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XRT is a raytracing based programmable rendering system for photorealistic image synthesis built around a plugin architecture design.
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Surge or slump ?
Posted: 23 Dec 2011 12:42
Tags: examples
I have slightly expanded the XRT examples archive.
 The source for the global illumination example seen here is now included.
 The Menger Sponge procedural example has been updated.
 For those puzzled by fractals, I offer this alternative:


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Two CGKit animations
Posted: 03 Dec 2011 09:34
Tags: animation cgkit
Last month, I have stumbled upon two animations from Roger Stuckey made with CGKit. CGKit is a really neat collection of Python modules dedicated to 3D graphics. Amongst a plethora of features, you can run rigid body dynamics simulations and export them to RenderMan RIB format. Here they are, rendered with 256 ambient occlusion samples (a rather high number required to avoid noise flickering across frames). I have tweaked a bit the original scripts mainly to reduce the size ot the generated RIBs (instead of a 500+ MB file, the teapots animation RIB is now less than 3 MB). If you want to pay a look, here are links to the files: pyode_render_ex3.py for the blocks animation and pyode_render_ex4.py for the teapots animation.
Falling Blocks
Teapots
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It's a wonderful world ...
Posted: 04 Nov 2011 23:59
Tags: gallery hair
While browsing the Internet for free models, the harvest has been very good and I wish to thank all these people who kindly made all this data available.
 Ingo Wald (The Utah 3D Animation Repository)
 Robert Sumner (Deformation Transfer Data)
 Cem Yuksel (Hair Model Files)
 Morgan McGuire (McGuire GraphicsData)
New eye candies
I have created new galleries for all of them:
 The Utah 3D Animation Repository (here)
 Deformation Transfer Data (here)
 Hair Model Files (here)
 McGuire GraphicsData (here)
Some of them are far from being complete (especially McGuire GraphicsData gallery). Properly setting up cameras and lights is awfully time consuming, and XRT runs out of memory when scenes contain more than 1.5 million primitives.
Hair raising (literaly)
I also spent quite some time on the Hair Models gallery. Rendering curves is not trivial but shading them so that they look like real hair is really challenging. I started with an old Renderman shader from the 1999 Siggraph course "Advanced renderman: Beyond the Companion" but it did not really cut it. A couple of Google searches later, I knew that my shader was based on a KayKajiya hair model [1] which has been superseded by the Marschner hair model [2]. Not being discouraged by hairy formulas, I found a Marschner shader here itself based on an open source implementation from the Cortex project.
All pictures of the gallery are based on this shader, slightly extended: to account for hair selfshadowing, all shading calculations are modulated by ambient occlusion which I believe greatly improves realism. Nevertheless, it is still very far from what you can see in Disney's "Tangled" for instance. The state of the art seems to be Zinke hair model [3] from which Sadeghi derived the Renderman shader [4] used in that movie. I have not yet found enough courage to dig the formulas …
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XRT 1.2.0 released
Posted: 02 Oct 2011 13:22
Tags: downloads
Apart from the improved BVH accelerator highlighted in my previous post, there are two other important changes in this version:
 the code is now compiled with Visual C++ 2010. That affects the list of dependencies (listed in the Downloads page) required to run XRT.
 a new OBJ generator imports geometry and materials from the Alias/Wavefront object format. There are still some rough edges (especially, I need to extend the list of supported illumination models) but I can already render nice scenes such as the Crytek Sponza Atrium or the Ajax model from Jotero. Given the wealth of OBJ models available on the Internet, the gallery will get even bigger!
For a complete list of changes, see the change log.
Post scriptum
The BVH accelerator code makes heavy use of SSE4 instructions. As a result, it will bomb on older processors that do not support this instruction set. If this is a problem for anyone, I can still provide an SSE4less accelerator.
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To SAH or not to SAH ?
Posted: 16 Sep 2011 19:39
Tags: bvh embree sah
This post should have dealt with CatmullClark subdivision mesh texturing but this development is not yet completed.
The "culprit" is Intel which has recently released Embree, an opensource renderer. While it's a pretty decent renderer in its own right, do not expect fancy shaders, motion blur, or high quality texturing: its main purpose is to demonstrate how to efficiently build and traverse ray tracing acceleration structures on Intel architectures in parallel fashion. In this area, it fares very well.
Embree features four different acceleration structures based on BVH (bounding volume hierarchies): two different object partitionings (SAH^{1} and split BVH^{2}) times two branching factors (the number of children per node in the tree) for the BVH (2 and 4). To complete the comparison, I have added a median cut object partitioning which makes for a total of six different acceleration structures. The benchmarks are unambiguous: median cut is slower than SAH which in turn is slower than split BVH and BVH4 is faster than BVH2 whatever is the object partitioning.
