Depth Map Building and Enhancement using a Monocular Camera, Object Shape Priors and Variational Methods

Authors

  • Andres Alejandro Diaz Toro Universidad del Valle
  • Eduardo Francisco Caicedo Bravo Universidad del Valle
  • Lina María Paz Pérez Intel corporation
  • Pedro Piniés Rodríguez Intel Corporarion

DOI:

https://doi.org/10.13053/cys-24-2-3021

Keywords:

Dense mapping, shape priors, variational methods, primal-dual algorithm, depth integration, depth denoising

Abstract

We present a monocular system that uses shape priors for improving the quality of estimated depth maps, specially in the region of an object of interest, when the environment presents complex conditions like changes in light, with low-textured, very reflective and translucent objects. A depth map is built by solving a non-convex optimization problem using the primal-dual algorithm and a coupling term. The energy functional consists of a photometric term for a set of images with common elements in the scene and a regularization term that allows smooth solutions. The camera is moved by hand and tracked using ORB-SLAM2. The resulting depth map is enhanced by integrating, with a novel variational formulation, depth data coming from the 3D model that best fits to observed data, optimized w.r.t. shape, pose and scale (shape prior). We also present an alternative algorithm that simultane ously builds a depth map and integrates a previously estimated shape prior. We quantify the improvements in accuracy and in noise reduction of the final depth map.

Author Biographies

Andres Alejandro Diaz Toro, Universidad del Valle

Department of Electrical and Electronic EngineeringPh.D student

Eduardo Francisco Caicedo Bravo, Universidad del Valle

Department of Electrical and Electronic EngineeringProfessor and Researcher 

Lina María Paz Pérez, Intel corporation

Researcher at Intel Corporation  

Pedro Piniés Rodríguez, Intel Corporarion

Researcher at Intel Corporarion

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Published

2020-06-23

Issue

Vol. 24 No. 2 (2020): Thematic Issue on Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez)

Section

Articles

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