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 Teams waste hours chasing down the right version, manually stitching together datasets, and fighting with outdated storage systems.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Reality capture data is growing exponentially in the construction industry, yet the methods for managing it remain outdated and inefficient.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 Learn how we’re tackling the challenges of exploding data volumes, outdated tools, and scattered workflows to build a future where reality capture data is easily accessible.