Markus Murschitz

Michael Schwingshackl, Fabio F. Oberweger, Mario Niedermeyer et al.

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.

Michael Schwingshackl, Fabio Francisco Oberweger, Markus Murschitz

This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network (GCN) to accurately segment machinery parts. By providing 1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset depicting a truck-mounted loading crane, achieves effective segmentation across various levels of detail. Training times are kept under five minutes on consumer GPUs. The model demonstrates robust generalization to real data, achieving a qualitative synthetic-to-real generalization with a $J\&F$ score of 92.2 on real data using 10 synthetic support samples. When benchmarked on the DAVIS 2017 dataset, it achieves a $J\&F$ score of 71.5 in semi-supervised video segmentation with three support samples. This method's fast training times and effective generalization to real data make it a valuable tool for autonomous systems interacting with machinery and infrastructure, and illustrate the potential of combined and orchestrated foundation models for few-shot segmentation tasks.

Huy Hoang Nguyen, Johannes Huemer, Markus Murschitz et al.

The logistics and construction industries face persistent challenges in automating pallet handling, especially in outdoor environments with variable payloads, inconsistencies in pallet quality and dimensions, and unstructured surroundings. In this paper, we tackle automation of a critical step in pallet transport: the pallet pick-up operation. Our work is motivated by labor shortages, safety concerns, and inefficiencies in manually locating and retrieving pallets under such conditions. We present Lang2Lift, a framework that leverages foundation models for natural language-guided pallet detection and 6D pose estimation, enabling operators to specify targets through intuitive commands such as "pick up the steel beam pallet near the crane." The perception pipeline integrates Florence-2 and SAM-2 for language-grounded segmentation with FoundationPose for robust pose estimation in cluttered, multi-pallet outdoor scenes under variable lighting. The resulting poses feed into a motion planning module for fully autonomous forklift operation. We validate Lang2Lift on the ADAPT autonomous forklift platform, achieving 0.76 mIoU pallet segmentation accuracy on a real-world test dataset. Timing and error analysis demonstrate the system's robustness and confirm its feasibility for deployment in operational logistics and construction environments. Video demonstrations are available at https://eric-nguyen1402.github.io/lang2lift.github.io/