NURBS-Diff: A Differentiable Programming Module for NURBS
WebMay 1, 2022 · We propose a differentiable NURBS module to integrate NURBS representations of CAD models with deep learning methods. We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters (control points, weights, and the knot vector).
WebWe mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters (control points, weights, and the knot vector). These derivatives are used to define an ap-proximate Jacobian used for performing the “backward” evaluation to train the deep learning models.
WebWe mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters. These derivatives are used to define an approximate Jacobian that can be used to perform the “backward” evaluation used while training deep learning models.
NURBS-Diff: A Differentiable Programming Module for NURBS
WebMay 1, 2022 · We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters (control points, weights, and the knot vector). These derivatives are used to define an approximate Jacobian used for performing the “backward” evaluation to train the deep learning models.
NURBS-Diff: A Differentiable Programming Module for NURBS
WebJan 17, 2022 · NURBS-Diff: A Differentiable Programming Module for NURBS. January 2022. Computer-Aided Design 146 (4):103199. DOI: 10.1016/j.cad.2022.103199. Authors: Anjana Deva Prasad. Aditya Balu....
NURBS-Diff: A Differentiable Programming Module for NURBS
WebApr 29, 2021 · NURBS-Diff: A Differentiable Programming Module for NURBS. Anjana Prasad, Aditya Balu, +3 authors. A. Krishnamurthy. Published in Comput. Aided Des. 29 April 2021. Computer Science, Engineering. Comput. Aided Des. [PDF] Semantic Reader. Save to Library. Create Alert. Cite. Figures and Tables from this paper. figure 1. table 1. …
WebWe mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters (control points, weights, and the knot vector). These derivatives are used to define an approximate Jacobian used for performing the ‘‘backward’’ evaluation to train the deep learning models.
NURBS-Diff: A Differentiable NURBS Layer for Machine …
WebApr 29, 2021 · NURBS-Diff: A Differentiable NURBS Layer for Machine Learning CAD Applications. April 2021. License. CC BY-NC-ND 4.0. Authors: Anjana Deva Prasad. Aditya Balu. Birla Institute of...
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B-Splines and NURBS
WebB-spline Blending Functions. is a step function that is 1 in the interval. spans two intervals and is a piecewise linear function that goes from 0 to 1 (and back) spans three intervals and is a piecewise quadratic that grows from 0 to 1/4, then up to 3/4 in the middle of the second interval, back to 1/4, and back to 0.
NURBS-Diff: A Differentiable Programming Module for NURBS
WebApr 29, 2021 · We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters (control points, weights, and the knot vector). These derivatives are used to define an approximate Jacobian used for performing the "backward" evaluation to train the deep learning models.
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