Jigsaw++: Imagining Complete Shape Priors for Object Reassembly
Keywords: Fracture Reassembly, Object Reassembly, Generative Model
TL;DR: We propose Jigsaw++, a generative model designed for reconstruction for the object reassembly problem.
Abstract: The automatic assembly problem has attracted increasing interest due to its complex challenges that involve 3D representation. This paper introduces Jigsaw++, a novel generative method designed to tackle the multifaceted challenges of reconstruction for the reassembly problem. Existing approach focusing primarily on piecewise information for both part and fracture assembly, often overlooking the integration of complete object prior. Jigsaw++ distinguishes itself by learning a category-agnostic shape prior of complete objects. It employs the proposed "retargeting" strategy that effectively leverages the output of any existing assembly method to generate complete shape reconstructions. This capability allows it to function orthogonally to the current methods. Through extensive evaluations on Breaking Bad dataset and PartNet, Jigsaw++ has demonstrated its effectiveness, reducing reconstruction errors and enhancing the precision of shape reconstruction, which sets a new direction for future reassembly model developments.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1969
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