Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, IROS 2021

Related tags

Deep LearningSHEF
Overview

For academic use only.

Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception

Ziwei Wang, Liyuan Pan, Yonhon Ng, Zheyu Zhuang and Robert Mahony

The paper was accepted by the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) in Prague, Czech Republic.

Publications

PDF

@inproceedings{wang2021stereo,
  title={Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception},
  author={Wang, Ziwei and Pan, Liyuan and Ng, Yonhon and Zhuang, Zheyu and Mahony, Robert},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2021},
  organization={IEEE}
}

IROS-video

https://www.youtube.com/watch?v=Azu7rJSPGNc

Data

Events Image Raw Data:

Three scenarios: picnic, complex boxes, and simple boxes. Each scenario includes at least 6 sequences with different camera speeds and lighting conditions.

From FLIR RGB camera From Prophesee event camera Description
Intensity images yes no Synchronised intensity images from FLIR RGB camera
images_ts.txt no yes Timestamps of the ynchronised intensity images. We synchronise the two cameras by sending a trigger signal from the FLIR RGB camera to the event camera.
log_td.dat no yes Event data, includes event x, y, ts, p

Notes:

Events are decompressed from .raw to .dat format. To convert raw data to .dat or .csv format, we used the Prophesee tools in Prophesee_tools You can also install the last Prophesee software version follow the instructions on the website If you need, you can find all tools in /usr/share/prophesee_driver/samples or /usr/share/metavision/sdk/driver/samples, depending on what version you are using.

Processed stereo event-frame dataset

Parameters for each sequence

Stereo hybrid event-frame calibration data

Point cloud

UR5 robot arm pose

Notes

  1. If you have any questions regarding this code and the corresponding results, please contact [email protected]
Owner
Ziwei Wang
Ziwei Wang
3D-aware GANs based on NeRF (arXiv).

CIPS-3D This repository will contain the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.

Peterou 563 Dec 31, 2022
The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also pr

Meta Research 1 Dec 02, 2021
Improved Fitness Optimization Landscapes for Sequence Design

ReLSO Improved Fitness Optimization Landscapes for Sequence Design Description Citation How to run Training models Original data source Description In

Krishnaswamy Lab 44 Dec 20, 2022
Code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language"

The repository provides the source code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language" submitted to HA

Sherzod Hakimov 3 Aug 04, 2022
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Microsoft 13.8k Jan 03, 2023
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
LSUN Dataset Documentation and Demo Code

LSUN Please check LSUN webpage for more information about the dataset. Data Release All the images in one category are stored in one lmdb database fil

Fisher Yu 426 Jan 02, 2023
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 01, 2022
git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction Code for the ECCV 2020 paper by Yiming Qian and Yasutaka Furukawa Getting

37 Dec 04, 2022
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
A general-purpose programming language, focused on simplicity, safety and stability.

The Rivet programming language A general-purpose programming language, focused on simplicity, safety and stability. Rivet's goal is to be a very power

The Rivet programming language 17 Dec 29, 2022
Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

gans-collection.torch Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN). Note that EBGAN and

Minchul Shin 53 Jan 22, 2022
YuNetのPythonでのONNX、TensorFlow-Lite推論サンプル

YuNet-ONNX-TFLite-Sample YuNetのPythonでのONNX、TensorFlow-Lite推論サンプルです。 TensorFlow-LiteモデルはPINTO0309/PINTO_model_zoo/144_YuNetのものを使用しています。 Requirement Op

KazuhitoTakahashi 8 Nov 17, 2021
U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Yihui He 401 Nov 21, 2022
[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning [MedIA or Arxiv] and [Demo] This repository pr

Healthcare Intelligence Laboratory 92 Dec 08, 2022
Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

FGHV Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing Requirements Python 3.6 Pytorch 1.5.0 Cud

5 Jun 02, 2022
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022
Autonomous Movement from Simultaneous Localization and Mapping

Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor

14 Nov 07, 2022
Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel

<a href=[email protected]"> 4 Oct 14, 2022
HarDNeXt: Official HarDNeXt repository

HarDNeXt-Pytorch HarDNeXt: A Stage Receptive Field and Connectivity Aware Convolution Neural Network HarDNeXt-MSEG for Medical Image Segmentation in 0

5 May 26, 2022