Vehicle Detection from 3D Lidar Using Fully Convolutional Network

TL;DR

This paper presents a novel approach for vehicle detection from 3D lidar data using a 2D fully convolutional network that predicts 3D bounding boxes, achieving state-of-the-art results on the KITTI dataset.

Contribution

It introduces a method to encode 3D bounding boxes for 2D convolutional networks, enabling effective 3D vehicle detection from lidar range data.

Findings

Achieves state-of-the-art performance on KITTI dataset.

Successfully predicts full 3D bounding boxes using 2D convolutional networks.

Abstract

Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.