We present GeoNeRF, a generalizable photorealistic novel view synthesis
method based on neural radiance fields. Our approach consists of two main
stages: a geometry reasoner and a renderer. To render a novel view, the
geometry reasoner first constructs cascaded cost volumes for each nearby source
view. Then, using a Transformer-based attention mechanism and the cascaded cost
volumes, the renderer infers geometry and appearance, and renders detailed
images via classical volume rendering techniques. This architecture, in
particular, allows sophisticated occlusion reasoning, gathering information
from consistent source views. Moreover, our method can easily be fine-tuned on
a single scene, and renders competitive results with per-scene optimized neural
rendering methods with a fraction of computational cost. Experiments show that
GeoNeRF outperforms state-of-the-art generalizable neural rendering models on
various synthetic and real datasets. Lastly, with a slight modification to the
geometry reasoner, we also propose an alternative model that adapts to RGBD
images. This model directly exploits the depth information often available
thanks to depth sensors. The implementation code is available at
this https URL

该论文提出了一种基于神经辐射场的通用逼真新视角合成方法，该方法包括几何推理和渲染两个主要阶段，可用于各种合成和实际数据集。