116,883 research outputs found

    View Selection with Geometric Uncertainty Modeling

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    Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking,simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane G\mathcal{G} viewed from a parallel viewing plane S\mathcal{S} above it. Such instances commonly arise, for example, in aerial photography. Consider a world point gGg \in \mathcal{G} and its worst case reconstruction uncertainty ε(g,S)\varepsilon(g,\mathcal{S}) obtained by merging \emph{all} possible views of gg chosen from S\mathcal{S}. We first show that one can pick two views sps_p and sqs_q such that the uncertainty ε(g,{sp,sq})\varepsilon(g,\{s_p,s_q\}) obtained using only these two views is almost as good as (i.e. within a small constant factor of) ε(g,S)\varepsilon(g,\mathcal{S}). Next, we extend the result to the entire ground plane G\mathcal{G} and show that one can pick a small subset of SS\mathcal{S'} \subseteq \mathcal{S} (which grows only linearly with the area of G\mathcal{G}) and still obtain a constant factor approximation, for every point gGg \in \mathcal{G}, to the minimum worst case estimate obtained by merging all views in S\mathcal{S}. Finally, we present a multi-resolution view selection method which extends our techniques to non-planar scenes. We show that the method can produce rich and accurate dense reconstructions with a small number of views. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards

    Cross-View Image Synthesis using Conditional GANs

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    Learning to generate natural scenes has always been a challenging task in computer vision. It is even more painstaking when the generation is conditioned on images with drastically different views. This is mainly because understanding, corresponding, and transforming appearance and semantic information across the views is not trivial. In this paper, we attempt to solve the novel problem of cross-view image synthesis, aerial to street-view and vice versa, using conditional generative adversarial networks (cGAN). Two new architectures called Crossview Fork (X-Fork) and Crossview Sequential (X-Seq) are proposed to generate scenes with resolutions of 64x64 and 256x256 pixels. X-Fork architecture has a single discriminator and a single generator. The generator hallucinates both the image and its semantic segmentation in the target view. X-Seq architecture utilizes two cGANs. The first one generates the target image which is subsequently fed to the second cGAN for generating its corresponding semantic segmentation map. The feedback from the second cGAN helps the first cGAN generate sharper images. Both of our proposed architectures learn to generate natural images as well as their semantic segmentation maps. The proposed methods show that they are able to capture and maintain the true semantics of objects in source and target views better than the traditional image-to-image translation method which considers only the visual appearance of the scene. Extensive qualitative and quantitative evaluations support the effectiveness of our frameworks, compared to two state of the art methods, for natural scene generation across drastically different views.Comment: Accepted at CVPR 201

    UA1C1 Views - WKU Archives Photograph Collection

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    Views of Western Kentucky University and its founding institutions showing multiple buildings. Includes aerial photographs and maps
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