Andrea Ramazzina

Since 2021, I'm a PhD student at Mercedes-Benz and Technical University of Munich, supervised by Federico Tombari, Felix Heide and Mario Bijelic. My main research focus is on understanding the world through generative models.

Before that, from 2019 to 2021 I worked at Volvo Autonomous Solutions developing their V&V analytics and other data science projects. Here, I was also part of the Engineering Graduate Program 2019.

I have a MS in Physics of Complex Systems from Chalmers University of Technology where I worked with Fredrik Kahl, and a double BS degree from Politecnico di Milano and Tongji University. During my studies I was recipient of the PoliTong Scholarship (2015), Adlerbert Hospitality Foundation Scholarship (2018) and Taiwan Elite Internship Program Scholarship (2018).

Email / CV / Scholar / Linkedin / Github

Research

I'm interested in computer vision, machine learning, optimization, and complex adaptive systems.

	Gated Fields: Learning Scene Reconstruction from Gated Videos Andrea Ramazzina, Stefanie Walz, Mario Bijelic, Pragyan Dahal, Felix Heide CVPR, 2024 Project Page / Code / PDF We propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions.
	Learning Complete Human Neural Representations from Limited Viewpoints Alessandro Sanvito, Andrea Ramazzina, Stefanie Walz, Mario Bijelic, Felix Heide IEEE IV, 2024 ArXiv We introduce HINT, a novel method able to learn a robust representation of a human captured only in a limited range of views augmenting existing sequences with novel views of the person and poses.
	Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar David Borts, Erich Liang, Tim Brödermann, Andrea Ramazzina, Stefanie Walz, Edoardo Palladin, David Bruggemann, Christos Sakaridis, Luc Van Gool, Mario Bijelic, Felix Heide SIGGRAPH, 2024 Project Page / ACM / Code / ArXiv We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements and extract scene occupancy.
	ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering Andrea Ramazzina, Mario Bijelic, Stefanie Walz, Alessandro Sanvito, Dominik Scheuble, Felix Heide ICCV, 2023 Project Page / Code / ArXiv We learn a foggy scene from a single video, disjointly representing scattering volume and scene objects through physics-inspired losses. This allows for accurate rendering of novel views with full control over the scattering volume, as well as a more accurate estimation of the 3D scene.
	Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues Stefanie Walz, Mario Bijelic, Andrea Ramazzina, Amanpreet Walia, Fahim Mannan, Felix Heide CVPR, 2023 (Highlight) Project Page / Code / ArXiv We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images.
	Single scattering models for radiative transfer of isotropic and cone-shaped light sources in fog Simeon Geiger, André Liemert, Dominik Reitzle, Mario Bijelic, Andrea Ramazzina, Werner Ritter, Felix Heide, Alwin Kienle Optics Express, 2022 Paper We present an improved solution to calculate the so called air-light integral that can be evaluated fast and robustly for an isotropic point source in homogeneous media.

Previously Supervised Students

Alessandro Sanvito - M.Sc - Now at Optiver
Tina Höflich - B.Sc - Now at Mercedes-Benz

Design and source code from Jon Barron's website