3D surface reconstruction from a single uncalibrated 2D image

This paper described a simple computation to reconstruct 3D surface using a single uncalibrated 2D image from a digital camera as an image acquisition device that also focused on fast processing. An object is placed on a table with black background for the digital camera to shoot an image of the object. Image segmentation methods are applied in order to obtain the shape of the object from silhouette. The concept Radon transform is adopted to generate sinograms of the object and it is then inverse Radon transform is used to construct 2D cross-section of the object layer by layer. Canny edge detection helps to get the outline of each cross-section and coordinate points are extracted forming 3D point cloud from the image slices. 3D surface of the object is then reconstructed using Delaunay triangulation to connect each point with another. The results obtained from this project are satisfying regarding the processing time with recognizable shape and also strengthened with considerably low percentage error in the calculation for all six objects used in the experiment

Implicit Meshes for Effective Silhouette Handling

Using silhouettes in uncontrolled environments typically requires handling occlusions as well as changing or cluttered backgrounds, which limits the applicability of most silhouette based methods. For the purpose of 3-D shape modeling, we show that representing generic 3-D surfaces as implicit surfaces lets us effectively address these issues. This desirable behavior is completely independent from the way the surface deformations are parame-trized. To show this, we demonstrate our technique in three very different cases: Modeling the deformations of a piece of paper represented by an ordinary triangulated mesh; reconstruction and tracking a person's shoulders whose deformations are expressed in terms of Dirichlet Free Form Deformations; reconstructing the shape of a human face parametrized in terms of a Principal Component Analysis mode

On Using Gait in Forensic Biometrics

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is our new differentiable parametric decoder that encapsulates image formation analytically based on a generative model. Our decoder takes as input a code vector with exactly defined semantic meaning that encodes detailed face pose, shape, expression, skin reflectance and scene illumination. Due to this new way of combining CNN-based with model-based face reconstruction, the CNN-based encoder learns to extract semantically meaningful parameters from a single monocular input image. For the first time, a CNN encoder and an expert-designed generative model can be trained end-to-end in an unsupervised manner, which renders training on very large (unlabeled) real world data feasible. The obtained reconstructions compare favorably to current state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13 page

In-loop Feature Tracking for Structure and Motion with Out-of-core Optimization

In this paper, a novel and approach for obtaining 3D models from video sequences captured with hand-held cameras is addressed. We define a pipeline that robustly deals with different types of sequences and acquiring devices. Our system follows a divide and conquer approach: after a frame decimation that pre-conditions the input sequence, the video is split into short-length clips. This allows to parallelize the reconstruction step which translates into a reduction in the amount of computational resources required. The short length of the clips allows an intensive search for the best solution at each step of reconstruction which robustifies the system. The process of feature tracking is embedded within the reconstruction loop for each clip as opposed to other approaches. A final registration step, merges all the processed clips to the same coordinate fram

Using 3D morphable models for face recognition in video

The 3D Morphable Face Model (3DMM) has been used for over a decade for creating 3D models from single images of faces. This model is based on a PCA model of the 3D shape and texture generated from a limited number of 3D scans. The goal of fitting a 3DMM to an image is to find the model coefficients, the lighting and other imaging variables from which we can remodel that image as accurately as possible. The model coefficients consist of texture and of shape descriptors, and can without further processing be used in verification and recognition experiments. Until now little research has been performed into the influence of the diverse parameters of the 3DMM on the recognition performance. In this paper we will introduce a Bayesian-based method for texture backmapping from multiple images. Using the information from multiple (non-frontal) views we construct a frontal view which can be used as input to 2D face recognition software. We also show how the number of triangles used in the fitting proces influences the recognition performance using the shape descriptors. The verification results of the 3DMM are compared to state-of-the-art 2D face recognition software on the MultiPIE dataset. The 2D FR software outperforms the Morphable Model, but the Morphable Model can be useful as a preprocesser to synthesize a frontal view from a non-frontal view and also combine images with multiple views to a single frontal view. We show results for this preprocessing technique by using an average face shape, a fitted face shape, with a MM texture, with the original texture and with a hybrid texture. The preprocessor has improved the verification results significantly on the dataset

On Using Gait Biometrics to Enhance Face Pose Estimation

Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so a process is required to handle the pose variation of the human head, change in illumination, and low frame rate of input image sequences. This has been achieved using scale invariant features and 3D models to determine the pose of the human subject. Then, a gait trajectory model is generated to obtain the correct the face region whilst handing the looming effect. In this way, we describe a new approach aimed to estimate accurate face pose. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face pose estimation process