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Improve 3D object reconstruction using unsupervised learning on image collection with TARS

Improve 3D object reconstruction using unsupervised learning on image collection with TARS


Topologically-Aware Deformation Fields for Single-View 3D Reconstruction

arXiv paper abstract https://arxiv.org/abs/2205.06267



... present a new framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection.


The 3D shapes are generated ... as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections without any 3D supervision.


... image collections ... contain several intra-category ... variations ... Because of this, prior works either focus on ... shape individually without modeling ... correspondences or perform ... correspondence ... on categories with minimal ... variations.


... overcome these restrictions by learning a topologically-aware implicit deformation field that maps a 3D point in the object space to a higher dimensional point in the category-specific canonical space.


At inference ... given a single image, ... reconstruct the underlying 3D shape by first implicitly deforming each 3D point in the object space to the learned category-specific canonical space using the topologically-aware deformation field and then reconstructing the 3D shape as a canonical signed distance field.


... approach ... TARS, achieves state-of-the-art reconstruction fidelity on several datasets: ShapeNet, Pascal3D+, CUB, and Pix3D chairs. ...



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