epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module

TL;DR

The paper introduces epBRM, a lightweight endpoint box regression module that enhances 3D object detection accuracy using LiDAR data by employing spatial transformations, with minimal inference time and broad applicability.

Contribution

epBRM is a novel, efficient module that improves 3D bounding box prediction accuracy by integrating spatial transformations into lightweight networks for LiDAR-based detection.

Findings

epBRM improves 3D detection performance on KITTI dataset.

The method enhances overlap between predicted and ground truth boxes.

epBRM outperforms current state-of-the-art approaches.

Abstract

We present an endpoint box regression module(epBRM), which is designed for predicting precise 3D bounding boxes using raw LiDAR 3D point clouds. The proposed epBRM is built with sequence of small networks and is computationally lightweight. Our approach can improve a 3D object detection performance by predicting more precise 3D bounding box coordinates. The proposed approach requires 40 minutes of training to improve the detection performance. Moreover, epBRM imposes less than 12ms to network inference time for up-to 20 objects. The proposed approach utilizes a spatial transformation mechanism to simplify the box regression task. Adopting spatial transformation mechanism into epBRM makes it possible to improve the quality of detection with a small sized network. We conduct in-depth analysis of the effect of various spatial transformation mechanisms applied on raw LiDAR 3D point clouds. We also evaluate the proposed epBRM by applying it to several state-of-the-art 3D object detection systems. We evaluate our approach on KITTI dataset, a standard 3D object detection benchmark for autonomous vehicles. The proposed epBRM enhances the overlaps between ground truth bounding boxes and detected bounding boxes, and improves 3D object detection. Our proposed method evaluated in KITTI test server outperforms current state-of-the-art approaches.