We present a deep convolutional decoder architecture that can generate volumetric 3D
outputs in a compute-and memory-efficient manner by using an octree representation. The …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

In this work we propose a novel model-based deep convolutional autoencoder that
addresses the highly challenging problem of reconstructing a 3D human face from a single …

Modern 3D human pose estimation techniques rely on deep networks, which require large
amounts of training data. While weakly-supervised methods require less supervision, by …

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete
3D structure of a given object from a single arbitrary depth view using generative adversarial …

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the
complete 3D structure of a given object from a single arbitrary depth view using generative …

Assembly-based tools provide a powerful modeling paradigm for non-expert shape
designers. However, choosing a component from a large shape repository and aligning it to …

In this work, we propose a novel model-based deep convolutional autoencoder that
addresses the highly challenging problem of reconstructing a 3D human face from a single …

Learning general image representations has proven key to the success of many computer
vision tasks. For example, many approaches to image understanding problems rely on deep …

We present 3DeepCT, a deep neural network for computed tomography, which performs 3D
reconstruction of scattering volumes from multi-view images. The architecture is dictated by …