Essays about: "3D Rekonstruktion"
Showing result 1 - 5 of 28 essays containing the words 3D Rekonstruktion.
-
1. Assessing the Efficiency of COLMAP, DROID-SLAM, and NeRF-SLAM in 3D Road Scene Reconstruction
University essay from Lunds universitet/Matematik LTHAbstract : 3D reconstruction is a field in computer vision which has evolved rapidly as a result of the recent advancements in deep learning. As 3D reconstruction pipelines now can run in real-time, this has opened up new possibilities for teams developing Advanced Driver Assistance Systems (ADAS), which rely on the camera system of the vehicle to enhance the safety and driving experience. READ MORE
-
2. Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction
University essay from KTH/Matematisk statistikAbstract : Cone Beam Computed Tomography is a technology to visualize the 3D interior anatomy of a patient. It is important for image-guided radiation therapy in cancer treatment. During a scan, iterative methods are often used for the image reconstruction step. READ MORE
-
3. Augmented Reality-Assisted Techniques for Sustainable Lithium-Ion EV Battery Dismantling
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The increasing adoption of electric vehicles (EVs) brings forth the challenge of effectively managing the second-life and end-of-life cycles for lithium-ion batteries. Augmented Reality (AR) offers a promising solution to sustainably and efficiently dismantle these batteries. READ MORE
-
4. Respiratory Motion Correction in PET Imaging: Comparative Analysis of External Device and Data-driven Gating Approaches
University essay from KTH/FysikAbstract : Positron Emission Tomography (PET) is pivotal in medical imaging but is prone to artifactsfrom physiological movements, notably respiration. These motion artifacts both degradeimage quality and compromise precise attenuation correction. READ MORE
-
5. Spatiotemporal PET reconstruction with Learned Registration
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Because of the long acquisition time of Positron Emission Tomography scanners, the reconstructed images are blurred by motion. We hereby propose a novel motion-correction maximum-likelihood expectation-maximization algorithm integrating 3D movements between the different gates estimated by a neural network trained on synthetic data with contrast invariance. READ MORE