I am working on filtering point cloud data, I read in multiple scientifical publications that it's preferred to work on organized point cloud since the the filtering/segmenting/so on can be more efficient if the cloud is organized. So I organized it using spherical projection and it works fine.
After passing the projected cloud to multiple filters like voxel, crop, outlier removal, it deorganize back to the former format!!! Which I don't want I still want to segment and treat the surface and so on but on the organized format to profit computationally from the organized format!!
I could apply the projection on it but it upsamples it again which is logical, yet I don't genuinely want after downsampling through filters!!
Is there any way by instantiation the filters to handle the data in different way that doesn't deorganize the data?
Here some info out the data pipeline
Projected cloud Size: 65536
The projected cloud is organized: 1
The projected cloud is dense: 1
Projected PointCloud width: 1024
Projected PointCloud height: 64
Projecting took 11 milliseconds
PointCloud after cropping: 51145 data points (x y z intensity).
PointCloud after extracting the ego: 51115 data points (x y z intensity).
PointCloud after filtering with voxel grid: 17340 data points (x y z intensity).
PointCloud size after regarding Radius Outlier Removal filter: 17303 data points (x y z intensity).
Filtered Cloud Size: 51145
The filtered Cloud is organized: 0
Filtered PointCloud width: 51145
Filtered PointCloud height: 1
Filtering took 21 milliseconds