Computer vision is gradually merging with other visual sciences, under the general umbrella of Visual Computing. In academia, we are seeing deep learning for computer vision and neuroscience programs merging topics together. In this chapter, we cover computer vision developments that are changing computer graphics and changing the GPU itself, as well developments in imaging sciences that are incorporating computer vision methods into silicon inside cameras, bringing new capabilities and applications. The GPU is becoming a visual computing processor, assisted by special-purpose computer vision and machine learning processors, as all the pixels are processed and combined for display inside the GPU. This chapter covers a selected range of applied computer vision technologies which have a broad impact, pointing to a future of mixed synthetic objects and real objects through the merger of computer vision, AI, computer graphics, and imaging. We examine the future of computer vision and the third wave of AI: Continuous Learning and Associative Multi-Modal Learning (Visual Genomes for Synthetic Vision, Scott Krig, TBP (2016)). In this area, we will see AI that keeps learning and growing to assist in computer vision classification using multi-modal data and associative feature models that grow over time and with use, and we explore how multimodal [caption: image] NLP image view synthesis and [video: audio] video synthesis is only the start. We examine various methods for image super-resolution, high dynamic range imaging, panoramic imaging and image stitching, animated avatars in MR spaces, as well as view synthesis and neural radiance fields. We highlight specific background concepts and provide an introduction to the fundamentals of view synthesis models and computer graphics volume rendering methods. In the commercial realm, we highlight areas where computer vision is now an expected and familiar commodity, with mostly free default apps on any smartphone such as face recognition, selfie-images turned into 3D avatars, interactive scenic tours overlaying objects onto the video scene in a mixed-reality fashion, view synthesis, text-to-image synthesis, and much more. Scientific imaging systems using computer vision are also introduced, including polarimetric imaging, applied confocal and tomographic imaging, fluorescence microscopy, X-Ray tomography, and various related technologies.