# Identifying Unknown Instances for Autonomous Driving Source: [Identifying Unknown Instances for Autonomous Driving]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:52:21.192Z Authors: Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun Published: 2019-10-24T17:24:43.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Panoptic Segmentation on Lost&Found unseen (test)](): Panoptic Segmentation; Lost&Found unseen (test); PQ: 145. - [Open-set 3D Object Detection on KITTI (test)](): Open-set 3D Object Detection; KITTI (test); AP (Unknown): 1.1. ## Original sources and implementation [Original paper]() [PDF]()