Drone Imaging 3D Reconstruction With Hybrid Photogrammetry and NeRF
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Solution Overview
Problem
Current drone imaging technologies face challenges in efficiently generating high-fidelity, high-dimensional models due to limitations in computational efficiency, accuracy, and the inability to effectively utilize drone-specific knowledge, leading to issues with reconstruction accuracy and scalability.
Innovation Solution
The system employs kernel computation, data compression, and graphical rendering alongside drone-specific heuristics to optimize high-dimensional model synthesis, leveraging the absolute and relative positional understanding of drones to enhance the efficiency and accuracy of model generation, and combines traditional photogrammetry and Neural Radiance Fields (NeRF) approaches for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional photogrammetry methods are used for 3D model generation, then the process is well-established and relatively simple to implement, but computational efficiency is low and high-fidelity models require significant computational resources and time
Solution Approach 1:
The patent combines traditional photogrammetry methods with Neural Radiance Fields (NeRF) to create a hybrid approach that leverages the strengths of both techniques. The photogrammetry component provides robust geometric structure from drone imagery, while NeRF adds high-fidelity rendering capabilities, achieving both speed and accuracy simultaneously
Solution Approach 2:
The system dynamically adjusts computational parameters based on the specific reconstruction task requirements. By modifying parameters such as resolution levels, sampling densities, and processing iterations, the system can optimize between speed and accuracy depending on the application context
2Manufacturing precision
If high-resolution drone imagery is processed to create detailed 3D models, then model fidelity is improved, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent divides the 3D model generation process into distinct segments: photogrammetry-based geometry extraction, NeRF-based texture and appearance modeling, and hierarchical rendering stages. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining high fidelity
Solution Approach 2:
The system transitions from traditional 3D mesh representations to a 4D representation by incorporating temporal dimensions in NeRF, allowing high-fidelity models to be generated and rendered more efficiently through implicit neural representations that encode spatial and appearance information in a compressed manner
3Adaptability or versatility
If existing 3D modeling systems are used, then implementation is straightforward with available tools, but they cannot effectively utilize drone-specific knowledge such as absolute and relative positional understanding
Solution Approach 1:
The system automatically extracts and utilizes drone-specific metadata including absolute GPS positions, relative positions between multiple drones, orientation data, and timing information. This self-service approach eliminates the need for manual intervention to incorporate drone characteristics, seamlessly integrating them into the reconstruction process
Solution Approach 2:
The patent introduces specialized intermediary modules that act as bridges between raw drone sensor data and the 3D reconstruction algorithms. These intermediaries process drone-specific knowledge (positions, orientations, timestamps) and translate them into formats that enhance the reconstruction process without requiring changes to the core algorithms
4Area of stationary object
If multiple drones are used for large-scale imaging, then coverage and data richness are improved, but coordination complexity and processing challenges increase
Solution Approach 1:
The system employs a universal processing framework that handles data from multiple drones with different configurations, positions, and sensor types through a common pipeline. This multi-functional approach allows the same system to process diverse drone data without requiring drone-specific processing logic, reducing coordination complexity
Data Source
AI summary
A method comprising receiving a plurality of images of a scene captured by at least one drone; identifying features within the plurality of images; identifying similar images of the plurality of images based on the features identified within the plurality of images; comparing the similar images based on the features identified within the similar images to determine a proportion of features shared by the similar images; selecting a subset of the plurality of images that have a proportion of shared features that meets a predetermined range; generating a first 3D model of the scene from the subset of images using a first 3D model building algorithm; generating a second 3D model of the scene from the subset of images using a second 3D model building algorithm; computing errors for the first and second 3D models; and selecting as the model of the scene the first or second 3D model.