SuperOcc: Toward Cohesive Temporal Modeling for Superquadric-based Occupancy Prediction
This work addresses a domain-specific problem in autonomous driving by improving sparse scene representation for occupancy prediction, though it appears incremental as it builds on existing superquadric frameworks.
The paper tackles the problem of insufficient temporal modeling and inefficiency in superquadric-based 3D occupancy prediction for autonomous driving, proposing SuperOcc, which achieves state-of-the-art performance on benchmarks like SurroundOcc and Occ3D while maintaining superior efficiency.
3D occupancy prediction plays a pivotal role in the realm of autonomous driving, as it provides a comprehensive understanding of the driving environment. Most existing methods construct dense scene representations for occupancy prediction, overlooking the inherent sparsity of real-world driving scenes. Recently, 3D superquadric representation has emerged as a promising sparse alternative to dense scene representations due to the strong geometric expressiveness of superquadrics. However, existing superquadric frameworks still suffer from insufficient temporal modeling, a challenging trade-off between query sparsity and geometric expressiveness, and inefficient superquadric-to-voxel splatting. To address these issues, we propose SuperOcc, a novel framework for superquadric-based 3D occupancy prediction. SuperOcc incorporates three key designs: (1) a cohesive temporal modeling mechanism to simultaneously exploit view-centric and object-centric temporal cues; (2) a multi-superquadric decoding strategy to enhance geometric expressiveness without sacrificing query sparsity; and (3) an efficient superquadric-to-voxel splatting scheme to improve computational efficiency. Extensive experiments on the SurroundOcc and Occ3D benchmarks demonstrate that SuperOcc achieves state-of-the-art performance while maintaining superior efficiency. The code is available at https://github.com/Yzichen/SuperOcc.