The burgeoning complexity and scale of 3D geometry models pose significant challenges for deployment on resource-constrained platforms. While Post-Training Quantization (PTQ) enables efficient inference without retraining, conventional methods, primarily optimized for 2D Vision Transformers, fail to transfer effectively to 3D models due to intricate feature distributions and prohibitive calibration overhead. To address these challenges, we propose TAPTQ, a Tail-Aware Post-Training Quantization pipeline specifically engineered for 3D geometric learning. Our contribution is threefold: (1) To overcome the data-scale bottleneck in 3D datasets, we develop a progressive coarse-to-fine calibration construction strategy that constructs a highly compact subset to achieve both statistical purity and geometric representativeness. (2) We reformulate the quantization interval search as an optimization problem and introduce a ternary-search-based solver, reducing the computational complexity from $\mathcal{O}(N)$ to $\mathcal{O}(\log N)$ for accelerated deployment. (3) To mitigate quantization error accumulation, we propose TRE-Guided Module-wise Compensation, which utilizes a Tail Relative Error (TRE) metric to adaptively identify and rectify distortions in modules sensitive to long-tailed activation outliers. Extensive experiments on the VGGT and Pi3 benchmarks demonstrate that TAPTQ consistently outperforms state-of-the-art PTQ methods in accuracy while significantly reducing calibration time. The code will be released soon.

翻译：三维几何模型日益增长的复杂性和规模对资源受限平台上的部署提出了重大挑战。虽然训练后量化（PTQ）能够在不重新训练的情况下实现高效推理，但传统方法主要针对二维视觉Transformer优化，由于复杂的特征分布和过高的校准开销，无法有效迁移至三维模型。为解决这些挑战，我们提出了TAPTQ，一种专门为三维几何学习设计的尾部感知训练后量化流程。我们的贡献包括三个方面：（1）为克服三维数据集中的数据规模瓶颈，我们开发了一种渐进式由粗到细的校准构建策略，该策略构建了一个高度紧凑的子集，以实现统计纯度和几何代表性的双重目标。（2）我们将量化区间搜索重新表述为一个优化问题，并引入了一种基于三分查找的求解器，将计算复杂度从 $\mathcal{O}(N)$ 降低到 $\mathcal{O}(\log N)$，从而加速部署。（3）为减轻量化误差累积，我们提出了TRE引导的模块级补偿，该方法利用尾部相对误差（TRE）指标自适应地识别并纠正对长尾激活离群值敏感的模块中的失真。在VGGT和Pi3基准测试上进行的大量实验表明，TAPTQ在精度上始终优于最先进的PTQ方法，同时显著减少了校准时间。代码即将发布。