In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutional neural network to project points into a category-agnostic embedding space in which they can be clustered into instances irrespective of their semantics. Experiments on two large-scale self-driving datasets validate the effectiveness of our proposed method.
@article{arxiv.1910.11296,
title = {Identifying Unknown Instances for Autonomous Driving},
author = {Kelvin Wong and Shenlong Wang and Mengye Ren and Ming Liang and Raquel Urtasun},
journal= {arXiv preprint arXiv:1910.11296},
year = {2019}
}