Depth-guided NeRF Training via Earth Mover's Distance: Improving Neural Radiance Fields with Uncertainty

Conceitos Básicos

Leveraging uncertainty in depth priors through Earth Mover's Distance improves NeRF training.

Resumo

The content discusses the challenges of using photometric loss alone in Neural Radiance Fields (NeRF) training and proposes a novel approach to incorporating depth supervision. The authors introduce a method that utilizes off-the-shelf pre-trained diffusion models to predict depth and capture uncertainty during the denoising process. By supervising the ray termination distance distribution with Earth Mover's Distance, they outperform baselines on standard depth metrics while maintaining performance on photometric measures. The article includes an introduction, related work, method overview, experimental setup, results, conclusions, limitations, and future work.

Structure:

1. Introduction to NeRFs and challenges.
2. Previous work on depth supervision in NeRFs.
3. Proposed method using Earth Mover's Distance for supervision.
4. Experimental setup and evaluation on ScanNet scenes.
5. Results showing improved geometric understanding with maintained photometric quality.
6. Conclusions and future directions.

Estatísticas

"Our method reduces all depth metrics of all baselines by at least 11%."
"Our model reduces the error of DäRF by up to 54% on relative error metric."
"Our method outperforms DDPrior which has in-domain pretrained depth maps."

Citações

"Depth priors should be a suggestion."
"We propose a new way to think about uncertainty in depth supervised NeRF."

Principais Insights Extraídos De

Depth-guided NeRF Training via Earth Mover's Distance

by Anita Rau,Jo... às arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13206.pdf

Perguntas Mais Profundas

How can the proposed method impact other applications beyond view synthesis

The proposed method's impact extends beyond view synthesis to various applications in computer vision and robotics. By incorporating uncertainty into depth-guided NeRF training, the model can better understand the underlying scene geometry, leading to more accurate 3D reconstructions. This improved geometric understanding can benefit tasks like object recognition, scene segmentation, and robotic navigation. For instance, in robotics, a robot equipped with a neural radiance field trained using this method could navigate complex indoor environments more effectively by accurately perceiving obstacles and objects in its surroundings.

What are potential drawbacks of relying solely on off-the-shelf pre-trained models for depth prediction

Relying solely on off-the-shelf pre-trained models for depth prediction has some potential drawbacks. One drawback is that these models may not be optimized for specific use cases or datasets, leading to suboptimal performance when applied out-of-the-box. Additionally, pre-trained models may have biases or limitations based on the data they were trained on, which could affect their accuracy and generalization capabilities across different scenarios. Moreover, using off-the-shelf models may limit flexibility in adapting the depth estimation process to suit specific requirements or constraints of a particular application.

How might incorporating uncertainty into other neural network training processes improve their performance

Incorporating uncertainty into other neural network training processes can enhance their performance in several ways. Firstly, by considering uncertainty estimates during training, models can make more informed decisions about how much weight to assign to different sources of information or features. This adaptive weighting based on uncertainty levels can lead to more robust and reliable predictions under varying conditions or input scenarios.
Secondly,
by capturing uncertainties inherent in data inputs or model outputs,
neural networks can learn when to trust certain information
more than others
and adjust their predictions accordingly.
This ability
to quantify confidence levels allows models
to provide probabilistic outputs rather than deterministic ones,
enabling them
to convey the level of certainty associated with each prediction.
Overall,
incorporating uncertainty into neural network training processes helps improve model reliability,
robustness,
and adaptability across diverse tasks and domains.