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Point Transformer

This repository contains my code and report for the NPM3D Project (MVA 2025/2026) by, building upon the unofficial Point Transformer implementation (the code for the shape classification part of the model was never added).

Project summary

This project proposes a study of the Point Transformer, a transformer-based architecture for 3D point cloud understanding.

  • My primary goal is to reproduce the results of the original paper on the ModelNet40 shape classification task. This implementation achieves an overall accuracy of 92.7%, which is close to the 93.7% reported by the authors.
  • Beyond reproduction, the project includes a series of ablation studies and architectural modifications to better understand the behavior of the model.

Experimental results

Shape classification on ModelNet40:

Model Overall accuracy (OA) Mean class accuracy (mAcc)
Current implementation 92.67% 90.25%
Original paper 93.7% 90.6%

My codebase additions and modifications

  • Created model/pointtransformer/pointtransformer_cls.py (massively adapted from the existing segmentation model pointtransformer_cls.py).
  • Created util/modelnet40.py to handle loading data specifically for the ModelNet40 dataset.
  • Created util/profiler.py in order to measure the computational latency of each component of my implementation during the evaluation.
  • Modified tool/train.py and tool/test.py to support classification tasks and configuration file selections.

Dependencies

  • Ubuntu: 18.04 or higher
  • PyTorch: 1.9.0
  • CUDA: 11.1
  • Hardware: GPUs required to reproduce Point Transformer
  • To create conda environment, command as follows: 
    bash env_setup.sh pt

Dataset preparation

Download the pre-processed modelnet40_normal_resampled dataset from Kaggle: ModelNet Normal Resampled

Unzip the downloaded file and save it in dataset/modelnet40_normal_resampled.

Note: Loading the dataset takes a long time during the first session because it caches the arrays directly into shared memory for fast access.

Shape classification on ModelNet40:

  • Train a model (6 mn of caching): sh tool/train.sh modelnet40 pointtransformer_cls
  • Modify hyperparameters: Edit the configuration file located at config/modelnet40/modelnet40_pointtransformer_cls.yaml.
  • Test the best model (2 mn of caching): sh tool/test.sh modelnet40 pointtransformer_cls

Semantic segmentation on S3DIS:
Take a look at the base unofficial implementation repository.

Checkpoints and training logs

All experiments, model checkpoints, and training logs can be viewed and downloaded from the following link: Download Checkpoints & Logs (Google Drive)

Once downloaded and unzipped, you can easily visualize the training curves and metrics using TensorBoard. Navigate to the extracted folder in your terminal and run:

tensorboard --logdir=.

The following window will appear:

Tensorboard

Reproducing figures

To reproduce the plots and graphs I used in the project report, run the following scripts:

  • figures/treemap.py
  • figures/pareto.py
  • figures/graphs_generation_for_report.py

References

@inproceedings{zhao2021point,
  title={Point transformer},
  author={Zhao, Hengshuang and Jiang, Li and Jia, Jiaya and Torr, Philip HS and Koltun, Vladlen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={16259--16268},
  year={2021}
}