Publication | ACM SIGGRAPH

Neural Shape Diameter Function for Efficient Mesh Segmentation


Neural Shape Diameter Function for Efficient Mesh Segmentation

Bruno Roy


Partitioning a polygonal mesh into meaningful parts 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, these state-of-the-art methods are often hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a data-driven approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a neighborhood map using our knowledge of the Shape Diameter Function (SDF) method using similarities among vertex neighborhoods. Our approach is resolution-agnostic as we downsample the input meshes and query the full-resolution structure solely for neighborhood contributions. Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.

Download publication

Related Resources



A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly

In this work we propose a learning approach to high-precision robotic…




An investigation of new user interface designs and interaction…



Software Learning

This learning project investigates advanced techniques for assisting…



3D User Interfaces: Human Experience in 3D Environments

Designing user interfaces for interacting with 3D data involves a…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us