Accelerating map_pointcloud_to_image by loading LIDAR data only once

[xhluca]

This following function performs a pretty expensive computation: https://github.com/lyft/nuscenes-devkit/blob/e4efd52a9630959a5b890e1575b58cab145e2441/lyft_dataset_sdk/lyftdataset.py#L653-L715

I am curious: does this function take so much time to run because of the process of loading the lidar cloud points, or transforming the 3D coordinates into 2D for a specific camera?

I am personally using this function in my public kernel for the Kaggle competition, and I realized that running this for all cameras, and across multiple timestamps takes a considerable amount of time, which could be partially caused by the data loading, I could cache it so that the lidar data is not redundantly loaded for every camera for a single timestamp. Thanks!

[ternaus]

I will look at it after the weekend, but if you know how to implement caching - feel free to create PR, it would be great and may help other competitors.

[rrklearn2020]

It would be great if you can share an update on Accelerating loading of LiDAR data using 'map_pointcloud_to_image'