# Learning Shape Templates with Structured Implicit Functions

## TL;DR

This paper introduces a method to learn a versatile 3D shape template using structured implicit functions, enabling various shape analysis tasks without relying on predefined templates.

## Contribution

It presents a novel approach to learn shape templates directly from data with a structured implicit surface representation, handling diverse geometries and topologies.

## Key findings

- Successfully learned a general shape template from data.
- Enabled shape exploration, correspondence, and segmentation from RGB images.
- Demonstrated smooth fitting of multiple shape classes.

## Abstract

Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements. While long known to computer graphics, this representation has not yet been explored in the context of machine learning for vision. We show that structured implicit functions are suitable for learning and allow a network to smoothly and simultaneously fit multiple classes of shapes. The learned shape template supports applications such as shape exploration, correspondence, abstraction, interpolation, and semantic segmentation from an RGB image.