Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. The linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency.

基于序列化的方法在3D物体检测中展示了其有效性，但是将三维体素序列化为一维序列会不可避免地牺牲体素的空间接近性。本文引入了一种名为Voxel Mamba的体素状态空间模型（Voxel SSM），采用无分组策略将全部体素空间序列化为单一序列，并提出了一种双尺度SSM块以建立分层结构，增强1D序列化曲线的感受野和三维空间的完整局部区域。利用位置编码隐式地应用窗口分割，进一步增强了体素的空间接近性。在Waymo Open Dataset和nuScenes数据集上的实验证明，Voxel Mamba不仅实现了比现有方法更高的准确性，并且在计算效率方面具有显著优势。

体素眼镜蛇：基于点云的三维物体检测的无群组状态空间模型