Awesome-LVLM-Attack

A continual collection of papers related to Text-guided 3D Visual Grounding (T-3DVG).

Text-guided 3D visual grounding (T-3DVG) aims to locate a specific object that semantically corresponds to a language query from a complicated 3D scene, has drawn increasing attention in the 3D research community over the past few years. T-3DVG presents great potential and challenges due to its closer proximity to the real world and the complexity of data collection and 3D point cloud source processing.

In the T-3DVG community, we've summarized existing T-3DVG methods in our survey paper👍.

If you find some important work missed, it would be super helpful to let me know (dzliu@stu.pku.edu.cn). Thanks!

If you find our survey useful for your research, please consider citing:

@article{liu2024survey,
  title={A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions},
  author={Liu, Daizong and Liu, Yang and Huang, Wencan and Hu, Wei},
  journal={arXiv preprint arXiv},
  year={2024}
}

Table of Contents

• Fully-supervised two-stage approach
• Fully-supervised one-stage approach
• Weakly-supervised approach
• Approaches using No Point Cloud Input
• Approaches using LLMs

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Fully-Supervised-Two-Stage

• ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language | Github
  • Dave Zhenyu Chen, Angel X. Chang, Matthias Nießner
  • Technical University of Munich, Simon Fraser University
  • [ECCV2020] https://arxiv.org/abs/1912.08830
  • A dataset, two-stage approach, proposal-then-selection