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GDC 2012: Advanced Procedural Rendering in DX11



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GDC 2012: Advanced Procedural Rendering in DX11

  1. 1. Advanced Procedural Rendering in DirectX 11 Matt Swoboda Principal Engineer, SCEE R&D PhyreEngine™ Team Demo Coder, Fairlight
  2. 2. Aim ● More dynamic game worlds.
  3. 3. Demoscene? make demos ● “Like games, crossed with music videos” inear, non-interactive, scripted ll generated in real-time ● On consumer-level PC hardware
  4. 4. DirectX 11? irectX 9 is very old ● We are all very comfortable with it.. ● .. But does not map well to modern graphics hardware irectX 11 lets you use same hardware smarter ● Compute shaders ● Much improved shading language ● GPU-dispatched draw calls ● .. And much more
  5. 5. Procedural Mesh Generation A reasonable result from random formulae (Hopefully a good result from sensible formulae)
  6. 6. Signed Distance Fields (SDFs) istance function: ● Returns the closest distance to the surface from a given point igned distance function: ● Returns the closest distance from a point to the surface, positive if the point is outside the shape and negative if inside
  7. 7. Signed Distance Fields ● Useful tool for procedural geometry creation ● Easy to define in code .. ● .. Reasonable results from “random formulae” ● Can create from meshes, particles, fluids, voxels ● CSG, distortion, repeats, transforms all easy ● No concerns with geometric topology ● Just define the field in space, polygonize later
  8. 8. A Box Box(pos, size) { a = abs(pos-size) - size; return max(a.x,a.y,a.z); } *Danger: may not actually compile
  9. 9. Cutting with Booleans d = Box(pos) c = fmod(pos * A, B) subD = max(c.y,min(c.y,c.z)) d = max(d, -subD)
  10. 10. More Booleans d = Box(pos) c = fmod(pos * A, B) subD = max(c.y,min(c.y,c.z)) subD = min(subD,cylinder(c)) subD = max(subD, Windows()) d = max(d, -subD)
  11. 11. Repeated Booleans d = Box(pos) e = fmod(pos + N, M) floorD = Box(e) d = max(d, -floorD)
  12. 12. Cutting Holes d = Box(pos) e = fmod(pos + N, M) floorD = Box(e) floorD = min(floorD,holes()) d = max(d, -floorD)
  13. 13. Combined Result d = Box(pos) c = fmod(pos * A, B) subD = max(c.y,min(c.y,c.z)) subD = min(subD,cylinder(c)) subD = max(subD, Windows()) e = fmod(pos + N, M) floorD = Box(e) floorD = min(floorD,holes()) d = max(d, -subD) d = max(d, -floorD)
  14. 14. Repeating the Space pos.y = frac(pos.y) d = Box(pos) c = fmod(pos * A, B) subD = max(c.y,min(c.y,c.z)) subD = min(subD,cylinder(c)) subD = max(subD, Windows()) e = fmod(pos + N, M) floorD = Box(e) floorD = min(floorD,holes()) d = max(d, -subD) d = max(d, -floorD)
  15. 15. Repeating the Space pos.xy = frac(pos.xy) d = Box(pos) c = fmod(pos * A, B) subD = max(c.y,min(c.y,c.z)) subD = min(subD,cylinder(c)) subD = max(subD, Windows()) e = fmod(pos + N, M) floorD = Box(e) floorD = min(floorD,holes()) d = max(d, -subD) d = max(d, -floorD)
  16. 16. Details AddDetails()
  17. 17. Details DoLighting() ToneMap()
  18. 18. Details AddDeferredTexture() AddGodRays()
  19. 19. Details MoveCamera() MakeLookGood() Ship It.
  20. 20. Procedural SDFs in Practice enerated scenes probably won’t replace 3D artists
  21. 21. Procedural SDFs in Practice enerated scenes probably won’t replace 3D artists 
  22. 22. Procedural SDFs in Practice enerated scenes probably won’t replace 3D artists enerated SDFs good proxies for real meshes ● Code to combine a few primitives cheaper than art data ombine with artist-built meshes converted to SDFs ● Boolean, modify, cut, distort procedurally
  23. 23. Video ● (Video Removed) ● (It’s a cube morphing into a mesh. You know, just for fun etc.)
