[ECCV 2024] PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training

This is the Pytorch implementation of our ECCV2024 paper PointRegGPT.

Introduction

Data plays a crucial role in training learning-based methods for 3D point cloud registration. However, the real-world dataset is expensive to build, while rendering-based synthetic data suffers from domain gaps. In this work, we present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point clouds gives a training pair. To enhance the data realism, we formulate a generative model as a depth inpainting diffusion to process the target depth map with the re-projected source depth map as the condition. Also, we design a depth correction module to alleviate artifacts caused by point penetration during the re-projection. To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration. When equipped with our approach, several recent algorithms can improve their performance significantly and achieve SOTA consistently on two common benchmarks.

Installation

Please use the following commands for installation.

# Download the codes
git clone https://github.com/Chen-Suyi/PointRegGPT.git
cd PointRegGPT

# It is recommended to create a new environment
conda create -n pointreggpt python==3.9
conda activate pointreggpt

# Install packages
pip install -r requirements.txt

Data Preparation

3DMatch

The 3DMatch RGB-D dataset can be found on 3DMatch: RGB-D Reconstruction Datasets.

Please follow the official training and testing scenes split to organize the downloaded files as follows:

--3DMatch--train--7-scenes-chess--camera-intrinsics.txt
        |      |               |--seq-XX--frame-XXXXXX.color.png
        |      |                       |--frame-XXXXXX.depth.png
        |      |                       |--frame-XXXXXX.pose.txt
        |      |--...
        |--test--7-scenes-redkitchen--camera-intrinsics.txt
              |                    |--seq-XX--frame-XXXXXX.color.png
              |                            |--frame-XXXXXX.depth.png
              |                            |--frame-XXXXXX.pose.txt
              |--... 

The 3DMatch data for point cloud registration can be downloaded from OverlapPredator.

Please put it in PointRegGPT/dataset/indoor/data. The data should be organized as follows:

--dataset--indoor--metadata
                |--data--train--7-scenes-chess--cloud_bin_0.pth
                      |      |               |--...
                      |      |--...
                      |--test--7-scenes-redkitchen--cloud_bin_0.pth
                            |                    |--...
                            |--...

Depth Correction

The training dataset for depth correction can be downloaded from here.

The unzipped data should be organized as follows:

--dataset--depth_correction--metadata--train.json
                          |         |--val.json
                          |--data--train--XXXXXX-input.depth.png
                                |      |--XXXXXX-label.depth.png
                                |--val--XXXXXX-input.depth.png
                                     |--XXXXXX-label.depth.png

Official Generative Dataset

The generative dataset used in the experiments in our paper can be downloaded from here.

The unzipped data should be organized as follows:

--generated_dataset--metadata--gt.log
                  |--data--scene-XXXXXX--sample-000000.cloud.ply
                                      |--sample-000001.cloud.ply

Pre-trained Weights

We will provide pre-trained weights on the releases page.

• successive_ddnm_diffusion_results.zip contains the pre-trained weights for our diffusion model.
  Please unzip and put it to PointRegGPT/successive_ddnm_diffusion_results.

• depth_correction_results.zip contains the pre-trained weights for our depth correction module.
  Please unzip and put it to PointRegGPT/depth_correction_results.

Quick Start

To begin with, please set the path to your downloaded 3DMatch RGB-D training data in generate_dataset.py#L48.

Use the following commands to create generative data:

# To generate point cloud pairs (No.000000 ~ No.000009 for example)
python generate_dataset.py --resume=official -start=0 -stop=10

# To generate a gt.log for the pairs
python generate_gt.py -start=0 -stop=10

By default, the generated data will be saved in PointRegGPT/generated_dataset.

Diffusion Model

Please set the path to your downloaded 3DMatch RGB-D training data in train_successive_ddnm_diffusion.py#L28.

Training

You can try training your own diffusion model using the following command:

python train_successive_ddnm_diffusion.py

By default, the checkpoints will be saved in PointRegGPT/successive_ddnm_diffusion_results with names formatted in model-SUFFIX.pt.

Multi-GPU Training

The trainer has been equipped with 🤗Accelerator, please refer to the link if you are not yet familiar with it.

Multi-GPU training can be simply set up by using the following commands:

# First, configure the Accelerator on your own machine
accelerate config

# Then, launch your training
accelerate launch train_successive_ddnm_diffusion.py

Testing

You can check the state of a diffusion model during training by:

# Replace SUFFIX with the suffix of checkpoint
python test_successive_ddnm_diffusion.py --resume=SUFFIX

By default, the results will be saved in PointRegGPT/successive_ddnm_diffusion_samples.

Depth Correction

Training

You can also try training your own depth correction module using the following command:

python train_depth_correction.py

By default, the checkpoints and train.log will be saved in PointRegGPT/depth_correction_results.

Testing

Please set the path to your downloaded 3DMatch RGB-D testing data in test_depth_correction.py#L15.

You can qualitatively test a depth correction module on 3DMatch RGB-D test split using the following command:

python test_depth_correction.py --resume=SUFFIX

By default, the results will be saved as GIFs in PointRegGPT/depth_correction_samples.

Point Cloud Registration

To load generated data during training, we provide examples in PointRegGPT/example_dataloader for the baselines (PREDATOR, CoFiNet, and GeoTransformer) we used in our paper.

We also provide pre-trained weights on the releases page, which are trained on our official generative dataset. You can simply load them using the official code of each baseline.

Citations

@inproceedings{chen2025pointreggpt,
  author={Chen, Suyi and Xu, Hao and Li, Haipeng and Luo, Kunming and Liu, Guanghui and Fu, Chi-Wing and Tan, Ping and Liu, Shuaicheng},
  title={PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training},
  booktitle={European Conference on Computer Vision},
  pages={272-289},
  year={2025},
  organization={Springer}
}

Achnowledgement

Denoising Diffusion Probabilistic Model, in Pytorch