Abstract
Nowadays, depth data has an important role in many applications. The sensors which can capture depth data became essential parts of autonomous vehicles. These sensors record a huge amount of 3D data (point clouds with x, y, and z coordinates). Furthermore, for many point cloud processing applications, it is important to calculate feature vectors that aim at describing the neighborhood of each point. Usually, a feature vector has high dimensionality, and storing it in a database is a difficult task. One of the most common operations on feature descriptors is the nearest neighbor search. However, earlier works show that nearest neighbor search with spatial index structures in high dimensions could be outperformed by sequential scan. In this work, we investigate how dimensionality reduction on 3D feature descriptors affects the descriptiveness.
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Acknowledgements
The authors thank the support of project “Application Domain Specific Highly Reliable IT Solutions” that has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.
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Varga, D., Szalai-Gindl, J.M., Laki, S. (2021). The Descriptiveness of Feature Descriptors with Reduced Dimensionality. In: Bellatreche, L., et al. New Trends in Database and Information Systems. ADBIS 2021. Communications in Computer and Information Science, vol 1450. Springer, Cham. https://doi.org/10.1007/978-3-030-85082-1_29
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