Overview

Stunad Spatial is a research-driven project focused on working with point cloud data as a primary design and computation medium. The project explores how raw spatial data can be structured, filtered, and transformed into usable representations for real-time and design-oriented workflows.

Core Focus

• Point cloud processing and spatial data pipelines
• Structuring unorganized spatial data into meaningful representations
• Performance-aware handling of large spatial datasets
• Bridging point clouds with real-time engines and interactive systems
• Exploration of spatial abstraction beyond mesh-based workflows

Approach

Instead of immediately converting point clouds into meshes, the project treats point data as first-class geometry. This allows operations such as filtering, clustering, sampling, and spatial querying to remain flexible and performant across different stages of the workflow.

Current Status

Stunad Spatial is an ongoing experimental project. The focus is on understanding performance limits, spatial data structures, and interaction possibilities when working directly with point clouds in real-time environments.

Why It Matters

Point clouds are increasingly central to spatial computing, scanning, and XR pipelines. This project reflects my interest in treating spatial data not just as visual output, but as a computational and design resource that can drive future tools and workflows.