Reconstructing the shape and spatially varying surface appearances of a
physical-world object as well as its surrounding illumination based on 2D
images (e.g., photographs) of the object has been a long-standing problem in
computer vision and graphics. In this paper, we introduce a robust object
reconstruction pipeline combining neural based object reconstruction and
physics-based inverse rendering (PBIR). Specifically, our pipeline firstly
leverages a neural stage to produce high-quality but potentially imperfect
predictions of object shape, reflectance, and illumination. Then, in the later
stage, initialized by the neural predictions, we perform PBIR to refine the
initial results and obtain the final high-quality reconstruction. Experimental
results demonstrate our pipeline significantly outperforms existing
reconstruction methods quality-wise and performance-wise