pdf : arxiv.org/abs/1804.09915.pdf citation : F. Piewak, P. Pinggera, M. Schafer, D. Peter, B. Schwarz, N. Schneider, ¨ M. Enzweiler, D. Pfeiffer, and M. Zollner, “Boosting LIDAR-based ¨ semantic labeling by cross-modal training data generation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11134 LNC..

pdf : arxiv.org/pdf/1912.03874.pdf Input : 3D lidar point cloud output : point-wise classification (noise/de-noise) 논문 citation : R. Heinzler, F. Piewak, P. Schindler and W. Stork, "CNN-Based Lidar Point Cloud De-Noising in Adverse Weather," in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2514-2521, April 2020, doi: 10.1109/LRA.2020.2972865. summary : 2D convolution을 활용하여 3d point cl..

Input : 3D lidar point cloud output : point-wise classification summary : 2020 CVPR에 등재된 최신 point-wise segmentation 논문 본래 segmentation 은 point 단위로 classification하기 때문에 point의 양이 많은 경우 real-time 수행이 어려움 본 논문에서는 random point selection 과 Perceptive field를 점차적으로 넓혀가는 방법을 통해 매우 빠른 속도와 높은 성능으로 point 단위 classification 수행 : 앞선 LiLaNet과는 달리 camera domain이 필요하지 않으며 voxelization, grid 등의 기법을 활용하지 않기 때문에 정보..