SuperOcc: Toward Cohesive Temporal Modeling for Superquadric-based 3D Occupancy Prediction

TL;DR

SuperOcc introduces a novel framework for 3D occupancy prediction that leverages cohesive temporal modeling, enhanced geometric expressiveness, and efficient superquadric-to-voxel conversion, achieving state-of-the-art results in autonomous driving scenarios.

Contribution

It presents a new superquadric-based approach with integrated temporal modeling and efficient decoding strategies for improved 3D occupancy prediction.

Findings

Achieves state-of-the-art performance on SurroundOcc and Occ3D benchmarks.

Demonstrates superior efficiency compared to existing methods.

Effectively models temporal cues for better scene understanding.

Abstract

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.