Abstract
Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the role of different computational blocks in 3D point cloud networks and find an intuitive behaviour: convolution is adequate to extract low-level geometry at high-resolution in early layers, where attention is expensive without bringing any benefits; attention captures high-level semantics and context in low-resolution, deep layers more efficiently. Guided by this design principle, we propose a new, improved 3D point cloud backbone that employs convolutions in early stages and switches to attention for deeper layers. To avoid the loss of spatial layout information when discarding redundant convolution layers, we introduce a novel, training-free 3D positional encoding, PointROPE. The resulting LitePT model has $3.6\times$ fewer parameters, runs $2\times$ faster, and uses $2\times$ less memory than the state-of-the-art Point Transformer V3, but nonetheless matches or even outperforms it on a range of tasks and datasets. Code and models are available at: this https URL.
Abstract (translated)
现代用于处理三维点云的神经网络架构包含卷积层和注意力模块，但最佳的组装方式尚不明确。我们分析了3D点云网络中不同计算块的作用，并发现了一种直观的行为：在早期层次的高分辨率下，卷积足以提取低级几何信息，在这种情况下使用注意力机制成本高昂且没有带来任何好处；而注意力则可以更有效地捕捉低分辨率深层中的高级语义和上下文信息。遵循这一设计原则，我们提出了一种新的、改进的3D点云骨干网络，它在早期阶段采用卷积，并在深层切换为注意力模块。为了避免丢弃冗余卷积层时空间布局信息丢失的问题，我们引入了一种新颖且无需训练的三维位置编码方法——PointROPE。最终得到的LitePT模型相比最先进的Point Transformer V3，在参数量上减少了3.6倍，运行速度提升了2倍，内存使用减少了2倍，但在一系列任务和数据集上的表现却相匹配甚至更优。代码和模型可在以下网址获取：this https URL。
URL
https://arxiv.org/abs/2512.13689
