Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10458-10467


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 correspondences and accuracy of surface reconstruction.

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@InProceedings{Bednarik_2021_ICCV, author = {Bednarik, Jan and Kim, Vladimir G. and Chaudhuri, Siddhartha and Parashar, Shaifali and Salzmann, Mathieu and Fua, Pascal and Aigerman, Noam}, title = {Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {10458-10467} }