Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation  

作　　者：Yuanxun Lu Xinya Ji Hao Zhu Xun Cao 

机构地区：[1]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China [2]School of Electronic Science and Engineering,Nanjing University,Nanjing 210023,China [3]School of Intelligence Science and Technology,Nanjing University,Suzhou 215163,China

出　　处：《Machine Intelligence Research》2025年第2期304-323,共20页机器智能研究(英文版)

基　　金：supported by the National Natural Science Foundation of China(Nos.62001213 and 62025108).

摘　　要：Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the representations.While recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes well.In this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape representation.Specifically,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query points.In addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher levels.These formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high accuracy.Extensive qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.

关 键 词：Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]