Objects as Audio-Visual Modal Sound Fields

While modern 3D reconstruction excels at modeling objectgeometry and appearance, it largely ignores the rich acoustic cues re-vealed through physical interaction. Object impact sounds convey mate-rial, stiffness, and structural properties that complement vision, yet exist-ing impact sound modeling approaches either rely on expensive physics-based simulation or require large datasets to generalize in a purely data-driven manner. We introduce Audio-Visual Modal Sound Field (AV-MSF), a novel object-level acoustic representation reconstructed frommulti-view images and only a few impact sound recordings. AV-MSFbuilds on 3D Gaussian Splatting integrated with dense 3D visual featureto provide a strong geometry-aware prior, and represents the impactsound field using compact, physically meaningful modal parameters, en-abling robust few-shot reconstruction. Experiments on two real-worlddatasets show that AV-MSF achieves state-of-the-art impact sound ren-dering, outperforming both physics-based and data-driven baselines. Fur-thermore, we demonstrate downstream applications enabled by our rep-resentation, including contact localization and object sound editing.