Depth Completion Evaluation



The depth completion and depth prediction evaluation are related to our work published in Sparsity Invariant CNNs (THREEDV 2017). It
contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images, aligned with the "raw data" of the KITTI dataset.
Given the large amount of training data, this dataset shall allow a training of complex deep learning models for the tasks of depth completion
and single image depth prediction. Also, we provide manually selected images with unpublished depth maps to serve as a benchmark for those
two challenging tasks.

Make sure to unzip annotated depth maps and raw LiDaR scans into the same directory so that all corresponding files end up in the same folder
structure. The structure of all provided depth maps is aligned with the structure of our raw data to easily find corresponding left and right images,
or other provided information.


Note: On 12.04.2018 we have fixed a small error in the file data_depth_velodyne.zip, please download this file again if you have an old version.


All methods providing less than 100 % density have been interpolated using simple background interpolation as explained in the corresponding header file in the development kit.

    Our evaluation table ranks all methods according to the root mean squared error (RMSE) of the inverse depth maps.
    However, we also provide some other metrics:
  • iRMSE:  Root mean squared error of the inverse depth [1/km]
  • iMAE:    Mean absolute error of the inverse depth [1/km]
  • RMSE:   Root mean squared error [mm]
  • MAE:     Mean absolute error [mm]


Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Additional training data: Use of additional data sources for training (see details)
  • RGB image: Use of RGB images for depth completion

