We introduce IndustryShapes, a new RGB-D benchmark dataset of industrial tools and components, designed for both instance-level and novel object 6D pose estimation approaches. The dataset provides a realistic and application-relevant testbed for benchmarking these methods in the context of industrial robotics bridging the gap between lab-based research and deployment in real-world manufacturing scenarios. Unlike many previous datasets that focus on household or consumer products or use synthetic, clean tabletop datasets, or objects captured solely in controlled lab environments, IndustryShapes introduces five new object types with challenging properties, also captured in realistic industrial assembly settings. The dataset has diverse complexity, from simple to more challenging scenes, with single and multiple objects, including scenes with multiple instances of the same object and it is organized in two parts: the classic set and the extended set. The classic set includes a total of 4,6k images and 6k annotated poses. The extended set introduces additional data modalities to support the evaluation of model-free and sequence-based approaches. To the best of our knowledge, IndustryShapes is the first dataset to offer RGB-D static onboarding sequences. We further evaluate the dataset on a representative set of state-of-the art methods for instance-based and novel object 6D pose estimation, including also object detection, segmentation, showing that there is room for improvement in this domain. The dataset page can be found in https://pose-lab.github.io/IndustryShapes.

翻译：本文介绍IndustryShapes——一个面向工业工具与部件的新型RGB-D基准数据集，专为实例级及新物体6D位姿估计方法设计。该数据集为工业机器人领域的相关方法评估提供了真实且具应用价值的测试平台，弥合了实验室研究与实际制造场景部署之间的鸿沟。与以往聚焦家居消费品、使用合成桌面数据集或仅在受控实验室环境采集的数据集不同，IndustryShapes引入了五种具有挑战性特性的新型物体类别，并在真实工业装配场景中完成采集。数据集涵盖从简单到复杂的多样化场景，包含单物体与多物体配置（含同物体多实例场景），并分为经典集与扩展集两部分：经典集包含总计4.6千幅图像与6千个标注位姿；扩展集则提供额外数据模态以支持无模型与序列化方法的评估。据我们所知，IndustryShapes是首个提供RGB-D静态上料序列的数据集。我们进一步采用代表性前沿方法对数据集进行评估，涵盖实例级与新物体6D位姿估计（包括物体检测与分割任务），结果表明该领域仍有提升空间。数据集页面详见https://pose-lab.github.io/IndustryShapes。