Robotic manipulation systems frequently utilize RGB-D cam- eras based on infrared projection to perceive three-dimensional environ ments. Unfortunately, this technique often fails on transparent objects such as glasses, bottles and plastic containers. We present methods to exploit the perceived infrared camera images to detect and reconstruct volumetric shapes of arbitrary transparent objects. Our reconstruction pipeline first segments transparent surfaces based on pattern scattering and absorption, followed by optimization-based multi-view reconstruc tion of volumetric object models. Outputs from the segmentation stage can also be utilized for single-view transparent object detection. The presented methods improve on previous work by analyzing infrared camera images directly and by successfully reconstructing cavities in objects such as drinking glasses. A dataset of recorded transparent objects, autonomously gathered by a robotic camera-in-hand setup, is published together with this work.
This repository contains code for generating volumetric 3D reconstructions of transparent objects. For detecting transparent objects, see: https://github.com/TAMS-Group/tams_bartender_glass_recognition. The dataset will be published here: https://tams.informatik.uni-hamburg.de/research/datasets/index.php.
@inproceedings{iccsip2020,
title={Detection and Reconstruction of Transparent Objects with Infrared Projection-based RGB-D Cameras},
author={Ruppel, Philipp and Görner, Michael and Hendrich, Norman and Zhang, Jianwei},
booktitle={International Conference on Cognitive Systems and Information Processing (ICCSIP)},
year={2020}
}