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Robust Depth Estimation Using Auto-Exposure Bracketing
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DOI：10.1109/TIP.2018.2886777.png)
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Abstract
En 中文
As the computing power of handheld devices grows, there has been increasing interest in the capture of depth information to enable a variety of photographic applications. However, under low-light conditions, most devices still suffer from low imaging quality and inaccurate depth acquisition. To address the problem, we present a robust depth estimation method from a short burst shot with varied intensity (i.e., auto-exposure bracketing) and/or strong noise (i.e., high ISO). Our key idea synergistically combines deep convolutional neural networks with a geometric understanding of the scene. We introduce a geometric transformation between optical flow and depth tailored for burst images, enabling our learning-based multi-view stereo matching to be performed effectively. We then describe our depth estimation pipeline that incorporates this geometric transformation into our residual-flow network. It allows our framework to produce an accurate depth map even with a bracketed image sequence. We demonstrate that our method outperforms the state-of-the-art methods for various datasets captured by a smartphone and a DSLR camera. Moreover, we show that the estimated depth is applicable for image quality enhancement and photographic editing.
Keywords:
Depth estimation
exposure fusion
image denoising
3D reconstruction
geometry
convolutional neural network
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