Sparse Point Pose Estimation via Spatial Hierarchical Encoding
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Solution Overview
Problem
Existing methods for processing sparse point sets, such as those generated by dynamic vision sensors, are inefficient due to the lack of texture and contour features, making it difficult to apply deep learning techniques effectively for pose estimation, especially for unordered and sparse point sets without clear texture or color.
Innovation Solution
A method that determines spatial hierarchical point data and relationship feature data by encoding spatial relationships between points, generating global and local features through max pooling and concatenation operations, and using these features to produce a processing result, including pose estimation, suitable for sparse 2D point sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used to process sparse point sets, then pose estimation accuracy is improved, but the method becomes inapplicable to unordered sparse point sets without texture or contour features
Solution Approach 1:
The patent divides the sparse point set into key point sets and local point sets, creating a hierarchical structure. This segmentation allows the method to process unordered sparse points by organizing them into meaningful groups with spatial relationships, making deep learning applicable where it previously failed.
Solution Approach 2:
The patent transforms 2D sparse point coordinates into 3D spatial hierarchical relationships by introducing depth information through key-point-based local point grouping. This dimensional transformation enables the processing of sparse points without relying on texture or contour features.
2Reliability
If traditional processing methods are used for sparse point sets, then the process is complex requiring feature extraction, registration, and optimization, but computing resources are consumed and noise is generated
Solution Approach 1:
The patent replaces the mechanical processing pipeline (feature extraction → registration → optimization) with a data-driven deep learning approach that directly processes spatial hierarchical relationships. This substitution simplifies the overall process while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameters of processing by moving from pixel-based feature extraction to spatial relationship encoding. Instead of extracting texture and contour features, the method encodes spatial hierarchical relationships, fundamentally altering the processing approach.
3Measurement precision
If spatial hierarchical relationships are encoded for all points, then processing accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the point set into key points and local points, computing spatial relationships only within local neighborhoods rather than for all point pairs. This segmentation dramatically reduces computational complexity while maintaining accuracy through the hierarchical structure.
Solution Approach 2:
The patent computes spatial hierarchical relationships partially, focusing only on local point sets around key points rather than exhaustive pairwise relationships. This partial action achieves sufficient accuracy for pose estimation without the full computational burden.
Data Source
AI summary
A method and apparatus for processing sparse points. The method includes determining spatial hierarchical point data based on a key point set and a local point set of a sparse point set, determining relationship feature data by encoding a spatial hierarchical relationship between points of the spatial hierarchical point data, generating a global feature and a local feature of the sparse point set through a conversion operation associated with the relationship feature data, and generating a processing result for the sparse point set based on the global feature and the local feature.