Objects Can Move: 3D Change Detection by Geometric Transformation Consistency
Abstract
AR/VR applications and robots need to know when the scene has changed. An example is when objects are moved, added, or removed from the scene. We propose a 3D object discovery method that is based only on scene changes. Our method does not need to encode any assumptions about what is an object, but rather discovers objects by exploiting their coherent move. Changes are initially detected as differences in the depth maps and segmented as objects if they undergo rigid motions. A graph cut optimization propagates the changing labels to geometrically consistent regions. Experiments show that our method achieves state-of-the-art performance on the 3RScan dataset against competitive baselines. The source code of our method can be found at https://github.com/katadam/ObjectsCanMove.
Cite
Text
Adam et al. "Objects Can Move: 3D Change Detection by Geometric Transformation Consistency." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19827-4_7Markdown
[Adam et al. "Objects Can Move: 3D Change Detection by Geometric Transformation Consistency." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/adam2022eccv-objects/) doi:10.1007/978-3-031-19827-4_7BibTeX
@inproceedings{adam2022eccv-objects,
title = {{Objects Can Move: 3D Change Detection by Geometric Transformation Consistency}},
author = {Adam, Aikaterini and Sattler, Torsten and Karantzalos, Konstantinos and Pajdla, Tomas},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-19827-4_7},
url = {https://mlanthology.org/eccv/2022/adam2022eccv-objects/}
}