I’ve started an effort to clean up, rewrite, and enhance my ObjCore library, and part of that effort includes taking my KD-Tree viewer from Takua Render and making it just a standard component of ObjCore. As a result, I can now plug the latest version of ObjCore into any of my projects that use it and quickly wire up support for viewing the KD-Tree view for that project. Here’s the jello sim from a few months back visualized as a KD-Tree:

I’ve adopted a new standard grey background for OpenGL tests, since I’ve found that the higher amount of contrast this darker grey provides plays nicer with Vimeo’s compression for a clearer result. But of course I’ll still post stills too.

Hopefully at the end of this clean up process, I’ll have ObjCore in a solid enough of a state to post to Github.

I’ve been meaning to add animation support to my volume renderer for demoreel purposes for a while now, so I did that this week! Here’s a rotating cloud animation:

…and of course, a still or two:

Instead of just rotating the camera around the cloud, I wanted for the cloud itself to rotate but have the noise field it samples stay stationary, resulting in a cool kind of morphing effect with the cloud’s actual shape. In order to author animations easily, I implemented a fairly rough, crude version of Maya integration. I wrote a script that will take spheres and point lights in Maya and build a scene file for my volume renderer using the Maya spheres to define cloud clusters and the point lights to define… well… lights. With an easy bit of scripting, I can do this for each frame in a keyframed animation in Maya and then simply call the volume renderer once for each frame. Here’s what the above animation’s Maya scene file looks like:

Also, since my pseudo-blackbody trick was originally intended to simulate the appearance of a fireball, I tried creating an animation of a fireball by just scaling a sphere:

…and as usual again, stills:

So that’s that for the volume renderer for now! I think this might be the end of the line for this particular incarnation of the volume renderer (it remains the only piece of tech I’m keeping around that is more or less unmodified from its original CIS460/560 state). I think the next time I revisit the volume renderer, I’m either going to port it entirely to CUDA, as my good friend Adam Mally did with his, or I’m going to integrate it into my renderer project, Peter Kutz style.

Every once in a while, I return to trying to make a good looking tree. Here’s a frame from my latest attempt:

Have I finally managed to create a tree that I’m happy with? Well….. no. But I do think this batch comes closer than previous attempts! I’ve had a workflow for creating base tree geometry for a while now that I’m fairly pleased with, which is centered around using OnyxTREE as a starting point and then custom sculpting in Maya and Mudbox. However, I haven’t tried actually animating trees before, and shading trees properly has remained a challenge. So, my goal this time around was to see if I could make any progress in animating and shading trees.

As a starting point, I played with just using the built in wind simulation tools in OnyxTREE, which was admittedly difficult to control. I found that having medium to high windspeeds usually led to random branches glitching out and jumping all over the place. I also wanted to make a weeping willow style tree, and even medium-low windspeeds often resulted in the hilarious results:

A bigger problem though was the sheer amount of storage space exporting animated tree sequences from Onyx to Maya requires. The only way to bring Onyx simulations into programs that aren’t 3ds Max is to export the simulation as an obj sequence from Onyx and then import the sequence into whatever program. Maya doesn’t have a native method to import obj sequences, so I wrote a custom Python script to take care of it for me. Here’s a short compilation of some results:

One important thing I discovered was that the vertex numbering in each obj frame exported from Onyx remains consistent; this fact allowed for an important improvement. Instead of storing a gazillion individual frames of obj meshes, I experimented with dropping a large number of intermediate frames and leaving a relatively smaller number of keyframes which I then used as blendshape frames with more scripting hackery. This method works rather well; in the above video, the weeping willow at the end uses this approach. There is, however, a significant flaw with this entire Onyx based animation workflow: geometry clipping. Onyx’s system does not resolve cases where leaves and entire branches clip through each other… while from a distance the trees look fine, up close the clipping can become quite apparent. For this reason, I’m thinking about abandoning the Onyx approach altogether down the line and perhaps experimenting with building my own tree rigs and procedurally animating them. That’s a project for another day, however.

