Point-PQAE (ICCV 2025)

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views

Xiangdong Zhang*, Shaofeng Zhang* and Junchi Yan

Point-PQAE: Self-Supervised Cross Reconstruction with Decoupled Views for Point Cloud Learning

TL;DR: We introduce a cross-reconstruction method Point-PQAE, a new learning paradigm for point cloud self-supervised learning. By reconstructing one decoupled view from another, it creates a more challenging task than Point-MAE's self-reconstruction, leading to significantly better performance.

📰 News

If you find our project helpful, please consider giving us a star ⭐ on GitHub for the latest update.

• 🎉 Sept, 2024: Our previous work PCP-MAE is accepted by NeurIPS 2024 as Spotlight, check out the code
• 🎉 Jun, 2025: Point-PQAE is accepted by ICCV 2025.
• 💥 Sept, 2025: The Point-PQAE Paper is now available in arxiv.
• 📌 Sept, 2025: The pytorch implementation of Point-PQAE has been released.

✅ TODO List

• Release the training and inference code.
• Release the checkpoints.

Introduction

Point-PQAE introduces a more challenging and effective pre-training paradigm for 3D point cloud learning. While most self-supervised methods focus on reconstructing masked parts of a single point cloud view, we propose a more difficult and informative pre-training task called cross-reconstruction.

The model is then tasked with reconstructing one view given another view, which encourages the learning of more robust and meaningful representations by understanding both intra-view and inter-view relationships. To achieve this, we designed three novel modules 1) decoupled views generation, 2) VRPE generation, and 3) positional query block. To our knowledge, we are the first to design and apply crop mechanism to point cloud self-supervised learning.

Point-PQAE surpasses previous single-modal self-reconstruction methods on challenging benchmarks. For instance, it outperforms the Point-MAE baseline by 6.5%, 7.0%, and 6.7% on three variants of the ScanObjectNN dataset, demonstrating the superior quality of the learned representations.

Point-PQAE Models

Task	Dataset	Config	Acc.	Checkpoints
Pre-training	ShapeNet	base.yaml	N.A.	Pre-train
Classification	ScanObjectNN	finetune_scan_objbg.yaml	95.0%	-
Classification	ScanObjectNN	finetune_scan_objonly.yaml	93.6%	-
Classification	ScanObjectNN	finetune_scan_hardest.yaml	89.6%	-
Classification	ModelNet40(1k)	finetune_modelnet.yaml	94.0%	-
Classification	ModelNet40(8k)	finetune_modelnet.yaml	94.3%	-
Part Segmentation	ShapeNetPart	segmentation	84.6% Cls.mIoU	-
Scene Segmentation	S3DIS	semantic_segmentataion	61.4% mIoU	-

Task	Dataset	Config	5w10s (%)	5w20s (%)	10w10s (%)	10w20s (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.9±3.2	98.9±1.0	94.1±4.2	96.3±2.7

To fully reproduce our reported results, we recommend fine-tuning the pre-trained ckpt-300 with different random seeds (typically 8 different seeds) and recording the best performance which is also adopted by other peer methods (e.g. Point-MAE, ReCon and PCP-MAE).

Requirements

PyTorch >= 1.7.0 < 1.11.0; python >= 3.7; CUDA >= 9.0; GCC >= 4.9; torchvision;

# Quick Start
conda create -n pcpmae python=3.10 -y
conda activate pcpmae

# Install pytorch
conda install pytorch==2.0.1 torchvision==0.15.2 cudatoolkit=11.8 -c pytorch -c nvidia
# pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 -f https://download.pytorch.org/whl/torch_stable.html

# Install required packages
pip install -r requirements.txt

# Install the extensions
# Chamfer Distance & emd
cd ./extensions/chamfer_dist
python setup.py install --user
cd ./extensions/emd
python setup.py install --user
# PointNet++
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"

Datasets

We use ShapeNet, ScanObjectNN, ModelNet40, ShapeNetPart and S3DIS in this work. See DATASET.md for details.

Training/Inference Scripts

Pre-training

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/pretrain/base.yaml --exp_name <output_file_name>

# For example
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/pretrain/base.yaml --exp_name Point-PQAE

Fine-tuning

Fine-tuning on ScanObjectNN:

# Select one config from finetune_scan_objbg/objonly/hardest.yaml

# Full
python main.py --config cfgs/full/finetune_scan_hardest.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --model-prefix PQAE_encoder --seed $RANDOM

# Linear
python main.py --config cfgs/linear/finetune_scan_hardest.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --model-prefix PQAE_encoder --seed $RANDOM

# MLP-3
python main.py --config cfgs/mlp3/finetune_scan_hardest.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --model-prefix PQAE_encoder --seed $RANDOM

ModelNet40:

# full/linear/mlp3
# 1K points
python main.py --config cfgs/full/finetune_modelnet.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --seed $RANDOM

# 8K points
python main.py --config cfgs/full/finetune_modelnet_8k.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --seed $RANDOM

Few-shot learning:

# full/linear/mlp3
python main.py --config cfgs/full/fewshot.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path_to_pretrained_model> --way <5 or 10> --shot <10 or 20> --fold <0-9> --seed $RANDOM

Part segmentation on ShapeNetPart:

cd segmentation
python main.py --gpu <gpu_id> --ckpts <path_to_pretrained_model> \
--log_dir <log_dir> --learning_rate 0.0002 --epoch 300 \
--root <your_data_path>/data/shapenetcore_partanno_segmentation_benchmark_v0_normal/ \
--seed $RANDOM --model-prefix PQAE_encoder

Semantic segmentation on S3DIS:

cd semantic_segmentation
python main.py --ckpts <path_to_pretrained_model> \
--root <your_data_path>/data/s3dis/stanford_indoor3d --learning_rate 0.0002 --epoch 60 --gpu <gpu_id>

Contact

If you have any questions related to the code or the paper, feel free to email Xiangdong (zhangxiangdong@sjtu.edu.cn) or Shaofeng (sherrylone@sjtu.edu.cn).

Acknowledgements

This codebase is built upon Point-MAE, ReCon, Pointnet2_PyTorch.

Citation

If you find our work useful in your research, please consider citing:

@misc{zhang2025diversechallengingpretrainingpoint,
      title={Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views}, 
      author={Xiangdong Zhang and Shaofeng Zhang and Junchi Yan},
      year={2025},
      eprint={2509.01250},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.01250}, 
}