53,284 research outputs found
SEGCloud: Semantic Segmentation of 3D Point Clouds
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision
(3DV 2017
Application of Generalized Partial Volume Estimation for Mutual Information based Registration of High Resolution SAR and Optical Imagery
Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance has been thoroughly explored for registration of high resolution SAR (TerraSAR-X) and optical (IKONOS-2) satellite images. Our experiments highlight possibility of an inconsistent MI behavior with different joint histogram bin size which gets reduced with an increase in order of B-spline kernel employed in GPVE. In general, bin size reduction and/or increasing B-spline order have a smoothing affect on MI surfaces and even the lowest order B-spline with a suitable histogram bin size can achieve same pixel level accuracy as achieved by the higher order kernels more consistently
Automatic alignment for three-dimensional tomographic reconstruction
In tomographic reconstruction, the goal is to reconstruct an unknown object
from a collection of line integrals. Given a complete sampling of such line
integrals for various angles and directions, explicit inverse formulas exist to
reconstruct the object. Given noisy and incomplete measurements, the inverse
problem is typically solved through a regularized least-squares approach. A
challenge for both approaches is that in practice the exact directions and
offsets of the x-rays are only known approximately due to, e.g. calibration
errors. Such errors lead to artifacts in the reconstructed image. In the case
of sufficient sampling and geometrically simple misalignment, the measurements
can be corrected by exploiting so-called consistency conditions. In other
cases, such conditions may not apply and we have to solve an additional inverse
problem to retrieve the angles and shifts. In this paper we propose a general
algorithmic framework for retrieving these parameters in conjunction with an
algebraic reconstruction technique. The proposed approach is illustrated by
numerical examples for both simulated data and an electron tomography dataset
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
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