.91 149	78.79 235	98.57 153	81.50 272	89.98 222	94.44 276
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		90.93 212	89.85 221	94.17 177	93.34 285	90.79 122	94.60 263	96.02 225	84.62 272	87.45 247	95.15 189	81.88 179	97.45 267	87.70 177	87.87 241	94.27 282

XVG-ACMP-BASELINE		90.93 212	90.21 209	93.09 234	94.31 242	85.89 254	95.33 249	97.26 147	91.06 125	89.38 212	95.44 180	68.61 302	98.60 150	89.46 143	91.05 209	94.79 265

v1921920		90.85 214	90.03 214	93.29 228	93.55 277	86.96 243	96.74 156	97.04 171	87.36 228	89.52 209	94.34 226	80.23 208	97.97 222	86.27 204	85.21 262	94.94 251

CR-MVSNet		90.82 215	89.77 224	93.95 189	94.45 237	87.19 236	90.23 320	95.68 243	86.89 244	92.40 127	92.36 283	80.91 194	97.05 282	81.09 283	93.95 165	97.60 145

v7n		90.76 216	89.86 220	93.45 222	93.54 278	87.60 229	97.70 64	97.37 139	88.85 183	87.65 245	94.08 243	81.08 188	98.10 191	84.68 230	83.79 287	94.66 270

DWT-MVSNet_test		90.76 216	89.89 219	93.38 224	95.04 213	83.70 279	95.85 227	94.30 299	88.19 209	90.46 170	92.80 272	73.61 281	98.50 159	88.16 167	90.58 215	97.95 127

RPSCF		90.75 218	90.86 181	90.42 295	96.84 132	76.29 319	95.61 239	96.34 212	83.89 279	91.38 148	97.87 53	76.45 259	98.78 137	87.16 195	92.23 187	96.20 181

MVP-Stereo		90.74 219	90.08 211	92.71 245	93.19 294	88.20 202	95.86 226	96.27 215	86.07 255	84.86 274	94.76 205	77.84 253	97.75 248	83.88 246	98.01 86	92.17 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 220	89.65 230	93.96 188	94.29 243	89.63 152	97.79 52	96.82 193	89.07 173	86.12 267	95.48 179	78.61 236	97.78 245	86.97 197	81.67 299	94.46 275

V4		90.71 221	90.00 215	92.82 239	93.21 292	87.03 239	97.59 80	97.16 155	88.21 207	87.69 243	93.92 248	80.93 193	98.06 203	87.39 187	83.90 285	93.39 292

v1240		90.70 222	89.85 221	93.23 230	93.51 280	86.80 244	96.61 175	97.02 174	87.16 233	89.58 205	94.31 229	79.55 217	97.98 219	85.52 219	85.44 257	94.90 254

v52		90.70 222	90.00 215	92.82 239	93.24 289	87.03 239	97.60 78	97.14 156	88.21 207	87.69 243	93.94 246	80.91 194	98.07 198	87.39 187	83.87 286	93.36 294

EPMVS		90.70 222	89.81 223	93.37 225	94.73 228	84.21 273	93.67 284	88.02 338	89.50 158	92.38 129	93.49 262	77.82 254	97.78 245	86.03 211	92.68 182	98.11 124

DTE-MVSNet		90.56 225	89.75 226	93.01 236	93.95 266	87.25 233	97.64 72	97.65 105	90.74 129	87.12 255	95.68 166	79.97 211	97.00 287	83.33 251	81.66 300	94.78 266

ACMH	87.59 16	90.53 226	89.42 233	93.87 194	96.21 160	87.92 221	97.24 108	96.94 184	88.45 197	83.91 284	96.27 136	71.92 285	98.62 149	84.43 235	89.43 227	95.05 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		90.51 227	90.19 210	91.44 280	93.41 283	81.25 295	96.98 131	96.28 214	91.68 107	86.55 263	96.30 134	74.20 276	97.98 219	88.96 156	87.40 247	95.09 240

