Complex Room Renders

Rendered in Takua a0.5 using VCM. Model credits in the post below.

I realize I have not posted in some weeks now, which means I still haven’t gotten around to writing up Takua a0.5’s architecture and VCM integrator. I’m hoping to get to that once I’m finished with my thesis work. In the meantime, here are some more pretty pictures rendered using Takua a0.5.

A few months back, I made a high-complexity scene designed to test Takua a0.5’s capability for handling “real-world” workloads. The scene was also designed to have an extremely difficult illumination setup. The scene is an indoor room that is lit primarily from outside through glass windows. Yes, the windows are actually modeled as geometry with a glass BSDF! This means everything seen in these renders is being lit primarily through caustics! Of course, no real production scene would be set up in this manner, but I chose this difficult setup specifically to test the VCM integrator. There is a secondary source of light from a metal cylindrical lamp, but this light source is also difficult since the actual light is emitted from a sphere light inside of a reflective metal cylinder that blocks primary visibility from most angles.

The flowers and glass vase are the same ones from my earlier Flower Vase Renders post. The original flowers and vase are by Andrei Mikhalenko, with custom textures of my own. The amazing, colorful Takua poster on the back wall is by my good friend Alice Yang. The two main furniture pieces are by ODESD2, and the Braun SK4 record player model is by one of my favorite archviz artists, Bertrand Benoit. The teapot is, of course, the famous Utah teapot. All textures, shading, and other models are my own.

As usual, all depth of field is completely in-camera and in-renderer. Also, all BSDFs in this scene are fairly complex; there is not a single simple diffuse surface anywhere in the scene! Instancing is used very heavily; the wicker baskets, notebooks, textbooks, chess pieces, teacups, and tea dishes are all instanced from single pieces of geometry. The floorboards are individually modeled but not instanced, since they all vary in length and slightly in width.

A few more pretty renders, all rendered in Takua a0.5 using VCM:

Closeup of Braun SK4 record player with DOF. Rendered using VCM.

Flower vase and tea set. Rendered using VCM

Floorboards, textbooks, and rough metal bin with DOF. The book covers are entirely made up. Rendered using VCM.

Note On Images

Just a quick note on images on this blog. So far, I’ve generally been embedding full resolution, losslessly compressed PNG format images in the blog. I prefer having the full resolution, lossless images available on the blog since they are the exact output from my renderer. However, full resolution lossless PNGs can get fairly large (several MB for a single 1920x1080 frame), which is dragging down the load times for the blog.

Going forward, I’ll be embedding lossy compressed JPG images in blog posts, but the JPGs will link through to the full resolution, lossless PNG originals. Fortunately, high quality JPG compression is quite good these days at fitting an image with nearly imperceptible compression differences into a much smaller footprint. I’ll also be going back and applying this scheme to old posts too at some point.

Addendum 04/08/2016: Now that I am doing some renders in 4K resolution (3840x2160), it’s time for an addendum to this policy. I won’t be uploading full resolution lossless PNGs for 4K images, due to the overwhelming file size (>30MB for a single image, which means a post with just a handful of 4K images can easily add up to hundreds of MB). Instead, for 4K renders, I will embed a downsampled 1080P JPG image in the post, and link through to a 4K JPG compressed to balance image quality and file size.


Just a quick update on future plans. Starting in July, I’m going to be working full time for Walt Disney Animation Studios as a software engineer on their custom, in-house Hyperion Renderer. I couldn’t be more excited about working with everyone on the Hyperion team; ever since the Sorted Deferred Shading paper was published two years ago, I’ve thought that the Hyperion team is doing some of the most interesting work there is in the rendering field right now.

I owe an enormous thanks to everyone that’s advised and supported and encouraged me to continue exploring the rendering and graphics world. Thanks, Joe, Don, Peter, Tony, Mark, Christophe, Amy, Fran, Gabriel, Harmony, and everyone else!

Normally as a rule I only post images to this blog that I made or have a contribution to, but this time I’ll make an exception. Here’s one of my favorite stills from Big Hero 6, rendered entirely using Hyperion and lit by Angela McBride, a friend from PUPs 2011! Images like this one are an enormous source of inspiration to me, so I absolutely can’t wait to get started at Disney and help generate more gorgeous imagery like this!

A still from Big Hero 6, rendered entirely using Hyperion. Property of Walt Disney Animation Studios.

