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、
论文第一次将transformer应用到了LiDAR-camera 3D detection中。整体算法如下,首先分别使用2d,3d backbones来提取lidar bev features和image features。第一层transformer decoder使用object queries基于lidar信息生成initial predictions。第二层decoder则进行了fusion的操作,将有用的image features融合到object queries中,进一步...
3D object detectionLiDAR-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 paradi...
DeepFusion是一种深度学习框架,旨在通过融合来自不同传感器的数据来提高目标检测的准确性。在LiDAR-相机深度融合的背景下,DeepFusion利用LiDAR提供的高精度深度信息和相机提供的丰富颜色纹理信息,共同构建一个更加全面和准确的目标表示。 二、LiDAR和相机在3D目标检测中的应用 LiDAR:LiDAR通过发射激光并接收反射回来的信号来...
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...
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection CLOCs is a novel Camera-LiDAR Object Candidates fusion network. It provides a low-complexity multi-modal fusion framework that improves the performance of single-modality detectors. CLOCs operates on the combined output candidates of...
LiDAR–camera fusion for road detection using a recurrent conditional random field model Article Open access 05 July 2022 Dense projection fusion for 3D object detection Article Open access 08 October 2024 Introduction Object detection is one of the most important tasks that needs to be handled...
PyTorch implementation of TransFusion for CVPR'2022 paper "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers", by Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai. This paper focus on LiDAR-camera fusion for 3D object dete...
由于3D点云在做camera view投影的时候丢失了原来的3D结构信息,引入了图像中的尺度变化和遮挡两个问题,因此少有方法直接在这种模式下作3D目标检测,一般需要在网络输出基础上做比较多的后处理。但是camera view的表达模式,极大的增加了远处点云的上下文信息,也是一种极好的提高点云特征表达能力的方式。
近期一些sparse 3d detector由于其高效的计算收到了关注,但是这些sparse 3d detector在检测结果上有时却不如对应的dense 3d detector。论文提出了一种全稀疏的,端到端的,多模态3d检测器--SparseLIF,在性能上超越了所有dense和sparse检测器。 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...