Method
Setting
Code
iRMSE
iMAE
RMSE
MAE
Runtime
Environment
1
MSTDC
1.96
0.88
708.82
203.39
0.19 s
GPU @ 2.5 Ghz (Python)
2
DySPN
1.88
0.82
709.12
192.71
0.16 s
GPU @ 2.0 Ghz (Python)
Y. Lin, T. Cheng, Q. Zhong, W. Zhou and H. Yang: Dynamic Spatial Propagation Network for Depth Completion . Accepted by AAAI 2022.
3
SemAttNet
2.03
0.90
709.41
205.49
0.2 s
1 core @ 2.5 Ghz (C/C++)
D. Nazir, M. Liwicki, D. Stricker and M. Afzal: SemAttNet: Towards Attention-based
Semantic Aware Guided Depth Completion . 2022.
4
MCF
2.03
0.90
710.11
205.12
0.09 s
GPU @ 2.5 Ghz (Python)
5
RigNet
2.08
0.90
712.66
203.25
0.20 s
GPU @ 2.5 Ghz (Python)
Z. Yan, K. Wang, X. Li, Z. Zhang, B. Xu, J. Li and J. Yang: RigNet: Repetitive Image Guided Network
for Depth Completion . 2021.
6
SEHLNet
2.07
0.91
714.71
207.51
0.07 s
GPU @ 2.5 Ghz (Python)
7
MFDFR-Net
2.21
0.94
719.85
208.11
0.1 s
GPU @ 2.5 Ghz (Python)
8
FC
2.35
0.91
722.01
205.44
0.1 s
GPU @ 2.5 Ghz (Python)
9
DC
2.09
0.92
723.35
210.06
0.01 s
1 core @ 2.5 Ghz (C/C++)
10
SGSNet
2.11
0.92
723.67
209.54
0.02 s
GPU @ 2.5 Ghz (Python)
11
NNNet
1.99
0.88
724.14
205.57
0.03 s
1 core @ 2.5 Ghz (Python)
12
DFN
2.24
0.97
724.32
214.34
0.15 s
GPU @ 1.5 Ghz (Python)
13
Decompose
2.20
0.96
724.51
213.87
0.04 s
1 core @ 2.5 Ghz (C/C++)
14
Test
2.08
0.90
726.71
207.25
0.01 s
1 core @ 2.5 Ghz (C/C++)
15
AMFv1
2.00
0.84
727.00
196.08
0.03 s
1 core @ 2.5 Ghz (C/C++)
16
3DCGNet
2.26
1.00
727.40
219.10
0.14 s
GPU @ 1.5 Ghz (Python)
17
GSPN
2.42
1.05
727.62
216.10
0.06 s
1 core @ 2.5 Ghz (C/C++)
18
DC_BB_SMT
2.15
0.95
728.47
213.33
1 s
1 core @ 2.5 Ghz (C/C++)
19
DepthNet
2.05
0.91
728.93
208.86
0.03 s
GPU @ 2.5 Ghz (Python)
20
ACBC
2.15
0.93
729.11
210.01
0.02 s
GPU @ 2.0 Ghz (Python)
21
DenseLConv-64
2.10
0.93
729.88
210.06
0.12 s
GPU @ 2.5 Ghz (Python)
22
PENet
code
2.17
0.94
730.08
210.55
0.032s
GPU @ 2.5 Ghz (Python)
M. Hu, S. Wang, B. Li, S. Ning, L. Fan and X. Gong: PENet: Towards Precise and Efficient
Image
Guided Depth Completion . ICRA 2021.
23
SN
2.04
0.85
730.72
200.65
0.03 s
1 core @ 2.5 Ghz (Python)
24
dd
2.20
1.03
731.53
219.23
dd s
1 core @ 2.5 Ghz (C/C++)
25
DC_BB
2.07
0.92
731.97
211.84
0.1 s
1 core @ 2.5 Ghz (C/C++)
26
CFN
2.12
0.93
732.08
211.21
0.05 s
1 core @ 2.5 Ghz (Python)
27
AMF
1.99
0.84
732.48
198.14
0.03 s
1 core @ 2.5 Ghz (C/C++)
28
ACMNet
code
2.08
0.90
732.99
206.80
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
S. Zhao, M. Gong, H. Fu and D. Tao: Adaptive context-aware multi-modal
network
for depth completion . IEEE Transactions on Image
Processing 2021.
29
SPL
2.09
0.93
733.44
212.49
0.03 s
1 core @ 2.5 Ghz (Python)
X.Liang and C.Jung: Selective Progressive Learning for Sparse Depth Completion . Proceedings of the International Conference on Pattern Recognition (ICPR2022). 2022.
30
FMPN
2.11
0.92
733.69
211.15
0.015 s
1 core @ 2.5 Ghz (Python)
31
AGK
2.38
1.02
733.70
216.34
0.05 s
1 core @ 2.5 Ghz (C/C++)
32
FCFR-Net
2.20
0.98
735.81
217.15
0.13 s
GPU @ 2.5 Ghz (Python)
L. Liu, X. Song, X. Lyu, J. Diao, M. Wang, Y. Liu and L. Zhang: FCFR-Net: Feature Fusion based Coarse-
to-Fine Residual Learning for Depth Completion . Proceedings of the AAAI Conference
on Artificial Intelligence 2021.
33
repp
2.21
0.95
735.94
211.89
0.02 s
GPU @ 2.5 Ghz (Python)
34
DC
2.15
0.95
736.09
214.77
0.01 s
1 core @ 2.5 Ghz (C/C++)
35
GuideNet
code
2.25
0.99
736.24
218.83
0.14 s
GPU @ 1.5 Ghz (Python + C/C++)
J. Tang, F. Tian, W. Feng, J. Li and P. Tan: Learning Guided Convolutional Network for
Depth Completion . IEEE Transactions on Image
Processing(TIP) 2020.
36
MDANet
code
2.12
0.99
738.23
214.99
0.03 s
GPU @ 2.5 Ghz (Python)
Y. Ke, K. Li, W. Yang, Z. Xu, D. Hao, L. Huang and G. Wang: MDANet:
Multi-Modal Deep Aggregation Network for Depth
Completion . 2021 IEEE International Conference on
Robotics and Automation (ICRA) 2021.
37
ENet
code
2.14
0.95
741.30
216.26
0.019 s
GPU @ 2.5 Ghz (Python)
M. Hu, S. Wang, B. Li, S. Ning, L. Fan and X. Gong: PENet: Towards Precise and
Efficient
Image Guided Depth Completion . ICRA 2021.
38
NLSPN
code
1.99
0.84
741.68
199.59
0.22 s
GPU @ 1.5 Ghz (Python)
J. Park, K. Joo, Z. Hu, C. Liu and I. Kweon: Non-Local Spatial Propagation Network for
Depth Completion . European Conference on Computer
Vision (ECCV) 2020.
39
try1
2.15
0.94
743.46
213.41
0.1 s
1 core @ 2.5 Ghz (C/C++)
40
CSPN++
2.07
0.90
743.69
209.28
0.2 s
1 core @ 2.5 Ghz (C/C++)
X. Cheng, P. Wang, G. Chenye and R. Yang: CSPN++: Learning Context and Resource
Aware
Convolutional Spatial Propagation Networks for
Depth
Completion . Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20) 2020.
41
ACMNet
code
2.08
0.90
744.91
206.09
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Zhao, M. Gong, H. Fu and D. Tao: Adaptive context-aware multi-modal network
for depth completion . IEEE Transactions on Image Processing 2021.
42
idtnet
2.16
0.95
745.79
217.53
0.02 s
GPU @ 2.5 Ghz (Python)
43
CFN
code
2.15
0.95
745.91
215.64
0.2 s
GPU @ 2.5 Ghz (Python)
44
IDNet
code
2.15
0.95
748.23
215.92
0.01 s
GPU @ 2.5 Ghz (Python)
45
cf
1.99
0.86
750.11
201.49
0.14 s
GPU @ 2.5 Ghz (Python)
46
SPKD-MSG-CHN_64
2.16
0.92
750.90
212.66
0.04 s
1 core @ 2.5 Ghz (Python)
47
Ms_Unc_UARes-B
code
1.98
0.85
751.59
198.09
0.1 s
GPU @ 2.5 Ghz (Python)
Y. Zhu, W. Dong, L. Li, J. Wu, X. Li and G. Shi: Robust Depth Completion with Uncertainty-Driven Loss Functions . accepted by AAAI2022 .
