Densification of Semi-Dense Reconstructions for Novel View Generation of Live Scenes
Abstract
In this paper, we consider the problem of rendering novel views of a live unprepared scene from video input, important to many application scenarios (such as telepresence and remote collaboration). We present an optimization approach to improving incomplete scene reconstructions captured in real time with a single moving monocular camera. We take semi-dense depth maps and convert them into a dense scene model, suitable for rendering plausible novel views of the scene using conventional image-based rendering. Our implementation densifies depth maps at the rate they are generated, and enables us to generate novel views of live scenes with no pre-capture or preprocessing. In evaluations comparing with other approaches, our method performs well even on difficult scenes, and results in higher-quality novel views.
Cite
Text
Baricevic et al. "Densification of Semi-Dense Reconstructions for Novel View Generation of Live Scenes." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.99Markdown
[Baricevic et al. "Densification of Semi-Dense Reconstructions for Novel View Generation of Live Scenes." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/baricevic2017wacv-densification/) doi:10.1109/WACV.2017.99BibTeX
@inproceedings{baricevic2017wacv-densification,
title = {{Densification of Semi-Dense Reconstructions for Novel View Generation of Live Scenes}},
author = {Baricevic, Domagoj and Höllerer, Tobias and Turk, Matthew},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2017},
pages = {842-851},
doi = {10.1109/WACV.2017.99},
url = {https://mlanthology.org/wacv/2017/baricevic2017wacv-densification/}
}