Method Setting Code iRMSE iMAE RMSE MAE Runtime Environment
1 DMD^3C code 1.82 0.85 678.12 194.46 0.01 s 1 core @ 2.5 Ghz (C/C++)
Y. Liang, Y. Hu, W. Shao and Y. Fu: Distilling Monocular Foundation Model for Fine-grained Depth Completion. CVPR 2025.
2 UDeerDC3 1.81 0.84 679.28 193.29 0.01 s 1 core @ 2.5 Ghz (C/C++)
3 GAC-Net 1.81 0.84 680.82 193.85 0.17 s 1 core @ 2.5 Ghz (C/C++)
4 ae 1.83 0.82 681.09 190.61 0.11 s GPU @ >3.5 Ghz (Python)
5 SAE-SPN 1.84 0.82 681.63 190.21 0.12 s GPU @ >3.5 Ghz (Python + C/C++)
6 GBPN 1.78 0.82 682.20 192.14 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
7 CAD 1.82 0.84 682.34 194.96 0.01 s 1 core @ 2.5 Ghz (Python)
8 DB-SPN 1.84 0.84 683.83 195.55 0.01 s 1 core @ 2.5 Ghz (C/C++)
9 PS-Net 1.86 0.85 684.27 196.96 0.01 s 1 core @ 2.5 Ghz (Python)
10 DPG 1.84 0.83 684.39 194.25 0.01 s 1 core @ 2.5 Ghz (C/C++)
11 CAD 1.86 0.85 684.54 195.65 0.03 s 1 core @ 2.5 Ghz (C/C++)
12 LP-Net 1.81 0.80 684.71 186.63 0.06 s GPU @ 2.5 Ghz (Python)
K. Wang, Z. Yan, J. Fan, J. Li and J. Yang: Learning Inverse Laplacian Pyramid for Progressive Depth Completion. 2025.
13 BP-Net code 1.82 0.84 684.90 194.69 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Tang, F. Tian, B. An, J. Li and P. Tan: Bilateral Propagation Network for Depth Completion. CVPR 2024.
14 HSPN 1.85 0.82 684.90 191.25 0.13 s 1 core @ 2.5 Ghz (Python)
15 ImprovingDC code 1.83 0.81 686.46 187.95 0.1 s 8 cores @ 2.5 Ghz (Python)
Y. Wang, G. Zhang, S. Wang, B. Li, Q. Liu, L. Hui and Y. Dai: Improving Depth Completion via Depth Feature Upsampling. CVPR 2024.
16 SPN 1.86 0.83 687.65 191.85 0.3 s GPU @ 2.5 Ghz (Python)
17 UDeerDCDC 1.87 0.86 688.41 196.38 0.01 s 1 core @ 2.5 Ghz (C/C++)
18 GNNNet 1.89 0.87 693.23 198.24 0.09 s 1 core @ 2.5 Ghz (C/C++)
19 TPVD code 1.82 0.81 693.97 188.60 0.01 s GPU @ 2.5 Ghz (Python)
Z. Yan, Y. Lin, K. Wang, Y. Zheng, Y. Wang, Z. Zhang, J. Li and J. Yang: Tri-Perspective View Decomposition for Geometry-Aware Depth Completion. CVPR (oral) 2024.
20 RigNet++ 1.82 0.81 694.24 188.62 0.06 s GPU @ 2.5 Ghz (Python)
Z. Yan, X. Li, Z. Zhang, J. Li and J. Yang: RigNet++: Efficient Repetitive Image Guided Network for Depth Completion. arXiv preprint arXiv:2309.00655 2023.
21 HFFNet 1.95 0.88 694.90 201.54 0.03 s 1 core @ 2.5 Ghz (C/C++)
22 LRRU-Base-L2 code 2.18 0.86 695.67 198.31 0.12 s 8 cores @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
23 LRRU-Base-L2+L1 code 1.87 0.81 696.51 189.96 0.12 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
24 BEV@DC 1.83 0.82 697.44 189.44 0.1 s 1 core @ 2.5 Ghz (Python)
W. Zhou, X. Yan, Y. Liao, Y. Lin, J. Huang, G. Zhao, S. Cui and Z. Li: BEVDC: Bird's-Eye View Assisted Training for Depth Completion. CVPR 2023.
25 MAENet 1.90 0.87 698.21 198.47 0.12 s 1 core @ 2.5 Ghz (C/C++)
26 NDDepth 1.89 0.83 698.71 192.75 0.1 s GPU @ 2.5 Ghz (Python)
S. Shao, Z. Pei, W. Chen, P. Chen and Z. Li: NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion. arXiv:2311.07166 2023.
27 CWATNet 1.97 0.87 703.84 199.64 0.12 s GPU @ 2.5 Ghz (Python)
28 DP code 1.85 0.85 704.06 197.18 0.06 s 1 core @ 2.5 Ghz (Python)
29 WTAttNet 1.92 0.87 704.22 199.88 0.12 s GPU @ 2.5 Ghz (Python)
30 WATNet 1.92 0.87 704.76 199.26 0.1 s GPU @ 2.5 Ghz (Python)
31 TANet 1.95 0.88 707.17 200.09 0.14 s 1 core @ 2.5 Ghz (C/C++)
32 HNASNet 2.14 0.94 707.84 207.31 0.0198 s GPU @ A100
33 Decomposition B 2.05 0.91 707.93 205.11 0.1 s GPU @ 2.5 Ghz (Python)
Y. Wang, Y. Mao, Q. Liu and Y. Dai: Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion. TCSVT 2023.
34 Decomposition A 2.04 0.91 708.30 205.01 0.1 s GPU @ 2.5 Ghz (Python)
Y. Wang, Y. Mao, Q. Liu and Y. Dai: Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion. TCSVT 2023.
35 OGNI-DC L1+L2 code 1.86 0.83 708.38 193.20 0.2 s GPU @ 2.5 Ghz (Python)
Y. Zuo and J. Deng: OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations. ECCV 2024.
36 WTNet 1.93 0.87 708.69 200.52 0.14 s 1 core @ 2.5 Ghz (C/C++)
37 CompletionFormer code 2.01 0.88 708.87 203.45 0.12 s GPU @ 2.5 Ghz (Python)
Y. Zhang, X. Guo, M. Poggi, Z. Zhu, G. Huang and S. Mattoccia: CompletionFormer: Depth Completion with Convolutions and Vision Transformers. CVPR 2023.
38 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.
39 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.
40 RCDformer 2.12 0.98 709.59 220.49 1 s 1 core @ 2.5 Ghz (Python)
41 PFDC 1.88 0.84 710.54 195.78 0.09 s GPU @ 2.5 Ghz (Python)
42 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. ECCV 2022.
43 LRRU-Small 2.01 0.88 713.64 203.60 0.05 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
44 MEDO-n 2.03 0.89 714.02 207.00 0.04 s GPU @ 2.5 Ghz (Python)
45 NSnet-test 2.44 1.20 714.28 225.08 0.02 s 1 core @ 2.5 Ghz (C/C++)
46 BPATT-1 1.98 0.88 716.05 202.66 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 LRRU-Small-L2+L1 1.96 0.85 717.50 197.72 0.06 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
48 NSNet_T 2.29 1.05 718.84 219.05 0.02 s 1 core @ 2.5 Ghz (C/C++)
49 BPRay-1 1.99 0.88 719.51 203.25 0.04 s GPU @ 2.5 Ghz (Python)
