page_title: Path tracing
section_title: Performance
hierarchical_section_title: Path tracing / Performance
caption_attribution_description: English: Each image uses a different samples per pixel value, starting at 1 sample per pixel and doubling from left to right to 32,768 samples.
context_page_description: Path tracing is a computer graphics Monte Carlo method of rendering images of three-dimensional scenes such that the global illumination is faithful to reality. Fundamentally, the algorithm is integrating over all the illuminance arriving to a single point on the surface of an object. This illuminance is then reduced by a surface reflectance function to determine how much of it will go towards the viewpoint camera. This integration procedure is repeated for every pixel in the output image. When combined with physically accurate models of surfaces, accurate models of real light sources, and optically correct cameras, path tracing can produce still images that are indistinguishable from photographs.
  Path tracing naturally simulates many effects that have to be specifically added to other methods, such as soft shadows, depth of field, motion blur, caustics, ambient occlusion, and indirect lighting. Implementation of a renderer including these effects is correspondingly simpler. An extended version of the algorithm is realized by volumetric path tracing, which considers the light scattering of a scene.
context_section_description: A path tracer continuously samples pixels of an image. The image starts to become recognisable after only a few samples per pixel, perhaps 100. However, for the image to "converge" and reduce noise to acceptable levels usually takes around 5000 samples for most images, and many more for pathological cases. Noise is particularly a problem for animations, giving them a normally unwanted "film-grain" quality of random speckling.
  The central performance bottleneck in Path Tracing is the complex geometrical calculation of casting a ray. Importance Sampling is a technique which is motivated to cast fewer rays through the scene while still converging correctly to outgoing luminance on the surface point. This is done by casting more rays in directions in which the luminance would have been greater anyway. If the density of rays cast in certain directions matches the strength of contributions in those directions, the result is identical, but far fewer rays were actually cast. Importance Sampling is used to match ray density to Lambert's Cosine law, and also used to match BRDFs.
  Metropolis light transport can result in a lower-noise image with fewer samples. This algorithm was created in order to get faster convergence in scenes in which the light must pass through odd corridors or small holes in order to reach the part of the scene that the camera is viewing. It has also shown promise in correctly rendering pathological situations with caustics. Instead of generating random paths, new sampling paths are created as slight mutations of existing ones. In this sense, the algorithm "remembers" the successful paths from light sources to the camera.
caption: Noise decreases as the number of samples per pixel increases. The top left shows 1 sample per pixel, and doubles from left to right each square.
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