Difference between revisions of "Point Cloud"

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[http://www.google.com/search?q=Point+Cloud+Convolutional+Neural+Network+CNN+deep+machine+learning+ML ...Google search]
 
[http://www.google.com/search?q=Point+Cloud+Convolutional+Neural+Network+CNN+deep+machine+learning+ML ...Google search]
  
* [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[3D Simulation Environments]] ... [[Simulated Environment Learning]] ... [[Point Cloud]]
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* [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]]
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* [[Simulation]] ... [[Simulated Environment Learning]]
 
** [[3D Model#3DCNN | 3DCNN]]
 
** [[3D Model#3DCNN | 3DCNN]]
 
* [[Hyperdimensional Computing (HDC)]]
 
* [[Hyperdimensional Computing (HDC)]]

Revision as of 07:13, 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