MonoEM: Object-Level Monocular 3D Object Detection Based on Equirectangular Map under Inclement Weather

Abstract

Monocular 3D object detection has received growing recognition in contemporary research due to its reduced hardware complexity and lower deployment cost compared to multi-sensor-based approaches. Prior research has primarily addressed ideal environmental settings, neglecting the influence of diverse weather scenarios, including rain, snow, and fog, that significantly hinder detection reliability. To enhance robustness under inclement weather conditions, we introduce MonoEM, a monocular 3D object detection framework that leverages object-level image representations and equirectangular maps. Starting from 2D detection results, MonoEM derives equirectangular maps through an equirectangular object-level reconstruction. Furthermore, MonoEM suppresses inclement weather noise in object-level images through image restoration. Subsequently, MonoEM fuses the reconstructed equirectangular map with the restored image and performs 3D bounding box prediction using a visual-range fusion detector. The integration of 2D-3D box alignment loss between 2D and 3D bounding boxes improves the geometric alignment and 3D object detection accuracy. Experimental results across various inclement weather conditions validate the notable accuracy and robustness of MonoEM compared to existing monocular 3D baselines. The source code is provided at https://anonymous.4open.science/r/MonoEM-00AC.