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		95.46 1	95.64 1	94.91 11	98.26 19	86.29 37	97.46 2	97.40 9	89.03 47	96.20 3	98.10 1	89.39 6	99.34 21	95.88 1	99.03 1	99.10 1

CNVR-MVS		95.40 2	95.37 3	95.50 3	98.11 24	88.51 3	95.29 61	96.96 37	92.09 3	95.32 8	97.08 23	89.49 5	99.33 24	95.10 2	98.85 7	98.66 5

HSP-MVS		95.30 3	95.48 2	94.76 23	98.49 9	86.52 27	96.91 15	96.73 54	91.73 9	96.10 4	96.69 36	89.90 2	99.30 27	94.70 3	98.04 48	98.45 17

SteuartSystems-ACMMP		95.20 4	95.32 5	94.85 15	96.99 54	86.33 33	97.33 3	97.30 17	91.38 12	95.39 7	97.46 7	88.98 9	99.40 19	94.12 7	98.89 6	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 5	94.91 7	95.83 1	98.25 20	89.65 1	95.92 38	96.96 37	91.75 8	94.02 18	96.83 30	88.12 10	99.55 5	93.41 15	98.94 4	98.28 27

SD-MVS		94.96 6	95.33 4	93.88 48	97.25 51	86.69 20	96.19 29	97.11 28	90.42 24	96.95 1	97.27 11	89.53 4	96.91 213	94.38 5	98.85 7	98.03 48

TSAR-MVS + MP.		94.85 7	94.94 6	94.58 30	98.25 20	86.33 33	96.11 31	96.62 65	88.14 68	96.10 4	96.96 26	89.09 8	98.94 63	94.48 4	98.68 23	98.48 12

NCCC		94.81 8	94.69 9	95.17 6	97.83 31	87.46 9	95.66 49	96.93 40	92.34 2	93.94 19	96.58 43	87.74 13	99.44 18	92.83 20	98.40 38	98.62 6

ACMMP_Plus		94.74 9	94.56 10	95.28 4	98.02 29	87.70 4	95.68 47	97.34 11	88.28 65	95.30 9	97.67 3	85.90 32	99.54 8	93.91 9	98.95 3	98.60 7

HFP-MVS		94.52 10	94.40 11	94.86 13	98.61 3	86.81 15	96.94 10	97.34 11	88.63 56	93.65 22	97.21 16	86.10 28	99.49 14	92.35 27	98.77 13	98.30 25

MPTG		94.47 11	94.30 13	95.00 8	98.42 13	86.95 11	95.06 80	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

XVS		94.45 12	94.32 12	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	97.16 21	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

MCST-MVS		94.45 12	94.20 19	95.19 5	98.46 11	87.50 8	95.00 83	97.12 26	87.13 87	92.51 49	96.30 52	89.24 7	99.34 21	93.46 12	98.62 31	98.73 3

region2R		94.43 14	94.27 15	94.92 10	98.65 1	86.67 22	96.92 14	97.23 21	88.60 58	93.58 26	97.27 11	85.22 38	99.54 8	92.21 29	98.74 17	98.56 9

ACMMPR		94.43 14	94.28 14	94.91 11	98.63 2	86.69 20	96.94 10	97.32 16	88.63 56	93.53 29	97.26 13	85.04 41	99.54 8	92.35 27	98.78 12	98.50 10

MTAPA		94.42 16	94.22 16	95.00 8	98.42 13	86.95 11	94.36 134	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

CP-MVS		94.34 17	94.21 18	94.74 25	98.39 15	86.64 24	97.60 1	97.24 19	88.53 60	92.73 42	97.23 14	85.20 39	99.32 25	92.15 32	98.83 9	98.25 33

Regformer-2		94.33 18	94.22 16	94.68 26	95.54 99	86.75 19	94.57 113	96.70 58	91.84 6	94.41 11	96.56 45	87.19 19	99.13 38	93.50 11	97.65 56	98.16 37

#test#		94.32 19	94.14 20	94.86 13	98.61 3	86.81 15	96.43 23	97.34 11	87.51 82	93.65 22	97.21 16	86.10 28	99.49 14	91.68 45	98.77 13	98.30 25

MP-MVS		94.25 20	94.07 23	94.77 22	98.47 10	86.31 35	96.71 20	96.98 33	89.04 46	91.98 59	97.19 18	85.43 36	99.56 1	92.06 35	98.79 10	98.44 18

APD-MVS		94.24 21	94.07 23	94.75 24	98.06 27	86.90 14	95.88 39	96.94 39	85.68 118	95.05 10	97.18 19	87.31 18	99.07 42	91.90 43	98.61 32	98.28 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-1		94.22 22	94.13 21	94.51 33	95.54 99	86.36 32	94.57 113	96.44 72	91.69 10	94.32 13	96.56 45	87.05 21	99.03 48	93.35 16	97.65 56	98.15 38

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 23	86.65 23	94.82 94	97.17 24	86.26 108	92.83 37	97.87 2	85.57 35	99.56 1	94.37 6	98.92 5	98.34 22
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 65	80.00 171	94.00 161	97.08 29	90.05 26	95.65 6	97.29 10	89.66 3	98.97 59	93.95 8	98.71 18	98.50 10

DeepC-MVS_fast	89.43 2	94.04 25	93.79 28	94.80 21	97.48 40	86.78 17	95.65 51	96.89 42	89.40 38	92.81 38	96.97 25	85.37 37	99.24 29	90.87 56	98.69 20	98.38 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		94.02 26	93.88 26	94.43 36	98.39 15	85.78 48	97.25 5	97.07 30	86.90 98	92.62 46	96.80 33	84.85 45	99.17 33	92.43 24	98.65 29	98.33 23

mPP-MVS		93.99 27	93.78 29	94.63 28	98.50 8	85.90 46	96.87 16	96.91 41	88.70 54	91.83 63	97.17 20	83.96 51	99.55 5	91.44 49	98.64 30	98.43 19

PGM-MVS		93.96 28	93.72 31	94.68 26	98.43 12	86.22 38	95.30 59	97.78 1	87.45 83	93.26 30	97.33 9	84.62 46	99.51 12	90.75 58	98.57 33	98.32 24

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 105	85.47 50	94.68 105	96.41 75	91.60 11	93.75 21	96.71 34	85.95 31	99.10 41	93.21 17	96.65 71	98.01 50

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 57	86.43 30	96.69 21	97.49 4	85.15 129	93.56 28	96.28 53	85.60 34	99.31 26	92.45 23	98.79 10	98.12 41

APD-MVS_3200maxsize		93.78 31	93.77 30	93.80 53	97.92 30	84.19 74	96.30 26	96.87 45	86.96 94	93.92 20	97.47 6	83.88 52	98.96 62	92.71 22	97.87 51	98.26 32

MSLP-MVS++		93.72 32	94.08 22	92.65 81	97.31 45	83.43 90	95.79 42	97.33 14	90.03 27	93.58 26	96.96 26	84.87 44	97.76 136	92.19 31	98.66 27	96.76 93

Regformer-3		93.68 33	93.64 33	93.81 52	95.36 105	84.61 59	94.68 105	95.83 113	91.27 13	93.60 25	96.71 34	85.75 33	98.86 68	92.87 19	96.65 71	97.96 51

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 62	86.78 17	94.40 124	93.93 217	89.77 32	94.21 14	95.59 79	87.35 17	98.61 83	92.72 21	96.15 79	97.83 60

test_prior3		93.60 35	93.53 34	93.82 50	97.29 47	84.49 63	94.12 145	96.88 43	87.67 79	92.63 44	96.39 50	86.62 24	98.87 65	91.50 47	98.67 25	98.11 42

CANet		93.54 36	93.20 39	94.55 31	95.65 96	85.73 49	94.94 86	96.69 60	91.89 5	90.69 76	95.88 70	81.99 70	99.54 8	93.14 18	97.95 50	98.39 20

MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 54	84.84 55	93.24 201	97.24 19	88.76 53	91.60 67	95.85 71	86.07 30	98.66 78	91.91 40	98.16 44	98.03 48

train_agg		93.44 38	93.08 40	94.52 32	97.53 35	86.49 28	94.07 153	96.78 50	81.86 215	92.77 39	96.20 57	87.63 15	99.12 39	92.14 33	98.69 20	97.94 52

DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 104	85.04 54	93.06 208	97.13 25	90.74 20	91.84 61	95.09 90	86.32 27	99.21 30	91.22 50	98.45 37	97.65 64
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 17	84.83 56	97.15 7	96.80 49	85.77 115	92.47 50	97.13 22	82.38 60	99.07 42	90.51 60	98.40 38	97.92 56

DeepC-MVS	88.79 3	93.31 41	92.99 43	94.26 41	96.07 83	85.83 47	94.89 89	96.99 32	89.02 48	89.56 86	97.37 8	82.51 59	99.38 20	92.20 30	98.30 40	97.57 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		93.29 42	92.97 44	94.26 41	97.38 42	85.92 43	93.92 164	96.72 56	81.96 205	92.16 55	96.23 55	87.85 11	98.97 59	91.95 39	98.55 36	97.90 57

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 38	86.12 40	94.07 153	96.73 54	81.46 223	92.46 51	96.05 65	86.90 22	99.15 36	92.14 33	98.69 20	97.94 52

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 95	87.66 5	96.33 25	96.41 75	90.00 28	94.09 16	94.60 104	82.33 61	98.62 82	92.40 26	92.86 133	98.27 30

