3D Object Separation Using Multi-Camera Fusion and Calibration
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Solution Overview
Problem
Existing object recognition systems struggle to quickly identify objects from a large number of possible objects in uncontrolled environments, particularly when objects are not in specific positions or lack distinctive marks, such as barcodes.
Innovation Solution
The use of multiple three-dimensional (3D) cameras to capture objects from different angles, combining image information to create a 3D model, and employing machine-learning algorithms for identification, with continuous calibration to maintain synchronicity amidst ambient changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple 3D cameras are used to capture objects from different angles, then object recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines data from multiple 3D cameras to create a unified 3D model of objects. The system merges image information from different camera angles and integrates depth data to produce a comprehensive representation, enabling accurate recognition without requiring a single complex camera system.
Solution Approach 2:
The patent segments the object recognition task into separate processing stages: capturing images from multiple cameras, combining the data into a unified representation, separating the object from the background, and performing recognition. This segmentation allows each stage to be optimized independently while working together toward the overall goal.
2Adaptability or versatility
If 3D models are created by combining image information from multiple cameras, then object recognition capability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by capturing images from multiple cameras and pre-processing the data to create a unified 3D model before the actual recognition task. This preparation can be done in advance or in parallel, reducing the computational burden during real-time recognition operations.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific regions and features of the 3D model that are most important for recognition. Rather than processing the entire model uniformly, the system identifies and emphasizes key local characteristics that enable accurate object identification.
3Reliability
If continuous calibration is performed to maintain synchronicity, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements continuous calibration through feedback mechanisms that monitor the synchronization status of multiple cameras and adjust their operation accordingly. The system detects drift or misalignment and automatically performs calibration corrections, ensuring reliable operation without requiring manual intervention.
Solution Approach 2:
The calibration system performs self-service by automatically detecting and correcting synchronization issues without external intervention. The system monitors its own performance and adjusts its calibration parameters to maintain optimal operation, reducing the need for complex manual calibration procedures.
4Measurement precision
If objects are separated in 3D space, then recognition accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments objects from the background and from each other by exploiting their spatial separation in 3D space. The system identifies distinct 3D regions corresponding to different objects and processes them independently, which simplifies the recognition task compared to analyzing overlapping 2D images while maintaining high accuracy.
Data Source
AI summary
Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.