论文标题
结构感知3D VR草图到3D形状检索
Structure-Aware 3D VR Sketch to 3D Shape Retrieval
论文作者
论文摘要
我们研究了基于3D-VR-Sketch的细粒度3D形状检索的实际任务。此任务特别令人感兴趣，因为2D草图被证明是2D图像的有效查询。但是，由于域间隙，很难从2D草图中以3D形状的检索获得强大的性能。最近的工作证明了3D VR素描在此任务上的优势。在我们的工作中，我们专注于3D VR草图中固有的不准确性造成的挑战。我们观察到，以固定边缘值的三重损失获得的检索结果，通常用于检索任务，包含许多不相关的形状，通常只有一个或几个或几个具有与查询相似的结构。为了减轻此问题，我们首次在自适应边距值和形状相似性之间建立联系。特别是，我们建议使用由“拟合差距”驱动的自适应边缘值的三重损失损失，这是在结构保护变形下的两个形状的相似性。我们还进行了一项用户研究，证实这种拟合差距确实是评估形状结构相似性的合适标准。此外，我们为202个3D形状的数据集引入了一个数据集，该数据集是从内存而不是从观察中绘制的。代码和数据可在https://github.com/rowl1ng/structure-aware-aware-vr-sketch-shape-retrieval中找到。
We study the practical task of fine-grained 3D-VR-sketch-based 3D shape retrieval. This task is of particular interest as 2D sketches were shown to be effective queries for 2D images. However, due to the domain gap, it remains hard to achieve strong performance in 3D shape retrieval from 2D sketches. Recent work demonstrated the advantage of 3D VR sketching on this task. In our work, we focus on the challenge caused by inherent inaccuracies in 3D VR sketches. We observe that retrieval results obtained with a triplet loss with a fixed margin value, commonly used for retrieval tasks, contain many irrelevant shapes and often just one or few with a similar structure to the query. To mitigate this problem, we for the first time draw a connection between adaptive margin values and shape similarities. In particular, we propose to use a triplet loss with an adaptive margin value driven by a "fitting gap", which is the similarity of two shapes under structure-preserving deformations. We also conduct a user study which confirms that this fitting gap is indeed a suitable criterion to evaluate the structural similarity of shapes. Furthermore, we introduce a dataset of 202 VR sketches for 202 3D shapes drawn from memory rather than from observation. The code and data are available at https://github.com/Rowl1ng/Structure-Aware-VR-Sketch-Shape-Retrieval.