BSDF System

Takua a0.5’s BSDF system was particularly interesting to build, especially because in previous versions of Takua Render, I never really had a good BSDF system. Previously, my BSDFs were written in a pretty ad-hoc way and were somewhat hardcoded into the pathtracing integrator, which made BSDF extensibility very difficult and multi-integrator support nearly impossible without significant duplication of BSDF code. In Takua a0.5, I’ve written a new, extensible, modularized BSDF system that is inspired by Mitsuba and Renderman 19/RIS. In this post, I’ll write about how Takua a0.5’s BSDF system works and show some pretty test images generated during development with some interesting models and props.

First, here’s a still-life sort of render showcasing a number of models with a number of interesting materials, all using Takua a0.5’s BSDF system and rendered using my VCM integrator. All of the renders in this post are rendered either using my BDPT integrator or my VCM integrator.

Still-life scene with a number of interesting, complex materials created using Takua a0.5's BSDF system. The chess pieces and notebooks make use of instancing. Rendered in Takua a0.5 using VCM.

BSDFs in Takua a0.5 are designed to support bidirectional evaluation and importance sampling natively. Basically, this means that all BSDFs need to implement five basic functions. These five basic functions are:

  • Evaluate, which takes input and output directions of light and a normal, and returns the BSDF weight, cosine of the angle of the input direction, and color absorption of the scattering event. Evaluate can also optionally return the probability of the output direction given the input direction, with respect to solid angle.
  • CalculatePDFW, which takes the input and output directions of light and a normal, and returns the forward probability of the output direction given the input direction. In order to make the BSDF operate bidirectionally, this function also needs to be able to return the backwards probability if the input and output are reversed.
  • Sample, which takes in an input direction, a normal, and a random number generator and returns an output direction, the BSDF weight, the forward probability of the output direction, and the cosine of the input angle.
  • IsDelta, which returns true if the BSDF’s probability distribution function is a Dirac delta function and false otherwise. This attribute is important for allowing BDPT and VCM to handle perfectly specular BSDFs correctly, since perfectly specular BSDFs are something of a special case.
  • GetContinuationProbability, which takes in an input direction and normal and returns the probability of ending a ray path at this BSDF. This function is used for Russian Roulette early path termination.

In order to be correct and bididirectional, each of these functions should return results that agree with the other functions. For example, taking the output direction generated by Sample and calling Evaluate with the Sample output direction should produce the same color absorption and forward probability and other attributes as Sample. Sample, Evaluate, and CalculatePDFW are all very similar functions and often can share a large amount of common code, but each one is tailored to a slightly different purpose. For example, Sample is useful for figuring out a new random ray direction along a ray path, while Evaluate is used for calculating BSDF weights while importance sampling light sources.

Small note: I wrote that these five functions all take in a normal, which is technically all they need in terms of differential geometry. However, in practice, passing in a surface point and UV and other differential geometry information is very useful since that allows for various properties to be driven by 2D and 3D textures. In Takua a0.5, I pass in a normal, surface point, UV coordinate, and a geom and primitive ID for future PTEX support, and allow every BSDF attribute to be driven by a texture.

One of the test props I made is the PBRT book, since I thought rendering the Physically Based Rendering book with a physically based renderer and physically based shading would be amusing. The base diffuse color is driven by a texture map, and the interesting rippled and variation in the glossiness of the book cover comes from driving additional gloss and specular properties with more texture maps.

Physically Based Rendering book, rendered with my physically based renderer. Note the texture-driven gloss and specular properties. Rendered using BDPT.

In order to be physically correct, BSDFs should also fulfill the following three properties:

  • Positivity, meaning that the return value of the BSDF should always be positive or equal to 0.
  • Helmholtz Reciprocity, which means the return value of the BSDF should not be changed by switching the input and output directions (although switching the input and output CAN change how things are calculated internally, such as in perfectly specular refractive materials).
  • Energy Conservation, meaning the surface cannot reflect more light than arrives.

At the moment, my base BSDFs are not actually the best physically based BSDFs in the world… I just have Lambertian diffuse, normalized Blinn-Phong, and Fresnel-based perfectly specular reflection/refraction. At a later point I’m planning on adding Beckmann and Disney’s Principled BSDF, and possibly others such as GGX and Ward. However, for the time being, I can still create highly complex and interesting materials because of the modular nature of Takua a0.5’s BSDF system; one of the most powerful uses of this modular system is combining base BSDFs into more complex BSDFs. For example, I have another BSDF called FresnelPhong, which internally calls normalized Blinn-Phong BSDF but also calls the Fresnel code from my Fresnel specular BSDF to account for an output direction with the Fresnel effect with glossy surfaces. Since the Fresnel specular BSDF handles refractive materials, FresnelPhong allows for creating glossy transmissive surfaces such as frosted glass (albeit not as accurate to reality as one would get with Beckmann or GGX).

