Existing neural field representations for 3D object reconstruction either (1) utilize object-level representations, but suffer from low-quality details due to conditioning on a global latent code, or (2) are able to perfectly reconstruct the observations, but fail to utilize object-level prior knowledge to infer unobserved regions. We present SimNP, a method to learn category-level self-similarities, which combines the advantages of both worlds by connecting neural point radiance fields with a category-level self-similarity representation. Our contribution is two-fold. (1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds. The resulting neural point radiance fields store a high level of detail for locally supported object regions. (2) We learn how information is shared between neural points in an unconstrained and unsupervised fashion, which allows to derive unobserved regions of an object during the reconstruction process from given observations. We show that SimNP is able to outperform previous methods in reconstructing symmetric unseen object regions, surpassing methods that build upon category-level or pixel-aligned radiance fields, while providing semantic correspondences between instances

翻译：现有用于三维物体重建的神经场表示方法要么（1）利用物体级表示，但由于依赖于全局潜在编码而导致细节质量低下，要么（2）能够完美重建观测结果，但无法利用物体级先验知识推断未观测区域。我们提出SimNP方法，通过将神经点辐射场与类别级自相似性表示相结合，融合两类方法的优势来学习类别级自相似性。我们的贡献有两方面：（1）利用相干点云概念，首次设计了类别级神经点表示，所得神经点辐射场为局部支撑的物体区域存储了高细节信息。（2）我们以无约束无监督方式学习如何在神经点间共享信息，从而在重建过程中根据给定观测结果推导物体的未观测区域。实验表明，SimNP在重建对称性未知物体区域方面优于先前方法，超越了基于类别级或像素对齐辐射场的方法，同时提供了实例间的语义对应关系。