Reconstruction of normal and albedo of convex Lambertian objects by solving ambiguity matrices using SVD and optimization method

Yujuan Sun, Muwei Jian, Xiaofeng Zhang, Junyu Dong, Linlin Shen, Beijing Chen

Research output: Journal PublicationArticlepeer-review

9 Citations (Scopus)

Abstract

Photometric stereo (PMS) can reconstruct shape and albedo of an object by using multiple images captured under varied illumination directions. However, PMS may fail if light intensity is varied across different images captured under different unknown lighting directions. This paper presents a method that can estimate shapes and albedo of inhomogeneous Lambertian objects with much less constrained lighting conditions, i.e. the illumination directions are unknown and there can be arbitrary combination of different light sources and ambient light; meanwhile the light intensity can be different in different images. By placing a reference object alongside an object, the ambiguous matrix produced by SVD can be estimated effectively. This matrix is then used to generate more accurate shape and albedo. The reconstructed results are further refined using an optimization algorithm. Both synthetic and real objects are used in our experiments and the results show the effectiveness of our method.

Original languageEnglish
Pages (from-to)95-104
Number of pages10
JournalNeurocomputing
Volume207
DOIs
Publication statusPublished - 26 Sep 2016
Externally publishedYes

Keywords

  • 3D Reconstruction
  • Ambiguity matrix
  • Photometric stereo

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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