Interactive Fusion and Tracking For Multi‐Modal Spatial Data Visualization

Abstract

Scientific data acquired through sensors which monitor natural phenomena, as well as simulation data that imitate time-identified events, have fueled the need for interactive techniques to successfully analyze and understand trends and patterns across space and time. We present a novel interactive visualization technique that fuses ground truth measurements with simulation results in real-time to support the continuous tracking and analysis of spatiotemporal patterns. We start by constructing a reference model which densely represents the expected temporal behavior, and then use GPU parallelism to advect measurements on the model and track their location at any given point in time. Our results show that users can interactively fill the spatio-temporal gaps in real world observations, and generate animations that accurately describe physical phenomena.

Curve Complexity Heuristic KD‐trees for Neighborhood‐based Exploration of 3D Curves

“…First, our method requires considerable memory to store the point samples and the KD‐trees and thus we will develop an out‐of‐core version to handle data with more than 200K curves. Second, inspired by the GPU construction algorithm for SAH KD‐tree [ZHWG08] and GPU‐based spatial data visualization [EGG*15], we will explore a GPU version of our CCH KD‐tree as a potential avenue for future research. Last, we will explore more neighborhood‐based visualization applications.…”