A neural network for simultaneously reconstructing transparent and opaque surfaces

Mohamad Ivan Fanany, Itsuo Kumazawa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a neural network (NN) to recover three-dimensional (3D) shape of an object from its multiple view images. The object may contain non-overlapping transparent and opaque surfaces. The challenge is to simultaneously reconstruct the transparent and opaque surfaces given only a limited number of views. By minimizing the pixel error between the output images of this NN and teacher images, we want to refine vertices position of an initial 3D polyhedron model to approximate the true shape of the object. For that purpose, we incorporate a ray tracing formulation into our NN's mapping and learning. At the implementation stage, we develop a practical regularization learning method using texture mapping instead of ray tracing. By choosing an appropriate regularization parameter and optimizing using hierarchical learning and annealing strategies, our NN gives more approximate shape.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - Third International Conference, ICIAR 2006, Proceedings
PublisherSpringer Verlag
Pages157-168
Number of pages12
ISBN (Print)3540448942, 9783540448945
Publication statusPublished - 1 Jan 2006
Event3rd International Conference on Image Analysis and Recognition, ICIAR 2006 - Povoa de Varzim, Portugal
Duration: 18 Sep 200620 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4142 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Image Analysis and Recognition, ICIAR 2006
Country/TerritoryPortugal
CityPovoa de Varzim
Period18/09/0620/09/06

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