Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent
<|reference_start|>Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent: In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams. Unlike popular point cloud registration techniques, the alignment of the point cloud and the bounding box can rely on the properties of the bounding box, such as size, shape, and temporal information, which provides substantial support and performance gains. Motivated by this, we propose a new approach to tackle this problem. Specifically, we model the registration process through an overall objective function that includes the final goal and all constraints. We then optimize the function using gradient descent. Our experiments show that the proposed method performs remarkably well with a 40\% improvement in IoU and demonstrates more robust registration between point cloud streams and sequential bounding boxes<|reference_end|>
@article{li2024registration,
    title={Registration between Point Cloud Streams and Sequential Bounding Boxes
  via Gradient Descent},
    author={Xuesong Li and Xinge Zhu and Yuexin Ma and Subhan Khan and Jose
  Guivant},
    journal={arXiv preprint arXiv:2409.09312},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09312},
    primaryClass={cs.CV cs.RO}
}