论文标题
Skeletonnet：从RGB图像中学习对象表面的网格重建的拓扑的解决方案
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images
论文作者
论文摘要
本文着重于从RGB图像中学习3D对象表面重建的挑战任务。现有的方法通过使用不同的表面表示实现了不同程度的成功程度。但是，它们都有自己的缺点，并且无法正确重建复杂拓扑的表面形状，这可以说是由于其学习框架中拓扑结构缺乏约束。为此，我们建议学习和使用拓扑保存的骨骼形状表示，以帮助RGB图像中对象表面重建的下游任务。 Technically, we propose the novelSkeletonNetdesign that learns a volumetric representation of a skeleton via a bridged learning of a skeletal point set, where we use paralleldecoders each responsible for the learning of points on 1D skeletal curves and 2D skeletal sheets, as well as an efficient module ofglobally guided subvolume synthesis for a refined, high-resolution skeletal volume;我们提出了一个微小的点2 Voxellayer Tomake Skeletonnet端到端，可训练。借助学习的骨骼量，我们提出了两种模型，即基于骨架的图形跨度神经网络（SkeGCNN）和骨骼规范化的深层隐式表面网络（SKEDISN），它们在现有的网格变形和隐含的现有实地学习框架上进行了构建，并改善了下面学习的现有框架。我们进行了彻底的实验，以验证我们提出的骨架的功效。 skegcnn和skedisnoutperform现有的方法，通过不同的指标衡量时，它们具有自己的优点。其他结果inneralized任务设置进一步证明了我们提出的方法的有用性。我们已经将实施方式编码为shapenet-skeleton数据集，网址为https://github.com/tangjiapeng/skeletonnet。
This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. Existingmethods achieve varying degrees of success by using different surface representations. However, they all have their own drawbacks,and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topologicalstructures in their learning frameworks. To this end, we propose to learn and use the topology-preserved, skeletal shape representationto assist the downstream task of object surface reconstruction from RGB images. Technically, we propose the novelSkeletonNetdesign that learns a volumetric representation of a skeleton via a bridged learning of a skeletal point set, where we use paralleldecoders each responsible for the learning of points on 1D skeletal curves and 2D skeletal sheets, as well as an efficient module ofglobally guided subvolume synthesis for a refined, high-resolution skeletal volume; we present a differentiablePoint2Voxellayer tomake SkeletonNet end-to-end and trainable. With the learned skeletal volumes, we propose two models, the Skeleton-Based GraphConvolutional Neural Network (SkeGCNN) and the Skeleton-Regularized Deep Implicit Surface Network (SkeDISN), which respectivelybuild upon and improve over the existing frameworks of explicit mesh deformation and implicit field learning for the downstream surfacereconstruction task. We conduct thorough experiments that verify the efficacy of our proposed SkeletonNet. SkeGCNN and SkeDISNoutperform existing methods as well, and they have their own merits when measured by different metrics. Additional results ingeneralized task settings further demonstrate the usefulness of our proposed methods. We have made both our implementation codeand the ShapeNet-Skeleton dataset publicly available at ble at https://github.com/tangjiapeng/SkeletonNet.
