Search Results for author: Philipp Henzler

Found 7 papers, 2 papers with code

Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction

1 code implementation1 Sep 2021 Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotny

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-centric data.

3D Reconstruction Neural Rendering

Unsupervised Learning of 3D Object Categories from Videos in the Wild

no code implementations CVPR 2021 Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny

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.

Generative Modelling of BRDF Textures from Flash Images

no code implementations23 Feb 2021 Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra, Tobias Ritschel

We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance.

Femtosecond Transfer and Manipulation of Persistent Hot-Trion Coherence in a Single CdSe/ZnSe Quantum Dot

no code implementations20 Nov 2020 Philipp Henzler, Christian Traum, Matthias Holtkemper, David Nabben, Marcel Erbe, Doris E. Reiter, Tilmann Kuhn, Suddhassatta Mahapatra, Karl Brunner, Denis V. Seletskiy, Alfred Leitenstorfer

Ultrafast transmission changes around the fundamental trion resonance are studied after exciting a p-shell exciton in a negatively charged II-VI quantum dot.

Mesoscale and Nanoscale Physics Quantum Physics

Learning a Neural 3D Texture Space from 2D Exemplars

1 code implementation CVPR 2020 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.

Escaping Plato's Cave: 3D Shape From Adversarial Rendering

no code implementations ICCV 2019 Philipp Henzler, Niloy Mitra, Tobias Ritschel

We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PlatonicGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods.

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