In recent years, supervised learning has become the dominant paradigm for training deep-learning based methods for 3D object detection. Lately, the academic community has studied 3D object detection in the context of autonomous vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse 2.0. However, these datasets may have incomplete annotations, often only labeling a small subset of objects in a scene. Although commercial services exists for 3D bounding box annotation, these are often prohibitively expensive. To address these limitations, we propose ReBound, an open-source 3D visualization and dataset re-annotation tool that works across different datasets. In this paper, we detail the design of our tool and present survey results that highlight the usability of our software. Further, we show that ReBound is effective for exploratory data analysis and can facilitate active-learning. Our code and documentation is available at https://github.com/ajedgley/ReBound
@article{arxiv.2303.06250,
title = {ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning},
author = {Wesley Chen and Andrew Edgley and Raunak Hota and Joshua Liu and Ezra Schwartz and Aminah Yizar and Neehar Peri and James Purtilo},
journal= {arXiv preprint arXiv:2303.06250},
year = {2023}
}
Comments
Accepted to CHI 2023 Workshop - Intervening, Teaming, Delegating: Creating Engaging Automation Experiences (AutomationXP)