تقرير
Stereo Frustums: A Siamese Pipeline for 3D Object Detection
| العنوان: | Stereo Frustums: A Siamese Pipeline for 3D Object Detection |
|---|---|
| المؤلفون: | Mo, Xi, Sajid, Usman, Wang, Guanghui |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | The paper proposes a light-weighted stereo frustums matching module for 3D objection detection. The proposed framework takes advantage of a high-performance 2D detector and a point cloud segmentation network to regress 3D bounding boxes for autonomous driving vehicles. Instead of performing traditional stereo matching to compute disparities, the module directly takes the 2D proposals from both the left and the right views as input. Based on the epipolar constraints recovered from the well-calibrated stereo cameras, we propose four matching algorithms to search for the best match for each proposal between the stereo image pairs. Each matching pair proposes a segmentation of the scene which is then fed into a 3D bounding box regression network. Results of extensive experiments on KITTI dataset demonstrate that the proposed Siamese pipeline outperforms the state-of-the-art stereo-based 3D bounding box regression methods. Comment: Accepted by Journal of Intelligent & Robotic Systems (JIRS) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2010.14599 |
| رقم الأكسشن: | edsarx.2010.14599 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |