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EDRIC: Embracing 1D Autoencoder for Real-Time Lossy LiDAR Reflectance Compression
Summary
EDRIC achieves state-of-the-art LiDAR reflectance compression with real-time processing. This novel neural framework significantly improves efficiency and speed for 3D point cloud data.
Area of Science:
- Computer Vision
- Signal Processing
- Machine Learning
Background:
- LiDAR reflectance compression advancements exist but struggle with real-time processing.
- Conventional 3D frameworks face suboptimal downsampling and inefficient feature extraction.
Purpose of the Study:
- Introduce EDRIC, a neural compression framework for efficient and real-time LiDAR reflectance processing.
- Overcome limitations of existing 3D point cloud compression methods.
Main Methods:
- Serialize 3D point clouds into 1D sequences for efficient compression.
- Utilize a lightweight 1D autoencoder for LiDAR reflectance signal compression.
- Incorporate geometric priors via a geometry-aware entropy model.
Main Results:
- Achieve 8.38%–9.85% BD-BR reduction compared to G-PCCv23 (RAHT).
- Operate 22× faster than existing standards, exceeding 50 FPS on an RTX 4090.
- EDRIC has only 2.3M parameters, enabling practical real-world deployment.
Conclusions:
- EDRIC offers state-of-the-art compression efficiency and real-time capability for LiDAR reflectance.
- The framework effectively exploits geometric priors and interdependencies.
- EDRIC presents a practical and efficient solution for real-world LiDAR data processing.
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