On the shading front, my basic approach is still the same: use a Vray double sided material with a waxier, more specular shader for the “front” of the leaves and a more diffuse shader for the “back”. In real life, leaves of course display an enormous amount of subsurface scattering, but leaves are a special case for subsurface scatter: they’re really really thin! Normally subsurface scattering is a rather expensive effect to render, but for thin material cases, the Vray double sided material can quite efficiently approximate the subsurface effect for a fraction of the rendertime.

Bark is fairly straightforward to, it all comes down to the displacement and bump mapping. Unfortunately, the limbs in the tree models I made this time around were straight because I forgot to go in and vary them up/sculpt them. Because of the straightness, my tree twigs don’t look very good this time, even with a decent shader. Must remember for next time! Creating displacement bark maps from photographs or images sourced from Google Image Search or whatever is really simple; take your color texture into Photoshop, slam it to black and white, and adjust contrast as necessary:

Here’s a few seconds of rendered output with the camera inside of the tree’s leaf canopy, pointed skyward. It’s not exactly totally realistic looking, meaning it needs more work of course, but I do like the green-ess of the whole thing. More importantly, you can see the subsurface effect on the leaves from the double sided material!

Something that continues to prove challenging is how my shaders hold up at various distances. The same exact shader (with a different leaf texture), looks great from a distance, but loses realism when the camera is closer. I did a test render of the weeping willow from further away using the same shader, and it looks a lot better. Still not perfect, but closer than previous attempts:

…and of course, a pretty still or two:

A fun experiment I tried was building a shader that can imitate the color change that occurs as fall comes around. This shader is in no way physically based, it’s using just a pure mix function controlled through keyframes. Here’s a quick test showing the result:

Eventually building a physically based leaf BSSDF system might be a fun project for my own renderer. Speaking of which, I couldn’t resist throwing the weeping willow model through my KD-tree library to get a tree KD-tree:

Since the Vimeo compression kind of borks thin lines, here’s a few stills:

Alright, that’s all for this time! I will most likely return to trees yet again perhaps a few weeks or months from now, but for now, much has been learned!

Just a heads up, this post is admittedly more of a brain dump for myself than it is anything else.

A while back I implemented a couple of fast methods to generate random points on geometry surfaces, which will be useful for a number of applications, such as direct lighting calculations involving area lights.

The way I’m sampling random points varies by geometry type, but all methods are pretty simple. Right now the system is implemented such that I can give the renderer a global point density to follow, and points will be generated according to that density value. This means the number of points generated on each piece of geometry is directly linked to the geometry’s surface area.

For spheres, the method I use is super simple: get the surface area of the sphere, generate random UV coordinates, and map those coordinates back to the surface of the sphere. This method is directly pulled from this Wolfram Mathworld page, which also describes why the most naive approach to point picking on a sphere is actually wrong.

My approach for ellipsoids unfortunately is a bit brute force. Since getting the actual surface area for an ellipsoid is actually fairly mathematically tricky, I just approximate it and then use plain old rejection sampling to get a point.

Boxes are the easiest of the bunch; find the surface area of each face, randomly select a face weighted by the proportion of the total surface area that face comprises, and then pick a random x and y coordinate on that face. The method I use for meshes is similar, just on potentially a larger scale: find the surface area of all of the faces in the mesh and select a face randomly weighted by the face’s proportion of the total surface area. Then instead of generating random cartesian coordinates, I generate a random barycentric coordinate, and I’m done.

The method that I’m using right now is purely random, so there’s no guarantee of equal spacing between points initially. Of course, as one picks more and more points, the spacing between any given set of points will converge on something like equally spaced, but that would take a lot of random points. I’ve been looking at this Dart Throwing On Surfaces Paper for ideas, but at least for now, this solution should work well enough for what I want it for (direct lighting). But we shall see!

Also, as I am sure you can guess from the window chrome on that last screenshot, I’ve successfully tested Takua Render on Linux! Specifically, on Fedora!

I finally got around to doing a long overdue piece of analysis on Takua Render: looking at the impact of ray bounce depth on performance and on the final image.

Of course, in real life, light can bounce around (almost) indefinitely before it is either totally absorbed or enters our eyeballs. Unfortunately, simulating this behavior completely is extremely difficult in any type of raytracing solution because in a raytrace solution, letting a ray bounce around indefinitely until it does something interesting can lead to extremely extremely long render times. Thus, one of the first shortcuts that most raytracing (and therefore pathtracing) systems take is cutting off rays after they bounce a certain number of times. This strategy should not have much of an impact on the final visual quality of a rendered image, since the more a light ray bounces around, the less each successive bounce contributes to the final image anyway.

