为工业金属物体6自由度位姿估计建立新基准，解决现有数据集泛化不足问题。
IMD: A 6-DoF Pose Estimation Benchmark for Industrial Metallic Objects
- 构建45个真实尺寸工业部件的RGB-D数据集，模拟真实车间环境。
- 在金属、无纹理、高反光条件下，现有模型性能显著下降。
- 支持三类任务，适合工业机器人感知算法研发与评估。
6D位姿估计对工业机器人感知至关重要，但现有基准多基于日常有纹理、低反光物体，难以适配金属、无纹理、高反射的工业场景。为此，我们提出面向工业应用的新数据集与基准——工业金属数据集（IMD）。该数据集包含45个真实尺寸的工业部件，采用RGB-D相机在自然室内光照下、不同摆放条件下采集，复现真实作业环境。基准涵盖视频目标分割、6D位姿跟踪和单次6D位姿估计三项任务。我们评估了XMem、SAM2等分割模型以及BundleTrack、BundleSDF等位姿估计模型在工业场景下的表现。结果表明，本数据集比现有家居类数据集更具挑战性。该基准为开发更适用于工业机器人的分割与位姿估计算法提供了统一基线。
原文摘要 · Abstract (English)
Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。