Abstract Robust Moving Least-squares Fitting with Sharp Features

We introduce a robust moving least-squares technique for reconstructing a piecewise smooth surface from a potentially noisy point cloud. We use techniques from robust statistics to guide the creation of the neighborhoods used by the moving least squares (MLS) computation. This leads to a conceptually simple approach that provides a unified framework for not only dealing with noise, but also for enabling the modeling of surfaces with sharp features. Our technique is based on a new robust statistics method for outlier detection: the forward-search paradigm. Using this powerful technique, we locally classify regions of a point-set to multiple outlier-free smooth regions. This classification allows us to project points on a locally smooth region rather than a surface that is smooth everywhere, thus defining a piecewise smooth surface and increasing the numerical stability of the projection operator. Furthermore, by treating the points across the discontinuities as outliers, we are able to define sharp features. One of the nice features of our approach is that it automatically disregards outliers during the surface-fitting phase

Deep Compression for Streaming Texture Intensive Animations

This paper presents a streaming technique for synthetic texture intensive 3D animation sequences. There is a short latency time while downloading the animation, until an initial fraction of the compressed data is read by the client. As the animation is played, the remainder of the data is streamed online seamlessly to the client. The technique exploits frame-to-frame coherence for transmitting geometric and texture streams. Instead of using the original textures of the model, the texture stream consists of view-dependent textures which are generated by rendering offline nearby views. These textures have a strong temporal coherency and can thus be well compressed. As a consequence, the bandwidth of the stream of the view-dependent textures is narrow enough to be transmitted together with the geometry stream over a low bandwidth network. These two streams maintain a small online cache of geometry and view-dependent textures from which the client renders the walkthrough sequence in real-time. The overall data transmitted over the network is an order of magnitude smaller than an MPEG postrendered sequence with an equivalent image quality

Bilateral Mesh Denoising

We present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is accomplished by filtering vertices of the mesh in the normal direction using local neighborhoods. Motivated by the impressive results of bilateral filtering for image denoising, we adopt it to denoise 3D meshes; addressing the specific issues required in the transition from two-dimensions to manifolds in three dimensions. We show that the proposed method successfully removes noise from meshes while preserving features. Furthermore, the presented algorithm excels in its simplicity both in concept and implementation