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Deep implicit functions have shown remarkable shape modeling ability invarious 3D computer vision tasks. One drawback is that it is hard for them torepresent a 3D shape as multiple parts. Current solutions learn variousprimitives and blend the primitives directly in the spatial space, which stillstruggle to approximate the 3D shape accurately. To resolve this problem, weintroduce a novel implicit representation to represent a single 3D shape as aset of parts in the latent space, towards both highly accurate and plausiblyinterpretable shape modeling. Our insight here is that both the part learningand the part blending can be conducted much easier in the latent space than inthe spatial space. We name our method Latent Partition Implicit (LPI), becauseof its ability of casting the global shape modeling into multiple local partmodeling, which partitions the global shape unity. LPI represents a shape asSigned Distance Functions (SDFs) using surface codes. Each surface code is alatent code representing a part whose center is on the surface, which enablesus to flexibly employ intrinsic attributes of shapes or additional surfaceproperties. Eventually, LPI can reconstruct both the shape and the parts on theshape, both of which are plausible meshes. LPI is a multi-level representation,which can partition a shape into different numbers of parts after training. LPIcan be learned without ground truth signed distances, point normals or anysupervision for part partition. LPI outperforms the latest methods under thewidely used benchmarks in terms of reconstruction accuracy and modelinginterpretability. Our code, data and models are available athttps://github.com/chenchao15/LPI.

Latent Partition Implicit with Surface Codes for 3D Representation