Real-time 3D point cloud processing algorithms in your browser. Featuring distance-aware downsampling and advanced segmentation techniques.
Choose from publicly available point cloud scans
Shortcut: press L to reload dataset.
For uniform and random sampling
This technique, featured in research papers, adaptively samples points based on local geometry. It preserves more points in areas with high geometric variation (edges, corners) while aggressively downsampling flat regions.
Points within this distance are considered inliers
Random Sample Consensus (RANSAC) is a robust method for fitting geometric models. It iteratively selects random subsets of points, fits a model, and counts inliers to find the best-fitting plane or shape.
K-Means: Partitions points into K clusters by minimizing within-cluster variance.
DBSCAN: Density-based clustering that can find arbitrarily-shaped clusters and identify noise points.
| Method | Time (ms) | Points | Reduction |
|---|---|---|---|
| No performance data yet. Apply methods to see metrics. | |||
Compare the execution time and point reduction of different downsampling methods. Distance-aware downsampling typically provides the best quality-to-reduction ratio for geometric features.
Point clouds are 3D representations of physical objects or environments, typically captured using LiDAR sensors or depth cameras. Processing these massive datasets efficiently is crucial for applications in autonomous driving, robotics, and 3D reconstruction.
This interactive demo showcases various techniques for point cloud processing, including distance-aware downsampling (featured in research papers), RANSAC-based segmentation, and clustering algorithms.
This advanced technique, featured in research publications, adaptively samples points based on local geometric complexity. Unlike uniform methods, it preserves critical features (edges, corners) while aggressively reducing points in flat or redundant regions.
The algorithm computes local curvature or normal variation for each point and adjusts sampling density accordingly, resulting in better preservation of geometric details with lower point counts.