Semantic segmentation, a fundamental task in image processing, is applied to various significant applications such as visual navigation of a robot, where accurate results and high speed processing are required. GPUs play an important role in high speed implementation but other options should be shown because various heavy procedures run in the process of the visual navigation. In this study, we propose a novel method to improve the calculation speed of PSPNet on the latest AMD Ryzen 9950X CPU. First, in order to improve the calculation speed of the Convolution layer, which accounts for the majority of PSPNet calculations, we utilize SIMD instructions (AVX-512), multi-core parallelization, and L1, L2, and L3 caches to accelerate matrix multiplication calculations using im2col. Next, we improve the calculation speed of the entire model by fusing the Convolution layer and the Batch Normalization layer. Compared with the PyTorch implementation, the proposed method achieved more than $79 \%$ of the peak performance in matrix multiplication while maintaining accuracy and succeeded in speeding up inference time by $47.712 \%$ on the Cityscapes dataset and $39.096 \%$ on the Visual Navigation dataset.

The authors attempt to actualize 3D reconstruction from a single view for previewing a room in virtual space using results of semantic segmentation. The segmentation accuracy has been drastically improved by state-of-the-art method, which enables pixel-wise classification of walls, floors, ceilings, and objects in the target room with sufficient accuracy. Assuming a room can be represented as a cuboid, its parameters can be computed analytically when semantic edges of the room structure are accurately obtained. In the actual process of the estimation, lines constructing the cuboid are estimated from detected edges. These edges are derived by spatial filtering to a semantic map corresponding to an input image. To enhance the effectiveness of edge detection on a semantic map, we adopted a simple differential filter that incorporates only two active values as filter coefficients, utilizing the smallest filter size. Experimental results using synthetic datasets showed no significant difference in the accuracy of line parameter estimation when comparing our method with a typical filter, despite using half the samples during the optimization process though sample numbers for parameter estimation became smaller obviously.