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Omnidirectional video is an essential component of Virtual Reality. Although various methods have been proposed to generate content that can be viewed with six degrees of freedom (6-DoF), existing systems usually involve complex depth estimation, image in-painting or stitching pre-processing. In this paper, we propose a system that uses a 3D ConvNet to generate a multi-sphere images (MSI) representation that can be experienced in 6-DoF VR. The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition. By using a newly designed weighted sphere sweep volume (WSSV) fusing technique, our approach is compatible with most panoramic VR camera setups. A ground truth generation approach for high-quality artifact-free 6-DoF contents is proposed and can be used by the research and development community for 6-DoF content generation.
When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this pape
Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manually collecting a new training dataset that contains adequate hard sampl
We introduce a method to convert stereo 360{deg} (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 360{deg} imagery can be captured from multi-camera systems for virt
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers. These models, however, are both monolithic and static in the sense that they typically use a sp
State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. Firs