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Required Image:
BVH: 0.815224 sec (4 threads) 2.13323 sec (1 thread) Build: 0.965906 sec Grid 70x70x40: 0.804214 sec (4 threads) 2.09292 sec (1 thread) Build: 0.77698 sec |
Cook filter,
3 spp support 3. |
Cook filter,
3 spp support 3. Dielectric Material. |
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This is the
Budda model, 1087716 faces. |
This is the Budda
model rendered with an ambient occlusion technique. |
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This
is a model of a toy truck (59792 faces) that I made some time ago and
recently exported from an open source modelling package. Unfortunately
the package did not order the faces correctly and in some cases did not
produce connected, closed meshes. As a result it wasn't extremely easy
to automatically correct the normals in the mesh and there are some
artifacts. The image was produced with an ambient shader on the floor and vehicle body. I used 100 secondary bounces for each shader. I'm not too thrilled about the "shadows" adjacent to studs on the roof line and hud and I'm not convinced that the technique did a good job on the windshield and hood. |
This
is the same toy truck model. The entire model has a dielectric material
with Beer's law attenuation. |
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This is a closeup of
the JP8 dataset (approx 1.1M particles). I have color mapped it with
invrain16.nrrd colormap using the first scalar value. This uses a
Lancos Sinc filter, 3spp width 2. With a BVH and 1spp box filter, this image renders in 2.08981 sec. Render time with a grid is around 1.5304 sec. I didn't spend a great deal of time improving the performance with these data sets since I work with them on a regular basis. |
This is the HMX
dataset containing around 800,000 particles. I've applied the same
colormap as before. This image is 5spp with a Lancos Sinc filter width
2. The performance viewing the extents of the model, rather then
the details provided much better performance, even though more geometry
was visible. |