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.

翻译：物体在不同扰动、环境条件以及相对于听者的姿态下会发出独特的声音。尽管已有研究在仿真中模拟撞击声和声音传播，但我们仍缺乏一个用于视听学习及模拟-现实差距校准的真实物体撞击声场标准数据集。我们提出了RealImpact——一个在受控条件下记录的大规模真实物体撞击声数据集。RealImpact包含50个日常用品的15万条撞击声记录，并附带详细标注，包括撞击位置、麦克风位置、接触力轮廓、材料标签及RGBD图像。我们初步尝试将该数据集作为参考，用于改进现有仿真方法以生成与真实世界匹配的物体撞击声。此外，通过评估两个基准任务（听者位置分类与视觉声学匹配），我们证明了该数据集作为声学与视听学习测试平台的有效性。