Our course provides an essential overview of 3D reconstruction and generative AI-based 3D generation approaches that have transformed the field. The course provides an understanding of the fundamentals of photogrammetry, neural radiance fields-based techniques such as Instant NeRF, and generative AI-based 3D generation such as text-to-3D, with a focus on reflecting on these approaches, their applications in 3D world building, and how participants can integrate them into their research.

DIY cardboard-based VR headset that enfolds a commodity smartphone is a popular means of achieving low cost immersive stereoscopic viewing experiences. However, existing applications for these devices have limited user-input capability, allowing mainly look-at control by head tilting. CardboardSense is a set of novel tap-gesture-based inputs that take advantage of the motion sensing capabilities already available in most smartphones. The key premise is that finger contacts with the cardboard surface produce incidental but detectable vibrations that can be reliably differentiated from typical user head motion. Moreover, tapping on different surface locations produces differentiable temporal signatures in the sensor signals. We present methods to extract and analyze these temporal patterns, and experimentally determine a set of tapping gestures that can be accurately recognized. Experimental results show promising potential for CardboardSense to extend the interactivity of future cardboard-based VR headset applications.