Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent

TL;DR

This paper introduces a gradient descent-based algorithm for registering point cloud streams with sequential bounding boxes, leveraging bounding box properties for improved accuracy and robustness.

Contribution

It presents a novel registration method that models the process with an objective function incorporating constraints, optimized via gradient descent.

Findings

40% improvement in IoU

More robust registration performance

Effective use of bounding box properties

Abstract

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