EDRIC: Embracing 1D Autoencoder for Real-Time Lossy LiDAR Reflectance Compression

While recent advancements in LiDAR reflectance compression have improved rate-distortion performance, real-time processing remains an unresolved challenge. In this work, we introduce EDRIC, a highly effective neural compression framework that offers state-of-the-art compression efficiency while achieving real-time capability. To overcome the suboptimal downsampling scheme and inefficient feature extraction in conventional 3D frameworks, EDRIC serializes a 3D point cloud into a 1D sequence and introduces a lightweight 1D autoencoder to efficiently compress the serialized LiDAR reflectance signal. In addition, we explicitly incorporate geometric priors through a geometry-aware entropy model, effectively exploiting the interdependencies between reflectance attributes and underlying geometry. Extensive experiments on representative datasets (e.g., KITTI, Ford, nuScenes, and QNX) demonstrate that EDRIC achieves 8.38%~9.85% BD-BR reduction compared to the latest G-PCCv23 (RAHT) standard while operating $22\times $ faster (e.g., >50 frames per second on an RTX 4090 GPU). Furthermore, EDRIC comprises merely 2.3M parameters, making it a practical solution for real-world deployment.