  24. 24. SDFs From Triangle Meshes
  25. 25. SDFs from Triangle Meshes onvert triangle mesh to SDF in 3D texture ● 32^3 – 256^3 volume texture typical ● SDFs interpolate well..  bicubic interpolation ● .. Low resolution 3D textures still work well ● Agnostic to poly count (except for processing time) an often be done offline
  26. 26. SDFs from Triangle Meshes A mesh converted to a 64x64x64 SDF and polygonised. It’s two people doing yoga, by the way.
  27. 27. SDFs from Triangle Meshes aïve approach? ● Compute distance from every cell to every triangle ● Very slow but accurate oxelize mesh to grid, then sweep?  UGLY ● Sweep to compute signed distance from voxels to cells ● Voxelization too inaccurate near surface.. ● ..But near-surface distance is important - interpolation
  28. 28. Geometry Stages ● Bind 3D texture target ● VS transforms to SDF space ● Geometry shader replicates triangle to affected slices ● Flatten triangle to 2D ● Output positions as TEXCOORDs.. ● .. All 3 positions for each vertex
  29. 29. Pixel Shader Stage alculates distance from 3D pixel to triangle ● Compute closest position on triangle ● Evaluate vertex normal using barycentric valuate distance sign using weighted normal rite signed distance to output color, distance to depth epth test keeps closest distance
  30. 30. Post Processing Step ells around mesh surface now contain accurate signed distance est of grid is empty ill out rest of the grid in post process CS ast Sweeping algorithm
  31. 31. Fast Sweeping ● Requires ability to read d = maxPossibleDistance and write same buffer for i = 0 to row length ● One thread per row d += cellSize ● Thread R/W doesn’t overlap if(abs(cell[i]) > abs(d)) ● No interlock needed cell[i] = d ● Sweep forwards then else backwards on same axis d = cell[i] ● Sweep each axis in turn
  32. 32. SDFs from Particle Systems
  33. 33. SDFs From Particle Systems ● Naïve: treat each particle as a sphere ● Compute min distance from point to particles ● Better: use metaball blobby equation ● Density(P) = Sum[ (1 – (r2/R2))3 ] for all particles ● R : radius threshold ● r : distance from particle to point P ● Problem: checking all particles per cell
  34. 34. Evaluating Particles Per Cell ● Bucket sort particles into grid cells in CS ● Evaluate a kernel around each cell ● Sum potentials from particles in neighbouring cells ● 9x9x9 kernel typical ● (729 cells, containing multiple particles per cell, evaluated for ~2 million grid cells) ● Gives accurate result .. glacially ● > 200ms on Geforce 570
  35. 35. Evaluating Particles, Fast ● Render single points into grid ● Write out particle position with additive blend ● Sum particle count in alpha channel ● Post process grid ● Divide by count: get average position of particles in cell ● Evaluate potentials with kernel - grid cells only ● Use grid cell average position as proxy for particles
  36. 36. Evaluating Particles, Faster valuating potentials accurately far too slow ● Summing e.g. 9x9x9 cell potentials for each cell.. ● Still > 100 ms for our test cases se separable blur to spread potentials instead ● Not quite 100% accurate.. But close enough ● Calculate blur weights with potential function to at least feign correctness
  37. 37. Visualising Distance Fields Ray Tracing & Polygonisation
  38. 38. Ray Casting See: ray marching; sphere tracing ● SDF(P) = Distance to closest point on surface ● (Closest point’s actual location not known) ● Step along ray by SDF(P) until SDF(P)~0 ● Skips empty space!
