Colored Transparent Object Matting from a Single Image Using Deep Learning
This addresses a practical limitation for applications involving real-world transparent objects by reducing the need for multiple images and long processing times, though it is incremental as it builds on an existing method.
The paper tackles the problem of colored transparent object matting from a single image by extending TOM-Net to model intrinsic color with a color filter, achieving promising results on synthetic and real datasets.
This paper proposes a deep learning based method for colored transparent object matting from a single image. Existing approaches for transparent object matting often require multiple images and long processing times, which greatly hinder their applications on real-world transparent objects. The recently proposed TOM-Net can produce a matte for a colorless transparent object from a single image in a single fast feed-forward pass. In this paper, we extend TOM-Net to handle colored transparent object by modeling the intrinsic color of a transparent object with a color filter. We formulate the problem of colored transparent object matting as simultaneously estimating an object mask, a color filter, and a refractive flow field from a single image, and present a deep learning framework for learning this task. We create a large-scale synthetic dataset for training our network. We also capture a real dataset for evaluation. Experiments on both synthetic and real datasets show promising results, which demonstrate the effectiveness of our method.