# DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2007.02438 · HTML: https://mlsanity.com/paper/2007.02438/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion](https://mlsanity.com/paper/2204.13635/) (2022)
- [GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs](https://mlsanity.com/paper/2210.10758/) (2022)
- [DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions](https://mlsanity.com/paper/2403.12202/) (2024)
- [Prior based Sampling for Adaptive LiDAR](https://mlsanity.com/paper/2304.07099/) (2023)
- [Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching](https://mlsanity.com/paper/2210.01436/) (2022)
- [Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation](https://mlsanity.com/paper/2309.08289/) (2023)
- [Single-Shot Metric Depth from Focused Plenoptic Cameras](https://mlsanity.com/paper/2412.02386/) (2024)
- [Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency](https://mlsanity.com/paper/2312.15263/) (2023)
- [All-day Depth Completion via Thermal-LiDAR Fusion](https://mlsanity.com/paper/2504.02356/) (2025)
- [Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission](https://mlsanity.com/paper/2601.01210/) (2026)

## Abstract (arXiv)

Autonomous vehicles rely heavily on sensors such as camera and LiDAR, which provide real-time information about their surroundings for the tasks of perception, planning and control. Typically a LiDAR can only provide sparse point cloud owing to a limited number of scanning lines. By employing depth completion, a dense depth map can be generated by assigning each camera pixel a corresponding depth value. However, the existing depth completion convolutional neural networks are very complex that requires high-end GPUs for processing, and thus they are not applicable to real-time autonomous driving. In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion. With an astonishing 96.2% reduction in the number of parameters, it still achieves comparable performance (9.3% better in MAE but 3.9% worse in RMSE) to the state-of-the-art network. For real-time embedded platforms, depthwise separable technique is applied to both convolution and deconvolution operations and the number of parameters decreases further by a factor of 7.3, with only a small percentage increase in RMSE and MAE performance. Moreover, a system-on-chip architecture for depth completion is developed on a PYNQ-based FPGA platform that achieves real-time processing for HDL-64E LiDAR at the speed 11.1 frame per second.