Translator Disclaimer
Presentation
4 March 2019 High-quality texture reconstruction for three-dimensional multi-view imaging (Conference Presentation)
Author Affiliations +
Abstract
Three-dimensional (3D) model with high-quality texture is a powerful way to interact between virtual and reality 3D scene. It has significant applications in digital preservation of cultural heritage, virtual reality / augmented reality (VR/AR), medical imaging and other domains. High-quality texture reconstruction is one of the essential elements for 3D model. Due to the camera pose error and inaccurate model, current texture reconstruction methods could not eliminate the artifacts like blurring, ghosting and discontinuity. In this work, multi-view high-definition images are captured for texture mapping. Normal-weight, depth-weight and edge-weight parameter are introduced to evaluate texture color confidence, respectively. The normalized weight factors are multiplied to generate a comprehensive weight parameter. By taking a weighted average of projected texture images, discontinuity of texture can be smoothed to a large extent. For large misalignment, bidirectional similarity (BDS) function, which represent the structural similarity between two images, are utilized to improve the texture image. The energy function is composed of two terms. One is Euclidean distance between target texture image and merged texture image. The other is BDS between target texture image and original texture image. By minimizing the energy function, the texture image could generate small local displacement while retaining the original structural information. The target and merged images are optimized alternately, the target image is calculated by patch-match algorithm, and the merged image is derived from weighted average of target images. The method we proposed could produce seamless texture comparing with Markov random field (MRF) algorithm. The definition could be higher than the camera parameter optimization algorithm. The experimental results show that structural similarity (SSIM) between the reconstructed images and the ground truth is higher than traditional algorithms. When dealing with inaccurate models with less than 10,000 facets, the SSIM value could be doubled compared with current algorithms.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jiachen Wu, Liangcai Cao, and Guofan Jin "High-quality texture reconstruction for three-dimensional multi-view imaging (Conference Presentation)", Proc. SPIE 10943, Ultra-High-Definition Imaging Systems II, 1094307 (4 March 2019); https://doi.org/10.1117/12.2509615
PROCEEDINGS
PRESENTATION


SHARE
Advertisement
Advertisement
RELATED CONTENT

Augmented reality: past, present, future
Proceedings of SPIE (March 04 2013)
Augmented reality system
Proceedings of SPIE (September 07 2010)
Feature detector and descriptor for medical images
Proceedings of SPIE (March 27 2009)
Quality improving techniques for free-viewpoint DIBR
Proceedings of SPIE (February 24 2010)

Back to Top