MambaOcc：用于基于BEV的占用预测并带有局部自适应重排序的视觉状态空间模型
MambaOcc: Visual State Space Model for BEV-based Occupancy Prediction with Local Adaptive Reordering
AI总结：
本文提出MambaOcc，一种基于BEV特征和线性Mamba注意力的占用预测方法，并引入局部自适应重排序（LAR）机制与混合BEV编码器，在降低参数与计算成本的同时实现了最先进的准确性与效率。
摘要：
占用预测引起了广泛关注，并在自动驾驶系统的发展中展现出巨大优势。占用预测在几何和语义信息方面带来的细粒度环境表示，促进了开放场景下的通用感知和安全规划。然而，在利用基于体素的3D密集表示和基于Transformer的二次注意力的现有工作中，它也带来了高计算成本和沉重的参数。为了解决这些挑战，本文提出了一种基于Mamba的占用预测方法（MambaOcc），采用BEV特征来减轻3D场景表示的负担，并利用线性的Mamba风格注意力实现高效的长距离感知。此外，为了解决Mamba对序列顺序的敏感性，我们提出了一种具有可变形卷积的局部自适应重排序（LAR）机制，并设计了一个由卷积层和Mamba组成的混合BEV编码器。在Occ3D-nuScenes数据集上的大量实验表明，MambaOcc在准确性和计算效率方面均达到了最先进的性能。例如，与FlashOcc相比，MambaOcc在提供更优结果的同时，将参数数量减少了42%，计算成本降低了39%。代码将在https://github.com/Hub-Tian/MambaOcc 上提供。
英文摘要：
Occupancy prediction has attracted intensive attention and shown great superiority in the development of autonomous driving systems. The fine-grained environmental representation brought by occupancy prediction in terms of both geometry and semantic information has facilitated the general perception and safe planning under open scenarios. However, it also brings high computation costs and heavy parameters in existing works that utilize voxel-based 3d dense representation and Transformer-based quadratic attention. To address these challenges, in this paper, we propose a Mamba-based occupancy prediction method (MambaOcc) adopting BEV features to ease the burden of 3D scenario representation, and linear Mamba-style attention to achieve efficient long-range perception. Besides, to address the sensitivity of Mamba to sequence order, we propose a local adaptive reordering (LAR) mechanism with deformable convolution and design a hybrid BEV encoder comprised of convolution layers and Mamba. Extensive experiments on the Occ3D-nuScenes dataset demonstrate that MambaOcc achieves state-of-the-art performance in terms of both accuracy and computational efficiency. For example, compared to FlashOcc, MambaOcc delivers superior results while reducing the number of parameters by 42\% and computational costs by 39\%. Code will be available at https://github.com/Hub-Tian/MambaOcc.
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