What are the best tools and techniques for rendering volumetric effects?

1 Ray marching

Ray marching is a technique that samples a volume along a ray, starting from the camera and moving towards the scene. At each sample point, the ray accumulates the color and opacity of the volume, as well as the lighting and shading effects . Ray marching can produce realistic and detailed volumetric effects, but it can also be very slow and noisy, especially for high-resolution volumes or complex lighting. To speed up ray marching, you can use adaptive sampling, which adjusts the sample rate based on the local variation of the volume, or precomputed lookup tables, which store the results of ray marching for a fixed set of directions and distances.

2 Voxel-based methods

Voxel-based methods are techniques that store the volume data in a 3D grid of voxels, which are small cubic cells that have a color and opacity value. Voxel-based methods can render volumetric effects faster than ray marching, because they avoid the expensive sampling and integration steps. However, voxel-based methods also have some drawbacks, such as memory consumption, aliasing, and low resolution. To overcome these issues, you can use techniques such as sparse voxel octrees, which compress the voxel data into a hierarchical structure that only stores the non-empty voxels, or voxel cone tracing, which approximates the lighting and shading effects by tracing cones instead of rays through the voxel grid.

3 Screen-space methods

Screen-space methods are techniques that render volumetric effects in screen space, which means that they only use the information that is visible on the screen, such as the depth buffer, the normal buffer, or the color buffer. Screen-space methods can be very fast and easy to implement, because they do not require any volume data or ray tracing. However, screen-space methods also have some limitations, such as missing occlusions, artifacts at depth discontinuities, or lack of self-shadowing. To improve screen-space methods, you can use techniques such as screen-space ambient occlusion, which simulates the darkening of corners and crevices, or screen-space reflections, which add realistic reflections of the volume on other surfaces.

4 Deep learning methods

Deep learning methods are techniques that use neural networks to generate or enhance volumetric effects. Deep learning methods can leverage the power of machine learning to create realistic and diverse volumetric effects, without relying on physical models or hand-crafted parameters. However, deep learning methods also have some challenges, such as data acquisition, training time, or generalization. To apply deep learning methods, you can use techniques such as generative adversarial networks, which create synthetic volumes from random noise or latent codes, or neural rendering, which modify existing volumes based on user input or scene context.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?