Better 3D object reconstruction by segmentation to learn signed distance functions with ObjectSDF
Better 3D object reconstruction by segmentation to learn signed distance functions with ObjectSDF
Object-Compositional Neural Implicit Surfaces
arXiv paper abstract https://arxiv.org/abs/2207.09686v1
arXiv PDF paper https://arxiv.org/pdf/2207.09686v1.pdf
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images.
However, most approaches focus on holistic scene representation yet ignore individual objects inside it
... proposes a novel framework, ObjectSDF, to build an object-compositional neural implicit representation with high fidelity in 3D reconstruction and object representation.
... model the scene by combining the Signed Distance Functions (SDF) of individual object to exert explicit surface constraint.
... convert the semantic information to a function of object SDF and develop a unified and compact representation for scene and objects.
... show the superiority of ObjectSDF framework in representing both the holistic object-compositional scene and the individual instances ...
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