Into the Twilight Zone: Depth Estimation using
Joint Structure-Stereo Optimization
Abstract. We present a joint Structure-Stereo optimization model that
is robust for disparity estimation under low-light conditions. Eschewing
the traditional denoising approach – which we show to be ineffective
for stereo due to its artefacts and the questionable use of the PSNR
metric, we propose to instead rely on structures comprising of piecewise
constant regions and principal edges in the given image, as these are
the important regions for extracting disparity information. We also judiciously retain the coarser textures for stereo matching, discarding the
finer textures as they are apt to be inextricably mixed with noise. This
selection process in the structure-texture decomposition step is aided by
the stereo matching constraint in our joint Structure-Stereo formulation.
The resulting optimization problem is complex but we are able to decompose it into sub-problems that admit relatively standard solutions.
Our experiments confirm that our joint model significantly outperforms
the baseline methods on both synthetic and real noise datasets.