Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

• URL: http://arxiv.org/abs/2104.06950v1
• Date: Wed, 14 Apr 2021 16:21:22 GMT
• Title: Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
• Authors: Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman
• Abstract summary: We represent the reconstructed surface as an atlas, using a neural network. We empirically show that our method achieves results that exceed that state of the art in the accuracy of unsupervised correspondences and accuracy of surface reconstruction.
• Score: 131.50372468579067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes. We represent the reconstructed surface as an atlas, using a neural network. Using canonical correspondences defined via the atlas, we encourage the reconstruction to be as isometric as possible across frames, leading to semantically-meaningful reconstruction. Through experiments and comparisons, we empirically show that our method achieves results that exceed that state of the art in the accuracy of unsupervised correspondences and accuracy of surface reconstruction.

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