The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

Although point-based networks are demonstrated to be accurate for 3D point cloud
modeling, they are still falling behind their voxel-based competitors in 3D detection. We …

Road segmentation from remote-sensing images is a challenging task with wide ranges of
application potentials. Deep neural networks have advanced this field by leveraging the …

Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a
segmentation model trained on the labeled source domain to the unlabeled target domain …

Many CNN-based segmentation methods have been applied in lane marking detection
recently and gain excellent success for a strong ability in modeling semantic information …