Table of Contents

Interface IPointCloudClassification<T>

Namespace
AiDotNet.PointCloud.Interfaces
Assembly
AiDotNet.dll

Defines functionality for point cloud classification tasks.

public interface IPointCloudClassification<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 classification determines what object or category an entire point cloud represents.

Think of classification as recognizing what an object is:

  • Input: A complete point cloud of an object
  • Output: The category the object belongs to
  • It's like looking at a 3D scan and saying "this is a chair" or "this is a table"

Common classification benchmarks:

  • ModelNet40: 40 categories of 3D objects (chair, table, car, airplane, etc.)
  • ShapeNet: Large-scale dataset with many object categories
  • ScanNet: Real-world scanned objects and scenes

Applications:

  • Object recognition in 3D scans
  • Quality control in manufacturing (identify defective parts)
  • Archaeological artifact classification
  • Medical imaging (classify anatomical structures)

Methods

ClassifyPointCloud(Tensor<T>)

Classifies a point cloud into one of the predefined categories.

Vector<T> ClassifyPointCloud(Tensor<T> pointCloud)

Parameters

pointCloud Tensor<T>

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

Returns

Vector<T>

A vector of class probabilities of length C, where C is the number of classes.

Remarks

For Beginners: This method determines what object the point cloud represents.

The classification process:

  • Takes the entire point cloud as input
  • Processes all points together to understand the overall shape
  • Returns probabilities for each possible category

Example with furniture classification:

  • Input: Point cloud of a furniture piece
  • Output: [0.85 chair, 0.10 stool, 0.03 table, 0.02 bench]
  • The model predicts it's most likely a chair (85% confidence)

The returned probabilities sum to 1.0, allowing you to:

  • Pick the most likely category (highest probability)
  • Understand the model's confidence in its prediction
  • Consider alternative possibilities if the top prediction has low confidence
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