3D Non-Rigid Reconstruction with Prior Shape Constraints

3D non-rigid shape recovery from a single uncalibrated camera is a challenging, under-constrained problem in computer vision. Although tremendous progress has been achieved towards solving the problem, two main limitations still exist in most previous solutions. First, current methods focus on non-incremental solutions, that is, the algorithms require collection of all the measurement data before the reconstruction takes place. This methodology is inherently unsuitable for applications requiring real-time solutions. At the same time, most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. Note that specific types of shape variation might be governed by only a small number of parameters and therefore can be well-represented in a low dimensional manifold. The methods proposed in this thesis aim to estimate the non-rigid shapes and the corresponding camera trajectories, based on both the observations and the prior learned manifold. Firstly, an incremental approach is proposed for estimating the deformable objects. An important advantage of this method is the ability to reconstruct the 3D shape from a newly observed image and update the parameters in 3D shape space. However, this recursive method assumes the deformable shapes only have small variations from a mean shape, thus is still not feasible for objects subject to large scale deformations. To address this problem, a series of approaches are proposed, all based on non-linear manifold learning techniques. Such manifold is used as a shape prior, with the reconstructed shapes constrained to lie within the manifold. Those non-linear manifold based approaches significantly improve the quality of reconstructed results and are well-adapted to different types of shapes undergoing significant and complex deformations. Throughout the thesis, methods are validated quantitatively on 2D points sequences projected from the 3D motion capture data for a ground truth comparison, and are qualitatively demonstrated on real example of 2D video sequences. Comparisons are made for the proposed methods against several state-of-the-art techniques, with results shown for a variety of challenging deformable objects. Extensive experiments also demonstrate the robustness of the proposed algorithms with respect to measurement noise and missing data

Shape basis interpretation for monocular deformable 3D reconstruction

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft

Fine-Scaled 3D Geometry Recovery from Single RGB Images

3D geometry recovery from single RGB images is a highly ill-posed and inherently ambiguous problem, which has been a challenging research topic in computer vision for several decades. When fine-scaled 3D geometry is required, the problem become even more difficult. 3D geometry recovery from single images has the objective of recovering geometric information from a single photograph of an object or a scene with multiple objects. The geometric information that is to be retrieved can be of different representations such as surface meshes, voxels, depth maps or 3D primitives, etc. In this thesis, we investigate fine-scaled 3D geometry recovery from single RGB images for three categories: facial wrinkles, indoor scenes and man-made objects. Since each category has its own particular features, styles and also variations in representation, we propose different strategies to handle different 3D geometry estimates respectively. We present a lightweight non-parametric method to generate wrinkles from monocular Kinect RGB images. The key lightweight feature of the method is that it can generate plausible wrinkles using exemplars from one high quality 3D face model with textures. The local geometric patches from the source could be copied to synthesize different wrinkles on the blendshapes of specific users in an offline stage. During online tracking, facial animations with high quality wrinkle details can be recovered in real-time as a linear combination of these personalized wrinkled blendshapes. We propose a fast-to-train two-streamed CNN with multi-scales, which predicts both dense depth map and depth gradient for single indoor scene images.The depth and depth gradient are then fused together into a more accurate and detailed depth map. We introduce a novel set loss over multiple related images. By regularizing the estimation between a common set of images, the network is less prone to overfitting and achieves better accuracy than competing methods. Fine-scaled 3D point cloud could be produced by re-projection to 3D using the known camera parameters. To handle highly structured man-made objects, we introduce a novel neural network architecture for 3D shape recovering from a single image. We develop a convolutional encoder to map a given image to a compact code. Then an associated recursive decoder maps this code back to a full hierarchy, resulting a set of bounding boxes to represent the estimated shape. Finally, we train a second network to predict the fine-scaled geometry in each bounding box at voxel level. The per-box volumes are then embedded into a global one, and from which we reconstruct the final meshed model. Experiments on a variety of datasets show that our approaches can estimate fine-scaled geometry from single RGB images for each category successfully, and surpass state-of-the-art performance in recovering faithful 3D local details with high resolution mesh surface or point cloud

Adaptive control of large space structures using recursive lattice filters

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance