High Quality Texture Mapping Process Aimed at the Optimization of 3d Structured Light Models

Abstract

<p><strong>Abstract.</strong> This article presents the evaluation of a pipeline to develop a high-quality texture mapping implementation which makes it possible to carry out a semantic high-quality 3D textured model. Due to geometric errors such as camera parameters or limited image resolution or varying environmental parameters, the calculation of a surface texture from 2D images could present several color errors. And, sometimes, it needs adjustments to the RGB or lightness information on a defined part of the texture. The texture mapping procedure is composed of mesh parameterization, mesh partitioning, mesh segmentation unwraps, UV map and projection of island, UV layout optimization, mesh packing and mesh baking. The study focuses attention to the mesh partitioning that essentially assigns a weight to each mesh, which reveals a mesh’s weight calculated by considering the flatness and distance of the mesh with respect to a chart. The 3D texture mapping has been developed in <i>Blender</i> and implemented in <i>Python</i>. In this paper we present a flowchart that resumes the procedure which aims to achieve a high-quality mesh and texture 3D model starting from the 3D <i>Spider</i> acquire, integrated with the <i>SfM</i> texture and using the texture mapping to reduce the color errors according to a semantic interpretation.</p>

Linear target change detection from a single image based on three‐dimensional real scene

“…Change detection in the 2D-2D domain can be categorized based on the granularity of the analysis into pixel-, object-and scene-level approaches. However, the reliability of texture information is compromised of environmental variables such as lighting, seasonal shifts and weather conditions (Inzerillo et al, 2019). As image resolution increases, variations in imaging perspectives exacerbate issues with perspective deformation (Xu et al, 2016).…”

The survey of the precious one of Constance. Tools and techniques for three-dimensional restitution of complex surfaces at sub-millimetre resolution

“…This can, of course, also be influenced by the surface and morphological characteristics of the object, as well as the boundary conditions and the acquiring operator. In this regard, some research focuses on correct acquisition processes, proposing data verifications [12][13][14], innovative processes, and improving the texture of the final model [15][16][17].…”

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

“…The second method segments the mesh surface and places image textures into each segment. The surface is segmented using criteria such as similar area size, angle preservation between directions in the same region, and segmentation that allows the least deformation of each surface [113][114][115][116]. For each segmented surface, a textured image is projected [117] and different deformations applied [118], so it fits best onto the surface.…”