1,509 research outputs found
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
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