Ground-Aware Monocular 3D Object Detection for Autonomous Driving

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

15 Scopus Citations
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Original languageEnglish
Pages (from-to)919-926
Journal / PublicationIEEE Robotics and Automation Letters
Issue number2
Online published18 Jan 2021
Publication statusPublished - Apr 2021


Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We first identify how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes. Based on this observation, we then improve the processing of 3D anchors and introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning. Finally, we introduce an efficient neural network embedded with the proposed module for 3D object detection. We further verify the power of the proposed module with a neural network designed for monocular depth prediction. The two proposed networks achieve state-of-the-art performances on the KITTI 3D object detection and depth prediction benchmarks, respectively.

Research Area(s)

  • Automation Technologies for Smart Cities, Cameras, Convolution, Deep Learning for Visual Perception, Feature extraction, Neural networks, Object detection, Object Detection, Segmentation and Categorization, Three-dimensional displays, Two dimensional displays