A related paper

  A week ago, the professor gave a related paper to me as an important reference, which has been published recently on PROCAMS 2012. The link is  http://www.cs.cmu.edu/~ILIM/publications/PDFs/MKSN-PROCAMS12.pdf.I have read it in detail, and found that this paper was built on a device more sophisticated than ours, which could control red, green and blue lasers at high frame rates (18kHz horizontally and 60Hz vertically), and thus could use a filter to easily block the unwanted ambient light. Although they use different device to complete different goals,  the method they are using can give me a lot of cues to conduct my experiment.

The structure of the paper is as follows:

•  types

 "We discuss how the line-striping acts as a kind of “light-probe”, creating distinctive patterns of light scattered by different types of materials.
We investigate visual features that can be computed from these patterns and can reliably identify the dominant material characteristic of a scene, i.e. where most of the objects consist of either diffuse (wood), translucent (wax), reflective (metal) or transparent (glass) materials."
 
The types they are looking into:

1. diffuse (wood)----------lambertian materials
2. translucent (wax)-------------dispersive media and subsurface scatteringmaterials
3. reflective (metal)--------------------reflective surfaces
4. transparent (glass) materials---------------refractive surfaces

•  goals

1. The first is low-power and low-cost reconstruction of diffuse scenes under strong ambient lighting (e.g. direct sun-light).
2. The other application of our sensor relates to the scene’s material properties.

•  method we can use for reference

1. Ambient light suppression

  "Lastly the background can be suppressed by taking an image with the projector on and one with the projector off.  It is not  actually necessary to shut the projector off; instead, we choose a different trigger delay which effectively moves the location of the projected line. In this way, one gets two images with the same back-ground but with different projected lines. Subtracting one from the other and keeping only the positive values gives us a single line-stripe."

 I can use the different exposures in different positions to subtract the background from the response.

   2. Fast per-frame analysis

"Fig. 6 shows a per-frame analysis of a scene with milky
water bottles and another with glass objects. Our method has five steps:
(1) For each column, find the maximum intensity pixel
(2) At this pixel, apply two filters (see figure inset),
(3) If filter 1’s response is greater than a threshold,it is glass
(4) Otherwise, if the response to the second filter
is greater than a second threshold, label as milk. If there is no labeling, then it is a diffuse material. In the figure, we have marked glass as red, milk as blue and diffuse as green.
The biggest errors are for clear glass when the camera sees
mostly the background. This is a fast classification, since
for each column the filters are only applied once."

I can use the similar method to classify glass and others.

 3.Full scan analysis

• diffuse(plastic)

"First we point out that simple detection of a single, sharp peak in a scene point’s appearance profile
[13] strongly suggests lambertian/diffuse reflectance. If the
profile has no peak, then the projector is not illuminating this pixel and therefore it is in shadow. "

They identify the diffuse materia by looking at the number of intensity maxima, which may not be useful in our experiment.

• scattering and subsurface scattering(wax)

"Figure 8. We take the power spectrum of the three dimensional Fourier transform of each scan video, and integrate the time frequency dimension. The resulting 2D matrix is mostly sparse. Low non-zero support gives an indication of scattering and subsurface scattering."

  There may not be spectrums in our dataset, but I may try variance to detect this feature.

•  distinguishing between reflective or refractive surfaces(metal and glass)

  "We have empirically found that the number of intensity maxima in the appearance profile at each pixel can be very discriminative. An intuitive explanation is that since reflective caustics are caused by opaque objects, the number of observed caustics at each scene point is less than in a refractive material, where the camera can view through the material onto the diffuse background, allowing the observance of many more caustics."

Glass has more reflective caustics than metal, I can use this feature too.

 "In (c) we show the raw features obtained from a low-res histogram of gradients (HOG). The top three discriminative features (d) for metal and glass show promise, but we believe more data is needed before a
discriminative hyperplane can be learned."

Another way to discriminate metal and glass, but maybe can't be used in my experiment.

• methods my experiment can use

1. We need to do ambient light suppression
2. Can use filters to detect main difference
3. The number of intensity maxima can also be an important feature.