3D Point Cloud Representation via Segmentation and Spatial Features
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Solution Overview
Problem
Existing machine learning models struggle to process unstructured and unordered 3D point clouds, as they are typically trained for structured data and fail to accurately represent scenes with multiple objects in various poses.
Innovation Solution
A computer-implemented method is developed to train a machine learning model by executing a neural network on a point cloud to generate segmentations, computing spatial and quantized context features, and modifying the network based on the likelihood of reconstructing the point cloud, enabling the model to map 3D scenes to structured representations of 3D geometry instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a permutation-invariant neural network is trained to map scene point clouds to numerical features describing a single object in canonical pose, then the model achieves consistency across different orderings of the same points, but the model fails to accurately process point clouds representing multiple objects in various poses
Solution Approach 1:
The patent applies segmentation by dividing the scene point cloud into multiple object-specific point clouds. The neural network processes each segmented object cloud separately to generate its own set of numerical features, then combines these features to represent the entire scene. This allows the model to handle multiple objects in various poses while maintaining permutation invariance within each object's feature generation.
2Ease of operation
If traditional machine learning models are used to process unstructured and unordered 3D point cloud data, then the models can handle the raw input format, but the models generate inconsistent results when processing different orderings of the same underlying input data
Solution Approach 1:
The patent introduces an intermediary processing step where the unstructured point cloud data is first organized into structured representations (such as segmented object clouds or ordered feature vectors) before being fed to the neural network. This intermediary structure serves as a bridge that preserves the unstructured input capability while enabling consistent processing through permutation-invariant operations.
3Reliability
If the neural network is designed to be permutation-invariant to ensure consistent mapping, then the model works reliably for single objects, but the model cannot process point clouds with multiple objects in various poses
Solution Approach 1:
The patent applies segmentation by dividing the scene point cloud into multiple object-specific point clouds. The neural network processes each segmented object cloud separately to generate its own set of numerical features, then combines these features to represent the entire scene. This allows the model to handle multiple objects in various poses while maintaining permutation invariance within each object's feature generation.
Solution Approach 2:
The patent extends the feature space by introducing additional dimensions that capture spatial relationships and configurations of multiple objects. Instead of limiting the output to features describing a single object in canonical pose, the model generates features that include positional information, orientation data, and inter-object relationships, thereby handling complex scenes with multiple objects in various poses.
Data Source
AI summary
In various embodiments, an unsupervised training application trains a machine learning model to generate representations of point clouds. The unsupervised training application executes a neural network on a first point cloud representing a first three-dimensional (3D) scene to generate segmentations. Based on the segmentations, the unsupervised training application computes spatial features. The unsupervised training application computes quantized context features based on the segmentations and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model that includes the updated neural network and a second set of codes representing a second set of 3D geometry blocks maps a point cloud representing a 3D scene to a representation of 3D geometry instances.