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	73.01 125	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	73.07 124	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 99	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 99	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	68.69 196	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	72.45 136	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	68.96 193	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	73.54 111	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	68.29 202	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	88.95 114	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 94	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	88.49 130	67.93 107	85.52 196	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	68.32 201	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	88.95 114	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	73.51 112	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	73.89 100	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	88.18 139	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	86.77 173	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	79.28 196	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	87.87 146	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	68.75 195	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	88.38 135	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	86.86 170	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	88.79 122	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	84.22 205	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	88.80 121	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	84.47 214	89.78 137	76.36 64	84.07 45	91.88 59	64.71 85	90.26 224	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	73.82 105	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	87.94 145	72.94 22	90.64 37	92.14 56	77.21 42	75.47 167	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	88.82 119	71.58 42	85.15 201	86.16 216	74.69 88	80.47 83	91.04 77	62.29 128	90.55 222	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	70.53 164	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	87.35 160	70.21 59	90.50 40	90.38 112	68.55 198	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	88.42 134	55.97 279	87.95 110	93.42 13	77.10 45	77.38 129	90.98 82	69.96 45	91.79 183	68.46 158	84.50 133	92.33 79

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 99	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	88.75 124	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	69.39 179	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	88.47 131	67.73 111	85.81 183	92.35 49	75.78 70	78.33 108	86.58 189	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	88.46 132	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	76.46 229	92.25 83

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 175	67.27 118	89.27 66	91.51 84	71.75 146	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 165	91.49 103

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 107	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	81.85 265	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	71.51 152	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 233

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 135	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	76.49 228	91.60 98

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 110	68.03 106	84.46 216	90.02 131	70.67 162	81.30 76	86.53 192	63.17 100	94.19 90	75.60 97	88.54 93	88.57 211

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 94	68.21 103	84.28 222	90.09 127	70.79 159	81.26 77	85.62 214	63.15 101	94.29 83	75.62 96	88.87 85	88.59 209

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 76	68.84 194	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	69.77 174	78.50 100	86.21 201	62.36 127	94.52 79	65.36 181	92.05 54	89.77 173
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	71.11 155	83.18 54	93.48 31	50.54 244	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 96	68.69 94	89.97 51	91.50 86	74.46 90	75.04 183	90.41 88	53.82 204	94.54 77	77.56 74	82.91 157	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	88.07 141	60.80 226	86.86 150	91.58 81	75.67 73	80.24 84	89.45 110	63.34 95	90.25 225	70.51 143	79.22 197	91.23 108

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 149	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	76.46 229	92.20 85

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 116	65.40 146	84.43 218	92.00 62	67.62 206	78.11 117	85.05 226	66.02 76	94.27 85	71.52 138	89.50 79	89.01 191

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 147	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	75.24 247	92.30 81

jason		81.39 91	80.29 95	84.70 76	86.63 174	69.90 66	85.95 176	86.77 207	63.24 244	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	87.35 160	70.21 59	85.55 192	86.41 211	62.85 250	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	88.85 116	65.40 146	86.16 171	92.00 62	69.34 182	78.11 117	86.09 204	66.02 76	94.27 85	71.52 138	82.06 166	87.39 235

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 202	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	80.57 182	90.74 121

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 116	64.51 171	85.53 194	89.39 147	70.79 159	78.49 101	85.06 225	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	85.60 185	63.17 205	87.36 128	88.65 179	76.37 63	75.88 159	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	89.31 104	68.37 98	89.99 50	91.60 80	70.28 168	77.25 132	89.66 101	53.37 207	93.53 126	74.24 108	82.85 158	88.85 195
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	84.66 208	85.42 222	57.37 292	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 240

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 185	68.78 86	83.54 233	90.50 109	70.66 163	76.71 141	91.66 61	60.69 153	91.26 205	76.94 82	81.58 171	91.83 94

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 160	70.19 61	85.56 189	88.77 175	69.06 188	81.83 67	88.16 137	50.91 238	92.85 154	78.29 69	87.56 103	89.06 184

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 160	70.19 61	85.56 189	88.77 175	69.06 188	81.83 67	88.16 137	50.91 238	92.85 154	78.29 69	87.56 103	89.06 184

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 160	70.19 61	85.56 189	88.77 175	69.06 188	81.83 67	88.16 137	50.91 238	92.85 154	78.29 69	87.56 103	89.06 184

UGNet		80.83 99	79.59 107	84.54 78	88.04 142	68.09 104	89.42 63	88.16 185	76.95 48	76.22 153	89.46 108	49.30 252	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	84.10 213	64.51 171	87.57 119	90.22 121	73.25 117	78.47 102	86.65 184	62.83 109	93.86 106	75.72 92	77.02 215	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 213	64.51 171	87.57 119	90.22 121	73.25 117	78.47 102	86.65 184	62.83 109	93.86 106	75.72 92	77.02 215	90.58 132

v6		80.40 113	79.54 108	82.98 132	84.09 215	64.50 175	87.57 119	90.22 121	73.25 117	78.47 102	86.63 186	62.84 108	93.86 106	75.73 91	77.02 215	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	58.75 282	74.08 190	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	88.01 144	68.97 83	91.59 24	92.00 62	66.63 215	75.15 180	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	69.27 183	75.70 166	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 234

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 192	69.75 69	89.28 65	88.10 188	74.99 84	76.44 148	86.72 176	57.27 176	94.26 88	73.53 115	83.18 155	91.87 93

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 149	63.34 198	86.31 168	91.09 96	79.01 26	72.17 208	89.07 115	67.20 66	92.81 158	66.08 176	75.65 238	92.20 85

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 180	63.20 203	86.79 153	89.34 148	74.19 93	75.45 169	86.72 176	66.62 69	92.39 167	72.58 126	76.86 220	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	83.98 222	65.37 149	90.50 40	90.38 112	68.55 198	76.19 154	88.70 121	56.44 183	93.46 128	78.98 61	80.14 189	90.97 114

v1141		80.19 122	79.31 118	82.85 141	83.84 225	64.12 184	87.14 137	90.08 128	73.13 120	78.27 110	86.39 195	62.67 118	93.75 116	75.40 99	76.83 223	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 225	64.11 186	87.13 140	90.08 128	73.13 120	78.27 110	86.39 195	62.69 116	93.75 116	75.40 99	76.82 224	90.68 123

v1		80.19 122	79.31 118	82.85 141	83.83 227	64.12 184	87.14 137	90.07 130	73.13 120	78.27 110	86.38 199	62.72 115	93.75 116	75.41 98	76.82 224	90.68 123

v2v482		80.23 120	79.29 121	83.05 128	83.62 230	64.14 182	87.04 144	89.97 132	73.61 108	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 226	91.18 109

v7		80.24 119	79.26 122	83.15 122	84.07 219	64.94 160	87.56 122	90.67 102	72.26 141	78.28 109	86.51 193	61.45 139	94.03 97	75.14 102	77.41 209	90.49 137