COLMAP_ROB	87.81 15	90.40 228	89.28 235	93.79 197	97.95 87	87.13 238	96.92 139	95.89 236	82.83 289	86.88 262	97.18 93	73.77 280	99.29 92	78.44 296	93.62 171	94.95 249
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v748		90.34 229	89.54 231	92.75 244	93.25 288	85.71 257	97.61 77	97.17 152	88.54 196	87.20 254	93.54 259	81.02 189	98.01 215	85.73 217	81.80 297	94.52 273

MS-PatchMatch		90.27 230	89.77 224	91.78 273	94.33 241	84.72 270	95.55 240	96.73 195	86.17 254	86.36 264	95.28 186	71.28 290	97.80 243	84.09 240	98.14 84	92.81 299

tpm		90.25 231	89.74 227	91.76 275	93.92 267	79.73 309	93.98 278	93.54 313	88.28 205	91.99 138	93.25 268	77.51 256	97.44 268	87.30 191	87.94 240	98.12 121

AllTest		90.23 232	88.98 239	93.98 185	97.94 88	86.64 246	96.51 182	95.54 248	85.38 260	85.49 271	96.77 106	70.28 296	99.15 102	80.02 287	92.87 179	96.15 185

ACMH+	87.92 14	90.20 233	89.18 237	93.25 229	96.48 150	86.45 250	96.99 130	96.68 201	88.83 185	84.79 275	96.22 137	70.16 298	98.53 156	84.42 236	88.04 239	94.77 267

test-mter		90.19 234	89.54 231	92.12 262	94.59 232	80.66 298	94.29 271	92.98 319	87.68 222	90.76 166	92.37 280	67.67 306	98.07 198	88.81 161	96.74 118	97.63 140

IterMVS		90.15 235	89.67 228	91.61 277	95.48 187	83.72 277	94.33 270	96.12 223	89.99 148	87.31 253	94.15 241	75.78 264	96.27 294	86.97 197	86.89 249	94.83 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 236	89.42 233	91.97 266	94.41 239	80.62 300	94.29 271	91.97 328	87.28 231	90.44 171	92.47 279	68.79 301	97.67 253	88.50 165	96.60 123	97.61 144

tpm2		89.96 237	89.21 236	92.23 256	94.91 221	81.25 295	93.78 281	94.42 294	80.62 307	91.56 145	93.44 265	76.44 260	97.94 228	85.60 218	92.08 194	97.49 149

IB-MVS	87.33 17	89.91 238	88.28 249	94.79 154	95.26 201	87.70 227	95.12 258	93.95 307	89.35 161	87.03 258	92.49 278	70.74 294	99.19 97	89.18 150	81.37 301	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		89.89 239	88.68 243	93.53 217	95.86 174	84.89 268	90.93 315	95.07 271	83.23 287	91.28 157	91.81 290	79.01 227	97.85 238	79.52 289	91.39 204	97.84 132

FMVSNet1		89.88 240	88.31 248	94.59 161	95.41 189	91.18 108	97.50 86	96.93 185	86.62 248	87.41 249	94.51 214	65.94 314	97.29 278	83.04 254	87.43 245	95.31 230

pmmvs5		89.86 241	88.87 241	92.82 239	92.86 298	86.23 252	96.26 204	95.39 252	84.24 275	87.12 255	94.51 214	74.27 275	97.36 275	87.61 184	87.57 243	94.86 256

tpmvs		89.83 242	89.15 238	91.89 268	94.92 219	80.30 304	93.11 295	95.46 250	86.28 251	88.08 237	92.65 274	80.44 203	98.52 157	81.47 273	89.92 224	96.84 167

tfpnnormal		89.70 243	88.40 247	93.60 212	95.15 207	90.10 132	97.56 82	98.16 37	87.28 231	86.16 266	94.63 211	77.57 255	98.05 206	74.48 306	84.59 276	92.65 300

tpmp4_e23		89.58 244	88.59 244	92.54 249	95.16 206	81.53 293	94.11 276	95.09 269	81.66 297	88.60 227	93.44 265	75.11 268	98.33 175	82.45 262	91.72 197	97.75 136

Test4		89.48 245	87.50 255	95.44 124	90.76 314	89.72 147	95.78 232	97.09 162	90.28 143	77.67 319	91.74 292	55.42 332	98.08 194	91.92 106	96.83 115	98.52 97

