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

• URL: http://arxiv.org/abs/2106.05662v1
• Date: Thu, 10 Jun 2021 11:21:14 GMT
• Title: To The Point: Correspondence-driven monocular 3D category reconstruction
• Authors: Filippos Kokkinos and Iasonas Kokkinos
• Abstract summary: To The Point (TTP) is a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision. We replace CNN-based regression of camera pose and non-rigid deformation and obtain substantially more accurate 3D reconstructions.
• Score: 39.811816510186475
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://fkokkinos.github.io/to_the_point/.

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