基于PP-Matting抠图和增量式SfM的三维重建方法

3D Reconstruction Method Based on PP-Matting and Incremental Structure-from-Motion

基于视觉的三维重建技术通过获取物体的真实图像来还原其三维模型。然而,这些获取的图像通常包含大量无用的背景信息,直接使用这样的图像进行三维重建将导致计算资源和存储空间的浪费。为了解决上述问题,本文提出了一种融合PP-Matting抠图和增量式SfM的三维重建方法,该方法在使用SfM和MVS算法完成三维重建之前,对物体的原始图像进行抠图。本文利用Distinctions-646等多个图像集对PP-Matting抠图模型进行微调训练,得到仅包含待重建物体的图像。实验结果表明,本文提出的方法在重建效率方面取得显著提升,并且能够降低存储空间需求。

Visual-based 3D reconstruction techniques aim to restore the three-dimensional models of objects by capturing their real images. However, these captured images often contain a significant amount of irrelevant background information, and directly using such images for 3D reconstruction results in wastage of computational resources and storage space. To address these issues, this paper pro-poses a three-dimensional reconstruction method that combines PP-Matting image matting and in-cremental Structure-from-Motion (SfM). The proposed method performs image matting on the original images of the objects before utilizing SfM and MVS algorithms for 3D reconstruction. The PP-Matting model is fine-tuned using multiple image datasets, including Distinctions-646, to obtain images that solely contain the objects to be reconstructed. Experimental results demonstrate that the proposed method significantly improves reconstruction efficiency and reduces storage space requirements.