Accurate depth-map refinement by per-pixel plane fitting for stereo vision

Masashi Yokozuka, Kohji Tomita, Osamu Matsumoto, Atsuhiko Banno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper discusses the refinement of sparse and noisy depth-maps to improve stereo measurements. Our method functions as a post-filter for stereo measurements, to remove outliers and interpolate the depths of invalid pixels. Per-pixel plane fitting is employed to estimate the normals of an object's surface in a depth-map. These normals provide information regarding the interpolation of depth and the removal of outliers by evaluating the directions of surfaces. In our experiments, our method successfully reconstructed a dense and accurate geometry from a sparse and noisy depth-map, even where several dozen percent of pixels were outliers and only a few percent were from the original correct geometry. This result indicates a novel method of fast stereo measurement, because dense reconstruction can be performed without stereo matching for all pixels.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2807-2812
Number of pages6
ISBN (Electronic)9781509048472
DOIs
Publication statusPublished - 2016 Jan 1
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 2016 Dec 42016 Dec 8

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume0
ISSN (Print)1051-4651

Other

Other23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period16/12/416/12/8

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Accurate depth-map refinement by per-pixel plane fitting for stereo vision'. Together they form a unique fingerprint.

Cite this