title: Rectified Point Flow: Generic Point Cloud Pose Estimation
summary: We introduce Rectified Point Flow, a unified parameterization that formulates
  pairwise point cloud registration and multi-part shape assembly as a single
  conditional generative problem. Given unposed point clouds, our method learns a
  continuous point-wise velocity field that transports noisy points toward their
  target positions, from which part poses are recovered. In contrast to prior
  work that regresses part-wise poses with ad-hoc symmetry handling, our method
  intrinsically learns assembly symmetries without symmetry labels. Together with
  a self-supervised encoder focused on overlapping points, our method achieves a
  new state-of-the-art performance on six benchmarks spanning pairwise
  registration and shape assembly. Notably, our unified formulation enables
  effective joint training on diverse datasets, facilitating the learning of
  shared geometric priors and consequently boosting accuracy. Project page:
  https://rectified-pointflow.github.io/.
paper_authors._id: 68425fce548d527097ac00bb
paper_authors.name: Tao Sun
paper_authors._id: 68425fce548d527097ac00bc
paper_authors.name: Liyuan Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:49.305Z
paper_authors._id: 6503916e0905dd866fd129cb
paper_authors.avatarUrl: /avatars/818716460d9c9aaa056e0f1b43816c6a.svg
paper_authors.fullname: Liyuan Zhu
paper_authors.type: user
paper_authors.user: liyzzz
paper_authors._id: 68425fce548d527097ac00bd
paper_authors.name: Shengyu Huang
paper_authors._id: 68425fce548d527097ac00be
paper_authors.name: Shuran Song
paper_authors._id: 68425fce548d527097ac00bf
paper_authors.name: Iro Armeni
paper_title: Rectified Point Flow: Generic Point Cloud Pose Estimation
paper_summary: We introduce Rectified Point Flow, a unified parameterization that formulates
  pairwise point cloud registration and multi-part shape assembly as a single
  conditional generative problem. Given unposed point clouds, our method learns a
  continuous point-wise velocity field that transports noisy points toward their
  target positions, from which part poses are recovered. In contrast to prior
  work that regresses part-wise poses with ad-hoc symmetry handling, our method
  intrinsically learns assembly symmetries without symmetry labels. Together with
  a self-supervised encoder focused on overlapping points, our method achieves a
  new state-of-the-art performance on six benchmarks spanning pairwise
  registration and shape assembly. Notably, our unified formulation enables
  effective joint training on diverse datasets, facilitating the learning of
  shared geometric priors and consequently boosting accuracy. Project page:
  https://rectified-pointflow.github.io/.
paper_ai_summary: Rectified Point Flow unifies pairwise point cloud registration and multi-part shape assembly through a continuous point-wise velocity field, achieving state-of-the-art performance on various benchmarks.
paper_ai_keywords: Rectified Point Flow pairwise point cloud registration multi-part shape assembly continuous point-wise velocity field self-supervised encoder overlapping points geometric priors