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lyft / nuscenes-devkit

Devkit for the public 2019 Lyft Level 5 AV Dataset (fork of https://github.com/nutonomy/nuscenes-devkit)
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Accelerating map_pointcloud_to_image by loading LIDAR data only once #19

Open xhluca opened 4 years ago

xhluca commented 4 years ago

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 commented 4 years ago

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 commented 3 years ago

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