Many applications such as UV-mapping, shape analysis, shape interpolation, sparse to dense reconstruction and partial scan-completion rely on the availability of a surface representation

that is coherent across different instances, ie, each point on one surface maps to a point with the same semantic meaning on another. In the literature, the most common way to achieve

coherence consists of explicitly computing and establishing correspondences between input representations, such as 3D meshes1 or 3D point clouds2. Such a registration is discrete, and

obtaining a continuous, smooth registration between the input representations is difficult and non-accurate. Figure 1: 4D registration performed in previous works

In our previous works3, we tackled this problem more directly by learning to reconstruct temporally-coherent surfaces from a sequence of 3D point clouds representing a shape

deforming over time (see Figure 1). This allows a compact, coherent representation of objects which can be easily used in the above-mentioned applications. In order to learn a dense

registration, we rely on the preservation of intrinsic geometric properties of the shapes in addition to a global coherence by minimising distances between corresponding point sets.

Such a formulation is accurate, robust but quite expensive which prohibits its usage in real-life scenarios. In this project, we aim to speed up the process. Our goal is to develop fast

matching techniques that loosely preserve the intrinsic geometric properties so that a quick and decently accurate registration can be obtained. This requires approximation of intrinsic

geometric properties using simpler mathematical formulations, such as normal and curvature analysis. Our focus will be on identifying and matching local structures with relatively unique surface properties. The project will be supervised by Shaifali Parashar (shaifali.parashar@liris.cnrs.fr), Dr. Julie Digne (julie.digne@cnrs.fr) and Prof. Liming Chen (liming.chen@ec-lyon.fr) at the LIRIS laboratory (INSA/Ecole Centrale/University Lyon 1) in Lyon. Interested students should drop an email with CV, transcript and two reference letters