TY - JOUR

T1 - A Human-Perceived Softness Measure of Virtual 3D Objects

AU - Lau, Manfred Chung Man

AU - Dev, Kapil

AU - Dorsey, Julie

AU - Rushmeier, Holly

N1 - © ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Applied Perception (TAP) http://dx.doi.org/10.1145/3193107

PY - 2018/8/1

Y1 - 2018/8/1

N2 - We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications.

AB - We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications.

U2 - 10.1145/3193107

DO - 10.1145/3193107

M3 - Journal article

VL - 15

JO - ACM Transactions on Applied Perception

JF - ACM Transactions on Applied Perception

SN - 1544-3558

IS - 3

M1 - 19

ER -