Gofinge
commented
1 month ago Given the scale of my point cloud, should I tune some parameter related to the model
10M^2 is not large compared with the outdoor scene. Just adjust the grid size (in both augmentations and model).
Should I normalize the colors between 0 and 1?
We have a unified strategy to normalize color; please refer to "NormalizeColor" in the data.transform. Just make sure your color range is [0, 255]. But this is important only when you want to engage multiple datasets for training.
I didn't understand how the trainer processes the input shape. It seems that the model is fed with inputs of size 200K. Should I divide the point clouds into multiple files?
Currently, we don't need to try to adjust the grid size, and if we fail to train the model efficiently, we can further chunk the data.
How should I change the configuration file if I want to ignore one or more classes?
In this case, you need to rewrite the dataset to map the classes you want to ignore to -1.
The dataset is unbalanced. Does the code manage this? Should I assign different weights to the classes? If so, how?
Lovasz loss, Weighted CE (provide a weight), CAC Segmentor.
FSet89
I am training a custom dataset using the Pointcept codebase. My dataset consists of a large point cloud of a building facade spanning ~10 meters in width and height. I divided it into two files train.txt and val.txt including coordinates, colors, normals and label. The total amount of points is ~8M. I have a few questions.