QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction
Sicheng Zuo, Wenzhao Zheng, Xiaoyong Han, Longchao Yang, Yong Pan, Jiwen Lu
3D occupancy prediction is crucial for robust autonomous driving systems due to its comprehensive perception of environmental structures and semantics. Most existing methods employ dense grid-based scene representations, which ignore the inherent sparsity of driving scenes and suffer from low efficiency. Recent works explore object-centric representations based on sparse Gaussians, but the ellipsoidal shape prior of Gaussians limits their ability to model diverse structures. In real-world driving scenes, objects exhibit rich geometries (e.g., planes, cuboids, and irregular shapes), requiring excessive overlapping ellipsoidal Gaussians for accurate representation, which leads to inefficient scene representation. To address this, we propose to use geometrically expressive superquadrics as scene representation primitives instead of Gaussians, which can efficiently represent complex structures without much overlap. We develop a probabilistic superquadric mixture model, which interprets each superquadric as an occupancy probability distribution of its neighborhood with corresponding geometry, and calculates semantics through probabilistic mixture. We then employ a superquadric-based model (QuadricFormer) for efficient 3D occupancy prediction, and design a pruning-and-splitting module to further improve modeling efficiency by concentrating superquadrics in occupied regions. Extensive experiments on the nuScenes dataset demonstrate that QuadricFormer achieves state-of-the-art performance while maintaining superior efficiency.