3DZip：面向3D问答的空间感知特征多样性引导的Token压缩

摘要

近期三维视觉-语言模型（3D VLMs）通过将二维视觉特征投影到世界坐标来构建几何感知令牌，从而实现对三维问答等任务的空间推理。然而，这种设计在每个场景中会生成数千个令牌，导致可观的计算和内存开销。尽管令牌压缩已在二维VLM中得到广泛研究，但现有方法依赖语义相关性或基于注意力的选择，忽视了三维令牌的结构化空间特性。此外，三维表示中的冗余无法仅通过空间邻近性解决，因为即使在空间聚合之后，物体级令牌不平衡仍然存在。为解决这一问题，我们提出3DZip，一种三阶段令牌压缩框架：首先应用粗体素化以去除点级冗余，然后通过行列式点过程基于特征空间多样性选择锚定令牌，最后在空间约束下合并剩余令牌以保持几何一致性。在三个三维问答基准上的实验表明，3DZip持续优于现有压缩方法，仅用128个令牌即可保留94.7%的原始性能，推理速度提升1.92倍。

English

Recent 3D vision-language models (3D VLMs) construct geometry aware tokens by projecting 2D visual features into world coordinates, enabling spatial reasoning for tasks such as 3D question answering. However, this design generates thousands of tokens per scene, resulting in substantial computational and memory overhead. While token compression has been extensively studied in 2D VLMs, existing approaches rely on semantic relevance or attention-based selection that overlook the structured spatial nature of 3D tokens. Moreover, redundancy in 3D representations cannot be resolved by spatial proximity alone, as object-level token imbalance persists even after spatial aggregation. To address this, we propose 3DZip, a three-stage token compression framework that first applies coarse voxelization to remove point-level redundancy, then selects anchor tokens based on feature-space diversity via a Determinantal Point Process, and finally merges remaining tokens under spatial constraints to preserve geometric coherence. Experiments on three 3D question answering benchmarks demonstrate that 3DZip consistently outperforms existing compression methods, retaining 94.7% of the original performance with only 128 tokens, achieving a 1.92times faster inference speed.