Vision-Based Boundary Detection Using Delaunay Triangulation
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Solution Overview
Problem
Current 3D imaging and virtual reality systems lack effective methods for automatically detecting and generating boundaries within a physical environment, leading to potential collisions with obstacles such as walls and furniture, which can disrupt user experience and safety.
Innovation Solution
The use of computer vision and 3D representation to detect safe and unsafe points in a space, employing Delaunay triangulation or symbolic perturbation methods to create a mesh of safe and unsafe regions, allowing for the generation of boundaries that guide users around obstacles without manual setup, and enabling visualization techniques to balance obstacle avoidance with immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual boundary setup is used in virtual reality systems, then boundary detection accuracy can be ensured, but setup complexity and time consumption increase significantly
Solution Approach 1:
The system performs automatic boundary detection using computer vision and Delaunay triangulation algorithms, eliminating the need for manual setup by users. The headset device autonomously captures images, processes feature points, and generates boundary definitions without human intervention, making the system self-configuring and self-adapting to the physical environment.
Solution Approach 2:
The patent replaces manual mechanical setup processes with automated computer vision and computational geometry algorithms. Instead of requiring users to manually position and configure boundary markers or perform complex calibration procedures, the system uses image processing and Delaunay triangulation to automatically define boundaries based on captured environmental features.
2Ease of operation
If automatic boundary detection is implemented, then setup time is reduced and ease of operation improves, but system complexity increases
Solution Approach 1:
The system creates a virtual copy or representation of the physical environment by capturing images with the headset camera, extracting feature points, and generating a computational model through Delaunay triangulation. This virtual model serves as a simplified representation that enables automatic boundary detection without requiring complex physical setup infrastructure.
Solution Approach 2:
The patent transforms the boundary detection problem from a complex manual configuration task into an automated computational process by changing the parameters from manual coordinates and physical markers to image-based feature points and algorithmic boundary generation. This parameter transformation simplifies user interaction while managing system complexity through software-based solutions.
3Reliability
If comprehensive obstacle detection is performed, then safety is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the boundary detection process into distinct stages: image capture, feature point extraction, Delaunay triangulation computation, and boundary definition generation. This segmentation allows for optimized processing at each stage, improving overall efficiency while maintaining comprehensive safety detection through systematic analysis of environmental features.
Data Source
AI summary
A system configured to determine one or more boundaries of the space, for example, in 3D applications. In some cases, the system may generate point data including a set of safe points in a space and a set of unsafe points in the space, the space surrounding a user device of the system, generate a triangulation over a union of the set of safe points and the set of unsafe points, determine triangles of the triangulation that include at least one safe point and determine edges of determined triangles which are part of a single triangle that include at least one safe point. The system may then determine one or more boundaries of the space using the determined edges.