Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manhattan World: Compass Direction from a Single Image by Bayesian Inference
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ACM SIGGRAPH 2005 Papers
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fast Automatic Single-View 3-d Reconstruction of Urban Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
A fast approximation of the bilateral filter using a signal processing approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Robust photometric invariant features from the color tensor
IEEE Transactions on Image Processing
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Using the Manhattan world assumption we propose a new method for global 2${\!}^{{1}}/{2}$D geometry estimation of indoor environments from single low quality RGB-D images. This method exploits both color and depth information at the same time and allows to obtain a full representation of an indoor scene from only a single shot of the Kinect sensor. The main novelty of our proposal is that it allows estimating geometry of a whole environment from a single Kinect RGB-D image and does not rely on complex optimization methods. This method performs robustly even in the conditions of low resolution, significant depth distortion, nonlinearity of depth accuracy and presence of noise.