LiDAR Object Detection Augmentation for Occlusion-Aware Point Clouds
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Solution Overview
Problem
Existing data augmentation techniques for 3D object detection in LiDAR point clouds often deteriorate data quality due to random application of augmentation methods without considering object characteristics, particularly in scenarios with high occlusion or sparse point distributions.
Innovation Solution
An object detection apparatus that segments objects into partitions based on density and occlusion, adjusts augmentation intensity based on object characteristics, and applies techniques like dropout, sparse, and noise to enhance data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If random object augmentation is applied to increase data quantity, then training data amount is improved, but data quality deteriorates
Solution Approach 1:
The patent applies different augmentation strategies to different regions of objects based on their characteristics. Objects with high occlusion or sparse point distributions receive different augmentation treatments compared to objects with complete and dense point clouds. This local differentiation ensures that augmentation enhances data quality rather than degrading it, while still increasing the overall training data quantity.
Solution Approach 2:
The patent dynamically adjusts augmentation parameters based on object characteristics such as occlusion level and point density. By changing parameters like augmentation intensity, type, and application method according to these parameters, the system maintains data quality while increasing data quantity. The augmentation strength is scaled according to the object's specific properties.
2Adaptability or versatility
If high intensity augmentation is applied to all objects, then data diversity is improved, but data quality deteriorates for occluded objects
Solution Approach 1:
The patent makes the augmentation process dynamic by adjusting augmentation intensity and type based on real-time analysis of object characteristics. Objects with high occlusion or sparse points receive lower intensity or different types of augmentation compared to objects with complete data. This dynamic adaptation ensures data diversity is maintained while preventing quality degradation in challenging scenarios.
Solution Approach 2:
Instead of applying full-strength augmentation uniformly to all objects, the patent applies partial augmentation only where needed. For objects with sufficient data quality, minimal or no augmentation is applied. For objects with occlusion or sparsity, targeted augmentation is applied at appropriate intensity levels. This partial action approach maintains diversity while preserving quality.
3Ease of manufacture
If uniform augmentation technique is applied to all objects, then processing simplicity is improved, but training performance deteriorates
Solution Approach 1:
The patent segments objects into different categories based on their characteristics (e.g., occlusion level, point density, object type) and applies different augmentation techniques to each segment. This segmentation allows complex, performance-optimized augmentation strategies to be implemented in a manageable way, with each segment receiving tailored treatment that maximizes training performance.
Solution Approach 2:
The patent performs preliminary analysis of object characteristics before applying augmentation. By pre-characterizing objects based on occlusion, point density, and other attributes, the system can prepare and select appropriate augmentation techniques in advance. This preliminary action enables complex, performance-optimized augmentation to be executed efficiently without sacrificing processing simplicity.
Data Source
AI summary
An object detection apparatus for detecting a three-dimensional (3D) object using a light detection and ranging (LiDAR) point cloud and a data augmentation method thereof are provided. The object detection apparatus may obtain point cloud data that includes an object, perform data augmentation of the point cloud data by applying an augmentation technique that is determined based on a characteristic of the object, and control a vehicle based on the augmented point cloud data.