taketwo / pcl-mean-and-covariance Goto Github PK

I'm reading through the PCL code in the pcl/test folder, and I see that there are a number of example point clouds in there. I also see that there is an existing test for PCL point normals in the file test_normal_estimation.cpp but this file either specifies the exact points or loads one of the pre-existing point clouds: bun0.pcd which appears to be one of the original scans of the Stanford Bunny.

Specifying exact points doesn't scale well, and the Stanford Bunny seems like a limited example by itself, and doesn't contain any ground-truth normals. So I'd like to propose creating a folder of purpose-built point clouds that can be loaded and run through the test framework. I'm initially thinking of 15 or more clouds spread across three tiers:

1. Primitives: Planes, cubes, spheres etc for initial testing and visualization purposes.
2. Shapes: Clouds like the tum_soda_bottle of stand-alone yet complex objects.
3. Scans: Point clouds that resemble the output of a real (or simulated) LiDAR or stereo camera unit.

In each case, the cloud will contain XYZ points and ground-truth normals. So rather than comparing the calculated normals to hard-coded values, you can load the cloud, copy it without normals, re-calculate them using the test functions, and then compare the calculated normals in the second (copied) cloud to the ground-truth normals in the first (original) cloud.

The first two tiers of clouds can be created using Blender, which can export point clouds with vertex normals, e.g.

The third tier is harder. I'm looking into a Unity-based simulation that could potentially simulate LiDAR scans in a known environment, and export them with the ground-truth normals. But if that falls through I have a lot of data from a survey-grade total station that I could calculate normals for and then decimate to make it look like a LiDAR scan.

What are everyone's thoughts on this? I'll also open another issue to discuss the types of tests I could implement once I've finished reading the googletest documentation.