1. LODManager A framework for rendering multiresolution models in real-time applications J. Gumbau O. Ripollés M. Chover

2. Introduction LOD techniques are important on highly populated scenes. Continuous multiresolution methods reduce popping –But extracting a LOD has a cost (extracting overhead) Use of CLOD on massive scenes without control: –Is completely inefficient –Drastically reduces interactivity Our aim: manage efficiently the LOD of those scenes.

3. Previous work Solutions to the LOD management problem: –Methods based on static heuristics: Static metric: distance or screen-space area Easy and cheap to evaluate Not adaptive and unstable frame rates –Methods based on feedback algorithms: Good when there’s coherence between frames Present oscillations on discontinuous environments –Methods based on predictive algorithms: Very accurate but has a great computational cost Scenes with lots of objects can become too slow They are inefficient solutions on massive scenes

4. Motivation Nowadays GPUs are extremely powerful but very delicate: –Overload the CPU can become a bottleneck –Change GPU data can cause a stall Our aim is to create a CLOD manager for massive scenes which: –Minimizes CPU time used to decide the LOD for each object –Minimizes GPU data changes

5. Method overview We present a method which: –Is designed for massively populated scenes with thousands of dynamic objects –Has very low CPU requirements to keep the bottleneck on the GPU –Provides frame rate feedback to globally adjust the LOD of the scene –Is based on sharing already calculated LODs to minimize GPU data changes minimize CPU usage

6. Method details The user defines a set of n LOD slots –This is called the LOD snapshots list –A slot i refers to an object with a certain level of detail. If an object must change it’s LOD: –If another object changed to a similar LOD: Borrow its already calculated level of detail –Otherwise, calculate its own level of detail And insert itself into the LOD snapshot’s list

7. Method improvements Use a non-linear distributed LOD snapshots list –So nearer objects will have higher LOD granularity –Makes less perceptible discretization artifacts

8. Feedback LOD factor is calculated using a static metric Uses frame rate feedback to globally adjust LOD –Uses a perturbation function The perturbation parameter n depends on the difference between the real and the desired LOD

9. Test and Results Test scene: 3000 LOD objects The graph shows a CPU bottleneck when the LOD Manager is disabled. time

10. Conclusions Efficient LOD management on massive scenes –Algorithm with very low CPU requirements: Cost of querying and borrowing a similar LOD is negligible Drastically optimizes real changes in levels of detail –LOD Borrowing minimizes GPU updating reduces unnecessary GPU stalls optimizes bus traffic Drawback: Dynamic LOD discretization of a multiresolution model, but: –Has less memory consumption –The number of virtual discretizations is user defined –The system can decide whether to borrow or not?

11. Any questions?