学习点云的高维嵌入，实现非刚性形状的精准对应匹配。
CoE: Deep Coupled Embedding for Non-Rigid Point Cloud Correspondences
- 通过学习点级高维嵌入，捕捉形状几何与语义相似性。
- 在多个挑战性基准上达到最新水平，对噪声和部分缺失鲁棒。
- 适合需要高精度点对应的任务，如分割与三维分析。
由于低成本3D传感器的普及，基于原始点云匹配非刚性变形形状的兴趣日益增长。然而，该任务极具挑战性，因点云具有不规则性且缺乏内在形状信息。本文提出一种新方法：学习每点的高维嵌入表示，在嵌入空间中语义相似的点拥有相似嵌入。所学嵌入具备多重优势：能感知底层形状几何，对形变及各类形状瑕疵（如噪声、部分缺失）具有鲁棒性。因此，可直接通过嵌入空间中的最近邻搜索获取高质量稠密对应。大量实验表明，该方法在多个具有挑战性的非刚性形状匹配基准上达到新最优性能，并展现出在分割等其他形状分析任务中的巨大潜力。
原文摘要 · Abstract (English)
The interest in matching non-rigidly deformed shapes represented as raw point clouds is rising due to the proliferation of low-cost 3D sensors. Yet, the task is challenging since point clouds are irregular and there is a lack of intrinsic shape information. We propose to tackle these challenges by learning a new shape representation -- a per-point high dimensional embedding, in an embedding space where semantically similar points share similar embeddings. The learned embedding has multiple beneficial properties: it is aware of the underlying shape geometry and is robust to shape deformations and various shape artefacts, such as noise and partiality. Consequently, this embedding can be directly employed to retrieve high-quality dense correspondences through a simple nearest neighbor search in the embedding space. Extensive experiments demonstrate new state-of-the-art results and robustness in numerous challenging non-rigid shape matching benchmarks and show its great potential in other shape analysis tasks, such as segmentation.
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