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 184	69.02 80	83.03 237	90.39 111	71.09 156	77.63 126	91.49 69	54.62 198	91.35 203	75.71 94	83.47 147	91.54 100

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 122	58.35 241	85.06 202	88.61 181	78.56 29	77.65 125	88.34 133	63.81 93	90.66 221	64.98 185	77.22 212	91.80 97

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 201	57.97 248	82.59 238	87.62 197	67.40 210	76.17 157	88.56 128	68.47 56	89.59 234	70.65 142	86.05 122	93.47 48

v1144		80.03 126	79.03 126	83.01 130	83.78 228	64.51 171	87.11 142	90.57 107	71.96 145	78.08 119	86.20 202	61.41 140	93.94 100	74.93 103	77.23 211	90.60 129

v8		79.97 128	79.02 127	82.80 146	84.09 215	64.50 175	87.96 109	90.29 120	74.13 96	75.24 178	86.81 173	62.88 106	93.89 105	74.39 106	75.40 243	90.00 158

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 132	60.91 224	83.84 228	89.24 154	70.36 166	79.03 92	88.87 119	63.23 99	90.21 226	65.12 182	82.57 163	92.28 82

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 153	68.99 82	83.65 230	91.46 87	63.00 247	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	84.06 220	64.95 159	87.88 114	90.62 105	73.11 123	75.11 181	86.56 190	61.46 138	94.05 96	73.68 111	75.55 240	89.90 166

VPNet		78.69 152	78.66 131	78.76 231	88.31 137	55.72 285	84.45 217	86.63 209	76.79 51	78.26 113	90.55 87	59.30 163	89.70 233	66.63 171	77.05 214	90.88 116

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 109	65.91 137	86.07 174	88.52 182	72.18 142	75.42 170	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 253

diffmvs		79.51 135	78.59 133	82.25 159	83.31 237	62.66 211	84.17 223	88.11 186	67.64 204	76.09 158	87.47 156	64.01 90	91.15 208	71.71 137	84.82 131	92.94 67

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 250	65.32 150	86.12 172	89.55 142	69.64 178	70.55 224	85.82 209	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	85.13 192	68.74 89	88.77 82	88.10 188	74.99 84	74.97 184	83.49 243	57.27 176	93.36 133	73.53 115	80.88 176	91.18 109

WR-MVS_H		78.51 154	78.49 136	78.56 234	88.02 143	56.38 274	88.43 92	92.67 39	77.14 43	73.89 191	87.55 153	66.25 72	89.24 240	58.92 230	73.55 262	90.06 156

test_normal		79.81 130	78.45 137	83.89 103	82.70 254	65.40 146	85.82 182	89.48 145	69.39 179	70.12 233	85.66 212	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	78.07 242	88.64 126	51.78 300	86.70 157	79.63 285	74.14 95	75.11 181	90.83 83	61.29 143	89.75 231	58.10 239	91.60 57	92.69 71

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 98	65.93 136	84.95 204	87.15 204	73.56 110	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	83.55 232	64.52 169	86.93 148	90.58 106	70.83 158	77.78 123	85.90 205	59.15 164	93.94 100	73.96 110	77.19 213	90.76 119

v144192		79.47 138	78.37 141	82.78 149	83.35 235	63.96 188	86.96 146	90.36 115	69.99 171	77.50 127	85.67 211	60.66 154	93.77 114	74.27 107	76.58 227	90.62 127

CP-MVSNet		78.22 158	78.34 142	77.84 244	87.83 148	54.54 289	87.94 111	91.17 94	77.65 33	73.48 193	88.49 129	62.24 130	88.43 253	62.19 202	74.07 255	90.55 135

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 181	56.21 277	86.78 154	85.76 220	73.60 109	77.93 121	87.57 152	65.02 83	88.99 244	67.14 168	75.33 244	87.63 230

OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 189	69.91 65	90.57 38	90.97 97	66.70 211	72.17 208	91.91 57	54.70 196	93.96 98	61.81 208	90.95 65	88.41 217

V42		79.38 141	78.24 145	82.83 144	81.10 276	65.50 145	85.55 192	89.82 136	71.57 151	78.21 114	86.12 203	60.66 154	93.18 141	75.64 95	75.46 242	89.81 171

PS-CasMVS		78.01 166	78.09 146	77.77 246	87.71 153	54.39 291	88.02 107	91.22 91	77.50 40	73.26 195	88.64 124	60.73 151	88.41 254	61.88 206	73.88 259	90.53 136

v1921920		79.22 143	78.03 147	82.80 146	83.30 238	63.94 189	86.80 152	90.33 117	69.91 172	77.48 128	85.53 216	58.44 168	93.75 116	73.60 114	76.85 221	90.71 122

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 198	66.57 128	89.25 67	90.16 125	69.20 185	75.46 168	89.49 105	45.75 272	93.13 144	76.84 84	80.80 178	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	65.77 223	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	83.07 244	64.80 163	84.08 227	88.95 168	69.01 192	78.69 96	87.17 166	54.70 196	92.43 165	74.69 104	80.57 182	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	12.47 340	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	82.73 253	61.96 219	86.30 169	88.08 190	73.26 116	76.18 155	85.47 218	62.46 126	92.36 169	71.92 136	73.82 260	90.09 152

v1240		78.99 148	77.78 153	82.64 153	83.21 239	63.54 192	86.62 159	90.30 119	69.74 177	77.33 130	85.68 210	57.04 181	93.76 115	73.13 120	76.92 218	90.62 127

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 197	66.37 130	89.17 68	90.19 124	69.38 181	75.40 171	89.46 108	44.17 278	93.15 142	76.78 85	80.70 180	90.14 148

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 155	68.23 102	84.40 220	86.20 215	67.49 208	76.36 149	86.54 191	61.54 137	90.79 219	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	77.84 244	87.07 168	53.91 293	87.91 113	91.18 93	77.56 37	73.14 197	88.82 120	61.23 144	89.17 241	59.95 221	72.37 268	90.43 140

v7n		78.97 149	77.58 157	83.14 123	83.45 234	65.51 144	88.32 99	91.21 92	73.69 107	72.41 205	86.32 200	57.93 171	93.81 110	69.18 153	75.65 238	90.11 150

TAMVS		78.89 150	77.51 158	83.03 129	87.80 149	67.79 110	84.72 207	85.05 226	67.63 205	76.75 140	87.70 148	62.25 129	90.82 218	58.53 235	87.13 109	90.49 137

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 155	63.01 206	88.39 96	89.28 150	71.63 148	75.34 173	87.28 159	54.80 192	91.11 209	62.72 196	79.57 191	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 155	63.01 206	88.39 96	89.28 150	71.63 148	75.34 173	87.28 159	54.80 192	91.11 209	62.72 196	79.57 191	90.09 152

