We present PI3DETR, an end-to-end framework that directly predicts 3D parametric curve instances from raw point clouds, avoiding the intermediate representations and multi-stage processing common in prior work. Extending 3DETR, our model introduces a geometry-aware matching strategy and specialized loss functions that enable unified detection of differently parameterized curve types, including cubic Bézier curves, line segments, circles, and arcs, in a single forward pass. Optional post-processing steps further refine predictions without adding complexity. This streamlined design improves robustness to noise and varying sampling densities, addressing critical challenges in real world LiDAR and 3D sensing scenarios. PI3DETR sets a new state-of-the-art on the ABC dataset and generalizes effectively to real sensor data, offering a simple yet powerful solution for 3D edge and curve estimation.

翻译：本文提出PI3DETR，一种端到端框架，可直接从原始点云预测三维参数化曲线实例，避免了先前工作中常见的中间表示与多阶段处理流程。本模型在3DETR基础上扩展，引入几何感知匹配策略与专用损失函数，能够在前向传播中统一检测不同参数化类型的曲线，包括三次贝塞尔曲线、线段、圆形及圆弧。可选的后续处理步骤可进一步优化预测结果而不增加复杂度。这种精简设计提升了对噪声及变化采样密度的鲁棒性，解决了真实世界LiDAR与三维传感场景中的关键挑战。PI3DETR在ABC数据集上实现了新的最优性能，并能有效泛化至真实传感器数据，为三维边缘与曲线估计提供了简洁而强大的解决方案。