Despite advances in 3D generation, the direct creation of 3D objects within an existing 3D scene represented as NeRF remains underexplored. This process requires not only high-quality 3D object generation but also seamless composition of the generated 3D content into the existing NeRF. To this end, we propose a new method, GO-NeRF, capable of utilizing scene context for high-quality and harmonious 3D object generation within an existing NeRF. Our method employs a compositional rendering formulation that allows the generated 3D objects to be seamlessly composited into the scene utilizing learned 3D-aware opacity maps without introducing unintended scene modification. Moreover, we also develop tailored optimization objectives and training strategies to enhance the model's ability to exploit scene context and mitigate artifacts, such as floaters, originating from 3D object generation within a scene. Extensive experiments on both feed-forward and $360^o$ scenes show the superior performance of our proposed GO-NeRF in generating objects harmoniously composited with surrounding scenes and synthesizing high-quality novel view images. Project page at {\url{https://daipengwa.github.io/GO-NeRF/}.

翻译：尽管三维生成领域取得了进展，但如何在以NeRF表示的现有三维场景中直接生成三维对象仍未得到充分探索。这一过程不仅需要高质量的三维对象生成，还需要将生成的三维内容无缝合成到现有NeRF中。为此，我们提出了一种新方法GO-NeRF，该方法能够利用场景上下文，在现有NeRF内实现高质量且和谐的三维对象生成。我们的方法采用组合渲染公式，使得生成的三维对象能够通过已学习的3D感知透明度图无缝合成到场景中，而不会引入意外的场景修改。此外，我们还开发了定制的优化目标和训练策略，以增强模型利用场景上下文的能力，并减轻源自场景内三维对象生成的伪影（如浮动粒子）。在前馈场景和360°场景上的大量实验表明，我们提出的GO-NeRF在与周围场景和谐生成对象以及合成高质量新视角图像方面具有优越性能。项目页面：{\url{https://daipengwa.github.io/GO-NeRF/}。