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.
Video
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}
}