Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent
This addresses a specific registration challenge in computer vision, likely for applications like autonomous driving or robotics, but appears incremental as it builds on existing point cloud registration techniques.
The paper tackles the problem of registering sequential bounding boxes with point cloud streams by proposing a gradient descent-based algorithm that leverages bounding box properties like size, shape, and temporal information, resulting in a 40% improvement in IoU and more robust registration.
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