Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection

TL;DR

This paper introduces GPENet, a novel approach for monocular 3D object detection that effectively utilizes ground plane prior by addressing key issues with a new network, leading to state-of-the-art results on KITTI and nuScenes datasets.

Contribution

GPENet is the first to jointly solve projection localization and ground plane tilt issues in monocular 3D detection, using contact points and horizon line estimation.

Findings

Outperforms existing methods on KITTI benchmark

Achieves superior cross-dataset performance on nuScenes

No extra data annotation required for contact points and horizon line

Abstract

The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD). However, it has been neglected by most mainstream methods. In this paper, we identify two key factors that limit the applicability of ground plane prior: the projection point localization issue and the ground plane tilt issue. To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. For the projection point localization issue, instead of using the bottom vertices or bottom center of the 3D bounding box (BBox), we leverage the object's ground contact points, which are explicit pixels in the image and easy for the neural network to detect. For the ground plane tilt problem, our GPENet estimates the horizon line in the image and derives a novel mathematical expression to accurately estimate the ground plane equation. An unsupervised vertical edge mining algorithm is also proposed to address the occlusion of the horizon line. Furthermore, we design a novel 3D bounding box deduction method based on a dynamic back projection algorithm, which could take advantage of the accurate contact points and the ground plane equation. Additionally, using only M3OD labels, contact point and horizon line pseudo labels can be easily generated with NO extra data collection and label annotation cost. Extensive experiments on the popular KITTI benchmark show that our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach. Moreover, our GPENet works better than other methods in cross-dataset evaluation on the nuScenes dataset. Our code and models will be published.