OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Prediction

TL;DR

OccLoff is a novel framework that optimizes feature fusion for 3D occupancy prediction, improving accuracy and efficiency in autonomous driving perception tasks.

Contribution

It introduces a sparse fusion encoder with entropy masks, a proxy-based loss, and an adaptive weighting algorithm, enhancing transferability and performance across models.

Findings

Outperforms existing methods on nuScenes and SemanticKITTI benchmarks.

Reduces computational costs while improving accuracy.

Effective ablation results confirm each module's contribution.

Abstract

3D semantic occupancy prediction is crucial for finely representing the surrounding environment, which is essential for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features, followed by computationally intensive 3D operations to fuse these with LiDAR features, leading to high computational costs and reduced accuracy. Moreover, current research on occupancy prediction predominantly focuses on designing specific network architectures, often tailored to particular models, with limited attention given to the more fundamental aspect of semantic feature learning. This gap hinders the development of more transferable methods that could enhance the performance of various occupancy models. To address these challenges, we propose OccLoff, a framework that Learns to Optimize Feature Fusion for 3D occupancy prediction. Specifically, we introduce a sparse fusion encoder with entropy masks that directly fuses 3D and 2D features, improving model accuracy while reducing computational overhead. Additionally, we propose a transferable proxy-based loss function and an adaptive hard sample weighting algorithm, which enhance the performance of several state-of-the-art methods. Extensive evaluations on the nuScenes and SemanticKITTI benchmarks demonstrate the superiority of our framework, and ablation studies confirm the effectiveness of each proposed module.