Christopher Lee
``` >>> list(reader.copc_config.las_header.min) Traceback (most recent call last): File "", line 1, in TypeError: 'copclib._core.Vector3' object is not iterable ```
```python point.red = 65537 ``` The above code will throw this error: > TypeError: (): incompatible function arguments. The following argument types are supported: > 1. (arg0: copclib._core.Point, arg1: int)...
> The Paris-Lille-3D is a Dataset and a Benchmark on Point Cloud Classification. The data has been produced by a Mobile Laser System (MLS) in two different cities in France...
Provide support on https://github.com/guochengqian/PointNeXt/issues/79, i.e. enabling OpenPoints to become a pip and conda-installable package.
Issue for tracking how to incorporate testing within the repo. ## Unit testing - [x] Ensure datasets remain downloadable and usable - [x] Ensure datasets are loaded in the correct...
OpenPoints contains many learning rate schedulers that we can add hydra config files to support: https://github.com/guochengqian/openpoints/tree/master/scheduler
> We present the Dayton Annotated Laser Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with nearly a half-billion points spanning 10 square kilometers of area....
> Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and...
Semantic-3d is a terrestrially-acquired semantic segmentation dataset with 4 billion points in an urban landscape. http://www.semantic3d.net/
Provide a means of exploring datasets before, during and after training (i.e. with ground truth labels and with predicted labels). Maybe use the Open3d visualizer API? https://github.com/isl-org/Open3D-ML/blob/21951adc23831ee0597c4f49b47deb8f122fda07/examples/visualize.py Other alternatives are...