Non-rigid 3D reconstruction aims at endowing computers with the ability to capture and reconstruct 3D non-rigid scenes from multiple 2D image observations from different viewpoints. This project aims at handling complex deformation in non-rigid structure from motion, thus enabling dense non-rigid reconstruction in in real world applications. Previous non-rigid structure from motion techniques manily take sparse correspondences as input and there are few methods to deal with dense correspondences. Recent work shows that extensions of sparse non-rigid structure from motion method can be applied in dense non-rigid reconstruction with simple non-rigid deformation. This project targets to fill the gap by exploiting dense non-rigid structure from motion methods under complex deformation scenarios.
Through investigating local non-linear/linear deformation representation, global adjustment and optimization method, and scalability algorithms, this project aims at alleviating major difficulties (dense correspondences, long sequences, and complex deformations) associated with current non-rigid reconstruction methods.
Background in image processing and computer vision.