NeROIC: Neural Rendering of Objects from Online Image Collections

Autor:	Kuang, Zhengfei, Olszewski, Kyle, Chai, Menglei, Huang, Zeng, Achlioptas, Panos, Tulyakov, Sergey
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: SIGGRAPH 2022 (Journal Track). Project page: https://formyfamily.github.io/NeROIC/ Code repository: https://github.com/snap-research/NeROIC/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.02533 Zobrazit plný text záznamu View this record from Arxiv