3D Point Cloud Segmentation Using Spatial Hash Clustering
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Solution Overview
Problem
Conventional 3D point cloud segmentation algorithms are unsuitable for real-time, high-resolution, and multiple-input scenarios due to linearithmic time complexity, noise sensitivity, non-determinism, and lack of static memory, making them inadequate for autonomous vehicles and other embedded applications.
Innovation Solution
A scalable clustering algorithm that uses a spatial hash data structure and aggressive down-sampling to segment 3D point clouds in real-time, handling multiple inputs and wide distance ranges while requiring only static memory, by adding points to clusters based on computed threshold metrics and pruning unnecessary data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D point cloud segmentation algorithms are used, then segmentation accuracy can be achieved, but the time complexity becomes linearithmic (O(n log n)) which is too slow for real-time applications
Solution Approach 1:
The patent changes the computational parameters by using fixed-radius neighborhood searches with constant time complexity O(1) instead of sorting-based methods. The algorithm uses a spatial hash data structure to enable constant-time point access and clustering operations, transforming the time complexity from O(n log n) to O(n), thereby achieving real-time processing while maintaining segmentation accuracy through adaptive radius adjustment.
Solution Approach 2:
The patent replaces the traditional mechanical sorting and iterative refinement processes with a hash-based spatial indexing system. Instead of using comparison-based sorting algorithms that require O(n log n) time, the system uses hash table lookups that provide O(1) average-case access time, enabling linear-time clustering that is suitable for real-time autonomous vehicle applications.
2Adaptability or versatility
If algorithms work with points over wide distance ranges, then comprehensive environment coverage is achieved, but points that are farther apart cause algorithmic issues and reduced performance
Solution Approach 1:
The patent implements dynamic clustering radius adjustment where the search radius adapts based on the local point density and distance from the sensor. The algorithm dynamically modifies the neighborhood radius for each cluster center to accommodate varying distances, ensuring reliable clustering whether points are close to or far from the sensor, thereby maintaining robustness across wide distance ranges.
Solution Approach 2:
The patent applies different clustering parameters locally based on the spatial position of points. Instead of using a fixed global radius, the algorithm adjusts the search radius locally for each cluster center based on its distance from the sensor and local point density, enabling reliable segmentation across varying distances while maintaining algorithmic robustness in each local region.
3Adaptability or versatility
If multiple sensor inputs are processed simultaneously, then comprehensive environmental understanding is achieved, but conventional algorithms are not amenable to multiple input sources
Solution Approach 1:
The patent creates a universal clustering algorithm that can process multiple sensor inputs simultaneously through a unified spatial hash data structure. The same clustering logic and data structure handle points from multiple sensors without requiring separate processing pipelines, achieving multi-input capability while keeping the algorithm relatively simple and efficient compared to conventional approaches.
4Stability of the object's composition
If static memory is used for embedded applications, then memory predictability is achieved, but upfront project planning and engineering discipline are required
Solution Approach 1:
The patent performs preliminary actions by pre-allocating the spatial hash data structure with a predetermined maximum size based on expected point cloud density and sensor characteristics. This upfront allocation ensures that the algorithm uses only static memory with predictable bounds, suitable for embedded systems, while the modular design keeps the implementation manageable through systematic memory planning.
Data Source
AI summary
Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).