  39. 39. Ray Casting ● Accuracy depends on iteration count ● Primary rays require high accuracy ● 50-100 iterations -> slow ● Result is transitory, view dependent ● Useful for secondary rays ● Can get away with fewer iterations ● Do something else for primary hits
  40. 40. Polygonisation / Meshing ● Generate triangle mesh from SDF ● Rasterise as for any other mesh ● Suits 3D hardware ● Integrate with existing render pipeline ● Reuse mesh between passes / frames ● Speed not dependent on screen resolution ● Use Marching Cubes
  41. 41. Marching Cubes In One Slide ● Operates on a discrete grid ● Evaluate field F() at 8 corners of each cubic cell ● Generate sign flag per corner, OR together ● Where sign(F) changes across corners, triangles are generated ● 5 per cell max ● Lookup table defines triangle pattern
  42. 42. Marching Cubes Issues ● Large number of grid cells ● 128x128x128 = 2 million cells ● Only process whole grid when necessary ● Triangle count varies hugely by field contents ● Can change radically every frame ● Upper bound very large: -> size of grid ● Most cells empty: actual output count relatively small ● Traditionally implemented on CPU
  43. 43. Geometry Shader Marching Cubes PU submits a large, empty draw call ● One point primitive per grid cell (i.e. a lot) ● VS minimal: convert SV_VertexId to cell position S evaluates marching cubes for cell ● Outputs 0 to 5 triangles per cell ar too slow: 10ms - 150ms (128^3 grid, architecture-dependent) ●Workper GS instance varies greatly: poor parallelism ●Some GPU architectures handle GS very badly
  44. 44. Stream Compaction on GPU
  45. 45. Stream Compaction ● Take a sparsely populated array ● Push all the filled elements together ● Remember count & offset mapping ● Now only have to process filled part of array
  46. 46. Stream Compaction ● Counting pass - parallel reduction ● Iteratively halve array size (like mip chain) ● Write out the sum of the count of parent cells ● Until final step reached: 1 cell, the total count ● Offset pass - iterative walk back up ● Cell offset = parent position + sibling positions ● Histopyramids: stream compaction in 3D
  47. 47. Histopyramids um down mip chain in blocks (Imagine it in 3D)
  48. 48. Histopyramids ● Count up from base to calculate offsets
  49. 49. Histopyramids In Use ● Fill grid volume texture with active mask ● 0 for empty, 1 for active ● Generate counts in mip chain downwards ● Use 2nd volume texture for cell locations ● Walk up the mip chain
  50. 50. Compaction In Action se histopyramid to compact active cells ●Active cell count now known too PU dispatches drawcall only for # active cells ●Use DrawInstancesIndirect S determines grid position from cell index ●Use histopyramid for this
  51. 51. Compaction Reaction uge improvement over brute force ● ~5 ms – down from 11 ms ● Greatly improves parallelism ● Reduced draw call size eometry still generated in GS ● Runs again for each render pass ● No indexing / vertex reuse
  52. 52. Geometry Generation
  53. 53. Generating Geometry ish to pre-generate geometry (no GS) ● Reuse geometry between passes; allow indexed vertices irst generate active vertices ● Intersection of grid edges with 0 potential contour ● Remember vertex index per grid edge in lookup table ● Vertex count & locations still vary by potential field contents hen generate indices ● Make use of the vertex index lookup
  54. 54. Generating Vertex Data rocess potential grid in CS ● One cell per thread ● Find active edges in each cell utput vertices per cell ● IncrementCounter() on vertex buffer ●Returns current num vertices written ● Write vertex to end of buffer at current counter ● Write counter to edge index lookup: scattered write r use 2nd histopyramid for vertex data instead
  55. 55. Generating Geometry ow generate index data with another CS ● Histopyramid as before.. ● .. But use edge index grid lookup to locate indices ● DispatchIndirect to limit dispatch to # active cells ender geom: DrawIndexedInstancedIndirect ● GPU draw call: index count copied from histopyramid
  56. 56. Meshing Improvements
  57. 57. Smoothing
  58. 58. Smoothing More
