  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
https://github.com/vinits5/pointnet-registrationframework.
