Robust ego-motion estimation and 3d model refinement using depth based parallax model

We present an iterative algorithm for robustly estimating the egomotion and refining and updating a coarse, noisy and partial depth map using a depth based parallax model and brightness derivatives extracted from an image pair. Given a coarse, noisy and partial depth map acquired by a range-finder or obtained from a Digital Elevation Map (DEM), we first estimate the ego-motion by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth map estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. Instead of assuming a smooth parallax field or locally smooth depth models, we locally model the parallax magnitude using the depth map, formulate the problem as a generalized eigen-value analysis and obta...

• Type: Conference
• Year: 2004
• Where: ICIP
• Authors: Amit K. Agrawal, Rama Chellappa
• Keywords: Available Depth Map, Depth Based Parallax, Depth Estimates, Depth Map Estimate, ICIP 2004, Image Processing, Partial Depth Map