A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks

by | Jul 3, 2020

Authors: Sangpil Kim*, Hyung-gun Chi*, Xiao Hu, Qixing Huang, Karthik Ramani
In proceedings of 16th European Conference on Computer Vision (ECCV)

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Mechanical Components annotation tool for ECCV Paper, 2020

We main_figureintroduce 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 the 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.

Project Page: https://mechanical-components.herokuapp.com/

MCB Dataset Download:  https://app.box.com/s/m3cl7fq5n28pofnwh9uh5pg8wowmx3de

Sangpil Kim

Sangpil Kim

Sangpil Kim is a Ph.D. student in the School of Computer Engineering at Purdue University. He is working on the deep learning algorithm and virtual reality. To be more specific, he develops the generative model, video segmentation, and hand pose estimation with a depth sensor. Currently, he is working on combining virtual reality and deep learning algorithm.