5MB Magic: How NeRF Compresses 3D Scenes into 2D Images

Neural Radiance Fields (NeRF), introduced in a 2020 paper, revolutionizes 3D scene rendering by compressing scenes into about 5MB of data. Unlike traditional methods like ray casting and ray tracing, which are computationally intensive, NeRF uses an overfitted multi-layer perceptron (MLP) to encode scene information. This MLP can generate 2D images from any viewing direction, significantly reducing the need for extensive computational resources. NeRF requires multiple images from different angles to learn and interpolate these images for novel views. The process involves casting rays through each pixel, sampling points along these rays, and using the MLP to predict color and density for each point. These predictions are then integrated to compute the final pixel color, allowing for the creation of photorealistic images from new viewpoints. The MLP’s training involves backpropagation, optimizing the system based on pixel-wise Mean Squared Error (MSE) loss.

Source: towardsdatascience.com

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