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
Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

OG-SPACE Introduction Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE) is a computational framewo

Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca) 0 Nov 17, 2021
Tutorial page of the Climate Hack, the greatest hackathon ever

Tutorial page of the Climate Hack, the greatest hackathon ever

UCL Artificial Intelligence Society 12 Jul 02, 2022
Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❤️

Hello 🤟 #Team-Greider The team of 20 people for HackBio'2021 Virtual Bioinformatics Internship 💝 🖨️ 👨‍💻 HackBio: https://thehackbio.com 💬 Ask us

Siddhant Sharma 7 Oct 20, 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
This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning It includes /bert, which is the original BERT repos

Mitchell Gordon 11 Nov 15, 2022
Implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networks, using PyTorch

C-CNN: Contourlet Convolutional Neural Networks This repo implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networ

Goh Kun Shun (KHUN) 10 Nov 03, 2022
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022
Implementation of Bottleneck Transformer in Pytorch

Bottleneck Transformer - Pytorch Implementation of Bottleneck Transformer, SotA visual recognition model with convolution + attention that outperforms

Phil Wang 621 Jan 06, 2023
Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

On the Generative Utility of Cyclic Conditionals This repository is the official implementation of "On the Generative Utility of Cyclic Conditionals"

Chang Liu 44 Nov 16, 2022
MonoRCNN is a monocular 3D object detection method for automonous driving

MonoRCNN MonoRCNN is a monocular 3D object detection method for automonous driving, published at ICCV 2021. This project is an implementation of MonoR

87 Dec 27, 2022
TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Microsoft 1.3k Dec 30, 2022
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr

Edward Oughton 32 Jan 03, 2023
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Zilong Huang 245 Dec 13, 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
Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

RNN-for-Joint-NLU Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

Kim SungDong 194 Dec 28, 2022
MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

ZhengChang 20 Nov 25, 2022
Learning nonlinear operators via DeepONet

DeepONet: Learning nonlinear operators The source code for the paper Learning nonlinear operators via DeepONet based on the universal approximation th

Lu Lu 239 Jan 02, 2023
Self-Supervised Methods for Noise-Removal

SSMNR | Self-Supervised Methods for Noise Removal Image denoising is the task of removing noise from an image, which can be formulated as the task of

1 Jan 16, 2022
Unofficial implementation of Perceiver IO: A General Architecture for Structured Inputs & Outputs

Perceiver IO Unofficial implementation of Perceiver IO: A General Architecture for Structured Inputs & Outputs Usage import torch from src.perceiver.

Timur Ganiev 111 Nov 15, 2022
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022