Learning from the Giants: A Practical Approach to Underwater Depth and Surface Normals Estimation
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely either on discriminative models, which struggle with transparent or reflective surfaces, or generative models, which, while a...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
02.10.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks
such as 3D reconstruction, autonomous navigation, and underwater exploration.
Current methods rely either on discriminative models, which struggle with
transparent or reflective surfaces, or generative models, which, while
accurate, are computationally expensive. This paper presents a novel deep
learning model for MDSNE, specifically tailored for underwater environments,
using a hybrid architecture that integrates Convolutional Neural Networks
(CNNs) with Transformers, leveraging the strengths of both approaches. Training
effective MDSNE models is often hampered by noisy real-world datasets and the
limited generalization of synthetic datasets. To address this, we generate
pseudo-labeled real data using multiple pre-trained MDSNE models. To ensure the
quality of this data, we propose the Depth Normal Evaluation and Selection
Algorithm (DNESA), which evaluates and selects the most reliable pseudo-labeled
samples using domain-specific metrics. A lightweight student model is then
trained on this curated dataset. Our model reduces parameters by 90% and
training costs by 80%, allowing real-time 3D perception on resource-constrained
devices. Key contributions include: a novel and efficient MDSNE model, the
DNESA algorithm, a domain-specific data pipeline, and a focus on real-time
performance and scalability. Designed for real-world underwater applications,
our model facilitates low-cost deployments in underwater robots and autonomous
vehicles, bridging the gap between research and practical implementation. |
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DOI: | 10.48550/arxiv.2410.02072 |