BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift
This provides a domain-specific dataset for researchers in 3D computer vision to study object detection under domain shift, though it is incremental as it focuses on a new dataset rather than a novel method.
The paper tackles the problem of domain shift in 3D object detection by introducing the BugNIST dataset, which includes 9,154 micro-CT volumes of 12 bug types and 388 volumes of tightly packed mixtures, enabling training on individually scanned objects and testing on mixtures to address annotation challenges.
Domain shift significantly influences the performance of deep learning algorithms, particularly for object detection within volumetric 3D images. Annotated training data is essential for deep learning-based object detection. However, annotating densely packed objects is time-consuming and costly. Instead, we suggest training models on individually scanned objects, causing a domain shift between training and detection data. To address this challenge, we introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types and 388 volumes of tightly packed bug mixtures. This dataset is characterized by having objects with the same appearance in the source and target domains, which is uncommon for other benchmark datasets for domain shift. During training, individual bug volumes labeled by class are utilized, while testing employs mixtures with center point annotations and bug type labels. Together with the dataset, we provide a baseline detection analysis, with the aim of advancing the field of 3D object detection methods.