Learning to Importance Sample in Primary Sample Space

What is it about?

This paper shows how to use AI to make computer-generated images look better and render faster. It uses a neural network to learn how light behaves in a scene and helps the renderer make smarter guesses about where light goes. This reduces noise and improves image quality. The method works with any existing renderer and doesn't need to know how it works internally, making it flexible and easy to use for various lighting effects.

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Why is it important?

This paper proposes a neural importance sampling method towards Monte Carlo variance reduction and better rendering. This paper uses AI to improve rendering without changing the renderer itself. By learning better sampling in a flexible way, it reduces image noise and speeds up rendering—making high-quality graphics more accessible and efficient.