Instance-Aware Object Detection for Accurate CGR Scene Understanding
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Solution Overview
Problem
Existing techniques for detecting objects and understanding physical environments in computer-generated reality (CGR) environments are inefficient and inaccurate, particularly in identifying specific instances of objects for enhancing the quality of CGR environments.
Innovation Solution
Combining object detection with instance detection using machine learning models to identify object types and determine precise models or particular instances, accessing a database for characteristics, and integrating these characteristics into CGR environments for improved scene understanding and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detection techniques are used to detect objects in physical environments, then basic object identification can be achieved, but the accuracy and precision of identifying specific object instances and models is insufficient
Solution Approach 1:
The detection process is divided into two distinct stages: first, a neural network detects and identifies the general object type (e.g., chair, table); second, instance detection techniques are applied to identify the specific model or instance of that object type. This segmentation allows each stage to specialize, improving overall precision without requiring a single overly complex system.
Solution Approach 2:
Object type identification serves as an intermediary step between basic object detection and specific instance identification. The neural network first identifies the object type, which then guides the instance detection process by providing context and constraints, making the overall system more efficient and accurate.
2Reliability
If instance detection is performed to identify precise object models and characteristics, then the quality of CGR environments is improved, but the computational time and processing requirements increase
Solution Approach 1:
Object type identification is performed as a preliminary action before instance detection. By first identifying the general object type using a neural network, the system narrows down the search space for instance detection, reducing the computational burden and time required for the more detailed analysis that follows.
Solution Approach 2:
The system applies instance detection selectively - not all objects require the same level of detailed instance identification. The system performs partial instance detection based on the specific requirements of the CGR environment, balancing quality improvements with computational efficiency.
3Loss of information
If comprehensive object characteristics are extracted and integrated into CGR environments, then scene understanding and reconstruction quality are enhanced, but the data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts only the specific characteristics and properties of identified object instances that are relevant for CGR environment construction. Rather than processing all possible object attributes, the system selectively extracts pertinent information such as dimensions, materials, and visual properties, reducing data processing complexity while maintaining scene understanding completeness.
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 obtained. 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.