我发现了一些有趣的链接。您可以使用 MIT 的方法使用CodAC。他们在这篇文章中列出了很多事实，我将在这里发布最重要的事实。
9. What are limitations of this technique?
The main limitation of our framework is inapplicability to scenes with curvilinear
objects, which would require extensions of the current mathematical model.
Another limitation is that a periodic light source creates a wrap-around error
as it does in other TOF devices. For scenes in which surfaces have high reflectance
or texture variations, availability of a traditional 2D image prior to our data
acquisition allows for improved depth map reconstruction as discussed in our paper.
10. What are advantages of this technique/device and how does it
compare with existing TOF-based range sensing techniques?
In laser scanning, spatial resolution is limited by the scanning time.
TOF cameras do not provide high spatial resolution because they rely on a
low-resolution 2D pixel array of range-sensing pixels. CoDAC is a single-sensor,
high spatial resolution depth camera which works by exploiting the sparsity of natural
scene structure.
11. What is the range resolution and spatial resolution of the CoDAC system?
We have demonstrated sub-centimeter range resolution in our experiments.
This is significantly better than fundamental limit of about 10 cm that would
arise from using a detector with 0.7 nanosecond rise time if we were not using
parametric signal modeling. The improvement in range resolution comes from the
parametric modeling and deconvolution in our framework. We refer the reader to
our publications for complete details and analysis.
We have demonstrated 64-by-64 pixel spatial resolution,
as this is the spatial resolution of our spatial light modulator.
Spatially patterning with a digital micromirror device (DMD) will enable
much higher spatial resolution. Our experiments use only 205 projection patterns,
which correspond to just 5% of number of pixels in the reconstructed depth map.
This is a significant improvement over raster scanning in LIDAR, and it is
obtained without the 2D sensor array used in TOF cameras.
我在Youtube 上发现的另一个有趣的项目使用libfreenect和libusb
还有dSensingNI一个被描述为
This work presents an approach to overcome the disadvantages of existing interaction
frameworks and technologies for touch detection and object interaction. The robust and
easy to use framework dSensingNI (Depth Sensing Natural Interaction) is described,
which supports multitouch and tangible interaction with arbitrary objects. It uses
images from a depth-sensing camera and provides tracking of users fingers of palm of
hands and combines this with object interaction, such as grasping, grouping and
stacking, which can be used for advanced interaction techniques.
所以你已经找到了其中的大多数，尤其是那些使用 Kinect 的，但还有其他一些选择！希望这可以帮助！