Enhancing Generalizability via Utilization of Unlabeled Data for Occupancy Perception

Abstract

3D occupancy perception accurately estimates the volumetric status and semantic labels of a scene, attracting significant attention in the field of autonomous driving. However, enhancing the model's ability to generalize across different driving scenarios or sensing systems, often requires redesigning the model or extra-expensive annotations. To this end, following a comprehensive analysis of the occupancy model architecture, we proposed the UGOCC method that utilizes domain adaptation to efficiently harness unlabeled autonomous driving data, thereby enhancing the model's generalizability. Specifically, we design the depth fusion module by employing self-supervised depth estimation, and propose a strategy based on semantic attention and domain adversarial learning to improve the generalizability of the learnable fusion module. Additionally, we propose an OCC-specific pseudo-label selection tailored for semi-supervised learning, which optimizes the overall network's generalizability. Our experiment results on two challenging datasets nuScenes and Waymo, demonstrate that our method not only achieves state-of-the-art generalizability but also enhances the model's perceptual capabilities within the source domain by utilizing unlabeled data.