Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
This work addresses the gap between geometric and deep learning methods in computer vision, offering a hybrid approach for more reliable 3D reconstruction.
The paper tackles the problem of 3D shape reconstruction from images by integrating learned shape priors from deep shape generators into Photometric Bundle Adjustment, demonstrating impressive results.
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve this problem through projected point displacement \& bundle adjustment. More recently, deep methods have attempted to solve this problem by directly learning a relationship between geometry and appearance. There is, however, a significant gap between these two strategies. SfM tackles the problem from purely a geometric perspective, taking no account of the object shape prior. Modern deep methods more often throw away geometric constraints altogether, rendering the results unreliable. In this paper we make an effort to bring these two seemingly disparate strategies together. We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.