PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments
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Abstract (arXiv)
Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this paper, we introduce visual prompt learning for predicting depth across different environments within a unified model, and present a self-supervised learning framework called PromptMono. It employs a set of learnable parameters as visual prompts to capture domain-specific knowledge. To integrate prompting information into image representations, a novel gated cross prompting attention (GCPA) module is proposed, which enhances the depth estimation in diverse conditions. We evaluate the proposed PromptMono on the Oxford Robotcar dataset and the nuScenes dataset. Experimental results demonstrate the superior performance of the proposed method.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 23 Jan 2025.
Builds on
32 of 41 references.
References · 32
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20 more
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- 2023Visual Prompt Multi-Modal Trackingcited by 141
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- 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
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- 2021Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Darkcited by 57
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- 2020Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptationcited by 35
- 2021Unsupervised Monocular Depth Estimation in Highly Complex Environmentscited by 28
- 2022When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimationcited by 24
- 2023WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditionscited by 15
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