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
In the AI for TSP competition we try to solve optimization problems using machine learning.

AI for TSP Competition Goal In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted

Paulo da Costa 11 Nov 27, 2022
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Deep Hedging Demo Pricing Derivatives using Machine Learning 1) Jupyter version: Run ./colab/deep_hedging_colab.ipynb on Colab. 2) Gui version: Run py

Yu Man Tam 102 Jan 06, 2023
Pytorch ImageNet1k Loader with Bounding Boxes.

ImageNet 1K Bounding Boxes For some experiments, you might wanna pass only the background of imagenet images vs passing only the foreground. Here, I'v

Amin Ghiasi 11 Oct 15, 2022
DilatedNet in Keras for image segmentation

Keras implementation of DilatedNet for semantic segmentation A native Keras implementation of semantic segmentation according to Multi-Scale Context A

303 Mar 15, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

FFD Source Code Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face M

88 Nov 22, 2022
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface.

Gym-TORCS Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic

naoto yoshida 400 Dec 27, 2022
ALBERT-pytorch-implementation - ALBERT pytorch implementation

ALBERT-pytorch-implementation developing... 모델의 개념이해를 돕기 위한 구현물로 현재 변수명을 상세히 적었고

BG Kim 3 Oct 06, 2022
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
Subdivision-based Mesh Convolutional Networks

Subdivision-based Mesh Convolutional Networks The official implementation of SubdivNet in our paper, Subdivion-based Mesh Convolutional Networks Requi

Zheng-Ning Liu 181 Dec 28, 2022
Official repository for the paper "Instance-Conditioned GAN"

Official repository for the paper "Instance-Conditioned GAN" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.

Facebook Research 510 Dec 30, 2022
An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

Kakao Brain 72 Dec 28, 2022
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
PyTorch implementations of algorithms for density estimation

pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invert

Ilya Kostrikov 546 Dec 05, 2022
Related resources for our EMNLP 2021 paper

Plan-then-Generate: Controlled Data-to-Text Generation via Planning Authors: Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier Code

Yixuan Su 61 Jan 03, 2023
🐦 Opytimizer is a Python library consisting of meta-heuristic optimization techniques.

Opytimizer: A Nature-Inspired Python Optimizer Welcome to Opytimizer. Did you ever reach a bottleneck in your computational experiments? Are you tired

Gustavo Rosa 546 Dec 31, 2022
This is the code used in the paper "Entity Embeddings of Categorical Variables".

This is the code used in the paper "Entity Embeddings of Categorical Variables". If you want to get the original version of the code used for the Kagg

Cheng Guo 845 Nov 29, 2022
Official PyTorch implementation of PS-KD

Self-Knowledge Distillation with Progressive Refinement of Targets (PS-KD) Accepted at ICCV 2021, oral presentation Official PyTorch implementation of

61 Dec 28, 2022