PointNet has recently emerged as a popular representation for unstructured
point cloud data, allowing application of deep learning to tasks such as object
detection, segmentation and shape completion. However, recent works in
literature have shown the sensitivity of the PointNet representation to pose
misalignment. This paper presents a novel framework that uses PointNet encoding
to align point clouds and perform registration for applications such as 3D
reconstruction, tracking and pose estimation. We develop a framework that
compares PointNet features of template and source point clouds to find the
transformation that aligns them accurately. In doing so, we avoid
computationally expensive correspondence finding steps, that are central to
popular registration methods such as ICP and its variants. Depending on the
prior information about the shape of the object formed by the point clouds, our
framework can produce approaches that are shape specific or general to unseen
shapes. Our framework produces approaches that are robust to noise and initial
misalignment in data and work robustly with sparse as well as partial point
clouds. We perform extensive simulation and real-world experiments to validate
the efficacy of our approach and compare the performance with state-of-art
approaches. Code is available at
this https URL

本文提出了一种新的基于 PointNet 编码的点云对齐和配准框架，避免了 ICP 等流行配准方法中的计算昂贵的对应点搜索步骤，适用于 3D 重建、跟踪和姿态估计等应用，能够处理噪声、初始错位以及粗糙点云，并通过大量模拟和真实实验验证了算法的有效性和性能优势。

一种框架以注册它们所有: PointNet 编码用于点云对齐

One Framework to Register Them All: PointNet Encoding for Point Cloud  Alignment

PointNet has recently emerged as a popular representation for unstructured
point cloud data, allowing application of deep learning to tasks such as object
detection, segmentation and shape completion. However, recent works in
literature have shown the sensitivity of the PointNet representation to pose
misalignment. This paper presents a novel framework that uses the PointNet
representation to align point clouds and perform registration for applications
such as tracking, 3D reconstruction and pose estimation. We develop a framework
that compares PointNet features of template and source point clouds to find the
transformation that aligns them accurately. Depending on the prior information
about the shape of the object formed by the point clouds, our framework can
produce approaches that are shape specific or general to unseen shapes. The
shape specific approach uses a Siamese architecture with fully connected (FC)
layers and is robust to noise and initial misalignment in data. We perform
extensive simulation and real-world experiments to validate the efficacy of our
approach and compare the performance with state-of-art approaches.

该论文提出了一个新框架，利用 PointNet 表征对齐点云和执行注册，以实现跟踪，3D 重建和姿态估计等应用，能够根据点云的形状信息生成特定形状或通用的方法，并具有噪声和数据初始化错位的鲁棒性。