3D Facade Reconstruction Using the Fusion of Images and LiDAR: A Review

Xu, Haotian; Chen, Chia‐Yen

doi:10.1007/978-981-13-9190-3_18

Cited by 1 publication

(1 citation statement)

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“…The technologies used to reconstruct a 3D model are typically based on RGB cameras [ 7 ], RGB-D cameras [ 8 , 9 ], and light detection and ranging (LiDAR) [ 10 , 11 ]. Point clouds from LiDAR sensors contain quite precise depth information, but suffer from problems such as occlusions, sparsity, and noise, and images from RGB cameras provide color and high-resolution data, but no depth information [ 12 ]. Nonetheless, the fusion of LiDAR point clouds and RGB images can achieve better 3D reconstruction results than a single data modality [ 13 ] and, for this reason, is often used to create textured 3D models.…”

Section: Introductionmentioning

confidence: 99%

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Dal’Col

Coelho

Madeira

et al. 2023

Sensors

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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.

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