Registration Loss Learning for Deep Probabilistic Point Set Registration

Related tags

Deep LearningRLLReg
Overview

RLLReg

This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV 2020 paper "Registration Loss Learning for Deep Probabilistic Point Set Registration".

ArXiv: [paper]

If you find the code useful, please cite using

@InProceedings{Lawin_2020_3DV,
    author = {Felix J\"aremo Lawin and Per-Erik Forss\'en},
    title = {Registration Loss Learning for Deep Probabilistic Point Set Registration},
    booktitle = {{IEEE/CVF} International Virtual Conference on 3D Vision ({3DV})},
    month = {November},
    year = {2020}} 

Installation

  • Clone the repository: git clone https://github.com/felja633/RLLReg.git
  • Create a conda environment and install the following dependencies:
conda create -n rllreg python=3.7
conda activate rllreg
conda install -y numpy pathlib mkl-include pyyaml
conda install -y pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
conda install -y -c conda-forge cudatoolkit-dev
pip install easydict visdom
pip install git+https://github.com/jonbarron/robust_loss_pytorch
conda install -y -c open3d-admin open3d
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --cuda_home=/path/to/conda/rllreg 
pip install torch-scatter==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip install torch-sparse==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip install torch-cluster==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip install torch-spline-conv==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip install torch-geometric

Datasets

Kitti

Download and unpack Velodyne scans from http://www.cvlibs.net/download.php?file=data_odometry_velodyne.zip

3DMatch

Download RGB-D scenes from http://3dmatch.cs.princeton.edu/ using http://vision.princeton.edu/projects/2016/3DMatch/downloads/rgbd-datasets/download.sh and unpack the file. Download train.txt and test.txt. These contain the official train/test splits which can be found in the file https://vision.princeton.edu/projects/2016/3DMatch/downloads/rgbd-datasets/split.txt. Place these text files in the 3DMatch dataset folder.

Configuration

Set up your local environment by setting the correct paths for your system in config.py. Here you should set the paths to the datasets and pre-trained models.

Models

The following pre-trained models are available for download:

Name Training set Weights
RLLReg_threedmatch.pth 3DMatch download
RLLReg_threedmatch_multi.pth 3DMatch download
RLLReg_kitti.pth Kitti download
RLLReg_kitti_multi.pth Kitti download

For the version trained with contrastive loss, use the following models from https://github.com/chrischoy/FCGF

Name Training set Weights
2019-08-16_19-21-47.pth 3DMatch download
KITTI-v0.3-ResUNetBN2C-conv1-5-nout16.pth Kitti download

To further enable comparisons to DGR, download the weights for 3DMatch and Kitti.

Place all pre-trained weights in the same folder and set pretrained_networks to the path of that folder in config.py.

Running evaluations

Scripts for evaluation are available at experiments/. For an evaluation of pairwise registration as described in the paper run:

python experiments/evaluation_kitti.py

Training

Scripts for training are available at experiments/. If you want to train RLLReg for pairwise registration run:

python experiments/train_rll_kitti.py

Additional implementations

This repository also includes a pytorch version of Density Adaptive Point Set Registration (DARE) and Joint Registration of Multiple Point Clouds (JRMPC). Further, models/feature_reg_model_fcgf_fppsr.py and models/feature_reg_model_fpfh_fppsr.py contain pytorch implementations of FPPSR using FCGF and FPFH features respectively.

Under external/DeepGLobalRegistration the official implementation of DGR is located. The code is copied from the original repository but it is modified to use relative paths.

Contact

Felix Järemo Lawin

email: [email protected]

Acknowledgements

Special thanks go to Shivangi Srivastava who helped with initial implementations of the work!

Owner
Felix Järemo Lawin
Felix Järemo Lawin
Reimplementation of the paper `Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL2020)`

Human Attention for Text Classification Re-implementation of the paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Foc

Shunsuke KITADA 15 Dec 13, 2021
Camera ready code repo for the NeuRIPS 2021 paper: "Impression learning: Online representation learning with synaptic plasticity".

Impression-Learning-Camera-Ready Camera ready code repo for the NeuRIPS 2021 paper: "Impression learning: Online representation learning with synaptic

2 Feb 09, 2022
This program writes christmas wish programmatically. It is using turtle as a pen pointer draw christmas trees and stars.

Introduction This is a simple program is written in python and turtle library. The objective of this program is to wish merry Christmas programmatical

Gunarakulan Gunaretnam 1 Dec 25, 2021
Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information" Notes I probabl

Berkeley Expert System Technologies Lab 0 Jul 01, 2021
This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

📈 Automated Time Series Forecasting Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to gene

Zach Renwick 42 Jan 04, 2023
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Rahul Vigneswaran 1 Jan 17, 2022
Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch

Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch; pre-processing and post-processing using numpy instead of pytroch.

炼丹去了 21 Dec 12, 2022
AI pipelines for Nvidia Jetson Platform

Jetson Multicamera Pipelines Easy-to-use realtime CV/AI pipelines for Nvidia Jetson Platform. This project: Builds a typical multi-camera pipeline, i.

NVIDIA AI IOT 96 Dec 23, 2022
QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

QR2Pass This is a proof of concept for an alternative (passwordless) authenticat

4 Dec 09, 2022
Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Dataloader for CIFAR-N CIFAR-10N noise_label = torch.load('./data/CIFAR-10_human.pt') clean_label = noise_label['clean_label'] worst_label = noise_lab

<a href=[email protected]"> 117 Nov 30, 2022
A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

Benedek Rozemberczki 259 Dec 05, 2022
Neural implicit reconstruction experiments for the Vector Neuron paper

Neural Implicit Reconstruction with Vector Neurons This repository contains code for the neural implicit reconstruction experiments in the paper Vecto

Congyue Deng 35 Jan 02, 2023
Tensorflow implementation for Self-supervised Graph Learning for Recommendation

If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.

152 Jan 07, 2023
"Domain Adaptive Semantic Segmentation without Source Data" (ACM MM 2021)

LDBE Pytorch implementation for two papers (the paper will be released soon): "Domain Adaptive Semantic Segmentation without Source Data", ACM MM2021.

benfour 16 Sep 28, 2022
The official pytorch implemention of the CVPR paper "Temporal Modulation Network for Controllable Space-Time Video Super-Resolution".

This is the official PyTorch implementation of TMNet in the CVPR 2021 paper "Temporal Modulation Network for Controllable Space-Time VideoSuper-Resolu

Gang Xu 95 Oct 24, 2022
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
Code for Discriminative Sounding Objects Localization (NeurIPS 2020)

Discriminative Sounding Objects Localization Code for our NeurIPS 2020 paper Discriminative Sounding Objects Localization via Self-supervised Audiovis

51 Dec 11, 2022
Compute FID scores with PyTorch.

FID score for PyTorch This is a port of the official implementation of Fréchet Inception Distance to PyTorch. See https://github.com/bioinf-jku/TTUR f

2.1k Jan 06, 2023
Official pytorch implementation of "Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization" ACMMM 2021 (Oral)

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization This is an official implementation of "Feature Stylization and Domain-

22 Sep 22, 2022
Highway networks implemented in PyTorch.

PyTorch Highway Networks Highway networks implemented in PyTorch. Just the MNIST example from PyTorch hacked to work with Highway layers. Todo Make th

Conner Vercellino 56 Dec 14, 2022