MRLabeling: Create RGB-D Datasets On-The-Fly using Mixed Reality

• Published in: 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) pp. 706 - 711
• Main Authors: Nguyen, Richard, Baghezza, Rani, Georges, Benjamin, Gouin-Vallerand, Charles, Bouchard, Kevin, Amiri, Maryam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16-10-2023
• Subjects: Annotations; Computing methodologies-Artificial intelligence-Computer vision; Headphones; Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality; Image segmentation; Mixed reality; Object detection; Prototypes; Three-dimensional displays
• ISSN: 2771-1110
• DOI: 10.1109/ISMAR-Adjunct60411.2023.00151

One of the best ways to build better vision models is to train large models on big datasets. However, the process of building such datasets is often costly and tedious. With the ever increasing adoption of Mixed Reality in professional settings, and with the performance improvements of headsets in recent years, we see an opportunity for a tool that combines data collection and annotation in a single process, and that leverages both RGB and depth data provided by the headset sensors. Moreover, assisting machine learning predictive models with user inputs through natural mixed reality interactions is a promising prospective for human-artificial intelligence interactions. In this paper, we present MRLabeling, an application developped for the Microsoft Hololens 2 that allows the easy creation and annotation of datasets directly in Mixed Reality. We first describe the design of the system, the way 3D bounding boxes drawn by the user are projected in 2D to create annotated images on the fly, and the use of segmentation algorithms to go beyond bounding boxes. After that, we explore the use of depth data, and the current limitations of the system, as well as avenues for future work.