SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining

2025-03-25

AI Summary

Existing LiDAR-camera cross-modal pretraining methods largely neglect temporal dynamics, leading to inadequate motion modeling and scene continuity. To address this, we propose the first pretraining framework explicitly modeling spatiotemporal consistency, comprising four core components: fused-view consistency alignment, density-aware sparse-dense regularization, optical-flow-guided contrastive learning, and cross-frame semantic voting. Our method integrates multi-view geometric alignment, point-cloud-density-aware regularization, temporal optical flow modeling, and synchronized scaling of 2D and 3D backbone networks. Evaluated on 11 heterogeneous LiDAR datasets, it consistently surpasses state-of-the-art methods. Downstream 3D detection and segmentation tasks demonstrate substantial gains in accuracy, robustness, and generalization, while maintaining computational efficiency. Notably, we are the first to reveal scalable emergent properties of 3D foundation models under spatiotemporal pretraining—highlighting a critical advancement toward temporally grounded multimodal perception.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

LiDAR representation learning has emerged as a promising approach to reducing reliance on costly and labor-intensive human annotations. While existing methods primarily focus on spatial alignment between LiDAR and camera sensors, they often overlook the temporal dynamics critical for capturing motion and scene continuity in driving scenarios. To address this limitation, we propose SuperFlow++, a novel framework that integrates spatiotemporal cues in both pretraining and downstream tasks using consecutive LiDAR-camera pairs. SuperFlow++ introduces four key components: (1) a view consistency alignment module to unify semantic information across camera views, (2) a dense-to-sparse consistency regularization mechanism to enhance feature robustness across varying point cloud densities, (3) a flow-based contrastive learning approach that models temporal relationships for improved scene understanding, and (4) a temporal voting strategy that propagates semantic information across LiDAR scans to improve prediction consistency. Extensive evaluations on 11 heterogeneous LiDAR datasets demonstrate that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions. Furthermore, by scaling both 2D and 3D backbones during pretraining, we uncover emergent properties that provide deeper insights into developing scalable 3D foundation models. With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving. The code is publicly available at https://github.com/Xiangxu-0103/SuperFlow

Problem

• Enhances spatiotemporal consistency in LiDAR-camera data pretraining
• Addresses overlooked temporal dynamics in sensor alignment methods
• Improves feature robustness across varying point cloud densities

Innovation

• Integrates spatiotemporal cues in pretraining
• Uses flow-based contrastive learning approach
• Introduces temporal voting strategy for consistency