@inproceedings{a80639cc1dde4f629cb32014e9507ff1,
title = "A Large-Scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks",
abstract = "We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named Mechanical Components Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components. The dataset enables data-driven feature learning for mechanical components. Exploring the shape descriptor for mechanical components is essential to computer vision and manufacturing applications. However, not much attention has been given on creating annotated mechanical components datasets on a large scale. This is because acquiring 3D models is challenging and annotating mechanical components requires engineering knowledge. Our main contributions are the creation of a large-scale annotated mechanical component benchmark, defining hierarchy taxonomy of mechanical components, and benchmarking the effectiveness of deep learning shape classifiers on the mechanical components. We created an annotated dataset and benchmarked seven state-of-the-art deep learning classification methods in three categories, namely: (1) point clouds, (2) volumetric representation in voxel grids, and (3) view-based representation.",
keywords = "3D objects, Benchmark, Classification, Deep learning, Mechanical components, Retrieval",
author = "Sangpil Kim and Chi, {Hyung gun} and Xiao Hu and Qixing Huang and Karthik Ramani",
note = "Funding Information: Acknowledgment. We wish to give a special thanks to the reviewers for their invaluable feedback. Additionally, we thank TraceParts for providing CAD models of mechanical components. This work is partially supported by NSF under the grants FW-HTF 1839971, OIA 1937036, and CRI 1729486. We also acknowledge the Feddersen Chair Funds. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agency. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58523-5_11",
language = "English",
isbn = "9783030585228",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "175--191",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",
address = "Germany",
}