Language-Enhanced RNR-Map: Querying Renderable Neural Radiance Field Maps with Natural Language

Francesco Taioli, Federico Cunico, Federico Girella, Riccardo Bologna, Alessandro Farinelli, Marco Cristani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 4669-4674

Abstract

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at the link https://intelligolabs.github.io/Le-RNR-Map/

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[bibtex]
@InProceedings{Taioli_2023_ICCV, author = {Taioli, Francesco and Cunico, Federico and Girella, Federico and Bologna, Riccardo and Farinelli, Alessandro and Cristani, Marco}, title = {Language-Enhanced RNR-Map: Querying Renderable Neural Radiance Field Maps with Natural Language}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2023}, pages = {4669-4674} }