48
UberATG-FuseNet
2.34
1.14
752.88
221.19
0.09 s
GPU @ 2.5 Ghz (Python)
Y. Chen, B. Yang, M. Liang and R. Urtasun: Learning Joint 2D-3D Representations
for Depth Completion . ICCV 2019.
49
DenseLConv-32
2.22
0.94
753.78
216.15
0.06 s
GPU @ 2.5 Ghz (Python)
50
DenseLiDAR
2.25
0.96
755.41
214.13
0.02 s
1 core @ 2.5 Ghz (Python)
J. Gu, Z. Xiang, Y. Ye and L. Wang: DenseLiDAR: A Real-Time Pseudo Dense
Depth Guided Depth Completion Network . IEEE Robotics and Automation Letters 2021.
51
DeepLiDAR
code
2.56
1.15
758.38
226.50
0.07s
GPU @ 1.5 Ghz (Python)
J. Qiu, Z. Cui, Y. Zhang, X. Zhang, S. Liu, B. Zeng and M. Pollefeys: DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image . The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
52
DANConv
code
2.17
0.92
759.65
213.68
0.05 s
GPU @ 2.5 Ghz (Python)
L. Yan, K. Liu and G. Long: DAN-Conv: Depth aware non-local convolution for LiDAR depth completion . Electronics Letters 2021.
53
MSG-CHN
code
2.30
0.98
762.19
220.41
0.01 s
GPU @ 2.5 Ghz (Python + C/C++)
A. Li, Z. Yuan, Y. Ling, W. Chi, C. Zhang and others: A Multi-Scale Guided Cascade Hourglass Network for Depth Completion . The IEEE Winter Conference on Applications of Computer Vision 2020.
54
ABCD
code
2.29
0.97
764.61
220.86
0.02 s
1 core @ 2.5 Ghz (C/C++)
Y. Jeon, H. Kim and S. Seo: ABCD: Attentive Bilateral Convolutional
Network for Robust Depth Completion . IEEE Robotics and Automation Letters 2021.
55
DSPN
2.47
1.03
766.74
220.36
0.34 s
1 core @ 2.5 Ghz (Python)
Z. Xu, H. Yin and J. Yao: Deformable Spatial Propagation Networks
For Depth Completion . 2020 IEEE International Conference
on Image Processing (ICIP) 2020.
56
CIN_UnRefine
code
2.08
0.98
770.24
233.75
0.01 s
1 core @ 2.5 Ghz (C/C++)
57
two-rendering
2.17
0.95
770.98
216.19
0.4 s
8 cores @ 2.5 Ghz (C/C++)
58
RGB_guide&certainty
code
2.19
0.93
772.87
215.02
0.02 s
GPU @ 1.5 Ghz (Python)
W. Van Gansbeke, D. Neven, B. De Brabandere and L. Van Gool: Sparse and noisy LiDAR completion with
RGB guidance and uncertainty . International Conference on Machine
Vision Applications (MVA) 2019.
59
GC&BA
2.19
0.93
772.87
215.02
0.05 s
GPU @ 1.5 Ghz (Python)
60
GAENet(Full)
code
2.29
1.08
773.90
231.29
0.05 s
GPU @ 2.5 Ghz (Python)
W. Du, H. Chen, H. Yang and Y. Zhang: Depth Completion using Geometry-Aware
Embedding . 2022 IEEE International Conference on
Robotics and Automation (ICRA) 2022.
61
DVMN
2.21
0.94
776.31
220.37
0.12 s
GPU @ 1.5 Ghz (Python)
L. Reichardt, P. Mangat and O. Wasenmüller: DVMN: Dense Validity Mask Network for Depth
Completion . IEEE International Conference on
Intelligent Transportation (ITSC) 2021.
