  We present a new dataset called Real Acoustic Fields (RAF) that captures real
acoustic room data from multiple modalities. The dataset includes high-quality
and densely captured room impulse response data paired with multi-view images,
and precise 6DoF pose tracking data for sound emitters and listeners in the
rooms. We used this dataset to evaluate existing methods for novel-view
acoustic synthesis and impulse response generation which previously relied on
synthetic data. In our evaluation, we thoroughly assessed existing audio and
audio-visual models against multiple criteria and proposed settings to enhance
their performance on real-world data. We also conducted experiments to
investigate the impact of incorporating visual data (i.e., images and depth)
into neural acoustic field models. Additionally, we demonstrated the
effectiveness of a simple sim2real approach, where a model is pre-trained with
simulated data and fine-tuned with sparse real-world data, resulting in
significant improvements in the few-shot learning approach. RAF is the first
dataset to provide densely captured room acoustic data, making it an ideal
resource for researchers working on audio and audio-visual neural acoustic
field modeling techniques. Demos and datasets are available on our project
page: https://facebookresearch.github.io/real-acoustic-fields/
