@@ -16,6 +16,8 @@ date: 22 June 2024
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bibliography : paper.bib
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---
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+ \def\CC{{C\nolinebreak[ 4] \hspace{-.05em}\raisebox{.4ex}{\tiny\bf ++}}}
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+
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# Summary
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Point cloud registration is a task of aligning two point clouds measured by 3D ranging
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fast point cloud registration pipeline. It offers parallel implementations of
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downsampling, nearest neighbor search, local feature extraction, and registration
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to accelerate the entire process.
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- small_gicp is implemented as a header-only C++ library with minimal dependencies
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+ small_gicp is implemented as a header-only \CC library with minimal dependencies
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to offer efficiency, portability, and customizability.
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# Statement of need
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offering up to 2x speed gain in single-threaded scenarios and better scalability in multi-core
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environments.
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- - A modular and customizable framework using C++ templates, allowing easy customization of the
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+ - A modular and customizable framework using \CC templates, allowing easy customization of the
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algorithm's internal workings while maintaining efficiency.
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- - A header-only C++ library implementation for easy integration into user projects, with Python bindings
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+ - A header-only \CC library implementation for easy integration into user projects, with Python bindings
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provided for collaborative use with other libraries (e.g., Open3D).
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# Functionalities
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- Multi-threaded construction of ` small_gicp::KdTree ` can be up to 6x faster than that of ` nanoflann ` .
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- Single-threaded ` small_gicp::GICP ` is about 2.4x faster than ` pcl::GICP ` , with the multi-threaded version showing better scalability.
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- More details can be found at https://github.com/koide3/small_gicp/blob/master/BENCHMARK.md .
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+ More details can be found at \url{ https://github.com/koide3/small_gicp/blob/master/BENCHMARK.md} .
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# Future work
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