In this paper, we present a channel based framework to represent directional information. The chosen basis functions allow to exactly determine solutions, i.e. peak positions, between samples and to detect multiple hypothesizes. We show how such a representation can be used to detect and describe multiple line-endings in local neighbourhoods. We also use this method to locally determine multiple intensity levels in different directions. As an example application we use the latter to devise a novel adaptive smoothing technique.

The combined use of intensity and depth information greatly helps in the estimation of the local 3D movements (range flow) of moving surfaces. We demonstrate how the two can be combined in both: a local total least squares algorithm, and an iterative global variational technique. While the former assumes locally constant flow, the latter relies on a smoothly varying flow field. The improvement achieved through incorporating intensity is illustrated qualitatively and quantitatively on synthetic and real test data.