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

Non-resonant dot-cavity coupling and its applications in resonant quantum dot spectroscopy

We present experimental investigations on the non-resonant dot-cavity coupling of a single quantum dot inside a micro-pillar where the dot has been resonantly excited in the s-shell, thereby avoiding the generation of additional charges in the QD and its surrounding. As a direct proof of the pure single dot-cavity system, strong photon anti-bunching is consistently observed in the autocorrelation functions of the QD and the mode emission, as well as in the cross-correlation function between the dot and mode signals. Strong Stokes and anti-Stokes-like emission is observed for energetic QD-mode detunings of up to ~100 times the QD linewidth. Furthermore, we demonstrate that non-resonant dot-cavity coupling can be utilized to directly monitor and study relevant QD s-shell properties like fine-structure splittings, emission saturation and power broadening, as well as photon statistics with negligible background contributions. Our results open a new perspective on the understanding and implementation of dot-cavity systems for single-photon sources, single and multiple quantum dot lasers, semiconductor cavity quantum electrodynamics, and their implementation, e.g. in quantum information technology.Comment: 17 pages, 4 figure

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. For easy color calibration and improved assessment of dehazing algorithms, each scene include a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM

Interaction Between Real and Virtual Humans: Playing Checkers

. For some years, we have been able to integrate virtual humans into virtual environments. As the demand for Augmented Reality systems grows, so will the need for these synthetic humans to coexist and interact with humans who live in the real world. In this paper, we use the example of a checkers game between a real and a virtual human to demonstrate the integration of techniques required to achieve a realistic-looking interaction in real-time. We do not use cumbersome devices such as a magnetic motion capture system. Instead, we rely on purely image-based techniques to address the registration issue, when the camera or the objects move, and to drive the virtual human's behavior. 1 Introduction Recent developments in Virtual Reality and Human Animation have led to the integration of Virtual Humans into synthetic environments. We can now interact with them and represent ourselves as avatars in the Virtual World [4]. Fast workstations make it feasible to animate them in realtime..

Physically Plausible Dehazing for Non-physical Dehazing Algorithms

Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods

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 will be made publicly available.ISSN:0302-9743ISSN:1611-334