3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering

Recent 3D vision-language models (3D VLMs) construct ge-ometry aware tokens by projecting 2D visual features into world coordi-nates, enabling spatial reasoning for tasks such as 3D question answering.However, this design generates thousands of tokens per scene, resultingin substantial computational and memory overhead. While token com-pression has been extensively studied in 2D VLMs, existing approachesrely on semantic relevance or attention-based selection that overlook thestructured spatial nature of 3D tokens. Moreover, redundancy in 3D rep-resentations cannot be resolved by spatial proximity alone, as object-leveltoken imbalance persists even after spatial aggregation. To address this,we propose 3DZip, a three-stage token compression framework that firstapplies coarse voxelization to remove point-level redundancy, then selectsanchor tokens based on feature-space diversity via a Determinantal PointProcess, and finally merges remaining tokens under spatial constraintsto preserve geometric coherence. Experiments on three 3D question an-swering benchmarks demonstrate that 3DZip consistently outperformsexisting compression methods, retaining 94.7% of the original perfor-mance with only 128 tokens, achieving a 1.92× faster inference speed.