LEADER 01090nam 2200253la 450 001 9910481825303321 005 20221108075704.0 035 $a(UK-CbPIL)2090343911 035 $a(CKB)5500000000085618 035 $a(EXLCZ)995500000000085618 100 $a20210618d1547 uy | 101 0 $alat 135 $aurcn||||a|bb| 200 10$aIoannis Saxonii Hattestedii Commentarioli duo, ad T. Livii Patavini historiarum ab Urbe co[n]dita librum XXI. & XXII. qui libri sunt de secundo bello Punico nunc primům in lucem editi$b[electronic resource] 210 $aBasel $cJohann Oporinus$d1547 215 $aOnline resource ([14], 171, [2] s.) 300 $aReproduction of original in Det Kongelige Bibliotek / The Royal Library (Copenhagen). 700 $aAnon.$0815482 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910481825303321 996 $aIoannis Saxonii Hattestedii Commentarioli duo, ad T. Livii Patavini historiarum ab Urbe codita librum XXI. & XXII. qui libri sunt de secundo bello Punico nunc primům in lucem editi$92059441 997 $aUNINA LEADER 01548nam 2200409 450 001 9910162996903321 005 20220630094823.0 010 $a3-0357-3091-1 035 $a(CKB)3710000001045406 035 $a(MiAaPQ)EBC4865604 035 $a(EXLCZ)993710000001045406 100 $a20170622h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aOcean science and coastal engineering $eselected, peer reviewed papers form the 3rd International Seminar on Oceanand Coastal Engineering, Environemtnal and Natural Disaster Management (ISOCEEN 2015), December 10th, 2015, Surabaya, Indonesia /$fedited by Suntoyo, PhD and Agro Wisudawan, MT 210 1$aSwitzerland :$cTrans Tech Publications Ltd,$d2017. 210 4$d©2017 215 $a1 online resource (343 pages) $cillustrations (some color), tables 225 1 $aAdvanced Materials Research,$x1662-8985 ;$vVolume 862 311 $a3-0357-1091-0 320 $aIncludes bibliographical references at the end of each chapters and index. 410 0$aAdvanced materials research ;$vVolume 862. 606 $aCoastal engineering$xEnvironmental aspects 615 0$aCoastal engineering$xEnvironmental aspects. 676 $a627.58 702 $aSuntoyo 702 $aWisudawan$b Agro 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910162996903321 996 $aOcean science and coastal engineering$92907493 997 $aUNINA LEADER 04630nam 22007095 450 001 9910983334603321 005 20241207115232.0 010 $a9789819795703$b(electronic bk.) 010 $z9789819795697 024 7 $a10.1007/978-981-97-9570-3 035 $a(MiAaPQ)EBC31821754 035 $a(Au-PeEL)EBL31821754 035 $a(CKB)36841090800041 035 $a(DE-He213)978-981-97-9570-3 035 $a(OCoLC)1478700414 035 $a(EXLCZ)9936841090800041 100 $a20241207d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for 3D Point Clouds /$fby Wei Gao, Ge Li 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (330 pages) 311 08$aPrint version: Gao, Wei Deep Learning for 3D Point Clouds Singapore : Springer,c2025 9789819795697 327 $aChapter 1. Introduction to 3D Point Clouds: Datasets and Perception -- Chapter 2. Learning Basics for 3D Point Clouds -- Chapter 3. Deep Learning-based Point Cloud Enhancement I -- Chapter 4. Deep Learning-based Point Cloud Enhancement II -- Chapter 5. Deep Learning-based Point Cloud Analysis I -- Chapter 6. Deep Learning-based Point Cloud Analysis II -- Chapter 7. Point Cloud Pre-trained Models and Large Models -- Chapter 8. Point Cloud-Language Multi-modal Learning -- Chapter 9. Open Source Projects for 3D Point Clouds -- Chapter 10. Typical Engineering Applications of 3D Point Clouds -- Chapter 11. FutureWork on Deep Learning-based Point Cloud Technologies. 330 $aAs 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. 606 $aComputer vision 606 $aVirtual reality 606 $aAugmented reality 606 $aComputer graphics 606 $aImage processing 606 $aCoding theory 606 $aInformation theory 606 $aComputer Vision 606 $aVirtual and Augmented Reality 606 $aComputer Graphics 606 $aImage Processing 606 $aCoding and Information Theory 615 0$aComputer vision. 615 0$aVirtual reality. 615 0$aAugmented reality. 615 0$aComputer graphics. 615 0$aImage processing. 615 0$aCoding theory. 615 0$aInformation theory. 615 14$aComputer Vision. 615 24$aVirtual and Augmented Reality. 615 24$aComputer Graphics. 615 24$aImage Processing. 615 24$aCoding and Information Theory. 676 $a006.37 700 $aGao$b Wei$0868641 701 $aLi$b Ge$01739395 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910983334603321 996 $aDeep Learning for 3D Point Clouds$94317588 997 $aUNINA