Neural Radiance Field Dynamic Scene Reconstruction
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Solution Overview
Problem
Current methods for determining vehicle damage after a collision rely on human observation, leading to inaccurate, incomplete, and lengthy diagnosis processes.
Innovation Solution
A system utilizing Neural Radiance Field (NeRF) technology to generate a dynamic 3-dimensional scene from video clips captured by sensors, combined with neural networks to analyze the scene and determine damage, required repairs, and estimated costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observation methods are used to determine vehicle damage, then the process is simple to operate, but the accuracy and completeness of damage assessment deteriorates
Solution Approach 1:
The patent creates a digital 3D copy of the collision scene using neural radiance field technology. Multiple video clips from different angles are processed to generate a continuous 3D representation of the environment and vehicles involved in the collision. This digital copy allows for accurate damage assessment without physical inspection, resolving the contradiction by providing high measurement precision through virtual modeling rather than simple visual observation.
Solution Approach 2:
The patent replaces the mechanical/physical inspection process with an automated neural network-based visual analysis system. The neural radiance field technology substitutes human observers and physical measurement tools with AI algorithms that process video data to assess damage accurately, eliminating the need for complex manual inspection procedures while improving assessment accuracy.
2Productivity
If human observation is used for damage determination, then the equipment required is simple, but the time required for assessment increases
Solution Approach 1:
The patent performs preliminary actions by capturing multiple video clips of the collision scene from various angles before the assessment process begins. These video clips are pre-processed and stored, allowing the neural network to quickly generate the 3D scene reconstruction and damage assessment without requiring time-consuming on-site inspections later. The preliminary video capture enables rapid automated analysis, significantly reducing assessment time while maintaining high productivity.
3Measurement precision
If multiple video clips are processed to generate dynamic 3D scene, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The patent segments the complex task of 3D scene reconstruction into manageable components: first processing individual video clips separately, then integrating them through neural radiance field technology to create the continuous 3D representation. The neural network divides the computational workload by handling different aspects of scene understanding independently before combining results, reducing overall computational complexity while maintaining high measurement precision through systematic processing of multiple video inputs.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to reconstructing a dynamic scene using Neural Radiance Field (NeRF) technology. In one embodiment, a method includes receiving, from one or more sensors, a plurality of video clips of an environment. The method includes generating a second plurality of video clips based on the plurality of video clips, and reconstructing, using NeRF technology, a scene as a continuous function based on the second plurality of video clips.