50 Improving Single-bra 2.06 0.91 719.65 201.92 0.1 s 8 cores @ 2.5 Ghz (Python + C/C++)
Y. Wang, G. Zhang, S. Wang, B. Li, Q. Liu, L. Hui and Y. Dai: Improving Depth Completion via Depth Feature Upsampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
51 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.
52 Dual-branch 2.07 0.92 720.96 203.73 0.1 s 8 cores @ 2.5 Ghz (Python + C/C++)
Y. Wang, G. Zhang, S. Wang, B. Li, Q. Liu, L. Hui and Y. Dai: Improving Depth Completion via Depth Feature Upsampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
53 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.
54 ReDC code 2.05 0.89 728.31 204.60 0.02 s RTX 2080Ti GPU with 2.5GHz processor
X. Sun, J. Ponce and Y. Wang: Revisiting deformable convolution for depth completion. IEEE/RSJ International Conference on Intelligent Robots and Systems 2023.
55 GMDepth (L1) 1.83 0.79 728.91 179.09 0.1 s GPU @ 2.5 Ghz (Python)
56 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.
57 LRRU-Tiny-L2 2.09 0.90 732.43 209.14 0.04 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
58 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.
59 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.
60 CluDe code 2.08 0.88 734.59 200.48 0.14 s GPU @ 2.5 Ghz (Python)
S. Chen, H. Zhang, X. Ma, Z. Wang and H. Li: Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion. IEEE Transactions on Circuits and Systems for Video Technology 2024.
61 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.
62 UniDC Base 2.02 0.86 736.00 202.44 0.10 s GPU @ 2.5 Ghz (Python)
63 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.
64 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.
65 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.
66 LRRU-Tiny-L2+L1 2.04 0.85 738.86 200.28 0.04 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
67 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.
68 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.
69 CluDe* code 2.02 0.86 742.26 197.91 0.14 s GPU @ 2.5 Ghz (Python)
S. Chen, H. Zhang, X. Ma, Z. Wang and H. Li: Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion. IEEE Transactions on Circuits and Systems for Video Technology 2024.
70 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.
71 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.
72 Single-branch 2.22 0.95 745.16 209.86 0.1 s 8 cores @ 2.5 Ghz (Python + C/C++)
Y. Wang, G. Zhang, S. Wang, B. Li, Q. Liu, L. Hui and Y. Dai: Improving Depth Completion via Depth Feature Upsampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
73 OGNI-DC L1 code 1.81 0.79 747.64 182.29 0.2 s GPU @ 2.5 Ghz (Python)
Y. Zuo and J. Deng: OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations. ECCV 2024.
74 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.
75 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 .
76 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.
77 PFDC_small 1.96 0.84 753.05 197.88 0.01 s 1 core @ 2.5 Ghz (C/C++)
78 LDCNet code 2.33 0.98 753.15 218.02 0.05 s GPU @ 2.5 Ghz (Python)
Z. Yan, Y. Zheng, C. Li, J. Li and J. Yang: Learnable Differencing Center for Nighttime Depth Perception. 2023.
79 DepthPrompting 2.02 0.87 754.48 206.15 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Park, C. Jeong, J. Lee and H. Jeon: Depth Prompting for Sensor-Agnostic Depth Estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
80 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.
81 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.
82 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.
83 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.
84 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.
85 CompletionFormer code 1.89 0.80 764.87 183.88 0.12 s GPU @ 2.5 Ghz (Python)
Y. Zhang, X. Guo, M. Poggi, Z. Zhu, G. Huang and S. Mattoccia: CompletionFormer: Depth Completion with Convolutions and Vision Transformers. CVPR 2023.
86 LRRU-Mini-L2 code 2.26 0.94 765.95 218.31 0.03 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
87 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.
88 ADNet_Small 2.07 0.88 767.17 209.44 0.05 s 1 core @ 2.5 Ghz (Python)
J. Kim, J. Noh, M. Jeong, W. Lee, Y. Park and J. Park: ADNet: Non-Local Affinity Distillation Network for Lightweight Depth Completion With Guidance From Missing LiDAR Points. IEEE Robotics and Automation Letters 2024.
89 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.
90 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.
91 LRRU-Mini-L2+L1 2.21 0.90 774.43 210.87 0.03 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Li, G. Zhang, Q. Liu, G. Tao and Y. Dai: LRRU: Long-short Range Recurrent Updating Networks for Depth Completion. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2023.
92 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.
93 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.
94 LA-CHN 1.96 0.85 788.42 194.18 0.01 s 1 core @ 2.5 Ghz (C/C++)
95 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.
