Self-supervised Monocular Depth Estimation on Water Scenes via Specular
  Reflection Prior
Zhengyang Lu Ying Chen
Monocular depth estimation from a single image is an ill-posed problem for
computer vision due to insufficient reliable cues as the prior knowledge.
Besides the inter-frame supervision, namely stereo and adjacent frames,
extensive prior information is available in the same frame. Reflections from
specular surfaces, informative intra-frame priors, enable us to reformulate the
ill-posed depth estimation task as a multi-view synthesis. This paper proposes
the first self-supervision for deep-learning depth estimation on water scenes
via intra-frame priors, known as reflection supervision and geometrical
constraints. In the first stage, a water segmentation network is performed to
separate the reflection components from the entire image. Next, we construct a
self-supervised framework to predict the target appearance from reflections,
perceived as other perspectives. The photometric re-projection error,
incorporating SmoothL1 and a novel photometric adaptive SSIM, is formulated to
optimize pose and depth estimation by aligning the transformed virtual depths
and source ones. As a supplement, the water surface is determined from real and
virtual camera positions, which complement the depth of the water area.
Furthermore, to alleviate these laborious ground truth annotations, we
introduce a large-scale water reflection scene (WRS) dataset rendered from
Unreal Engine 4. Extensive experiments on the WRS dataset prove the feasibility
of the proposed method compared to state-of-the-art depth estimation
techniques.