Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding

Understand

Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has made significant process recently.

• Current state-of-the-art (SOTA) methods, are based on the learning framework of rigid structure-from-motion, where only 3D camera ego motion is modeled for geometry estimation.However, moving objects also exist in many videos, e.g.
• moving cars in a street scene.
• In this paper, we tackle such motion by additionally incorporating per-pixel 3D object motion into the learning framework, which provides holistic 3D scene flow understanding and helps single image geometry estimation.