D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video

Understand

Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence.

• Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment.
• We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.
• Our method represents the moving objects and the static background by two separate neural radiance fields with only one allowing for temporal changes.