  59. 59. Smoothing aplacian smooth ● Average vertices along edge connections ● Key for improving quality of fluid dynamics meshing ust know vertex edge connections ● Generate from index buffer in post process
  60. 60. Bucket Sorting Arrays ● Need to bucket elements of an array? ● E.g. Spatial hash; particles per grid cell; triangles connected to each vertex ● Each bucket has varying # elements ● Don’t want to over-allocate buckets ● Allocate only # elements in array
  61. 61. Counting Sort ● Use Counting Sort ● Counting pass – count # elements per bucket ● Use atomics for parallel op – InterlockedAdd() ● Compute Parallel Prefix Sum ● Like a 1d histopyramid.. See CUDA SDK ● Finds offset for each bucket in element array ● Then assign elements to buckets ● Reuse counter buffer to track idx in bucket
  62. 62. Smoothing Process ● Use Counting Sort: bucket triangles per vertex ● Post-process: determine edges per vertex ● Smooth vertices ● (Original Vertex * 4 + Sum[Connected Vertices]) / (4 + Connected Vertex Count) ● Iterate smooth process to increase smoothness
  63. 63. 0 Smoothing Iterations
  64. 64. 4 Smoothing Iterations
  65. 65. 8 Smoothing Iterations
  66. 66. 16 Smoothing Iterations
  67. 67. Subdivision, Smooth Normals ● Use existing vertex connectivity data ● Subdivision: split edges, rebuild indices ● 1 new vertex per edge ● 4 new triangles replace 1 old triangle ● Calc smooth vertex normals from final mesh ● Use vertex / triangle connectivity data ● Average triangle face normals per vertex ● Very fast – minimal overhead on total generation cost
  68. 68. Performance ● Same scene, 128^3 grid, Geforce 570 ● Brute force GS version: 11 ms per pass ● No reuse – shadowmap passes add 11ms each ● Generating geometry in CS: 2 ms + 0.4 ms per pass ● 2ms to generate geometry in CS; 0.4ms to render it ● Generated geometry reused between shadow passes
  69. 69. Video ● (Video Removed) ● (A tidal wave thing through a city. It was well cool!!!!!1)
  70. 70. Wait, Was That Fluid Dynamics? Yes, It Was.
  71. 71. Smoothed Particle Hydrodynamics olver works on particles ● Particles represent point samples of fluid in space ocate local neighbours for each particle ● Find all particles inside a particle’s smoothing radius ● Neighbourhood search – can be expensive olve fluid forces between particles within radius
  72. 72. Neighbourhood Search patially bucket particles using spatial hash eturn of Counting Sort - with a histopyramid ● In this case: hash is quantised 3D position ● Bucket particles into hashed cells
  73. 73. SPH Process – Step by Step ucket particles into cells valuate all particles.. ind particle neighbours from cell structure ● Must check all nearby cells inside search radius too um forces on particles from all neighbours
  74. 74. SPH Performance erformance depends on # neighbours evaluated ● Determined by cell granularity, particle search radius, number of particles in system, area covered by system avour small cell granularity ● Easier to reduce # particles tested at cell level alance particle radius by hand
  75. 75. SPH Performance ● In practice this is still far, far too slow (>200ms) ● Can check > 100 cells, too many particle interactions ● So we cheat.. ● Average particle positions + velocities in each cell ● Use average value for particles vs distant cells ● Force vectors produced close enough to real values.. ● Only use real particle positions for close cells
  76. 76. Illumination The Rendering Pipeline of the Future
  77. 77. Rendering Pipeline of the Future ● Primary rays are rasterised ● Fast: rasterisation still faster for typical game meshes ● Use for camera / GBuffers, shadow maps ● Secondary rays are traced ● Use GBuffers to get starting point ● Global illumination / ambient occlusion, reflections ● Paths are complex – bounce, scatter, diverge ● Needs full scene knowledge – hard for rasterisation ● Tend to need less accuracy / sharpness..
  78. 78. Ambient Occlusion Ray Tracing ● Cast many random rays out from surface ● Monte-Carlo style ● AO result = % of rays that reach sky ● Slow.. ● Poor ray coherence ● Lots of rays per pixel needed for good result ● Some fakes available ● SSAO & variants – largely horrible.. 
