Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducing the well-known implicit moving least-squares (IMLS) surface formulation, which naturally defines locally implicit functions on point sets. We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces. Our IMLSNet predicts an octree structure as a scaffold for generating MLS points where needed and characterizes shape geometry with learned local priors. Furthermore, our implicit function evaluation is independent of the neural network once the MLS points are predicted, thus enabling fast runtime evaluation. Our experiments on 3D object reconstruction demonstrate that IMLSNets outperform state-of-the-art learning-based methods in terms of reconstruction quality and computational efficiency. Extensive ablation tests also validate our network design and loss functions.
翻译:在这项工作中,我们通过引入众所周知的隐性移动最小平方表面配方(IMLS),将离散点组合变为光滑表面,这自然地界定了点数上隐含的功能。我们将IMLS表面生成纳入深层神经网络,以继承点数组的灵活性和高质量的隐含表面。我们的IMLSNet预测一个奥氏结构是产生MLS点的宝座,在需要的地方产生MLS点,并用当地已学过的前科来塑造几何形状。此外,一旦预测了MLS点,我们的隐含功能评价就独立于神经网络,从而使得能够快速运行时间评估。我们在3D对象重建方面的实验表明,IMLSNetes在重建质量和计算效率方面超越了基于状态的学习方法。我们的广泛对比测试还验证了我们的网络设计和损失功能。