Classification Tree Learning for Hidden Object Part Recognition
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Solution Overview
Problem
Current object recognition technologies face challenges in accurately identifying both visible and hidden parts of an object from a single depth image, limiting their ability to reconstruct the object's volume and pose effectively.
Innovation Solution
An object recognition apparatus and classification tree learning apparatus that utilize a classification tree to recognize visible and hidden object parts by generating and learning classification trees from training data associated with the object, employing a ray-casting scheme to decompose the object's volume and collect depth values and IDs, allowing for the recognition of both directly visible and occluded parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition methods are used, then visible object parts can be identified, but hidden object parts cannot be recognized
Solution Approach 1:
The patent creates virtual copies of visible object parts and projects them to estimate hidden parts. By generating virtual views from multiple angles and using ray-casting techniques, the system reconstructs hidden object regions that are not directly visible in the input image, effectively copying visible part information to infer hidden part geometry
Solution Approach 2:
The patent introduces classification trees as an intermediary mechanism between visible and hidden object parts. The classification tree learns from training data to predict hidden parts based on visible parts, acting as a mediator that transfers information from observable to unobservable regions through learned geometric relationships
2Ease of operation
If a single depth image is used as input, then processing is simplified, but recognition of hidden parts is limited
Solution Approach 1:
The patent transforms the problem from 2D image analysis to 3D volume reconstruction. By using ray-casting to project 3D object models into 2D image space and comparing with actual depth images, the system leverages the third dimension to infer hidden parts that cannot be seen from the current viewpoint, maintaining simple single-image input while achieving 3D-aware recognition
3Measurement precision
If classification trees are learned from training data, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary learning of classification trees during an offline training phase using labeled training data. By pre-training the classification tree with known object structures and relationships, the system captures geometric priors and contextual relationships in advance, enabling accurate hidden part prediction during actual operation without requiring complex real-time computation
Data Source
AI summary
An object recognition apparatus, a classification tree learning apparatus, an operation method of the object recognition apparatus, and an operation method of the classification tree learning apparatus are provided. The object recognition apparatus may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, using a classification tree.