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|a 10.1109/TPAMI.2020.3044749
|2 doi
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|a eng
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| 100 |
1 |
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|a Sharma, Gopal
|e verfasserin
|4 aut
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| 245 |
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|a Neural Shape Parsers for Constructive Solid Geometry
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|c 2022
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|a Text
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|a Date Completed 08.06.2022
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|a Date Revised 09.07.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Constructive solid geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinders. We present CSGNet, a deep network architecture that takes as input a 2D or 3D shape and outputs a CSG program that models it. Parsing shapes into CSG programs is desirable as it yields a compact and interpretable generative model. However, the task is challenging since the space of primitives and their combinations can be prohibitively large. CSGNet uses a convolutional encoder and recurrent decoder based on deep networks to map shapes to modeling instructions in a feed-forward manner and is significantly faster than bottom-up approaches. We investigate two architectures for this task-a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack. The stack augmentation improves the reconstruction quality of the generated shape and learning efficiency. Our approach is also more effective as a shape primitive detector compared to a state-of-the-art object detector. Finally, we demonstrate CSGNet can be trained on novel datasets without program annotations through policy gradient techniques
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Goyal, Rishabh
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Difan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kalogerakis, Evangelos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Maji, Subhransu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 5 vom: 14. Mai, Seite 2628-2640
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:44
|g year:2022
|g number:5
|g day:14
|g month:05
|g pages:2628-2640
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|u http://dx.doi.org/10.1109/TPAMI.2020.3044749
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