Depth Layers from Occlusions

by Arno Schödl and Irfan Essa

We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships between scene parts. Our approach does not rely on any strong assumptions about the object or the scene to aid in this segmentation into layers. In general, the problem of building relative depth relationships from occlusion events is underconstrained, even in the absence of observation noise. A minimum description length algorithm is used to reliably calculate layer opacities and their depth relationships in the absence of hard constraints. Our approach extends previously published approaches that are restricted to work with a certain type of moving object or require strong image edges to allow for an a-priori segmentation of the scene. We also discuss ideas on how to extend our algorithm to make use of a richer set of observations.

	Arno Schödl and Irfan A. Essa. Depth layers from occlusions. In IEEE Computer Vision and Pattern Recognition, pages I:339-644, 2001.
	The CVPR 2001 poster.