PIRATR: Parametric Object Inference for Robotic Applications with Transformers in 3D Point Clouds

• URL: http://arxiv.org/abs/2602.05557v1
• Date: Thu, 05 Feb 2026 11:29:09 GMT
• Title: PIRATR: Parametric Object Inference for Robotic Applications with Transformers in 3D Point Clouds
• Authors: Michael Schwingshackl, Fabio F. Oberweger, Mario Niedermeyer, Huemer Johannes, Markus Murschitz,
• Abstract summary: We present PIRATR, an end-to-end 3D object detection framework for robotic use cases in point clouds.<n>Our method streamlines parametric 3D object detection by jointly estimating multi-class 6-DoF poses and class-specific parametric attributes.<n>PIRATR generalizes effectively to real outdoor LiDAR scans, achieving a detection mAP of 0.919 without additional fine-tuning.
• Score: 0.9134244356393665
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present PIRATR, an end-to-end 3D object detection framework for robotic use cases in point clouds. Extending PI3DETR, our method streamlines parametric 3D object detection by jointly estimating multi-class 6-DoF poses and class-specific parametric attributes directly from occlusion-affected point cloud data. This formulation enables not only geometric localization but also the estimation of task-relevant properties for parametric objects, such as a gripper's opening, where the 3D model is adjusted according to simple, predefined rules. The architecture employs modular, class-specific heads, making it straightforward to extend to novel object types without re-designing the pipeline. We validate PIRATR on an automated forklift platform, focusing on three structurally and functionally diverse categories: crane grippers, loading platforms, and pallets. Trained entirely in a synthetic environment, PIRATR generalizes effectively to real outdoor LiDAR scans, achieving a detection mAP of 0.919 without additional fine-tuning. PIRATR establishes a new paradigm of pose-aware, parameterized perception. This bridges the gap between low-level geometric reasoning and actionable world models, paving the way for scalable, simulation-trained perception systems that can be deployed in dynamic robotic environments. Code available at https://github.com/swingaxe/piratr.

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