Archive for September, 2011


September 9, 2011 6 comments

Because this project also served as my MSc Individual Project at Imperial College London I have written a report and a presentation for it as well.

You can also see what this project does in less than 3 minutes from the following video:


Future work

Although the final deadline for this project has passed there are still a couple of things left to be done. I have split them into two main categories: implementation, improving and extending on the existing code / ideas and algorithm, introducing new ideas that should give better results.

  • Implementation

The existing render manager can be improved by extending it to support:

  1. Multiple lights – at the moment only one light is supported. Multiple lights can be added by using a 3D texture, because every light generates a maximum of up to 10 textures.
  2. Multiple mesh types – for now only genmesh is supported. Although this is the most generic type of mesh used in Crystal Space, support for materials from other types of meshes has to be added as well.
  3. Rendering smoke and clouds – this will involve adding a volumetric rendering technique and apply the algorithm from the current render manager without any further modifications.
  • Algorithm

A couple of ideas can be added to produce better renderings or make the application run faster.

  1. Different split ratio for each ray – at the moment the split ratio is computed globally and this causes problems when objects with different densities are present in the scene. One possible way of having a unique split function for each ray is by creating a split texture, storing the split ratios for each individual ray onto a corresponding pixel.
  2. Recomputing the split ratio in real-time – this is the current implementation bottleneck, because it is done on the CPU. If the split ratio will be computed for each ray, then no global information will be needed and the computation can be done faster on the GPU by adding a new image processing render pass.
  3. Compute the optimal number of layers – the number of layers is currently chosen by the user. However, a test scene can be created so that the maximum number of layers that keeps the application real-time will be automatically chosen.


As a final step for this project I tested the performance of the three algorithms I implemented throughout the summer: Opacity Shadow Maps (OSM), Deep Opacity Maps (DOM) and Bounding Opacity Maps (BOM).

I measured the performance in frames-per-second (FPS) using the average FPS provided by Fraps benchmarking tool for a period of 60 seconds. The GPU card (Nvidia GT 335M) had the most important contribution in the measures taken, because all three algorithms are GPU bound (they involve rendering to texture multiple times and no significantly computational task is done on the CPU).

The performance is shown in Figure 1

Figure 1 Plot generated using gnuplot. The variance between the number of layers and the FPS for the three algorithms implemented for this project. Even thought Bounding Opacity Maps have the worst performance they produce realistic renderings even when using the minimum number of layers tested because they follow the real-world light’s distribution (BOM).

Adding common lighting effects

  • Blinn-Phong shading model

The methods presented so far give only information about shadows cast by translucent objects on themselves.  In a Blinn-Phong shading model this would correspond to the diffuse lighting, but the specular and the ambient lighting have to be added as well. These two terms were used as described in the Blinn-Phong model (Figure 1), although for the specular component more advanced techniques such as Kajiya and Kay or Marschner could be used when rendering hair.

Figure 1 Picture from Crystal Space. Scene rendered without any specular or ambient lighting (a) and scene rendered using these two terms as described in the Blinn-Phong model (b).

  • Shadow mapping

Opaque objects were introduced in the scene by using the depth map that computes the initial splitting positions. This map is actually a regular shadow map, so adding opaque objects to the scene was just a matter of applying a common shadow mapping algorithm without any additional computational costs (Figure 2).

Figure 2 Picture from Crystal Space. Opaque objects don’t cast shadows (a) and cast shadows using shadow mapping (b).

Hybrid split

As I mentioned in the previous post a hybrid split between the linear and the logarithmic split can be a good idea because when the linear splitting scheme falls short the logarithmic one can be used and the other way around.

Because when multiple layers contain the same information artifacts may occur, the criteria for choosing the ratio between linear and logarithmic splitting is so that it produces consecutive layers as different from each other as possible. Or put in another way each new layer should bring new information. In terms of computer vision this can be translated to having the mutual information between these images as small as possible.

Two techniques of measuring mutual information were tested: sum of absolute differences and cross-correlation coefficient.

The sum of absolute differences is pretty straight forward to compute and it involves adding the absolute value of the difference between each two corresponding pixels from the two images.

The cross-correlation coefficient represents the ratio between the covariance of two images and the product of their standard deviation, and can be computed using the following formula:

where Ī(•) is the mean of image I. Another useful property about correlation is that it has values on a scale ranging from [-1, 1] and it gives a linear indication of the similarity between images.

As expected from the findings in the previous post the mutual information is smaller when choosing a more linear split for sparse objects and a more logarithmic one for denser objects (Figure 1).

Figure 1 Plot generated using gnuplot. Linear splitting corresponds to a split ratio of 0 while logarithmic splitting maps to the value of 1.

Because, as we can see from Figure 1, the cross-correlation coefficient (shown in green) covers a wider range of values, giving better estimate for each density value, it was chosen as the default method of computing the mutual information. The cross-correlation probably performs better due to the influence of standard deviation, which is completely neglected for the sum of absolute differences (shown in red).