Code for the RA-L (ICRA) 2021 paper "SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition"

Related tags

Deep Learninglocalizationdeep-learningsequentialsnowtemporalvisual-place-recognition1d-convolutionday-night
Overview

SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition

[ArXiv+Supplementary] [IEEE Xplore RA-L 2021] [ICRA 2021 YouTube Video]

and

SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition

[ArXiv] [CVPR 2021 Workshop 3DVR]


Sequence-Based Hierarchical Visual Place Recognition.

News:

Jun 23: CVPR 2021 Workshop 3DVR paper, "SeqNetVLAD vs PointNetVLAD", now available on arXiv. Oxford dataset to be released soon.

Jun 02: SeqNet code release with the Nordland dataset.

Setup (One time)

Conda

conda create -n seqnet python=3.8 mamba -c conda-forge -y
conda activate seqnet
mamba install numpy pytorch=1.8.0 torchvision tqdm scikit-learn faiss tensorboardx h5py -c conda-forge -y

Download

Run bash download.sh to download single image NetVLAD descriptors (3.4 GB) for the Nordland-clean dataset [a] and corresponding model files (1.5 GB) [b].

Run

Train

To train sequential descriptors through SeqNet:

python main.py --mode train --pooling seqnet --dataset nordland-sw --seqL 10 --w 5 --outDims 4096 --expName "w5"

To (re-)train single descriptors through SeqNet:

python main.py --mode train --pooling seqnet --dataset nordland-sw --seqL 1 --w 1 --outDims 4096 --expName "w1"

Test

python main.py --mode test --pooling seqnet --dataset nordland-sf --seqL 5 --split test --resume ./data/runs/Jun03_15-22-44_l10_w5/

The above will reproduce results for SeqNet (S5) as per Supp. Table III on Page 10.

To obtain other results from the same table, expand this. 
# Raw Single (NetVLAD) Descriptor
python main.py --mode test --pooling single --dataset nordland-sf --seqL 1 --split test

# SeqNet (S1)
python main.py --mode test --pooling seqnet --dataset nordland-sf --seqL 1 --split test --resume ./data/runs/Jun03_15-07-46_l1_w1/

# Raw + Smoothing
python main.py --mode test --pooling smooth --dataset nordland-sf --seqL 5 --split test

# Raw + Delta
python main.py --mode test --pooling delta --dataset nordland-sf --seqL 5 --split test

# Raw + SeqMatch
python main.py --mode test --pooling single+seqmatch --dataset nordland-sf --seqL 5 --split test

# SeqNet (S1) + SeqMatch
python main.py --mode test --pooling s1+seqmatch --dataset nordland-sf --seqL 5 --split test --resume ./data/runs/Jun03_15-07-46_l1_w1/

# HVPR (S5 to S1)
# Run S5 first and save its predictions by specifying `resultsPath`
python main.py --mode test --pooling seqnet --dataset nordland-sf --seqL 5 --split test --resume ./data/runs/Jun03_15-22-44_l10_w5/ --resultsPath ./data/results/
# Now run S1 + SeqMatch using results from above (the timestamp of `predictionsFile` would be different in your case)
python main.py --mode test --pooling s1+seqmatch --dataset nordland-sf --seqL 5 --split test --resume ./data/runs/Jun03_15-07-46_l1_w1/ --predictionsFile ./data/results/Jun03_16-07-36_l5_0.npz

Acknowledgement

The code in this repository is based on Nanne/pytorch-NetVlad. Thanks to Tobias Fischer for his contributions to this code during the development of our project QVPR/Patch-NetVLAD.

Citation

@article{garg2021seqnet,
  title={SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition},
  author={Garg, Sourav and Milford, Michael},
  journal={IEEE Robotics and Automation Letters},
  volume={6},
  number={3},
  pages={4305-4312},
  year={2021},
  publisher={IEEE},
  doi={10.1109/LRA.2021.3067633}
}

@misc{garg2021seqnetvlad,
  title={SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition},
  author={Garg, Sourav and Milford, Michael},
  howpublished={CVPR 2021 Workshop on 3D Vision and Robotics (3DVR)},
  month={Jun},
  year={2021},
}

Other Related Projects

Patch-NetVLAD (2021); Delta Descriptors (2020); CoarseHash (2020); seq2single (2019); LoST (2018)

[a] This is the clean version of the dataset that excludes images from the tunnels and red lights, exact image names can be obtained from here.

