shikhardevgupta

Mostly true. But the last part is incorrect. There are far more pixels in an image compared to the number of points in the point cloud area covered by that image. So you get 1 pixel per point. In addition there can be multiple points that map to the same pixel.

True. But my reference point is Lidars which mostly produce non-colored points.

Yes, I am working on using numpy to do the projection using matrices so we dont have to loop over each point and project it individually. That should be a big boost.

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.

Lidars are pretty powerful, but one big disadvantage of using point clouds for perception is that they are not colored. This makes identifying objects more difficult compared to camera images. However, by combining camera images with lidar data, we can enhance the point cloud by assigning colors to the points based on the corresponding camera image pixels. This makes visualizing and processing the point cloud much easier.

Labeling your data is hard work. We make it easier with an intuitive platform you can start using right away. Mindkosh lets you easily manage large labeling projects, triage labeling errors, and reduce the number of clicks it takes to label your image.

Sign up for free: https://app.mindkosh.com

Looks super cool. I read about Neural Pathways in grad school, and seeing all that theory in action feels great.

Looks super cool!

Learned C++ in school. Dont ever want to go back to it. It's just too complicated.