5 papers
RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation
Taiqin Chen, Zikun Zhou, Zheng Fang +5
The radio map represents the spatial distribution of spectrum resources within a region, supporting efficient resource allocation and interference mitigation. However, it is diffic…
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
Longkun Zou, Kangjun Liu, Ke Chen +3
Learning semantic representations from point sets of 3D object shapes is often challenged by significant geometric variations, primarily due to differences in data acquisition meth…
RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1\textpertenthousand Spatial Sampling
Zheng Fang, Kangjun Liu, Ke Chen +4
The task of radio map estimation aims to generate a dense representation of electromagnetic spectrum quantities, such as the received signal strength at each grid point within a ge…
Delving Deep into Semantic Relation Distillation
Zhaoyi Yan, Kangjun Liu, Qixiang Ye
Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional dist…
ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points
Qirui Huang, Runze Zhang, Kangjun Liu +3
We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically,…