BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning

2025-08-06

AI Summary

To address insufficient representation learning in BEV perception for autonomous driving, this paper proposes a dual-path contrastive learning framework that enforces semantic consistency at both the instance level (BEV features) and the view level (image backbone outputs). Specifically, it introduces an instance-feature contrastive module and a perspective-to-BEV contrastive module, integrated with dense pixel- and instance-level contrastive strategies. The approach enhances the discriminability and geometric robustness of BEV representations without increasing inference overhead. It seamlessly integrates into existing BEV detection pipelines by adding only a lightweight contrastive regularization term to the standard detection loss. Evaluated on the nuScenes dataset, the method achieves significant improvements across 3D detection, segmentation, and trajectory prediction—yielding up to +2.4% mAP gain over state-of-the-art baselines with negligible computational cost. This work provides the first systematic empirical validation that contrastive learning substantially improves BEV representation quality.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-down-view representation of the surrounding environment, making it crucial for 3D object detection, segmentation, and trajectory prediction tasks. While prior work has primarily focused on enhancing BEV encoders and task-specific heads, we address the underexplored potential of representation learning in BEV models. BEVCon introduces two contrastive learning modules: an instance feature contrast module for refining BEV features and a perspective view contrast module that enhances the image backbone. The dense contrastive learning designed on top of detection losses leads to improved feature representations across both the BEV encoder and the backbone. Extensive experiments on the nuScenes dataset demonstrate that BEVCon achieves consistent performance gains, achieving up to +2.4% mAP improvement over state-of-the-art baselines. Our results highlight the critical role of representation learning in BEV perception and offer a complementary avenue to conventional task-specific optimizations.

Problem

• Improving Bird's Eye View perception in autonomous driving
• Enhancing representation learning in BEV models
• Boosting feature quality via contrastive learning modules

Innovation

• Contrastive learning for BEV perception
• Instance feature contrast module
• Perspective view contrast module