GPU Accelerated Non-rigid ICP for surface registration

Related tags

Deep Learningpytorch-nicp
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

null 514 Dec 28, 2022
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

tianyuluan 3 Jun 18, 2022
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

null 44 Sep 15, 2022
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

VAMSHI CHOWDARY 3 Jun 22, 2022
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.

Gengshan Yang 157 Nov 21, 2022
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

Zan Gojcic 124 Dec 27, 2022
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

Yijia Weng 96 Dec 7, 2022
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).

Google 1.5k Jan 2, 2023
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

Bo Sun 132 Nov 28, 2022
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
GitHub Repository
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
A multi-scale unsupervised learning for deformable image registration

A multi-scale unsupervised learning for deformable image registration Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu and Baochang Zha

ShuweiShao 2 Apr 13, 2022
DL course co-developed by YSDA, HSE and Skoltech

Deep learning course This repo supplements Deep Learning course taught at YSDA and HSE @fall'21. For previous iteration visit the spring21 branch. Lec

Yandex School of Data Analysis 1.3k Dec 30, 2022
PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

Yonglong Tian 2.2k Jan 08, 2023
Fast Neural Style for Image Style Transform by Pytorch

FastNeuralStyle by Pytorch Fast Neural Style for Image Style Transform by Pytorch This is famous Fast Neural Style of Paper Perceptual Losses for Real

Bengxy 81 Sep 03, 2022
Pgn2tex - Scripts to convert pgn files to latex document. Useful to build books or pdf from pgn studies

Pgn2Latex (WIP) A simple script to make pdf from pgn files and studies. It's sti

12 Jul 23, 2022
https://arxiv.org/abs/2102.11005

LogME LogME: Practical Assessment of Pre-trained Models for Transfer Learning How to use Just feed the features f and labels y to the function, and yo

THUML: Machine Learning Group @ THSS 149 Dec 19, 2022
Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network Pytorch implementation for our DivCo. We propose a simple ye

64 Nov 22, 2022
WORD: Revisiting Organs Segmentation in the Whole Abdominal Region

WORD: Revisiting Organs Segmentation in the Whole Abdominal Region. This repository provides the codebase and dataset for our work WORD: Revisiting Or

Healthcare Intelligence Laboratory 71 Jan 07, 2023
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
Experiments for distributed optimization algorithms

Network-Distributed Algorithm Experiments -- This repository contains a set of optimization algorithms and objective functions, and all code needed to

Boyue Li 40 Dec 04, 2022
tensorrt int8 量化yolov5 4.0 onnx模型

onnx模型转换为 int8 tensorrt引擎

123 Dec 28, 2022
Cards Against Humanity AI

cah-ai This is a Cards Against Humanity AI implemented using a pre-trained Semantic Search model. How it works A player is described by a combination

Alex Nichol 2 Aug 22, 2022
Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

vanilla-rw-protonets-project Vanilla Prototypical Networks and PNs with Random Weights for image classification on Omniglot and mini-ImageNet. Made wi

Giovani Candido 8 Aug 31, 2022
Hyperbolic Image Segmentation, CVPR 2022

Hyperbolic Image Segmentation, CVPR 2022 This is the implementation of paper Hyperbolic Image Segmentation (CVPR 2022). Repository structure assets :

Mina Ghadimi Atigh 46 Dec 29, 2022
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation Efficient Self-Ensemble Framework for Semantic Segmentation by Walid Bousselham

61 Dec 26, 2022
Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

Description: This is the official implementation of our AAAI-21 accepted paper Label Confusion Learning to Enhance Text Classification Models. The str

101 Nov 25, 2022
Research on Event Accumulator Settings for Event-Based SLAM

Research on Event Accumulator Settings for Event-Based SLAM This is the source code for paper "Research on Event Accumulator Settings for Event-Based

Robin Shaun 26 Dec 21, 2022
Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

OG-SPACE Introduction Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE) is a computational framewo

Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca) 0 Nov 17, 2021
Download files from DSpace systems (because for some reason DSpace won't let you)

DSpaceDL A tool for downloading files from DSpace items. For some reason, DSpace systems have a dogshit UI, and Universities absolutely LOOOVE to use

Soumitra Shewale 5 Dec 01, 2022