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 120	65.13 156	84.87 205	87.85 194	69.75 175	74.52 188	84.74 231	61.34 141	93.11 145	58.24 238	85.84 125	84.27 279

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 155	62.89 210	87.47 126	89.02 159	71.63 148	75.29 177	87.28 159	54.80 192	91.10 212	62.38 200	79.38 194	89.61 176

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 282	67.08 120	87.24 135	89.53 143	65.66 225	75.16 179	87.19 165	52.52 209	92.25 172	77.17 79	79.34 195	89.61 176

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 159	60.21 230	83.37 235	87.78 195	66.11 219	75.37 172	87.06 171	63.27 97	90.48 223	61.38 212	82.43 164	90.40 142

MVS		78.19 161	76.99 165	81.78 167	85.66 183	66.99 121	84.66 208	90.47 110	55.08 302	72.02 212	85.27 221	63.83 92	94.11 95	66.10 175	89.80 77	84.24 280

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 251	87.20 166	51.60 301	80.06 258	80.46 277	75.20 82	67.69 262	86.72 176	62.48 125	88.98 245	63.44 192	89.25 82	91.51 101

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 171	62.36 213	86.52 162	88.77 175	71.13 154	75.34 173	86.66 183	54.07 202	91.10 212	62.72 196	79.57 191	89.45 178

DTE-MVSNet		76.99 191	76.80 168	77.54 250	86.24 177	53.06 297	87.52 124	90.66 104	77.08 46	72.50 203	88.67 123	60.48 157	89.52 235	57.33 246	70.74 279	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	66.03 222	72.38 206	89.64 102	57.56 174	86.04 271	59.61 224	83.35 152	88.79 198

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 205	58.62 239	86.41 165	88.36 184	71.37 153	73.31 194	88.01 141	61.22 145	89.15 242	64.24 189	73.01 264	89.03 190

v748		77.97 167	76.65 171	81.92 166	82.29 260	63.28 200	87.53 123	90.35 116	73.50 113	70.76 223	85.55 215	58.28 169	92.81 158	68.81 156	72.76 267	89.67 175

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 243	66.96 124	86.94 147	87.45 202	72.45 136	71.49 218	84.17 234	54.79 195	91.58 200	67.61 161	80.31 186	89.30 180

1112_ss		77.40 188	76.43 173	80.32 200	89.11 113	60.41 229	83.65 230	87.72 196	62.13 258	73.05 198	86.72 176	62.58 122	89.97 228	62.11 205	80.80 178	90.59 131

PAPM		77.68 175	76.40 174	81.51 180	87.29 165	61.85 220	83.78 229	89.59 141	64.74 232	71.23 219	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 191

v52		77.94 170	76.37 175	82.67 151	79.39 294	65.52 142	86.43 163	89.94 133	72.28 139	72.15 210	84.94 228	55.70 187	93.44 130	73.64 112	72.84 266	89.06 184

V4		77.95 168	76.37 175	82.67 151	79.40 293	65.52 142	86.43 163	89.94 133	72.28 139	72.14 211	84.95 227	55.72 186	93.44 130	73.64 112	72.86 265	89.05 188

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	64.33 237	69.87 239	88.38 132	53.66 205	93.58 123	58.86 231	82.73 160	87.86 226
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	84.15 210	64.63 168	87.33 130	88.99 163	72.69 134	69.31 247	82.08 256	62.80 112	91.79 183	72.70 124	67.23 290	88.63 203

v18		77.67 177	76.35 179	81.64 176	84.09 215	64.47 177	87.27 133	89.01 161	72.59 135	69.39 244	82.04 258	62.85 107	91.80 182	72.72 123	67.20 291	88.63 203

v17		77.68 175	76.35 179	81.69 173	84.15 210	64.65 166	87.33 130	88.99 163	72.70 133	69.25 248	82.07 257	62.82 111	91.79 183	72.69 125	67.15 292	88.63 203

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 138	63.24 201	84.61 212	86.40 212	67.55 207	77.81 122	86.48 194	54.10 201	93.15 142	57.75 242	82.72 161	87.20 241

v15		77.51 182	76.12 182	81.66 175	84.09 215	64.65 166	87.14 137	88.96 167	72.76 131	68.90 249	81.91 265	62.74 114	91.73 187	72.32 129	66.29 297	88.61 206

V14		77.52 180	76.12 182	81.70 172	84.15 210	64.77 164	87.21 136	88.95 168	72.80 130	68.79 250	81.94 264	62.69 116	91.72 189	72.31 130	66.27 298	88.60 207

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 172	63.01 206	88.39 96	89.28 150	70.49 165	74.39 189	87.28 159	49.06 255	91.11 209	60.91 215	78.52 199	90.09 152

V9		77.52 180	76.11 185	81.73 171	84.19 209	64.89 161	87.26 134	88.94 171	72.87 129	68.65 253	81.96 263	62.65 119	91.72 189	72.27 131	66.24 299	88.60 207

v12		77.51 182	76.09 186	81.76 170	84.22 205	64.99 158	87.30 132	88.93 172	72.92 126	68.48 257	81.97 261	62.54 123	91.70 192	72.24 132	66.21 301	88.58 210

v13		77.50 184	76.07 187	81.77 168	84.23 204	65.07 157	87.34 129	88.91 173	72.92 126	68.35 258	81.97 261	62.53 124	91.69 193	72.20 133	66.22 300	88.56 212

v11		77.45 185	76.06 188	81.59 179	84.22 205	64.52 169	87.11 142	89.02 159	72.76 131	68.76 251	81.90 266	62.09 132	91.71 191	71.98 134	66.73 293	88.56 212

Test4		77.83 172	75.90 189	83.62 106	80.24 284	65.25 152	85.27 198	90.67 102	69.03 191	66.48 274	83.75 239	43.07 283	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	78.70 272	87.50 200	56.38 297	75.80 161	86.84 172	58.67 166	91.40 202	61.58 210	85.75 126	90.34 143

WTY-MVS		75.65 215	75.68 191	75.57 265	86.40 176	56.82 265	77.92 278	82.40 254	65.10 229	76.18 155	87.72 147	63.13 104	80.90 293	60.31 219	81.96 167	89.00 193

