Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

C. Kozlov, M. Slavcheva, S. Ilic
Patch-based Non-rigid 3D Reconstruction from a Single Depth Stream
International Conference on 3D Vision (3DV), Verona, Italy, September 2018 (bib)

We propose an approach for 3D reconstruction and tracking of dynamic surfaces using a single depth sensor, without any prior knowledge of the scene. It is robust to rapid inter-frame motions due to the probabilistic expectation-maximization non-rigid registration framework. Our pipeline subdivides each input depth image into non-rigidly connected surface patches, and deforms it towards the canonical pose by estimating a rigid transformation for each patch. The combination of a data term imposing similarity between model and data, and a regularizer enforcing as-rigid-as-possible motion of neighboring patches ensures that we can handle large deformations, while coping with sensor noise. We employ a surfel-based fusion technique, which lets us circumvent the repeated conversion between mesh and signed distance field representations which are used by related techniques. Furthermore, a robust keyframe-based scheme allows us to keep track of correspondences throughout the entire sequence. Through a variety of qualitative and quantitative experiments, we demonstrate resistance to larger motion and achieving lower reconstruction errors than related approaches.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif