Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR

Abstract

Accurate 3D object detection is a key part of the perception module for autonomous vehicles. A better understanding of the objects in 3D facilitates better decision-making and path planning. RGB Cameras and LiDAR are the most commonly used sensors in autonomous vehicles for environment perception. Many approaches have shown promising results for 2D detection with RGB Images, but efficiently localizing small objects like pedestrians in the 3D point cloud of large scenes has remained a challenging area of research. We propose a novel method, Frustum-PointPillars, for 3D object detection using LiDAR data. Instead of solely relying on point cloud features, we leverage the mature field of 2D object detection to reduce the search space in the 3D space. Then, we use the Pillar Feature Encoding network for object localization in the reduced point cloud. We also propose a novel approach for masking point clouds to further improve the localization of objects. We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network. On the KITTI test set our method outperforms other multi-sensor SOTA approaches for 3D pedestrian localization (Bird's Eye View) while achieving a significantly faster runtime of 14 Hz.
Fichier principal
Vignette du fichier
Frustum_Pointpillars_ICCV.pdf (5.15 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03354114 , version 1 (24-09-2021)

Identifiers

Cite

Anshul Paigwar, David Sierra-Gonzalez, Özgür Erkent, Christian Laugier. Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. ICCVW 2021 - IEEE/CVF International Conference on Computer Vision Workshops, Oct 2021, California, United States. pp.1-9, ⟨10.1109/ICCVW54120.2021.00327⟩. ⟨hal-03354114⟩
236 View
931 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More