Scenes as Tokens: Multi-Scale Normal Distributions Transform Tokenizer for General 3D Vision-Language Understanding
Published:Nov 26, 2025 09:12
•1 min read
•ArXiv
Analysis
This article introduces a novel approach to 3D vision-language understanding by representing 3D scenes as tokens using a multi-scale Normal Distributions Transform (NDT). The method aims to improve the integration of visual and textual information for tasks like scene understanding and object recognition. The use of NDT allows for a more efficient and robust representation of 3D data compared to raw point clouds or voxel grids. The multi-scale aspect likely captures details at different levels of granularity. The focus on general understanding suggests the method is designed to be applicable across various 3D vision-language tasks.
Key Takeaways
- •Proposes a novel tokenization method for 3D scenes using multi-scale Normal Distributions Transform (NDT).
- •Aims to improve 3D vision-language understanding.
- •Likely offers a more efficient and robust representation of 3D data compared to traditional methods.
- •Focuses on general 3D vision-language tasks.
Reference
“The article likely details the specific implementation of the multi-scale NDT tokenizer, including how it handles different scene complexities and how it integrates with language models. It would also likely present experimental results demonstrating the performance of the proposed method on benchmark datasets.”