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

2024-05-08

IEEE Transactions on Pattern Analysis and Machine Intelligence

AI Summary

To address the high annotation cost and heavy reliance on manual labels in LiDAR-based autonomous driving perception, this paper proposes LaserMix++, a semi-supervised 3D scene understanding framework. Methodologically, it introduces the first multi-modal LaserMix data augmentation strategy, enabling fine-grained cross-modal fusion of LiDAR and camera features; incorporates cross-sensor feature distillation and large language model (LLM)-guided knowledge supervision to establish a 3D consistency regularization mechanism. Evaluated on benchmarks including nuScenes, LaserMix++ achieves full-supervision performance using only 20% labeled data, significantly outperforming existing semi-supervised approaches. This work is the first to integrate LLM-prior knowledge into multi-modal semi-supervised 3D understanding, empirically validating its effectiveness in improving annotation efficiency, generalization capability, and geometric consistency modeling in 3D space.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• Autonomous Vehicles
• Data Efficiency
• 3D Environment Recognition

Innovation

• LaserMix++
• Semi-supervised Learning
• Sensor Interaction