Abstract
The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representation of arbitrarily-shaped objects in motion. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While many methods have been proposed for the denoising of static point clouds, dynamic point cloud denoising has not been studied in the literature yet. Hence, we address this problem based on the proposed spatio-temporal graph modeling, exploiting both the intra-frame similarity and inter-frame consistency. Specifically, we first represent a point cloud sequence on graphs and model it via spatio-temporal Gaussian Markov Random Fields on defined patches. Then for each target patch, we pose a Maximum a Posteriori estimation, and propose the corresponding likelihood and prior functions via spectral graph theory, leveraging its similar patches within the same frame and corresponding patch in the previous frame. This leads to our problem formulation, which jointly optimizes the underlying dynamic point cloud and spatio-temporal graph. Finally, we propose an efficient algorithm for patch construction, similar/corresponding patch search, intra- and inter-frame graph construction, and the optimization of our problem formulation via alternating minimization. Experimental results show that the proposed method outperforms frame-by-frame denoising from state-of-the-art static point cloud denoising approaches.
Abstract (translated)
可接近深度传感和三维激光扫描技术的普及使三维动态点云的采集变得方便，这为运动中的任意形状物体提供了有效的表示。然而，由于硬件、软件或其他原因，动态点云常常受到噪声的干扰。静态点云的去噪方法很多，而动态点云的去噪在文献中还没有得到研究。因此，我们在所提出的时空图模型的基础上，利用帧内相似性和帧间一致性来解决这个问题。具体地说，我们首先在图上表示一个点云序列，然后在定义的补丁上通过时空高斯马尔可夫随机场对其进行建模。然后对每一个目标补丁，我们提出一个最大的后验估计，并通过谱图理论提出相应的似然和先验函数，利用其在同一帧内的相似补丁和在前一帧内的相应补丁。这导致了我们的问题公式化，它共同优化了底层动态点云和时空图。最后，我们提出了一个有效的补丁构造算法、相似/对应的补丁搜索、帧内和帧间图构造，以及通过交替最小化优化问题公式。实验结果表明，该方法优于现有静态点云去噪方法的逐帧去噪。
URL
https://arxiv.org/abs/1904.12284
