NeurIPS 2025

Can NeRFs “See” without Cameras?
Chaitanya Amballa1
Sattwik Basu1∗
Yu-Lin Wei1∗
amballa2@illinois.edu
sattwik2@illinois.edu
yulinlw2@illinois.edu
Zhijian Yang2
Mehmet Ergezer2
Romit Roy Choudhury1,2
yzhijian@amazon.com
mergezer@amazon.com
croy@illinois.edu
1University of Illinois Urbana–Champaign
2Amazon
Abstract
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.
1
Introduction
NeRFs [25, 43, 2, 45] have delivered impressive results in solving inverse problems, resulting in 3D
scene rendering. While NeRFs have mostly used pictures (from cameras or LIDARs) to infer a 3D
scene, we ask if the core ideas can generalize to the case of wireless signals (such as RF or audio).
Unlike camera pixels that receive line-of-sight (LoS) rays, a wireless receiver (e.g., a WiFi antenna
on a smartphone) would receive a mixture of LoS and many reflections, called multipath. If the
receiver moves, it receives a sequence of N measurements. Using these N wireless measurements, is
it possible to learn a ...