RGL-NET: a Recurrent Graph Learning framework for progressive part assembly

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dc.contributor.author Narayan Harish, Abhinav
dc.contributor.author Nagar, Rajendra
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2012-09-26T07:22:36Z
dc.date.available 2012-09-26T07:22:36Z
dc.date.issued 2021-07
dc.identifier.citation Narayan Harish, Abhinav; Nagar, Rajendra and Raman, Shanmuganathan, "RGL-NET: a Recurrent Graph Learning framework for progressive part assembly", arXiv, Cornell University Library, DOI: arXiv:2107.12859, Jul. 2021. en_US
dc.identifier.uri http://arxiv.org/abs/2107.12859
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/6767
dc.description.abstract Autonomous assembly of objects is an essential task in robotics and 3D computer vision. It has been studied extensively in robotics as a problem of motion planning, actuator control and obstacle avoidance. However, the task of developing a generalized framework for assembly robust to structural variants remains relatively unexplored. In this work, we tackle this problem using a recurrent graph learning framework considering inter-part relations and the progressive update of the part pose. Our network can learn more plausible predictions of shape structure by accounting for priorly assembled parts. Compared to the current state-of-the-art, our network yields up to 10% improvement in part accuracy and up to 15% improvement in connectivity accuracy on the PartNet dataset. Moreover, our resulting latent space facilitates exciting applications such as shape recovery from the point-cloud components. We conduct extensive experiments to justify our design choices and demonstrate the effectiveness of the proposed framework.
dc.description.statementofresponsibility by Abhinav Narayan Harish, Rajendra Nagar and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title RGL-NET: a Recurrent Graph Learning framework for progressive part assembly en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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