Keywords: 3D Object Detection, Multi-modal Fusion , Sensor Fusion ,Autonomous Driving 关键词:三维目标检测;多模态融合;传感器融合;自动驾驶 @[toc] 摘要 背景 自动驾驶技术过去10年发展迅速,实现全自动驾驶依然是一项艰巨的任务 挑战 驾驶场景不断变化且复杂,为了降低感知难度,自动驾驶车辆通常配备了一系列传感器(...
多传感器融合、多模态、BEV、3D Detection 摘要 研究目标:提出一种多模态3D物体检测模型,能够在一个或多个传感器输入缺失的情况下,保持鲁棒性和准确性。 模型架构:UniBEV由四个部分组成:特征提取器、统一的BEV编码器、融合模块和检测头。特征提取器分别从多视角图像和点云中提取特征,BEV编码器使用变形注意力机制将特...
3D object detectionMulti-modalAutonomous drivingFeature fusionPoint cloud3D object detection, of which the goal is to obtain the 3D spatial structure information of the object, is a challenging topic in many visual perception systems, e.g., autonomous driving, augmented reality, and robot navigation...
在上述代码中,FeatureFusion类负责将LiDAR和相机的特征进行融合,而ObjectDetection类则负责利用融合后的特征进行目标检测。这只是一个简化的示例,实际的算法设计可能更加复杂,需要考虑更多的细节和优化。 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...
摘要背景：自动驾驶技术在过去10年快速发展，实现全自动驾驶仍面临挑战。自动驾驶车辆通常配备多种传感器以减少感知难度，但融合传感器数据和利用其互补特性是当前趋势。然而，这一任务不容易处理，传感器数据可能互相影响或互为噪声。贡献：本研究深入研究了最近数十种多模态3D目标检测网络，尤其是相机与LiDAR的...
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...
Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D points and dense 2D pixels. Recent approaches either fuse the image featu
A Multi-Modal Feature Fusion Network for 3D Object Detection Code will be available Soon Environment Setup: Linux (tested on Ubuntu 22.04) Python 3.8 PyTorch 1.10 + CUDA-11.3 Installation: To deploy this project run git clone https://github.com/faziii0/LumiNet conda create -n liard python...
Deep Multi-scale and Multi-modal Fusion for 3D Object detection The perception of 3D objects in the scene is the basis of autonomous driving. Most autonomous driving cars are equipped with cameras and Lidar to obtain 3D... R Guo,D Li,Y Han - 《Pattern Recognition Letters》 被引量: 0...
In summary, our RoboFusion gradually reduces noise by leveraging the generalization and robustness of VFMs, thereby enhancing the resilience of multi-modal 3D object detection. Consequently, our RoboFusion achieves state-of-the-art performance in noisy scenarios, as demonstrated by the KITTI-C and ...