Hierarchical Object Recognition with Next-Best-View Planning
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Solution Overview
Problem
Current methods for fine-grained object recognition in large 3D model databases are inefficient, as they fail to effectively predict the next-best-view (NBV) and classify objects of varying fineness, especially when types are numerous, and existing technologies like Convolutional Deep Belief Networks (CDBN) cannot accurately predict NBV or classify different levels of object detail.
Innovation Solution
The proposed method involves a hierarchical classifier with a feature extraction unit, pooling layers, and a next-best-view generation unit, utilizing a fully connected layer and Gaussian Mixture Model for clustering, along with a Multi-view Recurrent Neural Network (MV-RNN) that integrates viewing parameters and generates partial regions for improved object recognition and NBV planning, while being fault-tolerant to sheltering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If volume presentation and CDBN are used for 3D model classification, then shape information can be captured, but next-best-view prediction capability is lost and fine-grained classification accuracy deteriorates
Solution Approach 1:
The patent segments the classification task into hierarchical levels (coarse-grained to fine-grained) and separates NBV prediction as an independent sub-task. The system divides the 3D model processing into shape representation, view prediction, and classification stages, allowing each component to be optimized independently while working together as a unified system.
Solution Approach 2:
The patent introduces an intermediary NBV prediction module that bridges the gap between current view and optimal classification views. This intermediary component selects intermediate viewing angles that maximize information gain for fine-grained classification, acting as a mediator between the current sensor position and the final classification result.
2Adaptability or versatility
If instance level classifier is trained for the whole database, then classification coverage is complete, but computational complexity and training time increase significantly
Solution Approach 1:
The patent segments the large-scale classification database into hierarchical categories, training separate classifiers for different levels of granularity. Instead of training one massive classifier for the entire database, the system divides it into manageable segments (coarse categories, then fine categories), reducing training complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent performs preliminary coarse-grained classification before fine-grained classification. By first categorizing objects into broad categories and then applying more specific classifiers only to relevant subsets, the system reduces the computational burden of training and inference while maintaining complete classification coverage across all object types.
3Measurement precision
If single viewing point is used for recognition, then energy consumption is low and processing is simple, but fine-grained object recognition accuracy is insufficient
Solution Approach 1:
The patent applies partial action by selecting only the necessary number of viewing points required for adequate fine-grained classification. Instead of capturing all possible views, the NBV prediction module identifies and processes only the most informative subset of viewing angles, achieving sufficient recognition accuracy with reduced energy expenditure.
Solution Approach 2:
The patent dynamically changes viewing parameters (angle, position, distance) based on the specific object being classified and the required classification granularity. The system adjusts viewing parameters adaptively, selecting optimal angles that maximize discriminative information for fine-grained categories while minimizing the total number of views required, thus reducing energy consumption.
4Measurement precision
If multiple viewing points are captured for fine-grained recognition, then classification accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent performs preliminary NBV prediction and view selection before actual classification processing. By pre-identifying the most informative viewing points based on object geometry and category characteristics, the system prepares an optimized view sequence that maximizes classification accuracy while minimizing the number of views that need to be processed, thereby improving overall recognition efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where classification uncertainty and information gain metrics guide the selection of subsequent viewing points. The system continuously evaluates the information obtained from each view and uses this feedback to determine whether additional views are necessary, adjusting the viewing sequence dynamically to achieve accurate classification with minimal processing load.
Data Source
AI summary
This inventor provides a kind of recognition method and device, including the following stages: S1: get the first feature vector by putting the depth image of current best view of the object to be recognized to feature extractor; S2: get the output of pooling layer by putting the first feature vector to first hidden layer, then get the classification by putting the output of pooling layer to a hierarchical classifier; S3: generate the partial region of current view according to the output of pooling payer and view observation vector; S4: get the second feature vector by putting the partial region to second hidden layer, and the second feature vector includes the output of pooling layer and information of current view; S5: generate the next best view using a fully connected layer and the second feature vector; S6: repeat S1 to S5 until reach the leaf node of the hierarchical classifier. This inventor scans and recognizes the unknown scene, while solving the problem of next best view.