Papers › A Large-scale Annotated Mechanical Components Benchmark for Classification and...
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
Sangpil Kim, Hyung-gun Chi, Xiao Hu, Qi-Xing Huang, Karthik Ramani
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components. The dataset enables data-driven feature learn-ing 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 an-notated 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 benchmark-ing 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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Retrieval | คลิปไวรัล!! ไอซ์ ปรีชญา ลืมปิดไลฟ์สดตอนอาบน้ำ ถูกแชร์กระหึ่มเน็ต | hello | 0L | 1,1,2,1,2,22 | #1 of 1 | Archive leaderboard | report |
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