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.