185,698 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Deep Neural Networks for Anatomical Brain Segmentation
We present a novel approach to automatically segment magnetic resonance (MR)
images of the human brain into anatomical regions. Our methodology is based on
a deep artificial neural network that assigns each voxel in an MR image of the
brain to its corresponding anatomical region. The inputs of the network capture
information at different scales around the voxel of interest: 3D and orthogonal
2D intensity patches capture the local spatial context while large, compressed
2D orthogonal patches and distances to the regional centroids enforce global
spatial consistency. Contrary to commonly used segmentation methods, our
technique does not require any non-linear registration of the MR images. To
benchmark our model, we used the dataset provided for the MICCAI 2012 challenge
on multi-atlas labelling, which consists of 35 manually segmented MR images of
the brain. We obtained competitive results (mean dice coefficient 0.725, error
rate 0.163) showing the potential of our approach. To our knowledge, our
technique is the first to tackle the anatomical segmentation of the whole brain
using deep neural networks
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