We scanned a lot of objects with our Kinect and we want to evaluate the quality of our scans. We decided us to validate the scan results. To be able to classify the scanned objects, we assign them to specific properties.
|270x267x150||perfect object to scan|
|70x70x150||impossible to scan because of the reflective surface|
|70x50x40||measuring tolerance are noticed (merging the point clouds) because the object is to small|
|200x150x150||perfect object to scan
only ‘spring legs’ get lost
|220x170x100||perfect object to scan|
|200x100x200||because of the fine structure, many details get lost|
|300x300x150||perfect object to scan|
|500x500x600||in the height the pointclouds are drifting apart|
After a few scans we noticed that the size of the scanned objects is an important factor. If the object is to small, the merging of the pointclouds is not perfect. But if it is to big, it is problematic too, because farther away from the origin the point clouds drifting more apart.
On the one hand the surface texture (smooth/rough) is almost meaningless, but on the other hand the reflectivity is very important. An object with a high reflection send us lesser depth information.
A course structure is a good precondition for a good result in contrast to a fine structure. A lot of details get lost by scanning objects with a fine structure.
All in all an object with the size and a broadly similar shape of a football is a perfect item to scan.