Data-Driven Mapping Function for Visual Effects Applications using Mesh Segmentation
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Abstract
Partitioning a polygonal mesh can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning has proven to be effective for the segmentation task on 3D structures. Nevertheless, the state-of-the-art methods introduced using deep learning to handle are hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a deep learning approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a mapping using our knowledge of the Shape Diameter Function (SDF) method to generate such a correspondence using similarities among vertex neighborhoods. Using our predicted mapping, we can inject the resulting structure to a graph cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.
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