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.
Publication
International Conference on 3D Vision (3DV)