Two-Stage Neural Network for CGR Object Instance Detection
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Solution Overview
Problem
Existing techniques for creating computer-generated reality (CGR) environments struggle with efficient and accurate detection of objects and understanding of physical environments, particularly in identifying specific instances of objects within these environments.
Innovation Solution
The implementation combines object detection with instance detection using machine learning models to identify object types and their specific instances, accessing characteristics such as dimensions and material properties from databases, which are then used to enhance the CGR environment by modifying it with precise object reconstructions and physics simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detection techniques are used to create CGR environments, then the basic object detection function is achieved, but the accuracy and efficiency of detecting specific object instances and understanding physical environments is insufficient
Solution Approach 1:
The patent segments the object detection process into two distinct stages: (1) a first neural network performs general object detection and classification to identify object types and their locations, and (2) a second neural network performs instance detection on detected objects to identify specific instances. This segmentation allows each network to be optimized for its specific task, improving both accuracy and efficiency.
Solution Approach 2:
The first neural network performs preliminary object detection and classification before the second neural network performs instance detection. By pre-identifying object types and locations, the system prepares the groundwork for more accurate instance-level detection, reducing the computational burden on the second network and improving overall efficiency.
2Productivity
If general object detection is performed without instance detection, then the detection process is simpler and faster, but the ability to identify specific object instances and access their characteristics is lost
Solution Approach 1:
The detection pipeline is divided into a fast first stage (general object detection) and a detailed second stage (instance detection). The first neural network quickly identifies object types and locations, while the second neural network processes only detected objects to extract instance-level information. This segmentation maintains speed while preserving detailed instance characteristics.
Solution Approach 2:
The system performs instance detection only on objects that are successfully detected and classified by the first neural network, rather than processing all image data at full detail. This partial application of instance detection maintains high speed while capturing all necessary instance characteristics for detected objects.
3Measurement precision
If instance detection is performed for all detected objects, then complete object instance information is obtained, but the computational complexity and processing time increase significantly
Solution Approach 1:
The system uses a two-stage neural network architecture where the first network handles general object detection and the second network handles instance detection. This segmentation allows the system to maintain complete instance detection for all detected objects while managing computational complexity through modular design and task specialization.
Solution Approach 2:
The first neural network performs preliminary filtering by detecting and classifying objects before the second network performs instance detection. This preliminary action reduces the input size and complexity for the second network, allowing complete instance detection without proportionally increasing overall system complexity.
4Reliability
If detailed scene understanding with instance detection is implemented, then the quality of CGR environments is improved, but the computational resources and processing time required increase
Solution Approach 1:
The computational workload is segmented into two specialized neural networks: the first network consumes energy for general object detection and classification, while the second network consumes energy for instance detection on detected objects only. This segmentation optimizes energy usage by matching computational resources to task requirements, improving CGR quality while managing energy consumption.
Solution Approach 2:
The system applies instance detection selectively only to detected objects rather than processing the entire image at full detail. This partial action reduces overall computational energy consumption while maintaining high CGR environment quality by focusing detailed analysis only where needed.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that determine a particular object instance in CGR environments. In some implementations, an object type of an object depicted in an image of a physical environment is identified. Then, a particular instance is determined based on the object type and the image. In some implementations, objects of the particular instance have a set of characteristics that differs from sets of characteristics associated with other instances of the object type. Then, the set of characteristics of the particular instance of the object depicted in the physical environment is obtained.