STEREO DEPTH ESTIMATION FROM VIDEO SEQUENCE

Bob Böggemann

Department of Information and Computing Sciences, Utrecht University

Abstract

This report is about an experimentation project performed by Master student Bob Böggemann under supervision of dr. Robby T. Tan. The goal is to recover consistent dense depth maps from a video sequence of static scenes recorded by a freely moving camera. The framework used for depth estimation is mainly derived from (Zhang, Jia, Wong, & Bao, Consistent Depth Maps Recovery from a Video Sequence, June 2009) and has been modified to fit our purpose. It consists of a structure from motion technique to derive camera parameters, a Markov Random Field to model the disparity likelihood and smoothness constraints, a segmentation step to better handle texture-less regions and a bundle optimization step to obtain temporal coherence. The photo consistency and geometric coherence constraints assure that after the bundle optimization step the resulting depth map sequence will be just as fluent as the original input video. Finally, the framework is evaluated by performing experiments on several video sequences.

Report

Stereo depth estimation from video sequence [PDF]
Bob Böggemann


System Pipeline

Results

References