Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Point cloud data has been extensively studied due to its compact form and flexibility in representing complex 3D structures. The ability of point cloud data to accurately capture and represent intricate 3D geometry makes it an ideal choice for a wide range of applications, including computer vision, robotics, and autonomous driving, all of which require an understanding of the underlying spatial structures. Given the challenges associated with annotating large-scale point clouds, self-supervised point cloud representation learning has attracted increasing attention in recent years. This approach aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotations. In this paper, we present a comprehensive survey of self-supervised point cloud representation learning using DNNs. We begin by presenting the motivation and general trends in recent research. We then briefly introduce the commonly used datasets and evaluation metrics. Following that, we delve into an extensive exploration of self-supervised point cloud representation learning methods based on these techniques. Finally, we share our thoughts on some of the challenges and potential issues that future research in self-supervised learning for pre-training 3D point clouds may encounter.
Code (0)
등록된 구현이 없습니다.
Tasks
3D geometryAutonomous DrivingRepresentation LearningSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Self-Supervised Learning for Point Clouds Data: A Survey
3D point clouds are a crucial type of data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization. Deep neural networks (DNNs) have achieved re…
Pedestrian DetectionSelf-Supervised LearningSurveySelf-Supervised Few-Shot Learning on Point Clouds
The increased availability of massive point clouds coupled with their utility in a wide variety of applications such as robotics, shape synthesis, and self-driving cars has attracted increased attention from both industr…
Few-Shot 3D Point Cloud ClassificationFew-Shot LearningGeneral ClassificationSelf-Driving Cars+1Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
We propose a novel scene flow method that captures 3D motions from point clouds without relying on ground-truth scene flow annotations. Due to the irregularity and sparsity of point clouds, it is expensive and time-consu…
Pseudo LabelSelf-Supervised LearningCross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has been a growing interest in self-supervise…
3D Object Detection3D Semantic SegmentationAutonomous DrivingContrastive Learning+4Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising res…
3D Point Cloud Linear ClassificationDeep LearningGeneral ClassificationPoint Cloud Pre-training+4