For COVID-19 prevention and treatment, it is essential to screen the pneumonia lesions in the lung region and analyze them in a qualitative and quantitative manner. Three-dimensional (3D) computed tomography (CT) volumes can provide sufficient information; however, extra boundaries of the lesions are also needed. The major challenge of automatic 3D segmentation of COVID-19 from CT volumes lies in the inadequacy of datasets and the wide variations of pneumonia lesions in their appearance, shape, and location. In this paper, we introduce a novel network called Comprehensive 3D UNet (C3D-UNet). Compared to 3D-UNet, an intact encoding (IE) strategy designed as residual dilated convolutional blocks with increased dilation rates is proposed to extract features from wider receptive fields. Moreover, a local attention (LA) mechanism is applied in skip connections for more robust and effective information fusion. We conduct five-fold cross-validation on a private dataset and independent offline evaluation on a public dataset. Experimental results demonstrate that our method outperforms other compared methods.