263 citations · 355 across the 2 of their papers we have counts for
6 papers · 1 filter
MetaSDF: Meta-learning Signed Distance Functions
Vincent Sitzmann, Eric R. Chan, Richard Tucker +2
Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high…
Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman +2
Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over con…
State of the Art on Neural Rendering
Ayush Tewari, Ohad Fried, Justus Thies +16
Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic imag…
Semantic Implicit Neural Scene Representations With Semi-Supervised Training
Amit Kohli, Vincent Sitzmann, Gordon Wetzstein
The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes. Unlike conventional 3D representations, such a…
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein
Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware repre…
DeepVoxels: Learning Persistent 3D Feature Embeddings
Vincent Sitzmann, Justus Thies, Felix Heide +3
In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose Dee…