147 citations · 355 across the 24 of their papers we have counts for
8 papers · 1 filter
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle +1
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently…
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Yiyi Liao, Katja Schwarz, Lars Mescheder +1
In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical renderi…
Attacking Optical Flow
Anurag Ranjan, Joel Janai, Andreas Geiger +1
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-drivin…
Texture Fields: Learning Texture Representations in Function Space
Michael Oechsle, Lars Mescheder, Michael Niemeyer +2
In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly r…
Robust Dense Mapping for Large-Scale Dynamic Environments
Ioan Andrei Bârsan, Peidong Liu, Marc Pollefeys +1
We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static backgr…
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…