IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction
This addresses the need for fast and accurate 3D reconstruction from single images in applications like robotics or AR/VR, though it is incremental as it builds on adversarial learning approaches.
The paper tackles the problem of non-rigid 3D surface reconstruction from a single monocular image, achieving real-time processing at over 250 Hz and reducing reconstruction error by 10-30% compared to state-of-the-art methods.
The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) - an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms several approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively.