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

**Authors:** Maria Parelli, Alexandros Delitzas, Nikolas Hars, Georgios Vlassis, Sotirios Anagnostidis, Gregor Bachmann, Thomas Hofmann  
**arXiv:** [2304.06061](https://arxiv.org/abs/2304.06061)  
**Date:** 2023-04-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.06061v1/

## Key points

- Researchers exploring application of linguistic knowledge and visual concepts from 2D images to 3D world understanding
- Proposal of a novel 3D pre-training Vision-Language method for learning semantically meaningful and transferable representations of 3D scene point clouds
- Incorporation of CLIP model's representational power into the 3D encoder to enhance reasoning about the 3D world
- Evaluation on a downstream task called 3D Visual Question Answering, outperforming existing state-of-the-art techniques
- Interpretable representation of 3D scene features for further analysis and exploration
- Additional details provided by authors including information about themselves and access code for associated study

## Summary

In recent years, researchers have begun exploring the application of linguistic knowledge and visual concepts from 2D images to 3D world understanding. In this study, the authors propose a novel 3D pre-training Vision-Language method that enables models to learn semantically meaningful and transferable representations of 3D scene point clouds. They achieve this by incorporating the representational power of the popular CLIP model into their 3D encoder. By aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP, they enhance the model's ability to reason about the 3D world. To evaluate the effectiveness of their approach, the authors assess their model on a downstream task known as 3D Visual Question Answering. The experimental results demonstrate that their pre-training method outperforms existing state-of-the-art techniques in this task. Additionally, their approach leads to an interpretable representation of 3D scene features which can be used for further analysis and exploration. The authors provide further details about their work including information about themselves and a link to access code associated with this study. Overall, this research contributes significantly towards advancing our understanding of how linguistic knowledge and visual concepts can be effectively applied in training models for 3D world understanding tasks.