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Jürgen Beyerer

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4 papers

1 author row

ICRA Conference 2024 Conference Paper

Synset Boulevard: A Synthetic Image Dataset for VMMR *

Anne Sielemann
Stefan Wolf
Masoud Roschani
Jens R. Ziehn
Jürgen Beyerer

We present and discuss the Synset Boulevard dataset, designed for the task of surveillance-nature vehicle make and model recognition (VMMR)—to the best of our knowledge the first entirely synthetically generated large-scale VMMR image dataset. Through the simulation of image data rather than the manual annotation of real data, we intend to mitigate common challenges in state-of-the-art VMMR datasets, namely bias, human error, privacy, and the challenge of providing systematic updates. On the other hand, the provision and use of synthetic data introduce individual challenges, such as potential domain gaps and a less pronounced intra-class variance. Our approach to address these challenges, using path tracing and physically-based, data-driven models, is evaluated on an existing large real-world dataset. Overall, our synthetic dataset contains 32 400 independent images (each with different imaging simulations and with/without masked license plates, leading to a total of 259 200 images) from 162 different vehicle models of 43 makes depicted in front view. It is split into 8 sub-datasets to investigate the influence of optical/imaging effects on the classification ability.

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ICRA Conference 2024 Conference Paper

SynthAct: Towards Generalizable Human Action Recognition based on Synthetic Data

David Schneider 0006
Marco Keller
Zeyun Zhong
Kunyu Peng
Alina Roitberg
Jürgen Beyerer
Rainer Stiefelhagen

Synthetic data generation is a proven method for augmenting training sets without the need for extensive setups, yet its application in human activity recognition is underexplored. This is particularly crucial for human-robot collaboration in household settings, where data collection is often privacy-sensitive. In this paper, we introduce SynthAct, a synthetic data generation pipeline designed to significantly minimize the reliance on real-world data. Leveraging modern 3D pose estimation techniques, SynthAct can be applied to arbitrary 2D or 3D video action recordings, making it applicable for uncontrolled in-the-field recordings by robotic agents or smarthome monitoring systems. We present two SynthAct datasets: AMARV, a large synthetic collection with over 800k multi-view action clips, and Synthetic Smarthome, mirroring the Toyota Smarthome dataset. SynthAct generates a rich set of data, including RGB videos and depth maps from four synchronized views, 3D body poses, normal maps, segmentation masks and bounding boxes. We validate the efficacy of our datasets through extensive synthetic-to-real experiments on NTU RGB+D and Toyota Smarthome. SynthAct is available on our project page 4.

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ICRA Conference 2022 Conference Paper

RangeBird: Multi View Panoptic Segmentation of 3D Point Clouds with Neighborhood Attention

Fabian Duerr
Hendrik Weigel
Jürgen Beyerer

Panoptic segmentation of point clouds is one of the key challenges of 3D scene understanding, requiring the simultaneous prediction of semantics and object instances. Tasks like autonomous driving strongly depend on these information to get a holistic understanding of their 3D environment. This work presents a novel proposal free framework for lidar-based panoptic segmentation, which exploits three different point cloud representations, leveraging their strengths and compensating their weaknesses. The efficient projection-based range view and bird's eye view are combined and further extended by a point-based network with a novel attention-based neighborhood aggregation for improved semantic features. Cluster-based object recognition in bird's eye view enables an efficient and high-quality instance segmentation. Semantic and instance segmentation are fused and further refined by a novel instance classification for the final panoptic segmentation. The results on two challenging large-scale datasets, nuScenes and SemanticKITTI, show the success of the proposed framework, which outperforms all existing approaches on nuScenes and achieves state-of-the-art results on SemanticKITTI.

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IROS Conference 2010 Conference Paper

Skill-based telemanipulation by means of intelligent robots

Simon Notheis
Giulio Milighetti
Björn Hein
Heinz Wörn
Jürgen Beyerer

In order to enable robots to execute highly dynamic tasks in dangerous or remote environments, a semiautomatic teleoperation concept has been developed and will be presented in this paper. It relies on a modular software architecture, which allows intuitive control over the robot and compensates latency-based risks by using Augmented Reality techniques together with path prediction and collision avoidance to provide the remote user with visual feedback about the tasks and skills that will be executed. Based on this architecture different skills with high dynamics are integrated in the robot control, so that they can be executed autonomously without the delayed feedback of the user. The skill-based grasping by adherence of smooth or fragile objects during a remote controlled picking and placing task will be exemplary presented.

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