TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

Overview

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++ is a novel multi-object TSDF formulation that can encode multiple object surfaces at each voxel. In a multiple dynamic object tracking and reconstruction scenario, a TSDF++ map representation allows maintaining accurate reconstruction of surfaces even while they become temporarily occluded by other objects moving in their proximity. At the same time, the representation allows maintaining a single volume for the entire scene and all the objects therein, thus solving the fundamental challenge of scalability with respect to the number of objects in the scene and removing the need for an explicit occlusion handling strategy.

Citing

When using TSDF++ in your research, please cite the following publication:

Margarita Grinvald, Federico Tombari, Roland Siegwart, and Juan Nieto, TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction, in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [Paper] [Video]

@article{grinvald2021tsdf,
  author={M. {Grinvald} and F. {Tombari} and R. {Siegwart} and J. {Nieto}},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  title={{TSDF++}: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction},
  year={2021},
}

Installation

The installation has been tested on Ubuntu 16.04 and Ubutnu 20.04.

Requirements

Install dependencies

Install ROS following the instructions at the ROS installation page. The full install (ros-kinetic-desktop-full, ros-melodic-desktop-full) are recommended.

Make sure to source your ROS setup.bash script by following the instructions on the ROS installation page.

Installation on Ubuntu

In your terminal, define the installed ROS version and name of the catkin workspace to use:

export ROS_VERSION=kinetic # (Ubuntu 16.04: kinetic, Ubuntu 18.04: melodic)
export CATKIN_WS=~/catkin_ws

If you don't have a catkin workspace yet, create a new one:

mkdir -p $CATKIN_WS/src && cd $CATKIN_WS
catkin init
catkin config --extend /opt/ros/$ROS_VERSION --merge-devel 
catkin config --cmake-args -DCMAKE_CXX_STANDARD=14 -DCMAKE_BUILD_TYPE=Release
wstool init src

Clone the tsdf-plusplus repository over HTTPS (no Github account required) and automatically fetch dependencies:

cd $CATKIN_WS/src
git clone https://github.com/ethz-asl/tsdf-plusplus.git
wstool merge -t . tsdf-plusplus/tsdf_plusplus_https.rosinstall
wstool update

Alternatively, clone over SSH (Github account required):

cd $CATKIN_WS/src
git clone [email protected]:ethz-asl/tsdf-plusplus.git
wstool merge -t . tsdf-plusplus/tsdf_plusplus_ssh.rosinstall
wstool update

Build and source the TSDF++ packages:

catkin build tsdf_plusplus_ros rgbd_segmentation mask_rcnn_ros cloud_segmentation
source ../devel/setup.bash # (bash shell: ../devel/setup.bash,  zsh shell: ../devel/setup.zsh)

Troubleshooting

Compilation freeze

By default catkin build on a computer with N CPU cores will run N make jobs simultaneously. If compilation seems to hang forever, it might be running low on RAM. Try limiting the number of maximum parallel build jobs through the -jN flag to a value way lower than your CPU count, i.e.

catkin build tsdf_plusplus_ros rgbd_segmentation mask_rcnn_ros cloud_segmentation -j4

If it still freezes at compilation time, you can go as far as limiting the maximum number of parallel build jobs and max load to 1 through the -lN flag:

catkin build tsdf_plusplus_ros rgbd_segmentation mask_rcnn_ros cloud_segmentation -j1 -l1

License

The code is available under the MIT license.

Owner
ETHZ ASL
ETHZ ASL
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.

NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne

Nebuly 1.7k Dec 31, 2022
Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Art041_NN_Feed_Forward Public repository containing materials used for Feed Forward (FF) Neural Networks article. -- Illustration of a very simple Fee

SolClover 2 Dec 29, 2021
(ICCV 2021) Official code of "Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing."

Dressing in Order (DiOr) 👚 [Paper] 👖 [Webpage] 👗 [Running this code] The official implementation of "Dressing in Order: Recurrent Person Image Gene

Aiyu Cui 277 Dec 28, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Paddle-PANet 目录 结果对比 论文介绍 快速安装 结果对比 CTW1500 Method Backbone Fine

7 Aug 08, 2022
Graph parsing approach to structured sentiment analysis.

Fine-grained Sentiment Analysis as Dependency Graph Parsing This repository contains the code and datasets described in following paper: Fine-grained

Jeremy Barnes 36 Dec 12, 2022
Scikit-learn compatible estimation of general graphical models

skggm : Gaussian graphical models using the scikit-learn API In the last decade, learning networks that encode conditional independence relationships

213 Jan 02, 2023
Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch.

Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch! Now, Rearrange and Reduce in einops.layers.jittor are support!!

130 Jan 08, 2023
RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation YouTube | BiliBili 16X interpolation results from two input images: Introd

旷视天元 MegEngine 28 Dec 09, 2022
Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

PGpoints Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021) Hyeontae Son, Young Min Kim Pre

Hyeontae Son 9 Jun 06, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our new data division is based on COCO2017. We divide the training set into

58 Dec 23, 2022
Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers.

Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers. It contains purchases, recurring

Ayodeji Yekeen 1 Jan 01, 2022
Unoffical implementation about Image Super-Resolution via Iterative Refinement by Pytorch

Image Super-Resolution via Iterative Refinement Paper | Project Brief This is a unoffical implementation about Image Super-Resolution via Iterative Re

LiangWei Jiang 2.5k Jan 02, 2023
Rust bindings for the C++ api of PyTorch.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

Laurent Mazare 2.3k Dec 30, 2022
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning This repository provides an implementation of the paper Beta S

Yongchan Kwon 28 Nov 10, 2022
Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Optimization for Oriented Object Detection via Representation Invariance Loss By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong. Th

ming71 56 Nov 28, 2022
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Nervana 3.9k Dec 20, 2022
EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

1 Jul 03, 2022
Weight initialization schemes for PyTorch nn.Modules

nninit Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin. ##Update This repo has been

Alykhan Tejani 69 Jan 26, 2021
Domain Generalization with MixStyle, ICLR'21.

MixStyle This repo contains the code of our ICLR'21 paper, "Domain Generalization with MixStyle". The OpenReview link is https://openreview.net/forum?

Kaiyang 208 Dec 28, 2022