GPU Accelerated Non-rigid ICP for surface registration

Overview

GPU Accelerated Non-rigid ICP for surface registration

Introduction

Preivous Non-rigid ICP algorithm is usually implemented on CPU, and needs to solve sparse least square problem, which is time consuming. In this repo, we implement a pytorch version NICP algorithm based on paper Amberg et al. Detailedly, we leverage the AMSGrad to optimize the linear regresssion, and then found nearest points iteratively. Additionally, we smooth the calculated mesh with laplacian smoothness term. With laplacian smoothness term, the wireframe is also more neat.


Quick Start

install

We use python3.8 and cuda10.2 for implementation. The code is tested on Ubuntu 20.04.

  • The pytorch3d cannot be installed directly from pip install pytorch3d, for the installation of pytorch3d, see pytorch3d.
  • For other packages, run
pip install -r requirements.txt
  • For the template face model, currently we use a processed version of BFM face model from 3DMMfitting-pytorch, download the BFM09_model_info.mat from 3DMMfitting-pytorch and put it into the ./BFM folder.
  • For demo, run
python demo_nicp.py

we show demo for NICP mesh2mesh and NICP mesh2pointcloud. We have two param sets for registration:

milestones = set([50, 80, 100, 110, 120, 130, 140])
stiffness_weights = np.array([50, 20, 5, 2, 0.8, 0.5, 0.35, 0.2])
landmark_weights = np.array([5, 2, 0.5, 0, 0, 0, 0, 0])

This param set is used for registration on fine grained mesh

milestones = set([50, 100])
stiffness_weights = np.array([50, 20, 5])
landmark_weights = np.array([50, 20, 5])

This param set is used for registration on noisy point clouds

Templated Model

You can also use your own templated face model with manually specified landmarks.

Todo

Currently we write some batchwise functions, but batchwise NICP is not supported now. We will support batch NICP in further releases.

You might also like...
High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

GrabGpu_py: a scripts for grab gpu when gpu is free

GrabGpu_py a scripts for grab gpu when gpu is free. WaitCondition: gpu_memory

A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection
A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection 1. 介绍 用以替代 NMS,在所有 bbox 中挑选出最优的集合。 NMS 仅考虑了 bbox 的得分,然后根据 IOU 来

A non-linear, non-parametric Machine Learning method capable of modeling complex datasets
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Fast Symbolic Regression Symbolic Regression is a non-linear, non-parametric Machine Learning method capable of modeling complex data sets. fastsr aim

Code for
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Weakly Supervised Learning of Rigid 3D Scene Flow
Weakly Supervised Learning of Rigid 3D Scene Flow

Weakly Supervised Learning of Rigid 3D Scene Flow This repository provides code and data to train and evaluate a weakly supervised method for rigid 3D

Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds Introduction This is the official PyTorch implementation of o

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators
Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators. It's also a suite of learning algorithms to train agents to operate in these environments (PPO, SAC, evolutionary strategy, and direct trajectory optimization are implemented).

Code for ICCV 2021 paper: ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators..
Code for ICCV 2021 paper: ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators..

ARAPReg Code for ICCV 2021 paper: ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators.. Installation The cod

Comments
  • Lack of file “BFM09_model_info.mat”

    Lack of file “BFM09_model_info.mat”

    Traceback (most recent call last): File "demo_nicp.py", line 28, in bfm_meshes, bfm_lm_index = load_bfm_model(torch.device('cuda:0')) File "/data/pytorch-nicp/bfm_model.py", line 15, in load_bfm_model bfm_meta_data = loadmat('BFM/BFM09_model_info.mat') File "/root/anaconda3/envs/pytorch3d/lib/python3.8/site-packages/scipy/io/matlab/mio.py", line 224, in loadmat with _open_file_context(file_name, appendmat) as f: File "/root/anaconda3/envs/pytorch3d/lib/python3.8/contextlib.py", line 113, in enter return next(self.gen) File "/root/anaconda3/envs/pytorch3d/lib/python3.8/site-packages/scipy/io/matlab/mio.py", line 17, in _open_file_context f, opened = _open_file(file_like, appendmat, mode) File "/root/anaconda3/envs/pytorch3d/lib/python3.8/site-packages/scipy/io/matlab/mio.py", line 45, in _open_file return open(file_like, mode), True FileNotFoundError: [Errno 2] No such file or directory: 'BFM/BFM09_model_info.mat'

    In 3DMMfitting-pytorch, there are only these files: BFM_exp_idx.mat BFM_front_idx.mat facemodel_info.mat README.md select_vertex_id.mat similarity_Lm3D_all.mat std_exp.txt

    opened by 675492062 2
  • What is the expected time needed for running demo_nicp.py?

    What is the expected time needed for running demo_nicp.py?

    Hello,

    On my computer it seems quite slow to run demo_nicp.py. At least it took more than 1 minutes to get final.obj. Is it correct?

    I ranAMM_NRR for non-rigit ICP registration with two 7000 vertices meshes. It needs ca 1 second with CPU on my computer. With GPU, it might be possible to do the same work in less than 100 ms?