XXY-MVS		75.41 218	75.56 192	74.96 269	83.59 231	57.82 252	80.59 255	83.87 235	66.54 216	74.93 185	88.31 134	63.24 98	80.09 297	62.16 203	76.85 221	86.97 247

conf200view11		76.55 196	75.55 193	79.57 216	89.52 92	56.99 262	85.83 180	83.23 244	73.94 98	76.32 150	87.12 167	51.89 223	91.95 177	48.33 282	83.75 141	89.78 172

thres100view900		76.50 198	75.55 193	79.33 218	89.52 92	56.99 262	85.83 180	83.23 244	73.94 98	76.32 150	87.12 167	51.89 223	91.95 177	48.33 282	83.75 141	89.07 182

thres600view7		76.50 198	75.44 195	79.68 211	89.40 97	57.16 259	85.53 194	83.23 244	73.79 106	76.26 152	87.09 169	51.89 223	91.89 181	48.05 288	83.72 145	90.00 158

Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 105	58.09 244	81.69 246	87.07 205	59.53 276	72.48 204	86.67 182	61.30 142	89.33 238	60.81 217	80.15 188	90.41 141

HyFIR lowres test		77.53 179	75.40 197	83.94 102	89.59 90	66.62 126	80.36 256	88.64 180	56.29 298	76.45 145	85.17 222	57.64 173	93.28 135	61.34 213	83.10 156	91.91 92

tfpn200view9		76.42 201	75.37 198	79.55 217	89.13 111	57.65 254	85.17 199	83.60 237	73.41 114	76.45 145	86.39 195	52.12 217	91.95 177	48.33 282	83.75 141	89.07 182

thres400		76.50 198	75.37 198	79.86 207	89.13 111	57.65 254	85.17 199	83.60 237	73.41 114	76.45 145	86.39 195	52.12 217	91.95 177	48.33 282	83.75 141	90.00 158

1314		76.53 197	75.30 200	80.21 203	83.93 223	62.32 215	84.66 208	88.81 174	60.23 270	70.16 232	84.07 236	55.30 190	90.73 220	67.37 164	83.21 154	87.59 232

view600		76.20 205	75.21 201	79.16 223	89.64 85	55.82 280	85.74 184	82.06 260	73.88 101	75.74 162	87.85 143	51.84 226	91.66 194	46.75 292	83.42 148	90.00 158

view800		76.20 205	75.21 201	79.16 223	89.64 85	55.82 280	85.74 184	82.06 260	73.88 101	75.74 162	87.85 143	51.84 226	91.66 194	46.75 292	83.42 148	90.00 158

conf0.05thres1000		76.20 205	75.21 201	79.16 223	89.64 85	55.82 280	85.74 184	82.06 260	73.88 101	75.74 162	87.85 143	51.84 226	91.66 194	46.75 292	83.42 148	90.00 158

tfpn		76.20 205	75.21 201	79.16 223	89.64 85	55.82 280	85.74 184	82.06 260	73.88 101	75.74 162	87.85 143	51.84 226	91.66 194	46.75 292	83.42 148	90.00 158

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 252	62.58 212	81.44 250	86.35 214	72.16 144	74.74 186	82.89 246	46.20 267	92.02 176	68.85 155	81.09 174	91.30 107

EPNet_dtu		75.46 217	74.86 206	77.23 254	82.57 257	54.60 288	86.89 149	83.09 248	71.64 147	66.25 276	85.86 207	55.99 185	88.04 258	54.92 256	86.55 116	89.05 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 192	74.82 207	83.37 115	90.45 70	67.36 117	89.15 72	86.94 206	61.87 260	69.52 242	90.61 86	51.71 231	94.53 78	46.38 299	86.71 114	88.21 219

cascas		76.72 195	74.64 208	82.99 131	85.78 182	65.88 138	82.33 240	89.21 155	60.85 266	72.74 200	81.02 273	47.28 261	93.75 116	67.48 163	85.02 127	89.34 179

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 31	69.46 76	88.55 91	83.70 236	63.98 241	70.20 229	88.89 118	54.01 203	94.80 73	46.66 296	81.88 169	86.01 264

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 187	57.10 261	86.78 154	86.09 218	72.17 143	71.53 217	87.34 158	63.01 105	89.31 239	56.84 249	61.83 309	87.17 242

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 127	64.38 180	79.24 266	89.12 157	70.76 161	69.79 241	87.86 142	49.09 254	93.20 139	56.21 252	80.16 187	86.65 254
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		75.55 216	74.47 212	78.82 230	87.78 152	57.85 251	83.07 236	83.51 240	72.44 138	75.84 160	84.42 233	52.08 219	91.75 186	47.41 290	83.64 146	86.86 249

MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 188	69.79 67	84.42 219	87.95 192	65.03 230	67.46 264	85.33 220	53.28 208	91.73 187	58.01 240	83.27 153	81.85 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 105	66.95 125	88.41 95	89.03 158	64.05 239	66.83 270	88.61 125	46.78 264	92.89 153	57.48 243	78.55 198	87.67 229

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 240	64.67 165	83.60 232	89.75 138	69.75 175	71.85 213	87.09 169	32.78 315	92.11 175	69.99 147	80.43 185	88.09 221

ACMH+	68.96 14	76.01 211	74.01 216	82.03 163	88.60 127	65.31 151	88.86 78	87.55 198	70.25 169	67.75 261	87.47 156	41.27 293	93.19 140	58.37 236	75.94 234	87.60 231

ACMH	67.68 16	75.89 212	73.93 217	81.77 168	88.71 125	66.61 127	88.62 88	89.01 161	69.81 173	66.78 271	86.70 181	41.95 292	91.51 201	55.64 253	78.14 204	87.17 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 220	73.90 218	79.27 219	82.65 256	58.27 243	80.80 251	82.73 252	61.57 261	75.33 176	83.13 245	55.52 188	91.07 215	64.98 185	78.34 203	88.45 215

sss		73.60 230	73.64 219	73.51 280	82.80 251	55.01 287	76.12 284	81.69 266	62.47 255	74.68 187	85.85 208	57.32 175	78.11 305	60.86 216	80.93 175	87.39 235

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 271	57.48 257	86.64 158	87.95 192	64.99 231	70.18 230	86.61 187	50.43 245	89.52 235	62.12 204	70.18 281	88.83 196

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 196	66.03 133	83.38 234	85.06 225	70.21 170	69.40 243	81.05 272	45.76 271	94.66 76	65.10 183	75.49 241	89.25 181
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		75.73 214	73.34 222	82.89 140	77.37 302	65.22 153	84.10 226	90.54 108	69.09 187	60.46 298	81.15 271	40.48 296	92.84 157	76.36 86	80.54 184	90.60 129

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 179	58.05 245	84.65 211	87.53 199	70.32 167	71.22 220	85.63 213	54.97 191	89.86 229	43.03 308	75.02 248	86.32 257

PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 248	59.55 235	81.65 247	82.30 256	67.44 209	71.62 216	78.15 292	52.34 213	88.92 249	65.05 184	75.90 235	88.12 220

