Recalibration of Neural Networks for Point Cloud Analysis
- URL: http://arxiv.org/abs/2011.12888v1
- Date: Wed, 25 Nov 2020 17:14:34 GMT
- Title: Recalibration of Neural Networks for Point Cloud Analysis
- Authors: Ignacio Sarasua, Sebastian Poelsterl, Christian Wachinger
- Abstract summary: We introduce re-calibration modules on deep neural networks for 3D point clouds.
We demonstrate the benefit and versatility of our proposed modules by incorporating them into three state-of-the-art networks for 3D point cloud analysis.
In the second set of experiments, we investigate the benefits of re-calibration blocks on Alzheimer's Disease diagnosis.
- Score: 3.7814216736076434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spatial and channel re-calibration have become powerful concepts in computer
vision. Their ability to capture long-range dependencies is especially useful
for those networks that extract local features, such as CNNs. While
re-calibration has been widely studied for image analysis, it has not yet been
used on shape representations. In this work, we introduce re-calibration
modules on deep neural networks for 3D point clouds. We propose a set of
re-calibration blocks that extend Squeeze and Excitation blocks and that can be
added to any network for 3D point cloud analysis that builds a global
descriptor by hierarchically combining features from multiple local
neighborhoods. We run two sets of experiments to validate our approach. First,
we demonstrate the benefit and versatility of our proposed modules by
incorporating them into three state-of-the-art networks for 3D point cloud
analysis: PointNet++, DGCNN, and RSCNN. We evaluate each network on two tasks:
object classification on ModelNet40, and object part segmentation on ShapeNet.
Our results show an improvement of up to 1% in accuracy for ModelNet40 compared
to the baseline method. In the second set of experiments, we investigate the
benefits of re-calibration blocks on Alzheimer's Disease (AD) diagnosis. Our
results demonstrate that our proposed methods yield a 2% increase in accuracy
for diagnosing AD and a 2.3% increase in concordance index for predicting AD
onset with time-to-event analysis. Concluding, re-calibration improves the
accuracy of point cloud architectures, while only minimally increasing the
number of parameters.
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