类似于2D对象建议[25、33、2],3D对象建议方法会生成一小组3D候选框,以便覆盖3D空间中的大多数对象。为此,3DOP [4]在立体点云中设计了一些深度特征,以对大量3D候选框进行评分。Mono3D[3]利用地平面先验知识,利用一些分割特征,从单个图像中生成3D方案。3DOP和Mono3D都使用了手工制作的功能。深层滑动形状[23]利用了更强大的
[论文解读]Multi-View 3D Object Detection Network for Autonomous Driving,程序员大本营,技术文章内容聚合第一站。
Multi-View 3D Object Detection Network for Autonomous Driving 贡献点 利用多模态信息进行3D目标检测预测,融合主要思想是进行基于区域的特征融合。(不同view的ROI特征进行融合) MV3D包含两部分网络组成,剩下的就是对融合后的特征进行3D目标检测 3D Proposal Network 输入BEV视角下的点云,预测高质量的3D候选框。 3D...
2.DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries DETR3D通过几何反投影和相机变换矩阵将 2D feature extraction 和3D object prediction 联系起来,在无需密集深度估计的情况下完成3D目标检测任务。将多视图检测转化为 set-to-set prediction 任务。 图2 DETR3D backbone:输入6个视角下...
Self-supervised learningMulti-view optimizationAutomotive applicationMonocular systemSelf-supervised 3D object detection from RGB videos using geometric constraints.Temporal continuity repaces additional sensors in training.Self-supervision enables fine-tuning on real data without labels....
3D object detection in images 3DVP 利用3D voxel模式和利用ACF检测器进行2D检测和3D姿态估计。3DOP 使用熵最小化的方法从双目图像重构深度,然后输入到R-CNN用于目标识别。Mono3D和3DOP具有同样的pipeline,只不过是利用单目图像生成3D proposal。为了融合时序信息,一些工作结合运动中的结构以及地面估计将2D 目标检测迁...
3D点云做detection的一篇milestone paper。经典的two-stage方法(region proposal-based method)。思路来自于经典的faster rcnn。 整个模型如下图 图一. 整体模型 3D Point Cloud Representation 这篇文章可以归纳为是multiview-based method,multi-view的方法是指将三维点云按照不同view进行映射,得到很多的2D图像,因为...
Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D detection further, in this work, we introduce...
Multi-View 3D Object Detection Network for Autonomous Driving Xiaozhi Chen1, Huimin Ma1, Ji Wan2, Bo Li2, Tian Xia2 1Department of Electronic Engineering, Tsinghua University 2Baidu Inc. {chenxz12@mails., mhmpub@}tsinghua.edu.cn, {wanji, libo24, xiatian}@baidu.com Abstract This paper ...
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection Kaixin Xiong*,1, Shi Gong∗,2, Xiaoqing Ye∗,2, Xiao Tan2, Ji Wan2, Errui Ding2, Jingdong Wang†,2, Xiang Bai1 1Huazhong University of Science and Technology, 2Baidu Inc. ...