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Found 11 papers, 2 papers with code
PoNQ: a Neural QEM-based Mesh Representation
Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications.
PASOA- PArticle baSed Bayesian Optimal Adaptive design
This novel combination of stochastic optimization and tempered SMC allows to jointly handle design optimization and parameter inference.
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge.
BPNet: Bézier Primitive Segmentation on 3D Point Clouds
This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds.
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images.
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions.
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data.
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations.
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
The key points of the proposed approach are adapting the network to learn new as well as old classes on the new training data, and allowing it to remember the previously learned information for the old classes.
High-Resolution Semantic Labeling with Convolutional Neural Networks
We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties.
Recurrent Neural Networks to Correct Satellite Image Classification Maps
Instead, our goal is to directly learn the iterative process itself.