62
PwP
2.42
1.13
777.05
235.17
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Yan Xu: Depth Completion from Sparse LiDAR Data
with Depth-Normal Constraints . Proceedings of the IEEE International
Conference on Computer Vision 2019.
63
pear
2.12
0.97
777.37
220.94
0.4 s
8 cores @ 2.5 Ghz (Python)
64
DenseTeacher
2.08
0.89
778.96
211.41
0.1 s
1 core @ 2.5 Ghz (Python)
65
MonDi-1
2.11
0.92
785.06
218.60
0.01 s
1 core @ 2.0 Ghz (Python)
66
TRTE1
5.67
2.41
788.83
291.39
0.5 s
1 core @ 2.5 Ghz (C/C++)
67
TRSTE1
5.67
2.41
788.83
291.40
2 s
1 core @ 2.5 Ghz (C/C++)
68
rendering
2.27
0.97
790.15
215.22
0.04 s
1 core @ 2.5 Ghz (Python)
69
Revisiting
code
2.42
0.99
792.80
225.81
0.05 s
GPU @ 2.0 Ghz (Python)
L. Yan, K. Liu and E. Belyaev: Revisiting Sparsity Invariant Convolution:
A Network for Image Guided Depth Completion . IEEE Access 2020.
70
Ms_Unc_UARes
code
1.98
0.83
795.61
190.88
0.08 s
GPU @ 2.5 Ghz (Python)
Y. Zhu, W. Dong, L. Li, J. Wu, X. Li and G. Shi: Robust Depth Completion with Uncertainty-Driven Loss Functions . accepted by AAAI2022 .
71
BA&GC
2.44
1.05
799.31
232.98
0.05 s
GPU @ 2.5 Ghz (Python)
K. Liu, Q. Li and Y. Zhou: An adaptive converged depth
completion network based on efficient RGB
guidance . Multimedia Tools and
Applications 2022.
72
CrossGuidance
2.73
1.33
807.42
253.98
0.2 s
1 core @ 2.5 Ghz (Python)
S. Lee, J. Lee, D. Kim and J. Kim: Deep Architecture with Cross Guidance
Between Single Image and Sparse LiDAR Data for Depth
Completion . IEEE Access 2020.
73
Sparse-to-Dense (gd)
code
2.80
1.21
814.73
249.95
0.08 s
GPU @ 1.5 Ghz (Python)
F. Ma, G. Cavalheiro and S. Karaman: Self-supervised Sparse-to-Dense: Self-
supervised Depth Completion from LiDAR and
Monocular Camera . 2019 IEEE International Conference on Robotics
and Automation (ICRA) 2019.
74
MonDi
2.21
0.96
815.64
231.07
0.01 s
1 core @ 1.5 Ghz (C/C++)
75
NConv-CNN-L2 (gd)
code
2.60
1.03
829.98
233.26
0.02 s
GPU @ 1.5 Ghz (Python)
A. Eldesokey, M. Felsberg and F. Khan: Confidence propagation through cnns for
guided sparse depth regression . IEEE transactions on pattern analysis
and machine intelligence 2019.
76
DDP
2.10
0.85
832.94
203.96
0.08 s
GPU @ 1.5 Ghz (Python)
Y. Yang, A. Wong and S. Soatto: Dense depth posterior (ddp) from single image and sparse
range . Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition 2019.
77
SSGP
2.51
1.09
838.22
244.70
0.14 s
RTX 2080 Ti
R. Schuster, O. Wasenmüller, C. Unger and D. Stricker: SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation . IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
78
TWISE
code
2.08
0.82
840.20
195.58
0.02 s
GPU @ 2.5 Ghz (Python)
S. Imran, X. Liu and D. Morris: Depth Completion With Twin
Surface Extrapolation at Occlusion Boundaries . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2021.
