I'm a PhD student at the University of Bonn (Germany) where I work on problems at the intersection of traditional 3D processing and deep learning. My interests are in 3D reconstruction, novel-view rendering, implicit representations and semantic segmentation.
During my PhD, I interned at Facebook Reality Labs: at Pittsburgh (supervised by Giljoo Nam). During the internship I worked on 3D hair reconstruction from images which culminated with the article Neural Strands presented at ECCV 2022 (Tel Aviv).
I obtained my bachelor degree from University of Salamanca (Spain) in 2015, where I majored in Computer Science. I worked with Iván Álvarez Navia on "Reconstruction of 3D Figures from Computerized Tomography".
I grew up in Targoviste (Romania) and later in Aranda de Duero (Spain).
We introduce the permutohedral lattice in the context of neural surface reconstruction and recover geometry and color of a scene given posed RGB images in as little as 30min.
We propose a real-time novel-view synthesis method that runs at interactive speeds and generalizes to novel objects after training on a general dataset.
Real-time physically-based renderer with an emphasis on ease-of-use. Actively used for synthetic dataset creation, data visualization, figure creation, etc. Supports deffered rendering, ambient occlusion, bloom, image-based lighting, shader hotloading and various other features.
We create a system for real-time semantic segmentation of general 3D point clouds by embedding points into a permutohedral lattice where convolutions are defined using custom and highly optimized CUDA kernels.
Based on laser data, RGB and thermal images we reconstruct 3D scenes as a textured mesh colored by RGB and thermal information on which we detect potential fires for the purpose of firefigher intervention.