Two-edge resolved NLOS imaging scenario and hidden scene representation. A Representation of the imaging scenario and proposed spherical projected elevation coordinates. With the origin in the upper left corner of the door frame, a hidden scene point is identified by its range. ρazimuth θand projected elevation ψ. b Shows projected elevation ψ in the proposed projected elevation spherical coordinate system, it is the projection of the conventional elevation angle of the spherical coordinates onto the Xz-plane and is such that tan(ψ)=tan(φ)sec(θ)\tan (\psi )=\tan (\varphi )\sec (\theta ). (For greater clarity, the z-the axis is reversed by (A) to point upwards.) vs Representation of the elementary surface resulting from 10 equal divisions of azimuth axes and projected elevation at fixed range, ρ. The red dot represents an example of a surface element whose center is at (ρ,θ,ψ) = (1, 11 π/40, 13 π/40) and the angular extents are equal π/20 along the azimuth and projected elevation. of Plot the changes in the observed measurement due to a hidden point source (red dot) moving from its position in (d) to a new position in (e) such that its range and projected elevation angle are fixed and only its azimuthal angle changes. Light from a hidden stage point is screened by the edges of the door to create a trapezoidally shaped illuminated region on the ceiling. The observation in (d) has an illuminated trapezoidal region whose inclined edge is steeper than that of (e) because the azimuthal angle of the point source increases from (d) has (e); the heights of the trapezoidal parts illuminated in (d) And (e) are otherwise equal because the projected elevation angle is unchanged. Credit: Natural communications (2024). DOI: 10.1038/s41467-024-45397-7
After a recent car accident, John Murray-Bruce wished the other car would arrive. The accident reaffirmed the USF assistant professor of computer science and engineering’s mission to create technology that could do just that: see around obstacles and, ultimately, expand its field of vision.
Using a single photograph, Murray-Bruce and her doctoral student, Robinson Czajkowski, created an algorithm that calculates highly accurate, full-color, three-dimensional reconstructions of areas behind obstacles – a concept that can not only help prevent car accidents, but also help law enforcement. experts in hostage-taking, search and rescue, and strategic military efforts.
“We turn ordinary surfaces into mirrors to reveal regions, objects and rooms that are outside our field of vision,” Murray-Bruce said. “We live in a 3D world, so getting a more complete 3D picture of a scenario can be essential in a number of situations and applications.”
As published inNatural communications, Czajkowski and Murray-Bruce’s research is the first of its kind to successfully reconstruct a hidden scene in 3D using a regular digital camera. The algorithm works by using photo information from faint shadows cast on nearby surfaces to create a high-quality reconstruction of the scene. Although it is more technical for the average person, it could have broad applications.
“These shadows are all around us,” Czajkowski said. “Just because we can’t see them with the naked eye doesn’t mean they’re not there.”
The idea of seeing around obstacles has been a subject of science fiction films and books for decades. Murray-Bruce says this research makes significant progress toward bringing this concept to life.
Before this work, researchers only used ordinary cameras to create rough 2D reconstructions of small spaces. The most successful demonstrations of 3D imaging of hidden scenes all required specialized and expensive equipment.
“Our work achieves a similar result using much less,” Czajkowski said. “You don’t need to spend a million dollars on equipment for this anymore.”
Czajkowski and Murray-Bruce expect it will take 10 to 20 years before the technology is robust enough to be adopted by law enforcement and automakers. Currently, they plan to continue their research to further improve the speed and accuracy of the technology to expand its applications in the future, including self-driving cars to improve their safety and situational awareness.
“In just over a decade since the idea of seeing around corners emerged, remarkable progress has been made, and interest and research activity in this area is accelerating,” said Murray- Bruce. “This increased activity, along with access to better, more sensitive cameras and faster computing power, provides the basis for my optimism about how quickly this technology will become practical for a wide range of scenarios.”
Although the algorithm is still in the development phase, other researchers can test and replicate it in their own space.
More information:
Robinson Czajkowski et al, Three-dimensional no-line-of-sight imaging resolved at two edges with an ordinary camera,Natural communications (2024). DOI: 10.1038/s41467-024-45397-7
Provided by University of South Florida
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