Colorize Lidar point clouds with camera images
medium.com
medium.com
Regarding the coloring of each 3d point, it might be feasible to not use one camera image, but a weighted sum of all camera images that can see the same point in the scene. Each pixel color is then weighted with the scalar product of the points normal and the viewing direction of the camera. This would also regard for noise and specular reflections (which can mess up the original color).
The way I handle the different camera images is to simply see which one provides a lower depth and use - with the idea that if the camera is closer, it would provide better information. But what you are suggesting is pretty interestint. I'm going to try that as well.
That depends entirely on the capture device.
Edit: Just realized they're probably the author too of the post
LLM's are getting heavy use from the ESL (and even English as a third or fourth language) crowd.
It is pretty cool, we use it for detection of humidity degree or for species discrimination (e.g. plants, minerals, chemicals…).
https://github.com/leggedrobotics/open3d_slam
Its not AI, but it is simple and you can re-use a point cloud to re-localise against (ie once the map has been generated you can just localise rather than have to map the same time.)
Some places use ML to make a more robust descriptor (ie the thing that identifies the point in a point cloud) which mostly practical. I've not yet seen a practical "deep" SLAM pipeline. (but I'd not looked recently. )
* Mathematically align the photograph and the lidar point cloud.
* For each photograph pixel, colour whichever aligned lidar point is closest to the camera.
So you end up with one coloured lidar point per photograph pixel?