The Skin of Venice: Automatic Facade Extraction from Point Clouds

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The representation of Arctic clouds and their phase distributions, i.e., the amount of ice and supercooled water, influences predictions of future Arctic warming. Therefore, it is essential that cloud phase is correctly captured by models in order to accur ...

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

The Arctic is one of the most rapidly warming regions of the globe. Low-level clouds and fog modify the energy transfer from and to space and play a key role in the observed strong Arctic surface warming, a phenomenon commonly termed "Arctic amplification" ...

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Appropriate waste and resource management are essential for a sustainable circular economy with reduced environmental impact. With critical resources, e-waste may serve as indirect raw material. For example, with NdFeB permanent magnets, Neodymium (Nd) and ...

Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that ...

The dataset contains point clouds from 3D scans, reconstructed 3D data, and statistical data analysis, which are intended as complementary material for the publication "AR-assisted timber drilling with smart retrofitted tools". ...

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Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardizati ...

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The Skin of Venice: Automatic Facade Extraction from Point Clouds

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Highly Hygroscopic Aerosols Facilitate Summer and Early‐Autumn Cloud Formation at Extremely Low Concentrations Over the Central Arctic Ocean

Simulations of primary and secondary ice production during an Arctic mixed-phase cloud case from the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) campaign

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

Multi-Source Fusion for Voxel-Based 7-DoF Grasping Pose Estimation

The role of secondary ice production in mixed-phase clouds

Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds

Brown carbon absorption in the Mediterranean basin from local and long-range transported biomass burning air masses

A Case Study on Drivers of the Isotopic Composition of Water Vapor at the Coast of East Antarctica

Surfactant-based enrichment of rare earth elements from NdFeB magnet e-waste: Optimisation of cloud formation and rare earths extraction

Incompleteness of graph neural networks for points clouds in three dimensions

Supplementary material for "AR-assisted timber drilling with smart retrofitted tools"

A kernel‐based change detection method to map shifts in phytoplankton communities measured by flow cytometry

Modeling Extreme Warm-Air Advection in the Arctic During Summer: The Effect of Mid-Latitude Pollution Inflow on Cloud Properties

On Block Prediction For Learning-Based Point Cloud Compression

Leveraging meteorological radars to investigate the influence of atmospheric dynamics on snowfall microphysics

Properties of Arctic liquid and mixed-phase clouds from shipborne Cloudnet observations during ACSE 2014

Aerosol absorption over the Aegean Sea under northern summer winds

Improved Representation of Clouds in the Atmospheric Component LMDZ6A of the IPSL-CM6A Earth System Model