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Christoph Holst

Full Professor and Head of Chair

Technical University of Munich

Germany

Research Interests

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Professors in Germany

About

Professor Christoph Holst is a faculty member at the Technical University of Munich, Germany. His research focuses on advanced methodologies in deep learning, particularly the efficiency of deep convolutional neural networks, as well as spatio-temporal models for vibration monitoring and terrestrial laser scanning technologies. His recent publications highlight innovative approaches to enhancing monitoring techniques for complex structures and environments.

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Collaborators (5)

Florian Schill

Professor

Hochschule Mainz

GERMANY

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Thomas H. Kolbe

Full Professor

Ludwig-Maximilians-Universität München

GERMANY

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Ziyang Xu

Research Associate

Technical University of Munich

GERMANY

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Jens-André Paffenholz

Professor (Full) Geomatics for Underground Systems

Clausthal University of Technology

GERMANY

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Matteo Stagni

ITALY

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Christoph Holst

Research Interests

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About

Articles (25)

L2M-Reg: Building-level uncertainty-aware registration of outdoor LiDAR point clouds and semantic 3D city models

TUM2TWIN: Introducing the large-scale multimodal urban digital twin benchmark dataset

Quality-controlled registration of urban MLS point clouds reducing drift effects by adaptive fragmentation

Improved 4D feature-based deformation tracking for high-resolution real-time landslide and slope deformation monitoring based on terrestrial laser scanning

Feature-based multi-epoch rock slope monitoring using images and terrestrial laser scans

HovoPhoto40 – A high-resolution multi-epoch photogrammetry dataset for geomonitoring of the Mt. Hochvogel (5 epochs, 40 images each)

Identification and interpretation of systematic influences caused by changing atmospheric conditions in multitemporal Permanent Laser Scanning

Intensity-based stochastic model of terrestrial laser scanners: Methodological workflow, empirical derivation and practical benefit

Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds

PL4U: Automated plane-based uncertainty evaluation and reduction method for indoor Mobile Laser Scanning systems

Uncertainty-aware Evaluation and Fusion of Point Clouds for Simultaneous Scanning of Two State-of-the-art Indoor MLS Systems

Segment-wise ICP for Enhanced Point Cloud Registration in Low-Cost Photogrammetric Landslide Monitoring

The Hartebeesthoek Terrestrial Laser Scanning Project – Concept and First Results

RoFlex: Robust and flexible filtering of non-semantic landmarks for automotive applications

Piecewise-ICP: Efficient and robust registration for 4D point clouds in permanent laser scanning

Comparison of TLS and photogrammetric workflows for tracking Alpine rock slope failures

How to Find Geometric Changes in Laser Scanning Point Clouds? A Perspective on Correspondence Definitions

Extending geodetic networks for geo-monitoring by supervised point cloud matching

AImon5.0 - Real-time monitoring of gravitational mass movements for critical infrastructure risk management with AI-assisted 3D metrology

Quality-controlled deformation analysis of the 26-m HartRAO radio telescope’s main reflector: First results

KdO-Net: Towards Improving the Efficiency of Deep Convolutional Neural Networks Applied in the 3D Pairwise Point Feature Matching

Spatio-Temporal Models for Vibration Monitoring of Elongated Structures Using Profile Laser Scans

Increasing Spatio-Temporal Resolution for Monitoring Alpine Solifluction Using Terrestrial Laser Scanners and 3D Vector Fields

Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

Investigating the resolution capability of terrestrial laser scanners and its impact on the effective number of measurements

Collaborators (5)

Florian Schill

Thomas H. Kolbe

Ziyang Xu

Jens-André Paffenholz

Matteo Stagni

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