Title
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Abstract
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. Experi-ments demonstrate that DCC-DIF achieves better performance over previous methods. Code and data are available at https://github.com/lity20/DCCDIF.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01250
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
3D from multi-view and sensors
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Tianyang Li100.34
Xin Wen261.42
Yu-shen Liu331923.20
Hua Su400.34
Han Zhizhong519818.28