Therefore, I have decided to remove the dust from my BVH accelerator (based on a median cut object partitionning) by reusing Embree software. The results are somewhat mixed: overall rendering times are decreased (the larger the numbers of primitives, the larger the gains), BVH4 is slightly faster than BVH2 but median cut is faster than SAH which is faster than split BVH. That completely contradicts available computer graphics litterature and means there is something fishy.
To be honest, I am not so surprised: I had already tried my luck with my own SAH BVH implementation with the same results. The difference is that I had concluded that I was to blame and that I had better to wait for a model implementation before drawing any conclusion. Now I can say the problem is not the implementation.
A possible explanation is that the traversal and intersection costs for my architecture have the wrong values which leads to wrong decisions and a low quality hierarchy. Embree deals only with triangles which it intersect four at a time using SSE. XRT handles a large array of primitives types (some of them are really expensive to intersect) and intersects them one at a time using plain x86 code. However, I get the same result with triangles only scenes which makes me doubt it is the right reason. This requires further testing.
In the mean time, the tree traversal is much faster than before and this new BVH implementation will supersede the previous.
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CatmullClark subdivision surfaces: holes and boundaries
Posted: 12 Jun 2011 10:37
Tags: catmull clark subdivision surface
As promised, this post describes the remaining features in CatmullClark subdivision surfaces: holes and boundaries. This is going to be a lot more straightforward than the previous posts
Holes
A hole is a face or a group of faces that will not generate geometry to be drawn. You could say, why bother and specify data that will not be seen? Remember that a face depends on its 1neighbourhood. Therefore, when a subdivision iteration occurs, a face next to a hole is influenced by it even if it is not to be seen.
Putting it all together 
crease, hole and two corners 
Boundaries
So far, so good but for faces abutting the boundary of an open mesh, their 1neighbourhood is not complete and smooth subdivision rules as defined in this post do not apply. There are three possible interpolation strategies to handle this particular case:
 no interpolation: boundary faces are treated like holes and will not be rendered
 edge interpolation: boundary edges are tagged as infinitely sharp creases (which do not require a 1neighbourhood for subdivision). All boundary vertices become implicitely crease vertices.
 full interpolation: boundary edges are tagged as infinitely sharp creases and boundary vertices of valence 2 (in general, these correspond to geometric corners) are tagged as infinitely sharp corners. If you have abutting control meshes, you get abutting limit surfaces but they are only C0continuous.
Let's have a quick example to illustrate all of these:



Not yet ready …
This ends the series of posts on CatmullClark but it does not mean I am ready for delivery. Although I have made good progress with the implementation, there are still rough edges to be ironed out, the biggest one being the lack of texturing support for this primitive.
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Gallery slideshow
Posted: 09 Jun 2011 21:14
Tags: gallery
As the gallery is growing, there are more and more pages to browse. If you just want to skim rapidly through all pictures, there is now a slideshow available in the main Gallery page. For good measure, a thumbnail version of it is also available on the Blog page to wet the appetite of any visitor. Kudos to Wikidot for making embedding gadgets so easy !
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CatmullClark subdivision surfaces: corners and creases
Posted: 28 May 2011 12:41
Tags: catmull clark subdivision surface
This post delves a bit further into the realm of subdivision surfaces. Building upon the lore gathered in the previous post, it describes extensions to the CatmullClark scheme for modeling fillets and blends.
Sharp features
Because smooth only surfaces are of limited use, Pixar people have introduced sharp features into CatmullClark subdivision surfaces[1]: corners and creases. A few more rules (let's call them sharp rules) have been added to the smooth subdivision rules.
If you remember my previous post, the subdivision produces three types of vertices:
 face vertex. The vertex uses always the smooth rule
 edge vertex. For each edge tagged as sharp, the new vertex is the average of the edge's endpoints. The new subedges are also tagged as sharp. The other edges use the smooth rule.
 control vertex. For each control vertex of the mesh, the vertex is moved to a new location that depends on the number of sharp edges incident at the vertex.
 If this number is less than 2, the vertex uses the smooth rule.
 If the number equals 2, the vertex uses the crease vertex rule: the new vertex is a weighted average of the old vertex location (3/4) and of the two other endpoints of the incident creases (1/8).
 If the number is more than 2, the vertex uses the corner rule: the vertex does not move under subdivision.
 Of course, if a vertex is tagged as sharp, it uses the corner rule whatever is the number of incident sharp edges.
Here is a small example of how a surface changes, just by tagging edges or vertices. The control mesh is the same for each picture.



In this example, four edges meet at the same vertex, implicitely making a corner out of it.
Implicit corner 
Semisharp features
In the real world, surfaces are never infinitely sharp. Anything when viewed sufficiently closely is smooth. Another refinement of the subdivision rules is needed to obtain semisharp features. For that purpose, sharp edges and corners are weighted by a sharpness parameter. This parameter is decreased by 1 at each iteration of the subdivision process and controls which rules are applied to sharp features.
