3 papers
cs.LG2026
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben +4
Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying…
cs.GR2026
Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering
Landon Dyken, Andres Sewell, Will Usher +3
Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user sy…
cs.CV2025
Enabling Fast and Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping
Landon Dyken, Saugat Adhikari, Pravin Poudel +4
Mapping the extent of flood events is a necessary and important aspect of disaster management. In recent years, deep learning methods have evolved as an effective tool to quickly l…