Multi-Domain Neighborhood Embedding for Sparse Point Cloud Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud processing techniques are ineffective for sparse data, leading to performance degradation, especially in automotive systems that require accurate interpretation of three-dimensional scenes for autonomous driving and perception tasks, as they fail to capture and maintain local contextual information effectively.
Innovation Solution
The Multi-domain Neighborhood Embedding and Weighting (MNEW) process, which embeds adaptive weighting factors based on geometric distance, feature similarity, and local sparsity in point cloud data, using a hierarchical encoder-decoder structure to enhance point cloud processing, enabling efficient handling of both sparse and dense data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional point cloud processing techniques are used, then processing of dense point cloud data is effective, but performance degrades when processing sparse point cloud data
Solution Approach 1:
The patent applies local quality by computing geometric distances and feature similarities for each point's local neighborhood, then generating adaptive weighting factors specific to each point based on its local sparsity and neighborhood characteristics. This allows the processing to be optimized locally for each region's density characteristics rather than applying a uniform approach.
Solution Approach 2:
The patent implements dynamics by making the weighting factors adaptive and dynamic rather than static. The weighting for each point is computed based on its geometric distance to neighbors, local sparsity, and feature similarity, allowing the processing to dynamically adjust to varying data density conditions across different regions of the point cloud.
2Ease of manufacture
If uniform processing is applied to all points, then implementation is simple, but local contextual information is not effectively captured
Solution Approach 1:
The patent segments the point cloud processing into local neighborhoods for each point, computing geometric distances and feature similarities within each local region. This segmentation allows local contextual information to be captured while maintaining a systematic processing framework that can be implemented through structured computation.
Solution Approach 2:
The patent changes parameters by computing multiple characteristics (geometric distance, feature similarity, local sparsity) for each point's neighborhood and using these to generate adaptive weighting factors. This transforms the processing from a single uniform operation to a multi-parameter adaptive system that preserves local contextual information.
3Measurement precision
If adaptive weighting based on multiple factors is computed for each point, then processing accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by computing geometric distances and feature similarities only for points within local neighborhoods rather than all points in the cloud. This limits the computational scope to relevant local regions, achieving high accuracy for each point without the excessive computational cost of global processing.
Solution Approach 2:
The patent manages computational complexity through parameter changes by computing a limited set of key characteristics (geometric distance, feature similarity, local sparsity) for each local neighborhood. These computed parameters are then used to generate weighting factors, transforming a complex multi-factor problem into a structured computation based on a defined set of parameters.
Data Source
AI summary
This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.