

Neroic
Overview :
NeROIC is a novel method for obtaining object representations from online image collections. It can capture high-quality geometric and material properties of any object in photographs with varying cameras, lighting, and backgrounds. It can be used for novel view synthesis, relighting, and harmonious background synthesis, among other object-centric rendering applications. Building upon the multi-stage approach of neural radiance fields, we first infer surface geometry and refine rough initial camera parameters. Simultaneously, we leverage a coarse foreground object mask to enhance training efficiency and geometric quality. We also introduce a robust normal estimation technique that mitigates the impact of geometric noise while preserving crucial details. Finally, we extract surface material properties and environmental lighting, represented using spherical harmonics, and handle transient elements such as sharp shadows. The combination of these components forms a highly modular and efficient framework for object acquisition. Extensive evaluations and comparisons demonstrate the superiority of our method in capturing high-quality geometry and appearance properties for rendering applications.
Target Users :
Suitable for applications needing to obtain object representations from online image collections
Use Cases
Synthesize a novel view of an object with different lighting conditions using NeROIC
Parse the material properties and surface normals of an object using NeROIC
Render an object in a new lighting environment using NeROIC
Features
Obtain high-quality geometric and material properties of objects from online image collections
Novel view synthesis
Relighting
Harmonious background synthesis
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