63 citations · 67 across the 6 of their papers we have counts for
6 papers · 1 filter
DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization
Jianxin Huang, Jiahang Li, Sergey Vityazev +2
RGB-D scene parsing methods effectively capture both semantic and geometric features of the environment, demonstrating great potential under challenging conditions such as extreme…
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
Yikang Zhang, Chuang-Wei Liu, Jiahang Li +3
Road inspection is crucial for maintaining road serviceability and ensuring traffic safety, as road defects gradually develop and compromise functionality. Traditional inspection m…
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Jianxin Huang, Jiahang Li, Ning Jia +4
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts hetero…
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
Guanfeng Tang, Zhiyuan Wu, Jiahang Li +5
Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception syst…
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
Jiahang Li, Peng Yun, Yang Xu +5
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogene…
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Jiahang Li, Yikang Zhang, Peng Yun +3
The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily o…