Interactive Segmentation of Radiance Fields
Abstract
Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show promise but don't scale to complex objects with diverse appearance. We present the ISRF method to interactively segment objects with fine structure and appearance. Nearest neighbor feature matching using distilled semantic features identifies high-confidence seed regions. Bilateral search in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., and an interactive segmentation tool that others can use.
Cite
Text
Goel et al. "Interactive Segmentation of Radiance Fields." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00409Markdown
[Goel et al. "Interactive Segmentation of Radiance Fields." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/goel2023cvpr-interactive/) doi:10.1109/CVPR52729.2023.00409BibTeX
@inproceedings{goel2023cvpr-interactive,
title = {{Interactive Segmentation of Radiance Fields}},
author = {Goel, Rahul and Sirikonda, Dhawal and Saini, Saurabh and Narayanan, P. J.},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2023},
pages = {4201-4211},
doi = {10.1109/CVPR52729.2023.00409},
url = {https://mlanthology.org/cvpr/2023/goel2023cvpr-interactive/}
}