DOI: 10.7763/IJCTE.2026.V18.1393
Self Determining Structure Model for Depth Map Completion
2. Data and AI Department, Nucleo Digital School, Madrid, Spain
Email: vanel.lazcano@umayor.cl (V.L.); anthony.cho@umayor.cl (A.D.C.); felipecalderero@gmail.com (F.C.)
*Corresponding author
Manuscript received January 7, 2025; revised March 18, 2025; accepted November 7, 2025; published May 15, 2026
Abstract—In this paper, we present a comprehensive approach for depth map completion using a variable pipeline model. The pipeline is designed to be flexible, and its structure is dynamically determined during the training phase by minimizing a fitness function. To achieve this objective, we used the Particle Swarm Optimization (PSO) technique, which efficiently explores the solution space to optimize key parameters. The pipeline itself consists of three main stages: a convolutional stage (Lab Convolution), an Interpolation Model (IM), and a post-Convolutional Stage (SC2). During training, our model automatically adjusts several aspects, including the selection of filter parameters, interpolator settings, and the number of filters used at each stage. Moreover, the pipeline is capable of interchanging the execution order between the stages, either processing in the sequence of Lab Convolution-IM-SC2 or SC2-IM-Lab Convolution. This flexible structure allows for the discovery of better configurations that significantly improve the performance of depth map completion tasks. Our approach demonstrates superior results by adapting to the specific characteristics of the input data.
Keywords—variable pipeline, particle swarm optimization, depth map completion
Cite: Vanel Lazcano, Anthony D. Cho, and Felipe Calderero, "Self Determining Structure Model for Depth Map Completion," International Journal of Computer Theory and Engineering, vol. 18, no. 2, pp. 110-117, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).