Home > Research > Publications & Outputs > Tactile mesh saliency

Electronic data

  • TactileMeshSaliency_SIGGRAPH

    Rights statement: "© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn"

    Accepted author manuscript, 11.9 MB, PDF document

Links

Text available via DOI:

View graph of relations

Tactile mesh saliency

Research output: Contribution to journalJournal articlepeer-review

Published
  • Manfred Lau
  • Kapil Dev
  • Weiqi Shi
  • Julie Dorsey
  • Holly Rushmeier
Close
Article numbera52
<mark>Journal publication date</mark>11/07/2016
<mark>Journal</mark>ACM Transactions on Graphics
Issue number4
Volume35
Number of pages11
Publication StatusPublished
<mark>Original language</mark>English

Abstract

While the concept of visual saliency has been previously explored in the areas of mesh and image processing, saliency detection also applies to other sensory stimuli. In this paper, we explore the problem of tactile mesh saliency, where we define salient points on a virtual mesh as those that a human is more likely to grasp, press, or touch if the mesh were a real-world object. We solve the problem of taking as input a 3D mesh and computing the relative tactile saliency of every mesh vertex. Since it is difficult to manually define a tactile saliency measure, we introduce a crowdsourcing and learning framework. It is typically easy for humans to provide relative rankings of saliency between vertices rather than absolute values. We thereby collect crowdsourced data of such relative rankings and take a learning-to-rank approach. We develop a new formulation to combine deep learning and learning-to-rank methods to compute a tactile saliency measure. We demonstrate our framework with a variety of 3D meshes and various applications including material suggestion for rendering and fabrication