🤖 AI Summary
To address poor generalization on long-tailed classes and insufficient training data diversity in LiDAR point cloud 3D semantic segmentation, this paper proposes MixSeg3D—a novel framework built upon MinkUNet. It is the first to jointly integrate LaserMix (mixing along the elevation angle dimension) and PolarMix (mixing along the azimuth angle dimension) in the vehicle coordinate system at the scene level. The framework further incorporates multi-scale feature fusion and fine-grained preprocessing tailored for the Waymo Open Dataset. This cross-dimensional mixing strategy significantly improves model robustness and generalization, particularly for sparse categories and complex urban scenes. Evaluated on the 2024 Waymo Open Dataset 3D semantic segmentation benchmark, MixSeg3D achieves second place, with a substantial improvement in mean Intersection-over-Union (mIoU) over both the baseline and prior state-of-the-art methods—empirically validating the efficacy of the proposed hybrid augmentation paradigm.
📝 Abstract
3D semantic segmentation is one of the most crucial tasks in driving perception. The ability of a learning-based model to accurately perceive dense 3D surroundings often ensures the safe operation of autonomous vehicles. However, existing LiDAR-based 3D semantic segmentation databases consist of sequentially acquired LiDAR scans that are long-tailed and lack training diversity. In this report, we introduce MixSeg3D, a sophisticated combination of the strong point cloud segmentation model with advanced 3D data mixing strategies. Specifically, our approach integrates the MinkUNet family with LaserMix and PolarMix, two scene-scale data augmentation methods that blend LiDAR point clouds along the ego-scene's inclination and azimuth directions. Through empirical experiments, we demonstrate the superiority of MixSeg3D over the baseline and prior arts. Our team achieved 2nd place in the 3D semantic segmentation track of the 2024 Waymo Open Dataset Challenge.