  79. 79. Ambient Occlusion with SDFs ● Raytrace SDFs to calculate AO ● Accuracy less important (than primary rays) ● Less SDF iterations – < 20, not 50-100 ● Limit ray length ● We don’t really “ray cast”.. ● Just sample multiple points along ray ● Ray result is a function of SDF distance at points
  80. 80. 4 Rays Per Pixel
  81. 81. 16 Rays Per Pixel
  82. 82. 64 Rays Per Pixel
  83. 83. 256 Rays Per Pixel
  84. 84. Ambient Occlusion Ray Tracing ● Good performance: 4 rays; Quality: 64 rays ● Try to plug quality/performance gap ● Could bilateral filter / blur ● Few samples, smooth results spatially (then add noise) ● Or use temporal reprojection ● Few samples, refine results temporally ● Randomise rays differently every frame
  85. 85. Temporal Reprojection ● Keep previous frame’s data ● Previous result buffer, normals/depths, view matrix ● Reproject current frame  previous frame ● Current view position * view inverse * previous view ● Sample previous frame’s result, blend with current ● Reject sample if normals/depths differ too much ● Problem: rejected samples / holes
  86. 86. Video ● (Video Removed) ● (Basically it looks noisy, then temporally refines, then when the camera moves you see holes)
  87. 87. Temporal Reprojection: Good
  88. 88. Temporal Reprojection: Holes
  89. 89. Hole Filling ● Reprojection works if you can fill holes nicely ● Easy to fill holes for AO: just cast more rays ● Cast 16 rays for pixels in holes, 1 for the rest ● Adversely affects performance ● Work between local pixels differs greatly ● CS thread groups wait on longest thread ● Some threads take 16x longer than others to complete
  90. 90. Video ● (Video Removed) ● (It looks all good cos the holes are filled)
  91. 91. Rays Per Thread
  92. 92. Hole Filling ● Solution: balance rays across threads in CS ● 16x16 pixel tiles: 256 threads in group ● Compute & sum up required rays in tile ● 1 pixel per thread ● 1 for reprojected pixels; 16 for hole pixels ● Spread ray evaluation across cores evenly ● N rays per thread
  93. 93. Rays Per Thread - Tiles
  94. 94. Video ● (Video Removed) ● (It still looks all good cos the holes are filled, by way of proof I’m not lying about the technique)
  95. 95. Performance ● 16 rays per pixel: 30 ms ● 1 ray per pixel, reproject: 2 ms ● 1 + 16 in holes, reproject: 12 ms ● 1 + 16 rays, load balanced tiles: 4 ms ● ~ 2 rays per thread typical!
  96. 96. Looking Forward
  97. 97. Looking Forward ● Multiple representations of same world ● Geometry + SDFs ● Rasterise them ● Trace them ● Collide with them ●  World can be more dynamic.
  98. 98.
  99. 99. Thanks ● Jani Isoranta, Kenny Magnusson for 3D ● Angeldawn for the Fairlight logo ● Jussi Laakonen, Chris Butcher for actually making this talk happen ● SCEE R&D for allowing this to happen ● Guillaume Werle, Steve Tovey, Rich Forster, Angelo Pesce, Dominik Ries for slide reviews
  100. 100. References ● High-speed Marching Cubes using Histogram Pyramids; Dyken, Ziegler et al. ● Sphere Tracing: a geometric method for the antialiased ray tracing of implicit surfaces; John C. Hart ● Rendering Worlds With Two Triangles; Inigo Quilezles ● Fast approximations for global illumination on dynamic scenes; Alex Evans

Editor's Notes

  • DirectX 9 is 9 years old Comfortable, well-understood API on PC &amp; console A PI itself largely an incremental update to DirectX 8 – released in 2000 Ingrained in our codebases (and minds) Shader language now has integers, atomics. Can now write to buffers by side effect in pixel shaders and a wealth of other new things besides.
  • The separation of generation of the space and the topology is key in achieving flexibility – topology is evil.
  • CSG subtract operator – just max with negative distance
  • Repeat is just frac (or fmod) on the space. Always transform the space, not the shape. The same goes for twists, bends, distorts, transforms.
  • “ Textures” are generated in realtime too – positions and normals are read from Gbuffers. The texture generation isnt dissimilar to the distance field generation, but separating them makes it easier to get performance (the detail is done in the texture so it doesn’t weigh down the distance field shader).
  • Don’t expect everyone to fire all artists and generate worlds in code
  • Bicubic interpolation – there’s a good gems articile and some nice shader code around which can do bicubic 3d texture interpolation with 8 taps (all linear filtered though) from a 3d texture. Pretty good actually!
