CoverPrune: Coverage-Driven Token Pruning for 3D VLMs via Optimal Transport
Score
72首次系统提出覆盖驱动剪枝范式并引入最优传输，突破了传统多样性剪枝的局限。
方法设计有理论支撑和高效算法，多基准实验验证，写作文风规范。
无需额外训练即可直接用于3D VLM推理加速，并提供低开销加速版本，工程落地潜力大。
针对3D VLM当前的效率痛点，但论文编号等信息可能存在虚构，时效性评估以内容立意为主。
来源为arXiv预印本，已开源，但尚未经同行评审且链接信息存疑。
Base 80 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
It is the first to introduce coverage-driven pruning with Optimal Transport for 3D VLMs, with novel methodology and comprehensive experiments that significantly benefit 3D visual reasoning efficiency.
AI commentary
「The most compelling takeaway is the shift from “select diverse tokens” to “preserve evidence coverage,” which matters even more for 3D tasks than for 2D. For researchers working on 3D VLM inference acceleration or multimodal model compression, this coverage-centric angle could open a new path.」
Quick overview
3D Vision-Language Models (3D VLMs) excel in spatial reasoning but suffer from massive visual token counts that create severe computational bottlenecks during inference. Existing token pruning methods mostly rely on diversity-based selection, which often drops representative prototype tokens in 3D scenes, breaking multi-view consistency and geometric structures. To address this, the team proposes CoverPrune, a training-free framework that formulates inference-time token pruning as an Optimal Transport problem, featuring Feature-Spatial-Temporal (FST) transport cost and a Spatial-Guided Greedy Selection (SGS) algorithm. An accelerated variant, CoverPrune-Lite, is also introduced. Extensive experiments on multiple 3D spatial reasoning benchmarks show state-of-the-art token efficiency while maintaining robust performance even under aggressive pruning budgets.
- Proposes a paradigm shift for 3D VLM token pruning: from maximizing diversity to preserving visual evidence coverage.
- Introduces CoverPrune, a training-free framework built on Optimal Transport, plus an accelerated variant CoverPrune-Lite.
- Achieves state-of-the-art token efficiency across multiple 3D spatial reasoning benchmarks while maintaining robust performance under aggressive pruning budgets.
Full text
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