  The problem of obtaining dense reconstruction of an object in a natural
sequence of images has been long studied in computer vision. Classically this
problem has been solved through the application of bundle adjustment (BA). More
recently, excellent results have been attained through the application of
photometric bundle adjustment (PBA) methods -- which directly minimize the
photometric error across frames. A fundamental drawback to BA & PBA, however,
is: (i) their reliance on having to view all points on the object, and (ii) for
the object surface to be well textured. To circumvent these limitations we
propose semantic PBA which incorporates a 3D object prior, obtained through
deep learning, within the photometric bundle adjustment problem. We demonstrate
state of the art performance in comparison to leading methods for object
reconstruction across numerous natural sequences.
