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Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program
3D shapes have complementary abstractions from low-level geometry to
part-based hierarchies to languages, which convey different levels of
information. This paper presents a unified framework to translate between pairs
of shape abstractions: . We propose to model the abstraction transformation as a conditional
generation process. It converts 3D shapes of three abstract types into unified
discrete shape code, transforms each shape code into code of other abstract
types through the proposed , and decodes them
to output the target shape abstraction. Point Cloud code is obtained in a
class-agnostic way by the proposed VQVAE. On Text2Shape,
ShapeGlot, ABO, Genre, and Program Synthetic datasets, Neural Shape Compiler
shows strengths in ,
, , and Point Cloud Completion tasks.
Additionally, Neural Shape Compiler benefits from jointly training on all
heterogeneous data and tasks.Comment: project page:
https://tiangeluo.github.io/projectpages/shapecompiler.htm
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