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 94	87.58 7	94.71 104	96.59 67	91.78 7	91.46 68	96.18 61	75.45 145	99.55 5	93.53 10	98.19 43	98.28 27

ACMMP		93.24 46	92.88 47	94.30 40	98.09 26	85.33 52	96.86 17	97.45 7	88.33 63	90.15 82	97.03 24	81.44 73	99.51 12	90.85 57	95.74 82	98.04 47
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CSCG		93.23 47	93.05 41	93.76 54	98.04 28	84.07 76	96.22 28	97.37 10	84.15 149	90.05 83	95.66 77	87.77 12	99.15 36	89.91 63	98.27 41	98.07 44

abl_6		93.18 48	93.05 41	93.57 57	97.52 37	84.27 73	95.53 54	96.67 61	87.85 74	93.20 32	97.22 15	80.35 80	99.18 32	91.91 40	97.21 61	97.26 74

alignmvs		93.08 49	92.50 52	94.81 20	95.62 98	87.61 6	95.99 35	96.07 96	89.77 32	94.12 15	94.87 94	80.56 79	98.66 78	92.42 25	93.10 128	98.15 38

EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 118	83.51 89	94.48 116	95.77 117	90.87 16	92.52 48	96.67 38	84.50 47	99.00 56	91.99 36	94.44 106	97.36 73

UA-Net		92.83 51	92.54 51	93.68 55	96.10 81	84.71 58	95.66 49	96.39 77	91.92 4	93.22 31	96.49 47	83.16 54	98.87 65	84.47 119	95.47 87	97.45 72

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 32	86.11 41	94.06 156	96.66 62	80.09 234	92.77 39	96.63 40	86.62 24	99.04 47	87.40 88	98.66 27	98.17 36

EI-MVSNet-UG-set		92.74 53	92.62 49	93.12 64	94.86 126	83.20 95	94.40 124	95.74 120	90.71 21	92.05 58	96.60 42	84.00 50	98.99 57	91.55 46	93.63 115	97.17 80

MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 86	84.43 70	93.08 206	96.09 94	88.20 67	91.12 73	95.72 76	81.33 75	97.76 136	91.74 44	97.37 60	96.75 94

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 97	88.73 2	97.07 8	96.77 52	90.84 17	84.02 207	96.62 41	75.95 134	99.34 21	87.77 83	97.68 54	98.59 8

VNet		92.24 56	91.91 55	93.24 60	96.59 62	83.43 90	94.84 93	96.44 72	89.19 43	94.08 17	95.90 69	77.85 111	98.17 103	88.90 70	93.38 122	98.13 40

CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 22	81.98 125	96.76 19	96.49 71	81.89 210	90.24 80	96.44 49	78.59 100	98.61 83	89.68 64	97.85 52	97.06 85

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 18	85.92 43	94.66 108	96.66 62	82.69 194	90.03 84	95.82 72	82.30 62	99.03 48	84.57 118	96.48 76	96.91 89

EPNet		91.79 59	91.02 66	94.10 44	90.10 284	85.25 53	96.03 34	92.05 250	92.83 1	87.39 120	95.78 73	79.39 94	99.01 53	88.13 79	97.48 58	98.05 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 60	91.70 57	92.00 105	97.08 53	80.03 170	93.60 185	95.18 167	87.85 74	90.89 75	96.47 48	82.06 68	98.36 93	85.07 110	97.04 64	97.62 65

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 108	83.78 81	96.14 30	95.98 101	89.89 29	90.45 78	96.58 43	75.09 149	98.31 99	84.75 116	96.90 65	97.78 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 136	86.37 31	97.18 6	97.02 31	89.20 42	84.31 203	96.66 39	73.74 168	99.17 33	86.74 98	97.96 49	97.79 62

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 89	80.50 161	97.33 3	95.25 160	86.15 110	89.76 85	95.60 78	83.42 53	98.32 98	87.37 90	93.25 125	97.56 69

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 163	83.88 79	95.96 36	95.90 108	84.66 138	91.76 64	94.91 92	77.92 108	97.30 180	89.64 65	97.11 62	97.24 75

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 147	83.39 92	94.60 110	95.10 169	87.10 88	90.57 77	93.10 151	81.43 74	98.07 121	89.29 67	94.48 103	97.59 67

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 91	81.38 136	96.95 9	93.69 222	89.72 34	89.50 88	95.98 66	78.57 101	97.77 135	83.02 137	96.50 75	98.22 34

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 67	83.17 96	94.87 92	96.66 62	83.29 171	89.27 89	94.46 106	80.29 82	99.17 33	87.57 86	95.37 89	96.05 113

MVS_Test		91.31 68	91.11 63	91.93 109	94.37 144	80.14 165	93.46 190	95.80 115	86.46 104	91.35 70	93.77 133	82.21 64	98.09 119	87.57 86	94.95 94	97.55 70

OMC-MVS		91.23 69	90.62 72	93.08 66	96.27 70	84.07 76	93.52 187	95.93 104	86.95 95	89.51 87	96.13 63	78.50 102	98.35 95	85.84 105	92.90 132	96.83 92

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 34	81.46 133	94.37 130	96.24 85	86.39 106	87.41 118	94.80 99	82.06 68	98.48 89	82.80 141	95.37 89	97.61 66

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 111	82.60 117	92.09 239	95.70 122	86.27 107	91.84 61	92.46 171	79.70 89	98.99 57	89.08 68	95.86 81	94.29 183

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 121	82.42 118	92.24 233	95.64 128	86.11 112	91.74 66	93.14 149	79.67 92	98.89 64	89.06 69	95.46 88	94.28 184

nrg030		91.08 73	90.39 73	93.17 63	93.07 185	86.91 13	96.41 24	96.26 82	88.30 64	88.37 99	94.85 97	82.19 65	97.64 143	91.09 51	82.95 241	94.96 147

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 163	83.88 79	92.81 215	93.86 218	79.84 236	91.76 64	94.29 111	77.92 108	98.04 123	90.48 61	97.11 62	97.17 80

jason		90.80 75	90.10 79	92.90 74	93.04 187	83.53 88	93.08 206	94.15 204	80.22 232	91.41 69	94.91 92	76.87 114	97.93 130	90.28 62	96.90 65	97.24 75
jason: jason.
VDD-MVS		90.74 76	89.92 84	93.20 61	96.27 70	83.02 101	95.73 44	93.86 218	88.42 62	92.53 47	96.84 29	62.09 280	98.64 80	90.95 55	92.62 135	97.93 55

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 77	81.25 138	92.55 224	96.83 46	82.04 204	89.10 91	92.56 170	81.04 77	98.85 71	86.72 101	95.91 80	95.84 120

API-MVS		90.66 78	90.07 80	92.45 89	96.36 68	84.57 61	96.06 33	95.22 166	82.39 196	89.13 90	94.27 114	80.32 81	98.46 90	80.16 184	96.71 69	94.33 182

xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 160	84.46 66	93.32 192	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 160	84.46 66	93.32 192	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 160	84.46 66	93.32 192	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 132	82.73 111	95.85 40	96.22 86	90.81 18	86.91 127	94.86 95	74.23 157	98.12 106	88.15 77	89.99 163	94.63 164

FIs		90.51 83	90.35 74	90.99 143	93.99 159	80.98 147	95.73 44	97.54 3	89.15 44	86.72 131	94.68 100	81.83 72	97.24 188	85.18 109	88.31 195	94.76 161

1121		90.42 84	89.49 88	93.20 61	97.27 49	84.46 66	92.63 220	95.51 138	71.01 311	91.20 72	96.21 56	82.92 56	99.05 44	80.56 175	98.07 47	96.10 109

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 61	84.48 65	95.68 47	95.67 123	82.36 198	87.85 107	92.85 160	76.63 119	98.80 74	80.01 185	96.68 70	95.91 116
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
FC-MVSNet-test		90.27 86	90.18 78	90.53 152	93.71 169	79.85 175	95.77 43	97.59 2	89.31 40	86.27 140	94.67 101	81.93 71	97.01 205	84.26 124	88.09 198	94.71 162

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 175	83.01 102	93.48 188	94.47 194	89.43 37	87.76 115	94.23 115	70.54 210	99.03 48	84.97 111	96.39 77	96.38 101

OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 183	83.90 78	94.16 144	95.74 120	88.96 49	87.86 106	95.43 81	72.48 185	97.91 131	88.10 80	90.18 162	93.65 221

LFMVS		90.08 89	89.13 98	92.95 72	96.71 59	82.32 120	96.08 32	89.91 303	86.79 99	92.15 57	96.81 31	62.60 277	98.34 96	87.18 92	93.90 111	98.19 35

PAPR		90.02 90	89.27 96	92.29 96	95.78 92	80.95 149	92.68 219	96.22 86	81.91 208	86.66 132	93.75 135	82.23 63	98.44 92	79.40 202	94.79 95	97.48 71

PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 77	81.25 138	93.92 164	96.83 46	83.49 165	89.10 91	92.26 181	81.04 77	98.85 71	86.72 101	87.86 200	92.35 267

PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 207	80.85 152	95.26 68	95.98 101	86.26 108	86.21 141	94.29 111	79.70 89	97.65 141	88.87 71	88.10 196	94.57 170

XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 158	81.21 141	91.87 241	96.06 98	85.78 114	88.55 96	95.73 75	74.67 153	97.27 184	88.71 72	89.64 170	95.91 116

UGNet		89.95 93	88.95 102	92.95 72	94.51 139	83.31 93	95.70 46	95.23 164	89.37 39	87.58 117	93.94 124	64.00 273	98.78 75	83.92 129	96.31 78	96.74 95
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 176	83.72 82	94.43 122	97.12 26	89.80 31	86.46 134	93.32 140	83.16 54	97.23 190	84.92 112	81.02 269	94.49 177

