1,790 research outputs found
Building Footprint Generation Using Improved Generative Adversarial Networks
Building footprint information is an essential ingredient for 3-D
reconstruction of urban models. The automatic generation of building footprints
from satellite images presents a considerable challenge due to the complexity
of building shapes. In this work, we have proposed improved generative
adversarial networks (GANs) for the automatic generation of building footprints
from satellite images. We used a conditional GAN with a cost function derived
from the Wasserstein distance and added a gradient penalty term. The achieved
results indicated that the proposed method can significantly improve the
quality of building footprint generation compared to conditional generative
adversarial networks, the U-Net, and other networks. In addition, our method
nearly removes all hyperparameters tuning.Comment: 5 page
Pal-GAN: Palette-conditioned Generative Adversarial Networks
Recent advances in Generative Adversarial Networks (GANs) have shown great progress on a large variety of tasks. A common technique used to yield greater diversity of samples is conditioning on class labels. Conditioning on high-dimensional structured or unstructured information has also been shown to improve generation results, e.g. Image-to-Image translation. The conditioning information is provided in the form of human annotations, which can be expensive and difficult to obtain in cases where domain knowledge experts are needed. In this paper, we present an alternative: conditioning on low-dimensional structured information that can be automatically extracted from the input without the need for human annotators. Specifically, we propose a Palette-conditioned Generative Adversarial Network (Pal-GAN), an architecture-agnostic model that conditions on both a colour palette and a segmentation mask for high quality image synthesis. We show improvements on conditional consistency, intersection-over-union, and Fréchet inception distance scores. Additionally, we show that sampling colour palettes significantly changes the style of the generated images
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In this work, we generate 2048x1024 visually appealing results
with a novel adversarial loss, as well as new multi-scale generator and
discriminator architectures. Furthermore, we extend our framework to
interactive visual manipulation with two additional features. First, we
incorporate object instance segmentation information, which enables object
manipulations such as removing/adding objects and changing the object category.
Second, we propose a method to generate diverse results given the same input,
allowing users to edit the object appearance interactively. Human opinion
studies demonstrate that our method significantly outperforms existing methods,
advancing both the quality and the resolution of deep image synthesis and
editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example
Poly-GAN: Regularizing Polygons with Generative Adversarial Networks
Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features predicted by Machine Learning (ML) algorithms in the GIScience domain, where information overload remains a significant problem in many cartographic/LBS applications. It addresses the limitations of traditional cartographic regularization/generalization algorithms, which can struggle with producing both accurate and minimal representations of multisided built environment objects. Furthermore, future work proposes a way to test the method on even more complex object shapes to address this limitation
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Self-Organizing Floor Plans
© 2021 by the author(s). The editorial is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode)This article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers. The first part of this work contextualizes self-organizing floor plans in architecture and computational design, highlighting their importance and potential for designers as well as software developers. The central part discusses some of the most common techniques with concrete examples, including Neuro Evolution of Augmenting Topologies (NEAT) and Generative Adversarial Networks (GAN). The final section of the article provides some general comments considering pitfalls and possible future developments, as well as speculating on the future of this trend.Peer reviewe
Shape Generation using Spatially Partitioned Point Clouds
We propose a method to generate 3D shapes using point clouds. Given a
point-cloud representation of a 3D shape, our method builds a kd-tree to
spatially partition the points. This orders them consistently across all
shapes, resulting in reasonably good correspondences across all shapes. We then
use PCA analysis to derive a linear shape basis across the spatially
partitioned points, and optimize the point ordering by iteratively minimizing
the PCA reconstruction error. Even with the spatial sorting, the point clouds
are inherently noisy and the resulting distribution over the shape coefficients
can be highly multi-modal. We propose to use the expressive power of neural
networks to learn a distribution over the shape coefficients in a
generative-adversarial framework. Compared to 3D shape generative models
trained on voxel-representations, our point-based method is considerably more
light-weight and scalable, with little loss of quality. It also outperforms
simpler linear factor models such as Probabilistic PCA, both qualitatively and
quantitatively, on a number of categories from the ShapeNet dataset.
Furthermore, our method can easily incorporate other point attributes such as
normal and color information, an additional advantage over voxel-based
representations.Comment: To appear at BMVC 201
Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation
The integration of information acquired with different modalities, spatial
resolution and spectral bands has shown to improve predictive accuracies. Data
fusion is therefore one of the key challenges in remote sensing. Most prior
work focusing on multi-modal fusion, assumes that modalities are always
available during inference. This assumption limits the applications of
multi-modal models since in practice the data collection process is likely to
generate data with missing, incomplete or corrupted modalities. In this paper,
we show that Generative Adversarial Networks can be effectively used to
overcome the problems that arise when modalities are missing or incomplete.
Focusing on semantic segmentation of building footprints with missing
modalities, our approach achieves an improvement of about 2% on the
Intersection over Union (IoU) against the same network that relies only on the
available modality
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
We describe the workflow of a digital surface models (DSMs) refinement
algorithm using a hybrid conditional generative adversarial network (cGAN)
where the generative part consists of two parallel networks merged at the last
stage forming a WNet architecture. The inputs to the so-called WNet-cGAN are
stereo DSMs and panchromatic (PAN) half-meter resolution satellite images.
Fusing these helps to propagate fine detailed information from a spectral image
and complete the missing 3D knowledge from a stereo DSM about building shapes.
Besides, it refines the building outlines and edges making them more
rectangular and sharp
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