Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 260 for “"Point Cloud"”.
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Accelerating point cloud cleaning
… Secondly, design constraints in generalised point cloud editing software result in inefficient abstraction of layers that may extend a task duration due to memory pressure. Finally, existing semi-automated segmentation tools have difficulty targeting the diverse set of segmentation targets in …
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Learning robust and efficient point cloud representations
L'abstract è presente nell'allegato / the abstract is in the attachment
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Enhancing point cloud processing using audio cues
… airborne and terrestrial acquisitions capture point clouds of scenes or objects to be modelled. But before modelling can be done point clouds need to be taken through processing steps such as registration, cleaning, simpli_cation, etc. These point clouds are usually manually processed before …
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Point cloud segmentation for mobile robot manipulation
… Given as input a sequence of observed RGB-D point clouds of a scene, a list of known objects in the scene and their pose distributions as a prior, and a black-box object detector, our system outputs a belief state of what is believed to be in the scene. This belief state consists of the …
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On the 3D point cloud for human-pose estimation
… for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty …
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3D point cloud learning: a survey and a toolbox
… robotics, has brought increasing attention to 3D point cloud understanding. However, while deep learning methods obtained remarkable success in 2D image tasks, deep models on point clouds still suffer from unique challenges in processing unstructured points with deep neural networks. This thesis …
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DGCNN : learning point cloud representations by dynamic graph CNN
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent …
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Covariance based point cloud descriptors for object detection and classification
Processing 3D point data is of primary interest in many areas of computer vision, including object grasping, robot navigation, and 3D object recognition. The recent introduction of cheap range sensors like the Microsoft Kinect has created a great interest in the computer vision community towards …
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Registration of Arbitrary 3D Point Cloud Using Statistical Head Model
… techniques (i.e. the Iterative Closest Point algorithm) and a given statistical head mesh model. During the process, missing information in the original head mesh can also be recovered based on the information contained in the statistical model. The project also seeks to automate the …
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River channel dynamics mapped with dense lidar point cloud time series
… surfaces in airborne- and drone-derived 3D lidar point clouds. The other reconstructs 3D surfaces between point cloud pairs to obtain measurements of volume change. Within this context, I examine channel migration rates and bank erosion volumes calculated with different approaches, highlighting …
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Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data
… body that might indicate signs of illness. 3D point cloud data represents some unique challenges. Consider an object scanned with a laser scanner. The scanner returns the surface points of the object, but nothing more. Using the tool Qhull, a researcher can quickly compute the convex hull of an …
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3D diffusion based generation model for point cloud annotation and generation
… 3D shape to the 3D vision problem are desired. Point cloud, as one of the most popular representations of 3D, is facing the same desire. Point cloud generative model is one type of model that can be used to synthesize a new point cloud. The characteristic of the point cloud generative model …
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Quality assessment of 3D printed concrete through point cloud data analysis
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation
… and sensors, involve segmenting sparse sets of points (point clouds) for which point density is the only discriminating feature. The segmentation of these point clouds is challenging for several reasons, including the fact that the points are not associated with a regular grid. Moreover, the …
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Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data
Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning …
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Automated Geometric Digital Twin Construction for Existing Buildings from Point Cloud Datasets
… DTs or gDTs). This process involves capturing Point Cloud Datasets (PCDs) and modelling these datasets to represent current building geometry accurately. However, this process requires extensive manual labour and remains a barrier to the broader adoption of DTs for the operation and maintenance …
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3-D Point Cloud Generation from Rigid and Flexible Stereo Vision Systems
… capture images of a scene from two or more viewpoints, to create 3-D point clouds. A point cloud is a set of un-gridded 3-D points corresponding to a 2-D image, and is used to build gridded surface models. Designing a stereo system for distant terrain modeling requires an extended baseline, or …
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The Use of Image and Point Cloud Data in Statistical Process Control
… size, and time of occurrence; and 3) shows how point cloud data (3D laser scans) can be used to detect and locate unknown faults in complex geometries. Overall, the research goal is to create new quality control tools that utilize high density data available in manufacturing environments to …
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