http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
인접 픽셀 정보를 이용한 Shift-Convolution 기반의 3D LiDAR 깊이 완성
유병준(Byeong-Jun Yu),기석철(Seok-Cheol Kee) 한국자동차공학회 2021 한국 자동차공학회논문집 Vol.29 No.9
In an automated driving system, recognizing range information is essential in order to understand the surrounding environment. As a result, we proposed depth completion method, filling the area with depth information of point cloud, which is projected onto the image plane, and high resolution color data from the image. The projected point cloud is placed into the shift-convolution network which expands received sparse LiDAR data to the pixel level, and then it is inserted synchronously into the convolutional neuron network(CNN) with image. Fully completed ground truth is formed by using max and median filters sequentially, and it is taken as input of shift-convolution to make an expanded point cloud that focuses more on completing an empty area than section the contour off. Finally, CNN uses point cloud to get the exact depth information and image for separating objects along the outline. The system that uses expanded point cloud has approved almost 9 % more than the system that does not.