Researchers at Saarland University and the Max Planck Institute for Informatics in Germany say they have developed a computational method for reconstructing a digital object from incomplete images.
Their method uses a neural network to reconstruct digital objects from incomplete datasets.
"Our method requires no supervision during the learning phase, which is novel for this type," notes Max Planck researcher Mario Fritz. For example, the researchers could use the new model to reconstruct a flat monitor, whose digital representation after a three-dimensional scan looked more like a paneled wall, so viewers could once again recognize a monitor in the digital object.
The team intends to further develop the method so it also will work on deformable objects and larger scenes.
"In the future, it will have to be possible to capture real-world objects simply and quickly, and project them in a realistic way into the digital world," says Saarland University professor Philipp Slusallek.
From Saarland University
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