IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain

TL;DR

IROAM introduces a contrastive learning framework that enhances roadside monocular 3D object detection by effectively bridging the viewpoint domain gap between vehicle and roadside cameras.

Contribution

The paper presents IROAM, a novel semantic-geometry decoupled contrastive learning method specifically designed for roadside monocular 3D detection, addressing domain gap challenges.

Findings

IROAM improves roadside detector performance.

Effective cross-domain feature learning demonstrated.

Significant accuracy gains over baseline methods.

Abstract

In autonomous driving, The perception capabilities of the ego-vehicle can be improved with roadside sensors, which can provide a holistic view of the environment. However, existing monocular detection methods designed for vehicle cameras are not suitable for roadside cameras due to viewpoint domain gaps. To bridge this gap and Improve ROAdside Monocular 3D object detection, we propose IROAM, a semantic-geometry decoupled contrastive learning framework, which takes vehicle-side and roadside data as input simultaneously. IROAM has two significant modules. In-Domain Query Interaction module utilizes a transformer to learn content and depth information for each domain and outputs object queries. Cross-Domain Query Enhancement To learn better feature representations from two domains, Cross-Domain Query Enhancement decouples queries into semantic and geometry parts and only the former is used for contrastive learning. Experiments demonstrate the effectiveness of IROAM in improving roadside detector's performance. The results validate that IROAM has the capabilities to learn cross-domain information.