Inference and Training

[abhigoku10]

@mbredif @pbias thanks for sharing your code , i have following few queries
Q1. can we use the inference on custom dataset ? is so what is the changes to be made
Q2. Does sematic segmentation performed on other object class like tree building poles and barrier classes
Q3. to train the custom dataset is it should be in semantic kitti format ?
Q4 can you share your pretrained model to test he model on custom dataset and transfer learning
Q5 what is the performance achieved on live data

THnaks in advance

[pbias]

Hi,

Q1. You can use the inference on a custom dataset. You just need to generate a TFRecords in the same way as it is done during the data preparation.
Q2. LU-Net has only been tested on the SqueezeSeg dataset (created from KITTI) and its 3 classes "car", "pedestrian", "cyclist". However it is possible to train it on different datasets such as SemanticKITTI to be able to distinguish more classes.
Q3. If you want to use the code as is, the custom dataset has to match the standards of the SqueezeSeg dataset, meaning that each sample should be a 64x512x6 matrix whose channels are respectively: x, y, z, depth, reflectance, label
Q4. I do not plan on sharing the pretrained model, but you can follow the training protocol of the README to retrain the model, which takes several hours on a good GPU, and achieve similar results as in the paper.
Q5. I'm not sure I understand the question, the performances of the model are described in the associated publication. The validation scores are computed on real data.

Hope it helps :)