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In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape...
We investigate the problem of estimating the dense 3D shape of an object, given a set of 2D landmarks and silhouette in a single image. An obvious prior to employ in such a problem is a dictionary of dense CAD models. Employing a sufficiently large enough dictionary of CAD models, however, is in general computationally infeasible. A common strategy in dictionary learning to encourage generalization...
We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors. To this end, we tackle the problem of non-rigid registration by level set evolution without explicit correspondence search. Given a pair of signed distance fields (SDFs) representing the shapes of interest, we estimate a dense deformation field...
The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied in the wild, their 3D estimates are either unstable and change for different photos...
Highly effective optimization frameworks have been developed for traditional multiview stereo relying on lambertian photoconsistency. However, they do not account for complex material properties. On the other hand, recent works have explored PDE invariants for shape recovery with complex BRDFs, but they have not been incorporated into robust numerical optimization frameworks. We present a variational...
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense...
We present a method for the fast 3D face reconstruction of people wearing glasses. Our method explicitly and robustly models the case in which a face to be reconstructed is partially occluded by glasses. We propose a simple and generic model for glasses that copes with a wide variety of different shapes, colors and styles, without the need for any database or learning. Our algorithm is simple, fast...
We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different...
The Gromov-Hausdorff (GH) distance is traditionally used for measuring distances between metric spaces. It was adapted for non-rigid shape comparison and matching of isometric surfaces, and is defined as the minimal distortion of embedding one surface into the other, while the optimal correspondence can be described as the map that minimizes this distortion. Solving such a minimization is a hard combinatorial...
We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from the original, that could have generated the same samples. In other words, the error can be arbitrarily large. Motivated by this, we prove that, for...
Three-dimensional surface reconstruction based on photometric stereo requires accurate positioning of the light source or estimation of the lighting parameters, which increases the complexity of the operation in experiments. Moreover, the actual light environment is composed of many kinds of complex optical components, and the accurate lighting parameters cannot be measured. In this paper, a reference...
Fragment reconstruction aims to restore broken images and documents via matching spatial adjacent fragments. As the existing solutions in the literature still remain problematic, we present a novel feature descriptor, Normal Direction Local Binary Pattern (termed as ND-LBP), for document/image fragment matching. ND-LBP is based on the conventional LBP descriptor, however, it outstands LBP by introducing...
The through-the-wall inverse scattering problem is analyzed via the linear sampling method. The reciprocity gap formulation of the linear sampling method is used to reconstruct an object within a closed-off room. In order to apply this method, a numerical method to model a point source is needed, along with knowledge of how the numerical fundamental solution differs from the analytic one. Application...
Statistical craniofacial reconstruction methods have become an important research aspect in computer-aided craniofacial reconstruction. The existing joint statistical craniofacial reconstruction method based on PCA modeling is not scientific in terms of forensic anthropology, and its description ability for the shape correlation of skull and face is inadequate. So, partial least squares regression...
Identification of intended movement type and movement phase of hand grasp shaping are critical features for the control of volitional neuroprosthetics. We demonstrate that neural dynamics during visually-guided imagined grasp shaping can encode intended movement. We apply Procrustes analysis and LASSO regression to achieve 72% accuracy (chance = 25%) in distinguishing between visually-guided imagined...
We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in shape priors, which are learnt...
Can we reconstruct the entire internal shape of a room if all we can directly observe is a small portion of one internal wall, presumably through a window in the room? While conventional wisdom may indicate that this is not possible, motivated by recent work on ‘looking around corners’, we show that one can exploit light echoes to reconstruct the internal shape of hidden rooms. Existing techniques...
In this work, we leverage the reflection property explored in our experimental results and propose an efficient and effective algorithm for the reconstruction of the layout. The proposed algorithm identifies the reflective path, which is caused by obstacle, and evaluated by the difference of Angle of Arrival (AoA) at each transceiver deployed at the room. Then with the knowledge of AoA corresponding...
We introduce a novel segmentation method for time-lapse image stacks of neurites based on the co-segmentation principle. Our method aggregates information from multiple stacks to improve the segmentation task, using a neurite model and a tree similarity term. The neurite model takes into account branching characteristics, such as local shape smoothness and continuity, while the tree similarity term...
Restoration of shredded signals remains a relevant and significant challenge in archaeological and forensic efforts. In this work, we present a novel approach for reconstruction of shredded signals (including text documents and images) within a context of general multidimensional sparse signals. To this end, we present a generic efficient non-convex optimization method that employs iterative sparsity...
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