Six degrees-of-freedom (6-DoF) video provides telepresence by enabling users to move around in the captured scene with a wide field of regard. Compared to methods requiring sophisticated camera setups, the image-based rendering method based on photogrammetry can work with images captured with any poses, which is more suitable for casual users. However, existing image-based rendering methods are based on perspective images. When used to reconstruct 6-DoF views, it often requires capturing hundreds of images, making data capture a tedious and time-consuming process. In contrast to traditional perspective images, 360° images capture the entire surrounding view in a single shot, thus, providing a faster capturing process for 6-DoF view reconstruction. This article presents a novel method to provide 6-DoF experiences over a wide area using an unstructured collection of 360° panoramas captured by a conventional 360° camera. Our method consists of 360° data capturing, novel depth estimation to produce a high-quality spherical depth panorama, and high-fidelity free-viewpoint generation. We compared our method against state-of-the-art methods, using data captured in various environments. Our method shows better visual quality and robustness in the tested scenes.

In this paper, we present a Convolutional AutoEncoder (CAE) for single image dehazing. Our CAE makes use of Densely Connection Networks as its encoder and decoder. It is trained with the corresponding hazy and clean images at the input and output, enabling it to remove the haze without having to rely on an atmospheric scattering model. The CAE is trained and tested with the RESIDE dataset. Experiment results show that this CAE outperforms eight state-of-art methods. The trained CAE is also applied to some real-life hazy images, and decent dehazing results are obtained. Moreover, our method is computationally efficient enough to run on computers without GPU units.