# VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations

Source: [VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations](<https://www.sota2.com/research/paper/68fa6f7c1e209131cea45e38>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:21:27.030Z

Authors: Nikolaos Tsagkas, Oisin Mac Aodha, Chris Xiaoxuan Lu

Published: 2023-05-21T10:55:27.000Z

## Abstract

We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries. Our model encodes and fuses the geometry of a scene with vision-language trained latent features by distilling information from a language-driven segmentation model. VL-Fields is trained without requiring any prior knowledge of the scene object classes, which makes it a promising representation for the field of robotics. Our model outperformed the similar CLIP-Fields model in the task of semantic segmentation by almost 10%.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.12427>)

[PDF](<https://arxiv.org/pdf/2305.12427.pdf>)