[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

Overview

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

YouTube | arXiv

                 

Prerequisites

Kaolin is available here: https://github.com/NVIDIAGameWorks/kaolin

Running

Examples

The code can be easily run by:

python optimize.py

Running with your inputs:

python optimize.py --im examples/vol_im.png --bgr examples/vol_bgr.png
python optimize.py --im examples/aerobie_im.png --bgr examples/aerobie_bgr.png
python optimize.py --im examples/pen_im.png --bgr examples/pen_bgr.png

The results will be written to the output folder.

Reference

Examples If you use this repository, please cite the following publication:

@misc{sfb,
  title = {Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects},
  author = {Denys Rozumnyi and Martin R. Oswald and Vittorio Ferrari and Marc Pollefeys},
  booktitle = {NeurIPS},
  month = {Dec},
  year = {2021}
}
You might also like...
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Code for Discriminative Sounding Objects Localization (NeurIPS 2020)
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

A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up/down.

HandTrackingBrightnessControl A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up

Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

DeepMLS: Deep Implicit Moving Least-Squares Functions for 3D Reconstruction This repository contains the implementation of the paper: Deep Implicit Mo

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

LiDAR-MOS: Moving Object Segmentation in 3D LiDAR Data This repo contains the code for our paper: Moving Object Segmentation in 3D LiDAR Data: A Learn

Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

High-performance moving least squares material point method (MLS-MPM) solver.
High-performance moving least squares material point method (MLS-MPM) solver.

High-Performance MLS-MPM Solver with Cutting and Coupling (CPIC) (MIT License) A Moving Least Squares Material Point Method with Displacement Disconti

Predict multi paths to a moving person depending on his trajectory history.
Predict multi paths to a moving person depending on his trajectory history.

Multi-future Trajectory Prediction The project is about using the Multiverse model to make possible multible-future trajectory prediction for a seen p

Comments
  • cannot import name 'total_variation' from 'kornia'

    cannot import name 'total_variation' from 'kornia'

    hi!

    what an interesting work!

    I have some trouble running the test demo. I have downloaded the newest kornia from pip and tried this https://github.com/kornia/kornia/discussions/1290, but still, get the problem. I wonder if you could give your environment requirements to run the test.

    thx :)

    Traceback (most recent call last):
      File "optimize.py", line 7, in <module>
        from shapefromblur import *
      File "/dev-data/xxx/projects/SfB/ShapeFromBlur-main/shapefromblur.py", line 12, in <module>
        from models.loss import *
      File "/dev-data/xxx/projects/SfB/ShapeFromBlur-main/models/loss.py", line 3, in <module>
        from kornia import total_variation
    ImportError: cannot import name 'total_variation' from 'kornia'
    
    opened by Ayxm1412 2
Releases(v1.0)
Owner
Denys Rozumnyi
PhD student at ETH Zurich.
Denys Rozumnyi
A web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks

This project is a web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks. Thanks for NVlabs' excelle

K.L. 150 Dec 15, 2022
null

DeformingThings4D dataset Video | Paper DeformingThings4D is an synthetic dataset containing 1,972 animation sequences spanning 31 categories of human

208 Jan 03, 2023
ISNAS-DIP: Image Specific Neural Architecture Search for Deep Image Prior [CVPR 2022]

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior (CVPR 2022) Metin Ersin Arican*, Ozgur Kara*, Gustav Bredell, Ender Konukogl

Özgür Kara 24 Dec 18, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
MIRACLE (Missing data Imputation Refinement And Causal LEarning)

MIRACLE (Missing data Imputation Refinement And Causal LEarning) Code Author: Trent Kyono This repository contains the code used for the "MIRACLE: Cau

van_der_Schaar \LAB 15 Dec 29, 2022
1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

Lihe Yang 209 Jan 01, 2023
DABO: Data Augmentation with Bilevel Optimization

DABO: Data Augmentation with Bilevel Optimization [Paper] The goal is to automatically learn an efficient data augmentation regime for image classific

ElementAI 24 Aug 12, 2022
On the Adversarial Robustness of Visual Transformer

On the Adversarial Robustness of Visual Transformer Code for our paper "On the Adversarial Robustness of Visual Transformers"

Rulin Shao 35 Dec 14, 2022
基于pytorch构建cyclegan示例

cyclegan-demo 基于Pytorch构建CycleGAN示例 如何运行 准备数据集 将数据集整理成4个文件,分别命名为 trainA, trainB:训练集,A、B代表两类图片 testA, testB:测试集,A、B代表两类图片 例如 D:\CODE\CYCLEGAN-DEMO\DATA

Koorye 3 Oct 18, 2022
Deep Reinforcement Learning with pytorch & visdom

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Jingwei Zhang 783 Jan 04, 2023
This repository contains the reference implementation for our proposed Convolutional CRFs.

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

Marvin Teichmann 553 Dec 07, 2022
Implementation of 'X-Linear Attention Networks for Image Captioning' [CVPR 2020]

Introduction This repository is for X-Linear Attention Networks for Image Captioning (CVPR 2020). The original paper can be found here. Please cite wi

JDAI-CV 240 Dec 17, 2022
Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper

Continual Learning With Filter Atom Swapping Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper If find t

11 Aug 29, 2022
From Perceptron model to Deep Neural Network from scratch in Python.

Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N

Aditya Kahol 1 Jan 14, 2022
NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models

NaturalCC NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks,

159 Dec 28, 2022
This repo contains the official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis This repo contains the official implementations of EigenDamage: Structured Prunin

Chaoqi Wang 107 Apr 20, 2022
HeartRate detector with ArduinoandPython - Use Arduino and Python create a heartrate detector.

Syllabus of Contents Syllabus of Contents Introduction Of Project Features Develop With Python code introduction Installation License Developer Contac

1 Jan 05, 2022
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Context Encoders: Feature Learning by Inpainting CVPR 2016 [Project Website] [Imagenet Results] Sample results on held-out images: This is the trainin

Deepak Pathak 829 Dec 31, 2022
Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

Eder Santana 7 Jan 24, 2022
"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021

undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f

Yichen Jiang 0 Mar 25, 2022