Summary of Omni6d: Large-vocabulary 3d Object Dataset For Category-level 6d Object Pose Estimation, by Mengchen Zhang et al.
Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation
by Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, Dahua Lin
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel RGBD dataset called Omni6D is introduced to facilitate comprehensive 6D object pose estimation. This paper addresses the limitations of existing datasets by providing a wide range of categories and varied backgrounds, enabling models to generalize better. The Omni6D dataset contains 166 categories, 4688 instances, and over 0.8 million captures, making it an extensive resource for evaluation. A symmetry-aware metric is also proposed, along with systematic benchmarks of existing algorithms on the new dataset. Furthermore, a fine-tuning approach is presented to adapt models from previous datasets to this new setting. This initiative aims to push forward the boundaries of general 6D pose estimation and contribute to substantial progress in both industrial and academic fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces Omni6D, a new dataset for estimating object poses. Currently, we can estimate an object’s position and orientation from one image, but this task is limited by the types of objects it has been trained on. To solve this problem, the authors created a huge dataset with many different categories of objects and lots of different backgrounds. This means that models can learn to recognize objects in all sorts of situations, not just the ones they were trained on. The new dataset also includes ways to measure how well algorithms do at estimating poses, which will help us understand what’s working and what’s not. |
Keywords
» Artificial intelligence » Fine tuning » Pose estimation