IterMVS		74.29 224	72.94 225	78.35 239	81.53 268	63.49 194	81.58 248	82.49 253	68.06 203	69.99 236	83.69 241	51.66 232	85.54 274	65.85 178	71.64 274	86.01 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		73.83 228	72.67 226	77.30 253	83.87 224	66.02 134	81.82 243	84.66 228	61.37 264	68.61 255	82.82 248	47.29 260	88.21 255	59.27 227	84.32 136	77.68 312

CVMVSNet		72.99 240	72.58 227	74.25 276	84.28 202	50.85 307	86.41 165	83.45 242	44.56 323	73.23 196	87.54 154	49.38 250	85.70 273	65.90 177	78.44 201	86.19 259

test-LLR		72.94 241	72.43 228	74.48 273	81.35 272	58.04 246	78.38 273	77.46 294	66.66 212	69.95 237	79.00 288	48.06 258	79.24 299	66.13 173	84.83 129	86.15 260

OurMVSNet-221017-0		74.26 225	72.42 229	79.80 209	83.76 229	59.59 232	85.92 178	86.64 208	66.39 217	66.96 269	87.58 151	39.46 299	91.60 199	65.76 179	69.27 283	88.22 218

tpmrst		72.39 243	72.13 230	73.18 282	80.54 281	49.91 311	79.91 261	79.08 288	63.11 245	71.69 215	79.95 281	55.32 189	82.77 288	65.66 180	73.89 258	86.87 248

pmmvs4		74.03 227	71.91 231	80.39 197	81.96 263	68.32 99	81.45 249	82.14 258	59.32 277	69.87 239	85.13 223	52.40 212	88.13 257	60.21 220	74.74 251	84.73 277

DWT-MVSNet_test		73.70 229	71.86 232	79.21 221	82.91 249	58.94 237	82.34 239	82.17 257	65.21 227	71.05 222	78.31 290	44.21 277	90.17 227	63.29 194	77.28 210	88.53 214

Patchmatch-test1		73.49 231	71.85 233	78.41 238	84.05 221	62.17 217	79.96 260	79.29 287	66.30 218	72.38 206	79.58 285	51.95 222	85.08 278	55.46 254	77.67 207	87.99 222

EG-PatchMatch MVS		74.04 226	71.82 234	80.71 195	84.92 195	67.42 114	85.86 179	88.08 190	66.04 221	64.22 287	83.85 237	35.10 314	92.56 163	57.44 244	80.83 177	82.16 298

tpm		72.37 245	71.71 235	74.35 275	82.19 261	52.00 298	79.22 267	77.29 296	64.56 234	72.95 199	83.68 242	51.35 233	83.26 287	58.33 237	75.80 236	87.81 227

tpm2		73.26 236	71.46 236	78.63 232	83.34 236	56.71 268	80.65 254	80.40 278	56.63 296	73.55 192	82.02 259	51.80 230	91.24 206	56.35 251	78.42 202	87.95 223

RPSCF		73.23 237	71.46 236	78.54 235	82.50 258	59.85 231	82.18 241	82.84 251	58.96 279	71.15 221	89.41 112	45.48 274	84.77 280	58.82 232	71.83 273	91.02 113

PatchmatchNet		73.12 238	71.33 238	78.49 237	83.18 241	60.85 225	79.63 262	78.57 289	64.13 238	71.73 214	79.81 284	51.20 235	85.97 272	57.40 245	76.36 231	88.66 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 233	71.27 239	79.67 212	81.32 274	65.19 154	75.92 286	80.30 279	59.92 273	72.73 201	81.19 269	52.50 210	86.69 265	59.84 222	77.71 205	87.11 245

SixPastTwentyTwo		73.37 233	71.26 240	79.70 210	85.08 194	57.89 250	85.57 188	83.56 239	71.03 157	65.66 278	85.88 206	42.10 290	92.57 162	59.11 229	63.34 306	88.65 202

tpmp4_e23		73.45 232	71.17 241	80.31 201	83.55 232	59.56 234	81.88 242	82.33 255	57.94 287	70.51 226	81.62 267	51.19 236	91.63 198	53.96 260	77.51 208	89.75 174

MSDG		73.36 235	70.99 242	80.49 196	84.51 200	65.80 139	80.71 253	86.13 217	65.70 224	65.46 279	83.74 240	44.60 275	90.91 217	51.13 270	76.89 219	84.74 276

PatchMatch-RL		72.38 244	70.90 243	76.80 257	88.60 127	67.38 116	79.53 263	76.17 300	62.75 252	69.36 245	82.00 260	45.51 273	84.89 279	53.62 262	80.58 181	78.12 310

PVSNet	64.34 18	72.08 246	70.87 244	75.69 263	86.21 178	56.44 272	74.37 296	80.73 274	62.06 259	70.17 231	82.23 254	42.86 285	83.31 286	54.77 257	84.45 135	87.32 238

test_0402		72.79 242	70.44 245	79.84 208	88.13 140	65.99 135	85.93 177	84.29 231	65.57 226	67.40 266	85.49 217	46.92 263	92.61 161	35.88 319	74.38 254	80.94 302

COLMAP_ROB	66.92 17	73.01 239	70.41 246	80.81 193	87.13 167	65.63 141	88.30 100	84.19 233	62.96 248	63.80 290	87.69 149	38.04 305	92.56 163	46.66 296	74.91 249	84.24 280
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 249	70.39 247	74.48 273	81.35 272	58.04 246	78.38 273	77.46 294	60.32 269	69.95 237	79.00 288	36.08 312	79.24 299	66.13 173	84.83 129	86.15 260

pmmvs5		71.55 248	70.20 248	75.61 264	77.83 299	56.39 273	81.74 245	80.89 271	57.76 288	67.46 264	84.49 232	49.26 253	85.32 277	57.08 248	75.29 245	85.11 273

MDTV_nov1_ep13			69.97 249		83.18 241	53.48 295	77.10 282	80.18 282	60.45 267	69.33 246	80.44 277	48.89 256	86.90 264	51.60 268	78.51 200

MIMVSNet		70.69 254	69.30 250	74.88 270	84.52 199	56.35 275	75.87 288	79.42 286	64.59 233	67.76 260	82.41 251	41.10 294	81.54 292	46.64 298	81.34 172	86.75 252

tpmvs		71.09 251	69.29 251	76.49 258	82.04 262	56.04 278	78.92 270	81.37 270	64.05 239	67.18 268	78.28 291	49.74 249	89.77 230	49.67 278	72.37 268	83.67 284

Patchmtry		70.74 253	69.16 252	75.49 266	80.72 278	54.07 292	74.94 295	80.30 279	58.34 283	70.01 234	81.19 269	52.50 210	86.54 267	53.37 263	71.09 277	85.87 266

TESTMET0.1,1		69.89 262	69.00 253	72.55 283	79.27 296	56.85 264	78.38 273	74.71 310	57.64 289	68.09 259	77.19 299	37.75 306	76.70 310	63.92 190	84.09 137	84.10 283

