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We propose CLIP-Fields, an implicit scene model that can be used for avariety of tasks, such as segmentation, instance identification, semanticsearch over space, and view localization. CLIP-Fields learns a mapping fromspatial locations to semantic embedding vectors. Importantly, we show that thismapping can be trained with supervision coming only from web-image and web-texttrained models such as CLIP, Detic, and Sentence-BERT; and thus uses no directhuman supervision. When compared to baselines like Mask-RCNN, our methodoutperforms on few-shot instance identification or semantic segmentation on theHM3D dataset with only a fraction of the examples. Finally, we show that usingCLIP-Fields as a scene memory, robots can perform semantic navigation inreal-world environments. Our code and demonstration videos are available here:https://mahis.life/clip-fields

CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory