Uncertainty-aware NeRF Fusion for Large-scale 3D Reconstruction
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Solution Overview
Problem
Neural Radiance Field (NeRF) models are limited to small-scale scenes due to the scale-resolution trade-off caused by the limited capacity of a single multilayer perceptron (MLP), restricting their ability to handle large-scale 3D scene reconstruction and novel view synthesis effectively.
Innovation Solution
The method involves grouping images into clusters, training separate NeRFs for each cluster, and using an uncertainty multilayer perceptron to fuse these NeRFs, allowing for high-fidelity novel view synthesis and 3D reconstruction in large-scale scenes by regressing a 6D continuous uncertainty field and applying a rendering loss and entropy loss for improved stitching and geometry estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If a single NeRF model is used to represent the entire scene, then the model capacity is limited, but the scene coverage area is restricted to small-scale only
Solution Approach 1:
The patent divides the large-scale scene into multiple image clusters, each representing a local region. Separate NeRF models are trained for each cluster, allowing the system to cover large-scale scenes by composing multiple local models. This segmentation approach resolves the contradiction by enabling extended scene coverage without requiring a single overly complex model.
Solution Approach 2:
The patent merges multiple local NeRF models into a unified fused NeRF that represents the entire large-scale scene. The fusion process combines the strengths of individual local models while maintaining computational efficiency, thus achieving large scene coverage without proportionally increasing overall model complexity.
2Volume of stationary object
If the NeRF model capacity is increased to handle large-scale scenes, then the scene coverage area expands, but the training time and computational resources increase significantly
Solution Approach 1:
By segmenting the scene into multiple smaller image clusters, each NeRF model trains on a smaller subset of data independently and in parallel. This reduces the training time for each individual model compared to training a single comprehensive model on all data sequentially.
Solution Approach 2:
The patent performs preliminary clustering of images into groups before training the NeRF models. This preliminary organization allows for efficient parallel training of multiple smaller models, reducing overall training time compared to training one large model from scratch.
3Volume of stationary object
If multiple separate NeRF models are trained for different scene regions, then the scene coverage area increases, but the stitching quality between regions deteriorates
Solution Approach 1:
The patent introduces an uncertainty field as an intermediary mechanism between multiple local NeRF models. This uncertainty field guides the fusion process by determining the reliability of each local model's predictions, enabling seamless stitching of regional models into a coherent large-scale scene representation with high quality transitions.
Solution Approach 2:
The patent uses uncertainty values as dynamic parameters to control the fusion of multiple NeRF models. By adjusting the weighting of different local models based on their uncertainty metrics, the system achieves high-quality stitching that adapts to the reliability of each region's data.
4Measurement precision
If a single NeRF model is used, then the training process is simple, but the resolution and detail quality must be reduced for large-scale scenes
Solution Approach 1:
By dividing the scene into multiple local clusters, each NeRF model can be trained to achieve high rendering resolution for its specific region without being diluted by the need to cover the entire large scene. This segmentation allows each model to maintain high detail quality while the collection of models covers the full scene.
Solution Approach 2:
The patent applies local quality by training each NeRF model to achieve high-fidelity rendering for its specific local region. Each model optimizes for the details and characteristics of its assigned cluster, ensuring high resolution and quality where needed rather than compromising overall resolution for scene scale.
Data Source
AI summary
A method for achieving high-fidelity novel view synthesis and 3D reconstruction for large-scale scenes is presented. The method includes obtaining images from a video stream received from a plurality of video image capturing devices, grouping the images into different image clusters representing a large-scale 3D scene, training a neural radiance field (NeRF) and an uncertainty multilayer perceptron (MLP) for each of the image clusters to generate a plurality of NeRFs and a plurality of uncertainty MLPs for the large-scale 3D scene, applying a rendering loss and an entropy loss to the plurality of NeRFs, performing uncertainty-based fusion to the plurality of NeRFs to define a fused NeRF, and jointly fine-tuning the plurality of NeRFs and the plurality of uncertainty MLPs, and during inference, applying the fused NeRF for novel view synthesis of the large-scale 3D scene.