From Points to Surfaces
A point cloud is the raw geometric output of photogrammetric reconstruction — millions of individual 3D coordinates that collectively describe the shape of an object or landscape. Unlike mesh models, point clouds make no assumptions about surface connectivity, making them flexible but requiring further processing for visualization and measurement. Modern photogrammetry pipelines routinely produce clouds with billions of points from drone imagery alone.
Density and Detail
Point density determines the finest feature that can be resolved. At 100 points per square meter, you capture building outlines; at 1000 points, architectural details emerge; at 10,000+, you resolve individual bricks. This simulation lets you see how increasing point count progressively reveals surface structure, and how the relationship between density and noise level determines the effective resolution of the reconstruction.
Noise and Filtering
Every measurement system introduces noise. In photogrammetry, matching errors, calibration imprecision, and image noise all contribute to scatter around the true surface. Statistical outlier removal identifies points too far from their neighbors, while moving least squares fitting produces smooth surfaces from noisy input. The signal-to-noise ratio quantifies whether meaningful geometry survives the noise floor.
Visualization and Rendering
Displaying millions of 3D points efficiently requires level-of-detail techniques like octree-based point budgeting, where distant regions show fewer points while nearby areas render at full density. Point splatting assigns each point a disk radius that covers gaps between neighbors, creating the appearance of a continuous surface. Modern web viewers like Potree handle billions of points in real-time through hierarchical streaming.