  We introduce k-planes, a white-box model for radiance fields in arbitrary
dimensions. Our model uses d choose 2 planes to represent a d-dimensional
scene, providing a seamless way to go from static (d=3) to dynamic (d=4)
scenes. This planar factorization makes adding dimension-specific priors easy,
e.g. temporal smoothness and multi-resolution spatial structure, and induces a
natural decomposition of static and dynamic components of a scene. We use a
linear feature decoder with a learned color basis that yields similar
performance as a nonlinear black-box MLP decoder. Across a range of synthetic
and real, static and dynamic, fixed and varying appearance scenes, k-planes
yields competitive and often state-of-the-art reconstruction fidelity with low
memory usage, achieving 1000x compression over a full 4D grid, and fast
optimization with a pure PyTorch implementation. For video results and code,
please see https://sarafridov.github.io/K-Planes.
