Currently, most point cloud based detection pipelines are focused on producing high accuracy results while requiring significant computational resources and a high-end GPU. Our research explores how to reduce the computational overhead by improving a key element of detection: bounding box regression. We demonstrate a fast and iterative method of bounding box regression which has significant run-time performance advantages over existing leading methods. The iterative structure of our method also gives the system control over the trade-offs between accuracy and speed at run-time. We furthermore integrate our bounding box regression method into an existing detection pipeline and motivate additional research into how the first stage of the pipeline can be modified to take better advantage of the performance characteristics of our bounding box regression method.

This was done within the CI2CV lab.