79
HiReNet
2.38
0.99
842.48
229.69
0.18 s
GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
80
ScaffFusion-SSL
code
3.24
0.88
847.22
205.75
0.03 s
1 core @ 1.5 Ghz (Python)
A. Wong, S. Cicek and S. Soatto: Learning topology from synthetic data for
unsupervised depth completion . IEEE Robotics and Automation Letters 2021.
81
NConv-CNN-L1 (gd)
code
2.52
0.92
859.22
207.77
0.02 s
GPU @ 1.5 Ghz (Python)
A. Eldesokey, M. Felsberg and F. Khan: Confidence propagation through cnns for
guided sparse depth regression . IEEE transactions on pattern analysis
and machine intelligence 2019.
82
IR_L2
4.92
1.35
901.43
292.36
0.05 s
GPU @ 2.5 Ghz (Python)
K. Lu, N. Barnes, S. Anwar and L. Zheng: From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020.
83
Spade-RGBsD
2.17
0.95
917.64
234.81
0.07 s
GPU @ 2.5 Ghz (Python)
M. Jaritz, R. Charette, E. Wirbel, X. Perrotton and F. Nashashibi: Sparse and Dense Data with CNNs: Depth
Completion and Semantic Segmentation . International Conference on 3D Vision
(3DV) 2018.
84
CU-Net
code
2.69
1.04
917.76
244.36
0.03 s
GPU @ 2.5 Ghz (Python)
85
glob_guide&certainty
code
2.80
1.07
922.93
249.11
0.02 s
GPU @ 1.5 Ghz (Python)
W. Van Gansbeke, D. Neven, B. De Brabandere and L. Van Gool: Sparse and noisy LiDAR completion with
RGB guidance and uncertainty . International Conference on Machine
Vision Applications (MVA) 2019.
86
DNet
code
2.71
1.05
923.13
246.59
15 s
1 core @ 2.5 Ghz (C/C++)
87
SNet
code
2.95
1.13
933.59
257.81
15 s
1 core @ 2.5 Ghz (C/C++)
88
DFineNet
code
3.21
1.39
943.89
304.17
0.02 s
GPU @ 2.5 Ghz (Python)
Y. Zhang, T. Nguyen, I. Miller, S. Shivakumar, S. Chen, C. Taylor and V. Kumar: DFineNet: Ego-Motion Estimation and
Depth Refinement from Sparse, Noisy Depth Input
with RGB Guidance . CoRR 2019.
89
Sparse-to-Dense (d)
code
3.21
1.35
954.36
288.64
0.04 s
GPU @ 1.5 Ghz (Python)
F. Ma, G. Cavalheiro and S. Karaman: Self-supervised Sparse-to-Dense: Self-
supervised Depth Completion from LiDAR and
Monocular Camera . 2019 IEEE International Conference on Robotics
and Automation (ICRA) 2019.
90
SIUNet_L2
2.88
1.10
958.91
256.19
0.01 s
GPU @ 2.5 Ghz (Python)
91
pNCNN (d)
code
3.37
1.05
960.05
251.77
0.02 s
1 core @ 2.5 Ghz (Python)
A. Eldesokey, M. Felsberg, K. Holmquist and M. Persson: Uncertainty-Aware CNNs for Depth
Completion: Uncertainty from Beginning to End . IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2020.
92
Conf-Net
code
3.10
1.09
962.28
257.54
0.02 s
GPU @ 2.5 Ghz (Python)
H. Hekmatian, S. Al-Stouhi and J. Jin: Conf-Net: Predicting Depth Completion
Error-Map For High-Confidence Dense 3D Point-
Cloud . 2019.
93
DCrgb_80b_3coef
2.43
0.98
965.87
215.75
0.15 s
1 core @ 2.5 Ghz (C/C++)
S. Imran, Y. Long, X. Liu and D. Morris: Depth coefficients for depth
completion . 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
94
UDC_delving
3.05
1.21
972.70
273.82
0.01 s
GPU @ 2.5 Ghz (Python)
95
DCd_all
2.87
1.13
988.38
252.21
0.1 s
1 core @ 2.5 Ghz (C/C++)
S. Imran, Y. Long, X. Liu and D. Morris: Depth coefficients for depth
completion . 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
96
LW-DepthNet
2.99
1.09
991.88
261.67
0.09 s
GPU @ 2.5 Ghz (Python)
L. Bai, Y. Zhao, M. Elhousni and X. Huang: DepthNet: Real-Time LiDAR Point Cloud
Depth Completion for Autonomous Vehicles . arXiv preprint arXiv:2007.02438 2020.