RPMNet		71.62 247	68.94 254	79.67 212	81.32 274	65.19 154	75.92 286	78.30 291	57.60 290	72.73 201	76.45 302	52.30 214	86.69 265	48.14 287	77.71 205	87.11 245

PMMVS		69.34 264	68.67 255	71.35 290	75.67 309	62.03 218	75.17 290	73.46 315	50.00 319	68.68 252	79.05 286	52.07 220	78.13 304	61.16 214	82.77 159	73.90 320

K. test v3		71.19 250	68.51 256	79.21 221	83.04 246	57.78 253	84.35 221	76.91 298	72.90 128	62.99 293	82.86 247	39.27 300	91.09 214	61.65 209	52.66 324	88.75 199

USDC		70.33 258	68.37 257	76.21 260	80.60 280	56.23 276	79.19 268	86.49 210	60.89 265	61.29 295	85.47 218	31.78 318	89.47 237	53.37 263	76.21 232	82.94 295

tpm cat1		70.57 255	68.31 258	77.35 252	82.41 259	57.95 249	78.08 277	80.22 281	52.04 314	68.54 256	77.66 297	52.00 221	87.84 260	51.77 267	72.07 272	86.25 258

OpenMVS_ROB	64.09 19	70.56 256	68.19 259	77.65 247	80.26 283	59.41 236	85.01 203	82.96 250	58.76 281	65.43 280	82.33 252	37.63 308	91.23 207	45.34 304	76.03 233	82.32 296

EPMVS		69.02 265	68.16 260	71.59 286	79.61 290	49.80 313	77.40 280	66.93 331	62.82 251	70.01 234	79.05 286	45.79 270	77.86 307	56.58 250	75.26 246	87.13 244

CMPMVS	51.72 21	70.19 260	68.16 260	76.28 259	73.15 318	57.55 256	79.47 264	83.92 234	48.02 321	56.48 313	84.81 229	43.13 282	86.42 269	62.67 199	81.81 170	84.89 274
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 252	68.09 262	79.58 214	85.15 190	63.62 190	84.58 213	79.83 283	62.31 256	60.32 299	86.73 174	32.02 316	88.96 247	50.28 273	71.57 275	86.15 260

gg-mvs-nofinetune		69.95 261	67.96 263	75.94 261	83.07 244	54.51 290	77.23 281	70.29 322	63.11 245	70.32 228	62.33 324	43.62 280	88.69 251	53.88 261	87.76 100	84.62 278

FMVSNet5		69.50 263	67.96 263	74.15 277	82.97 247	55.35 286	80.01 259	82.12 259	62.56 254	63.02 291	81.53 268	36.92 309	81.92 290	48.42 281	74.06 256	85.17 272

PatchT		68.46 269	67.85 265	70.29 294	80.70 279	43.93 321	72.47 299	74.88 306	60.15 271	70.55 224	76.57 301	49.94 248	81.59 291	50.58 271	74.83 250	85.34 269

pmmvs-eth3d		70.50 257	67.83 266	78.52 236	77.37 302	66.18 132	81.82 243	81.51 268	58.90 280	63.90 289	80.42 278	42.69 286	86.28 270	58.56 234	65.30 303	83.11 290

Anonymous20231206		68.60 266	67.80 267	71.02 292	80.23 285	50.75 308	78.30 276	80.47 276	56.79 295	66.11 277	82.63 250	46.35 265	78.95 301	43.62 307	75.70 237	83.36 287

Patchmatch-RL test		70.24 259	67.78 268	77.61 248	77.43 301	59.57 233	71.16 301	70.33 321	62.94 249	68.65 253	72.77 312	50.62 243	85.49 275	69.58 150	66.58 295	87.77 228

test0.0.03 1		68.00 270	67.69 269	68.90 299	77.55 300	47.43 315	75.70 289	72.95 317	66.66 212	66.56 272	82.29 253	48.06 258	75.87 314	44.97 305	74.51 253	83.41 286

EU-MVSNet		68.53 268	67.61 270	71.31 291	78.51 298	47.01 317	84.47 214	84.27 232	42.27 324	66.44 275	84.79 230	40.44 297	83.76 282	58.76 233	68.54 289	83.17 288

test20.03		67.45 272	66.95 271	68.94 298	75.48 312	44.84 319	77.50 279	77.67 293	66.66 212	63.01 292	83.80 238	47.02 262	78.40 303	42.53 310	68.86 287	83.58 285

MIMVSNet1		68.58 267	66.78 272	73.98 278	80.07 286	51.82 299	80.77 252	84.37 230	64.40 236	59.75 302	82.16 255	36.47 310	83.63 284	42.73 309	70.33 280	86.48 256

testgi		66.67 277	66.53 273	67.08 304	75.62 310	41.69 326	75.93 285	76.50 299	66.11 219	65.20 283	86.59 188	35.72 313	74.71 318	43.71 306	73.38 263	84.84 275

UnsupCasMVSNet_eth		67.33 273	65.99 274	71.37 288	73.48 315	51.47 303	75.16 291	85.19 224	65.20 228	60.78 297	80.93 276	42.35 287	77.20 309	57.12 247	53.69 323	85.44 268

dp		66.80 275	65.43 275	70.90 293	79.74 289	48.82 314	75.12 293	74.77 308	59.61 275	64.08 288	77.23 298	42.89 284	80.72 294	48.86 280	66.58 295	83.16 289

TinyColmap		67.30 274	64.81 276	74.76 272	81.92 264	56.68 269	80.29 257	81.49 269	60.33 268	56.27 314	83.22 244	24.77 325	87.66 262	45.52 302	69.47 282	79.95 306

CHOSEN 280x420		66.51 278	64.71 277	71.90 285	81.45 269	63.52 193	57.98 330	68.95 329	53.57 309	62.59 294	76.70 300	46.22 266	75.29 317	55.25 255	79.68 190	76.88 318

TDRefinement		67.49 271	64.34 278	76.92 255	73.47 316	61.07 222	84.86 206	82.98 249	59.77 274	58.30 305	85.13 223	26.06 323	87.89 259	47.92 289	60.59 314	81.81 300

PM-MVS		66.41 279	64.14 279	73.20 281	73.92 313	56.45 271	78.97 269	64.96 335	63.88 243	64.72 284	80.24 279	19.84 331	83.44 285	66.24 172	64.52 305	79.71 307

MDA-MVSNet-bldmvs		66.68 276	63.66 280	75.75 262	79.28 295	60.56 228	73.92 297	78.35 290	64.43 235	50.13 324	79.87 283	44.02 279	83.67 283	46.10 300	56.86 318	83.03 292

ADS-MVSNet2		66.20 281	63.33 281	74.82 271	79.92 287	58.75 238	67.55 318	75.19 304	53.37 310	65.25 281	75.86 303	42.32 288	80.53 295	41.57 311	68.91 285	85.18 270

