PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 20 Nov 2022.
Builds on
23 of 29 references.
References · 23
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11 more
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