用几何投影与自适应融合提升点云补全的结构完整性
ProjFormer: Point Cloud Completion via Geometric-Projective Transformer and Cross-Modal Semantic Constraints
- 通过显式投影实现2D-3D特征对齐，保证几何一致性
- 轻量级设计下完成度优于现有方法，结构更完整
- 适合需要高精度点云重建的三维视觉任务
点云补全因观测数据稀疏和语义模糊而本质病态。现有多视角方法虽引入2D语义缓解问题，但依赖学习注意力与固定融合，缺乏几何一致性与适应性。本文提出ProjFormer，一种跨模态框架，通过显式投影与自适应特征路由，实现几何一致的2D-3D交互。投影引导视图注意力模块通过确定性投影将3D点与多视角特征对齐，实现高效且几何一致的聚合。在此基础上，几何感知路由网络实现点级自适应融合，结合结构与观测驱动特征，逐步优化补全结果。实验表明，在轻量级设计下，ProjFormer在结构完整性上表现优异，性能具有竞争力。
原文摘要 · Abstract (English)
Point cloud completion is inherently ill-posed due to severe sparsity and ambiguity in partial observations. Existing multi-view methods alleviate this by incorporating 2D semantics, but often rely on learned attention and fixed fusion, which lack geometric consistency and adaptability. We propose ProjFormer, a cross-modal framework that enforces geometry-consistent 2D-3D interaction through explicit projection and adaptive feature routing. A Projective Guided View Attention module aligns 3D points with multi-view features via deterministic projection, enabling efficient and geometrically consistent aggregation. Building on this, a geometry-aware routing network performs point-wise adaptive fusion of structural and observation-driven features for progressive refinement. Experiments show that, under a lightweight design, ProjFormer delivers competitive performance with improved structural completeness.
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