2 citations · 3 across the 3 of their papers we have counts for
5 papers
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Biao Gao, Xijun Zhao, Huijing Zhao
Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-…
An Image-based Approach of Task-driven Driving Scene Categorization
Shaochi Hu, Hanwei Fan, Biao Gao +2
Categorizing driving scenes via visual perception is a key technology for safe driving and the downstream tasks of autonomous vehicles. Traditional methods infer scene category by…
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
Biao Gao, Shaochi Hu, Xijun Zhao +1
Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as…
Off-Road Drivable Area Extraction Using 3D LiDAR Data
Biao Gao, Anran Xu, Yancheng Pan +3
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to dea…
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
Jilin Mei, Biao Gao, Donghao Xu +3
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including…