VeloView vs. Scale Data Engine

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.

The Scale Data Engine equips machine learning teams with the necessary tools to effectively enhance their datasets. By unifying your data, verifying it against ground truth, and integrating model predictions, you can effectively tackle issues related to model performance and data quality. You can make the most of your labeling budget by identifying class imbalances, errors, and edge cases within your dataset through the Scale Data Engine. This platform has the potential to significantly boost model performance by pinpointing and addressing areas of failure. Implementing active learning and edge case mining allows for the efficient discovery and labeling of high-value data. By fostering collaboration among machine learning engineers, labelers, and data operations within a single platform, you can assemble the most impactful datasets. Furthermore, the platform offers straightforward visualization and exploration of your data, facilitating the rapid identification of edge cases that need attention. You have the ability to closely track your models' performance to ensure that you are consistently deploying the optimal version. The comprehensive overlays within our robust interface provide an all-encompassing view of your data, including metadata and aggregate statistics for deeper analysis. Additionally, Scale Data Engine supports the visualization of diverse formats such as images, videos, and lidar scenes, all enriched with pertinent labels, predictions, and metadata for a detailed comprehension of your datasets. This functionality not only streamlines your workflow but also makes Scale Data Engine an essential asset for any data-driven initiative. Ultimately, its capabilities foster a more efficient approach to managing and enhancing data quality across projects.