@inproceedings{55dc8aef7bba4eabb57a158fa96fde7a,
title = "Teach it Yourself - Fast Modeling of Industrial Objects for 6D Pose Estimation",
abstract = "In this paper, we present a vision system that allows a human to create new 3D models of novel industrial parts by placing the part in two different positions in the scene. The two shot modeling framework generates models with a precision that allows the model to be used for 6D pose estimation without loss in pose accuracy. We quantitatively show that our modeling framework reconstructs noisy but adequate object models with a mean RMS error at 2. 7 mm, a mean standard deviation at 0. 025mm and a completeness of 70. 3% over all 14 reconstructed models, compared to the ground truth CAD models. In addition, the models are applied in a pose estimation application, evaluated with 37 different scenes with 61 unique object poses. The pose estimation results show a mean translation error on 4. 97mm and a mean rotation error on 3. 38 degrees.",
keywords = "3D modeling, Flexible automation, Pose estimation, Robot manipulation",
author = "S{\o}lund Thomas and Savarimuthu, {Thiusius Rajeeth} and Buch, {Anders Glent} and Beck, {Anders Billes{\o}} and Norbert Kr{\"u}ger and Henrik Aanaes",
year = "2015",
doi = "10.1007/978-3-319-20904-3_27",
language = "English",
isbn = "978-3-319-20903-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "289--302",
editor = "Antonios Gasteratos and Lazaros Nalpantidis and Volker Kruger and Jan-Olof Eklundh",
booktitle = "Computer Vision Systems",
address = "Germany",
note = "null ; Conference date: 06-07-2015 Through 09-07-2015",
}