A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Model Acquisition from Extended Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Frame Decimation for Structure and Motion
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Amodal volume completion: 3D visual completion
Computer Vision and Image Understanding
Parsing Images into Regions, Curves, and Curve Groups
International Journal of Computer Vision
3D target recognition using cooperative feature map binding under Markov Chain Monte Carlo
Pattern Recognition Letters
Amodal volume completion: 3D visual completion
Computer Vision and Image Understanding
Extraction and integration of window in a 3D building model from ground view images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper investigates the use of an implicit prior in Bayesian Model-based 3D reconstruction of architecture from image sequences. In our previous work architecture is represented as a combination of basic primitives such as windows and doors etc, each with their own prior. The contribution of this work is to Provide a global prior for the spatial organization of the basic primitives. However, it is difficult ot explicitly formulate the prior on spatial organization. Instead we define an implicit representation that favours global regularities prevalent in architeture (e.g. windows lie in rows etc.). Specifying exact parameter values for this prior is problematic at best, however it is demonstrated that for a broad range of values the prior provides reasonable results. The validity of the prior is tested visually by generating synthetic buildings as draws from the prior simulated using MCMC. The result is a fully Bayesian method for structure from motion in the domain of architecture.