Lmitchell11's Stars
AlexeyAB/Yolo_mark
GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
mosaic-addons/ns3-federate
ms-van3t-devs/ms-van3t
A multi-stack, ETSI compliant, V2X framework for ns-3.
sedgecloud/ECSNeTpp
ECSNeT++ is a simulation framework built on OMNeT++ for Distributed Stream Processing applications running on Edge and Cloud computing environments.
brianmc95/OpenCV2X
OMNeT++ V2X simulation framework for ETSI ITS-G5
Monish6864/Big-Data-Analytics-and-Visualization-Using-PySpark
Every day, more than 2.5 quintillion bytes of data are produced, according to studies, and the number is continuously growing. In fact, by 2020, every individual on the world is expected to create 1.7MB of data each second . Google is the largest player in the sector, with 87.35 percent of the global search engine market share in 2021. This equates to 1.2 trillion searches each year and over 40,000 queries every second . This produced huge amounts of data, known as big data. Massive data sets generated from a number of sources are referred to as big data. These data sets, due to their magnitude and complexity, cannot be collected, stored, or analysed using any of the existing traditional procedures. As a result, several tools, including as NoSQL, Hadoop, and Spark, are used to investigate massive data sets. Using big data analysis technologies, we collect various types of data from more diverse sources, such as digital media, web services, business applications, machine log data, and so on. Artificial intelligence, particularly machine learning, has become increasingly important in today's fast-paced world of information technology. A diverse set of resources has been discovered using a variety of techniques in order to process products and services quickly while maintaining high standards. Machine learning methods, on the other hand, are difficult to implement because to their high cost and massive storage requirements for data, CPUs, and memory. To deal with huge amounts of data, sophisticated systems began to emerge. Spark is a well-known distributed data research computer that boasts impressive capabilities such as improved performance in huge datasets. While Spark supports a number of programming languages, for this study Python was chosen because of its unique features such as screen analysis, data visualisation, and quicker framework processing. Thereafter, PySpark, a Spark and Python hybrid, is used for managing Spark data in this study. Jupyter Notebook is a free online tool for writing and sharing live code, equations, visualisations, and text documents. Jupyter Project is in charge of Jupyter Notebook upkeep. Jupyter Notebooks is an IPython fork with an IPython Notebook project. Jupyter is called for the three primary programming languages that it supports: Julia, Python, and R. Jupyter comes with the IPython kernel, which allows you to write Python programmes, however there are presently over 100 additional kernels available. This open source tool includes Jupyter Notebook, Jupyter Lab, and Jupyter Hub, as well as a variety of plug-ins and modules to aid in the development of a collaborative application. The data is based on a Google Play Store app. This data set was derived from the Kaggle data set and utilised in this study to analyse and visualise massive amounts of data. While the data obtained comprised 24 attributes , only attributes were chosen as the most appropriate for this study. PySpark was started using the installation step number once the dataset was chosen. Following that, the data set's pre-loading was done. After that, duplicates were eliminated, as well as columns with incomplete data. After then, the investigation was carried out. The discussion section includes an explanation of the graphics and associated algorithms.
gallenszl/CFNet
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching（CVPR2021）
cardwing/Codes-for-PVKD
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)
Tuxacker/semantic_calibration
hku-mars/joint-lidar-camera-calib
Joint intrinsic and extrinsic LiDAR-camera calibration.
ika-rwth-aachen/MultiCorrupt
MultiCorrupt: A benchmark for robust multi-modal 3D object detection, evaluating LiDAR-Camera fusion models in autonomous driving. Includes diverse corruption types (e.g., misalignment, miscalibration, weather) and severity levels. Assess model performance under challenging conditions.
BraveGroup/PointSAM-for-MixSup
Codes for ICLR 2024: "MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection"
tensorflow/lingvo
Lingvo
OpenCalib/CalibAnything
michaelmengistu/cryptography-algorithms-for-TI-Nspire
I made some programs and functions to support Encryption/Decryption techniques for the TI-Nspire.
darrenjkt/MS3D
Auto-labeling of point cloud sequences for 3D object detection using an ensemble of experts and temporal refinement
darrenjkt/SEE-MTDA
(RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation.
IIPCVLAB/LCCNet
Official PyTorch implementation of the paper “LCCNet: Lidar and Camera Self-Calibration usingCost Volume Network”.
KleinYuan/RGGNet
[RA-L 2020] Official Tensorflow Implementation for "RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model", IEEE Robotics and Automation Letters 5.4 (2020): 6956-6963
open-mmlab/OpenPCDet
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
kaitheuser/Multiple-LiDARs-Merger
This repository is to merge multiple LiDAR point cloud data into one frame for each timestep.
darrenjkt/SEE-VCN
(ICRA 2023) Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
yinwu33/multi_lidar_calibration
Calibrate extrinsic parameters of multi-lidars, based on ICP or NDT, etc.
saadjahangir/multi_lidar_calibration
Mult-LiDAR calibration by extracting planar features
acfr/cam_lidar_calibration
(ITSC 2021) Optimising the selection of samples for robust lidar camera calibration. This package estimates the calibration parameters from camera to lidar frame.
facebookresearch/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Yangzhangcst/Transformer-in-Computer-Vision
A paper list of some recent Transformer-based CV works.
Amit10311/Codes-link-for-Lidar-and-Camera
Codes links for implementation on LiDar and Camera
jialeli1/lidarseg3d
A repository for LiDAR 3D semantic segmentation in autonomous driving scenarios. Also the official implementations of our ECCV 2022 paper (SDSeg3D) and CVPR 2023 paper (MSeg3D).
KangLiao929/Awesome-Deep-Camera-Calibration
Deep Learning for Camera Calibration and Beyond: A Survey