@inproceedings{4565bb7992674cfc989981558496e67d,
title = "DeepVoxels: Learning persistent 3D feature embeddings",
abstract = "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 DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes.",
keywords = "Deep Learning, Image and Video Synthesis",
author = "Vincent Sitzmann and Justus Thies and Felix Heide and Matthias Niebner and Gordon Wetzstein and Michael Zollhofer",
note = "Funding Information: Acknowledgements: We thank Robert Konrad, Nitish Padmanaban, and Ludwig Schubert for fruitful discussions, and Robert Konrad for the video voiceover. Vincent Sitzmann was supported by a Stanford Graduate Fellowship. Michael Zollh{\"o}fer and Vincent Sitzmann were supported by the Max Planck Center for Visual Computing and Communication (MPC-VCC). Gordon Wetzstein was supported by a National Science Foundation CAREER award (IIS 1553333), by a Sloan Fellowship, and by an Okawa Research Grant. Matthias Nie{\ss}ner and Justus Thies were supported by a Google Research Grant, the ERC Starting Grant Scan2CAD (804724), a TUM-IAS Rudolf M{\"o}{\ss}bauer Fellowship (Focus Group Visual Computing), and a Google Faculty Award. Publisher Copyright: {\textcopyright} 2019 IEEE.; 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CVPR.2019.00254",
language = "English (US)",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "2432--2441",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019",
address = "United States",
}