AR Annotation Interface With Dynamic Guides for 3D Feature Anchoring
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Solution Overview
Problem
Conventional augmented and virtual reality methods for annotating, measuring, and modeling environments are cumbersome, inefficient, and limited in functionality, often requiring multiple user inputs, failing to provide dynamic guides, and not accounting for surface curvature, leading to inefficient energy consumption in battery-operated devices.
Innovation Solution
The system provides a computer interface that automatically detects features in a physical space, offers intelligent annotation placement guides, and updates the user interface based on detected features, allowing for fewer user inputs and improved feedback, while maintaining relevant annotations and measurements in view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional augmented reality methods are used for annotating and measuring, then basic functionality is provided, but the methods are cumbersome, inefficient, and require multiple user inputs
Solution Approach 1:
The system automatically detects features in the physical environment and performs annotation without requiring multiple user inputs. The computational camera and processing system autonomously identify objects, surfaces, and spatial relationships, then place annotations automatically, allowing the system to serve itself rather than requiring continuous user direction.
Solution Approach 2:
The system performs preliminary detection and analysis of the physical environment before annotation occurs. By pre-identifying features, calculating measurements, and determining optimal annotation placements in advance, the system prepares all necessary information before the user interacts with the interface, significantly reducing interaction time.
2Adaptability or versatility
If conventional augmented reality methods are used for measuring, then basic measurements can be taken, but the methods are limited in functionality and require users to specify measurement types
Solution Approach 1:
The computational camera system automatically determines what measurements are relevant based on the detected physical features and context. Instead of requiring users to specify measurement types, the system autonomously identifies appropriate measurements to take (e.g., distance between objects, dimensions of surfaces, spatial relationships) and performs them automatically.
Solution Approach 2:
The system provides a unified measurement framework that can handle multiple types of measurements (distance, dimension, spatial relationships, angles) through a single interface. The computational camera and processing system are designed to perform various measurement functions adaptively based on the detected environment, eliminating the need for separate functions or complex user specifications.
3Ease of operation
If conventional augmented reality methods are used for annotating, then annotations can be added, but dynamic guides are not provided to help users
Solution Approach 1:
The system provides real-time feedback to users during the annotation process by displaying dynamic guides that respond to user actions and environmental context. As users move the device or interact with the interface, the system continuously updates guidance information to indicate optimal annotation placements, measured distances, and relevant features, ensuring users have current information throughout the process.
Solution Approach 2:
The annotation guides are dynamic rather than static, automatically adjusting based on the detected physical environment, camera movement, and user interactions. The guides adapt their position, orientation, and content in real-time to provide contextually relevant assistance, making the annotation process more intuitive and easier to perform correctly.
4Manufacturing precision
If conventional augmented reality methods are used, then annotations can be made, but surface curvature and shape are not taken into account
Solution Approach 1:
The system explicitly accounts for surface curvature and three-dimensional shape when placing annotations and making measurements. The computational camera captures depth information and spatial geometry, allowing the system to adjust annotation placement and measurement calculations to accommodate curved surfaces, angled planes, and complex geometries rather than assuming flat surfaces only.
Solution Approach 2:
The system dynamically adjusts annotation and measurement parameters based on the detected surface properties. When surfaces with specific characteristics (curvature, orientation, texture) are detected, the system modifies its measurement approach and annotation placement parameters to maintain accuracy across diverse surface types and geometries.
5Productivity
If conventional augmented reality methods are used, then each annotation requires separate user inputs, but this increases the number of inputs needed
Solution Approach 1:
The system merges multiple annotation operations into a single user action. By detecting features automatically and pre-calculating measurement information, the system allows users to add multiple annotations or measurements with fewer inputs. Related annotations are grouped and can be applied simultaneously, reducing the total number of separate user actions required.
Solution Approach 2:
The system performs preliminary detection, feature identification, and measurement calculation before user annotation input is required. By preparing all necessary information in advance, the system enables users to add annotations more quickly without needing to provide separate inputs for each measurement parameter or feature identification step.
6Use of energy by moving object
If conventional augmented reality methods are used in battery-operated devices, then functionality is provided, but energy is wasted due to longer operation times
Solution Approach 1:
The computational camera and processing system perform preliminary detection and analysis of the physical environment during brief processing intervals, preparing measurement data and feature information in advance. This allows the device to spend more time in low-power states while still maintaining annotation functionality, reducing overall power consumption without sacrificing operational capability.
Solution Approach 2:
The system uses periodic processing cycles where the computational camera actively processes environmental data at intervals rather than continuously. During active processing periods, measurements and feature detection are performed; during idle periods, the device can enter lower-power states. This periodic operation reduces average power consumption while maintaining annotation functionality when needed.
Data Source
Figure 1A
Figure 1B
Figure 2A
AI summary
A computer system displays an annotation placement user interface that includes a representation of a field of view of one or more cameras that is updated over time based on changes in the field of view, and a placement user interface element indicating a virtual annotation placement location. If the placement user interface element is over a representation of a first type of feature in the physical environment, the appearance of the placement user interface element changes to indicate an anchor point corresponding to the first type of feature, and the system displays a first set of guides. If the placement user interface element is over a representation of a second, different type of feature in the physical environment, the appearance of the placement user interface element changes to indicate an anchor point corresponding to the second type of feature, and the system displays a second, different set of guides.