Neural Fields in Visual Computing and Beyond

Abstract: Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time. These methods, which we call neural fields, have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation. However, due to rapid progress in a short time, many papers exist but a comprehensive review and formulation of the problem has not yet emerged. In this report, we address this limitation by providing context, mathematical grounding, and an extensive review of literature on neural fields. This report covers research along two dimensions. In Part I, we focus on techniques in neural fields by identifying common components of neural field methods, including different representations, architectures, forward mapping, and generalization methods. In Part II, we focus on applications of neural fields to different problems in visual computing, and beyond (e.g., robotics, audio). Our review shows the breadth of topics already covered in visual computing, both historically and in current incarnations, demonstrating the improved quality, flexibility, and capability brought by neural fields methods. Finally, we present a companion website that contributes a living version of this review that can be continually updated by the community.

• The paper presents a comprehensive survey of neural fields, outlining key techniques like prior learning, hybrid representations, and differentiable rendering.
• It demonstrates the effective application of neural fields in 3D reconstruction, dynamic scene modeling, and generative tasks across visual computing and related domains.
• Future directions include enhancing generalization through robust priors and standardized benchmarking for diverse real-world applications.

Neural Fields in Visual Computing and Beyond: An Expert Overview

The paper "Neural Fields in Visual Computing and Beyond" offers a comprehensive survey of techniques and applications of neural fields, a class of methods employing coordinate-based neural networks. These methods parameterize physical properties of scenes or objects across space and time and have shown effectiveness in various visual computing tasks, such as 3D shape reconstruction and generative modeling.

Techniques in Neural Fields

The authors classify techniques into five core areas:

1. Prior Learning and Conditioning: This involves learning priors from data to address under-constrained problems, using methods like global and local conditioning with latent variables to influence neural field dynamics.
2. Hybrid Representations: Combining neural fields with discrete data structures can enhance memory efficiency and computation. These hybrid approaches utilize data structures like voxel grids and meshes to decompose the input space.
3. Forward Maps: These operators map reconstructed domains to sensor domains and are essential in solving inverse problems. Neural fields often leverage differentiable renderers for tasks like 3D reconstruction from 2D images.
4. Network Architecture: Proper architectural choices can address inherent biases, such as spectral biases, in neural fields. Techniques like positional encoding and periodic activation functions are used to capture high-frequency details.
5. Manipulating Neural Fields: This includes editing through coordinate remapping or direct modifications of neural network parameters. These methods allow alteration of shape and appearance in applications such as animation and editing.

Applications in Visual Computing

Neural fields find diverse applications across visual computing:

• 3D Scene Reconstruction: By utilizing differentiable rendering, neural fields enable reconstruction from 2D observations, significantly enhancing accessibility and potential applications in fields like autonomous navigation and virtual reality.
• Material and Lighting: They facilitate the reconstruction of complex interactions of materials with light, allowing realistic relighting and rendering.
• Dynamic Scenes and Digital Humans: Modeling time-varying scenes with neural fields supports dynamic scene reconstruction, capturing transitions and motion.
• Generative Modeling: Neural fields represent arbitrary scenes and objects, providing a continuous domain for generative tasks, with significant advances in image and 3D content generation.
• 2D Image Processing: The resolution independence of neural fields is leveraged for tasks in super-resolution, inpainting, and other image transformations.

Broader Impact and Beyond Visual Computing

The exploration of neural fields extends beyond conventional domains, with significant impact observed in:

• Robotics: Applications include camera pose estimation for localization and path planning, demonstrating the integration of neural fields for enhanced perception and control.
• Medical Imaging: Techniques for limited-angle tomography and MRI reconstruction utilize neural fields to better solve ill-posed problems, improving medical diagnostics.
• Physics-informed Problems: Neural fields parameterize solutions constrained by physical laws, addressing PDEs in computational physics and offering efficient approximations for complex phenomena.

Discussion

The paper highlights several promising directions for future research, such as improving the generalization of neural fields through stronger priors and multi-modal integration. Additionally, efforts should focus on benchmarking and method comparison to establish standardized evaluations within this rapidly evolving field.

Conclusion

This comprehensive survey illustrates the potential and versatility of neural fields across various domains, suggesting a broad scope for future applications. The paper serves as a pivotal resource for researchers seeking in-depth understanding and advancement within this burgeoning area of study.