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3D object reconstructions of transparent and concave structured objects, withinferred material properties, remains an open research problem for robotnavigation in unstructured environments. In this paper, we propose a multimodalsingle- and multi-frame neural network for 3D reconstructions usingaudio-visual inputs. Our trained reconstruction LSTM autoencoder 3D-MOV acceptsmultiple inputs to account for a variety of surface types and views. Our neuralnetwork produces high-quality 3D reconstructions using voxel representation.Based on Intersection-over-Union (IoU), we evaluate against other baselinemethods using synthetic audio-visual datasets ShapeNet and Sound20K with impactsounds and bounding box annotations. To the best of our knowledge, our single-and multi-frame model is the first audio-visual reconstruction neural networkfor 3D geometry and material representation.

3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video