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To The Point: Correspondence-driven monocular 3D category reconstruction

2021-06-10 11:21:14
Filippos Kokkinos, Iasonas Kokkinos
arXiv_CV
             
Abstract

We present To The Point (TTP), a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision. We recover a 3D shape from a 2D image by first regressing the 2D positions corresponding to the 3D template vertices and then jointly estimating a rigid camera transform and non-rigid template deformation that optimally explain the 2D positions through the 3D shape projection. By relying on 3D-2D correspondences we use a simple per-sample optimization problem to replace CNN-based regression of camera pose and non-rigid deformation and thereby obtain substantially more accurate 3D reconstructions. We treat this optimization as a differentiable layer and train the whole system in an end-to-end manner. We report systematic quantitative improvements on multiple categories and provide qualitative results comprising diverse shape, pose and texture prediction examples. Project website: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2106.05662

PDF

https://arxiv.org/pdf/2106.05662.pdf

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