LumiDB blog

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.

We have added support for streaming point clouds directly from LumiDB into QGIS, allowing you to use your data for map creation or geospatial analysis without downloading massive files. By utilizing EPT links, users can instantly stream datasets with full Level of Detail support. This feature takes advantage of LumiDB’s runtime transformation, meaning all existing data is ready to use immediately without time-consuming re-indexing or pre-processing.

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.

We’ve released a C client library for importing data into LumiDB. It wraps our HTTP API with a simple C interface. Ideal for integrating with existing C or C++ systems.

We’ve brought progressive 3D Tiles streaming to the LumiDB Viewer, letting users instantly see full 3D datasets and get live query results without waiting. This major upgrade is part of a broader summer rollout focused on delivering a smooth, high-performance experience for exploring and analyzing massive point cloud datasets.

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.

LumiDB now supports adaptive level-of-detail streaming via 3D Tiles. This means easy integration into any Cesium.js-based tool, or any tool requiring adaptive streaming of reality capture datasets, even massive ones. We’re also bringing LOD streaming into our own viewer app.

This post shows you how to import reality capture files into a LumiDB table, by walking through our simple import API.

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?

Managing and using a terabyte-scale point cloud dataset becomes painful when working with traditional file-based tools and methods. LumiDB might be able to help you here! In this case study we investigate how such a dataset is imported into LumiDB.

Managing large 3D scan datasets efficiently is challenging—especially when dealing with strict memory constraints. In this post, we explore how metadata queries in LumiDB let you interactively enable and disable scans without ever loading the full dataset into memory. We’ll walk through a real-world example, where a building scan is split into multiple scanner positions, and show how LumiDB’s built-in filtering and level-of-detail (LOD) handling can keep your application fast and responsive. 🚀

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.

We’re releasing our minimal-yet-powerful runtime API. Whether you’re visualizing in Three.js or exporting data to 3rd party tools, LumiDB is built to make managing reality capture data easy and efficient.

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.