The neural implicit representation has shown its effectiveness in novel view
synthesis and high-quality 3D reconstruction from multi-view images. However,
most approaches focus on holistic scene representation yet ignore individual
objects inside it, thus limiting potential downstream applications. In order to
learn object-compositional representation, a few works incorporate the 2D
semantic map as a cue in training to grasp the difference between objects. But
they neglect the strong connections between object geometry and instance
semantic information, which leads to inaccurate modeling of individual
instance. This paper proposes a novel framework, ObjectSDF, to build an
object-compositional neural implicit representation with high fidelity in 3D
reconstruction and object representation. Observing the ambiguity of
conventional volume rendering pipelines, we model the scene by combining the
Signed Distance Functions (SDF) of individual object to exert explicit surface
constraint. The key in distinguishing different instances is to revisit the
strong association between an individual object's SDF and semantic label.
Particularly, we convert the semantic information to a function of object SDF
and develop a unified and compact representation for scene and objects.
Experimental results show the superiority of ObjectSDF framework in
representing both the holistic object-compositional scene and the individual
instances. Code can be found at this https URL

该论文提出了一种新的框架 ObjectSDF 来构建具有高保真度的对象组合神经隐式表示，它结合了各个对象的有符号距离函数（SDF），并将语义信息转换为对象 SDF 的函数，以及开发了统一且紧凑的场景和对象表示。实验结果表明该框架在表示整体物体组合场景和单个实例方面具有卓越的优势。