Local-consistent Transformation Learning for Rotation-invariant Point
  Cloud Analysis
Yiyang Chen Lunhao Duan Shanshan Zhao Changxing Ding Dacheng Tao
Rotation invariance is an important requirement for point shape analysis. To
achieve this, current state-of-the-art methods attempt to construct the local
rotation-invariant representation through learning or defining the local
reference frame (LRF). Although efficient, these LRF-based methods suffer from
perturbation of local geometric relations, resulting in suboptimal local
rotation invariance. To alleviate this issue, we propose a Local-consistent
Transformation (LocoTrans) learning strategy. Specifically, we first construct
the local-consistent reference frame (LCRF) by considering the symmetry of the
two axes in LRF. In comparison with previous LRFs, our LCRF is able to preserve
local geometric relationships better through performing local-consistent
transformation. However, as the consistency only exists in local regions, the
relative pose information is still lost in the intermediate layers of the
network. We mitigate such a relative pose issue by developing a relative pose
recovery (RPR) module. RPR aims to restore the relative pose between adjacent
transformed patches. Equipped with LCRF and RPR, our LocoTrans is capable of
learning local-consistent transformation and preserving local geometry, which
benefits rotation invariance learning. Competitive performance under arbitrary
rotations on both shape classification and part segmentation tasks and
ablations can demonstrate the effectiveness of our method. Code will be
available publicly at https://github.com/wdttt/LocoTrans.