Neural Radiance Field (NeRF): A Gentle Introduction to Cutting-Edge 3D Scene Generation and View Synthesis



Aug 22, 20235 min read


Neural Radiance Field (NeRF): A Gentle Introduction to Cutting-Edge 3D Scene Generation and View Synthesis

In recent years, there have been significant advancements in the field of 3D scene generation and view synthesis. One such breakthrough is the development of Neural Radiance Field (NeRF), a fully-connected neural network capable of generating novel views of complex 3D scenes based on a partial set of 2D images. In this article, we will explore the concept of NeRF, its applications, and its various extensions and improvements.

To understand NeRF, it is essential to grasp the fundamental concept of rendering. Rendering is the process of creating an image from a 3D model, incorporating features such as textures, shading, shadows, lighting, and viewpoints. Traditionally, rendering engines utilize algorithms such as rasterization, ray casting, or ray tracing to achieve realistic images. However, these methods have their limitations and may not be suitable for generating novel views of a scene.

This is where NeRF comes into play. Unlike traditional rendering algorithms, NeRF utilizes a neural network to map the viewing direction and spatial location (5D input) to opacity and color (4D output). By training the NeRF network using volume rendering techniques, it becomes capable of rendering new views of the scene. The result is a powerful tool that can generate realistic and novel perspectives of complex 3D scenes.

The original NeRF model did have its drawbacks. It was slow to train and render, limited to handling static scenes, and lacked flexibility as a model trained on one scene couldn't be used for other scenes. However, researchers have developed several extensions and improvements to overcome these limitations.

One such extension is RegNeRF, which stands for regularizing neural radiance fields. It addresses the performance issue of NeRF when the number of input views is low. By introducing regularization techniques, RegNeRF improves the quality of view synthesis even with sparse input views.

Another exciting development is the pixelNeRF learning framework. This framework allows the prediction of a continuous neural scene representation based on one or several input images. Unlike traditional NeRF, which requires optimizing the representation of each scene independently, pixelNeRF uses a fully convolutional approach to condition the NeRF on image inputs. This approach significantly reduces the computational burden and enables faster training and rendering.

Mega-NeRF takes NeRF to a new level by building interactive 3D environments from large-scale visual captures, such as buildings and city blocks. By analyzing visibility statistics for these scenes, Mega-NeRF optimizes the network structure to handle the complexity of large-scale scenes efficiently. This specialization of parameters to different scene regions allows for faster rendering and improved performance.

LOLNeRF, which stands for Learn from One Look, is another noteworthy extension. This learning method focuses on generative 3D modeling and trains primarily from data with single views of each object. LOLNeRF enables the production of corresponding 3D structures of objects, which can then be rendered from different viewpoints. This approach has exciting implications for various applications, such as virtual reality and augmented reality.

While NeRF and its extensions have revolutionized the field of 3D scene generation and view synthesis, other approaches have also emerged. One such approach is NSVF, a neural scene representation that enables fast, high-quality rendering independent of a specific viewpoint. NSVF utilizes voxel-bounded implicit fields organized in a sparse network of cells to progressively learn voxel structures. This results in efficient rendering and high-quality outputs.

To address blurring effects and visual artifacts in NeRF, researchers have developed Mip-NeRF. This extension introduces a conical frustum to render each pixel instead of a traditional ray. By doing so, aliasing is significantly reduced, fine details are preserved, and error rates are reduced by a considerable margin. Mip-NeRF offers a more refined and visually pleasing rendering experience.

KiloNeRF tackles the issue of slow rendering in NeRF by distributing the workload among thousands of small MLPs (Multi-Layer Perceptrons) instead of querying one large MLP repeatedly. By separating the workload, KiloNeRF reduces the computational burden and allows for faster rendering without sacrificing quality.

Finally, Plenoptic voxels (Plenoxels) provide an alternative approach to NeRF. Plenoxels replace the MLP in the center of NeRF with a sparse 3D grid. Instead of running a neural network for each query point, new 2D views can be rendered by interpolating from the surrounding voxels. This approach significantly reduces complexity and computational requirements.

In conclusion, Neural Radiance Field (NeRF) and its various extensions and improvements have revolutionized the field of 3D scene generation and view synthesis. These advancements enable the generation of novel and realistic views of complex scenes, opening up exciting possibilities in fields such as virtual reality, augmented reality, and computer graphics. While the original NeRF model had its limitations, researchers have developed RegNeRF, pixelNeRF, Mega-NeRF, LOLNeRF, NSVF, Mip-NeRF, KiloNeRF, and Plenoxels to overcome these challenges. By combining these advancements and exploring their unique features, we can continue pushing the boundaries of 3D scene generation and view synthesis.

Actionable Advice:

  • 1. Experiment with NeRF and its extensions: Dive into the world of NeRF and its various extensions to gain hands-on experience. By experimenting with these models, you can explore their capabilities and discover new possibilities for your projects.
  • 2. Optimize the input views: To achieve the best results with NeRF, ensure that your input views are carefully selected and provide sufficient coverage of the scene. Adding more views can help improve the quality of view synthesis and generate more accurate and realistic outputs.
  • 3. Stay updated with the latest research: The field of 3D scene generation and view synthesis is constantly evolving. Keep up with the latest research and advancements in this area to stay at the forefront of innovation. By staying updated, you can leverage the newest techniques and approaches to enhance your own work.

In summary, Neural Radiance Field (NeRF) and its extensions have transformed the field of 3D scene generation and view synthesis. Through advancements like RegNeRF, pixelNeRF, Mega-NeRF, LOLNeRF, NSVF, Mip-NeRF, KiloNeRF, and Plenoxels, researchers have addressed the limitations of the original NeRF model and opened up new possibilities for generating realistic and novel views of complex scenes. By exploring and experimenting with these advancements, staying updated with the latest research, and optimizing your input views, you can harness the power of NeRF and its extensions to create stunning 3D visualizations and immersive experiences.


  1. "Neural Radiance Field (NeRF): A Gentle Introduction", (Glasp)
  2. "Is it Good Design? Well, yeah. - Library - Google Design", (Glasp)

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