The Power of Reading Books and the Advancements in Neural Radiance Field (NeRF) Technology


Hatched by Glasp

Jul 11, 2023

5 min read


The Power of Reading Books and the Advancements in Neural Radiance Field (NeRF) Technology


In today's fast-paced world, where information is readily available at our fingertips, it may seem that people no longer have the patience or inclination to read. However, the truth is that reading books remains one of the easiest ways to find good ideas to write about. In fact, many writers derive 90% of their ideas from books, while their original thought makes up the remaining 10%. This highlights the importance of reading books sequentially and the advantage it provides in understanding the rarity of making significant leaps in knowledge. Moreover, reading old books can offer a unique perspective and give us a significant advantage in today's world. Let's delve into why reading books matters and how it can enhance our intellectual growth.

The Power of Reading Books:

Books are a treasure trove of knowledge, experiences, and ideas passed down through generations. They are a testament to the fact that much of our understanding of the world comes from the collective experiences of others. By immersing ourselves in books, we gain access to a vast array of perspectives and insights that can enrich our own thinking. Books allow us to explore different cultures, eras, and disciplines, broadening our horizons and fostering empathy. They serve as a bridge between generations, enabling us to learn from the wisdom of those who came before us. Therefore, reading books is not only a means of acquiring knowledge but also a way of preserving and passing on the wisdom of humanity.

The Advancements in Neural Radiance Field (NeRF) Technology:

In recent years, there have been significant advancements in the field of computer graphics and rendering techniques. One such breakthrough is the development of Neural Radiance Field (NeRF), a fully-connected neural network that can generate novel views of complex 3D scenes based on a partial set of 2D images. NeRF networks are trained to map viewing direction and spatial location to opacity and color, using volume rendering to render new views.

Rendering refers to the process of creating an image from a 3D model, incorporating features such as textures, shading, shadows, lighting, and viewpoints to create a realistic image. There are various rendering algorithms, including rasterization, ray casting, and ray tracing, each with its own advantages and limitations. NeRF falls under the category of ray casting, utilizing Monte Carlo techniques to achieve realistic images efficiently.

NeRF's ability to synthesize new views of a scene from a sparse set of input views has revolutionized the field of view synthesis. This technique involves creating a 3D view from a series of 2D images, enabling us to explore and visualize complex scenes from different angles. NeRF's original model, however, had limitations in terms of training and rendering speed and its ability to handle dynamic scenes. Researchers have since developed various extensions and frameworks to overcome these challenges.

RegNeRF, or regularizing neural radiance fields, addresses the problem of low input views' performance. It optimizes the continuous volumetric scene function by using a sparse set of input views, resulting in the ability to produce novel views of complex scenes. This regularization technique enhances the quality and fidelity of the rendered images.

PixelNeRF introduces a learning framework that predicts a continuous neural scene representation based on one or several input images. It overcomes the computational challenges of constructing NeRFs by using a fully convolutional approach to condition the NeRF on image inputs. This significantly reduces the compute time required for training and rendering NeRF models.

Mega-NeRF tackles the challenge of building interactive 3D environments from large-scale visual captures, such as city blocks and buildings. This framework analyzes visibility statistics for large-scale scenes and employs a sparse network structure with specialized parameters for different scene regions. Mega-NeRF enables the creation of immersive and realistic virtual environments.

LOLNeRF, or Learn from One Look, focuses on generative 3D modeling based on primarily single views of each object. By training only from data with limited viewpoints, LOLNeRF produces the corresponding 3D structure of objects, allowing them to be rendered from different perspectives. This learning method expands the possibilities of NeRF technology and its applications in various fields.

NSVF, or neural scene representation with implicit fields, enables fast, high-quality rendering that is viewpoint-independent. It achieves this by defining voxel-bounded implicit fields organized in a sparse network of cells. The progressive learning process allows for efficient voxel structure learning, resulting in realistic and visually appealing renderings.

Mip-NeRF is an extension of the original NeRF model that addresses blurring effects and visual artifacts in the rendered images. By using a conical frustum instead of a single ray per pixel, Mip-NeRF reduces aliasing, enables the depiction of fine details, and significantly reduces error rates. This improvement enhances the overall quality of NeRF renderings.

KiloNeRF tackles the issue of slow rendering in NeRF by distributing the workload among thousands of small MLPs (multilayer perceptrons) instead of querying a single large MLP multiple times. This parallelization technique improves rendering speed and efficiency, making NeRF more practical for real-time applications.


The power of reading books lies in its ability to provide a wealth of knowledge and ideas. By immersing ourselves in books, we tap into the collective wisdom of humanity and gain a broader perspective on the world. Reading books sequentially and exploring older works can offer unique insights and sharpen our critical thinking skills.

Simultaneously, the advancements in Neural Radiance Field (NeRF) technology have revolutionized the field of computer graphics and rendering. NeRF's ability to generate novel views of complex 3D scenes has opened up new possibilities in virtual reality, gaming, and architectural design. The various extensions and frameworks developed for NeRF have addressed its limitations and enhanced its performance, making it a powerful tool for visualizing and exploring virtual environments.

In conclusion, here are three actionable pieces of advice:

  • 1. Cultivate the habit of reading books regularly to broaden your knowledge, gain new perspectives, and enhance your writing skills.
  • 2. Stay updated with the advancements in NeRF technology and explore its applications in various fields such as virtual reality, gaming, and architectural design.
  • 3. Experiment with NeRF frameworks and extensions to enhance your 3D modeling and rendering capabilities, and explore the possibilities of creating immersive and realistic virtual environments.

By combining the power of reading books and leveraging the advancements in NeRF technology, we can expand our intellectual horizons and unlock new creative possibilities. Let us embrace these opportunities and continue to learn, grow, and innovate.

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