OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction

• URL: http://arxiv.org/abs/2403.05329v2
• Date: Wed, 10 Jul 2024 11:08:14 GMT
• Title: OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction
• Authors: Ji Zhang, Yiran Ding, Zixin Liu,
• Abstract summary: 3D occupancy prediction based on multi-sensor fusion,crucial for a reliable autonomous driving system. Previous fusion-based 3D occupancy predictions relied on depth estimation for processing 2D image features. We propose OccFusion, a depth estimation free multi-modal fusion framework.
• Score: 5.285847977231642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D occupancy prediction based on multi-sensor fusion,crucial for a reliable autonomous driving system, enables fine-grained understanding of 3D scenes. Previous fusion-based 3D occupancy predictions relied on depth estimation for processing 2D image features. However, depth estimation is an ill-posed problem, hindering the accuracy and robustness of these methods. Furthermore, fine-grained occupancy prediction demands extensive computational resources. To address these issues, we propose OccFusion, a depth estimation free multi-modal fusion framework. Additionally, we introduce a generalizable active training method and an active decoder that can be applied to any occupancy prediction model, with the potential to enhance their performance. Experiments conducted on nuScenes-Occupancy and nuScenes-Occ3D demonstrate our framework's superior performance. Detailed ablation studies highlight the effectiveness of each proposed method.

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