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
AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)

AOT-GAN for High-Resolution Image Inpainting Arxiv Paper | AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting Yanhong

Multimedia Research 214 Jan 03, 2023
SphereFace: Deep Hypersphere Embedding for Face Recognition

SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa

Weiyang Liu 1.5k Dec 29, 2022
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions This is the official repository of PRIME, the data agumentation method introduced i

Apostolos Modas 34 Oct 30, 2022
This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation

This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation (Guillaume Couairon, Holger

Meta Research 31 Oct 17, 2022
Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

Jhacson Meza 47 Nov 18, 2022
A simple Rock-Paper-Scissors game using CV in python

ML18_Rock-Paper-Scissors-using-CV A simple Rock-Paper-Scissors game using CV in python For IITISOC-21 Rules and procedure to play the interactive game

Anirudha Bhagwat 3 Aug 08, 2021
Scalable, event-driven, deep-learning-friendly backtesting library

...Minimizing the mean square error on future experience. - Richard S. Sutton BTGym Scalable event-driven RL-friendly backtesting library. Build on

Andrew 922 Dec 27, 2022
PAWS 🐾 Predicting View-Assignments with Support Samples

This repo provides a PyTorch implementation of PAWS (predicting view assignments with support samples), as described in the paper Semi-Supervised Learning of Visual Features by Non-Parametrically Pre

Facebook Research 437 Dec 23, 2022
code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

PyTorch implementation of UAGAN(U-net Attention Generative Adversarial Networks) This repository contains the source code for the paper "A High-precis

Tong 8 Apr 25, 2022
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically. The collected data will then be used to train a deep neural network that can

Martin Valchev 3 Apr 24, 2022
BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

BitPack is a practical tool that can efficiently save quantized neural network models with mixed bitwidth.

Zhen Dong 36 Dec 02, 2022
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy for sma

THUDM 540 Dec 30, 2022
AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

AsymmetricGAN for Image-to-Image Translation AsymmetricGAN Framework for Multi-Domain Image-to-Image Translation AsymmetricGAN Framework for Hand Gest

Hao Tang 42 Jan 15, 2022
CvT-ASSD: Convolutional vision-Transformerbased Attentive Single Shot MultiBox Detector (ICTAI 2021 CCF-C 会议)The 33rd IEEE International Conference on Tools with Artificial Intelligence

CvT-ASSD including extra CvT, CvT-SSD, VGG-ASSD models original-code-website: https://github.com/albert-jin/CvT-SSD new-code-website: https://github.c

金伟强 -上海大学人工智能小渣渣~ 5 Mar 07, 2022
Discovering and Achieving Goals via World Models

Discovering and Achieving Goals via World Models [Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper] Russell Mendonca*1, Ole

Oleg Rybkin 71 Dec 22, 2022
PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased

Kaihua Tang 824 Jan 03, 2023
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.

Attendance-System-based-on-Facial-recognition-Attendance-data-stored-in-csv-file- Simple Python project using Opencv and datetime package to recognise

3 Aug 09, 2022
StyleMapGAN - Official PyTorch Implementation

StyleMapGAN - Official PyTorch Implementation StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing Hyunsu Kim, Yunj

NAVER AI 425 Dec 23, 2022