Statistical Convolution On Unordered Point Set
| Autor: | Sanghoon Lee, Anh-Duc Nguyen, Weisi Lin, Seonghwa Choi, Woojae Kim |
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| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Artificial neural network Computer science Point set Feature extraction Point cloud 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolution Operator (computer programming) 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Segmentation 0105 earth and related environmental sciences |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip40778.2020.9190709 |
| Popis: | In this paper, we propose a new convolutional layer for neural networks on unordered and irregular point set. Most research advanced to date usually face multiple problem related to point cloud density and may require ad-hoc neural network architectures, which overlooks the huge treasure of architectures from computer vision or language processing. To mitigate these shortcomings, we process a point set at its distribution level by introducing statistical convolution (StatsConv). The spotlight feature of StatsConv is that it extracts various statistics to characterize the distribution of the input point set, which makes it highly scalable compared to existing point convolution operators. StatsConv is fundamentally simple, and can be used as a drop-in in any contemporary neural network architecture with negligible changes. Thorough experiments on point cloud classification and segmentation demonstrate the competence of StatsConv compared to the state of the art. |
| Databáze: | OpenAIRE |
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