NeRF Regularization With Diffusion Priors for Sparse-View 3D Scenes
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Solution Overview
Problem
Neural radiance fields (NeRFs) struggle with under-constrained scene geometry and color fields, leading to artifacts when trained with few input views, especially during novel view synthesis tasks.
Innovation Solution
Utilize a denoising diffusion model to learn a prior over scene geometry and color, using RGBD patches from a synthetic dataset to regularize NeRF training by backpropagating log gradients during the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If NeRFs are trained with few input views, then training efficiency is improved, but geometry reconstruction quality deteriorates due to under-constrained scene fields
Solution Approach 1:
The denoising diffusion prior is pre-trained on a large dataset of synthetic RGBD images before being applied to the actual scene reconstruction. This preliminary training enables the prior to capture general geometric and color patterns, which then guides the NeRF training process when only few input views are available, resolving the contradiction between training efficiency and geometry quality
Solution Approach 2:
The denoising diffusion prior acts as an intermediary that bridges the gap between limited input views and complete scene understanding. By introducing this learned prior as an additional constraint during NeRF training, the system can reconstruct high-quality geometry even with few input views, as the prior provides missing geometric information that would otherwise require many more training views
2Manufacturing precision
If NeRFs are trained with more input views, then geometry reconstruction quality is improved, but training time increases
Solution Approach 1:
The denoising diffusion prior provides continuous feedback during NeRF training by regularizing the color and density fields. This feedback mechanism guides the optimization process toward solutions that are consistent with learned geometric priors, achieving high-quality reconstruction faster than traditional methods that would require processing many more input views
3Device complexity
If scene geometry and color fields are under-constrained, then training complexity is reduced, but artifacts appear in novel view synthesis
Solution Approach 1:
The denoising diffusion prior changes the parameter space by introducing learned constraints on color and density fields. These parameter constraints prevent the optimization from converging to artifact-prone solutions, maintaining training simplicity while significantly improving novel view synthesis reliability through the regularizing effect of the prior
Data Source
AI summary
Methods and systems for creating accurate three-dimensional representations of environments using neural radiance fields regularized by denoising diffusion models are disclosed. Receiving a plurality of images representing an environment, a scene representation model is trained to create the three-dimensional model of the environment. Using these images, the virtual rays are sampled from training viewpoints within the environment. The scene representation model is then applied to these rays to generate simulated images of the environment from the training viewpoints. These simulated images undergo a regularization process that uses a denoising diffusion model to determine color gradients and depth gradients in each simulated image. The scene representation model is trained with this data to create the final three-dimensional model of the environment. This model is provided to the requesting client device to generate the three-dimensional representation and create a virtual object within the environment.