ADS-MVSNet2		89.45 246	88.59 244	92.03 265	95.86 174	82.26 289	90.93 315	94.32 298	83.23 287	91.28 157	91.81 290	79.01 227	95.99 304	79.52 289	91.39 204	97.84 132

Patchmatch-test		89.42 247	87.99 251	93.70 207	95.27 198	85.11 263	88.98 326	94.37 296	81.11 302	87.10 257	93.69 253	82.28 169	97.50 264	74.37 308	94.76 149	98.48 105

test0.0.03 1		89.37 248	88.70 242	91.41 281	92.47 305	85.63 258	95.22 256	92.70 324	91.11 123	86.91 261	93.65 256	79.02 225	93.19 325	78.00 297	89.18 229	95.41 220

SixPastTwentyTwo		89.15 249	88.54 246	90.98 284	93.49 281	80.28 305	96.70 165	94.70 283	90.78 128	84.15 281	95.57 170	71.78 287	97.71 251	84.63 231	85.07 267	94.94 251

TransMVSNet (Re)		88.94 250	87.56 253	93.08 235	94.35 240	88.45 193	97.73 58	95.23 263	87.47 225	84.26 279	95.29 184	79.86 212	97.33 276	79.44 292	74.44 327	93.45 291

USDC		88.94 250	87.83 252	92.27 252	94.66 229	84.96 266	93.86 280	95.90 231	87.34 229	83.40 286	95.56 171	67.43 308	98.19 183	82.64 261	89.67 226	93.66 288

dp		88.90 252	88.26 250	90.81 288	94.58 234	76.62 318	92.85 299	94.93 277	85.12 265	90.07 187	93.07 269	75.81 262	98.12 189	80.53 285	87.42 246	97.71 138

PatchT		88.87 253	87.42 258	93.22 231	94.08 259	85.10 264	89.51 324	94.64 287	81.92 295	92.36 130	88.15 319	80.05 210	97.01 286	72.43 313	93.65 170	97.54 148

EU-MVSNet		88.72 254	88.90 240	88.20 304	93.15 295	74.21 322	96.63 174	94.22 302	85.18 263	87.32 252	95.97 145	76.16 261	94.98 317	85.27 222	86.17 251	95.41 220

v18		88.71 255	87.52 254	92.27 252	94.16 248	88.11 211	96.82 148	95.96 226	87.03 235	80.76 300	89.81 300	83.15 133	96.22 295	84.69 229	75.31 318	92.49 304

v16		88.69 256	87.50 255	92.26 254	94.19 245	88.11 211	96.81 149	95.95 227	87.01 237	80.71 302	89.80 301	83.08 139	96.20 296	84.61 232	75.34 317	92.48 306

v17		88.67 257	87.47 257	92.26 254	94.13 251	88.09 213	96.81 149	95.95 227	87.02 236	80.72 301	89.75 302	83.11 136	96.20 296	84.61 232	75.15 320	92.49 304

Patchmtry		88.64 258	87.25 262	92.78 243	94.09 257	86.64 246	89.82 323	95.68 243	80.81 306	87.63 246	92.36 283	80.91 194	97.03 284	78.86 294	85.12 263	94.67 269

v15		88.53 259	87.31 259	92.20 257	94.09 257	88.05 214	96.72 160	95.90 231	87.01 237	80.53 305	89.60 306	83.02 145	96.13 298	84.29 237	74.64 321	92.41 310

V14		88.52 260	87.30 260	92.17 259	94.12 253	87.99 216	96.72 160	95.91 230	86.98 239	80.50 306	89.63 303	83.03 144	96.12 300	84.23 238	74.60 323	92.40 311

RPMNet		88.52 260	86.72 272	93.95 189	94.45 237	87.19 236	90.23 320	94.99 274	77.87 319	92.40 127	87.55 324	80.17 209	97.05 282	68.84 321	93.95 165	97.60 145

MIMVSNet		88.50 262	86.76 270	93.72 206	94.84 223	87.77 225	91.39 310	94.05 304	86.41 250	87.99 239	92.59 276	63.27 318	95.82 308	77.44 298	92.84 181	97.57 147

