Geometry-aware Deep Network for Single-Image Novel View Synthesis

This paper tackles the problem of novel view synthesis from a single image. In particular, we target real-world scenes with rich geometric structure, a challenging task due to the large appearance variations of such scenes and the lack of simple 3D models to represent them. Modern, learning-based approaches mostly focus on appearance to synthesize novel views and thus tend to generate predictions that are inconsistent with the underlying scene structure. By contrast, in this paper, we propose to exploit the 3D geometry of the scene to synthesize a novel view. Specifically, we approximate a real-world scene by a fixed number of planes, and learn to predict a set of homographies and their corresponding region masks to transform the input image into a novel view. To this end, we develop a new region-aware geometric transform network that performs these multiple tasks in a common framework. Our results on the outdoor KITTI and the indoor ScanNet datasets demonstrate the effectiveness of our network in generating high quality synthetic views that respect the scene geometry, thus outperforming the state-of-the-art methods.


Published in:
2018 Ieee/Cvf Conference On Computer Vision And Pattern Recognition (Cvpr), 4616-4624
Presented at:
31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, Jun 18-23, 2018
Year:
Jan 01 2018
Publisher:
New York, IEEE
ISSN:
1063-6919
ISBN:
978-1-5386-6420-9
Laboratories:




 Record created 2019-06-18, last modified 2019-08-12


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