April 2025
Intermediate to advanced
690 pages
19h 37m
Portuguese (Portugal, Brazil)
Content preview from Ciência de dados 3D com Python
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Capítulo 12. Aprendizagem automática 3D: Agrupamento
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Extrair o significado de dados 3D não é um objetivo simples. Começamos com conjuntos de dados maciços, ricos em detalhes geométricos, dos quais queremos extrair uma compreensão semântica(Capítulo 1) necessária para tarefas de alto nível. O primeiro desafio é a disparidade entre os dados em bruto e o conhecimento utilizável que limita o potencial da aprendizagem automática 3D em várias disciplinas, incluindo a robótica, a navegação autónoma e a compreensão de cenas.
De facto, apesar de beneficiarmos de um imenso corpus acessível para treinar modelos supervisionados para modalidades de imagem ou de texto, são raros os conjuntos de dados 3D rotulados acessíveis abertamente. A falta de etiquetas inerentes torna os métodos de aprendizagem supervisionada difíceis ou demasiado dispendiosos para serem implementados em grande escala.
Precisamos de um método para colmatar esta lacuna sem depender extensivamente da rotulagem humana, que é dispendiosa e trabalhosa. Isto alimenta a ideia de permitir às máquinas discernir autonomamente a estrutura intrínseca dos dados 3D. Isto aumentaria a eficiência e permitir-nos-ia resolver inúmeras dificuldades. Quero ensinar-te como conseguir isto utilizando o poder da aprendizagem não supervisionada através de algoritmos de agrupamento (ver Figura 12-1).
Neste capítulo, centramo-nos ...
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