V9		88.49 263	87.26 261	92.18 258	94.12 253	87.97 219	96.73 157	95.90 231	86.95 241	80.40 308	89.61 304	82.98 148	96.13 298	84.14 239	74.55 324	92.44 308

v12		88.46 264	87.23 264	92.17 259	94.10 256	87.99 216	96.71 162	95.90 231	86.91 242	80.34 310	89.58 307	82.92 152	96.11 302	84.09 240	74.50 326	92.42 309

v13		88.45 265	87.22 265	92.16 261	94.08 259	87.95 220	96.71 162	95.90 231	86.86 246	80.27 312	89.55 308	82.92 152	96.12 300	84.02 242	74.63 322	92.40 311

v11		88.41 266	87.19 268	92.08 264	94.08 259	87.77 225	96.75 155	95.85 237	86.74 247	80.50 306	89.50 309	82.49 164	96.08 303	83.55 248	75.20 319	92.38 313

tpm cat1		88.36 267	87.21 266	91.81 271	95.13 209	80.55 301	92.58 302	95.70 241	74.97 325	87.45 247	91.96 288	78.01 252	98.17 185	80.39 286	88.74 234	96.72 170

JIA-IIPM		88.26 268	87.04 269	91.91 267	93.52 279	81.42 294	89.38 325	94.38 295	80.84 305	90.93 165	80.74 331	79.22 221	97.92 232	82.76 258	91.62 199	96.38 179

testgi		87.97 269	87.21 266	90.24 297	92.86 298	80.76 297	96.67 169	94.97 275	91.74 105	85.52 270	95.83 153	62.66 320	94.47 319	76.25 303	88.36 238	95.48 214

LF4IMVS		87.94 270	87.25 262	89.98 299	92.38 306	80.05 308	94.38 268	95.25 262	87.59 224	84.34 277	94.74 207	64.31 317	97.66 255	84.83 226	87.45 244	92.23 315

gg-mvs-nofinetune		87.82 271	85.61 278	94.44 168	94.46 236	89.27 178	91.21 314	84.61 344	80.88 304	89.89 191	74.98 334	71.50 288	97.53 262	85.75 216	97.21 108	96.51 174

pmmvs6		87.81 272	86.19 274	92.69 246	91.32 311	86.30 251	97.34 100	96.41 210	80.59 308	84.05 283	94.37 225	67.37 309	97.67 253	84.75 228	79.51 307	94.09 284

K. test v3		87.64 273	86.75 271	90.32 296	93.02 297	79.48 311	96.61 175	92.08 327	90.66 134	80.25 313	94.09 242	67.21 310	96.65 290	85.96 213	80.83 304	94.83 259

Patchmatch-RL test		87.38 274	86.24 273	90.81 288	88.74 322	78.40 316	88.12 329	93.17 315	87.11 234	82.17 289	89.29 310	81.95 177	95.60 311	88.64 164	77.02 311	98.41 110

testing_2		87.33 275	85.03 282	94.22 175	87.77 326	89.32 174	94.97 259	97.11 161	89.22 164	71.64 328	88.73 313	55.16 333	97.94 228	91.95 105	88.73 235	95.41 220

FMVSNet5		87.29 276	85.79 277	91.78 273	94.80 225	87.28 231	95.49 244	95.28 259	84.09 277	83.85 285	91.82 289	62.95 319	94.17 320	78.48 295	85.34 260	93.91 286

Anonymous20231206		87.09 277	86.14 275	89.93 300	91.22 312	80.35 302	96.11 213	95.35 255	83.57 284	84.16 280	93.02 270	73.54 282	95.61 310	72.16 314	86.14 252	93.84 287

EG-PatchMatch MVS		87.02 278	85.44 279	91.76 275	92.67 302	85.00 265	96.08 216	96.45 209	83.41 286	79.52 315	93.49 262	57.10 328	97.72 250	79.34 293	90.87 212	92.56 302

TinyColmap		86.82 279	85.35 281	91.21 282	94.91 221	82.99 284	93.94 279	94.02 306	83.58 283	81.56 295	94.68 208	62.34 321	98.13 187	75.78 304	87.35 248	92.52 303

