10 citations · 15 across the 10 of their papers we have counts for
10 papers
LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
Hannah Schieber, Dominik Frischmann, Victor Schaack +2
Autonomous robots in unknown indoor environments require both reliable collision avoidance and object-level understanding. Classical representations such as TSDF support safe plann…
Supercharging Thermal Gaussian Splatting with Depth Estimation
Manoj Biswanath, Chenxin Cai, Hannah Schieber +2
Efficient and robust 3D scene representation is crucial in autonomous driving, robotics, and related fields. While RGB images provide valuable content for 3D reconstruction, other…
CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus
Hannah Schieber, Dominik Frischmann, Victor Schaack +4
Fast and efficient photorealistic 3D reconstruction with (semantic) Gaussian Splatting (GS) is crucial for time-critical robotic perception and navigation, where robots may need to…
Locality-Sensitive Hashing for Efficient Hard Negative Sampling in Contrastive Learning
Fabian Deuser, Philipp Hausenblas, Hannah Schieber +3
Contrastive learning is a representational learning paradigm in which a neural network maps data elements to feature vectors. It improves the feature space by forming lots with an…
Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality
Hannah Schieber, Jacob Young, Tobias Langlotz +2
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…
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation
Hannah Schieber, Shiyu Li, Niklas Corell +3
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 consequence…
GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
Shiyu Li, Hannah Schieber, Niklas Corell +3
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 i…
DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields
Nicolas Schischka, Hannah Schieber, Mert Asim Karaoglu +6
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…
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters
Hannah Schieber, Fabian Deuser, Bernhard Egger +2
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 prio…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Guangyao Zhai, Shun-Cheng Wu +10
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…