LOD continued

Although the algorithm introduced above covers a whole range of level of detail management techniques, real world applications usually employ different methods according the information being rendered. Because of the appearance of the considered objects, two main algorithm families are used.[3] The first is based on subdividing the space in a finite number of regions, each with a certain level of detail. The result is discrete number of detail levels, from which the name Discrete LoD (DLOD). There's no way to support a smooth transition between LOD levels at this level, although alpha blending or morphing can be used to avoid visual popping. The second considers the polygon mesh being rendered as a function which must be evaluated requiring to avoid excessive errors which are a function of some heuristic (usually distance) themselves. The given "mesh" function is then continuously evaluated and an optimized version is produced according to a tradeoff between visual quality and performance. These types of algorithms are usually referred as Continuous LOD (CLOD). Details on Discrete LOD An example of various DLOD ranges. Darker areas are meant to be rendered with higher detail. An additional culling operation is run, discarding all the information outside the frustum (colored areas). The basic concept of discrete LOD (DLOD) is to provide various models to represent the same object. Obtaining those models requires an external algorithm which is often non-trivial and subject of many polygon reduction techniques. Successive LOD-ing algorithms will simply assume those models are available. DLOD algorithms are often used in performance-intensive applications with small data sets which can easily fit in memory. Although out-of-core algorithms could be used, the information granularity is not well suited to this kind of application. This kind of algorithm is usually easier to get working, providing both faster performance and lower CPU usage because of the few operations involved. DLOD methods are often used for "stand-alone" moving objects, possibly including complex animation methods. A different approach is used for geomipmapping[4], a popular terrain rendering algorithm because this applies to terrain meshes which are both graphically and topologically different from "object" meshes. Instead of computing an error and simplify the mesh according to this, geomipmapping takes a fixed reduction method, evaluates the error introduced and computes a distance at which the error is acceptable. Although straightforward, the algorithm provides decent performance. A discrete LOD example As a simple example, consider the following sphere. A discrete LOD approach would cache a certain number of models to be used at different distances. Because the model can trivially be procedurally generated by its mathematical formulation, using a different amount of sample points distributed on the surface is sufficient to generate the various models required. This pass is not a LOD-ing algorithm. Visual impact comparisons and measurements Image Vertices ~5500 ~2880 ~1580 ~670 140 Notes Maximum detail, for closeups. Minimum detail, very far objects. To simulate a realistic transform bound scenario, we'll use an ad-hoc written application. We'll make sure we're not CPU bound by using simple algorithms and minimum fragment operations. Each frame, the program will compute each sphere's distance and choose a model from a pool according to this information. To easily show the concept, the distance at which each model is used is hard coded in the source. A more involved method would compute adequate models according to the usage distance chosen. We use OpenGL for rendering because its high efficiency in managing small batches, storing each model in a display list thus avoiding communication overheads. Additional vertex load is given by applying two directional light sources ideally located infinitely far away. The following table compares the performance of LoD aware rendering and a full detail (brute force) method. Visual impact comparisons and measurements Brute DLOD Comparison Rendered images Render time 27.27 ms 1.29 ms 21 × reduction Scene vertices (thousands) 2328.48 109.44 21 × reduction Hierarchical LOD Because hardware is geared towards large amounts of detail, rendering low polygon objects may score sub-optimal performances. HLOD avoids the problem by grouping different objects together[5]. This allows for higher efficiency as well as taking advantage of proximity considerations.