Start ordering with Uber Eats

Order now
AI, Engineering

Identifying Unknown Instances for Autonomous Driving

October 24, 2019 / Global

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.

Authors

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

Conference

CoRL 2019

Full Paper

‘Identifying Unknown Instances for Autonomous Driving’ (PDF)

Uber ATG