NeRF Depth Rendering for Automatic 3D Mesh Reconstruction
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Solution Overview
Problem
Existing 3D modeling methods face challenges such as high memory usage, lack of connectivity information, and high costs due to complex hardware and software requirements, making it difficult to generate high-quality meshes automatically from 2D images.
Innovation Solution
Utilizing a neural radiance field (NeRF) model trained on a set of 2D images to generate a depth field, which is then postprocessed to create high-quality 3D polygonal meshes, leveraging a fully-connected neural network to represent scenes implicitly and render images from arbitrary viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If voxel representation is used to represent 3D models, then the model can be stored in a structured grid format, but it occupies very large memory
Solution Approach 1:
The patent extracts only the essential information needed for 3D reconstruction by using neural radiance fields to represent scene geometry and appearance implicitly, rather than storing complete voxel grids. This selective extraction of critical data reduces memory requirements while preserving reconstruction quality.
Solution Approach 2:
The patent transitions from explicit 3D voxel representation to a continuous implicit representation using neural networks that map 3D spatial coordinates and viewing directions to radiance values. This dimensional transformation allows efficient memory usage by storing network parameters instead of dense voxel data.
2Quantity of substance
If point cloud representation is used to represent 3D models, then the model can be stored with fewer data points, but it lacks connectivity information
Solution Approach 1:
The patent replaces traditional geometric data structures (point clouds, meshes) with a neural radiance field system that implicitly encodes both surface geometry and connectivity through continuous density and radiance functions. This substitution eliminates the need for explicit connectivity storage while preserving topological information.
3Manufacturing precision
If manual 3D modeling methods are used, then high quality 3D models can be created, but it is time-consuming and requires a lot of effort from artists
Solution Approach 1:
The patent enables the system to automatically perform 3D reconstruction by training neural radiance fields on input images, eliminating the need for manual artist intervention. The system serves itself by learning scene representations directly from data, achieving both high quality and efficiency.
Solution Approach 2:
The patent changes the fundamental parameters of 3D modeling from manual vertex manipulation to automated neural network optimization. By adjusting network architecture, loss functions, and training parameters, the system automatically generates high-quality models without manual effort.
4Manufacturing precision
If existing photogrammetry systems are used to construct 3D models, then 3D reconstruction can be achieved, but it involves complicated hardware and software resulting in high cost
Solution Approach 1:
The patent replaces expensive, specialized photogrammetry hardware with standard cameras and computational methods. By using readily available imaging devices combined with neural radiance field algorithms, the system achieves 3D reconstruction at low cost without complex equipment.
Solution Approach 2:
The patent substitutes complex mechanical photogrammetry systems with a computational approach using neural networks. This replacement eliminates the need for specialized hardware rigs and complex software pipelines, simplifying the overall system while maintaining reconstruction quality.
Data Source
AI summary
A method of generating a three-dimensional (3D) model includes obtaining a set of two-dimensional (2D) images of a scene acquired by one or more cameras from a set of camera angles and camera positions, obtaining the set of camera angles and camera positions based on obtaining, for each 2D image in the set of 2D images, a respective camera angle and a respective camera position for the 2D image, training a neural radiance field (NeRF) model, using the set of 2D images and the set of camera angles and camera positions as a training dataset, to obtain a trained NeRF model, generating a set of 2D depth maps based on the trained NeRF model, and generating a 3D polygonal mesh representing at least one object of one or more objects in the scene based on the set of 2D depth maps.