Sampling for View Synthesis: From Local Light Field Fusion to Neural Radiance Fields and Beyond (2408.04586v1)

Abstract: Capturing and rendering novel views of complex real-world scenes is a long-standing problem in computer graphics and vision, with applications in augmented and virtual reality, immersive experiences and 3D photography. The advent of deep learning has enabled revolutionary advances in this area, classically known as image-based rendering. However, previous approaches require intractably dense view sampling or provide little or no guidance for how users should sample views of a scene to reliably render high-quality novel views. Local light field fusion proposes an algorithm for practical view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image scene representation, then renders novel views by blending adjacent local light fields. Crucially, we extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. We achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. Subsequent developments have led to new scene representations for deep learning with view synthesis, notably neural radiance fields, but the problem of sparse view synthesis from a small number of images has only grown in importance. We reprise some of the recent results on sparse and even single image view synthesis, while posing the question of whether prescriptive sampling guidelines are feasible for the new generation of image-based rendering algorithms.

• The paper introduces a Local Light Field Fusion algorithm that achieves Nyquist perceptual quality using MPI representations with up to 4096x fewer views.
• It extends plenoptic sampling theory to include occlusion handling, merging theoretical insights with deep learning for reliable view synthesis.
• The work bridges sparse sampling techniques and NeRF advancements, laying foundations for immersive AR, VR, and 3D photography applications.

Sampling for View Synthesis: From Local Light Field Fusion to Neural Radiance Fields and Beyond

The paper "Sampling for View Synthesis: From Local Light Field Fusion to Neural Radiance Fields and Beyond" authored by Ravi Ramamoorthi et al., explores novel methodologies for view synthesis within image-based rendering (IBR). The primary aim is to capture and render novel views of real-world scenes while reducing the traditionally intractable number of required views. This is crucial for applications in augmented and virtual reality, immersive experiences, and 3D photography.

Local Light Field Fusion (LLFF)

The core contribution of LLFF is an algorithm that facilitates practical view synthesis from an irregular grid of sampled views. Each sampled view is expanded into a local light field using a multiplane image (MPI) scene representation. Subsequently, novel views are synthesized by blending adjacent local light fields.

Significant Contributions:

• Extended Plenoptic Sampling Theory: The paper extends traditional plenoptic sampling theory to provide prescriptive guidelines for view sampling, enabling high-quality rendering with significantly fewer views.
• Reduction in View Sampling Rate: By exploiting advanced light field sampling analysis and MPI representation, the method achieves Nyquist rate perceptual quality using up to 4000x fewer views. For a scene with a minimum depth of 0.5 meters, this translates to reducing the required views from 2.5 million per square meter to just approximately 20-50 views.
• Occlusion Handling: The theory is further extended to accurately account for occlusions. This involves considering the frequency spectrum as being convoluted with the occluder spectrum, effectively transforming the sampling requirements.

Practical Algorithm

LLFF relies on developing a simple convolutional neural network for constructing the MPI from input views. Despite the use of deep learning, the method distinguishes itself by providing theoretical guarantees, a rarity in the context of deep learning-based approaches.

Key components of the algorithm include:

• MPI Construction: Each view is transformed into an MPI using a neural network, marginalizing around five views input.
• Blending Local Light Fields: A generalization of blending local light fields reconstructs a comprehensive light field over varied trajectories.

Validation and Practical Application

The efficacy of the LLFF algorithm is validated through rigorous tests, demonstrating the ability to render novel views at Nyquist level perceptual quality with a substantial reduction in required views. Practical use cases are also discussed, including the implementation of an iOS smartphone application that leverages ARKit for capturing requisite views, thereby making this technology accessible and user-friendly.

Numerical Results:

LLFF achieves Nyquist level perceptual quality with a disparity of up to 64 pixels between input views by predicting a 64-layer MPI, effectively reducing the required number of views by a factor of 4096x.

Implications and Future Developments

LLFF's theoretical contributions provide a benchmark for setting sampling guidelines in view synthesis. This bridges the gap between practical capture methods using sparse image sets and the boundary conditions set by the Nyquist rate.

Neural Radiance Fields (NeRF) and Beyond

Following LLFF, further advancements led to Neural Radiance Fields (NeRF), which employs continuous volumetric scene representations. This method captures finer details and achieves even higher visual fidelity than MPIs. Further innovations such as Instant Neural Graphics Primitives and Gaussian-splatted Radiance Fields leverage hybrid grid-MLP methods and continuous volume representations, marking notable strides in view synthesis.

However, it is pertinent to note that despite these advancements, current NeRF-based methods do not offer the same theoretical sampling guarantees as LLFF. Establishing rigorous sampling-error curves and providing guarantees for these new methods remains an open challenge.

Conclusion

The paper on Local Light Field Fusion represents a critical advancement in the domain of view synthesis by providing precise sampling guidelines and significantly reducing the requisite sampling rate while ensuring high-fidelity renders. The work sets a foundational precedent for subsequent developments, including NeRF, which push the boundaries of view synthesis further into the field of practical and commercial use.

Future endeavors must aim to bridge the gap between practical application and theoretical foundations, ensuring that advancements in view synthesis not only achieve high visual fidelity but also come with predictable and reliable sampling guidelines.