RealityScan 2.1 is now available
RealityScan 2.1 is now available for download! This release puts a major emphasis on automation and advanced LiDAR capabilities, with a host of other powerful new features included to boot. Let’s dive straight in.
Enhanced automation for greater productivity
The latest version of RealityScan takes automation to the next level. We’re publishing a comprehensive set of automation documentation on the Epic Developer Community, including reference material for Command Line Interface (CLI). It includes everything you need to master your RealityScan automation workflows—from the commands you already know and love, to brand new guides, samples, and expanded functionality.
You’ll find extensive documentation and examples for our new templating system, which is a complete rebrand of the reporting system.
Templating takes your pipelines to the next level, eliminating busywork by automating data extraction, formatting results, and generating reports directly from your projects.
RealityScan 2.1 also introduces new REST and gRPC plugins, complete with a suite of Python samples and full Linux support. These make it easier than ever to connect and automate RealityScan across multiple PCs on your network.
The samples on EDC demonstrate how to spin up REST and gRPC servers and clients. You can even create a multi-asset client capable of communicating with machines running either protocol. In addition, there are templating samples that show how to generate JSON files and pull structured data directly from your projects.
Powerful new LiDAR capabilities
RealityScan 2.1 also expands what’s possible for LiDAR based workflows. You can now import SLAM (Simultaneous Localization and Mapping) data, such as trajectories, images, and point clouds, and merge the data with photogrammetry or terrestrial laser scans.
SLAM is a technique that enables a robot, drone, or vehicle to build a map of an unknown environment and track its own position within that map at the same time, without relying on GPS.
Working with SLAM data has a number of advantages. It enables fast data acquisition with live tracking and coverage visualization, so you can capture progress and fill missing areas during scanning. It also enables you to achieve cleaner geometry on surfaces problematic for photogrammetry.
On top of SLAM data import, RealityScan 2.1 includes the capability to import classified point clouds in LAS and LAZ formats.
Classified point clouds can help RealityScan generate cleaner meshes; enable selective meshes of specific classes of objects to reduce processing time; automatically remove unwanted elements such as cars or trees; and much more.
These additions make RealityScan a more flexible and powerful tool for professional LiDAR users.
But wait…there’s more!
You’ll also find a range of quality-of-life updates and creative tools in RealityScan 2.1.
There are more export options for registration, such as OpenCV format; the ability to render from the exact positions of your cameras, with matching distortion—not only for textured models, but also for normals; and a brand-new colored checker map for UVs, making it easier to visualize and refine unwraps.
Start exploring RealityScan 2.1 today.
You’ll also find a range of quality-of-life updates and creative tools in RealityScan 2.1.
There are more export options for registration, such as OpenCV format; the ability to render from the exact positions of your cameras, with matching distortion—not only for textured models, but also for normals; and a brand-new colored checker map for UVs, making it easier to visualize and refine unwraps.
Start exploring RealityScan 2.1 today.
Download RealityScan 2.1 today!
Unlock powerful automation and advanced LiDAR capabilities with RealityScan 2.1, including the ability to automate repetitive tasks, work with SLAM data, and import point clouds with classification.