Making 3d Point Cloud Reconstruction Computationally Efficient
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Making 3d Point Cloud Reconstruction Computationally Efficient
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Author : Chih-Hsiang Chang
language : en
Publisher:
Release Date : 2016
Making 3d Point Cloud Reconstruction Computationally Efficient written by Chih-Hsiang Chang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computational photography categories.
This dissertation covers the problem of 3D reconstruction of a scene from a number of images taken from that scene. Although this problem has been previously addressed in the computer vision literature, what differentiates this dissertation research from what has already been done is the computational efficiency aspect of various computer vision modules. The contributions made in this dissertation to the computational efficiency aspect of 3D reconstruction are published or submitted as five papers that appear as five chapters in this dissertation. Each chapter provides an abstract of the contribution made, an introduction and literature review, the methodology developed, the results obtained together with their discussions, and conclusion associated with that contribution. Chapter 1 targets the bundle adjustment module of 3D reconstruction where it is shown how a local bundle adjustment approach improves the computational complexity. In Chapter 2, a two- stage scheme is developed for camera pose estimation. The main advantage of this scheme is that the computation burden caused by the Levenberg-Marquardt optimization is avoided. Chapter 3 targets the frame selection and camera rotation registration aspects of 3D reconstruction. Initially, the translation vector is estimated by using the relative camera pose and 3D correspondences. Then, a rotation registration is considered to generate the camera rotation matrix. The developed approach reduces the re-projection error in each frame at a lower computational complexity compared to the conventional approach. Chapter 4 targets the computational efficiency aspect of the entire 3D reconstruction pipeline by providing a new absolute camera pose recovery approach in a computationally efficient manner. The experimental results show the developed pipeline generates lower re-projection errors and higher frame rates towards 3D reconstruction. Finally, in Chapter 5, the camera pose estimation is applied to the problem of vanishing point detection for camera orientation applications. A fast J-linkage algorithm is developed to perform vanishing point detection. Then, this algorithm is used to recover the camera rotation in a computationally efficient manner. The contributions presented in the chapters offer a computationally efficient framework towards making 3D reconstruction from video sequences feasible on laptop devices.
Fine Feature Reconstruction In Point Set Surfaces Using Deep Learning
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Author : Prashant Raina
language : en
Publisher:
Release Date : 2020
Fine Feature Reconstruction In Point Set Surfaces Using Deep Learning written by Prashant Raina 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.
Point clouds are typically captured from surfaces of real-world objects using scanning devices. This scanning process invariably results in the loss of sharp edges as well as other geometric features of the original surface, which we collectively refer to as "fine features". This thesis explores leveraging recent advances in deep learning in order to recover fine features of surfaces which were lost when acquiring the point cloud. We first focus on reconstructing sharp edges of the original surface. We define a new concept - a sharpness field - over the underlying surface of a point cloud, whose ridges give the locations of sharp edges even at points not originally sampled from the surface. We then demonstrate that with appropriate training data, deep neural networks can be trained to compute the sharpness field for a point cloud. We evaluate several different local neighborhood representations and deep learning models to improve the accuracy of sharpness field computation across different neighborhood scales. Some applications of the sharpness field are then described. The most important such application is feature-aware smoothing: using the computed sharpness field to preserve sharp edges while removing noise from point clouds. Our novel smoothing algorithm shows superior performance in reconstructing sharp edges and corners compared to the state-of-the-art RIMLS algorithm, while also yielding points lying on sharp edges. Other applications of the sharpness field are also presented: generating a graphical representation of sharp edges and segementing a point cloud into smooth surface patches. We then expand the scope of the problem from sharp edges to undersampled fine features in general. We tackle this by developing a unique deep learning approach to point cloud super-resolution using an innovative application of generative adversarial networks (GANs). The novelty of our super-resolution method lies in framing point cloud super-resolution as a domain translation task between heightmaps obtained from point clouds and heightmaps obtained from triangular meshes. By using recent developments in domain translation using GANs, we obtain results qualitatively and quantitatively superior to state-of-the-art point cloud super-resolution methods, all while using a radically different deep learning approach which is also more computationally efficient. The main contributions of this thesis are: 1. Establishing sharpness field computation as a novel method for localizing sharp edges in 3D point clouds. 