LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Abstract We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric
representations from images of in-the-wild scenes. Once trained, our model can render color …

Query-based transformer has shown great potential in constructing long-range attention in
many image-domain tasks, but has rarely been considered in LiDAR-based 3D object …

Image segmentation for video analysis plays an essential role in different research fields
such as smart city, healthcare, computer vision and geoscience, and remote sensing …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

LiDAR and camera are two modalities available for 3D semantic segmentation in
autonomous driving. The popular LiDAR-only methods severely suffer from inferior …

Panoptic scene understanding and tracking of dynamic agents are essential for robots and
automated vehicles to navigate in urban environments. As LiDARs provide accurate …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Comprehensive modeling of the surrounding 3D world is crucial for the success of
autonomous driving. However existing perception tasks like object detection road structure …