The code will be released soon.
Authors: Remco Royen, Leon Denis, and Adrian Munteanu - Vrije Universiteit Brussel (VUB). The first author has been funded by a FWO-SB scholarship.
This repository is the code release of the work from our [electronic letter] (link not yet available), published in ??? 2024. We propose a 3D instance segmentation method which simultaneously learns coefficients and prototypes.
3D instance segmentation is crucial for applications demanding comprehensive 3D scene understanding. In this paper, we introduce a novel method that simultaneously learns coefficients and prototypes. Employing an overcomplete sampling strategy, our method produces an overcomplete set of instance predictions, from which the optimal ones are selected through a Non-Maximum Suppression (NMS) algorithm during inference. The obtained prototypes are visualizable and interpretable. Our method demonstrates superior performance on S3DIS-blocks, consistently outperforming existing methods in mRec and mPrec. Moreover, it operates 32.9% faster than the state-of-the-art. Notably, with only 0.8% of the total inference time, our method exhibits an over 20-fold reduction in the variance of inference time compared to existing methods. These attributes render our method well-suited for practical applications requiring both rapid inference and high reliability.
If you find our work useful in your research, please consider citing:
@article{royen2024joint,
title={Joint prototype and coefficient prediction for 3D instance segmentation},
author={Royen, Remco and Denis, Leon and Munteanu, Adrian},
journal={Electronics Letters},
volume={60},
number={5},
pages={e13137},
year={2024},
publisher={Wiley Online Library}
}
Our code is released under MIT License (see LICENSE file for details).
If you have any questions or suggestions regarding this repo or the paper, feel free to contact me (remco.royen@vub.be).