提升金属物体6D姿态估计精度，解决反光与高光问题
Improving 6D Object Pose Estimation of metallic Household and Industry Objects
- 构建BOP兼容的金属物体数据集，包含多种光照与背景条件
- 引入关键点预测和材质估计算法，使金属物体姿态估计准确率提升
- 适合工业场景中金属物体识别与机器人抓取应用
6D物体姿态估计在金属物体上表现不佳，主要受反射和高光影响。本文提出一个BOP兼容的新数据集，包含多种金属物体（如罐头、家用及工业物品），覆盖不同光照与背景条件，提供额外的几何与视觉线索。实验表明这些线索可有效提升性能。为验证其价值，改进GDRNPP算法，增加关键点预测与材质估计算头，增强空间场景理解。在新数据集上的评估显示，金属物体的姿态估计精度显著提高，证实额外几何与视觉线索有助于学习。
原文摘要 · Abstract (English)
6D object pose estimation suffers from reduced accuracy when applied to metallic objects. We set out to improve the state-of-the-art by addressing challenges such as reflections and specular highlights in industrial applications. Our novel BOP-compatible dataset, featuring a diverse set of metallic objects (cans, household, and industrial items) under various lighting and background conditions, provides additional geometric and visual cues. We demonstrate that these cues can be effectively leveraged to enhance overall performance. To illustrate the usefulness of the additional features, we improve upon the GDRNPP algorithm by introducing an additional keypoint prediction and material estimator head in order to improve spatial scene understanding. Evaluations on the new dataset show improved accuracy for metallic objects, supporting the hypothesis that additional geometric and visual cues can improve learning.
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