Cornell researchers taught a robot to take Airbnb photos
wheeled robot upon which the camera is affixed; and the AutoPhoto algorithm itself, which serves as a sort of firmware, translating the results from the image grading process into drive commands for the physical robot and effectively automating the optimized image capture process.
For its image evaluation algorithm, the Cornell team led by second year Masters student Hadi AlZayer, leveraged an existing, which had been trained on a dataset of more than a million human-ranked photographs. AutoPhoto itself was virtually trained on dozens of 3D images of interior room scenes to spot the optimally composed angle before the team attached it to the Jackal.
“You can essentially take incremental improvements to the current commands,” AlZayer told Engadget. “You can do it one step at a time, meaning you can formulate it as a reinforcement learning problem.” This way, the algorithm doesn’t have to conform to traditional heuristics likebecause it already knows what people will like as it was taught to match the look and feel of the shots it takes with the highest-ranked pictures from its training data, AlZayer explained.
“The most challenging part was the fact there was no existing baseline number we were trying to improve,” AlZayer noted to theLooking ahead, AlZayer hopes to adapt the AutoPhoto system for outdoor use, potentially swapping out the terrestrial Jackal for a UAV. “Simulating high quality realistic outdoor scenes is very hard,” AlZayer said, “just because it's harder to perform reconstruction of a controlled scene.
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