Identifying Unknown Instances for Autonomous Driving

Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)384-393
Number of pages10
JournalProceedings of Machine Learning Research
Volume100
StatePublished - 2019
Event3rd Conference on Robot Learning, CoRL 2019 - Osaka, Japan
Duration: Oct 30 2019Nov 1 2019

Keywords

  • Autonomous Driving
  • Instance Segmentation
  • Open-Set Perception

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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