Another one of my test props is a glass chessboard, where half of the pieces and board squares are using frosted glass. Needless to say, this scene is very difficult to render using unidirectional pathtracing. I only have one model of each chess piece type, and all of the pieces on the board are instances with varying materials per instance.

Chessboard with ground glass squares and clear glass squares. Rendered using BDPT.

Chessboard with ground glass and clear glass pieces. Rendered using BDPT.

Another interesting use of modular BSDFs and embedding BSDFs inside of other BSDFs is in implementing bump mapping. Takua a0.5 implements bump mapping as a simple BSDF wrapper that calculates the bump mapped normal and passes that normal into whatever the underlying BSDF is. This approach allows for any BSDF to have a bump map, and even allows for applying multiple bump maps to the same piece of geometry. In addition to specifying bump maps as wrapper BSDFs, Takua a0.5 also allows attaching bump maps to individual geometry so that the same BSDF can be reused with a number of different bump maps attached to a number of different geometries, but under the hood this system works exactly the same as the BSDF wrapper bump map.

This notebook prop’s leathery surface detail comes entirely from a BSDF wrapper bump map:

Notebook with a leathery surface. All surface detail comes from bump mapping. Rendered using BDPT.

Finally, one of the most useful and interesting features of Takua a0.5’s BSDF system is the layered BSDF. The layered BSDF is a special BSDF that allows arbitrary combining, layering, and mixing between different BSDFs, much like Vray’s BlendMtl or Renderman 19/RIS’s LM shader system. Any BSDF can be used as a layer in a layered BSDF, including entire other layered BSDF networks. The Takua layered BSDF consists of a base substrate BSDF, and an arbitrary number of coat layers on top of the substrate. Each coat is given a texture-drive weight which determines how much of the final output BSDF is from the current coat layer versus from all of the layers and substrate below the current coat layer. Since the weight for each coat layer must be between 0 and 1, the result layered BSDF maintains physical correctness as long as all of the component BSDFs are also physically correct. Practically, the layered BSDF is implemented so that with each iteration, only one of the component BSDFs is evaluated and sampled, with the particular component BSDF per iteration chosen randomly based on each component BSDF’s weighting.

The layered BSDF system is what allows the creation of truly interesting and complex materials, since objects in reality often have complex materials consisting of a number of different scattering event types. For example, a real object may have a diffuse base with a glossy clear coat, but there may also be dust and fingerprints on top of the clear coat contributing to the final appearance. The globe model seen in my adaptive sampling post uses a complex layered BSDF; the base BSDF is ground glass, with the continents layered on top as a perfectly specular mirror BSDF, and then an additional dirt and fingerprints layer on top made up of diffuse and varying glossy BSDFs:

Glass globe using Takua's layered BSDF system. The globe has a base ground glass layer, a mirror layer for continents, and a dirt/fingerprints layer for additional detail. Rendered using VCM.

Here’s an additional close-up render of the globe that better shows off some of the complex surface detail:

Close-up of the globe. Rendered using VCM.

Going forward, I’m planning on adding a number of better BSDFs to Takua a0.5 (as mentioned before). Since the BSDF system is so modular and extensible, adding new BSDFs should be relatively simple and should require little to no additional work to integrate into the renderer. Because of how I designed BSDF wrappers, any new BSDF I add will automatically work with the bump map BSDF wrapper and the layered BSDF system. I’m also planning on adding interesting effects to the refractive/transmission BSDF, such as absorption based on Beer’s law and spectral diffraction.

After I finish work on my thesis, I also intend on adding more complex materials for subsurface scattering and volume rendering. These additions will be much more involved than just adding GGX or Beckmann, but I have a rough roadmap for how to proceed and I’ve already built a lot of supporting infrastructure into Takua a0.5. The plan for now is to implement a unified SSS/volume system based on the Unified Points, Beams, and Paths presented at SIGGRAPH 2014. UPBP can be thought of as extending VCM to combine a number of different volumetric rendering techniques. I can’t wait to get started on that over the summer!