    Thank you!

    opened by 1939938853 0
  • Hi, with landmarks: `landmarks = torch.from_numpy(np.array(landmarks)).to(device).long()`, maybe you can  reshape landmarks from torch.Size([1, 1, 68, 2]) to  torch.Size([1, 68, 2])

    Hi, with landmarks: `landmarks = torch.from_numpy(np.array(landmarks)).to(device).long()`, maybe you can reshape landmarks from torch.Size([1, 1, 68, 2]) to torch.Size([1, 68, 2])

    Hi, with landmarks: landmarks = torch.from_numpy(np.array(landmarks)).to(device).long(), maybe you can reshape landmarks from torch.Size([1, 1, 68, 2]) to torch.Size([1, 68, 2])

    Originally posted by @wuhaozhe in https://github.com/wuhaozhe/pytorch-nicp/issues/3#issuecomment-971453681 hi!I got output as torch.Size([1, 68, 512, 3]) torch.Size([1, 68, 2]) torch.Size([1, 512, 512, 3]) I think the shape of following tensors are right, but I meet the same problem. lm_vertex = torch.gather(lm_vertex, 2, column_index) RuntimeError: CUDA error: device-side assert triggered

    landmarks = torch.from_numpy(np.array(landmarks)).to(device).long()
    
    row_index = landmarks[:, :, 1].view(landmarks.shape[0], -1)
    column_index = landmarks[:, :, 0].view(landmarks.shape[0], -1)
    row_index = row_index.unsqueeze(2).unsqueeze(3).expand(landmarks.shape[0], landmarks.shape[1], shape_img.shape[2], shape_img.shape[3])
    column_index = column_index.unsqueeze(1).unsqueeze(3).expand(landmarks.shape[0], landmarks.shape[1], landmarks.shape[1], shape_img.shape[3])
    print(row_index.shape, landmarks.shape, shape_img.shape)
    
    opened by alicedingyueming 1
  • RuntimeError

    RuntimeError

    Traceback (most recent call last): File "demo_nicp.py", line 27, in target_lm_index, lm_mask = get_mesh_landmark(norm_meshes, dummy_render) File "/data/pytorch-nicp/landmark.py", line 37, in get_mesh_landmark row_index = row_index.unsqueeze(2).unsqueeze(3).expand(landmarks.shape[0], landmarks.shape[1], shape_img.shape[2], shape_img.shape[3]) RuntimeError: The expanded size of the tensor (1) must match the existing size (2) at non-singleton dimension 1. Target sizes: [1, 1, 512, 3]. Tensor sizes: [1, 2, 1, 1]

    I have already configure the environment,but it seems have some problems in the code.What can I do to solve this problem.

    opened by 675492062 8
Releases(v0.1)
Owner
Haozhe Wu
Research interests in Computer Vision and Machine Learning.
Haozhe Wu
Personalized Federated Learning using Pytorch (pFedMe)

Personalized Federated Learning with Moreau Envelopes (NeurIPS 2020) This repository implements all experiments in the paper Personalized Federated Le

Charlie Dinh 226 Dec 30, 2022
OntoProtein: Protein Pretraining With Ontology Embedding

OntoProtein This is the implement of the paper "OntoProtein: Protein Pretraining With Ontology Embedding". OntoProtein is an effective method that mak

ZJUNLP 80 Dec 14, 2022
Unsupervised Image Generation with Infinite Generative Adversarial Networks

Unsupervised Image Generation with Infinite Generative Adversarial Networks Here is the implementation of MICGANs using DCGAN architecture on MNIST da

16 Dec 24, 2021
Saeed Lotfi 28 Dec 12, 2022
Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis Fast & Low Memory requirement & Enhanced implementation of Local Context F

YangHeng 567 Jan 07, 2023
Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline

Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline. The pipeline accepts english text as input and returns the French translation.

Afropunk Technologist 1 Jan 24, 2022
AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models

AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models Description

Angel de Paula 0 Jun 08, 2022
Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

Rana Hanocka 1.4k Jan 04, 2023
TensorFlow-based implementation of "Pyramid Scene Parsing Network".

PSPNet_tensorflow Important Code is fine for inference. However, the training code is just for reference and might be only used for fine-tuning. If yo

HsuanKung Yang 323 Dec 20, 2022
Video Corpus Moment Retrieval with Contrastive Learning (SIGIR 2021)

Video Corpus Moment Retrieval with Contrastive Learning PyTorch implementation for the paper "Video Corpus Moment Retrieval with Contrastive Learning"

ZHANG HAO 42 Dec 29, 2022
Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

You Only Cut Once (YOCO) YOCO is a simple method/strategy of performing augmenta

88 Dec 28, 2022
[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"

KPAC: Kernel-Sharing Parallel Atrous Convolutional block This repository contains the official Tensorflow implementation of the following paper: Singl

Hyeongseok Son 50 Dec 29, 2022
Model Zoo for MindSpore

Welcome to the Model Zoo for MindSpore In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical

MindSpore 226 Jan 07, 2023
A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering.

DeepFilterNet A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering. libDF contains Rust code used for dat

Hendrik Schröter 292 Dec 25, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
This repository contains the code for the paper in EMNLP 2021: "HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression".

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression This repository contains the code for the paper in EM

Chenhe Dong 2 Mar 24, 2022
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [toc] 1. Introduction This repository provides the code for our paper at

13 Dec 08, 2022
Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation

Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation Overview This example will show how to validate the status of our firewall before and a

Calvin Remsburg 1 Jan 07, 2022
[AAAI 2021] EMLight: Lighting Estimation via Spherical Distribution Approximation and [ICCV 2021] Sparse Needlets for Lighting Estimation with Spherical Transport Loss

EMLight: Lighting Estimation via Spherical Distribution Approximation (AAAI 2021) Update 12/2021: We release our Virtual Object Relighting (VOR) Datas

Fangneng Zhan 144 Jan 06, 2023
Learn about Spice.ai with in-depth samples

Samples Learn about Spice.ai with in-depth samples ServerOps - Learn when to run server maintainance during periods of low load Gardener - Intelligent

Spice.ai 16 Mar 23, 2022