* If the sharpness is greater or equal to 1, sharp subdivsion rules are applied
* If the sharpness is between 0 and 1, the result is a linear interpolation of the smooth rule and of the sharp rule using the sharpness value.
* If the sharpness is lower or equal to 0, smooth subdivision rules are applied.
This is a very intuitive mechanism which behaves like levels of detail. At coarser levels of the subdivision, features are sharp and become smooth at finer levels.
Here is how the smooth surface of the first paragraph is affected by various sharpness values.



In the following animation, the sharpness varies between 0 and 10 with a 0.1 step.
Notice that a crease does not need to be not a closed contour. In the next example, only three of the four upper edges are tagged as creases with a sharpness of 4.0.
Open crease 
More to come
We are not yet done with subdivision surfaces. The next post will describe how to deal with holes in the control mesh and boundaries for open meshes.
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CatmullClark subdivision surfaces: the basics
Posted: 04 May 2011 21:21
Tags: catmull clark subdivision surface
Introduction
The basic idea of subdivision surface is to construct a surface from an arbitrary polyhedron (the control mesh) by repeatedly subdividing each of the faces. If the subdivision is done appropriately, the limit of this subdivision process will be a smooth surface. Many subdivision schemes (or set of rules) have been defined over the years (Loop, DooSabin, …) but CatmullClark scheme [1] and its extensions [2] are by far the most popular.
Why do modelers prefer subdivision surfaces over NURBS ?
A NURBS surface, like any other parametric surface, is limited to representing surfaces which are topologically equivalent to a sheet, a cylinder or a torus. Because the control mesh is rectangular, refining a NURBS affects the whole surface. By contrast, subdivision surfaces can be refined locally by adding points to the control mesh, giving more freedom to the modeler. Therefore, support for this primitive is now ubiquitous in all highend renderers.
CatmullClark scheme
This subdivision produces three types of vertices:
 face vertex. For each face, the new vertex is the average of the face's vertices
 edge vertex. For each edge, the new vertex is the average of the edge's endpoints and the new face vertices of the two faces that share the edge.
 control vertex. For each control vertex of the mesh, the vertex is moved to a new location that is (n2)/n times the old vertex location, plus 1/n times the average of the n adjacent new edge vertex positions, plus 1/n times the average of the n adjacent new face vertex positions, where n is the valence of the vertex^{1}.
For each face, new faces are created by connecting these new vertices as follows: each new face vertex is connected to the new edge vertices of the boundary of the face; each new control vertex is connected to the new edge vertices created for the edges incident with the old control vertex.
These rules are valid only for closed meshes (which have no boundaries). They must be extended for open meshes. This will be the subject for another post.
A few nice properties
This looks complicated and really is. Studying how these rules work, a number of properties can be observed:
 For CatmullClark subdivision surfaces, each face has an associated limit surface patch that is completely defined by the face and its 1neighborhood. That means a subdivision surface can be broken down into individual components that are rendered separately, just like a polygon mesh can be rendered as individual polygons.
 Each new face is rectangular with two edge vertices, one control vertex and one face vertex.
 The face vertices corresponding to the original non rectangular faces are extraordinary vertices^{2}.
 The new control vertices keep their valence.
 Since there are only rectangular faces after the first iteration, the number of extraordinary vertices remain constant and there is at most one extraordinary vertex per face from the second iteration onward .
 When subdividing an irregular patch^{3}, the result is another irregular patch whose extraordinary vertex has the same valence and three regular patches.
Rendering
Rendering subdivision surfaces is difficult: it is not too hard to implement subdivision rules but the limit surface has no analytical expression and the control mesh can have any arbitrary topology. There are different schools of thought: some tesselate away the control mesh using subdivision rules until the resulting polygons are small enough at the expense of considerable memory usage (at each iteration, a CatmullClark scheme quadruples the number of polygons), others try to approximate the surface using bicubic patches.
XRT implements a third approach advocated in [3]. The subdivision is performed on the fly with the required precision which saves memory and avoids artifacts but increases rendering times.
Algorithm outline
The algorithm makes use of the CatmullClark scheme properties to simplify the topology of the control mesh. It performs one or two levels of subdivision to obtain a control mesh made of quads with at most one extraordinary vertex (this cuts down drastically the number of special cases) and breaks down the mesh into individual faces with their 1neighborhood. In most cases, these are regular patches with 16 control points. During the intersection calculation, if the ray misses the patch bounding box, the patch is discarded, otherwise it is subdivided until it looks flat or small enough. The face can then be safely approximated as a bilinear patch which is checked for intersection.
