PI3DETR: Parametric Instance Detection of 3D Point Cloud Edges with a Geometry-Aware 3DETR

Fabio F. Oberweger, Michael Schwingshackl, Vanessa Staderini

Keywords: 3D Edge Detection, Parametric Inference, Point Clouds, 3DETR, Detections, Deep Learning

TL;DR: PI3DETR predicts parametric 3D curve instance from point clouds end-to-end, achieving SOTA on ABC Dataset.

Abstract: 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.

Submission Number: 208