Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation

LiDAR semantic segmentation frameworks predominantly use geometry-based features to differentiate objects within a scan. These methods excel with clear boundaries but struggle in ambiguous environments, especially of-road. Recent 3D segmentation advances use raw LiDAR intensity for better prediction accuracy. Nonetheless, existing models face challenges in relating raw intensity to distance, angle, reflectivity, and atmospheric conditions. Building on prior work Viswanath et al. (2024), we examine the benefits of calibrated intensity (reflectivity) in learning-based LiDAR segmentation. Adding reflectivity as input enhances data representation, resulting in a 4% mIoU improvement on the Rellis-3d of-road dataset. We also explore calibrated intensity benefits for urban segmentation (SemanticKITTI) and cross-sensor adaptation. Testing the Segment Anything Model (SAM) Kirillov et al. (2023) with reflectivity led to improved masks for LiDAR images. Github repository