论文标题
大规模数据库中的准确实例级CAD模型检索
Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database
论文作者
论文摘要
我们为从大规模数据库中清洁CAD模型的细粒度检索提供了一种新解决方案，以恢复RGBD扫描的详细对象形状几何形状。与以前的工作不同，只需使用对象形状描述符并接受顶部检索结果，将其索引到中等小的数据库中，我们认为在大规模数据库的情况下，可以在描述符的社区中找到更准确的模型。更重要的是，我们建议在实例级别上形状描述符的独特性缺陷可以通过基于几何形状的邻居的重新排列来补偿。我们的方法首先利用了学到的表示形式的判别能力来区分不同类别的模型，然后使用一种新颖的稳健点设置距离度量度量来对CAD邻域进行重新列入，从而在大型数据库中实现了细粒度的检索。对现实世界数据集的评估表明，我们基于几何的重新排列是一种概念上简单但高效的方法，与最先进的方法相比，检索准确性可以显着提高。
We present a new solution to the fine-grained retrieval of clean CAD models from a large-scale database in order to recover detailed object shape geometries for RGBD scans. Unlike previous work simply indexing into a moderately small database using an object shape descriptor and accepting the top retrieval result, we argue that in the case of a large-scale database a more accurate model may be found within a neighborhood of the descriptor. More importantly, we propose that the distinctiveness deficiency of shape descriptors at the instance level can be compensated by a geometry-based re-ranking of its neighborhood. Our approach first leverages the discriminative power of learned representations to distinguish between different categories of models and then uses a novel robust point set distance metric to re-rank the CAD neighborhood, enabling fine-grained retrieval in a large shape database. Evaluation on a real-world dataset shows that our geometry-based re-ranking is a conceptually simple but highly effective method that can lead to a significant improvement in retrieval accuracy compared to the state-of-the-art.
