Hybrid 3D Point Cloud Segmentation for Scalable Semantic Modeling
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Solution Overview
Problem
Current 3D semantic segmentation methods for large-scale outdoor point cloud data are either costly and manual or rely on deep neural networks that are hard to explain and scale, or rule-based algorithms that become intractable beyond a certain complexity level, lacking a reliable, fully automated solution for handling large-scale scans effectively.
Innovation Solution
A hybrid pipeline combining deep neural networks and rule-based algorithms for semantic segmentation, where machine learning components extract knowledge from annotations, and logical rules provide reliability, generalizability, and predictability, enabling the identification of semantic attributes and construction of a 3D semantics model by determining ground and non-ground data points, applying logical rules to segmented features, and constructing a 3D semantics model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for automated 3D semantic segmentation, then segmentation accuracy is improved, but model complexity and difficulty of explanation increase
Solution Approach 1:
The patent segments the segmentation pipeline into distinct modules: ground point determination, non-ground point categorization, feature extraction, and semantic labeling. This modular approach reduces overall model complexity by allowing each component to be optimized independently while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary feature representation layer that bridges raw point cloud data and final semantic labels. This intermediate feature space allows the system to achieve high segmentation accuracy through learned representations while maintaining better interpretability by connecting abstract neural network outputs to concrete geometric features and spatial relationships.
2Measurement precision
If deep neural networks are used for 3D semantic segmentation, then segmentation accuracy is improved, but scalability to large scenes deteriorates
Solution Approach 1:
The patent divides the large-scale scene segmentation into hierarchical levels: global ground determination, local non-ground categorization, and fine-grained semantic labeling. This hierarchical segmentation enables the system to handle large scenes by processing them in manageable chunks while maintaining consistency across the entire scene through shared ground references and unified feature spaces.
Solution Approach 2:
The patent transitions from processing individual points in 3D space to processing feature representations in a higher-dimensional semantic space. This dimensional transformation enables the system to capture global scene context and maintain scalability by operating in an abstract feature space that can accommodate large scenes without proportionally increasing computational complexity.
3Ease of operation
If rule-based algorithms are used for 3D semantic segmentation, then explainability is improved, but computational tractability deteriorates beyond certain complexity levels
Solution Approach 1:
The patent merges rule-based reasoning with machine learning by combining logical rules for ground determination and spatial relationships with neural networks for feature extraction and semantic classification. This hybrid approach maintains explainability through logical rules while achieving computational tractability through efficient neural network processing, resolving the contradiction between interpretability and scalability.
4Measurement precision
If manual 3D semantic segmentation is performed, then accuracy is improved, but time cost increases
Solution Approach 1:
The patent implements a fully automated pipeline where the system performs semantic segmentation without human intervention. The automated ground determination, non-ground categorization, and semantic labeling processes eliminate manual annotation requirements, reducing time cost to near zero while maintaining high accuracy through sophisticated algorithms that automatically learn from training data and apply consistent segmentation across large scenes.
Data Source
AI summary
A method, machine readable medium and system for semantic segmentation of 3D point cloud data includes determining ground data points of the 3D point cloud data, categorizing non-ground data points relative to a ground surface determined from the ground data points to determine legitimate non-ground data points, segmenting the determined legitimate non-ground and ground data points based on a set of common features, applying logical rules to a data structure of the features built on the segmented determined non-ground and ground data points based on their spatial relationships and incorporated within a machine learning system, and constructing a 3D semantics model from the application of the logical rules to the data structure.