3D Lighting Fixture Model Generation for Setup Optimization
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Solution Overview
Problem
Determining the operational capabilities of lighting fixtures is challenging due to the complexity of kinematic and lighting capabilities, requiring extensive knowledge and experience, leading to inefficiencies and potential errors in setting up optimal lighting arrangements for events.
Innovation Solution
A system and method for generating a three-dimensional model of a lighting fixture by scanning and comparing its configurations to identify operational capabilities, allowing for visualization and optimization of lighting arrangements in a virtual environment without the need for expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually assesses lighting fixture capabilities through experience and testing, then the assessment may be accurate for known fixtures, but the process is time-consuming and requires exceptional skill
Solution Approach 1:
The system performs capability assessment in advance by automatically detecting and storing the operational capabilities of lighting fixtures before they are needed for an event. The processor identifies pan/tilt ranges, beam angles, and lighting parameters through automated scanning and data collection, creating a ready-to-use capability database that eliminates the need for on-site manual assessment.
Solution Approach 2:
The lighting fixture itself provides the capability data through automated detection systems. The processor communicates with the fixture to automatically extract operational parameters such as pan/tilt limits, beam angles, and lighting capabilities without requiring external expert intervention. The system self-documented the fixture's capabilities through programmed interaction.
2Reliability
If an experienced technician manually configures lighting arrangements, then the setup may achieve satisfactory results, but the process requires guesswork and cannot explore all possible arrangements
Solution Approach 1:
The system dynamically evaluates multiple lighting arrangements by programmatically adjusting fixture positions and configurations based on detected capabilities. The processor automatically generates and tests various pan/tilt combinations, beam angle settings, and lighting parameters to identify optimal arrangements that satisfy scene requirements, replacing static expert judgment with dynamic computational exploration.
Solution Approach 2:
The system uses feedback from automated capability detection to inform arrangement optimization. The processor continuously refines lighting configurations based on measured performance data, adjusting parameters to achieve desired lighting effects while respecting fixture capabilities. This closed-loop approach ensures reliable results while exploring multiple possibilities.
3Loss of information
If multiple lighting fixtures are manually analyzed and logged by experienced users, then comprehensive capability data may be obtained, but the process is costly and prone to human error
Solution Approach 1:
The system replaces manual mechanical assessment with automated electronic detection. The processor communicates electronically with lighting fixtures to extract capability data through programmed protocols, eliminating the need for physical manipulation and expert observation. This substitution ensures consistent, error-free data collection at minimal cost.
Solution Approach 2:
The system creates digital copies of fixture capability data through automated detection and storage. Instead of relying on human memory or handwritten logs, the processor generates and stores precise digital representations of each fixture's capabilities, ensuring accurate replication and consistent usage across multiple projects and users.
4Adaptability or versatility
If the system considers hypothetical lighting fixtures not present at the venue, then more optimal arrangements may be discovered, but the number of variables and complexity increases significantly
Solution Approach 1:
The system creates a universal capability database that stores standardized operational parameters for various lighting fixture types. This universal framework allows the processor to evaluate both existing and hypothetical fixtures using the same detection and comparison logic, reducing complexity by applying consistent evaluation criteria across different scenarios.
Data Source
AI summary
Systems and methods for generating a three-dimensional model of a lighting fixture. The systems include a controller that includes an electronic processor coupled to a memory. The memory is configured to store instructions that when executed by the electronic processor configure the controller to receive first scanning data related to a lighting fixture while the lighting fixture is in a first configuration, receive second scanning data related to the lighting fixture after an adjustment to the lighting fixture to a second configuration, compare the first scanning data and the second scanning data, perform three-dimensional mesh reconstruction based on the first scanning data and the second scanning data, and generate the three-dimensional model based on the first configuration of the lighting fixture and the second configuration of the lighting fixture representing an operational capability of the lighting fixture.