FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin

• URL: http://arxiv.org/abs/2311.12058v1
• Date: Sat, 18 Nov 2023 15:28:09 GMT
• Title: FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin
• Authors: Zichen Yu, Changyong Shu, Jiajun Deng, Kangjie Lu, Zongdai Liu, Jiangyong Yu, Dawei Yang, Hui Li, Yan Chen
• Abstract summary: FlashOCC consolidates rapid and memory-efficient occupancy prediction. Channel-to-height transformation is introduced to lift the output logits from the BEV into the 3D space. Results substantiate the superiority of our plug-and-play paradigm over previous state-of-the-art methods.
• Score: 32.172269679513285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the capability of mitigating the long-tail deficiencies and intricate-shaped absence prevalent in 3D object detection, occupancy prediction has become a pivotal component in autonomous driving systems. However, the procession of three-dimensional voxel-level representations inevitably introduces large overhead in both memory and computation, obstructing the deployment of to-date occupancy prediction approaches. In contrast to the trend of making the model larger and more complicated, we argue that a desirable framework should be deployment-friendly to diverse chips while maintaining high precision. To this end, we propose a plug-and-play paradigm, namely FlashOCC, to consolidate rapid and memory-efficient occupancy prediction while maintaining high precision. Particularly, our FlashOCC makes two improvements based on the contemporary voxel-level occupancy prediction approaches. Firstly, the features are kept in the BEV, enabling the employment of efficient 2D convolutional layers for feature extraction. Secondly, a channel-to-height transformation is introduced to lift the output logits from the BEV into the 3D space. We apply the FlashOCC to diverse occupancy prediction baselines on the challenging Occ3D-nuScenes benchmarks and conduct extensive experiments to validate the effectiveness. The results substantiate the superiority of our plug-and-play paradigm over previous state-of-the-art methods in terms of precision, runtime efficiency, and memory costs, demonstrating its potential for deployment. The code will be made available.

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