Paper presentation at CVPR 2019 & Ranked 1st in the “Long-Term Visual Localization” Challenge
NAVER LABS presented its paper “Did it change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates” at CVPR 2019, the world’s largest conference on computer vision and pattern recognition, sponsored by IEEE. The paper imparted the research results of the self-updating map conducted jointly by NAVER LABS and the European research team of NAVER LABS over a period of about one year. Core technologies of NAVER LABS, such as robotics, computer vision and deep learning, were utilized to update map information to the latest state by having autonomous robots collect and analyze the data of large indoor spaces and recognize the spatial changes.
Meanwhile, NAVER LABS Europe ranked first in the “Local Feature Challenge” category of the "Long-Term Visual Localization challenge". The challenge was to determine the current location of the nighttime photograph based on the daytime photograph and the shooting location of a particular landmark. This time, the Europe researchers of NAVER LABS successfully developed a deep learning-based feature that surpasses the scale-invariant feature transform (SIFT) feature that has been used for nearly 20 years in the field of local feature detection. Henceforth, it is expected to be applicable to various fields related to computer vision other than just visual localization.
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