3D scene analysis through non-visual cues
The wide applicability of scene analysis from as few viewpoints as possible attracts the attention of many scientific fields, ranging from augmented reality to autonomous driving and robotics. When approaching 3D problems in the wild, one has to admit, that the problems to solve are particularly challenging due to a monocular setup being severely under-constrained. One has to design algorithmic solutions that resourcefully take advantage of abundant prior knowledge, much alike the way human reasoning is performed. I propose the utilization of non-visual cues to interpret visual data. I investigate, how making non-restrictive assumptions about the scene, such as “obeys Newtonian physics” or “is made by or for humans” greatly improves the quality of information retrievable from the same type of data. I successfully reason about the hidden constraints that shaped the acquired scene to come up with abstractions that represent likely estimates about the unobservable or difficult to acquire parts of scenes. I hypothesize, that jointly reasoning about these hidden processes and the observed scene allows for more accurate inference and lays the way for prediction through understanding. Applications of the retrieved information range from image and video editing (e.g., visual effects) through robotic navigation to assisted living.