Deep Learning On Point Clouds For 3d Scene Understanding

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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.

Multimodal Scene Understanding

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Author : Michael Ying Yang
language : en
Publisher: Academic Press
Release Date : 2019-07-16

Multimodal Scene Understanding written by Michael Ying Yang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 with Technology & Engineering categories.

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Computer Vision Eccv 2022

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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-10-20

Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Computers categories.

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

3d Scene Modeling And Robotics Interaction

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Author : Xin Yang
language : en
Publisher: Springer Nature
Release Date : 2025-05-18

3d Scene Modeling And Robotics Interaction written by Xin Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-18 with Computers categories.

This book focuses on the intelligent perception and interaction module in intelligent robotic systems, establishes a multidisciplinary cross-fertilization knowledge system, explores the related technology frontiers and research frontiers as comprehensively as possible from the perspective of scene modeling and understanding, and develops a practical exposition of practical application tasks such as robotic navigation, obstacle avoidance, and grasping. The main contents of this book include 3D reconstruction, scene exploration, scene understanding, robot navigation and obstacle avoidance, robot grasping and comprehensive project practice. Combining theory and practice, the book contains both basic algorithms and covers the latest technologies with detailed code or pseudo-code resources. This book can be used as a teaching reference book for information and intelligence related majors in higher education institutions, computer graphics, computer vision and intelligent robotics and other related fields, as well as a reference book for technicians engaged in related fields. This book takes intelligent robots as the carrier, focuses on the technologies of environment perception and understanding and applying them to practical tasks such as robot navigation, obstacle avoidance and grasping. The book consists of six chapters. Chapters 1 to 3 provide a comprehensive introduction to the development and application of scene modeling and understanding technologies, including 3D reconstruction, scene exploration, and scene understanding. Chapters 4 and Chapter 5 provide a comprehensive introduction to the development and application of robot perception technologies, including visual relocalization and robot navigation, obstacle avoidance and grasping. Chapter 6 introduces comprehensive project practice with 3D scene modeling and understanding for robot tasks as an example, which facilitates readers to have a comprehensive understanding and mastery of theory and practice. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

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 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.

Computer Vision Eccv 2024

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Author : Aleš Leonardis
language : en
Publisher: Springer Nature
Release Date : 2024-10-24

Computer Vision Eccv 2024 written by Aleš Leonardis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-24 with Computers categories.

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.

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.

Computer Vision Eccv 2018

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Author : Vittorio Ferrari
language : en
Publisher: Springer
Release Date : 2018-10-08

Computer Vision Eccv 2018 written by Vittorio Ferrari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Deep Learning For Multi Sensor Earth Observation

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Author : Sudipan Saha
language : en
Publisher: Elsevier
Release Date : 2025-02-03

Deep Learning For Multi Sensor Earth Observation written by Sudipan Saha and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Technology & Engineering categories.

Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning. - Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality - Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences - Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice