This page is for adverse weather supplementary videos Contents are divided into two parts 1. Videos from CVPR 2020 papers 2. Videos from Google Waymo 1. “Seeing Through Fog Without Seeing Fog: Deep Sensor Fusion in the Absence of Labeled Training Data,” 2019. [Online]. Available: M. Bijelic, F. Mannan, T. Gruber, W. Ritter, K. Dietmayer, and F. Heide, 1.1 pdf of above paper : www.researchgate.ne..

본 글의 목적 : [31] 논문을 CNN-based Lidar Point Cloud De-Noising in Adverse Weather 논문이 활용한것이 많았다. 그리하여 [31] 논문 중 활용한 부분을 나열하는 과정을 거친다. 그리하여 블로그의 글 : [paper review,deatail] CNN-based Lidar Point Cloud De-Noising in Adverse Weather 논문의 내용 중 [31] 이 포함된 것을 나열한다. [LiLaNet 제안 논문], [31] F. Piewak, P. Pinggera, M. Schafer, D. Peter, B. Schwarz, N. Schneider, ¨ M. Enzweiler, D. Pfeiffer, and M. Zollner, “Boost..

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..

용어 asymmetric distortions: 비대칭 외곡 distort the sensor streams asymmetrically : 센서 데이터를 비대칭적으로 비틀다, 외곡하다 intertwine : 뒤얽히다 , 엮이다 인트로 좋은 날씨에 biased 되어 학습되었다. [Existing object detection methods, including efficient Single Shot detectors (SSD) [41], are trained on auto motive datasets that are biased towards good weather con ditions. While these methods work well in good condi tions [19, 59], they fa..