Hashmod: A Hashing Method for Scalable 3D Object Detection

Wadim Kehl, Federico Tombari, Nassir Navab, Slobodan Ilic and Vincent Lepetit

Abstract

We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.

Session

Poster 1

Files

Extended Abstract (PDF, 916K)
Paper (PDF, 1827K)

DOI

10.5244/C.29.36
https://dx.doi.org/10.5244/C.29.36

Citation

Bibtex

@inproceedings{BMVC2015_36,
	title={Hashmod: A Hashing Method for Scalable 3D Object Detection},
	author={Wadim Kehl and Federico Tombari and Nassir Navab and Slobodan Ilic and Vincent Lepetit},
	year={2015},
	month={September},
	pages={36.1-36.12},
	articleno={36},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.36},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.36}
}