NDLI: Multi-view Occlusion Reasoning for Probabilistic Silhouette-Based Dynamic Scene Reconstruction

Content Provider	Springer Nature Link
Author	Guan, Li Franco, Jean Sébastien Pollefeys, Marc
Copyright Year	2010
Abstract	In this paper, we present an algorithm to probabilistically estimate object shapes in a 3D dynamic scene using their silhouette information derived from multiple geometrically calibrated video camcorders. The scene is represented by a 3D volume. Every object in the scene is associated with a distinctive label to represent its existence at every voxel location. The label links together automatically-learned view-specific appearance models of the respective object, so as to avoid the photometric calibration of the cameras. Generative probabilistic sensor models can be derived by analyzing the dependencies between the sensor observations and object labels. Bayesian reasoning is then applied to achieve robust reconstruction against real-world environment challenges, such as lighting variations, changing background etc. Our main contribution is to explicitly model the visual occlusion process and show: (1) static objects (such as trees or lamp posts), as parts of the pre-learned background model, can be automatically recovered as a byproduct of the inference; (2) ambiguities due to inter-occlusion between multiple dynamic objects can be alleviated, and the final reconstruction quality is drastically improved. Several indoor and outdoor real-world datasets are evaluated to verify our framework.
Starting Page	283
Ending Page	303
Page Count	21
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	90
Issue Number	3
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2010-04-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-view 3D reconstruction Bayesian inference Graphical model Shape-from-silhouette Occlusion Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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