150 citations · 365 across the 15 of their papers we have counts for
19 papers
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
Francesco Milano, Jen Jen Chung, Hermann Blum +2
State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines f…
Unsupervised Continual Semantic Adaptation through Neural Rendering
Zhizheng Liu, Francesco Milano, Jonas Frey +3
An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deploym…
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
Hermann Blum, Marcus G. Müller, Abel Gawel +2
In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps. Autonomous deployment to such…
Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning
René Zurbrügg, Hermann Blum, Cesar Cadena +2
This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks f…
SL Sensor: An Open-Source, ROS-Based, Real-Time Structured Light Sensor for High Accuracy Construction Robotic Applications
Teng Foong Lam, Hermann Blum, Roland Siegwart +1
High accuracy 3D surface information is required for many construction robotics tasks such as automated cement polishing or robotic plaster spraying. However, consumer-grade depth…
Continual Adaptation of Semantic Segmentation using Complementary 2D-3D Data Representations
Jonas Frey, Hermann Blum, Francesco Milano +2
Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the traini…
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
Boyang Sun, Jiaxu Xing, Hermann Blum +2
Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth esti…
Self-Improving Semantic Perception for Indoor Localisation
Hermann Blum, Francesco Milano, René Zurbrügg +3
We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fi…
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer +6
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle pre…
Pixel-wise Anomaly Detection in Complex Driving Scenes
Giancarlo Di Biase, Hermann Blum, Roland Siegwart +1
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as au…
The Hidden Uncertainty in a Neural Networks Activations
Janis Postels, Hermann Blum, Yannick Strümpler +4
The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution…
Precise Robot Localization in Architectural 3D Plans
Hermann Blum, Julian Stiefel, Cesar Cadena +2
This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inhe…
Accurate Mapping and Planning for Autonomous Racing
Leiv Andresen, Adrian Brandemuehl, Alex Hönger +11
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student G…
A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction
Abel Gawel, Hermann Blum, Johannes Pankert +9
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction s…
This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity
David Haldimann, Hermann Blum, Roland Siegwart +1
There has been a remarkable progress in the accuracy of semantic segmentation due to the capabilities of deep learning. Unfortunately, these methods are not able to generalize much…
Active Learning for UAV-based Semantic Mapping
Hermann Blum, Silvan Rohrbach, Marija Popovic +2
Unmanned aerial vehicles combined with computer vision systems, such as convolutional neural networks, offer a flexible and affordable solution for terrain monitoring, mapping, and…
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes
Nicolas Marchal, Charlotte Moraldo, Roland Siegwart +3
Deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed…
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto +2
Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical appli…
Modular Sensor Fusion for Semantic Segmentation
Hermann Blum, Abel Gawel, Roland Siegwart +1
Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations. Current multi-sensor deep learning b…