TDRefinement		86.53 280	84.76 285	91.85 269	82.23 336	84.25 272	96.38 193	95.35 255	84.97 268	84.09 282	94.94 193	65.76 315	98.34 174	84.60 234	74.52 325	92.97 295

test_0402		86.46 281	84.79 284	91.45 279	95.02 214	85.55 259	96.29 202	94.89 278	80.90 303	82.21 288	93.97 245	68.21 305	97.29 278	62.98 327	88.68 236	91.51 321

DSMNet-mixed		86.34 282	86.12 276	87.00 309	89.88 318	70.43 327	94.93 260	90.08 335	77.97 318	85.42 273	92.78 273	74.44 274	93.96 321	74.43 307	95.14 143	96.62 172

pmmvs-eth3d		86.22 283	84.45 286	91.53 278	88.34 323	87.25 233	94.47 267	95.01 272	83.47 285	79.51 316	89.61 304	69.75 299	95.71 309	83.13 253	76.73 313	91.64 319

test20.03		86.14 284	85.40 280	88.35 302	90.12 315	80.06 307	95.90 225	95.20 264	88.59 192	81.29 296	93.62 257	71.43 289	92.65 326	71.26 318	81.17 302	92.34 314

UnsupCasMVSNet_eth		85.99 285	84.45 286	90.62 292	89.97 317	82.40 288	93.62 286	97.37 139	89.86 150	78.59 318	92.37 280	65.25 316	95.35 315	82.27 265	70.75 330	94.10 283

YYNet1		85.87 286	84.23 288	90.78 291	92.38 306	82.46 287	93.17 292	95.14 267	82.12 294	67.69 329	92.36 283	78.16 244	95.50 314	77.31 300	79.73 306	94.39 277

MDA-MVSNet_test_wron		85.87 286	84.23 288	90.80 290	92.38 306	82.57 285	93.17 292	95.15 266	82.15 293	67.65 330	92.33 286	78.20 241	95.51 313	77.33 299	79.74 305	94.31 281

CMPMVS	62.92 21	85.62 288	84.92 283	87.74 306	89.14 321	73.12 325	94.17 274	96.80 194	73.98 327	73.65 324	94.93 194	66.36 311	97.61 258	83.95 245	91.28 206	92.48 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 289	83.64 290	90.92 286	95.27 198	79.49 310	90.55 318	95.60 245	83.76 282	83.00 287	89.95 297	71.09 291	97.97 222	82.75 259	60.79 337	95.31 230

MDA-MVSNet-bldmvs		85.00 290	82.95 292	91.17 283	93.13 296	83.33 282	94.56 265	95.00 273	84.57 273	65.13 334	92.65 274	70.45 295	95.85 306	73.57 311	77.49 310	94.33 279

MIMVSNet1		84.93 291	83.05 291	90.56 293	89.56 320	84.84 269	95.40 247	95.35 255	83.91 278	80.38 309	92.21 287	57.23 327	93.34 324	70.69 320	82.75 296	93.50 289

OpenMVS_ROB	81.14 20	84.42 292	82.28 293	90.83 287	90.06 316	84.05 276	95.73 233	94.04 305	73.89 328	80.17 314	91.53 294	59.15 325	97.64 256	66.92 323	89.05 230	90.80 324

LP		84.13 293	81.85 298	90.97 285	93.20 293	82.12 290	87.68 330	94.27 301	76.80 320	81.93 291	88.52 314	72.97 284	95.95 305	59.53 332	81.73 298	94.84 257

