activity
20172023
most citedCounting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation

58 citations · 91 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2023★ 3 cited

AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation

Siqi Du, Weixi Wang, Renzhong Guo +3

Understanding indoor scenes is crucial for urban studies. Considering the dynamic nature of indoor environments, effective semantic segmentation requires both real-time operation a…

cs.CV2023

HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration

Canhui Tang, Yiheng Li, Shaoyi Du +2

Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of loca…

cs.CV2023★ 8 cited

HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching

Yiheng Li, Canhui Tang, Runzhao Yao +3

Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes,…

cs.RO2023

CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence

Pengyu Yin, Di Wang, Shaoyi Du +3

In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global nois…

cs.CV2022★ 2 cited

MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos

Fengrui Tian, Shaoyi Du, Yueqi Duan

In this paper, we target at the problem of learning a generalizable dynamic radiance field from monocular videos. Different from most existing NeRF methods that are based on multip…

cs.CV2022★ 58 cited

Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation

Yuehai Chen, Jing Yang, Badong Chen +1

In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing density variation, humans tend to locate and count the targets in low-density r…