Joint Texture and Geometry Optimization for RGB-D Reconstruction

Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping results. Most of the 3D reconstruction methods focus on either geometry refinement or...

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Bibliographic Details
Main Authors: Fu, Yanping, Yan, Qingan, Liao, Jie, Xiao, Chunxia
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Color
Geometry
Image reconstruction
Optimization
Solid modeling
Three-dimensional displays
Online Access:Request full text
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Summary:Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping results. Most of the 3D reconstruction methods focus on either geometry refinement or texture improvement respectively, which subjectively decouples the inter-relationship between geometry and texture. In this paper, we propose a novel approach that can jointly optimize the camera poses, texture and geometry of the reconstructed model, and color consistency between the key-frames. Instead of computing Shape-From-Shading (SFS) expensively, our method directly optimizes the reconstructed mesh according to color and geometric consistency and high-boost normal cues, which can effectively overcome the texture-copy problem generated by SFS and achieve more detailed shape reconstruction. As the joint optimization involves multiple correlated terms, therefore, we further introduce an iterative framework to interleave the optimal state. The experiments demonstrate that our method can recover not only fine-scale geometry but also high-fidelity texture.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00599