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In recent years, supervised learning has become the dominant paradigm fortraining deep-learning based methods for 3D object detection. Lately, theacademic community has studied 3D object detection in the context of autonomousvehicles (AVs) using publicly available datasets such as nuScenes and Argoverse2.0. However, these datasets may have incomplete annotations, often onlylabeling a small subset of objects in a scene. Although commercial servicesexists for 3D bounding box annotation, these are often prohibitively expensive.To address these limitations, we propose ReBound, an open-source 3Dvisualization and dataset re-annotation tool that works across differentdatasets. In this paper, we detail the design of our tool and present surveyresults that highlight the usability of our software. Further, we show thatReBound is effective for exploratory data analysis and can facilitateactive-learning. Our code and documentation is available athttps://github.com/ajedgley/ReBound

ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning