Abstract
Superpoint-based pipelines provide an efficient alternative to point- or voxel-based 3D semantic segmentation, but are often bottlenecked by their CPU-bound partition step. We propose a learnable, fully GPU partitioning algorithm that generates geometrically and semantically coherent superpoints 13$\times$ faster than prior methods. Our module is compact (under 60k parameters), trains in under 20 minutes with a differentiable surrogate loss, and requires no handcrafted features. Combine with a lightweight superpoint classifier, the full pipeline fits in $<$2 MB of VRAM, scales to multi-million-point scenes, and supports real-time inference. With 72$\times$ faster inference and 120$\times$ fewer parameters, EZ-SP matches the accuracy of point-based SOTA models across three domains: indoor scans (S3DIS), autonomous driving (KITTI-360), and aerial LiDAR (DALES). Code and pretrained models are accessible at this http URL.
Abstract (translated)
基于Superpoint的管道为点或体素基的三维语义分割提供了一种高效的替代方案，但常常受限于其CPU绑定的分区步骤。我们提出了一种可学习、完全在GPU上运行的分区算法，该算法能以比先前方法快13倍的速度生成几何和语义连贯性良好的超点。我们的模块小巧（参数数量低于60k），仅需不到20分钟即可使用可微分代理损失进行训练，并且不需要手工制作的功能特征。结合轻量级的超点分类器，整个管道占用的VRAM小于2MB，能够扩展到包含数百万个点的场景，并支持实时推理。通过实现72倍的速度提升和参数减少至原来的1/120倍，EZ-SP在三个领域（室内扫描[S3DIS]、自动驾驶[KITTI-360]以及航空激光雷达[DALES]）上与基于点的最佳模型精度相匹配。代码及预训练模型可在提供的链接处访问。 请注意，原文中的“this http URL”是一个占位符，实际使用时应该替换为具体的网址链接以便读者访问相关资源。
URL
https://arxiv.org/abs/2512.00385
