Section 2, Section 3 and Section 4 provide more visualization results on a number of 3D modelingtasks,includingshapereconstruction,generationandinterpolation. Note that all the hierarchical aggregators {Ei} share the same networkparameters. Hierarchical decoder D includes an implicit octant decoder and some hierarchical local decoders {Di}, which maps the decoded shape code and a sample point (x,y,z) to the local geometry and the local latent feature, respectively. IN means the instance normalization 3D operator[9]. We provide two tables (Table 1 and Table 2) to detail the network structures of 3D voxel encoder andimplicitdecoder,respectively.

In this supplemental material, we provide more details on network architecture and more visualization results, including shape reconstruction/comparison, shape Generation, and shape Interpolations. Furthermore, some results on scene reconstruction and comparison with Local Implicit Grid [3] are presented to demonstrate our superiority on large data representation thanks to the hierarchical tree structure of our proposed OctField representation. All sections are listed as follows: Section 1 provides the details of network architecture and training. Section 2, Section 3 and Section 4 provide more visualization results on a number of 3D modeling tasks, including shape reconstruction, generation and interpolation. Section 5 conducts four ablation studies, including with or without overlapping of adjacent octants, the training strategy, the distinction of latent codes and the subdivision parameter τ .