This paper addresses the problem of transparent object matting. Existing image matting approaches for transparent objects often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we first formulate transparent object matting as a refractive flow estimation problem. We then propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 158K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also collect a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.
@cite_5: This article introduces a new approach for matting and compositing transparent and refractive objects in photographs. The key to our work is an image-based matting model, termed the Attenuation-Refraction Matte (ARM), that encodes plausible refractive properties of a transparent object along with its observed specularities and transmissive properties. We show that an object's ARM can be extracted directly from a photograph using simple user markup. Once extracted, the ARM is used to paste the object onto a new background with a variety of effects, including compound compositing, Fresnel effect, scene depth, and even caustic shadows. User studies find our results favorable to those obtained with Photoshop as well as perceptually valid in most cases. Our approach allows photo editing of transparent and refractive objects in a manner that produces realistic effects previously only possible via 3D models or environment matting.
Yeung al @cite_5 proposed an interactive way to estimate an environment matte given an image containing a transparent object. Their method requires users to manually mark the foreground and background in the image, and models the refractive effect using a thin-plate-spline transformation. Their method does not produce an accurate environment matte, but instead a visually pleasing refractive effect. Our method shares the same spirit, but does not involve any human interaction.