Windowed Factorization and Merging

In this work, an online 3D reconstruction algorithm is proposed which attempts to solve the structure from motion problem for occluded and degenerate data. To deal with occlusion the temporal consistency of data within a limited window is used to compute local reconstructions. These local reconstructions are transformed and merged to obtain an estimation of the 3D object shape. The algorithm is shown to accurately reconstruct a rotating and translating artificial sphere and a rotating toy dinosaur from a video. The proposed algorithm (WIFAME) provides a versatile framework to deal with missing data in the structure from motion problem

Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment

With the increasing demands of applications in virtual reality such as 3D films, virtual Human-Machine Interactions and virtual agents, the analysis of 3D human face analysis is considered to be more and more important as a fundamental step for those virtual reality tasks. Due to information provided by an additional dimension, 3D facial reconstruction enables aforementioned tasks to be achieved with higher accuracy than those based on 2D facial analysis. The denser the 3D facial model is, the more information it could provide. However, most existing dense 3D facial reconstruction methods require complicated processing and high system cost. To this end, this paper presents a novel method that simplifies the process of dense 3D facial reconstruction by employing only one frame of depth data obtained with an off-the-shelf RGB-D sensor. The experiments showed competitive results with real world data

3D Face Reconstruction from Single 2D Image Using Distinctive Features

3D face reconstruction is considered to be a useful computer vision tool, though it is difficult to build. This paper proposes a 3D face reconstruction method, which is easy to implement and computationally efficient. It takes a single 2D image as input, and gives 3D reconstructed images as output. Our method primarily consists of three main steps: feature extraction, depth calculation, and creation of a 3D image from the processed image using a Basel face model (BFM). First, the features of a single 2D image are extracted using a two-step process. Before distinctive-features extraction, a face must be detected to confirm whether one is present in the input image or not. For this purpose, facial features like eyes, nose, and mouth are extracted. Then, distinctive features are mined by using scale-invariant feature transform (SIFT), which will be used for 3D face reconstruction at a later stage. Second step comprises of depth calculation, to assign the image a third dimension. Multivariate Gaussian distribution helps to find the third dimension, which is further tuned using shading cues that are obtained by the shape from shading (SFS) technique. Thirdly, the data obtained from the above two steps will be used to create a 3D image using BFM. The proposed method does not rely on multiple images, lightening the computation burden. Experiments were carried out on different 2D images to validate the proposed method and compared its performance to those of the latest approaches. Experiment results demonstrate that the proposed method is time efficient and robust in nature, and it outperformed all of the tested methods in terms of detail recovery and accuracy