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

RealImpact: A Dataset of Impact Sound Fields for Real Objects