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...
In the field of multi-sensor-based object detection, Li et al.15 developed a 3D detection model named DeepFusion to fuse camera features with deep lidar features to perform object detection. This model was proposed based on two novel techniques: Inverse Aug and Learnable Align. The results sho...
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。
2.提出了一个新的基于transformer的lidar-camera融合模型,实现了退化图像质量和传感器校准错位情况下的鲁棒检测 3.提出了一些简单使用的adjustments来对object queries 进行初始化,从而得到更加精确的初始bounding box 的检测结果;image-guided initialize module使得能够在点云中检测到一些 hard objects。