论文标题
通过学习预测上下文先验,从点云进行的表面重建
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
论文作者
论文摘要
点云的表面重建对于3D计算机视觉至关重要。最先进的方法利用大型数据集首先学习以神经网络的签名距离功能(SDF)表示的本地上下文先验,其中一些参数编码本地上下文。要在推理时间在特定查询位置重建表面,这些方法然后通过在给定查询位置搜索本地先前空间中的最佳匹配(通过优化编码本地上下文的参数)来匹配本地重建目标。但是,这需要在概括到各种看不见的目标区域之前的本地环境,这很难实现。为了解决此问题,我们通过在推理时间为每个特定点云学习预测性查询来介绍预测上下文先验。具体来说,我们首先使用与以前的技术相似的大点云数据集进行训练。但是,对于推理时间的表面重建,我们通过学习预测性查询将局部环境提前为先验的局部环境,该查询可以预测调整后的空间查询位置作为原始位置的位移。这导致了一个适合特定点云的全局SDF。直观地,查询预测使我们能够在整个以前的空间中灵活地搜索所学的本地上下文,而不是仅限于固定查询位置,这可以提高可推广性。我们的方法不需要地面真相签名的距离,正常距离或在重叠区域内签名的距离融合的任何其他程序。我们对单一形状或复杂场景的表面重建的实验结果表明,在广泛使用的基准下,最先进的结果显着改善。
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented as neural network-based signed distance functions (SDFs) with some parameters encoding the local contexts. To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location. However, this requires the local context prior to generalize to a wide variety of unseen target regions, which is hard to achieve. To resolve this issue, we introduce Predictive Context Priors by learning Predictive Queries for each specific point cloud at inference time. Specifically, we first train a local context prior using a large point cloud dataset similar to previous techniques. For surface reconstruction at inference time, however, we specialize the local context prior into our Predictive Context Prior by learning Predictive Queries, which predict adjusted spatial query locations as displacements of the original locations. This leads to a global SDF that fits the specific point cloud the best. Intuitively, the query prediction enables us to flexibly search the learned local context prior over the entire prior space, rather than being restricted to the fixed query locations, and this improves the generalizability. Our method does not require ground truth signed distances, normals, or any additional procedure of signed distance fusion across overlapping regions. Our experimental results in surface reconstruction for single shapes or complex scenes show significant improvements over the state-of-the-art under widely used benchmarks.