2020
DOI: 10.1109/access.2020.3013595
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3D Model Retrieval Based on a 3D Shape Knowledge Graph

Abstract: A development of 3D construction technology has led to 3D models being applied in many fields, and the number of 3D models has exploded in recent years. Thus, 3D model retrieval has become a popular topic with many proposed approaches. However, all of the methods focus on the 3D model's global structural descriptor design based on various deep learning networks and ignore the local structural information of the 3D model and the correlation of the local structures. In this paper, we propose a novel 3D model ret… Show more

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Cited by 7 publications
(3 citation statements)
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References 97 publications
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“…In [QRS*16] and [QYSG17], Qi et al directly used point cloud data, represented by three coordinates to feed a network able to perform feature transformations and aggregate data points by max pooling. In [NWSL20], authors utilized the PointNet++ model to segment each 3D model into a set of shapes and extracted their features. The K-means method was then utilized to construct from each different shape a node of a 3D shape knowledge graph.…”
Section: Learning-based Methodsmentioning
confidence: 99%
“…In [QRS*16] and [QYSG17], Qi et al directly used point cloud data, represented by three coordinates to feed a network able to perform feature transformations and aggregate data points by max pooling. In [NWSL20], authors utilized the PointNet++ model to segment each 3D model into a set of shapes and extracted their features. The K-means method was then utilized to construct from each different shape a node of a 3D shape knowledge graph.…”
Section: Learning-based Methodsmentioning
confidence: 99%
“…A classificação de objetos tridimensionais, necessária para a percepção do ambiente, normalmente se baseia em encontrar uma correspondência entre duas formas baseada em alguma métrica de similaridade, normalmente a similaridade visual, ou seja, duas formas que são visualmente parecidas (BUSTOS; SIPIRAN, 2012). Esse processo normalmente inclui as seguintes etapas (NIE et al, 2020):…”
Section: Revisão Bibliográficaunclassified
“…Ainda, sistemas autônomos críticos possuem um risco muito maior do que sistemas simulados -um erro em um robô ou um veículo autônomo pode causar danos permanentes ao próprio sistema, à propriedade de terceiros ou atingir pessoas. É extremamente necessário que o sistema seja robusto e que forneça respostas em tempo real para aumentar a segurança (NIE et al, 2020). Em muitos estudos, não é informada a taxa de atualização necessária para a detecção/classificação do objeto, ou mostram taxas muito distantes do ideal para respostas em tempo real.…”
Section: Visão Computacional Em Sistemas Autônomosunclassified