Paper Info
Title
Localized scene interpretation from 3D models, range, and optical data
Abstract
How an object appears in an image is determined in part by interactions with other objects in the scene. Occlusion is the most obvious from of interaction. Here we present a system which uses 3D CAD models in combination with optical and range data to recognize partially occluded objects. Recognition uses a hypothesize, perturb, render, and match cycle to arrive at a scene-optimized prediction of model appearance. This final scene-optimized prediction is based upon an iterative search algorithm converging to the optimal 3D pose of the object. During recognition, evidence of terrain occlusion in range imagery is mapped through the model into the optical imagery in order to explain the absence of model features. A similar process predicts the structure of occluding contours. Highly occluded military vehicles are successfully matched using this approach.
Year
DOI
Venue
2000
10.1006/cviu.2000.0821
Computer Vision and Image Understanding
Keywords
Field
DocType
localized scene interpretation,optical data,object recognition,search algorithm
CAD,Computer vision,3D optical data storage,Occlusion,Pattern recognition,Iterative search,Terrain,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
80
2
Computer Vision and Image Understanding
Citations 
PageRank 
References 
3
0.43
11
Authors
2
Name
Order
Citations
PageRank
Mark R. Stevens1758.93
J. Ross Beveridge217711.30