%0 Generic %1 willis2020fusion %A Willis, Karl D. D. %A Pu, Yewen %A Luo, Jieliang %A Chu, Hang %A Du, Tao %A Lambourne, Joseph G. %A Solar-Lezama, Armando %A Matusik, Wojciech %D 2020 %K 2020 cad dataset reconstruction %T Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction %U http://arxiv.org/abs/2010.02392 %X Parametric computer-aided design (CAD) is a standard paradigm used for the design of manufactured objects. CAD designers perform modeling operations, such as sketch and extrude, to form a construction sequence that makes up a final design. Despite the pervasiveness of parametric CAD and growing interest from the research community, a dataset of human designed 3D CAD construction sequences has not been available to-date. In this paper we present the Fusion 360 Gallery reconstruction dataset and environment for learning CAD reconstruction. We provide a dataset of 8,625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction. We outline a standard CAD reconstruction task, together with evaluation metrics, and present results from a novel method using neurally guided search to recover a construction sequence from raw geometry.

@misc{willis2020fusion, abstract = {Parametric computer-aided design (CAD) is a standard paradigm used for the design of manufactured objects. CAD designers perform modeling operations, such as sketch and extrude, to form a construction sequence that makes up a final design. Despite the pervasiveness of parametric CAD and growing interest from the research community, a dataset of human designed 3D CAD construction sequences has not been available to-date. In this paper we present the Fusion 360 Gallery reconstruction dataset and environment for learning CAD reconstruction. We provide a dataset of 8,625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction. We outline a standard CAD reconstruction task, together with evaluation metrics, and present results from a novel method using neurally guided search to recover a construction sequence from raw geometry.}, added-at = {2021-05-06T15:08:15.000+0200}, author = {Willis, Karl D. D. and Pu, Yewen and Luo, Jieliang and Chu, Hang and Du, Tao and Lambourne, Joseph G. and Solar-Lezama, Armando and Matusik, Wojciech}, biburl = {https://www.bibsonomy.org/bibtex/29056a7de54f407b00fbcbf4ef50a8f36/analyst}, description = {[2010.02392] Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction}, interhash = {3fe5adaa0f950afe1a152d084b19f0a7}, intrahash = {9056a7de54f407b00fbcbf4ef50a8f36}, keywords = {2020 cad dataset reconstruction}, note = {cite arxiv:2010.02392}, timestamp = {2021-05-06T15:08:15.000+0200}, title = {Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction}, url = {http://arxiv.org/abs/2010.02392}, year = 2020 }