I am a PhD candidate at Matthias Nießner’s Visual Computing Group, at the Technical University of Munich.
I’m interested in computer vision, deep learning and optimization. My research is mostly focused on robust correspondence estimation for rigid 3D reconstruction.
Before joining the group, I received a Master’s Degree in Computer Science from the Technical University of Munich and a Bachelor’s Degree in Computer Science from the Polytechnical University of Catalonia.
In this project, I worked on the SLAM pipeline for an autonomous driving vehicle.
The tools I used for this project are, ROS, C++, PCL library, Ceres Solver, and Google Cartographer.
In addition, I created a ROS package that generates a cost map from a point cloud generated by SLAM. A cost map is a matrix where each cell contains a cost value. This value expresses how likely it is that the cell is occupied by an object. The cost value is also used to express “preferred” surfaces: for example, asphalt is preferred over grass.
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.
Bachelor’s thesis
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.
Writing about StirHack16, the first hackathon I ever attended, brings me very good memories. In this page I will focus on the project itself, although some day I should write about the hackathon experience. 😃
Don’t hesitate to reach out in any of the following ways!