Patchmatch-test		64.82 284	63.24 282	69.57 296	79.42 292	49.82 312	63.49 325	69.05 328	51.98 315	59.95 301	80.13 280	50.91 238	70.98 328	40.66 313	73.57 261	87.90 225

MDA-MVSNet_test_wron		65.03 282	62.92 283	71.37 288	75.93 307	56.73 266	69.09 313	74.73 309	57.28 293	54.03 317	77.89 294	45.88 268	74.39 320	49.89 277	61.55 310	82.99 293

YYNet1		65.03 282	62.91 284	71.38 287	75.85 308	56.60 270	69.12 312	74.66 312	57.28 293	54.12 316	77.87 295	45.85 269	74.48 319	49.95 276	61.52 311	83.05 291

ADS-MVSNet		64.36 286	62.88 285	68.78 301	79.92 287	47.17 316	67.55 318	71.18 320	53.37 310	65.25 281	75.86 303	42.32 288	73.99 322	41.57 311	68.91 285	85.18 270

JIA-IIPM		66.32 280	62.82 286	76.82 256	77.09 305	61.72 221	65.34 322	75.38 302	58.04 286	64.51 285	62.32 325	42.05 291	86.51 268	51.45 269	69.22 284	82.21 297

LF4IMVS		64.02 287	62.19 287	69.50 297	70.90 323	53.29 296	76.13 283	77.18 297	52.65 313	58.59 303	80.98 274	23.55 326	76.52 311	53.06 265	66.66 294	78.68 309

Anonymous20231211		64.82 284	61.79 288	73.91 279	77.11 304	50.92 306	85.29 197	81.53 267	54.19 304	57.98 306	78.03 293	26.90 321	87.83 261	37.92 316	57.12 317	82.99 293

new-patchmatchnet		61.73 289	61.73 289	61.70 312	72.74 319	24.50 342	69.16 311	78.03 292	61.40 262	56.72 312	75.53 305	38.42 303	76.48 312	45.95 301	57.67 316	84.13 282

UnsupCasMVSNet_bld		63.70 288	61.53 290	70.21 295	73.69 314	51.39 304	72.82 298	81.89 264	55.63 300	57.81 307	71.80 314	38.67 302	78.61 302	49.26 279	52.21 325	80.63 303

PVSNet_0	57.27 20	61.67 290	59.27 291	68.85 300	79.61 290	57.44 258	68.01 316	73.44 316	55.93 299	58.54 304	70.41 317	44.58 276	77.55 308	47.01 291	35.91 330	71.55 322

test2356		59.50 292	58.08 292	63.74 308	71.23 322	41.88 324	67.59 317	72.42 319	53.72 308	57.65 308	70.74 316	26.31 322	72.40 325	32.03 326	71.06 278	76.93 316

testus		59.00 294	57.91 293	62.25 311	72.25 320	39.09 329	69.74 306	75.02 305	53.04 312	57.21 310	73.72 310	18.76 333	70.33 329	32.86 322	68.57 288	77.35 313

LP		61.36 291	57.78 294	72.09 284	75.54 311	58.53 240	67.16 320	75.22 303	51.90 316	54.13 315	69.97 318	37.73 307	80.45 296	32.74 323	55.63 320	77.29 314

MVS-HIRNet		59.14 293	57.67 295	63.57 309	81.65 266	43.50 322	71.73 300	65.06 334	39.59 328	51.43 322	57.73 328	38.34 304	82.58 289	39.53 314	73.95 257	64.62 327

testpf		56.51 299	57.58 296	53.30 319	71.99 321	41.19 327	46.89 335	69.32 327	58.06 285	52.87 321	69.45 320	27.99 320	72.73 324	59.59 225	62.07 308	45.98 332

DSMNet-mixed		57.77 297	56.90 297	60.38 313	67.70 328	35.61 332	69.18 310	53.97 338	32.30 334	57.49 309	79.88 282	40.39 298	68.57 332	38.78 315	72.37 268	76.97 315

test1235678		58.74 295	56.89 298	64.30 306	69.70 324	41.87 325	71.05 302	74.87 307	54.06 305	50.63 323	71.53 315	25.30 324	74.10 321	31.80 327	63.10 307	76.93 316

1111		57.11 298	56.82 299	57.97 316	69.10 325	28.28 337	68.90 314	74.54 313	54.01 306	53.71 318	74.51 307	23.09 327	67.90 333	32.28 324	61.26 312	77.73 311

pmmvs3		57.79 296	54.26 300	68.37 302	64.02 330	56.72 267	75.12 293	65.17 333	40.20 326	52.93 320	69.86 319	20.36 330	75.48 316	45.45 303	55.25 322	72.90 321

N_pmnet		52.79 303	53.26 301	51.40 322	78.99 297	7.68 346	69.52 308	3.89 347	51.63 317	57.01 311	74.98 306	40.83 295	65.96 335	37.78 317	64.67 304	80.56 305

FPMVS		53.68 302	51.64 302	59.81 314	65.08 329	51.03 305	69.48 309	69.58 325	41.46 325	40.67 327	72.32 313	16.46 336	70.00 330	24.24 334	65.42 302	58.40 329

testmv		53.85 301	51.03 303	62.31 310	61.46 332	38.88 330	70.95 305	74.69 311	51.11 318	41.26 326	66.85 321	14.28 337	72.13 326	29.19 329	49.51 327	75.93 319

new_pmnet		50.91 305	50.29 304	52.78 320	68.58 327	34.94 335	63.71 324	56.63 337	39.73 327	44.95 325	65.47 323	21.93 329	58.48 337	34.98 320	56.62 319	64.92 326

.test1245		45.55 308	50.02 305	32.14 328	69.10 325	28.28 337	68.90 314	74.54 313	54.01 306	53.71 318	74.51 307	23.09 327	67.90 333	32.28 324	0.02 342	0.25 341

LCM-MVSNet		54.25 300	49.68 306	67.97 303	53.73 337	45.28 318	66.85 321	80.78 273	35.96 330	39.45 329	62.23 326	8.70 343	78.06 306	48.24 286	51.20 326	80.57 304

test12356		49.28 307	48.51 307	51.59 321	62.06 331	19.11 343	60.40 327	72.45 318	47.60 322	40.64 328	65.68 322	13.84 338	68.72 331	27.29 331	46.67 329	66.94 325

ANet_high		50.57 306	46.10 308	63.99 307	48.67 340	39.13 328	70.99 304	80.85 272	61.39 263	31.18 332	57.70 329	17.02 335	73.65 323	31.22 328	15.89 339	79.18 308

no-one		51.08 304	45.79 309	66.95 305	57.92 335	50.49 310	59.63 329	76.04 301	48.04 320	31.85 330	56.10 331	19.12 332	80.08 298	36.89 318	26.52 332	70.29 323

