Toronto-3D and OpenGF dataset code for RandLA-Net

Code for Toronto-3D has been uploaded. Try it for building your own network.

Will release code for OpenGF later

Train and test RandLA-Net on Toronto-3D

1. Set up environment and compile the operations - exactly the same as the RandLA-Net environment
2. Create a folder called data and move the .ply files into data/Tronto_3D/original_ply/
3. Change parameters according to your preference in data_prepare_toronto3d.py and run to preprocess point clouds
4. Change parameters according to your preference in helper_tool.ply to build the network
5. Train the network by running python main_Toronto3D.py --mode train
6. Test and evaluate on L002 by running python main_Toronto3D.py --mode test --test_eval True
7. Modify the code to find a good parameter set or test on your own data

Sample results of Toronto-3D

The highest results reported are from RandLA-Net in Hu et al. (2021). Here are some results I got on my code with the default parameters. The largest factor in mIoU is the accuracy of Road Markings, which is impossible to be classified with XYZ only.

Features	OA	mIoU	Road	Road mrk.	Natural	Bldg	Util. line	Pole	Car	Fence
Hu et al. (2021)	92.95	77.71	94.61	42.62	96.89	93.01	86.51	78.07	92.85	37.12
Hu et al. (2021) with RGB	94.37	81.77	96.69	64.21	96.92	94.24	88.06	77.84	93.37	42.86
XYZRGBI	96.57	81.00	95.61	58.04	97.22	93.45	87.58	82.64	91.06	42.39
XYZRGB	96.71	80.89	95.88	60.75	97.02	94.04	86.71	83.30	87.66	41.80
XYZI	95.65	80.03	94.59	50.14	95.90	92.76	87.70	77.77	91.10	50.30
XYZ	94.94	74.13	93.53	12.52	96.67	92.34	86.25	80.10	88.04	43.57