通过空间哈希对齐硬件与几何局部性，实现3D点云模型的高效扩展。
Flash3D: Super-scaling Point Transformers through Joint Hardware-Geometry Locality
- 基于完美空间哈希设计几何局部性机制，与GPU分块自然对齐。
- 相比PTv3提升2.25倍速度、2.4倍内存效率，支持更大模型与注意力范围。
- 适合追求3D点云模型高效率扩展的研究者与工业应用开发者。
近期研究认识到规模在3D学习（如PTv3）和注意力机制（如FlashAttention）中的重要性。然而，现有点云骨干网络未能全面整合几何局部性、注意力机制与GPU架构。本文提出Flash3D Transformer，通过基于完美空间哈希（PSH）的原理性局部性机制，对齐几何局部性与GPU分块。该对齐使PSH局部性机制与FlashAttention以可忽略开销融合。这一机制赋予骨干网络灵活的设计选择，带来更优下游任务表现。Flash3D在基准数据集上超越当前最优的PTv3，实现2.25倍提速和2.4倍内存效率提升。此效率使模型可在无额外开销下扩大注意力范围与规模，从而在相同计算预算下获得更高任务精度。
原文摘要 · Abstract (English)
Recent efforts recognize the power of scale in 3D learning (e.g. PTv3) and attention mechanisms (e.g. FlashAttention). However, current point cloud backbones fail to holistically unify geometric locality, attention mechanisms, and GPU architectures in one view. In this paper, we introduce Flash3D Transformer, which aligns geometric locality and GPU tiling through a principled locality mechanism based on Perfect Spatial Hashing (PSH). The common alignment with GPU tiling naturally fuses our PSH locality mechanism with FlashAttention at negligible extra cost. This mechanism affords flexible design choices throughout the backbone that result in superior downstream task results. Flash3D outperforms state-of-the-art PTv3 results on benchmark datasets, delivering a 2.25x speed increase and 2.4x memory efficiency boost. This efficiency enables scaling to wider attention scopes and larger models without additional overhead. Such scaling allows Flash3D to achieve even higher task accuracies than PTv3 under the same compute budget.
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