Neural representations are popular for representing shapes, as they can be learned form sensor data and used for data cleanup, model completion, shape editing, and shape synthesis. Current neural representations can be categorized as either overfitting to a single object instance, or representing a collection of objects. However, neither allows accurate editing of neural scene representations: on the one hand, methods that overfit objects achieve highly accurate reconstructions, but do not generalize to unseen object configurations and thus cannot support editing; on the other hand, methods that represent a family of objects with variations do generalize but produce only approximate reconstructions. We propose NEUFORM to combine the advantages of both overfitted and generalizable representations by adaptively using the one most appropriate for each shape region: the overfitted representation where reliable data is available, and the generalizable representation everywhere else. We achieve this with a carefully designed architecture and an approach that blends the network weights of the two representations, avoiding seams and other artifacts. We demonstrate edits that successfully reconfigure parts of human-designed shapes, such as chairs, tables, and lamps, while preserving semantic integrity and the accuracy of an overfitted shape representation. We compare with two state-of-the-art competitors and demonstrate clear improvements in terms of plausibility and fidelity of the resultant edits.
翻译:神经场面的表示方式很受欢迎,因为它们可以被学习成传感器数据的形式,并用于数据清理、模型完成、形状编辑和形状合成。当前的神经面貌可以分为以下两类:过分适合单一对象实例,或代表一个物体的集合。然而,两者都不允许对神经场面的表示方式进行准确编辑:一方面,过分适合物体的方法可以实现高度准确的重建,但不能推广到看不见的物体配置,从而无法支持编辑;另一方面,代表具有变异的物体的组合的方法可以概括化,但只能产生大致的重建。我们建议NEGBIOMM将超合适和可概括的表示方式的优点结合起来,采用适应性地对每个形状区域最合适的表示方式:在有可靠数据的情况下,过分适合的表示方式,以及其它地方的通用表示方式。我们通过精心设计的构架和方式,将两种表达方式的网络权重结合起来,避免接缝和其他艺术品。我们展示了成功重新配置人设计的形状部分,例如椅子、桌子和灯具的重建效果。我们将两张的品面的准确性与真实性相比,我们将两幅直观地展示了。