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 41	83.03 100	94.42 123	95.92 105	82.81 190	86.34 139	94.65 102	73.89 164	99.02 51	80.69 172	95.51 85	95.05 141

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 172	84.52 62	94.78 97	97.47 5	89.26 41	86.44 137	92.32 177	82.10 66	97.39 176	84.81 115	80.84 273	94.12 189

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 149	81.56 128	94.39 126	96.04 99	88.81 50	85.43 170	93.97 123	73.83 166	97.96 127	87.11 95	89.77 168	94.50 175

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 163	82.89 106	95.46 55	97.33 14	87.91 71	88.43 98	93.31 141	74.17 160	97.40 173	87.32 91	82.86 243	94.52 173

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 103	81.15 143	92.38 229	94.78 187	83.11 174	89.06 93	94.32 109	78.67 99	96.61 228	81.57 161	90.89 154	97.24 75

Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 189	95.74 93	75.85 266	95.61 52	90.80 287	87.66 81	87.83 112	95.40 82	76.79 116	96.46 236	78.37 208	96.73 68	97.80 61

VDDNet		89.56 102	88.49 113	92.76 79	95.07 117	82.09 122	96.30 26	93.19 228	81.05 228	91.88 60	96.86 28	61.16 289	98.33 97	88.43 75	92.49 136	97.84 59

114514_t		89.51 103	88.50 111	92.54 85	98.11 24	81.99 124	95.16 74	96.36 79	70.19 313	85.81 146	95.25 85	76.70 117	98.63 81	82.07 152	96.86 67	97.00 86

QAPM		89.51 103	88.15 123	93.59 56	94.92 123	84.58 60	96.82 18	96.70 58	78.43 252	83.41 221	96.19 60	73.18 175	99.30 27	77.11 223	96.54 74	96.89 91

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 148	82.07 123	92.13 237	96.09 94	87.90 72	85.37 177	92.45 172	74.38 155	97.56 146	87.15 93	90.43 156	93.93 198
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
mvs-test1		89.45 106	89.14 97	90.38 167	93.33 177	77.63 249	94.95 85	94.36 197	87.70 77	87.10 124	92.81 164	73.45 171	98.03 124	85.57 107	93.04 129	95.48 131

LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 140	81.49 131	95.30 59	96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 171	83.61 87	93.01 209	94.68 189	81.95 206	87.82 113	93.24 145	78.69 98	96.99 206	80.34 180	93.23 126	96.28 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 128	80.81 153	93.54 186	95.10 169	83.11 174	86.82 130	90.67 237	79.74 88	97.75 139	80.51 177	93.55 116	96.57 98

ab-mvs		89.41 109	88.35 115	92.60 82	95.15 116	82.65 115	92.20 235	95.60 129	83.97 151	88.55 96	93.70 136	74.16 161	98.21 102	82.46 147	89.37 173	96.94 88

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 152	81.46 133	91.27 254	96.07 96	86.14 111	88.89 94	95.77 74	68.73 238	97.26 186	87.39 89	89.96 165	95.83 121

mvs_anonymous		89.37 112	89.32 93	89.51 209	93.47 174	74.22 272	91.65 248	94.83 185	82.91 188	85.45 167	93.79 132	81.23 76	96.36 241	86.47 104	94.09 109	97.94 52

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 187	83.72 82	94.47 119	96.59 67	89.50 36	86.46 134	93.29 143	77.25 112	97.23 190	84.92 112	81.02 269	94.59 168

TAMVS		89.21 114	88.29 120	91.96 107	93.71 169	82.62 116	93.30 196	94.19 202	82.22 199	87.78 114	93.94 124	78.83 96	96.95 210	77.70 216	92.98 130	96.32 102

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 135	81.22 140	95.31 57	96.12 93	85.31 125	85.92 145	94.34 107	70.19 214	98.06 122	85.65 106	88.86 185	94.08 193
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EI-MVSNet		89.10 116	88.86 106	89.80 199	91.84 209	78.30 229	93.70 180	95.01 172	85.73 116	87.15 122	95.28 83	79.87 86	97.21 192	83.81 131	87.36 204	93.88 202

diffmvs		89.07 117	88.32 118	91.34 128	93.24 180	79.79 176	92.29 232	94.98 175	80.24 231	87.38 121	92.45 172	78.02 106	97.33 178	83.29 134	92.93 131	96.91 89

CNLPA		89.07 117	87.98 126	92.34 94	96.87 56	84.78 57	94.08 151	93.24 227	81.41 224	84.46 195	95.13 89	75.57 142	96.62 226	77.21 221	93.84 113	95.61 129

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 49	82.69 114	94.29 135	95.44 147	79.71 238	84.01 208	94.18 116	76.68 118	98.75 76	77.28 220	93.41 121	95.02 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 120	88.64 108	90.21 172	90.74 268	79.28 201	95.96 36	95.90 108	84.66 138	85.33 179	92.94 159	74.02 163	97.30 180	89.64 65	88.53 188	94.05 194

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 126	82.77 107	92.08 240	94.49 193	81.52 222	86.93 126	92.79 166	78.32 105	98.23 100	79.93 188	90.55 155	95.88 118

ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 129	81.27 137	95.07 78	95.89 110	86.48 103	83.67 215	94.30 110	69.33 222	97.99 126	87.10 97	88.55 187	93.72 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 123	88.26 122	90.94 145	94.05 153	80.78 154	91.71 245	95.38 151	81.55 221	88.63 95	93.91 128	75.04 150	95.47 275	82.47 146	91.61 140	96.57 98

TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 196	83.01 102	94.92 88	96.31 80	89.88 30	85.53 161	93.85 131	76.63 119	96.96 209	81.91 156	79.87 288	94.50 175

CHOSEN 1792x2688		88.84 124	87.69 130	92.30 95	96.14 76	81.42 135	90.01 266	95.86 112	74.52 285	87.41 118	93.94 124	75.46 144	98.36 93	80.36 179	95.53 84	97.12 83

MVSTER		88.84 124	88.29 120	90.51 159	92.95 191	80.44 162	93.73 176	95.01 172	84.66 138	87.15 122	93.12 150	72.79 179	97.21 192	87.86 82	87.36 204	93.87 203

OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 195	85.39 51	96.57 22	96.43 74	78.74 249	80.85 251	96.07 64	69.64 219	99.01 53	78.01 214	96.65 71	94.83 158

Effi-MVS+-dtu		88.65 128	88.35 115	89.54 206	93.33 177	76.39 261	94.47 119	94.36 197	87.70 77	85.43 170	89.56 259	73.45 171	97.26 186	85.57 107	91.28 142	94.97 144

BH-untuned		88.60 129	88.13 124	90.01 191	95.24 115	78.50 224	93.29 197	94.15 204	84.75 136	84.46 195	93.40 137	75.76 139	97.40 173	77.59 217	94.52 102	94.12 189

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 187	84.16 75	94.77 98	96.25 84	89.05 45	80.04 263	93.29 143	79.02 95	97.05 203	81.71 160	80.05 283	94.59 168

1112_ss		88.42 131	87.33 137	91.72 118	94.92 123	80.98 147	92.97 212	94.54 192	78.16 257	83.82 211	93.88 129	78.78 97	97.91 131	79.45 198	89.41 172	96.26 104

WR-MVS		88.38 132	87.67 131	90.52 158	93.30 179	80.18 163	93.26 199	95.96 103	88.57 59	85.47 166	92.81 164	76.12 123	96.91 213	81.24 163	82.29 247	94.47 180

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 109	79.45 187	92.89 214	93.07 230	85.45 122	86.91 127	94.84 98	70.35 211	97.76 136	73.97 248	94.59 100	95.85 119

IterMVS-LS		88.36 134	87.91 129	89.70 202	93.80 166	78.29 230	93.73 176	95.08 171	85.73 116	84.75 189	91.90 196	79.88 85	96.92 212	83.83 130	82.51 245	93.89 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 135	86.13 181	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	23.41 345	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

LCM-MVSNet-Re		88.30 136	88.32 118	88.27 249	94.71 131	72.41 292	93.15 202	90.98 282	87.77 76	79.25 269	91.96 193	78.35 104	95.75 264	83.04 136	95.62 83	96.65 96

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 263	80.14 165	95.31 57	95.65 127	84.97 132	84.24 205	94.02 120	65.31 267	97.42 166	88.56 73	88.52 189	93.89 200

VPNet		88.20 138	87.47 134	90.39 165	93.56 173	79.46 185	94.04 157	95.54 134	88.67 55	86.96 125	94.58 105	69.33 222	97.15 194	84.05 128	80.53 278	94.56 171

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 60	80.65 155	94.39 126	96.21 89	76.38 267	86.19 142	95.44 80	79.75 87	98.08 120	62.75 310	95.29 91	96.13 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DI_MVS_plusplus_test		88.15 140	86.82 155	92.14 101	90.67 271	81.07 144	93.01 209	94.59 191	83.83 155	77.78 276	90.63 238	68.51 241	98.16 104	88.02 81	94.37 107	97.17 80

test_normal		88.13 141	86.78 159	92.18 99	90.55 276	81.19 142	92.74 217	94.64 190	83.84 153	77.49 279	90.51 244	68.49 242	98.16 104	88.22 76	94.55 101	97.21 78

HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 85	80.63 156	90.01 266	95.79 116	73.42 291	87.68 116	92.10 187	73.86 165	97.96 127	80.75 171	91.70 139	97.19 79

mvs_tets		88.06 143	87.28 139	90.38 167	90.94 259	79.88 173	95.22 70	95.66 125	85.10 130	84.21 206	93.94 124	63.53 275	97.40 173	88.50 74	88.40 194	93.87 203

v1neww		87.98 144	87.25 141	90.16 174	91.38 228	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 254	93.61 226

v7new		87.98 144	87.25 141	90.16 174	91.38 228	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 254	93.61 226

v6		87.98 144	87.25 141	90.16 174	91.36 231	79.39 194	94.37 130	95.27 159	84.48 141	85.78 147	91.51 212	76.15 122	97.46 153	84.46 120	81.88 256	93.62 225

F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 69	80.88 151	94.73 99	95.45 145	79.65 239	82.04 238	94.61 103	71.13 197	98.50 88	76.24 230	91.05 148	94.80 160

LS3D		87.89 148	86.32 177	92.59 83	96.07 83	82.92 105	95.23 69	94.92 180	75.66 274	82.89 226	95.98 66	72.48 185	99.21 30	68.43 284	95.23 93	95.64 128

v1		87.85 149	87.10 144	90.11 185	91.21 245	79.24 205	94.09 149	95.24 161	84.44 145	85.70 153	91.31 220	75.96 133	97.45 155	84.18 125	81.73 261	93.64 222

anonymousdsp		87.84 150	87.09 145	90.12 180	89.13 295	80.54 159	94.67 107	95.55 132	82.05 202	83.82 211	92.12 184	71.47 195	97.15 194	87.15 93	87.80 201	92.67 256

v1141		87.84 150	87.09 145	90.11 185	91.23 243	79.25 203	94.08 151	95.24 161	84.44 145	85.69 155	91.31 220	75.91 135	97.44 162	84.17 126	81.74 260	93.63 224

divwei89l23v2f112		87.84 150	87.09 145	90.10 187	91.23 243	79.24 205	94.09 149	95.24 161	84.44 145	85.70 153	91.31 220	75.91 135	97.44 162	84.17 126	81.73 261	93.64 222

v2v482		87.84 150	87.06 148	90.17 173	90.99 255	79.23 207	94.00 161	95.13 168	84.87 133	85.53 161	92.07 190	74.45 154	97.45 155	84.71 117	81.75 259	93.85 206

WR-MVS_H		87.80 154	87.37 136	89.10 225	93.23 181	78.12 234	95.61 52	97.30 17	87.90 72	83.72 213	92.01 192	79.65 93	96.01 253	76.36 227	80.54 277	93.16 242

v7		87.75 155	86.96 151	90.12 180	91.20 246	79.50 180	94.28 136	95.46 141	83.45 166	85.75 150	91.56 209	75.13 147	97.43 164	83.60 132	82.18 249	93.42 235

PCF-MVS	84.11 10	87.74 156	86.08 184	92.70 80	94.02 154	84.43 70	89.27 277	95.87 111	73.62 290	84.43 197	94.33 108	78.48 103	98.86 68	70.27 265	94.45 105	94.81 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.68 157	86.86 153	90.15 178	90.58 273	80.14 165	94.24 138	95.28 156	83.66 158	85.67 156	91.33 217	74.73 152	97.41 171	84.43 123	81.83 257	92.89 250

conf200view11		87.65 158	86.71 161	90.46 163	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.71 162

thres600view7		87.65 158	86.67 164	90.59 149	96.08 82	78.72 212	94.88 91	91.58 263	87.06 93	88.08 102	92.30 178	68.91 230	98.10 112	70.05 273	91.10 143	94.96 147

XXY-MVS		87.65 158	86.85 154	90.03 189	92.14 203	80.60 158	93.76 173	95.23 164	82.94 186	84.60 191	94.02 120	74.27 156	95.49 274	81.04 165	83.68 234	94.01 197

Test_1112_low_res		87.65 158	86.51 173	91.08 137	94.94 122	79.28 201	91.77 242	94.30 200	76.04 272	83.51 219	92.37 175	77.86 110	97.73 140	78.69 207	89.13 182	96.22 105

thres100view900		87.63 162	86.71 161	90.38 167	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.48 178

CP-MVSNet		87.63 162	87.26 140	88.74 230	93.12 184	76.59 260	95.29 61	96.58 69	88.43 61	83.49 220	92.98 158	75.28 146	95.83 260	78.97 204	81.15 266	93.79 208

view600		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

view800		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

conf0.05thres1000		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

tfpn		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

thres400		87.62 164	86.64 169	90.57 150	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.96 147

v1144		87.61 169	86.79 158	90.06 188	91.01 254	79.34 197	93.95 163	95.42 150	83.36 170	85.66 157	91.31 220	74.98 151	97.42 166	83.37 133	82.06 250	93.42 235

tfpn200view9		87.58 170	86.64 169	90.41 164	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.48 178

BH-w/o		87.57 171	87.05 149	89.12 223	94.90 125	77.90 239	92.41 227	93.51 224	82.89 189	83.70 214	91.34 216	75.75 140	97.07 201	75.49 234	93.49 118	92.39 265

1314		87.51 172	86.57 172	90.34 170	92.42 199	79.74 178	92.63 220	95.35 155	78.35 253	80.14 261	91.62 205	74.05 162	97.15 194	81.05 164	93.53 117	94.12 189

v8		87.50 173	86.71 161	89.89 194	91.37 230	79.40 193	94.50 115	95.38 151	84.81 135	83.60 217	91.33 217	76.05 127	97.42 166	82.84 140	80.51 280	92.84 252

Fast-Effi-MVS+-dtu		87.44 174	86.72 160	89.63 204	92.04 206	77.68 248	94.03 158	93.94 216	85.81 113	82.42 230	91.32 219	70.33 212	97.06 202	80.33 181	90.23 161	94.14 188

MVS		87.44 174	86.10 183	91.44 126	92.61 197	83.62 86	92.63 220	95.66 125	67.26 321	81.47 243	92.15 183	77.95 107	98.22 101	79.71 194	95.48 86	92.47 262

FMVSNet3		87.40 176	86.11 182	91.30 130	93.79 168	83.64 85	94.20 143	94.81 186	83.89 152	84.37 198	91.87 197	68.45 244	96.56 229	78.23 211	85.36 218	93.70 217

PS-CasMVS		87.32 177	86.88 152	88.63 233	92.99 190	76.33 263	95.33 56	96.61 66	88.22 66	83.30 223	93.07 152	73.03 177	95.79 263	78.36 209	81.00 271	93.75 214

GBi-Net		87.26 178	85.98 186	91.08 137	94.01 155	83.10 97	95.14 75	94.94 176	83.57 161	84.37 198	91.64 201	66.59 257	96.34 242	78.23 211	85.36 218	93.79 208

test1		87.26 178	85.98 186	91.08 137	94.01 155	83.10 97	95.14 75	94.94 176	83.57 161	84.37 198	91.64 201	66.59 257	96.34 242	78.23 211	85.36 218	93.79 208

v1192		87.25 180	86.33 176	90.00 192	90.76 267	79.04 209	93.80 170	95.48 140	82.57 195	85.48 165	91.18 226	73.38 174	97.42 166	82.30 149	82.06 250	93.53 230

v10		87.25 180	86.38 174	89.85 195	91.19 248	79.50 180	94.48 116	95.45 145	83.79 156	83.62 216	91.19 225	75.13 147	97.42 166	81.94 155	80.60 275	92.63 258

DP-MVS		87.25 180	85.36 200	92.90 74	97.65 33	83.24 94	94.81 95	92.00 252	74.99 280	81.92 240	95.00 91	72.66 181	99.05 44	66.92 293	92.33 137	96.40 100

thres200		87.21 183	86.24 180	90.12 180	95.36 105	78.53 218	93.26 199	92.10 247	86.42 105	88.00 105	91.11 230	69.24 226	98.00 125	69.58 274	91.04 149	93.83 207

v144192		87.19 184	86.35 175	89.74 200	90.64 272	78.24 232	93.92 164	95.43 148	81.93 207	85.51 163	91.05 232	74.21 159	97.45 155	82.86 139	81.56 263	93.53 230

FMVSNet2		87.19 184	85.82 190	91.30 130	94.01 155	83.67 84	94.79 96	94.94 176	83.57 161	83.88 209	92.05 191	66.59 257	96.51 232	77.56 218	85.01 222	93.73 215

Baseline_NR-MVSNet		87.07 186	86.63 171	88.40 246	91.44 221	77.87 241	94.23 139	92.57 240	84.12 150	85.74 152	92.08 188	77.25 112	96.04 250	82.29 150	79.94 286	91.30 285

v148		87.04 187	86.32 177	89.21 221	90.94 259	77.26 254	93.71 179	94.43 195	84.84 134	84.36 201	90.80 235	76.04 129	97.05 203	82.12 151	79.60 289	93.31 237

v1921920		86.97 188	86.06 185	89.69 203	90.53 277	78.11 235	93.80 170	95.43 148	81.90 209	85.33 179	91.05 232	72.66 181	97.41 171	82.05 153	81.80 258	93.53 230

v7n		86.81 189	85.76 191	89.95 193	90.72 269	79.25 203	95.07 78	95.92 105	84.45 144	82.29 231	90.86 234	72.60 183	97.53 148	79.42 201	80.52 279	93.08 247

PEN-MVS		86.80 190	86.27 179	88.40 246	92.32 201	75.71 267	95.18 72	96.38 78	87.97 69	82.82 227	93.15 148	73.39 173	95.92 256	76.15 231	79.03 291	93.59 228

v1240		86.78 191	85.85 189	89.56 205	90.45 278	77.79 243	93.61 184	95.37 153	81.65 217	85.43 170	91.15 228	71.50 194	97.43 164	81.47 162	82.05 252	93.47 234

TR-MVS		86.78 191	85.76 191	89.82 196	94.37 144	78.41 226	92.47 226	92.83 233	81.11 227	86.36 138	92.40 174	68.73 238	97.48 151	73.75 251	89.85 167	93.57 229

PatchMatch-RL		86.77 193	85.54 193	90.47 162	95.88 89	82.71 113	90.54 259	92.31 243	79.82 237	84.32 202	91.57 208	68.77 237	96.39 239	73.16 253	93.48 120	92.32 268

PAPM		86.68 194	85.39 199	90.53 152	93.05 186	79.33 200	89.79 270	94.77 188	78.82 246	81.95 239	93.24 145	76.81 115	97.30 180	66.94 291	93.16 127	94.95 154

pm-mvs1		86.61 195	85.54 193	89.82 196	91.44 221	80.18 163	95.28 67	94.85 183	83.84 153	81.66 242	92.62 169	72.45 187	96.48 234	79.67 195	78.06 293	92.82 254

GA-MVS		86.61 195	85.27 202	90.66 148	91.33 236	78.71 213	90.40 260	93.81 221	85.34 124	85.12 181	89.57 258	61.25 286	97.11 198	80.99 168	89.59 171	96.15 106

v52		86.50 197	85.53 196	89.39 213	89.17 294	78.99 210	94.72 102	95.54 134	83.59 159	82.10 235	90.60 240	71.59 192	97.45 155	82.52 143	79.99 285	91.73 277

V4		86.50 197	85.54 193	89.39 213	89.13 295	78.99 210	94.73 99	95.54 134	83.59 159	82.10 235	90.61 239	71.60 191	97.45 155	82.52 143	80.01 284	91.74 276

EPNet_dtu		86.49 199	85.94 188	88.14 254	90.24 282	72.82 284	94.11 147	92.20 246	86.66 102	79.42 268	92.36 176	73.52 169	95.81 262	71.26 260	93.66 114	95.80 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 200	84.98 205	90.80 147	92.10 205	80.92 150	90.24 262	95.91 107	73.10 294	83.57 218	88.39 273	65.15 268	97.46 153	84.90 114	91.43 141	94.03 195

v748		86.27 201	85.28 201	89.25 220	90.26 281	77.58 253	94.89 89	95.50 139	84.28 148	81.41 245	90.46 245	72.57 184	97.32 179	79.81 193	78.36 292	92.84 252

LTVRE_ROB	82.13 13	86.26 202	84.90 210	90.34 170	94.44 143	81.50 130	92.31 231	94.89 181	83.03 181	79.63 266	92.67 167	69.69 218	97.79 134	71.20 261	86.26 212	91.72 278
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		86.11 203	85.48 197	87.98 256	91.65 217	74.92 270	94.93 87	95.75 119	87.36 84	82.26 232	93.04 153	72.85 178	95.82 261	74.04 247	77.46 296	93.20 240

tfpn_ndepth		86.10 204	84.98 205	89.43 212	95.52 102	78.29 230	94.62 109	89.60 309	81.88 214	85.43 170	90.54 241	68.47 243	96.85 217	68.46 283	90.34 159	93.15 244

tfpn1000		86.06 205	84.92 209	89.49 210	95.54 99	77.79 243	94.72 102	89.07 315	82.05 202	85.36 178	91.94 194	68.32 249	96.65 224	67.04 290	90.24 160	94.02 196

PatchFormer-LS_test		86.02 206	85.13 203	88.70 231	91.52 218	74.12 275	91.19 256	92.09 248	82.71 193	84.30 204	87.24 288	70.87 201	96.98 207	81.04 165	85.17 221	95.00 143

XVG-ACMP-BASELINE		86.00 207	84.84 212	89.45 211	91.20 246	78.00 236	91.70 246	95.55 132	85.05 131	82.97 225	92.25 182	54.49 312	97.48 151	82.93 138	87.45 203	92.89 250

MVP-Stereo		85.97 208	84.86 211	89.32 218	90.92 261	82.19 121	92.11 238	94.19 202	78.76 248	78.77 271	91.63 204	68.38 248	96.56 229	75.01 241	93.95 110	89.20 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test-LLR		85.87 209	85.41 198	87.25 271	90.95 257	71.67 295	89.55 271	89.88 304	83.41 167	84.54 193	87.95 279	67.25 252	95.11 288	81.82 157	93.37 123	94.97 144

FMVSNet1		85.85 210	84.11 223	91.08 137	92.81 193	83.10 97	95.14 75	94.94 176	81.64 218	82.68 228	91.64 201	59.01 299	96.34 242	75.37 236	83.78 231	93.79 208

PatchmatchNet		85.85 210	84.70 215	89.29 219	91.76 212	75.54 268	88.49 287	91.30 272	81.63 219	85.05 182	88.70 268	71.71 189	96.24 245	74.61 244	89.05 183	96.08 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		85.81 212	84.71 214	89.12 223	92.15 202	76.60 259	91.12 257	91.69 261	83.53 164	85.50 164	88.56 271	66.79 255	95.00 291	72.69 255	90.35 158	95.76 124

CostFormer		85.77 213	84.94 208	88.26 250	91.16 251	72.58 291	89.47 275	91.04 281	76.26 270	86.45 136	89.97 252	70.74 204	96.86 216	82.35 148	87.07 209	95.34 137

tfpn_n400		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpnconf		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpnview11		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

Test4		85.75 214	83.72 231	91.83 114	88.08 307	81.03 146	92.48 225	95.54 134	83.38 169	73.40 307	88.57 270	50.99 319	97.37 177	86.61 103	94.47 104	97.09 84

PMMVS		85.71 218	84.96 207	87.95 257	88.90 299	77.09 255	88.68 285	90.06 299	72.32 301	86.47 133	90.76 236	72.15 188	94.40 295	81.78 159	93.49 118	92.36 266

PVSNet	78.82 18	85.55 219	84.65 216	88.23 252	94.72 130	71.93 293	87.12 298	92.75 236	78.80 247	84.95 184	90.53 243	64.43 272	96.71 223	74.74 242	93.86 112	96.06 112

pmmvs4		85.43 220	83.86 227	90.16 174	90.02 287	82.97 104	90.27 261	92.67 238	75.93 273	80.73 252	91.74 200	71.05 198	95.73 265	78.85 205	83.46 238	91.78 275

ACMH	80.38 17	85.36 221	83.68 233	90.39 165	94.45 142	80.63 156	94.73 99	94.85 183	82.09 201	77.24 280	92.65 168	60.01 295	97.58 144	72.25 257	84.87 223	92.96 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 222	84.64 217	87.49 266	90.77 266	72.59 290	94.01 160	94.40 196	84.72 137	79.62 267	93.17 147	61.91 282	96.72 221	81.99 154	81.16 264	93.16 242

CR-MVSNet		85.35 222	83.76 228	90.12 180	90.58 273	79.34 197	85.24 310	91.96 256	78.27 254	85.55 159	87.87 282	71.03 199	95.61 266	73.96 249	89.36 174	95.40 134

tpmrst		85.35 222	84.99 204	86.43 284	90.88 264	67.88 315	88.71 284	91.43 270	80.13 233	86.08 144	88.80 266	73.05 176	96.02 252	82.48 145	83.40 240	95.40 134

IB-MVS	80.51 15	85.24 225	83.26 244	91.19 132	92.13 204	79.86 174	91.75 243	91.29 273	83.28 172	80.66 254	88.49 272	61.28 285	98.46 90	80.99 168	79.46 290	95.25 138
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CHOSEN 280x420		85.15 226	83.99 225	88.65 232	92.47 198	78.40 227	79.68 330	92.76 235	74.90 282	81.41 245	89.59 257	69.85 217	95.51 271	79.92 189	95.29 91	92.03 272

RPSCF		85.07 227	84.27 221	87.48 267	92.91 192	70.62 305	91.69 247	92.46 241	76.20 271	82.67 229	95.22 86	63.94 274	97.29 183	77.51 219	85.80 215	94.53 172

MS-PatchMatch		85.05 228	84.16 222	87.73 260	91.42 225	78.51 223	91.25 255	93.53 223	77.50 259	80.15 260	91.58 206	61.99 281	95.51 271	75.69 233	94.35 108	89.16 311

ACMH+	81.04 14	85.05 228	83.46 240	89.82 196	94.66 134	79.37 195	94.44 121	94.12 206	82.19 200	78.04 274	92.82 163	58.23 301	97.54 147	73.77 250	82.90 242	92.54 259

v18		84.97 230	83.76 228	88.60 236	91.36 231	79.41 189	93.82 169	94.04 207	83.00 184	76.61 283	86.60 291	76.19 121	95.43 276	80.39 178	71.79 310	90.96 290

v16		84.96 231	83.74 230	88.62 234	91.40 226	79.48 183	93.83 167	94.04 207	83.03 181	76.54 284	86.59 292	76.11 126	95.42 277	80.33 181	71.80 309	90.95 292

DWT-MVSNet_test		84.95 232	83.68 233	88.77 228	91.43 224	73.75 278	91.74 244	90.98 282	80.66 230	83.84 210	87.36 286	62.44 278	97.11 198	78.84 206	85.81 214	95.46 132

v17		84.93 233	83.70 232	88.62 234	91.36 231	79.48 183	93.83 167	94.03 209	83.04 180	76.51 285	86.57 293	76.05 127	95.42 277	80.31 183	71.65 311	90.96 290

