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- W4383109509 abstract "General scene understanding for robotics requires flexible semantic representation, so that novel objects and structures which may not have been known at training time can be identified, segmented and grouped. We present an algorithm which fuses general learned features from a standard pre-trained network into a highly efficient 3D geometric neural field representation during real-time SLAM. The fused 3D feature maps inherit the coherence of the neural field's geometry representation. This means that tiny amounts of human labelling interacting at runtime enable objects or even parts of objects to be robustly and accurately segmented in an open set manner. Project page: https://makezur.github.io/FeatureRealisticFusion/" @default.
- W4383109509 created "2023-07-05" @default.
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- W4383109509 date "2023-05-29" @default.
- W4383109509 modified "2023-10-16" @default.
- W4383109509 title "Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding" @default.
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- W4383109509 doi "https://doi.org/10.1109/icra48891.2023.10160800" @default.
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