Markus Murschitz — accepted AI-conference papers

2026

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

Michael Schwingshackl, Fabio Francisco Oberweger, Mario Niedermeyer, Johannes Huemer, Markus Murschitz

ICRA 2026poster

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…

Cited by 0 Source ↗PDF ↗Scholar ↗

2022

Unifying Panoptic Segmentation for Autonomous Driving

Oliver Zendel, Matthias Schörghuber, Bernhard Rainer, Markus Murschitz, Csaba Beleznai

CVPR 2022poster

This paper aims to improve panoptic segmentation for real-world applications in three ways. First, we present a label policy that unifies four of the most popular panoptic segmentation datasets for autonomous driving. We also clean up label confusion by adding the new vehicle labels pickup and van.…

Cited by 57 PDF ↗code ↗Scholar ↗

2018

WildDash - Creating Hazard-Aware Benchmarks

Oliver Zendel, Katrin Honauer, Markus Murschitz, Daniel Steininger, Gustavo Fernandez Dominguez

ECCV 2018poster

Test datasets should contain many different challenging aspects so that the robustness and real-world applicability of algorithms can be assessed. In this work, we present a new test dataset for semantic and instance segmentation for the automotive domain. We have conducted a thorough risk analysis…

Cited by 217 Source ↗PDF ↗Scholar ↗

2017

Analyzing Computer Vision Data - The Good, the Bad and the Ugly

Oliver Zendel, Katrin Honauer, Markus Murschitz, Martin Humenberger, Gustavo Fernandez Dominguez

CVPR 2017poster

In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. These valuable contributions helped to push CV solutions to a level where they can be used for safety-relevant applications, such as autonomous driving. However, major questions concerni…

Cited by 36 PDF ↗Scholar ↗

2015

CV-HAZOP: Introducing Test Data Validation for Computer Vision

Oliver Zendel, Markus Murschitz, Martin Humenberger, Wolfgang Herzner

ICCV 2015oral

Test data plays an important role in computer vision (CV) but is plagued by two questions: Which situations should be covered by the test data and have we tested enough to reach a conclusion? In this paper we propose a new solution answering these questions using a standard procedure devised by the…

Cited by 57 PDF ↗Scholar ↗