Multi-Domain Point Cloud Embedding for Sparse Sensor Perception
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Solution Overview
Problem
Existing point cloud processing techniques are ineffective for sparse data, leading to performance degradation, especially in automotive systems that rely on lidar, camera, and radar sensors for autonomous driving and perception tasks, as they fail to capture and maintain information effectively due to occlusions and computational expenses.
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, utilizing a hierarchical encoder-decoder structure to enhance point cloud processing for both sparse and dense data, enabling robust performance across various benchmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional point cloud processing techniques are used on sparse data, then processing speed is maintained, but performance and accuracy degrade significantly
Solution Approach 1:
The patent applies local quality by making the processing approach adaptive to local data density characteristics. The system identifies sparse regions versus dense regions in the point cloud and applies different processing strategies to each, rather than using a uniform approach. This allows the system to maintain high performance in dense regions while applying specialized techniques in sparse regions to prevent performance degradation.
Solution Approach 2:
The patent implements dynamics by making the processing technique adaptive and flexible rather than static. The system dynamically adjusts its processing approach based on the actual density characteristics of the input point cloud data, enabling it to effectively handle varying degrees of sparsity across different scenes and sensor conditions.
2Reliability
If more sophisticated processing techniques are applied to maintain performance on sparse data, then accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud processing into distinct stages and components. The system segments the processing pipeline to handle different aspects (feature extraction, neighborhood analysis, weighting) separately, which allows for optimized implementation of each component and avoids the need for overly complex monolithic processing algorithms.
Solution Approach 2:
The patent implements partial action by applying sophisticated processing techniques selectively rather than uniformly to all points. The system identifies and applies advanced processing methods primarily to regions or points where they are most needed, rather than applying the full computational overhead to every point in the cloud, thus reducing overall complexity while maintaining accuracy.
3Ease of manufacture
If uniform processing is applied to all point cloud data, then implementation is simple, but performance degrades on sparse regions
Solution Approach 1:
The patent applies local quality by making the processing approach adaptive to local data density characteristics. The system identifies sparse regions versus dense regions in the point cloud and applies different processing strategies to each, rather than using a uniform approach. This allows the system to maintain high performance in dense regions while applying specialized techniques in sparse regions to prevent performance degradation.
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.