Probabilistic Models And Inference For Multi View People Detection In Overlapping Depth Images
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Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
Author: Wetzel, Johannes
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-07-12
In this work, the task of wide-area indoor people detection in a network of depth sensors is examined. In particular, we investigate how the redundant and complementary multi-view information, including the temporal context, can be jointly leveraged to improve the detection performance. We recast the problem of multi-view people detection in overlapping depth images as an inverse problem and present a generative probabilistic framework to jointly exploit the temporal multi-view image evidence.
Computational, label, and data efficiency in deep learning for sparse 3D data
Author: Li, Lanxiao
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-05-13
Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Light Field Imaging for Deflectometry
Author: Uhlig, David
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-07-14
Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.