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Training models to apply linguistic knowledge and visual concepts from 2Dimages to 3D world understanding is a promising direction that researchers haveonly recently started to explore. In this work, we design a novel 3Dpre-training Vision-Language method that helps a model learn semanticallymeaningful and transferable 3D scene point cloud representations. We inject therepresentational power of the popular CLIP model into our 3D encoder byaligning the encoded 3D scene features with the corresponding 2D image and textembeddings produced by CLIP. To assess our model's 3D world reasoningcapability, we evaluate it on the downstream task of 3D Visual QuestionAnswering. Experimental quantitative and qualitative results show that ourpre-training method outperforms state-of-the-art works in this task and leadsto an interpretable representation of 3D scene features.

CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes