SpecBEV-IR: Illumination-Robust Front-End Enhancement for Multi-View BEV 3D Object Detection

Abstract

Multi-view visual BEV 3D object detection projects image information from different camera views into a unified bird’s-eye-view space and has become an important paradigm for autonomous driving perception due to its low cost, flexible deployment, and rich semantic information. However, under complex lighting conditions such as nighttime, backlighting, local overexposure, and uneven illumination, multi-view input images often suffer from degraded brightness distribution, local contrast, and structural details, which further affects image feature extraction, view transformation, and unified spatial modeling. To address this issue, this paper proposes SpecBEV-IR, an illumination-robust multi-view BEV 3D object detection method. Built upon the SpecBEV framework, the proposed method introduces an illumination-robust image front-end enhancement module, termed ICF, between the multi-view input images and the shared 2D encoder. The ICF module consists of an invariant cue extraction unit (ICE) and a fusion convolution unit (Fuse Conv). ICE extracts more stable illumination-invariant cues from raw images, while Fuse Conv integrates these cues with the original image content to generate enhanced input representations for subsequent feature encoding and view transformation. Different from conventional enhancement methods that mainly improve visual appearance, SpecBEV-IR emphasizes structural stability and cross-view consistency for downstream 3D detection. Experiments on the nuScenes dataset show that SpecBEV-IR achieves 0.4121 mAP and 0.5174 NDS on the validation set, while also obtaining better or more balanced performance on multiple error metrics, including mATE, mASE, mAOE, and mAAE. The results demonstrate that the proposed method effectively improves the overall robustness and detection performance of multi-view visual 3D object detection under complex lighting conditions.