Predicting Ground-Level Scene Layout from Aerial Imagery

CVPR 2017 Menghua ZhaiZachary BessingerScott WorkmanNathan Jacobs

We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Cross-View Image-to-Image Translation cvusa CrossNet SSIM 0.4147 # 6