ViPFormer

This repository is the official implementation of "ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding", created by Hongyu Sun, Yongcai Wang, Xudong Cai, Xuewei Bai and Deying Li.

Point cloud classification, segmentation and object detection are fundamental tasks in robot perception. Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoint follows the contrastive learning framework and exploits image and point cloud data for unsupervised point cloud understanding. Although the impressive performance is presented, the unbalanced architecture makes it unnecessarily complex and inefficient. For example, the image branch in CrossPoint is ~8.3x heavier than the point cloud branch leading to higher complexity and latency. To address this problem, in this paper, we propose a lightweight Vision-and-Pointcloud Transformer (ViPFormer) to unify image and point cloud processing in a single architecture. ViPFormer learns in an unsupervised manner by optimizing intra-modal and cross-modal contrastive objectives. Then the pretrained model is transferred to various downstream tasks, including 3D shape classification and semantic segmentation. Experiments on different datasets show ViPFormer surpasses previous state-of-the-art unsupervised methods with higher accuracy, lower model complexity and runtime latency. Finally, the effectiveness of each component in ViPFormer is validated by extensive ablation studies.

Preparation

Package Setup

• Ubuntu 18.04
• Python 3.7.11
• PyTorch 1.11.0
• CUDA 10.2
• torchvision 0.12.0
• wandb 0.12.11
• lightly 1.1.21
• einops 0.4.1
• h5py 3.6.0
• setuptools 59.5.0
• pueue & pueued 2.0.4

  conda create -n vipformer python=3.7.11
  codna activate vipformer

  pip install torch==1.11.0+cu102 torchvision==0.12.0+cu102 --extra-index-url https://download.pytorch.org/whl/cu102
  pip install -r requirements.txt

pueue is a shell command management software, we use it for scheduling the model pre-training & fine-tuning tasks, please refer to the official page for installation and basic usage. We recommend it because you can run the experiments at scale with this tool thus save your time.

W&B Server Setup

We track the model training and fine-tuning with W&B tools. The official W&B tools may be slow and unstable since they are on remote servers, we install the local version by running the following command.

  sudo groupadd docker  # create `docker` group on Ubuntu
  sudo usermod -aG docker <username>  # add <username> to `docker` group to use docker, replace <username> with yours
  docker run --rm -d -v wandb:/vol -p 28282:8080 --name wandb-local wandb/local:0.9.41

If you do not have Docker installed on your computer before, referring to the official document to finish Docker installation on Ubuntu.

Dataset Download

Download the following datasets and extract them to the desired location on your computer.

1. ShapeNetRender
2. ModelNet40
3. ScanObjectNN
4. ShapeNetPart

Usage

Pre-train

1. We pre-train ViPFormer on the ShapeNetRender dataset, which consists of image and point cloud pairs.

2. We provide the different shell files to pre-train ViPFormer with different architectures, for example,

  ./scripts/pretrain/pt-E1CL6SL-H4D256-L96-MR2-0.sh

The filename of shell has specific meaning, pt refers to pre-train, and E1CL6SL-H4D256-L96-MR2-0 specifies its architecture with 1 cross-attention layer, 6 self-attention layer, 4 attention heads, model dimension of 256, 96 point or image patches, MLP ratio of 2, and the ending 0 indicates the first run with this architecture. So we can ablate the model architecture by changing these parameters in the shell files.

Fine-tune

1. We fine-tune the pre-trained ViPFormer model on various target datasets, including ModelNet40, ScanObjectNN and ShapeNetPart.

2. We also provide the shell files to fine-tune the pre-trained model, for example,

  ./scripts/finetune/ft-E1CL6SL-H4D256-L96-MR2-0.sh

Here ft means fine-tune and its following string points out the model architecture and corresponding running order.

3. Optionally, you can download our pre-trained models for evaluation.

Architecture	Acc. (MN)	ACC. (SO)	Code
E1CL8SL-H4D256-L128-MR2	92.48	90.72	9wfb
E1CL8SL-H6D384-L128-MR4	93.93	89.69	v843

Few-shot Classification

1. The performance of few-shot point cloud classification is tested on two datasets. We follow the K-way, N-shot convention to conduct the experiments.

2. We provide the running scripts, for example

  ./scripts/fewshot/eval_fewshot-MN.sh

In eval_fewshot-MN.sh, MN represents the ModelNet40 dataset. You can switch MN to SO to evaluate on ScanObjectNN.

Results

Model Complexity, Latency and Performance

a) 3D Point Cloud Classification

b) 3D Shape Part Segmentation

c) Few-shot Point Cloud Classification

Visualization

a) Pre-trained & Fine-tuned Feature Distribution

b) 3D Shape Part Segmentation

Ablation Studies

a) Model Architecture

b) Contrastive Objectives

c) Learning Strategies

Citation

If you find our paper and codebase are helpful for your research or project, please consider cite ViPFormer as follows.

@inproceedings{sun23vipformer,
  title={ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding},
  author={Hongyu Sun, Yongcai Wang, Xudong Cai, Xuewei Bai and Deying Li},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023}
}

Acknowledgement

Our implementation and visualization are partially inspired by the following repositories:

1. CrossPoint
2. PerceiverIO
3. PointNet++