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.

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 15	73.46 14	92.90 8	92.73 38	80.27 13	91.35 1	94.16 20	78.35 2	96.77 9	89.59 1	94.22 42	93.33 52

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 8	71.69 41	93.83 2	93.96 3	75.70 72	91.06 2	96.03 1	76.84 3	97.03 5	89.09 2	95.65 13	94.47 11

HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 1	76.03 1	92.38 14	92.85 33	80.26 14	87.78 11	94.27 16	75.89 7	96.81 8	87.45 9	96.44 1	93.05 62

CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 2	73.82 6	90.51 39	93.00 25	80.90 10	88.06 9	94.06 24	76.43 4	96.84 7	88.48 4	95.99 4	94.34 15

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 52	72.96 19	93.73 3	93.67 7	80.19 15	88.10 8	94.80 4	73.76 20	97.11 3	87.51 8	95.82 8	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 50	69.03 79	89.57 62	93.39 14	77.53 39	89.79 4	94.12 22	78.98 1	96.58 20	85.66 12	95.72 9	94.58 7

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 48	72.71 24	91.81 23	93.19 19	77.87 32	90.32 3	94.00 25	74.83 10	93.78 112	87.63 7	94.27 40	93.65 41

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 25	72.13 37	91.41 26	92.35 49	74.62 89	88.90 5	93.85 27	75.75 8	96.00 33	87.80 5	94.63 31	95.04 2

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 23	73.05 18	90.86 33	93.59 8	76.27 66	88.14 7	95.09 3	71.06 36	96.67 13	87.67 6	96.37 2	94.09 22

NCCC		88.06 7	88.01 10	88.24 5	94.41 12	73.62 7	91.22 30	92.83 34	81.50 7	85.79 21	93.47 33	73.02 24	97.00 6	84.90 17	94.94 24	94.10 21

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 26	72.04 38	89.80 55	93.50 10	75.17 83	86.34 16	95.29 2	70.86 37	96.00 33	88.78 3	96.04 3	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 11	87.64 12	87.93 14	94.36 14	73.88 4	92.71 13	92.65 41	77.57 35	83.84 47	94.40 15	72.24 30	96.28 25	85.65 13	95.30 21	93.62 43

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 16	72.39 32	91.86 22	92.83 34	73.01 123	88.58 6	94.52 7	73.36 21	96.49 21	84.26 27	95.01 23	92.70 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 5	73.54 11	93.04 5	93.24 16	76.78 52	84.91 29	94.44 12	70.78 38	96.61 16	84.53 23	94.89 26	93.66 36

MPTG		87.53 14	87.41 15	87.90 15	94.18 19	74.25 2	90.23 47	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 7	72.46 31	89.82 53	93.82 5	73.07 122	84.86 34	92.89 45	76.22 5	96.33 23	84.89 19	95.13 22	94.40 12

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 3	73.59 8	93.04 5	93.20 18	76.78 52	84.66 35	94.52 7	68.81 55	96.65 14	84.53 23	94.90 25	94.00 28

region2R		87.42 17	87.20 18	88.09 6	94.63 4	73.55 9	93.03 7	93.12 21	76.73 55	84.45 38	94.52 7	69.09 53	96.70 12	84.37 26	94.83 28	94.03 25

#test#		87.33 19	87.13 19	87.94 11	94.58 5	73.54 11	92.34 15	93.24 16	75.23 80	84.91 29	94.44 12	70.78 38	96.61 16	83.75 31	94.89 26	93.66 36

MTAPA		87.23 20	87.00 20	87.90 15	94.18 19	74.25 2	86.58 160	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 22	72.16 36	92.19 18	93.33 15	76.07 69	83.81 48	93.95 26	69.77 48	96.01 32	85.15 14	94.66 30	94.32 17

CP-MVS		87.11 22	86.92 22	87.68 25	94.20 18	73.86 5	93.98 1	92.82 36	76.62 57	83.68 49	94.46 11	67.93 59	95.95 35	84.20 29	94.39 36	93.23 54

XVS		87.18 21	86.91 23	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	94.17 19	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 62	72.32 35	90.31 45	93.94 4	77.12 44	82.82 59	94.23 18	72.13 31	97.09 4	84.83 20	95.37 16	93.65 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		86.73 26	86.86 25	86.33 46	92.61 46	69.59 71	88.85 79	92.97 30	75.41 76	84.91 29	93.54 29	74.28 17	95.48 43	83.31 32	95.86 6	93.91 30

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 28	72.37 34	91.26 27	93.04 22	76.62 57	84.22 43	93.36 35	71.44 34	96.76 10	80.82 51	95.33 19	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		86.63 29	86.53 27	86.95 36	89.33 97	71.24 44	88.43 92	92.05 58	82.50 1	86.88 14	90.09 94	74.45 12	95.61 39	84.38 25	90.63 68	94.01 27

Regformer-1		86.41 33	86.33 28	86.64 41	89.33 97	70.93 50	88.43 92	91.39 88	82.14 3	86.65 15	90.09 94	74.39 15	95.01 64	83.97 30	90.63 68	93.97 29

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 12	73.55 9	92.74 11	92.22 52	76.87 50	82.81 60	94.25 17	66.44 71	96.24 26	82.88 40	94.28 39	93.38 49

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 17	73.38 15	90.22 48	93.04 22	75.53 74	83.86 46	94.42 14	67.87 61	96.64 15	82.70 41	94.57 32	93.66 36

train_agg		86.43 31	86.20 31	87.13 33	93.26 33	72.96 19	88.75 84	91.89 68	68.69 194	85.00 27	93.10 39	74.43 13	95.41 48	84.97 15	95.71 10	93.02 63

CSCG		86.41 33	86.19 32	87.07 35	92.91 40	72.48 30	90.81 34	93.56 9	73.95 97	83.16 55	91.07 76	75.94 6	95.19 55	79.94 58	94.38 37	93.55 45

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 67	70.96 47	92.27 17	94.07 2	72.45 134	85.22 25	91.90 58	69.47 50	96.42 22	83.28 34	95.94 5	94.35 14

CANet		86.45 30	86.10 34	87.51 27	90.09 77	70.94 49	89.70 59	92.59 42	81.78 4	81.32 73	91.43 71	70.34 41	97.23 2	84.26 27	93.36 46	94.37 13

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 41	71.94 39	88.59 89	91.78 74	68.96 191	84.41 39	93.18 38	74.94 9	94.93 65	84.75 22	95.33 19	93.01 65

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 44	69.53 73	91.93 21	92.99 27	73.54 109	85.94 17	94.51 10	65.80 78	95.61 39	83.04 38	92.51 53	93.53 47

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 83	69.44 77	90.45 43	93.00 25	76.70 56	88.01 10	91.23 73	73.28 22	93.91 103	81.50 47	88.80 86	94.77 5

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 30	72.86 23	88.77 82	91.68 78	68.29 200	84.26 42	92.83 47	72.83 25	95.42 47	84.97 15	95.71 10	93.02 63

MVS_0304		86.37 35	85.81 39	88.02 8	90.13 75	72.39 32	89.66 60	92.75 37	81.64 6	82.66 63	92.04 54	64.44 86	97.35 1	84.76 21	94.25 41	94.33 16

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 55	70.24 58	90.71 36	92.86 32	77.46 41	84.22 43	92.81 50	67.16 67	92.94 152	80.36 54	94.35 38	90.16 147

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 112	69.67 70	88.29 101	91.29 90	81.73 5	85.36 23	90.01 96	72.62 27	95.35 53	83.28 34	87.57 101	94.03 25

ACMMP		85.89 40	85.39 42	87.38 29	93.59 27	72.63 26	92.74 11	93.18 20	76.78 52	80.73 82	93.82 28	64.33 87	96.29 24	82.67 42	90.69 67	93.23 54
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
TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 60	72.50 29	89.07 74	87.28 203	76.41 59	85.80 20	90.22 92	74.15 19	95.37 52	81.82 45	91.88 55	92.65 72

alignmvs		85.48 44	85.32 44	85.96 54	89.51 92	69.47 75	89.74 57	92.47 43	76.17 67	87.73 12	91.46 70	70.32 42	93.78 112	81.51 46	88.95 83	94.63 6

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 128	67.93 107	85.52 194	93.44 12	78.70 28	83.63 52	89.03 117	74.57 11	95.71 38	80.26 56	94.04 43	93.66 36
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
CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 29	71.08 45	88.58 90	92.42 47	68.32 199	84.61 36	93.48 31	72.32 29	96.15 30	79.00 60	95.43 15	94.28 18

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 112	69.01 81	88.29 101	89.91 135	80.95 9	85.01 26	90.01 96	72.45 28	94.19 90	82.50 43	87.57 101	93.90 32

UA-Net		85.08 52	84.96 48	85.45 57	92.07 53	68.07 105	89.78 56	90.86 100	82.48 2	84.60 37	93.20 37	69.35 51	95.22 54	71.39 140	90.88 66	93.07 61

abl_6		85.23 49	84.95 49	86.07 52	92.23 51	70.48 57	90.80 35	92.08 57	73.51 110	85.26 24	94.16 20	62.75 113	95.92 36	82.46 44	91.30 62	91.81 96

