Efficient Encoder-Free Fourier-based 3D Large Multimodal Model

📰 ArXiv cs.AI

Efficient encoder-free Fourier-based 3D large multimodal model for processing unordered 3D data

advanced Published 31 Mar 2026

Action Steps

Replace traditional visual encoders with Fourier-based transforms to extract geometric features from 3D data
Utilize unordered point cloud data to train large multimodal models
Implement efficient tokenization methods for 3D data to enable scalability
Evaluate the performance of the proposed model on various 3D multimodal tasks

Who Needs to Know This

AI engineers and researchers working on 3D multimodal models can benefit from this approach to improve efficiency and scalability, and ml-researchers can apply these findings to develop more effective models

Key Insight

💡 Fourier-based transforms can efficiently extract geometric features from unordered 3D data, eliminating the need for heavy pre-trained visual encoders

Key Takeaways

Efficient encoder-free Fourier-based 3D large multimodal model for processing unordered 3D data

Full Article

Title: Efficient Encoder-Free Fourier-based 3D Large Multimodal Model

Abstract:
arXiv:2602.23153v2 Announce Type: replace-cross Abstract: Large Multimodal Models (LMMs) that process 3D data typically rely on heavy, pre-trained visual encoders to extract geometric features. While recent 2D LMMs have begun to eliminate such encoders for efficiency and scalability, extending this paradigm to 3D remains challenging due to the unordered and large-scale nature of point clouds. This leaves a critical unanswered question: How can we design an LMM that tokenizes unordered 3D data ef

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