activity
20172023
most citedCommon Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction

1 citations · 3 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV2023★ 1 cited

CamP: Camera Preconditioning for Neural Radiance Fields

Keunhong Park, Philipp Henzler, Ben Mildenhall +2

Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D scene reconstructions of objects and large-scale scenes. However, NeRFs require accurate camera parameters…

cs.CV2021★ 1 cited

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…

cs.CV2021

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…

cs.GR2021

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…

cs.CV2019

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…

cs.CV2018

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

Philipp Henzler, Niloy Mitra, Tobias Ritschel

We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e., where no relation between photos is known, except that…

cs.RO2018

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…

cs.CV2018

Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation

Stefan Hoermann, Philipp Henzler, Martin Bach +1

We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360° coverage were fused in a dyna…

cs.GR2017★ 1 cited

Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

Philipp Henzler, Volker Rasche, Timo Ropinski +1

As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can…