April 2025
Intermediate to advanced
690 pages
20h 12m
German
Content preview from 3D-Datenwissenschaft mit Python
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Kapitel 7. Erstellen von 3D-Analyse-Apps
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Wir suchen ständig nach Möglichkeiten, 3D-Rohdaten in verwertbare Erkenntnisse umzuwandeln. Herkömmliche Analysemethoden liefern zwar wertvolle Informationen, lassen aber oft die Flexibilität und Interaktivität vermissen, die für eine gründliche Datenuntersuchung erforderlich sind. Statische Visualisierungen und manuelle Datenmanipulation behindern deine Fähigkeit, komplexe 3D-Datensätze effektiv zu segmentieren, zu beschriften und Wissen daraus zu extrahieren. Es wäre hilfreich, wenn du dynamische Werkzeuge hättest, um Merkmale in Echtzeit zu visualisieren und zu manipulieren.
Stell dir vor, du analysierst eine Punktwolke einer alten Fabrik, um bestimmte Maschinen zu isolieren und ihren räumlichen Kontext zu verstehen. Herkömmliche Methoden können eine mühsame manuelle Segmentierung oder Skripterstellung beinhalten, was unsere Möglichkeiten einschränkt, schnell verschiedene Merkmalskombinationen und Schwellenwerte zu untersuchen. Wir brauchen eine interaktive Anwendung zur Visualisierung von Merkmalen wie Ebenheit, Linearität und Höhenunterschiede. Dies würde die Festlegung von Schwellenwerten ermöglichen, die wir zur Strukturierung unseres Datensatzes auf der Grundlage von Echtzeit-Feedback verwenden können .
Unser Ziel in diesem Kapitel ist es, eine solche interaktive Schwellenwertmethode zu entwickeln, ...
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