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👋 Hola! Hello! Hallo!
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.
👷♂ Experience
Research Fellowship
- Fellowship awarded to outstanding Master students to allow them developing state-of-the-art models. See page.
- See project’s page.
- Built a high-performance GPU cluster for the Deep Learning laboratory.
- Built a frontend with Vue.js and a backend for the cluster’s users.
Software Engineer
- Designed and built a cluster for the BE-OP-LHC team at CERN using Kubernetes.
Research Assistant
- Worked on a novel conversion of Binary Decision Trees to Conjunctive Normal Forms.
- See project’s page.
Intern
- Frontend and backend development.
Android Instructor
- Taught a two-week course to introduction to Android development to 25+ students.
🏗️ Projects
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We present an end-to-end learnable, differentiable method for pairwise relative pose registration of RGB-D frames. Our method is robust to big camera motions thanks to a self-supervised weighting of the predicted correspondences between the frames. Given a pair of frames, our method estimates matches of points and their visibility score. A self-supervised model predicts a confidence weight for visible matches. Finally, visible matches and their weight are fed into a differentiable weighted Procrustes aligner which estimates the rigid transformation between the input frames.
Implementation of the paper “Kinect Fusion: Real-Time Dense Surface Mapping and Tracking”
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 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 $\mathbb{R}^D$.
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.
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.
📫 Contact
Don’t hesitate to reach out in any of the following ways!