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A Memory- and Accuracy-Aware Gaussian Parameter-Based Stereo Matching Using Confidence Measure

Accurate stereo matching requires a large amount of memory at a high bandwidth, which restricts its use in resource-limited systems such as mobile devices. This problem is compounded by the recent trend of applications requiring significantly high pixel resolution and disparity levels. To alleviate this, we present a memory-efficient and robust stereo matching algorithm. For cost aggregation, we employ the semiglobal parametric approach, which significantly reduces the memory bandwidth by representing the costs of all disparities as a Gaussian mixture model. All costs on multiple paths in an image are aggregated by updating the Gaussian parameters. The aggregation is performed during the scanning in the forward and backward directions. To reduce the amount of memory for the intermediate results during the forward scan, we suggest to store only the Gaussian parameters which contribute significantly to the final disparity selection. We also propose a method to enhance the overall procedure through a learning-based confidence measure. The random forest framework is used to train various features which are extracted from the cost and intensity profile. The experimental results on KITTI dataset show that the proposed method reduces the memory requirement to less than 3 percent of that of semiglobal matching (SGM) while providing a robust depth map compared to those of state-of-the-art SGM-based algorithms.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.6 June 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1845 - 1858

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

STEREO MATCHING
CONFIDENCE MEASURE
SEMIGLOBAL MATCHING
COST AGGREGATION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2019.2959613

Pernyataan Tanggungjawab

Yeongmin Lee, Chong-Min Kyung

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