[b] These will automatically save to ./data/, you can modify this path in download.sh and get_datasets.py to specify your workdir.

Owner
Sourav Garg
Sourav Garg
GitHub Repository
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
Demonstration of the Model Training as a CI/CD System in Vertex AI

Model Training as a CI/CD System This project demonstrates the machine model training as a CI/CD system in GCP platform. You will see more detailed wo

Chansung Park 19 Dec 28, 2022
Hierarchical probabilistic 3D U-Net, with attention mechanisms (β€”π˜ˆπ˜΅π˜΅π˜¦π˜―π˜΅π˜ͺ𝘰𝘯 𝘜-π˜•π˜¦π˜΅, π˜šπ˜Œπ˜™π˜¦π˜΄π˜•π˜¦π˜΅) and a nested decoder structure with deep supervision (β€”π˜œπ˜•π˜¦π˜΅++).

Hierarchical probabilistic 3D U-Net, with attention mechanisms (β€”π˜ˆπ˜΅π˜΅π˜¦π˜―π˜΅π˜ͺ𝘰𝘯 𝘜-π˜•π˜¦π˜΅, π˜šπ˜Œπ˜™π˜¦π˜΄π˜•π˜¦π˜΅) and a nested decoder structure with deep supervision (β€”π˜œπ˜•π˜¦π˜΅++). Built in TensorFlow 2.5. Configured for vox

Diagnostic Image Analysis Group 32 Dec 08, 2022
An example of semantic segmentation using tensorflow in eager execution.

Semantic segmentation using Tensorflow eager execution Requirement Python 2.7+ Tensorflow-gpu OpenCv H5py Scikit-learn Numpy Imgaug Train with eager e

IΓ±igo Alonso Ruiz 25 Sep 29, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and

Salesforce 72 Dec 05, 2022
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

DensePose: Dense Human Pose Estimation In The Wild RΔ±za Alp GΓΌler, Natalia Neverova, Iasonas Kokkinos [densepose.org] [arXiv] [BibTeX] Dense human pos

Meta Research 6.4k Jan 01, 2023
Course materials for Fall 2021 "CIS6930 Topics in Computing for Data Science" at New College of Florida

Fall 2021 CIS6930 Topics in Computing for Data Science This repository hosts course materials used for a 13-week course "CIS6930 Topics in Computing f

Yoshi Suhara 101 Nov 30, 2022
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AIST

Marcelo Hartmann 2 May 06, 2022
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)

A simple API for working with University of California, Irvine (UCI) Machine Learning (ML) repository Table of Contents Introduction About Page of the

Tirthajyoti Sarkar 223 Dec 05, 2022
PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

mlp-mixer-pytorch PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021) Usage import torch from mlp_mixer

isaac 27 Jul 09, 2022
RL Algorithms with examples in Python / Pytorch / Unity ML agents

Reinforcement Learning Project This project was created to make it easier to get started with Reinforcement Learning. It now contains: An implementati

Rogier Wachters 3 Aug 19, 2022
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Sta

Charles R. Qi 4k Dec 30, 2022
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

The Hypersim Dataset For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real i

Apple 1.3k Jan 04, 2023
Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD @ NeurIPS 2021.

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models Code and supplementary materials Repository of the p

Daniel Bogdoll 4 Jul 13, 2022
Style transfer between images was performed using the VGG19 model

Style transfer between images was performed using the VGG19 model. The necessary codes, libraries and all other information of this project are available below

Onur yΔ±lmaz 2 May 09, 2022
HuSpaCy: industrial-strength Hungarian natural language processing

HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti

HuSpaCy 120 Dec 14, 2022
Learning embeddings for classification, retrieval and ranking.

StarSpace StarSpace is a general-purpose neural model for efficient learning of entity embeddings for solving a wide variety of problems: Learning wor

Facebook Research 3.8k Dec 22, 2022
Source Code for AAAI 2022 paper "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching"

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching This repository is an official implementation of

HKUST-KnowComp 13 Sep 08, 2022
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

AndrΓ©s Romero 37 Nov 27, 2022