IterMVS		84.88 234	83.98 226	87.60 262	91.44 221	76.03 265	90.18 264	92.41 242	83.24 173	81.06 250	90.42 246	66.60 256	94.28 296	79.46 197	80.98 272	92.48 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 235	83.09 246	90.14 179	93.80 166	80.05 168	89.18 280	93.09 229	78.89 244	78.19 272	91.91 195	65.86 266	97.27 184	68.47 282	88.45 191	93.11 245

v15		84.79 236	83.53 237	88.57 240	91.30 242	79.41 189	93.70 180	94.01 210	83.06 177	76.27 286	86.42 297	76.03 130	95.38 279	80.01 185	71.00 314	90.92 293

V14		84.79 236	83.52 238	88.57 240	91.32 238	79.43 188	93.72 178	94.01 210	83.06 177	76.22 287	86.43 294	76.01 131	95.37 280	79.96 187	70.99 315	90.91 294

V9		84.77 238	83.50 239	88.58 237	91.33 236	79.46 185	93.75 174	94.00 213	83.07 176	76.07 292	86.43 294	75.97 132	95.37 280	79.91 190	70.93 317	90.91 294

v12		84.74 239	83.46 240	88.58 237	91.32 238	79.50 180	93.75 174	94.01 210	83.06 177	75.98 294	86.41 298	75.82 138	95.36 282	79.87 191	70.89 318	90.89 296

tpm		84.73 240	84.02 224	86.87 281	90.33 279	68.90 312	89.06 281	89.94 302	80.85 229	85.75 150	89.86 254	68.54 240	95.97 254	77.76 215	84.05 230	95.75 125

tfpnnormal		84.72 241	83.23 245	89.20 222	92.79 194	80.05 168	94.48 116	95.81 114	82.38 197	81.08 249	91.21 224	69.01 229	96.95 210	61.69 312	80.59 276	90.58 303

v13		84.72 241	83.44 242	88.58 237	91.31 241	79.52 179	93.77 172	94.00 213	83.03 181	75.85 295	86.38 299	75.84 137	95.35 283	79.83 192	70.95 316	90.87 297

CVMVSNet		84.69 243	84.79 213	84.37 299	91.84 209	64.92 323	93.70 180	91.47 269	66.19 323	86.16 143	95.28 83	67.18 254	93.33 306	80.89 170	90.42 157	94.88 156

v11		84.67 244	83.41 243	88.44 245	91.32 238	79.13 208	93.69 183	93.99 215	82.81 190	76.20 288	86.24 301	75.48 143	95.35 283	79.53 196	71.48 313	90.85 298

test-mter		84.54 245	83.64 235	87.25 271	90.95 257	71.67 295	89.55 271	89.88 304	79.17 241	84.54 193	87.95 279	55.56 308	95.11 288	81.82 157	93.37 123	94.97 144

TransMVSNet (Re)		84.43 246	83.06 247	88.54 242	91.72 213	78.44 225	95.18 72	92.82 234	82.73 192	79.67 265	92.12 184	73.49 170	95.96 255	71.10 264	68.73 325	91.21 286

pmmvs5		84.21 247	82.84 251	88.34 248	88.95 298	76.94 257	92.41 227	91.91 258	75.63 275	80.28 258	91.18 226	64.59 271	95.57 268	77.09 224	83.47 237	92.53 260

tpm2		84.08 248	82.94 248	87.48 267	91.39 227	71.27 297	89.23 279	90.37 292	71.95 304	84.64 190	89.33 260	67.30 251	96.55 231	75.17 238	87.09 208	94.63 164

COLMAP_ROB	80.39 16	83.96 249	82.04 255	89.74 200	95.28 109	79.75 177	94.25 137	92.28 244	75.17 278	78.02 275	93.77 133	58.60 300	97.84 133	65.06 303	85.92 213	91.63 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		83.91 250	82.90 249	86.92 278	90.99 255	70.67 304	93.48 188	91.99 253	85.54 120	77.62 278	92.11 186	60.59 291	96.87 215	76.05 232	77.75 294	93.20 240

EPMVS		83.90 251	82.70 252	87.51 264	90.23 283	72.67 287	88.62 286	81.96 338	81.37 225	85.01 183	88.34 274	66.31 260	94.45 294	75.30 237	87.12 207	95.43 133

tpmp4_e23		83.87 252	82.33 253	88.48 243	91.46 220	72.82 284	89.82 269	91.57 266	73.02 296	81.86 241	89.05 262	66.20 262	96.97 208	71.57 259	86.39 211	95.66 127

TESTMET0.1,1		83.74 253	82.85 250	86.42 285	89.96 288	71.21 299	89.55 271	87.88 322	77.41 260	83.37 222	87.31 287	56.71 305	93.65 302	80.62 174	92.85 134	94.40 181

pmmvs6		83.42 254	81.60 257	88.87 227	88.01 308	77.87 241	94.96 84	94.24 201	74.67 284	78.80 270	91.09 231	60.17 294	96.49 233	77.06 225	75.40 301	92.23 270

AllTest		83.42 254	81.39 258	89.52 207	95.01 118	77.79 243	93.12 203	90.89 285	77.41 260	76.12 290	93.34 138	54.08 314	97.51 149	68.31 285	84.27 228	93.26 238

testing_2		83.40 256	81.02 261	90.56 151	85.06 318	80.51 160	91.37 252	95.57 130	82.92 187	67.06 323	85.54 305	49.47 322	97.24 188	86.74 98	85.44 217	93.93 198

tpmvs		83.35 257	82.07 254	87.20 275	91.07 253	71.00 302	88.31 289	91.70 260	78.91 243	80.49 257	87.18 289	69.30 225	97.08 200	68.12 288	83.56 236	93.51 233

RPMNet		83.18 258	80.87 264	90.12 180	90.58 273	79.34 197	85.24 310	90.78 288	71.44 306	85.55 159	82.97 315	70.87 201	95.61 266	61.01 314	89.36 174	95.40 134

USDC		82.76 259	81.26 260	87.26 270	91.17 249	74.55 271	89.27 277	93.39 226	78.26 255	75.30 297	92.08 188	54.43 313	96.63 225	71.64 258	85.79 216	90.61 300

Patchmtry		82.71 260	80.93 263	88.06 255	90.05 286	76.37 262	84.74 312	91.96 256	72.28 302	81.32 247	87.87 282	71.03 199	95.50 273	68.97 280	80.15 282	92.32 268

PatchT		82.68 261	81.27 259	86.89 280	90.09 285	70.94 303	84.06 317	90.15 296	74.91 281	85.63 158	83.57 311	69.37 221	94.87 293	65.19 301	88.50 190	94.84 157

MIMVSNet		82.59 262	80.53 265	88.76 229	91.51 219	78.32 228	86.57 301	90.13 297	79.32 240	80.70 253	88.69 269	52.98 316	93.07 311	66.03 299	88.86 185	94.90 155

test0.0.03 1		82.41 263	81.69 256	84.59 297	88.23 304	72.89 283	90.24 262	87.83 323	83.41 167	79.86 264	89.78 255	67.25 252	88.99 324	65.18 302	83.42 239	91.90 274

EG-PatchMatch MVS		82.37 264	80.34 266	88.46 244	90.27 280	79.35 196	92.80 216	94.33 199	77.14 264	73.26 308	90.18 249	47.47 326	96.72 221	70.25 266	87.32 206	89.30 308

tpm cat1		81.96 265	80.27 267	87.01 276	91.09 252	71.02 301	87.38 297	91.53 268	66.25 322	80.17 259	86.35 300	68.22 250	96.15 248	69.16 279	82.29 247	93.86 205

gg-mvs-nofinetune		81.77 266	79.37 276	88.99 226	90.85 265	77.73 247	86.29 302	79.63 342	74.88 283	83.19 224	69.05 333	60.34 292	96.11 249	75.46 235	94.64 99	93.11 245

Patchmatch-RL test		81.67 267	79.96 271	86.81 282	85.42 316	71.23 298	82.17 325	87.50 327	78.47 251	77.19 281	82.50 316	70.81 203	93.48 304	82.66 142	72.89 306	95.71 126

ADS-MVSNet2		81.66 268	79.71 274	87.50 265	91.35 234	74.19 273	83.33 321	88.48 319	72.90 297	82.24 233	85.77 303	64.98 269	93.20 308	64.57 304	83.74 232	95.12 139

K. test v3		81.59 269	80.15 270	85.91 288	89.89 290	69.42 311	92.57 223	87.71 324	85.56 119	73.44 306	89.71 256	55.58 307	95.52 270	77.17 222	69.76 321	92.78 255

ADS-MVSNet		81.56 270	79.78 272	86.90 279	91.35 234	71.82 294	83.33 321	89.16 314	72.90 297	82.24 233	85.77 303	64.98 269	93.76 300	64.57 304	83.74 232	95.12 139

FMVSNet5		81.52 271	79.60 275	87.27 269	91.17 249	77.95 237	91.49 250	92.26 245	76.87 265	76.16 289	87.91 281	51.67 317	92.34 313	67.74 289	81.16 264	91.52 280

dp		81.47 272	80.23 268	85.17 294	89.92 289	65.49 322	86.74 299	90.10 298	76.30 269	81.10 248	87.12 290	62.81 276	95.92 256	68.13 287	79.88 287	94.09 192

