Can NeRFs See without Cameras?
This work addresses the challenge of environment inference without cameras for applications in indoor mapping and signal analysis, representing an incremental adaptation of existing methods to new data types.
The paper tackled the problem of inferring indoor environments from sparse WiFi measurements by adapting Neural Radiance Fields (NeRFs) to learn from multipath radio frequency signals, resulting in promising implicitly learnt floorplans that enable applications like indoor signal prediction and ray tracing.
Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how optical rays bring color information from a 3D object to the camera pixels. Radio frequency (RF) or audio signals can also be viewed as a vehicle for delivering information about the environment to a sensor. However, unlike camera pixels, an RF/audio sensor receives a mixture of signals that contain many environmental reflections (also called "multipath"). Is it still possible to infer the environment using such multipath signals? We show that with redesign, NeRFs can be taught to learn from multipath signals, and thereby "see" the environment. As a grounding application, we aim to infer the indoor floorplan of a home from sparse WiFi measurements made at multiple locations inside the home. Although a difficult inverse problem, our implicitly learnt floorplans look promising, and enables forward applications, such as indoor signal prediction and basic ray tracing.