16 citations · 19 across the 2 of their papers we have counts for
8 papers · 1 filter
Learning Neural Light Transport
Paul Sanzenbacher, Lars Mescheder, Andreas Geiger
In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data au…
Learning Implicit Surface Light Fields
Michael Oechsle, Michael Niemeyer, Lars Mescheder +2
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to repres…
Convolutional Occupancy Networks
Songyou Peng, Michael Niemeyer, Lars Mescheder +2
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited t…
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…
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…