无需训练的3D生成加速框架，保持几何精度同时提升速度
Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration
- 基于动态缓存调度与时空稳定性筛选，避免3D结构失真
- 最高提速27.12%，FLOPs降低54.83%，几何误差仅增2.48%（Chamfer）
- 适合追求高效3D生成且需保真的研究者和应用开发者
扩散模型在2D图像、视频和3D形状生成中表现卓越，但推理过程因迭代去噪而计算昂贵。尽管基于缓存的方法能有效重用冗余计算以加速2D和视频生成，但直接应用于3D扩散模型会严重破坏几何一致性。在3D合成中，缓存潜在特征的微小数值误差会累积，导致结构伪影和拓扑不一致。为此，我们提出Fast3Dcache，一种无需训练的几何感知缓存框架，在加速3D扩散推理的同时保持几何保真度。该方法引入预测性缓存调度约束（PCSC），根据体素稳定模式动态分配缓存配额；并设计时空稳定性准则（SSC），依据速度幅值与加速度标准选择可重用的稳定特征。全面实验表明，Fast3Dcache显著加速推理，最高实现27.12%的速度提升和54.83%的浮点运算量减少，几何质量下降极小：切比雪夫距离仅增加2.48%，F-Score下降1.95%。
原文摘要 · Abstract (English)
Diffusion models have achieved impressive generative quality across modalities like 2D images, videos, and 3D shapes, but their inference remains computationally expensive due to the iterative denoising process. While recent caching-based methods effectively reuse redundant computations to speed up 2D and video generation, directly applying these techniques to 3D diffusion models can severely disrupt geometric consistency. In 3D synthesis, even minor numerical errors in cached latent features accumulate, causing structural artifacts and topological inconsistencies. To overcome this limitation, we propose Fast3Dcache, a training-free geometry-aware caching framework that accelerates 3D diffusion inference while preserving geometric fidelity. Our method introduces a Predictive Caching Scheduler Constraint (PCSC) to dynamically determine cache quotas according to voxel stabilization patterns and a Spatiotemporal Stability Criterion (SSC) to select stable features for reuse based on velocity magnitude and acceleration criterion. Comprehensive experiments show that Fast3Dcache accelerates inference significantly, achieving up to a 27.12% speed-up and a 54.83% reduction in FLOPs, with minimal degradation in geometric quality as measured by Chamfer Distance (2.48%) and F-Score (1.95%).
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