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D scene graphs are an emerging 3D scene representation, that models both theobjects present in the scene as well as their relationships. However, learning3D scene graphs is a challenging task because it requires not only objectlabels but also relationship annotations, which are very scarce in datasets.While it is widely accepted that pre-training is an effective approach toimprove model performance in low data regimes, in this paper, we find thatexisting pre-training methods are ill-suited for 3D scene graphs. To solve thisissue, we present the first language-based pre-training approach for 3D scenegraphs, whereby we exploit the strong relationship between scene graphs andlanguage. To this end, we leverage the language encoder of CLIP, a popularvision-language model, to distill its knowledge into our graph-based network.We formulate a contrastive pre-training, which aligns text embeddings ofrelationships (subject-predicate-object triplets) and predicted 3D graphfeatures. Our method achieves state-of-the-art results on the main semantic 3Dscene graph benchmark by showing improved effectiveness over pre-trainingbaselines and outperforming all the existing fully supervised scene graphprediction methods by a significant margin. Furthermore, since our scene graphfeatures are language-aligned, it allows us to query the language space of thefeatures in a zero-shot manner. In this paper, we show an example of utilizingthis property of the features to predict the room type of a scene withoutfurther training.

Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction