Three-dimensional reconstruction image generation of traditional Chinese painting elements

This paper presents a comprehensive pipeline for generating detailed three-dimensional (3D) models from single images of traditional Chinese painting elements. This task is particularly challenging due to the lack of 3D datasets for Chinese paintings and the limited research on their 3D reconstruction. As a result, direct access to multiple views is precluded. We propose a novel method for the 3D reconstruction of Traditional Chinese Painting Elements, termed TCPE-3D, which has three components of the process. The first component is a multi-view synthesis module named One To Six (OTX) - Multi-View Generating (MVG) Module. This module creates six fixed-view images through a series of preprocessing steps. These images are used to generate the Local Light Field Fusion (LLFF) dataset within the Neural Radiance Fields (NeRF) synthesis module. This process leads to the creation of detailed mesh structures in the final Mesh Generation module. Comparison with several state-of-the-art 3D reconstruction methods shows that our framework achieves better visualization results and higher technical specifications. Additionally, it solves the Janus problem encountered by other algorithms for Chinese painting data. Our dataset is available at https://github.com/LPDLG/3DTCP-Dataset.