Point Cloud Neighborhood Weighting for Sparse Data Perception
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Solution Overview
Problem
Existing point cloud processing techniques struggle with sparse data, leading to inefficiencies in autonomous driving and perception systems due to loss of information and computational expense, particularly when using lidar data for outdoor scenes.
Innovation Solution
Multi-domain Neighborhood Embedding and Weighting (MNEW) technique that embeds adaptive weightings based on geometric distance, feature similarity, and neighborhood sparsity, using a hierarchical encoder-decoder structure to enhance point cloud data processing for both sparse and dense data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional point cloud processing techniques are used on sparse data, then processing speed is maintained, but information loss increases and effectiveness depreciates
Solution Approach 1:
The patent applies local quality by computing adaptive weighting factors for each point based on its local neighborhood characteristics. The weighting factor for each point is determined by its geometric distance to neighboring points, feature similarity, and local sparsity, allowing different regions of the point cloud to be processed with locally optimized weights that preserve important local contextual information while maintaining processing efficiency.
Solution Approach 2:
The patent transforms multiple parameters (geometric distance, feature similarity, local sparsity) into a composite weighting factor that dynamically adjusts the importance of each point. This parameter transformation approach allows the system to adaptively emphasize or de-emphasize points based on their local characteristics, thereby reducing information loss in sparse regions without sacrificing overall processing effectiveness.
2Measurement precision
If dense point cloud data is processed using conventional techniques, then processing accuracy is maintained, but computational expense increases
Solution Approach 1:
The patent computes weighting factors based on local sparsity and geometric properties, which allows the system to process dense point clouds more efficiently by identifying and emphasizing only the most informative points. This parameter-based weighting reduces the effective computational load while maintaining processing accuracy, as not all points in dense regions require equal processing attention.
Solution Approach 2:
The patent extracts and emphasizes key geometric and feature properties (local sparsity, geometric distance, feature similarity) that are most relevant for accurate processing. By focusing computational resources on these extracted key properties rather than processing all points uniformly, the system reduces computational expense while maintaining measurement precision.
3Loss of information
If adaptive weighting based on multiple factors is applied, then information preservation improves, but computational complexity increases
Solution Approach 1:
The patent segments the point cloud processing into distinct computational stages: computing geometric distances to neighboring points, evaluating feature similarities, determining local sparsity, and finally combining these into weighting factors. This segmentation of the processing pipeline allows each component to be optimized independently and facilitates efficient implementation while preserving local contextual information through the multi-factor weighting approach.
Data Source
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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.