97
CSPN
2.93
1.15
1019.64
279.46
1 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Cheng, P. Wang and R. Yang: Depth estimation via affinity learned
with convolutional spatial propagation network . Proceedings of the European
Conference on Computer Vision (ECCV) 2018. X. Cheng, P. Wang and R. Yang: Learning Depth with Convolutional
Spatial
Propagation Network . arXiv preprint arXiv:1810.02695 2018.
98
SIUNet_L1
2.73
0.96
1026.61
227.28
0.01 s
GPU (Python)
99
Spade-sD
2.60
0.98
1035.29
248.32
0.04 s
GPU @ 2.5 Ghz (Python)
M. Jaritz, R. Charette, E. Wirbel, X. Perrotton and F. Nashashibi: Sparse and Dense Data with CNNs: Depth
Completion and Semantic Segmentation . International Conference on 3D Vision
(3DV) 2018.
100
Morph-Net
3.84
1.57
1045.45
310.49
0.17 s
GPU @ 1.5 Ghz (Matlab + C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Learning morphological operators for depth completion . Advanced Concepts for Intelligent Vision Systems 2018.
101
SynthProjV
3.12
1.13
1062.48
268.37
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Lopez-Rodriguez, B. Busam and K. Mikolajczyk: Project to Adapt: Domain Adaptation for
Depth Completion from Noisy and Sparse Sensor
Data . Asian Conference on Computer Vision
(ACCV) 2020.
102
KBNet
code
2.95
1.02
1069.47
256.76
0.01 s
1 core @ 2.5 Ghz (C/C++)
A. Wong and S. Soatto: Unsupervised Depth Completion with
Calibrated Backprojection Layers . Proceedings of the IEEE International
Conference on Computer Vision (ICCV) 2021.
103
VLW-DepthNet
3.43
1.21
1077.22
282.02
0.09
GPU @ 2.5 Ghz (Python)
L. Bai, Y. Zhao, M. Elhousni and X. Huang: DepthNet: Real-Time LiDAR Point Cloud
Depth Completion for Autonomous Vehicles . arXiv preprint arXiv:2007.02438 2020.
104
SynthProj
3.53
1.19
1095.26
280.42
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Lopez-Rodriguez, B. Busam and K. Mikolajczyk: Project to Adapt: Domain Adaptation for
Depth Completion from Noisy and Sparse Sensor
Data . Asian Conference on Computer Vision
(ACCV) 2020.
105
DCd_3
2.95
1.07
1109.04
234.01
0.1 s
1 core @ 2.5 Ghz (C/C++)
S. Imran, Y. Long, X. Liu and D. Morris: Depth coefficients for depth
completion . 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
106
ScaffFusion
code
3.32
1.17
1121.89
282.86
0.03 s
1 core @ 1.5 Ghz (Python)
A. Wong, S. Cicek and S. Soatto: Learning topology from synthetic data for
unsupervised depth completion . IEEE Robotics and Automation Letters 2021.
107
AdaFrame-VGG8
code
3.32
1.16
1125.67
291.62
0.02 s
GPU @ 1.5 Ghz (Python)
A. Wong, X. Fei, B. Hong and S. Soatto: An Adaptive Framework for Learning
Unsupervised Depth Completion . IEEE Robotics and Automation Letters 2021.
108
VOICED
code
3.56
1.20
1169.97
299.41
0.02 s
1 core @ 2.5 Ghz (C/C++)
A. Wong, X. Fei, S. Tsuei and S. Soatto: Unsupervised Depth Completion from Visual
Inertial Odometry . IEEE Robotics and Automation Letters 2020.
