Hannah Schieber
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CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus Open
Mobile reconstruction has the potential to support time-critical tasks such as tele-guidance and disaster response, where operators must quickly gain an accurate understanding of the environment. Full high-fidelity scene reconstruction is …
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization Open
In medical image visualization, path tracing of volumetric medical data like computed tomography (CT) scans produces lifelike three-dimensional visualizations. Immersive virtual reality (VR) displays can further enhance the understanding o…
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization Open
In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike three-dimensional visualizations. Immersive VR displays can further enhance the understanding of complex anatomies. Going beyond the di…
NeRFtrinsic Four: An end-to-end trainable NeRF jointly optimizing diverse intrinsic and extrinsic camera parameters Open
Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high- quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera paramete…
Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality Open
Advancements in 3D rendering like Gaussian Splatting (GS) allow novel view synthesis and real-time rendering in virtual reality (VR). However, GS-created 3D environments are often difficult to edit. For scene enhancement or to incorporate …
AMOR: Ambiguous Authorship Order Open
As we all know, writing scientific papers together with our beloved colleagues is a truly remarkable experience (partially): endless discussions about the same useless paragraph over and over again, followed by long days and long nights --…
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation Open
In medical and industrial domains, providing guidance for assembly processes can be critical to ensure efficiency and safety. Errors in assembly can lead to significant consequences such as extended surgery times and prolonged manufacturin…
GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance Open
Guidance for assemblable parts is a promising field for augmented reality. Augmented reality assembly guidance requires 6D object poses of target objects in real time. Especially in time-critical medical or industrial settings, continuous …
Indoor Synthetic Data Generation: A Systematic Review Open
Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene understanding require a large amount of training data to achieve generalization. Time-consuming annotation processes, privacy, and security aspects l…
Injured Avatars: The Impact of Embodied Anatomies and Virtual Injuries on Well-Being and Performance Open
Human cognition relies on embodiment as a fundamental mechanism. Virtual avatars allow users to experience the adaptation, control, and perceptual illusion of alternative bodies. Although virtual bodies have medical applications in motor r…
DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields Open
The accurate reconstruction of dynamic scenes with neural radiance fields is significantly dependent on the estimation of camera poses. Widely used structure-from-motion pipelines encounter difficulties in accurately tracking the camera tr…
Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition Open
The present study provides a comprehensive review of recent methods in surgical workflow analysis, summarizes commonly used architectures, datasets, and discusses challenges.
Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition Open
Objective: In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware i…
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters Open
Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high-quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera parameter…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios Open
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current category-level dataset…
Deep Learning in Surgical Workflow Analysis: A review Open
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible Objective: In the last two decades, there has been a growing interest in…
View article: Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation
Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation Open
Robust environment perception for autonomous vehicles is a tremendous challenge, which makes a diverse sensor set with e.g. camera, lidar and radar crucial. In the process of understanding the recorded sensor data, 3D semantic segmentation…
Surgical Phase Recognition: A Review and Evaluation of Current Approaches Open
Objective: In the last decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations in different se?mantic levels. This information is necessary for developing context-aware inte…
Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition Open
Objective: In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware i…