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 twostage 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.

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 twostage 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.

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There has been a lot of research and industrial effort on building XQuery engines with different kinds of XML storage and index models. However, most of these efforts focus on building either an efficient XQuery engine with one kind of XML storage, index, view model in mind or a general XQuery engine without any consideration of the underlying XML storage, index and view model. We need an underlying framework to build an XQuery engine that can work with and provide optimization for different XML storage, index and view models. Besides XQuery, RDBMSs also support SQL/XML, a standard language that integrates XML and relational processing. There are industrial efforts for building hybrid XQuery and SQL/XML engines that support both languages so that users can manage and query both relational and XML data on one platform. However, we need a theoretical framework to optimize both SQL/XML and XQuery languages in one RDBMS. In this paper, we show our industrial work of building a combined XQuery and SQL/XML engine that is able to work and provide optimization for different kinds of XML storage and index models in Oracle XMLDB. This work is based on XML extended relational algebra as the underlying tuple-based logical algebra and incorporates tree and automata based physical algebra into the logical tuple-based algebra so as to provide optimization for different physical XML formulations. This results in logical and physical rewrite techniques to optimize XQuery and SQL/XML over a variety of physical XML storage, index and view models, including schema aware object relational XML storage with relational indexes, binary XML storage with schema agnostic path-value-order key XMLIndex, SQL/XML view over relational data and relational view over XML. Furthermore, we show the approach of leveraging cost based XML physical rewrite strategy to evaluate different physical rewrite plans.