Neural Network Controlled 3D Capture Rig
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Solution Overview
Problem
Conventional 3D volumetric studios are static and costly, requiring specialized equipment and manual reconfiguration for different scenes, making them time-consuming and expensive to set up and adapt.
Innovation Solution
An electronic device and method for programmable rig control using a neural network to dynamically adjust the arrangement of image sensors, audio capture devices, and light sources based on real-time analysis of the scene, allowing for flexible and automated reconfiguration of the volumetric capture volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional volumetric studios use static rig configurations with specialized image sensors and devices, then the system provides stable 3D capture capability, but the device complexity and cost increase significantly
Solution Approach 1:
The patent applies universality by using standard, commercially available image sensors and devices that can serve multiple functions across different scenes and applications. Instead of requiring specialized dedicated equipment for each volumetric capture scenario, the system uses versatile components that can be dynamically reconfigured through software control and neural network optimization, thereby reducing device complexity while maintaining capture reliability
Solution Approach 2:
The system dynamically changes operational parameters such as sensor positions, orientations, and selection based on scene characteristics analyzed by a neural network. This allows the rig to adapt its configuration for different capture scenarios without requiring physically different specialized equipment, resolving the contradiction between reliable capture and device complexity
2Adaptability or versatility
If manual reconfiguration of image sensors and devices is performed for different scenes, then the system adapts to various capture scenarios, but the time and cost for setup increase
Solution Approach 1:
The system employs a neural network that automatically analyzes scene characteristics and determines the optimal rig configuration without human intervention. The neural network selects appropriate image sensors, determines their positions and orientations, and controls the rig's movement autonomously, enabling the system to reconfigure itself for different scenes instantly, thereby eliminating manual reconfiguration time while maintaining scene-specific adaptability
Solution Approach 2:
The patent replaces manual mechanical reconfiguration with an automated control system based on neural network algorithms. Instead of physically moving and adjusting components by hand, the system uses software-based scene analysis and automated actuation, substituting mechanical manual operations with intelligent automated control to reduce reconfiguration time
3Device complexity
If a fixed capture volume is used in static studios, then the system structure is simplified, but the adaptability to accommodate different volumetric subjects is limited
Solution Approach 1:
The patent implements a dynamic rig system where the capture volume can be dynamically adjusted based on the size, shape, and position of the volumetric subject. The neural network analyzes the scene and automatically reconfigures the rig's structure and sensor arrangement to match the subject's dimensions, transforming a static fixed-volume system into a dynamic adaptive system that maintains structural simplicity while achieving volume flexibility
Data Source
AI summary
An electronic device for programmable rig control for three-dimensional (3D) reconstruction is provided. The electronic device controls a first image sensor to capture a first image of a first scene which includes a set of subjects. The electronic device further feed a first input to a neural network. The electronic device further receives a first output from the neural network based on the fed first input. The electronic device further selects one or more image sensors based on the received first output. The electronic device further controls a first set of structures associated with the selected one or more image sensors to re-arrange a rig around a three-dimensional (3D) physical space. The electronic device further controls a first set of image sensors, in the re-arranged rig, to capture one or more images for generation of one or more three-dimensional (3D) models of a first subject in the 3D physical space.