  The benefit of transformers in large-scale 3D point cloud perception tasks,
such as 3D object detection, is limited by their quadratic computation cost
when modeling long-range relationships. In contrast, linear RNNs have low
computational complexity and are suitable for long-range modeling. Toward this
goal, we propose a simple and effective window-based framework built on LInear
grOup RNN (i.e., perform linear RNN for grouped features) for accurate 3D
object detection, called LION. The key property is to allow sufficient feature
interaction in a much larger group than transformer-based methods. However,
effectively applying linear group RNN to 3D object detection in highly sparse
point clouds is not trivial due to its limitation in handling spatial modeling.
To tackle this problem, we simply introduce a 3D spatial feature descriptor and
integrate it into the linear group RNN operators to enhance their spatial
features rather than blindly increasing the number of scanning orders for voxel
features. To further address the challenge in highly sparse point clouds, we
propose a 3D voxel generation strategy to densify foreground features thanks to
linear group RNN as a natural property of auto-regressive models. Extensive
experiments verify the effectiveness of the proposed components and the
generalization of our LION on different linear group RNN operators including
Mamba, RWKV, and RetNet. Furthermore, it is worth mentioning that our
LION-Mamba achieves state-of-the-art on Waymo, nuScenes, Argoverse V2, and ONCE
dataset. Last but not least, our method supports kinds of advanced linear RNN
operators (e.g., RetNet, RWKV, Mamba, xLSTM and TTT) on small but popular KITTI
dataset for a quick experience with our linear RNN-based framework.