Gipuma		45.18 309	41.86 310	55.16 318	77.03 306	51.52 302	32.50 338	80.52 275	32.46 332	27.12 333	35.02 335	9.52 342	75.50 315	22.31 335	60.21 315	38.45 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 310	40.28 311	55.82 317	40.82 343	42.54 323	65.12 323	63.99 336	34.43 331	24.48 334	57.12 330	3.92 345	76.17 313	17.10 337	55.52 321	48.75 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 311	38.86 312	46.69 324	53.84 336	16.45 344	48.61 334	49.92 340	37.49 329	31.67 331	60.97 327	8.14 344	56.42 338	28.42 330	30.72 331	67.19 324

PNet_i23d		38.26 313	35.42 313	46.79 323	58.74 333	35.48 333	59.65 328	51.25 339	32.45 333	23.44 337	47.53 333	2.04 347	58.96 336	25.60 333	18.09 337	45.92 333

pcd1.5k->3k		34.07 314	35.26 314	30.50 329	86.92 169	0.00 349	0.00 340	91.58 81	0.00 344	0.00 345	0.00 346	56.23 184	0.00 347	0.00 344	82.60 162	91.49 103

wuykxyi23d		39.76 312	33.18 315	59.51 315	46.98 341	44.01 320	57.70 331	67.74 330	24.13 336	13.98 341	34.33 336	1.27 348	71.33 327	34.23 321	18.23 335	63.18 328

E-PMN		31.77 315	30.64 316	35.15 326	52.87 338	27.67 339	57.09 332	47.86 341	24.64 335	16.40 339	33.05 337	11.23 340	54.90 339	14.46 339	18.15 336	22.87 336

EMVS		30.81 316	29.65 317	34.27 327	50.96 339	25.95 341	56.58 333	46.80 342	24.01 337	15.53 340	30.68 338	12.47 339	54.43 340	12.81 340	17.05 338	22.43 337

cdsmvs_eth3d_5k		19.96 318	26.61 318	0.00 335	0.00 348	0.00 349	0.00 340	89.26 153	0.00 344	0.00 345	88.61 125	61.62 136	0.00 347	0.00 344	0.00 345	0.00 343

MVE	26.22 23	30.37 317	25.89 319	43.81 325	44.55 342	35.46 334	28.87 339	39.07 343	18.20 338	18.58 338	40.18 334	2.68 346	47.37 341	17.07 338	23.78 334	48.60 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 319	21.40 320	10.23 332	4.82 345	10.11 345	34.70 337	30.74 345	1.48 341	23.91 336	26.07 339	28.42 319	13.41 344	27.12 332	15.35 340	7.17 338

wuyk23d		16.82 320	15.94 321	19.46 331	58.74 333	31.45 336	39.22 336	3.74 348	6.84 340	6.04 342	2.70 343	1.27 348	24.29 343	10.54 341	14.40 341	2.63 339

ab-mvs-re		7.23 321	9.64 322	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	86.72 176	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

test123		6.12 322	8.11 323	0.14 333	0.06 347	0.09 347	71.05 302	0.03 350	0.04 343	0.25 344	1.30 345	0.05 350	0.03 346	0.21 343	0.01 344	0.29 340

testmvs		6.04 323	8.02 324	0.10 334	0.08 346	0.03 348	69.74 306	0.04 349	0.05 342	0.31 343	1.68 344	0.02 351	0.04 345	0.24 342	0.02 342	0.25 341

pcd_1.5k_mvsjas		5.26 324	7.02 325	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	63.15 101	0.00 347	0.00 344	0.00 345	0.00 343

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ESAPD								94.22 1

sam_mvs1												51.32 234

sam_mvs												50.01 247

semantic-postprocess				80.11 204	82.69 255	64.85 162		83.47 241	69.16 186	70.49 227	84.15 235	50.83 242	88.15 256	69.23 152	72.14 271	87.34 237

ambc				75.24 268	73.16 317	50.51 309	63.05 326	87.47 201		64.28 286	77.81 296	17.80 334	89.73 232	57.88 241	60.64 313	85.49 267

MTGPA								92.02 59

test_post1							78.90 271				5.43 342	48.81 257	85.44 276	59.25 228

test_post											5.46 341	50.36 246	84.24 281

patchmatchnet-post											74.00 309	51.12 237	88.60 252

GG-mvs-BLEND				75.38 267	81.59 267	55.80 284	79.32 265	69.63 324		67.19 267	73.67 311	43.24 281	88.90 250	50.41 272	84.50 133	81.45 301

MTMP								32.83 344

gm-plane-assit					81.40 270	53.83 294			62.72 253		80.94 275		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	68.44 200	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	68.69 196	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	85.15 190	63.62 190		79.83 283	62.31 256	60.32 299	86.73 174	32.02 316	88.96 247	50.28 273	71.57 275	86.15 260

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		58.10 284	87.04 13			88.98 245	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	57.43 291	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 239

旧先验1					91.96 54	65.79 140		86.37 213			93.08 43	69.31 52			92.74 50	88.74 200

无先验							87.48 125	88.98 165	60.00 272				94.12 93	67.28 165		88.97 194

原ACMM2							86.86 150

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 44	63.96 242	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 197

test222					91.50 59	68.26 101	84.16 224	83.20 247	54.63 303	79.74 85	91.63 64	58.97 165			91.42 60	86.77 251

testdata2													91.01 216	62.37 201

segment_acmp												73.08 23

testdata				79.97 206	90.90 66	64.21 181		84.71 227	59.27 278	85.40 22	92.91 44	62.02 133	89.08 243	68.95 154	91.37 61	86.63 255

testdata1							84.14 225		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								0.00 351

nn								0.00 351

door-mid								69.98 323

lessismore_v0				78.97 227	81.01 277	57.15 260		65.99 332		61.16 296	82.82 248	39.12 301	91.34 204	59.67 223	46.92 328	88.43 216

LGP-MVS_train				84.50 79	89.23 107	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								69.44 326

HQP5-MVS						66.98 122

HQP-NCC					89.33 99		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 99		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						37.79 331	75.16 291		55.10 301	66.53 273		49.34 251		53.98 259		87.94 224

ACMMP++_ref															81.95 168

ACMMP++															81.25 173

Test By Simon												64.33 87

ITE_SJBPF				78.22 240	81.77 265	60.57 227		83.30 243	69.25 184	67.54 263	87.20 164	36.33 311	87.28 263	54.34 258	74.62 252	86.80 250

DeepMVS_CX				27.40 330	40.17 344	26.90 340		24.59 346	17.44 339	23.95 335	48.61 332	9.77 341	26.48 342	18.06 336	24.47 333	28.83 335

High-resolution multi-view benchmark