LST-BEV: Generating a Long-Term Spatial–Temporal Bird’s-Eye-View Feature for Multi-View 3D Object Detection
This paper presents a novel multi-view 3D object detection framework, Long-Term Spatial–Temporal Bird’s-Eye View (LST-BEV), designed to improve performance in autonomous driving. Traditional 3D detection relies on sensors like LiDAR, but visual perception using multi-camera systems is emerging as a...
Saved in:
| Main Authors: | Qijun Feng, Chunyang Zhao, Pengfei Liu, Zhichao Zhang, Yue Jin, Wanglin Tian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/13/4040 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Multi-Task BEV Perception Algorithm Based on LiDAR for Autonomous-Rail Rapid Trams
by: YAO Gang, et al.
Published: (2024-08-01) -
BETAV: A Unified BEV-Transformer and Bézier Optimization Framework for Jointly Optimized End-to-End Autonomous Driving
by: Rui Zhao, et al.
Published: (2025-05-01) -
DMformer: a transformer with denoising and multi-modal data fusion for enhancing BEV perception
by: Xuefeng Bao, et al.
Published: (2025-07-01) -
Comparative and pharmacological investigation of bEVs from eight Lactobacillales strains
by: Seoah Park, et al.
Published: (2025-07-01) -
Kalman Filter-Based Fusion of LiDAR and Camera Data in Bird’s Eye View for Multi-Object Tracking in Autonomous Vehicles
by: Loay Alfeqy, et al.
Published: (2024-12-01)