论文标题
使用未签名的距离场基于卷积神经网络的高效致密点云产生
Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields
论文作者
论文摘要
从稀疏或不完整的点云中产生密集的点云是3D计算机视觉和计算机图形学中的一个至关重要且具有挑战性的问题。到目前为止，现有方法要么在计算上太昂贵，分辨率有限或两者兼而有之。此外，某些方法严格限于水密表面 - 许多应用的另一个主要障碍。为了解决这些问题，我们提出了一个轻巧的卷积神经网络，该网络使用最近出现的隐含功能学习的概念来学习和预测任意3D形状的无符号距离字段。实验表明，所提出的体系结构的表现优于较小的模型参数，与最新的ART相比，推理时间更快2.4倍，发电质量提高了24.8％。
Dense point cloud generation from a sparse or incomplete point cloud is a crucial and challenging problem in 3D computer vision and computer graphics. So far, the existing methods are either computationally too expensive, suffer from limited resolution, or both. In addition, some methods are strictly limited to watertight surfaces -- another major obstacle for a number of applications. To address these issues, we propose a lightweight Convolutional Neural Network that learns and predicts the unsigned distance field for arbitrary 3D shapes for dense point cloud generation using the recently emerged concept of implicit function learning. Experiments demonstrate that the proposed architecture outperforms the state of the art by 7.8x less model parameters, 2.4x faster inference time and up to 24.8% improved generation quality compared to the state-of-the-art.
