April 2025
Intermediate to advanced
690 pages
20h 12m
German
Content preview from 3D-Datenwissenschaft mit Python
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Kapitel 6. Punktwolken-Daten-Engineering
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ist es an der Zeit, die Feinheiten des 3D-Daten-Engineerings - insbesondere der Punktwolken - zu erforschen. Punktwolken bieten zwar eine reichhaltige Darstellung der 3D-Geometrie, aber um aussagekräftige Informationen zu extrahieren, bedarf es spezieller Techniken, um Rauschen und Redundanz zu bewältigen und um relevante Muster zu erkennen. Wir brauchen einen Weg, um rohe Punktwolkendaten in eine verfeinerte Form umzuwandeln und wesentliche Merkmale zu extrahieren, die die Leistung nachgelagerter Aufgaben wie 3D-Formerkennung(Kapitel 9), Klassifizierung(Kapitel 15) und Objekterkennung(Kapitel 16) verbessern. Die Basiseinheit sind unsere Daten. Wir wollen sie so verfeinern, dass sie für unsere Endanwendung sinnvoll sind.
Betrachten wir ein kurzes Beispiel. Stell dir vor, du möchtest einen MRT-Scan des Gehirns (eine Auswahl von 2D-Schichten des Gehirns) analysieren, um festzustellen, ob bestimmte Zonen von einem Tumor bedroht sind. Wir könnten die Bilder, aus denen unser Datensatz besteht, direkt an einen erfahrenen Neurochirurgen weitergeben. Es liegt an seinem Fachwissen, das erste Bild zu interpretieren, diese Vorhersage für die verschiedenen Schichten zu wiederholen und dann seine Diagnose und Analyse zu veröffentlichen. Das bedeutet eine hohe Verantwortung für die Qualität der Daten. Wenn die Daten stark ...
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