Deep Learning For 3d Point Clouds
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Deep Learning For 3d Point Clouds
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Author : Wei Gao
language : en
Publisher: Springer Nature
Release Date : 2024-12-06
Deep Learning For 3d Point Clouds written by Wei Gao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Computers categories.
As an efficient 3D vision solution, point clouds have been widely applied into diverse engineering scenarios, including immersive media communication, autonomous driving, reverse engineering, robots, topography mapping, digital twin city, medical analysis, digital museum, etc. Thanks to the great developments of deep learning theories and methods, 3D point cloud technologies have undergone fast growth during the past few years, including diverse processing and understanding tasks. Human and machine perception can be benefited from the success of using deep learning approaches, which can significantly improve 3D perception modeling and optimization, as well as 3D pre-trained and large models. This book delves into these research frontiers of deep learning-based point cloud technologies. The subject of this book focuses on diverse intelligent processing technologies for the fast-growing 3D point cloud applications, especially using deep learning-based approaches. The deep learning-based enhancement and analysis methods are elaborated in detail, as well as the pre-trained and large models with 3D point clouds. This book carefully presents and discusses the newest progresses in the field of deep learning-based point cloud technologies, including basic concepts, fundamental background knowledge, enhancement, analysis, 3D pre-trained and large models, multi-modal learning, open source projects, engineering applications, and future prospects. Readers can systematically learn the knowledge and the latest developments in the field of deep learning-based point cloud technologies. This book provides vivid illustrations and examples, and the intelligent processing methods for 3D point clouds. Readers can be equipped with an in-depth understanding of the latest advancements of this rapidly developing research field.
Deep Learning For 3d Point Clouds
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Author : Wei Gao
language : en
Publisher:
Release Date : 2024-12-07
Deep Learning For 3d Point Clouds written by Wei Gao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-07 with Computers categories.
3d Point Cloud Analysis
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Author : Shan Liu
language : en
Publisher: Springer Nature
Release Date : 2021-12-10
3d Point Cloud Analysis written by Shan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Computers categories.
This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
Deep Learning For 3d Object Detection On Point Clouds For Autonomous Driving
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Author : Weihao Lu
language : en
Publisher:
Release Date : 2023
Deep Learning For 3d Object Detection On Point Clouds For Autonomous Driving written by Weihao Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Multimodal Panoptic Segmentation Of 3d Point Clouds
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Author : Dürr, Fabian
language : en
Publisher: KIT Scientific Publishing
Release Date : 2023-10-09
Multimodal Panoptic Segmentation Of 3d Point Clouds written by Dürr, Fabian and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-09 with categories.
The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Deep Learning For 3d Vision Algorithms And Applications
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Author : Xiaoli Li
language : en
Publisher: World Scientific
Release Date : 2024-08-27
Deep Learning For 3d Vision Algorithms And Applications written by Xiaoli Li and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Computers categories.
3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.
Deep Learning On Point Clouds For 3d Scene Understanding
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Author : Ruizhongtai Qi
language : en
Publisher:
Release Date : 2018
Deep Learning On Point Clouds For 3d Scene Understanding written by Ruizhongtai Qi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Point cloud is a commonly used geometric data type with many applications in computer vision, computer graphics and robotics. The availability of inexpensive 3D sensors has made point cloud data widely available and the current interest in self-driving vehicles has highlighted the importance of reliable and efficient point cloud processing. Due to its irregular format, however, current convolutional deep learning methods cannot be directly used with point clouds. Most researchers transform such data to regular 3D voxel grids or collections of images, which renders data unnecessarily voluminous and causes quantization and other issues. In this thesis, we present novel types of neural networks (PointNet and PointNet++) that directly consume point clouds, in ways that respect the permutation invariance of points in the input. Our network provides a unified architecture for applications ranging from object classification and part segmentation to semantic scene parsing, while being efficient and robust against various input perturbations and data corruption. We provide a theoretical analysis of our approach, showing that our network can approximate any set function that is continuous, and explain its robustness. In PointNet++, we further exploit local contexts in point clouds, investigate the challenge of non-uniform sampling density in common 3D scans, and design new layers that learn to adapt to varying sampling densities. The proposed architectures have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointNets to two important perception problems in robotics: 3D object detection and 3D scene flow estimation. In 3D object detection, we propose a new frustum-based detection framework that achieves 3D instance segmentation and 3D amodal box estimation in point clouds. Our model, called Frustum PointNets, benefits from accurate geometry provided by 3D points and is able to canonicalize the learning problem by applying both non-parametric and data-driven geometric transformations on the inputs. Evaluated on large-scale indoor and outdoor datasets, our real-time detector significantly advances state of the art. In scene flow estimation, we propose a new deep network called FlowNet3D that learns to recover 3D motion flow from two frames of point clouds. Compared with previous work that focuses on 2D representations and optimizes for optical flow, our model directly optimizes 3D scene flow and shows great advantages in evaluations on real LiDAR scans. As point clouds are prevalent, our architectures are not restricted to the above two applications or even 3D scene understanding. This thesis concludes with a discussion on other potential application domains and directions for future research.
