all AI news
Large receptive field strategy and important feature extraction strategy in 3D object detection
March 12, 2024, 4:50 a.m. | Leichao Cui, Xiuxian Li, Min Meng, Guangyu Jia
cs.CV updates on arXiv.org arxiv.org
Abstract: The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a crucial information for this purpose. Our study focuses on key challenges in 3D target detection. To tackle the challenge of expanding the receptive field of a 3D convolutional kernel, we introduce the Dynamic Feature Fusion Module (DFFM). This module achieves adaptive expansion of the 3D …
3d object 3d object detection abstract arxiv autonomous autonomous driving capabilities challenges cs.ai cs.cv detection driving environmental extraction feature feature extraction information key lidar object perception pivotal serve strategy study type
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US