PointResNet: Residual Network for 3D Point Cloud Segmentation and
Classification
- URL: http://arxiv.org/abs/2211.11040v1
- Date: Sun, 20 Nov 2022 17:39:48 GMT
- Title: PointResNet: Residual Network for 3D Point Cloud Segmentation and
Classification
- Authors: Aadesh Desai, Saagar Parikh, Seema Kumari, Shanmuganathan Raman
- Abstract summary: Point cloud segmentation and classification are some of the primary tasks in 3D computer vision.
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.
- Score: 18.466814193413487
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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