C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation

2026-07-02 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

The C2E (Co-Perception to Eo-Perception) paradigm introduces a Multi-to-Single (M2S) agent contrastive knowledge distillation framework designed to enhance Ego-only Perception (Eo-Perception) in LiDAR-based 3D object detection for autonomous driving. Traditional Eo-Perception faces limitations from restricted perspectives and occlusions, while multi-agent Collaborative Perception (Co-Perception) offers superior performance but incurs high communication costs and pose errors. The M2S framework addresses these issues by incorporating a Multi-Level Feature Enhancement module for stable features, alongside Auxiliary Point Cloud Reconstruction and Multi-Teacher Contrastive Distillation mechanisms to bridge domain gaps in point cloud and feature distributions. This approach allows M2S to maintain Co-Perception's performance advantages without its communication and positioning drawbacks. Evaluated on V2XSet, V2V4Real, and DAIR-V2X datasets, the M2S framework demonstrates effectiveness and generalizability, boosting 3D mAP performance by up to 8.64% when integrated with models like CoSDH, without adding communication overhead.

Key takeaway

For autonomous driving engineers seeking to enhance 3D object detection without incurring high communication costs, the C2E M2S framework offers a compelling solution. You should consider integrating this knowledge distillation approach to improve ego-only perception, potentially boosting 3D mAP performance by up to 8.64%. This allows you to achieve collaborative perception benefits while avoiding communication delays and positioning errors inherent in multi-agent systems.

Key insights

The C2E M2S framework boosts ego-only 3D object detection by distilling knowledge from multi-agent collaborative perception, avoiding communication costs.

Principles

• Ego-only perception benefits from collaborative knowledge.
• Distillation can bridge perception domain gaps.
• Stable features improve 3D object detection.

Method

The M2S framework uses Multi-Level Feature Enhancement, Auxiliary Point Cloud Reconstruction, and Multi-Teacher Contrastive Distillation to transfer collaborative perception knowledge to ego-only systems, mitigating domain gaps and improving feature stability.

In practice

• Improve 3D mAP by 8.64% in ego-only systems.
• Enhance autonomous driving perception.
• Reduce communication costs in V2X systems.

Topics

• 3D Object Detection
• Autonomous Driving
• Knowledge Distillation
• Ego-only Perception
• Collaborative Perception
• LiDAR Perception
• V2X Systems

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.