FMFNet: Improve the 3D Object Detection and Tracking via Feature Map Flow

Three-dimensional object detection and tracking from point clouds are important computer vision tasks for robots and vehicles where objects can be represented as 3D boxes. Improving the accuracy of understanding the environment is critical for successful autonomous driving. This paper presents a simple yet efficient method called “Feature Map Flow, FMF” for 3D object detection and tracking, considering time-spatial feature map aggregation from different timesteps of deep neural model inference. Several versions of the FMF are proposed: from common concatenation to context-based feature map fusion and odometry usage for previous feature map affine transform. The proposed approach significantly improves the quality of 3D detection and tracking baseline on the nuScenes and Waymo benchmarks. The software implementation of the proposed method has been carried out for the server platform and the NVidia Jetson AGX Xavier single-board computer. Its results have demonstrated high application prospects both for automated 3D point cloud labeling and for embedded on-board vehicle systems. The developed code is publicly available at: https://github.com/YoushaaMurhij/FMFNet.

I. Introduction

High-quality 3D object detection and tracking are important tasks in computer vision. Solutions to them make it possible to build safe on-board control systems for robots and autonomous vehicles.