DART: Depth-Enhanced Accurate and Real-Time Background Matting
Hanxi Li Guofeng Li Bo Li Lin Wu Yan Cheng
Matting with a static background, often referred to as ``Background Matting"
(BGM), has garnered significant attention within the computer vision community
due to its pivotal role in various practical applications like webcasting and
photo editing. Nevertheless, achieving highly accurate background matting
remains a formidable challenge, primarily owing to the limitations inherent in
conventional RGB images. These limitations manifest in the form of
susceptibility to varying lighting conditions and unforeseen shadows.
  In this paper, we leverage the rich depth information provided by the
RGB-Depth (RGB-D) cameras to enhance background matting performance in
real-time, dubbed DART. Firstly, we adapt the original RGB-based BGM algorithm
to incorporate depth information. The resulting model's output undergoes
refinement through Bayesian inference, incorporating a background depth prior.
The posterior prediction is then translated into a "trimap," which is
subsequently fed into a state-of-the-art matting algorithm to generate more
precise alpha mattes. To ensure real-time matting capabilities, a critical
requirement for many real-world applications, we distill the backbone of our
model from a larger and more versatile BGM network. Our experiments demonstrate
the superior performance of the proposed method. Moreover, thanks to the
distillation operation, our method achieves a remarkable processing speed of 33
frames per second (fps) on a mid-range edge-computing device. This high
efficiency underscores DART's immense potential for deployment in mobile
applications}