Visualizing 3D trajectories to extract insights about their similarities and spatial conﬁguration is a critical task in several domains. Air trafﬁc controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of ﬁbers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with speciﬁc geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air trafﬁc control and neurology.
FiberClay is an immersive multidimensional visualization system. The user can navigate into trail sets to gain a better understanding of dense and complex datasets. Left, the user activated the union brushing interaction to select trails intersected by the two beams in a recorded aircraft trajectory dataset. Middle, the user investigates DTI ﬁber tracks connecting the two hemisphere of the brain. Right, the user reﬁne a data selection by visually sculpting the query with the two brush interactions while navigating into the different visual mappings (e.g. presets) of the investigated dataset.
Immersive visualization of DTI ﬁber extraction from a brain Scan. The small multiple visualization shows the original dataset and two different 3D edge bundling algorithm (an extended 3D version of KDEEB  and Functional Decomposition Edge Bundling ). Our system helps to select a subset of the 3D ﬁbers with our 3D brushing techniques (KDEEB and FDBEB). Then the user can investigate how this subset is bundled through the two different 3D edge bundling methods. As a result, FDBEB proposed less distortion compared to KDEEB but with less visual simpliﬁcation. Our system help to deﬁne the correct balance between edge bundling simpliﬁcation and distortion in a 3D space.