Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

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

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.Comment: final version, ECCV 201

Realistic Noise Synthesis with Diffusion Models

Deep learning-based approaches have achieved remarkable performance in single-image denoising. However, training denoising models typically requires a large amount of data, which can be difficult to obtain in real-world scenarios. Furthermore, synthetic noise used in the past has often produced significant differences compared to real-world noise due to the complexity of the latter and the poor modeling ability of noise distributions of Generative Adversarial Network (GAN) models, resulting in residual noise and artifacts within denoising models. To address these challenges, we propose a novel method for synthesizing realistic noise using diffusion models. This approach enables us to generate large amounts of high-quality data for training denoising models by controlling camera settings to simulate different environmental conditions and employing guided multi-scale content information to ensure that our method is more capable of generating real noise with multi-frequency spatial correlations. In particular, we design an inversion mechanism for the setting, which extends our method to more public datasets without setting information. Based on the noise dataset we synthesized, we have conducted sufficient experiments on multiple benchmarks, and experimental results demonstrate that our method outperforms state-of-the-art methods on multiple benchmarks and metrics, demonstrating its effectiveness in synthesizing realistic noise for training denoising models