Objects make unique sounds under different perturbations, environment
conditions, and poses relative to the listener. While prior works have modeled
impact sounds and sound propagation in simulation, we lack a standard dataset
of impact sound fields of real objects for audio-visual learning and
calibration of the sim-to-real gap. We present RealImpact, a large-scale
dataset of real object impact sounds recorded under controlled conditions.
RealImpact contains 150,000 recordings of impact sounds of 50 everyday objects
with detailed annotations, including their impact locations, microphone
locations, contact force profiles, material labels, and RGBD images. We make
preliminary attempts to use our dataset as a reference to current simulation
methods for estimating object impact sounds that match the real world.
Moreover, we demonstrate the usefulness of our dataset as a testbed for
acoustic and audio-visual learning via the evaluation of two benchmark tasks,
including listener location classification and visual acoustic matching.

研究团队构建了一个大规模的现实物体撞击声音数据集（RealImpact），并使用该数据集作为参照标准来校准仿真模型与真实模型之间的差距，同时还证明了该数据集的可用性来测试声学和视听学习。