DA-Occ: Efficient 3D Voxel Occupancy Prediction via Directional 2D for Geometric Structure Preservation

• URL: http://arxiv.org/abs/2507.23599v1
• Date: Thu, 31 Jul 2025 14:39:31 GMT
• Title: DA-Occ: Efficient 3D Voxel Occupancy Prediction via Directional 2D for Geometric Structure Preservation
• Authors: Yuchen Zhou, Yan Luo, Xiangang Wang, Xingjian Gu, Mingzhou Lu,
• Abstract summary: Efficient and high-accuracy 3D occupancy prediction is crucial for ensuring the performance of autonomous driving systems.<n>Our method involves slicing 3D voxel features to preserve complete vertical geometric information.<n>This strategy compensates for the loss of height cues in Bird's-Eye View representations, thereby maintaining the integrity of the 3D geometric structure.
• Score: 13.792614780020061
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient and high-accuracy 3D occupancy prediction is crucial for ensuring the performance of autonomous driving (AD) systems. However, many current methods focus on high accuracy at the expense of real-time processing needs. To address this challenge of balancing accuracy and inference speed, we propose a directional pure 2D approach. Our method involves slicing 3D voxel features to preserve complete vertical geometric information. This strategy compensates for the loss of height cues in Bird's-Eye View (BEV) representations, thereby maintaining the integrity of the 3D geometric structure. By employing a directional attention mechanism, we efficiently extract geometric features from different orientations, striking a balance between accuracy and computational efficiency. Experimental results highlight the significant advantages of our approach for autonomous driving. On the Occ3D-nuScenes, the proposed method achieves an mIoU of 39.3% and an inference speed of 27.7 FPS, effectively balancing accuracy and efficiency. In simulations on edge devices, the inference speed reaches 14.8 FPS, further demonstrating the method's applicability for real-time deployment in resource-constrained environments.

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