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 24	70.58 56	92.15 19	91.62 79	73.89 98	82.67 62	94.09 23	62.60 120	95.54 42	80.93 49	92.93 48	93.57 44

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 58	72.70 25	85.98 175	90.33 117	76.11 68	82.08 66	91.61 65	71.36 35	94.17 92	81.02 48	92.58 52	92.08 89

3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 63	73.49 13	93.18 4	93.78 6	80.79 11	76.66 142	93.37 34	60.40 160	96.75 11	77.20 78	93.73 45	95.29 1

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 137	67.85 108	87.66 116	89.73 139	80.05 17	82.95 56	89.59 103	70.74 40	94.82 72	80.66 53	84.72 132	93.28 53

nrg030		83.88 54	83.53 54	84.96 69	86.77 171	69.28 78	90.46 42	92.67 39	74.79 87	82.95 56	91.33 72	72.70 26	93.09 146	80.79 52	79.28 194	92.50 75

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 43	62.28 216	88.17 105	89.50 144	75.22 81	81.49 72	92.74 51	66.75 68	95.11 58	72.85 121	91.58 58	92.45 76

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 144	67.53 112	87.44 127	89.66 140	79.74 18	82.23 65	89.41 112	70.24 43	94.74 74	79.95 57	83.92 138	92.99 66

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 32	70.86 52	92.09 20	90.38 112	68.75 193	79.57 87	92.83 47	60.60 156	93.04 150	80.92 50	91.56 59	90.86 117

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 78	68.71 91	91.25 28	92.44 44	79.12 23	78.92 94	91.00 80	60.42 158	95.38 50	78.71 63	86.32 119	91.33 105

Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 133	67.45 113	88.89 77	89.15 156	75.50 75	82.27 64	88.28 135	69.61 49	94.45 81	77.81 72	87.84 99	93.84 34

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 168	63.21 202	86.11 173	92.00 62	74.31 92	82.87 58	89.44 111	70.03 44	93.21 137	77.39 77	88.50 95	93.81 35

EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 75	64.47 177	92.32 16	90.73 101	74.45 91	79.35 90	91.10 74	69.05 54	95.12 57	72.78 122	87.22 108	94.13 20

OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 120	70.95 48	89.13 73	91.52 83	77.55 38	80.96 80	91.75 60	60.71 152	94.50 80	79.67 59	86.51 117	89.97 165

EPNet		83.72 57	82.92 63	86.14 51	84.22 203	69.48 74	91.05 32	85.27 223	81.30 8	76.83 139	91.65 62	66.09 74	95.56 41	76.00 89	93.85 44	93.38 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 81	63.30 199	91.59 24	88.46 183	79.04 25	79.49 88	92.16 52	65.10 82	94.28 84	67.71 160	91.86 56	94.95 3

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 74	68.74 89	90.30 46	90.13 126	76.33 65	80.87 81	92.89 45	61.00 149	94.20 89	72.45 128	90.97 64	93.35 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FIs		82.07 78	82.42 66	81.04 190	88.80 119	58.34 242	88.26 103	93.49 11	76.93 49	78.47 102	91.04 77	69.92 46	92.34 170	69.87 148	84.97 128	92.44 77

VNet		82.21 76	82.41 67	81.62 177	90.82 68	60.93 223	84.47 212	89.78 137	76.36 64	84.07 45	91.88 59	64.71 85	90.26 222	70.68 141	88.89 84	93.66 36

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 49	66.37 130	87.93 112	91.80 72	73.82 103	77.32 131	90.66 85	67.90 60	94.90 69	70.37 144	89.48 80	93.19 58

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 64	66.40 129	88.91 76	88.11 186	77.57 35	84.39 41	93.29 36	52.19 216	93.91 103	77.05 81	88.70 88	94.57 9

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 143	72.94 22	90.64 37	92.14 56	77.21 42	75.47 165	92.83 47	58.56 167	94.72 75	73.24 119	92.71 51	92.13 88

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 117	71.58 42	85.15 199	86.16 216	74.69 88	80.47 83	91.04 77	62.29 128	90.55 220	80.33 55	90.08 74	90.20 146

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 9	70.92 51	88.79 81	92.20 53	70.53 162	79.17 91	91.03 79	64.12 89	96.03 31	68.39 159	90.14 73	91.50 102

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 158	70.21 59	90.50 40	90.38 112	68.55 196	81.32 73	89.47 106	61.68 134	93.46 128	78.98 61	90.26 71	92.05 90

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 132	55.97 277	87.95 110	93.42 13	77.10 45	77.38 129	90.98 82	69.96 45	91.79 181	68.46 158	84.50 133	92.33 79

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 97	66.98 122	89.17 68	92.19 54	76.41 59	77.23 134	90.23 91	60.17 161	95.11 58	77.47 75	85.99 123	91.03 111

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 122	67.42 114	87.98 108	90.87 99	74.92 86	79.72 86	91.65 62	62.19 131	93.96 98	75.26 101	86.42 118	93.16 59

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 69	69.76 68	88.74 86	91.70 77	69.39 177	78.96 93	88.46 130	65.47 79	94.87 71	74.42 105	88.57 91	90.24 145

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 129	67.73 111	85.81 181	92.35 49	75.78 70	78.33 108	86.58 187	64.01 90	94.35 82	76.05 88	87.48 106	90.79 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 130	63.46 195	87.13 140	92.37 48	80.19 15	78.38 106	89.14 114	71.66 33	93.05 148	70.05 145	76.46 227	92.25 83

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 173	67.27 118	89.27 66	91.51 84	71.75 144	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 163	91.49 103

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 105	68.76 87	90.22 48	91.94 66	75.37 78	76.64 143	91.51 67	54.29 199	94.91 67	78.44 65	83.78 139	89.83 169

LFMVS		81.82 83	81.23 82	83.57 109	91.89 56	63.43 197	89.84 52	81.85 263	77.04 47	83.21 53	93.10 39	52.26 215	93.43 132	71.98 134	89.95 76	93.85 33

API-MVS		81.99 80	81.23 82	84.26 88	90.94 65	70.18 64	91.10 31	89.32 149	71.51 150	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 231

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 133	64.41 179	87.60 117	93.02 24	78.42 31	78.56 99	88.16 137	69.78 47	93.26 136	69.58 150	76.49 226	91.60 98

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 108	68.03 106	84.46 214	90.02 131	70.67 160	81.30 76	86.53 190	63.17 100	94.19 90	75.60 97	88.54 93	88.57 209

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 92	68.21 103	84.28 220	90.09 127	70.79 157	81.26 77	85.62 212	63.15 101	94.29 83	75.62 96	88.87 85	88.59 207

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 76	68.84 192	77.13 138	89.50 104	67.63 62	94.88 70	67.55 162	88.52 94	93.09 60

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 61	71.53 43	89.82 53	90.92 98	69.77 172	78.50 100	86.21 199	62.36 127	94.52 79	65.36 181	92.05 54	89.77 172
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
VDDNet		81.52 88	80.67 89	84.05 94	90.44 71	64.13 183	89.73 58	85.91 219	71.11 153	83.18 54	93.48 31	50.54 242	93.49 127	73.40 117	88.25 97	94.54 10

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 94	68.69 94	89.97 51	91.50 86	74.46 90	75.04 181	90.41 88	53.82 204	94.54 77	77.56 74	82.91 155	89.86 168
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 139	60.80 226	86.86 150	91.58 81	75.67 73	80.24 84	89.45 110	63.34 95	90.25 223	70.51 143	79.22 195	91.23 108

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 147	63.46 195	87.02 145	91.87 70	79.01 26	78.38 106	89.07 115	65.02 83	93.05 148	70.05 145	76.46 227	92.20 85

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 114	65.40 146	84.43 216	92.00 62	67.62 204	78.11 117	85.05 224	66.02 76	94.27 85	71.52 138	89.50 79	89.01 189

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 145	62.33 214	87.74 115	91.33 89	80.55 12	77.99 120	89.86 98	65.23 81	92.62 160	67.05 169	75.24 245	92.30 81

jason		81.39 91	80.29 95	84.70 76	86.63 172	69.90 66	85.95 176	86.77 207	63.24 242	81.07 79	89.47 106	61.08 148	92.15 174	78.33 68	90.07 75	92.05 90
jason: jason.
lupinMVS		81.39 91	80.27 96	84.76 75	87.35 158	70.21 59	85.55 190	86.41 211	62.85 248	81.32 73	88.61 125	61.68 134	92.24 173	78.41 67	90.26 71	91.83 94

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 114	65.40 146	86.16 171	92.00 62	69.34 180	78.11 117	86.09 202	66.02 76	94.27 85	71.52 138	82.06 164	87.39 233

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 200	63.19 204	86.41 165	88.95 168	74.18 94	78.69 96	87.54 154	66.62 69	92.43 165	72.57 127	80.57 180	90.74 121

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 114	64.51 171	85.53 192	89.39 147	70.79 157	78.49 101	85.06 223	67.54 63	93.58 123	67.03 170	86.58 115	92.32 80

