Unsupervised point cloud representation learning with deep neural networks: A survey
Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …
deep learning (DL). However, the latter faces various issues, including the lack of data or …
Robo3d: Towards robust and reliable 3d perception against corruptions
The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …
A survey on safety-critical driving scenario generation—A methodological perspective
Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …
thanks to the advance in machine learning-enabled sensing and decision-making …
Polarmix: A general data augmentation technique for lidar point clouds
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …
capture precise geometry of the surrounding environment and are crucial to many …
Less: Label-efficient semantic segmentation for lidar point clouds
Semantic segmentation of LiDAR point clouds is an important task in autonomous driving.
However, training deep models via conventional supervised methods requires large …
However, training deep models via conventional supervised methods requires large …
Pttr: Relational 3d point cloud object tracking with transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of
an object in the current search point cloud given a template point cloud. Motivated by the …
an object in the current search point cloud given a template point cloud. Motivated by the …
Cosmix: Compositional semantic mix for domain adaptation in 3d lidar segmentation
Abstract 3D LiDAR semantic segmentation is fundamental for autonomous driving. Several
Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently …
Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently …
Segment any point cloud sequences by distilling vision foundation models
Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …
3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds
Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …