Thanks for all this informations, I’ll start working and checking on this. 🙂
We already know off Kintinuous, our project is something similar to this, or like Matterport.
Thanks again ! !
]]>Hi,
Sounds like a fun project.
I guess you want to do something similar to Kintinuous and maybe you could take a look at some of the tools used for smoothing models in e.g. Skanect.
My suggestion would be to start looking into using ROS to obtain your data, and then some of the awesome features of PCL to treat and simplify the data to less dense geometry structures. Maybe something like this example.
Good luck with your project!
I’m part of a 5 students team, working on an indoor maping project.
The project main point is to map an all indoor bulding, flat or others indoor places, creating 3D models for video games (at first) but can be used too for others things.
Using kinect as hardware part for mapping many rooms, and then creating a software which can modelize one big mesh from all the little one created before.
What are you thinking about this project, did you think it can be a good idea ?
Is it possible to do it ?
Did you know others similar projects ?
If you have any tracks that we could follow, please answer this post.
]]>I agree.
The raw best case values are not of much use in evaluating actual operation performance and capabilities. It would be interesting and useful to experiment with viewing highly detailed objects and determine how small depth detail features can be considered as reliably detectable and distinguishable.
Hi,
I’m glad that you find it useful.
For your queries:
1. What you are referring to seems to be related to the Kinect for Windows. All of my works was done with the original Kinect for Xbox360 with the OpenNI driver in Linux. I have not looked into the official SDK released by Microsoft, but i bet the data is formatted very differently than the raw value measurements I had with the early OpenNI driver.
Thus, I cannot help you with the range data format from the Windows SDK. Sorry.
2. I will actually be looking into this in my current project. Currently, I am not sure exactly how the PCL handles the Kinect data and the resolution. The experimental conclusion was based on observation of the raw data resolution, which may be filtered in some way in the Windows SDK. I am not sure how much filtering is done to the raw depth data before it can be reached from the Windows SDK, but it is very likely that it has been put through some smoothing.
2.Spatial resolution along X & Y as reproted by prime-sense & observed by you experimentally is ~3mm @2m but whether ‘definition’ of spatial resolution described in your paper is useful,as i cannot distinguish between 2 points spaced 3mm apart at 2m distance from sensor in the point cloud(experimetally observed)?
]]>Hi Mivia,
that sounds reasonable. It might be interesting to find out about the function for different devices. Thank you very much again for the helpful answers! Good luck with your studies!
Regards,
Chris
Hi Chris, I am glad that you find my answer satisfactory.
Regarding the function, I approximated it by analyzing my experiment results, after the advising professor on the project suggested that it might be somewhat similar to the corresponding function for stereo camera setups.
I just realized that I didn’t include the raw experiment data in the report. I will consider to add it in an upcoming revised version.
But if you do a representative batch of tests and obtain sensor values for a number of distances, you should be able to recreate a similar approximated function. Actually, I didn’t look into how much, or which parameters, the function differs from one Kinect device to another.
By the way, I am very curious how you discovered this function as I’ve not come across it anywhere else so far and also did not find a corresponding documentation by PrimeSense or rather OpenNI (this makes sense as OpenNI is just the Natural Interaction framework). It certainly is very valuable to have!
Best Regards,
Chris