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What is VeloView?

VeloView is an adaptable open-source platform designed specifically for the real-time visualization and analysis of 3D LiDAR data collected from a range of Velodyne HDL sensor models, including but not limited to the HDL-64E, HDL-32E, VLP-32, VLP-16, Puck, Puck LITE, Puck Hi-Res, Ultra Alpha, Alpha Prime, Velarray, Velabit, and Veladome. This software allows users to visualize live data streams or access previously recorded data stored in .pcap files, in addition to providing the capability to record live streams for later review. Equipped with tools for displaying, selecting, and measuring data from captured points, VeloView supports customizable color maps based on various criteria such as intensity-of-return, time, distance, azimuth, dual return type, and laser ID. Additionally, users can export data in CSV format as XYZ coordinates or take screenshots of the point cloud visualizations for their records. Recent updates have introduced features like support for Velarray sensors, the ability to run multiple sensors at once, and independent transformation of live data for each sensor, significantly broadening its functionality and range of application. The combination of these capabilities makes VeloView an exceptional resource for professionals engaged in 3D LiDAR technology, ultimately enhancing their analytical and visualization tasks. As such, it is increasingly recognized as an essential tool in the toolkit of those who work with cutting-edge LiDAR systems.

What is SKY ENGINE AI?

SKY ENGINE AI is a comprehensive synthetic data platform engineered to deliver large-scale 3D generative content for Vision AI development. It unifies simulation, rendering, annotation, and model-training infrastructure into a single managed system, removing the typical fragmentation found in AI workflows. Using physics-based rendering and multispectrum support, the platform generates highly realistic synthetic images tailored to complex perception tasks across multiple sensors. Its domain processor aligns synthetic output with real-world data through GAN post-processing, texture adaptation, and automated gap-analysis tools. Developers benefit from an integrated code environment that connects directly to GPU memory, offering smooth compatibility with PyTorch, TensorFlow, and enterprise MLOps stacks. SKY ENGINE AI’s distributed rendering system enables fast generation of millions of samples by scaling scenes, models, and training plans across compute clusters. Built-in blueprints for automotive, robotics, drones, manufacturing, and human analytics allow users to generate rich, scenario-specific datasets instantly. Powerful randomization controls provide complete variability for lighting, materials, motion, and environment physics, ensuring robust generalization in Vision AI models. With automated cloud resource management and continuous data iteration capability, teams can test model hypotheses, synthesize edge cases, and refine datasets with unprecedented speed. The platform ultimately reduces cost, accelerates development cycles, and delivers enterprise-grade synthetic datasets for production-ready AI systems.

Media

Media

Integrations Supported

Google Sheets
Kitware Glance
Microsoft Excel

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Contact us
Free Trial Offered?

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Kitware

Date Founded

1998

Company Location

United States

Company Website

www.paraview.org/veloview/

Company Facts

Organization Name

SKY ENGINE AI

Date Founded

2018

Company Location

United Kingdom

Company Website

www.skyengine.ai

Categories and Features

LiDAR

Not specified

Categories and Features

AI Development

Not specified

Artificial Intelligence

For Healthcare
Image Recognition
Machine Learning
Predictive Analytics

Computer Vision

Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Deep Learning

Convolutional Neural Networks
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Image Recognition

Not specified

LiDAR

Not specified

Virtual Reality

Not specified

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