1 citations · 1 across the 1 of their papers we have counts for
5 papers
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler +3
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category…
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Philipp Henzler, Jeremy Reizenstein, Patrick Labatut +4
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D. While several recent works have obtained analogous…
Generative Modelling of BRDF Textures from Flash Images
Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra +1
We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural mat…
Learning a Neural 3D Texture Space from 2D Exemplars
Philipp Henzler, Niloy J. Mitra, Tobias Ritschel
We propose a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend…
Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs
Nico Engel, Stefan Hoermann, Philipp Henzler +1
The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a n…