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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 Apollo Autonomous Vehicle Platform?

Various sensors such as LiDAR, cameras, and radar collect data about the surrounding environment of the vehicle. Utilizing sensor fusion technology, advanced perception algorithms are capable of accurately detecting, positioning, evaluating the velocity, and establishing the orientation of objects on the road in real-time. This autonomous perception framework is bolstered by Baidu's vast big data resources and deep learning expertise, complemented by an extensive collection of labeled driving data derived from actual driving experiences. Furthermore, the comprehensive deep-learning platform, along with GPU clusters, supports simulation, allowing for the virtual navigation of millions of kilometers each day through a range of real-world traffic and autonomous driving scenarios. This simulation service provides partners with a multitude of autonomous driving situations, enabling rapid testing, validation, and refinement of models while emphasizing safety and efficiency. In essence, this cutting-edge methodology not only improves the dependability of autonomous systems but also significantly hastens their development timelines, fostering innovation in the industry. As a result, the integration of these technologies sets a new standard for future advancements in autonomous driving.

Media

Media

Integrations Supported

Google Sheets
Kitware Glance
Microsoft Excel

Integrations Supported

Dragon Metrics

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

Kitware

Date Founded

1998

Company Location

United States

Company Website

www.paraview.org/veloview/

Company Facts

Organization Name

Baidu

Date Founded

2000

Company Location

China

Company Website

apollo.auto/

Categories and Features

LiDAR

Not specified

Categories and Features

Autonomous Driving

Not specified

LiDAR

Not specified

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