Adaptive Sampling

Adaptive sampling is a relatively small and simple but very powerful feature, so I thought I’d write briefly about how adaptive sampling works in Takua a0.5. Before diving into the details though, I’ll start with a picture. The scene I’ll be using for comparisons in this post is a globe of the Earth, made of a polished ground glass with reflective metal insets for the landmasses and with a rough scratched metal stand. The globe is on a white backdrop and is lit by two off-camera area lights. The following render is the fully converged reference baseline for everything else in the post, rendered using VCM:

Fully converged reference baseline. Rendered in Takua a0.5 using VCM.

As mentioned before, in pathtracing based renderers, we solve the path integral through Monte Carlo sampling, which gives us an estimate of the total integral per sample thrown. As we throw more and more samples at the scene, we get a better and better estimate of the total integral, which explains why pathtracing based integrators start out producing a noisy image but eventually converge to a nice, smooth image if enough rays are traced per pixel.

In a naive renderer, the number of samples traced per pixel is usually just a fixed number, equal for all pixels. However, not all parts of the image are necessarily equally difficult to sample; for example, in the globe scene, the backdrop should require fewer samples than the ground glass globe to converge, and the ground glass globe in turn should require fewer samples than the two caustics on the ground. This observation means that a fixed sampling strategy can potentially be quite wasteful. Instead, computation can be used much more efficiently if the sampling strategy can adapt and drive more samples towards pixels that require more work to converge, while driving fewer samples towards pixels that have already converged mid-render. Such a sample can also be used to automatically stop the renderer once the sampler has detected that the entire render has converged, without needing user guesswork for how many samples to use.

The following image is the same globe scene as above, but limited to 5120 samples per pixel using bidirectional pathtracing and a fixed sampler. Note that most of the image is reasonable converged, but there is still noise visible in the caustics:

Fixed sampling, 5120 samples per pixel, BDPT.

Since it may be difficult to see the difference between this image and the baseline image on smaller screens, here is a close-up crop of the same caustic area between the two images:

500% crop. Left: converged baseline render. Right: fixed sampling, 5120 samples per pixel, BDPT.

The difficult part of implementing an adaptive sampler is, of course, figuring out a metric for convergence. The PBRT book presents a very simple adaptive sampling strategy on page 388 of the 2nd edition: for each pixel, generate some minimum number of initial samples and record the radiances returned by each initial sample. Then, take the average of the luminances of the returned radiances, and compute the contrast between each initial sample’s radiance and the average luminance. If any initial sample has a contrast from the average luminance above some threshold (say, 0.5), generate more samples for the pixel up until some maximum number of samples per pixel is reached. If all of the initial samples have contrasts below the threshold, then the sampler can mark the pixel as finished and move onto the next pixel. The idea behind this strategy is to try to eliminate fireflies, since fireflies result from statistically improbably samples that are significantly above the true value of the pixel.

The PBRT adaptive sampler works decently, but has a number of shortcomings. First, the need to draw a large number of samples per pixel simultaneously makes this approach less than ideal for progressive rendering; while well suited to a bucketed renderer, a progressive renderer prefers to draw a small number of samples per pixel per iteration, and return to each pixel to draw more samples in subsequent iterations. In theory, the PBRT adaptive sampler could be made to work with a progressive renderer if sample information was stored from each iteration until enough samples were accumulated to run an adaptive sampling check, but this approach would require storing a lot of extra information. Second, while the PBRT approach can guarantee some degree of per-pixel variance minimization, each pixel isn’t actually aware of what its neighbours look like, meaning that there still can be visual noise across the image. A better, global approach would have to take into account neighbouring pixel radiance values as a second check for whether or not a pixel is sufficiently sampled.

My first attempt at a global approach (the test scene in this post is a globe, but that pun was not intended) was to simply have the adaptive sampler check the contrast of each pixel with it’s immediate neighbours. Every N samples, the adaptive sampler would pull the accumulated radiances buffer and flag each pixel as unconverged if the pixel has a contrast greater than some threshold from at least one of its neighbours. Pixels marked unconverged are sampled for N more iterations, while pixels marked as converged are skipped for the next N iterations. After another N iterations, the adaptive sampler would go back and reflag every pixel, meaning that a pixel previously marked as converged could be reflagged as unconverged if its neighbours changed enormously. Generally N should be a rather large number (say, 128 samples per pixel), since doing convergence checks is meaningless if the image is too noisy at the time of the check.

