1 / 61

R-LODs: Fast LOD-based Ray Tracing of Massive Models

R-LODs: Fast LOD-based Ray Tracing of Massive Models. Sung-Eui Yoon Lawrence Livermore National Lab. Christian Lauterbach Dinesh Manocha Univ. of North Carolina-Chapel Hill. Forest model (32M). Goal. Perform an interactive ray tracing of massive models

betha
Download Presentation

R-LODs: Fast LOD-based Ray Tracing of Massive Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. R-LODs: Fast LOD-based Ray Tracing of Massive Models Sung-Eui Yoon Lawrence Livermore National Lab. Christian Lauterbach Dinesh Manocha Univ. of North Carolina-Chapel Hill

  2. Forest model (32M) Goal • Perform an interactive ray tracing of massive models • Handles various kinds of polygonal meshes (e.g., scanned data and CAD) Double eagle tanker 82M triangles St. Matthew 372M triangles

  3. Recent Advances for Interactive Ray Tracing • Hardware improvements • Exponential growth of computation power • Multi-core architectures • Algorithmic improvements • Efficient ray coherence techniques [Wald et al. 01, Reshetov et al. 05]

  4. Hierarchical Acceleration Data Structures • kd-trees for interactive ray tracing [Wald 04] • Simplicity and efficiency • Used for efficient object culling Axis-aligned bounding box kd-node

  5. Ray Tracing of Massive Models • Logarithmic asymptotic behavior • Very useful for dealing with massive models • Due to the hierarchical data structures • Observed only in in-core cases

  6. Performance of Ray Tracing with Different Model Complexity • Measured with 2GB main memory Render time (log scale) Memory thrashing! Working set size 2GB Model complexity (M tri) - log scale

  7. Low Growth Rate of Data Access Time Growth rate during 1993 – 2004 47X 20X 2X Courtesy: http://www.hcibook.com/e3/online/moores-law/

  8. Inefficient Memory Accesses and Temporal Aliasing • St. Matthew (256M triangles) • Around 100M visible triangles • 1K by 1K image resolution • 1M primary rays • Hundreds of triangle per pixel • Each triangle likely in differentarea of memory

  9. Main Contributions • Propose an LOD (level-of-detail)-based ray tracing of massive models • R-LOD, a novel LOD representation for Ray tracing • Efficient LOD error metric for primary and secondary rays • Integrate ray and cache coherent techniques

  10. Performance of Ray Tracing with Different Model Complexity • Measured with 2GB main memory Render time (log scale) Memory thrashing! Working set size 2GB Model complexity (M tri) - log scale

  11. Performance of LOD-based Ray Tracing Achieved up to three order of magnitude speedup! • Measured with 2GB main memory Render time (log scale) Working set size Model complexity (M tri) - log scale

  12. Real-time Captured Video – St. Matthew Model 512 by 512 and 2x2 super-sampling, 4 pixels of LOD error in image space

  13. Related Work • Interactive ray tracing • LOD and out-of-core techniques • LOD-based ray tracing

  14. Interactive Ray Tracing • Ray coherences • [Heckbert and Hanrahan 84, Wald et al. 01, Reshetov et al. 05] • Parallel computing • [Parker et al. 99, DeMarle et al. 04, Dietrich et al. 05] • Hardware acceleration • [Purcell et al. 02, Schmittler et al. 04, Woop et al. 05] • Large dataset • [Pharr et al. 97, Wald et al. 04]

  15. LOD and Out-of-Core Techniques • Widely researched • LOD book [Luebke et al. 02] • Out-core algorithm course [Chiang et al. 03] • LOD algorithms combined with out-of-core techniques • Points clouds [Rusinkiewicz and Levoy 00] • Regular meshes [Hwa et al. 04, Losasso and Hoppe 04] • General meshes [Lindstrom 03, Cignoni et al. 04, Yoon et al. 04, Gobbetti and Marton 05] Not clear whether LOD techniques for rasterization is applicable to ray tracing

  16. LOD-based Ray Tracing • Ray differentials [Igehy 99] • Subdivision meshes [Christensen et al. 03, Stoll et al. 06] • Point clouds [Wand and Straβer 03] Image plane Footprint size of ray Viewpoint Ray beam for one pixel

  17. Outline • R-LODs for ray tracing • Results

  18. Outline • R-LODs for ray tracing • Results

  19. Rays No intersection Normal Intersection R-LOD Representation • Tightly integrated with kd-nodes • A plane, material attributes, and surface deviation kd-node Plane Valid extent of the plane

  20. Properties of R-LODs • Compact and efficient LOD representation • Add only 4 bytes to (8 bytes) kd-node • Drastic simplification • Useful for performance improvement • Error-controllable LOD rendering • Error is measured in a screen-space in terms of pixels-of-error (PoE) • Provides interactive rendering framework

