Scalable multi-view stereo using CMA-ES and distance transform-based depth map refinement

Nirmal S. Nair; Madhu S Nair

doi:10.1117/12.2587241

4 January 2021 Scalable multi-view stereo using CMA-ES and distance transform-based depth map refinement

Nirmal S. Nair, Madhu S Nair

Author Affiliations +

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050Q (2021) https://doi.org/10.1117/12.2587241
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Recovering three-dimensional structure from images is a long-standing ill-posed inverse problem in computer vision. This paper presents a simple and highly scalable method to reconstruct dense 3D point cloud from multi-view images by estimating per-pixel depth using an evolutionary computation technique – CMA-ES. The proposed method uses ZNCCbased template matching to reconstruct fine details of textured regions and DAISY-based feature matching to reconstruct smooth surface of homogeneous regions. We handle the problem of reconstructing large homogeneous regions using distance transform-based adaptive median filtering. The proposed method is highly scalable since pixels are processed independently at all stages of reconstruction – depth map estimation, refinement, and fusion. This enables the proposed method to be parallelized at the pixel-level, unlike most existing methods that can only be parallelized at the image-level. Experimental results on Middlebury benchmark dataset demonstrate the robustness and efficacy of the proposed method in reconstructing textured as well as homogeneous regions.

Citation Download Citation

Nirmal S. Nair and Madhu S Nair "Scalable multi-view stereo using CMA-ES and distance transform-based depth map refinement", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050Q (4 January 2021); https://doi.org/10.1117/12.2587241

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE