论文标题
使用RGB-D融合和综合数据将沉重的混乱中的看不见的工业组件分割
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
论文作者
论文摘要
对自主工业系统的看不见的工业零件的细分至关重要。但是，工业组件是无纹理的，反光的，并且经常在堵塞的混乱和非结构化的环境中发现，这使得处理看不见的物体更具挑战性。为了解决此问题，我们提出了一个合成数据生成管道，该管道通过域随机化将纹理随机随机化，以关注形状信息。此外，我们提出了一个带有置信图估计器的RGB-D融合蒙版R-CNN，该估计器利用了多个特征级别的可靠深度信息。我们将经过训练的模型转移到了现实世界的情况下，并通过与基准和消融研究进行比较来评估其性能。我们证明，仅使用合成数据的方法可能是对看不见的工业组件细分的有效解决方案。
Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information. In addition, we propose an RGB-D Fusion Mask R-CNN with a confidence map estimator, which exploits reliable depth information in multiple feature levels. We transferred the trained model to real-world scenarios and evaluated its performance by making comparisons with baselines and ablation studies. We demonstrate that our methods, which use only synthetic data, could be effective solutions for unseen industrial components segmentation.