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 183	63.17 205	87.36 128	88.65 179	76.37 63	75.88 157	88.44 131	53.51 206	93.07 147	73.30 118	89.74 78	92.25 83

ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 102	68.37 98	89.99 50	91.60 80	70.28 166	77.25 132	89.66 101	53.37 207	93.53 126	74.24 108	82.85 156	88.85 193
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1121		80.84 97	79.77 102	84.05 94	93.11 37	70.78 53	84.66 206	85.42 222	57.37 290	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 238

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 183	68.78 86	83.54 231	90.50 109	70.66 161	76.71 141	91.66 61	60.69 153	91.26 203	76.94 82	81.58 169	91.83 94

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 158	70.19 61	85.56 187	88.77 175	69.06 186	81.83 67	88.16 137	50.91 236	92.85 154	78.29 69	87.56 103	89.06 182

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 158	70.19 61	85.56 187	88.77 175	69.06 186	81.83 67	88.16 137	50.91 236	92.85 154	78.29 69	87.56 103	89.06 182

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 158	70.19 61	85.56 187	88.77 175	69.06 186	81.83 67	88.16 137	50.91 236	92.85 154	78.29 69	87.56 103	89.06 182

UGNet		80.83 99	79.59 107	84.54 78	88.04 140	68.09 104	89.42 63	88.16 185	76.95 48	76.22 151	89.46 108	49.30 250	93.94 100	68.48 157	90.31 70	91.60 98
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
v1neww		80.40 113	79.54 108	82.98 132	84.10 211	64.51 171	87.57 119	90.22 121	73.25 115	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 213	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 211	64.51 171	87.57 119	90.22 121	73.25 115	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 213	90.58 132

v6		80.40 113	79.54 108	82.98 132	84.09 213	64.50 175	87.57 119	90.22 121	73.25 115	78.47 102	86.63 184	62.84 108	93.86 106	75.73 91	77.02 213	90.58 132

114514_t		80.68 106	79.51 111	84.20 89	94.09 21	67.27 118	89.64 61	91.11 95	58.75 280	74.08 188	90.72 84	58.10 170	95.04 63	69.70 149	89.42 81	90.30 144

QAPM		80.88 95	79.50 112	85.03 67	88.01 142	68.97 83	91.59 24	92.00 62	66.63 213	75.15 178	92.16 52	57.70 172	95.45 45	63.52 191	88.76 87	90.66 126

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 38	68.91 84	89.36 64	88.97 166	69.27 181	75.70 164	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 232

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 190	69.75 69	89.28 65	88.10 188	74.99 84	76.44 148	86.72 174	57.27 176	94.26 88	73.53 115	83.18 153	91.87 93

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 147	63.34 198	86.31 168	91.09 96	79.01 26	72.17 206	89.07 115	67.20 66	92.81 158	66.08 176	75.65 236	92.20 85

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 178	63.20 203	86.79 153	89.34 148	74.19 93	75.45 167	86.72 174	66.62 69	92.39 167	72.58 126	76.86 218	90.75 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 118	79.32 117	83.27 118	83.98 220	65.37 149	90.50 40	90.38 112	68.55 196	76.19 152	88.70 121	56.44 183	93.46 128	78.98 61	80.14 187	90.97 114

v1141		80.19 122	79.31 118	82.85 141	83.84 223	64.12 184	87.14 137	90.08 128	73.13 118	78.27 110	86.39 193	62.67 118	93.75 116	75.40 99	76.83 221	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 223	64.11 186	87.13 140	90.08 128	73.13 118	78.27 110	86.39 193	62.69 116	93.75 116	75.40 99	76.82 222	90.68 123

v1		80.19 122	79.31 118	82.85 141	83.83 225	64.12 184	87.14 137	90.07 130	73.13 118	78.27 110	86.38 197	62.72 115	93.75 116	75.41 98	76.82 222	90.68 123

v2v482		80.23 120	79.29 121	83.05 128	83.62 228	64.14 182	87.04 144	89.97 132	73.61 106	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 224	91.18 109

v7		80.24 119	79.26 122	83.15 122	84.07 217	64.94 160	87.56 122	90.67 102	72.26 139	78.28 109	86.51 191	61.45 139	94.03 97	75.14 102	77.41 207	90.49 137

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 182	69.02 80	83.03 235	90.39 111	71.09 154	77.63 126	91.49 69	54.62 198	91.35 201	75.71 94	83.47 145	91.54 100

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 120	58.35 241	85.06 200	88.61 181	78.56 29	77.65 125	88.34 133	63.81 93	90.66 219	64.98 185	77.22 210	91.80 97

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 199	57.97 248	82.59 236	87.62 197	67.40 208	76.17 155	88.56 128	68.47 56	89.59 232	70.65 142	86.05 122	93.47 48

v1144		80.03 126	79.03 126	83.01 130	83.78 226	64.51 171	87.11 142	90.57 107	71.96 143	78.08 119	86.20 200	61.41 140	93.94 100	74.93 103	77.23 209	90.60 129

v8		79.97 128	79.02 127	82.80 146	84.09 213	64.50 175	87.96 109	90.29 120	74.13 96	75.24 176	86.81 171	62.88 106	93.89 105	74.39 106	75.40 241	90.00 158

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 130	60.91 224	83.84 226	89.24 154	70.36 164	79.03 92	88.87 119	63.23 99	90.21 224	65.12 182	82.57 161	92.28 82

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 151	68.99 82	83.65 228	91.46 87	63.00 245	77.77 124	90.28 89	66.10 73	95.09 62	61.40 211	88.22 98	90.94 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 132	78.67 130	82.97 136	84.06 218	64.95 159	87.88 114	90.62 105	73.11 121	75.11 179	86.56 188	61.46 138	94.05 96	73.68 111	75.55 238	89.90 166

VPNet		78.69 152	78.66 131	78.76 229	88.31 135	55.72 283	84.45 215	86.63 209	76.79 51	78.26 113	90.55 87	59.30 163	89.70 231	66.63 171	77.05 212	90.88 116

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 107	65.91 137	86.07 174	88.52 182	72.18 140	75.42 168	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 251

diffmvs		79.51 135	78.59 133	82.25 159	83.31 235	62.66 211	84.17 221	88.11 186	67.64 202	76.09 156	87.47 156	64.01 90	91.15 206	71.71 137	84.82 131	92.94 67

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 248	65.32 150	86.12 172	89.55 142	69.64 176	70.55 222	85.82 207	57.24 178	93.81 110	76.85 83	88.55 92	92.41 78

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 190	68.74 89	88.77 82	88.10 188	74.99 84	74.97 182	83.49 241	57.27 176	93.36 133	73.53 115	80.88 174	91.18 109

WR-MVS_H		78.51 154	78.49 136	78.56 232	88.02 141	56.38 272	88.43 92	92.67 39	77.14 43	73.89 189	87.55 153	66.25 72	89.24 238	58.92 230	73.55 260	90.06 156

test_normal		79.81 130	78.45 137	83.89 103	82.70 252	65.40 146	85.82 180	89.48 145	69.39 177	70.12 231	85.66 210	57.15 180	93.71 121	77.08 80	88.62 90	92.56 74

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 240	88.64 124	51.78 298	86.70 157	79.63 283	74.14 95	75.11 179	90.83 83	61.29 143	89.75 229	58.10 239	91.60 57	92.69 71

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 96	65.93 136	84.95 202	87.15 204	73.56 108	78.19 115	89.79 99	56.67 182	93.36 133	59.53 226	86.74 113	90.13 149

v1192		79.59 134	78.43 140	83.07 127	83.55 230	64.52 169	86.93 148	90.58 106	70.83 156	77.78 123	85.90 203	59.15 164	93.94 100	73.96 110	77.19 211	90.76 119

v144192		79.47 138	78.37 141	82.78 149	83.35 233	63.96 188	86.96 146	90.36 115	69.99 169	77.50 127	85.67 209	60.66 154	93.77 114	74.27 107	76.58 225	90.62 127

CP-MVSNet		78.22 158	78.34 142	77.84 242	87.83 146	54.54 287	87.94 111	91.17 94	77.65 33	73.48 191	88.49 129	62.24 130	88.43 251	62.19 202	74.07 253	90.55 135

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 246	85.79 179	56.21 275	86.78 154	85.76 220	73.60 107	77.93 121	87.57 152	65.02 83	88.99 242	67.14 168	75.33 242	87.63 228

OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 187	69.91 65	90.57 38	90.97 97	66.70 209	72.17 206	91.91 57	54.70 196	93.96 98	61.81 208	90.95 65	88.41 215

V42		79.38 141	78.24 145	82.83 144	81.10 274	65.50 145	85.55 190	89.82 136	71.57 149	78.21 114	86.12 201	60.66 154	93.18 141	75.64 95	75.46 240	89.81 171

PS-CasMVS		78.01 166	78.09 146	77.77 244	87.71 151	54.39 289	88.02 107	91.22 91	77.50 40	73.26 193	88.64 124	60.73 151	88.41 252	61.88 206	73.88 257	90.53 136

v1921920		79.22 143	78.03 147	82.80 146	83.30 236	63.94 189	86.80 152	90.33 117	69.91 170	77.48 128	85.53 214	58.44 168	93.75 116	73.60 114	76.85 219	90.71 122

