MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes

• URL: http://arxiv.org/abs/2304.12592v1
• Date: Tue, 25 Apr 2023 05:55:29 GMT
• Title: MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes
• Authors: Han Wang, Jiayuan Zhang, Lipeng Wan, Xingyu Chen, Xuguang Lan, Nanning Zheng
• Abstract summary: We propose a novel multi-view fusion framework, namely multi-view MRD network (MMRDN) We project the 2D data from different views into a common hidden space and fit the embeddings with a set of Von-Mises-Fisher distributions. We select a set of $K$ Maximum Vertical Neighbors (KMVN) points from the point cloud of each object pair, which encodes the relative position of these two objects.
• Score: 62.20046129613934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes. Previous works infer manipulation relationship by deep neural network trained with data collected from a predefined view, which has limitation in visual dislocation in unstructured environments. Multi-view data provide more comprehensive information in space, while a challenge of multi-view MRD is domain shift. In this paper, we propose a novel multi-view fusion framework, namely multi-view MRD network (MMRDN), which is trained by 2D and 3D multi-view data. We project the 2D data from different views into a common hidden space and fit the embeddings with a set of Von-Mises-Fisher distributions to learn the consistent representations. Besides, taking advantage of position information within the 3D data, we select a set of $K$ Maximum Vertical Neighbors (KMVN) points from the point cloud of each object pair, which encodes the relative position of these two objects. Finally, the features of multi-view 2D and 3D data are concatenated to predict the pairwise relationship of objects. Experimental results on the challenging REGRAD dataset show that MMRDN outperforms the state-of-the-art methods in multi-view MRD tasks. The results also demonstrate that our model trained by synthetic data is capable to transfer to real-world scenarios.

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