Scene understanding from 3D point clouds and RGB images for autonomous driving

Rolando de Sousa Chichorro Avides Moreira

doi:10.34626/hxcs-gj21

Utilize este identificador para referenciar este registo: https://hdl.handle.net/10216/135288

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Campo DC	Valor	Idioma
dc.creator	Rolando de Sousa Chichorro Avides Moreira
dc.date.accessioned	2026-01-12T05:10:10Z	-
dc.date.available	2026-01-12T05:10:10Z	-
dc.date.issued	2021-07-22
dc.date.submitted	2021-08-03
dc.identifier.other	sigarra:486175
dc.identifier.uri	https://hdl.handle.net/10216/135288	-
dc.description	Autonomous cars are often equipped with 3D data acquisition sensors and devices, e.g., LiDAR, which provide a 3D point cloud that describes the surroundings. Direct acquisition of 3D data from these sensors is commonly used for obstacle avoidance and mapping. Analysing 3D point clouds is complex since point clouds are unstructured, unordered, and contain a varying number of points. The most common approach used for scene understanding in images is the Convolutional Neural Network. Although CNNs achieve high performance in image analysis, they cannot be applied naturally on point clouds. Several methods for extending CNNs to 3D point cloud analysis have been proposed, such as rasterization into a 3D voxel grid to use directly a CNN or using a Graph Convolutional Network. The main goal of this dissertation is to study and compare different approaches for scene understanding from 3D point clouds within the scope of driving automation systems. Moreover, the project contemplates the study of sensor fusion approaches, namely how to combine 3D point clouds and images. In light of this, this project uses a sensor fusion technique called pointpainting, which uses images segmentation to enhance 3D object detection on point clouds.
dc.language.iso	eng
dc.rights	openAccess
dc.subject	Engenharia electrotécnica, electrónica e informática
dc.subject	Electrical engineering, Electronic engineering, Information engineering
dc.title	Scene understanding from 3D point clouds and RGB images for autonomous driving
dc.type	Dissertação
dc.contributor.uporto	Faculdade de Engenharia
dc.identifier.doi	10.34626/hxcs-gj21
dc.identifier.tid	202825248
dc.subject.fos	Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fos	Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.discipline	Mestrado Integrado em Engenharia Electrotécnica e de Computadores
thesis.degree.grantor	Faculdade de Engenharia
thesis.degree.grantor	Universidade do Porto
thesis.degree.level	1
Aparece nas coleções:	FEUP - Dissertação

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