10,252 research outputs found
Iterative graph cuts for image segmentation with a nonlinear statistical shape prior
Shape-based regularization has proven to be a useful method for delineating
objects within noisy images where one has prior knowledge of the shape of the
targeted object. When a collection of possible shapes is available, the
specification of a shape prior using kernel density estimation is a natural
technique. Unfortunately, energy functionals arising from kernel density
estimation are of a form that makes them impossible to directly minimize using
efficient optimization algorithms such as graph cuts. Our main contribution is
to show how one may recast the energy functional into a form that is
minimizable iteratively and efficiently using graph cuts.Comment: Revision submitted to JMIV (02/24/13
3D Shape Segmentation with Projective Convolutional Networks
This paper introduces a deep architecture for segmenting 3D objects into
their labeled semantic parts. Our architecture combines image-based Fully
Convolutional Networks (FCNs) and surface-based Conditional Random Fields
(CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are
used for efficient view-based reasoning about 3D object parts. Through a
special projection layer, FCN outputs are effectively aggregated across
multiple views and scales, then are projected onto the 3D object surfaces.
Finally, a surface-based CRF combines the projected outputs with geometric
consistency cues to yield coherent segmentations. The whole architecture
(multi-view FCNs and CRF) is trained end-to-end. Our approach significantly
outperforms the existing state-of-the-art methods in the currently largest
segmentation benchmark (ShapeNet). Finally, we demonstrate promising
segmentation results on noisy 3D shapes acquired from consumer-grade depth
cameras.Comment: This is an updated version of our CVPR 2017 paper. We incorporated
new experiments that demonstrate ShapePFCN performance under the case of
consistent *upright* orientation and an additional input channel in our
rendered images for encoding height from the ground plane (upright axis
coordinate values). Performance is improved in this settin
General Dynamic Scene Reconstruction from Multiple View Video
This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance
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