The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

This paper describes a multiresolution method for the acquisition of a complete, relatively noise-free, and high-resolution depth map from a low-resolution laser range image and a stereo pair of high resolution intensity images. Depth information from the laser range data is used to constrain the initial search in stereo matching. The inter- and intralevel linkings of edges in the pyramid allow a process where the coarse laser depth information drives a multiresolution stereo-matching process to construct a high-resolution depth map. The motivation for this new approach to multi-sensor integration is to offset the advantages and disadvantages of traditional stereo matching and triangulation range finding approaches.>