HaLo-NeRF offers a novel technique for connecting unique architectural elements across different modalities of text, images, and 3D volumetric representations of a scene. The method leverages inter-view coverage of a scene in multiple modalities to distill concepts using a language model and bootstrap spatial understanding through view correspondences. This knowledge is used to guide a neural 3D representation that enforces view consistency, demonstrating its performance on a new benchmark for concept localization in large-scale scenes of tourist landmarks.

The implementation involves training a language model to distill image metadata into concise textual pseudo-labels, which describe prominent architectural features within the images. These pseudo-labels are then used to fine-tune a vision-and-language model (CLIPFT) on images, enabling the model to understand and localize domain-specific semantics. The fine-tuning process involves preprocessing the data to remove irrelevant pairs and direction words, ensuring the model focuses on visually informative content. This project also uses CLIPSeg for image segmentation, showcasing its ability to segment salient objects even when lacking fine-grained understanding of the pseudo-labels.

Furthermore, the method incorporates an optimization-based pipeline for each textual query, which fits the segmentation field for the specific term. This approach allows for the segmentation of semantically or geometrically related regions based on the input query, enhancing the model's ability to identify and localize architectural elements in diverse scenes.