Optimization | Marc B

Learning Robust Correspondences Estimation

LRCE addresses the challenging problem of relative camera pose estimation in computer vision, essential for applications like 3D reconstruction, Structure from Motion (SfM), and Simultaneous Localization and Mapping (SLAM). Estimating a camera’s position and orientation from a sequence of images has been a long-standing issue in the field.

Divergence-Free Shape Correspondence with Time Dependent Vector Fields

In this project, we extended the work of Eisenberger, Zorah, Cremers, “Divergence-Free Shape Interpolation and Correspondence” 1. In their work, they present a method to calculate deformation fields between shapes embedded in $\mathbb{R}^D$. To do so, they compute a divergence-free deformation field represented in a coarse-to-fine basis using the Karhunen-Loéve expansion.

Design of an Environment for Solving pseudo-Boolean Optimization Problems

Boolean Satisfiability problems (SAT) consists of finding a valid assignment (model) for a set of Boolean variables. It was the first problem proven to be NP-Complete which allowed reducing many NP-Complete problems to it. Because of this, it is one of the pillars of Computer Science.