Structure Perception in 3D Point Clouds: Preprint

Research output: Contribution to conferencePaper


Understanding human perception is critical to the design of ef- fective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the liter- ature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projec- tions of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rota- tion, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under ro- tation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns per- ceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.
Original languageAmerican English
Number of pages12
StatePublished - 2021
EventACM Symposium on Applied Perception (ACM SAP) -
Duration: 16 Sep 202117 Sep 2021


ConferenceACM Symposium on Applied Perception (ACM SAP)

NREL Publication Number

  • NREL/CP-2C00-80547


  • data analysis
  • data visualization
  • image generation
  • scatterplots
  • visual perception


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