Difference between revisions of "Point Cloud"

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[http://www.google.com/search?q=Passenger+Screening+Challenge+kaggle+deep+machine+learning+ML ...Google search]
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|title=PRIMO.ai
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools 
  
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* [[Graph Convolutional Network (GCN)]]
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[http://www.youtube.com/results?search_query=Point+Cloud+Convolutional+Neural+Network+CNN+deep+machine+learning+ML Youtube search...]
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[http://www.google.com/search?q=Point+Cloud+Convolutional+Neural+Network+CNN+deep+machine+learning+ML ...Google search]
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* [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]]
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* [[Simulation]] ... [[Simulated Environment Learning]] ... [[World Models]] ... [[Minecraft]]: [[Minecraft#Voyager|Voyager]]
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** [[3D Model#3DCNN | 3DCNN]]
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* [[Hyperdimensional Computing (HDC)]]
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* [http://www.qwertee.io/blog/deep-learning-with-point-clouds/ Deep learning with point clouds | Romain Thalineau - qwertee.io]
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* [http://arxiv.org/pdf/1904.07601.pdf RS-CNN: Relation-Shape Convolutional Neural Network for Point Cloud Analysis | Y. Liu, B. Fin, S. Xiang, and C. Pan - University of [[Government Services#China|Chinese]] Academy of Sciences]
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* [http://pointclouds.org/ Point Cloud Library (PCL)] is a standalone, large scale, open project for 2D/3D image and point cloud processing.
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* [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]
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* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
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* [http://info.vercator.com/blog/what-are-the-most-common-3d-point-cloud-file-formats-and-how-to-solve-interoperability-issues Common 3D point cloud file formats & solving interoperability issues | Charles Thomson - Vercator]
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A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners, which measure a large number of points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications. [A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners, which measure a large number of points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications. [http://en.wikipedia.org/wiki/Point_cloud Point Cloud] and [http://en.wikipedia.org/wiki/List_of_programs_for_point_cloud_processing List of programs for Point Cloud processing | Wikipedia]
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Although Convolutional Neural Networks are the state of the art techniques for 2D object detection, they do not perform well on 3D point cloud due to the sparse sensor data, therefore new techniques are needed. [http://medium.com/@SmartLabAI/3d-object-detection-from-lidar-data-with-deep-learning-95f6d400399a 3D Object Detection from LiDAR Data with Deep Learning | SmartLab AI - Medium]
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<img src="http://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Point_cloud_torus.gif/220px-Point_cloud_torus.gif" width="400" height="400">
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<youtube>PL6wD8jczkE</youtube>
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<youtube>KHYnC6t0boQ</youtube>
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<youtube>pV-4xtirZCc</youtube>
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<youtube>Jz2jXect_7I</youtube>
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<youtube>XkbauvReJFA</youtube>
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<youtube>VRIVeq6RWA8</youtube>
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== SPLATNet ==
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* [http://github.com/NVlabs/splatnet SPLATNet: Sparse Lattice Networks for Point Cloud Processing | NVIDIA - GitHub]
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<youtube>rKL7cmJP0bg</youtube>
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<img src="http://news.developer.nvidia.com/wp-content/uploads/2018/06/splatnet.png" width="1000" height="600">
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== [http://github.com/charlesq34/pointnet PointNet]  ==
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* [http://github.com/charlesq34/pointnet PointNet | C. Qi, H. Su, K. Mo, and L. Guibas - Stanford -GitHub] deep learning on point sets for 3D classification and segmentation
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* [http://stanford.edu/~rqi/pointnet2/ PointNet++: deep hierarchical feature learning on point sets in a metric space |  C. Qi, L. Yi, H. Su, and L. Guibas - Stanford]
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<youtube>Cge-hot0Oc0</youtube>
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<img src="http://www-cdn.qwertee.io/media/uploads/blog/deep_learning_with_point_clouds/pointnet_architecture.png" width="1000" height="700">
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<img src="http://stanford.edu/~rqi/pointnet2/images/pnpp.jpg" width="1000" height="700">
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== [http://arxiv.org/abs/1609.06666 Vote3Deep] ==
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* [http://github.com/lijiannuist/Vote3Deep_lidar  Vote3Deep LiDAR]
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<youtube>WUOSmAfeXIw</youtube>
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== Point-GNN ==
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a graph neural network to detect objects from a [[3D Model#LiDAR|LiDAR]] point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms. Our results demonstrate the potential of using the graph neural network as a new approach for 3D object detection. [http://github.com/WeijingShi/Point-GNN | Weijing Shi and Raj Rajkumar - GitHub]
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<youtube>jBowq7ubCtg</youtube>
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== [http://github.com/BichenWuUCB/SqueezeSeg  SqueezeSeg] ==
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<youtube>Xyn5Zd3lm6s</youtube>
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== Neural Point-Based Graphics ==
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* [http://arxiv.org/abs/1906.08240 Neural Point-Based Graphics | Kara-Ali Aliev, Dmitry Ulyanov, and Victor Lempitsky]
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<youtube>lbObxT7lJrI</youtube>
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<youtube>7s3BYGok7wU</youtube>
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== Kd-Networks ==
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* [http://arxiv.org/pdf/1704.01222.pdf Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models | Roman Klokov and Victor Lempitsky] ... [http://github.com/Regenerator/kdnets GitHub] This code uses python2.7 with theano(0.9.0) installed over Cuda8.0 with CuDNN5.1 and Lasagne(0.2.dev1).
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<youtube>6UV_ASKa2RM</youtube>
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== Vote3Deep ==
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Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
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<youtube>WUOSmAfeXIw</youtube>
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== 3D Point Cloud Classification, Segmentation and Normal estimation ==
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using Modified Fisher Vector and [[(Deep) Convolutional Neural Network (DCNN/CNN)|CNN]]s
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* [http://arxiv.org/pdf/1711.08241.pdf 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks | Y. Ben-Shabat, M. Lindenbaum, and A. Fischer
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<youtube>PSVmTDzXPpc</youtube>
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=== Modified Fisher Vector (3DmFV) ===
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* [http://www.itzikbs.com/what-is-3d-modified-fisher-vector-3dmfv-representation-for-3d-point-clouds What Is 3D Modified Fisher Vector (3DMFV) Representation For 3D Point Clouds | Itzik Ben Shabat]
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http://i1.wp.com/www.itzikbs.com/wp-content/uploads/2018/09/3d_fv_smaller-compressor.gif
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<youtube>HIUGOKSLTcE</youtube>

Latest revision as of 08:07, 16 June 2024

Youtube search... ...Google search

A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners, which measure a large number of points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications. [A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners, which measure a large number of points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications. Point Cloud and List of programs for Point Cloud processing | Wikipedia

Although Convolutional Neural Networks are the state of the art techniques for 2D object detection, they do not perform well on 3D point cloud due to the sparse sensor data, therefore new techniques are needed. 3D Object Detection from LiDAR Data with Deep Learning | SmartLab AI - Medium



SPLATNet

PointNet



Vote3Deep

Point-GNN

a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms. Our results demonstrate the potential of using the graph neural network as a new approach for 3D object detection. | Weijing Shi and Raj Rajkumar - GitHub

SqueezeSeg

Neural Point-Based Graphics

Kd-Networks


Vote3Deep

Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

3D Point Cloud Classification, Segmentation and Normal estimation

using Modified Fisher Vector and CNNs

  • [http://arxiv.org/pdf/1711.08241.pdf 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks | Y. Ben-Shabat, M. Lindenbaum, and A. Fischer

Modified Fisher Vector (3DmFV)

3d_fv_smaller-compressor.gif

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