CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913548059279360 |
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| author | Gupta, Pranav Rengarajan, Rishabh Bankapur, Viren Mannem, Vedansh Ahuja, Lakshit Vijay, Surya Wang, Kevin |
| author_facet | Gupta, Pranav Rengarajan, Rishabh Bankapur, Viren Mannem, Vedansh Ahuja, Lakshit Vijay, Surya Wang, Kevin |
| contents | Combining LiDAR and Camera-view data has become a common approach for 3D Object Detection. However, previous approaches combine the two input streams at a point-level, throwing away semantic information derived from camera features. In this paper we propose Cross-View Center Point-Fusion, a state-of-the-art model to perform 3D object detection by combining camera and LiDAR-derived features in the BEV space to preserve semantic density from the camera stream while incorporating spacial data from the LiDAR stream. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint, and runs their backbones in parallel, allowing efficient computation for real-time processing and application. In this paper we find that while an implicitly calculated depth-estimate may be sufficiently accurate in a 2D map-view representation, explicitly calculated geometric and spacial information is needed for precise bounding box prediction in the 3D world-view space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction Gupta, Pranav Rengarajan, Rishabh Bankapur, Viren Mannem, Vedansh Ahuja, Lakshit Vijay, Surya Wang, Kevin Computer Vision and Pattern Recognition Machine Learning Combining LiDAR and Camera-view data has become a common approach for 3D Object Detection. However, previous approaches combine the two input streams at a point-level, throwing away semantic information derived from camera features. In this paper we propose Cross-View Center Point-Fusion, a state-of-the-art model to perform 3D object detection by combining camera and LiDAR-derived features in the BEV space to preserve semantic density from the camera stream while incorporating spacial data from the LiDAR stream. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint, and runs their backbones in parallel, allowing efficient computation for real-time processing and application. In this paper we find that while an implicitly calculated depth-estimate may be sufficiently accurate in a 2D map-view representation, explicitly calculated geometric and spacial information is needed for precise bounding box prediction in the 3D world-view space. |
| title | CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.11211 |