Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
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DOI：
10.1109/tip.2022.3214077
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发表时间：
2021-11
影响因子：
10.6
通讯作者：
Xue Zhang;Gene Cheung;Jiahao Pang;Yash Sanghvi;Abhiram Gnanasambandam;Stanley H. Chan
中科院分区：
文献类型：
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作者：
Xue Zhang;Gene Cheung;Jiahao Pang;Yash Sanghvi;Abhiram Gnanasambandam;Stanley H. Chan
3D点云通常由传感器在一个或多个视点处获取的深度测量来构造。测量遭受量化和噪声破坏。为了提高质量，以前的作品去噪点云后，投影到3D空间的不完美的深度数据。相反，我们在合成3D点云之前，先验地直接在感测图像上增强深度测量。通过增强近物理感测过程，我们在随后的处理步骤之前将我们的优化调整到我们的深度地层模型，从而掩盖测量误差。具体来说，我们模型的深度形成信号相关的噪声添加和非均匀的基于对数的量化的组合过程。使用从代表性深度传感器收集的经验数据来验证所设计的模型（具有拟合的参数）。为了增强深度图像中的每个像素行，我们首先通过特征图学习将可用行像素之间的视图内相似性编码为边缘权重。接下来，我们通过视点映射和稀疏线性插值来建立与另一个校正深度图像的视图间相似性。这导致最大后验概率（MAP）图过滤目标是凸的和可微的。我们使用加速梯度下降（AGD）有效地最小化目标，其中最佳步长通过Gershgorin圆定理（GCT）近似。实验表明，我们的方法显着优于最近的点云去噪方案和国家的最先进的图像去噪方案在两个既定的点云质量指标。
A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images a priori, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics.