Lars M. Mescheder

28 papers A* 7A 1Journal 15Unranked 3

Year	Rank	Type	Title / Venue / Authors
2020	—	`conf`	Convolutional Occupancy Networks. ECCV (3) Songyou Peng, Michael Niemeyer, Lars M. Mescheder, Marc Pollefeys, Andreas Geiger
2020	J	`jnl`	Convolutional Occupancy Networks. CoRR Songyou Peng, Michael Niemeyer, Lars M. Mescheder, Marc Pollefeys, Andreas Geiger
2020	A*	`conf`	Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision. CVPR Michael Niemeyer, Lars M. Mescheder, Michael Oechsle, Andreas Geiger
2020	—	`conf`	Learning Implicit Surface Light Fields. 3DV Michael Oechsle, Michael Niemeyer, Christian Reiser, Lars M. Mescheder, Thilo Strauss, Andreas Geiger
2020	J	`jnl`	Learning Implicit Surface Light Fields. CoRR Michael Oechsle, Michael Niemeyer, Lars M. Mescheder, Thilo Strauss, Andreas Geiger
2020	J	`jnl`	Learning Neural Light Transport. CoRR Paul Sanzenbacher, Lars M. Mescheder, Andreas Geiger
2020	—	`—`	Stability and Expressiveness of Deep Generative Models. Lars M. Mescheder
2020	—	`ch.`	Stabilität und Expressivität von tiefen generativen Modellen. Ausgezeichnete Informatikdissertationen Lars M. Mescheder
2020	A*	`conf`	Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis. CVPR Yiyi Liao, Katja Schwarz, Lars M. Mescheder, Andreas Geiger
2019	J	`jnl`	Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision. CoRR Michael Niemeyer, Lars M. Mescheder, Michael Oechsle, Andreas Geiger
2019	A*	`conf`	Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics. ICCV Michael Niemeyer, Lars M. Mescheder, Michael Oechsle, Andreas Geiger
2019	A*	`conf`	Occupancy Networks: Learning 3D Reconstruction in Function Space. CVPR Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger
2019	A*	`conf`	Texture Fields: Learning Texture Representations in Function Space. ICCV Michael Oechsle, Lars M. Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger
2019	J	`jnl`	Texture Fields: Learning Texture Representations in Function Space. CoRR Michael Oechsle, Lars M. Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger
2019	J	`jnl`	Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis. CoRR Yiyi Liao, Katja Schwarz, Lars M. Mescheder, Andreas Geiger
2018	J	`jnl`	An Extended Perona-Malik Model Based on Probabilistic Models. J. Math. Imaging Vis. Lars M. Mescheder, Dirk A. Lorenz
2018	J	`jnl`	Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes. Int. J. Comput. Vis. Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars M. Mescheder, Andreas Geiger, Carsten Rother
2018	J	`jnl`	Occupancy Networks: Learning 3D Reconstruction in Function Space. CoRR Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger
2018	J	`jnl`	On the convergence properties of GAN training. CoRR Lars M. Mescheder
2018	A*	`conf`	Which Training Methods for GANs do actually Converge? ICML Lars M. Mescheder, Andreas Geiger, Sebastian Nowozin
2017	A*	`conf`	Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. ICML Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger
2017	J	`jnl`	Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. CoRR Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger
2017	J	`jnl`	Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes. CoRR Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars M. Mescheder, Andreas Geiger, Carsten Rother
2017	A	`conf`	Augmented Reality meets Deep Learning. BMVC Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars M. Mescheder, Andreas Geiger, Carsten Rother
2017	—	`conf`	The Numerics of GANs. NIPS Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger
2017	J	`jnl`	The Numerics of GANs. CoRR Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger
2016	J	`jnl`	An extended Perona-Malik model based on probabilistic models. CoRR Lars M. Mescheder, Dirk A. Lorenz
2016	J	`jnl`	Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring. CoRR Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.