2. Several new data-driven methods to compute the sharpness field for a point cloud using different local neighborhood representations and machine learning models. 3. A novel feature-aware smoothing algorithm for denoising point clouds while preserving sharp edges, using the aforementioned sharpness field. This method produces denoising results superior to the state-of-the-art RIMLS smoothing method. 4. An innovative application of GAN-based domain translation, in order to transform sparse heightmaps obtained from point cloud neighborhoods into dense heightmaps obtained from triangular meshes. 5. A unique method for reconstrucing fine features in point clouds, by using heightmap domain translation to perform point cloud super-resolution. The reconstructed surfaces are qualitatively and quantitatively superior to those produced by state-of-the-art point cloud super-resolution methods. Other contributions include: 1. An algorithm for extracting an explicit graphical representation of the sharp edges of a point cloud, using the sharpness field. 2. An algorithm for segmenting a point cloud into smooth patches, using the sharpness field. 3. Feature-aware smoothing algorithms which incorporate the aforementioned edge graph and patch segmentation.
Reconstruction And Analysis Of 3d Scenes
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Author : Martin Weinmann
language : en
Publisher: Springer
Release Date : 2016-03-17
Reconstruction And Analysis Of 3d Scenes written by Martin Weinmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-17 with Computers categories.
This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.
Volumetric Change Detection Using Uncalibrated 3d Reconstruction Models
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Author : Yakov Diskin
language : en
Publisher:
Release Date : 2015
Volumetric Change Detection Using Uncalibrated 3d Reconstruction Models written by Yakov Diskin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Detectors categories.
We present a 3D change detection technique designed to support various wide-area-surveillance (WAS) applications in changing environmental conditions. The novelty of the work lies in our approach of creating an illumination invariant system tasked with detecting changes in a scene. Previous efforts have focused on image enhancement techniques that manipulate the intensity values of the image to create a more controlled and unnatural illumination. Since most applications require detecting changes in a scene irrespective of the time of day, (lighting conditions or weather conditions present at the time of the frame capture), image enhancement algorithms fail to suppress the illumination differences enough for Background Model (BM) subtraction to be effective. A more effective change detection technique utilizes the 3D scene reconstruction capabilities of structure from motion to create a 3D background model of the environment. By rotating and computing the projectile of the 3D model, previous work has been shown to effectively eliminate the background by subtracting the newly captured dataset from the BM projectile leaving only the changes within the scene. Although previous techniques have proven to work in some cases, these techniques fail when the illumination significantly changes between the capture of the datasets. Our approach completely eliminates the illumination challenges from the change detection problem. The algorithm is based on our previous work in which we have shown a capability to reconstruct a surrounding environment in near real-time speeds. The algorithm, namely Dense Point-Cloud Representation (DPR), allows for a 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the relative depths in a scene. The reconstruction process resulting in a point-cloud is computed based on SURF feature matching and depth triangulation analysis. We utilized optical flow features and a single image super resolution technique to create an extremely dense model. The accuracy of DPR is independent of the environmental changes that may be present between the datasets, since DPR only operates on images within one dataset to create the 3D model for each dataset. Our change detection technique utilizes a unique scheme to register the two 3D models. The technique uses an opportunistic approach to compute the optimal feature extraction and matching scheme to compute a fundamental matrix needed to transform a 3D point-cloud model from one dataset to align with the 3D model produced by another. Next, in order to eliminate any effects of the illumination change we convert each point-cloud model into a 3D binary voxel grid. A `oneʹ is assigned to voxels containing points from the model while a `zeroʹ is assigned to voxels with no points. In our final step, we detect the changes between the two environments by geometrically subtracting the registered 3D binary voxel models. This process is computationally efficient due to logic-based operation available when handling binary models. We measure the success of our technique by evaluating the detection outputs, false alarm rate and computational expense when comparing with state-of-the-art change detection techniques.
