공부/Deep Learning9 [Paper Review] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 논문 : Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving URL : arxiv.org/abs/1812.07179 Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input da.. 2021. 5. 1. [Paper Review] MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization 논문 : MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization URL : arxiv.org/abs/1811.10247 MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss duri.. 2021. 5. 1. [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] Disentangling Monocular 3D Object Detection (MonoDis) 논문 : Disentangling Monocular 3D Object Detection (MonoDis) URL : arxiv.org/abs/1905.12365 Disentangling Monocular 3D Object Detection In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes. Our pro arxiv.o.. 2021. 5. 1. 이전 1 2 3 다음 반응형
