Showing 1-8 of 8 results
- Sort
Filter Results
Show
Sort By
Results
A Novel Teacher–Student Framework With Degradation Model for Infrared–Visible Image Fusion
The fusion performance of infrared (IR) and visible (VIS) images depends on the quality of the source images, which are often affected by some factors in real-world scenarios, such as environmental changes, hardware limitations, and image compression. The influence of these factors can be minimized by training a neural network capable of generating high-quality (HQ) fused images from low-quality (...Show More
Real-Time Semantic Segmentation of LiDAR Point Clouds on Edge Devices for Unmanned Systems
Real-time semantic segmentation of LiDAR measurements is crucial for high-level perception in unmanned systems, such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The limited computation and memory capacity of onboard devices, however, restricts most existing methods to offline analyses. To solve this problem, this article proposes an attention-based 3-D semantic segmenta...Show More
A Context-Aware Method for Indexing Large-Scale SpatioTemporal Data
2022 IEEE International Conference on Big Data (Big Data)
Year: 2022 | Conference Paper |
With the rise of mobile terminals and the maturity of positioning technology, the amount of available spatiotemporal data continues to grow rapidly, so it is crucial to be able to process it efficiently. This paper proposes a multi-level indexing technique based on context dimension awareness. It first selects the partition order by the unit scale of each context dimension in the dataset. Second, ...Show More
A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment
With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the a...Show More
Real-Time 3-D Semantic Scene Parsing With LiDAR Sensors
IEEE Transactions on Cybernetics
Cited by: Papers (8)
This article proposes a novel deep-learning framework, called RSSP, for real-time 3-D scene understanding with LiDAR sensors. To this end, we introduce new sparse strided operations based on the sparse tensor representation of point clouds. Compared with conventional convolution operations, the time and space complexity of our sparse strided operations are proportional to the number of occupied vo...Show More
OctreeNet: A Novel Sparse 3-D Convolutional Neural Network for Real-Time 3-D Outdoor Scene Analysis
Convolutional neural networks (CNNs) for 3-D data analyses require a large size of memory and fast computation power, making real-time applications difficult. This article proposes a novel OctreeNet (a sparse 3-D CNN) to analyze the sparse 3-D laser scanning data gathered from outdoor environments. It uses a collection of shallow octrees for 3-D scene representation to reduce the memory footprint ...Show More
A Novel Real-time Semantic-Assisted Lidar Odometry and Mapping System
2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Recently, rich semantic information has proven to be an enabling factor for a wide variety of applications in mobile robots. In this paper, we explore the integration of semantics into lidar odometry and mapping approaches and present a novel real-time semantic-assisted system. To this end, a sparse 3D-CNN model is designed to perform per-frame semantic segmentation of lidar points. Transformation...Show More
Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis
The recent success of deep learning in 3-D data analysis relies upon the availability of large annotated data sets. However, creating 3-D data sets with point-level labels are extremely challenging and require a huge amount of human efforts. This paper presents a novel open-sourced method to extract light detection and ranging point clouds with ground truth annotations from a simulator automatical...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.