Robust Pooling through the Data Mode
Autor: | Ayman Mukhaimar, Ruwan Tennakoon, Reza Hoseinnezhad, Chow Yin Lai, Alireza Bab-Hadiashar |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
History Polymers and Plastics Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Signal Processing Computer Science (miscellaneous) Computer Science - Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Business and International Management Industrial and Manufacturing Engineering Computer Science Applications |
DOI: | 10.48550/arxiv.2106.10850 |
Popis: | The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ability to classify or segment objects. While there are some robust deep learning approaches, they are computationally too expensive for real-time applications. This paper proposes a deep learning solution that includes a novel robust pooling layer which greatly enhances network robustness and performs significantly faster than state-of-the-art approaches. The proposed pooling layer looks for data a mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models. We tested the pooling layer into frameworks such as Point-based and graph-based neural networks, and the tests showed enhanced robustness as compared to robust state-of-the-art methods. Comment: under consideration at Computer Vision and Image Understanding |
Databáze: | OpenAIRE |
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