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 196	66.57 128	89.25 67	90.16 125	69.20 183	75.46 166	89.49 105	45.75 270	93.13 144	76.84 84	80.80 176	90.11 150

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 90	62.99 209	88.16 106	91.51 84	65.77 221	77.14 137	91.09 75	60.91 150	93.21 137	50.26 275	87.05 110	92.17 87
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSTER		79.01 147	77.88 150	82.38 157	83.07 242	64.80 163	84.08 225	88.95 168	69.01 190	78.69 96	87.17 166	54.70 196	92.43 165	74.69 104	80.57 180	89.89 167

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	12.47 338	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

v148		78.72 151	77.80 152	81.47 181	82.73 251	61.96 219	86.30 169	88.08 190	73.26 114	76.18 153	85.47 216	62.46 126	92.36 169	71.92 136	73.82 258	90.09 152

v1240		78.99 148	77.78 153	82.64 153	83.21 237	63.54 192	86.62 159	90.30 119	69.74 175	77.33 130	85.68 208	57.04 181	93.76 115	73.13 120	76.92 216	90.62 127

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 195	66.37 130	89.17 68	90.19 124	69.38 179	75.40 169	89.46 108	44.17 276	93.15 142	76.78 85	80.70 178	90.14 148

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 153	68.23 102	84.40 218	86.20 215	67.49 206	76.36 149	86.54 189	61.54 137	90.79 217	61.86 207	87.33 107	90.49 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PEN-MVS		77.73 173	77.69 156	77.84 242	87.07 166	53.91 291	87.91 113	91.18 93	77.56 37	73.14 195	88.82 120	61.23 144	89.17 239	59.95 221	72.37 266	90.43 140

v7n		78.97 149	77.58 157	83.14 123	83.45 232	65.51 144	88.32 99	91.21 92	73.69 105	72.41 203	86.32 198	57.93 171	93.81 110	69.18 153	75.65 236	90.11 150

TAMVS		78.89 150	77.51 158	83.03 129	87.80 147	67.79 110	84.72 205	85.05 226	67.63 203	76.75 140	87.70 148	62.25 129	90.82 216	58.53 235	87.13 109	90.49 137

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 153	63.01 206	88.39 96	89.28 150	71.63 146	75.34 171	87.28 159	54.80 192	91.11 207	62.72 196	79.57 189	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 153	63.01 206	88.39 96	89.28 150	71.63 146	75.34 171	87.28 159	54.80 192	91.11 207	62.72 196	79.57 189	90.09 152

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 118	65.13 156	84.87 203	87.85 194	69.75 173	74.52 186	84.74 229	61.34 141	93.11 145	58.24 238	85.84 125	84.27 277

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 153	62.89 210	87.47 126	89.02 159	71.63 146	75.29 175	87.28 159	54.80 192	91.10 210	62.38 200	79.38 192	89.61 175

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 280	67.08 120	87.24 135	89.53 143	65.66 223	75.16 177	87.19 165	52.52 209	92.25 172	77.17 79	79.34 193	89.61 175

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 157	60.21 230	83.37 233	87.78 195	66.11 217	75.37 170	87.06 169	63.27 97	90.48 221	61.38 212	82.43 162	90.40 142

MVS		78.19 161	76.99 165	81.78 167	85.66 181	66.99 121	84.66 206	90.47 110	55.08 300	72.02 210	85.27 219	63.83 92	94.11 95	66.10 175	89.80 77	84.24 278

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 249	87.20 164	51.60 299	80.06 256	80.46 275	75.20 82	67.69 260	86.72 174	62.48 125	88.98 243	63.44 192	89.25 82	91.51 101

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 169	62.36 213	86.52 162	88.77 175	71.13 152	75.34 171	86.66 181	54.07 202	91.10 210	62.72 196	79.57 189	89.45 177

DTE-MVSNet		76.99 191	76.80 168	77.54 248	86.24 175	53.06 295	87.52 124	90.66 104	77.08 46	72.50 201	88.67 123	60.48 157	89.52 233	57.33 246	70.74 277	90.05 157

CNLPA		78.08 163	76.79 169	81.97 164	90.40 72	71.07 46	87.59 118	84.55 229	66.03 220	72.38 204	89.64 102	57.56 174	86.04 269	59.61 224	83.35 150	88.79 196

pm-mvs1		77.25 189	76.68 170	78.93 226	84.22 203	58.62 239	86.41 165	88.36 184	71.37 151	73.31 192	88.01 141	61.22 145	89.15 240	64.24 189	73.01 262	89.03 188

v748		77.97 167	76.65 171	81.92 166	82.29 258	63.28 200	87.53 123	90.35 116	73.50 111	70.76 221	85.55 213	58.28 169	92.81 158	68.81 156	72.76 265	89.67 174

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 241	66.96 124	86.94 147	87.45 202	72.45 134	71.49 216	84.17 232	54.79 195	91.58 198	67.61 161	80.31 184	89.30 179

1112_ss		77.40 188	76.43 173	80.32 200	89.11 111	60.41 229	83.65 228	87.72 196	62.13 256	73.05 196	86.72 174	62.58 122	89.97 226	62.11 205	80.80 176	90.59 131

PAPM		77.68 175	76.40 174	81.51 180	87.29 163	61.85 220	83.78 227	89.59 141	64.74 230	71.23 217	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 189

v52		77.94 170	76.37 175	82.67 151	79.39 292	65.52 142	86.43 163	89.94 133	72.28 137	72.15 208	84.94 226	55.70 187	93.44 130	73.64 112	72.84 264	89.06 182

V4		77.95 168	76.37 175	82.67 151	79.40 291	65.52 142	86.43 163	89.94 133	72.28 137	72.14 209	84.95 225	55.72 186	93.44 130	73.64 112	72.86 263	89.05 186

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 57	67.80 109	88.19 104	89.46 146	64.33 235	69.87 237	88.38 132	53.66 205	93.58 123	58.86 231	82.73 158	87.86 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v16		77.69 174	76.36 178	81.68 174	84.15 208	64.63 168	87.33 130	88.99 163	72.69 132	69.31 245	82.08 254	62.80 112	91.79 181	72.70 124	67.23 288	88.63 201

v18		77.67 177	76.35 179	81.64 176	84.09 213	64.47 177	87.27 133	89.01 161	72.59 133	69.39 242	82.04 256	62.85 107	91.80 180	72.72 123	67.20 289	88.63 201

v17		77.68 175	76.35 179	81.69 173	84.15 208	64.65 166	87.33 130	88.99 163	72.70 131	69.25 246	82.07 255	62.82 111	91.79 181	72.69 125	67.15 290	88.63 201

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 136	63.24 201	84.61 210	86.40 212	67.55 205	77.81 122	86.48 192	54.10 201	93.15 142	57.75 242	82.72 159	87.20 239

v15		77.51 182	76.12 182	81.66 175	84.09 213	64.65 166	87.14 137	88.96 167	72.76 129	68.90 247	81.91 263	62.74 114	91.73 185	72.32 129	66.29 295	88.61 204

V14		77.52 180	76.12 182	81.70 172	84.15 208	64.77 164	87.21 136	88.95 168	72.80 128	68.79 248	81.94 262	62.69 116	91.72 187	72.31 130	66.27 296	88.60 205

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 170	63.01 206	88.39 96	89.28 150	70.49 163	74.39 187	87.28 159	49.06 253	91.11 207	60.91 215	78.52 197	90.09 152

V9		77.52 180	76.11 185	81.73 171	84.19 207	64.89 161	87.26 134	88.94 171	72.87 127	68.65 251	81.96 261	62.65 119	91.72 187	72.27 131	66.24 297	88.60 205

v12		77.51 182	76.09 186	81.76 170	84.22 203	64.99 158	87.30 132	88.93 172	72.92 124	68.48 255	81.97 259	62.54 123	91.70 190	72.24 132	66.21 299	88.58 208

v13		77.50 184	76.07 187	81.77 168	84.23 202	65.07 157	87.34 129	88.91 173	72.92 124	68.35 256	81.97 259	62.53 124	91.69 191	72.20 133	66.22 298	88.56 210

v11		77.45 185	76.06 188	81.59 179	84.22 203	64.52 169	87.11 142	89.02 159	72.76 129	68.76 249	81.90 264	62.09 132	91.71 189	71.98 134	66.73 291	88.56 210

Test4		77.83 172	75.90 189	83.62 106	80.24 282	65.25 152	85.27 196	90.67 102	69.03 189	66.48 272	83.75 237	43.07 281	93.00 151	75.93 90	88.66 89	92.62 73

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 80	68.58 96	78.70 270	87.50 200	56.38 295	75.80 159	86.84 170	58.67 166	91.40 200	61.58 210	85.75 126	90.34 143

WTY-MVS		75.65 213	75.68 191	75.57 263	86.40 174	56.82 263	77.92 276	82.40 252	65.10 227	76.18 153	87.72 147	63.13 104	80.90 291	60.31 219	81.96 165	89.00 191

