Scene understanding is a significant research topic in computer vision, especially for robots to understand their environment intelligently. Semantic scene segmentation can help robots to identify the objects that are present in their surroundings, while semantic scene completion can enhance the ability of the robot to infer the object shape, which is pivotal for several high-level tasks. With dense Conditional Random Field (CRF), one key issue is how to construct the long-range interactions between nodes with Gaussian pairwise potentials. Another issue is what effective and efficient inference algorithms can be adapted to resolve the optimization. In this paper, we focus on semantic scene segmentation and completion optimization technology simultaneously using dense CRF based on a single depth image only. Firstly, we convert the single depth image into different down-sampled Truncated Signed Distance Function (TSDF) or flipped TSDF voxel formats, and formulate the pairwise potentials terms with such a representation. Secondly, we use the output results of an end-to-end 3D convolutional neural network named SSCNet to obtain the unary potentials. Finally, we pursue the efficiency of different CRF inference algorithms (the mean-field inference, the negative semi-definite specific difference of convex relaxation, the proximal minimization of linear programming and its variants, etc.). The proposed dense CRF and inference algorithms are evaluated on three different datasets (SUNCG, NYU, and NYUCAD). Experimental results demonstrate that the voxel-level intersection over union (IoU) of predicted voxel's semantic and completion can reach to state-of-the-art. Specifically, for voxel semantic segmentation, the highest IoU improvements are 2.6%, 1.3%, 3.1%, and for scene completion, the highest IoU improvements are 2.5%, 3.7%, 5.4%, respectively for SUNCG, NYU, and NYUCAD datasets.