# 3D Object Reconstruction from Hand-Object Interactions

## TL;DR

This paper introduces a novel method for 3D object reconstruction that leverages hand motion data during in-hand scanning to effectively reconstruct featureless and symmetric objects, overcoming limitations of existing techniques.

## Contribution

The work demonstrates that incorporating hand motion information significantly improves the reconstruction of textureless and symmetric objects in in-hand scanning systems.

## Key findings

- Hand motion extraction enhances reconstruction quality.
- Method outperforms existing approaches on symmetric objects.
- Significant contribution to in-hand scanning techniques.

## Abstract

Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.