DANCE能精准补全点云缺失部分，且不依赖固定密度和图像信息。
DANCE: Density-agnostic and Class-aware Network for Point Cloud Completion
- 通过多视角射线采样生成候选点，再用Transformer优化位置与有效性
- 在PCN和MVP数据集上精度优于现有方法，对稀疏输入和噪声鲁棒
- 直接从几何特征学习类别信息，无需图像监督，适合真实场景
点云补全旨在恢复因遮挡或传感器视角限制导致的不完整3D扫描中的缺失几何结构。现有方法通常假设固定的输入/输出密度，或依赖图像表示，难以适应实际中变化的稀疏性与有限监督。本文提出密度无关且类别感知的DANCE框架，仅补全缺失区域并保留已观测几何结构。DANCE通过多视角射线采样生成候选点，再由Transformer解码器优化其位置并预测透明度分数，决定点是否纳入最终表面。为引入语义指导，设计轻量级分类头直接基于几何特征训练，实现类别一致的补全，无需外部图像监督。在PCN和MVP基准上的大量实验表明，DANCE在准确性和结构一致性上优于当前最优方法，且对输入密度变化和噪声具有鲁棒性。
原文摘要 · Abstract (English)
Point cloud completion aims to recover missing geometric structures from incomplete 3D scans, which often suffer from occlusions or limited sensor viewpoints. Existing methods typically assume fixed input/output densities or rely on image-based representations, making them less suitable for real-world scenarios with variable sparsity and limited supervision. In this paper, we introduce Density-agnostic and Class-aware Network (DANCE), a novel framework that completes only the missing regions while preserving the observed geometry. DANCE generates candidate points via ray-based sampling from multiple viewpoints. A transformer decoder then refines their positions and predicts opacity scores, which determine the validity of each point for inclusion in the final surface. To incorporate semantic guidance, a lightweight classification head is trained directly on geometric features, enabling category-consistent completion without external image supervision. Extensive experiments on the PCN and MVP benchmarks show that DANCE outperforms state-of-the-art methods in accuracy and structural consistency, while remaining robust to varying input densities and noise levels.
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