RUHSNet: 3D Object Detection Using Lidar Data in Real Time

TL;DR

RUHSNet is a real-time 3D object detection neural network for lidar data, achieving high accuracy and speed suitable for autonomous vehicle perception systems.

Contribution

The paper introduces a novel neural network architecture optimized for real-time 3D object detection from lidar point clouds, outperforming existing methods in accuracy and inference speed.

Findings

Achieves >30 FPS in 3D object detection

Surpasses state-of-the-art in average precision

Validated on KITTI dataset

Abstract

In this work, we address the problem of 3D object detection from point cloud data in real time. For autonomous vehicles to work, it is very important for the perception component to detect the real world objects with both high accuracy and fast inference. We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects in point cloud data. We compare the results with different backbone architectures including the standard ones like VGG, ResNet, Inception with our backbone. Also we present the optimization and ablation studies including designing an efficient anchor. We use the Kitti 3D Birds Eye View dataset for benchmarking and validating our results. Our work surpasses the state of the art in this domain both in terms of average precision and speed running at > 30 FPS. This makes it a feasible option to be deployed in real time applications including self driving cars.