Retrographic sensing for the measurement of surface texture and shape

By Micah K. Johnson, Edward H. Adelson. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

361 citations recorded by the source corpus.

We describe a novel device that can be used as a 2.5D “scanner” for acquiring surface texture and shape. The device consists of a slab of clear elastomer covered with a reflective skin. When an object presses on the skin, the skin distorts to take on the shape of the object's surface. When viewed from behind (through the elastomer slab), the skin appears as a relief replica of the surface. A camera records an image of this relief, using illumination from red, green, and blue light sources at three different positions. A photometric stereo algorithm that is tailored to the device is then used to reconstruct the surface. There is no problem dealing with transparent or specular materials because the skin supplies its own BRDF. Complete information is recorded in a single frame; therefore we can record video of the changing deformation of the skin, and then generate an animation of the changing surface. Our sensor has no moving parts (other than the elastomer slab), uses inexpensive materials, and can be made into a portable device that can be used “in the field” to record surface shape and texture.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c93d8351d1d2f2648e06efc542727e38d72dd641

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In Computer Graphics and Computer-Aided Design

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Citations in the corpus

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger (2018, methodology): “The original, larger GelSight sensor [14], [15] was designed to measure the 3D topography of the contact surface with micrometer-level spatial resolution.”
GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force (2017, methodology): “The ﬁrst GelSight prototype was developed in 2009 by Johnson and Adelson [7].”
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor With Application to In-Hand Manipulation (2020, background): “In addition, DIGIT retains the rich and sensitive measurements characteristic of previous vision-based sensors [10]–[12], [16].”
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch (2018, background): “The GelSight sensor is an optical tactile sensor that measures high-resolution topography of the contact surface [33], [34].”
Rearrangement: A Challenge for Embodied AI (2020, background): “A variety of sensor types are common: color vision cameras, depth sensors, contact/collision sensors, tactile sensors [50], Li-DAR, microphones, and elastomeric sensors such as Gel-Sight [38, 80] are prominent examples.”
Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation (2017, background): “A motivating example is what to do when a highfidelity contact sensor like Gelsight [27] is available.”
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? (2017, neutral): “The GelSight sensor is an optical tactile sensor that measures highresolution topography of the contact surface [30, 10].”