用WiFi信号复原室内布局，让NeRF突破摄像头限制。
Can NeRFs See without Cameras?
- 改造NeRF模型，从多径信号中学习环境信息。
- 仅用稀疏WiFi测量即可还原室内布局，效果初步可信。
- 适合对无线感知与3D重建交叉研究者参考。
神经辐射场（NeRF）通过优化体积场景函数，在合成3D场景新视角方面表现卓越。该函数建模了光学光线如何将颜色信息从三维物体传递至相机像素。射频（RF）或音频信号也可作为环境信息的载体，但与相机像素不同，RF/音频传感器接收的是包含多个环境反射（即“多径”）的混合信号。能否利用此类多径信号推断环境？我们证明，通过重构设计，NeRF可被训练以多径信号为输入，从而“看见”环境。以室内平面图重建为例，仅需在家中多个位置采集稀疏的WiFi测量数据，便能隐式推断出房间布局。尽管这是一个困难的逆问题，但所生成的布局结果已具潜力，并可支持后续应用，如室内信号预测和基础光线追踪。
原文摘要 · Abstract (English)
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.
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