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RelocNet and RelocDB
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Continuous Metric Learning Relocalisation Using Neural Nets
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2026-01-26 23:21:30
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RelocNet and RelocDB Home Dataset Tutorials Active Vision Lab AVLCode GitHub RelocNet : Continuous Metric Learning Relocalisation Using Neural Nets RelocNet is a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors. We introduce information from camera frusta overlaps between pairs of images to optimise our feature embedding network. Thus, the final camera pose descriptor differences represent camera pose changes. In addition, we build a pose regressor that is trained with a geometric loss to infer finer relative poses between a query and nearest neighbour images. RelocDB is our relocalisation dataset, comprising of 500 sequences filmed on a Google Tango-equipped mobile phone. Each sequence captures a single room twice, and provides 30 fps RGB data, 5 fps depth data, along with intrinsic camera calibration, timestamps and poses for data stream...
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