3D point cloud is an efficient and flexible representation of 3D structures.
Recently, neural networks operating on point clouds have shown superior
performance on 3D understanding tasks such as shape classification and part
segmentation. However, performance on such tasks is evaluated on complete
shapes aligned in a canonical frame, while real world 3D data are partial and
unaligned. A key challenge in learning from partial, unaligned point cloud data
is to learn features that are invariant or equivariant with respect to
geometric transformations. To address this challenge, we propose the Iterative
Transformer Network (IT-Net), a network module that canonicalizes the pose of a
partial object with a series of 3D rigid transformations predicted in an
iterative fashion. We demonstrate the efficacy of IT-Net as an anytime pose
estimator from partial point clouds without using complete object models.
Further, we show that IT-Net achieves superior performance over alternative 3D
transformer networks on various tasks, such as partial shape classification and
object part segmentation.

提出了使用迭代变换网络 (IT-Net) 模块来完成无完整对象模型的局部点云的姿态估计，进而在形状分类和物体部分分割等任务上实现了卓越的性能。