Point Cloud

3D Vision Processing

vitreous covers 3D point cloud processing: computing geometric properties, clustering and segmenting points, filtering and denoising, moving/combining clouds, converting between clouds and meshes, and registering multiple clouds together with ICP. Pick the group that matches what you're trying to do.

Geometric Measurements

These skills compute basic geometric properties of a point cloud - its bounding volume or its center of mass. Use them as building blocks for pose estimation, grasp planning, or reference-frame computation.

Calculate Axis-Aligned Bounding Box

Learn how to compute the smallest 3D axis-aligned bounding box (AABB) for a point cloud using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, and scene understanding pipelines.

Calculate Oriented Bounding Box

Learn how to compute a tightly-fitted, orientation-aware 3D bounding box (OBB) for a point cloud using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, pose estimation, and grasp planning.

Calculate Point Cloud Centroid

Learn how to compute the geometric center (centroid) of a 3D point cloud using Telekinesis Vitreous. Useful for robotics, Physical AI, object localization, grasp planning, and reference frame computation.

Clustering & Segmentation

Clustering and segmentation skills split a point cloud into meaningful groups - either by finding density jumps or spatial clusters, or by isolating points that share a color or lie on a common plane. Use them to tell distinct objects or surfaces apart before processing them individually.

Cluster Point Cloud Based on Density Jump

Segment a point cloud into clusters by detecting sharp density changes along a chosen axis using Telekinesis Agentic Skill Library. Ideal for stacked objects, layered structures, or closely packed items in robotics and Physical AI pipelines.

Cluster Point Cloud Using DBSCAN

Segment a 3D point cloud into clusters using DBSCAN from the Telekinesis Agentic Skill Library. Ideal for detecting distinct objects, handling noise, and separating spatially distributed items in robotics and perception pipelines.

Segment Point Cloud Using Color

Learn how to segment point clouds by color using the Vitreous SDK. Ideal for identifying and isolating objects based on color cues in robotics, industrial pipelines, and 3D perception.

Segment Point Cloud Using Plane

Learn how to segment planar surfaces from point clouds using the Vitreous SDK. Ideal for detecting floors, walls, tables, and other flat surfaces in robotics, industrial pipelines, and 3D perception.

Filtering & Denoising

Filtering skills crop, clean, or thin out a point cloud before it goes further down the pipeline - by bounding volume, mask, plane proximity, outlier removal, or downsampling. This is usually the first step applied to a raw sensor capture.

Filter Point Cloud Using Bounding Box

Extract points within a 3D axis-aligned bounding box (AABB) to isolate regions of interest in point clouds for robotics, grasp planning, and object detection.

Filter Point Cloud Using Cylinder Base Removal

Remove base points from cylindrical meshes to isolate object geometry for robotics applications like pose estimation, grasp planning, and cylinder alignment.

Filter Point Cloud Using Mask

Apply a 2D boolean mask to selectively filter points from an organized point cloud for robotics tasks like object segmentation, ROI extraction, and grasp planning.

Filter Point Cloud Using Oriented Bounding Box

Crop a point cloud to a rotated 3D region using an oriented bounding box (OBB) for robotics tasks like object isolation, pose estimation, and manipulation.

Filter Point Cloud Using Plane Defined by Point Normal Proximity

Extract points near a plane defined by a reference point and normal vector for robotics tasks like planar surface extraction, ground removal, and tabletop detection.

Filter Point Cloud Using Plane Proximity

Extract points near a plane defined by coefficients [a, b, c, d] for robotics tasks like planar surface extraction, ground removal, and tabletop detection.

Filter Point Cloud Using Plane Splitting

Retain points on one side of a plane using coefficients [a, b, c, d]. Ideal for robotics tasks like ground removal, workspace segmentation, and object separation.

Filter Point Cloud Using Radius Outlier Removal

Remove isolated or noisy points in a point cloud using radius-based neighbor filtering. Ideal for denoising, preprocessing, and improving 3D perception in robotics.

Filter Point Cloud Using Statistical Outlier Removal

Remove noise from point clouds using statistical analysis of neighbor distances. Ideal for denoising, preprocessing, and improving 3D perception in robotics pipelines.

