15 citations · 18 across the 4 of their papers we have counts for
7 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…
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Biao Gao, Yancheng Pan, Chengkun Li +2
3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing…
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…
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
Yancheng Pan, Biao Gao, Jilin Mei +3
3D semantic segmentation is one of the key tasks for autonomous driving system. Recently, deep learning models for 3D semantic segmentation task have been widely researched, but th…