I am trying to use a stereo camera for scene reconstruction, but I can usually only obtain sparse point clouds (i.e. over half the image does not have any proper depth information).
I realize that stereo processing algorithms rely on the presence of texture in the images and have a few parameters that can be tweaked to obtain better results, such as the disparity range or correlation window size. As much as I tune these parameters, though, I am never able to get results that are even remotely close to what can be obtained using an active sensor such as the Kinect.
The reason why I want that is because very often point clouds corresponding to adjacent regions don't have enough overlap for me to obtain a match, so reconstruction is severely impaired.
My question to the Computer Vision experts out there is the following: what can I do to obtain denser point clouds in general (without arbitrarily modifying my office environment)?