Kavli Affiliate: Michael Wimmer
| First 5 Authors: Philipp Erler, Paul Guerrero, Stefan Ohrhallinger, Michael Wimmer, Niloy J. Mitra
| Summary:
A key step in any scanning-based asset creation workflow is to convert
unordered point clouds to a surface. Classical methods (e.g., Poisson
reconstruction) start to degrade in the presence of noisy and partial scans.
Hence, deep learning based methods have recently been proposed to produce
complete surfaces, even from partial scans. However, such data-driven methods
struggle to generalize to new shapes with large geometric and topological
variations. We present Points2Surf, a novel patch-based learning framework that
produces accurate surfaces directly from raw scans without normals. Learning a
prior over a combination of detailed local patches and coarse global
information improves generalization performance and reconstruction accuracy.
Our extensive comparison on both synthetic and real data demonstrates a clear
advantage of our method over state-of-the-art alternatives on previously unseen
classes (on average, Points2Surf brings down reconstruction error by 30% over
SPR and by 270%+ over deep learning based SotA methods) at the cost of longer
computation times and a slight increase in small-scale topological noise in
some cases. Our source code, pre-trained model, and dataset are available on:
https://github.com/ErlerPhilipp/points2surf
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