Nara Institute of Science and Technology (NAIST), Ikoma 630-0192, Nara, Japan.
Sensors (Basel). 2022 Feb 19;22(4):1631. doi: 10.3390/s22041631.
The truncated signed distance function (TSDF) fusion is one of the key operations in the 3D reconstruction process. However, existing TSDF fusion methods usually suffer from the inevitable sensor noises. In this paper, we propose a new TSDF fusion network, named DFusion, to minimize the influences from the two most common sensor noises, i.e., depth noises and pose noises. To the best of our knowledge, this is the first depth fusion for resolving both depth noises and pose noises. DFusion consists of a fusion module, which fuses depth maps together and generates a TSDF volume, as well as the following denoising module, which takes the TSDF volume as the input and removes both depth noises and pose noises. To utilize the 3D structural information of the TSDF volume, 3D convolutional layers are used in the encoder and decoder parts of the denoising module. In addition, a specially-designed loss function is adopted to improve the fusion performance in object and surface regions. The experiments are conducted on a synthetic dataset as well as a real-scene dataset. The results prove that our method outperforms existing methods.
截断符号距离函数 (TSDF) 融合是 3D 重建过程中的关键操作之一。然而，现有的 TSDF 融合方法通常会受到不可避免的传感器噪声的影响。在本文中，我们提出了一种新的 TSDF 融合网络，称为 DFusion，以最小化两种最常见的传感器噪声（即深度噪声和姿态噪声）的影响。据我们所知，这是第一个用于解决深度噪声和姿态噪声的深度融合方法。DFusion 由一个融合模块组成，该模块将深度图融合在一起并生成一个 TSDF 体，以及随后的去噪模块，该模块以 TSDF 体作为输入，去除深度噪声和姿态噪声。为了利用 TSDF 体的 3D 结构信息，在去噪模块的编码器和解码器部分使用了 3D 卷积层。此外，还采用了一种专门设计的损失函数来提高在物体和表面区域的融合性能。实验在合成数据集和真实场景数据集上进行。结果证明，我们的方法优于现有方法。
