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
Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022
This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

RGB2NIR_Experimental This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models

5 Jan 04, 2023
A High-Performance Distributed Library for Large-Scale Bundle Adjustment

MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment This repo contains an official implementation of MegBA. MegBA is a

旷视研究院 3D 组 336 Dec 27, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels.

AutoDSP TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels. About Adaptive filtering algorithms are commonplace in sign

Jonah Casebeer 48 Sep 19, 2022
Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”

GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is

Jiacheng Ye 12 Nov 24, 2022
Nonnegative spatial factorization for multivariate count data

Nonnegative spatial factorization for multivariate count data This repository contains supporting code to facilitate reproducible analysis. For detail

Will Townes 24 Dec 19, 2022
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
Adaout is a practical and flexible regularization method with high generalization and interpretability

Adaout Adaout is a practical and flexible regularization method with high generalization and interpretability. Requirements python 3.6 (Anaconda versi

lambett 1 Feb 09, 2022
LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs.

LocUNet LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs. The method utilizes accura

4 Oct 05, 2022
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A

17 Nov 30, 2022
Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

Pau Riba 310 Nov 07, 2022
This is a code repository for the paper "Graph Auto-Encoders for Financial Clustering".

Repository for the paper "Graph Auto-Encoders for Financial Clustering" Requirements Python 3.6 torch torch_geometric Instructions This is a simple c

Edward Turner 1 Dec 02, 2021
Convolutional Neural Network to detect deforestation in the Amazon Rainforest

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

5 Feb 17, 2022
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022
Your interactive network visualizing dashboard

Your interactive network visualizing dashboard Documentation: Here What is Jaal Jaal is a python based interactive network visualizing tool built usin

Mohit 177 Jan 04, 2023
Simple Tensorflow implementation of Toward Spatially Unbiased Generative Models (ICCV 2021)

Spatial unbiased GANs — Simple TensorFlow Implementation [Paper] : Toward Spatially Unbiased Generative Models (ICCV 2021) Abstract Recent image gener

Junho Kim 16 Apr 15, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
Pytorch implementation of SimSiam Architecture

SimSiam-pytorch A simple pytorch implementation of Exploring Simple Siamese Representation Learning which is developed by Facebook AI Research (FAIR)

Saeed Shurrab 1 Oct 20, 2021
[cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

PS-MT [cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation by Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasile

Yuyuan Liu 132 Jan 03, 2023