Authors:
Nicolas Wagner
and
Ulrich Schwanecke
Affiliation:
RheinMain University of Applied Sciences, Wiesbaden, Germany
Keyword(s):
Point Clouds, Compression, Deep Learning.
Abstract:
In this paper, we propose NeuralQAAD, a differentiable point cloud compression framework that is fast, robust to sampling, and applicable to consistent shapes with high detail resolution. Previous work that is able to handle complex and non-smooth topologies is hardly scaleable to more than just a few thousand points. We tackle the task with a novel neural network architecture characterized by weight sharing and autodecoding. Our architecture uses parameters far more efficiently than previous work, allowing it to be deeper and more scalable. We also show that the currently only tractable training criterion for point cloud compression, the Chamfer distance, performances poorly for high resolutions. To overcome this issue, we pair our architecture with a new training procedure based on a quadratic assignment problem. This procedure acts as a surrogate loss and allows to implicitly minimize the more expressive Earth Movers Distance (EMD) even for point clouds with way more than 106 poin
ts. As directly evaluating the EMD on high resolution point clouds is intractable, we propose a new divide-and-conquer approach based on k-d trees, which we call EM-kD. The EM-kD is shown to be a scaleable and fast but still reliable upper bound for the EMD. NeuralQAAD demonstrates on three datasets (COMA, D-FAUST and Skulls) that it significantly outperforms the current state-of-the-art both visually and qualitatively in terms of EM-kD.
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