Using this strategy, I got the following image, which was set to run for a maximum of 5120 samples per pixel but wound up averaging 4500 samples per pixel, or about a 12.1% reduction in samples needed:

Adaptive sampling per pixel, average 4500 samples per pixel, BDPT.

At an initial glance, this looks pretty good! However, as soon as I examined where the actual samples went, I realized that this strategy doesn’t work. The following image is a heatmap showing where samples were driven, with brighter areas indicating more samples per pixel:

Sampling heatmap for adaptive sampling per pixel. Brighter areas indicate more samples.

Generally, my per-pixel adaptive sampler did correctly identify the caustic areas as needing more samples, but a problem becomes apparent in the backdrop areas: the per-pixel adaptive sampler drove samples at clustered “chunks” evenly, but not evenly across different clusters. This behavior happens because while the per-pixel sampler is now taking into account variance across neighbours, it still doesn’t have any sort of global sense across the entire image! Instead, the sampler is finding localized pockets where variance seems even across pixels, but those pockets can be quite disconnected from further out areas. While the resultant render looks okay at a glance, clustered variance patterns becomes apparent if the image contrast is increased:

Adaptive sampling per pixel, with enhanced contrast. Note the local clustering artifacts.

Interestingly, these artifacts are reminiscent of the artifacts that show up in not-fully-converged Metropolis Light Transport renders. This similarity makes sense, since in both cases they arise from uneven localized convergence.

The next approach that I tried is a more global approach adapted from Dammertz et al.’s paper, “A Hierarchical Automatic Stopping Condition for Monte Carlo Global Illumination”. For the sake of simplicity, I’ll refer to the approach in this paper as Dammertz for the rest of this post. Dammertz works by considering the variance across an entire block of pixels at once and flagging the entire block as converged or unconverged, allowing for much more global analysis. At the first variance check, the only block considered is the entire image as one enormous block; if the total variance eb in the entire block is below a termination threshold et, the block is flagged as converged and no longer needs to be sampled further. If eb is greater than et but still less than a splitting threshold es, then the block will be split into two non-overlapping child blocks for the next round of variance checking after N iterations have passed. At each variance check, this process is repeated for each block, meaning the image eventually becomes split into an ocean of smaller blocks. Blocks are kept inside of a simple unsorted list, require no relational information to each other, and are removed from the list once marked as converged, making the memory requirements very simple. Blocks are split along their major axis, with the exact split point chosen to keep error as equal as possible across the two sides of the split.

The actual variance metric used is also very straightforward; instead of trying to calculate an estimate of variance based on neighbouring pixels, Dammertz stores two framebuffers: one buffer I for all accumulated radiances so far, and a second buffer A for accumulated radiances from every other iteration. As the image approaches full convergence, the differences between I and A should shrink, so an estimation of variance can be found simply by comparing radiance values between I and A. The specific details and formulations can be found in section 2.1 of the paper.

I made a single modification to the paper’s algorithm: I added a lower bound to the block size. Instead of allowing blocks to split all the way to a single pixel, I stop splitting after a block reaches 64 pixels in a 8x8 square. I found that splitting down to single pixels could sometimes cause false positives in convergence flagging, leading to missed pixels similar to in the PBRT approach. Forcing blocks to stop splitting at 64 pixels means there is a chance of false negatives for convergence, but a small amount of unnecessary oversampling is preferable to undersampling.

Using this per-block adaptive sampler, I got the following image, which again is superficially extremely similar to the fixed sampler result. This render was also set to run for a maximum of 5120 samples, but wound up averaging just 2920 samples per pixel, or about a 42.9% reduction in samples needed:

Adaptive sampling per block, average 2920 samples per pixel, BDPT.

The sample heatmap looks good too! The heatmap shows that the sampler correctly identified the caustic and highlight areas as needing more samples, and doesn’t have clustering issues in areas that needed fewer samples:

Sampling heatmap for adaptive sampling per block. Brighter areas indicate more samples.

Boosting the image contrast shows that the image is free of local clustering artifacts and noise is even across the entire image, which is what we would expect:

Adaptive sampling per block, with enhanced contrast. Note the even noise spread and lack of local clustering artifacts.

Looking at the same 500% crop area as earlier, the adaptive per-block and fixed sampling renders are indistinguishable:

500% crop. Left: fixed sampling, 5120 samples per pixel, BDPT. Right: adaptive per-block sampling, average 2920 samples per pixel, BDPT.

So with that, I think Dammertz works pretty well! Also, the computational and memory overhead required for the Dammertz approach is basically negligible relative to the actual rendering process. This approach is the one that is currently in Takua a0.5.

