IMPNet

This is a Pytorch-Lightning implementation of the paper "**Efficient Instance Motion-Aware Point Cloud Scene Prediction **" submitted to IROS 2025.

ATPPNet Architecture. **

Table of Contents

1. Installation
2. Data
3. Training
4. Testing
5. Download
6. Acknowledgment

Installation

Clone this repository and run

cd impnet
git submodule update --init

to install the Chamfer distance submodule. The Chamfer distance submodule is originally taken from here with some modifications to use it as a submodule. All parameters are stored in config/parameters.yaml.

In our project, all our dependencies are managed by miniconda. Use the following command to create the conda environment:

conda env create -f impnet.yml

Then activate the environment using the command conda activate atppnet

Data

Download the SemanticKITTI data from the official website.

We process the data in advance to speed up training. To prepare the dataset from the our dataset, set the value of GENERATE_FILES to true in config/parameters.yaml.

Training

After following the data preparation tutorial, the model can be trained in the following way:

The training script can be run by

python train.py

using the parameters defined in config/parameters.yaml. Pass the flag --help if you want to see more options like resuming from a checkpoint or initializing the weights from a pre-trained model. A directory will be created in pcf/runs which makes it easier to discriminate between different runs and to avoid overwriting existing logs. The script saves everything like the used config, logs and checkpoints into a path pcf/runs/COMMIT/EXPERIMENT_DATE_TIME consisting of the current git commit ID (this allows you to checkout at the last git commit used for training), the specified experiment ID (pcf by default) and the date and time.

Testing

Test your model by running

python test.py -m COMMIT/EXPERIMENT_DATE_TIME

Download

Please download the model file from (here)

Acknowledgment