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A signed distance function (SDF) as the 3D shape description is one of themost effective approaches to represent 3D geometry for rendering andreconstruction. Our work is inspired by the state-of-the-art method DeepSDFthat learns and analyzes the 3D shape as the iso-surface of its shell and thismethod has shown promising results especially in the 3D shape reconstructionand compression domain. In this paper, we consider the degeneration problem ofreconstruction coming from the capacity decrease of the DeepSDF model, whichapproximates the SDF with a neural network and a single latent code. We proposeLocal Geometry Code Learning (LGCL), a model that improves the original DeepSDFresults by learning from a local shape geometry of the full 3D shape. We add anextra graph neural network to split the single transmittable latent code into aset of local latent codes distributed on the 3D shape. Mentioned latent codesare used to approximate the SDF in their local regions, which will alleviatethe complexity of the approximation compared to the original DeepSDF.Furthermore, we introduce a new geometric loss function to facilitate thetraining of these local latent codes. Note that other local shape adjustingmethods use the 3D voxel representation, which in turn is a problem highlydifficult to solve or even is insolvable. In contrast, our architecture isbased on graph processing implicitly and performs the learning regressionprocess directly in the latent code space, thus make the proposed architecturemore flexible and also simple for realization. Our experiments on 3D shapereconstruction demonstrate that our LGCL method can keep more details with asignificantly smaller size of the SDF decoder and outperforms considerably theoriginal DeepSDF method under the most important quantitative metrics.

3D Shapes Local Geometry Codes Learning with SDF