ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression

arXiv:2608.00345v1 Announce Type: new Abstract: 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 extbf{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 information a downstream model needs. ORCA is training-free and plug-and-play, producing an adjustable token set without any model change or text query. We evaluate it across two datasets (CT-RATE and Merlin) and five encoders. The evaluation spans two task types: attribute prediction over five families (size, density, location, texture, and disease) and text generation (visual question answering and report generation). At matched token budgets, ORCA improves consistently over existing compression methods. It shrinks the visual context 64imes and its KV-cache 50imes, and is 31imes faster to process each volume. Code released at https://github.com/renjie-liang/ORCA-3DCT.

Related

• VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression
• CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models
• Towards Joint Quantization and Token Pruning of Vision-Language Models
• Decoupling semantics from vision: A framework for faithful visual-text compression evaluation

Source: arXiv cs.CV | 2026-08-04