Patchmatch-test		81.37 273	79.30 277	87.58 263	90.92 261	74.16 274	80.99 327	87.68 325	70.52 312	76.63 282	88.81 265	71.21 196	92.76 312	60.01 318	86.93 210	95.83 121

EU-MVSNet		81.32 274	80.95 262	82.42 307	88.50 302	63.67 324	93.32 192	91.33 271	64.02 327	80.57 256	92.83 162	61.21 288	92.27 314	76.34 228	80.38 281	91.32 284

test_0402		81.30 275	79.17 279	87.67 261	93.19 182	78.17 233	92.98 211	91.71 259	75.25 277	76.02 293	90.31 247	59.23 298	96.37 240	50.22 328	83.63 235	88.47 321

JIA-IIPM		81.04 276	78.98 282	87.25 271	88.64 300	73.48 280	81.75 326	89.61 308	73.19 293	82.05 237	73.71 330	66.07 265	95.87 259	71.18 263	84.60 225	92.41 264

Anonymous20231206		81.03 277	79.77 273	84.82 296	87.85 311	70.26 307	91.42 251	92.08 249	73.67 289	77.75 277	89.25 261	62.43 279	93.08 310	61.50 313	82.00 253	91.12 288

pmmvs-eth3d		80.97 278	78.72 283	87.74 259	84.99 319	79.97 172	90.11 265	91.65 262	75.36 276	73.51 305	86.03 302	59.45 297	93.96 299	75.17 238	72.21 307	89.29 309

testgi		80.94 279	80.20 269	83.18 303	87.96 309	66.29 319	91.28 253	90.70 290	83.70 157	78.12 273	92.84 161	51.37 318	90.82 321	63.34 307	82.46 246	92.43 263

CMPMVS	59.16 21	80.52 280	79.20 278	84.48 298	83.98 321	67.63 317	89.95 268	93.84 220	64.79 326	66.81 324	91.14 229	57.93 303	95.17 286	76.25 229	88.10 196	90.65 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		80.37 281	79.07 281	84.27 301	86.64 313	69.87 310	89.39 276	91.05 280	76.38 267	74.97 299	90.00 251	47.85 325	94.25 297	74.55 245	80.82 274	88.69 316

UnsupCasMVSNet_eth		80.07 282	78.27 284	85.46 291	85.24 317	72.63 289	88.45 288	94.87 182	82.99 185	71.64 315	88.07 278	56.34 306	91.75 318	73.48 252	63.36 332	92.01 273

test20.03		79.95 283	79.08 280	82.55 306	85.79 315	67.74 316	91.09 258	91.08 278	81.23 226	74.48 302	89.96 253	61.63 283	90.15 322	60.08 316	76.38 298	89.76 305

TDRefinement		79.81 284	77.34 286	87.22 274	79.24 333	75.48 269	93.12 203	92.03 251	76.45 266	75.01 298	91.58 206	49.19 323	96.44 237	70.22 268	69.18 322	89.75 306

TinyColmap		79.76 285	77.69 285	85.97 287	91.71 214	73.12 281	89.55 271	90.36 293	75.03 279	72.03 313	90.19 248	46.22 328	96.19 247	63.11 308	81.03 268	88.59 317

OpenMVS_ROB	74.94 19	79.51 286	77.03 290	86.93 277	87.00 312	76.23 264	92.33 230	90.74 289	68.93 316	74.52 301	88.23 276	49.58 321	96.62 226	57.64 320	84.29 227	87.94 323

MIMVSNet1		79.38 287	77.28 287	85.69 289	86.35 314	73.67 279	91.61 249	92.75 236	78.11 258	72.64 311	88.12 277	48.16 324	91.97 317	60.32 315	77.49 295	91.43 283

YYNet1		79.22 288	77.20 288	85.28 293	88.20 306	72.66 288	85.87 305	90.05 301	74.33 287	62.70 329	87.61 284	66.09 264	92.03 315	66.94 291	72.97 305	91.15 287

MDA-MVSNet_test_wron		79.21 289	77.19 289	85.29 292	88.22 305	72.77 286	85.87 305	90.06 299	74.34 286	62.62 330	87.56 285	66.14 263	91.99 316	66.90 294	73.01 304	91.10 289

MDA-MVSNet-bldmvs		78.85 290	76.31 291	86.46 283	89.76 291	73.88 277	88.79 283	90.42 291	79.16 242	59.18 331	88.33 275	60.20 293	94.04 298	62.00 311	68.96 323	91.48 282

PM-MVS		78.11 291	76.12 293	84.09 302	83.54 323	70.08 308	88.97 282	85.27 332	79.93 235	74.73 300	86.43 294	34.70 337	93.48 304	79.43 200	72.06 308	88.72 315

PVSNet_0	73.20 20	77.22 292	74.83 295	84.37 299	90.70 270	71.10 300	83.09 323	89.67 307	72.81 299	73.93 304	83.13 314	60.79 290	93.70 301	68.54 281	50.84 337	88.30 322

DSMNet-mixed		76.94 293	76.29 292	78.89 310	83.10 324	56.11 336	87.78 293	79.77 341	60.65 331	75.64 296	88.71 267	61.56 284	88.34 326	60.07 317	89.29 176	92.21 271

new-patchmatchnet		76.41 294	75.17 294	80.13 309	82.65 327	59.61 329	87.66 295	91.08 278	78.23 256	69.85 317	83.22 313	54.76 311	91.63 320	64.14 306	64.89 328	89.16 311

UnsupCasMVSNet_bld		76.23 295	73.27 297	85.09 295	83.79 322	72.92 282	85.65 309	93.47 225	71.52 305	68.84 319	79.08 326	49.77 320	93.21 307	66.81 295	60.52 334	89.13 313

LP		75.51 296	72.15 300	85.61 290	87.86 310	73.93 276	80.20 329	88.43 320	67.39 318	70.05 316	80.56 323	58.18 302	93.18 309	46.28 334	70.36 320	89.71 307

test2356		74.50 297	73.27 297	78.20 311	80.81 329	59.84 327	83.76 320	88.33 321	71.43 307	72.37 312	81.84 319	45.60 329	86.26 332	50.97 326	84.32 226	88.50 318

testus		74.41 298	73.35 296	77.59 315	82.49 328	57.08 332	86.02 303	90.21 295	72.28 302	72.89 310	84.32 308	37.08 335	86.96 330	52.24 324	82.65 244	88.73 314

MVS-HIRNet		73.70 299	72.20 299	78.18 313	91.81 211	56.42 335	82.94 324	82.58 336	55.24 333	68.88 318	66.48 334	55.32 310	95.13 287	58.12 319	88.42 193	83.01 328

Anonymous20231211		72.97 300	69.63 305	83.00 305	83.05 325	66.91 318	92.69 218	89.45 310	61.06 330	67.50 322	83.46 312	34.34 338	93.61 303	51.11 325	63.97 330	88.48 320

test1235678		72.22 301	70.31 302	77.93 314	78.04 334	58.04 331	85.76 307	89.80 306	70.15 314	63.43 328	80.20 324	42.24 332	87.24 329	48.68 330	74.50 302	88.50 318

new_pmnet		72.15 302	70.13 303	78.20 311	82.95 326	65.68 320	83.91 318	82.40 337	62.94 329	64.47 327	79.82 325	42.85 331	86.26 332	57.41 321	74.44 303	82.65 329

pmmvs3		71.81 303	68.71 306	81.11 308	75.86 335	70.42 306	86.74 299	83.66 334	58.95 332	68.64 321	80.89 322	36.93 336	89.52 323	63.10 309	63.59 331	83.39 327

testpf		71.41 304	72.11 301	69.30 324	84.53 320	59.79 328	62.74 340	83.14 335	71.11 309	68.83 320	81.57 321	46.70 327	84.83 337	74.51 246	75.86 300	63.30 336

1111		70.54 305	69.71 304	73.04 319	79.30 331	44.83 344	84.23 315	88.96 316	67.33 319	65.42 325	82.28 317	41.11 333	88.11 327	47.12 332	71.60 312	86.19 325

N_pmnet		68.89 306	68.44 307	70.23 322	89.07 297	28.79 351	88.06 290	19.50 353	69.47 315	71.86 314	84.93 306	61.24 287	91.75 318	54.70 322	77.15 297	90.15 304

LCM-MVSNet		66.00 307	62.16 311	77.51 316	64.51 345	58.29 330	83.87 319	90.90 284	48.17 336	54.69 333	73.31 331	16.83 350	86.75 331	65.47 300	61.67 333	87.48 324

testmv		65.49 308	62.66 309	73.96 318	68.78 340	53.14 339	84.70 313	88.56 318	65.94 324	52.35 334	74.65 329	25.02 343	85.14 335	43.54 336	60.40 335	83.60 326

test12356		64.99 309	63.78 308	68.61 326	72.69 337	39.14 347	78.46 331	87.61 326	64.91 325	55.77 332	77.48 327	28.10 340	85.59 334	44.69 335	64.35 329	81.12 331

FPMVS		64.63 310	62.55 310	70.88 321	70.80 338	56.71 333	84.42 314	84.42 333	51.78 335	49.57 335	81.61 320	23.49 344	81.48 339	40.61 339	76.25 299	74.46 335

no-one		61.56 311	56.58 313	76.49 317	67.80 343	62.76 326	78.13 332	86.11 328	63.16 328	43.24 338	64.70 336	26.12 342	88.95 325	50.84 327	29.15 340	77.77 333

