  Implicit neural 3D representation has achieved impressive results in surface
or scene reconstruction and novel view synthesis, which typically uses the
coordinate-based multi-layer perceptrons (MLPs) to learn a continuous scene
representation. However, existing approaches, such as Neural Radiance Field
(NeRF) and its variants, usually require dense input views (i.e. 50-150) to
obtain decent results. To relive the over-dependence on massive calibrated
images and enrich the coordinate-based feature representation, we explore
injecting the prior information into the coordinate-based network and introduce
a novel coordinate-based model, CoCo-INR, for implicit neural 3D
representation. The cores of our method are two attention modules: codebook
attention and coordinate attention. The former extracts the useful prototypes
containing rich geometry and appearance information from the prior codebook,
and the latter propagates such prior information into each coordinate and
enriches its feature representation for a scene or object surface. With the
help of the prior information, our method can render 3D views with more
photo-realistic appearance and geometries than the current methods using fewer
calibrated images available. Experiments on various scene reconstruction
datasets, including DTU and BlendedMVS, and the full 3D head reconstruction
dataset, H3DS, demonstrate the robustness under fewer input views and fine
detail-preserving capability of our proposed method.
