  We present a novel method to acquire object representations from online image
collections, capturing high-quality geometry and material properties of
arbitrary objects from photographs with varying cameras, illumination, and
backgrounds. This enables various object-centric rendering applications such as
novel-view synthesis, relighting, and harmonized background composition from
challenging in-the-wild input. Using a multi-stage approach extending neural
radiance fields, we first infer the surface geometry and refine the coarsely
estimated initial camera parameters, while leveraging coarse foreground object
masks to improve the training efficiency and geometry quality. We also
introduce a robust normal estimation technique which eliminates the effect of
geometric noise while retaining crucial details. Lastly, we extract surface
material properties and ambient illumination, represented in spherical
harmonics with extensions that handle transient elements, e.g. sharp shadows.
The union of these components results in a highly modular and efficient object
acquisition framework. Extensive evaluations and comparisons demonstrate the
advantages of our approach in capturing high-quality geometry and appearance
properties useful for rendering applications.