PMMVS2		59.60 312	56.40 314	69.21 325	68.83 339	46.58 342	73.02 338	77.48 345	55.07 334	49.21 336	72.95 332	17.43 349	80.04 340	49.32 329	44.33 338	80.99 332

ANet_high		58.88 313	54.22 316	72.86 320	56.50 349	56.67 334	80.75 328	86.00 329	73.09 295	37.39 340	64.63 337	22.17 345	79.49 342	43.51 337	23.96 344	82.43 330

Gipuma		57.99 314	54.91 315	67.24 327	88.51 301	65.59 321	52.21 343	90.33 294	43.58 339	42.84 339	51.18 341	20.29 347	85.07 336	34.77 341	70.45 319	51.05 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		57.63 315	61.79 312	45.14 333	79.30 331	44.83 344	84.23 315	88.96 316	67.33 319	65.42 325	82.28 317	41.11 333	88.11 327	47.12 332	0.39 348	2.46 347

PMVS	47.18 22	52.22 316	48.46 317	63.48 328	45.72 350	46.20 343	73.41 336	78.31 343	41.03 340	30.06 343	65.68 335	6.05 352	83.43 338	30.04 342	65.86 326	60.80 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		50.55 317	44.13 319	69.81 323	56.77 347	54.58 338	73.22 337	80.78 339	39.79 341	22.08 347	46.69 343	4.03 354	79.71 341	47.65 331	26.13 342	75.14 334

PNet_i23d		50.48 318	47.18 318	60.36 329	68.59 341	44.56 346	72.75 339	72.61 346	43.92 338	33.91 342	60.19 339	6.16 351	73.52 343	38.50 340	28.04 341	63.01 337

MVE	39.65 23	43.39 319	38.59 324	57.77 330	56.52 348	48.77 341	55.38 342	58.64 350	29.33 344	28.96 344	52.65 340	4.68 353	64.62 346	28.11 343	33.07 339	59.93 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 320	42.29 320	46.03 332	65.58 344	37.41 348	73.51 335	64.62 347	33.99 342	28.47 345	47.87 342	19.90 348	67.91 344	22.23 344	24.45 343	32.77 342

EMVS		42.07 321	41.12 321	44.92 334	63.45 346	35.56 350	73.65 334	63.48 348	33.05 343	26.88 346	45.45 344	21.27 346	67.14 345	19.80 345	23.02 345	32.06 343

pcd1.5k->3k		37.02 322	38.84 323	31.53 335	92.33 200	0.00 355	0.00 345	96.13 92	0.00 349	0.00 351	0.00 351	72.70 180	0.00 352	0.00 349	88.43 192	94.60 167

tmp_tt		35.64 323	39.24 322	24.84 336	14.87 351	23.90 352	62.71 341	51.51 352	6.58 346	36.66 341	62.08 338	44.37 330	30.34 349	52.40 323	22.00 346	20.27 344

cdsmvs_eth3d_5k		22.14 324	29.52 325	0.00 340	0.00 354	0.00 355	0.00 345	95.76 118	0.00 349	0.00 351	94.29 111	75.66 141	0.00 352	0.00 349	0.00 351	0.00 349

wuyk23d		21.27 325	20.48 326	23.63 337	68.59 341	36.41 349	49.57 344	6.85 354	9.37 345	7.89 348	4.46 350	4.03 354	31.37 348	17.47 346	16.07 347	3.12 345

testmvs		8.92 326	11.52 327	1.12 339	1.06 352	0.46 354	86.02 303	0.65 355	0.62 347	2.74 349	9.52 348	0.31 357	0.45 351	2.38 347	0.39 348	2.46 347

test123		8.76 327	11.22 328	1.39 338	0.85 353	0.97 353	85.76 307	0.35 356	0.54 348	2.45 350	8.14 349	0.60 356	0.48 350	2.16 348	0.17 350	2.71 346

ab-mvs-re		7.82 328	10.43 329	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	93.88 129	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

pcd_1.5k_mvsjas		6.64 329	8.86 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	79.70 89	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					98.55 5	87.22 10				96.40 2

test_part1								97.45 7				91.93 1			99.02 2	98.67 4

test_all								97.46 6

sam_mvs1												71.70 190

sam_mvs												70.60 205

semantic-postprocess				88.18 253	91.71 214	76.87 258		92.65 239	85.40 123	81.44 244	90.54 241	66.21 261	95.00 291	81.04 165	81.05 267	92.66 257

ambc				83.06 304	79.99 330	63.51 325	77.47 333	92.86 232		74.34 303	84.45 307	28.74 339	95.06 290	73.06 254	68.89 324	90.61 300

MTGPA								96.97 34

test_post1							88.00 291				9.81 347	69.31 224	95.53 269	76.65 226

test_post											10.29 346	70.57 209	95.91 258

patchmatchnet-post											83.76 310	71.53 193	96.48 234

GG-mvs-BLEND				87.94 258	89.73 292	77.91 238	87.80 292	78.23 344		80.58 255	83.86 309	59.88 296	95.33 285	71.20 261	92.22 138	90.60 302

MTMP								60.64 349

gm-plane-assit					89.60 293	68.00 314			77.28 263		88.99 263		97.57 145	79.44 199

test9_res														91.91 40	98.71 18	98.07 44

TEST9					97.53 35	86.49 28	94.07 153	96.78 50	81.61 220	92.77 39	96.20 57	87.71 14	99.12 39

test_8					97.49 38	86.30 36	94.02 159	96.76 53	81.86 215	92.70 43	96.20 57	87.63 15	99.02 51

agg_prior2														90.54 59	98.68 23	98.27 30

agg_prior					97.38 42	85.92 43		96.72 56		92.16 55			98.97 59

TestCases				89.52 207	95.01 118	77.79 243		90.89 285	77.41 260	76.12 290	93.34 138	54.08 314	97.51 149	68.31 285	84.27 228	93.26 238

test_prior4						85.96 42	94.11 147

test_prior2							94.12 145		87.67 79	92.63 44	96.39 50	86.62 24		91.50 47	98.67 25

test_prior				93.82 50	97.29 47	84.49 63		96.88 43					98.87 65			98.11 42

旧先验2							93.36 191		71.25 308	94.37 12			97.13 197	86.74 98

新几何2							93.11 205

新几何1				93.10 65	97.30 46	84.35 72		95.56 131	71.09 310	91.26 71	96.24 54	82.87 57	98.86 68	79.19 203	98.10 46	96.07 111

旧先验1					96.79 58	81.81 126		95.67 123			96.81 31	86.69 23			97.66 55	96.97 87

无先验							93.28 198	96.26 82	73.95 288				99.05 44	80.56 175		96.59 97

原ACMM2							92.94 213

原ACMM1				92.01 103	97.34 44	81.05 145		96.81 48	78.89 244	90.45 78	95.92 68	82.65 58	98.84 73	80.68 173	98.26 42	96.14 107

test222					96.55 64	81.70 127	92.22 234	95.01 172	68.36 317	90.20 81	96.14 62	80.26 83			97.80 53	96.05 113

testdata2													98.75 76	78.30 210

segment_acmp												87.16 20

testdata				90.49 160	96.40 66	77.89 240		95.37 153	72.51 300	93.63 24	96.69 36	82.08 67	97.65 141	83.08 135	97.39 59	95.94 115

testdata1							92.15 236		87.94 70

test12				94.34 39	97.13 52	86.15 39		96.29 81		91.04 74		85.08 40	99.01 53		98.13 45	97.86 58

plane_prior7					94.70 132	82.74 110

plane_prior6					94.52 138	82.75 108						74.23 157

plane_prior5								96.22 86					98.12 106	88.15 77	89.99 163	94.63 164

plane_prior4											94.86 95

plane_prior3						82.75 108			90.26 25	86.91 127

plane_prior2							95.85 40		90.81 18

plane_prior1					94.59 136

plane_prior						82.73 111	95.21 71		89.66 35						89.88 166

n2								0.00 357

nn								0.00 357

door-mid								85.49 330

lessismore_v0				86.04 286	88.46 303	68.78 313		80.59 340		73.01 309	90.11 250	55.39 309	96.43 238	75.06 240	65.06 327	92.90 249

LGP-MVS_train				91.12 134	94.47 140	81.49 131		96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

test11								96.57 70

door								85.33 331

HQP5-MVS						81.56 128

HQP-NCC					94.17 149		94.39 126		88.81 50	85.43 170

ACMP_Plane					94.17 149		94.39 126		88.81 50	85.43 170

BP-MVS														87.11 95

HQP4-MVS										85.43 170			97.96 127			94.51 174

HQP3-MVS								96.04 99							89.77 168

HQP2-MVS												73.83 166

NP-MVS					94.37 144	82.42 118					93.98 122

MDTV_nov1_ep13_2view						55.91 337	87.62 296		73.32 292	84.59 192		70.33 212		74.65 243		95.50 130

MDTV_nov1_ep13			83.56 236		91.69 216	69.93 309	87.75 294	91.54 267	78.60 250	84.86 188	88.90 264	69.54 220	96.03 251	70.25 266	88.93 184

ACMMP++_ref															87.47 202

ACMMP++															88.01 199

Test By Simon												80.02 84

ITE_SJBPF				88.24 251	91.88 208	77.05 256		92.92 231	85.54 120	80.13 262	93.30 142	57.29 304	96.20 246	72.46 256	84.71 224	91.49 281

DeepMVS_CX				56.31 331	74.23 336	51.81 340		56.67 351	44.85 337	48.54 337	75.16 328	27.87 341	58.74 347	40.92 338	52.22 336	58.39 340

High-resolution multi-view benchmark