32,210 research outputs found
PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification
Point cloud segmentation and classification are some of the primary tasks in
3D computer vision with applications ranging from augmented reality to
robotics. However, processing point clouds using deep learning-based algorithms
is quite challenging due to the irregular point formats. Voxelization or 3D
grid-based representation are different ways of applying deep neural networks
to this problem. In this paper, we propose PointResNet, a residual block-based
approach. Our model directly processes the 3D points, using a deep neural
network for the segmentation and classification tasks. The main components of
the architecture are: 1) residual blocks and 2) multi-layered perceptron (MLP).
We show that it preserves profound features and structural information, which
are useful for segmentation and classification tasks. The experimental
evaluations demonstrate that the proposed model produces the best results for
segmentation and comparable results for classification in comparison to the
conventional baselines.Comment: Paper Under Review at IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP) 202
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