PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
Hao Yang, Qianyu Zhou, Haijia Sun, Xiangtai Li, Xuequan Lu, Lizhuang Ma, Shuicheng YAN
Abstract
Domain Generalization (DG) has been recently explored to enhance the generalizability of Point Cloud Classification (PCC) models toward unseen domains. Prior works are based on convolutional networks, Transformer or Mamba architectures, either suffering from limited receptive fields or high computational cost, or insufficient long-range dependency modeling. RWKV, as an emerging architecture, possesses superior linear complexity, global receptive fields, and long-range dependency. In this paper, we present the first work that studies the generalizability of RWKV models in DG PCC. We find that directly applying RWKV to DG PCC encounters two significant challenges: RWKV
BibTeX
@inproceedings{aaai2026_pointdgrwkvgener,
title = {PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification},
author = {Hao Yang and Qianyu Zhou and Haijia Sun and Xiangtai Li and Xuequan Lu and Lizhuang Ma and Shuicheng YAN},
booktitle = {AAAI 2026},
year = {2026}
}