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Single-View 3D Mesh Reconstruction and Generation

Paper Title: Single-View 3D Mesh Reconstruction and Generation Authors: George Fahim, Khalid Amin, Sameh Zarif presented at: The International Conference on Artificial Intelligence and Computer Vision 2021 (AICV2021) Paper Abstract: Single-view 3D shape reconstruction is an ill-posed problem that poses a major challenge to the field of Computer Vision. With the advent of the deep learning era, many methods are being continuously proposed to tackle this problem because of its importance. This paper proposes a data-driven method for single-view mesh-based 3D reconstruction and generation. Specifically, it presents a framework that is capable of reconstructing shapes and generating novel variants of these shapes from a single RGB image. The proposed framework consists of a mesh reconstruction network and a mesh autoencoder network. The framework is trained in two stages on synthetic data with 3D supervision. The quantitative and qualitative evaluations show promising results in the direction of solving this long-standing problem. Paper Link: https://link.springer.com/chapter/10....