Computational Science Iccs 2021
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Author : Maciej Paszynski
language : en
Publisher: Springer Nature
Release Date : 2021-06-09
Computational Science Iccs 2021 written by Maciej Paszynski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-09 with Computers categories.
The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually.
Computational Label And Data Efficiency In Deep Learning For Sparse 3d Data
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Author : Li, Lanxiao
language : en
Publisher: KIT Scientific Publishing
Release Date : 2024-05-13
Computational Label And Data Efficiency In Deep Learning For Sparse 3d Data written by Li, Lanxiao 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 2024-05-13 with categories.
Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Towards Optimal Point Cloud Processing For 3d Reconstruction
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Author : Guoxiang Zhang
language : en
Publisher: Springer Nature
Release Date : 2022-06-03
Towards Optimal Point Cloud Processing For 3d Reconstruction written by Guoxiang Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-03 with Technology & Engineering categories.
This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods. The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data. The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.
Cave Investigation
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Author : Savas Karabulut
language : en
Publisher: BoD – Books on Demand
Release Date : 2017-07-12
Cave Investigation written by Savas Karabulut and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Science categories.
Cave investigation is one of the most interesting research field in natural and earth sciences, showing a rapid development with technological progress. It coincides with a number of scientific disciplines to address the early human histrory, habitats of living lives and specimens. This book will be useful to students and researchers as well as to earth scientists, archeologist, biologist, natural sciences, and other experts in a other related of disciplines. The volume consists of three sections sorted thematically, each focusing on a certain aspect. The book presents results on the determination and definition of caves discussed by means of geographic, geophysical, and geological applications. Geomorphometric analysis using GIS and laser scanning, the importance of electric tomography method in cave detection, the use of groundwater sources in agricultural areas, and the habitats of bats and species are studied on several cave studies.
Medical Image Computing And Computer Assisted Intervention Miccai 2023 Workshops
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Author : M. Emre Celebi
language : en
Publisher: Springer Nature
Release Date : 2023-11-30
Medical Image Computing And Computer Assisted Intervention Miccai 2023 Workshops written by M. Emre Celebi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-30 with Computers categories.
This double volume set LNCS 14393-14394 constitutes the proceedings from the workshops held at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC, Canada, in October 2023. The 54 full papers together with 14 short papers presented in this volume were carefully reviewed and selected from 123 submissions from all workshops. The papers of the workshops are presenting the topical sections: Eighth International Skin Imaging Collaboration Workshop (ISIC 2023) First Clinically-Oriented and Responsible AI for Medical Data Analysis (Care-AI 2023) Workshop First International Workshop on Foundation Models for Medical Artificial General Intelligence (MedAGI 2023) Fourth Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2023) First MICCAI Workshop on Time-Series Data Analytics and Learning First MICCAI Workshop on Lesion Evaluation and Assessment with Follow-Up (LEAF) AI For Treatment Response Assessment and predicTion Workshop (AI4Treat 2023) Fourth International Workshop on Multiscale Multimodal Medical Imaging (MMMI 2023) Second International Workshop on Resource-Effcient Medical Multimodal Medical Imaging Image Analysis (REMIA 2023)
Pattern Recognition Computer Vision And Image Processing Icpr 2022 International Workshops And Challenges
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Author : Jean-Jacques Rousseau
language : en
Publisher: Springer Nature
Release Date : 2023-08-01
Pattern Recognition Computer Vision And Image Processing Icpr 2022 International Workshops And Challenges written by Jean-Jacques Rousseau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-01 with Computers categories.
This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.