Filter Point Cloud Using Uniform Downsampling

Reduce point cloud density by selecting every Nth point for faster processing. Ideal for robotics pipelines, preview generation, and preprocessing of large 3D datasets.

Filter Point Cloud Using Voxel Downsampling

Reduce point cloud density using voxel downsampling. Ideal for preprocessing, registration, clustering, and segmentation in robotics and 3D point cloud workflows.

Transform & Arithmetic

These skills combine or reshape point clouds directly - fusing multiple captures together, isolating what changed between two scans, or moving, rotating, and resizing a cloud in space.

Add Point Clouds

Learn how to combine multiple 3D point clouds into a single unified cloud using the Telekinesis Agentic Skill Library. Perfect for multi-view fusion, sensor aggregation, and robotics pipelines.

Subtract Point Clouds

Learn how to subtract one point cloud from another using the Vitreous SDK. Ideal for isolating objects, removing backgrounds, or filtering known surfaces in robotics, industrial pipelines, and 3D perception.

Scale Point Cloud

Learn how to uniformly scale a point cloud about a center point using the Vitreous SDK. Ideal for resizing objects and scenes in robotics, industrial pipelines, and simulations.

Apply Transform to Point Cloud

Learn how to move, rotate, or align 3D point clouds using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, and multi-view sensor pipelines.

Projection & Mesh Reconstruction

These skills move between representations - flattening a cloud onto a plane, converting a mesh to a point cloud, or reconstructing a mesh surface from a cloud. Use them when you need a different geometric representation than the one you started with.

Project Point Cloud to Plane

Learn how to project a 3D point cloud onto a plane using general plane coefficients [a, b, c, d] with the Vitreous SDK. Useful for robotics, AI, and physical pipelines in planar alignment, ground plane removal, and feature extraction.

Project Point Cloud to Plane Defined by Point Normal

Learn how to project a 3D point cloud onto a plane using a reference point and normal vector with the Vitreous SDK. Useful for robotics, AI, and physical pipelines in planar alignment, registration, and feature extraction.

Convert Mesh to Point Cloud

Convert 3D triangle meshes into point clouds using the Telekinesis SDK. Ideal for 6D pose estimation, registration, and robotic perception pipelines with uniform point sampling and high fidelity geometry.

Reconstruct Mesh Using Convex Hull

Learn how to reconstruct a convex mesh from a 3D point cloud using the Vitreous SDK. Ideal for robotics, AI, and physical pipelines in collision geometry, grasp planning, and simplified object modeling.

Reconstruct Mesh Using Poisson

Learn how to reconstruct a smooth, watertight 3D mesh from a point cloud using Poisson surface reconstruction with the Vitreous SDK. Ideal for high-quality object surfaces in robotics, AI, and industrial pipelines.

Registration (ICP)

Registration skills align two point clouds into a common frame - useful when you have a captured scene and a reference model (or two overlapping scans) and need to find the transform that lines them up. Different variants trade off speed, robustness to unknown initial pose, and final accuracy.

Register Point Clouds Using Cuboid Translation Sampler ICP

Learn how to align two point clouds with uncertain initial translation using Cuboid Translation Sampler ICP in the Vitreous SDK. Ideal for 6D pose estimation, object alignment, and industrial robotics.

Register Point Clouds Using Fast Global Registration

Learn how to quickly align two point clouds using Fast Global Registration (FGR) with FPFH features in the Vitreous SDK. Ideal for initial 6D pose estimation, coarse alignment, and industrial robotics.

Register Point Clouds Using Point-to-Plane ICP

Learn how to refine point cloud alignment using Point-to-Plane ICP in the Vitreous SDK. Ideal for high-accuracy registration, fine 6D pose estimation, and industrial robotics applications.

Register Point Clouds Using Point-to-Point ICP

Learn how to perform fine-grained point cloud registration using Point-to-Point ICP in the Vitreous SDK. Ideal for precise 6D pose estimation and object alignment in robotics and industrial applications.

Register Point Clouds Using Rotation Sampler ICP

Learn how to perform robust point cloud registration with unknown initial rotations using Rotation Sampler ICP in the Vitreous SDK. Ideal for 6D pose estimation and object alignment in robotics and industrial applications.

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