Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior

• URL: http://arxiv.org/abs/2404.07176v1
• Date: Wed, 10 Apr 2024 17:25:42 GMT
• Title: Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior
• Authors: Zhengyang Lu, Ying Chen,
• Abstract summary: This paper proposes the first self-supervision for deep-learning depth estimation on water scenes via intra-frame priors. In the first stage, a water segmentation network is performed to separate the reflection components from the entire image. The photometric re-projection error, incorporating SmoothL1 and a novel photometric adaptive SSIM, is formulated to optimize pose and depth estimation.
• Score: 3.2120448116996103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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