3D Point Cloud Processing and Learning for Autonomous Driving

• URL: http://arxiv.org/abs/2003.00601v1
• Date: Sun, 1 Mar 2020 22:13:46 GMT
• Title: 3D Point Cloud Processing and Learning for Autonomous Driving
• Authors: Siheng Chen and Baoan Liu and Chen Feng and Carlos Vallespi-Gonzalez and Carl Wellington
• Abstract summary: We present a review of 3D point cloud processing and learning for autonomous driving. LiDAR sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes.
• Score: 26.285659927609213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles, light detection and ranging (LiDAR) sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an autonomous vehicle. While much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of LiDAR in autonomous driving and have proposed processing and learning algorithms to exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe autonomous vehicles. We also offer perspectives on open issues that are needed to be solved in the future.

Related papers

• Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds [34.99995524090838]
  3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. There has been a growing interest in self-supervised pre-training of 3D perception models. We propose the instance-aware and similarity-balanced contrastive units that are tailored for self-driving point clouds.
  arXiv  Detail & Related papers  (2024-09-10T19:11:45Z)
• Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks [10.780826266192621]
  3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space. Recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms. This review looks back and analyzes the current state of achievements in the field of 3D lane detection research.
  arXiv  Detail & Related papers  (2024-04-10T09:35:50Z)
• Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey [3.224562109592693]
  3D point cloud learning has been attracting more and more attention among all other forms of self-driving data. This paper shows recent advances in deep learning methods for 3D object detection and tracking.
  arXiv  Detail & Related papers  (2023-11-10T13:03:37Z)
• HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving [95.42203932627102]
  3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
  arXiv  Detail & Related papers  (2022-12-15T11:15:14Z)
• Real-Time And Robust 3D Object Detection with Roadside LiDARs [20.10416681832639]
  We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. We make a significant contribution with our LiDAR-based 3D detector that can be used for smart city applications.
  arXiv  Detail & Related papers  (2022-07-11T21:33:42Z)
• 3D Object Detection for Autonomous Driving: A Comprehensive Survey [48.30753402458884]
  3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-06-19T19:43:11Z)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking (Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21) [68.12101204123422]
  A point cloud is a dense compilation of spatial data in 3D coordinates. We propose a PointNet-based approach for 3D Multi-Object Tracking (MOT)
  arXiv  Detail & Related papers  (2021-06-03T05:36:39Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review [33.56857661598032]
  We provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds. More than 140 key contributions in the recent five years are summarized in this survey.
  arXiv  Detail & Related papers  (2020-05-20T03:01:40Z)
• Deep Learning for 3D Point Clouds: A Survey [58.954684611055]
  This paper presents a review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets.
  arXiv  Detail & Related papers  (2019-12-27T09:15:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.