ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning
This tool addresses annotation challenges for researchers and developers in 3D object detection, particularly in autonomous vehicles, but it is incremental as it builds on existing annotation methods.
The authors tackled the problem of incomplete annotations and high costs in 3D bounding box annotation for autonomous vehicle datasets by proposing ReBound, an open-source tool that enables re-annotation and active learning, with survey results demonstrating its usability.
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