# Joint Object-Material Category Segmentation from Audio-Visual Cues

arXiv: https://arxiv.org/abs/1601.02220 · HTML: https://mlsanity.com/paper/1601.02220/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction](https://mlsanity.com/paper/2110.02405/) (2021)
- [Segmenting Collision Sound Sources in Egocentric Videos](https://mlsanity.com/paper/2511.13863/) (2025)

## Abstract (arXiv)

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials. In this paper, we therefore present an approach that augments the available dense visual cues with sparse auditory cues in order to estimate dense object and material labels. Since estimates of object class and material properties are mutually informative, we optimise our multi-output labelling jointly using a random-field framework. We evaluate our system on a new dataset with paired visual and auditory data that we make publicly available. We demonstrate that this joint estimation of object and material labels significantly outperforms the estimation of either category in isolation.