Towards Real-Time Full-Waveform LiDAR Transformers via Intensity-Guided Token Reduction and Physics-Aware Augmentation

LiDAR is a critical sensor for autonomous driving and robotics, yet it remains vulnerable to adverse conditions such as fog, rain, transparent objects, and low-reflectance surfaces. Full-waveform LiDAR (FWL) addresses these limitations by capturing the complete return waveform as a histogram, preserving rich reflection characteristics including multi-path echoes and weak signals. While recent Transformer-based approaches have demonstrated promise on FWL data, two fundamental bottlenecks remain: high computational cost from redundant tokens in low-intensity background regions, and limited generalization due to scarce annotated training data. We address both bottlenecks by exploiting the physical structure inherent to FWL data. First, we propose FWL-ToPM, a token pruning and merging mechanism that leverages intensity distributions to selectively reduce tokens while retaining critical waveform content. Second, we introduce FWLAug, a physically consistent data augmentation framework that preserves time-of-flight waveform structure and frustum geometry to improve generalization across diverse environments. Evaluated on the Ghost-FWL benchmark, FWL-ToPM achieves over 8$\times$ speedup at real-time throughput alongside a nearly 9-point F1 gain, establishing the first real-time FWL Transformer that simultaneously improves accuracy. FWLAug further improves generalization to both standard and out-of-distribution environments. Together, these methods push FWL-based perception toward practical deployment.