Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding (1808.01265v1)

Abstract: This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.

• The paper introduces the Curriculum Model Adaptation method that gradually transitions models from synthetic to real fog data.
• It presents a semantic-aware fog simulation technique that transforms clear-weather datasets to accurately simulate dense fog conditions.
• The study achieves significant segmentation performance gains on benchmarks like Foggy Zurich and Foggy Driving, enhancing reliability in adverse weather.

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

The paper addresses an important yet underexplored area within computer vision: semantic scene understanding in dense fog conditions. Traditional semantic segmentation models are typically evaluated on clear-weather datasets, leaving a gap in performance when applied to scenes obscured by adverse weather conditions like dense fog. To address this, the authors propose a novel method, named Curriculum Model Adaptation (CMAda), which enhances the performance of semantic segmentation models in foggy environments by leveraging both synthetic and real foggy data.

Synthetic Fog Simulation and Dataset Contribution

One of the seminal contributions of this paper is the novel fog simulation technique. This method simulates high-quality synthetic fog in clear-weather images using a semantic-aware filtering approach, thereby enabling the exploitation of existing datasets that lack adverse weather conditions. This technique demonstrated a slight performance improvement over the competing state-of-the-art method, reinforcing its efficacy in generating realistic fog effects crucial for model adaptation.

In support of model training and evaluation, the paper also introduces the Foggy Zurich dataset, a comprehensive compilation of 3,808 real foggy images, with a subset of 16 images annotated with pixel-level semantic details under dense fog conditions. This dataset fills a critical need by providing robust, real-world data necessary for testing model performance in realistic adverse conditions.

Curriculum Model Adaptation and Results

Central to the findings is the Curriculum Model Adaptation strategy, which leverages the continuity between synthetic and real-world fog data. The method gradually transitions a segmentation model from training on lighter synthetic fog to training on real, dense fog conditions using a curriculum learning approach. This helps the model to increasingly adapt to harder tasks, ultimately improving its robustness and performance in realistic scenarios as demonstrated on Foggy Zurich and Foggy Driving datasets, where the adapted models achieved significant improvements against non-adapted baselines.

Theoretical and Practical Implications

From a theoretical perspective, the authors propose a novel dual-reference filtering method for depth completion using both color and semantic references. This methodological advancement addresses the challenge of preserving semantic and depth boundaries during the fog simulation process, minimizing the structural discrepancies between synthetic and real-world images. Practically, this work has significant implications for enhancing autonomous driving systems, where accurate scene understanding under various weather conditions is essential for safety and functionality.

Future Directions

Looking forward, the paper suggests numerous directions for continued research and development. Given that CMAda employs synthetic fog simulations and unlabeled real fog data, there is room for refining these techniques to further narrow the domain gap. Integrating more advanced learning paradigms such as domain adaptation and unsupervised learning could bolster model performance further. Additionally, expanding the scope of real-world adverse weather datasets will be vital in driving future advancements in this field.

In conclusion, this paper presents a comprehensive approach for addressing the semantic understanding of foggy scenes, enhancing adaptability, and performance of existing models in such challenging conditions. The innovative use of curriculum model adaptation and the introduction of new datasets pave the way for future research and practical applications in varied weather environments. This work not only highlights the effectiveness of integrating synthetic and real-world data but also underscores the importance of comprehensive datasets and robust model adaptation techniques in advancing outdoor scene understanding technologies.