[show abstract][hide abstract] ABSTRACT: We propose a new approach to progressively compress time-dependent geometry. Our approach exploits correlations in motion vectors to achieve better compression. We use unsupervised learning techniques to detect good clusters of motion vectors. For each detected cluster, we build a hierarchy of motion vectors using pairwise agglomerative clustering, and succinctly encode the hierarchy using entropy encod- ing. We demonstrate our approach on a client-server system that we have built for downloading time-dependent geometry.
[show abstract][hide abstract] ABSTRACT: We propose a simple lighting model to incorporate subsurface scattering effects within the local illumination framework. Subsurface scattering is relatively local due to its exponential falloff and has little effect on the appearance of neighboring objects. These observations have motivated us to approximate the BSSRDF model and to model subsurface scattering effects by using only local illumination. Our model is able to capture the most important features of subsurface scattering: reflection and transmission due to multiple scattering.In our approach we build the neighborhood information as a preprocess and modify the traditional local illumination model into a run-time two-stage process. In the first stage we compute the reflection and transmission of light on the surface. The second stage involves bleeding the scattering effects from a vertex's neighborhood to produce the final result. We then show how to merge the run-time two-stage process into a run-time single-stage process using precomputed integral. The complexity of our run-time algorithm is O(N), where N is the number of vertices. Using this approach, we achieve interactive frame rates with about one to two orders of magnitude speedup compared with the state-of-the-art methods.