Stereo-consistent screen-space ambient occlusion

Screen-space ambient occlusion (SSAO) shows high efficiency and is widely used in real-time 3D applications. However, using SSAO algorithms in stereo rendering can lead to inconsistencies due to the differences in the screen-space information captured by the left and right eye. This will affect the perception of the scene and may be a source of viewer discomfort. In this paper, we show that the raw obscurance estimation part and subsequent filtering are both sources of inconsistencies. We developed a screen-space method involving both views in conjunction, leading to a stereo-aware raw obscurance estimation method and a stereo-aware bilateral filter. The results show that our method reduces stereo inconsistencies to a level comparable to geometry-based AO solutions, while maintaining the performance benefits of a screen-space approach

Advances in 3D Neural Stylization: A Survey

Modern artificial intelligence provides a novel way of producing digital art in styles. The expressive power of neural networks enables the realm of visual style transfer methods, which can be used to edit images, videos, and 3D data to make them more artistic and diverse. This paper reports on recent advances in neural stylization for 3D data. We provide a taxonomy for neural stylization by considering several important design choices, including scene representation, guidance data, optimization strategies, and output styles. Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then provides in-depth discussions on recent neural stylization methods for 3D data, where we also provide a mini-benchmark on artistic stylization methods. Based on the insights gained from the survey, we then discuss open challenges, future research, and potential applications and impacts of neural stylization.Comment: 26 page