813 citations · 1.2k across the 10 of their papers we have counts for
13 papers
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…