CatFree3D: Category-agnostic 3D Object Detection with Diffusion
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913477246844928 |
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| author | Bian, Wenjing Wang, Zirui Vedaldi, Andrea |
| author_facet | Bian, Wenjing Wang, Zirui Vedaldi, Andrea |
| contents | Image-based 3D object detection is widely employed in applications such as autonomous vehicles and robotics, yet current systems struggle with generalisation due to complex problem setup and limited training data. We introduce a novel pipeline that decouples 3D detection from 2D detection and depth prediction, using a diffusion-based approach to improve accuracy and support category-agnostic detection. Additionally, we introduce the Normalised Hungarian Distance (NHD) metric for an accurate evaluation of 3D detection results, addressing the limitations of traditional IoU and GIoU metrics. Experimental results demonstrate that our method achieves state-of-the-art accuracy and strong generalisation across various object categories and datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CatFree3D: Category-agnostic 3D Object Detection with Diffusion Bian, Wenjing Wang, Zirui Vedaldi, Andrea Computer Vision and Pattern Recognition Image-based 3D object detection is widely employed in applications such as autonomous vehicles and robotics, yet current systems struggle with generalisation due to complex problem setup and limited training data. We introduce a novel pipeline that decouples 3D detection from 2D detection and depth prediction, using a diffusion-based approach to improve accuracy and support category-agnostic detection. Additionally, we introduce the Normalised Hungarian Distance (NHD) metric for an accurate evaluation of 3D detection results, addressing the limitations of traditional IoU and GIoU metrics. Experimental results demonstrate that our method achieves state-of-the-art accuracy and strong generalisation across various object categories and datasets. |
| title | CatFree3D: Category-agnostic 3D Object Detection with Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12747 |