In the ever-evolving landscape of computer graphics and artificial intelligence research, a groundbreaking technology gained attention rapidly, and now is shaping the way we perceive and interact with digital imagery.

Neural Radiance Fields (NeRFs) is a cutting-edge approach that promises to revolutionize the field of computer graphics and computer vision. In this blog post, we’ll delve into the fascinating domain of NeRF technologies, exploring principles, applications, and potential implications for various industries.

The Relevance

When you search for images of a monument in the XReco repository, the images can come from different sources, taken at different times of day or with obstacles such as people or objects in the foreground. CERTH’s research and development work in the XReco project wants to solve this problem. The team is focussing on training NeRF algorithms to overcome such challenges and achieve clear results without occlusions in the final trained NeRF. In addition, the trained NeRF can adjust its appearance based on the original lighting conditions in the source images.

Understanding NeRFs

At its core, NeRF is a way of 3D scene representation. Unlike traditional explicit geometric representations, such as voxel grids, triangle meshes, or point clouds, NeRFs leverage the power of Neural Networks to learn a continuous 3D scene representation from 2D images. So, the algorithm is trained to predict the radiance (i.e., outbound color from a surface) and the opacity of a scene at any given 3D point along a ray cast from the camera. This is enabled by an inverse rendering process. Inverse rendering means that we seek to estimate 3D scene parameters (e.g. a camera’s pose) given the 2D rendered image.

So, what do we need to train a NeRF?

The magic behind NeRF lies in its ability to reconstruct detailed 3D scenes from a sparse set of 2D images, capturing intricate lighting effects, surface textures, and object shapes with very high realism. Therefore, we need a collection of images capturing the subject scene and their viewpoint positions and orientation. How to get that you ask? Well, many of the datasets used for training NeRF algorithms are synthetic (e.g. rendered with Blender [blender2024]). When someone needs to learn NeRF representation from real data (real scenes), they can use of-the-self Structure from Motion (SfM) algorithms, such as Colmap [schoen2016sfm], which is used in most of the research works in recent research literature.

Figure 1: Example of a colmap dataset. The camera viewpoints and the estimated pointcloud for the trex scene of the nerf-llff dataset [mild2019local]

How are NeRF algorithms trained?

As already mentioned, an inverse rendering approach is utilized to train NeRF algorithms. Rays are cast from the known camera viewpoint, and points are sampled on that ray. We provide these points as input to our neural networks, and they output a color and opacity value corresponding to that point. When we do this for many points along a ray, the result is aggregated via a volumetric rendering technique [kajiya1984ray], to compute the final color for that ray. Check Figure 1 to get an intuition about what’s happening during training. 

XReco_Figure 2: Ray-casting and ray sampling

Figure 2: Ray-casting and ray sampling

Now, once we do this process for all the images in our collection, many times, the neural networks learn to output a color and opacity value, for any given point in the scene. Of course, the real procedure is more complicated, but this is the real gist of it.

Applications across industries

The applications of NeRFs span a wide range of industries, offering transformative solutions in areas such as:

Entertainment and Gaming: NeRF technology is poised to revolutionize the creation of virtual worlds, enabling game developers and filmmakers to produce immersive experiences and lifelike environments, characters, and special effects.

Architecture and design: Architects and designers can leverage NeRFs to visualize and iterate on building designs in a photorealistic manner, facilitating better communication with clients and stakeholders.

E-commerce and retail: Online retailers can use NeRF-based rendering to offer interactive product experiences, allowing customers to view items from all angles and under various lighting conditions before making a purchase.

Healthcare and education: NeRFs hold potential in medical imaging and educational simulations, enabling the creation of detailed anatomical models and virtual training environments for medical professionals.

Challenges

Despite the promise, NeRFs still face several challenges, and will be a long way until such technologies are standardized in the industry. These include scalability issues, training complexity, and time, and limitations in handling dynamic scenes. However, ongoing research efforts are focused on addressing these challenges and extending the capabilities of NeRF technology.

Looking ahead, the future of NeRFs appears bright, with exciting opportunities for innovation and cross-disciplinary collaboration. As researchers continue to push the boundaries of what’s possible with NeRFs, we can expect to see even more remarkable applications to emerge, further blurring the lines between the virtual and real worlds.

In conclusion, NeRFs represent a paradigm shift in digital imaging, offering a glimpse into a future where virtual experiences rival reality in terms of fidelity and immersion. NeRF technology is poised to leave an indelible mark on countless industries, paving the way for a new era of visual storytelling and exploration.

Summary

Neural Radiance Fields (NeRFs) are a revolutionary technology in computer graphics, using neural networks to generate realistic 3D scenes from 2D images. Unlike traditional 3D representations, NeRFs predict the color and opacity of points in a scene, enabling highly detailed and lifelike imagery through a process called inverse rendering.

Training a NeRF requires a set of images from different viewpoints, often generated synthetically with tools like Blender or through real-world methods like Structure from Motion (SfM). NeRFs have transformative applications across industries: they enhance immersive virtual environments in gaming and entertainment, improve architectural visualization, enable interactive product views in e-commerce, and support detailed simulations in healthcare.

While challenges remain, such as scalability and training complexity, ongoing research is advancing NeRF technology, making it a promising tool for creating realistic digital experiences that closely mimic reality.

CERTH’s work in XReco

In XReco, CERTH is advancing research on NeRF training for real-world in-the-wild scenarios.

Authors: Antonis Karakottas (ankarako@iti.gr), Research Associate, Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Visual Computing Lab (VCL)

 Sources:

[mild2021nerf] Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2021). Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1), 99-106.

[mild2019local] Mildenhall, B., Srinivasan, P. P., Ortiz-Cayon, R., Kalantari, N. K., Ramamoorthi, R., Ng, R., & Kar, A. (2019). Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (ToG), 38(4), 1-14.

[blender2024] https://www.blender.org/

[scoen2016sfm] Schonberger, J. L., & Frahm, J. M. (2016). Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4104-4113).

[kajiya1984ray] Kajiya, J. T., & Von Herzen, B. P. (1984). Ray tracing volume densities. ACM SIGGRAPH computer graphics, 18(3), 165-174.

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