all AI news
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
April 22, 2024, 4:45 a.m. | Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada
cs.CV updates on arXiv.org arxiv.org
Abstract: Multi-task networks can potentially improve performance and computational efficiency compared to single-task networks, facilitating online deployment. However, current multi-task architectures in point cloud perception combine multiple task-specific point cloud representations, each requiring a separate feature encoder and making the network structures bulky and slow. We propose PAttFormer, an efficient multi-task architecture for joint semantic segmentation and object detection in point clouds that only relies on a point-based representation. The network builds on transformer-based feature encoders …
abstract architectures arxiv cloud computational cs.cv current deployment efficiency encoder feature however lidar making multiple network networks perception performance type
More from arxiv.org / cs.CV updates on arXiv.org
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
1 day, 10 hours ago |
arxiv.org
Fingerprint Matching with Localized Deep Representation
1 day, 10 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne