Abstract
Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.
Highlights
As an important part of underwater robotics and 3D reconstruction, underwater depth prediction is crucial for underwater vision research
We propose a novel jointtraining generative adversarial network for both multi-style underwater image synthesis and depth estimation performed in an end-to-end manner
We propose a novel joint-training generative adversarial network, which can simultaneously handle the controllable translation from the hazy RGB-D images to the multi-style realistic underwater images by combining one additional label, and the depth prediction from both the synthetic and real underwater images
Summary
As an important part of underwater robotics and 3D reconstruction, underwater depth prediction is crucial for underwater vision research. The quality of collected images is restricted by light refraction and absorption, suspended particles in the water, and color distortion, making it difficult and challenging to obtain reliable underwater depth maps. As quite a few underwater RGB-D datasets (Akkaynak and Treibitz, 2019; Gomez Chavez et al, 2019; Berman et al, 2020) are currently available, many researchers have sought to adopt image processing methods to estimate the depth from a single monocular underwater image or a consecutive underwater image sequence. To perform single monocular underwater depth prediction, several restoration-based methods have been developed. It is extremely laborious to select and measure these parameters relevant to the physical process (Abas et al, 2019), and limited to some special task
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