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

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 results using synthetic data or assuming the availability of 2D …

Generative Modelling of BRDF Textures from Flash Images

We learn a latent space for easy capture, semantic editing, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flash light illumination, it is …

Learning a Neural 3D Texture Space from 2D Exemplars

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 ideas from classic stochastic procedural texturing (Perlin noise) to …

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

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 they are showing instances of the same category}. The key idea is to …

Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

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 new approach to establish an environment model containing a …

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

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 dynamic occupancy grid map (DOGMa). A single-stage deep convolutional …

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

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 solve this task. As the main challenge in learning is the sheer …

Unconstrained Pedestrian Navigationbased on Vibro-tactile Feedback around the Wristband of a Smartwatch

We present a bearing-based pedestrian navigation approach that utilizes vibro-tactile feedback around the user’s wrist to convey information about the general direction of a target. Unlike traditional navigation, no route is pre-defined so that users …