Ground-aware Monocular 3D Object Detection for Autonomous Driving

TL;DR

This paper introduces a ground-aware neural network module that leverages ground plane information to improve monocular 3D object detection accuracy for autonomous driving, achieving state-of-the-art results.

Contribution

It proposes a novel ground-aware module that enhances deep learning-based 3D detection by utilizing ground plane priors, improving performance over existing methods.

Findings

Achieved state-of-the-art results on KITTI 3D detection benchmark.

Enhanced monocular depth prediction accuracy.

Demonstrated the effectiveness of ground priors in 3D reasoning.

Abstract

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. The code will be published in https://www.github.com/Owen-Liuyuxuan/visualDet3D