Image-Based 3D Reconstruction of Dynamic Objects Using Instance-Aware Multibody Structure from Motion - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Image-Based 3D Reconstruction of Dynamic Objects Using Instance-Aware Multibody Structure from Motion write by Bullinger, Sebastian. This book was released on 2020-08-26. Image-Based 3D Reconstruction of Dynamic Objects Using Instance-Aware Multibody Structure from Motion available in PDF, EPUB and Kindle. "This work proposes a Multibody Structure from Motion (MSfM) algorithm for moving object reconstruction that incorporates instance-aware semantic segmentation and multiple view geometry methods. The MSfM pipeline tracks two-dimensional object shapes on pixel level to determine object specific feature correspondences, in order to reconstruct 3D object shapes as well as 3D object motion trajectories" -- Publicaciones de Arquitectura y Arte.
Dynamic Switching State Systems for Visual Tracking
Dynamic Switching State Systems for Visual Tracking - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Switching State Systems for Visual Tracking write by Becker, Stefan. This book was released on 2020-12-02. Dynamic Switching State Systems for Visual Tracking available in PDF, EPUB and Kindle. This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together.
Deep Learning based Vehicle Detection in Aerial Imagery
Deep Learning based Vehicle Detection in Aerial Imagery - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning based Vehicle Detection in Aerial Imagery write by Sommer, Lars Wilko. This book was released on 2022-02-09. Deep Learning based Vehicle Detection in Aerial Imagery available in PDF, EPUB and Kindle. This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.
Multimodal Panoptic Segmentation of 3D Point Clouds
Multimodal Panoptic Segmentation of 3D Point Clouds - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multimodal Panoptic Segmentation of 3D Point Clouds write by Dürr, Fabian. This book was released on 2023-10-09. Multimodal Panoptic Segmentation of 3D Point Clouds available in PDF, EPUB and Kindle. The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Probabilistic Parametric Curves for Sequence Modeling
Probabilistic Parametric Curves for Sequence Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Parametric Curves for Sequence Modeling write by Hug, Ronny. This book was released on 2022-07-12. Probabilistic Parametric Curves for Sequence Modeling available in PDF, EPUB and Kindle. This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation.
