Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
This provides a dataset and environment for researchers working on programmatic CAD construction, addressing a gap in the field, but it is incremental as it builds on existing CAD and program synthesis paradigms.
The authors tackled the lack of a realistic dataset for parametric CAD by introducing the Fusion 360 Gallery, a dataset of 8,625 human design sequences in a simple programmatic language, and the Fusion 360 Gym environment for machine learning applications, and they applied state-of-the-art program synthesis methods to the CAD reconstruction task.
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despite the pervasiveness of parametric CAD and a growing interest from the research community, currently there does not exist a dataset of realistic CAD models in a concise programmatic form. In this paper we present the Fusion 360 Gallery, consisting of a simple language with just the sketch and extrude modeling operations, and a dataset of 8,625 human design sequences expressed in this language. We also present an interactive environment called the Fusion 360 Gym, which exposes the sequential construction of a CAD program as a Markov decision process, making it amendable to machine learning approaches. As a use case for our dataset and environment, we define the CAD reconstruction task of recovering a CAD program from a target geometry. We report results of applying state-of-the-art methods of program synthesis with neurally guided search on this task.