A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds
CoRR(2023)
Abstract
We present a comprehensive survey and benchmark of both traditional and
learning-based methods for surface reconstruction from point clouds. This task
is particularly challenging for real-world acquisitions due to factors like
noise, outliers, non-uniform sampling, and missing data. Traditional approaches
often simplify the problem by imposing handcrafted priors on either the input
point clouds or the resulting surface, a process that can necessitate tedious
hyperparameter tuning. Conversely, deep learning models have the capability to
directly learn the properties of input point clouds and desired surfaces from
data. We study the influence of these handcrafted and learned priors on the
precision and robustness of surface reconstruction techniques. We evaluate
various time-tested and contemporary methods in a standardized manner. When
both trained and evaluated on point clouds with identical characteristics, the
learning-based models consistently produce superior surfaces compared to their
traditional counterpartsx2013even in scenarios involving novel
shape categories. However, traditional methods demonstrate greater resilience
to the diverse array of point cloud anomalies commonly found in real-world 3D
acquisitions. For the benefit of the research community, we make our code and
datasets available, inviting further enhancements to learning-based surface
reconstruction. This can be accessed at
https://github.com/raphaelsulzer/dsr-benchmark .
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Key words
automatic surface reconstruction,benchmark
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