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Dense Material Segmentation with Context-Aware Network

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1815))

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Abstract

The dense material segmentation task aims at recognising the material for every pixel in daily images. It is beneficial to applications such as robot manipulation and spatial audio synthesis. Modern deep-learning methods combine material features with contextual features. Material features can generalise to unseen images regardless of appearance properties such as material shape and colour. Contextual features can reduce the segmentation uncertainty by providing extra global or semi-global information about the image. Recent studies proposed to crop the images into patches, which forces the network to learn material features from local visual clues. Typical contextual information includes extracted feature maps from networks targeting object and place related tasks. However, due to the lack of contextual labels, existing methods use pre-trained networks to provide contextual features. As a consequence, the trained networks do not give a promising performance. Their accuracy is below 70%, and the predicted segments have coarse boundaries. Considering this problem, this chapter introduces the Context-Aware Material Segmentation Network (CAM-SegNet). The CAM-SegNet is a hybrid network architecture to simultaneously learn from contextual and material features jointly with labelled materials. The effectiveness of the CAM-SegNet is demonstrated by training the network to learn boundary-related contextual features. Since the existing material datasets are sparsely labelled, a self-training approach is adopted to fill in the unlabelled pixels. Experiments show that CAM-SegNet can identify materials correctly, even with similar appearances. The network improves the pixel accuracy by 3–20% and raises the Mean IoU by 6–28%.

Supported by the University of Southampton.

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Notes

  1. 1.

    https://vision.cs.uiuc.edu/attributes/.

  2. 2.

    https://kylezheng.org/research-projects/densesegattobj/.

  3. 3.

    http://opensurfaces.cs.cornell.edu/.

  4. 4.

    http://opensurfaces.cs.cornell.edu/publications/minc/.

  5. 5.

    https://vision.ist.i.kyoto-u.ac.jp/codeanddata/localmatdb/.

  6. 6.

    The patch size is 23.3% of the smaller image dimension and can cover up to 5.29% of the area of the image.

  7. 7.

    https://groups.csail.mit.edu/vision/datasets/ADE20K/.

  8. 8.

    https://vision.princeton.edu/projects/2010/SUN/.

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Acknowledgements

This work was supported by the EPSRC Programme Grant Immersive Audio-Visual 3D Scene Reproduction Using a Single 360 Camera (EP/V03538X/1).

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Authors and Affiliations

  1. Vision, Learning and Control (VLC) Research Group, School of Electronics and Computer Science (ECS), University of Southampton, Southampton, UK

    Yuwen Heng, Yihong Wu, Srinandan Dasmahapatra & Hansung Kim

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  1. Yuwen Heng
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  2. Yihong Wu
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  3. Srinandan Dasmahapatra
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  4. Hansung Kim
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Correspondence to Hansung Kim .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Augusto de Sousa

  2. University of Warwick, Coventry, UK

    Kurt Debattista

  3. Mines ParisTech, Paris, France

    Alexis Paljic

  4. Bentley University, Waltham, USA

    Mounia Ziat

  5. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  6. Monash University, Melbourne, VIC, Australia

    Helen Purchase

  7. Department of Mathematics, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  8. Computer Vision Center, University of Barcelona, Barcelona, Spain

    Petia Radeva

  9. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

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Heng, Y., Wu, Y., Dasmahapatra, S., Kim, H. (2023). Dense Material Segmentation with Context-Aware Network. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2022. Communications in Computer and Information Science, vol 1815. Springer, Cham. https://doi.org/10.1007/978-3-031-45725-8_4

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