96 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 .
97 DenseFormer 2.37 1.16 796.16 250.26 0.3 s GPU @ 2.5 Ghz (Python)
98 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.
99 UniDC code 2.15 0.88 804.33 211.11 0.56 s 1 core @ 2.5 Ghz (C/C++)
100 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.
101 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.
102 TFDCNet 3.27 1.22 826.08 243.69 0.17 s 1 core @ 2.5 Ghz (Python)
103 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.
104 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.
105 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.
106 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.
107 EMNet 2.13 0.84 841.70 196.17 0.01 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 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.
110 DUGC 2.44 0.95 882.63 228.32 0.23 s GPU @ 2.5 Ghz (Python)
111 AGC 2.45 0.95 884.92 228.79 0.23 s GPU @ 2.5 Ghz (Python)
112 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.
113 AHSPN 2.66 1.06 912.17 251.18 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
114 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.
115 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.
116 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. AAAI (oral) 2023.
117 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.
118 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.
119 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.
120 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.
121 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.
122 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.
123 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.
124 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.
125 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.
126 UDCM 2.89 1.04 1041.55 257.53 0.10 s 1 core @ 2.5 Ghz (Python)
127 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.
128 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.
129 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.
130 DCPlugin 3.45 1.18 1069.88 272.55 0.01 s 1 core @ 2.5 Ghz (C/C++)
L. Xiao, J. Wu, J. Hu, Z. Li and W. Yang: Domain adaptive depth completion via spatial-error consistency. Pattern Recognition 2025.
131 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.
132 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.
133 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.
134 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.
135 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.
136 OMNI-DC (Zero-shot) 3.23 1.09 1154.71 269.15 0.2 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 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.
139 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.
140 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.
141 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.
142 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.
143 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.
144 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.
145 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.
146 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.
147 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.
148 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.
149 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.
150 VD 5.82 2.31 1625.06 546.56 0.2 s 1 core @ 2.5 Ghz (Python)
151 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.
152 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.
Table as LaTeX | Only published Methods




Related Datasets

  • SYNTHIA Dataset: SYNTHIA is a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations as well as pixel-wise depth information. The dataset consists of +200,000 HD images from video streams and +20,000 HD images from independent snapshots.
  • Middlebury Stereo Evaluation: The classic stereo evaluation benchmark, featuring four test images in version 2 of the benchmark, with very accurate ground truth from a structured light system. 38 image pairs are provided in total.
  • Make3D Range Image Data: Images with small-resolution ground truth used to learn and evaluate depth from single monocular images.
  • Virtual KITTI Dataset: Virtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions.
  • Scene Flow Dataset: The Freiburg Scene Flow Dataset collection has been used to train convolutional networks for disparity, optical flow, and scene flow estimation. The collection contains more than 39000 stereo frames in 960x540 pixel resolution, rendered from various synthetic sequences.

Citation

When using this dataset in your research, we will be happy if you cite us:
@inproceedings{Uhrig2017THREEDV,
  author = {Jonas Uhrig and Nick Schneider and Lukas Schneider and Uwe Franke and Thomas Brox and Andreas Geiger},
  title = {Sparsity Invariant CNNs},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2017}
}



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