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Deep Implicit Function (DIF) has gained popularity as an efficient 3D shaperepresentation. To capture geometry details, current methods usually learn DIFusing local latent codes, which discretize the space into a regular 3D grid (oroctree) and store local codes in grid points (or octree nodes). Given a querypoint, the local feature is computed by interpolating its neighboring localcodes with their positions. However, the local codes are constrained atdiscrete and regular positions like grid points, which makes the code positionsdifficult to be optimized and limits their representation ability. To solvethis problem, we propose to learn DIF with Dynamic Code Cloud, named DCC-DIF.Our method explicitly associates local codes with learnable position vectors,and the position vectors are continuous and can be dynamically optimized, whichimproves the representation ability. In addition, we propose a novel codeposition loss to optimize the code positions, which heuristically guides morelocal codes to be distributed around complex geometric details. In contrast toprevious methods, our DCC-DIF represents 3D shapes more efficiently with asmall amount of local codes, and improves the reconstruction quality.Experiments demonstrate that DCC-DIF achieves better performance over previousmethods. Code and data are available at https://github.com/lity20/DCCDIF.

Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds