2019/01/12 by Jun Gao, Gao, Jun, Chengcheng Tang +11 · 36 citations
Computer Science · Engineering · Mathematics · #3D Shape Modeling and Analysis #Advanced Numerical Analysis Techniques #Advanced Vision and Imaging #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer graphics #Computer graphics (images) #Computer science #Computer vision #Engineering #FOS: Computer and information sciences #Geometry #Graphics #Initialization #Mathematical optimization #Mathematics #Parametric statistics #Parametric surface #Point (geometry) #Point cloud #Spline (mechanical) #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.03781
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/01/12 · openalex publication_date 2019/01/12 · arxiv updated 2019/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have traditionally involved the use of spline-based representations at their core. Most such methods attempt to solve optimization problems that minimize an output-target mismatch. However, these optimization techniques require an initialization that is close enough, as they are local methods by nature. We propose a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods. We showcase the performance of our approach, by reconstructing spline curves and surfaces based on input images or point clouds.