D-Compress: Detail-Preserving LiDAR Range Image Compression for Real-Time Streaming on Resource-Constrained Robots
Shengqian Wang, Chang Tu, and He Chen∗
AI summary
Problem
Standard LiDAR compression methods either demand too much compute for edge robots or discard geometric details crucial for perception tasks due to human-vision-centric design. Furthermore, effective rate control for range image compression under fluctuating network bandwidth remains largely unaddressed.
Approach
The framework projects point clouds into range images and uses gradient-based intra-frame and odometry-driven inter-frame prediction, followed by an adaptive discrete wavelet transform that prioritizes machine-level precision. It also introduces a novel rate-distortion model and optimization algorithm to dynamically adjust compression levels for real-time streaming under varying bandwidth.
Key results
- Surpasses state-of-the-art codecs in geometric accuracy (67.6 dB PSNR) and 3D object detection (53.6% AP) at 1.55 bpp
- Achieves real-time processing (25.1 FPS) on low-cost mini PC hardware
- Maintains robust streaming quality under dynamic bandwidth via adaptive rate control
- Delivers over 100× compression ratios while preserving fine-grained LiDAR details
Why it matters
Provides a practical, high-fidelity compression pipeline that enables reliable real-time robotic perception and control on resource-constrained edge platforms.
Abstract
Efficient 3D LiDAR point cloud compression (LPCC) and streaming are critical for edge server-assisted robotic systems, enabling real-time communication with com- pact data representations. A widely adopted approach rep- resents LiDAR point clouds as range images, enabling the direct use of mature image and video compression codecs. However, because these codecs are designed with human vi- sual perception in mind, they often compromise geometric details, which downgrades the performance of downstream robotic tasks such as mapping and object detection. Further- more, rate-distortion optimization (RDO)-based rate control remains largely underexplored for range image compression (RIC) under dynamic bandwidth conditions. To address these limitations, we propose D-Compress, a new detail-preserving and fast RIC framework tailored for real-time streaming. D- Compress integrates both intra- and inter-frame prediction with an adaptive discrete wavelet transform approach for precise residual compression. Additionally, we introduce a new RDO-based rate control algorithm for RIC through new rate- distortion modeling. Extensive evaluations on various datasets demonstrate the superiority of D-Compress, which outperforms state-of-the-art (SOTA) compression methods in both geometric accuracy and downstream task performance, particularly at compression ratios exceeding 100×, while maintaining real- time execution on resource-constrained hardware. Moreover, evaluations under dynamic bandwidth conditions validate the robustness of its rate control mechanism.