3D Object Reconstruction using Multi-View Calibrated Images

In this study, two models are proposed, one is a visual hull model and another one is a 3D object reconstruction model. The proposed visual hull model, which is based on bounding edge representation, obtains high time performance which makes it to be one of the best methods. The main contribution of the proposed visual hull model is to provide bounding surfaces over the bounding edges, which results a complete triangular surface mesh. Moreover, the proposed visual hull model can be computed over the camera networks distributedly. The second model is a depth map based 3D object reconstruction model which results a watertight triangular surface mesh. The proposed model produces the result with acceptable accuracy as well as high completeness, only using stereo matching and triangulation. The contribution of this model is to playing with the 3D points to find the best reliable ones and fitting a surface over them

An Iterative Surface Evolution Algorithm for Multiview Stereo

We propose a new iterative surface evolution algorithm for multiview stereo. Starting from an embedding space such as the visual hull, we will first conduct robust 3D depth estimation (represented as 3D points) based on image correlation. A fast implicit distance function-based region growing method is then employed to extract an initial shape estimation based on these 3D points. Next, an explicit surface evolution will be conducted to recover the finer geometry details of the recovered shape. The recovered shape will be further improved by several iterations between depth estimation and shape reconstruction, similar to the Expectation Maximization (EM) approach. The experiments on the benchmark datasets show that our algorithm can obtain high-quality reconstruction results that are comparable with the state-of-art methods, with considerable less computational time and complexity.</p