Panorama Image Localization on Building Floor Plans Without Depth Sensors
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Solution Overview
Problem
Existing methods for determining the acquisition location of images within building interiors are inefficient and inaccurate, often requiring depth sensors or manual construction of floor plans, and struggle to capture and represent building interior information effectively for remote users.
Innovation Solution
The use of a computing system that analyzes visual data from images and compares it to floor plan information using trained neural networks to generate and match circular descriptors, determining the image's acquisition location without depth sensors, and updating floor plan information with visual data for improved navigation and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensors or manual construction methods are used to determine image acquisition locations, then measurement precision may be improved, but device complexity and ease of manufacture deteriorate
Solution Approach 1:
The patent replaces depth sensors and manual construction methods with a neural network-based image analysis system. The neural network automatically analyzes visual data from images to determine acquisition locations by comparing visual features with floor plan features, eliminating the need for complex depth sensing hardware and manual floor plan construction while maintaining or improving location determination accuracy.
Solution Approach 2:
The system enables automated determination of image acquisition locations through self-service mechanisms. The neural network autonomously processes images, extracts visual features, matches them with floor plan data, and determines locations without requiring manual intervention or additional specialized sensors, thereby reducing device complexity while preserving measurement precision.
2Measurement precision
If depth sensors are used to capture building interior information, then measurement precision improves, but device complexity and ease of operation worsen
Solution Approach 1:
The patent substitutes depth sensors with standard image capture devices combined with neural network analysis. The system captures building interior information using regular cameras and automatically processes the images through neural networks to extract spatial and semantic features, eliminating the need for specialized depth sensing equipment and simplifying the operational process while maintaining measurement precision.
Solution Approach 2:
The system creates detailed digital representations (copies) of building interiors from standard images through neural network processing. The neural network extracts and reconstructs three-dimensional spatial information, furniture layouts, and semantic features from two-dimensional images, producing accurate building interior models without requiring depth sensors or complex capture procedures.
3Manufacturing precision
If manual construction of floor plans is performed, then manufacturing precision may be improved, but productivity and ease of manufacture deteriorate
Solution Approach 1:
The patent replaces manual floor plan construction with automated neural network-based processing. The neural network automatically analyzes images, extracts spatial and semantic features, and generates or updates floor plans digitally, eliminating manual drafting work while maintaining or improving floor plan accuracy through consistent algorithmic processing.
Solution Approach 2:
The system performs preliminary automated processing of images to extract features and generate floor plan data before finalization. The neural network pre-processes visual data, identifies key spatial relationships, and prepares structured floor plan information, significantly reducing the time required for complete floor plan construction while ensuring accuracy through systematic feature extraction.
Data Source
AI summary
Techniques are described for using computing devices to perform automated operations for determining the acquisition location of an image using an analysis of the image's visual contents. In at least some situations, images to be analyzed include panorama images acquired at acquisition locations in an interior of a multi-room building, and the determined acquisition location information includes a location on a floor plan of the building and in some cases orientation direction information—in at least some such situations, the acquisition location determination is performed without having or using information from any distance-measuring devices about distances from an image's acquisition location to objects in the surrounding building. The acquisition location information may be used in various automated manners, including for controlling navigation of devices (e.g., autonomous vehicles), for display on one or more client devices in corresponding graphical user interfaces, etc.