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SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs
arXiv – CS AI|Guanting Ye, Qiyan Zhao, Wenhao Yu, Liangyu Yuan, Mingkai Li, Xiaofeng Zhang, Jianmin Ji, Yanyong Zhang, Qing Jiang, Ka-Veng Yuen||5 views
🤖AI Summary
Researchers introduce SoPE (Spherical Coordinate-based Positional Embedding), a new method that enhances 3D Large Vision-Language Models by mapping point-cloud data into spherical coordinate space. This approach overcomes limitations of existing Rotary Position Embedding (RoPE) by better preserving spatial structures and directional variations in 3D multimodal understanding.
Key Takeaways
- →SoPE addresses fundamental limitations in current 3D Large Vision-Language Models' position encoding mechanisms.
- →The method maps point-cloud tokens into spherical coordinate space for unified spatial and directional modeling.
- →Multi-scale frequency mixing strategy is introduced to fuse information across different frequency domains.
- →Experimental validation shows improved performance on multiple 3D scene benchmarks.
- →Real-world deployment demonstrates strong generalization capabilities for practical applications.
#3d-vision#large-language-models#computer-vision#multimodal-ai#spatial-computing#machine-learning#research#embedding#point-cloud
Read Original →via arXiv – CS AI
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