JetsonCompletion: Real-Time Depth Completion on Resource-Constrained Edge Devices

Abstract

Depth completion from sparse LiDAR points and images is a key perception task for autonomous robots, enabling dense 3D understanding in challenging environments. However, most recent researches achieve accuracy gains by greatly enlarging network size, making them unsuitable for realtime deployment on power- and compute-constrained platforms. This paper proposes an ultra-lightweight depth completion framework optimized for embedded systems. Our approach integrates a re-parameterized encoder–decoder with fewer than 5M parameters and a two-stage hybrid distillation strategy. The first stage progressively densifies sparse depth supervision, while the second preserves edge fidelity through a combination of metric and structural losses. A full TensorRT FP16 pipeline further ensures efficient deployment. Extensive experiments on KITTI Depth Completion, NYU-v2 . demonstrate that our method achieves competitive accuracy while maintaining high efficiency. On a Jetson Xavier NX, the system runs at over 30 FPS with sub-33 ms latency within a 20 W power envelope, showing strong potential for real-world micro-robotic platforms. We will open-source the code to benefit the community. Our open source website: https://github.com/2463450186Q/JetsonCompletion.git