Self-correction of 3D reconstruction from multi-view stereo images

We present a self-correction approach to improving the 3D reconstruction of a multi-view 3D photogrammetry system. The self-correction approach has been able to repair the reconstructed 3D surface damaged by depth discontinuities. Due to self-occlusion, multi-view range images have to be acquired and integrated into a watertight nonredundant mesh model in order to cover the extended surface of an imaged object. The integrated surface often suffers from “dent” artifacts produced by depth discontinuities in the multi-view range images. In this paper we propose a novel approach to correcting the 3D integrated surface such that the dent artifacts can be repaired automatically. We show examples of 3D reconstruction to demonstrate the improvement that can be achieved by the self-correction approach. This self-correction approach can be extended to integrate range images obtained from alternative range capture devices

Dictionary Learning-based Inpainting on Triangular Meshes

The problem of inpainting consists of filling missing or damaged regions in images and videos in such a way that the filling pattern does not produce artifacts that deviate from the original data. In addition to restoring the missing data, the inpainting technique can also be used to remove undesired objects. In this work, we address the problem of inpainting on surfaces through a new method based on dictionary learning and sparse coding. Our method learns the dictionary through the subdivision of the mesh into patches and rebuilds the mesh via a method of reconstruction inspired by the Non-local Means method on the computed sparse codes. One of the advantages of our method is that it is capable of filling the missing regions and simultaneously removes noise and enhances important features of the mesh. Moreover, the inpainting result is globally coherent as the representation based on the dictionaries captures all the geometric information in the transformed domain. We present two variations of the method: a direct one, in which the model is reconstructed and restored directly from the representation in the transformed domain and a second one, adaptive, in which the missing regions are recreated iteratively through the successive propagation of the sparse code computed in the hole boundaries, which guides the local reconstructions. The second method produces better results for large regions because the sparse codes of the patches are adapted according to the sparse codes of the boundary patches. Finally, we present and analyze experimental results that demonstrate the performance of our method compared to the literature

Structured light in the digital reconstruction of architectural details

[EN] The interest in cataloguing historical heritage has involved the growing development of techniques of three-dimensional scanning in recent years. The present work introduces the technique of structured light as a digitalization method that despite its limitations provides a powerful tool for surveying architectural details. The results found with structured light in the architectural survey of the Hospital Real of the Universidad de Granada (Spain) have been satisfactory as a substitute or a complement to the laser scanner and digital photography.[ES] El interés en la catalogación del patrimonio histórico ha supuesto un creciente desarrollo de las técnicas de escaneado en tres dimensiones en los últimos años. En el presente trabajo se introduce la técnica de luz estructurada como un método de digitalización que, a pesar de sus limitaciones, es una potente herramienta para el levantamiento de detalles arquitectónicos. Los resultados obtenidos con la luz estructurada en el levantamiento arquitectónico del Hospital Real de la Universidad de Granada han sido satisfactorios como sustitutivo o complemento al escáner láser y la fotogrametría digital.León Robles, C.; Reinoso Gordo, JF.; Mataix Sanjuán, J. (2018). Luz estructurada en la reconstrucción digital de detalles arquitectónicos. EGA. Revista de Expresión Gráfica Arquitectónica. 23(32):198-207. doi:10.4995/ega.2018.9810SWORD198207233