LiDAR Point Cloud Classification results not good with real data

[manishmaruthi]

Dear all,

I am using DGCNN to classify LiDAR pointClouds. I have trained the model using ModelNet40 train data(2048 points, 250 epochs) and results are good when I try to classify objects using ModelNet40 test data.

But when I try to classify real data collected by velodyne sensor the prediction is mostly wrong. Please find the attached example. Most of the times I get output as Plant, Guitar or Stairs. I have shifted my objects to center of the coordinate frame and have normalized the values[-1,1]. I have even tried to clean the boundaries.

Can somebody suggest me what I could be doing wrong?

[manishmaruthi]

I found that I have to feed the point cloud in the right orientation or else result can be bad. The network is invariant to geometric transform like rotation only up to an extent