论文标题
弱监督的端到端CAD检索到扫描对象
Weakly-Supervised End-to-End CAD Retrieval to Scan Objects
论文作者
论文摘要
CAD模型检索到现实世界的场景观察结果已显示出强烈的希望，作为对物体3D感知的基础和干净，轻巧的网格场景表示。但是，当前检索CAD模型进行查询扫描的方法取决于CAD-SCAN对象的1：1关联的昂贵手动注释，该注释通常包含强大的下层几何差异。因此，我们提出了一种新的弱监督方法，以将语义和结构相似的CAD模型检索到查询3D扫描场景的情况下，而无需任何CAD-SCAN关联，并且仅作为对象检测信息作为方向的边界框。我们的方法利用了完全不同的顶部 - $ K $检索层，从而使端到端的训练能够以几何和感知相似性为指导，而最高的CAD模型则可以在扫描查询中进行。我们证明，我们弱监督的方法可以胜过对现实世界中的扫描扫描的挑战，并保持对看不见的班级类别的鲁棒性，从而在零拍摄的CAD回收中实现完全提高的ART状态。
CAD model retrieval to real-world scene observations has shown strong promise as a basis for 3D perception of objects and a clean, lightweight mesh-based scene representation; however, current approaches to retrieve CAD models to a query scan rely on expensive manual annotations of 1:1 associations of CAD-scan objects, which typically contain strong lower-level geometric differences. We thus propose a new weakly-supervised approach to retrieve semantically and structurally similar CAD models to a query 3D scanned scene without requiring any CAD-scan associations, and only object detection information as oriented bounding boxes. Our approach leverages a fully-differentiable top-$k$ retrieval layer, enabling end-to-end training guided by geometric and perceptual similarity of the top retrieved CAD models to the scan queries. We demonstrate that our weakly-supervised approach can outperform fully-supervised retrieval methods on challenging real-world ScanNet scans, and maintain robustness for unseen class categories, achieving significantly improved performance over fully-supervised state of the art in zero-shot CAD retrieval.