109
DFuseNet
code
3.62
1.79
1206.66
429.93
0.08 s
GPU @ 2.0 Ghz (C/C++)
S. Shivakumar, T. Nguyen, S. Chen and C. Taylor: DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion . arXiv preprint arXiv:1902.00761 2019.
110
NonLearning Complete
3.63
1.23
1222.00
303.82
0.84 s
1 core @ 3.5 Ghz (Python)
B. Krauss, G. Schroeder, M. Gustke and A. Hussein: Deterministic Guided LiDAR
Depth Map Completion . 2021 IEEE Intelligent Vehicles Symposium
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111
PDC
3.89
1.26
1227.96
288.55
10 s
1 core @ 2.5 Ghz (Python)
D. Teutscher, P. Mangat and O. Wasenmüller: PDC: Piecewise Depth Completion
utilizing Superpixels . IEEE International Conference on
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Physical_Surface_Mod
code
3.76
1.21
1239.84
298.30
0.06 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhao, L. Bai, Z. Zhang and X. Huang: A Surface Geometry Model for LiDAR Depth Completion . IEEE Robotics and Automation Letters 2021.
113
NG_Depth
code
14.93
1.38
1266.22
305.98
0.8 s
1 core @ 2.5 Ghz (C/C++)
P. An, Y. Gao, W. Fu, J. Ma, B. Fang and K. Yu: Lambertian Model Based Normal Guided Depth
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114
NConv-CNN (d)
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4.67
1.52
1268.22
360.28
0.01 s
GPU @ 1.5 Ghz (Python)
A. Eldesokey, M. Felsberg and F. Khan: Propagating Confidences through CNNs
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IP-Basic
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3.78
1.29
1288.46
302.60
0.011 s
1 core @ >3.5 Ghz (Python)
J. Ku, A. Harakeh and S. Waslander: In Defense of Classical Image
Processing: Fast Depth Completion on the CPU . 2018 15th Conference on Computer and
Robot Vision (CRV) 2018.
116
Sparse2Dense(w/o gt)
code
4.07
1.57
1299.85
350.32
0.08 s
GPU @ 1.5 Ghz (Python + C/C++)
F. Ma, G. Cavalheiro and S. Karaman: Self-supervised Sparse-to-Dense: Self-
supervised Depth Completion from LiDAR and
Monocular Camera . 2019 IEEE International Conference on Robotics
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ADNN
code
59.39
3.19
1325.37
439.48
.04 s
GPU @ 2.5 Ghz (Python)
S. Nathaniel Chodosh: Deep Convolutional Compressed Sensing for LiDAR Depth Completion . Asian Conference on Computer Vision (ACCV) 2018.
118
NN+CNN
3.25
1.29
1419.75
416.14
0.02 s
GPU
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox and A. Geiger: Sparsity Invariant CNNs . International Conference on 3D Vision (3DV) 2017.
119
B-ADT
4.16
1.23
1480.36
298.72
0.120 sec.
GPU
Y. Yao, M. Roxas, R. Ishikawa, S. Ando, j. shimamura and T. Oishi: Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor . IEEE Robotics and Automation Letters 2020.
120
DC
10.13
6.84
1533.74
972.26
1 s
1 core @ 2.5 Ghz (C/C++)
121
SparseConvs
code
4.94
1.78
1601.33
481.27
0.01 s
GPU
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox and A. Geiger: Sparsity Invariant CNNs . International Conference on 3D Vision (3DV) 2017.
122
SIUNet_L1_Zero-shot
5.49
1.55
1613.10
361.46
0.01 s
GPU @ 2.5 Ghz (Python)
123
NadarayaW
6.34
1.84
1852.60
416.77
0.05 s
1 core @ 2.5 Ghz (Python)
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox and A. Geiger: Sparsity Invariant CNNs . International Conference on 3D Vision (3DV) 2017.
124
SGDU
7.38
2.05
2312.57
605.47
0.2 s
4 cores @ 2.5 Ghz (C/C++)
N. Schneider, L. Schneider, P. Pinggera, U. Franke, M. Pollefeys and C. Stiller: Semantically Guided Depth Upsampling . German Conference on Pattern Recognition 2016.