Scalable Point Cloud-based Reconstruction with Local Implicit Functions
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Surface reconstruction from point clouds has been a well-studied research topic with applications in computer vision and computer graphics. Recently, several learningbased methods were proposed for 3D shape representation through implicit functions which among others can be used for point cloud-based reconstruction. Although delivering compelling results for synthetic object datasets of overseeable size, they fail to represent larger scenes accurately, presumably due to the use of only one global latent code for encoding an entire scene or object. We propose to encode only parts of objects with features attached to unstructured point clouds. To this end we use a hierarchical feature map in 3D space, extracted from the input point clouds, with which local latent shape encodings can be queried at arbitrary positions. We use a permutohedral lattice to process the hierarchical feature maps sparsely and efficiently. This enables accurate and detailed point cloud-based reconstructions for large amounts of points in a time-efficient manner, showing good generalization capabilities across different datasets. Experiments on synthetic and real world datasets demonstrate the reconstruction capability of our method and compare favorably to state-of-the-art methods.
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published
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Book title
2020 International Conference on 3D Vision (3DV)
Journal / series
Volume
Pages / Article No.
997 - 1007
Publisher
IEEE
Event
International Conference on 3D Vision (3DV 2020) (virtual)
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Date created
Subject
3D reconstruction; Deep learning
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.