BEVCorner: Enhancing Bird’s-Eye View Object Detection with Monocular Features via Depth Fusion

BEVCorner: Enhancing Bird’s-Eye View Object Detection with Monocular Features via Depth Fusion

This research paper presents BEVCorner, a novel framework that synergistically integrates monocular and multi-view pipelines for enhanced 3D object detection in autonomous driving. By fusing depth maps from Bird’s-Eye View (BEV) with object-centric depth estimates from monocular detection, BEVCorner...

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Bibliographic Details
Main Authors: Jesslyn Nathania, Qiyuan Liu, Zhiheng Li, Liming Liu, Yipeng Gao
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
bird’s eye view (BEV)
monocular 3D object detection
depth fusion
depth estimation
autonomous driving
Online Access:https://www.mdpi.com/2076-3417/15/7/3896
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