Modular design

Physics

Mathematics

Global illumination

Gene

Operating system

Optics

Computer graphics (images)

Mathematical analysis

Artificial intelligence

Image-based lighting

Chemistry

Rendering (computer graphics)

Radiance

View synthesis

Spherical harmonics

Computer science

Computer vision

Image-based modeling and rendering

Biochemistry

Robustness (evolution)

ACM Transactions on Graphics

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.

NeROIC