Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving

TL;DR

This paper introduces LaserMix++, a semi-supervised framework that leverages multi-modal data and novel augmentation techniques to improve 3D scene understanding in autonomous driving with significantly less labeled data.

Contribution

The study presents LaserMix++, a new semi-supervised learning framework that integrates multi-modal data, laser beam manipulations, and language-driven guidance for efficient 3D scene understanding.

Findings

Outperforms fully supervised methods with five times fewer annotations.

Achieves comparable accuracy to fully supervised models.

Significantly improves baseline semi-supervised approaches.

Abstract

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supervised methods. Addressing this, our study extends into semi-supervised learning for LiDAR semantic segmentation, leveraging the intrinsic spatial priors of driving scenes and multi-sensor complements to augment the efficacy of unlabeled datasets. We introduce LaserMix++, an evolved framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to further assist data-efficient learning. Our framework is tailored to enhance 3D scene consistency regularization by incorporating multi-modality, including 1) multi-modal LaserMix operation for fine-grained cross-sensor interactions; 2) camera-to-LiDAR feature distillation that enhances LiDAR feature learning; and 3) language-driven knowledge guidance generating auxiliary supervisions using open-vocabulary models. The versatility of LaserMix++ enables applications across LiDAR representations, establishing it as a universally applicable solution. Our framework is rigorously validated through theoretical analysis and extensive experiments on popular driving perception datasets. Results demonstrate that LaserMix++ markedly outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations and significantly improving the supervised-only baselines. This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.