PointAtMe: Efficient 3D Point Cloud Labeling in Virtual Reality

摘要

Generating annotations which can be used to train new models has become an independent field of research within machine learning. Its goal is producing highly accurate annotations as cost efficient as possible. 3D point clouds are the common sensor output when recording 3D data from a mobile platform. The latest ways of annotating 3D point clouds include their visualization on a 2D screen. This method contradicts the goal of time-efficient annotating since it is unintuitive and therefore unnecessarily time consuming. We present a novel labeling technique in Virtual Reality. Using our tool, we accelerate the process of data annotation significantly compared to existing approaches. Furthermore, we will give the machine learning community access to our tool and create a new community-labeled dataset for autonomous driving. Furthermore we plan to set up an annotation benchmark in which primarily commercial annotation companies but also researchers active in annotation can take part in. We present results from an experimental plattform based on Oculus Rift indicating a huge potential for VR annotations.