This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints.

We present a novel non-rigid reconstruction method using a moving RGB-D camera. Current approaches use only non-rigid part of the scene and completely ignore the rigid background. Non-rigid parts often lack sufficient geometric and photometric information for tracking large frame-to-frame motion. Our approach uses camera pose estimated from the rigid background for foreground tracking. This enables robust foreground tracking in situations where large frame-to-frame motion occurs. Moreover, we are proposing a multi-scale deformation graph which improves non-rigid tracking without compromising the quality of the reconstruction. We are also contributing a synthetic dataset which is made publically available for evaluating non-rigid reconstruction methods. The dataset provides frame-by-frame ground truth geometry of the scene, the camera trajectory, and masks for background foreground. Experimental results show that our approach is more robust in handling larger frame-to-frame motions and provides better reconstruction compared to state-of-the-art approaches.

This paper addresses the problem of 3D motion reconstruction from a series of 2D projections under low reconstructibility. Reconstructibility defines the accuracy of a 3D reconstruction from 2D projections given a particular trajectory basis, 3D point trajectory, and 3D camera center trajectory. Reconstructibility accuracy is inherently related to the correlation between point and camera trajectories. Poor correlation leads to good reconstruction, high correlation leads to poor reconstruction. Unfortunately, in most real-world situations involving non-rigid objects (e.g. bodies), camera and point motions are highly correlated (i.e., slow and smooth) resulting in poor reconstructibility. In this paper, we propose a novel approach for 3D motion reconstruction of non-rigid body motion in the presence of real-world camera motion. Specifically we: (i) propose the inclusion of a small number of keyframes in the video sequence from which 3D coordinates are inferred/estimated to circumvent ambiguities between point and camera motion, and (ii) employ a L1penalty term to enforce a sparsity constraint on the trajectory basis coefficients so as to ensure our reconstructions are consistent with the natural compressibility of human motion. We demonstrate impressive 3D motion reconstruction for 2D projection sequences with hitherto low reconstructibility.

In this paper we pursue the task of aligning an ensemble of images in an unsupervised manner. This task has been commonly referred to as “congealing” in literature. A form of congealing, using a least-squares criteria, has been recently demonstrated to have desirable properties over conventional congealing. Least-squares congealing can be viewed as an extension of the Lucas & Kanade (LK) image alignment algorithm. It is well understood that the alignment performance for the LK algorithm, when aligning a single image with another, is theoretically and empirically equivalent for additive and compositional warps. In this paper we: (i) demonstrate that this equivalence does not hold for the extended case of congealing, (ii) characterize the inherent drawbacks associated with least-squares congealing when dealing with large numbers of images, and (iii) propose a novel method for circumventing these limitations through the application of an inverse-compositional strategy that maintains the attractive properties of the original method while being able to handle very large numbers of images.

In this paper, we present an approach we refer to as "least squares congealing" which provides a solution to the problem of aligning an ensemble of images in an unsupervised manner. Our approach circumvents many of the limitations existing in the canonical "congealing" algorithm. Specifically, we present an algorithm that:- (i) is able to simultaneously, rather than sequentially, estimate warp parameter updates, (ii) exhibits fast convergence and (iii) requires no pre-defined step size. We present alignment results which show an improvement in performance for the removal of unwanted spatial variation when compared with the related work of Learned-Miller on two datasets, the MNIST hand written digit database and the MultiPIE face database.

A common problem that affects object alignment algorithms is when they have to deal with objects with unseen intra-class appearance variation. Several variants based on gradient-decent algorithms, such as the Lucas-Kanade (or forward-additive) and inverse-compositional algorithms, have been proposed to deal with this issue by solving for both alignment and appearance simultaneously. In [1], Baker and Matthews showed that without appearance variation, the inverse-compositional (IC) algorithm was theoretically and empirically equivalent to the forward-additive (FA) algorithm, whilst achieving significant improvement in computational efficiency. With appearance variation, it would be intuitive that a similar benefit of the IC algorithm would be experienced over the FA counterpart. However, to date no such comparison has been performed. In this paper we remedy this situation by performing such a comparison. In this comparison we show that the two algorithms are not equivalent due to the inclusion of the appearance variation parameters. Through a number of experiments on the MultiPIE face database, we show that we can gain greater refinement using the FA algorithm due to it being a truer solution than the IC approach.