ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
A 3D CT scan entering a vision-language model produces a long sequence of visual tokens, often thousands to tens of thousands per volume, and this sequence must be compressed before a language model can consume it. Token compression is well studied in general vision, but little of it targets 3D CT specifically. A common baseline is grid average, which pools regular grid cells and can blend distinct anatomy, lesion, and air into one token. We present \textbf{ORCA} (ORgan-Centroid Aggregation), a token compressor for 3D CT. It merges adjacent tokens with organ guidance and adds a sinusoidal encoding of each region's centroid to preserve spatial layout. This preserves the anatomical informat...
| Authors | Renjie Liang, Zijian Xu, Jinqian Pan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
Connected in the knowledge graph
- LiangRenjie/ORCA-3DCTDataset · cited by
- microsoft/colipriModel · cited by
Models and datasets referencing this paper
- microsoft/colipriModel
- LiangRenjie/ORCA-3DCTDataset
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