The task of point cloud segmentation, comprising semantic, instance, and panoptic
segmentation, has been mainly tackled by designing task-specific network architectures …

Semantic segmentation of point clouds in autonomous driving datasets requires techniques
that can process large numbers of points efficiently. Sparse 3D convolutions have become …

Abstract Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception. End-to-
end transformer-based algorithms, which detect and track objects simultaneously, show …

Point cloud analysis has a wide range of applications in many areas such as computer
vision, robotic manipulation, and autonomous driving. While deep learning has achieved …

In this paper an exhaustive review and comprehensive analysis of recent and former deep
learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related …

LiDAR is one of the most crucial sensors for autonomous vehicle perception. However,
current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous …

LiDAR-based 3D pedestrian detection has recently been extensively applied in autonomous
driving and intelligent mobile robots. However, it remains a highly challenging perceptual …

Reliable object detection using cameras plays a crucial role in enabling autonomous
vehicles to perceive their surroundings. However, existing camera-based object detection …

Chart comprehension presents significant challenges for machine learning models due to
the diverse and intricate shapes of charts. Existing multimodal methods often overlook these …

Supervised 3D part segmentation models are tailored for a fixed set of objects and parts,
limiting their transferability to open-set, real-world scenarios. Recent works have explored …