Deep Learning For Object Tracking In 3d Point Clouds
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Author : Jaume Colom Hernández
language : en
Publisher:
Release Date : 2020
Deep Learning For Object Tracking In 3d Point Clouds written by Jaume Colom Hernández and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Great progress has been achieved in computer vision tasks within image and video; however, technological advances in LiDAR sensors have created a whole new area of computer vision research devoted to it. With applications in many industries, such as transportation, agriculture, or healthcare. This thesis studies object tracking in 3D point clouds. Pairs of point cloud observations are feed to a neural network to estimate pose and translation between the observations. Then these estimations, together with external features, are processed with Kalman Filter and RNN to extract spatial-temporal redundancies and improve the results. The system has been tested in the KITTI dataset, with pre-segmented observations, on different types of objects and paths. The results show that the neural network estimated pose gives a very accurate tracking. Still, the best results are achieved when combining the estimated pose and translations with a recurrent neural network.
Deep Learning Based Point Cloud Processing And Compression
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Author : Anique Akhtar
language : en
Publisher:
Release Date : 2022
Deep Learning Based Point Cloud Processing And Compression written by Anique Akhtar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Data compression (Computer science) categories.
A point cloud is a 3D data representation that is becoming increasingly popular. Recent significant advances in 3D sensors and capturing techniques have led to a surge in the usage of 3D point clouds in virtual reality/augmented reality (VR/AR) content creation, as well as 3D sensing for robotics, smart cities, telepresence, and automated driving applications. With an increase in point cloud applications and improved capturing technologies, we now have high-resolution point clouds with millions of points per frame. However, due to the large size of a point cloud, efficient techniques for the transmission, compression, and processing of point cloud content are still widely sought. This thesis addresses multiple issues in the transmission, compression, and processing pipeline for point cloud data. We employ a deep learning solution to process 3D dense as well as sparse point cloud data for both static as well as dynamic contents. Employing deep learning on point cloud data which is inherently sparse is a challenging task. We propose multiple deep learning-based frameworks that address each of the following problems: Point Cloud Compression Artifact Removal. V-PCC is the current state-of-the-art for dynamic point cloud compression. However, at lower bitrates, there are unpleasant artifacts introduced by V-PCC. We propose a deep learning solution for V-PCC artifact removal by leveraging the direction of projection property in V-PCC to remove quantization noise. Point Cloud Geometry Prediction. The current point cloud lossy compression and processing techniques suffer from quantization loss which results in a coarser sub-sampled representation of the point cloud. We solve the problem of points lost during voxelization by performing geometry prediction across spatial scales using deep learning architecture. Point Cloud Geometry Upsampling. Loss of details and irregularities in point cloud geometry can occur during the capturing, processing, and compression pipeline. We present a novel geometry upsampling technique, PU-Dense, which can process a diverse set of point clouds including synthetic mesh-based point clouds, real-world high-resolution point clouds, real-world indoor LiDAR scanned objects, as well as outdoor dynamically acquired LiDAR-based point clouds. Dynamic Point Cloud Interpolation. Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. We also propose the first point cloud interpolation framework for photorealistic dynamic point clouds. Inter-frame Compression for Dynamic Point Clouds. Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. We propose a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. In each case, our method achieves state-of-the-art results with significant improvement to the current technologies.
Proceedings Of 3rd 2023 International Conference On Autonomous Unmanned Systems 3rd Icaus 2023
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Author : Yi Qu
language : en
Publisher: Springer Nature
Release Date : 2024-04-22
Proceedings Of 3rd 2023 International Conference On Autonomous Unmanned Systems 3rd Icaus 2023 written by Yi Qu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-22 with Technology & Engineering categories.
This book includes original, peer-reviewed research papers from the 3rd ICAUS 2023, which provides a unique and engaging platform for scientists, engineers and practitioners from all over the world to present and share their most recent research results and innovative ideas. The 3rd ICAUS 2023 aims to stimulate researchers working in areas relevant to intelligent unmanned systems. Topics covered include but are not limited to: Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and their Application in Unmanned Systems. The papers presented here share the latest findings in unmanned systems, robotics, automation, intelligent systems, control systems, integrated networks, modelling and simulation. This makes the book a valuable resource for researchers, engineers and students alike.