Search Results for author:
Found 9 papers, 0 papers with code
Task-Oriented Hybrid Beamforming for OFDM-DFRC Systems with Flexibly Controlled Space-Frequency Spectra
This paper investigates the issues of the hybrid beamforming design for the orthogonal frequency division multiplexing dual-function radar-communication (DFRC) system in multiple task scenarios involving the radar scanning and detection task and the target tracking task.
Enhancing Physical Layer Security in Dual-Function Radar-Communication Systems with Hybrid Beamforming Architecture
Additionally, the location of the radar target is also imperfectly known by the BS.
Temporal Point Cloud Completion with Pose Disturbance
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
Graph-Guided Deformation for Point Cloud Completion
In this paper, we propose a Graph-Guided Deformation Network, which respectively regards the input data and intermediate generation as controlling and supporting points, and models the optimization guided by a graph convolutional network(GCN) for the point cloud completion task.
PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding
Accurate localization on autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available.
3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training.
i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions
Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors.
MRS-VPR: a multi-resolution sampling based global visual place recognition method
We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching.
A Multi-Domain Feature Learning Method for Visual Place Recognition
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically.
