Surfels: Surface Elements as Rendering Primitives

Surface elements (surfels) are a powerful paradigm to efficiently render complex geometric objects at interactive frame rates. Unlike classical surface discretizations, i.e., triangles or quadrilateral meshes, surfels are point primitives without explicit connectivity. Surfel attributes comprise depth, texture color, normal, and others. As a pre-process, an octree-based surfel representation of a geometric object is computed. During sampling, surfel positions and normals are optionally perturbed, and different levels of texture colors are prefiltered and stored per surfel. During rendering, a hierarchical forward warping algorithm projects surfels to a z-buffer. A novel method called visibility splatting determines visible surfels and holes in the z-buffer. Visible surfels are shaded using texture filtering, Phong illumination, and environment mapping using per-surfel normals. Several methods of image reconstruction, including supersampling, offer flexible speed-quality tradeoffs. Due to the simplicity of the operations, the surfel rendering pipeline is amenable for hardware implementation. Surfel objects offer complex shape, low rendering cost and high image quality, which makes them specifically suited for low-cost, real-time graphics, such as games.Engineering and Applied Science

3D performance capture for facial animation

This work describes how a photogrammetry based 3D capture system can be used as an input device for animation. The 3D Dynamic Capture System is used to capture the motion of a human face, which is extracted from a sequence of 3D models captured at TV frame rate. Initially the positions of a set of landmarks on the face are extracted. These landmarks are then used to provide motion data in two different ways. First, a high level description of the movements is extracted, and these can be used as input to a procedural animation package (i.e. CreaToon). Second the landmarks can be used as registration points for a conformation process where the model to be animated is modified to match the captured model. This approach gives a new sequence of models, which have the structure of the drawn model but the movement of the captured sequence

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table