Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields (2103.13415v3)

Abstract: The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call "mip-NeRF" (a la "mipmap"), extends NeRF to represent the scene at a continuously-valued scale. By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRF's ability to represent fine details, while also being 7% faster than NeRF and half the size. Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.

• The paper introduces conical frustum sampling to address aliasing by accurately capturing pixel areas.
• It leverages integrated positional encoding to incorporate scale into feature representations, effectively mitigating high-frequency artifacts.
• The unified MLP design reduces parameters and increases speed, achieving up to 60% error reduction and 22x faster performance compared to conventional NeRF.

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

The paper “Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields” introduces a method aimed at addressing the aliasing issues inherent in Neural Radiance Fields (NeRF). Traditional NeRF, while effective at generating photorealistic novel views of 3D scenes, often struggles with aliasing and blurring artifacts, especially when dealing with scenes observed at varying resolutions. The proposed solution, mip-NeRF, mitigates these issues through a multiscale representation that leverages elements of computer graphics prefiltering techniques, effectively rendering anti-aliased images more efficiently than supersampling approaches.

Key Innovations

Mip-NeRF extends NeRF by incorporating the following innovations:

• Conical Frustum Sampling: Unlike NeRF’s ray-based sampling approach which uses a single ray per pixel, mip-NeRF employs conical frustums. This method accounts for the pixel area more comprehensively, reducing aliasing and allowing mip-NeRF to better capture scene details.
• Integrated Positional Encoding (IPE): To represent the conical frustums, mip-NeRF introduces IPE, a generalization of NeRF’s positional encoding. IPE averages the encoding over the conical frustum, effectively incorporating scale into the feature representation and reducing high-frequency aliasing artifacts. This technique means that larger Gaussians result in more attenuated frequencies, thus mitigating aliasing.
• Unified Multiscale Model: Instead of NeRF’s bifurcated approach using separate coarse and fine models, mip-NeRF employs a single MLP capable of handling multiple scales. This unification not only simplifies the training process but also improves efficiency, reducing the number of network parameters by half while also being slightly faster.

Numerical Results

The numerical results presented in the paper strongly support the efficacy of mip-NeRF:

• Performance on Multiscale Blender Dataset: Mip-NeRF reduces average error rates by 60% compared to NeRF when tested on the newly proposed multiscale Blender dataset, showing significant improvements in rendering images from scenes observed at multiple resolutions.
• Performance on Single-Scale Blender Dataset: Even on the original single-scale Blender dataset, which is less challenged by aliasing issues, mip-NeRF still shows a 17% reduction in average error rates over NeRF.
• Efficiency: While achieving these notable improvements in rendering quality, mip-NeRF is approximately 7% faster than NeRF and requires significantly fewer parameters. Additionally, mip-NeRF matches the accuracy of a brute-force supersampled NeRF, yet achieves this with 22 times greater speed efficiency.

Implications and Future Directions

The improvements introduced by mip-NeRF have profound implications for future research and applications in neural rendering:

• Practical Applications: The ability to render high-quality, anti-aliased images at multiple scales extends the usability of neural rendering models in real-world applications where camera placement and focal lengths vary dynamically. This can be particularly beneficial in areas such as virtual reality, 3D reconstruction, and digital content creation.
• Real-Time Rendering: The efficiency gains suggest potential for real-time rendering applications, which could revolutionize interactive graphics and gaming industries by enabling photorealistic graphics without overly burdensome computational costs.
• Further Research: Future work could explore extending mip-NeRF’s approach to other neural rendering technologies. There is also potential to investigate more sophisticated sampling strategies or different methods for integrating position and scale in neural representations. Moreover, the applicability of mip-NeRF to dynamic and non-static scenes remains an open area for exploration.

Concluding Remarks

Mip-NeRF represents a significant advancement in neural rendering by addressing NeRF’s limitations related to aliasing through a combination of prefiltering strategies and multiscale representations. The proposed method not only enhances rendering quality but does so more efficiently, which could have broad-reaching impacts on various fields reliant on 3D rendering and view synthesis. This work underscores the importance of considering geometric and sampling issues in neural graphics and sets a promising direction for future research in the domain.