Apr 29, 2021 · We propose a differentiable NURBS module to integrate NURBS representations of CAD models with deep learning methods. We mathematically define ...
The NURBS-Diff module takes as input the control points, weights, and knot vectors for a batch of NURBS surfaces. We define a parameter to control the number of ...
NURBSDiff. This repo contains code for fitting curves and surfaces to any input point cloud. Requirements and Install dependencies ...
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NURBS-Diff: A Differentiable Programming Module for NURBS. Publication source: CAD Computer Aided Design. 2022. (146). C. 103199. Publication authors:.
Apr 29, 2021 · We demonstrate the efficacy of our NURBS layer by automatically incorporating it with the stochastic gradient descent algorithm and performing ...
A differentiable NURBS layer is proposed for evaluating the curve or surface given a set of NURBS parameters and its utility in deep learning applications ...
NURBS-diff is a differentiable layer that can be run as a standalone layer for CAD applications like curve fitting, surface fitting, surface offseting, ...
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer · Computer Science. Neural Information Processing Systems · 2019.
After defining NURBS-Diff, we validate our approach by performing traditional CAD operations such as curve fitting, surface fitting, and surface offsetting.
NURBS, Non-Uniform Rational B-Splines, are mathematical representations of 3D geometry that can accurately describe any shape from a simple 2D line, circle, ...