1 citations · 1 across the 9 of their papers we have counts for
9 papers
Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm
Feng Qiu, Zheng Fang, Shuhang Zhang +4
Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network optimization. Most current…
UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection
Shenghui Huang, Menghao Hu, Longkun Zou +6
Detecting Unmanned Aerial Vehicles (UAVs) in low-altitude environments is essential for perception and defense systems but remains highly challenging due to complex backgrounds, ca…
UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data
Longkun Zou, Jiale Wang, Rongqin Liang +3
Accurate perception of UAVs in complex low-altitude environments is critical for airspace security and related intelligent systems. Developing reliable solutions requires large-sca…
LAA3D: A Benchmark of Detecting and Tracking Low-Altitude Aircraft in 3D Space
Hai Wu, Shuai Tang, Jiale Wang +5
Perception of Low-Altitude Aircraft (LAA) in 3D space enables precise 3D object localization and behavior understanding. However, datasets tailored for 3D LAA perception remain sca…
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…
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation
Li Yu, Hongchao Zhong, Longkun Zou +2
Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy fr…
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers
Longkun Zou, Wanru Zhu, Ke Chen +4
Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to larg…
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
Yongwei Chen, Zihao Wang, Longkun Zou +2
Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. Howe…
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
Longkun Zou, Hui Tang, Ke Chen +1
The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to d…