  Neural fields have recently enjoyed great success in representing and
rendering 3D scenes. However, most state-of-the-art implicit representations
model static or dynamic scenes as a whole, with minor variations. Existing work
on learning disentangled world and object neural fields do not consider the
problem of composing objects into different world neural fields in a
lighting-aware manner. We present Lighting-Aware Neural Field (LANe) for the
compositional synthesis of driving scenes in a physically consistent manner.
Specifically, we learn a scene representation that disentangles the static
background and transient elements into a world-NeRF and class-specific
object-NeRFs to allow compositional synthesis of multiple objects in the scene.
Furthermore, we explicitly designed both the world and object models to handle
lighting variation, which allows us to compose objects into scenes with
spatially varying lighting. This is achieved by constructing a light field of
the scene and using it in conjunction with a learned shader to modulate the
appearance of the object NeRFs. We demonstrate the performance of our model on
a synthetic dataset of diverse lighting conditions rendered with the CARLA
simulator, as well as a novel real-world dataset of cars collected at different
times of the day. Our approach shows that it outperforms state-of-the-art
compositional scene synthesis on the challenging dataset setup, via composing
object-NeRFs learned from one scene into an entirely different scene whilst
still respecting the lighting variations in the novel scene. For more results,
please visit our project website https://lane-composition.github.io/.
