论文标题
表示形状范围
Representation-Agnostic Shape Fields
论文作者
论文摘要
在深度学习时代，已经广泛探索了3D形状分析。已经为各种3D数据表示格式开发了许多模型，例如网格的MeshCNN，点云的点网和Voxnet的Voxnet。在这项研究中，我们提出了表示形状的形状字段（RASF），这是一种可推广且计算有效的形状嵌入模块，用于3D深度学习。 RASF具有可学习的3D网格，并具有多个存储本地几何形状的渠道。基于RASF，通过坐标索引检索了各种3D形状表示（点云，网格和体素）的形状嵌入。尽管有多种方法可以优化RASF的可学习参数，但我们在本文中提供了两个有效的方案，用于RASF预训练：形状重建和正常估计。一旦受过培训，RASF就会成为插件的性能助推器，成本可忽略不计。对不同3D表示格式，网络和应用的广泛实验验证了拟议的RASF的普遍有效性。代码和预培训模型可公开可用https://github.com/seanywang0408/rasf
3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning. RASF is implemented with a learnable 3D grid with multiple channels to store local geometry. Based on RASF, shape embeddings for various 3D shape representations (point clouds, meshes and voxels) are retrieved by coordinate indexing. While there are multiple ways to optimize the learnable parameters of RASF, we provide two effective schemes among all in this paper for RASF pre-training: shape reconstruction and normal estimation. Once trained, RASF becomes a plug-and-play performance booster with negligible cost. Extensive experiments on diverse 3D representation formats, networks and applications, validate the universal effectiveness of the proposed RASF. Code and pre-trained models are publicly available https://github.com/seanywang0408/RASF
