This paper presents a vector HD-mapping algorithm that formulates the mapping as a
tracking task and uses a history of memory latents to ensure consistent reconstructions over …

This paper investigates the semantic segmentation problem for large-scale point clouds.
Recent segmentation methods usually employ an encoder–decoder architecture. However …

Abstract 4D panoptic segmentation is a challenging but practically useful task that requires
every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and …

Abstract 3D point clouds play a pivotal role in outdoor scene perception, especially in the
context of autonomous driving. Recent advancements in 3D LiDAR segmentation often …

Long-term memory often plays a pivotal role in human cognition through the analysis of
contextual information. Machine learning researchers have attempted to emulate this …

In the domain of supervised scene flow estimation, the process of manual labeling is both
time-intensive and financially demanding. This paper introduces SSFlowNet, a semi …

Semantic segmentation of LiDAR points has significant value for autonomous driving and
mobile robot systems. Most approaches explore spatio-temporal information of multi-scan to …

3D point clouds are essential for perceiving outdoor scenes, especially within the realm of
autonomous driving. Recent advances in 3D LiDAR Object Detection focus primarily on the …

Unsupervised intrinsic image decomposition (IID) is the process of separating a natural
image into albedo and shade without these ground truths. A recent model employing light …

Defocus blur estimation requires high-precision detection between the homogeneous region
and transition edge. This paper develops a novel progressive design that effectively …