(SOTA)^2! A Unified Framework for Efficient Visual 3D Perception

Autonomous Driving systems rely heavily on accurate 3D scene understanding to plan and navigate safely. Progress has been made in recent years in visual 3D detection via feature transformation, temporal fusion, and supervision signal design. However, detection focuses on objects and struggles with representing complete spatial occupancy. Meanwhile, occupancy prediction methods can represent geometry and semantics more comprehensively but less efficiently. Exploring the interplay between detection and occupancy prediction could lead to unified, efficient 3D perception. But ensuring shared representation …

autonomous autonomous driving autonomous driving systems computervision design detection driving feature framework fusion objects perception prediction progress signal sota spatial supervision systems temporal transformation understanding via visual