Room Model Generation via Skeleton Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for room mapping in advanced gaming applications are either time-consuming and error-prone when using hand-held cameras or expensive and resource-intensive when using LIDAR devices, making them unsuitable for ordinary users.
Innovation Solution
A computer-implemented method that determines a path of a moving object within a user space based on a tracking dataset from a stationary camera, infers a walking space, and generates a model of the user space, reducing the need for manual camera movement and minimizing computing resources compared to LIDAR-based approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a hand-held camera is used to manually capture images around the room, then a room model can be generated, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system uses the user's natural movement through the room (captured by the stationary camera) to automatically generate the room model. The user simply walks through the room as normal, and the system self-services by capturing images from multiple angles and reconstructing the 3D model without requiring manual camera operation.
Solution Approach 2:
Instead of moving the camera around the room to capture images (traditional approach), the invention inverts the approach by placing a stationary camera in the room and moving the user through the space. The user's movement provides the varying perspectives needed for 3D reconstruction, eliminating the need for manual camera handling.
2Measurement precision
If scanning LIDAR devices are used to generate room models, then accurate surface point data is obtained, but the devices are expensive and require significant computing resources
Solution Approach 1:
The invention replaces expensive, complex LIDAR devices with inexpensive stationary cameras. Multiple standard cameras can be positioned around the room to capture images, providing sufficient data for 3D reconstruction without the high cost and computational burden of LIDAR systems.
Solution Approach 2:
The system uses visual copying through photographing the room from multiple angles using standard cameras, then reconstructs the 3D model computationally. This approach copies the spatial information visually rather than using active sensing like LIDAR, reducing hardware complexity and cost while maintaining adequate measurement precision.
Data Source
AI summary
In various embodiments, a map inference application automatically maps a user space. A camera is positioned within the user space. In operation, the map inference application determines a path of a first moving object within the user space based on a tracking dataset generated from images captured by the camera. Subsequently, the map inference application infers a walking space within the user space based on the path. The map inference application then generates a model of at least a portion of the user space based on the walking space. One or more movements of a second object within the user space are based on the model. Advantageously, unlike prior art solutions, the map inference application enables a model of a user space to be automatically and efficiently generated based on images from a single stationary camera.