  21. Two Main Design Criteria for LOD Metric • Controllability of visual errors • Efficiency • Performed per ray (not per object) • More than tens of million times evaluation

  22. Ray with original mesh Ray with LODs Visual Artifacts Surface deviation • Visibility difference • Illumination difference • Path difference for secondary rays Projected area Curvature difference View direction Original mesh LODs Image plane

  23. R-LOD Error Metric • Consider two factors • Projected screen-space area of a kd-node • Surface deviation

  24. dmin ? C (B) dmin > R LOD metric: Conservative Projection Method • Measures the screen-space area affected by using an R-LOD Image plane R Viewpoint kd-node B { PoE error bound One ray beam

  25. R-LODs with Different PoE Values PoE: Original 1.85 5 10 (512x512, no anti-aliasing)

  26. LOD Metric for Secondary Rays • Applicable to any linear transformation • Shadow • Planar reflection • Not applicable to non-linear transformation • Refraction • Uses more general, but expensive ray differentials [Igehy 99]

  27. Ray C0 Discontinuity between R-LODs • Possible solutions • Posing dependencies [Lindstrom 03, Hwa et al. 04, Yoon et al. 04, Cignoni et al. 05] • Implicit surfaces [Wald and Seidel 05]

  28. Ray Expansion of R-LODs • Expansion of the extent of the plane • Inspired by hole-free point clouds rendering [Kalaiah and Varshney 03] • A function of the surface deviation (20% of the surface deviation)

  29. Impact of Expansions of R-LODs Hole Before expansion After expansion PoE = 5 at 512 by 512 Original model

  30. R-LOD Construction • Principal component analysis (PCA) • Compute the covariance matrix for the plane of R-LODs • Hierarchical PCA computation • Has linear time complexity • Accesses the original data only one time with virtually no memory overhead Normal (= Eigenvector)

  31. Utilizing Coherence • Ray coherence • Using LOD improve the utilization of SIMD traversal/intersection • Cache coherence • Use cache-oblivious layouts of bounding volume hierarchies [Yoon and Manocha 06] • 10% ~ 60% performance improvement

  32. Outline • R-LODs for ray tracing • Results

  33. Implementation • Uses common optimized kd-tree construction methods • Based on surface-area heuristic [MacDonald and Booth 90, Havran 00] • Out-of-core computation • Decompose an input model into a set of clusters [Yoon et al. 04]

  34. Preprocessing • Simplification computation speed • Very fast due to its linear complexity (3M triangles per min) • Memory overhead • Requires 33% more storage over the optimized kd-tree representation [Wald 04] • Runtime overhead • 5% compared to non-LOD version of the same efficient ray tracer

  35. PoE = 0 (No LOD) PoE = 2.5 Impact of R-LODs 10X speedup # of intersected nodes per ray Render time Working set size

  36. Real-time Captured Video – St. Matthew Model 512 x 512, 2 x 2 anti-aliasing, PoE = 4

  37. Pros and Cons • Limitations • Does not handle advanced materials such as BRDF • No guarantee there is no holes • Advantages • Simplicity • Interactivity • Efficiency

  38. Conclusion • LOD-based ray tracing method • R-LOD representation • Efficient LOD error metric • Hierarchical LOD construction method with a linear time complexity • Reduce the working set size

  39. Ongoing and Future Work • Investigate an efficient use of implicit surfaces • Allow approximate visibility • Extend to global illumination

  40. Acknowledgements • Model contributors • Funding agencies • Army Research Office • DARPA • Lawrence Livermore National Laboratory • National Science Foundation • Office of Naval Research • RDECOM • Intel • Microsoft

  41. Acknowledgements • Eric Haines • Martin Isenburg • Dawoon Jung • David Kasik • Peter Lindstrom • Matt Pharr • Ingo Wald • Anonymous reviewers

  42. Questions? Thanks!

  43. UCRL-PRES-223086 This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-ENG-48.

  44. Additional slides

  45. Double eagle tanker 82M triangles Isosurface (472M) St. Matthew 372M triangles Goal • Perform an interactive ray tracing of massive models • Handles various kinds of polygonal meshes (e.g., scanned data and CAD)

  46. Speed Size 100 ns 1KB Register 101 ns 1MB Caches 102ns 1GB Main memory 104ns Disk storage > 1GB Memory Hierarchies

  47. + Hierarchical R-LOD Computation with Linear Time Complexity where , are x, y coordinates of kth points

  48. Performance Comparison – St. Matthew Model 2 ~ 20X improvements Non-LOD Render time (sec) LOD Approaching the model for every frame

  49. Image Quality Comparison – St. Matthew Model 512 x 512, no anti-aliasing LOD (PoE = 4) Non-LOD

  50. Further Information • R-LODs: Fast LOD-based Ray Tracing of Massive Models • S. Yoon, C. Lauterbach, and D. Manocha • (To appear at) Pacific graphics (The Visual Computer) 2006

More Related