Sampo Lappalainen

Infrakit partnered with LumiDB to turn drone and LiDAR scans into actionable field measurements. By replacing file-based workflows with a database built for large point clouds, the team can complete depth and distance checks directly from the office. The approach is at least 50% faster than traditional site-based methods and produces more consistent documentation for regulatory requirements.

The City of Helsinki’s GIS Centre partnered with LumiDB to test whether its growing archive of point-cloud data could be made broadly usable through browser-based access. The pilot showed faster loading, simpler workflows, and reduced dependency on specialist desktop tools. This case study outlines the problem, the approach, and what changed for planners and GIS teams.

Infrakit partnered with LumiDB to streamline its point-cloud workflows and replace file-based LOD pipeline. The integration delivered faster rendering, sub-second cross-sections, and a simpler, more stable architecture for handling large LiDAR and drone datasets. This case study outlines how the change was implemented and what improved for engineers and end users.

Swipe Compare: visually slide between two scans. See changes in construction, vegetation, or infrastructure with aligned point clouds.

National infrastructure operators are generating massive volumes of 3D scan data from drones, mobile scanners, and contractors. This post outlines how LumiDB helps integrate point clouds into GIS, CAD, and BIM workflows as part of a unified digital twin strategy.

Reality capture data is a powerful asset, until it turns into a liability. Scans pile up across cloud drives and servers, buried in endless folders with no clear way to retrieve, compare, or integrate them. Teams waste hours chasing down the right version, manually stitching together datasets, and fighting with outdated storage systems. It’s an expensive mess. But what if data didn’t have to be scattered and frustrating? What if it were instantly accessible, easy to explore, and seamlessly connected to your workflows?

Visualizing large 3D point cloud datasets can be a daunting task. With LumiDB, users store their data in a special purpose database that enables efficient querying based on point budget or density, eliminating the need for preprocessing. Beyond visualization, the stored points remain fully usable for other workflows. This post explores the challenges of visualizing massive point cloud datasets and how LumiDB helps.

Reality capture data is growing exponentially in the construction industry, yet the methods for managing it remain outdated and inefficient. At LumiDB, we’re building a solution to tackle this problem, paving the way for a future where construction sites are fully integrated and autonomous.

From hacking together data management software for autonomous robots at Amazon to starting LumiDB, this is the story of how we set out to fix reality capture data. Learn how we’re tackling the challenges of exploding data volumes, outdated tools, and scattered workflows to build a future where reality capture data is easily accessible.