AI Depth-Map 3D Video Reconstruction for Real-Time Mobile Parallax
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Solution Overview
Problem
Current 3D reconstruction technologies require specialized hardware and technical expertise, limiting their accessibility and usability on consumer-grade devices, and fail to provide immersive content experiences on widely available devices like smartphones and tablets.
Innovation Solution
A system leveraging AI algorithms for real-time 3D reconstruction and rendering, using depth maps and sensor data to create interactive 3D content on devices like smartphones and VR/AR systems, with adaptive streaming and optimized shader programs for enhanced depth perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional 3D reconstruction methods using depth sensors and sophisticated computer vision algorithms are employed, then manufacturing precision and measurement precision are improved, but device complexity and ease of manufacture deteriorate
Solution Approach 1:
The patent replaces complex mechanical depth sensing systems with AI-based computational methods. The system uses neural networks to infer depth information from monocular video frames, substituting physical depth sensors with software-based solutions that run on standard consumer devices.
Solution Approach 2:
The patent creates simplified 3D models by copying and processing video frames through AI algorithms. Instead of requiring complex hardware to capture 3D data directly, the system captures 2D video and generates 3D representations through computational copying and transformation of the visual data.
2Manufacturing precision
If high computational power and specialized hardware are used, then 3D reconstruction quality is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The patent creates a system that works across multiple device types and operating systems. The AI-based 3D reconstruction methodology is implemented as software that can run on smartphones, tablets, and computers regardless of the specific hardware platform, making the technology universally accessible.
Solution Approach 2:
The patent replaces expensive specialized 3D capture hardware with standard consumer devices. By using off-the-shelf smartphones and computers with basic cameras, the system eliminates the need for costly dedicated equipment while achieving comparable 3D reconstruction results.
3Productivity
If real-time processing is implemented on consumer-grade devices, then productivity and ease of operation are improved, but manufacturing precision and reliability may deteriorate
Solution Approach 1:
The patent processes key features and salient objects in the video stream rather than every pixel uniformly. The AI algorithm focuses computational resources on identifying and reconstructing important 3D structures, achieving acceptable accuracy for real-time applications without processing the entire scene at maximum detail.
Solution Approach 2:
The patent implements adaptive processing that adjusts computational intensity based on scene complexity and device capabilities. The system dynamically balances processing speed and reconstruction quality, allowing real-time operation on consumer devices while maintaining acceptable 3D accuracy through adaptive algorithm execution.
Data Source
AI summary
A system and method for real-time 3D reconstruction of videos, converting 2D video frames into 3D video frames by generating depth maps using an artificial intelligence (AI) algorithm. The system separates depth maps from 2D video frames into RGB/A and depth components, maps the RGB/A component onto a 3D mesh based on UV coordinates, and adjusts the vertices according to the depth component. The rendered 3D video frames are displayed in real-time. The system integrates real-time sensor data to create dynamic parallax effects, locks the camera position onto target transforms within a 3D environment, and updates the camera's position and rotation based on device motion. Features include colorized depth maps, compression for efficient transmission, curved 3D meshes, shader programs for enhanced depth perception, gradient borders, dynamic orientation switching, and a user interface optimized for right-handed and left-handed users. Adaptive streaming technologies such as DASH and HLS are supported.