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Found 3 papers, 1 papers with code
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape.
Learning Part Generation and Assembly for Structure-aware Shape Synthesis
Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet.
Im2Struct: Recovering 3D Shape Structure from a Single RGB Image
We propose to recover 3D shape structures from single RGB images, where structure refers to shape parts represented by cuboids and part relations encompassing connectivity and symmetry.
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