XXY-MVS		75.41 216	75.56 192	74.96 267	83.59 229	57.82 252	80.59 253	83.87 235	66.54 214	74.93 183	88.31 134	63.24 98	80.09 295	62.16 203	76.85 219	86.97 245

thres600view7		76.50 197	75.44 193	79.68 211	89.40 95	57.16 259	85.53 192	83.23 244	73.79 104	76.26 150	87.09 167	51.89 223	91.89 179	48.05 286	83.72 143	90.00 158

Test_1112_low_res		76.40 200	75.44 193	79.27 217	89.28 103	58.09 244	81.69 244	87.07 205	59.53 274	72.48 202	86.67 180	61.30 142	89.33 236	60.81 217	80.15 186	90.41 141

HyFIR lowres test		77.53 179	75.40 195	83.94 102	89.59 90	66.62 126	80.36 254	88.64 180	56.29 296	76.45 145	85.17 220	57.64 173	93.28 135	61.34 213	83.10 154	91.91 92

tfpn200view9		76.42 199	75.37 196	79.55 216	89.13 109	57.65 254	85.17 197	83.60 237	73.41 112	76.45 145	86.39 193	52.12 217	91.95 177	48.33 282	83.75 141	89.07 181

thres400		76.50 197	75.37 196	79.86 207	89.13 109	57.65 254	85.17 197	83.60 237	73.41 112	76.45 145	86.39 193	52.12 217	91.95 177	48.33 282	83.75 141	90.00 158

1314		76.53 196	75.30 198	80.21 203	83.93 221	62.32 215	84.66 206	88.81 174	60.23 268	70.16 230	84.07 234	55.30 190	90.73 218	67.37 164	83.21 152	87.59 230

view600		76.20 203	75.21 199	79.16 221	89.64 85	55.82 278	85.74 182	82.06 258	73.88 99	75.74 160	87.85 143	51.84 224	91.66 192	46.75 290	83.42 146	90.00 158

view800		76.20 203	75.21 199	79.16 221	89.64 85	55.82 278	85.74 182	82.06 258	73.88 99	75.74 160	87.85 143	51.84 224	91.66 192	46.75 290	83.42 146	90.00 158

conf0.05thres1000		76.20 203	75.21 199	79.16 221	89.64 85	55.82 278	85.74 182	82.06 258	73.88 99	75.74 160	87.85 143	51.84 224	91.66 192	46.75 290	83.42 146	90.00 158

tfpn		76.20 203	75.21 199	79.16 221	89.64 85	55.82 278	85.74 182	82.06 258	73.88 99	75.74 160	87.85 143	51.84 224	91.66 192	46.75 290	83.42 146	90.00 158

GA-MVS		76.87 193	75.17 203	81.97 164	82.75 250	62.58 212	81.44 248	86.35 214	72.16 142	74.74 184	82.89 244	46.20 265	92.02 176	68.85 155	81.09 172	91.30 107

EPNet_dtu		75.46 215	74.86 204	77.23 252	82.57 255	54.60 286	86.89 149	83.09 246	71.64 145	66.25 274	85.86 205	55.99 185	88.04 256	54.92 256	86.55 116	89.05 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 192	74.82 205	83.37 115	90.45 70	67.36 117	89.15 72	86.94 206	61.87 258	69.52 240	90.61 86	51.71 229	94.53 78	46.38 297	86.71 114	88.21 217

cascas		76.72 195	74.64 206	82.99 131	85.78 180	65.88 138	82.33 238	89.21 155	60.85 264	72.74 198	81.02 271	47.28 259	93.75 116	67.48 163	85.02 127	89.34 178

DP-MVS		76.78 194	74.57 207	83.42 112	93.29 31	69.46 76	88.55 91	83.70 236	63.98 239	70.20 227	88.89 118	54.01 203	94.80 73	46.66 294	81.88 167	86.01 262

TransMVSNet (Re)		75.39 217	74.56 208	77.86 241	85.50 185	57.10 261	86.78 154	86.09 218	72.17 141	71.53 215	87.34 158	63.01 105	89.31 237	56.84 249	61.83 307	87.17 240

LTVRE_ROB	69.57 13	76.25 202	74.54 209	81.41 182	88.60 125	64.38 180	79.24 264	89.12 157	70.76 159	69.79 239	87.86 142	49.09 252	93.20 139	56.21 252	80.16 185	86.65 252
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
thres200		75.55 214	74.47 210	78.82 228	87.78 150	57.85 251	83.07 234	83.51 240	72.44 136	75.84 158	84.42 231	52.08 219	91.75 184	47.41 288	83.64 144	86.86 247

MVP-Stereo		76.12 207	74.46 211	81.13 189	85.37 186	69.79 67	84.42 217	87.95 192	65.03 228	67.46 262	85.33 218	53.28 208	91.73 185	58.01 240	83.27 151	81.85 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 201	74.33 212	82.50 155	89.28 103	66.95 125	88.41 95	89.03 158	64.05 237	66.83 268	88.61 125	46.78 262	92.89 153	57.48 243	78.55 196	87.67 227

XVG-ACMP-BASELINE		76.11 208	74.27 213	81.62 177	83.20 238	64.67 165	83.60 230	89.75 138	69.75 173	71.85 211	87.09 167	32.78 313	92.11 175	69.99 147	80.43 183	88.09 219

ACMH+	68.96 14	76.01 209	74.01 214	82.03 163	88.60 125	65.31 151	88.86 78	87.55 198	70.25 167	67.75 259	87.47 156	41.27 291	93.19 140	58.37 236	75.94 232	87.60 229

ACMH	67.68 16	75.89 210	73.93 215	81.77 168	88.71 123	66.61 127	88.62 88	89.01 161	69.81 171	66.78 269	86.70 179	41.95 290	91.51 199	55.64 253	78.14 202	87.17 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 218	73.90 216	79.27 217	82.65 254	58.27 243	80.80 249	82.73 250	61.57 259	75.33 174	83.13 243	55.52 188	91.07 213	64.98 185	78.34 201	88.45 213

sss		73.60 228	73.64 217	73.51 278	82.80 249	55.01 285	76.12 282	81.69 264	62.47 253	74.68 185	85.85 206	57.32 175	78.11 303	60.86 216	80.93 173	87.39 233

pmmvs6		74.69 219	73.39 218	78.61 231	81.38 269	57.48 257	86.64 158	87.95 192	64.99 229	70.18 228	86.61 185	50.43 243	89.52 233	62.12 204	70.18 279	88.83 194

IB-MVS	68.01 15	75.85 211	73.36 219	83.31 116	84.76 194	66.03 133	83.38 232	85.06 225	70.21 168	69.40 241	81.05 270	45.76 269	94.66 76	65.10 183	75.49 239	89.25 180
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
testing_2		75.73 212	73.34 220	82.89 140	77.37 300	65.22 153	84.10 224	90.54 108	69.09 185	60.46 296	81.15 269	40.48 294	92.84 157	76.36 86	80.54 182	90.60 129

tfpnnormal		74.39 221	73.16 221	78.08 239	86.10 177	58.05 245	84.65 209	87.53 199	70.32 165	71.22 218	85.63 211	54.97 191	89.86 227	43.03 306	75.02 246	86.32 255

PatchFormer-LS_test		74.50 220	73.05 222	78.86 227	82.95 246	59.55 235	81.65 245	82.30 254	67.44 207	71.62 214	78.15 290	52.34 213	88.92 247	65.05 184	75.90 233	88.12 218

IterMVS		74.29 222	72.94 223	78.35 237	81.53 266	63.49 194	81.58 246	82.49 251	68.06 201	69.99 234	83.69 239	51.66 230	85.54 272	65.85 178	71.64 272	86.01 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		73.83 226	72.67 224	77.30 251	83.87 222	66.02 134	81.82 241	84.66 228	61.37 262	68.61 253	82.82 246	47.29 258	88.21 253	59.27 227	84.32 136	77.68 310

CVMVSNet		72.99 238	72.58 225	74.25 274	84.28 200	50.85 305	86.41 165	83.45 242	44.56 321	73.23 194	87.54 154	49.38 248	85.70 271	65.90 177	78.44 199	86.19 257

test-LLR		72.94 239	72.43 226	74.48 271	81.35 270	58.04 246	78.38 271	77.46 292	66.66 210	69.95 235	79.00 286	48.06 256	79.24 297	66.13 173	84.83 129	86.15 258

OurMVSNet-221017-0		74.26 223	72.42 227	79.80 209	83.76 227	59.59 232	85.92 178	86.64 208	66.39 215	66.96 267	87.58 151	39.46 297	91.60 197	65.76 179	69.27 281	88.22 216

tpmrst		72.39 241	72.13 228	73.18 280	80.54 279	49.91 309	79.91 259	79.08 286	63.11 243	71.69 213	79.95 279	55.32 189	82.77 286	65.66 180	73.89 256	86.87 246

pmmvs4		74.03 225	71.91 229	80.39 197	81.96 261	68.32 99	81.45 247	82.14 256	59.32 275	69.87 237	85.13 221	52.40 212	88.13 255	60.21 220	74.74 249	84.73 275

