3.3 Uncertainty-Aware Fusion 四、实验结果 论文链接:SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection 代码链接:暂无 作者:Hongcheng Zhang, Liu Liang, Pengxin Zeng, Xiao Song, Zhe Wang 发表单位:商汤科技、四川大学 会议/期刊:无 一、研究背景 人们提出了各种方法来彻底探索激...
LiDAR-Camera Fusion Label Assignment and Losses Image-Guided Query Initialization 论文链接:arxiv.org/pdf/2203.1149 Introduction TransFusion由convolutional backbones和基于transformer decoder的detection head组成。 decoder的第一层利用object queries从点云中预测出初步的box;decoder的第二层则进一步将object queries与...
LiDAR-camera fusiontwo-stageAccurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on ...
总的来说,DeepFusion为LiDAR-相机深度融合的多模态3D目标检测提供了一种有效的解决方案。通过充分利用LiDAR和相机的互补优势,DeepFusion能够显著提高目标检测的准确性和鲁棒性。
Autonomous vehicles can detect and recognize their surroundings by using a variety of sensors, including camera, LiDAR, or multi-sensor fusion. In the field of camera-based object detection, Sinan et al.6 investigated the image quality and the object detection accuracy rate under extremely harsh ...
多模态融合: DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection，这研究探讨了如何在自动驾驶领域中，通过整合激光雷达和相机数据进行三维物体检测，以提高检测精度和可靠性。面对现有技术的局限性，研究团队提出了一种创新的深度特征融合策略，旨在解决传统方法中出现的挑战，包括特征...
基于lidar的目标检测方法可以分成3个部分:lidar representation,network backbone,detection head,如下图所示。 根据lidar不同的特征表达方式,可以将目标检测方法分成以下4种:基于BEV(bird’s eye view)的目标检测方法,基于camera view的目标检测方法,基于point-wise feature的目标检测方法,基于融合特征的目标检测方法。如...
www.nature.com/scientificreports OPEN Real time object detection using LiDAR and camera fusion for autonomous driving Haibin Liu , Chao Wu & Huanjie Wang * Autonomous driving has been widely applied in commercial and industrial applications, along with the upgrade of ...
Currently, the technology of fusing LiDAR point cloud and camera image data for 3D object detection is gaining popularity in this field. In this paper, we introduce a query feature generation strategy based on multi-scale image features in the cross-attention-based feature fusion module to ...
目前自动驾驶场景中一般会同时使用camera和image这两种模态的数据,lidar更擅长获取准确的几何信息,camera则能提供丰富的语义信息。因此很多算法尝试将两个模态的数据融合起来实现multi-modality 3d detector。传统的fusion方法一般是将两个模态产生的信息转换到同一个特征空间进行feature fusion。如PointPainting,使用image的语义...