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
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

Wenxuan Zhou 27 Oct 28, 2022
OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

OpenFace 2.2.0: a facial behavior analysis toolkit Over the past few years, there has been an increased interest in automatic facial behavior analysis

Tadas Baltrusaitis 5.8k Dec 31, 2022
Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning

isvd Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning If you find this code useful, you may cite us as: @inprocee

Sami Abu-El-Haija 16 Jan 08, 2023
simple demo codes for Learning to Teach with Dynamic Loss Functions

Learning to Teach with Dynamic Loss Functions This repo contains the simple demo for the NeurIPS-18 paper: Learning to Teach with Dynamic Loss Functio

Lijun Wu 15 Dec 30, 2021
Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

ETSformer - Pytorch Implementation of ETSformer, state of the art time-series Transformer, in Pytorch Install $ pip install etsformer-pytorch Usage im

Phil Wang 121 Dec 30, 2022
This is the code used in the paper "Entity Embeddings of Categorical Variables".

This is the code used in the paper "Entity Embeddings of Categorical Variables". If you want to get the original version of the code used for the Kagg

Cheng Guo 845 Nov 29, 2022
Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

LASR Installation Build with conda conda env create -f lasr.yml conda activate lasr # install softras cd third_party/softras; python setup.py install;

Google 157 Dec 26, 2022
K-Nearest Neighbor in Pytorch

Pytorch KNN CUDA 2019/11/02 This repository will no longer be maintained as pytorch supports sort() and kthvalue on tensors. git clone https://github.

Chris Choy 65 Dec 01, 2022
A curated list of resources for Image and Video Deblurring

A curated list of resources for Image and Video Deblurring

Subeesh Vasu 1.7k Jan 01, 2023
Neuralnetwork - Basic Multilayer Perceptron Neural Network for deep learning

Neural Network Just a basic Neural Network module Usage Example Importing Module

andreecy 0 Nov 01, 2022
GANmouflage: 3D Object Nondetection with Texture Fields

GANmouflage: 3D Object Nondetection with Texture Fields Rui Guo1 Jasmine Collins

29 Aug 10, 2022
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [BCNet, CVPR 2021] This is the official pytorch implementation of BCNet built on

Lei Ke 434 Dec 01, 2022
A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash

Duplicate Image Detection Getting Started Install dependencies pip install -r requirements.txt Run service python main.py Testing Test with pytest How

Matthew Podolak 21 Nov 11, 2022
An End-to-End Machine Learning Library to Optimize AUC (AUROC, AUPRC).

Logo by Zhuoning Yuan LibAUC: A Machine Learning Library for AUC Optimization Website | Updates | Installation | Tutorial | Research | Github LibAUC a

Optimization for AI 176 Jan 07, 2023
The code for paper Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm

Quantum Qubit Rotation Algorithm Single qubit rotation gates $$ U(\Theta)=\bigotimes_{i=1}^n R_x (\phi_i) $$ QQRA for the max-cut problem This code wa

SheffieldWang 0 Oct 18, 2021
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Wenbo Huang 1 May 17, 2022
The final project for "Applying AI to Wearable Device Data" course from "AI for Healthcare" - Udacity.

Motion Compensated Pulse Rate Estimation Overview This project has 2 main parts. Develop a Pulse Rate Algorithm on the given training data. Then Test

Omar Laham 2 Oct 25, 2022
Predict halo masses from simulations via graph neural networks

HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati

Pablo Villanueva Domingo 20 Nov 15, 2022
Dynamical Wasserstein Barycenters for Time Series Modeling

Dynamical Wasserstein Barycenters for Time Series Modeling This is the code related for the Dynamical Wasserstein Barycenter model published in Neurip

8 Sep 09, 2022
Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Retrieval.

Targeted Trojan-Horse Attacks on Language-based Image Retrieval Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Re

fine 7 Aug 23, 2022