Multi-user, Scalable 3D Object Detection in AR Cloud

PubDate: June 2020

Teams: Magic Leap

Writers: Siddarth Choudhary, Nitesh Sekhar, Siddharth Mahendran, Prateek Singhal

PDF: Multi-user, Scalable 3D Object Detection in AR Cloud

Project: Multi-user, Scalable 3D Object Detection in AR Cloud

Abstract

As AR Cloud gains importance, one key challenge is large scale, multi-user 3D object detection. Current approaches typically focus on the single-room, single-user scenarios. In this work, we present an approach for multi-user and scalable 3D object detection, based on distributed data association and fusion. We use an off-the-shelf detector to detect object instances in 2D and then combine them in 3D, per object while allowing asynchronous updates to the map. The distributed data association and fusion allows us to scale the detection to a large number of users concurrently, while maintaining a lower memory footprint without loss in accuracy. We show empirical results, where the distributed and centralized approaches achieve comparable accuracy on the ScanNet dataset while reducing the memory consumption by a factor of 15.