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

Last update: Dec 12, 2022

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.

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

Related tags

Overview

SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition

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

News:

Setup (One time)

Conda

Download

Run

Train

Test

Acknowledgement

Citation

Other Related Projects

Owner

Sourav Garg

Python-experiments - A Repository which contains python scripts to automate things and make your life easier with python

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Text-Based Ideal Points

ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Unofficial Implement PU-Transformer

Manage the availability of workspaces within Frappe/ ERPNext (sidebar) based on user-roles

PyTorch implementation of EigenGAN

Simulation-based inference for the Galactic Center Excess

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Unpaired Caricature Generation with Multiple Exaggerations

Repository for MDPGT

The repository offers the official implementation of our BMVC 2021 paper in PyTorch.

Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships.

Solver for Large-Scale Rank-One Semidefinite Relaxations

Code for Environment Dynamics Decomposition (ED2).

An Unsupervised Detection Framework for Chinese Jargons in the Darknet

Python scripts form performing stereo depth estimation using the HITNET model in ONNX.