AirCode

Abstract

air pocket optimization fabricated model AirCode embeded input mesh user-specified region & data goo.gl/1ph6g2 global component a c d e b Figure 1. (a) AirCode tagging tool takes as inputs a mesh, a user-specified region, and embedded data. (b) It first determines the air pocket parameters, including the depth d and thickness h, for a fabrication material. (c) An AirCode tag is embedded inside the object, without changing its geometry or appearance. (d) The fabricated tag is invisible under environmental lighting. (e) Using our imaging system that separates out the global scattering effects, the user detects the embedded tag and retrieve the data. "Moai" by gravityisweak / CC BY 3.0 / modified from original. ABSTRACTWe present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An Air-Code tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.

Near-infrared Imaging for Information Embedding and Extraction with Layered Structures

“…Existing works using dielectric materials involve at least two materials that have di erent properties such as scattering or absorbance. For instance, Li et al present an Air-Code scheme to embed information such as a QR code, inside a 3D printed object, using a group of air pockets as the information material with sophisticated designs under the surface [Li et al 2017]. The air pockets are unnoticeable for human eyes, while readable using an o -the-shelf projector as a patterned light source, and a monochrome linear camera at 700 nm wavelength (i.e., red light).…”

OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs

“…), it is crucial that systems are able to understand the context of one's environment and the information that is available to users. One way to obtain such an understanding is to directly retrieve information that is embedded in barcodes, fiducial markers [22], human faces [2], or objects during fabrication processes [19,20,38]. Researchers have also explored retrieving "raw" information such as visible text [54,66], physical objects [23,51], multimodal scenes [65], human speech (e.g., Google API 4 ), and music (e.g., Shazam).…”

Embedding QR Code onto Triangulated Meshes using Horizon Based Ambient Occlusion

“…Besides, objects can also be tagged with a QR code to describe and provide useful information. For example, AirCode method [LNNZ17] embeds the user-defined information by placing structured air pockets under the surface of the item. The resulting code of the method is only readable by computers, and the process takes minutes to provide results.…”