296 citations · 1.4k across the 42 of their papers we have counts for
6 papers · 1 filter
Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds
Yujia Liu, Anton Obukhov, Jan Dirk Wegner +1
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision, graphics, and machine learning; it saves the desi…
Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields
Alexander Becker, Rodrigo Caye Daudt, Dominik Narnhofer +4
Recent approaches to arbitrary-scale single image super-resolution (ASR) use neural fields to represent continuous signals that can be sampled at arbitrary resolutions. However, po…
Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain
Yuchang Jiang, Marius Rüetschi, Vivien Sainte Fare Garnot +4
Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide…
Uncertainty Voting Ensemble for Imbalanced Deep Regression
Yuchang Jiang, Vivien Sainte Fare Garnot, Konrad Schindler +1
Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by…
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt +2
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to…
An evaluation of deep learning models for predicting water depth evolution in urban floods
Stefania Russo, Nathanaël Perraudin, Steven Stalder +4
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water…