  Transformers have revolutionized the point cloud learning task, but the
quadratic complexity hinders its extension to long sequence and makes a burden
on limited computational resources. The recent advent of RWKV, a fresh breed of
deep sequence models, has shown immense potential for sequence modeling in NLP
tasks. In this paper, we present PointRWKV, a model of linear complexity
derived from the RWKV model in the NLP field with necessary modifications for
point cloud learning tasks. Specifically, taking the embedded point patches as
input, we first propose to explore the global processing capabilities within
PointRWKV blocks using modified multi-headed matrix-valued states and a dynamic
attention recurrence mechanism. To extract local geometric features
simultaneously, we design a parallel branch to encode the point cloud
efficiently in a fixed radius near-neighbors graph with a graph stabilizer.
Furthermore, we design PointRWKV as a multi-scale framework for hierarchical
feature learning of 3D point clouds, facilitating various downstream tasks.
Extensive experiments on different point cloud learning tasks show our proposed
PointRWKV outperforms the transformer- and mamba-based counterparts, while
significantly saving about 46\% FLOPs, demonstrating the potential option for
constructing foundational 3D models.
