Tangible user interfaces for physically-based deformation: design principles and first prototype

Takouachet, Nawel; Couture, Nadine; Reuter, Patrick; Joyot, Pierre; Rivière, Guillaume; Verdon, Nicolas

doi:10.1007/s00371-012-0695-y

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…An example-based approach [34] simulates deformation behaviors by taking a few example poses of the deformation. There are also user interfaces for interacting with physics-based simulations, and there are design principles for building tangible user interfaces for the interactive physics-based deformation of 3D models [48]. A recent work solves the problem of computing a shape that when fabricated deforms naturally to a target shape [9].…”

Section: Physics-based Deformations Of 3d Shapesmentioning

confidence: 99%

A Human-Perceived Softness Measure of Virtual 3D Objects

Lau

Dev

Dorsey

et al. 2018

ACM Trans. Appl. Percept.

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Fig. 1. An input 3D object and softness map computed by our approach (front and back views). The jet colormap corresponds to relative softness values (blue is least soft and red is most soft). 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.

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Section: Physics-based Deformations Of 3d Shapesmentioning

confidence: 99%

A Human-Perceived Softness Measure of Virtual 3D Objects

Lau

Dev

Dorsey

et al. 2018

ACM Trans. Appl. Percept.

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“…So it is important to precede modification and optimization of the prototype model. At present, it is usually used the offset correction method, equidistance correction method and rotary angle correction method [5] [6]. Considering the constraint conditions in the modification.…”

Section: Cycloid Gear Profile Modificationmentioning

confidence: 99%

Study on Planetary Reduction Gears Motion Simulation and Profile Correction Based on Virtual Prototype

Cai

2014

AMM

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Aiming at the current situation of long cycle and high cost for planetary reduction gears designing and manufacturing in the field of engineering machinery, analysis on the environment has been established by using reducer virtual prototype in this paper, simultaneously the article brings forward a strong realization system from virtual simulation to finite element analysis. The results show that the virtual prototype model possesses high reliability and rapid correction method, it can shorten the design cycle greatly, and the method has certain practical application value.

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Passive Stylus Tracking: A Systematic Literature Review

Burnah,

Imtiaz,

Guesgen

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Passive stylus systems offer a simple and cost-effective solution for digital input, compatible with a wide range of surfaces and devices. This study reviews the domain of passive stylus tracking on passive surfaces, a topic previously underexplored in existing literature. We answer four key research questions: what type of systems exist in this domain, what methods do they use for tracking styli, how accurate are they, and what are their limitations? A systematic literature review resulted in 24 papers describing passive stylus systems. Their methods primarily fall into four categories: monocular cameras with image processing, multiple camera systems with image processing, machine learning systems using high-speed cameras or motion capture hardware, and radio frequency signal-based systems with signal processing. We found the system with the highest accuracy used a single monocular camera. In many systems, markers such as retroreflective spheres, tape, or fiducial markers were used to enhance the feature matching. We have also found stagnation and in some cases, regression in the precision and reliability of these systems over time. The limitations in these systems include the lack of varied stylus form factor support, the restriction to specific camera positions and angles, and the requirement of expensive hardware. Given these findings, we discuss the important characteristics and features of passive stylus systems and propose ways forward in this field.

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Tangible user interfaces for physically-based deformation: design principles and first prototype

Cited by 3 publications

References 19 publications

A Human-Perceived Softness Measure of Virtual 3D Objects

A Human-Perceived Softness Measure of Virtual 3D Objects

Study on Planetary Reduction Gears Motion Simulation and Profile Correction Based on Virtual Prototype

Passive Stylus Tracking: A Systematic Literature Review

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