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Exploiting uncertainty in regression forests for accurate camera relocalization
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure. In these methods, each tree associates one pixel with a point in the scene's 3D world coordinate frame. In previous work, these predictions were point estimates and the subsequent camera pose optimization implicitly assumed an isotropic distribution of these estimates. In this paper, we train a regression forest to predict mixtures of anisotropic 3D Gaussians and
doi:10.1109/cvpr.2015.7299069
dblp:conf/cvpr/ValentinNSFIT15
fatcat:rihvn3n4jjfebaihlccqif7wqq