CMU talk: 3-D Point Cloud Classification with Max-Margin Markov Networks

Speaker: Daniel Munoz (RI@CMU)
Venue: NSH 1507
Date: Monday, December 1, 2008

Title: 3-D Point Cloud Classification with Max-Margin Markov Networks

Abstract:
Point clouds extracted from laser range finders are hard to classify due to variable and noisy returns due to pose, occlusions, surface reflectance, and sensor type. Conditional Random Fields (CRFs) is a popular framework for performing contextual classification that produce improved and "smooth" classification over local classifiers. In this talk, I will present some recent extensions to the max-margin CRF model from Taskar et al. 2004 that is used in this application.