Holger Dammertz's thesis is rather sketchy and only details regular patch subdivision. Nevertheless, the algorithm can be extended to handle open meshes with corners and creases.
Conclusion
This post has been rather long and technical. It was needed to setup the foundations. Next posts will describe the incremental changes required for a full fledged implementation of CatmullClark subdivision surfaces.
For now, XRT only supports closed meshes: open meshes require a set of additional rules which are being implemented. Many thanks to Kevin Beason for putting its subdivision software in the public domain. It has been a great starting point to build upon.
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XRT 1.1.1 released
Posted: 13 Feb 2011 13:31
Tags: downloads gallery luxrays
There is nothing really striking in this new release, this is just a collection of bug fixes and improvements that I have brought to XRT while trying to render the classroom scene (for a larger rendering look into the LuxRays gallery) and a bunch of other scenes from Brigham Young University (BYU) CS655 Courseware (gallery here).
Here is a quick summary (for a complete list, see the change log):
 improved sampling for area lights and distributed specular rays. My previous post explains in detail the ins and outs of proper sample generation.
 improved ply generator. Depending on a user attribute (int user:plyGenerateNormals), it is now possible to average normals on vertices of a PLY mesh. This is mainly useful to give a rounded appearance to curved objects that have been tesselated (for instance, the chairs in the classroom).
 deprecated LookAt call. It was not orthogonal with Camera and World calls and I have never able to come up with a clear and concise explanation on how to use it. These are genuine signs of a flawed design. When I finally figured out that, in most cases, I could do the same thing with Python, it was just about time to remove it. The StructureSynth exporter and a few other generators have been overhauled to take this change into account. There is now a tiny script in $(XRT_HOME)/inputs, lookat.py, that takes care of computing the viewing transformation. A nice side effect is that, now, the StructureSynth exporter is compatible with Gelato.
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Bring da noise down
Posted: 08 Jan 2011 22:16
Tags: sampling
Computing a picture is basically sampling the world to reconstruct a signal (the color information). The question is: how many samples do you need ? Claude Shannon, in 1949, replied: "A signal can be reconstructed exactly if it is sampled, at least, at twice its maximum frequency". This minimum sampling frequency is called the Nyquist frequency. What happens if your sampling frequency is below that threshold ? You get geometrical, color or temporal aliasing (jaggies, Moiré patterns, wagon wheel effect) which is visually disturbing.
In the real world, geometric details range from kilometers to sub millimeters and light comes from all directions, there are abrupt geometric or color transitions which set the Nyquist frequency to very high values. Theoretically, you would need zillions of samples to remove aliasing. However, if you consider that your eye has even less sensors than the CCD of the average cellphone camera but still never ever aliases, it seems like Mother Nature has found a good solution. In 1983, Yellott had a decisive interview with a monkey [1].
Monkey eye cone distribution 
At first sight, the cones only look like random dots. However, the frequency analysis of the spatial distribution is much more revealing.
Fourier transform 
This is a blue noise spectrum (or Poisson disk distribution) which has two interesting properties:
 there is no low frequency energy (the empty ring around the spike): there is always a minimum distance between samples.
 there is an equal amount of energy in higher frequencies (the white ring): samples are noisy but uniformly distributed
Thanks to its cone distribution, our eye converts aliasing energy into high frequency noise for which our visual system is less sensitive. You cannot remove aliasing but you can mitigate its impact on the quality of the final image. Therefore, a good sample distribution should strive to mimic a Poisson disk distribution pattern.
However, generating a Poisson distribution in a efficient manner, with a accurate control of the number of samples is still an open problem. There are only approximating distributions available. Armed with these bits of theoretical knowledge, we can now explain the behaviour of various sample distributions^{1}.
A uniform distribution satisfies the minimum distance requirement but fails to distribute evenly the aliasing energy. To make the matter worse, it concentrates it in a coherent manner producing repeating patterns. Conversely, a random distribution distributes evenly the aliasing energy but fails to respect the minimum distance requirement: this results in low frequency noise which is distracting for the eye.
A stratified jittered distribution does a better job with respect to the minimum distance requirement but does not completely fullfill it: there is no way to avoid that two samples from adjacent strata do not come too close. Most of low frequency noise is eliminated but not all. Finally, lowdiscrepancy^{2} distributions try to obtain stratified samples while avoiding clumps. They exhibit the best results.
There are many lowdiscrepancy distributions (Halton, Hammersley, Van Der Corput, Sobol', …). The next XRT release will use (0,2) sequence^{3} to sample things like area lights, soft shadows, glossiness or translucency.
As a concrete example, let's compare how an area light shadow is rendered when sampled with a random distribution and with a (0,2) sequence.


The area light is sampled with only 4 samples to highlight sampling efficiency problems. Even without zooming the pictures, it is obvious that the shadows that surround the dresser are noisier with the random sampler. Quality matters !