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%.

翻译：我们提出视觉-语言场（VL-Fields），这是一种神经隐式空间表征，支持开放词汇语义查询。该模型通过从语言驱动的分割模型中蒸馏信息，将场景几何结构与经过视觉-语言训练的潜在特征进行编码与融合。VL-Fields在无需任何场景物体类别先验知识的情况下进行训练，这使其成为机器人领域颇具前景的表征方法。在语义分割任务中，我们的模型性能较同类模型CLIP-Fields提升了近10%。