PM-MVS		83.48 294	81.86 297	88.31 303	87.83 325	77.59 317	93.43 287	91.75 329	86.91 242	80.63 303	89.91 298	44.42 339	95.84 307	85.17 225	76.73 313	91.50 322

new-patchmatchnet		83.18 295	81.87 296	87.11 308	86.88 328	75.99 320	93.70 282	95.18 265	85.02 267	77.30 320	88.40 316	65.99 313	93.88 322	74.19 310	70.18 331	91.47 323

new_pmnet		82.89 296	81.12 301	88.18 305	89.63 319	80.18 306	91.77 309	92.57 325	76.79 321	75.56 322	88.23 318	61.22 323	94.48 318	71.43 316	82.92 294	89.87 326

test2356		82.77 297	82.14 295	84.65 313	85.77 330	70.36 328	91.22 313	93.69 312	81.58 299	81.82 292	89.00 312	60.63 324	90.77 332	64.74 325	90.80 213	92.82 297

testus		82.63 298	82.15 294	84.07 314	87.31 327	67.67 333	93.18 290	94.29 300	82.47 291	82.14 290	90.69 295	53.01 334	91.94 329	66.30 324	89.96 223	92.62 301

MVS-HIRNet		82.47 299	81.21 300	86.26 312	95.38 191	69.21 332	88.96 327	89.49 337	66.28 333	80.79 299	74.08 336	68.48 303	97.39 273	71.93 315	95.47 139	92.18 316

UnsupCasMVSNet_bld		82.13 300	79.46 302	90.14 298	88.00 324	82.47 286	90.89 317	96.62 207	78.94 313	75.61 321	84.40 329	56.63 329	96.31 293	77.30 301	66.77 336	91.63 320

testpf		80.97 301	81.40 299	79.65 320	91.53 310	72.43 326	73.47 341	89.55 336	78.63 314	80.81 298	89.06 311	61.36 322	91.36 331	83.34 250	84.89 274	75.15 337

pmmvs3		79.97 302	77.50 306	87.39 307	82.80 334	79.38 312	92.70 301	90.75 333	70.69 331	78.66 317	87.47 325	51.34 336	93.40 323	73.39 312	69.65 332	89.38 327

test1235678		79.82 303	78.53 304	83.69 315	82.55 335	67.55 334	92.50 304	94.13 303	79.28 311	72.10 327	86.45 327	57.27 326	90.68 333	61.60 330	80.90 303	92.82 297

N_pmnet		78.73 304	78.71 303	78.79 322	92.80 300	46.50 349	94.14 275	43.71 353	78.61 315	80.83 297	91.66 293	74.94 272	96.36 292	67.24 322	84.45 278	93.50 289

1111		78.29 305	77.55 305	80.50 318	83.89 331	59.98 341	91.89 307	93.71 309	75.06 323	73.60 325	87.67 322	55.66 330	92.60 327	58.54 334	77.92 309	88.93 328

Anonymous20231211		78.22 306	75.30 307	86.99 310	86.14 329	74.16 323	95.62 238	93.88 308	66.43 332	74.44 323	87.86 321	41.39 340	95.11 316	62.49 328	69.46 333	91.71 318

test12356		74.97 307	74.13 308	77.49 323	78.81 337	56.23 345	88.53 328	92.75 323	75.14 322	67.50 331	85.07 328	44.88 338	89.96 334	58.71 333	75.75 315	86.26 329

LCM-MVSNet		72.55 308	69.39 311	82.03 316	70.81 346	65.42 337	90.12 322	94.36 297	55.02 337	65.88 333	81.72 330	24.16 350	89.96 334	74.32 309	68.10 334	90.71 325

testmv		72.22 309	70.02 309	78.82 321	73.06 344	61.75 339	91.24 312	92.31 326	74.45 326	61.06 336	80.51 332	34.21 342	88.63 337	55.31 337	68.07 335	86.06 330

FPMVS		71.27 310	69.85 310	75.50 325	74.64 339	59.03 343	91.30 311	91.50 330	58.80 336	57.92 337	88.28 317	29.98 346	85.53 340	53.43 338	82.84 295	81.95 333

PMMVS2		70.19 311	66.92 313	80.01 319	76.35 338	65.67 336	86.22 332	87.58 340	64.83 335	62.38 335	80.29 333	26.78 348	88.49 338	63.79 326	54.07 338	85.88 331

no-one		68.12 312	63.78 315	81.13 317	74.01 341	70.22 330	87.61 331	90.71 334	72.63 330	53.13 339	71.89 337	30.29 344	91.45 330	61.53 331	32.21 342	81.72 334