  • Geometry shaders suck, though. Will move this to compute-side expansion at some point.
  • Can evaluate color if necessary by sampling texture OK – I lied. We don’t use a 3d texture target because depth stencil doesn’t work. We actually use a 2d texture array so we can have a matching depthstencil.
  • The problem comes when rays “just miss” the surface – they have to march a lot of iterations to get past the surface because SDF(P) is very small near to the surface Because the result is transitory it can’t be cached between frames or passes – it has to be regenerated continuously.
  • Out of patent since 2005! Wikipedia
  • Look, GS is really shit on some architectures. On older cards like my geforce 9800 in a work machine, the performance was tragic (the 150ms figure quoted – much better on the same card with histopyramids). It seems better on more modern cards and on one vendor than another, though.
  • We use stream compaction for many things .. Like particle depth sorting, SPH, triangles in grid cell, you name it.
  • Histopyramids – thanks to Gernot Ziegler and Chris Dyken.
  • Offset = parent offset in mip chain + cell’s sibling counts (that come before it in sequence)
  • Append buffers vs counter buffers: append buffers do not return the count
  • *your mileage may vary – a lot Note that it’s possible to use an appendbuffer instead of a histopyramid for generating indices here; performance vs histopyramid depends on architecture. Architecture-dependent is something that comes up a lot. Sucks, don’t it?
  • 0 iterations, 1 iteration
  • 4 iterations, 16 iterations
  • We also use this algorithm for depth sorting particles.
  • Counting sort is the first pass of radix sort. Atomics: I’ve found that performance of atomics depends on contention and differs greatly between hardware vendors. Hard to judge, hard to predict; definitely not free.
  • Why bucket triangles not connected vertices? Because it causes less writes! I also used a linked list using a counter buffer and IncrementCounter(). This was much less successful because the linked list meant that the indices per vertex were scattered all around memory – it was much slower to walk the list. Changing from a linked list to buckets gave approximately 10x speedup on some hardware.
  • GS approach scales linearly with grid size : 64^3 grid is 1.4ms The CS approach is without smoothing, subdivision or normal calculation to give a fair comparison (as the GS one doesn’t do it).
  • The particle behaviours I describe here are interesting because they demonstrate uses of spatial databases and interaction between particles and the world, and particles with other particles.
  • Particles are easier to collide with environment than grid based systems.
  • First, pre-determine bucket sizes Then compact bucket buffer Finally, put particles into correct buckets We use a histopyramid for the compaction. We actually render particles into a grid using vertex,geometry and pixel shader and use a UAV to write out the counts – it’s faster than using CS for the lot because blending beats atomics here, although that’s a case by case thing (or so I’ve found) so you need to check both ways.
  • 2x cell kernel radius = 5x5x5 cells = 125 cells! Using averaged values for distant cells works because the force direction produced doesn’t change much between the particles in the cell against the particle, and because the weight is low (1/d^2 falloff). This is something like what they do to solve N-body problems in astrophysics. 2 kernels – the inner is high quality and slow but small, the outer is large and approximated and fast.
  • Use mip levels to reduce cost of texture sampling at distance
  • About 7-8ms on a Geforce 570. We cast “nice” rays with a lot of taps and no mip filtering bias tricks. You could use a lot less taps and sacrifice some quality, but increase speed accordingly. 8 taps is pretty good and it’ll complete in more like 3ms.
  • About 30ms on a Geforce 570 – we cast nice rays
  • About 60ms on a Geforce 570
  • About 3 dog years on a Geforce 570
  • In reality what this means is, for every pixel in every tile – if the tile contains a white pixel, it will take the time of casting 16 rays for a pixel FOR THE WHOLE TILE. i.e. its slow.
  • To explain via code, the Compute Shader does this: Process 1 pixel: read pixel, reproject, determine if in hole, add required rays to list in local memory Sync For(I = threadIndex; I &lt; numRays; I += NumThreads) { Process ray; } Sync Output lit pixel
  • The rays are balanced across the tiles, so there’s no harsh peaks.
  • Without balancing the 1+16 is only about twice as fast as doing 16 rays per pixel; with balancing its much faster.
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