text: Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: An automatic method for initial coarse reconstruction to initialize joint estimation; Sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to free-viewpoint rendering and virtual reality.
  @cite_44: In this paper, we present a method for extracting consistent foreground regions when multiple views of a scene are available. We propose a framework that automatically identifies such regions in images under the assumption that, in each image, background and foreground regions present different color properties. To achieve this task, monocular color information is not sufficient and we exploit the spatial consistency constraint that several image projections of the same space region must satisfy. Combining the monocular color consistency constraint with multiview spatial constraints allows us to automatically and simultaneously segment the foreground and background regions in multiview images. In contrast to standard background subtraction methods, the proposed approach does not require a priori knowledge of the background nor user interaction. Experimental results under realistic scenarios demonstrate the effectiveness of the method for multiple camera set ups.
  @cite_16: This paper introduces a statistical inference framework to temporally propagate trimap labels from sparsely defined key frames to estimate trimaps for the entire video sequence. Trimap is a fundamental requirement for digital image and video matting approaches. Statistical inference is coupled with Bayesian statistics to allow robust trimap labelling in the presence of shadows, illumination variation and overlap between the foreground and background appearance. Results demonstrate that trimaps are sufficiently accurate to allow high quality video matting using existing natural image matting algorithms. Quantitative evaluation against ground-truth demonstrates that the approach achieves accurate matte estimation with less amount of user interaction compared to the state-of-the-art techniques.
  @cite_20: This invention relates to a novel energy absorbing isolation device which will absorb and dissipate a major portion of the energy associated with vehicle collisions. The present invention comprises a cylindrical tube, housing a plurality of Belleville spring washers which are compressed on impact by the wide portion of a movable shaft having a relatively wide portion and a relatively narrow portion. The relatively narrow portion of the shaft advances axially into the cylindrical tube as the Belleville washers are compressed. The energy of impact is absorbed and dissipated by compression of the Belleville washers and by interactions between the washers, the inside surface of the cylindrical tube, and the narrow portion of the shaft.
  @cite_2: Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. Second, we show how other modalities such as depth may be seamlessly integrated in the model and benefit the segmentation. The paper exposes a detailed set of experiments used to validate the algorithm, showing results comparable with the state of art, with reduced computational complexity. We also discuss the use of different modalities for specific situations, such as dealing with a low number of viewpoints or a scene with color ambiguities between foreground and background.
summary: Many of the existing multi-view reconstruction approaches rely on a two-stage sequential pipeline where foreground or background segmentation is initially performed independently with respect to each camera, and then used as input to obtain visual hull for multi-view reconstruction. The problem with this approach is that the errors introduced at the segmentation stage cannot be recovered and are propagated to the reconstruction stage reducing the final reconstruction quality. Segmentation from multiple wide-baseline views has been proposed by exploiting appearance similarity @cite_2 @cite_44 @cite_20 . These approaches assume static backgrounds and different colour distributions for the foreground and background @cite_16 @cite_2 which limits applicability for general scenes.