I actually have an additional adaptive sampling trick designed specifically for targeting fireflies. This additional trick works in conjunction with the Dammertz approach. However, this post is already much longer than I originally planned, so I’ll save that discussion for a later post. I’ll also be getting back to the PPM/VCM posts in my series of integrator posts shortly; I have not had much time to write on my blog since the vast majority of my time is currently focused on my thesis, but I’ll try to get something posted soon!

Flower Vase Renders

Rendered in Takua a0.5 using BDPT. Nearly a quarter of a billion triangles.

In order to test Takua a0.5, I’ve been using my renderer on some quick little “pretty picture” projects. I recently ran across a fantastic flower vase model by artist Andrei Mikhalenko and used Andrei’s model as the basis for a shading exercise. The above and following images are rendered entirely in Takua a0.5 using bidirectional pathtracing. I textured and shaded everything using Takua a0.5’s layered material system, and also made some small modifications to the model (moved some flowers around, extended the stems to the bottom of the vase, and thickened the bottom of the vase). Additionally, I further subdivided the flower petals to gain additional detail and smoothness, meaning the final rendered model weighs in at nearly a quarter of a billion triangles. Obviously using such heavy models is not practical for a single prop in real world production, but I wanted to push the amount of geometry my renderer can handle. Overall, total memory usage for each of these renders hovered around 10.5 GB. All images were rendered at 1920x1080 resolution; click on each image to see the full resolution renders.

For the flowers, I split all of the flowers into five randomly distributed groups and assigned each group a different flower material. Each material is a two-sided material with a different BSDF assigned to each side, with side determined by the surface normal direction. For each flower, the outside BSDF has a slightly darker reflectance than the inner BSDF, which efficiently approximates the subsurface scattering effect real flowers have, but without actually having to use subsurface scattering. In this case, using a two-sided material to fake the effect of subsurface scattering is desirable since the model is so complex and heavy. Also, the stems and branches are all bump mapped.

Rendered in Takua a0.5 using BDPT. Note the complex caustics from the vase and water.

This set of renders was a good test for bidirectional pathtracing because of the complex nature of the caustics in the vase and water; note that the branches inside of the vase and water cannot be efficiently rendered by unidirectional pathtracing since they are in glass and therefore cannot directly sample the light sources. The scene is lit by a pair of rectlights, one warmer and one cooler in temperature. This lighting setup, combined with the thick glass and water volume at the bottom of the vase, produces some interesting caustic on the ground beneath the vase.

The combination of the complex caustics and the complex geometry in the bouquet itself meant that a fairly deep maximum ray path length was required (16 bounces). Using BDPT helped immensely with resolving the complex bounce lighting inside of the bouquet, but the caustics proved to be difficult for BDPT; in all of these renders, everything except for the caustics converged within about 30 minutes on a quad-core Intel Core i7 machine, but the caustics took a few hours to converge in the top image, and a day to converge for the second image. I’ll discuss caustic performance in BDPT compared to PPM and VCM in some upcoming posts.

Rendered in Takua a0.5 using BDPT. Depth of field and circular bokeh entirely in-camera.

All depth of field is completely in-camera and in-renderer as well. No post processed depth of field whatsoever! For the time being, Takua a0.5 only supports circular apertures and therefore only circular bokeh, but I plan on adding custom aperture shapes after I finish my thesis work. In general, I think that testing my own renderer with plausibly real-world production quality scenes is very important. After all, having just a toy renderer with pictures of spheres is not very fun… the whole point of a renderer is to generate some really pretty pictures! For my next couple of posts, I’m planning on showing some more complex material/scene tests, and then moving onto discussing the PPM and VCM integrators in Takua.

Addendum: I should comment on the memory usage a bit more, since some folks have expressed interest in what I’m doing there. By default, the geometry actually weighs in closer to 30 GB in memory usage, so I had to implement some hackery to get this scene to fit in memory on a 16 GB machine. The hack is really simple: I added an optional half-float mode for geometry storage. In practice, using half-floats for geometry is usually not advisable due to precision loss, but in this particular scene, that precision loss becomes more acceptable due to a combination of depth of field hiding most alignment issues closer to camera, and sheer visual complexity making other alignment issues hard to spot without looking too closely. Additionally, for the flowers I also threw away all of the normals and recompute them on the fly at render-time. Recomputing normals on the fly results in a small performance hit, but it vastly preferable to going out of core.