  Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape
representation. To capture geometry details, current methods usually learn DIF
using local latent codes, which discretize the space into a regular 3D grid (or
octree) and store local codes in grid points (or octree nodes). Given a query
point, the local feature is computed by interpolating its neighboring local
codes with their positions. However, the local codes are constrained at
discrete and regular positions like grid points, which makes the code positions
difficult to be optimized and limits their representation ability. To solve
this 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, which
improves the representation ability. In addition, we propose a novel code
position loss to optimize the code positions, which heuristically guides more
local codes to be distributed around complex geometric details. In contrast to
previous methods, our DCC-DIF represents 3D shapes more efficiently with a
small amount of local codes, and improves the reconstruction quality.
Experiments demonstrate that DCC-DIF achieves better performance over previous
methods. Code and data are available at https://github.com/lity20/DCCDIF.
