
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.

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 Learn how we’re tackling the challenges of exploding data volumes, outdated tools, and scattered workflows to build a future where reality capture data is easily accessible.