Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization

Aashish Sharma, Loong-Fah Cheong; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 103-118

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.