8 papers
NVGS: Neural Visibility for Occlusion Culling in 3D Gaussian Splatting
Brent Zoomers, Florian Hahlbohm, Joni Vanherck +3
3D Gaussian Splatting can exploit frustum culling and level-of-detail strategies to accelerate rendering of scenes containing a large number of primitives. However, the semi-transp…
Learning-Based Estimation of Spatially Resolved Scatter Radiation Fields in Interventional Radiology
Felix Lehner, Pasquale Lombardo, Susana Castillo +2
We present three variants of a lightweight, fully connected artificial neural network, suited for interactive estimation of three-dimensional, spatially resolved volumes of scatter…
RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications
Felix Lehner, Pasquale Lombardo, Susana Castillo +2
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for d…
Faster-GS: Analyzing and Improving Gaussian Splatting Optimization
Florian Hahlbohm, Linus Franke, Martin Eisemann +1
Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implement…
A Bag of Tricks for Efficient Implicit Neural Point Clouds
Florian Hahlbohm, Linus Franke, Leon Overkämping +4
Implicit Neural Point Cloud (INPC) is a recent hybrid representation that combines the expressiveness of neural fields with the efficiency of point-based rendering, achieving state…
INPC: Implicit Neural Point Clouds for Radiance Field Rendering
Florian Hahlbohm, Linus Franke, Moritz Kappel +4
We introduce a new approach for reconstruction and novel view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based m…