3D Vehicle Detection Using Camera and Low-Resolution LiDAR

• URL: http://arxiv.org/abs/2105.01765v1
• Date: Tue, 4 May 2021 21:08:20 GMT
• Title: 3D Vehicle Detection Using Camera and Low-Resolution LiDAR
• Authors: Lin Bai, Yiming Zhao and Xinming Huang
• Abstract summary: We propose a novel framework for 3D object detection in Bird-Eye View (BEV) using a low-resolution LiDAR and a monocular camera. Taking the low-resolution LiDAR point cloud and the monocular image as input, our depth completion network is able to produce dense point cloud. For both easy and moderate cases, our detection results are comparable to those from 64-line high-resolution LiDAR.
• Score: 6.293059137498174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively expensive, while its low-resolution counterpart is much more affordable. Therefore, using low-resolution LiDAR for autonomous driving perception tasks instead of high-resolution LiDAR is an economically feasible solution. In this paper, we propose a novel framework for 3D object detection in Bird-Eye View (BEV) using a low-resolution LiDAR and a monocular camera. Taking the low-resolution LiDAR point cloud and the monocular image as input, our depth completion network is able to produce dense point cloud that is subsequently processed by a voxel-based network for 3D object detection. Evaluated with KITTI dataset, the experimental results shows that the proposed approach performs significantly better than directly applying the 16-line LiDAR point cloud for object detection. For both easy and moderate cases, our detection results are comparable to those from 64-line high-resolution LiDAR. The network architecture and performance evaluations are analyzed in detail.

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