This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

Overview

Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans.

The approach builds on top of an arbitrary single-scan Panoptic Segmentation network and extends it to the temporal domain by associating instances across time using our Contrastive Aggregation network that leverages the point-wise features from the panoptic network.

Requirements

  • Install this package: go to the root directory of this repo and run:
pip3 install -U -e .

Data preparation

Download the SemanticKITTI dataset inside the directory data/kitti/. The directory structure should look like this:

./
└── data/
    └── kitti
        └── sequences
            ├── 00/           
            │   ├── velodyne/	
            |   |	├── 000000.bin
            |   |	├── 000001.bin
            |   |	└── ...
            │   └── labels/ 
            |       ├── 000000.label
            |       ├── 000001.label
            |       └── ...
            ├── 08/ # for validation
            ├── 11/ # 11-21 for testing
            └── 21/
                └── ...

Pretrained models

Reproducing the results

Run the evaluation script, which will compute the metrics for the validation set:

python evaluate_4dpanoptic.py --ckpt_ps path/to/panoptic_weights --ckpt_ag path/to/aggregation_weights 

Training

Create instances dataset

Since we use a frozen Panoptic Segmentation Network, to avoid running the forward pass during training, we save the instance predictions and the point features in advance running:

python save_panoptic_features.py --ckpt path/to/panoptic_weights

This will create a directory in cont_assoc/data/instance_features with the same structure as Kitti but containing, for each sequence of the train set, npy files containing the instance points, labels and features for each scan.

Save validation predictions

To get the 4D Panoptic Segmentation performance for the validation step during training, we save the full predictions for the validation set (sequence 08) running:

python save_panoptic_features.py --ckpt path/to/panoptic_weights --save_val_pred

This will create a directory in cont_assoc/data/validation_predictions with npy files for each scan of the validation sequence containing the semantic and instance predictions for each point.

Train Contrastive Aggregation Network

Once the instance dataset and the validation predictions are generated, we're ready to train the Contrastive Aggregation Network running:

python train_aggregation.py 

All the configurations are in the config/contrastive_instances.yaml file.

Citation

If you use this repo, please cite as :

@article{marcuzzi2022ral,
  author = {Rodrigo Marcuzzi and Lucas Nunes and Louis Wiesmann and Ignacio Vizzo and Jens Behley and Cyrill Stachniss},
  title = {{Contrastive Instance Association for 4D Panoptic Segmentation \\ using Sequences of 3D LiDAR Scans}},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  year = 2022,
  volume={7},
  number={2},
  pages={1550-1557},
}

Acknowledgments

The Panoptic Segmentation Network used in this repo is DS-Net.

The loss function it's a modified version of SupContrast.

License

Copyright 2022, Rodrigo Marcuzzi, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.

This project is free software made available under the MIT License. For details see the LICENSE file.

Owner
Photogrammetry & Robotics Bonn
Photogrammetry & Robotics Lab at the University of Bonn
Photogrammetry & Robotics Bonn
A containerized REST API around OpenAI's CLIP model.

OpenAI's CLIP — REST API This is a container wrapping OpenAI's CLIP model in a RESTful interface. Running the container locally First, build the conta

Santiago Valdarrama 48 Nov 06, 2022
This repository contains the code to replicate the analysis from the paper "Moving On - Investigating Inventors' Ethnic Origins Using Supervised Learning"

Replication Code for 'Moving On' - Investigating Inventors' Ethnic Origins Using Supervised Learning This repository contains the code to replicate th

Matthias Niggli 0 Jan 04, 2022
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors This package provides a simulator for vision-based

Facebook Research 255 Dec 27, 2022
Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Huaxiu Yao 16 Dec 26, 2022
Official Repository for Machine Learning class - Physics Without Frontiers 2021

PWF 2021 Física Sin Fronteras es un proyecto del Centro Internacional de Física Teórica (ICTP) en Trieste Italia. El ICTP es un centro dedicado a fome

36 Aug 06, 2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

[ICCV2021] TransReID: Transformer-based Object Re-Identification [pdf] The official repository for TransReID: Transformer-based Object Re-Identificati

DamoCV 569 Dec 30, 2022
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Awesome production machine learning This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, versi

The Institute for Ethical Machine Learning 12.9k Jan 04, 2023
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

Computer Vision Insitute, SZU 113 Dec 27, 2022
Implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hashing by Maximizing Bit Entropy

Deep Unsupervised Image Hashing by Maximizing Bit Entropy This is the PyTorch implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hash

62 Dec 30, 2022
Accelerated deep learning R&D

Accelerated deep learning R&D PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and

Catalyst-Team 3.1k Jan 06, 2023
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

SimMIM By Zhenda Xie*, Zheng Zhang*, Yue Cao*, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai and Han Hu*. This repo is the official implementation of

Microsoft 674 Dec 26, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Human-Level Control through Deep Reinforcement Learning Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning. This imp

Devsisters Corp. 2.4k Dec 26, 2022
YouRefIt: Embodied Reference Understanding with Language and Gesture

YouRefIt: Embodied Reference Understanding with Language and Gesture YouRefIt: Embodied Reference Understanding with Language and Gesture by Yixin Che

16 Jul 11, 2022
A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

Convolutional Neural Network (CNN). This repository contains a source code of a deep learning network built with TensorFlow and Keras to classify gend

Pawel Dziemiach 1 Dec 19, 2021
Solutions of Reinforcement Learning 2nd Edition

Solutions of Reinforcement Learning, An Introduction

YIFAN WANG 1.4k Dec 30, 2022
Text Summarization - WCN — Weighted Contextual N-gram method for evaluation of Text Summarization

Text Summarization WCN — Weighted Contextual N-gram method for evaluation of Text Summarization In this project, I fine tune T5 model on Extreme Summa

Aditya Shah 1 Jan 03, 2022
This git repo contains the implementation of my ML project on Heart Disease Prediction

Introduction This git repo contains the implementation of my ML project on Heart Disease Prediction. This is a real-world machine learning model/proje

Aryan Dutta 1 Feb 02, 2022
The Video-based Accident Detection System built in Python

Accident-detection-system About the Project This Repository contains the Video-based Accident Detection System built in Python. Contributors Yukta Gop

SURYAVANSHI SNEHAL BALKRISHNA 50 Dec 07, 2022