DWT-MVSNet_test		73.70 227	71.86 230	79.21 219	82.91 247	58.94 237	82.34 237	82.17 255	65.21 225	71.05 220	78.31 288	44.21 275	90.17 225	63.29 194	77.28 208	88.53 212

Patchmatch-test1		73.49 229	71.85 231	78.41 236	84.05 219	62.17 217	79.96 258	79.29 285	66.30 216	72.38 204	79.58 283	51.95 222	85.08 276	55.46 254	77.67 205	87.99 220

EG-PatchMatch MVS		74.04 224	71.82 232	80.71 195	84.92 193	67.42 114	85.86 179	88.08 190	66.04 219	64.22 285	83.85 235	35.10 312	92.56 163	57.44 244	80.83 175	82.16 296

tpm		72.37 243	71.71 233	74.35 273	82.19 259	52.00 296	79.22 265	77.29 294	64.56 232	72.95 197	83.68 240	51.35 231	83.26 285	58.33 237	75.80 234	87.81 225

tpm2		73.26 234	71.46 234	78.63 230	83.34 234	56.71 266	80.65 252	80.40 276	56.63 294	73.55 190	82.02 257	51.80 228	91.24 204	56.35 251	78.42 200	87.95 221

RPSCF		73.23 235	71.46 234	78.54 233	82.50 256	59.85 231	82.18 239	82.84 249	58.96 277	71.15 219	89.41 112	45.48 272	84.77 278	58.82 232	71.83 271	91.02 113

PatchmatchNet		73.12 236	71.33 236	78.49 235	83.18 239	60.85 225	79.63 260	78.57 287	64.13 236	71.73 212	79.81 282	51.20 233	85.97 270	57.40 245	76.36 229	88.66 199
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 231	71.27 237	79.67 212	81.32 272	65.19 154	75.92 284	80.30 277	59.92 271	72.73 199	81.19 267	52.50 210	86.69 263	59.84 222	77.71 203	87.11 243

SixPastTwentyTwo		73.37 231	71.26 238	79.70 210	85.08 192	57.89 250	85.57 186	83.56 239	71.03 155	65.66 276	85.88 204	42.10 288	92.57 162	59.11 229	63.34 304	88.65 200

tpmp4_e23		73.45 230	71.17 239	80.31 201	83.55 230	59.56 234	81.88 240	82.33 253	57.94 285	70.51 224	81.62 265	51.19 234	91.63 196	53.96 260	77.51 206	89.75 173

MSDG		73.36 233	70.99 240	80.49 196	84.51 198	65.80 139	80.71 251	86.13 217	65.70 222	65.46 277	83.74 238	44.60 273	90.91 215	51.13 270	76.89 217	84.74 274

PatchMatch-RL		72.38 242	70.90 241	76.80 255	88.60 125	67.38 116	79.53 261	76.17 298	62.75 250	69.36 243	82.00 258	45.51 271	84.89 277	53.62 262	80.58 179	78.12 308

PVSNet	64.34 18	72.08 244	70.87 242	75.69 261	86.21 176	56.44 270	74.37 294	80.73 272	62.06 257	70.17 229	82.23 252	42.86 283	83.31 284	54.77 257	84.45 135	87.32 236

test_0402		72.79 240	70.44 243	79.84 208	88.13 138	65.99 135	85.93 177	84.29 231	65.57 224	67.40 264	85.49 215	46.92 261	92.61 161	35.88 317	74.38 252	80.94 300

COLMAP_ROB	66.92 17	73.01 237	70.41 244	80.81 193	87.13 165	65.63 141	88.30 100	84.19 233	62.96 246	63.80 288	87.69 149	38.04 303	92.56 163	46.66 294	74.91 247	84.24 278
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 247	70.39 245	74.48 271	81.35 270	58.04 246	78.38 271	77.46 292	60.32 267	69.95 235	79.00 286	36.08 310	79.24 297	66.13 173	84.83 129	86.15 258

pmmvs5		71.55 246	70.20 246	75.61 262	77.83 297	56.39 271	81.74 243	80.89 269	57.76 286	67.46 262	84.49 230	49.26 251	85.32 275	57.08 248	75.29 243	85.11 271

MDTV_nov1_ep13			69.97 247		83.18 239	53.48 293	77.10 280	80.18 280	60.45 265	69.33 244	80.44 275	48.89 254	86.90 262	51.60 268	78.51 198

MIMVSNet		70.69 252	69.30 248	74.88 268	84.52 197	56.35 273	75.87 286	79.42 284	64.59 231	67.76 258	82.41 249	41.10 292	81.54 290	46.64 296	81.34 170	86.75 250

tpmvs		71.09 249	69.29 249	76.49 256	82.04 260	56.04 276	78.92 268	81.37 268	64.05 237	67.18 266	78.28 289	49.74 247	89.77 228	49.67 278	72.37 266	83.67 282

Patchmtry		70.74 251	69.16 250	75.49 264	80.72 276	54.07 290	74.94 293	80.30 277	58.34 281	70.01 232	81.19 267	52.50 210	86.54 265	53.37 263	71.09 275	85.87 264

TESTMET0.1,1		69.89 260	69.00 251	72.55 281	79.27 294	56.85 262	78.38 271	74.71 308	57.64 287	68.09 257	77.19 297	37.75 304	76.70 308	63.92 190	84.09 137	84.10 281

RPMNet		71.62 245	68.94 252	79.67 212	81.32 272	65.19 154	75.92 284	78.30 289	57.60 288	72.73 199	76.45 300	52.30 214	86.69 263	48.14 285	77.71 203	87.11 243

PMMVS		69.34 262	68.67 253	71.35 288	75.67 307	62.03 218	75.17 288	73.46 313	50.00 317	68.68 250	79.05 284	52.07 220	78.13 302	61.16 214	82.77 157	73.90 318

K. test v3		71.19 248	68.51 254	79.21 219	83.04 244	57.78 253	84.35 219	76.91 296	72.90 126	62.99 291	82.86 245	39.27 298	91.09 212	61.65 209	52.66 322	88.75 197

USDC		70.33 256	68.37 255	76.21 258	80.60 278	56.23 274	79.19 266	86.49 210	60.89 263	61.29 293	85.47 216	31.78 316	89.47 235	53.37 263	76.21 230	82.94 293

tpm cat1		70.57 253	68.31 256	77.35 250	82.41 257	57.95 249	78.08 275	80.22 279	52.04 312	68.54 254	77.66 295	52.00 221	87.84 258	51.77 267	72.07 270	86.25 256

OpenMVS_ROB	64.09 19	70.56 254	68.19 257	77.65 245	80.26 281	59.41 236	85.01 201	82.96 248	58.76 279	65.43 278	82.33 250	37.63 306	91.23 205	45.34 302	76.03 231	82.32 294

EPMVS		69.02 263	68.16 258	71.59 284	79.61 288	49.80 311	77.40 278	66.93 329	62.82 249	70.01 232	79.05 284	45.79 268	77.86 305	56.58 250	75.26 244	87.13 242

CMPMVS	51.72 21	70.19 258	68.16 258	76.28 257	73.15 316	57.55 256	79.47 262	83.92 234	48.02 319	56.48 311	84.81 227	43.13 280	86.42 267	62.67 199	81.81 168	84.89 272
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 250	68.09 260	79.58 214	85.15 188	63.62 190	84.58 211	79.83 281	62.31 254	60.32 297	86.73 172	32.02 314	88.96 245	50.28 273	71.57 273	86.15 258

gg-mvs-nofinetune		69.95 259	67.96 261	75.94 259	83.07 242	54.51 288	77.23 279	70.29 320	63.11 243	70.32 226	62.33 322	43.62 278	88.69 249	53.88 261	87.76 100	84.62 276

FMVSNet5		69.50 261	67.96 261	74.15 275	82.97 245	55.35 284	80.01 257	82.12 257	62.56 252	63.02 289	81.53 266	36.92 307	81.92 288	48.42 281	74.06 254	85.17 270

PatchT		68.46 267	67.85 263	70.29 292	80.70 277	43.93 319	72.47 297	74.88 304	60.15 269	70.55 222	76.57 299	49.94 246	81.59 289	50.58 271	74.83 248	85.34 267

pmmvs-eth3d		70.50 255	67.83 264	78.52 234	77.37 300	66.18 132	81.82 241	81.51 266	58.90 278	63.90 287	80.42 276	42.69 284	86.28 268	58.56 234	65.30 301	83.11 288

Anonymous20231206		68.60 264	67.80 265	71.02 290	80.23 283	50.75 306	78.30 274	80.47 274	56.79 293	66.11 275	82.63 248	46.35 263	78.95 299	43.62 305	75.70 235	83.36 285

Patchmatch-RL test		70.24 257	67.78 266	77.61 246	77.43 299	59.57 233	71.16 299	70.33 319	62.94 247	68.65 251	72.77 310	50.62 241	85.49 273	69.58 150	66.58 293	87.77 226

test0.0.03 1		68.00 268	67.69 267	68.90 297	77.55 298	47.43 313	75.70 287	72.95 315	66.66 210	66.56 270	82.29 251	48.06 256	75.87 312	44.97 303	74.51 251	83.41 284

