34 citations · 62 across the 3 of their papers we have counts for
4 papers
Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
Despoina Paschalidou, Ali Osman Ulusoy, Andreas Geiger
Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this p…
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
Despoina Paschalidou, Ali Osman Ulusoy, Carolin Schmitt +2
In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) al…
OctNetFusion: Learning Depth Fusion from Data
Gernot Riegler, Ali Osman Ulusoy, Horst Bischof +1
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on…
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep…