Slide Deformable Transformer for High-Precision LiDAR Point Cloud Compression.

Abstract

Dynamic LiDAR point cloud compression with range images aims to reduce storage and transmission costs while preserving both spatial accuracy and temporal consistency across frames. Vision Transformers (ViTs) are commonly used for cross-frame dependency modeling. However, they suffer from feature misalignment under cross-frame displacement due to fixed patch partitioning, and their global attention across all patches is costly yet ineffective for local motions. High-precision sequences also face precision loss when 16-bit range data are quantized in a single channel. To address these limitations, we propose a Slide Deformable Transformer framework for high-precision dynamic LiDAR point cloud compression, termed SDT-PCC. At its core, the proposed SDT layer restricts attention to local sliding windows, capturing fine-grained correspondences across consecutive frames. It integrates deformable convolution into cross-frame attention to adaptively sample motion-offset locations, thereby enhancing temporal alignment and motion modeling. We also propose a Radix-Decomposition Multi-Channel Quantizer (RDMCQ), which decomposes range values into multiple channels and progressively refines precision across radix levels. Consequently, these designs can produce more temporally-coherent, accurate and stable reconstructions. Experiments on the SemanticKITTI dataset show that SDT-PCC achieves high efficiency in dynamic point cloud compression. The code is available on https://github.com/SYSU-SAIL/SDT-PCC.