Method
Setting
Code
iRMSE
iMAE
RMSE
MAE
Runtime
Environment
1
LRRU
2.18
0.86
695.67
198.31
0.1 s
8 cores @ 2.5 Ghz (Python)
2
LRRU-Base
1.87
0.81
696.51
189.96
0.1 s
GPU @ 2.5 Ghz (Python)
3
BEV@DC
1.83
0.82
697.44
189.44
0.1 s
1 core @ 2.5 Ghz (Python)
4
DySPN*
1.84
0.81
700.16
189.70
0.16 s
1 core @ 2.5 Ghz (C/C++)
5
MRLTNet
2.14
0.97
707.53
213.04
0.6s
1 core @ 2.5 Ghz (C/C++)
6
Decomposition B
2.05
0.91
707.93
205.11
0.1 s
GPU @ 2.5 Ghz (Python)
7
Decomposition A
2.04
0.91
708.30
205.01
0.1 s
GPU @ 2.5 Ghz (Python)
8
CompletionFormer
2.01
0.88
708.87
203.45
0.12 s
GPU @ 2.5 Ghz (Python)
9
DySPN
code
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 . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
10
SemAttNet
code
2.03
0.90
709.41
205.49
0.2 s
1 core @ 2.5 Ghz (C/C++)
D. Nazir, A. Pagani, M. Liwicki, D. Stricker and M. Afzal: SemAttNet: Towards Attention-based Semantic
Aware Guided Depth Completion . IEEE Access 2022.
11
GeNetv2
2.08
0.92
709.56
205.91
0.13 s
GPU @ 2.5 Ghz (Python)
12
LpNet
2.18
0.96
710.74
212.51
0.05 s
GPU @ 2.5 Ghz (Python)
13
GeNet
2.10
0.94
711.27
208.85
0.1 s
1 core @ 2.5 Ghz (Python)
14
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, J. Li and J. Yang: RigNet: Repetitive Image Guided
Network
for Depth Completion . ECCV2022.
15
SAFNet
1.91
0.84
712.75
196.15
0.01 s
1 core @ 2.5 Ghz (C/C++)
16
LRRU-Small
2.01
0.88
713.64
203.60
0.05 s
GPU @ 2.5 Ghz (Python)
17
GuET
2.19
0.96
714.19
210.27
0.2 s
1 core @ 2.5 Ghz (C/C++)
18
GTER
2.16
0.94
715.25
209.61
2 s
1 core @ 2.5 Ghz (C/C++)
19
AMFv1
1.92
0.84
715.62
195.60
0.03 s
1 core @ 2.5 Ghz (C/C++)
20
LTR
2.09
0.92
716.72
208.79
0.02 s
1 core @ 2.5 Ghz (C/C++)
21
MRTCSPN
2.11
0.93
718.31
208.24
2 s
1 core @ 2.5 Ghz (C/C++)
22
MRTNet
2.11
0.93
718.31
208.24
0.2 s
GPU @ 2.5 Ghz (Python)
23
GSPN
2.41
1.03
718.50
212.57
0.06 s
1 core @ 2.5 Ghz (C/C++)
24
MFF-Net
2.21
0.94
719.85
208.11
0.05 s
GPU @ 2.5 Ghz (Python)
L. Liu, X. Song, J. Sun, X. Lyu, L. Li, Y. Liu and L. Zhang: MFF-Net: Towards Efficient Monocular Depth
Completion with Multi-modal Feature Fusion . IEEE Robotics and Automation Letters 2023.
25
GuTr2
2.15
0.94
720.81
212.64
2 s
1 core @ 2.5 Ghz (C/C++)
26
GCSPN
2.09
0.91
722.49
206.96
0.26 s
GPU @ 2.5 Ghz (Python + C/C++)
27
SN
1.96
0.85
723.36
195.87
0.03 s
1 core @ 2.5 Ghz (Python)
28
NNNet
1.99
0.88
724.14
205.57
0.03 s
1 core @ 2.5 Ghz (Python)
J. Liu and C. Jung: NNNet: New Normal Guided Depth Completion
from Sparse LiDAR Data and Single Color Image . IEEE Access 2022.
29
Decompose
2.20
0.96
724.51
213.87
0.04 s
1 core @ 2.5 Ghz (C/C++)
30
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.
31
GuTfNet
2.33
1.03
732.29
218.47
0.2 s
1 core @ 2.5 Ghz (C/C++)
32
LRRU-Tiny
2.09
0.90
732.43
209.14
0.04 s
GPU @ 2.5 Ghz (Python)
33
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.
34
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.
35
AGK
2.38
1.02
733.70
216.34
0.05 s
1 core @ 2.5 Ghz (C/C++)
36
TbNet
code
2.14
0.93
734.39
208.62
0.07 s
GPU @ 2.5 Ghz (Python)
37
FCFR-Net
2.20
0.98
735.81
217.15
0.1 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.
38
CluDe
2.07
0.87
735.92
201.09
0.2 s
GPU @ 2.5 Ghz (Python)
39
GraphCSPN
1.92
0.82
736.24
193.25
0.12 s
GPU @ 2.5 Ghz (Python)
40
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.
41
testclude55
2.10
0.89
736.39
202.84
0.12 s
GPU @ 2.5 Ghz (Python)
42
clude2
2.07
0.87
736.69
201.64
0.12 s
GPU @ 2.5 Ghz (Python + C/C++)
43
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.
44
CDCNet
2.18
0.99
738.26
216.05
0.06 s
GPU @ 2.5 Ghz (C/C++)
R. Fan, Z. Li, M. Poggi and S. Mattoccia: A Cascade Dense Connection Fusion Network
for Depth Completion . BMVC 2022.
45
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.
46
clude
2.08
0.88
741.31
203.58
0.1 s
GPU @ 2.5 Ghz (Python)
47
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.
48
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.
49
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.
50
testrefine
2.10
0.90
745.78
206.12
0.06 s
GPU @ 2.5 Ghz (C/C++)
51
CFN
code
2.15
0.95
745.91
215.64
0.2 s
GPU @ 2.5 Ghz (Python)
52
IDNet
code
2.15
0.95
748.23
215.92
0.01 s
GPU @ 2.5 Ghz (Python)
53
CDCNet-lite
2.22
0.95
748.99
215.38
0.04 s
GPU @ 2.5 Ghz (C/C++)
R. Fan, Z. Li, M. Poggi and S. Mattoccia: A Cascade Dense Connection Fusion Network
for Depth Completion . BMVC 2022.
54
af39
2.10
0.89
749.44
204.95
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
55
testaf
2.13
0.90
749.68
206.99
0.13 s
GPU @ 2.5 Ghz (Python + C/C++)
56
alignnet
2.13
0.90
749.68
206.99
0.13 s
GPU @ 2.5 Ghz (Python + C/C++)
57
abftest
2.15
0.92
749.89
208.25
0.13 s
GPU @ 2.5 Ghz (Python)
58
af2
2.09
0.88
750.40
204.88
0.06 s
GPU @ 2.5 Ghz (Python)
59
finetuneaf
2.08
0.88
750.96
203.86
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
60
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 .
61
ASPN
2.01
0.89
752.69
207.09
0.156 s
GPU @ 2.5 Ghz (Python)
62
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.
63
custkitti
2.23
0.97
753.72
218.42
0.09 s
GPU @ 2.5 Ghz (Python)
64
AlignFuse
2.14
0.93
755.06
212.83
0.08 s
8 cores @ 2.5 Ghz (Python)
65
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.
66
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.
67
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.
68
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.
69
self-setting
2.34
1.02
762.80
227.09
0.13 s
4 cores @ 2.5 Ghz (Python)
70
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.
71
CompletionFormer
1.89
0.80
764.87
183.88
0.12 s
GPU @ 2.5 Ghz (Python)
72
LRRU-Mini
2.26
0.94
765.95
218.31
0.03 s
GPU @ 2.5 Ghz (Python)
73
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.
74
CIN_UnRefine
code
2.08
0.98
770.24
233.75
0.01 s
1 core @ 2.5 Ghz (C/C++)
75
two-rendering
2.17
0.95
770.98
216.19
0.4 s
8 cores @ 2.5 Ghz (C/C++)
76
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.
77
GC&BA
2.19
0.93
772.87
215.02
0.05 s
GPU @ 1.5 Ghz (Python)
78
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.
79
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.
80
21ARcat
2.31
0.99
776.72
222.84
0.12 s
8 cores @ 2.5 Ghz (Python)
81
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.
82
05AR32
2.34
0.99
777.18
225.29
0.12 s
8 cores @ 2.5 Ghz (Python)
83
pear
2.12
0.97
777.37
220.94
0.4 s
8 cores @ 2.5 Ghz (Python)
84
CDSPN
2.63
1.07
779.36
227.71
0.01 s
1 core @ 2.5 Ghz (Python)
85
PS-DP Net
2.38
0.99
779.91
223.49
0.02s
GPU @ 2.5 Ghz (Python)
86
ddcnet
code
2.15
0.91
788.18
213.44
0.28 s
GPU @ 2.5 Ghz (Python)
87
TRTE1
5.67
2.41
788.83
291.39
0.5 s
1 core @ 2.5 Ghz (C/C++)
88
TRSTE1
5.67
2.41
788.83
291.40
2 s
1 core @ 2.5 Ghz (C/C++)
89
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.
90
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 .
91
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.
92
FDPNet_v2
2.73
1.18
806.41
245.11
0.04 s
GPU @ 2.5 Ghz (Python)
93
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.
94
FDPNet
2.61
1.04
810.61
230.48
0.04 s
GPU @ 2.5 Ghz (Python)
95
ASPN
2.09
0.89
814.17
217.78
0.15 s
GPU @ 2.5 Ghz (Python)
96
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.
97
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.
98
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.
99
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.
100
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.
101
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.
102
nlspn_bs8_retrain
code
2.26
0.96
848.21
237.11
0.27 s
GPU @ 2.5 Ghz (Python)
103
twins_scratch
code
2.27
0.96
850.09
235.69
0.29 s
GPU @ 2.5 Ghz (Python)
104
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.
105
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.
106
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.
107
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.
108
DesNet
2.95
1.13
938.45
266.24
0.01 s
GPU @ 2.5 Ghz (Python)
Z. Yan, K. Wang, X. Li, Z. Zhang, J. Li and J. Yang: DesNet: Decomposed Scale-Consistent
Network
for Unsupervised Depth Completion . AAAI2023 (Oral).
109
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.
110
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.
111
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.
112
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.
113
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.
114
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.
115
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.
116
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.
117
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.
118
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.
119
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.
120
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.
121
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.
122
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.
123
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.
124
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.
125
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.
126
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.
127
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.
128
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
(IV) 2021.
129
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
Intelligent Transportation (ITSC) 2021.
130
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.
131
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
Completion for LiDAR-Camera System . IEEE GRSL 2021.
132
NConv-CNN (d)
code
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
for Sparse Data Regression . 2018.
133
IP-Basic
code
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.
134
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
and Automation (ICRA) 2019.
135
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.
136
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.
137
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.
138
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.
139
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.
140
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.