Review4 [Paper Review] GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving 논문 : GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving URL : arxiv.org/abs/1903.10955 GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the.. 2021. 5. 1. [Paper Review] Pyramid Stereo Matching Network(PSMNet) 논문 : Pyramid Stereo Matching Network (PSMNet) URL : arxiv.org/abs/1803.08669 Pyramid Stereo Matching Network Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking arxiv.org 저자 : Jia-Ren Chang, Yon.. 2021. 5. 1. [Paper Review] Unsupervised Learning of Depth and Ego-Motion from Video 논문 : Unsupervised Learning of Depth and Ego-Motion from Video (Sfm Learner) URL : arxiv.org/abs/1704.07813 Unsupervised Learning of Depth and Ego-Motion from Video We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. We achieve this by simultaneously training depth and camera pose estimation networks using t.. 2021. 5. 1. [Papers Review] ArcFace: Additive Angular Margin Loss for Deep Face Recognition 논문 : ArcFace: Additive Angular Margin Loss for Deep Face Recognition(2019) , CVPR URL : https://arxiv.org/abs/1801.07698 ArcFace: Additive Angular Margin Loss for Deep Face Recognition One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power. Centr.. 2020. 10. 11. 이전 1 다음 반응형
