Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving

Understand

Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception.

• Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion.
• In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps from multi-view image sequences.
• To achieve streaming dense reconstruction, we maintain a memory pool to reason both spatial relationships across sensors and dynamic temporal contexts to enhance multi-view 3D consistency and temporal integration.