D-MO: Depth from Motion and Occlusion as a Visual Channel for Information Visualization

要旨

On a visualization, the position of the marks encoding data is the most expressive and effective visual channel. It conveys order and quantity without impairing the perception of other visual channels. In the field of Information Visualization, position is often restricted to two dimensions, because using the third dimension, \emph{depth}, usually affects the perception of size, which is also one of the most effective visual channels. We propose a new visual channel, D-MO (Depth from Motion and Occlusion), a combination of visual cues, \emph{motion} and \emph{occlusion}, with interactions, that induces a depth perception suitable for combined use with classic visual channels. We characterize the expressiveness and effectiveness of D-MO and show that it is a magnitude channel with good accuracy, acceptable discriminability, and is separable from size. Thus, D-MO opens up new areas for visualization design, which is limited by the scarceness of available visual channels.

著者
Carla Coutant
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
Adrien Chaffangeon Caillet
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
Renaud Blanch
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Perception & Cognition in Data Visualization

P1 - Room 123
6 件の発表
2026-04-14 18:00:00
2026-04-14 19:30:00