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Point cloud clustering
Mrkvička, Daniel ; Štarha, Pavel (referee) ; Procházková, Jana (advisor)
This bachelor's thesis deals with the point cloud clustering. It focuses on surface detection in three-dimensional space. It describes in particular the methods that are used for plane detection. It also describes the concrete implementation of one of these method, the RANSAC, and examines its practical application for roof detection.
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Tabletop Object Detection
Varga, Tomáš ; Zemčík, Pavel (referee) ; Španěl, Michal (advisor)
This work describes the issue of tabletop object detection in point cloud. Point cloud is recorded with Kinect sensor. Designed solution uses algorithm RANSAC for plane detection, algorithm Euclidean clustering for segmentation and ICP algorithm for object detection. Algorithm ICP is modified and mainly it can detect rotational symetric objects and objects without any transformation against it's models. The final package is build on platform ROS. The achieved results with own dataset are good despite of the limited functionality of the detector.
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3D Model of a Room Using Kinect
Zemek, Martin ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis is about finding significant planar surfaces in point cloud and their conversion to polygons. Which is important step for making a 3D model of a room. Input is point cloud, which was recorded by Kinect v2 sensor. Tool for capturing one snapshot from Kinect is included in this thesis. For recording more detailed point cloud is needed external program. Some of the programs are mentioned further in this thesis. For plane detection is used RANSAC. Inliers are divided using Euclidean Cluster Extraction. These clusters are converted to polygon using convex or concave hull. Application is capable of working with one snapshot or bigger point cloud assembled by registration of particular snapshots and detect primary and secondary planar surfaces. For the largest plane points can be prepared for creation of a texture and dimensions of this plan can be printed in CLI.
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Point cloud clustering
Mrkvička, Daniel ; Štarha, Pavel (referee) ; Procházková, Jana (advisor)
This bachelor's thesis deals with the point cloud clustering. It focuses on surface detection in three-dimensional space. It describes in particular the methods that are used for plane detection. It also describes the concrete implementation of one of these method, the RANSAC, and examines its practical application for roof detection.
|
|
|
3D Model of a Room Using Kinect
Zemek, Martin ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis is about finding significant planar surfaces in point cloud and their conversion to polygons. Which is important step for making a 3D model of a room. Input is point cloud, which was recorded by Kinect v2 sensor. Tool for capturing one snapshot from Kinect is included in this thesis. For recording more detailed point cloud is needed external program. Some of the programs are mentioned further in this thesis. For plane detection is used RANSAC. Inliers are divided using Euclidean Cluster Extraction. These clusters are converted to polygon using convex or concave hull. Application is capable of working with one snapshot or bigger point cloud assembled by registration of particular snapshots and detect primary and secondary planar surfaces. For the largest plane points can be prepared for creation of a texture and dimensions of this plan can be printed in CLI.
|
|
|
Tabletop Object Detection
Varga, Tomáš ; Zemčík, Pavel (referee) ; Španěl, Michal (advisor)
This work describes the issue of tabletop object detection in point cloud. Point cloud is recorded with Kinect sensor. Designed solution uses algorithm RANSAC for plane detection, algorithm Euclidean clustering for segmentation and ICP algorithm for object detection. Algorithm ICP is modified and mainly it can detect rotational symetric objects and objects without any transformation against it's models. The final package is build on platform ROS. The achieved results with own dataset are good despite of the limited functionality of the detector.
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