Table of Contents

Interface IPointCloudSegmentation<T>

Namespace
AiDotNet.PointCloud.Interfaces
Assembly
AiDotNet.dll

Defines functionality for point cloud segmentation tasks.

public interface IPointCloudSegmentation<T> : IPointCloudModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations (e.g., float, double).

Inherited Members
Extension Methods

Remarks

For Beginners: Point cloud segmentation assigns a label to each point in a 3D point cloud.

Think of segmentation as coloring a 3D model:

  • Each point in the cloud gets assigned to a category
  • Points belonging to the same object or part get the same label
  • This allows you to identify and separate different components

Common segmentation tasks:

  • Semantic segmentation: Label each point by object type (car, road, building, etc.)
  • Instance segmentation: Separate individual objects (this car vs that car)
  • Part segmentation: Identify parts of an object (chair leg, chair back, seat)

Applications:

  • Autonomous driving: Identify pedestrians, vehicles, road surfaces
  • Robotics: Recognize and grasp specific parts of objects
  • 3D scene understanding: Parse indoor/outdoor environments

Methods

SegmentPointCloud(Tensor<T>)

Performs semantic segmentation on a point cloud.

Tensor<T> SegmentPointCloud(Tensor<T> pointCloud)

Parameters

pointCloud Tensor<T>

Input point cloud tensor of shape [N, 3+F].

Returns

Tensor<T>

A tensor of shape [N, C] containing class probabilities for each point, where C is the number of classes.

Remarks

For Beginners: This method assigns a category label to each point.

For each point, it predicts which category it belongs to:

  • Input: Point cloud with N points
  • Output: For each point, probabilities for each possible category
  • The category with highest probability is the predicted label

Example for indoor scene segmentation:

  • Categories might be: floor, wall, ceiling, furniture, etc.
  • Each point gets probabilities: [0.01 floor, 0.95 wall, 0.02 ceiling, 0.02 furniture]
  • The point would be labeled as "wall" (highest probability)
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