Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations or missing parts from subtle intra-class variations. To explore this task, we leverage the power of computer-generated imagery to create, in a semi-automatic fashion, high-quality synthetic images of the same bike before and after a damage occurs. The resulting dataset, Bent & Broken Bicycles (BBBicycles), contains 39,200 images and 2,800 unique bike instances spanning 20 different bike models. As a baseline for this task, we propose TransReI3D, a multi-task, transformer-based deep network unifying damage detection (framed as a multi-label classification task) with object re-identification. The BBBicycles dataset is available at https://huggingface.co/datasets/GrainsPolito/BBBicycles

翻译：实例级物体重识别是计算机视觉中的一项基础任务，其应用涵盖图像检索、智能监控和欺诈检测等领域。本文提出了一项新颖的任务——受损物体重识别，旨在区分由形变或部件缺失导致的视觉外观变化与细微的类内差异。为探索该任务，我们利用计算机生成图像技术，以半自动方式创建了同一自行车在损伤前后的高质量合成图像。由此产生的数据集"弯曲与破损的自行车（BBBicycles）"包含39,200张图像和2,800个独特的自行车实例，涵盖20种不同的自行车模型。作为该任务的基线方法，我们提出了TransReI3D——一种基于Transformer的多任务深度网络，将损伤检测（建模为多标签分类任务）与物体重识别相融合。BBBicycles数据集可在https://huggingface.co/datasets/GrainsPolito/BBBicycles获取