EU-MVSNet		68.53 266	67.61 268	71.31 289	78.51 296	47.01 315	84.47 212	84.27 232	42.27 322	66.44 273	84.79 228	40.44 295	83.76 280	58.76 233	68.54 287	83.17 286

test20.03		67.45 270	66.95 269	68.94 296	75.48 310	44.84 317	77.50 277	77.67 291	66.66 210	63.01 290	83.80 236	47.02 260	78.40 301	42.53 308	68.86 285	83.58 283

MIMVSNet1		68.58 265	66.78 270	73.98 276	80.07 284	51.82 297	80.77 250	84.37 230	64.40 234	59.75 300	82.16 253	36.47 308	83.63 282	42.73 307	70.33 278	86.48 254

testgi		66.67 275	66.53 271	67.08 302	75.62 308	41.69 324	75.93 283	76.50 297	66.11 217	65.20 281	86.59 186	35.72 311	74.71 316	43.71 304	73.38 261	84.84 273

UnsupCasMVSNet_eth		67.33 271	65.99 272	71.37 286	73.48 313	51.47 301	75.16 289	85.19 224	65.20 226	60.78 295	80.93 274	42.35 285	77.20 307	57.12 247	53.69 321	85.44 266

dp		66.80 273	65.43 273	70.90 291	79.74 287	48.82 312	75.12 291	74.77 306	59.61 273	64.08 286	77.23 296	42.89 282	80.72 292	48.86 280	66.58 293	83.16 287

TinyColmap		67.30 272	64.81 274	74.76 270	81.92 262	56.68 267	80.29 255	81.49 267	60.33 266	56.27 312	83.22 242	24.77 323	87.66 260	45.52 300	69.47 280	79.95 304

CHOSEN 280x420		66.51 276	64.71 275	71.90 283	81.45 267	63.52 193	57.98 328	68.95 327	53.57 307	62.59 292	76.70 298	46.22 264	75.29 315	55.25 255	79.68 188	76.88 316

TDRefinement		67.49 269	64.34 276	76.92 253	73.47 314	61.07 222	84.86 204	82.98 247	59.77 272	58.30 303	85.13 221	26.06 321	87.89 257	47.92 287	60.59 312	81.81 298

PM-MVS		66.41 277	64.14 277	73.20 279	73.92 311	56.45 269	78.97 267	64.96 333	63.88 241	64.72 282	80.24 277	19.84 329	83.44 283	66.24 172	64.52 303	79.71 305

MDA-MVSNet-bldmvs		66.68 274	63.66 278	75.75 260	79.28 293	60.56 228	73.92 295	78.35 288	64.43 233	50.13 322	79.87 281	44.02 277	83.67 281	46.10 298	56.86 316	83.03 290

ADS-MVSNet2		66.20 279	63.33 279	74.82 269	79.92 285	58.75 238	67.55 316	75.19 302	53.37 308	65.25 279	75.86 301	42.32 286	80.53 293	41.57 309	68.91 283	85.18 268

Patchmatch-test		64.82 282	63.24 280	69.57 294	79.42 290	49.82 310	63.49 323	69.05 326	51.98 313	59.95 299	80.13 278	50.91 236	70.98 326	40.66 311	73.57 259	87.90 223

MDA-MVSNet_test_wron		65.03 280	62.92 281	71.37 286	75.93 305	56.73 264	69.09 311	74.73 307	57.28 291	54.03 315	77.89 292	45.88 266	74.39 318	49.89 277	61.55 308	82.99 291

YYNet1		65.03 280	62.91 282	71.38 285	75.85 306	56.60 268	69.12 310	74.66 310	57.28 291	54.12 314	77.87 293	45.85 267	74.48 317	49.95 276	61.52 309	83.05 289

ADS-MVSNet		64.36 284	62.88 283	68.78 299	79.92 285	47.17 314	67.55 316	71.18 318	53.37 308	65.25 279	75.86 301	42.32 286	73.99 320	41.57 309	68.91 283	85.18 268

JIA-IIPM		66.32 278	62.82 284	76.82 254	77.09 303	61.72 221	65.34 320	75.38 300	58.04 284	64.51 283	62.32 323	42.05 289	86.51 266	51.45 269	69.22 282	82.21 295

LF4IMVS		64.02 285	62.19 285	69.50 295	70.90 321	53.29 294	76.13 281	77.18 295	52.65 311	58.59 301	80.98 272	23.55 324	76.52 309	53.06 265	66.66 292	78.68 307

Anonymous20231211		64.82 282	61.79 286	73.91 277	77.11 302	50.92 304	85.29 195	81.53 265	54.19 302	57.98 304	78.03 291	26.90 319	87.83 259	37.92 314	57.12 315	82.99 291

new-patchmatchnet		61.73 287	61.73 287	61.70 310	72.74 317	24.50 340	69.16 309	78.03 290	61.40 260	56.72 310	75.53 303	38.42 301	76.48 310	45.95 299	57.67 314	84.13 280

UnsupCasMVSNet_bld		63.70 286	61.53 288	70.21 293	73.69 312	51.39 302	72.82 296	81.89 262	55.63 298	57.81 305	71.80 312	38.67 300	78.61 300	49.26 279	52.21 323	80.63 301

PVSNet_0	57.27 20	61.67 288	59.27 289	68.85 298	79.61 288	57.44 258	68.01 314	73.44 314	55.93 297	58.54 302	70.41 315	44.58 274	77.55 306	47.01 289	35.91 328	71.55 320

test2356		59.50 290	58.08 290	63.74 306	71.23 320	41.88 322	67.59 315	72.42 317	53.72 306	57.65 306	70.74 314	26.31 320	72.40 323	32.03 324	71.06 276	76.93 314

testus		59.00 292	57.91 291	62.25 309	72.25 318	39.09 327	69.74 304	75.02 303	53.04 310	57.21 308	73.72 308	18.76 331	70.33 327	32.86 320	68.57 286	77.35 311

LP		61.36 289	57.78 292	72.09 282	75.54 309	58.53 240	67.16 318	75.22 301	51.90 314	54.13 313	69.97 316	37.73 305	80.45 294	32.74 321	55.63 318	77.29 312

MVS-HIRNet		59.14 291	57.67 293	63.57 307	81.65 264	43.50 320	71.73 298	65.06 332	39.59 326	51.43 320	57.73 326	38.34 302	82.58 287	39.53 312	73.95 255	64.62 325

testpf		56.51 297	57.58 294	53.30 317	71.99 319	41.19 325	46.89 333	69.32 325	58.06 283	52.87 319	69.45 318	27.99 318	72.73 322	59.59 225	62.07 306	45.98 330

DSMNet-mixed		57.77 295	56.90 295	60.38 311	67.70 326	35.61 330	69.18 308	53.97 336	32.30 332	57.49 307	79.88 280	40.39 296	68.57 330	38.78 313	72.37 266	76.97 313

test1235678		58.74 293	56.89 296	64.30 304	69.70 322	41.87 323	71.05 300	74.87 305	54.06 303	50.63 321	71.53 313	25.30 322	74.10 319	31.80 325	63.10 305	76.93 314

1111		57.11 296	56.82 297	57.97 314	69.10 323	28.28 335	68.90 312	74.54 311	54.01 304	53.71 316	74.51 305	23.09 325	67.90 331	32.28 322	61.26 310	77.73 309

pmmvs3		57.79 294	54.26 298	68.37 300	64.02 328	56.72 265	75.12 291	65.17 331	40.20 324	52.93 318	69.86 317	20.36 328	75.48 314	45.45 301	55.25 320	72.90 319

N_pmnet		52.79 301	53.26 299	51.40 320	78.99 295	7.68 344	69.52 306	3.89 345	51.63 315	57.01 309	74.98 304	40.83 293	65.96 333	37.78 315	64.67 302	80.56 303

FPMVS		53.68 300	51.64 300	59.81 312	65.08 327	51.03 303	69.48 307	69.58 323	41.46 323	40.67 325	72.32 311	16.46 334	70.00 328	24.24 332	65.42 300	58.40 327

testmv		53.85 299	51.03 301	62.31 308	61.46 330	38.88 328	70.95 303	74.69 309	51.11 316	41.26 324	66.85 319	14.28 335	72.13 324	29.19 327	49.51 325	75.93 317

new_pmnet		50.91 303	50.29 302	52.78 318	68.58 325	34.94 333	63.71 322	56.63 335	39.73 325	44.95 323	65.47 321	21.93 327	58.48 335	34.98 318	56.62 317	64.92 324

.test1245		45.55 306	50.02 303	32.14 326	69.10 323	28.28 335	68.90 312	74.54 311	54.01 304	53.71 316	74.51 305	23.09 325	67.90 331	32.28 322	0.02 340	0.25 339

LCM-MVSNet		54.25 298	49.68 304	67.97 301	53.73 335	45.28 316	66.85 319	80.78 271	35.96 328	39.45 327	62.23 324	8.70 341	78.06 304	48.24 284	51.20 324	80.57 302

test12356		49.28 305	48.51 305	51.59 319	62.06 329	19.11 341	60.40 325	72.45 316	47.60 320	40.64 326	65.68 320	13.84 336	68.72 329	27.29 329	46.67 327	66.94 323

ANet_high		50.57 304	46.10 306	63.99 305	48.67 338	39.13 326	70.99 302	80.85 270	61.39 261	31.18 330	57.70 327	17.02 333	73.65 321	31.22 326	15.89 337	79.18 306

no-one		51.08 302	45.79 307	66.95 303	57.92 333	50.49 308	59.63 327	76.04 299	48.04 318	31.85 328	56.10 329	19.12 330	80.08 296	36.89 316	26.52 330	70.29 321

Gipuma		45.18 307	41.86 308	55.16 316	77.03 304	51.52 300	32.50 336	80.52 273	32.46 330	27.12 331	35.02 333	9.52 340	75.50 313	22.31 333	60.21 313	38.45 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 308	40.28 309	55.82 315	40.82 341	42.54 321	65.12 321	63.99 334	34.43 329	24.48 332	57.12 328	3.92 343	76.17 311	17.10 335	55.52 319	48.75 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 309	38.86 310	46.69 322	53.84 334	16.45 342	48.61 332	49.92 338	37.49 327	31.67 329	60.97 325	8.14 342	56.42 336	28.42 328	30.72 329	67.19 322

PNet_i23d		38.26 311	35.42 311	46.79 321	58.74 331	35.48 331	59.65 326	51.25 337	32.45 331	23.44 335	47.53 331	2.04 345	58.96 334	25.60 331	18.09 335	45.92 331

pcd1.5k->3k		34.07 312	35.26 312	30.50 327	86.92 167	0.00 347	0.00 338	91.58 81	0.00 342	0.00 343	0.00 344	56.23 184	0.00 345	0.00 342	82.60 160	91.49 103

wuykxyi23d		39.76 310	33.18 313	59.51 313	46.98 339	44.01 318	57.70 329	67.74 328	24.13 334	13.98 339	34.33 334	1.27 346	71.33 325	34.23 319	18.23 333	63.18 326

E-PMN		31.77 313	30.64 314	35.15 324	52.87 336	27.67 337	57.09 330	47.86 339	24.64 333	16.40 337	33.05 335	11.23 338	54.90 337	14.46 337	18.15 334	22.87 334

EMVS		30.81 314	29.65 315	34.27 325	50.96 337	25.95 339	56.58 331	46.80 340	24.01 335	15.53 338	30.68 336	12.47 337	54.43 338	12.81 338	17.05 336	22.43 335

cdsmvs_eth3d_5k		19.96 316	26.61 316	0.00 333	0.00 346	0.00 347	0.00 338	89.26 153	0.00 342	0.00 343	88.61 125	61.62 136	0.00 345	0.00 342	0.00 343	0.00 341

MVE	26.22 23	30.37 315	25.89 317	43.81 323	44.55 340	35.46 332	28.87 337	39.07 341	18.20 336	18.58 336	40.18 332	2.68 344	47.37 339	17.07 336	23.78 332	48.60 329
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 317	21.40 318	10.23 330	4.82 343	10.11 343	34.70 335	30.74 343	1.48 339	23.91 334	26.07 337	28.42 317	13.41 342	27.12 330	15.35 338	7.17 336

wuyk23d		16.82 318	15.94 319	19.46 329	58.74 331	31.45 334	39.22 334	3.74 346	6.84 338	6.04 340	2.70 341	1.27 346	24.29 341	10.54 339	14.40 339	2.63 337

ab-mvs-re		7.23 319	9.64 320	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	86.72 174	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

test123		6.12 320	8.11 321	0.14 331	0.06 345	0.09 345	71.05 300	0.03 348	0.04 341	0.25 342	1.30 343	0.05 348	0.03 344	0.21 341	0.01 342	0.29 338

testmvs		6.04 321	8.02 322	0.10 332	0.08 344	0.03 346	69.74 304	0.04 347	0.05 340	0.31 341	1.68 342	0.02 349	0.04 343	0.24 340	0.02 340	0.25 339

pcd_1.5k_mvsjas		5.26 322	7.02 323	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	63.15 101	0.00 345	0.00 342	0.00 343	0.00 341

sosnet-low-res		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

sosnet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uncertanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

Regformer		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ESAPD								94.22 1

sam_mvs1												51.32 232

sam_mvs												50.01 245

semantic-postprocess				80.11 204	82.69 253	64.85 162		83.47 241	69.16 184	70.49 225	84.15 233	50.83 240	88.15 254	69.23 152	72.14 269	87.34 235

ambc				75.24 266	73.16 315	50.51 307	63.05 324	87.47 201		64.28 284	77.81 294	17.80 332	89.73 230	57.88 241	60.64 311	85.49 265

MTGPA								92.02 59

test_post1							78.90 269				5.43 340	48.81 255	85.44 274	59.25 228

test_post											5.46 339	50.36 244	84.24 279

patchmatchnet-post											74.00 307	51.12 235	88.60 250

GG-mvs-BLEND				75.38 265	81.59 265	55.80 282	79.32 263	69.63 322		67.19 265	73.67 309	43.24 279	88.90 248	50.41 272	84.50 133	81.45 299

MTMP								32.83 342

gm-plane-assit					81.40 268	53.83 292			62.72 251		80.94 273		92.39 167	63.40 193

test9_res														84.90 17	95.70 12	92.87 68

TEST9					93.26 33	72.96 19	88.75 84	91.89 68	68.44 198	85.00 27	93.10 39	74.36 16	95.41 48

test_8					93.13 35	72.57 28	88.68 87	91.84 71	68.69 194	84.87 33	93.10 39	74.43 13	95.16 56

agg_prior2														82.91 39	95.45 14	92.70 69

agg_prior					92.85 41	71.94 39		91.78 74		84.41 39			94.93 65

TestCases				79.58 214	85.15 188	63.62 190		79.83 281	62.31 254	60.32 297	86.73 172	32.02 314	88.96 245	50.28 273	71.57 273	86.15 258

test_prior4						72.60 27	89.01 75

test_prior2							88.85 79		75.41 76	84.91 29	93.54 29	74.28 17		83.31 32	95.86 6

test_prior				86.33 46	92.61 46	69.59 71		92.97 30					95.48 43			93.91 30

旧先验2							86.56 161		58.10 282	87.04 13			88.98 243	74.07 109

新几何2							86.29 170

新几何1				83.42 112	93.13 35	70.71 54		85.48 221	57.43 289	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 237

旧先验1					91.96 54	65.79 140		86.37 213			93.08 43	69.31 52			92.74 50	88.74 198

无先验							87.48 125	88.98 165	60.00 270				94.12 93	67.28 165		88.97 192

原ACMM2							86.86 150

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 44	63.96 240	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 195

test222					91.50 59	68.26 101	84.16 222	83.20 245	54.63 301	79.74 85	91.63 64	58.97 165			91.42 60	86.77 249

testdata2													91.01 214	62.37 201

segment_acmp												73.08 23

testdata				79.97 206	90.90 66	64.21 181		84.71 227	59.27 276	85.40 22	92.91 44	62.02 133	89.08 241	68.95 154	91.37 61	86.63 253

testdata1							84.14 223		75.71 71

test12				86.80 38	92.63 45	70.70 55		91.79 73		82.71 61		71.67 32	96.16 29		94.50 33	93.54 46

plane_prior7					90.08 78	68.51 97

plane_prior6					89.84 83	68.70 93						60.42 158

plane_prior5								92.44 44					95.38 50	78.71 63	86.32 119	91.33 105

plane_prior4											91.00 80

plane_prior3						68.60 95			78.44 30	78.92 94

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 82

plane_prior						68.71 91	90.38 44		77.62 34						86.16 121

n2								0.00 349

nn								0.00 349

door-mid								69.98 321

lessismore_v0				78.97 225	81.01 275	57.15 260		65.99 330		61.16 294	82.82 246	39.12 299	91.34 202	59.67 223	46.92 326	88.43 214

LGP-MVS_train				84.50 79	89.23 105	68.76 87		91.94 66	75.37 78	76.64 143	91.51 67	54.29 199	94.91 67	78.44 65	83.78 139	89.83 169

test11								92.23 51

door								69.44 324

HQP5-MVS						66.98 122

HQP-NCC					89.33 97		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 97		89.17 68		76.41 59	77.23 134

BP-MVS														77.47 75

HQP4-MVS										77.24 133			95.11 58			91.03 111

HQP3-MVS								92.19 54							85.99 123

HQP2-MVS												60.17 161

NP-MVS					89.62 89	68.32 99					90.24 90

MDTV_nov1_ep13_2view						37.79 329	75.16 289		55.10 299	66.53 271		49.34 249		53.98 259		87.94 222

ACMMP++_ref															81.95 166

ACMMP++															81.25 171

Test By Simon												64.33 87

ITE_SJBPF				78.22 238	81.77 263	60.57 227		83.30 243	69.25 182	67.54 261	87.20 164	36.33 309	87.28 261	54.34 258	74.62 250	86.80 248

DeepMVS_CX				27.40 328	40.17 342	26.90 338		24.59 344	17.44 337	23.95 333	48.61 330	9.77 339	26.48 340	18.06 334	24.47 331	28.83 333

High-resolution multi-view benchmark