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

• URL: http://arxiv.org/abs/2305.12427v2
• Date: Thu, 25 May 2023 08:38:52 GMT
• Title: VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations
• Authors: Nikolaos Tsagkas, Oisin Mac Aodha and Chris Xiaoxuan Lu
• Abstract summary: 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.
• Score: 15.265341472149034
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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