Perceptual models of preference in 3D printing direction

Xiaoting Zhang, Xinyi Le, Athina Panotopoulou, Emily Whiting, Charlie C.L. Wang

Research output: Contribution to journalArticlepeer-review


This paper introduces a perceptual model for determining 3D printing orientations. Additive manufacturing methods involving lowcost 3D printers often require robust branching support structures
to prevent material collapse at overhangs. Although the designed
shape can successfully be made by adding supports, residual material remains at the contact points after the supports have been removed, resulting in unsightly surface artifacts. Moreover, fine surface details on the fabricated model can easily be damaged while
removing supports. To prevent the visual impact of these artifacts,
we present a method to find printing directions that avoid placing
supports in perceptually significant regions. Our model for preference in 3D printing direction is formulated as a combination of metrics including area of support, visual saliency, preferred viewpoint
and smoothness preservation. We develop a training-and-learning
methodology to obtain a closed-form solution for our perceptual
model and perform a large-scale study. We demonstrate the performance of this perceptual model on both natural and man-made
Original languageEnglish
Article number215
Number of pages12
JournalACM Transactions on Graphics
Issue number6
Publication statusPublished - Nov 2015


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