With that in mind, I did some tests with Takua Render in hopes of finding a good balance between ray bounce depth and quality/speed. The following images or a glossy white sphere in a Cornell Box were rendered on a quad-core 2.5 GhZ Core i5 machine.

For a reference, I started with a render with a maximum ray bounce depth of 50 and 200 samples per pixel:

Then I ran a test render with a maximum of just 2 bounces; essentially, this represents the direct lighting part of the solution only, albeit generated in a Monte Carlo fashion. Since I made the entire global limit 2 bounces, no reflections show up on the sphere of the walls, just the light overhead. Note the total lack of color bleeding and the dark shadow under the ball.

The next test was with a maximum of 5 bounces. In this test, nice effects like color bleeding and indirect illumination are back! However, compared to the reference render, the area under the sphere still has a bit of dark shadowing, much like what one would expect if an ambient occlusion pass had been added to the image. While not totally accurate to the reference render, this image under certain artistic guidelines might actually be acceptable, and renders considerably faster.

Differencing the 5 bounce render from the reference 50 bounce render shows that the 5 bounce one is ever so slightly dimmer and that most of the difference between the two images is in the shadow area under the sphere. Ignore the random fireflying pixels, which is just a result of standard pathtracing variance in the renders:

The next test was 10 bounces. At 10 bounces, the resultant images is essentially visually indistinguishable from the 50 bounce reference, as shown by the differenced image included. This result implies that beyond 10 bounces, the contributions of successive bounces to the final image are more or less negligible.

Finally, a test with a maximum of 20 bounces is still essentially indistinguishable from both the 10 bounce test and the 50 bounce reference:

Interestingly, render times do not scale linearly with maximum bounce depth! The reason for this relationship (or lack thereof) can be found in the fact that the longer a ray bounces around, the more likely it is to find a light source and terminate. At 20 bounces, the odds of a ray finding a light source is very very close to the odds of a ray finding a light source at 50 bounces, explaining the smallness of the gap in render time between 20 and 50 bounces (especially compared to the difference in render time between, say, 2 and 5 bounces).

Lately progress on my Takua Render project has slowed down a bit, since over this summer I am interning at Dreamworks Animation during weekdays. However, in the evenings and on weekends I am still been working at stuff!

Something that I never got around to doing for no particularly good reason was visualizing my KD-tree implementation. As such, I’ve known for a long time that my KD-tree is suboptimal, but have not actually been able to quickly determine to what degree my KD-tree is inefficient. However, since I now have a number of OpenGL based diagnostic views for Takua Render, I figured I no longer had a good excuse to not visualize my KD-tree. So last night I did just that! Here is what I got for the Stanford Dragon:

Just as I suspected, my KD-tree implementation was far from perfect. Some rough statistics I had my renderer output told me that even with the KD-tree, the renderer was still performing hundreds to even thousands of intersection tests against meshes. The above image explains why: each of those KD-tree leaf nodes are enormous, and therefore contain an enormous amount of objects!

Fortunately, after a bit of tinkering, I discovered that there’s nothing actually wrong with the KD-tree implementation itself. Instead, the sparseness of the tree is coming from how I tuned the tree building operation. With a bit of tinkering, I managed to get a fairly improved tree:

…and with a bit more of tuning and playing with maximum recursion depths:

Previously, my KD-tree construction routine based the construction on only a maximum recursion depth; after the tree reached a certain height, the construction would stop. I’ve now modified the construction routine to use three separate criteria: a maximum recursion depth, minimum node bounding box volume, and a minimum number of objects per node. If any node meets any of the above three conditions, it is turned into a leaf node. As a result, I can now get extremely dense KD-trees that only have on average a low-single-digit number of objects per leaf node, as opposed to the average hundreds of objects per leaf node before:

In theory, this improvement should allow for a fairly significant speedup, since the number of intersections per mesh should now be dramatically lower, leading to much higher ray throughput! I’m currently running some benchmarks to determine just how much of a performance boost better KD-trees will give me, and I’ll post about those results soon!