  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 an accurate and
highly efficient object reconstruction pipeline combining neural based object
reconstruction and physics-based inverse rendering (PBIR). Our pipeline firstly
leverages a neural SDF based shape reconstruction to produce high-quality but
potentially imperfect object shape. Then, we introduce a neural material and
lighting distillation stage to achieve high-quality predictions for material
and illumination. In the last stage, initialized by the neural predictions, we
perform PBIR to refine the initial results and obtain the final high-quality
reconstruction of object shape, material, and illumination. Experimental
results demonstrate our pipeline significantly outperforms existing methods
quality-wise and performance-wise.