Gipuma		67.86 313	65.41 314	75.18 326	92.66 303	73.45 324	66.50 343	94.52 292	53.33 338	57.80 338	66.07 340	30.81 343	89.20 336	48.15 341	78.88 308	62.90 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		65.38 314	69.22 312	53.86 334	83.89 331	59.98 341	91.89 307	93.71 309	75.06 323	73.60 325	87.67 322	55.66 330	92.60 327	58.54 334	2.96 348	9.00 346

ANet_high		63.94 315	59.58 316	77.02 324	61.24 349	66.06 335	85.66 334	87.93 339	78.53 316	42.94 341	71.04 338	25.42 349	80.71 342	52.60 339	30.83 344	84.28 332

PNet_i23d		59.01 316	55.87 317	68.44 329	73.98 342	51.37 346	81.36 337	82.41 346	52.37 339	42.49 343	70.39 339	11.39 351	79.99 344	49.77 340	38.71 340	73.97 338

PMVS	53.92 22	58.58 317	55.40 318	68.12 330	51.00 350	48.64 347	78.86 339	87.10 342	46.77 341	35.84 346	74.28 335	8.76 352	86.34 339	42.07 342	73.91 328	69.38 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 318	51.11 322	74.38 328	62.30 348	61.47 340	80.09 338	84.87 343	49.62 340	30.80 347	57.20 344	7.03 353	82.94 341	55.69 336	32.36 341	78.72 336

E-PMN		53.28 319	52.56 320	55.43 332	74.43 340	47.13 348	83.63 336	76.30 349	42.23 342	42.59 342	62.22 342	28.57 347	74.40 345	31.53 344	31.51 343	44.78 342

MVE	50.73 23	53.25 320	48.81 323	66.58 331	65.34 347	57.50 344	72.49 342	70.94 351	40.15 344	39.28 345	63.51 341	6.89 355	73.48 347	38.29 343	42.38 339	68.76 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 321	51.31 321	54.39 333	72.62 345	45.39 350	83.84 335	75.51 350	41.13 343	40.77 344	59.65 343	30.08 345	73.60 346	28.31 345	29.90 345	44.18 343

tmp_tt		51.94 322	53.82 319	46.29 335	33.73 351	45.30 351	78.32 340	67.24 352	18.02 345	50.93 340	87.05 326	52.99 335	53.11 348	70.76 319	25.29 346	40.46 344

pcd1.5k->3k		38.37 323	40.51 324	31.96 336	94.29 243	0.00 355	0.00 345	97.69 101	0.00 349	0.00 351	0.00 351	81.45 184	0.00 352	0.00 349	91.11 208	95.89 196

wuyk23d		25.11 324	24.57 326	26.74 337	73.98 342	39.89 352	57.88 344	9.80 354	12.27 346	10.39 348	6.97 350	7.03 353	36.44 349	25.43 346	17.39 347	3.89 348

cdsmvs_eth3d_5k		23.24 325	30.99 325	0.00 340	0.00 354	0.00 355	0.00 345	97.63 107	0.00 349	0.00 351	96.88 103	84.38 121	0.00 352	0.00 349	0.00 351	0.00 349

testmvs		13.36 326	16.33 327	4.48 339	5.04 352	2.26 354	93.18 290	3.28 355	2.70 347	8.24 349	21.66 346	2.29 357	2.19 350	7.58 347	2.96 348	9.00 346

test123		13.04 327	15.66 328	5.18 338	4.51 353	3.45 353	92.50 304	1.81 356	2.50 348	7.58 350	20.15 347	3.67 356	2.18 351	7.13 348	1.07 350	9.90 345

ab-mvs-re		8.06 328	10.74 329	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	96.69 112	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

pcd_1.5k_mvsjas		7.39 329	9.85 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	88.65 69	0.00 352	0.00 349	0.00 351	0.00 349

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

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

test_full								98.25 26

sam_mvs1												82.76 157

sam_mvs												81.94 178

semantic-postprocess				91.82 270	95.52 185	84.20 274		96.15 222	90.61 138	87.39 250	94.27 237	75.63 265	96.44 291	87.34 189	86.88 250	94.82 261

ambc				86.56 311	83.60 333	70.00 331	85.69 333	94.97 275		80.60 304	88.45 315	37.42 341	96.84 289	82.69 260	75.44 316	92.86 296

MTGPA								98.08 50

test_post1							92.81 300				16.58 349	80.53 201	97.68 252	86.20 206

test_post											17.58 348	81.76 180	98.08 194

patchmatchnet-post											90.45 296	82.65 161	98.10 191

GG-mvs-BLEND				93.62 211	93.69 275	89.20 179	92.39 306	83.33 345		87.98 240	89.84 299	71.00 292	96.87 288	82.08 266	95.40 140	94.80 263

MTMP								82.03 347

gm-plane-assit					93.22 291	78.89 315			84.82 270		93.52 260		98.64 146	87.72 176

test9_res														94.81 60	99.38 34	99.45 29

TEST9					98.70 37	94.19 23	96.41 187	98.02 67	88.17 211	96.03 53	97.56 81	92.74 13	99.59 50

test_8					98.67 39	94.06 29	96.37 194	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				93.98 185	97.94 88	86.64 246		95.54 248	85.38 260	85.49 271	96.77 106	70.28 296	99.15 102	80.02 287	92.87 179	96.15 185

test_prior4						93.66 40	96.42 186

test_prior2							96.35 195		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							95.94 223		81.66 297	97.34 16			98.82 134	92.26 95

新几何2							95.79 230

新几何1				97.32 42	98.60 46	93.59 42		97.75 92	81.58 299	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							95.79 230	97.87 85	83.87 281				99.65 39	87.68 179		98.89 79

原ACMM2							95.67 234

原ACMM1				96.38 80	98.59 47	91.09 112		97.89 82	87.41 227	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	95.33 249	97.60 108	79.22 312	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	88.90 185		97.66 103	82.73 290	97.03 28	98.07 42	90.06 56	98.85 132	89.67 138	98.98 64	98.64 92

testdata1							95.26 255		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					96.21 160	89.98 138

plane_prior6					96.10 170	90.00 134						81.32 186

plane_prior5								97.51 117					98.60 150	93.02 90	92.23 187	95.86 197

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					96.14 168

plane_prior						89.99 136	97.24 108		94.06 38						92.16 191

n2								0.00 357

nn								0.00 357

door-mid								91.06 332

lessismore_v0				90.45 294	91.96 309	79.09 314		87.19 341		80.32 311	94.39 223	66.31 312	97.55 261	84.00 244	76.84 312	94.70 268

LGP-MVS_train				94.10 179	96.16 165	88.26 196		97.46 124	91.29 117	90.12 182	97.16 94	79.05 223	98.73 142	92.25 97	91.89 195	95.31 230

test11								97.88 83

door								91.13 331

HQP5-MVS						89.33 172

HQP-NCC					95.86 174		96.65 170		93.55 48	90.14 176

ACMP_Plane					95.86 174		96.65 170		93.55 48	90.14 176

BP-MVS														92.13 101

HQP4-MVS										90.14 176			98.50 159			95.78 204

HQP3-MVS								97.39 136							92.10 192

HQP2-MVS												80.95 191

NP-MVS					95.99 173	89.81 145					95.87 150

MDTV_nov1_ep13_2view						70.35 329	93.10 296		83.88 280	93.55 100		82.47 166		86.25 205		98.38 114

MDTV_nov1_ep13			90.76 186		95.22 203	80.33 303	93.03 297	95.28 259	88.14 212	92.84 124	93.83 249	81.34 185	98.08 194	82.86 256	94.34 154

ACMMP++_ref															90.30 220

ACMMP++															91.02 210

Test By Simon												88.73 68

ITE_SJBPF				92.43 251	95.34 193	85.37 262		95.92 229	91.47 111	87.75 242	96.39 132	71.00 292	97.96 226	82.36 264	89.86 225	93.97 285

DeepMVS_CX				74.68 327	90.84 313	64.34 338		81.61 348	65.34 334	67.47 332	88.01 320	48.60 337	80.13 343	62.33 329	73.68 329	79.58 335

High-resolution multi-view benchmark