关键词: 点云重建, 模型检索与变形, 向量神经元, 特征仿射残差, 残差网络

Abstract: In robotic environmental perception tasks, single-viewpoint point clouds suffer from severe geometric information loss due to sensor viewpoint limitations. Point cloud reconstruction methods based on CAD model replacement effectively avoid the structural instability risks of direct point cloud reconstruction by retrieving similar models and applying deformation. The Unsupervised 3D Shape Retrieval and Deformation (U-RED) algorithm achieves topologically consistent CAD model replacement while maintaining editability of the reconstruction results. However, when dealing with objects of complex topology, it still faces challenges such as insufficient rotation and translation invariance representation of point clouds, difficulty distinguishing neighboring parts due to geometric similarity among homologous components, and parameter update failures caused by scattered attention weights and gradient vanishing or explosion. To address these challenges, this paper proposes the Vector Neuron Enhanced Unsupervised Retrieval and Deformation Framework with Feature Affine Residual (VU-RED-F) algorithm based on U-RED, incorporating part-level feature enhancement. A Vector Neuron Encoder (VNE) is constructed to improve the robustness of the feature extraction module in representing rotation and translation invariance of point clouds. A learnable affine transformation residual reconstruction feature mapping process is introduced to adaptively adjust feature distributions, enhancing the network’s discrimination ability of local geometric structures between parts. By integrating soft-threshold gating and residual correction, the algorithm constrains the sparsity of attention distribution while improving gradient propagation stability, thereby boosting network convergence efficiency and reducing loss during retrieval and deformation. Experimental results on the synthetic PartNet and ComplementMe datasets, as well as the real Scan2CAD dataset, show that the chamfer distance loss of the VU-RED-F algorithm decreases by 17.1%, 16.5%, and 16.4% respectively compared to U-RED, improving the fidelity of local geometric details in CAD models.

Key words: point cloud reconstruction, model retrieval and deformation, vector neurons, feature affine residuals, residual networks