论文标题
扩展DeepSDF以进行自动3D形状检索和相似性转换估计
Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
论文作者
论文摘要
计算机图形和计算机视觉的最新进展发现，基于签名的距离功能（SDFS）成功地应用了深神网络模型，这些模型可用于形状表示，检索和完成。但是，这种方法受到与训练期间观察到的规模相同的尺度和姿势的查询形状的限制，从而限制了其对现实世界场景的有效性。我们提出了一种通过共同估计形状和相似性变换参数来克服此问题的公式。我们进行实验以证明该公式对合成和实际数据集的有效性，并报告对艺术状态的有利比较。最后，我们还强调了这种方法作为3D模型压缩形式的生存力。
Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, retrieval, and completion. However, this approach has been limited by the need to have query shapes in the same canonical scale and pose as those observed during training, restricting its effectiveness on real world scenes. We present a formulation to overcome this issue by jointly estimating shape and similarity transform parameters. We conduct experiments to demonstrate the effectiveness of this formulation on synthetic and real datasets and report favorable comparisons to the state of the art. Finally, we also emphasize the viability of this approach as a form of 3D model compression.
